{
 "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_634/1774270832.py:10: DeprecationWarning: This package has reached its end of life. It is no longer maintained and will not receive any further updates or support. For further developments, please refer to the new Quantify repository: https://gitlab.com/quantify-os/quantify.All existing functionalities can be accessed via the new Quantify repository.\n",
      "  import quantify_core.visualization.pyqt_plotmon as pqm\n"
     ]
    }
   ],
   "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": "aad865e5566a410986e7cc6f4634c350",
       "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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",
      "text/plain": [
       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7bf846ff9c70>"
      ]
     },
     "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",
      "\n",
      "Prepared Resonator...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Prepared Resonator...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "519d7dad5fc145f99b1ac1d0bca3e985",
       "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",
      "\n",
      "Prepared Resonator...\n",
      "\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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RGeudUuWsrW9lS7h9/IVRGvgl4t41qUT0/VXopI6qwYJi/wgawtiv2+3WaDTeH9VqtZQZ9MEHH8yePVuv1/vF1KKiooKCgqFfO3369KqqqoyMDI1Gc/jwYcaY2+0moszMzI0bN0oBta6uThTFvLw8v+ykwa45tmDsFyDq5GfpJyRpvdm/Xuau0cj+rW4wX7D19C2hIW8nNTkeS2iihV8EDS1HybdPSUROp1Oj0bS2tjY0NEyZMqWzs9NqtXLOOzs7pdimUqm8MW/A1xLRwoULc3Nz9+7du2PHjqlTpzLGtFotEQmCIPVKXS7XoUOH8vPzOzs7jUYjEVkslu7u7iGuObagnwoQjRblJPsM/zJpJJYx+v2+U+HO/v1LRb007Mt7q0/0rpNNi9cN/UIYTaHFUV/JycneCcuurq6enp7k5GSTySQIwu7du4nI4/GIorhr165rrrlm0qRJw76WiARBWLx48eLFi4mopaWFMZaenu77QpfLpVarDx486D3y0Ucf5efnFxQUDHbNsUVpMdVbPR8lf2FMu//qaduqWzgJjIveFTVErNniCPdbH+tbluqbnUSM5U/GTKqi5ObmHjx4sLCwMCEhobq6OjU1NTk5OTk52Tup2djYuHPnzrvuukv68dy5cxaLZd68eYO9loi6u7vVarVKperq6jp48OC8efOkvqbZbK6trV26dGlcXJz3gqIovvDCC9ddd520lmawa44tSoupCKWgDPlZ+tQEdYfNfwK1yymGe0WNVDTfjyDQvcXTwvemMPpyc3Obm5u3bt2q1WoFQVi9evXQ57e0tDQ1NXlj6oCvbW5u3rt3r1ar7e7unjVr1hVXXCEdt1qt1dXVV1xxBWNssOsH257oxLy5fQpQXFxcUVER6VYAyOORLZXbqpulyg/S7yljjHN+c8Gk391RFKY3rW4w3/z8IY/YWxbRm6A0OTn20H9eG6Y3hQhyOBwOhyMxMXGIaBfUa51OZ1dXV1xcXGizoSNpTzRAjhJAlLr/6mlC3+bk3iKFjIV3Rc1fKupF7g2ovTWciGjx1JTwvSlEkFarTUpKCi2ADfhajUZjMBhCTi8aSXuiAWIqQJTKz9Inx2ukKVXvdqpEzNzjDt+bHjtv8flj1vtQiGEY+AUIBGIqQPRK0qmI/L+wt9tc4dtO1dLjErn/W6bFq1HqASAQSoup+/tEuiEAMpiXldT/oMh5mIoUSiUJqd9GNHrd2FsmCBARyPsFiF73Xz1tx9GW3npGPqtUwzSl6juZSn3/H6toAAKntH4qgJKM8pTqpZOpvbCKBiBwiKkAUc1nSpUzIkbEebimVDGZCjBCiKkAUU2aUuUkSItFOZHAuCjKP6Va3WA2dbkIk6kAI4CYChDVpFWqjC6WtOfEGKPTF+zyvtFbVU0eLva+AyZTAUKitBwl1PsFhcnP0s/N1B9r6vBLU0pLlLn72G51sL6+qZc6RsBkKkDglBZTEUpBeQxxqr5i+gLr20u1s0vmrSXN3b2Tqb5hdV5mAiZTAQKHsV+AaNdhcxIxvynVY40WedOUGozd7NLJVMbI5vDI+BYAioeYChDtpmfEc+J+U6oezt+uPC/ju/S43L0bpnIiKcGYBP+xYAAYEmIqQLS7/+ppaiGGiHyX03CidluPXG9R3WBut0kVlBgxRoxJw8BIUAIICmIqQLTLz9LPnpTkN/ZLnMzdsk2p/qWi3i16k357/4cEJYBgRTJHyWw2t7e3q1SqzMxMtVo97Pnt7e0Wi0WlUqWmpsbHx49CCwGihEcUGUk5SowTFzkTBN5u9d+xPGSn2+y8X4LSlGQdEpQAghKxmFpeXl5bWysIgiiKWq22pKQkOzt7sJPNZvOePXsuXLggnS8Iwty5c6+88sr+e+xhLQ0oUmqCxi/1V+RMiJFtj8m0BI2UneS70VtOapxc1wcYJyITU2tqampra5csWVJQUOBwOHbv3l1WVrZhw4bBep979+7t6OgoLS2dMmWK2+3+8MMPa2pqUlJS5syZ43cmQikokiFO7ZP6y4lIYPzEeWtNkyk/yyDXu3AiRowYcd5/izkAGF5k5lOrqqoyMjKKiooEQYiNjV2+fLnL5aqtrR3wZI/H09raOnXq1OzsbMaYWq2WeqhNTU2j3GyASElL0PZP/XV6RLlSf7/osDPpqn2TqYxxKWsJAAIXgZhqMplsNltOTo73iMFgMBgMjY2NA54fExOj1Wo9novr5DweD+c8Lg4DUzBerC/K0sSEK/W3usHcbO7x7ZoyRpwLuRkJI784wLgSgZhqNpuJyGC4ZMBKr9ebTIMuYF+0aNEXX3xx7Ngxm83W2dm5b98+nU6Xn58f9rYCRIf8LP2sieFK/fUm/foupFHHMCT9AgQrAvOpTqeTiDSaS6qVajQa6fiA8vPzPR7PwYMHKyoqiCghIWHt2rVJSUnhbipA9PBJ/SUiEjljTJ7UX5+k34vV85H0CxCCiOX9ch5EgZaPP/74008/nT9/fnZ2ttPprKmp2bZt2w033JCenu53ZnFxsd8RKQwDjHVS6i9xIpLSlDiRPJX0kfQLIJcIxFStVktEDofD96DD4dDpdAOeb7FYPv3003nz5i1btkw6kp2d/eqrrx46dOimm27yOxkRFJTKEKcmIt+uKifmdMtQPNAQr6F+Sb+GOGybChC0CMynJicnE5HRaPQ9aDQapeP9dXR0cM4nTpzoPaJSqdLS0trb28PaToCokpag7V9J/8hZ08gr6XOR90/6RaFfgBBEIKYmJiYmJyfX19d7h3/b2tqsVqtvzQen0+ly9SZfSP1X3xjMOTeZTIP1awEUaX1RlkYlMBKJ9ab+ipw53J6RL6dpszn6J/1ieSpACCKzPnXBggVGo7G8vNxisbS2tu7duzc2Nta3gMMrr7yyfft26fGECRP0en1VVdXx48ctFktHR8e+ffvMZvOMGTMi0niAiMjP0hdlJ/v1Uzkf6XKa6gZzZYOJXZr0y5iYmqCVq+UA40dkcpTy8vJsNtuRI0eOHz9ORHq9vrS0VJpn7U8QhNLS0oqKivfff186olKpFixYsHDhwtFrMUAUSE/U9E/9ZSOrePRWVZPT7fHb5U2jUt1cmCVHkwHGl4jl/RYWFs6bN89oNKpUqpSUFL9nv/GNb/j+qNfr165d63A4pBr6SUlJMb3r3/2h3i8oGfcvTyiK3uUvIWq3OvoW0vTWz+acFuUYsJAGIASR3JdGrVZnZGQEfr5Wq+2/eMYPQikoGeOy91OlizGffipxloGBX4CQYP9UgLGjt59KRH2raWik/VQp6bd30FeaTBWQ9AsQokj2UwEgOIxTvyWqbSMrpdTXT704mYqkX4CQoZ8KMGaEY4lqXz/1kqRf9FMBQoOYCjBmhGOJat/i1L5qD8TRTwUIGWIqwJgh+xJVLE4FkJfS5lOxlgaUTd4lqlicCiAvpcVUhFJQuIupv0REjDjnoaf+YnEqgLyUFlMBFE7qn5K37AORtJ9MaBfD4lQAWWE+FWAsSUvQ+uUoEVHI05+p8Zr+i1MxmQoQMvRTAcaS/Cw9sUt2JmeM5WclhXw1xhhx0TuZSiSEfDUAQD8VYCypaTJzTpd2Vend2tYRXI37Jv0SiTVNFnnbDDB+KK2firxfGAf8K+nv+6ytpsmUn2UI9kLtVgddmvRLxNptDnmbCzB+KC2mIpSCsq0vyvrH4XMut8t3OY1U9iGEmNpX8OFikhPnDAUfAEKGsV+AscSn7EPvEWk5TQhlH3wKPvTinFDwAWAklNZPBVC89EQNEY18OQ0KPgDIDv1UgDFGxuU03oIP3hylktnpKPgAEDLEVIAxxmc5DRERI84olOU0vQtpLimgT9fPmSBvawHGFaXF1P19It0QgHCRltNIFZU4EWPEiYewAKZvIQ15Cz4QC+U6AOCltPlU5P3CeOBfRj/Uer/UbyGNLM0DGLeUFlMBFE/esV/OfSNp6CWZAICUN/YLoHh9Y7/kHf4NbczWZ+yXiIhzQhElgBFCPxVgTPIb/m0ydYd6HYz9AsgG/VSAMWZ9UVaiTuNX9uHQqY6aJlNQ1+nL++1dSEOMYewXYIQQUwHGmPws/bLpKUTkm/pr6XG9XXk+qOtg7BdAdkob+928ebP38caNGyPYEoDwyTTEjjz1t8nYRRj7BZCV0mIq4iiMByNP/a1uMB8+0+lXQD9Bq0ZhQoCRwNgvwNgz8rIPb1U1mbtdfgX0r56RisKEACOBmAowJvUv+RvqdbyFCXmmPlbGFgKMQ0ob+wUYD0Y+9ttX8IH7bGmDpF+AkUI/FWDsGfnYL5J+AcIhxH4q57yrqys2NlYQoisqe6vno/AvKJssJX+R9AsgryBiqiiKH3zwQXl5eXV1dUtLiyiKgiCkp6dffvnly5Ytu/rqq9VqdfgaGiCEUhgP5Bv7RbFfADkFFFNFUXznnXf+9re/tbW1paamzp49e/HixfHx8Xa7vbOzs7KysqysLDk5+fbbb7/tttuiIbICKNulJX85ETHGa5os6wqDusLFkMo5MSYGdQUA6C+gmPrtb3/77Nmza9euvf7663Nycvqf0NjYWFZW9q9//Wv79u1btmyRuY0AMJCRl/zF2C+AvAKKqatWrbr22mtjYwfNs588efI999zzla98ZceOHfK1DQAGtr4o6/VPmqw9zv4lf/OzDIFcAWO/AOEQUIbR2rVrhwioXmq1+qabbhpxkwBgGD4lfy8KquQv8n4BwiGgfqrFYomPj4+JiQl3a0YOeb8wTmQapK+5vfOpAiMefOYvxn4B5BVQTC0vL//zn/98/fXXr1mzZsD51OiBUArjxPqirNc/OWt1iN751ARtTODVeqemxfqN/epjNSj2CzBCAcXU3NzcCRMmbNmyZcuWLXPmzFmzZk1JSUl8fHy4GwcAgxFFIhKIi9KPjDgjEgNbpVrdYP7dnlO+eb9E9MSqPBT7BRihgOZTZ82a9ac//emVV17ZsGFDa2vrM888s27dup/+9KeffPIJD2G8CQBG7K2qJmuP27eUks3hDnA+9a2qpk67k3yK/RLnZ9q6wtxkAOULouZDTk7OQw899MADDxw+fHjnzp379+8vKyubOHHi6tWr16xZM3HixPC1EgD6k6OUEsMsKoCMgq5NKAjC0qVLly5darVa33vvvV27dm3evPmll1664YYbvve974WjiQDQ30hKKfkU0PfCQhoAGYRerTcxMXH9+vXf+973Fi5cyDk/c+aMjM0CgKH5lFLqFXgZfSykAQiTEGvoG43GsrKynTt31tfXC4KwePHiW2+9Vd6WhQZraWCcCX0tDRbSAMguuJjqdrs/+OCDnTt3fvTRRx6PJysr6957712zZk16enqY2hcshFIYJ0aylmZ9Udb/HWmwO9zeSBqvUWMhDcDIBRpT6+rqdu7c+d5775nNZp1Ot3LlytLS0oKCgrA2DgAGM5K1NKJIAiPfAvqCwAN8LQAMIaCYunv37p/97GdENG/evAceeOCaa66Ji4sLc8MAYCh9a2kuktbSBFLvV3qt79ivtSfQ1wLAEAKKqXFxcXfeeWdpaWl2dna4GwQAQbo4pRrsCzGLCiCvgPJ+r7jiigcffDCQgOpyuUbcJAAY3vqiLEOchpFIjLPeUVwhJy2g6mbri7LitZd8n8Z8KoAsAoqpDz744Kuvvmq324c4p6en54033rj77rsDf2+z2Xz69OmzZ88GHokvXLhw6tSps2fPdnUNXPNlf5/AmwEwFuVn6b+zcobvElXOxd+UnaxpMg37Wu98at8LMZ8KII+Axn7vvvvu3//+9y+++OJVV121YMGCmTNnpqamxsfHd3d3d3R0fP7555WVleXl5XFxcffff3+Ab1xeXl5bWysIgiiKWq22pKRk6H5wR0fHnj17Ojs7vUduvPHGrCz/b9bI+4Xx40y73W/9jLHLGci0KOZTAcIkoJi6fPnyZcuWvfvuu2+++ea+ffv6nzB16tQHHnhgzZo1gWyzSkQ1NTW1tbVLliwpKChwOBy7d+8uKyvbsM+N4pMAACAASURBVGHDYHX5bTbbO++8Exsbe8MNN0yYMMHhcLS2tqKIPwARjWCJKuZTAWQW6FoajUZz44033njjjS0tLUePHm1pabHZbAkJCRMmTJg/f/6kSZOCeteqqqqMjIyioiIiio2NXb58+ZYtW2praxcvXjzg+YcPH3a73WvXrk1