{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "59b44241",
   "metadata": {},
   "source": [
    "# Coupled qubits characterization\n",
    "\n",
    "The experiments of this tutorial are meant to be executed with a Qblox Cluster controlling a flux-tunable transmon system.\n",
    "\n",
    "The experiments can also be executed using a dummy Qblox device that is created via an instance of the `Cluster` class, and is initialized with a dummy configuration. However, when using a dummy device, the analysis will not work because the experiments will return `np.nan` values. In this case, example data is loaded from the `\"./example_data/\"` directory."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2146c7c9",
   "metadata": {},
   "source": [
    "## Hardware setup\n",
    "In this section we configure the hardware configuration which specifies the connectivity of our system."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2c46dfb",
   "metadata": {},
   "source": [
    "### Configuration file\n",
    "\n",
    "This is a template hardware configuration file for a 2-qubit system (we name the qubits `q0` and `q1`), with dedicated flux-control lines.\n",
    "\n",
    "The hardware setup is as follows, by cluster slot:\n",
    "- **QCM-RF** (Slot 6)\n",
    "  - Drive line for `q0` using fixed 80 MHz IF.\n",
    "  - Drive line for `q1` using fixed 80 MHz IF.\n",
    "- **QCM** (Slot 2)\n",
    "  - Flux line for `q0`.\n",
    "  - Flux line for `q1`.\n",
    "- **QRM-RF** (Slot 8)\n",
    "  - Shared readout line for `q0`/`q1` using a fixed LO set at 7.5 GHz.\n",
    "\n",
    "Note that in the hardware configuration below the mixers are uncorrected, but for high fidelity experiments this should also be done for all the modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "386416fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "hardware_cfg = {\n",
    "    \"backend\": \"quantify_scheduler.backends.qblox_backend.hardware_compile\",\n",
    "    \"cluster0\": {\n",
    "        \"sequence_to_file\": False,  # Boolean flag which dumps waveforms and program dict to JSON file\n",
    "        \"ref\": \"internal\",  # Use shared clock reference of the cluster\n",
    "        \"instrument_type\": \"Cluster\",\n",
    "        # ============ DRIVE ============#\n",
    "        \"cluster0_module6\": {\n",
    "            \"instrument_type\": \"QCM_RF\",\n",
    "            \"complex_output_0\": {\n",
    "                \"output_att\": 10,\n",
    "                \"dc_mixer_offset_I\": 0.0,\n",
    "                \"dc_mixer_offset_Q\": 0.0,\n",
    "                \"portclock_configs\": [\n",
    "                    {\n",
    "                        \"port\": \"q0:mw\",\n",
    "                        \"clock\": \"q0.01\",\n",
    "                        \"interm_freq\": 80e6,\n",
    "                        \"mixer_amp_ratio\": 1.0,\n",
    "                        \"mixer_phase_error_deg\": 0.0,\n",
    "                    }\n",
    "                ],\n",
    "            },\n",
    "            \"complex_output_1\": {\n",
    "                \"output_att\": 10,\n",
    "                \"dc_mixer_offset_I\": 0.0,\n",
    "                \"dc_mixer_offset_Q\": 0.0,\n",
    "                \"portclock_configs\": [\n",
    "                    {\n",
    "                        \"port\": \"q1:mw\",\n",
    "                        \"clock\": \"q1.01\",\n",
    "                        \"interm_freq\": 80e6,\n",
    "                        \"mixer_amp_ratio\": 1.0,\n",
    "                        \"mixer_phase_error_deg\": 0.0,\n",
    "                    }\n",
    "                ],\n",
    "            },\n",
    "        },\n",
    "        # ============ FLUX ============#\n",
    "        \"cluster0_module2\": {\n",
    "            \"instrument_type\": \"QCM\",\n",
    "            \"real_output_0\": {\"portclock_configs\": [{\"port\": \"q0:fl\", \"clock\": \"cl0.baseband\"}]},\n",
    "            \"real_output_1\": {\"portclock_configs\": [{\"port\": \"q1:fl\", \"clock\": \"cl0.baseband\"}]},\n",
    "        },\n",
    "        # ============ READOUT ============#\n",
    "        \"cluster0_module8\": {\n",
    "            \"instrument_type\": \"QRM_RF\",\n",
    "            \"complex_output_0\": {\n",
    "                \"output_att\": 60,\n",
    "                \"input_att\": 0,\n",
    "                \"dc_mixer_offset_I\": 0.0,\n",
    "                \"dc_mixer_offset_Q\": 0.0,\n",
    "                \"lo_freq\": 7.5e9,\n",
    "                \"portclock_configs\": [\n",
    "                    {\n",
    "                        \"port\": \"q0:res\",\n",
    "                        \"clock\": \"q0.ro\",\n",
    "                        \"mixer_amp_ratio\": 1.0,\n",
    "                        \"mixer_phase_error_deg\": 0.0,\n",
    "                    },\n",
    "                    {\n",
    "                        \"port\": \"q1:res\",\n",
    "                        \"clock\": \"q1.ro\",\n",
    "                        \"mixer_amp_ratio\": 1.0,\n",
    "                        \"mixer_phase_error_deg\": 0.0,\n",
    "                    },\n",
    "                ],\n",
    "            },\n",
    "        },\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5beed061",
   "metadata": {},
   "source": [
    "### Scan For Clusters\n",
    "\n",
    "We scan for the available devices connected via ethernet using the Plug & Play functionality of the Qblox Instruments package (see [Plug & Play](https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/api_reference/tools.html#api-pnp) for more info)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "96f19dbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Devices:\n",
      " - 10.10.200.13: cluster_mm 0.6.2 with name \"QSE_1\" and serial number 00015_2321_005\n",
      " - 10.10.200.42: cluster_mm 0.7.0 with name \"cluster-mm\" and serial number 00015_2321_004\n",
      " - 10.10.200.43: cluster_mm 0.7.0 with name \"QAE-2\" and serial number 00015_2206_003\n",
      " - 10.10.200.50: cluster_mm 0.7.0 with name \"cluster-mm\" and serial number 00015_2219_003\n",
      " - 10.10.200.53: cluster_mm 0.7.0 with name \"cluster-mm\" and serial number 00015_2320_004\n",
      " - 10.10.200.70: cluster_mm 0.7.0 with name \"cluster-mm\" and serial number 123-456-789\n",
      " - 10.10.200.80: cluster_mm 0.6.1 with name \"cluster-mm\" and serial number not_valid\n"
     ]
    }
   ],
   "source": [
    "!qblox-pnp list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f1dcfdb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_ip = None  # To run this tutorial on hardware, fill in the IP address of the cluster here\n",
    "cluster_name = \"cluster0\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6738c4cb",
   "metadata": {},
   "source": [
    "### Connect to Cluster\n",
    "\n",
    "We now make a connection with the Cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca5048eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "from qcodes.instrument import find_or_create_instrument\n",
    "\n",
    "from qblox_instruments import Cluster, ClusterType\n",
    "\n",
    "cluster = find_or_create_instrument(\n",
    "    Cluster,\n",
    "    recreate=True,\n",
    "    name=cluster_name,\n",
    "    identifier=cluster_ip,\n",
    "    dummy_cfg=(\n",
    "        {\n",
    "            2: ClusterType.CLUSTER_QCM,\n",
    "            4: ClusterType.CLUSTER_QRM,\n",
    "            6: ClusterType.CLUSTER_QCM_RF,\n",
    "            8: ClusterType.CLUSTER_QRM_RF,\n",
    "        }\n",
    "        if cluster_ip is None\n",
    "        else None\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83814e22",
   "metadata": {},
   "source": [
    "### Reset the Cluster\n",
    "\n",
    "We reset the Cluster to enter a well-defined state. Note that resetting will clear all stored parameters and repeats startup calibration, so resetting between experiments is usually not desirable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "488c489a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: RESOLVED, Flags: TEMPERATURE_OUT_OF_RANGE, Slot flags: SLOT2_TEMPERATURE_OUT_OF_RANGE, SLOT4_TEMPERATURE_OUT_OF_RANGE, SLOT6_TEMPERATURE_OUT_OF_RANGE, SLOT8_TEMPERATURE_OUT_OF_RANGE\n"
