{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f9608b02",
   "metadata": {},
   "source": [
    "# Rabi experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "770934e0",
   "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": "0e6088b3",
   "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": "88bd7cea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:28.596545Z",
     "iopub.status.busy": "2024-10-17T13:10:28.596371Z",
     "iopub.status.idle": "2024-10-17T13:10:29.453402Z",
     "shell.execute_reply": "2024-10-17T13:10:29.452622Z"
    },
    "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": "d1a56130",
   "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": "markdown",
   "id": "d8a26427",
   "metadata": {},
   "source": [
    "`!qblox-pnp list`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bb672df9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:29.455999Z",
     "iopub.status.busy": "2024-10-17T13:10:29.455818Z",
     "iopub.status.idle": "2024-10-17T13:10:29.458809Z",
     "shell.execute_reply": "2024-10-17T13:10:29.458241Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster_ip = \"10.10.200.42\"\n",
    "cluster_name = \"cluster0\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46d7c8e6",
   "metadata": {},
   "source": [
    "### Connect to Cluster\n",
    "\n",
    "We now make a connection with the Cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aabc070e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:29.460808Z",
     "iopub.status.busy": "2024-10-17T13:10:29.460645Z",
     "iopub.status.idle": "2024-10-17T13:10:32.782494Z",
     "shell.execute_reply": "2024-10-17T13:10:32.781710Z"
    },
    "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": "e3b3dab3",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "#### Get connected modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "86a1f5f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:32.785326Z",
     "iopub.status.busy": "2024-10-17T13:10:32.785053Z",
     "iopub.status.idle": "2024-10-17T13:10:32.790142Z",
     "shell.execute_reply": "2024-10-17T13:10:32.789377Z"
    }
   },
   "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": 5,
   "id": "cda8975b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:32.792553Z",
     "iopub.status.busy": "2024-10-17T13:10:32.792208Z",
     "iopub.status.idle": "2024-10-17T13:10:32.821599Z",
     "shell.execute_reply": "2024-10-17T13:10:32.820752Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{4: <Module: cluster0_module4 of Cluster: cluster0>,\n",
       " 17: <Module: cluster0_module17 of Cluster: cluster0>}"
      ]
     },
     "execution_count": 5,
     "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": 6,
   "id": "a996d7fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:32.824357Z",
     "iopub.status.busy": "2024-10-17T13:10:32.823978Z",
     "iopub.status.idle": "2024-10-17T13:10:32.828693Z",
     "shell.execute_reply": "2024-10-17T13:10:32.827854Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module = readout_modules[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f61aa1e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:32.831332Z",
     "iopub.status.busy": "2024-10-17T13:10:32.831077Z",
     "iopub.status.idle": "2024-10-17T13:10:32.857108Z",
     "shell.execute_reply": "2024-10-17T13:10:32.856225Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: <Module: cluster0_module2 of Cluster: cluster0>}"
      ]
     },
     "execution_count": 7,
     "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": 8,
   "id": "ef70d353",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:32.860090Z",
     "iopub.status.busy": "2024-10-17T13:10:32.859788Z",
     "iopub.status.idle": "2024-10-17T13:10:32.863918Z",
     "shell.execute_reply": "2024-10-17T13:10:32.863023Z"
    }
   },
   "outputs": [],
   "source": [
    "control_module = control_modules[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05171d4b",
   "metadata": {},
   "source": [
    "## Sequencer Setup <a id='seq_setup'></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1678684a",
   "metadata": {},
   "source": [
    "Set `sync_en` to synchronize across modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f94e3c0a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:32.866719Z",
     "iopub.status.busy": "2024-10-17T13:10:32.866478Z",
     "iopub.status.idle": "2024-10-17T13:10:32.888663Z",
     "shell.execute_reply": "2024-10-17T13:10:32.887963Z"
    }
   },
   "outputs": [],
   "source": [
    "# Set sync_en\n",
    "readout_module.sequencer0.sync_en(True)\n",
    "control_module.sequencer0.sync_en(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a30a0df",
   "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": 10,
   "id": "31bb046c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:32.890900Z",
     "iopub.status.busy": "2024-10-17T13:10:32.890730Z",
     "iopub.status.idle": "2024-10-17T13:10:32.942855Z",
     "shell.execute_reply": "2024-10-17T13:10:32.941967Z"
    }
   },
   "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": "73323127",
   "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": 11,
   "id": "0d2749c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:32.945882Z",
     "iopub.status.busy": "2024-10-17T13:10:32.945626Z",
     "iopub.status.idle": "2024-10-17T13:10:33.039079Z",
     "shell.execute_reply": "2024-10-17T13:10:33.035812Z"
    }
   },
   "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": "543c942f",
   "metadata": {},
   "source": [
    "## Define waveforms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b344609a",
   "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": 12,
   "id": "c5140f9c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:33.048967Z",
     "iopub.status.busy": "2024-10-17T13:10:33.048098Z",
     "iopub.status.idle": "2024-10-17T13:10:33.065628Z",
     "shell.execute_reply": "2024-10-17T13:10:33.062754Z"
    }
   },
   "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": "1bbd3510",
   "metadata": {},
   "source": [
    "Hence we obtain the following waveforms for readout:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "de54a6aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:33.073991Z",
