{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d63e38d4",
   "metadata": {},
   "source": [
    "# Coupled qubits characterization\n",
    "\n",
    ".. Note::\n",
    "        This notebook uses some python helper functions and example data. You can find both in a zipfile accessible with this link: [gitlab](https://gitlab.com/qblox/packages/software/qblox_instruments_docs/-/blob/master/docs/applications/quantify/example_data_and_utils.zip).\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": "3e2f3b3a",
   "metadata": {},
   "source": [
    "## Hardware setup\n",
    "In this section we configure the hardware configuration which specifies the connectivity of our system."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28d63c78",
   "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",
    "1. **QCM-RF**\n",
    "    - Drive line for `q0` using fixed 80 MHz IF.\n",
    "    - Drive line for `q1` using fixed 80 MHz IF.\n",
    "2. **QCM**\n",
    "    - Flux line for `q0`.\n",
    "    - Flux line for `q1`.\n",
    "6. **QRM-RF**\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": "ddfa3103",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-06T16:53:35.349475Z",
     "iopub.status.busy": "2023-12-06T16:53:35.347968Z",
     "iopub.status.idle": "2023-12-06T16:53:35.374550Z",
     "shell.execute_reply": "2023-12-06T16:53:35.372975Z"
    }
   },
   "outputs": [],
   "source": [
    "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_module1\": {\n",
    "            \"instrument_type\": \"QCM_RF\",\n",
    "            \"complex_output_0\": {\n",
    "                \"output_att\": 0,\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\": 0,\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_module3\": {\n",
    "            \"instrument_type\": \"QRM_RF\",\n",
    "            \"complex_output_0\": {\n",
    "                \"output_att\": 0,\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": "d70cb0d0",
   "metadata": {},
   "source": [
    "### Scan For Clusters\n",
    "\n",
    "We scan for the available clusters on our network using the Plug & Play functionality of the Qblox Instruments package (see [Plug & Play](https://qblox-qblox-instruments.readthedocs-hosted.com/en/master/api_reference/tools.html#plug-play) for more info)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "90fe6cf3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-06T16:53:35.380262Z",
     "iopub.status.busy": "2023-12-06T16:53:35.380262Z",
     "iopub.status.idle": "2023-12-06T16:53:36.980403Z",
     "shell.execute_reply": "2023-12-06T16:53:36.979405Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ae1420ca9374a9e87c78bc7f2701f39",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Select Device', options=(['Dummy-Cluster', 'dummy_cluster'], ('dd0 @10.10.200.50', '0001…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display\n",
    "\n",
    "from qblox_instruments import PlugAndPlay\n",
    "\n",
    "warnings.simplefilter(\"ignore\")\n",
    "\n",
    "# Scan for available devices and display\n",
    "with PlugAndPlay() as p:\n",
    "    # Get info of all devices\n",
    "    device_list = p.list_devices()\n",
    "\n",
    "names = {dev_id: dev_info[\"description\"][\"name\"] for dev_id, dev_info in device_list.items()}\n",
    "names[\"dummy_cluster\"] = \"dummy_cluster\"\n",
    "ip_addresses = {dev_id: dev_info[\"identity\"][\"ip\"] for dev_id, dev_info in device_list.items()}\n",
    "\n",
    "\n",
    "# Create widget for names and ip addresses\n",
    "connect = widgets.Dropdown(\n",
    "    options=[[\"Dummy-Cluster\", \"dummy_cluster\"]]\n",
    "    + [(f\"{names[dev_id]} @{ip_addresses[dev_id]}\", dev_id) for dev_id in device_list],\n",
    "    description=\"Select Device\",\n",
    ")\n",
    "display(connect)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "306219eb",
   "metadata": {},
   "source": [
    "### Connect to Cluster\n",
    "\n",
    "We now make a connection with the Cluster selected in the dropdown widget. We also define a function to find the modules we're interested in. We select the readout and control module we want to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bcaed73b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-06T16:53:36.985937Z",
     "iopub.status.busy": "2023-12-06T16:53:36.984918Z",
     "iopub.status.idle": "2023-12-06T16:53:37.276918Z",
     "shell.execute_reply": "2023-12-06T16:53:37.275831Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dummy-Cluster connected\n"
     ]
    }
   ],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "from qcodes import Instrument\n",
    "\n",