ISCAitVotPQAYz0Jeotq30dvFCIyN3gBkEXQdpYkTJ46wXL7JZLLZbHPmzPEeMRgMBoOhsbFxwJjq8XhOnz49ffp0KY6KoigIoZdUBFCM0JaoYqM3gPAJsTbhSJjNZiIyGC6ZudHr9S0tLQOe39nZ6fF4UlJSDhw4UFdX53a7U1JSFi1aNG3atNFoLkC0Cm2J6qUbvfUexEZvALKIQEx1Op1EpNFofA9qNBrpeH/d3d1EVFlZmZCQ8KUvfYmIjh49unv37lWrVk2dOtXv5OLiYr8jFRUVcrUcICphPhUgWkQgpkqC3cycc37jjTfqdDoiysnJ+cc//nHkyJH+MRURFMaP0OZTLxb77YPFqQByicDEpFarJSKHw+F70OFwSPFysPMnTZrkPUGj0WRlZbW3t4uiGObGAkSvvvnU3h8DnE/F4lSA8IlATE1OTiYio9Hoe9BoNErHBzyfMeaXlxQTE0PBd3YBlGSw+dRAXsWIE+fEOSMuLU4NZ0sBxosIxNTExMTk5OT6+npvRGxra7Narb41H5xOp7e4kkajmTRp0oULFzwej3REFMWWlha9Xi9FVoDxjXEiTsSCmxxlxPr+BwAyCSWmiqJ4/PjxPXv2nDhxQjri8XiC6jIuWLDAaDSWl5dbLJbW1ta9e/fGxsb6rq555ZVXtm/f7v1x4cKFXV1de/fu7ezs7Ozs3Ldvn8ViwU5zMM6tL8pK1Aneer8iZ/FaVSDzqSj2CxAmQeconThxYtOmTY2NjUT05S9/efbs2R6PZ/369bfffvudd94Z4EXy8vJsNtuRI0eOHz9ORHq9vrS0VJo3HVBWVtY111xz8ODB06dPE5FKpbriiit8YzDAOBTa+lRsngoQPsHFVLPZ/B//8R/Z2dmPPvrojh07pIMxMTElJSUVFRWBx1QiKiwsnDdvntFoVKlUKSkpfs9+4xvf8DsyY8aM6dOnd3Z2cs5TUlJUqoFb7q2ej8K/oHghr09FsV+AMAkupu7Zs0cQhN/85jfx8fHvv/++9/i0adN2794d7Hur1eqMjIzAz4+JiUlPTx/6HIRSgMBgcSqA/IKbTz1//nxeXl7/4vXx8fFWq1W+VgHA8KQtVInIm6aUqBt+ZtTnVb1Q7BdALsHF1KSkpAsXLvQ/furUqdTUVJmaBAAByc/S/++dhb5pSpzI2D3MbsT5WfpvXp3DfNJ9H14xDcV+AWQRXEy94oorzp079/bbb/sePH369Ouvv37llVfK2jAAGF6iVs1YjDfByNbjeuTVyqG3Ja9uMP+54gvORWl9KnH+/IH6QHYyB4BhBTefOnPmzPXr1z/zzDNlZWUWi0Wn0/3whz/88MMPU1JSNm7cGKYmAsBg3qpqslzaMR12W/K+GvoX51MD3MkcAIYV9Fqaxx9/PC8v7/XXXz979iznvKGhYeXKld/85jf75+5GBPJ+YVwKuYw+AMgplBr6a9euXbt2rcvlcjqdcXFxLJrqsCCUwrgSQhl9bEgOED4BxVS73T5YmSS73S49iImJiY2Nla1dABCAYMs+YENygLAKKKZ+5StfaWtrG/qc/Pz8P/zhD3I0CQACFWzZB2xIDhBWAcXUe+65p6ur97eurq7u3XffXbhw4ezZs2NjY8+fP3/gwIGkpKRbbrklnO0EAHmh5gOA/AKKqWvXrpUetLa2vvTSS//93/991VVXeZ99+OGHH3744YaGhrA0EAAGt74o683KJlOXy5ujlKQbanK073yn9wgmUwFkFNz61D179mRnZ/sGVCJKSEi444473nrrLVkbFqL9fSLdEIDR0FvAQeit+UAkPDRkAQcUfAAIq+Dyfk0m04DJSpxzkykq1owj7xfGld4CDr0pSsS5+McD9cumpw42n+pT8KH3yPMH6pcOfj4ABCW4fmpubu5nn33mWz2fiMxm89atW3Nzc2VtGAAMz5tz5CUVcBju/Isbkg99PgAEJbh+aklJyTvvvPPkk08WFBTMmjVLp9O1tLSUl5e73e5nn302TE0EAAAYE4KLqTExMc8+++yrr7767rvvVlVVEVFcXNzChQvvueee6dOnh6eFADAo5CgBRBU2WDGHYTmdTpfLFVV1lIqLiysqKiLdCoBR9fyBul+VnZSmVBkTfrB6xjevHuoL7vMH6n61u877i//DNbPuH/J8AAhccPOpvjQaTXx8fPQEVIBxaMAcpSE2mcGmNABhFdzYr8fjcTqdAz4lCIJWq5WjSSOCGvowrgyWozRcHSVsSgMQFsHF1AMHDjz11FMDPhUltQkRSgEAIFKCi6l5eXkPPfSQ75H29vb3339fp9OtX79e1oYBwPCCzTlCjhJAWAUXU7Ozs7Ozs/0O3nfffQ888IDZbJavVQAQkPws/f/eWfjIlkqj3cWJa1UxT6yaMXQdpf+9s/DBf3xq7XYRUYJW9fzdhaijBCCX0HOUvHQ63bp167Zs2TLySwFAsK7KTXu8ZLpOIzAip9vzTFndwVPtQ5wvih7GehOUBMZE7GEOIB8ZYioRqdXqzs5OWS4FAEGpbjD/bs/pHqdH+tFodzzyauVgqbzVDebHXjtq6XJLRZQsPa4hTgaAYAU39jugs2fPbt26ddq0aSO/1Mht3rzZ+3jjxo0RbAnA6Agq9TfYPGEACEpwMfXgwYO//vWvfY90d3d3d3frdLpnnnlG1oaFCHEUAAAiJbiYmpGRsXz5ct8jOp1u0qRJK1as0OuR5gAQAUGVJ1xflPXPTxptPS7vEeT9AsgouJg6ZcqUu+66KyMjw++41Wpta2tLT0+Xr2EAEBBpS9RflZ2k3mpKQ22harT3MMZ572arFK9B3i+AnILLUfrggw++//3v9z/+xhtv/Nd//ZdMTQKAIARenlBKULJ2uxn15v3GMErUyZBUAQASefJ+3W63IMhzKQAISuBbqPqc2bt5qsXhxuapADIK9Cuq3W7nnPf09IiiaLPZfJ8ymUxHjhzpPyAcEaj3CwAAkRJoTL3lllvsdrv0ePXq1X7PCoLw1a9+Vc52hQqhFMabwHOUUJgQINwCjan33Xef0+k8derUp59+etttt3mPM8bi4uIuv/zyyy67LDwtBIChBJ6j1Humz+apDw+ezQQAIQiin0pEdXV1M2fO9I2pABBZA+YoLZue2r+Mg8/mqb1Hnj9Qv3SgMwEgNMElFuXl5SGgAkSV4HOUehOUiLHBzgSA0ATUT7VaraIo6vV6l8vV1dU18IVUqvj4eFnbBgAAMJYEFFO/9rWvdXR0vP/+++Xl5VG+JznAeBN45hFylADCLaCYet999/X09BDRzJkzH3vssQHPSUtLk7NdocJaGhhvpC1RH/p7pbnHSURJukFLI2HzVIBwCyimehfPTJ48efLkyeFsz0ghlMI4JIoeErhUbpAxYYgtUS9unkqEzVMBZIfiRwBjW9+WqL1l8S3dzsG2RMXmqQDhFkSO0jAXQo4SQCQEviUqNk8FCLdAc5Ta2tqGPgc5SgAAMM4FmqM02BIaryjJUQIYb5D3CxA9mLdKmQIUFxdv2rRJeoxkJRg/Dp5qf2RLpdHu4sS1qpj/umH2XUsGrhV68FS7b97vn766YNl0fBsGkE0oWye2tbXt3bu3vr7e5XKlp6cvXrx44cKFsrcsNAilMA5dlZv2eMn0p3ed7HF6nG7PM2V1l6XGX5U7QLBE3i9AWAXdT92+fftvf/tbp9Op0+l0Op3ZbOacL168+Kc//WlcXFyYWhmg4uLiioqKyLYBYPRVN5g3vvSxb/5Rcpzmb99Y5Jd8FOBpABCy4NbS1NXV/frXvy4qKvrb3/723nvvbdu2bffu3Y888sgnn3zy3HPPhamJADC0AEv+Bl4ZGABCE1xMLS8vT01Nffrpp6dOnSodiY2Nve222+666y5vASMAAIDxKbiY6nK5pk6dqlar/Y7PmDHD5XLJ1yoACML6oixDnMb3yIAJvQGeBgAhCy6mFhYWfv7551ar1e/4J598smDBgmDf22w2nz59+uzZs0HFY7vdbjKZHA7HgM/u7xNsYwDGLqmQb5JOw4k4UeIgJX/zs/TfWTlDq46RftTHqlHvF0BeweX9Llq0qKSk5NFHH924ceOsWbN0Ol1LS8v27dsPHz78q1/9yunsnapRq9WMsaEvVV5eXltbKwiCKIparbakpCQ7O3vYBtjt9tdee83hcCxdurSgoKD/Ccj7hfEpkJK/5Sdbf7f3pMPlJk5ateqJ62diIQ2AvILL+927d+9ge735+v3vfz9//vwhTqipqTl48OCSJUsKCgocDsfu3bvb29s3bNgwbHXDXbt2mc1mo9E4YExF3i+MT4Ek9CLpF2AUBNdPnT59+r333jvsaRMnThz6hKqqqoyMjKKiIiKKjY1dvnz5li1bamtrFy9ePMSr6urqmpubS0pKduzYEVSzAZQtkEK+KPYLMAqCi6k5OTk5OTkjfEuTyWSz2ebMmeM9YjAYDAZDY2PjEDG1u7v74MGDS5cujfgqWAAAgAFFYK83s9lMRAbDJd+O9Xq9yTTUnlPSMp7Zs2eHt3EAY1AgCb1I+gUYBUHXJmxra/vnP/958uRJo9HoOxc7Y8aMJ598MpArSKlMGs0lv94ajcab4tRffX39uXPnbrvttmBbCzAeSHm/D/290tzjJKKkgfJ+pXN8i/0i6RdAdsHF1Pb29nvvvddsNs+ePTszM9P3qYyMjKAuFXhulMPhKC8vX7hwoV4//O9/cXGx3xFkLcF4EEjeL4r9AoRbcDF1z5493d3dL7/88mWXDbzrRSC0Wi0R+S0wdTgcOp1uwPP//e9/E9GECROam5upb+jYarU2NzenpaX5FaBABIVxqLrB/NhrRy1dveu8Ld3OR16t7J/3+9hrRy1dbmKMiCw9rv7nAMAIBRdTTSZTXl7eSAIqESUnJxOR0Wj0PWg0GqXj/dnt9u7u7rffftv34LFjx44dO/blL38Z+7YCIO8XIEoEF1Pnz5+/a9cul8vVvzxh4BITE5OTk+vr6xctWiSVhmhra7NarXPnzvWe43Q6GWPSuxQXF19xxRXep4xG465duwoLC2fPnp2YmBhyMwAAAOQVXN7v0qVLFy9e/POf/7ytrW0k77pgwQKj0VheXm6xWFpbW/fu3RsbG+u7uuaVV17Zvn279DguLk7vIyEhgYh0Op1erxeECOQtA0Qb5P0CRIng+qmMsdtuu+273/3u+vXr4+LifHurc+bM+dWvfhXgdfLy8mw225EjR44fP05Eer2+tLRUmmcFgGBJOb2PbKk02p00SCHfQM4BgBEKrjbhuXPnvvGNb8TExCxevNhgMPgW9c3Kygp2rYvL5TIajSqVKiUlJagXDqa4uHjTpk3SYxT+hfHmjU8bXzz4xbEm8/9sKFhXMHAH9F+fNn7ntap5Wfr7i6etQycVQG5B5/2qVKq///3vqampI39vtVod7AqcYSGUwvhUfrL1ZztPdNqcjNFT206kJmivyvVP3ys/2frznScYY7XnLU9tP5GaOMA5ADASoeyfKktABQC5SOtkOm29ab1Gu+ORVytrmkzBngMAIxRcTF2wYMHZs2e7urrC1BoACMFg62SCPQcARii4mFpUVLRmzZof/OAHtbW1drvd6SOofcUBAACUJ7j51P3792/dupWIHnjgAb+n8vPz//CHP8jWLgAI2PqirDcrm0xdF7uhA66lGfYcABgh2fZPnTBhghztGan9+/dLD5CsBOOHzzoZFyeuVcU8sWoGaugDjL4I7J8aVgilMD5dlZv2eMn0p3ed7HF6nG7PM2V1l6XG+6X1ooY+QLjJVoeos7NTrksBQLCqG8y/23O6x+mRfuyf1ntJDX3GpBr6yPsFkNdIY6rdbn/77be/+c1v/uhHP5KlQQAQgmHTepH3CzAKgt6TXMI5r6ys3LFjx/vvv+9wONLT07FhOAAAjHNBx9SWlpadO3fu2rWrpaWFiHJzcx999NHLL7/ct04hAIyyYdN6kfcLMAoCHft1OBxlZWWPPvrobbfd9vLLL2dnZ//4xz8uKCiYNm1aQUEBAipAZElpvcnxvTvP9C+RP+wJADByAfVTP/744x//+Md2u33q1KkPPPDAddddJ+0E/u6774a5eUHDWhoYt67KTftR6ezHX6uel6W//+qpy6b71/JN1Kqvyk19p+r8zYVZ9xVPQ0AFkF1AMfXChQt2u33OnDnf+ta38vPzw92mkUAohXFLKqPPGNWeN/cvo19+svWx/zvaaXMyxt6v67h14ZQINhVAqQIa+124cOEtt9zS2Nj40EMP3XHHHS+99FJzc3O4WwYAgRu6RD4K6AOMjoBi6sSJEx977LG33nrrqaeeysrK2rx58+233/6tb32rsbEx3O0DgEAMvVQGC2kARkcQeb9qtfraa6+99tpr29rapNTf8+fPt7W1ORyOlStXLlu2TK1Wh6+hAAAAUS6Umg/p6elf+9rXtmzZ8txzz1177bWHDx9+8sknv/vd78reOAAI0PqiLEOcxveI71KZoZ8FALkwzkda9LOrq2vv3r1ffPHFI488IkubQlZcXLxp0ybpMZKVYLw5eKr9ob9XmnucRJSkU73wlQW+qb8HT7X7FtD/01cX9E8MBoARCrGOkq+4uLgbbrhh5NeRBUIpjFui6CGBS0vFGRP8SuSjgD7AKJCthj4ARFBfiXyX9KOl29k/7xcF9AHCDTEVQAmQ9wsQDRBTAQAA5IGYCqAEyPsFiAYy5P1Gj+Li4nvuucf748aNGyPYGIBRdvBU+yNbKo12J/WWyC/yy/sd4lkAkIXSYmpFRUWkWwEQMW982vjiwS+ONZn/Z0PBugL/bui/Pm3868Ezx5rM/3NH4Tp0UgHCQIa1NAAQDaQa+p02J2M0YA39n/c+y57afiI18ZJnAUAWmE8FUALU0AeIBoipAEqAtTQA0QAxFQAAQB6IqQBKgLU0ANFAaTF1f59INwRgVOVn6f/3zsLk+N7AqY9VP3934dwsfSDPAoBclBZTV/SJdEMARttVuWl/u2fRusuzOKcvzUhP1Kn9nn2ydDbnfG5m0qYb52JxKkA4KC2mAoxnRruj4nQbY/RO9fmv/vXfB0+1e5+S1tIwxmrPW57afsL3KQCQC2IqgEIMsWAGa2kARgdiKoBCDLFgBmtpAEYHYioAAIA8lBZTkfcL49YQC2awlgZgdKCGPoByDLH5DPalARgFSuunAoxnV+Wm/ah0Nuc0N1O/ad0lC2YSteqrclM55zcVZL567xIEVIBwQEwFUA5paxrGqPa8+altFxfMlJ9s3fjyx9uqmxlj79d1GLtdkW0ngFIhpgIoxGALZrCQBmDUYP9UAIUYbMGMh9OAx/OzDKPbQADlU1pM9Wb8ojwhAACMMqXFVIRSGLfWF2W9Wdlk6rrYJZUWzIhEAx6PRBsBFA7zqQAKMdjmM9iUBmDUIKYCKMdga2mkTWnmZiZxzrEpDUD4IKYCKMcQa2l+vvNE7XkLYwyb0gCED2IqgEJgLQ1AxEUyR8lsNre3t6tUqszMTLVaPfTJdru9s7PT6XQmJSWlpaUxxkankQBjBdbSAERcxGJqeXl5bW2tIAiiKGq12pKSkuzs7AHPtNls7777bltbm/dISkrK8uXLJ0yY0P9krKUBAIBIiUxMrampqa2tXbJkSUFBgcPh2L17d1lZ2YYNG+Lj4/uf3NPTo1arS0pKpO5sU1PT/v37d+7cedddd2k0Gr+TEUph3MJaGoCIi8x8alVVVUZGRlFRkSAIsbGxy5cvd7lctbW1A56cmpq6bt26vLy8+Ph4jUYzderURYsW9fT0NDY2jnKzAaIZ1tIARFwEYqrJZLLZbDk5Od4jBoPBYDAMFiP7T50mJCQQkSiKYWsjwJgkraWZm6nnnPqvpeGcz81MwloagPCJQEw1m81EZDBckh+h1+tNpkATEU+dOiUIwqRJk+RvHMBYJq2lqT1vZoz6r6VhjNWet2AtDUD4RCCmOp1OIvKbCtVoNNLxYZ06derUqVOFhYUDTr4CjFtYSwMQcRHL++Wch/Cqpqamffv25eTkLFq0aMATiouL/Y5UVFSE8EYAYw7W0gBEXARiqlarJSKHw+F70OFw6HS6oV/Y3Ny8a9euSZMmXXfddYOtT0UEBQCASInA2G9ycjIRGY1G34NGo1E6PpiWlpYdO3ZkZGSsXr06JiYmvE0EGIPWF2UZ4i6ZUpHWzAx2fHRbBzAuRCCmJiYmJicn19fXe4d/29rarFarb80Hp9Ppcrm8P7a2tm7fvj0tLW316tUqldL2pwOQhc+aGcaJNKqYJ1bN8K6lSYztLVWWoFVhLQ1AmERmfeqCBQuMRmN5ebnFYmltbd27d29sbOycOXO8J7zyyivbt2+XHlsslu3bt3POp02bdurUqRN9Ojo6ItJ4gKh1VW7a4yXTdRqBETndnmfK6qQUX1H0MMaJc+JcYEwMJZkBAIYXmT5fXl6ezWY7cuTI8ePHiUiv15eWlkrzrP1ZLBYpJfjQoUO+x5cuXZqamjoKrQUYK6obzL/bc7rH6ZF+lFJ8f7R29s92nLB0uYkxIrL0uB55tfJv31iEHCUA2bHQ8m9l4XK5jEajSqVKSUmR5YLFxcWbNm2SHqNIIYxDP9l2fPOhM34H52bqa8+b/Q7ee9XUJ9fOIQCQVSTnJtVqdUZGhrzXRCgFAIBIwf6pAMoxYIrv/VdPRd4vwOhATAVQjgHL5a8ryEINfYDRgZgKoChSGX3OaW6m3ltGP1Grvio3lXN+U0Hmq/cuQQ19gDDBWk8ARZHK6DNGtefNT207kZqgFUXPY/93tNPmZIy9X9dx68IpkW4jgGJFMu9XdsXFxahNCONZdYN540sf+1b3TdJpiHFL98UKKslxGiykAQgTpfVT9+/fLz1AAjCMQ/3L6Jt7nH6lsVFAHyB8lBZTEUoBACBSkKMEoBz919Ik6dSJOrXvESykAQgfxFQA5ZDW0iTpNJyIEyXqVC98pej5u4tQQB9gdCht7BdgnBNFDwlcmkNlTBC5TwF9IhTQBwgr5P0CKAfyfgEiS2n9VOT9wniGvF+AyFJaTEUoBQCASEGOEoByIO8XILIQUwGUA3m/AJGltLFfgHEOeb8AEYS8XwDlQN4vQGQprZ+KvF8Yz5D3CxBZSoupCKUAABApyFECUA7k/QJEFmIqgHJIeb/J8Roixok0qpgfrJ6JvF+AUYOYCqAoV+WmPV4yXacRGJHT7XmmrK6m0dib98s58n4BwgoxFUBRqhvMv9tzusfpkX402p2/2n3S0uUmxogxS4/rkVcra5pMkW0kgFIhpgIoil/qLyf/5XJS3u9oNwtgfFBa3u/mzZu9jzdu3BjBlgAAwHijtJiKOArj3PqirDcrm0xdvV1VgREnRj59VeT9AoQPxn4BFMWv5G+CVvX1pZdp1THSs/pYNfJ+AcIHMRVAabwlfxmRyMV/VTY6XG7iXKuKeeL6mcump0W6gQCKhZgKoCjVDebHXjtq6ZIK/DK7Q7R2u4kYMeZwe54pO4mkX4DwQUwFUBTfvF9O/mtRkfQLEFZKy1FCDX0AAIgUpcVUhFIY5/ryfl2cOGPEOfnuS4OkX4CwwtgvgKLkZ+m/eXUOk7Yl5yRIqUp9Hl4xDUm/AOGDmAqgKNUN5j9XfMFF6SfGOUmVfqX/PX+gHjlKAOGDmAqgKAPlKDGp2C8xhhwlgLBCTAUAAJCH0nKUkPcL4xxylAAiSGkxFaEUxjkpR+lXZSdJ7M1R4kTedarIUQIIK4z9AigKcpQAIggxFUBRkKMEEEGIqQBKxYY/BQBkhZgKoCjri7IMcRpGIjHeO5nqI16jRo4SQPggpgIoSn6W/jsrZxATiBPnjF3MTyLOSRC42K+wPgDIRWl5v1hLA3Cm3c45ETFO0i6qvfOqjMja43678nx+liHCTQRQKKXFVIRSACJiJHImMM6JGBHD1CrA6MDYL4DS5GfpiQnEGWPMb5w3QYv5VIAwQkwFUJqaJjPnJC1M9ZtPvXpGKmo+AISP0sZ+AYCksd++b8y+86mZ+thINgtA6RBTAZRGGvsViImcMyKf+VSWn5UUyZYBKN1Yiqlms7m9vV2lUmVmZqrV6gHPQd4vgHfsVxr4leIp58SYWNNkWVcY4eYBKNiYianl5eW1tbWCIIiiqNVqS0pKsrOz+5+GUApAPmO/vgO/qKwEEG5jI6bW1NTU1tYuWbKkoKDA4XDs3r27rKxsw4YN8fHxkW4aQNS5dOzXdyENxn4Bwmts5P1WVVVlZGQUFRUJghAbG7t8+XKXy1VbWxvpdgFEo96xXy76Jf0SiTVNlgg2DEDxxkBMNZlMNpstJyfHe8RgMBgMhsbGxsg1CiDKMal3yqh3lzdGnIi12xwRbheAoo2BmGo2m4nIYLikmpperzeZsA0kwADWF2WpmMB7Cz5c3OhNKv8LAOEzBuZTnU4nEWk0Gt+DGo1GOu6nuLjY70hFRUX42gYQhfKz9Ewg7hkg7zc1QRvhxgEo2hiIqRLOA9pMAxEUgIhiNTHuHo/I/fJ+BRQmBAirMTD2q9VqicjhuGQeyOFw6HS6CLUIINrdd/VUsbcwYd/AL7HbF2ShMCFAWI2BmJqcnExERqPR96DRaJSOA0B/j6zI3bBkCu/LUeKcl+ZP+OWXL490uwAUbgzE1MTExOTk5Pr6eu/wb1tbm9VqHbDmAwBIfnHz/P+4LjctXpuWoP3+dTP/cPfCSLcIQPlYgPOUkVVXV7dnz545c+YUFhZ2d3fv37+/p6dnw4YN0rCwV3FxMeZTAQAgUsZGjlJeXp7NZjtyTAZBWAAAFxNJREFU5Mjx48eJSK/Xl5aW+gVUAACAyBobMZWICgsL582bZzQaVSpVSkrKYKehhj4AAETKmImpRKRWqzMyMoY+B6EUAAAiZQzkKAEAAIwJiKkAAADyQEwFAACQB2IqAACAPJQWU/f3CfkK/avwwxBwuwKHexU43KvA4V4FbhTu1VjK+w0E8n4BACBSlNZPVarNmzdHugljCW5X4HCvAod7Fbhxe6+UGVOHHvsdycjwSK4cqVaN8K3H2+1Cq0bnfdEquZ4dGloVOFmurMyYCgAAMPoQUwEAAOQxNvalCRDy3wAAINyG2ABNUTEVAAAggjD2CwAAIA/EVAAAAHkgpgIAAMhDaXWUgIg8Hk9LS0tXV1dsbOyECRPUavXQJzc3N/f09CQnJ6empvY/oaenp7m5mXOekZGRkJDQ/4TW1lar1RoXFzdp0iTGWP8TLBaLKIpJSUmCMMB3OLPZ3N7erlKpMjMz/ZoqimJ7e7vVatVqtampqbGxscN88uBF273q6upyOp1xcXEajab/s0PcKy9RFC0WCxEZDIYhPksIgrpXNNyHjfi9CvbjBGWU79WwH3Yk98rj8bS1tdlstri4uLS0tAGvAF6IqUpz+vTp8vLynp4e6UdBEO69996YmJgBT25ra9u1a5fdbo+JifF4PFOnTl25cqXvyZ999ll5ebkoiowxzvmiRYsWLFjgfbanp2fXrl0tLS3Sy5OTk9esWZOUlCQ929zc/PHHH7e1tblcLiK688479Xq9XwPKy8tra2sFQRBFUavVlpSUZGdnS099+umnVVVVDofD+0Hy8/OXLl064J/X0ETPveru7t6/f39bW1tXVxcRLV++fPbs2X4NGOJe+Tp8+HBVVZVarb733ntHcG/8BXWvhv6wFAX3KqiPE6zRvFdDf9iR36uzZ8+Wl5fbbDbvCcuWLZs1a9aIb5JiIaYqytmzZ997773LLrts8eLFSUlJXV1d586dGywIud3uXbt2qdVqKdrV1dXt3bv38OHDy5Ytk05oa2s7cOBATk7ONddcExMT8+GHH3788cepqak5OTnSCQcOHGhvb1+7du2UKVPa29t37NhRVlZ26623Ss/a7XYimjNnjsViOXPmTP8G1NTU1NbWLlmypKCgwOFw7N69u6ysbMOGDfHx8URkMpnmzJkzffp0g8HQ3d19+PDh6upqrVbr9wdFGffK7XZ3dXXl5OSo1erq6upg75XXhQsXampq9Hq99DdULkHdq2E/bMTvVbAfJ5rv1dAfdoT3qqen57333ouPj7/99ttTUlJsNtt777134MCBCRMmJCcny3K7lAfzqcrBOa+oqEhNTV21alVqaqpardbr9fn5+QOOuBLRqVOn7Hb7lVdeKXUf8/LycnNzjx07JnUriejo0aOCIKxYsUKj0cTExFx55ZUJCQlVVVXSs1KkzM/PnzJlChGlpaUtWrSora2tsbFROiE3N3fdunXLli1LT08fsAFVVVUZGRlFRUWCIMTGxi5fvtzlctXW1krPrlix4oorrkhPT1er1UlJSddee21sbOzp06cVea8SExNvvfXWL33pS1OnTg3hXklEUdy/f//8+fNTUlLkuEm9gr1Xw37YyN6rYD9ONN+rYT/sCO+VNMh0+eWXS/+iEhISFi1axDlvbm4e+b1SKsRU5WhubrZarfPmzZPGiIY9v7GxUaVSTZ482Xvksssuk6YMvSdMmjRJq9VKPzLGsrOzW1papEAi/dpfdtll3pdL3529fw6GZjKZbDab9+s2ERkMBoPB4H2531d7QRDi4uI8Hk8gFx+Wwu6V5MiRIx6PZ9GiRYFcM3Ah3Csa8sNG9l4F+3GCMsr3KsB/GIMZ9uVxcXFE5Ha7vSdIj8OR2aAYGPtVjgsXLhCRTqfbtm1bU1MTYywzM3PZsmUDZtMQkclkSkxM9P0GLWW1mM1mInK5XF1dXX5fbw0GA+fcYrGkpqaaTCa6NBEmLi5OrVZLx4clvYtfHo1er29paRnwfKPR2NHRMW/evEAuPizl3av29vbKysobbrhBrklBrxDuFQ3+YSN+r4L9OEEZ5XsV7C+Rn2FfnpqaOnXq1MrKSoPBkJaWZjKZPvroo4yMDN8vAeAH/VTlkHIi9u/fr9ForrvuuiuvvLKjo+Ptt9+2Wq0Dnu90Ov1S+KQfpbQgp9PpPRLUCdLxYQX1crfbvWfPntjYWLkmUxV2r6RR35kzZ2ZmZgZywaCEcK+GaG3E71WwHycoCrtXRLRy5cpJkybt2LHj5Zdffvvtt3U6XWlpqSzj5EqFW6Mc0lhTSkrK9ddfP23atHnz5q1atcrhcNTU1AR1hSHyKXxPGPA0znlQuR6BjI+JolhWVmY0Gq+77jppMGrkFHavKisru7q6li5dGvjVgn3fwO9VCB92NO/VyP/TD/u+o3avfI+MsM0DEkVxx44dTU1NV1555Y033rh8+XKbzfbOO+94s/GhP8RU5dDpdNQ3HyOZOHFibGysNB414PnedH+J9KsiTd5I/9fvl0f6UXoj6QS/KzidTu/cz9AGu750cS9RFN97772GhoZVq1bJ2AlT0r3q6uo6cuTIjBkzOjs7m5ubpRW0UiKJ0WgM5PpDC/ZeDf1hI/7vKtiPE5SI3Kthf4kGM+zLT5482dTUtHz58vnz52dlZc2ePXv16tUdHR0DphCDBPOpyiHl5vlNp8XExIiiOOD5ycnJJ0+edLvdKlXvPwPpT7CUJa9SqRITE/3+KBuNRkEQpMVz0mlGo9G7RMFqtbrd7gCT7L0v97u+78tFUdyzZ88XX3xx/fXXD7gWM2RKulc9PT2iKFZVVfmmgxLRW2+9NXXq1FWrVgXyFkMI4V7R4B824v+ugv04QYnUvfI7Qa571dHRQUQTJ070PislM0vHYUDopypHZmamSqXyTXM3m802m82bH+HxeBwOh/fXOzs7WxTFL774wnt+fX29RqPx/gplZ2e3trbabDbpR7fbfe7cucmTJ0t/MqZMmcIYq6+v975cWugSYPBLTExMTk6ur6/3Dj21tbVZrVbvyznne/fuPXPmzMqVK32/+MtCSffKYDDceanJkyerVKo777zz6quvDuHm+An2Xg37YSP772rYjzMSw16cc+5wOLzp6yO8V8N+2KEN+3Kpw9rZ2el9ic1mc7lcAfaDx6eYp556KtJtAHnExMRwzo8dOyYIQkJCgtFo3L9/v8PhWLFihZT7fuzYsXfeeSczM1P6kmswGOrr68+cOZOamhoTE1NTU3P8+PGFCxdmZWVJFzQYDMePH29pacnIyHA6nRUVFe3t7StWrEhMTCQitVptt9tPnDgRGxsbHx/f2Nh46NChzMzMoqIi6eUOh6Ourq69vf38+fMmkykuLs5kMlmtVu+3YK1We/z48a6urpSUFJPJtG/fPsbYNddcI/UFDx48+Nlnn2VnZycmJrb36ezsTEtLU969IqKTJ09euHChtbW1paVFq9X29PS0t7enpKRI+SBD3CvGmO5SZ86csVqty5Ytk6XeXrD3atgPG8F7FcjHCeu96ujo+Mc//kFE0r+cEd6rYT/sCO9VXFzc8ePHz58/r9frVSpVe3v7gQMH7Hb70qVLfSs9gS/sn6oonPMPP/ywpqZG6jTEx8cvX77c+63z6NGjhw4duuGGG7zrLC0WS1lZWVtbGxEJgjBv3rxly5b5pj80NDTs27dPKsqj0WiKi4tnzJjhfdbtdh84cKCurk76cfLkyStXrvR+h+3s7Hzttdf8WpiSknL77bd7f6ysrDxy5Ii06E2v169cudJbIOLNN9/svyRAxpJ7UXWviGjz5s1+82pEtHHjRu85Q9wrP++++25jY6OMtQmDvVfDftjI3quhP84IDX3x9vb2f/7znwsWLFi8eHGAH3boezXshx3hvWpsbKyoqPAuZIqPj1+6dGleXt7IbpKSIaYqkMPhMJlMKpUqJSUlkGxJk8nkcDj0ev2AQzqiKHZ2dnLOU1JSBlz72NXVJZX/9n53DorL5TIajVJrQ3j5COFeBS7YezX0h434vQr24wRllO/VCP9hDPtym81mt9vj4uISEhJkv1cKg5gKAAAgD+QoAQAAyAMxFQAAQB6IqQAAAPJATAUAAJAHYioAAIA8EFMBAADkgXq/AMERRbGhocHvoEql8tZUGrf27t3b0NDw9a9/3e/42bNnz50719PTYzAYZsyYodfrvU9JN1Ov1/vt4mmxWIxGY2Zm5tCloFwu17