{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "71d00eeb",
   "metadata": {},
   "source": [
    "# Tutorial 2. Advanced capabilities of the MeasurementControl\n",
    "\n",
    "```{seealso}\n",
    "The complete source code of this tutorial can be found in\n",
    "\n",
    "{nb-download}`Tutorial 2. Advanced capabilities of the MeasurementControl.ipynb`\n",
    "```\n",
    "\n",
    "Following this Tutorial requires familiarity with the **core concepts** of Quantify, we **highly recommended** to consult the (short) {ref}`User guide` before proceeding (see Quantify documentation). If you have some difficulties following the tutorial it might be worth reviewing the {ref}`User guide`!\n",
    "\n",
    "We **highly recommended** beginning with {ref}`Tutorial 1. Controlling a basic experiment using MeasurementControl` before proceeding.\n",
    "\n",
    "In this tutorial, we will explore the more advanced features of Quantify. By the end of this tutorial, we will have covered:\n",
    "\n",
    "- Using hardware to drive experiments\n",
    "- Software averaging\n",
    "- Interrupting an experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f5bfac56",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Imports and auxiliary utilities"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1065/1774270832.py:10: DeprecationWarning: This package has reached its end of life. It is no longer maintained and will not receive any further updates or support. For further developments, please refer to the new Quantify repository: https://gitlab.com/quantify-os/quantify.All existing functionalities can be accessed via the new Quantify repository.\n",
      "  import quantify_core.visualization.pyqt_plotmon as pqm\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import signal\n",
    "import sys\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "from lmfit import Model\n",
    "from qcodes import ManualParameter\n",
    "\n",
    "import quantify_core.visualization.pyqt_plotmon as pqm\n",
    "from quantify_core.data.handling import set_datadir, default_datadir\n",
    "from quantify_core.measurement.control import MeasurementControl\n",
    "from quantify_core.visualization.instrument_monitor import InstrumentMonitor\n",
    "\n",
    "rng = np.random.default_rng(seed=222222)  # random number generator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b88a56",
   "metadata": {},
   "source": [
    "**Before instantiating any instruments or starting a measurement** we change the\n",
    "directory in which the experiments are saved using the\n",
    "{meth}`~quantify_core.data.handling.set_datadir`\n",
    "\\[{meth}`~quantify_core.data.handling.get_datadir`\\] functions.\n",
    "\n",
    "----------------------------------------------------------------------------------------\n",
    "\n",
    "⚠️ **Warning!**\n",
    "\n",
    "We recommend always setting the directory at the start of the Python kernel and sticking to a single common data directory for all notebooks/experiments within your\n",
    "measurement setup/PC.\n",
    "\n",
    "The cell below sets a default data directory (`~/quantify-data` on Linux/macOS or\n",
    "`$env:USERPROFILE\\\\quantify-data` on Windows) for tutorial purposes. Change it to your\n",
    "desired data directory. The utilities to find/search/extract data only work if\n",
    "all the experiment containers are located within the same directory.\n",
    "\n",
    "----------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "21867a1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data will be saved in:\n",
      "/root/quantify-data\n"
     ]
    }
   ],
   "source": [
    "set_datadir(default_datadir())  # change me!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d236c77d",
   "metadata": {
    "mystnb": {
     "remove-output": true
    }
   },
   "outputs": [],
   "source": [
    "meas_ctrl = MeasurementControl(\"meas_ctrl\")\n",
    "plotmon = pqm.PlotMonitor_pyqt(\"plotmon_meas_ctrl\")\n",
    "meas_ctrl.instr_plotmon(plotmon.name)\n",
    "insmon = InstrumentMonitor(\"InstrumentMonitor\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75bac5f5",
   "metadata": {},
   "source": [
    "## A 1D Batched loop: Resonator Spectroscopy\n",
    "\n",
    "### Defining a simple model\n",
    "\n",
    "In this example, we want to find the resonance of some devices. We expect to find its resonance somewhere in the low 6 GHz range, but manufacturing imperfections make it impossible to know exactly without inspection.\n",
    "\n",
    "We first create `freq`: a {class}`.Settable` with a {class}`~qcodes.parameters.Parameter` to represent the frequency of the signal probing the resonator, followed by a custom {class}`.Gettable` to mock (i.e. emulate) the resonator.\n",
    "The {class}`!Resonator` will return a Lorentzian shape centered on the resonant frequency. Our {class}`.Gettable` will read the setpoints from `freq`, in this case a 1D array.\n",
    "\n",
    "```{note}\n",
    "The `Resonator` {class}`.Gettable` has a new attribute `.batched` set to `True`. This property informs the {class}`.MeasurementControl` that it will not be in charge of iterating over the setpoints, instead the `Resonator` manages its own data acquisition. Similarly, the `freq` {class}`.Settable` must have a `.batched=True` so that the {class}`.MeasurementControl` hands over the setpoints correctly.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5c4c4fe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that in an actual experimental setup `freq` will be a QCoDeS parameter\n",
    "# contained in a QCoDeS Instrument\n",
    "freq = ManualParameter(name=\"frequency\", unit=\"Hz\", label=\"Frequency\")\n",
    "freq.batched = True  # Tells meas_ctrl that the setpoints are to be passed in batches\n",
    "\n",
    "\n",
