{ "cells": [ { "cell_type": "markdown", "id": "a7833fb6", "metadata": {}, "source": [ "(sec-tutorial-schedulegettable)=\n", "\n", "# Tutorial: ScheduleGettable\n", "\n", "```{seealso}\n", "The complete source code of this tutorial can be found in\n", "\n", "{nb-download}`ScheduleGettable.ipynb`\n", "```\n", "\n", "This tutorial covers the {class}`~quantify_scheduler.gettables.ScheduleGettable` in-depth. If you're looking for more information on how to set up an experiment in general, please see {ref}`sec-tutorial-experiment`.\n", "\n", "The {class}`~quantify_scheduler.gettables.ScheduleGettable` forms the top-level interface to {mod}`quantify_scheduler`. Under the hood, it uses `quantify-scheduler` modules to compile {ref}`Schedules `, run them on your hardware and retrieve measurement data from the hardware. As the {class}`~quantify_scheduler.gettables.ScheduleGettable` uses functions that return {class}`~quantify_scheduler.schedules.schedule.Schedule`s, you can dynamically set function parameters during experiments.\n", "\n", "For those familiar with [quantify-core](https://quantify-os.org/docs/quantify-core), the interface of the {class}`~quantify_scheduler.gettables.ScheduleGettable` is also designed to be used as a [gettable](https://quantify-os.org/docs/quantify-core/dev/user/concepts.html#settables-and-gettables) for [MeasurementControl](https://quantify-os.org/docs/quantify-core/dev/user/concepts.html#measurement-control). This is convenient for large, possibly multi-dimensional measurement loops, as is demonstrated in {ref}`2D (and ND) measurement loops`.\n", "\n", "Two things are always required when using a {class}`~quantify_scheduler.gettables.ScheduleGettable`: a {ref}`QuantumDevice ` and a function that returns a {class}`~quantify_scheduler.schedules.schedule.Schedule`. We will set these up in a few example scenarios below and show how to use the {class}`~quantify_scheduler.gettables.ScheduleGettable`. More commonly used schedule functions are also included in `quantify-scheduler` out of the box. You can find them in {mod}`~.quantify_scheduler.schedules.spectroscopy_schedules`, {mod}`~.quantify_scheduler.schedules.timedomain_schedules` and {mod}`~.quantify_scheduler.schedules.trace_schedules`.\n", "\n", "(sec-schedulegettable-1dsweep-usage)=\n", "\n", "## 1D iterative measurement loop\n", "\n", "\n", "```{admonition} Setup and hardware configuration\n", "The device setup and hardware configuration for this tutorial can be viewed in the collapsed code cells. In places where you would communicate with actual hardware, dummy objects have been used. If you want to learn more about how to set up the {class}`~quantify_scheduler.device_under_test.quantum_device.QuantumDevice` and hardware configuration, please see our other tutorials, in particular {ref}`sec-tutorial-experiment` and {ref}`sec-tutorial-compiling`.\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "id": "46210638", "metadata": { "mystnb": { "code_prompt_show": "Set up the quantum device, dummy hardware and hardware configuration" }, "tags": [ "hide-cell" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_2032/1151456847.py:2: 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", " from quantify_core.data import handling as dh\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Data will be saved in:\n", "/root/quantify-data\n" ] } ], "source": [ "from qblox_instruments import Cluster, ClusterType\n", "from quantify_core.data import handling as dh\n", "\n", "from quantify_scheduler import BasicTransmonElement, InstrumentCoordinator, QuantumDevice\n", "from quantify_scheduler.qblox import ClusterComponent, start_dummy_cluster_armed_sequencers\n", "\n", "# First, don't forget to set the data directory!\n", "dh.set_datadir() # change me!\n", "\n", "# We define a single transmon qubit as an element (BasicTransmonElement) of the\n", "# QuantumDevice, and populate the parameters with some reasonable values.\n", "\n", "# Device parameters\n", "ACQ_DELAY = 100e-9\n", "FREQ_01 = 4e9\n", "READOUT_AMP = 0.1\n", "READOUT_FREQ = 4.3e9\n", "PI_PULSE_AMP = 0.15\n", "LO_FREQ_QUBIT = 3.9e9\n", "LO_FREQ_READOUT = 4.5e9\n", "\n", "single_qubit_device = QuantumDevice(\"single_qubit_device\")\n", "\n", "q0 = BasicTransmonElement(\"q0\")\n", "single_qubit_device.add_element(q0)\n", "\n", "# Assign device parameters to transmon element\n", "q0.measure.pulse_amp(READOUT_AMP)\n", "q0.clock_freqs.readout(READOUT_FREQ)\n", "q0.clock_freqs.f01(FREQ_01)\n", "q0.measure.acq_delay(ACQ_DELAY)\n", "q0.rxy.amp180(PI_PULSE_AMP)\n", "\n", "# For this example, we will set up a Qblox cluster hardware setup with two modules: a\n", "# QRM-RF and a QCM-RF.\n", "\n", "# Note: if you are connecting to an actual cluster, you would provide the\n", "# 'identifier' argument (the ip address, device name or serial number) instead\n", "# of the 'dummy_cfg' argument.\n", "cluster = Cluster(\n", " \"cluster\",\n", " dummy_cfg={\n", " 1: ClusterType.CLUSTER_QRM_RF,\n", " 2: ClusterType.CLUSTER_QCM_RF,\n", " },\n", ")\n", "\n", "ic_cluster = ClusterComponent(cluster)\n", "\n", "# Temporarily fixing dummy cluster's deficiency.\n", "cluster.start_sequencer = lambda : start_dummy_cluster_armed_sequencers(ic_cluster)\n", "\n", "# We create an InstrumentCoordinator to control the cluster and add it to the\n", "# QuantumDevice.\n", "\n", "instrument_coordinator = InstrumentCoordinator(\"instrument_coordinator\")\n", "instrument_coordinator.add_component(ic_cluster)\n", "\n", "single_qubit_device.instr_instrument_coordinator(instrument_coordinator.name)\n", "\n", "# A basic hardware configuration will be used for the two cluster modules.\n", "\n", "hardware_cfg = {\n", " \"version\": \"0.2\",\n", " \"config_type\": \"quantify_scheduler.backends.qblox_backend.QbloxHardwareCompilationConfig\",\n", " \"hardware_description\": {\n", " f\"{cluster.name}\": {\n", " \"instrument_type\": \"Cluster\",\n", " \"modules\": {\n", " \"1\": {\n", " \"instrument_type\": \"QRM_RF\"\n", " },\n", " \"2\": {\n", " \"instrument_type\": \"QCM_RF\"\n", " }\n", " },\n", " \"ref\": \"internal\"\n", " }\n", " },\n", " \"hardware_options\": {\n", " \"modulation_frequencies\": {\n", " \"q0:res-q0.ro\": {\n", " \"lo_freq\": LO_FREQ_READOUT\n", " },\n", " \"q0:mw-q0.01\": {\n", " \"lo_freq\": LO_FREQ_QUBIT\n", " }\n", " }\n", " },\n", " \"connectivity\": {\n", " \"graph\": [\n", " [f\"{cluster.name}.module1.complex_output_0\", \"q0:res\"],\n", " [f\"{cluster.name}.module1.complex_input_0\", \"q0:res\"],\n", " [f\"{cluster.name}.module2.complex_output_0\", \"q0:mw\"]\n", " ]\n", " }\n", "}\n", "\n", "\n", "# This hardware configuration should also be added to the quantum device.