{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7630c6b0",
   "metadata": {},
   "source": [
    "# Rabi experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2170688c",
   "metadata": {
    "tags": []
   },
   "source": [
    "In this tutorial we will combine the techniques explained in the other tutorials and show how to perform a Rabi experiment. For this tutorial we will need one QCM to generate the Rabi pulses and one QRM to perform the readout, although the QCM could be replaced by another QRM if needed.\n",
    "\n",
    "Ports $\\text{O}^{[1-2]}$ of the QCM are used for the driving pulse, while $\\text{O}^{[1-2]}$ of the QRM are used for the readout pulse. Finally, ports $\\text{I}^{[1-2]}$ are used for the acquisition of the readout tone. In this tutorial it is assumed $\\text{O}^{1}$ of the QRM is connected to $\\text{I}^{1}$ of the QRM for time of flight calibration.\n",
    "Furthermore we assume that $\\text{O}^{1}$ of the QCM and $\\text{O}^{2}$ of the QRM are connected to an external oscilloscope to view the Rabi experiment pattern. The scope can be triggered of marker 1 of the QCM.\n",
    "\n",
    "The SYNQ technology synchronizes the programs in the two modules."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a17cfa7e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Setup\n",
    "\n",
    "\n",
    "First, we are going to import the required packages and connect to the instrument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "02c1c227",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:48.849466Z",
     "iopub.status.busy": "2024-09-18T14:49:48.848757Z",
     "iopub.status.idle": "2024-09-18T14:49:50.463456Z",
     "shell.execute_reply": "2024-09-18T14:49:50.462585Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "from __future__ import annotations\n",
    "\n",
    "import json\n",
    "from typing import TYPE_CHECKING, Callable\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from qcodes.instrument import find_or_create_instrument\n",
    "\n",
    "from qblox_instruments import Cluster, ClusterType\n",
    "\n",
    "if TYPE_CHECKING:\n",
    "    from qblox_instruments.qcodes_drivers.module import Module"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a446a223",
   "metadata": {},
   "source": [
    "### Scan For Clusters\n",
    "\n",
    "We scan for the available devices connected via ethernet using the Plug & Play functionality of the Qblox Instruments package (see [Plug & Play](https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/api_reference/tools.html#api-pnp) for more info)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f86b737",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:50.468379Z",
     "iopub.status.busy": "2024-09-18T14:49:50.467553Z",
     "iopub.status.idle": "2024-09-18T14:49:52.902960Z",
     "shell.execute_reply": "2024-09-18T14:49:52.902027Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No devices found\r\n"
     ]
    }
   ],
   "source": [
    "!qblox-pnp list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "53ed0f7f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:52.906310Z",
     "iopub.status.busy": "2024-09-18T14:49:52.906043Z",
     "iopub.status.idle": "2024-09-18T14:49:52.909508Z",
     "shell.execute_reply": "2024-09-18T14:49:52.908622Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster_ip = \"10.10.200.42\"\n",
    "cluster_name = \"cluster0\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c967925",
   "metadata": {},
   "source": [
    "### Connect to Cluster\n",
    "\n",
    "We now make a connection with the Cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8ba742d4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:52.913966Z",
     "iopub.status.busy": "2024-09-18T14:49:52.912959Z",
     "iopub.status.idle": "2024-09-18T14:49:56.469800Z",
     "shell.execute_reply": "2024-09-18T14:49:56.469085Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "cluster = find_or_create_instrument(\n",
    "    Cluster,\n",
    "    recreate=True,\n",
    "    name=cluster_name,\n",
    "    identifier=cluster_ip,\n",
    "    dummy_cfg=(\n",
    "        {\n",
    "            2: ClusterType.CLUSTER_QCM,\n",
    "            4: ClusterType.CLUSTER_QRM,\n",
    "            6: ClusterType.CLUSTER_QCM_RF,\n",
    "            8: ClusterType.CLUSTER_QRM_RF,\n",
    "        }\n",
    "        if cluster_ip is None\n",
    "        else None\n",
    "    ),\n",
    ")\n",
    "cluster.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e9d59d5",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "#### Get connected modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "63b01561",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:56.472490Z",
     "iopub.status.busy": "2024-09-18T14:49:56.471910Z",
     "iopub.status.idle": "2024-09-18T14:49:56.476247Z",