     ]
    }
   ],
   "source": [
    "cluster.reset()\n",
    "print(cluster.get_system_status())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff47d489",
   "metadata": {},
   "source": [
    "Note that a dummy cluster will raise error flags, this is expected behavior and can be ignored."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c9a82d3",
   "metadata": {},
   "source": [
    "## Experiment setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "075d0db2",
   "metadata": {},
   "source": [
    "### Quantum device settings\n",
    "Here we initialize our `QuantumDevice` and our qubit parameters,  checkout this [tutorial](https://quantify-os.org/docs/quantify-scheduler/dev/tutorials/Operations%20and%20Qubits.html) for further details.\n",
    "\n",
    "In short, a `QuantumDevice` contains device elements where we save our found parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0b3ba7db",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from quantify_scheduler.device_under_test.quantum_device import QuantumDevice\n",
    "from quantify_scheduler.device_under_test.transmon_element import BasicTransmonElement\n",
    "\n",
    "q0 = find_or_create_instrument(BasicTransmonElement, recreate=True, name=\"q0\")\n",
    "q1 = find_or_create_instrument(BasicTransmonElement, recreate=True, name=\"q1\")\n",
    "q0.measure.acq_channel(0)\n",
    "q1.measure.acq_channel(1)\n",
    "\n",
    "quantum_device = find_or_create_instrument(QuantumDevice, recreate=True, name=\"device_2q\")\n",
    "quantum_device.hardware_config(hardware_cfg)\n",
    "quantum_device.add_element(q0)\n",
    "quantum_device.add_element(q1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cdb3f76",
   "metadata": {},
   "source": [
    "### Set calibrations\n",
    "\n",
    "This tutorial explicitly only deals with 2-qubit experiments. As such, we assume that both the qubits and their resonators have already been characterized.\n",
    "For information on how to do this, please see the single transmon qubit tutorial.\n",
    "In order to use this tutorial on your own system, you must first change the calibrated values to match your own system.\n",
    "\n",
    "For the sake of this tutorial, we will use some template values for both qubits. If you have an old experiment with instrument settings\n",
    "that you want to load, checkout the helper function [load_settings_onto_instrument](https://quantify-os.org/docs/quantify-core/latest/api/utilities.html#quantify_core.utilities.experiment_helpers.load_settings_onto_instrument).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ee09c6e4",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# ============ READOUT ============ #\n",
    "q0.reset.duration(100e-6)\n",
    "q0.measure.acq_delay(100e-9)\n",
    "q0.measure.pulse_amp(0.05)\n",
    "q0.measure.pulse_duration(2e-6)\n",
    "q0.measure.integration_time(1.9e-6)\n",
    "\n",
    "q1.reset.duration(100e-6)\n",
    "q1.measure.acq_delay(100e-9)\n",
    "q1.measure.pulse_amp(0.05)\n",
    "q1.measure.pulse_duration(2e-6)\n",
    "q1.measure.integration_time(1.9e-6)\n",
    "\n",
    "q0.clock_freqs.readout(7.6e9)\n",
    "q1.clock_freqs.readout(7.7e9)\n",
    "\n",
    "# ============ DRIVE ============ #\n",
    "q0.rxy.amp180(0.1)\n",
    "q0.rxy.motzoi(0.05)\n",
    "q0.rxy.duration(40e-9)\n",
    "\n",
    "q1.rxy.amp180(0.1)\n",
    "q1.rxy.motzoi(0.05)\n",
    "q1.rxy.duration(40e-9)\n",
    "\n",
    "q0.clock_freqs.f01(5.1e9)\n",
    "q1.clock_freqs.f01(5.2e9)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8c20169",
   "metadata": {},
   "source": [
    "### Configure measurement control loop\n",
    "We will use a `MeasurementControl` object for data acquisition as well as an `InstrumentCoordinator` for controlling the instruments in our setup.\n",
    "\n",
    "The `PlotMonitor` is used for live plotting.\n",
    "\n",
    "All of these are then associated with the `QuantumDevice`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "adf2b10a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from quantify_core.measurement.control import MeasurementControl\n",
    "from quantify_core.visualization.pyqt_plotmon import PlotMonitor_pyqt as PlotMonitor\n",
    "from quantify_scheduler.instrument_coordinator import InstrumentCoordinator\n",
    "from quantify_scheduler.instrument_coordinator.components.qblox import ClusterComponent\n",
    "\n",
    "\n",
    "def configure_measurement_control_loop(\n",
    "    device: QuantumDevice, cluster: Cluster, live_plotting: bool = False\n",
    ") -> tuple[MeasurementControl, InstrumentCoordinator]:\n",
    "    meas_ctrl = find_or_create_instrument(MeasurementControl, recreate=True, name=\"meas_ctrl\")\n",
    "    ic = find_or_create_instrument(InstrumentCoordinator, recreate=True, name=\"ic\")\n",
    "\n",
    "    # Add cluster to instrument coordinator\n",
    "    ic_cluster = ClusterComponent(cluster)\n",
    "    ic.add_component(ic_cluster)\n",
    "\n",
    "    if live_plotting:\n",
    "        # Associate plot monitor with measurement controller\n",
    "        plotmon = find_or_create_instrument(PlotMonitor, recreate=False, name=\"PlotMonitor\")\n",
    "        meas_ctrl.instr_plotmon(plotmon.name)\n",
    "\n",
    "    # Associate measurement controller and instrument coordinator with the quantum device\n",
    "    device.instr_measurement_control(meas_ctrl.name)\n",
    "    device.instr_instrument_coordinator(ic.name)\n",
    "\n",
    "    return (meas_ctrl, ic)\n",
    "\n",
    "\n",
    "meas_ctrl, instrument_coordinator = configure_measurement_control_loop(quantum_device, cluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c10edfaf",
   "metadata": {},
   "source": [
    "### Set data directory\n",
    "This directory is where all of the experimental data as well as all of the post processing will go."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "134a7230",
   "metadata": {},
   "outputs": [],
   "source": [
    "import quantify_core.data.handling as dh\n",
    "\n",
    "# Enter your own dataset directory here!\n",
    "dh.set_datadir(Path(\"example_data\").resolve())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "211b6d51",
   "metadata": {},
   "source": [
    "### Configure external flux control\n",
    "We need to have some way of controlling the external flux.\n",
    "\n",
    "This can be done by setting an output bias on a module of the cluster which is then connected to the flux-control line.\n",
    "\n",
    "Here we are nullifying the external flux on both qubits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "680b825d",
   "metadata": {},
   "outputs": [],
   "source": [
    "flux_settables = {\"q0\": cluster.module2.out0_offset, \"q1\": cluster.module2.out1_offset}\n",
    "\n",
    "for flux_settable in flux_settables.values():\n",
    "    flux_settable.inter_delay = 100e-9  # Delay time in seconds between consecutive set operations.\n",
    "    flux_settable.step = 0.3e-3  # Stepsize in V that this Parameter uses during set operation.\n",
    "    flux_settable(0.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5dc5bf0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "flux_settables[q0.name](0.0)  # enter your own value here for qubit 0\n",
    "flux_settables[q1.name](0.0)  # enter your own value here for qubit 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da8feb4d",
   "metadata": {},
   "source": [
    "### Activate NCO delay compensation\n",
    "Compensate for the digital propagation delay for each qubit (i.e each sequencer)\n",
    "\n",
    "For more info, please see: https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/api_reference/sequencer.html#Sequencer.nco_prop_delay_comp\n",
    "\n",
