{
 "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": "912ef43fe82b4083a7cf99f16e249005",
       "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 0x7bec37359550>"
      ]
     },
     "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"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7544b8bf3e3c4c638ddafbb7e0183673",
       "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",
      "\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 0x7bec37359550>"
      ]
     },
     "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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       "Dimensions without coordinates: dim_0\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-5cc171d4-b053-4114-884c-590afe32af64' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5cc171d4-b053-4114-884c-590afe32af64' 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-47c5fe94-bfc2-4082-ac3f-5ba7f8f766af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-47c5fe94-bfc2-4082-ac3f-5ba7f8f766af' 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-818ae7ad-73a9-45e1-bdfe-ddf97aaf99fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-818ae7ad-73a9-45e1-bdfe-ddf97aaf99fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a71036b-1673-415d-b229-5ccf153388a2' class='xr-var-data-in' type='checkbox'><label for='data-7a71036b-1673-415d-b229-5ccf153388a2' 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-472171a1-0f8f-4b53-987a-8258cbbac849' class='xr-section-summary-in' type='checkbox'  checked><label for='section-472171a1-0f8f-4b53-987a-8258cbbac849' 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-40d17417-04ab-4891-ace9-b3e8d081ed16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40d17417-04ab-4891-ace9-b3e8d081ed16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f88dd50c-8c22-4427-a379-2823f46b68eb' class='xr-var-data-in' type='checkbox'><label for='data-f88dd50c-8c22-4427-a379-2823f46b68eb' 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-2758b86c-c924-4a96-99ef-66d3ed14c6e8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2758b86c-c924-4a96-99ef-66d3ed14c6e8' 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-000199f7-02e6-475c-82ee-ce9bc65d0368' class='xr-section-summary-in' type='checkbox'  checked><label for='section-000199f7-02e6-475c-82ee-ce9bc65d0368' 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>20240829-164521-904-b2f13e</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:                             20240829-164521-904-b2f13e\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 0x7bec37359550>"
      ]
     },
     "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": "e39018607b854e71a449eba43f44118a",
       "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 0x7bec37359550>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotmon.main_QtPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7ffa625",
   "metadata": {},
   "source": [
    "Success! We now have a smooth decay curve based on the characteristics of our qubit. All that remains is to run a fit against the expected values and we can solve for T1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bd8301b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tau': 5.994542423605272e-05}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Model(decay, independent_vars=[\"t\"])\n",
    "fit_res = model.fit(dset[\"y0\"].values, t=dset[\"x0\"].values, tau=1)\n",
    "\n",
    "fit_res.plot_fit(show_init=True)\n",
    "fit_res.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a36dec",
   "metadata": {},
   "source": [
    "## Interrupting\n",
    "Sometimes experiments, unfortunately, do not go as planned and it is desirable to interrupt and restart them with new parameters. In the following example, we have a long-running experiment where our Gettable is taking a long time to return data (maybe due to misconfiguration).\n",
    "Rather than waiting for this experiment to complete, instead we can interrupt any {class}`.MeasurementControl` loop using the standard interrupt signal.\n",
    "In a terminal environment this is usually achieved with a `ctrl` + `c` press on the keyboard or equivalent, whilst in a Jupyter environment interrupting the kernel (stop button) will cause the same result.\n",
    "\n",
    "When the {class}`.MeasurementControl` is interrupted, it will wait to obtain the results of the current iteration (or batch) and perform a final save of the data it has gathered, calling the `finish()` method on Settables & Gettables (if it exists) and return the partially completed dataset.\n",
    "\n",
    "```{note}\n",
    "The exact means of triggering an interrupt will differ depending on your platform and environment; the important part is to cause a `KeyboardInterrupt` exception to be raised in the Python process.\n",
    "```\n",
    "\n",
    "In case the current iteration is taking too long to complete (e.g. instruments not responding), you may force the execution of any python code to stop by signaling the same interrupt 5 times (e.g. pressing 5 times `ctrl` + `c`). Mind that performing this too fast might result in the `KeyboardInterrupt` not being properly handled and corrupting the dataset!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e1b46c8b",
   "metadata": {
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting iterative measurement...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a538a0af35144deb18fbeb9583bb70e",
       "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:406\u001b[0m, in \u001b[0;36mMeasurementControl.run\u001b[0;34m(self, name, soft_avg, lazy_set, save_data)\u001b[0m\n\u001b[1;32m    404\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    405\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_finish()\n\u001b[0;32m--> 406\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset_post()\n\u001b[1;32m    408\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:1075\u001b[0m, in \u001b[0;36m_KeyboardInterruptManager.__exit__\u001b[0;34m(self, exc_type, exc_val, exc_tb)\u001b[0m\n\u001b[1;32m   1073\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   1074\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-> 1075\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   1076\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   1077\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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