    "from qblox_instruments import Cluster, ClusterType\n",
    "\n",
    "# Close all existing QCoDeS Instrument instances\n",
    "Instrument.close_all()\n",
    "\n",
    "# Select the device\n",
    "dev_id = connect.value\n",
    "\n",
    "# Here we have the option to use a dummy device so that you can run your tests without a physical cluster\n",
    "dummy_cfg = (\n",
    "    {\n",
    "        1: ClusterType.CLUSTER_QCM_RF,\n",
    "        2: ClusterType.CLUSTER_QCM,\n",
    "        3: ClusterType.CLUSTER_QRM_RF,\n",
    "    }\n",
    "    if dev_id == \"dummy_cluster\"\n",
    "    else None\n",
    ")\n",
    "\n",
    "cluster = Cluster(name=\"cluster0\", identifier=ip_addresses.get(dev_id), dummy_cfg=dummy_cfg)\n",
    "\n",
    "print(f\"{connect.label} connected\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a9e6e5a",
   "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": 4,
   "id": "e06dd44b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-06T16:53:37.280460Z",
     "iopub.status.busy": "2023-12-06T16:53:37.280460Z",
     "iopub.status.idle": "2023-12-06T16:53:37.292258Z",
     "shell.execute_reply": "2023-12-06T16:53:37.291245Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: CRITICAL, Flags: TEMPERATURE_OUT_OF_RANGE, Slot flags: SLOT1_TEMPERATURE_OUT_OF_RANGE, SLOT2_TEMPERATURE_OUT_OF_RANGE, SLOT3_TEMPERATURE_OUT_OF_RANGE\n"
     ]
    }
   ],
   "source": [
    "cluster.reset()\n",
    "print(cluster.get_system_state())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "137d329b",
   "metadata": {},
   "source": [
    "Note that a dummy cluster will raise error flags, this is expected behavior and can be ignored."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26f14ca4",
   "metadata": {},
   "source": [
    "## Experiment setup"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5fb6c7a",
   "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": 5,
   "id": "72396c1a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-06T16:53:37.297335Z",
     "iopub.status.busy": "2023-12-06T16:53:37.296325Z",
     "iopub.status.idle": "2023-12-06T16:53:39.559468Z",
     "shell.execute_reply": "2023-12-06T16:53:39.558111Z"
    }
   },
   "outputs": [],
   "source": [
    "import contextlib\n",
    "\n",
    "from quantify_scheduler.device_under_test.quantum_device import QuantumDevice\n",
    "from quantify_scheduler.device_under_test.transmon_element import BasicTransmonElement\n",
    "\n",
    "# Close QCoDeS instruments with conflicting names\n",
    "for name in [\"device_2q\", \"q0\", \"q1\"]:\n",
    "    with contextlib.suppress(KeyError):\n",
    "        Instrument.find_instrument(name).close()\n",
    "\n",
    "\n",
    "q0 = BasicTransmonElement(\"q0\")\n",
    "q0.measure.acq_channel(0)\n",
    "\n",
    "q1 = BasicTransmonElement(\"q1\")\n",
    "q1.measure.acq_channel(1)\n",
    "\n",
    "quantum_device = QuantumDevice(\"device_2q\")\n",
    "quantum_device.hardware_config(hardware_cfg)\n",
    "\n",
    "quantum_device.add_element(q0)\n",
    "quantum_device.add_element(q1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f7d5387",
   "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc57981c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-06T16:53:39.564162Z",
     "iopub.status.busy": "2023-12-06T16:53:39.563094Z",
     "iopub.status.idle": "2023-12-06T16:53:39.590214Z",
     "shell.execute_reply": "2023-12-06T16:53:39.589057Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                  Type  Unit            q0            q1\n",
      "Parameter                                                               \n",
      "pulse_type                     measure         SquarePulse   SquarePulse\n",
      "pulse_amp                      measure                0.05          0.05\n",
      "pulse_duration                 measure   (s)      0.000002      0.000002\n",
      "acq_channel                    measure   (#)             0             1\n",
      "acq_delay                      measure   (s)     0.0000001     0.0000001\n",
      "integration_time               measure   (s)     0.0000019     0.0000019\n",
      "reset_clock_phase              measure                True          True\n",
      "acq_weights_a                  measure                None          None\n",
      "acq_weights_b                  measure                None          None\n",
      "acq_weights_sampling_rate      measure                None          None\n",
      "acq_weight_type                measure                 SSB           SSB\n",
      "acq_rotation                   measure                   0             0\n",