    "To avoid mismatches between modulation and demodulation, the delay between any readout frequency or phase changes and the next acquisition should be equal or greater than the total propagation delay (146ns + user defined value)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c95da7c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(6):\n",
    "    getattr(cluster.module8, f\"sequencer{i}\").nco_prop_delay_comp_en(True)\n",
    "    getattr(cluster.module8, f\"sequencer{i}\").nco_prop_delay_comp(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "103863b5",
   "metadata": {},
   "source": [
    "## Experiments\n",
    "\n",
    "As in the single qubit tuneup tutorial, the sweep setpoints for all experiments in this section are only examples. The sweep setpoints should be changed to match your own system.\n",
    "In this section we assume that each individual qubit has already been characterized, and that they have been biased to their sweetspots.\n",
    "\n",
    "We will examine the flux response of the 2-qubit system. By \"flux response\" we mean the measured response of the system when the parameterization of a flux pulse is varied.\n",
    "\n",
    "We consider two separate experiments which have their own separate parameterizations, they are:\n",
    "\n",
    "1. Controlled phase calibration (a.k.a. two-qubit Chevron experiment)\n",
    "    - Used to measure coupling strength between the two qubits.\n",
    "    - Used to find the location of the $|11\\rangle \\leftrightarrow |02\\rangle$ avoided crossing.\n",
    "2. Conditional oscillations\n",
    "    - Used to measure the conditional phase of the controlled phase gate.\n",
    "    - Used to estimate leakage to $|02\\rangle$.\n",
    "    - Can also be used to measure single-qubit phases on the individual qubits.\n",
    "\n",
    "In both of these experiments, we apply the flux pulse on the flux-control line of `q1` (on top of the sweetspot bias) which we take to be the qubit with the *higher* frequency, i.e. $\\omega_1$ > $\\omega_0$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7798e0cb",
   "metadata": {},
   "source": [
    "### Controlled phase calibration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4cd69735",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from qcodes.parameters import ManualParameter\n",
    "\n",
    "from quantify_scheduler.gettables import ScheduleGettable\n",
    "from quantify_scheduler.operations.gate_library import Measure, Reset, X\n",
    "from quantify_scheduler.schedules.schedule import Schedule\n",
    "from quantify_scheduler.schedules.two_qubit_transmon_schedules import chevron_cz_sched\n",
    "\n",
    "duration = ManualParameter(name=\"dur\", unit=\"Hz\", label=\"Duration of flux pulse\")\n",
    "amplitude = ManualParameter(name=\"amp\", unit=\"\", label=\"Amplitude of flux pulse\")\n",
    "\n",
    "amplitude.batched = True\n",
    "duration.batched = False\n",
    "\n",
    "chevron_cz_sched_kwargs = dict(\n",
    "    lf_qubit=q0.name, hf_qubit=q1.name, amplitudes=amplitude, duration=duration\n",
    ")\n",
    "\n",
    "gettable = ScheduleGettable(\n",
    "    quantum_device,\n",
    "    schedule_function=chevron_cz_sched,\n",
    "    schedule_kwargs=chevron_cz_sched_kwargs,\n",
    "    real_imag=False,\n",
    "    data_labels=[\n",
    "        \"Magnitude lf qubit\",\n",
    "        \"Phase lf qubit\",\n",
    "        \"Magnitude hf qubit\",\n",
    "        \"Phase hf qubit\",\n",
    "    ],\n",
    "    batched=True,\n",
    "    num_channels=2,\n",
    ")\n",
    "\n",
    "meas_ctrl.gettables(gettable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ec2f0230",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t dur \n",
      "Batched settable(s):\n",
      "\t amp \n",
      "Batch size limit: 1024\n",
      "\n"
     ]
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
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       "}\n",
       "\n",
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       "\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 115kB\n",
       "Dimensions:  (dim_0: 2400)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 19kB 4e-09 4e-09 4e-09 ... 9.6e-08 9.6e-08 9.6e-08\n",
       "    x1       (dim_0) float64 19kB 0.05 0.05152 0.05303 ... 0.197 0.1985 0.2\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 19kB nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y1       (dim_0) float64 19kB nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y2       (dim_0) float64 19kB nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y3       (dim_0) float64 19kB nan nan nan nan nan ... nan nan nan nan nan\n",
       "Attributes:\n",
       "    tuid:                             20240422-174415-275-feb0ba\n",
       "    name:                             chevron\n",
       "    grid_2d:                          True\n",
       "    grid_2d_uniformly_spaced:         True\n",
       "    1d_2_settables_uniformly_spaced:  False\n",
       "    xlen:                             24\n",
       "    ylen:                             100</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bc20614c-60ca-417e-8923-789dff8a1553' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bc20614c-60ca-417e-8923-789dff8a1553' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>dim_0</span>: 2400</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a31f9c60-5740-4b12-afa5-5e831154ca0a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a31f9c60-5740-4b12-afa5-5e831154ca0a' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>x0</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4e-09 4e-09 ... 9.6e-08 9.6e-08</div><input id='attrs-2fde3846-b872-49f7-aa85-6ba97423ae78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2fde3846-b872-49f7-aa85-6ba97423ae78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af91ad73-547a-42ab-accd-83a04f4eba7a' class='xr-var-data-in' type='checkbox'><label for='data-af91ad73-547a-42ab-accd-83a04f4eba7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dur</dd><dt><span>long_name :</span></dt><dd>Duration of flux pulse</dd><dt><span>units :</span></dt><dd>Hz</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([4.0e-09, 4.0e-09, 4.0e-09, ..., 9.6e-08, 9.6e-08, 9.6e-08])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x1</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.05 0.05152 0.05303 ... 0.1985 0.2</div><input id='attrs-cab852f8-183b-471c-8c85-deeb4e5bc1e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cab852f8-183b-471c-8c85-deeb4e5bc1e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ac18a74f-35c8-497f-b88d-5cfc0c498175' class='xr-var-data-in' type='checkbox'><label for='data-ac18a74f-35c8-497f-b88d-5cfc0c498175' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>amp</dd><dt><span>long_name :</span></dt><dd>Amplitude of flux pulse</dd><dt><span>units :</span></dt><dd></dd><dt><span>batched :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><pre>array([0.05      , 0.05151515, 0.0530303 , ..., 0.1969697 , 0.19848485,\n",
