{ "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": "48726e8b6358454ebb81363a741330c6", "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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Prepared Resonator...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1de7dd87e7034f34b368c9c337a339bc", "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": [ "" ] }, "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": "031f5316443643f18dc71eab9ea4b248", "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:                             20250320-201249-704-710273\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: 20250320-201249-704-710273\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": "f8d7b4b888564c998ed93f76c705a90b", "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": "a1d6ae15f38f48b3941e704d162d9fcf", "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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