      "acq_threshold                  measure                   0             0\n",
      "duration                         reset   (s)        0.0001        0.0001\n",
      "f01                        clock_freqs  (Hz)  5100000000.0  5200000000.0\n",
      "f12                        clock_freqs  (Hz)           NaN           NaN\n",
      "readout                    clock_freqs  (Hz)  7600000000.0  7700000000.0\n",
      "amp180                             rxy                 0.1           0.1\n",
      "motzoi                             rxy                0.05          0.05\n",
      "duration                           rxy   (s)    0.00000004    0.00000004\n",
      "microwave                        ports               q0:mw         q1:mw\n",
      "flux                             ports               q0:fl         q1:fl\n",
      "readout                          ports              q0:res        q1:res\n"
     ]
    }
   ],
   "source": [
    "from utils.tutorial_utils import show_parameters\n",
    "\n",
    "# ============ 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)\n",
    "\n",
    "show_parameters(q0, q1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c4bae08",
   "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": 7,
   "id": "a3f20f28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-06T16:53:39.594671Z",
     "iopub.status.busy": "2023-12-06T16:53:39.594671Z",
     "iopub.status.idle": "2023-12-06T16:53:40.454174Z",
     "shell.execute_reply": "2023-12-06T16:53:40.453662Z"
    }
   },
   "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",
    ") -> None:\n",
    "    # Close QCoDeS instruments with conflicting names\n",
    "    for name in [\n",
    "        \"PlotMonitor\",\n",
    "        \"meas_ctrl\",\n",
    "        \"ic\",\n",
    "        \"ic_generic\",\n",
    "        f\"ic_{cluster.name}\",\n",
    "    ] + [f\"ic_{module.name}\" for module in cluster.modules]:\n",
    "        with contextlib.suppress(KeyError):\n",
    "            Instrument.find_instrument(name).close()\n",
    "\n",
    "    meas_ctrl = MeasurementControl(\"meas_ctrl\")\n",
    "    ic = InstrumentCoordinator(\"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 = PlotMonitor(\"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": "9e5952ab",
   "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": 8,
   "id": "f79e9139",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-06T16:53:40.458710Z",
     "iopub.status.busy": "2023-12-06T16:53:40.458710Z",
     "iopub.status.idle": "2023-12-06T16:53:40.471145Z",
     "shell.execute_reply": "2023-12-06T16:53:40.469753Z"
    }
   },
   "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": "cbe9d14b",
   "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",
    "```python\n",
    "    # e.g. nullify external flux by setting current to 0 A\n",
    "    cluster.module2.out0_current(0.0)\n",
    "```\n",
    "\n",
    "Here we are nullifying the external flux on both qubits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3ade7761",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-06T16:53:40.475507Z",
     "iopub.status.busy": "2023-12-06T16:53:40.474233Z",
     "iopub.status.idle": "2023-12-06T16:53:40.486772Z",
     "shell.execute_reply": "2023-12-06T16:53:40.485199Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "q0 = cluster0_module2_out0_offset\n",
      "q1 = cluster0_module2_out1_offset\n"
     ]
    }
   ],
   "source": [
    "from utils.tutorial_utils import show_args\n",
    "\n",
    "from quantify_scheduler.helpers.collections import find_port_clock_path\n",
    "\n",
    "\n",
    "def find_flux_settable(qubit: BasicTransmonElement) -> callable:\n",
    "    \"\"\"Return flux port voltage offset for qubit.\"\"\"\n",
    "    path = find_port_clock_path(\n",
    "        quantum_device.hardware_config(), qubit.ports.flux(), \"cl0.baseband\"\n",
    "    )\n",
    "    module = getattr(cluster, path[1].split(\"_\")[1])\n",
    "    settable = getattr(module, f\"out{path[2].split('_')[-1]}_offset\")\n",
    "    return settable\n",
    "\n",
    "\n",
    "flux_settables = {q.name: find_flux_settable(q) for q in (q0, q1)}\n",
    "\n",
    "for flux_settable in flux_settables.values():\n",
    "    flux_settable(0.0)\n",
    "\n",
    "show_args(flux_settables)"
   ]
  },
  {
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   "execution_count": 10,
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   "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": "947a8fed",
   "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/master/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)."