     "shell.execute_reply": "2024-09-18T14:49:56.475435Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_connected_modules(cluster: Cluster, filter_fn: Callable | None = None) -> dict[int, Module]:\n",
    "    def checked_filter_fn(mod: ClusterType) -> bool:\n",
    "        if filter_fn is not None:\n",
    "            return filter_fn(mod)\n",
    "        return True\n",
    "\n",
    "    return {\n",
    "        mod.slot_idx: mod for mod in cluster.modules if mod.present() and checked_filter_fn(mod)\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "376ddbaf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:56.478692Z",
     "iopub.status.busy": "2024-09-18T14:49:56.478027Z",
     "iopub.status.idle": "2024-09-18T14:49:56.501460Z",
     "shell.execute_reply": "2024-09-18T14:49:56.500821Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{4: <Module: cluster0_module4 of Cluster: cluster0>}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# QRM baseband modules\n",
    "readout_modules = get_connected_modules(cluster, lambda mod: mod.is_qrm_type and not mod.is_rf_type)\n",
    "readout_modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ab390b90",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:56.503853Z",
     "iopub.status.busy": "2024-09-18T14:49:56.503202Z",
     "iopub.status.idle": "2024-09-18T14:49:56.506332Z",
     "shell.execute_reply": "2024-09-18T14:49:56.505703Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module = readout_modules[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0221edf1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:56.508489Z",
     "iopub.status.busy": "2024-09-18T14:49:56.507928Z",
     "iopub.status.idle": "2024-09-18T14:49:56.528804Z",
     "shell.execute_reply": "2024-09-18T14:49:56.528175Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: <Module: cluster0_module2 of Cluster: cluster0>}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# QCM baseband modules\n",
    "control_modules = get_connected_modules(\n",
    "    cluster, lambda mod: not mod.is_qrm_type and not mod.is_rf_type\n",
    ")\n",
    "control_modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d58ff73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:56.531017Z",
     "iopub.status.busy": "2024-09-18T14:49:56.530491Z",
     "iopub.status.idle": "2024-09-18T14:49:56.533396Z",
     "shell.execute_reply": "2024-09-18T14:49:56.532824Z"
    }
   },
   "outputs": [],
   "source": [
    "control_module = control_modules[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "851643b7",
   "metadata": {},
   "source": [
    "## Sequencer Setup <a id='seq_setup'></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ccb004",
   "metadata": {},
   "source": [
    "Set `sync_en` to synchronize across modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7dfd41c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:56.535807Z",
     "iopub.status.busy": "2024-09-18T14:49:56.535256Z",
     "iopub.status.idle": "2024-09-18T14:49:56.555443Z",
     "shell.execute_reply": "2024-09-18T14:49:56.554772Z"
    }
   },
   "outputs": [],
   "source": [
    "# Set sync_en\n",
    "readout_module.sequencer0.sync_en(True)\n",
    "control_module.sequencer0.sync_en(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8e83e45",
   "metadata": {},
   "source": [
    "Configure the `NCO` of both the QRM and QCM to 100 MHz and enable the up- and down-conversion in the sequencers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "78a8008b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:56.557950Z",
     "iopub.status.busy": "2024-09-18T14:49:56.557429Z",
     "iopub.status.idle": "2024-09-18T14:49:56.602582Z",
     "shell.execute_reply": "2024-09-18T14:49:56.601918Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.nco_freq(100e6)\n",
    "readout_module.sequencer0.mod_en_awg(True)\n",
    "readout_module.sequencer0.demod_en_acq(True)\n",
    "\n",
    "control_module.sequencer0.nco_freq(100e6)\n",
    "control_module.sequencer0.mod_en_awg(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64adf42b",
   "metadata": {},
   "source": [
    "Configure the outputs of the QRM and QCM such that sequencer0 is the only enabled sequencer and maps to $\\text{O}^{[1-2]}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "09e2e406",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:56.604940Z",
     "iopub.status.busy": "2024-09-18T14:49:56.604550Z",
     "iopub.status.idle": "2024-09-18T14:49:56.665358Z",
     "shell.execute_reply": "2024-09-18T14:49:56.664604Z"
    }
   },
   "outputs": [],
   "source": [
    "# Map sequencer of the QCM to specific outputs (but first disable all sequencer connections)\n",
    "control_module.disconnect_outputs()\n",
    "\n",
    "control_module.sequencer0.connect_sequencer(\"out0_1\")\n",
    "\n",
    "# Map sequencer of the QRM to specific outputs (but first disable all sequencer connections)\n",
    "readout_module.disconnect_outputs()\n",
    "readout_module.disconnect_inputs()\n",