\n", "single_qubit_device.hardware_config(hardware_cfg)" ] }, { "cell_type": "markdown", "id": "5918b2be", "metadata": {}, "source": [ "For this experiment, we have set up a basic {class}`~quantify_scheduler.device_under_test.quantum_device.QuantumDevice` called `single_qubit_device`, representing a single transmon qubit.\n", "\n", "We'll now define the schedule function. A valid schedule function must contain a `repetitions` (integer) parameter (see {ref}`Repetitions`), and can contain any number of additional parameters. It must return a {class}`~quantify_scheduler.schedules.schedule.Schedule` object.\n", "\n", "The schedule function can be parameterized to loop over different values. The parameters can be scalars or arrays. For example, the schedule function defined below takes an array of values for the parameter `times`. This is called a _batched_ measurement, as will be explained further in this tutorial." ] }, { "cell_type": "code", "execution_count": 2, "id": "be723bba", "metadata": {}, "outputs": [], "source": [ "from quantify_scheduler import Schedule\n", "from quantify_scheduler.operations import Measure, Reset, X\n", "\n", "def t1_sched(times, repetitions=1):\n", " schedule = Schedule(\"T1\", repetitions)\n", " for i, tau in enumerate(times):\n", " schedule.add(Reset(\"q0\"), label=f\"Reset {i}\")\n", " schedule.add(X(\"q0\"), label=f\"pi {i}\")\n", " # Measure tau seconds after the start of the X gate\n", " schedule.add(\n", " Measure(\"q0\"),\n", " ref_pt=\"start\",\n", " rel_time=tau,\n", " label=f\"Measurement {i}\",\n", " )\n", " return schedule" ] }, { "cell_type": "markdown", "id": "7a56b3f0", "metadata": {}, "source": [ "Now, let's create the {class}`~quantify_scheduler.gettables.ScheduleGettable`. The {class}`~quantify_scheduler.gettables.ScheduleGettable` provides a convenient way to compile and execute schedules in just a few lines of code. The parameters can be set directly, with arrays and scalars, or through QCoDeS parameters, as shown below." ] }, { "cell_type": "code", "execution_count": 3, "id": "e2be164d", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "from qcodes.instrument.parameter import ManualParameter\n", "\n", "from quantify_scheduler import ScheduleGettable\n", "\n", "# The points we want to measure.\n", "times = np.linspace(start=1.6e-7, stop=4.976e-5, num=125)\n", "\n", "# As QCoDeS parameter:\n", "time = ManualParameter(\"sample\", label=\"Sample time\", unit=\"s\")\n", "# Set the parameter. This can be done even after defining the gettable!\n", "time(times)\n", "\n", "# Or as array:\n", "time = times\n", "\n", "# Configure the gettable\n", "gettable = ScheduleGettable(\n", " quantum_device=single_qubit_device,\n", " schedule_function=t1_sched,\n", " schedule_kwargs={\"times\": time},\n", " batched=True\n", ")" ] }, { "cell_type": "markdown", "id": "5d200a0f", "metadata": {}, "source": [ "Note that `batched=True` here. This means we are doing a _batched_ measurement, which simply means that we tell the {class}`~quantify_scheduler.gettables.ScheduleGettable` to expect an array of values in the acquisition result. In this case, our schedule function creates one schedule for an array of times, and it includes an acquisition (the {class}`~quantify_scheduler.operations.gate_library.Measure` operation) for each point." ] }, { "cell_type": "code", "execution_count": 4, "id": "e47d66c0", "metadata": { "mystnb": { "code_prompt_show": "Provide the dummy hardware with acquisition data" }, "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "import xarray\n", "\n", "from quantify_scheduler.waveforms import soft_square\n", "\n", "from qblox_instruments import DummyScopeAcquisitionData, DummyBinnedAcquisitionData\n", "\n", "\n", "soft_sq_samples = round(q0.measure.integration_time() * 1e9)\n", "soft_sq_times = np.arange(soft_sq_samples)\n", "soft_sq = soft_square(t=soft_sq_times, amp=0.5)\n", "scope_data_real = np.zeros(16384) # Scope trace acquires 16384 points\n", "scope_data_real[:soft_sq_samples] = soft_sq\n", "scope_data_real += np.random.randn(16384) / 500 # add some \"noise\"\n", "\n", "# Create dummy scope data with the soft square pulse on the I path and noise on\n", "# the Q path\n", "scope_data = list(zip(scope_data_real, np.random.randn(16384) / 500))\n", "\n", "dummy_scope_acquisition_data = DummyScopeAcquisitionData(\n", " data=scope_data, out_of_range=(False, False), avg_cnt=(0, 0)\n", " )\n", "\n", "ic_cluster.instrument.set_dummy_scope_acquisition_data(\n", " slot_idx=1, sequencer=None, data=dummy_scope_acquisition_data\n", ")\n", "\n", "\n", "# Dataset with T1 experiment data\n", "example_dataset = xarray.open_dataset(\"../examples/dataset.hdf5\")\n", "\n", "def get_dummy_binned_acquisition_data(real: float, imag: float):\n", " return DummyBinnedAcquisitionData(data=(real, imag), thres=0, avg_cnt=0)\n", "\n", "ic_cluster.instrument.set_dummy_binned_acquisition_data(\n", " slot_idx=1,\n", " sequencer=0,\n", " acq_index_name=\"0\",\n", " data=[\n", " get_dummy_binned_acquisition_data(float(re) * 1024, float(im) * 1024)\n", " for re, im in zip(example_dataset[\"y0\"], example_dataset[\"y1\"])\n", " ],\n", ")" ] }, { "cell_type": "markdown", "id": "871e935b", "metadata": {}, "source": [ "Let's now run the experiment and retrieve the acquisitions using the {meth}`~quantify_scheduler.gettables.ScheduleGettable.get` method." ] }, { "cell_type": "code", "execution_count": 5, "id": "4bd8f0b1", "metadata": {}, "outputs": [ { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "\n", "data = gettable.get()\n", "\n", "plt.plot(times, data[0], '.', label=\"real\")\n", "plt.plot(times, data[1], '.', label=\"imaginary\")\n", "plt.legend()\n", "plt.xlabel(\"Idle time (s)\")\n", "plt.ylabel(\"Voltage (V)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "6ab4149b", "metadata": {}, "source": [ "Note that the data used here is the same as in {ref}`sec-tutorial-experiment`. The example dataset can be downloaded {download}`here <../examples/dataset.hdf5>`.