{
 "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": "ed970eddec0541fa8fa79e6a2d561a6d",
       "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 0x7da612e6fdf0>"
      ]
     },
     "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": "c07e600944c340e385698949d5b17919",
       "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 0x7da612e6fdf0>"
      ]
     },
     "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-80856875-c113-4b3f-a189-6cceb94f6611' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-80856875-c113-4b3f-a189-6cceb94f6611' 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-f82beb9e-7a70-4686-a1d9-7fe096ee0245' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f82beb9e-7a70-4686-a1d9-7fe096ee0245' 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-a379ba33-4e3f-443b-9e02-91490b106e42' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a379ba33-4e3f-443b-9e02-91490b106e42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd5ec8e0-c062-4646-989c-eb3eec8c4eb1' class='xr-var-data-in' type='checkbox'><label for='data-fd5ec8e0-c062-4646-989c-eb3eec8c4eb1' 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-7e367e3f-bc0c-4239-bd66-f56ef0d2c910' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7e367e3f-bc0c-4239-bd66-f56ef0d2c910' 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-b152a874-3533-4525-9748-7e98fad97aab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b152a874-3533-4525-9748-7e98fad97aab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-402cad79-811f-4015-88e0-8a7b6e282345' class='xr-var-data-in' type='checkbox'><label for='data-402cad79-811f-4015-88e0-8a7b6e282345' 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-203ac112-85c8-40e0-8f04-e7d4387dfd66' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-203ac112-85c8-40e0-8f04-e7d4387dfd66' 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-1583155a-cf28-44e2-8f43-b346d40dbe9c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1583155a-cf28-44e2-8f43-b346d40dbe9c' 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>20241014-175635-787-aa7fd6</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:                             20241014-175635-787-aa7fd6\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 0x7da612e6fdf0>"
      ]
     },
     "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": "87254041c4794459a9f3470cb04cc91f",
       "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 0x7da612e6fdf0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotmon.main_QtPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7ffa625",
   "metadata": {},
   "source": [
    "Success! We now have a smooth decay curve based on the characteristics of our qubit. All that remains is to run a fit against the expected values and we can solve for T1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bd8301b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tau': np.float64(5.994542423605272e-05)}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Model(decay, independent_vars=[\"t\"])\n",
    "fit_res = model.fit(dset[\"y0\"].values, t=dset[\"x0\"].values, tau=1)\n",
    "\n",
    "fit_res.plot_fit(show_init=True)\n",
    "fit_res.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a36dec",
   "metadata": {},
   "source": [
    "## Interrupting\n",
    "Sometimes experiments, unfortunately, do not go as planned and it is desirable to interrupt and restart them with new parameters. In the following example, we have a long-running experiment where our Gettable is taking a long time to return data (maybe due to misconfiguration).\n",
    "Rather than waiting for this experiment to complete, instead we can interrupt any {class}`.MeasurementControl` loop using the standard interrupt signal.\n",
    "In a terminal environment this is usually achieved with a `ctrl` + `c` press on the keyboard or equivalent, whilst in a Jupyter environment interrupting the kernel (stop button) will cause the same result.\n",
    "\n",
    "When the {class}`.MeasurementControl` is interrupted, it will wait to obtain the results of the current iteration (or batch) and perform a final save of the data it has gathered, calling the `finish()` method on Settables & Gettables (if it exists) and return the partially completed dataset.\n",
    "\n",
    "```{note}\n",
    "The exact means of triggering an interrupt will differ depending on your platform and environment; the important part is to cause a `KeyboardInterrupt` exception to be raised in the Python process.\n",
    "```\n",
    "\n",
    "In case the current iteration is taking too long to complete (e.g. instruments not responding), you may force the execution of any python code to stop by signaling the same interrupt 5 times (e.g. pressing 5 times `ctrl` + `c`). Mind that performing this too fast might result in the `KeyboardInterrupt` not being properly handled and corrupting the dataset!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e1b46c8b",
   "metadata": {
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting iterative measurement...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fe897890b7524f1c909ed691217ab8a0",
       "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:1080\u001b[0m, in \u001b[0;36m_KeyboardInterruptManager.__exit__\u001b[0;34m(self, exc_type, exc_val, exc_tb)\u001b[0m\n\u001b[1;32m   1078\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   1079\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-> 1080\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   1081\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   1082\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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