    "\n",
    "readout_module.sequencer0.connect_sequencer(\"io0_1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcf27627",
   "metadata": {},
   "source": [
    "## Define waveforms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e7d8aa0",
   "metadata": {},
   "source": [
    "To readout the systems we define constant pulses `one` and `zero` which will be up converted by the `NCO` to create the appropriate tones for an IQ mixer.\n",
    "Similarly for driving the qubit, we define a Gaussian pulse, together with a zero pulse of equal length to serve as inputs for an IQ mixer. In this tutorial we do not assume mixers to be connected to the inputs and outputs of the QCM/QRM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4143c50d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:56.668049Z",
     "iopub.status.busy": "2024-09-18T14:49:56.667549Z",
     "iopub.status.idle": "2024-09-18T14:49:56.671996Z",
     "shell.execute_reply": "2024-09-18T14:49:56.671369Z"
    }
   },
   "outputs": [],
   "source": [
    "t = np.arange(-80, 81, 1)\n",
    "sigma = 20\n",
    "wfs = {\n",
    "    \"zero\": {\"index\": 0, \"data\": [0.0] * 1024},\n",
    "    \"one\": {\"index\": 1, \"data\": [1.0] * 1024},\n",
    "    \"gauss\": {\"index\": 2, \"data\": list(np.exp(-(0.5 * t**2 / sigma**2)))},\n",
    "    \"empty\": {\"index\": 3, \"data\": list(0.0 * t)},\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0bc43af",
   "metadata": {},
   "source": [
    "Hence we obtain the following waveforms for readout:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b2a8acaa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:56.674167Z",
     "iopub.status.busy": "2024-09-18T14:49:56.673670Z",
     "iopub.status.idle": "2024-09-18T14:49:56.849475Z",
     "shell.execute_reply": "2024-09-18T14:49:56.848719Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7bc9c2c400>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(wfs[\"zero\"][\"data\"])\n",
    "plt.plot(wfs[\"one\"][\"data\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54cd7dfe",
   "metadata": {},
   "source": [
    "And for drive:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "538667bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:56.852734Z",
     "iopub.status.busy": "2024-09-18T14:49:56.852482Z",
     "iopub.status.idle": "2024-09-18T14:49:56.975636Z",
     "shell.execute_reply": "2024-09-18T14:49:56.974834Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f7bc9c13e50>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(wfs[\"gauss\"][\"data\"])\n",
    "plt.plot(wfs[\"empty\"][\"data\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d841b7bd",
   "metadata": {},
   "source": [
    "Finally, we define two acquisitions. A single readout to perform the calibration measurements. Secondly we create a `rabi` readout sequence that contains 50 different bins for saving the results, one for each of the different amplitudes used for the drive tone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bf3b391d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:56.979076Z",
     "iopub.status.busy": "2024-09-18T14:49:56.978705Z",
     "iopub.status.idle": "2024-09-18T14:49:56.982683Z",
     "shell.execute_reply": "2024-09-18T14:49:56.981645Z"
    }
   },
   "outputs": [],
   "source": [
    "num_bins = 50  # Number of amplitudes to be measured\n",
    "acquisitions = {\n",
    "    \"single\": {\"num_bins\": 1, \"index\": 0},\n",
    "    \"rabi\": {\"num_bins\": num_bins, \"index\": 1},\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc3655a5",
   "metadata": {},
   "source": [
    "## Calibration experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "097c0dcd",
   "metadata": {},
   "source": [
    "### TOF calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf4e1467",
   "metadata": {},
   "source": [
    "As a first step, we calibrate the time of flight (tof) for the QRM module. In order to do so, we play a readout pulse and analyze the obtained signal on the oscilloscope to find the travel time of the pulse through the system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d3fad6a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:56.984992Z",
     "iopub.status.busy": "2024-09-18T14:49:56.984788Z",
     "iopub.status.idle": "2024-09-18T14:49:56.988194Z",
     "shell.execute_reply": "2024-09-18T14:49:56.987361Z"
    }
   },
   "outputs": [],
   "source": [
    "qrm_prog = \"\"\"\n",
    "play    1, 0, 4     # start readout pulse\n",
    "acquire 0, 0, 16384 # start the 'single' acquisition sequence and wait for the length of the scope acquisition window\n",
    "stop\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79bef3f8",
   "metadata": {},
   "source": [
    "Upload the program, together with the waveforms and acquisitions to the QRM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0d0807b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:56.990876Z",