    "def lorenz(amplitude: float, fwhm: float, x: int, x_0: float):\n",
    "    \"\"\"Model of the frequency response.\"\"\"\n",
    "    return amplitude * ((fwhm / 2.0) ** 2) / ((x - x_0) ** 2 + (fwhm / 2.0) ** 2)\n",
    "\n",
    "\n",
    "class Resonator:\n",
    "    \"\"\"\n",
    "    Note that the Resonator is a valid Gettable not because of inheritance,\n",
    "    but because it has the expected attributes and methods.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self) -> None:\n",
    "        self.name = \"resonator\"\n",
    "        self.unit = \"V\"\n",
    "        self.label = \"Amplitude\"\n",
    "        self.batched = True\n",
    "        self.delay = 0.0\n",
    "\n",
    "        # hidden variables specifying the resonance\n",
    "        self._test_resonance = 6.0001048e9  # in Hz\n",
    "        self._test_width = 300  # FWHM in Hz\n",
    "\n",
    "    def get(self) -> float:\n",
    "        \"\"\"Emulation of the frequency response.\"\"\"\n",
    "        time.sleep(self.delay)\n",
    "        _lorenz = lambda x: lorenz(1, self._test_width, x, self._test_resonance)\n",
    "        return 1 - np.array(list(map(_lorenz, freq())))\n",
    "\n",
    "    def prepare(self) -> None:\n",
    "        \"\"\"Adding this print statement is not required but added for illustrative\n",
    "        purposes.\"\"\"\n",
    "        print(\"\\nPrepared Resonator...\")\n",
    "\n",
    "    def finish(self) -> None:\n",
    "        \"\"\"Adding this print statement is not required but added for illustrative\n",
    "        purposes.\"\"\"\n",
    "        print(\"\\nFinished Resonator...\")\n",
    "\n",
    "\n",
    "gettable_res = Resonator()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eba38ef",
   "metadata": {},
   "source": [
    "### Running the experiment\n",
    "\n",
    "Just like our Iterative 1D loop, our complete experiment is expressed in just four lines of code.\n",
    "\n",
    "The main difference is defining the `batched` property of our {class}`.Gettable` to `True`.\n",
    "The {class}`.MeasurementControl` will detect these settings and run in the appropriate mode.\n",
    "\n",
    "At this point the `freq` parameter is empty:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "98c44659",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(freq())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fbdd9d47",
   "metadata": {
    "mystnb": {
     "remove-output": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t --- (None) --- \n",
      "Batched settable(s):\n",
      "\t frequency \n",
      "Batch size limit: 2000\n",
      "\n",
      "\n",
      "Prepared Resonator...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "85e1a8867d1245bd8d2b6adb1aaa3bce",
       "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 0x7943a4b269a0>"
      ]
     },
     "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": "a7f5b88a0c664f2091ee13f3f7e4474c",
       "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 0x7943a4b269a0>"
      ]
     },
     "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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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 5kB\n",
       "Dimensions:  (dim_0: 300)\n",
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       "    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-db7c9cb3-fa2a-48b0-b698-6b46ce528e84' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-db7c9cb3-fa2a-48b0-b698-6b46ce528e84' 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-14da4074-7a43-470b-a711-67f726cf7642' class='xr-section-summary-in' type='checkbox'  checked><label for='section-14da4074-7a43-470b-a711-67f726cf7642' 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-81b19150-a128-4358-986e-edfe00078f0b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81b19150-a128-4358-986e-edfe00078f0b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93b9b963-f278-44a1-9b09-f00d3d29e964' class='xr-var-data-in' type='checkbox'><label for='data-93b9b963-f278-44a1-9b09-f00d3d29e964' 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-5ece9da3-e7d4-41b0-bc76-6d4c0d0e6dc5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5ece9da3-e7d4-41b0-bc76-6d4c0d0e6dc5' 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-4bb7538b-e33f-4001-8c22-7e214f1a015b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bb7538b-e33f-4001-8c22-7e214f1a015b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7896a217-08f2-4e8c-8322-49640a5a6505' class='xr-var-data-in' type='checkbox'><label for='data-7896a217-08f2-4e8c-8322-49640a5a6505' 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-5d34aab0-a3f2-46f7-8e73-cf6970dfaa50' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5d34aab0-a3f2-46f7-8e73-cf6970dfaa50' 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-0684dc85-48f8-4893-b542-3b976b150405' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0684dc85-48f8-4893-b542-3b976b150405' 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>20250818-113120-971-a43748</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:                             20250818-113120-971-a43748\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 0x7943a4b269a0>"
      ]
     },
     "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": "b0146280fb2a4a329de0c80ece92540f",
       "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 0x7943a4b269a0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotmon.main_QtPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7ffa625",
   "metadata": {},
   "source": [
    "Success! We now have a smooth decay curve based on the characteristics of our qubit. All that remains is to run a fit against the expected values and we can solve for T1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bd8301b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tau': 5.994542423605298e-05}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Model(decay, independent_vars=[\"t\"])\n",