\n", "\n", "As we defined 125 points in our `times` array which were measured in 125 different `acq_index`es, the acquisition result also contains 125 _I_ values (in `data[0][:]`) and 125 _Q_ values (in `data[1][:]`). The general format of the data returned by the {class}`~quantify_scheduler.gettables.ScheduleGettable` is also explained in the {ref}`user guide `.\n", "\n", "## Trace measurement\n", "\n", "The previous experiment's acquisition results had one data point for each acquisition in the schedule. For a trace measurement, the data format is slightly different. To illustrate this, let us set up an experiment with a trace measurement." ] }, { "cell_type": "code", "execution_count": 6, "id": "98a4a8c0", "metadata": {}, "outputs": [], "source": [ "from quantify_scheduler.operations import IdlePulse, SoftSquarePulse\n", "\n", "\n", "def trace_schedule(pulse_amp, acq_delay, port=\"q0:res\", clock=\"q0.ro\", repetitions=1):\n", " schedule = Schedule(\"Trace a pulse\", repetitions=repetitions)\n", "\n", " schedule.add(\n", " SoftSquarePulse(\n", " duration=q0.measure.integration_time(),\n", " amp=pulse_amp,\n", " port=port,\n", " clock=clock,\n", " ),\n", " label=\"trace_pulse\",\n", " )\n", "\n", " # Add acq_delay to compensate for time-of-flight of the pulse\n", " schedule.add(\n", " Measure(q0.name, acq_protocol=\"Trace\"),\n", " ref_pt=\"start\",\n", " rel_time=acq_delay,\n", " label=\"acquisition\"\n", " )\n", "\n", " return schedule" ] }, { "cell_type": "markdown", "id": "052d47e4", "metadata": {}, "source": [ "Again, we define the gettable and run the experiment." ] }, { "cell_type": "code", "execution_count": 7, "id": "4b6ea19d", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.9/site-packages/quantify_scheduler/backends/qblox/compiler_abc.py:632: RuntimeWarning: Operation is interrupting previous Pulse because it starts before the previous ends, offending operation: Pulse \"UpdateParameters\" (t0=1.0000000000000001e-07, duration=0)\n", " warnings.warn(\n" ] }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gettable = ScheduleGettable(\n", " quantum_device=single_qubit_device,\n", " schedule_function=trace_schedule,\n", " schedule_kwargs={\n", " \"pulse_amp\": 0.5,\n", " \"acq_delay\": 100e-9,\n", " },\n", " batched=True,\n", ")\n", "\n", "data = gettable.get()\n", "\n", "plt.plot(np.arange(1000)/1e9, data[0], '.', label=\"real\")\n", "plt.plot(np.arange(1000)/1e9, data[1], '.', label=\"imaginary\")\n", "plt.legend()\n", "plt.xlabel(\"Time (s)\")\n", "plt.ylabel(\"Voltage (V)\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "066523c5", "metadata": {}, "source": [ "This time, there is only one acquisition operation in the schedule. The measurement result contains two arrays: one for the _I_ values (`data[0][:]`), and one for the _Q_ values (`data[1][:]`) of the acquired trace.\n", "\n", "## MeasurementControl\n", "\n", "Iterating through different parameters can be done with some help from `quantify-core` as well, through the {class}`~quantify_core.measurement.control.MeasurementControl` class. The {class}`~quantify_core.measurement.control.MeasurementControl` uses settables (parameters to vary in an experiment) and gettables (in this case, our {class}`~quantify_scheduler.gettables.ScheduleGettable`). The settable must be a class that implements `set()`, such as a QCoDeS {class}`~qcodes.parameters.ManualParameter`.\n", "\n", "In this example, the settable is the `time` object, and the setpoints are a numpy array of values (`times`). These are added to the {class}`~quantify_core.measurement.control.MeasurementControl` as shown, together with the {class}`~quantify_scheduler.gettables.ScheduleGettable`. The {class}`~quantify_core.measurement.control.MeasurementControl` object will be in charge of setting the setpoints, and retrieving the measurement results through the gettable." ] }, { "cell_type": "code", "execution_count": 8, "id": "c8027a3d", "metadata": {}, "outputs": [], "source": [ "from quantify_core.measurement.control import MeasurementControl\n", "\n", "\n", "measurement_control = MeasurementControl(\"measurement_control\")\n", "\n", "# Configure the settable\n", "time = ManualParameter(\"sample\", label=\"Sample time\", unit=\"s\")\n", "time.batched = True\n", "\n", "times = np.linspace(start=1.6e-7, stop=4.976e-5, num=125)\n", "\n", "# Configure the gettable\n", "gettable = ScheduleGettable(\n", " quantum_device=single_qubit_device,\n", " schedule_function=t1_sched,\n", " schedule_kwargs={\"times\": time},\n", " batched=True\n", ")\n", "\n", "# Configure MeasurementControl\n", "measurement_control.settables(time)\n", "measurement_control.setpoints(times)\n", "measurement_control.gettables(gettable)" ] }, { "cell_type": "markdown", "id": "1688da08", "metadata": {}, "source": [ "The experiment is set to run fully in _batched_ mode. When using {class}`~quantify_core.measurement.control.MeasurementControl`, settables and gettables can be either batched (an array of points is set for each measurement iteration) or iterative (only one point is set per iteration). Combinations of batched and iterative settables are possible, as explained in detail in the [quantify-core documentation](https://quantify-os.org/docs/quantify-core/dev/user/concepts.html#mixing-iterative-and-batched-settables).\n", "\n", "Settables and gettables are batched if they have the attribute `batched=True`. In {class}`~quantify_scheduler.gettables.ScheduleGettable`, this can be controlled through the `batched` argument when creating the class. For other classes, the attribute can be added dynamically if needed, as shown above for the `time` parameter.