     "iopub.status.busy": "2024-09-18T14:49:56.990649Z",
     "iopub.status.idle": "2024-09-18T14:49:57.053047Z",
     "shell.execute_reply": "2024-09-18T14:49:57.052270Z"
    }
   },
   "outputs": [],
   "source": [
    "sequence = {\n",
    "    \"waveforms\": wfs,\n",
    "    \"weights\": {},\n",
    "    \"acquisitions\": acquisitions,\n",
    "    \"program\": qrm_prog,\n",
    "}\n",
    "with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "    json.dump(sequence, file, indent=4)\n",
    "    file.close()\n",
    "# Upload sequence.\n",
    "readout_module.sequencer0.sequence(\"sequence.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "053a1fea",
   "metadata": {},
   "source": [
    "Perform the calibration experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "774afe68",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:57.056541Z",
     "iopub.status.busy": "2024-09-18T14:49:57.056293Z",
     "iopub.status.idle": "2024-09-18T14:49:57.076516Z",
     "shell.execute_reply": "2024-09-18T14:49:57.075629Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, State: STOPPED, Info Flags: FORCED_STOP, ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_BINNING_DONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n"
     ]
    }
   ],
   "source": [
    "# Arm and start sequencer.\n",
    "readout_module.arm_sequencer(0)\n",
    "readout_module.start_sequencer()\n",
    "\n",
    "# Wait for the sequencer and acquisition to finish with a timeout period of one minute.\n",
    "readout_module.get_acquisition_status(0, 1)\n",
    "readout_module.store_scope_acquisition(0, \"single\")\n",
    "# Print status of sequencer.\n",
    "print(readout_module.get_sequencer_status(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "397ffeef",
   "metadata": {},
   "source": [
    "Analyze the resulting signal on the scope to find the tof"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5501df54",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:57.079042Z",
     "iopub.status.busy": "2024-09-18T14:49:57.078804Z",
     "iopub.status.idle": "2024-09-18T14:49:57.135299Z",
     "shell.execute_reply": "2024-09-18T14:49:57.134551Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "149.16666666666666\n"
     ]
    }
   ],
   "source": [
    "p0 = np.array(readout_module.get_acquisitions(0)[\"single\"][\"acquisition\"][\"scope\"][\"path0\"][\"data\"])\n",
    "p1 = np.array(readout_module.get_acquisitions(0)[\"single\"][\"acquisition\"][\"scope\"][\"path1\"][\"data\"])\n",
    "# Determine when the signal crosses half-max for the first time (in ns)\n",
    "t_halfmax = np.where(np.abs(p0) > np.max(p0) / 2)[0][0]\n",
    "\n",
    "# The time it takes for a sine wave to reach its half-max value is (in ns)\n",
    "correction = 1 / readout_module.sequencer0.nco_freq() * 1e9 / 12\n",
    "\n",
    "tof_measured = t_halfmax - correction\n",
    "print(tof_measured)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6925c170",
   "metadata": {},
   "source": [
    "Plot the signal on the scope, around the rising and falling edge of the acquisition signal, as determined by the tof analysis above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "976592cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:57.138307Z",
     "iopub.status.busy": "2024-09-18T14:49:57.138092Z",
     "iopub.status.idle": "2024-09-18T14:49:57.385664Z",
     "shell.execute_reply": "2024-09-18T14:49:57.384834Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = readout_module.get_acquisitions(0)[\"single\"][\"acquisition\"][\"scope\"]\n",
    "plt.plot(r[\"path0\"][\"data\"], \".-\")\n",
    "plt.plot(r[\"path1\"][\"data\"], \".-\")\n",
    "plt.axvline(tof_measured, c=\"k\")\n",
    "plt.xlim(\n",
    "    tof_measured - 10 / readout_module.sequencer0.nco_freq() * 1e9,\n",
    "    tof_measured + 10 / readout_module.sequencer0.nco_freq() * 1e9,\n",
    ")\n",
    "plt.show()\n",
    "\n",
    "plt.plot(r[\"path0\"][\"data\"], \".-\")\n",
    "plt.plot(r[\"path1\"][\"data\"], \".-\")\n",
    "plt.axvline(1024 + tof_measured, c=\"k\")\n",
    "plt.xlim(\n",
    "    1024 + tof_measured - 10 / readout_module.sequencer0.nco_freq() * 1e9,\n",
    "    1024 + tof_measured + 10 / readout_module.sequencer0.nco_freq() * 1e9,\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "435b86eb",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d205c642",
   "metadata": {},
   "source": [
    "Set the parameters for the Rabi experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4c2fa9f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:57.388638Z",
     "iopub.status.busy": "2024-09-18T14:49:57.388443Z",
     "iopub.status.idle": "2024-09-18T14:49:57.391998Z",
     "shell.execute_reply": "2024-09-18T14:49:57.391278Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# all times must be divisible by 4\n",
    "reset_time = 200  # reset time for the qubit in microseconds\n",