       "       0.2       ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f6416de4-dcc3-438c-bdee-1320d49d9887' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f6416de4-dcc3-438c-bdee-1320d49d9887' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y0</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-ba07f1ec-521d-4b35-aa39-9ce1fe75cde3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba07f1ec-521d-4b35-aa39-9ce1fe75cde3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4d312fd-2348-41dd-9103-6f6c8fd53278' class='xr-var-data-in' type='checkbox'><label for='data-d4d312fd-2348-41dd-9103-6f6c8fd53278' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>magn</dd><dt><span>long_name :</span></dt><dd>Magnitude lf qubit</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>batched :</span></dt><dd>True</dd><dt><span>batch_size :</span></dt><dd>1024</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y1</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-46d40f5b-b0d8-483f-8a5f-1506e610e83c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46d40f5b-b0d8-483f-8a5f-1506e610e83c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f701d755-7fc2-450d-b07a-6e74755621ce' class='xr-var-data-in' type='checkbox'><label for='data-f701d755-7fc2-450d-b07a-6e74755621ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>phase</dd><dt><span>long_name :</span></dt><dd>Phase lf qubit</dd><dt><span>units :</span></dt><dd>deg</dd><dt><span>batched :</span></dt><dd>True</dd><dt><span>batch_size :</span></dt><dd>1024</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y2</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-e2583274-db73-44db-b4e9-9f7416e13a25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2583274-db73-44db-b4e9-9f7416e13a25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4d2c008-ce82-4526-88c4-20d3e245b784' class='xr-var-data-in' type='checkbox'><label for='data-a4d2c008-ce82-4526-88c4-20d3e245b784' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>magn</dd><dt><span>long_name :</span></dt><dd>Magnitude hf qubit</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>batched :</span></dt><dd>True</dd><dt><span>batch_size :</span></dt><dd>1024</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y3</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-6104da6b-8162-4457-b5c1-4a5e2333c6c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6104da6b-8162-4457-b5c1-4a5e2333c6c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6041671-13ed-4cd3-a8ed-d67398a9a772' class='xr-var-data-in' type='checkbox'><label for='data-f6041671-13ed-4cd3-a8ed-d67398a9a772' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>phase</dd><dt><span>long_name :</span></dt><dd>Phase hf qubit</dd><dt><span>units :</span></dt><dd>deg</dd><dt><span>batched :</span></dt><dd>True</dd><dt><span>batch_size :</span></dt><dd>1024</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, ..., nan, nan, nan])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d5b96999-2e2e-4c22-9aca-18286c685e23' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d5b96999-2e2e-4c22-9aca-18286c685e23' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-a32d4ba7-521d-43ab-b751-6caef293d94f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a32d4ba7-521d-43ab-b751-6caef293d94f' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20240422-174415-275-feb0ba</dd><dt><span>name :</span></dt><dd>chevron</dd><dt><span>grid_2d :</span></dt><dd>True</dd><dt><span>grid_2d_uniformly_spaced :</span></dt><dd>True</dd><dt><span>1d_2_settables_uniformly_spaced :</span></dt><dd>False</dd><dt><span>xlen :</span></dt><dd>24</dd><dt><span>ylen :</span></dt><dd>100</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 115kB\n",
       "Dimensions:  (dim_0: 2400)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 19kB 4e-09 4e-09 4e-09 ... 9.6e-08 9.6e-08 9.6e-08\n",
       "    x1       (dim_0) float64 19kB 0.05 0.05152 0.05303 ... 0.197 0.1985 0.2\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 19kB nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y1       (dim_0) float64 19kB nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y2       (dim_0) float64 19kB nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y3       (dim_0) float64 19kB nan nan nan nan nan ... nan nan nan nan nan\n",
       "Attributes:\n",
       "    tuid:                             20240422-174415-275-feb0ba\n",
       "    name:                             chevron\n",
       "    grid_2d:                          True\n",
       "    grid_2d_uniformly_spaced:         True\n",
       "    1d_2_settables_uniformly_spaced:  False\n",
       "    xlen:                             24\n",
       "    ylen:                             100"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantum_device.cfg_sched_repetitions(400)\n",
    "\n",
    "duration_setpoints = np.arange(4e-9, 100e-9, 4e-9)\n",
    "amplitude_setpoints = np.linspace(0.05, 0.2, 100)\n",
    "\n",
    "meas_ctrl.settables([duration, amplitude])\n",
    "meas_ctrl.setpoints_grid((duration_setpoints, amplitude_setpoints))\n",
    "\n",
    "chevron_ds = meas_ctrl.run(\"chevron\")\n",
    "chevron_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c22db372",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If on dummy, override with old data for analysis\n",
    "if cluster_ip is None:\n",
    "    # NOTE: This dataset uses an old version of quantify which specified amplitude in volts.\n",
    "    chevron_ds = dh.to_gridded_dataset(dh.load_dataset(tuid=\"20230509-120110-134-51b044\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "18ef9ddc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, axs = plt.subplots(1, 2, figsize=plt.figaspect(1 / 2))\n",
    "\n",
    "# plot only magnitude data of both channels for simplicity\n",
    "chevron_ds.y0.plot(ax=axs[0])\n",
    "chevron_ds.y2.plot(ax=axs[1])\n",
    "\n",
    "axs[0].set_title(\"Low freq. qubit (q0)\")\n",
    "axs[1].set_title(\"High freq. qubit (q1)\")\n",
    "\n",
    "fig.suptitle(\"Controlled phase calibration\")\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
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   "source": [
    "### Conditional oscillations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "99fd903e",
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   "outputs": [],
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    "from typing import TYPE_CHECKING, Literal\n",
    "\n",
    "from quantify_scheduler.operations.gate_library import X90, Rxy\n",
    "from quantify_scheduler.operations.pulse_library import SuddenNetZeroPulse\n",
    "\n",
    "if TYPE_CHECKING:\n",
    "    from numpy.typing import NDArray\n",
    "\n",
    "\n",
    "def conditional_oscillation_sched(\n",
    "    target_qubit: str,\n",
    "    control_qubit: str,\n",
    "    phases: NDArray,\n",
    "    variant: Literal[\"OFF\", \"ON\"],\n",
    "    snz_A: float,\n",
    "    snz_B: float,\n",
    "    snz_scale: float,\n",
    "    snz_dur: float,\n",
    "    snz_t_phi: float,\n",
    "    snz_t_integral_correction: float,\n",
    "    flux_port: str | None = None,\n",
    "    repetitions: int = 1,\n",
    ") -> Schedule:\n",
    "    \"\"\"\n",
    "    Make a conditional oscillation schedule to measure conditional phase.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    target_qubit\n",
    "        The name of a qubit, e.g., \"q0\", the qubit with lower frequency.\n",
    "    control_qubit\n",
    "        The name of coupled qubit, the qubit with the higher frequency.\n",
    "    phases\n",
    "        An array (or scalar) of recovery phases in degrees.\n",
    "    variant\n",
    "        A string specifying whether to excite the control qubit.\n",
    "    snz_A\n",
    "        Unitless amplitude of the main square pulse.\n",
    "    snz_B\n",
    "        Unitless scaling correction for the final sample of the first\n",
    "        square and first sample of the second square pulse.\n",
    "    snz_scale\n",
    "        Amplitude scaling correction factor of the negative arm of the net-zero pulse.\n",
    "    snz_dur\n",
    "        The total duration of the two half square pulses.\n",
    "    snz_t_phi\n",
    "        The idling duration between the two half pulses.\n",
    "    snz_t_integral_correction\n",
    "        The duration in which any non-zero pulse amplitude needs to be corrected.\n",
    "    flux_port\n",
    "        An optional string for a flux port. Default is hf_qubit flux port.\n",
    "    repetitions\n",
    "        The amount of times the Schedule will be repeated.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    :\n",
    "        An experiment schedule.\n",
    "\n",
    "    \"\"\"\n",
    "    sched = Schedule(f\"ConditionalOscillation({variant})\", repetitions)\n",
    "\n",
    "    # Ensure phases is an iterable when passing a float\n",
    "    phases = np.asarray(phases)\n",
    "    phases = phases.reshape(phases.shape or (1,))\n",
    "\n",
    "    # Ensure that variant is uppercase for switch\n",
    "    variant = str.upper(variant)\n",
    "    if variant not in {\"OFF\", \"ON\"}:\n",