\n", "\n", "With both the gettable and the settable having `batched=True`, the {class}`~quantify_core.measurement.control.MeasurementControl` knows that it should set the entire `times` array as the settable's value (instead of repeating the experiment for each value in the array). All data points are measured without interruption and all measurement results are returned in one go. Now, let's run the experiment and retrieve the data." ] }, { "cell_type": "code", "execution_count": 9, "id": "cd9f2b5e", "metadata": { "tags": [ "hide-output" ] }, "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 sample \n", "Batch size limit: 125\n", "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1ae7dd9a0ada4aba8e7c70c85eb08065", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Completed: 0%| [ elapsed time: 00:00 | time left: ? ] it" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dataset = measurement_control.run()" ] }, { "cell_type": "markdown", "id": "eb098cf6", "metadata": {}, "source": [ "The {class}`~quantify_core.measurement.control.MeasurementControl` class processes the data returned by the {class}`~quantify_scheduler.gettables.ScheduleGettable`, and turns it into a {class}`~xarray.Dataset`. More information on the format of this dataset can be found in the [quantify-core documentation](https://quantify-os.org/docs/quantify-core/dev/user/concepts.html#dataset)." ] }, { "cell_type": "code", "execution_count": 10, "id": "44f5b719", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 3kB\n",
       "Dimensions:  (dim_0: 125)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 1kB 1.6e-07 5.6e-07 9.6e-07 ... 4.936e-05 4.976e-05\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 1kB -1.166 -1.141 -1.109 ... -0.737 -0.7305 -1.164\n",
       "    y1       (dim_0) float64 1kB 0.2569 0.2138 0.2035 ... -0.2533 -0.247 0.2671\n",
       "Attributes:\n",
       "    tuid:                             20260619-134740-442-c14a8b\n",
       "    name:                             \n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  False
" ], "text/plain": [ " Size: 3kB\n", "Dimensions: (dim_0: 125)\n", "Coordinates:\n", " x0 (dim_0) float64 1kB 1.6e-07 5.6e-07 9.6e-07 ... 4.936e-05 4.976e-05\n", "Dimensions without coordinates: dim_0\n", "Data variables:\n", " y0 (dim_0) float64 1kB -1.166 -1.141 -1.109 ... -0.737 -0.7305 -1.164\n", " y1 (dim_0) float64 1kB 0.2569 0.2138 0.2035 ... -0.2533 -0.247 0.2671\n", "Attributes:\n", " tuid: 20260619-134740-442-c14a8b\n", " name: \n", " grid_2d: False\n", " grid_2d_uniformly_spaced: False\n", " 1d_2_settables_uniformly_spaced: False" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset" ] }, { "cell_type": "markdown", "id": "0a672e1d", "metadata": {}, "source": [ "The dataset coordinates and data variables are named as generic `x` and `y` parameters. You can click on the 'Show/Hide attributes' button next to the coordinates and variables to see what they refer to.\n", "\n", "(sec-tutorial-schedulegettable-repetitions)=\n", "## Repetitions\n", "\n", "Repetition defines how many times the defined schedule will run on the hardware. Running the schedule multiple times can be useful for example if the user would like to reduce errors of acquisitions by averaging the result of multiple measurements.\n", "\n", "There are multiple ways the repetitions can be set. They are not completely independent of each other, and which setting will be taken into account depends on how you create and use the schedule.\n", "\n", "1. {class}`~quantify_scheduler.device_under_test.quantum_device.QuantumDevice`: via `cfg_sched_repetitions` (by default it is `1024`),\n", "2. schedule function: via the `repetitions` function argument,\n", "3. {class}`~quantify_scheduler.schedules.schedule.Schedule` object: via the `repetitions` function argument of its constructor, or via its `repetitions` attribute directly,\n", "4. ({class}`~quantify_scheduler.gettables.ScheduleGettable`: currently, it is not possible to supply `repetitions` via `schedule_kwargs`).\n", "\n", "Ultimately, the {class}`~quantify_scheduler.schedules.schedule.Schedule` object is what governs the repetitions, via its `repetitions` attribute.\n", "\n", "When using a schedule function, the `repetitions` function argument sets the repetitions, provided that the schedule function passes it to the `Schedule` object. This is true for the pre-defined schedules, see {mod}`!quantify_scheduler.schedules`.\n", "\n", "However, if the experiment is run using `ScheduleGettable`, this `repetitions` function argument is set to `QuantumDevice.cfg_sched_repetitions`. Hence, typically, the schedule will run `QuantumDevice.cfg_sched_repetitions` times.\n", "\n", "### Possible mistake: ignoring `repetitions` argument\n", "\n", "Keep in mind that the schedule function should pass the `repetitions` argument to the `Schedule` initializer, otherwise both `QuantumDevice.cfg_sched_repetitions` and the `repetitions` argument of the schedule function will be ignored. For example, in the following setup, the `repetitions` will always be `1` (default for the `Schedule` object), even if `cfg_sched_repetitions` is set to `2`." ] }, { "cell_type": "code", "execution_count": 11, "id": "6a30cf84", "metadata": {}, "outputs": [], "source": [ "from quantify_scheduler import QuantumDevice, ScheduleGettable\n", "\n", "def schedule_function(q0: str, repetitions: int):\n", " schedule = Schedule(\"Example schedule\")\n", " schedule.add(Measure(q0))\n", " return schedule\n", "\n", "quantum_device = QuantumDevice(name=\"quantum_sample\")\n", "quantum_device.cfg_sched_repetitions(2)\n", "\n", "schedule_gettable = ScheduleGettable(\n", " quantum_device=quantum_device,\n", " schedule_function=schedule_function,\n", " schedule_kwargs={\"q0\": \"q0\"},\n", ")" ] }, { "cell_type": "markdown", "id": "eb97c001", "metadata": {}, "source": [ "### Possible mistake: ignoring `cfg_sched_repetitions` default value\n", "\n", "Also note, that the default value of the `repetitions` argument of the schedule function will be ignored if `ScheduleGettable` is used, and it will be set to `QuantumDevice.cfg_sched_repetitions`. For example, in the following setup, `repetitions` will be `1024` (default for `QuantumDevice`), even if the default argument for `repetitions` is `2`." ] }, { "cell_type": "code", "execution_count": 12, "id": "b43b88ec", "metadata": { "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "quantum_device.close()" ] }, { "cell_type": "code", "execution_count": 13, "id": "9e5cff4b", "metadata": {}, "outputs": [], "source": [ "from quantify_scheduler import QuantumDevice, ScheduleGettable\n", "\n", "def schedule_function(q0: str, repetitions: int = 2):\n", " schedule = Schedule(\"Example