PPPnvjjTfOnj1bvs8BEDrEVIDgmEymu+++2+9genr6G2+8EZH2RAmz2fyb3/zmO9/5ju/Bf//7388999yZM2e8RwRBWLx48YMPPjht2jTqu5l33HHHww8/7PvCd95554UXXnj55Zel0wajVqu1Wu1vf/vbF154AbUIIBogpgKEYuXKlbfccov3R7+NncehrVu3xsfHX3PNNd4j+/bt+8lPfpKTk/P0008XFhbGxcV1dHR8/PHHW7duPXDgwNDBMnB33XXXrbfeevDgweLiYlkuCDASiKkAoUhNTZ07d67fQbPZLIpicnLyhQsX6urqJkyYkJubS0Sc87q6upaWlqSkpLlz5/qNZ7rd7pqamq6urlmzZqWmpnZ0dGi12oSEBCKy2+09PT2+W6Z0d3fbbLa0tDTfbpnFYjlx4oTL5Zo+ffqkSZO8x7u6uux2e1paWldXV01NjVarnTlzZv993VtaWurr6wVBuOyyy6SXt7e3a7Vavzp5ZrPZ4/EMWL7O5XJt27btpptukiqzSyf/4he/mDJlyh//+Edvbfr09PTS0tLrr7/+3LlzgdxkidPpNJvNfgdVKpW0GUN6enphYeGbb76JmArRADEVQDabNm0ym81Llix55ZVXOOerV6/+4Q9/eObMmaeeekraG87pdKalpf3kJz+ZP3++9JJz58498cQTTU1NOp3O5XI98sgjmzdvvvbaax9//HEievHFF8vKyrZv3+59i23btj333HO7du2Sgi7n/MUXX3z11VelvV3dbveaNWu++93vSvuKvPHGGy+88MKmTZt++ctf/v/27jWkye8PAPjZ3NSmTZlOJVdqRV5CrEzsYpiIpeWlKUkXSDQq6GpBYk5T0xBLSM3UZqZ4QaILXpKtEsWSLLTcRMii2ibrQs21R5m2tPl/cfg/PL9n6s9k2u/F9/Pq2Tlnz3Pmm6/nnO95zvj4uNFodHBwyM/P9/T0xHcbGRnJy8vr6upiMpkWFhYTExNxcXHJycm5ubnDw8O1tbXkcycmJg4ePBgYGJiRkWH6w3t7ewmC2Lx5M1ny6NGj8fHxpKQk08NeWCzWHw1SX758mZaWRitcvXp1VVUVvt6yZUtJSYlOp6MtygKw+CCmAjAfOp2OPEsEIeTs7IxPv1IoFAwGo7KycsWKFSMjIwRBnD171sHBAS8NajSanJycCxcuNDQ0cLlco9EoEokMBoNYLPb29v7w4YNIJCIPy5yLurq6mpqa48ePC4VCNpvd0dGRm5vr5OSUlJREtqmoqMjPz/f19VUoFOfPny8sLCwrK8NVIpFocHAwNTU1NDTU0tJSqVTi9CuhUJient7f30/G/s7OToIgoqOjp+1GX18fi8WinpfS39+PEAoICJjLryAIgnqGKPrnQdmbNm2i/lfR09Nz6dKldevWkSVr166dmpqSyWTbt2+fy+MAWDgQUwGYD6lUKpVKyY9paWkREREIocnJyczMTHzqGZ/Pr62t1Wq1paWly5YtQwg5OjpmZGTExsY+efIkLi6up6dHqVSmp6fjtNVVq1adOHHCdEw2k1+/ftXX14eHh+/btw+XhIaGvn79+sGDB4mJieTk8LFjx/z8/PD9hUKhWCw2GAxWVlYymUwmkx09enT37t245cqVK/EIMigoyNHRsampiYypzc3Nbm5u+D6mhoaGeDwedU5bq9XSZo+VSiU+cBshZGtrGxgYSFZJJBKJRDLTz2Sz2WSqsEKhKCgoCAgIOHnyJNkAz1erVKp//5MBsMAgpgIwH+Hh4fHx8eRHZ2dnfMHn88ljRBFCMpmMy+XK5XK5XE4WcjgcnAr75s0b9M/BHHms5ly8e/dOr9dbWFhQAxJeffzx4we58Onr60vW4tD+/ft3gUCAuxQSEmJ6ZwsLi6ioqPr6+jNnznC5XLVa3dfXd+rUqZl6QhAEbfGVyWTiA0RJnZ2dt27dwtceHh7UmLpjx469e/dSG0ul0vv379OeotVqU1JSXFxccnJyqEee4RkC0zVXABYfxFQA5sPe3n7ak5lN91kaDAZaeBAIBDi/Rq/X075iZWVlugA5k9HRUYSQTCajzkIjhDw9PX///k1+pN4QhyJci59OTYCiioqKqqmpkUql8fHxzc3NlpaW4eHhM/XEyspqZGSEWsLn8ycmJrRaLRnaExISEhISEEKJiYnU7iGEeDyel5cXtaS3t5f2CIPBkJqaOjk5eeXKFVqalcFgwH2YqXsALBqIqQAsIC6Xa2NjQ47PTGsRQlqt1tHREZf8/PlzfHycbIDTjqhfwXGU+vX9+/fPtMw5OxzLv3375ubmZlrL5/O3bt3a3NwsFAolEklISAh+3LQcHBzev39PLdmwYUNbW9vz588jIyPn0TeaqampnJwchUJx48YNJycnWi0eof6Vc9oBoIF3EwKwgDZu3Dg8PPzq1atpa/GsbFdXF1ny9OlTagMnJye9Xj88PEyWUAdwa9as4XK5jx8/npqamkff/P39EULUVWEaoVCoUqmKi4t1Ol1MTMwst/Lx8SEIQqPRkCVhYWE8Hu/27dvUzs9bWVnZs2fPsrOzqWlQJBzOTbc2AbD4IKYCsIBiYmIEAkF2drZEIvn69atGo5HL5deuXcNpsX5+ft7e3mKxuLOzU6fTdXd3l5eXUzN9AgMDmUxmQUGBSqX6+PHj1atXlUolWctms48cOSKXyzMzMwcHBwmCUCqVra2thYWFc+mbp6dncHBwQ0NDdXW1Wq3WaDQvXrxoaWkhG/j7+wsEgsbGRg8PD+qirCm8DDwwMECWWFtbZ2VljY6OHj58+M6dO2/fvlWr1QMDA3V1dV++fMFbfeaovb29oaFh165dPB5v8P+of4f+/n4ul0tuEALgL4K5XwAWEIfDKS4uLigoyMvLw6NJBoPh5eUVGxuLry9fviwSidLT0xFC1tbWKSkpRUVF5NeXL19++vTpkpISPJYNDg4+cOCAWCwmG+zZs4fFYlVUVHR0dOASLpcrFArn2L2MjIyioqLq6urKykqEEJvNPnToEFnLYDCio6NLS0tnH6Tifvr5+bW1tVF3s6xfv/7mzZvl5eVlZWXkAqqtrW1YWBheWJ0jnC388OFD6o4acn+q0Whsb2+PiIigZi0B8Lcw5jdrBAD4IwRBqNVqS0tLFxcXWoosQkilUo2Njbm7uy9ZsiQyMpJ85wM2NjY2NDRkb2/v4uIy7c2NRqNKpdLr9Twez9nZ+U+ji16vV6lUbDbb1dWVlv5z/fr1pqamxsZG/IqJWXR1dV28ePHu3bumSU9jY2OfPn0yGAx2dnaurq7ku5bMoru7WyQS1dfXU18gBcDfAuNUABaDnZ0d9TwWmmmzhEgcDoeWFkvDZDI9PDzm3TcbGxsfHx/Tco1G09raunPnzn8NqAihoKAgHx+f2tra5ORkWhWHw5k2R9osqqqqYmNjIaCC/wiIqQAAus+fP2dlZQ0NDbFYLNOz22aC32i4kP2im5ycPHfunLu7+2I+FIBZwNwvAP8t9+7dc3Nzm+Nb/RYIQRAtLS1Lly7dtm0b7FEBYO4gpgIAAADmAXtpAAAAAPOAmAoAAACYB8RUAAAAwDwgpgIAAADmATEVAAAAMA+IqQAAAIB5QEwFAAAAzANiKgAAAGAeEFMBAAAA84CYCgAAAJjH/wBq7pMIxpFOagAAAABJRU5ErkJggg==",
      "text/plain": [
       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7bf846ff9c70>"