    "tof = int(tof_measured / 4) * 4  # time of flight must be divisible by 4\n",
    "readout_delay = 164  # time to delay the readout pulse after the start of the rotation pulse\n",
    "\n",
    "navg = 1000  # number of averages\n",
    "stepsize = int(65535 / 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0422ee58",
   "metadata": {},
   "source": [
    "## Rabi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da251ab",
   "metadata": {},
   "source": [
    "Normally, a Rabi experiment would be performed by changing the amplitude in the inner loop, and averaging in the outer loop. To make the resulting experiment visible on an oscilloscope however, in this tutorial we swapped these two loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "224bc338",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:57.394284Z",
     "iopub.status.busy": "2024-09-18T14:49:57.394099Z",
     "iopub.status.idle": "2024-09-18T14:49:57.398587Z",
     "shell.execute_reply": "2024-09-18T14:49:57.397845Z"
    }
   },
   "outputs": [],
   "source": [
    "# QCM sequence program.\n",
    "qcm_seq_prog = f\"\"\"\n",
    "# Registers used:\n",
    "# R0 loops over the different awg amplitudes used for the rabi driving pulse\n",
    "# R2 is used to count the averages needed for a single amplitude\n",
    "# R3 contains the qubit reset time in microseconds\n",
    "\n",
    "           move          0, R0                       # start with awg amplitude 0\n",
    "           wait_sync     4                           # Synchronize the QRM with the QCM\n",
    "\n",
    "\n",
    "ampl_loop: add           R0, {stepsize}, R0          # increase the pulse amplitude by the stepsize\n",
    "           move          {navg}, R2                  # reset the number of averages and save in the R2 register\n",
    "\n",
    "           # let the qubit relax to its groundstate\n",
    "navg_loop: move          {reset_time}, R3            # reset the number of microseconds to wait and save in the R3 register\n",
    "rst_loop:  wait          1000                        # wait 1 microsecond\n",
    "           loop          R3,@rst_loop                # repeat the 1 microsecond wait as much as needed to let the qubit relax\n",
    "\n",
    "           set_awg_gain  R0, R0                      # Set the new amplitude used for the drive pulse\n",
    "           set_mrk       1                           # Set marker 1 high for to enable synchronization with external oscilloscope\n",
    "           wait_sync     4                           # Synchronize with the qrm to signify a measurement is coming\n",
    "           play          2,3,16384                   # Play waveforms and wait remaining duration of scope acquisition\n",
    "\n",
    "           set_mrk       0                           # Reset marker 1\n",
    "           upd_param     4\n",
    "\n",
    "           loop          R2,@navg_loop                        # Repeat the experiment to average, until R2 becomes 0\n",
    "           jlt           R0,{num_bins*stepsize},@ampl_loop    # Repeat the experiment for different pulse amplitudes from 0 to num_bins\n",
    "           stop                                               # Stop.\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "# QRM sequence program.\n",
    "qrm_seq_prog = f\"\"\"\n",
    "# Registers used:\n",
    "# R0 counts which bin to acquire into, a new bin for every new amplitude\n",
    "# R2 is used to count the averages needed for a single amplitude\n",
    "\n",
    "           wait_sync     4                           # Synchronize the QRM with the QCM.\n",
    "           move          0, R0                       # the first acquisition uses bin 0\n",
    "ampl_loop: move          {navg}, R2                  # reset the amount of averages to be taken to the initial value\n",
    "\n",
    "navg_loop: wait_sync     {readout_delay}             # wait for the QCM to signal a pulse is coming and wait the readout_delay\n",
    "           play          1,0,{tof}                   # play readout pulse and wait for the tof\n",
    "           acquire       1,R0,16384                  # Acquire waveforms and wait remaining duration of scope acquisition.\n",
    "\n",
    "           loop          R2, @navg_loop              # Repeat this measurement for every average\n",
    "           add           R0,1,R0                     # Increment the bin into which we are measuring\n",
    "           jmp           @ampl_loop                  # repeat\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "566399af",
   "metadata": {},
   "source": [
    "Upload programs and waveforms to QRM and QCM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "228214d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:57.401245Z",
     "iopub.status.busy": "2024-09-18T14:49:57.401067Z",
     "iopub.status.idle": "2024-09-18T14:49:57.503477Z",