schedule\", repetitions=repetitions)\n", " schedule.add(Measure(q0))\n", " return schedule\n", "\n", "quantum_device = QuantumDevice(name=\"quantum_sample\")\n", "\n", "schedule_gettable = ScheduleGettable(\n", " quantum_device=quantum_device,\n", " schedule_function=schedule_function,\n", " schedule_kwargs={\"q0\": \"q0\"},\n", ")" ] }, { "cell_type": "markdown", "id": "a92e7b67", "metadata": {}, "source": [ "(sec-schedulegettable-2dsweep-usage)=\n", "\n", "## 2D (and ND) measurement loops" ] }, { "cell_type": "code", "execution_count": 14, "id": "ec12c009", "metadata": { "mystnb": { "code_prompt_show": "Set up the quantum device, dummy hardware and hardware configuration" }, "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "from qblox_instruments import Cluster, ClusterType, DummyBinnedAcquisitionData\n", "\n", "from quantify_scheduler import BasicTransmonElement, InstrumentCoordinator, QuantumDevice\n", "from quantify_scheduler.qblox import ClusterComponent\n", "\n", "\n", "single_qubit_device.close_all()\n", "\n", "# Device parameters\n", "Q0_ACQ_DELAY = 100e-9\n", "Q0_FREQ_01 = 4e9\n", "Q0_READOUT_AMP = 0.1\n", "Q0_READOUT_FREQ = 4.3e9\n", "Q0_PI_PULSE_AMP = 0.15\n", "Q0_LO_FREQ_QUBIT = 3.9e9\n", "Q0_LO_FREQ_READOUT = 4.5e9\n", "\n", "Q1_ACQ_DELAY = 120e-9\n", "Q1_FREQ_01 = 4.1e9\n", "Q1_READOUT_AMP = 0.1\n", "Q1_READOUT_FREQ = 3.8e9\n", "Q1_PI_PULSE_AMP = 0.15\n", "Q1_LO_FREQ_QUBIT = 4.1e9\n", "Q1_LO_FREQ_READOUT = 3.8e9\n", "\n", "two_qubit_device = QuantumDevice(\"two_qubit_device\")\n", "\n", "q0 = BasicTransmonElement(\"q0\")\n", "q0.measure.acq_channel(0)\n", "q0.measure.pulse_amp(Q0_READOUT_AMP)\n", "q0.clock_freqs.readout(Q0_READOUT_FREQ)\n", "q0.clock_freqs.f01(Q0_FREQ_01)\n", "q0.measure.acq_delay(Q0_ACQ_DELAY)\n", "q0.rxy.amp180(Q0_PI_PULSE_AMP)\n", "\n", "q1 = BasicTransmonElement(\"q1\")\n", "q1.measure.acq_channel(1) # Note that we're specifying that measurements on q1 should use a different measurement channel\n", "q1.measure.pulse_amp(Q1_READOUT_AMP)\n", "q1.clock_freqs.readout(Q1_READOUT_FREQ)\n", "q1.clock_freqs.f01(Q1_FREQ_01)\n", "q1.measure.acq_delay(Q1_ACQ_DELAY)\n", "q1.rxy.amp180(Q1_PI_PULSE_AMP)\n", "\n", "two_qubit_device.add_element(q0)\n", "two_qubit_device.add_element(q1)\n", "\n", "# We will need to adjust the hardware configuration.\n", "\n", "# Note: if you are connecting to an actual cluster, you would provide the\n", "# 'identifier' argument (the ip address, device name or serial number) instead\n", "# of the 'dummy_cfg' argument.\n", "cluster = Cluster(\n", " \"cluster\",\n", " dummy_cfg={\n", " 1: ClusterType.CLUSTER_QRM_RF,\n", " 2: ClusterType.CLUSTER_QCM_RF,\n", " 3: ClusterType.CLUSTER_QRM_RF,\n", " 4: ClusterType.CLUSTER_QCM_RF,\n", " },\n", ")\n", "\n", "ic_cluster = ClusterComponent(cluster)\n", "\n", "# Temporarily fixing dummy cluster's deficiency.\n", "cluster.start_sequencer = lambda : start_dummy_cluster_armed_sequencers(ic_cluster)\n", "\n", "instrument_coordinator = InstrumentCoordinator(\"instrument_coordinator\")\n", "instrument_coordinator.add_component(ic_cluster)\n", "\n", "two_qubit_device.instr_instrument_coordinator(instrument_coordinator.name)\n", "\n", "hardware_cfg = {\n", " \"version\": \"0.2\",\n", " \"config_type\": \"quantify_scheduler.backends.qblox_backend.QbloxHardwareCompilationConfig\",\n", " \"hardware_description\": {\n", " f\"{cluster.name}\": {\n", " \"instrument_type\": \"Cluster\",\n", " \"modules\": {\n", " \"1\": {\n", " \"instrument_type\": \"QRM_RF\"\n", " },\n", " \"2\": {\n", " \"instrument_type\": \"QCM_RF\"\n", " },\n", " \"3\": {\n", " \"instrument_type\": \"QRM_RF\"\n", " },\n", " \"4\": {\n", " \"instrument_type\": \"QCM_RF\"\n", " }\n", " },\n", " \"ref\": \"internal\"\n", " }\n", " },\n", " \"hardware_options\": {\n", " \"modulation_frequencies\": {\n", " \"q0:res-q0.ro\": {\n", " \"lo_freq\": Q0_LO_FREQ_READOUT\n", " },\n", " \"q0:mw-q0.01\": {\n", " \"lo_freq\": Q0_LO_FREQ_QUBIT\n", " },\n", " \"q1:res-q1.ro\": {\n", " \"lo_freq\": Q1_LO_FREQ_READOUT\n", " },\n", " \"q1:mw-q1.01\": {\n", " \"lo_freq\": Q1_LO_FREQ_QUBIT\n", " }\n", " }\n", " },\n", " \"connectivity\": {\n", " \"graph\": [\n", " [f\"{cluster.name}.module1.complex_output_0\", \"q0:res\"],\n", " [f\"{cluster.name}.module1.complex_input_0\", \"q0:res\"],\n", " [f\"{cluster.name}.module2.complex_output_0\", \"q0:mw\"],\n", " [f\"{cluster.name}.module3.complex_output_0\", \"q1:res\"],\n", " [f\"{cluster.name}.module3.complex_input_0\", \"q1:res\"],\n", " [f\"{cluster.name}.module4.complex_output_0\", \"q1:mw\"]\n", " ]\n", " }\n", "}\n", "\n", "\n", "two_qubit_device.hardware_config(hardware_cfg)\n", "\n", "ic_cluster.instrument.set_dummy_binned_acquisition_data(\n", " slot_idx=1, sequencer=0, acq_index_name=\"0\", data=[DummyBinnedAcquisitionData(data=(0.0, 0.0), thres=0, avg_cnt=0)]\n", ")\n", "ic_cluster.instrument.set_dummy_binned_acquisition_data(\n", " slot_idx=3, sequencer=0, acq_index_name=\"0\", data=[DummyBinnedAcquisitionData(data=(0.0, 0.0), thres=0, avg_cnt=0)]\n", ")" ] }, { "cell_type": "markdown", "id": "c6cd0a38", "metadata": {}, "source": [ "2D and higher-dimensional measurement loops are easy to realize. Below we show an example Chevron experiment, which is a type of two-qubit experiment often performed on transmon qubits. The experiment includes a square pulse, and we want to vary both its amplitude and duration in a 2D grid. We have already set up a two-qubit {class}`~quantify_scheduler.device_under_test.quantum_device.QuantumDevice` under the variable name `two_qubit_device`.