{
 "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": "cfce544c03a043ca8481e83b51cdfd08",
       "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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Fi6SDFotl8uTJ0mO9Xq/X681mc1pamsVi+eijj6699lpB6LMjp9Fo3O6uPHOXy0VEZrM5OTk5so8QK4ipAMPOQ9dP2XG8mXNB4D6Rsa4pVc5aule2RNvHn5ukgV8i7l+TSkTfX4lO6pDqKyj2jKARh8+e8vPzFy5cqNfrL168uH//fpVKNWfOHCIqKioqKCjwn+bxeBoaGjo7O30+n9QxJSKv16vVav3naDQaqQubmZm5adOmfgIqEU2dOrWysjIjI0Or1R45coQx5vV65fpQQwZjvwDDTn6WYWyyzp/962dxDEX2b1W95bLN2b2Ehvyd1JQELKEZLoIiqIwBlYjGjh2blJSk0WgmTZpUUFDgH+9Vq9WB8TI5ObmkpGTdunVLly7du3ev1LMM7GsSkdvtll4iCII/7vZl3rx5ubm5+/fv37lz5+TJkxljOp1Oxs81NNBPBRiO5uekBAz/MmkkljH63YEz0c7+/Ut5nTTsy7uqT3Stk01PGOBvIgwleeNoX9Rq9YDLWsaPH+/1eh0Oh06nS0lJkSZQicjhcDidzpSUlBDfSxCEBQsWLFiwgIiam5sZY2PGjBlM42NCaf1U1NAHZXjo+ilEjJPAuL/DyIlYk9UV7bc+cdGfGHIlO4kYy5+AmVRF4Zx7PB5pfNXr9UqDtJzzpibpyxxZrdbq6urs7GzpxwsXLpw4cUJ6fPHiRSnWiqJYWVkZFxdnMBiIKDc39+zZs9LC06qqqrS0NCmmWiyWDz74oOu7Wm/vS0SdnZ3SQYfDcfjw4dmzZwd2i0cKpfVTUT0flCE/y5CWqGmzBU+gOtxitFfUSEXzgwgCPVA8JXpvCkOvra3ttddekx6/+OKLRHT//fdrNJo9e/ZI06KdnZ3Tpk2bN2+edE5zc3NjY+Ps2bOJ6OTJk++88058fLzT6TQYDKtWrZLmSnNzc5uamrZt26bT6QRBWLVqlfTajo6Oqqqq6667jjHW6/vqdLqmpqb9+/frdLrOzs5rrrnmuuuuG8q7IRfmz+1TgOLi4vLy8li3AkAej2+teKeqSar8IP2eMsY457cVjP/t3UVRetOqesttL7zvE7vKIvoTlCakxL3/f26M0pvCcONwODweT2Jiokql6uscj8fjcDj0en3PWU+Xy+VyuZKSkvzrbULkdrsdDkd8fPxI7KFKlNZPBVCMh66fsvN4c1fybcCUalRX1PylvE7k/oDatVUqES2YnBq9N4XhJj4+fsBzNBqNNN7bk06niyy9SKvVjtxoKlHafCqAYuRnGVIStD2nVC3OKC4wOHHRGtCz6HooqBgGfgFCgZgKMHwl69VEwaNnrTZP9LZTtTo9Ig9+y/QEDUo9AIRCaTEVeb+gJLOzeikiI3IepSKFUklC6rERjUE/sofjAIaM0uZTkfcLSjLEU6qBk6nU/f+xigYgdErrpwIoyRBPqV49mdoFq2gAQoeYCjCsBUypckbEiDiP1pQqJlMBBgkxFWBYk6ZUOQnSYlFOJDAuivJPqVbVW8wOD2EyFWAQEFMBhrWHrp8iMMboSkl7TowxOnvZLu8bvVXZ6ONi1ztgMhUgIkrLUfJn/CJZCZQhP8swK9NworEtKE0pPUnm7mNrh4t19039NCoBk6kAoVNaTEUoBeUxxqulNCXOBNa9l2q7o5eqvINh6eyaTA0Mq7MzEzGZChA6jP0CDHdtNre0R03glOqJBqu8aUr1pk529WQqY2RzDbDPFwAEQkwFGO6mZiRw4kFTqj7O3664KOO7OD3erg1TOZGUYExC8FgwAPQLMRVguHvo+ikaQdoe5MpyGk7UanPK9RZV9ZZWm1RBiRFjxJg0DIwEJYCwIKYCDHf5WYYZ45ODxn6Jk6VTtinVv5TXeUV/0m/Xf0hQAghXLHOULBZLa2urWq3OzMzUaDQDnt/a2mq1WtVqdVpaWkJCwhC0EGCY8IkiIylHiXHiImeCwFs7gncsj9jZFjvvkaA0MUWPBCWAsMQsppaVldXU1AiCIIqiTqcrKSnJzs7u62SLxbJv377Lly9L5wuCMGvWrCVLlvTc8BZraUCR0hK1Qam/ImeCKrwNn/uRnqiVspMCN3rLSRt4E00ACBSbmFpdXV1TU7Nw4cKCggKXy7Vnz57S0tKNGzf21fvcv39/W1vbmjVrJk6c6PV6P/zww+rq6tTU1JkzZwadiVAKimSM1wSk/nIiEhg/dbGjutGcn2WU6104ESNGjDjvucUcAAwsNvOplZWVGRkZRUVFgiDExcUtW7bM4/HU1NT0erLP57t06dLkyZOzs7MZYxqNRuqhNjY2DnGzAWIlPVHXM/XX7RPlSv39vM3OpKt2T6YyxqWsJQAIXQxiqtlsttlsOTk5/iNGo9FoNDY0NPR6vkql0ul0Pt+VdXI+n49zHh+PgSkYLTYUZWlV0Ur9raq3NFmcgV1TxohzITcjcfAXBxhVYhBTLRYLERmNVw1YGQwGs7nPBezz58///PPPT5w4YbPZ2tvbDxw4oNfr8/Pzo95WgOEhP8twzbhopf76k34DF9JoVAxJvwDhisF8qtvtJiKt9qpqpVqtVjreq/z8fJ/Pd/jw4fLyciJKTExcu3ZtcnJytJsKMHwEpP4SEYmcMSZP6m9A0u+V6vlI+gWIQMzyfjkPo0DLxx9//Mknn8yZMyc7O9vtdldXV7/zzjvr1q0bM2ZM0JnFxcVBR6QwDDDSSam/xIlISlPiRPJU0kfSL4BcYhBTdTodEblcrsCDLpdLr9f3er7Vav3kk09mz569ePFi6Uh2dvYrr7zy/vvv33rrrUEnI4KCUhnjNUQU2FXlxNxeGYoHGhO01CPp1xiPbVMBwhaD+dSUlBQiMplMgQdNJpN0vKe2tjbO+bhx4/xH1Gp1enp6a2trVNsJMKykJ+p6VtI/et48+Er6XOQ9k35R6BcgAjGIqUlJSSkpKXV1df7h35aWlo6OjsCaD2632+PpSr6Q+q+BMZhzbjab++rXAijShqIsrVpgJBLrSv0VOXN5fYNfTtNic/VM+sXyVIAIxGZ96ty5c00mU1lZmdVqvXTp0v79++Pi4gILOLz88ss7duyQHo8dO9ZgMFRWVp48edJqtba1tR04cMBisUybNi0mjQeIifwsQ1F2SlA/lfPBLqepqrdU1JvZ1Um/jIlpiTq5Wg4wesQmRykvL89msx09evTkyZNEZDAY1qxZI82z9iQIwpo1a8rLy9977z3piFqtnjt37rx584auxQDDwJgkbc/UXza4ikdvVTa6vb6gXd60avVthVlyNBlgdIlZ3m9hYeHs2bNNJpNarU5NTQ169mtf+1rgjwaDYe3atS6XS6qhn5ycrOpa/x4M9X5ByXhweUJR9C9/iVBrh6t7IU1X/WzOaX6OEQtpACIQy31pNBpNRkZG6OfrdLqei2eCIJSCkjEuez9VuhgL6KcSZxkY+AWICPZPBRg5uvqpRNS9moYG20+Vkn67Bn2lyVQBSb8AEYplPxUAwsM49Vii2jK4Ukrd/dQrk6lI+gWIGPqpACNGNJaodvdTr0r6RT8VIDKIqQAjRjSWqHYvTu2u9kAc/VSAiCGmAowYsi9RxeJUAHkpbT4Va2lA2eRdoorFqQDyUlpMRSgFhbuS+ktExIhzHnnqLxanAshLaTEVQOGk/in5yz4QSfvJRHYxLE4FkBXmUwFGkvREXVCOEhFFPP2ZlqDtuTgVk6kAEUM/FWAkyc8yELtqZ3LGWH5WcsRXY4wRF/2TqURCxFcDAPRTAUaS6kYL53R1V5Xerbk0iKvxwKRfIrG60SpvmwFGD6X1U5H3C6NAcCX9A5+2VDea87OM4V6otcNFVyf9ErFWm0ve5gKMHkqLqQiloGwbirL+eeSCx+sJXE4jlX2IIKZ2F3y4kuTEOUPBB4CIYewXYCQJKPvQdURaThNB2YeAgg9dOCcUfAAYDKX1UwEUb0ySlogGv5wGBR8AZId+KsAII+NyGn/BB3+OUsmMMSj4ABAxxFSAESZgOQ0RESPOKJLlNF0Laa4qoE83zxwrb2sBRhWlxdSD3WLdEIBokZbTSBWVOBFjxIlHsACmeyEN+Qs+EIvkOgDgp7T5VOT9wmgQXEY/0nq/1GMhjSzNAxi1lBZTARRP3rFfzgMjaeQlmQCAlDf2C6B43WO/5B/+jWzMNmDsl4iIc0IRJYBBQj8VYEQKGv5tNHdGeh2M/QLIBv1UgBFmQ1FWkl4bVPbh/TNt1Y3msK7TnffbtZCGGMPYL8AgIaYCjDD5WYbFU1OJKDD11+r0vF1xMazrYOwXQHZKG/vdsmWL//GmTZti2BKA6Mk0xg0+9bfR5CCM/QLISmkxFXEURoPBp/5W1VuOnGsPKqCfqNOgMCHAYGDsF2DkGXzZh7cqGy2dnqAC+tdPS0NhQoDBQEwFGJF6lvyN9Dr+woQ80xAnYwsBRiGljf0CjAaDH/vtLvjAA7a0QdIvwGChnwow8gx+7BdJvwDREGE/lXPucDji4uIEYXhFZX/1fBT+BWWTpeQvkn4B5BVGTBVF8YMPPigrK6uqqmpubhZFURCEMWPGXHvttYsXL77++us1Gk30GhoihFIYDeQb+0WxXwA5hRRTRVHcvn373//+95aWlrS0tBkzZixYsCAhIcFut7e3t1dUVJSWlqakpNx111133nnncIisAMp2dclfTkSM8epG6/rCsK5wJaRyToyJYV0BAHoKKaZ+85vfPH/+/Nq1a2+++eacnJyeJzQ0NJSWlv7rX//asWPH1q1bZW4jAPRm8CV/MfYLIK+QYurKlStvvPHGuLg+8+wnTJhw//33f+lLX9q5c6d8bQOA3m0oynr9WGOH092z5G9+ljGUK2DsFyAaQsowWrt2bT8B1U+j0dx6662DbhIADCCg5O8VYZX8Rd4vQDSE1E+1Wq0JCQkqlSrarRk85P3CKJFplL7mds2nCox4+Jm/GPsFkFdIMbWsrOzPf/7zzTffvHr16l7nU4cPhFIYJTYUZb1+7HyHS/TPpybqVKFX652cHhc09muI06LYL8AghRRTc3Nzx44du3Xr1q1bt86cOXP16tUlJSUJCQnRbhwA9EUUiUggLko/MuKMSAxtlWpVveW3+84E5v0S0fdW5qHYL8AghTSfes011/zpT396+eWXN27ceOnSpWeffXb9+vU//elPjx07xiMYbwKAQXursrHD6Q0spWRzeUOcT32rsrHd7qaAYr/E+bkWR5SbDKB8YdR8yMnJefTRRx9++OEjR47s2rXr4MGDpaWl48aNW7Vq1erVq8eNGxe9VgJAT3KUUmKYRQWQUdi1CQVBWLRo0aJFizo6Ovbu3bt79+4tW7a8+OKL69at++53vxuNJgJAT4MppRRQQN8PC2kAZBB5td6kpKQNGzZ897vfnTdvHuf83LlzMjYLAPoXUEqpS+hl9LGQBiBKIqyhbzKZSktLd+3aVVdXJwjCggUL7rjjDnlbFhmspYFRJvK1NFhIAyC78GKq1+v94IMPdu3a9dFHH/l8vqysrAceeGD16tVjxoyJUvvChVAKo8Rg1tJsKMr636P1dpfXH0kTtBospAEYvFBjam1t7a5du/bu3WuxWPR6/YoVK9asWVNQUBDVxgFAXwazlkYUSWAUWEBfEHiIrwWAfoQUU/fs2fOzn/2MiGbPnv3www/fcMMN8fHxUW4YAPSney3NFdJamlDq/UqvDRz77XCG+loA6EdIMTU+Pv6ee+5Zs2ZNdnZ2tBsEAGG6MqUa7gsxiwogr5Dyfq+77rpHHnkklIDq8XgG3SQAGNiGoixjvJaRSIyzrlFcISc9pOpmG4qyEnRXfZ/GfCqALEKKqY888sgrr7xit9v7OcfpdL7xxhv33Xdf6O9tsVjOnj17/vz50CPx5cuXz5w5c/78eYej95ovB7uF3gyAkSg/y/DtFdMCl6hyLv669HR1o3nA1/rnU7tfiPlUAHmENPZ73333/e53v/vrX/+6dOnSuXPnTp8+PS0tLSEhobOzs62t7bPPPquoqCgrK4uPj6IsXsgAACAASURBVH/ooYdCfOOysrKamhpBEERR1Ol0JSUl/feD29ra9u3b197e7j9yyy23ZGUFf7NG3i+MHuda7UHrZ0wOdyjTophPBYiSkGLqsmXLFi9e/O6777755psHDhzoecLkyZMffvjh1atXh7LNKhFVV1fX1NQsXLiwoKDA5XLt2bOntLR048aNfdXlt9ls27dvj4uLW7du3dixY10u16VLl1DEH4CIBrFEFfOpADILdS2NVqu95ZZbbrnllubm5uPHjzc3N9tstsTExLFjx86ZM2f8+PFhvWtlZWVGRkZRURERxcXFLVu2bOvWrTU1NQsWLOj1/CNHjni93rVr1yYmJhKRRqORHgCMZhEvUe3e6O1KBMZGbwCyCLuO0rhx4wZZLt9sNttstpkzZ/qPGI1Go9HY0NDQa0z1+Xxnz56dOnWqFEdFURSEyEsqAihGZEtUsdEbQPREWJtwMCwWCxEZjVfN3BgMhubm5l7Pb29v9/l8qamphw4dqq2t9Xq9qamp8+fPnzJlylA0F2C4imyJ6tUbvXUdxEZvALKIQUx1u91EpNVqAw9qtVrpeE+dnZ1EVFFRkZiY+IUvfIGIjh8/vmfPnpUrV06ePDno5OLi4qAj5eXlcrUcYFjCfCrAcBGDmCoJdzNzzvktt9yi1+uJKCcn55///OfRo0d7xlREUBg9IptPvVLstxsWpwLIJQYTkzqdjohcLlfgQZfLJcXLvs4fP368/wStVpuVldXa2iqKYpQbCzB8dc+ndv0Y4nwqFqcCRE8MYmpKSgoRmUymwIMmk0k63uv5jLGgvCSVSkXhd3YBlKSv+dRQXsWIE+fEOSMuLU6NZksBRosYxNSkpKSUlJS6ujp/RGxpaeno6Ais+eB2u/3FlbRa7fjx4y9fvuzz+aQjoig2NzcbDAYpsgKMbowTcSIW3uQoI9b9HwDIJJKYKoriyZMn9+3bd+rUKemIz+cLq8s4d+5ck8lUVlZmtVovXbq0f//+uLi4wNU1L7/88o4dO/w/zps3z+Fw7N+/v729vb29/cCBA1arFTvNwSi3oSgrSS/46/2KnCXo1KHMp6LYL0CUhJ2jdOrUqc2bNzc0NBDRF7/4xRkzZvh8vg0bNtx111333HNPiBfJy8uz2WxHjx49efIkERkMhjVr1kjzpr3Kysq64YYbDh8+fPbsWSJSq9XXXXddYAwGGIUiW5+KzVMBoie8mGqxWP7jP/4jOzv7iSee2Llzp3RQpVKVlJSUl5eHHlOJqLCwcPbs2SaTSa1Wp6amBj37ta99LejItGnTpk6d2t7ezjlPTU1Vq3tvub96Pgr/guJFvD4VxX4BoiS8mLpv3z5BEH79618nJCS89957/uNTpkzZs2dPuO+t0WgyMjJCP1+lUo0ZM6b/cxBKAUKDxakA8gtvPvXixYt5eXk9i9cnJCR0dHTI1yoAGJi0hSoR+dOUkvQDz4wGvKoLiv0CyCW8mJqcnHz58uWex8+cOZOWliZTkwAgJPlZhv+5pzAwTYkTmToH2I04P8vw9etzWEC672PLp6DYL4Aswoup11133YULF95+++3Ag2fPnn399deXLFkia8MAYGBJOg1jKn+Ckc3pefyViv63Ja+qt/y5/HPORWl9KnH+wqG6UHYyB4ABhTefOn369A0bNjz77LOlpaVWq1Wv1//whz/88MMPU1NTN23aFKUmAkBf3qpstF7dMR1wW/LuGvpX5lND3MkcAAYU9lqaJ598Mi8v7/XXXz9//jznvL6+fsWKFV//+td75u7GBPJ+YVSKuIw+AMgpkhr6a9euXbt2rcfjcbvd8fHxbDjVYUEohVElgjL62JAcIHpCiql2u72vMkl2u116oFKp4uLiZGsXAIQg3LIP2JAcIKpCiqlf+tKXWlpa+j8nPz//97//vRxNAoBQhVv2ARuSA0RVSDH1/vvvdzi6futqa2vffffdefPmzZgxIy4u7uLFi4cOHUpOTr799tuj2U4AkBdqPgDIL6SYunbtWunBpUuXXnzxxf/3//7f0qVL/c8+9thjjz32WH19fVQaCAB921CU9WZFo9nh8ecoJev7mxztPt/tP4LJVAAZhbc+dd++fdnZ2YEBlYgSExPvvvvut956S9aGRehgt1g3BGAodBVwELpqPhAJj/ZbwAEFHwCiKry8X7PZ3GuyEufcbB4Wa8aR9wujSlcBh64UJeJc/MOhusVT0/qaTw0o+NB15IVDdYv6Ph8AwhJePzU3N/fTTz8NrJ5PRBaLZdu2bbm5ubI2DAAG5s858pMKOAx0/pUNyfs/HwDCEl4/taSkZPv27U899VRBQcE111yj1+ubm5vLysq8Xu9zzz0XpSYCAACMCOHFVJVK9dxzz73yyivvvvtuZWUlEcXHx8+bN+/++++fOnVqdFoIAH1CjhLAsML6KuYwILfb7fF4hlUdpeLi4vLy8li3AmBIvXCo9pelp6UpVcaEH6ya9vXr+/uC+8Kh2l/uqfX/4v9w9TUP9Xs+AIQuvPnUQFqtNiEhYfgEVIBRqNccpX42mcGmNABRFd7Yr8/nc7vdvT4lCIJOp5OjSYOCGvowqvSVozRQHSVsSgMQFeHF1EOHDj399NO9PjVMahMilAIAQKyEF1Pz8vIeffTRwCOtra3vvfeeXq/fsGGDrA0DgIGFm3OEHCWAqAovpmZnZ2dnZwcdfPDBBx9++GGLxSJfqwAgJPlZhv+5p/DxrRUmu4cT16lV31s5rf86Sv9zT+Ej//yko9NDRIk69Qv3FaKOEoBcIs9R8tPr9evXr9+6devgLwUA4Vqam/5kyVS9VmBEbq/v2dLaw2da+zlfFH2MdSUoCYyJ2MMcQD4yxFQi0mg07e3tslwKAMJSVW/57b6zTrdP+tFkdz3+SkVfqbxV9ZZvvXrc6vBKRZSsTk8/JwNAuMIb++3V+fPnt23bNmXKlMFfavC2bNnif7xp06YYtgRgaISV+htunjAAhCW8mHr48OFf/epXgUc6Ozs7Ozv1ev2zzz4ra8MihDgKAACxEl5MzcjIWLZsWeARvV4/fvz45cuXGwxIcwCIgbDKE24oynrtWIPN6fEfQd4vgIzCi6kTJ0689957MzIygo53dHS0tLSMGTNGvoYBQEikLVF/WXqauqop9beFqsnuZIzzrs1WKUGLvF8AOYWXo/TBBx98//vf73n8jTfe+M///E+ZmgQAYQi9PKGUoNTR6WXUlferYpSklyGpAgAk8uT9er1eQZDnUgAQltC3UA04s2vzVKvLi81TAWQU6ldUu93OOXc6naIo2my2wKfMZvPRo0d7DgjHBOr9AgBArIQaU2+//Xa73S49XrVqVdCzgiB8+ctflrNdkUIohdEm9BwlFCYEiLZQY+qDDz7odrvPnDnzySef3Hnnnf7jjLH4+Phrr7120qRJ0WkhAPQn9BylrjMDNk99rO9sJgCIQBj9VCKqra2dPn16YEwFgNjqNUdp8dS0nmUcAjZP7TrywqG6Rb2dCQCRCS+xKC8vDwEVYFgJP0epK0GJGOvrTACITEj91I6ODlEUDQaDx+NxOBy9X0itTkhIkLVtAAAAI0lIMfUrX/lKW1vbe++9V1ZWNsz3JAcYbULPPEKOEkC0hRRTH3zwQafTSUTTp0//1re+1es56enpcrYrUlhLA6ONtCXqo/+osDjdRJSs77M0EjZPBYi2kGKqf/HMhAkTJkyYEM32DBZCKYxCougjgUvlBhkT+tkS9crmqUTYPBVAdih+BDCydW+J2lUW39rp7mtLVGyeChBtYeQoDXAh5CgBxELoW6Ji81SAaAs1R6mlpaX/c5CjBAAAo1yoOUp9LaHxGyY5SgCjDfJ+AYYP5q9SpgDFxcWbN2+WHiNZCUaPw2daH99aYbJ7OHGdWvWf62bcu7D3WqGHz7QG5v3+6ctzF0/Ft2EA2USydWJLS8v+/fvr6uo8Hs+YMWMWLFgwb9482VsWGYRSGIWW5qY/WTL1md2nnW6f2+t7trR2UlrC0txegiXyfgGiKux+6o4dO37zm9+43W69Xq/X6y0WC+d8wYIFP/3pT+Pj46PUyhAVFxeXl5fHtg0AQ6+q3rLpxY8D849S4rV//9r8oOSjEE8DgIiFt5amtrb2V7/6VVFR0d///ve9e/e+8847e/bsefzxx48dO/b8889HqYkA0L8QS/6GXhkYACITXkwtKytLS0t75plnJk+eLB2Ji4u788477733Xn8BIwAAgNEpvJjq8XgmT56s0WiCjk+bNs3j8cjXKgAIw4aiLGO8NvBIrwm9IZ4GABELL6YWFhZ+9tlnHR0dQcePHTs2d+7ccN/bYrGcPXv2/PnzYcVju91uNptdLlevzx7sFm5jAEYuqZBvsl7LiThRUh8lf/OzDN9eMU2nUUk/GuI0qPcLIK/w8n7nz59fUlLyxBNPbNq06ZprrtHr9c3NzTt27Dhy5Mgvf/lLt7trqkaj0TDG+r9UWVlZTU2NIAiiKOp0upKSkuzs7AEbYLfbX331VZfLtWjRooKCgp4nIO8XRqdQSv6Wnb702/2nXR4vcdJp1N+7eToW0gDIK7y83/379/e111ug3/3ud3PmzOnnhOrq6sOHDy9cuLCgoMDlcu3Zs6e1tXXjxo0DVjfcvXu3xWIxmUy9xlTk/cLoFEpCL5J+AYZAeP3UqVOnPvDAAwOeNm7cuP5PqKyszMjIKCoqIqK4uLhly5Zt3bq1pqZmwYIF/byqtra2qamppKRk586dYTUbQNlCKeSLYr8AQyC8mJqTk5OTkzPItzSbzTabbebMmf4jRqPRaDQ2NDT0E1M7OzsPHz68aNGimK+CBQAA6FUM9nqzWCxEZDRe9e3YYDCYzf3tOSUt45kxY0Z0GwcwAoWS0IukX4AhEHZtwpaWltdee+306dMmkylwLnbatGlPPfVUKFeQUpm02qt+vbVarT/Fqae6uroLFy7ceeed4bYWYDSQ8n4f/UeFxekmouTe8n6lcwKL/SLpF0B24cXU1tbWBx54wGKxzJgxIzMzM/CpjIyMsC4Vem6Uy+UqKyubN2+ewTDw739xcXHQEWQtwWgQSt4viv0CRFt4MXXfvn2dnZ0vvfTSpEm973oRCp1OR0RBC0xdLpder+/1/H//+99ENHbs2KamJuoeOu7o6GhqakpPTw8qQIEICqNQVb3lW68etzq61nlbO92Pv1LRM+/3W68etzq8xBgRWZ2enucAwCCFF1PNZnNeXt5gAioRpaSkEJHJZAo8aDKZpOM92e32zs7Ot99+O/DgiRMnTpw48cUvfhH7tgIg7xdgmAgvps6ZM2f37t0ej6dnecLQJSUlpaSk1NXVzZ8/XyoN0dLS0tHRMWvWLP85brebMSa9S3Fx8XXXXed/ymQy7d69u7CwcMaMGUlJSRE3AwAAQF7h5f0uWrRowYIFP//5z1taWgbzrnPnzjWZTGVlZVar9dKlS/v374+LiwtcXfPyyy/v2LFDehwfH28IkJiYSER6vd5gMAhCDPKWAYYb5P0CDBPh9VMZY3feeed3vvOdDRs2xMfHB/ZWZ86c+ctf/jLE6+Tl5dlstqNHj548eZKIDAbDmjVrpHlWAAiXlNP7+NYKk91NfRTyDeUcABik8GoTXrhw4Wtf+5pKpVqwYIHRaAws6puVlRXuWhePx2MymdRqdWpqalgv7EtxcfHmzZulxyj8C6PNG580/PXw5ycaLf+9sWB9Qe8d0H990vDtVytnZxkeKp6yHp1UALmFnferVqv/8Y9/pKWlDf69NRpNuCtwBoRQCqNT2elLP9t1qt3mZoyefudUWqJuaW5w+l7Z6Us/33WKMVZz0fr0jlNpSb2cAwCDEcn+qbIEVACQi7ROpt3WldZrsrsef6WiutEc7jkAMEjhxdS5c+eeP3/e4XBEqTUAEIG+1smEew4ADFJ4MbWoqGj16tU/+MEPampq7Ha7O0BY+4oDAAAoT3jzqQcPHty2bRsRPfzww0FP5efn//73v5etXQAQsg1FWW9WNJodV7qhva6lGfAcABgk2fZPHTt2rBztGayDBw9KD5CsBKNHwDoZDyeuU6u+t3IaaugDDL0Y7J8aVQilMDotzU1/smTqM7tPO90+t9f3bGntpLSEoLRe1NAHiDbZ6hC1t7fLdSkACFdVveW3+8463T7px55pvVfV0GdMqqGPvF8AeQ02ptrt9rfffvvrX//6j370I1kaBAARGDCtF3m/AEMg7D3JJZzzioqKnTt3vvfeey6Xa8yYMdgwHAAARrmwY2pzc/OuXbt2797d3NxMRLm5uU888cS1114bWKcQAIbYgGm9yPsFGAKhjv26XK7S0tInnnjizjvvfOmll7Kzs3/84x8XFBRMmTKloKAAARUgtqS03pSErp1nepbIH/AEABi8kPqpH3/88Y9//GO73T558uSHH374pptuknYCf/fdd6PcvLBhLQ2MWktz03+0ZsaTr1bNzjI8dP3kxVODa/km6TRLc9O2V168rTDrweIpCKgAsgsppl6+fNlut8+cOfMb3/hGfn5+tNs0GAilMGpJZfQZo5qLlp5l9MtOX/rW/x5vt7kZY+/Vtt0xb2IMmwqgVCGN/c6bN+/2229vaGh49NFH77777hdffLGpqSnaLQOA0PVfIh8F9AGGRkgxddy4cd/61rfeeuutp59+Oisra8uWLXfdddc3vvGNhoaGaLcPAELR/1IZLKQBGBph5P1qNJobb7zxxhtvbGlpkVJ/L1682NLS4nK5VqxYsXjxYo1GE72GAgAADHOR1HwYM2bMV77yla1btz7//PM33njjkSNHnnrqqe985zuyNw4AQrShKMsYrw08ErhUpv9nAUAujPPBFv10OBz79+///PPPH3/8cVnaFLHi4uLNmzdLj5GsBKPN4TOtj/6jwuJ0E1GyXv3HL80NTP09fKY1sID+n748t2diMAAMUoR1lALFx8evW7du8NeRBUIpjFqi6COBS0vFGROCSuSjgD7AEJCthj4AxFB3iXyP9KO1090z7xcF9AGiDTEVQAmQ9wswHCCmAgAAyAMxFUAJkPcLMBzIkPc7fBQXF99///3+Hzdt2hTDxgAMscNnWh/fWmGyu6mrRH5RUN5vP88CgCyUFlPLy8tj3QqAmHnjk4a/Hv78RKPlvzcWrC8I7ob+65OGvx0+d6LR8t93F65HJxUgCmRYSwMAw4FUQ7/d5maMeq2h//OuZ9nTO06lJV31LADIAvOpAEqAGvoAwwFiKoASYC0NwHCAmAoAACAPxFQAJcBaGoDhQGkx9WC3WDcEYEjlZxn+557ClISuwGmI07xwX+GsLEMozwKAXJQWU5d3i3VDAIba0tz0v98/f/21WZzTF6aNSdJrgp59as0MzvmszOTNt8zC4lSAaFBaTAUYzUx2V/nZFsZoe9XFL//t34fPtPqfktbSMMZqLlqf3nEq8CkAkAtiKoBC9LNgBmtpAIYGYiqAQvSzYAZraQCGBmIqAACAPJQWU5H3C6NWPwtmsJYGYGighj6AcvSz+Qz2pQEYAkrrpwKMZktz03+0ZgbnNCvTsHn9VQtmknSapblpnPNbCzJfeWAhAipANCCmAiiHtDUNY1Rz0fL0O1cWzJSdvrTppY/fqWpijL1X22bq9MS2nQBKhZgKoBB9LZjBQhqAIYP9UwEUoq8FMz5OvR7PzzIObQMBlE9pMdWf8YvyhAAAMMSUFlMRSmHU2lCU9WZFo9lxpUsqLZgRiXo9Hos2Aigc5lMBFKKvzWewKQ3AkEFMBVCOvtbSSJvSzMpM5pxjUxqA6EFMBVCOftbS/HzXqZqLVsYYNqUBiB7EVACFwFoagJiLZY6SxWJpbW1Vq9WZmZkajab/k+12e3t7u9vtTk5OTk9PZ4wNTSMBRgqspQGIuZjF1LKyspqaGkEQRFHU6XQlJSXZ2dm9nmmz2d59992Wlhb/kdTU1GXLlo0dO7bnyVhLAwAAsRKbmFpdXV1TU7Nw4cKCggKXy7Vnz57S0tKNGzcmJCT0PNnpdGo0mpKSEqk729jYePDgwV27dt17771arTboZIRSGLWwlgYg5mIzn1pZWZmRkVFUVCQIQlxc3LJlyzweT01NTa8np6WlrV+/Pi8vLyEhQavVTp48ef78+U6ns6GhYYibDTCcYS0NQMzFIKaazWabzZaTk+M/YjQajUZjXzGy59RpYmIiEYmiGLU2AoxI0lqaWZkGzqnnWhrO+azMZKylAYieGMRUi8VCREbjVfkRBoPBbA41EfHMmTOCIIwfP17+xgGMZNJampqLFsao51oaxljNRSvW0gBETwxiqtvtJqKgqVCtVisdH9CZM2fOnDlTWFjY6+QrwKiFtTQAMRezvF/OeQSvamxsPHDgQE5Ozvz583s9obi4OOhIeXl5BG8EMOJgLQ1AzMUgpup0OiJyuVyBB10ul16v7/+FTU1Nu3fvHj9+/E033dTX+lREUAAAiJUYjP2mpKQQkclkCjxoMpmk431pbm7euXNnRkbGqlWrVCpVdJsIMAJtKMoyxl81pSKtmenr+NC2DmBUiEFMTUpKSklJqaur8w//trS0dHR0BNZ8cLvdHo/H/+OlS5d27NiRnp6+atUqtVpp+9MByCJgzQzjRFq16nsrp/nX0iTFdZUqS9SpsZYGIEpisz517ty5JpOprKzMarVeunRp//79cXFxM2fO9J/w8ssv79ixQ3pstVp37NjBOZ8yZcqZM2dOdWtra4tJ4wGGraW56U+WTNVrBUbk9vqeLa2VUnxF0ccYJ86Jc4ExMZJkBgAYWGz6fHl5eTab7ejRoydPniQig8GwZs0aaZ61J6vVKqUEv//++4HHFy1alJaWNgStBRgpquotv9131un2ST9KKb4/WjvjZztPWR1eYoyIrE7P469U/P1r85GjBCA7Fln+rSw8Ho/JZFKr1ampqbJcsLi4ePPmzdJjFCmEUegn75zc8v65oIOzMg01Fy1BBx9YOvmptTMJAGQVy7lJjUaTkZEh7zURSgEAIFawfyqAcvSa4vvQ9ZOR9wswNBBTAZSj13L56wuyUEMfYGggpgIoilRGn3OalWnwl9FP0mmW5qZxzm8tyHzlgYWooQ8QJVjrCaAoUhl9xqjmouXpd06lJepE0fet/z3ebnMzxt6rbbtj3sRYtxFAsWKZ9yu74uJi1CaE0ayq3rLpxY8Dq/sm67XEuLXzSgWVlHgtFtIARInS+qkHDx6UHiABGEahnmX0LU53UGlsFNAHiB6lxVSEUgAAiBXkKAEoR8+1NMl6TZJeE3gEC2kAogcxFUA5pLU0yXotJ+JESXr1H79U9MJ9RSigDzA0lDb2CzDKiaKPBC7NoTImiDyggD4RCugDRBXyfgGUA3m/ALGltH4q8n5hNEPeL0BsKS2mIpQCAECsIEcJQDmQ9wsQW4ipAMqBvF+A2FLa2C/AKIe8X4AYQt4vgHIg7xcgtpTWT0XeL4xmyPsFiC2lxVSEUgAAiBXkKAEoB/J+AWILMRVAOaS835QELRHjRFq16gerpiPvF2DIIKYCKMrS3PQnS6bqtQIjcnt9z5bWVjeYuvJ+OUfeL0BUIaYCKEpVveW3+8463T7pR5Pd/cs9p60OLzFGjFmdnsdfqahuNMe2kQBKhZgKoChBqb+cgpfLSXm/Q90sgNFBaXm/W7Zs8T/etGlTDFsCAACjjdJiKuIojHIbirLerGg0O7q6qgIjTowC+qrI+wWIHoz9AihKUMnfRJ36q4sm6TQq6VlDnAZ5vwDRg5gKoDT+kr+MSOTivyoaXB4vca5Tq7538/TFU9Nj3UAAxUJMBVCUqnrLt149bnVIBX6Z3SV2dHqJGDHm8vqeLT2NpF+A6EFMBVCUwLxfTsFrUZH0CxBVSstRQg19AACIFaXFVIRSGOW68349nDhjxDkF7kuDpF+AqMLYL4Ci5GcZvn59DpO2JeckSKlK3R5bPgVJvwDRg5gKoChV9ZY/l3/OReknxjlJlX6l/144VIccJYDoQUwFUJTecpSYVOyXGEOOEkBUIaYCAADIQ2k5Ssj7hVEOOUoAMaS0mIpQCqOclKP0y9LTJHblKHEi/zpV5CgBRBXGfgEUBTlKADGEmAqgKMhRAoghxFQApWIDnwIAskJMBVCUDUVZxngtI5EY75pMDZCg1SBHCSB6EFMBFCU/y/DtFdOICcSJc8au5CcR5yQIXOxRWB8A5KK0vF+spQE412rnnIgYJ2kX1a55VUbU4fS+XXExP8sY4yYCKJTSYipCKQARMRI5ExjnRIyIYWoVYGhg7BdAafKzDMQE4owxFjTOm6jDfCpAFCGmAihNdaOFc5IWpgbNp14/LQ01HwCiR2ljvwBA0thv9zfmwPnUTENcLJsFoHSIqQBKI439CsREzhlRwHwqy89KjmXLAJRuJMVUi8XS2tqqVqszMzM1Gk2v5yDvF8A/9isN/ErxlHNiTKxutK4vjHHzABRsxMTUsrKympoaQRBEUdTpdCUlJdnZ2T1PQygFoICx38CBX1RWAoi2kRFTq6ura2pqFi5cWFBQ4HK59uzZU1paunHjxoSEhFg3DWDYuXrsN3AhDcZ+AaJrZOT9VlZWZmRkFBUVCYIQFxe3bNkyj8dTU1MT63YBDEddY79cDEr6JRKrG60xbBiA4o2AmGo2m202W05Ojv+I0Wg0Go0NDQ2xaxTAMMek3imjrl3eGHEi1mpzxbhdAIo2AmKqxWIhIqPxqmpqBoPBbMY2kAC92FCUpWYC7yr4cGWjN6n8LwBEzwiYT3W73USk1WoDD2q1Wul4kOLi4qAj5eXl0WsbwDCUn2VgAnFfL3m/aYm6GDcOQNFGQEyVcB7SZhqIoABEFKdVeZ0+kQfl/QooTAgQVSNg7Fen0xGRy3XVPJDL5dLr9TFqEcBw9+D1k8WuwoTdA7/E7pqbhcKEAFE1AmJqSkoKEZlMpsCDJpNJOg4APT2+hI2aqAAAF4NJREFUPHfjwom8O0eJc74mf+x/ffHaWLcLQOFGQExNSkpKSUmpq6vzD/+2tLR0dHT0WvMBACS/uG3Of9yUm56gS0/Uff+m6b+/b16sWwSgfCzEecrYqq2t3bdv38yZMwsLCzs7Ow8ePOh0Ojdu3CgNC/sVFxdjPhUAAGJlZOQo5eXl2Wy2o0ePnjx5kogMBsOaNWuCAioAAEBsjYyYSkSFhYWzZ882mUxqtTo1NbWv01BDHwAAYmXExFQi0mg0GRkZ/Z+DUAoAALEyAnKUAAAARgTEVAAAAHkgpgIAAMgDMRUAAEAeSoupB7tFfIWeVfihH7hdocO9Ch3uVehwr0I3BPdqJOX9hgJ5vwAAECtK66cq1ZYtW2LdhJEEtyt0uFehw70K3ai9V8qMqf2P/Q5mZHgwV45Vqwb51qPtdqFVQ/O+aJVcz/YPrQqdLFdWZkwFAAAYeoipAAAA8hgZ+9KECPlvAAAQbf1sgKaomAoAABBDGPsFAACQB2IqAACAPBBTAQAA5KG0OkpARD6fr7m52eFwxMXFjR07VqPR9H9yU1OT0+lMSUlJS0vreYLT6WxqauKcZ2RkJCYm9jzh0qVLHR0d8fHx48ePZ4z1PMFqtYqimJycLAi9fIezWCytra1qtTozMzOoqaIotra2dnR06HS6tLS0uLi4AT55+IbbvXI4HG63Oz4+XqvV9ny2n3vlJ4qi1WolIqPR2M9niUBY94oG+rAxv1fhfpywDPG9GvDDDuZe+Xy+lpYWm80WHx+fnp7e6xXADzFVac6ePVtWVuZ0OqUfBUF44IEHVCpVrye3tLTs3r3bbrerVCqfzzd58uQVK1YEnvzpp5+WlZWJosgY45zPnz9/7ty5/medTufu3bubm5ull6ekpKxevTo5OVl6tqmp6eOPP25pafF4PER0zz33GAyGoAaUlZXV1NQIgiCKok6nKykpyc7Olp765JNPKisrXS6X/4Pk5+cvWrSo1z+vkRk+96qzs/PgwYMtLS0Oh4OIli1bNmPGjKAG9HOvAh05cqSyslKj0TzwwAODuDfBwrpX/X9YGgb3KqyPE66hvFf9f9jB36vz58+XlZXZbDb/CYsXL77mmmsGfZMUCzFVUc6fP793795JkyYtWLAgOTnZ4XBcuHChryDk9Xp3796t0WikaFdbW7t///4jR44sXrxYOqGlpeXQoUM5OTk33HCDSqX68MMPP/7447S0tJycHOmEQ4cOtba2rl27duLEia2trTt37iwtLb3jjjukZ+12OxHNnDnTarWeO3euZwOqq6tramoWLlxYUFDgcrn27NlTWlq6cePGhIQEIjKbzTNnzpw6darRaOzs7Dxy5EhVVZVOpwv6g6KMe+X1eh0OR05OjkajqaqqCvde+V2+fLm6utpgMEh/Q+US1r0a8MPG/F6F+3GG873q/8MO8l45nc69e/cmJCTcddddqampNptt7969hw4dGjt2bEpKiiy3S3kwn6ocnPPy8vK0tLSVK1empaVpNBqDwZCfn9/riCsRnTlzxm63L1myROo+5uXl5ebmnjhxQupWEtHx48cFQVi+fLlWq1WpVEuWLElMTKysrJSelSJlfn7+xIkTiSg9PX3+/PktLS0NDQ3SCbm5uevXr1+8ePGYMWN6bUBlZWVGRkZRUZEgCHFxccuWLfN4PDU1NdKzy5cvv+6668aMGaPRaJKTk2+88ca4uLizZ88q8l4lJSXdcccdX/jCFyZPnhzBvZKIonjw4ME5c+akpqbKcZO6hHuvBvywsb1X4X6c4XyvBvywg7xX0iDTtddeK/2LSkxMnD9/Pue8qalp8PdKqRBTlaOpqamjo2P27NnSGNGA5zc0NKjV6gkTJviPTJo0SZoy9J8wfvx4nU4n/cgYy87Obm5ulgKJ9Gs/adIk/8ul787+Pwf