    "        raise ValueError(\"Schedule variant should be 'OFF' or 'ON'.\")\n",
    "\n",
    "    # Set flux port\n",
    "    flux_port = flux_port if flux_port is not None else f\"{control_qubit}:fl\"\n",
    "\n",
    "    for acq_index, phi in enumerate(phases):\n",
    "        # Reset to |00>\n",
    "        sched.add(Reset(target_qubit, control_qubit))\n",
    "\n",
    "        # Apply a pi/2 pulse on the target qubit\n",
    "        targ_eq_ref = sched.add(X90(target_qubit))\n",
    "\n",
    "        if variant == \"ON\":\n",
    "            # Also apply a pi pulse on the control qubit\n",
    "            sched.add(X(control_qubit), ref_op=targ_eq_ref, ref_pt=\"start\")\n",
    "\n",
    "        # Go to |11> <=> |02> avoided crossing on positive & negative sides\n",
    "        # using the SuddenNetZeroPulse\n",
    "        sched.add(\n",
    "            SuddenNetZeroPulse(\n",
    "                amp_A=snz_A,\n",
    "                amp_B=snz_B,\n",
    "                net_zero_A_scale=snz_scale,\n",
    "                t_pulse=snz_dur,\n",
    "                t_phi=snz_t_phi,\n",
    "                t_integral_correction=snz_t_integral_correction,\n",
    "                port=flux_port,\n",
    "                clock=\"cl0.baseband\",\n",
    "            )\n",
    "        )\n",
    "\n",
    "        # Apply a pi/2 recovery pulse on the target qubit\n",
    "        targ_rec_ref = sched.add(Rxy(theta=90.0, phi=phi, qubit=target_qubit))\n",
    "\n",
    "        if variant == \"ON\":\n",
    "            # Also apply a pi pulse on the control qubit\n",
    "            sched.add(X(control_qubit), ref_op=targ_rec_ref, ref_pt=\"start\")\n",
    "\n",
    "        # Measure system\n",
    "        sched.add(Measure(target_qubit, control_qubit, acq_index=acq_index))\n",
    "\n",
    "    return sched"
   ]
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   "id": "f483bf81",
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    "phase = ManualParameter(name=\"ph\", unit=\"deg\", label=\"Recovery phase\")\n",
    "flux_pulse_amplitude = ManualParameter(name=\"A\", unit=\"\", label=\"Flux pulse amplitude\")\n",
    "scaling_correction = ManualParameter(name=\"B\", unit=\"\", label=\"Scaling correction\")\n",
    "\n",
    "phase.batched = True\n",
    "flux_pulse_amplitude.batched = False\n",
    "scaling_correction.batched = False\n",
    "\n",
    "conditional_oscillation_sched_kwargs = dict(\n",
    "    target_qubit=q0.name,\n",
    "    control_qubit=q1.name,\n",
    "    phases=phase,\n",
    "    snz_A=flux_pulse_amplitude,\n",
    "    snz_B=scaling_correction,\n",
    "    snz_scale=1.0,\n",
    "    snz_dur=40e-9,\n",
    "    snz_t_phi=4e-9,\n",
    "    snz_t_integral_correction=0.0,\n",
    ")\n",
    "\n",
    "gettable_off = ScheduleGettable(\n",
    "    quantum_device,\n",
    "    conditional_oscillation_sched,\n",
    "    schedule_kwargs={**conditional_oscillation_sched_kwargs, **{\"variant\": \"OFF\"}},\n",
    "    real_imag=False,\n",
    "    batched=True,\n",
    "    num_channels=2,\n",
    ")\n",
    "gettable_on = ScheduleGettable(\n",
    "    quantum_device,\n",
    "    conditional_oscillation_sched,\n",
    "    schedule_kwargs={**conditional_oscillation_sched_kwargs, **{\"variant\": \"ON\"}},\n",
    "    real_imag=False,\n",
    "    batched=True,\n",
    "    num_channels=2,\n",
    ")\n",
    "\n",
    "meas_ctrl.gettables((gettable_off, gettable_on))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8a47b2c7",
   "metadata": {},
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     "name": "stdout",
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     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t A, B \n",
      "Batched settable(s):\n",
      "\t ph \n",
      "Batch size limit: 60\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Axel\\miniconda3\\envs\\tt_v2\\lib\\site-packages\\quantify_scheduler\\waveforms.py:317: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  integral_value = -sum(waveform) / num_corr_samples\n",
      "c:\\Users\\Axel\\miniconda3\\envs\\tt_v2\\lib\\site-packages\\quantify_scheduler\\waveforms.py:317: RuntimeWarning: divide by zero encountered in scalar divide\n",
      "  integral_value = -sum(waveform) / num_corr_samples\n"
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       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 5kB\n",
       "Dimensions:  (dim_0: 60)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 480B 0.0 6.102 12.2 18.31 ... 347.8 353.9 360.0\n",
       "    x1       (dim_0) float64 480B 0.8 0.8 0.8 0.8 0.8 ... 0.8 0.8 0.8 0.8 0.8\n",
       "    x2       (dim_0) float64 480B 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y1       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y2       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y3       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y4       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y5       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y6       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y7       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan\n",
       "Attributes:\n",
       "    tuid:                             20240422-174425-935-9d4fd3\n",
       "    name:                             conditional oscillation\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  False</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d3ed3ee1-2667-4c34-9f19-e661b25e99b8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d3ed3ee1-2667-4c34-9f19-e661b25e99b8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>dim_0</span>: 60</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fe9d1f84-2c41-41df-befa-b39d1a51790e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fe9d1f84-2c41-41df-befa-b39d1a51790e' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>x0</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 6.102 12.2 ... 353.9 360.0</div><input id='attrs-9c5fa310-ffc3-4cf3-b5b9-256045689875' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c5fa310-ffc3-4cf3-b5b9-256045689875' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dae08f14-7608-48d1-9e0f-89367980f27a' class='xr-var-data-in' type='checkbox'><label for='data-dae08f14-7608-48d1-9e0f-89367980f27a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>ph</dd><dt><span>long_name :</span></dt><dd>Recovery phase</dd><dt><span>units :</span></dt><dd>deg</dd><dt><span>batched :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><pre>array([  0.        ,   6.10169492,  12.20338983,  18.30508475,\n",
       "        24.40677966,  30.50847458,  36.61016949,  42.71186441,\n",
       "        48.81355932,  54.91525424,  61.01694915,  67.11864407,\n",
       "        73.22033898,  79.3220339 ,  85.42372881,  91.52542373,\n",
       "        97.62711864, 103.72881356, 109.83050847, 115.93220339,\n",
       "       122.03389831, 128.13559322, 134.23728814, 140.33898305,\n",
       "       146.44067797, 152.54237288, 158.6440678 , 164.74576271,\n",
       "       170.84745763, 176.94915254, 183.05084746, 189.15254237,\n",
       "       195.25423729, 201.3559322 , 207.45762712, 213.55932203,\n",
       "       219.66101695, 225.76271186, 231.86440678, 237.96610169,\n",
       "       244.06779661, 250.16949153, 256.27118644, 262.37288136,\n",
       "       268.47457627, 274.57627119, 280.6779661 , 286.77966102,\n",
       "       292.88135593, 298.98305085, 305.08474576, 311.18644068,\n",
       "       317.28813559, 323.38983051, 329.49152542, 335.59322034,\n",
       "       341.69491525, 347.79661017, 353.89830508, 360.        ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x1</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.8 0.8 0.8 0.8 ... 0.8 0.8 0.8 0.8</div><input id='attrs-8e7f01bf-dbe6-4d24-ac33-ebabb89c27e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e7f01bf-dbe6-4d24-ac33-ebabb89c27e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d290d01-ff67-40cc-b97a-3846d19bdd41' class='xr-var-data-in' type='checkbox'><label for='data-2d290d01-ff67-40cc-b97a-3846d19bdd41' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>A</dd><dt><span>long_name :</span></dt><dd>Flux pulse amplitude</dd><dt><span>units :</span></dt><dd></dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,\n",