\n", "\n", "We define simple schedule below with a parameterized amplitude and duration of the square pulse. `duration` and `amp` are scalars, so for each measurement point the schedule will be recompiled." ] }, { "cell_type": "code", "execution_count": 15, "id": "1d7f38ff", "metadata": {}, "outputs": [], "source": [ "from quantify_scheduler import Schedule\n", "from quantify_scheduler.operations import Measure, Reset, SquarePulse, X, X90\n", "\n", "def chevron_schedule_not_batched(duration, amp, repetitions=1):\n", " sched = Schedule(\"Chevron Experiment\", repetitions=repetitions)\n", "\n", " acq_idx = 0\n", "\n", " reset = sched.add(Reset(\"q0\", \"q1\"))\n", " sched.add(X(\"q0\"), ref_op=reset, ref_pt=\"end\") # Start at the end of the reset\n", " # We specify a clock for tutorial purposes, Chevron experiments do not necessarily use modulated square pulses\n", " square = sched.add(SquarePulse(amp=amp, duration=duration, port=\"q0:mw\", clock=\"q0.01\"))\n", " sched.add(X90(\"q0\"), ref_op=square) # Start at the end of the square pulse\n", " sched.add(X90(\"q1\"), ref_op=square)\n", " sched.add(Measure(\"q0\"), label=f\"M q0 {acq_idx}\")\n", " sched.add(\n", " Measure(\"q1\"),\n", " label=f\"M q1 {acq_idx}\",\n", " ref_pt=\"start\", # Start at the same time as the other measure\n", " )\n", "\n", " return sched" ] }, { "cell_type": "markdown", "id": "46b18198", "metadata": {}, "source": [ "We set up a non-batched measurement with {class}`~quantify_core.measurement.control.MeasurementControl` and a {class}`~quantify_scheduler.gettables.ScheduleGettable`. For this {class}`~quantify_scheduler.gettables.ScheduleGettable`, notice the keyword argument `num_channels=2`, which is needed since we specified in the quantum device elements that the measurements on `\"q0\"` and `\"q1\"` should end up in two different channels.\n", "\n", "In addition, we used another new argument in the {class}`~quantify_scheduler.gettables.ScheduleGettable`: `real_imag=False`. `real_imag` can be used to transform the acquisition data. If it is `True` (the default), the I and Q values will be returned, and if it is `False`, the data will be transformed to the absolute value and the phase (in degrees, in the interval `(-180, 180]`)." ] }, { "cell_type": "code", "execution_count": 16, "id": "0149fc44", "metadata": { "tags": [ "hide-output" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting iterative measurement...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f1c990719fca48e0816e44120b97dfab", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Completed: 0%| [ elapsed time: 00:00 | time left: ? ] it" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "measurement_control = MeasurementControl(\"measurement_control\")\n", "\n", "# Configure the settables\n", "duration = ManualParameter(\"duration\", label=\"Duration\", unit=\"s\")\n", "duration.batched = False\n", "\n", "durations = np.linspace(start=20e-9, stop=60e-9, num=6)\n", "\n", "amplitude = ManualParameter(\"amplitude\", label=\"Amplitude\", unit=\"V\")\n", "amplitude.batched = False\n", "\n", "amplitudes = np.linspace(start=0.1, stop=1.0, num=10)\n", "\n", "# Configure the gettable\n", "gettable = ScheduleGettable(\n", " quantum_device=two_qubit_device,\n", " schedule_function=chevron_schedule_not_batched,\n", " schedule_kwargs={\"duration\": duration, \"amp\": amplitude},\n", " batched=False,\n", " real_imag=False,\n", " num_channels=2,\n", ")\n", "\n", "# Configure MeasurementControl\n", "measurement_control.settables([duration, amplitude])\n", "measurement_control.setpoints_grid([durations, amplitudes]) # note: setpoints_grid instead of setpoints\n", "measurement_control.gettables(gettable)\n", "\n", "# Run!\n", "dataset = measurement_control.run()\n", "dset_grid = dh.to_gridded_dataset(dataset)" ] }, { "cell_type": "code", "execution_count": 17, "id": "7a30037e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 2kB\n",
       "Dimensions:  (x0: 6, x1: 10)\n",
       "Coordinates:\n",
       "  * x0       (x0) float64 48B 2e-08 2.8e-08 3.6e-08 4.4e-08 5.2e-08 6e-08\n",
       "  * x1       (x1) float64 80B 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0\n",
       "Data variables:\n",
       "    y0       (x0, x1) float64 480B 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
       "    y1       (x0, x1) float64 480B 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
       "    y2       (x0, x1) float64 480B 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
       "    y3       (x0, x1) float64 480B 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n",
       "Attributes:\n",
       "    tuid:                             20260619-134741-067-537b80\n",
       "    name:                             \n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         True\n",
       "    1d_2_settables_uniformly_spaced:  False\n",
       "    xlen:                             6\n",
       "    ylen:                             10
" ], "text/plain": [ " Size: 2kB\n", "Dimensions: (x0: 6, x1: 10)\n", "Coordinates:\n", " * x0 (x0) float64 48B 2e-08 2.8e-08 3.6e-08 4.4e-08 5.2e-08 6e-08\n", " * x1 (x1) float64 80B 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0\n", "Data variables:\n", " y0 (x0, x1) float64 480B 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n", " y1 (x0, x1) float64 480B 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n", " y2 (x0, x1) float64 480B 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n", " y3 (x0, x1) float64 480B 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0\n", "Attributes:\n", " tuid: 20260619-134741-067-537b80\n", " name: \n", " grid_2d: False\n", " grid_2d_uniformly_spaced: True\n", " 1d_2_settables_uniformly_spaced: False\n", " xlen: 6\n", " ylen: 10" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dset_grid" ] }, { "cell_type": "markdown", "id": "4440e965", "metadata": {}, "source": [ "As expected, this dataset contains double the amount of coordinates and data variables (note that the actual data has been mocked and set to 0.0). The two coordinates refer to the settables, `duration` and `amplitude`. With `real_imag` now set to `False`, the data variables contain the magnitude and phase (as opposed to the I and Q voltages) for measurements on `\"q0\"` and `\"q1\"`.\n", "\n", "### Batched 2D experiment\n", "\n", "Since this measurement is not batched, it's rather slow. Let's make this faster with (partial) batching.