   ]
  },
  {
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   "execution_count": 11,
   "id": "963a80a7",
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   "source": [
    "for i in range(6):\n",
    "    getattr(cluster.module3, f\"sequencer{i}\").nco_prop_delay_comp_en(True)\n",
    "    getattr(cluster.module3, f\"sequencer{i}\").nco_prop_delay_comp(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4951d4a6",
   "metadata": {},
   "source": [
    "### Set attenuation\n",
    "We ought to make sure that our excitation and readout pulses have the appropriate power. Since we are assuming that the qubits and resonators have already been individually characterised, we use the same attenuation that we used in the single transmon qubit tutorial."
   ]
  },
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   "id": "6f6e2eb9",
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   "source": [
    "from utils.tutorial_utils import set_drive_attenuation, set_readout_attenuation\n",
    "\n",
    "set_readout_attenuation(quantum_device, q0, out_att=50, in_att=0)\n",
    "set_readout_attenuation(quantum_device, q1, out_att=50, in_att=0)\n",
    "\n",
    "set_drive_attenuation(quantum_device, q0, out_att=18)\n",
    "set_drive_attenuation(quantum_device, q1, out_att=18)"
   ]
  },
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   "id": "3aa46059",
   "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": "1c0d614b",
   "metadata": {},
   "source": [
    "### Controlled phase calibration"
   ]
  },
  {
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   "id": "9b74ccb9",
   "metadata": {
    "execution": {
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   "source": [
    "import numpy as np\n",
    "\n",
    "from quantify_scheduler.operations.gate_library import Measure, Reset, X\n",
    "from quantify_scheduler.operations.pulse_library import SquarePulse\n",
    "from quantify_scheduler.schedules.schedule import Schedule\n",
    "\n",
    "\n",
    "def chevron_cz_sched(\n",
    "    lf_qubit: str,\n",
    "    hf_qubit: str,\n",
    "    amplitudes: np.ndarray,\n",
    "    duration: float,\n",
    "    flux_port: str = None,\n",
    "    repetitions: int = 1,\n",
    ") -> Schedule:\n",
    "    \"\"\"https://quantify-quantify-scheduler.readthedocs-hosted.com/en/latest/autoapi/quantify_scheduler/schedules/two_qubit_transmon_schedules.\"\"\"\n",
    "    sched = Schedule(\"Two-qubit Chevron CZ schedule\", repetitions)\n",
    "\n",
    "    # Ensure amplitudes is an iterable when passing a float\n",
    "    amplitudes = np.asarray(amplitudes)\n",
    "    amplitudes = amplitudes.reshape(amplitudes.shape or (1,))\n",
    "\n",
    "    # Set flux port\n",
    "    flux_port = flux_port if flux_port is not None else f\"{hf_qubit}:fl\"\n",
    "\n",
    "    for acq_index, amp in enumerate(amplitudes):\n",
    "        # Reset to |00>\n",
    "        sched.add(Reset(lf_qubit, hf_qubit), label=f\"Reset {acq_index}\")\n",
    "\n",
    "        # Prepare |11>\n",
    "        excite_lf = sched.add(X(lf_qubit), label=f\"X({lf_qubit}) {acq_index}\")\n",
    "        sched.add(\n",
    "            X(hf_qubit),\n",
    "            ref_op=excite_lf,\n",
    "            ref_pt=\"start\",\n",
    "            label=f\"X({hf_qubit}) {acq_index}\",\n",
    "        )\n",
    "\n",
    "        # Go to |11> <=> |02> avoided crossing and come back\n",
    "        sched.add(\n",
    "            SquarePulse(\n",
    "                amp=amp,\n",
    "                duration=duration,\n",
    "                port=flux_port,\n",
    "                clock=\"cl0.baseband\",\n",
    "            ),\n",
    "            label=f\"SquarePulse({flux_port}) {acq_index}\",\n",
    "        )\n",
    "\n",
    "        # Measure system\n",
    "        sched.add(\n",
    "            Measure(lf_qubit, hf_qubit, acq_index=acq_index),\n",
    "            label=f\"Measure({lf_qubit},{hf_qubit}) {acq_index}\",\n",
    "        )\n",
    "\n",
    "    return sched"
   ]
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     "text": [
      "chevron_cz_sched_kwargs\n",
      "=============\n",
      "lf_qubit   = q0\n",
      "hf_qubit   = q1\n",
      "amplitudes = amp\n",
      "duration   = dur\n"
     ]
    }
   ],
   "source": [
    "from qcodes.parameters import ManualParameter\n",
    "\n",
    "from quantify_scheduler.gettables import ScheduleGettable\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)\n",
    "show_args(chevron_cz_sched_kwargs, \"chevron_cz_sched_kwargs\")"
   ]
  },
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     "name": "stdout",
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     "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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       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  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;\n",
