{ "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": "9de4b9be01c74087aa8bf68fe8a4fa08", "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": { "image/png": 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", "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plotmon.main_QtPlot" ] }, { "cell_type": "markdown", "id": "f04a96a0", "metadata": {}, "source": [ "As expected, we find a Lorentzian spike in the readout at the resonant frequency, finding the peak of which is trivial.\n", "\n", "#### Memory-limited Settables/Gettables\n", "\n", "Instruments (either physical or virtual) operating in `batched` mode have an upper limit on how many datapoints can be processed at once.\n", "When an experiment is comprised of more datapoints than the instrument can handle, the {class}`.MeasurementControl` takes care of fulfilling the measurement of all the requested setpoints by running and an internal loop.\n", "\n", "By default the {class}`.MeasurementControl` assumes no limitations and passes all setpoints to the `batched` settable. However, as a best practice, the instrument limitation must be reflected by the `.batch_size` attribute of the `batched` settables. This is illustrated below." ] }, { "cell_type": "code", "execution_count": 8, "id": "11f48755", "metadata": { "mystnb": { "remove-output": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting batched measurement...\n", "Iterative settable(s) [outer loop(s)]:\n", "\t --- (None) --- \n", "Batched settable(s):\n", "\t frequency \n", "Batch size limit: 256\n", "\n", "\n", "Prepared Resonator...\n", "\n", "Prepared Resonator...\n", "\n", "Prepared Resonator...\n", "\n", "Prepared Resonator...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2c4811f219dd4b6f90ffb39f07e062a8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Completed: 0%| [ elapsed time: 00:00 | time left: ? ] it" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Prepared Resonator...\n", "\n", "Prepared Resonator...\n", "\n", "Prepared Resonator...\n", "\n", "Prepared Resonator...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Finished Resonator...\n" ] } ], "source": [ "# Tells meas_ctrl that only 256 datapoints can be processed at once\n", "freq.batch_size = 256\n", "\n", "gettable_res.delay = 0.05 # short delay for plotting\n", "meas_ctrl.settables(freq)\n", "meas_ctrl.setpoints(np.arange(6.0001e9, 6.00011e9, 5))\n", "meas_ctrl.gettables(gettable_res)\n", "dset = meas_ctrl.run()" ] }, { "cell_type": "code", "execution_count": 9, "id": "bd0cbdc5", "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "" ] }, "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" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e922bcfc95494a2da81d8944b0a286e8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Completed: 0%| [ elapsed time: 00:00 | time left: ? ] it" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
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<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:                             20250706-041117-632-2e91b5\n",
       "    name:                             noisy\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  False
" ], "text/plain": [ " 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: 20250706-041117-632-2e91b5\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": [ "" ] }, "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": "6c8d8c3a7571485fbedf3514b5a8488f", "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": [ "" ] }, "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": [ "
" ] }, "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": "666cfc25d90c439c9bb8146a157c417e", "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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", 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