{
 "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": "a53f129d3f144b349a4568a0aa544489",
       "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 0x7e6b58cdbca0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotmon.main_QtPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f04a96a0",
   "metadata": {},
   "source": [
    "As expected, we find a Lorentzian spike in the readout at the resonant frequency, finding the peak of which is trivial.\n",
    "\n",
    "#### Memory-limited Settables/Gettables\n",
    "\n",
    "Instruments (either physical or virtual) operating in `batched` mode have an upper limit on how many datapoints can be processed at once.\n",
    "When an experiment is comprised of more datapoints than the instrument can handle, the {class}`.MeasurementControl` takes care of fulfilling the measurement of all the requested setpoints by running and an internal loop.\n",
    "\n",
    "By default the {class}`.MeasurementControl` assumes no limitations and passes all setpoints to the `batched` settable. However, as a best practice, the instrument limitation must be reflected by the `.batch_size` attribute of the `batched` settables. This is illustrated below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "11f48755",
   "metadata": {
    "mystnb": {
     "remove-output": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t --- (None) --- \n",
      "Batched settable(s):\n",
      "\t frequency \n",
      "Batch size limit: 256\n",
      "\n",
      "\n",
      "Prepared Resonator...\n",
      "\n",
      "Prepared Resonator...\n",
      "\n",
      "Prepared Resonator...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b3dc29115a3447dd91844393ec9a6a7f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Prepared Resonator...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Prepared Resonator...\n",
      "\n",
      "Prepared Resonator...\n",
      "\n",
      "Prepared Resonator...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Prepared Resonator...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Finished Resonator...\n"
     ]
    }
   ],
   "source": [
    "# Tells meas_ctrl that only 256 datapoints can be processed at once\n",
    "freq.batch_size = 256\n",
    "\n",
    "gettable_res.delay = 0.05  # short delay for plotting\n",
    "meas_ctrl.settables(freq)\n",
    "meas_ctrl.setpoints(np.arange(6.0001e9, 6.00011e9, 5))\n",
    "meas_ctrl.gettables(gettable_res)\n",
    "dset = meas_ctrl.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bd0cbdc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7e6b58cdbca0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotmon.main_QtPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1a6c5b0",
   "metadata": {},
   "source": [
    "## Software Averaging: T1 Experiment\n",
    "\n",
    "In many cases it is desirable to run an experiment many times and average the result, such as when filtering noise on instruments or measuring probability.\n",
    "For this purpose, the {meth}`.MeasurementControl.run` provides the `soft_avg` argument.\n",
    "If set to *x*, the experiment will run *x* times whilst performing a running average over each setpoint.\n",
    "\n",
    "In this example, we want to find the relaxation time (aka T1) of a Qubit. As before, we define a {class}`.Settable` and {class}`.Gettable`, representing the varying timescales we will probe through and a mock Qubit emulated in software.\n",
    "The mock Qubit returns the expected decay sweep but with a small amount of noise (simulating the variable qubit characteristics). We set the qubit's T1 to 60 ms - obviously in a real experiment we would be trying to determine this, but for this illustration purposes in this tutorial we set it to a known value to verify our fit later on.\n",
    "\n",
    "Note that in this example meas_ctrl is still running in Batched mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a8fd9739",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decay(t, tau):\n",
    "    \"\"\"T1 experiment decay model.\"\"\"\n",
    "    return np.exp(-t / tau)\n",
    "\n",
    "\n",
    "time_par = ManualParameter(name=\"time\", unit=\"s\", label=\"Measurement Time\")\n",
    "# Tells meas_ctrl that the setpoints are to be passed in batches\n",
    "time_par.batched = True\n",
    "\n",
    "\n",
    "class MockQubit:\n",
    "    \"\"\"A mock qubit.\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        self.name = \"qubit\"\n",
    "        self.unit = \"%\"\n",
    "        self.label = \"High V\"\n",
    "        self.batched = True\n",
    "\n",
    "        self.delay = 0.01  # sleep time in secs\n",
    "        self.test_relaxation_time = 60e-6\n",
    "\n",
    "    def get(self):\n",
    "        \"\"\"Adds a delay to be able to appreciate the data acquisition.\"\"\"\n",
    "        time.sleep(self.delay)\n",
    "        rel_time = self.test_relaxation_time\n",
    "        _func = lambda x: decay(x, rel_time) + rng.uniform(-0.1, 0.1)\n",
    "        return np.array(list(map(_func, time_par())))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0ad9691",
   "metadata": {},
   "source": [
    "We will then sweep through 0 to 300 ms, getting our data from the mock Qubit. Let's first observe what a single run looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a04da706",
   "metadata": {
    "mystnb": "remove-output:true"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t --- (None) --- \n",
      "Batched settable(s):\n",
      "\t time \n",
      "Batch size limit: 300\n",
      "\n"
     ]
    },
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      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 5kB\n",
       "Dimensions:  (dim_0: 300)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 2kB 0.0 1.003e-06 2.007e-06 ... 0.000299 0.0003\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 2kB 0.9972 1.063 1.065 ... -0.04871 0.07343 0.01665\n",
       "Attributes:\n",
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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-66382bfc-a23d-4f3d-940f-e2b308fe93bb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-66382bfc-a23d-4f3d-940f-e2b308fe93bb' 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-37262e0c-b584-48da-abaa-45f828e77244' class='xr-section-summary-in' type='checkbox'  checked><label for='section-37262e0c-b584-48da-abaa-45f828e77244' 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-e793d8b6-a605-4784-9e4b-6580d77077d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e793d8b6-a605-4784-9e4b-6580d77077d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d62498f-7827-4a1e-8152-2647da2613f8' class='xr-var-data-in' type='checkbox'><label for='data-6d62498f-7827-4a1e-8152-2647da2613f8' 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-68b105fd-98b5-4cde-aa49-e5b5bd0df39d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-68b105fd-98b5-4cde-aa49-e5b5bd0df39d' 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-dc32c80a-9b16-493b-91da-17ac1c4775f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc32c80a-9b16-493b-91da-17ac1c4775f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79799f36-f715-4a56-8ff6-dc10424ec722' class='xr-var-data-in' type='checkbox'><label for='data-79799f36-f715-4a56-8ff6-dc10424ec722' 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-34135b59-687f-45cd-b629-a0f024059677' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-34135b59-687f-45cd-b629-a0f024059677' 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-81e5cb5b-b9c7-4a28-aea7-d83f89bf266c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-81e5cb5b-b9c7-4a28-aea7-d83f89bf266c' 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>20250708-040916-742-fe07c9</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:                             20250708-040916-742-fe07c9\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 0x7e6b58cdbca0>"
      ]
     },
     "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": "9749cc8f035640fab73dec57889e0580",
       "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 0x7e6b58cdbca0>"
      ]
     },
     "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": "f32b156695d94b7f84808e9f8f525e30",
       "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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",
      "text/plain": [
       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7e6b58cdbca0>"
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     },
     "execution_count": 17,
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   ],
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   ]
  }
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