       "Dimensions:  (dim_0: 2400)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 4e-09 4e-09 4e-09 4e-09 ... 9.6e-08 9.6e-08 9.6e-08\n",
       "    x1       (dim_0) float64 0.05 0.05152 0.05303 0.05455 ... 0.197 0.1985 0.2\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y1       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y2       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y3       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "Attributes:\n",
       "    tuid:                             20231206-175340-589-c5e22a\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-4d60cd18-9efa-4c43-9b3f-73cc0b356607' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4d60cd18-9efa-4c43-9b3f-73cc0b356607' 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-1507ccf3-11e3-4e3a-9af3-be95ec8db3e6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1507ccf3-11e3-4e3a-9af3-be95ec8db3e6' 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-bd21ff5e-c7e2-4976-9087-2ce296c4a1cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd21ff5e-c7e2-4976-9087-2ce296c4a1cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ab84304-54cc-42f3-b144-e40d0fdcab19' class='xr-var-data-in' type='checkbox'><label for='data-3ab84304-54cc-42f3-b144-e40d0fdcab19' 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-75753527-5372-46aa-9009-9d7f4d5f8e5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75753527-5372-46aa-9009-9d7f4d5f8e5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a15da405-8925-42cc-8fa3-61ffd4269917' class='xr-var-data-in' type='checkbox'><label for='data-a15da405-8925-42cc-8fa3-61ffd4269917' 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-02dab226-ad49-4863-80e1-88763efa8046' class='xr-section-summary-in' type='checkbox'  checked><label for='section-02dab226-ad49-4863-80e1-88763efa8046' 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-66be99e1-7051-4d3b-ab70-c4911892083d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66be99e1-7051-4d3b-ab70-c4911892083d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4297168a-e9d1-4b97-a8ca-3d86304bc425' class='xr-var-data-in' type='checkbox'><label for='data-4297168a-e9d1-4b97-a8ca-3d86304bc425' 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-6f34633b-e747-4110-83e1-c08ba2bfd0a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f34633b-e747-4110-83e1-c08ba2bfd0a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e4820a5-54d9-4c4e-a870-40e168e3e0dd' class='xr-var-data-in' type='checkbox'><label for='data-8e4820a5-54d9-4c4e-a870-40e168e3e0dd' 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-2579cc6b-22f4-4f42-9c63-c6d3db435bf4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2579cc6b-22f4-4f42-9c63-c6d3db435bf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15f80ced-c07f-4ae4-8869-5812240c6c1c' class='xr-var-data-in' type='checkbox'><label for='data-15f80ced-c07f-4ae4-8869-5812240c6c1c' 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-20f36b5a-f9af-4df5-b94e-bf27c76cddbf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20f36b5a-f9af-4df5-b94e-bf27c76cddbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6aa9725-f47c-48a8-b888-c5dc8e1febb1' class='xr-var-data-in' type='checkbox'><label for='data-d6aa9725-f47c-48a8-b888-c5dc8e1febb1' 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-799202f4-671f-4dcc-abba-11b22b8352f2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-799202f4-671f-4dcc-abba-11b22b8352f2' 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-48db331d-188c-4cc6-a5a9-0bfa33b92b56' class='xr-section-summary-in' type='checkbox'  checked><label for='section-48db331d-188c-4cc6-a5a9-0bfa33b92b56' 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>20231206-175340-589-c5e22a</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>\n",
       "Dimensions:  (dim_0: 2400)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 4e-09 4e-09 4e-09 4e-09 ... 9.6e-08 9.6e-08 9.6e-08\n",
       "    x1       (dim_0) float64 0.05 0.05152 0.05303 0.05455 ... 0.197 0.1985 0.2\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y1       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y2       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y3       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "Attributes:\n",
       "    tuid:                             20231206-175340-589-c5e22a\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": 15,
     "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": 16,
   "id": "660a8aa8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-06T16:53:55.256617Z",
     "iopub.status.busy": "2023-12-06T16:53:55.256617Z",
     "iopub.status.idle": "2023-12-06T16:53:55.361248Z",