       "       0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,\n",
       "       0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,\n",
       "       0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8,\n",
       "       0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x2</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5</div><input id='attrs-d6a3f225-8efb-4bae-be9e-0f66a94f7600' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6a3f225-8efb-4bae-be9e-0f66a94f7600' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-addb3438-4759-4560-86e5-789a840c78a3' class='xr-var-data-in' type='checkbox'><label for='data-addb3438-4759-4560-86e5-789a840c78a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>B</dd><dt><span>long_name :</span></dt><dd>Scaling correction</dd><dt><span>units :</span></dt><dd></dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
       "       0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b0d6ec76-bde9-4bb3-8f8d-d9c714b7ffb1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b0d6ec76-bde9-4bb3-8f8d-d9c714b7ffb1' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y0</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-4e699104-cc9e-4d24-9d80-ef2dfca2bd47' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e699104-cc9e-4d24-9d80-ef2dfca2bd47' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bb4dfef-0975-4ec0-b868-463ae28da5a9' class='xr-var-data-in' type='checkbox'><label for='data-2bb4dfef-0975-4ec0-b868-463ae28da5a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>magn</dd><dt><span>long_name :</span></dt><dd>Magnitude0</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>batched :</span></dt><dd>True</dd><dt><span>batch_size :</span></dt><dd>1024</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y1</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-dc980e78-73b2-450c-b4d0-0c5558c94a3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc980e78-73b2-450c-b4d0-0c5558c94a3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13e2d755-83ee-455b-8fe2-74a1555a0dd3' class='xr-var-data-in' type='checkbox'><label for='data-13e2d755-83ee-455b-8fe2-74a1555a0dd3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>phase</dd><dt><span>long_name :</span></dt><dd>Phase0</dd><dt><span>units :</span></dt><dd>deg</dd><dt><span>batched :</span></dt><dd>True</dd><dt><span>batch_size :</span></dt><dd>1024</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y2</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-9c037760-1d7c-447e-84a5-716199da9c52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c037760-1d7c-447e-84a5-716199da9c52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51a4a66d-2160-4bbc-b5df-99971e219f4c' class='xr-var-data-in' type='checkbox'><label for='data-51a4a66d-2160-4bbc-b5df-99971e219f4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>magn</dd><dt><span>long_name :</span></dt><dd>Magnitude1</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>batched :</span></dt><dd>True</dd><dt><span>batch_size :</span></dt><dd>1024</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y3</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-485b068a-4f5c-41b7-91b8-388cf7446c7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-485b068a-4f5c-41b7-91b8-388cf7446c7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18baad6a-1b1f-4bfc-905f-7c88ebb7181c' class='xr-var-data-in' type='checkbox'><label for='data-18baad6a-1b1f-4bfc-905f-7c88ebb7181c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>phase</dd><dt><span>long_name :</span></dt><dd>Phase1</dd><dt><span>units :</span></dt><dd>deg</dd><dt><span>batched :</span></dt><dd>True</dd><dt><span>batch_size :</span></dt><dd>1024</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y4</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-ab92a54b-4e86-4dcb-9246-eb9851bb4b06' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab92a54b-4e86-4dcb-9246-eb9851bb4b06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1425cdfb-8e47-44ca-869d-8121dc32588c' class='xr-var-data-in' type='checkbox'><label for='data-1425cdfb-8e47-44ca-869d-8121dc32588c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>magn</dd><dt><span>long_name :</span></dt><dd>Magnitude0</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>batched :</span></dt><dd>True</dd><dt><span>batch_size :</span></dt><dd>1024</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y5</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-525229a8-2879-4d89-8056-e4aa9cdbf6c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-525229a8-2879-4d89-8056-e4aa9cdbf6c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0e1433a-c47b-4e33-a252-34d8210c9084' class='xr-var-data-in' type='checkbox'><label for='data-b0e1433a-c47b-4e33-a252-34d8210c9084' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>phase</dd><dt><span>long_name :</span></dt><dd>Phase0</dd><dt><span>units :</span></dt><dd>deg</dd><dt><span>batched :</span></dt><dd>True</dd><dt><span>batch_size :</span></dt><dd>1024</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y6</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-6e06b4cc-c3e6-4658-aee5-ef000ef6ae02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e06b4cc-c3e6-4658-aee5-ef000ef6ae02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cd02ae4-d06a-45f6-b520-ae32986cb2ef' class='xr-var-data-in' type='checkbox'><label for='data-2cd02ae4-d06a-45f6-b520-ae32986cb2ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>magn</dd><dt><span>long_name :</span></dt><dd>Magnitude1</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>batched :</span></dt><dd>True</dd><dt><span>batch_size :</span></dt><dd>1024</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y7</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-b3a11684-0e05-47d6-baf7-9b8b294ca9a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3a11684-0e05-47d6-baf7-9b8b294ca9a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e43545b-86fc-4756-a37f-7214df2ba4f4' class='xr-var-data-in' type='checkbox'><label for='data-6e43545b-86fc-4756-a37f-7214df2ba4f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>phase</dd><dt><span>long_name :</span></dt><dd>Phase1</dd><dt><span>units :</span></dt><dd>deg</dd><dt><span>batched :</span></dt><dd>True</dd><dt><span>batch_size :</span></dt><dd>1024</dd></dl></div><div class='xr-var-data'><pre>array([nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan, nan,\n",
       "       nan, nan, nan, nan, nan, nan, nan, nan])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a4d7d54a-4dc1-4a09-b1a2-f186bba14c84' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a4d7d54a-4dc1-4a09-b1a2-f186bba14c84' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-e8253832-d1c0-478b-9209-d0293fbd2699' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e8253832-d1c0-478b-9209-d0293fbd2699' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20240422-174425-935-9d4fd3</dd><dt><span>name :</span></dt><dd>conditional oscillation</dd><dt><span>grid_2d :</span></dt><dd>False</dd><dt><span>grid_2d_uniformly_spaced :</span></dt><dd>False</dd><dt><span>1d_2_settables_uniformly_spaced :</span></dt><dd>False</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 5kB\n",
       "Dimensions:  (dim_0: 60)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 480B 0.0 6.102 12.2 18.31 ... 347.8 353.9 360.0\n",
       "    x1       (dim_0) float64 480B 0.8 0.8 0.8 0.8 0.8 ... 0.8 0.8 0.8 0.8 0.8\n",
       "    x2       (dim_0) float64 480B 0.5 0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5 0.5\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y1       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y2       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y3       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y4       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y5       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y6       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan\n",