    "fit_res = model.fit(dset[\"y0\"].values, t=dset[\"x0\"].values, tau=1)\n",
    "\n",
    "fit_res.plot_fit(show_init=True)\n",
    "fit_res.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a36dec",
   "metadata": {},
   "source": [
    "## Interrupting\n",
    "Sometimes experiments, unfortunately, do not go as planned and it is desirable to interrupt and restart them with new parameters. In the following example, we have a long-running experiment where our Gettable is taking a long time to return data (maybe due to misconfiguration).\n",
    "Rather than waiting for this experiment to complete, instead we can interrupt any {class}`.MeasurementControl` loop using the standard interrupt signal.\n",
    "In a terminal environment this is usually achieved with a `ctrl` + `c` press on the keyboard or equivalent, whilst in a Jupyter environment interrupting the kernel (stop button) will cause the same result.\n",
    "\n",
    "When the {class}`.MeasurementControl` is interrupted, it will wait to obtain the results of the current iteration (or batch) and perform a final save of the data it has gathered, calling the `finish()` method on Settables & Gettables (if it exists) and return the partially completed dataset.\n",
    "\n",
    "```{note}\n",
    "The exact means of triggering an interrupt will differ depending on your platform and environment; the important part is to cause a `KeyboardInterrupt` exception to be raised in the Python process.\n",
    "```\n",
    "\n",
    "In case the current iteration is taking too long to complete (e.g. instruments not responding), you may force the execution of any python code to stop by signaling the same interrupt 5 times (e.g. pressing 5 times `ctrl` + `c`). Mind that performing this too fast might result in the `KeyboardInterrupt` not being properly handled and corrupting the dataset!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e1b46c8b",
   "metadata": {
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting iterative measurement...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f664fe442e8c46b580e4d073c7a9d995",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "[!!!] 1 interruption(s) signaled. Stopping after this iteration/batch.\n",
      "[Send 4 more interruptions to forcestop (not safe!)].\n",
      "\n",
      "\n",
      "Interrupt signaled, exiting gracefully...\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[16], line 27\u001b[0m\n\u001b[1;32m     25\u001b[0m meas_ctrl\u001b[38;5;241m.\u001b[39mgettables(SlowGettable())\n\u001b[1;32m     26\u001b[0m \u001b[38;5;66;03m# Try interrupting me!\u001b[39;00m\n\u001b[0;32m---> 27\u001b[0m dset \u001b[38;5;241m=\u001b[39m \u001b[43mmeas_ctrl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mslow\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/quantify_core/measurement/control.py:561\u001b[0m, in \u001b[0;36mMeasurementControl.run\u001b[0;34m(self, name, soft_avg, lazy_set, save_data)\u001b[0m\n\u001b[1;32m    559\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_safe_write_dataset()  \u001b[38;5;66;03m# Wrap up experiment and store data\u001b[39;00m\n\u001b[1;32m    560\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_finish()\n\u001b[0;32m--> 561\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset_post()\n\u001b[1;32m    563\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/quantify_core/measurement/control.py:1255\u001b[0m, in \u001b[0;36m_KeyboardInterruptManager.__exit__\u001b[0;34m(self, exc_type, exc_val, exc_tb)\u001b[0m\n\u001b[1;32m   1253\u001b[0m signal\u001b[38;5;241m.\u001b[39msignal(signal\u001b[38;5;241m.\u001b[39mSIGINT, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_previous_handler)\n\u001b[1;32m   1254\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_interrupts \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:  \u001b[38;5;66;03m# call outside handler on exit\u001b[39;00m\n\u001b[0;32m-> 1255\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_previous_handler\u001b[49m\u001b[43m(\u001b[49m\u001b[43msignal\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSIGINT\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m   1256\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_interrupts \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m   1257\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_previous_handler \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "class SlowGettable:\n",
    "    \"\"\"A mock slow gettables.\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        self.name = \"slow\"\n",
    "        self.label = \"Amplitude\"\n",
    "        self.unit = \"V\"\n",
    "\n",
    "    def get(self):\n",
    "        \"\"\"Get method.\"\"\"\n",
    "        time.sleep(1.0)\n",
    "        if time_par() == 4:\n",
    "            # This same exception rises when pressing `ctrl` + `c`\n",
    "            # or the \"Stop kernel\" button is pressed in a Jupyter(Lab) notebook\n",
    "            if sys.platform == \"win32\":\n",
    "                # Emulating the kernel interrupt on windows might have side effects\n",
    "                raise KeyboardInterrupt\n",
    "            os.kill(os.getpid(), signal.SIGINT)\n",
    "        return time_par()\n",
    "\n",
    "\n",
    "time_par.batched = False\n",
    "meas_ctrl.settables(time_par)\n",
    "meas_ctrl.setpoints(np.arange(10))\n",
    "meas_ctrl.gettables(SlowGettable())\n",
    "# Try interrupting me!\n",
    "dset = meas_ctrl.run(\"slow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a33155c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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