9M5vNNpvN/3WbiIxGo9Fo9L886Ku9IAjx8fE+ny+Uiw9IYfdKcvToUZ/PN3/+/FCuGboI7hX1+2Fje6/C/ThhGeJ7FeI/jL4M+PL4+Hgi8nq9/hOkx9HIbFAMjP0qx+XLl4lIr9e/8847jY2NjLHMzMzFixf3mk1DRGazOSkpKfAbtJTVYrFYiMjj8TgcjqCvt0ajkXNutVrT0tLMZjNdnQgTHx+v0Wik4wOS3iUoj8ZgMDQ3N/d6vslkamtrmz17digXH5Dy7lVra2tFRcW6devkmhT0i+BeUd8fNub3KtyPE5Yhvlfh/hIFGfDlaWlpkydPrqioMBqN6enpZrP5o48+ysjICPwSAEHQT1UOKSfi4MGDWq32pptuWrJkSVtb29tvv93R0dHr+W63OyiFT/pRSgtyu93+I2GdIB0fUFgv93q9+/bti4uLk2syVWH3Shr1nT59emZmZigXDEsE96qf1sb8XoX7ccKisHtFRCtWrBg/fvzOnTtfeumlt99+W6/Xr1mzRpZxcqXCrVEOaawpNTX15ptvnjJlyuzZs1euXOlyuaqrq8O6Qj/5FIEn9Hoa5zysXI9QxsdEUSwtLTWZTDfddJM0GDV4CrtXFRUVDodj0aJFoV8t3PcN/V5F8GGH8l4N/n/6Ad93yO5V4JFBtrlXoiju3LmzsbFxyZIlt9xyy7Jly2w22/bt2/3Z+NATYqpy6PV66p6PkYwbNy4uLk4aj+r1fH+6v0T6VZEmb6T/G/TLI/0ovZF0QtAV3G63f+6nf31dX7q4nyiKe/fura+vX7lypYydMCXdK4fDcfTo0WnTprW3tzc1NUkraKVEEpPJFMr1+xfuver/w8b831W4HycsMblXA/4S9WXAl58+fbqxsXHZsmVz5szJysqaMWPGqlWr2traek0hBgnmU5VDys0Lmk5TqVSiKPZ6fkpKyunTp71er1rd9c9A+hMsZcmr1eqkpKSgP8omk0kQBGnxnHSayWTyL1Ho6Ojwer0hJtn7Xx50/cCXi6K4b9++zz///Oabb+51LWbElHSvnE6nKIqVlZWB6aBE9NZbb02ePHnlypWhvEU/IrhX1PeHjfm/q3A/Tlhida+CTpDrXrW1tRHRuHHj/M9KyczScegV+qnKkZmZqVarA9PcLRaLzWbz50f4fD6Xy+X/9c7OzhZF8fPPP/efX1dXp9Vq/b9C2dnZly5dstls0o9er/fChQsTJkyQ/mRMnDiRMVZXV+d/ubTQJcTgl5SUlJKSUldX5x96amlp6ejo8L+cc75///5z586tWLEi8Iu/LJR0r4xG4z1XmzBhglqtvueee66//voIbk6QcO/VgB82tv+uBvw4gzHgxTnnLpfLn74+yHs14Ift34Avlzqs7e3t/pfYbDaPxxNiP3h0Uj399NOxbgPIQ6VScc5PnDghCEJiYqLJZDp48KDL5Vq+fLmU+37ixInt27dnZmZKX3KNRmNdXd25c+fS0tJUKlV1dfXJkyfnzZuXlZUlXdBoNJ48ebK5uTkjI8PtdpeXl7e2ti5fvjwpKYmINBqN3W4/depUXFxcQkJCQ0PD+++/n5mZWVRUJL3c5XLV1ta2trZevHjRbDbHx8ebzeaOjg7/t2CdTnfy5EmHw5Gammo2mw8cOMAYu+GGG6S+4OHDhz/99NPs7OykpKTWbu3t7enp6cq7V0R0+vTpy5cvX7p0qbm5WafTOZ3O1tbW1NRUKR+kn3vFGNNf7dy5cx0dHYsXL5al3l6492rADxvDexXKx4nqvWpra/vnP/9JRNK/nEHeqwE/7CDvVXx8/MmTJy9evGgwGNRqdWtr66FDh+x2+6JFiwIrPUEg7J+qKJzzDz/8sLq6Wuo0JCQkLFu2zP+t8/jx4++///66dev86yytVmtpaWlLSwsRCYIwe/bsxYsXB6Y/1NfXHzhwQCrKo9Vqi4uLp02b5n/W6/UeOnSotrZW+nHChAkrVqzwf4dtb29/9dVXg1qYmpp61113+X+sqKg4evSotOjNYDCsWLHCXyDizTff7LkkQMaSe8PqXhHRli1bgubViGjTpk3+c/q5V0HefffdhoYGGWsThnuvBvywsb1X/X+cQer/4q2tra+99trcuXMXLFgQ4oft/14N+GEHea8aGhrKy8v9C5kSEhIWLVqUl5c3uJukZIipCuRyucxms1qtTk1NDSVb0mw2u1wug8HQ65COKIrt7e2c89TU1F7XPjocDqn8t/+7c1g8Ho/JZJJaG8HLBwn3KnTh3qv+P2zM71W4HycsQ3yvBvkPY8CX22w2u90eHx+fmJgo+71SGMRUAAAAeSBHCQAAQB6IqQAAAPJATAUAAJAHYioAAIA8EFMBAADkgZgKAAAgD9T7BQiPKIr19fVBB9Vqtb+m0qi1f//++vr6r371q0HHz58/f+HCBafTaTQap02bZjAY/E9JN9NgMATt4mm1Wk0mU2ZmZv+loDwez3PPPXfLLbfMmDFDvs8BEDnEVIDwmM3m++67L+jgmDFj3njjjZi0Z5iwWCy//vWvv/3tbwce/Pe///3888+fO3fOf0QQhAULFjzyyCNTpkyh7pt59913P/bYY4Ev3L59+x//+MeXXnpJOq0vGo1Gp9P95je/+eMf/4haBDAcIKYCRGLFihW33367/8egjZ1HoW3btiUkJNxwww3+IwcOHPjJT36Sk5PzzDPPFBYWxsfHt7W1ffzxx9u2bTt06FD/wTJ099577x133HH48OHi4mJZLggwGIipAJFIS0ubNWtW0EGLxSKKYkpKyuXLl2tra8eOHZubm0tEnPPa2trm5ubk5ORZs2YFjWd6vd7q6mqHw3HNNdekpaW1tbXpdLrExEQistvtTqczcMuUzs5Om82Wnp4e2C2zWq2nTp3yeDxTp04dP368/7jD4bDb7enp6Q6Ho7q6WqfTTZ8+vee+7s3NzXV1dYIgTJo0SXp5a2urTqcLqpNnsVh8Pl+v5es8Hs8777xz6623SpXZpZN/8YtfTJw48Q9/+IO/Nv2YMWPWrFlz8803X7hwIZSbLHG73RaLJeigWq2WNmMYM2ZMYWHhm2++iZgKwwFiKoBsNm/ebLFYFi5c+PL/b+9eQ5r8/gCAH6dTmzZlOpW01Iq8hFmZ2MUwEUvNS1OSLpBoVFBZGiXmNK8hlpC3vMyW4iWJSrwyM1EsyULLKUIW1S7YhZrLR5k2te3/4vB/eH6b9jOZ9nvx/bzaznkuZ775es75Ps+3ulqtVgcGBiYlJYlEorS0NFwbbmZmxtLSMj09fcuWLfgUqVSakJDw6dMnY2Pj2dnZ2NjYiooKPz+/+Ph4hBCfz29vb29paSFv0dzcXFhYKBAIcNBVq9V8Pv/evXu4tuvc3FxQUNDly5dxXZH6+vqysrKMjIycnJzp6WmVSmVhYZGTk+Pk5ISvNjExkZ2d3dPTQ6PR9PX1Z2dnIyIi4uLisrKyxsbGqquryfvOzs4eP37cy8srJSVF+4f39/cTBLFr1y6y5fHjx9PT0zExMdrFXgwMDP5okvry5cukpCSNxo0bN1ZUVODPu3fvLioqGh8f19iUBWDlQUwFYCnGx8fJWiIIIWtra1z9SiQS6enp8fn8devWTUxMEAQRHx9vYWGBtwZlMllmZubVq1fr6uqYTKZKpeJyuUqlksfjubi4fPjwgcvlksUyF6Ompqaqqurs2bMcDodOp3d1dWVlZVlZWcXExJDHlJeX5+TkuLm5iUSiK1eu5OXllZSU4C4ulzsyMpKYmOjn52doaCgWi3H6FYfDSU5OHhoaImN/d3c3QRChoaHzDmNgYMDAwIBaL2VoaAgh5OnpuZhfQRAEtYYo+meh7J07d1L/q+jr68vIyNi6dSvZsnnzZrVaLRQK9+3bt5jbAbB8IKYCsBRtbW1tbW3k16SkpMDAQITQ3NxcamoqrnrGZrOrq6vlcnlxcfGaNWsQQpaWlikpKeHh4U+ePImIiOjr6xOLxcnJyThtdcOGDefOndOeky1kZmamtrY2ICDgyJEjuMXPz+/169f19fXR0dHk4vCZM2fc3d3x9TkcDo/HUyqVRkZGQqFQKBSePn364MGD+Mj169fjGaS3t7elpWVjYyMZU5uamuzt7fF1tEmlUhaLRV3TlsvlGqvHYrEYF9xGCJmamnp5eZFdAoFAIBAs9DPpdDqZKiwSiXJzcz09Pc+fP08egNerJRLJv//JAFhmEFMBWIqAgIDIyEjyq7W1Nf7AZrPJMqIIIaFQyGQyBwcHBwcHyUYGg4FTYd+8eYP+OZkjy2ouxrt37xQKhb6+PjUg4d3HHz9+kBufbm5uZC8O7d+/f7ezs8ND8vX11b6yvr5+SEhIbW3txYsXmUzm6OjowMBAbGzsQiMhCEJj85VGo+ECoqTu7u47d+7gz46OjtSYun///sOHD1MPbmtre/TokcZd5HJ5QkKCjY1NZmYmteQZXiHQ3nMFYOVBTAVgKczNzeetzKz9nKVSqdQID3Z2dji/RqFQaJxiZGSkvQG5kMnJSYSQUCikrkIjhJycnH79+kV+pV4QhyLci+9OTYCiCgkJqaqqamtri4yMbGpqMjQ0DAgIWGgkRkZGExMT1BY2mz07OyuXy8nQHhUVFRUVhRCKjo6mDg8hxGKxnJ2dqS39/f0at1AqlYmJiXNzczdu3NBIs1IqlXgMCw0PgBUDMRWAZcRkMk1MTMj5mXYvQkgul1taWuKWnz9/Tk9PkwfgtCPqKTiOUk8/evToQtucv4dj+bdv3+zt7bV72Wz2nj17mpqaOByOQCDw9fXFt5uXhYXF+/fvqS3bt2/v6Oh4/vx5cHDwEsamQa1WZ2ZmikSi27dvW1lZafTiGepfqdMOgAZ4NyEAy2jHjh1jY2OvXr2atxevyvb09JAtT58+pR5gZWWlUCjGxsbIFuoEbtOmTUwms729Xa1WL2FsHh4eCCHqrrAGDocjkUgKCgrGx8fDwsJ+cylXV1eCIGQyGdni7+/PYrHu3r1LHfySlZSUPHv2LD09nZoGRcLhXPvRJgBWHsRUAJZRWFiYnZ1denq6QCD4+vWrTCYbHBy8desWTot1d3d3cXHh8Xjd3d3j4+O9vb2lpaXUTB8vLy8ajZabmyuRSD5+/Hjz5k2xWEz20un0U6dODQ4OpqamjoyMEAQhFotbW1vz8vIWMzYnJycfH5+6urrKysrR0VGZTPbixYvm5mbyAA8PDzs7u4aGBkdHR+qmrDa8DTw8PEy2GBsbp6WlTU5Onjx58v79+2/fvh0dHR0eHq6pqfny5Qt+1GeROjs76+rqgoKCWCzWyP9R/w5DQ0NMJpN8QAiAvwjWfgFYRgwGo6CgIDc3Nzs7G88m9fT0nJ2dw8PD8efr169zudzk5GSEkLGxcUJCQn5+Pnn62rVrL1y4UFRUhOeyPj4+x44d4/F45AGHDh0yMDAoLy/v6urCLUwmk8PhLHJ4KSkp+fn5lZWVfD4fIUSn00+cOEH26unphYaGFhcX/36Sisfp7u7e0dFBfZpl27ZtZWVlpaWlJSUl5Aaqqampv78/3lhdJJwt3NLSQn2ihnw+VaVSdXZ2BgYGUrOWAPhb9Ja2agQA+CMEQYyOjhoaGtrY2GikyCKEJBLJ1NSUg4PDqlWrgoODyXc+YFNTU1Kp1Nzc3MbGZt6Lq1QqiUSiUChYLJa1tfWfRheFQiGRSOh0uq2trUb6T2FhYWNjY0NDA37FxG/09PRcu3btwYMH2klPU1NTnz59UiqVZmZmtra25LuWdKK3t5fL5dbW1lJfIAXA3wLzVABWgpmZGbUei4Z5s4RIDAZDIy1WA41Gc3R0XPLYTExMXF1dtdtlMllra+uBAwf+NaAihLy9vV1dXaurq+Pi4jS6GAzGvDnSOlFRUREeHg4BFfxHQEwFAGj6/PlzWlqaVCo1MDDQrt22EPxGw+Ucl6a5ublLly45ODis5E0B+A1Y+wXgv+Xhw4f29vaLfKvfMiEIorm5efXq1Xv37oVnVABYPIipAAAAgG7AszQAAACAbkBMBQAAAHQDYioAAACgGxBTAQAAAN2AmAoAAADoBsRUAAAAQDcgpgIAAAC6ATEVAAAA0A2IqQAAAIBuQEwFAAAAdON/RYdS3QlWFf8AAAAASUVORK5CYII=",
      "text/plain": [
       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7eb975186880>"
      ]
     },
     "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": "c6724b89e94a467489fe27c4b37f6253",
       "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 0x7eb975186880>"
      ]
     },
     "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())))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0ad9691",
   "metadata": {},
   "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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      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 5kB\n",
       "Dimensions:  (dim_0: 300)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 2kB 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 2kB 0.9972 1.063 1.065 ... -0.04871 0.07343 0.01665\n",
       "Attributes:\n",
       "    tuid:                             20241106-153020-188-98e13e\n",