      ]
     },
     "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",
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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-01fbf586-d595-4e19-a7a6-d79a61e05a9d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-01fbf586-d595-4e19-a7a6-d79a61e05a9d' 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-ee25af44-cd15-4147-b45a-bb4b0c620224' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ee25af44-cd15-4147-b45a-bb4b0c620224' 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-25af8814-8f89-48ba-9782-788d4f549295' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25af8814-8f89-48ba-9782-788d4f549295' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca719a14-ca11-4d7d-8f12-1da848f69d7c' class='xr-var-data-in' type='checkbox'><label for='data-ca719a14-ca11-4d7d-8f12-1da848f69d7c' 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-b3aee9e6-2b0d-47ee-a799-0a9577f9c0d2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b3aee9e6-2b0d-47ee-a799-0a9577f9c0d2' 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-be68f96c-9cdb-48af-91c8-0637225eca22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be68f96c-9cdb-48af-91c8-0637225eca22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27b5adb8-93a9-4ace-8d3d-5e0b59f65648' class='xr-var-data-in' type='checkbox'><label for='data-27b5adb8-93a9-4ace-8d3d-5e0b59f65648' 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-bea56142-cf08-42e3-b319-299d1ec39aa6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bea56142-cf08-42e3-b319-299d1ec39aa6' 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-716a739f-a523-4a7f-bbc9-215e09f9e6f2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-716a739f-a523-4a7f-bbc9-215e09f9e6f2' 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>20250904-040940-842-2a7f5b</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:                             20250904-040940-842-2a7f5b\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 0x7bf846ff9c70>"
      ]
     },
     "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": "f78b9e183d204966b7e3f25b9e2e2edd",
       "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 0x7bf846ff9c70>"
      ]
     },
     "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': 5.994542423605298e-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": "0ec3845a58ee490496305264610b6b90",
       "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:561\u001b[0m, in \u001b[0;36mMeasurementControl.run\u001b[0;34m(self, name, soft_avg, lazy_set, save_data)\u001b[0m\n\u001b[1;32m    559\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    560\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_finish()\n\u001b[0;32m--> 561\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset_post()\n\u001b[1;32m    563\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:1255\u001b[0m, in \u001b[0;36m_KeyboardInterruptManager.__exit__\u001b[0;34m(self, exc_type, exc_val, exc_tb)\u001b[0m\n\u001b[1;32m   1253\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   1254\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-> 1255\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   1256\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   1257\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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       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7bf846ff9c70>"
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     },
     "execution_count": 17,
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   ],
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   ]
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