     "iopub.status.busy": "2024-10-17T13:10:33.073197Z",
     "iopub.status.idle": "2024-10-17T13:10:33.241353Z",
     "shell.execute_reply": "2024-10-17T13:10:33.240698Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc4ee4408e0>]"
      ]
     },
     "execution_count": 13,
     "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": "5be6c561",
   "metadata": {},
   "source": [
    "And for drive:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1a582410",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:33.243430Z",
     "iopub.status.busy": "2024-10-17T13:10:33.243234Z",
     "iopub.status.idle": "2024-10-17T13:10:33.351765Z",
     "shell.execute_reply": "2024-10-17T13:10:33.351111Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc4ee3b5eb0>]"
      ]
     },
     "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[\"gauss\"][\"data\"])\n",
    "plt.plot(wfs[\"empty\"][\"data\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe3bed9b",
   "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": 15,
   "id": "6f92c27d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:33.354100Z",
     "iopub.status.busy": "2024-10-17T13:10:33.353577Z",
     "iopub.status.idle": "2024-10-17T13:10:33.356969Z",
     "shell.execute_reply": "2024-10-17T13:10:33.356359Z"
    }
   },
   "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": "beb26e9d",
   "metadata": {},
   "source": [
    "## Calibration experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43355bf5",
   "metadata": {},
   "source": [
    "### TOF calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b16efeb",
   "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": 16,
   "id": "4f994d03",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:33.359006Z",
     "iopub.status.busy": "2024-10-17T13:10:33.358841Z",
     "iopub.status.idle": "2024-10-17T13:10:33.361784Z",
     "shell.execute_reply": "2024-10-17T13:10:33.361185Z"
    }
   },
   "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": "c13b9cea",
   "metadata": {},
   "source": [
    "Upload the program, together with the waveforms and acquisitions to the QRM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b33312aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:33.363530Z",
     "iopub.status.busy": "2024-10-17T13:10:33.363368Z",
     "iopub.status.idle": "2024-10-17T13:10:33.439768Z",
     "shell.execute_reply": "2024-10-17T13:10:33.436360Z"
    }
   },
   "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": "6d98afd7",
   "metadata": {},
   "source": [
    "Perform the calibration experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "98d950b3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:33.449201Z",
     "iopub.status.busy": "2024-10-17T13:10:33.448332Z",
     "iopub.status.idle": "2024-10-17T13:10:33.486772Z",
     "shell.execute_reply": "2024-10-17T13:10:33.483443Z"
    }
   },
   "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": "fa94de1a",
   "metadata": {},
   "source": [
    "Analyze the resulting signal on the scope to find the tof"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4200eb62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:33.495812Z",
     "iopub.status.busy": "2024-10-17T13:10:33.494844Z",
     "iopub.status.idle": "2024-10-17T13:10:33.573631Z",
     "shell.execute_reply": "2024-10-17T13:10:33.572974Z"
    }
   },
   "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": "3a8639bf",
   "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": 20,
   "id": "f7a7ff60",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:33.575559Z",
     "iopub.status.busy": "2024-10-17T13:10:33.575391Z",
     "iopub.status.idle": "2024-10-17T13:10:33.809476Z",
     "shell.execute_reply": "2024-10-17T13:10:33.808829Z"
    }
   },
   "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": "448844ac",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16211943",
   "metadata": {},
   "source": [
    "Set the parameters for the Rabi experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c82f10d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:33.811533Z",
     "iopub.status.busy": "2024-10-17T13:10:33.811353Z",
     "iopub.status.idle": "2024-10-17T13:10:33.814652Z",
     "shell.execute_reply": "2024-10-17T13:10:33.813992Z"
    },
    "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": "d5c1a67a",
   "metadata": {},
   "source": [
    "## Rabi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64164fa7",
   "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": 22,
   "id": "d4ee7894",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:33.816559Z",
     "iopub.status.busy": "2024-10-17T13:10:33.816392Z",
     "iopub.status.idle": "2024-10-17T13:10:33.820446Z",
     "shell.execute_reply": "2024-10-17T13:10:33.819785Z"
    }
   },
   "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": "5a569d1d",
   "metadata": {},
   "source": [
    "Upload programs and waveforms to QRM and QCM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "087a47e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:33.822144Z",
     "iopub.status.busy": "2024-10-17T13:10:33.821977Z",
     "iopub.status.idle": "2024-10-17T13:10:33.940600Z",
     "shell.execute_reply": "2024-10-17T13:10:33.937332Z"
    }
   },
   "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": "8a5dab5b",
   "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": 24,
   "id": "055ac5b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:33.947735Z",
     "iopub.status.busy": "2024-10-17T13:10:33.947516Z",
     "iopub.status.idle": "2024-10-17T13:10:33.964707Z",
     "shell.execute_reply": "2024-10-17T13:10:33.963894Z"
    }
   },
   "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": "be474374",
   "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": 25,
   "id": "fde97f06",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:10:33.967096Z",
     "iopub.status.busy": "2024-10-17T13:10:33.966903Z",
     "iopub.status.idle": "2024-10-17T13:10:42.634895Z",
     "shell.execute_reply": "2024-10-17T13:10:42.631764Z"
    }
   },
   "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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": "c0436709",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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