     "shell.execute_reply": "2024-09-18T14:49:57.502749Z"
    }
   },
   "outputs": [],
   "source": [
    "# Add QCM sequence to single dictionary and write to JSON file.\n",
    "sequence = {\n",
    "    \"waveforms\": wfs,\n",
    "    \"weights\": {},\n",
    "    \"acquisitions\": acquisitions,\n",
    "    \"program\": qcm_seq_prog,\n",
    "}\n",
    "with open(\"qcm_sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "    json.dump(sequence, file, indent=4)\n",
    "    file.close()\n",
    "\n",
    "# Add QRM sequence to single dictionary and write to JSON file.\n",
    "sequence = {\n",
    "    \"waveforms\": wfs,\n",
    "    \"weights\": {},\n",
    "    \"acquisitions\": acquisitions,\n",
    "    \"program\": qrm_seq_prog,\n",
    "}\n",
    "with open(\"qrm_sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "    json.dump(sequence, file, indent=4)\n",
    "    file.close()\n",
    "\n",
    "# Upload sequence to QCM.\n",
    "control_module.sequencer0.sequence(\"qcm_sequence.json\")\n",
    "\n",
    "# Upload sequence to QRM.\n",
    "readout_module.sequencer0.sequence(\"qrm_sequence.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01517c16",
   "metadata": {},
   "source": [
    "Arm and start sequencer0 of both the QCM and QRM. The `wait_sync` command together with the SYNQ technology ensures both modules start simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f822a3d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:57.506645Z",
     "iopub.status.busy": "2024-09-18T14:49:57.506448Z",
     "iopub.status.idle": "2024-09-18T14:49:57.521730Z",
     "shell.execute_reply": "2024-09-18T14:49:57.521046Z"
    }
   },
   "outputs": [],
   "source": [
    "# Arm and start sequencer of the QCM (only sequencer 0).\n",
    "control_module.arm_sequencer(0)\n",
    "control_module.start_sequencer(0)\n",
    "\n",
    "# Arm and start sequencer of the QRM (only sequencer 0).\n",
    "readout_module.arm_sequencer(0)\n",
    "readout_module.start_sequencer(0)\n",
    "\n",
    "# We didn't specify how many amplitudes would be measured, so we stop manually here.\n",
    "cluster.stop_sequencer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fbb764c",
   "metadata": {},
   "source": [
    "## Stop\n",
    "\n",
    "Finally, let's stop the sequencers if they haven't already and close the instrument connection. One can also display a detailed snapshot containing the instrument parameters before\n",
    "closing the connection by uncommenting the corresponding lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0994abee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-18T14:49:57.524665Z",
     "iopub.status.busy": "2024-09-18T14:49:57.524471Z",
     "iopub.status.idle": "2024-09-18T14:50:03.848316Z",
     "shell.execute_reply": "2024-09-18T14:50:03.847663Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QCM :\n",
      "Status: OKAY, State: STOPPED, Info Flags: FORCED_STOP, Warning Flags: NONE, Error Flags: NONE, Log: []\n",
      "\n",
      "QRM :\n",
      "Status: OKAY, State: STOPPED, Info Flags: FORCED_STOP, ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_OVERWRITTEN_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_SCOPE_OVERWRITTEN_PATH_1, ACQ_BINNING_DONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n",
      "\n",
      "QCM snapshot:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module2:\n",
      "\tparameter              value\n",
      "--------------------------------------------------------------------------------\n",
      "marker0_exp0_config     :\tbypassed \n",
      "marker0_exp1_config     :\tbypassed \n",
      "marker0_exp2_config     :\tbypassed \n",
      "marker0_exp3_config     :\tbypassed \n",
      "marker0_fir_config      :\tbypassed \n",
      "marker0_inv_en          :\tFalse \n",
      "marker1_exp0_config     :\tbypassed \n",
      "marker1_exp1_config     :\tbypassed \n",
      "marker1_exp2_config     :\tbypassed \n",
      "marker1_exp3_config     :\tbypassed \n",
      "marker1_fir_config      :\tbypassed \n",
      "marker1_inv_en          :\tFalse \n",
      "marker2_exp0_config     :\tbypassed \n",
      "marker2_exp1_config     :\tbypassed \n",
      "marker2_exp2_config     :\tbypassed \n",
      "marker2_exp3_config     :\tbypassed \n",
      "marker2_fir_config      :\tbypassed \n",
      "marker2_inv_en          :\tFalse \n",
      "marker3_exp0_config     :\tbypassed \n",
      "marker3_exp1_config     :\tbypassed \n",
      "marker3_exp2_config     :\tbypassed \n",
      "marker3_exp3_config     :\tbypassed \n",
      "marker3_fir_config      :\tbypassed \n",
      "marker3_inv_en          :\tFalse \n",
      "out0_exp0_amplitude     :\t0 \n",
      "out0_exp0_config        :\tbypassed \n",
      "out0_exp0_time_constant :\t0 \n",
      "out0_exp1_amplitude     :\t0 \n",
      "out0_exp1_config        :\tbypassed \n",
      "out0_exp1_time_constant :\t0 \n",