     "shell.execute_reply": "2023-12-06T16:53:55.360243Z"
    }
   },
   "outputs": [],
   "source": [
    "# If on dummy, override with old data for analysis\n",
    "if dev_id == \"dummy_cluster\":\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": 17,
   "id": "d7a3272e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-06T16:53:55.365729Z",
     "iopub.status.busy": "2023-12-06T16:53:55.365729Z",
     "iopub.status.idle": "2023-12-06T16:53:55.985099Z",
     "shell.execute_reply": "2023-12-06T16:53:55.984100Z"
    }
   },
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d31a89fa",
   "metadata": {},
   "source": [
    "### Conditional oscillations"
   ]
  },
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   "source": [
    "from typing import Literal\n",
    "\n",
    "from quantify_scheduler.operations.gate_library import X90, Rxy\n",
    "from quantify_scheduler.operations.pulse_library import SuddenNetZeroPulse\n",
    "\n",
    "\n",
    "def conditional_oscillation_sched(\n",
    "    target_qubit: str,\n",
    "    control_qubit: str,\n",
    "    phases: np.array,\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,\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",
    "    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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    {
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     "text": [
      "conditional_oscillation_sched_kwargs\n",
      "==============================\n",
      "target_qubit              = q0\n",
      "control_qubit             = q1\n",
      "phases                    = ph\n",
      "snz_A                     = A\n",
      "snz_B                     = B\n",
      "snz_scale                 = 1.0\n",
      "snz_dur                   = 4e-08\n",
      "snz_t_phi                 = 4e-09\n",
      "snz_t_integral_correction = 0.0\n"
     ]
    }
   ],
   "source": [
    "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))\n",
    "show_args(conditional_oscillation_sched_kwargs, title=\"conditional_oscillation_sched_kwargs\")"
   ]
  },
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   "id": "1503110c",
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     "shell.execute_reply": "2023-12-06T16:53:57.047836Z"
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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"
     ]
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       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
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       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  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;\n",
       "Dimensions:  (dim_0: 60)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 0.0 6.102 12.2 18.31 ... 341.7 347.8 353.9 360.0\n",
       "    x1       (dim_0) float64 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",
       "    x2       (dim_0) float64 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",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y1       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y2       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y3       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y4       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y5       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y6       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y7       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "Attributes:\n",
       "    tuid:                             20231206-175356-030-2b70af\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-816bbcd7-5553-43cc-99d0-46e10345f7a3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-816bbcd7-5553-43cc-99d0-46e10345f7a3' 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-95aca779-4954-4490-a46d-d04bcad6ae28' class='xr-section-summary-in' type='checkbox'  checked><label for='section-95aca779-4954-4490-a46d-d04bcad6ae28' 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-4a7816b8-6eda-4bc2-bb94-cc14090b9566' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a7816b8-6eda-4bc2-bb94-cc14090b9566' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d5a2dec-bf3e-4485-bd4e-2a48227bd999' class='xr-var-data-in' type='checkbox'><label for='data-1d5a2dec-bf3e-4485-bd4e-2a48227bd999' 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-20361ad9-6ffe-4094-a030-8c8b6a0d02fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20361ad9-6ffe-4094-a030-8c8b6a0d02fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0a187de-f0ee-4e70-b65d-34a446c337b5' class='xr-var-data-in' type='checkbox'><label for='data-a0a187de-f0ee-4e70-b65d-34a446c337b5' 