\n", "\n", "We will batch the amplitudes together, so we change the amplitudes parameter in the schedule function to an array." ] }, { "cell_type": "code", "execution_count": 18, "id": "7641b0ac", "metadata": { "mystnb": { "code_prompt_show": "Provide the dummy hardware with acquisition data" }, "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "from qblox_instruments import DummyBinnedAcquisitionData\n", "\n", "\n", "def get_dummy_binned_acquisition_data(real: float, imag: float):\n", " return DummyBinnedAcquisitionData(data=(real, imag), thres=0, avg_cnt=0)\n", "\n", "ic_cluster.instrument.set_dummy_binned_acquisition_data(\n", " slot_idx=1, sequencer=0, acq_index_name=\"0\", data=[get_dummy_binned_acquisition_data(re * 10, im * 10) for re, im in zip(range(-5, 5), range(5, -5, -1))]\n", ")\n", "ic_cluster.instrument.set_dummy_binned_acquisition_data(\n", " slot_idx=3, sequencer=0, acq_index_name=\"0\", data=[get_dummy_binned_acquisition_data(re * 10, im * 10) for re, im in zip(range(-5, 5), range(5, -5, -1))]\n", ")" ] }, { "cell_type": "code", "execution_count": 19, "id": "83c6f193", "metadata": {}, "outputs": [], "source": [ "def chevron_schedule_batched(duration, amps, repetitions=1):\n", " sched = Schedule(\"Chevron Experiment\", repetitions=repetitions)\n", "\n", " acq_idx = 0\n", "\n", " for amp in amps:\n", " reset = sched.add(Reset(\"q0\", \"q1\"))\n", " sched.add(X(\"q0\"), ref_op=reset, ref_pt=\"end\")\n", " square = sched.add(SquarePulse(amp=amp, duration=duration, port=\"q0:mw\", clock=\"q0.01\"))\n", " sched.add(X90(\"q0\"), ref_op=square)\n", " sched.add(X90(\"q1\"), ref_op=square)\n", " sched.add(Measure(\"q0\"), label=f\"M q0 {acq_idx}\")\n", " sched.add(\n", " Measure(\"q1\"),\n", " label=f\"M q1 {acq_idx}\",\n", " ref_pt=\"start\",\n", " )\n", "\n", " acq_idx += 1\n", "\n", " return sched" ] }, { "cell_type": "markdown", "id": "48cadb71", "metadata": {}, "source": [ "We specify that we want to batch the amplitudes by setting ``amplitude.batched = True`` and ``batched=True``as keyword argument for the gettable. Finally, we run this experiment just as before." ] }, { "cell_type": "code", "execution_count": 20, "id": "c12f0e5d", "metadata": { "tags": [ "hide-output" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting batched measurement...\n", "Iterative settable(s) [outer loop(s)]:\n", "\t duration \n", "Batched settable(s):\n", "\t amplitude \n", "Batch size limit: 60\n", "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1bc44377bc02486bbfe87ad843fb675f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Completed: 0%| [ elapsed time: 00:00 | time left: ? ] it" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Configure the settables\n", "duration = ManualParameter(\"duration\", label=\"Duration\", unit=\"s\")\n", "duration.batched = False\n", "\n", "durations = np.linspace(start=20e-9, stop=60e-9, num=6)\n", "\n", "amplitude = ManualParameter(\"amplitude\", label=\"Amplitude\", unit=\"V\")\n", "amplitude.batched = True\n", "\n", "amplitudes = np.linspace(start=0.1, stop=1.0, num=10)\n", "\n", "# Configure the gettable\n", "gettable = ScheduleGettable(\n", " quantum_device=two_qubit_device,\n", " schedule_function=chevron_schedule_batched,\n", " schedule_kwargs={\"duration\": duration, \"amps\": amplitude},\n", " batched=True,\n", " real_imag=False,\n", " data_labels=[\"Magnitude Q0\", \"Phase Q0\", \"Magnitude Q1\", \"Phase Q1\"],\n", " num_channels=2,\n", ")\n", "\n", "# Configure MeasurementControl\n", "measurement_control.settables([duration, amplitude])\n", "measurement_control.setpoints_grid([durations, amplitudes])\n", "measurement_control.gettables(gettable)\n", "\n", "# Run!\n", "dataset = measurement_control.run()\n", "dset_grid = dh.to_gridded_dataset(dataset)" ] }, { "cell_type": "code", "execution_count": 21, "id": "0af5b70f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset> Size: 2kB\n",
       "Dimensions:  (x0: 6, x1: 10)\n",
       "Coordinates:\n",
       "  * x0       (x0) float64 48B 2e-08 2.8e-08 3.6e-08 4.4e-08 5.2e-08 6e-08\n",
       "  * x1       (x1) float64 80B 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0\n",
       "Data variables:\n",
       "    y0       (x0, x1) float64 480B 0.07071 0.05657 0.04243 ... 0.04243 0.05657\n",
       "    y1       (x0, x1) float64 480B 135.0 135.0 135.0 135.0 ... -45.0 -45.0 -45.0\n",
       "    y2       (x0, x1) float64 480B 0.07071 0.05657 0.04243 ... 0.04243 0.05657\n",
       "    y3       (x0, x1) float64 480B 135.0 135.0 135.0 135.0 ... -45.0 -45.0 -45.0\n",
       "Attributes:\n",
       "    tuid:                             20260619-134744-538-ab59e2\n",
       "    name:                             \n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         True\n",
       "    1d_2_settables_uniformly_spaced:  False\n",
       "    xlen:                             6\n",
       "    ylen:                             10
" ], "text/plain": [ " Size: 2kB\n", "Dimensions: (x0: 6, x1: 10)\n", "Coordinates:\n", " * x0 (x0) float64 48B 2e-08 2.8e-08 3.6e-08 4.4e-08 5.2e-08 6e-08\n", " * x1 (x1) float64 80B 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0\n", "Data variables:\n", " y0 (x0, x1) float64 480B 0.07071 0.05657 0.04243 ... 0.04243 0.05657\n", " y1 (x0, x1) float64 480B 135.0 135.0 135.0 135.0 ... -45.0 -45.0 -45.0\n", " y2 (x0, x1) float64 480B 0.07071 0.05657 0.04243 ... 0.04243 0.05657\n", " y3 (x0, x1) float64 480B 135.0 135.0 135.0 135.0 ... -45.0 -45.0 -45.0\n", "Attributes:\n", " tuid: 20260619-134744-538-ab59e2\n", " name: \n", " grid_2d: False\n", " grid_2d_uniformly_spaced: True\n", " 1d_2_settables_uniformly_spaced: False\n", " xlen: 6\n", " ylen: 10" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dset_grid" ] }, { "cell_type": "markdown", "id": "971b9b82", "metadata": {}, "source": [ "The shape of the dataset is no different from the previous (non-batched) experiment, but the metadata is a little different. Some coordinates and data variables now have the `batched=True` attribute. We also introduced another keyword argument: the `data_labels`. These `data_labels` are picked up by the {class}`~quantify_core.measurement.control.MeasurementControl`and end up in the {class}`xarray.Dataset` as the \"long_name\" of attributes. For example, `\"y0\"`s label can be accessed through `dataset[\"y0\"].attrs[\"long_name\"]`. The various plotting features of `quantify-core` will use this name to get labels for the figure axes." ] } ], "metadata": { "file_format": "mystnb", "kernelspec": { "display_name": "python3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.25" }, "source_map": [ 6, 34, 141, 149, 166, 170, 195, 199, 247, 251, 263, 273, 299, 303, 322, 332, 356, 364, 369, 373, 375, 401, 417, 423, 430, 445, 451, 588, 594, 617, 623, 660, 662, 672, 692, 714, 718, 754, 756 ], "widgets": { "application/vnd.jupyter.widget-state+json": { "state": { "0565df4076724aa2b2e999dbbb4b10fe": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_5cdba1ac56954c868f987cc9cb2e9481", "max": 100.