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       "    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-5bb7dffa-1640-4f7e-8932-0f4099fd09a6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5bb7dffa-1640-4f7e-8932-0f4099fd09a6' 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-14331f5f-d6d4-46bb-bd47-19d481745fc1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-14331f5f-d6d4-46bb-bd47-19d481745fc1' 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-726bab31-a5e8-4fbd-b6d9-4fe778797893' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-726bab31-a5e8-4fbd-b6d9-4fe778797893' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9d77298-af7a-4850-8707-d8a6c43f961c' class='xr-var-data-in' type='checkbox'><label for='data-b9d77298-af7a-4850-8707-d8a6c43f961c' 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-067dd152-62d2-49bf-bec1-0dcd60a0356d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-067dd152-62d2-49bf-bec1-0dcd60a0356d' 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-ca65da9a-8537-49b9-b384-eb5167169a34' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca65da9a-8537-49b9-b384-eb5167169a34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f7f821e-b0bc-483c-a7b2-f9a811075270' class='xr-var-data-in' type='checkbox'><label for='data-9f7f821e-b0bc-483c-a7b2-f9a811075270' 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-bcbbaa68-23e4-4cbb-a3c9-cc2069df61bd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bcbbaa68-23e4-4cbb-a3c9-cc2069df61bd' 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-a35c22d9-2d40-4f07-9592-916c0b2b6c11' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a35c22d9-2d40-4f07-9592-916c0b2b6c11' 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>20241106-153020-188-98e13e</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> Size: 5kB\n",
       "Dimensions:  (dim_0: 300)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 2kB 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 2kB 0.9972 1.063 1.065 ... -0.04871 0.07343 0.01665\n",
       "Attributes:\n",
       "    tuid:                             20241106-153020-188-98e13e\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 0x7eb975186880>"
      ]
     },
     "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": "f497e83cecd2474face7408b766de549",
       "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 0x7eb975186880>"
      ]
     },
     "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': np.float64(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": "4df3d431f4ad4bc28fa3c20cc5626c59",
       "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": "",
     "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:546\u001b[0m, in \u001b[0;36mMeasurementControl.run\u001b[0;34m(self, name, soft_avg, lazy_set, save_data)\u001b[0m\n\u001b[1;32m    544\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_safe_write_dataset()  \u001b[38;5;66;03m# Wrap up experiment and store data\u001b[39;00m\n\u001b[1;32m    545\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_finish()\n\u001b[0;32m--> 546\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset_post()\n\u001b[1;32m    548\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:1240\u001b[0m, in \u001b[0;36m_KeyboardInterruptManager.__exit__\u001b[0;34m(self, exc_type, exc_val, exc_tb)\u001b[0m\n\u001b[1;32m   1238\u001b[0m signal\u001b[38;5;241m.\u001b[39msignal(signal\u001b[38;5;241m.\u001b[39mSIGINT, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_previous_handler)\n\u001b[1;32m   1239\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_interrupts \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:  \u001b[38;5;66;03m# call outside handler on exit\u001b[39;00m\n\u001b[0;32m-> 1240\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_previous_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43msignal\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSIGINT\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   1241\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_interrupts \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m   1242\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_previous_handler \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "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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