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-58ec164c-e53a-48aa-9b9a-4a89951c1e34' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58ec164c-e53a-48aa-9b9a-4a89951c1e34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9f76266-ad46-499f-9cca-7f762c44b299' class='xr-var-data-in' type='checkbox'><label for='data-f9f76266-ad46-499f-9cca-7f762c44b299' 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-af245a40-5fc1-4530-9d41-1c417de82c22' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af245a40-5fc1-4530-9d41-1c417de82c22' 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-ca737ab6-583f-4d32-8da0-0c3d058beced' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca737ab6-583f-4d32-8da0-0c3d058beced' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a222b45-db6c-40ac-b1df-0702d5f546f4' class='xr-var-data-in' type='checkbox'><label for='data-7a222b45-db6c-40ac-b1df-0702d5f546f4' 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-3ec7847b-0ec7-4803-b7a6-0cd780708f87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ec7847b-0ec7-4803-b7a6-0cd780708f87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19721844-aec4-4221-b2db-e6efcaf8ce09' class='xr-var-data-in' type='checkbox'><label for='data-19721844-aec4-4221-b2db-e6efcaf8ce09' 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-b4a75a87-bd1c-43f6-ac88-b94fedd1e937' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4a75a87-bd1c-43f6-ac88-b94fedd1e937' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43f3fca9-f3b7-4750-a441-efe6847d47b4' class='xr-var-data-in' type='checkbox'><label for='data-43f3fca9-f3b7-4750-a441-efe6847d47b4' 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-a2d5b32c-0af2-431e-a551-eabcb9d11780' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2d5b32c-0af2-431e-a551-eabcb9d11780' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bd61e5c-eb84-41ca-9659-83264e9290f7' class='xr-var-data-in' type='checkbox'><label for='data-9bd61e5c-eb84-41ca-9659-83264e9290f7' 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-a6b99dcf-e931-4913-9e13-50a5968d8cc2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6b99dcf-e931-4913-9e13-50a5968d8cc2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e6640d1-5ef9-4d07-b33b-e5bcf22ebc39' class='xr-var-data-in' type='checkbox'><label for='data-4e6640d1-5ef9-4d07-b33b-e5bcf22ebc39' 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-b792b867-c2f7-48fe-b7d2-8507f4cc6179' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b792b867-c2f7-48fe-b7d2-8507f4cc6179' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bf37af7-49d0-4486-9340-45b0c8740ce4' class='xr-var-data-in' type='checkbox'><label for='data-2bf37af7-49d0-4486-9340-45b0c8740ce4' 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-f768427c-c3b0-46b1-8460-9d62dcd7cbb4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f768427c-c3b0-46b1-8460-9d62dcd7cbb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d858e5e3-b371-485d-9ae5-6fd3652e4293' class='xr-var-data-in' type='checkbox'><label for='data-d858e5e3-b371-485d-9ae5-6fd3652e4293' 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-25b0b84a-3728-4e1a-b198-9005b3f7cea6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25b0b84a-3728-4e1a-b198-9005b3f7cea6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5133eaf8-85ca-48a3-9bc8-6387d0e4e446' class='xr-var-data-in' type='checkbox'><label for='data-5133eaf8-85ca-48a3-9bc8-6387d0e4e446' 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-ab097a55-f8c4-405c-a3b8-9b2ec0a02ff6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ab097a55-f8c4-405c-a3b8-9b2ec0a02ff6' 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-67a3c3d7-096a-49c5-940a-410b57eeb92f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-67a3c3d7-096a-49c5-940a-410b57eeb92f' 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>20231206-175356-030-2b70af</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>\n",
       "Dimensions:  (dim_0: 60)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 0.0 6.102 12.2 18.31 ... 341.7 347.8 353.9 360.0\n",
       "    x1       (dim_0) float64 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",
       "    x2       (dim_0) float64 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",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y1       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y2       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y3       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y4       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y5       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y6       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "    y7       (dim_0) float64 nan nan nan nan nan nan ... nan nan nan nan nan nan\n",
       "Attributes:\n",
       "    tuid:                             20231206-175356-030-2b70af\n",