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_37c538e12b7e499cb85dc50c9c5eeb9d", "tabbable": null, "tooltip": null, "value": 100.0 } }, "10acde55ace749869c7e443ecd4a49ba": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "12ba9bd53e0c49cab02a410a5c63a216": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null } }, "1ae7dd9a0ada4aba8e7c70c85eb08065": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_8696469a2bfe4ac68c19c66b2ee0c051", "IPY_MODEL_cc7c792bb8044affa6dd1bdd97b49b2b", "IPY_MODEL_d8a7c218692c4f828bf9781e4bdc8ee9" ], "layout": "IPY_MODEL_c4accf736e6d47d2a90a334dd52eb34a", "tabbable": null, "tooltip": null } }, "1bc44377bc02486bbfe87ad843fb675f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_513cdafc7e494c1692b285e68c507a40", "IPY_MODEL_ea63622e0faf40a0bc3ff5062a8ea5ca", "IPY_MODEL_497d26ccac6b4897b80cba3b810fcd7a" ], "layout": "IPY_MODEL_aa8c7a61797e4196bce3b97452f1885a", "tabbable": null, "tooltip": null } }, "3182b389448f43d7b930d8de02df25f6": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "37c538e12b7e499cb85dc50c9c5eeb9d": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "497d26ccac6b4897b80cba3b810fcd7a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_dde73c802af740248c5447aaed9426e8", "placeholder": "​", "style": "IPY_MODEL_8f877c472a134ea292a4272ee3836567", "tabbable": null, "tooltip": null, "value": " [ elapsed time: 00:00 | time left: 00:00 ]  last batch size: 10" } }, "513cdafc7e494c1692b285e68c507a40": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_c8c753b4f9684d5a9889871c9ebe75d4", "placeholder": "​", "style": "IPY_MODEL_80d1964242e640f083d35b746de29df5", "tabbable": null, "tooltip": null, "value": "Completed: 100%" } }, "5cdba1ac56954c868f987cc9cb2e9481": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "7bdd55556cbf4033b1633071bb37c555": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "7cedb2423eed405189152ebfd997604f": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_7bdd55556cbf4033b1633071bb37c555", "placeholder": "​", "style": "IPY_MODEL_12ba9bd53e0c49cab02a410a5c63a216", "tabbable": null, "tooltip": null, "value": "Completed: 100%" } }, "80d1964242e640f083d35b746de29df5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null } }, "8115dac1ae47443e8945b341ed5ba179": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "8696469a2bfe4ac68c19c66b2ee0c051": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_c20978bfac524837957b30eb714cc00c", "placeholder": "​", "style": "IPY_MODEL_8e5fb5e1e24649b18d2bb913e06a2032", "tabbable": null, "tooltip": null, "value": "Completed: 100%" } }, "8e5fb5e1e24649b18d2bb913e06a2032": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null } }, "8f877c472a134ea292a4272ee3836567": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null } }, "8f8f45e616b9416abb83a645c5fa0479": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null } }, "aa8c7a61797e4196bce3b97452f1885a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "c20978bfac524837957b30eb714cc00c": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "c4accf736e6d47d2a90a334dd52eb34a": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "c8c753b4f9684d5a9889871c9ebe75d4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "caac00e32fbe45cfba099bfc8a8c6767": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "cc7c792bb8044affa6dd1bdd97b49b2b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_3182b389448f43d7b930d8de02df25f6", "max": 100.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_10acde55ace749869c7e443ecd4a49ba", "tabbable": null, "tooltip": null, "value": 100.0 } }, "d8a7c218692c4f828bf9781e4bdc8ee9": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_ff33ea7752b54b1da3fe114daabac8ba", "placeholder": "​", "style": "IPY_MODEL_ee304a169afa455dbfa9423b24cc47c6", "tabbable": null, "tooltip": null, "value": " [ elapsed time: 00:00 | time left: 00:00 ]  last batch size: 125" } }, "dde73c802af740248c5447aaed9426e8": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "ea63622e0faf40a0bc3ff5062a8ea5ca": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_8115dac1ae47443e8945b341ed5ba179", "max": 100.0, "min": 0.0, "orientation": "horizontal", "style": "IPY_MODEL_f35eb8f97d2d450ba03a475bfdfb254b", "tabbable": null, "tooltip": null, "value": 100.0 } }, "ec53504d28c14b0c83320d4cc4fcdaf4": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "ee304a169afa455dbfa9423b24cc47c6": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "background": null, "description_width": "", "font_size": null, "text_color": null } }, "f1c990719fca48e0816e44120b97dfab": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HBoxModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_7cedb2423eed405189152ebfd997604f", "IPY_MODEL_0565df4076724aa2b2e999dbbb4b10fe", "IPY_MODEL_f85460a2d2144c29bf45f58fb54479e5" ], "layout": "IPY_MODEL_caac00e32fbe45cfba099bfc8a8c6767", "tabbable": null, "tooltip": null } }, "f35eb8f97d2d450ba03a475bfdfb254b": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "f85460a2d2144c29bf45f58fb54479e5": { "model_module": "@jupyter-widgets/controls", "model_module_version": "2.0.0", "model_name": "HTMLModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "2.0.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "2.0.0", "_view_name": "HTMLView", "description": "", "description_allow_html": false, "layout": "IPY_MODEL_ec53504d28c14b0c83320d4cc4fcdaf4", "placeholder": "​", "style": "IPY_MODEL_8f8f45e616b9416abb83a645c5fa0479", "tabbable": null, "tooltip": null, "value": " [ elapsed time: 00:02 | time left: 00:00 ] " } }, "ff33ea7752b54b1da3fe114daabac8ba": { "model_module": "@jupyter-widgets/base", "model_module_version": "2.0.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "2.0.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "2.0.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border_bottom": null, "border_left": null, "border_right": null, "border_top": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } } }, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 5 }