{
 "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": "e42b7365fe2b45059d11ab6f78ba2f62",
       "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 0x7d2501e921c0>"
      ]
     },
     "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": "faee9560334e48d8804b374550b72e41",
       "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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",
      "text/plain": [
       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7d2501e921c0>"
      ]
     },
     "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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       "version_minor": 0
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      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
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       ".xr-attrs dt,\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 5kB\n",
       "Dimensions:  (dim_0: 300)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 2kB 0.0 1.003e-06 2.007e-06 ... 0.000299 0.0003\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 2kB 0.9972 1.063 1.065 ... -0.04871 0.07343 0.01665\n",
       "Attributes:\n",
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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-b16e1f1a-81c1-4ff3-996f-23c524b07836' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b16e1f1a-81c1-4ff3-996f-23c524b07836' 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-9f2a41fe-920b-486e-96fd-5fe61f4b61ed' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9f2a41fe-920b-486e-96fd-5fe61f4b61ed' 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-fd8d0707-7fe0-4c1a-88c9-b64e4f8e953f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd8d0707-7fe0-4c1a-88c9-b64e4f8e953f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79b54d35-b857-4c61-abc3-dce176e88d33' class='xr-var-data-in' type='checkbox'><label for='data-79b54d35-b857-4c61-abc3-dce176e88d33' 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-cd89f7e4-e957-4088-94f8-a1464a2cfd5c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cd89f7e4-e957-4088-94f8-a1464a2cfd5c' 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-42ef29e7-cf74-4c6b-87ca-7017300dc05a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42ef29e7-cf74-4c6b-87ca-7017300dc05a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-220ed868-6c96-4f25-99c4-bfabef822faf' class='xr-var-data-in' type='checkbox'><label for='data-220ed868-6c96-4f25-99c4-bfabef822faf' 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-563edcec-ff41-4091-b44c-f49c8d826b53' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-563edcec-ff41-4091-b44c-f49c8d826b53' 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-98548184-ecc1-4a32-a4c7-02ff18337563' class='xr-section-summary-in' type='checkbox'  checked><label for='section-98548184-ecc1-4a32-a4c7-02ff18337563' 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>20250528-190519-056-2e2901</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:                             20250528-190519-056-2e2901\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 0x7d2501e921c0>"
      ]
     },
     "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": "151aa192997940e18fbf5794c9174b1e",
       "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 0x7d2501e921c0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotmon.main_QtPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7ffa625",
   "metadata": {},
   "source": [
    "Success! We now have a smooth decay curve based on the characteristics of our qubit. All that remains is to run a fit against the expected values and we can solve for T1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bd8301b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tau': np.float64(5.994542423605298e-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": "9c0ce3b3522f43a795730c408fe92adb",
       "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:559\u001b[0m, in \u001b[0;36mMeasurementControl.run\u001b[0;34m(self, name, soft_avg, lazy_set, save_data)\u001b[0m\n\u001b[1;32m    557\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    558\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_finish()\n\u001b[0;32m--> 559\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset_post()\n\u001b[1;32m    561\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:1253\u001b[0m, in \u001b[0;36m_KeyboardInterruptManager.__exit__\u001b[0;34m(self, exc_type, exc_val, exc_tb)\u001b[0m\n\u001b[1;32m   1251\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   1252\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-> 1253\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   1254\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   1255\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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