       "    name:                             conditional oscillation\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  False"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "id": "5c0b26d9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-06T16:53:57.052783Z",
     "iopub.status.busy": "2023-12-06T16:53:57.051781Z",
     "iopub.status.idle": "2023-12-06T16:53:57.095300Z",
     "shell.execute_reply": "2023-12-06T16:53:57.094245Z"
    }
   },
   "outputs": [],
   "source": [
    "# If on dummy, override with old data for analysis\n",
    "if dev_id == \"dummy_cluster\":\n",
    "    cond_osc_ds = dh.to_gridded_dataset(dh.load_dataset(tuid=\"20230509-164908-713-788626\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bbb35b3d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-06T16:53:57.098700Z",
     "iopub.status.busy": "2023-12-06T16:53:57.098700Z",
     "iopub.status.idle": "2023-12-06T16:53:58.748323Z",
     "shell.execute_reply": "2023-12-06T16:53:58.746876Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utils.tutorial_analysis_classes import ConditionalOscillationAnalysis\n",
    "\n",
    "cond_osc_analysis = ConditionalOscillationAnalysis(\n",
    "    tuid=cond_osc_ds.attrs[\"tuid\"], dataset=cond_osc_ds\n",
    ")\n",
    "cond_osc_analysis.run().display_figs_mpl()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ba60e22b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-06T16:53:58.752554Z",
     "iopub.status.busy": "2023-12-06T16:53:58.752033Z",
     "iopub.status.idle": "2023-12-06T16:53:58.764154Z",
     "shell.execute_reply": "2023-12-06T16:53:58.762857Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                  Type  Unit            q0            q1\n",
      "Parameter                                                               \n",
      "pulse_type                     measure         SquarePulse   SquarePulse\n",
      "pulse_amp                      measure                0.05          0.05\n",
      "pulse_duration                 measure   (s)      0.000002      0.000002\n",
      "acq_channel                    measure   (#)             0             1\n",
      "acq_delay                      measure   (s)     0.0000001     0.0000001\n",
      "integration_time               measure   (s)     0.0000019     0.0000019\n",
      "reset_clock_phase              measure                True          True\n",
      "acq_weights_a                  measure                None          None\n",
      "acq_weights_b                  measure                None          None\n",
      "acq_weights_sampling_rate      measure                None          None\n",
      "acq_weight_type                measure                 SSB           SSB\n",
      "acq_rotation                   measure                   0             0\n",
      "acq_threshold                  measure                   0             0\n",
      "duration                         reset   (s)        0.0001        0.0001\n",
      "f01                        clock_freqs  (Hz)  5100000000.0  5200000000.0\n",
      "f12                        clock_freqs  (Hz)           NaN           NaN\n",
      "readout                    clock_freqs  (Hz)  7600000000.0  7700000000.0\n",
      "amp180                             rxy                 0.1           0.1\n",
      "motzoi                             rxy                0.05          0.05\n",
      "duration                           rxy   (s)    0.00000004    0.00000004\n",
      "microwave                        ports               q0:mw         q1:mw\n",
      "flux                             ports               q0:fl         q1:fl\n",
      "readout                          ports              q0:res        q1:res\n"
     ]
    }
   ],
   "source": [
    "show_parameters(q0, q1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "89275272",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-12-06T16:53:58.768214Z",
     "iopub.status.busy": "2023-12-06T16:53:58.768214Z",
     "iopub.status.idle": "2023-12-06T16:53:58.827840Z",
     "shell.execute_reply": "2023-12-06T16:53:58.825739Z"
    }
   },
   "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_module1'</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\">18</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\">18</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_module3'</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\">50</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_module1'\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;36m18\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;36m18\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_module3'\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;36m50\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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