       "    y7       (dim_0) float64 480B nan nan nan nan nan ... nan nan nan nan nan\n",
       "Attributes:\n",
       "    tuid:                             20240422-174425-935-9d4fd3\n",
       "    name:                             conditional oscillation\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  False"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantum_device.cfg_sched_repetitions(400)\n",
    "\n",
    "meas_ctrl.settables([phase, flux_pulse_amplitude, scaling_correction])\n",
    "meas_ctrl.setpoints_grid((np.linspace(0.0, 360.0, 60), [0.8], [0.5]))\n",
    "\n",
    "cond_osc_ds = meas_ctrl.run(\"conditional oscillation\")\n",
    "cond_osc_ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eadf733e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# If on dummy, override with old data for analysis\n",
    "if cluster_ip is None:\n",
    "    cond_osc_ds = dh.load_dataset(tuid=\"20230509-164908-713-788626\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c5b46e66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from quantify_core.analysis.conditional_oscillation_analysis import ConditionalOscillationAnalysis\n",
    "\n",
    "cond_osc_analysis = ConditionalOscillationAnalysis(dataset=cond_osc_ds)\n",
    "cond_osc_analysis.run().display_figs_mpl()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9e4365db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'backend'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'quantify_scheduler.backends.qblox_backend.hardware_compile'</span>,\n",
       "    <span style=\"color: #008000; text-decoration-color: #008000\">'cluster0'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'sequence_to_file'</span>: <span style=\"color: #ff0000; text-decoration-color: #ff0000; font-style: italic\">False</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'ref'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'internal'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'instrument_type'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'Cluster'</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'cluster0_module6'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'instrument_type'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'QCM_RF'</span>,\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'complex_output_0'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "                <span style=\"color: #008000; text-decoration-color: #008000\">'output_att'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>,\n",
       "                <span style=\"color: #008000; text-decoration-color: #008000\">'dc_mixer_offset_I'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span>,\n",
       "                <span style=\"color: #008000; text-decoration-color: #008000\">'dc_mixer_offset_Q'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span>,\n",
       "                <span style=\"color: #008000; text-decoration-color: #008000\">'portclock_configs'</span>: <span style=\"font-weight: bold\">[</span>\n",
       "                    <span style=\"font-weight: bold\">{</span>\n",
       "                        <span style=\"color: #008000; text-decoration-color: #008000\">'port'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'q0:mw'</span>,\n",
       "                        <span style=\"color: #008000; text-decoration-color: #008000\">'clock'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'q0.01'</span>,\n",
       "                        <span style=\"color: #008000; text-decoration-color: #008000\">'interm_freq'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">80000000.0</span>,\n",
       "                        <span style=\"color: #008000; text-decoration-color: #008000\">'mixer_amp_ratio'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>,\n",
       "                        <span style=\"color: #008000; text-decoration-color: #008000\">'mixer_phase_error_deg'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span>\n",
       "                    <span style=\"font-weight: bold\">}</span>\n",
       "                <span style=\"font-weight: bold\">]</span>\n",
       "            <span style=\"font-weight: bold\">}</span>,\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'complex_output_1'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "                <span style=\"color: #008000; text-decoration-color: #008000\">'output_att'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>,\n",
       "                <span style=\"color: #008000; text-decoration-color: #008000\">'dc_mixer_offset_I'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span>,\n",
       "                <span style=\"color: #008000; text-decoration-color: #008000\">'dc_mixer_offset_Q'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span>,\n",
       "                <span style=\"color: #008000; text-decoration-color: #008000\">'portclock_configs'</span>: <span style=\"font-weight: bold\">[</span>\n",
       "                    <span style=\"font-weight: bold\">{</span>\n",
       "                        <span style=\"color: #008000; text-decoration-color: #008000\">'port'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'q1:mw'</span>,\n",
       "                        <span style=\"color: #008000; text-decoration-color: #008000\">'clock'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'q1.01'</span>,\n",
       "                        <span style=\"color: #008000; text-decoration-color: #008000\">'interm_freq'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">80000000.0</span>,\n",
       "                        <span style=\"color: #008000; text-decoration-color: #008000\">'mixer_amp_ratio'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>,\n",
       "                        <span style=\"color: #008000; text-decoration-color: #008000\">'mixer_phase_error_deg'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span>\n",
       "                    <span style=\"font-weight: bold\">}</span>\n",
       "                <span style=\"font-weight: bold\">]</span>\n",
       "            <span style=\"font-weight: bold\">}</span>\n",
       "        <span style=\"font-weight: bold\">}</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'cluster0_module2'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'instrument_type'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'QCM'</span>,\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'real_output_0'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'portclock_configs'</span>: <span style=\"font-weight: bold\">[{</span><span style=\"color: #008000; text-decoration-color: #008000\">'port'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'q0:fl'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'clock'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'cl0.baseband'</span><span style=\"font-weight: bold\">}]}</span>,\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'real_output_1'</span>: <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'portclock_configs'</span>: <span style=\"font-weight: bold\">[{</span><span style=\"color: #008000; text-decoration-color: #008000\">'port'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'q1:fl'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'clock'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'cl0.baseband'</span><span style=\"font-weight: bold\">}]}</span>\n",
       "        <span style=\"font-weight: bold\">}</span>,\n",
       "        <span style=\"color: #008000; text-decoration-color: #008000\">'cluster0_module8'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'instrument_type'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'QRM_RF'</span>,\n",
       "            <span style=\"color: #008000; text-decoration-color: #008000\">'complex_output_0'</span>: <span style=\"font-weight: bold\">{</span>\n",