      "out0_exp2_amplitude     :\t0 \n",
      "out0_exp2_config        :\tbypassed \n",
      "out0_exp2_time_constant :\t0 \n",
      "out0_exp3_amplitude     :\t0 \n",
      "out0_exp3_config        :\tbypassed \n",
      "out0_exp3_time_constant :\t0 \n",
      "out0_fir_coeffs         :\t[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
      "out0_fir_config         :\tbypassed \n",
      "out0_latency            :\t0 (s)\n",
      "out0_offset             :\t0 (V)\n",
      "out1_exp0_amplitude     :\t0 \n",
      "out1_exp0_config        :\tbypassed \n",
      "out1_exp0_time_constant :\t0 \n",
      "out1_exp1_amplitude     :\t0 \n",
      "out1_exp1_config        :\tbypassed \n",
      "out1_exp1_time_constant :\t0 \n",
      "out1_exp2_amplitude     :\t0 \n",
      "out1_exp2_config        :\tbypassed \n",
      "out1_exp2_time_constant :\t0 \n",
      "out1_exp3_amplitude     :\t0 \n",
      "out1_exp3_config        :\tbypassed \n",
      "out1_exp3_time_constant :\t0 \n",
      "out1_fir_coeffs         :\t[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
      "out1_fir_config         :\tbypassed \n",
      "out1_latency            :\t0 (s)\n",
      "out1_offset             :\t0 (V)\n",
      "out2_exp0_amplitude     :\t0 \n",
      "out2_exp0_config        :\tbypassed \n",
      "out2_exp0_time_constant :\t0 \n",
      "out2_exp1_amplitude     :\t0 \n",
      "out2_exp1_config        :\tbypassed \n",
      "out2_exp1_time_constant :\t0 \n",
      "out2_exp2_amplitude     :\t0 \n",
      "out2_exp2_config        :\tbypassed \n",
      "out2_exp2_time_constant :\t0 \n",
      "out2_exp3_amplitude     :\t0 \n",
      "out2_exp3_config        :\tbypassed \n",
      "out2_exp3_time_constant :\t0 \n",
      "out2_fir_coeffs         :\t[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
      "out2_fir_config         :\tbypassed \n",
      "out2_latency            :\t0 (s)\n",
      "out2_offset             :\t0 (V)\n",
      "out3_exp0_amplitude     :\t0 \n",
      "out3_exp0_config        :\tbypassed \n",
      "out3_exp0_time_constant :\t0 \n",
      "out3_exp1_amplitude     :\t0 \n",
      "out3_exp1_config        :\tbypassed \n",
      "out3_exp1_time_constant :\t0 \n",
      "out3_exp2_amplitude     :\t0 \n",
      "out3_exp2_config        :\tbypassed \n",
      "out3_exp2_time_constant :\t0 \n",
      "out3_exp3_amplitude     :\t0 \n",
      "out3_exp3_config        :\tbypassed \n",
      "out3_exp3_time_constant :\t0 \n",
      "out3_fir_coeffs         :\t[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
      "out3_fir_config         :\tbypassed \n",
      "out3_latency            :\t0 (s)\n",
      "out3_offset             :\t0 (V)\n",
      "present                 :\tTrue \n",
      "cluster0_module2_sequencer0:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_out0                     :\tI \n",
      "connect_out1                     :\tQ \n",
      "connect_out2                     :\toff \n",
      "connect_out3                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tTrue \n",
      "nco_freq                         :\t1e+08 (Hz)\n",
      "nco_freq_cal_type_default        :\toff (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tTrue \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module2_sequencer1:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "connect_out2                     :\toff \n",
      "connect_out3                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_freq_cal_type_default        :\toff (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n",
      "cluster0_module2_sequencer2:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "connect_out2                     :\toff \n",
      "connect_out3                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_freq_cal_type_default        :\toff (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module2_sequencer3:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "connect_out2                     :\toff \n",
      "connect_out3                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_freq_cal_type_default        :\toff (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n",
      "cluster0_module2_sequencer4:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "connect_out2                     :\toff \n",
      "connect_out3                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_freq_cal_type_default        :\toff (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module2_sequencer5:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "connect_out2                     :\toff \n",
      "connect_out3                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_freq_cal_type_default        :\toff (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n",
      "\n",
      "QRM snapshot:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4:\n",
      "\tparameter                    value\n",