       "                <span style=\"color: #008000; text-decoration-color: #008000\">'output_att'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">60</span>,\n",
       "                <span style=\"color: #008000; text-decoration-color: #008000\">'input_att'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>,\n",
       "                <span style=\"color: #008000; text-decoration-color: #008000\">'dc_mixer_offset_I'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span>,\n",
       "                <span style=\"color: #008000; text-decoration-color: #008000\">'dc_mixer_offset_Q'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span>,\n",
       "                <span style=\"color: #008000; text-decoration-color: #008000\">'lo_freq'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7500000000.0</span>,\n",
       "                <span style=\"color: #008000; text-decoration-color: #008000\">'portclock_configs'</span>: <span style=\"font-weight: bold\">[</span>\n",
       "                    <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'port'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'q0:res'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'clock'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'q0.ro'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'mixer_amp_ratio'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'mixer_phase_error_deg'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">}</span>,\n",
       "                    <span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'port'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'q1:res'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'clock'</span>: <span style=\"color: #008000; text-decoration-color: #008000\">'q1.ro'</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'mixer_amp_ratio'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'mixer_phase_error_deg'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.0</span><span style=\"font-weight: bold\">}</span>\n",
       "                <span style=\"font-weight: bold\">]</span>\n",
       "            <span style=\"font-weight: bold\">}</span>\n",
       "        <span style=\"font-weight: bold\">}</span>\n",
       "    <span style=\"font-weight: bold\">}</span>\n",
       "<span style=\"font-weight: bold\">}</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m{\u001b[0m\n",
       "    \u001b[32m'backend'\u001b[0m: \u001b[32m'quantify_scheduler.backends.qblox_backend.hardware_compile'\u001b[0m,\n",
       "    \u001b[32m'cluster0'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "        \u001b[32m'sequence_to_file'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
       "        \u001b[32m'ref'\u001b[0m: \u001b[32m'internal'\u001b[0m,\n",
       "        \u001b[32m'instrument_type'\u001b[0m: \u001b[32m'Cluster'\u001b[0m,\n",
       "        \u001b[32m'cluster0_module6'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "            \u001b[32m'instrument_type'\u001b[0m: \u001b[32m'QCM_RF'\u001b[0m,\n",
       "            \u001b[32m'complex_output_0'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "                \u001b[32m'output_att'\u001b[0m: \u001b[1;36m10\u001b[0m,\n",
       "                \u001b[32m'dc_mixer_offset_I'\u001b[0m: \u001b[1;36m0.0\u001b[0m,\n",
       "                \u001b[32m'dc_mixer_offset_Q'\u001b[0m: \u001b[1;36m0.0\u001b[0m,\n",
       "                \u001b[32m'portclock_configs'\u001b[0m: \u001b[1m[\u001b[0m\n",
       "                    \u001b[1m{\u001b[0m\n",
       "                        \u001b[32m'port'\u001b[0m: \u001b[32m'q0:mw'\u001b[0m,\n",
       "                        \u001b[32m'clock'\u001b[0m: \u001b[32m'q0.01'\u001b[0m,\n",
       "                        \u001b[32m'interm_freq'\u001b[0m: \u001b[1;36m80000000.0\u001b[0m,\n",
       "                        \u001b[32m'mixer_amp_ratio'\u001b[0m: \u001b[1;36m1.0\u001b[0m,\n",
       "                        \u001b[32m'mixer_phase_error_deg'\u001b[0m: \u001b[1;36m0.0\u001b[0m\n",
       "                    \u001b[1m}\u001b[0m\n",
       "                \u001b[1m]\u001b[0m\n",
       "            \u001b[1m}\u001b[0m,\n",
       "            \u001b[32m'complex_output_1'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "                \u001b[32m'output_att'\u001b[0m: \u001b[1;36m10\u001b[0m,\n",
       "                \u001b[32m'dc_mixer_offset_I'\u001b[0m: \u001b[1;36m0.0\u001b[0m,\n",
       "                \u001b[32m'dc_mixer_offset_Q'\u001b[0m: \u001b[1;36m0.0\u001b[0m,\n",
       "                \u001b[32m'portclock_configs'\u001b[0m: \u001b[1m[\u001b[0m\n",
       "                    \u001b[1m{\u001b[0m\n",
       "                        \u001b[32m'port'\u001b[0m: \u001b[32m'q1:mw'\u001b[0m,\n",
       "                        \u001b[32m'clock'\u001b[0m: \u001b[32m'q1.01'\u001b[0m,\n",
       "                        \u001b[32m'interm_freq'\u001b[0m: \u001b[1;36m80000000.0\u001b[0m,\n",
       "                        \u001b[32m'mixer_amp_ratio'\u001b[0m: \u001b[1;36m1.0\u001b[0m,\n",
       "                        \u001b[32m'mixer_phase_error_deg'\u001b[0m: \u001b[1;36m0.0\u001b[0m\n",
       "                    \u001b[1m}\u001b[0m\n",
       "                \u001b[1m]\u001b[0m\n",
       "            \u001b[1m}\u001b[0m\n",
       "        \u001b[1m}\u001b[0m,\n",
       "        \u001b[32m'cluster0_module2'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "            \u001b[32m'instrument_type'\u001b[0m: \u001b[32m'QCM'\u001b[0m,\n",
       "            \u001b[32m'real_output_0'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'portclock_configs'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m{\u001b[0m\u001b[32m'port'\u001b[0m: \u001b[32m'q0:fl'\u001b[0m, \u001b[32m'clock'\u001b[0m: \u001b[32m'cl0.baseband'\u001b[0m\u001b[1m}\u001b[0m\u001b[1m]\u001b[0m\u001b[1m}\u001b[0m,\n",
       "            \u001b[32m'real_output_1'\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'portclock_configs'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m{\u001b[0m\u001b[32m'port'\u001b[0m: \u001b[32m'q1:fl'\u001b[0m, \u001b[32m'clock'\u001b[0m: \u001b[32m'cl0.baseband'\u001b[0m\u001b[1m}\u001b[0m\u001b[1m]\u001b[0m\u001b[1m}\u001b[0m\n",
       "        \u001b[1m}\u001b[0m,\n",
       "        \u001b[32m'cluster0_module8'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "            \u001b[32m'instrument_type'\u001b[0m: \u001b[32m'QRM_RF'\u001b[0m,\n",
       "            \u001b[32m'complex_output_0'\u001b[0m: \u001b[1m{\u001b[0m\n",
       "                \u001b[32m'output_att'\u001b[0m: \u001b[1;36m60\u001b[0m,\n",
       "                \u001b[32m'input_att'\u001b[0m: \u001b[1;36m0\u001b[0m,\n",
       "                \u001b[32m'dc_mixer_offset_I'\u001b[0m: \u001b[1;36m0.0\u001b[0m,\n",
       "                \u001b[32m'dc_mixer_offset_Q'\u001b[0m: \u001b[1;36m0.0\u001b[0m,\n",
       "                \u001b[32m'lo_freq'\u001b[0m: \u001b[1;36m7500000000.0\u001b[0m,\n",
       "                \u001b[32m'portclock_configs'\u001b[0m: \u001b[1m[\u001b[0m\n",
       "                    \u001b[1m{\u001b[0m\u001b[32m'port'\u001b[0m: \u001b[32m'q0:res'\u001b[0m, \u001b[32m'clock'\u001b[0m: \u001b[32m'q0.ro'\u001b[0m, \u001b[32m'mixer_amp_ratio'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'mixer_phase_error_deg'\u001b[0m: \u001b[1;36m0.0\u001b[0m\u001b[1m}\u001b[0m,\n",
       "                    \u001b[1m{\u001b[0m\u001b[32m'port'\u001b[0m: \u001b[32m'q1:res'\u001b[0m, \u001b[32m'clock'\u001b[0m: \u001b[32m'q1.ro'\u001b[0m, \u001b[32m'mixer_amp_ratio'\u001b[0m: \u001b[1;36m1.0\u001b[0m, \u001b[32m'mixer_phase_error_deg'\u001b[0m: \u001b[1;36m0.0\u001b[0m\u001b[1m}\u001b[0m\n",
       "                \u001b[1m]\u001b[0m\n",
       "            \u001b[1m}\u001b[0m\n",
       "        \u001b[1m}\u001b[0m\n",
       "    \u001b[1m}\u001b[0m\n",
       "\u001b[1m}\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import rich\n",
    "\n",
    "rich.print(quantum_device.hardware_config())"
   ]
  }
 ],
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