      "--------------------------------------------------------------------------------\n",
      "in0_gain                      :\t-6 (dB)\n",
      "in0_offset                    :\t0 (V)\n",
      "in1_gain                      :\t-6 (dB)\n",
      "in1_offset                    :\t0 (V)\n",
      "marker0_exp0_config           :\tbypassed \n",
      "marker0_exp1_config           :\tbypassed \n",
      "marker0_exp2_config           :\tbypassed \n",
      "marker0_exp3_config           :\tbypassed \n",
      "marker0_fir_config            :\tbypassed \n",
      "marker0_inv_en                :\tFalse \n",
      "marker1_exp0_config           :\tbypassed \n",
      "marker1_exp1_config           :\tbypassed \n",
      "marker1_exp2_config           :\tbypassed \n",
      "marker1_exp3_config           :\tbypassed \n",
      "marker1_fir_config            :\tbypassed \n",
      "marker1_inv_en                :\tFalse \n",
      "marker2_exp0_config           :\tbypassed \n",
      "marker2_exp1_config           :\tbypassed \n",
      "marker2_exp2_config           :\tbypassed \n",
      "marker2_exp3_config           :\tbypassed \n",
      "marker2_fir_config            :\tbypassed \n",
      "marker2_inv_en                :\tFalse \n",
      "marker3_exp0_config           :\tbypassed \n",
      "marker3_exp1_config           :\tbypassed \n",
      "marker3_exp2_config           :\tbypassed \n",
      "marker3_exp3_config           :\tbypassed \n",
      "marker3_fir_config            :\tbypassed \n",
      "marker3_inv_en                :\tFalse \n",
      "out0_exp0_config              :\tbypassed \n",
      "out0_exp1_config              :\tbypassed \n",
      "out0_exp2_config              :\tbypassed \n",
      "out0_exp3_config              :\tbypassed \n",
      "out0_fir_config               :\tbypassed \n",
      "out0_latency                  :\t0 (s)\n",
      "out0_offset                   :\t0 (V)\n",
      "out1_exp0_config              :\tbypassed \n",
      "out1_exp1_config              :\tbypassed \n",
      "out1_exp2_config              :\tbypassed \n",
      "out1_exp3_config              :\tbypassed \n",
      "out1_fir_config               :\tbypassed \n",
      "out1_latency                  :\t0 (s)\n",
      "out1_offset                   :\t0 (V)\n",
      "present                       :\tTrue \n",
      "scope_acq_avg_mode_en_path0   :\tFalse \n",
      "scope_acq_avg_mode_en_path1   :\tFalse \n",
      "scope_acq_sequencer_select    :\t0 \n",
      "scope_acq_trigger_level_path0 :\t0 \n",
      "scope_acq_trigger_level_path1 :\t0 \n",
      "scope_acq_trigger_mode_path0  :\tsequencer \n",
      "scope_acq_trigger_mode_path1  :\tsequencer \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer0:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\tin0 \n",
      "connect_acq_Q                    :\tin1 \n",
      "connect_out0                     :\tI \n",
      "connect_out1                     :\tQ \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tTrue \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tTrue \n",
      "nco_freq                         :\t1e+08 (Hz)\n",
      "nco_freq_cal_type_default        :\toff (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tTrue \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer1:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_freq_cal_type_default        :\toff (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer2:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_freq_cal_type_default        :\toff (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer3:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_freq_cal_type_default        :\toff (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer4:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_freq_cal_type_default        :\toff (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer5:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_freq_cal_type_default        :\toff (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    }
   ],
   "source": [
    "# Print status of sequencers.\n",
    "print(\"QCM :\")\n",
    "print(control_module.get_sequencer_status(0))\n",
    "print()\n",
    "\n",
    "print(\"QRM :\")\n",
    "print(readout_module.get_sequencer_status(0))\n",
    "print()\n",
    "\n",
    "# Uncomment the following to print an overview of the instrument parameters.\n",
    "# Print an overview of instrument parameters.\n",
    "print(\"QCM snapshot:\")\n",
    "control_module.print_readable_snapshot(update=True)\n",
    "print()\n",
    "\n",
    "print(\"QRM snapshot:\")\n",
    "readout_module.print_readable_snapshot(update=True)\n",
    "\n",
    "# Close the instrument connections.\n",
    "cluster.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6403d863",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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