{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9de350ab",
   "metadata": {},
   "source": [
    "# Rabi experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c642e34a",
   "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": "6204056b",
   "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": "2138236f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:34:57.172238Z",
     "iopub.status.busy": "2024-11-13T02:34:57.171639Z",
     "iopub.status.idle": "2024-11-13T02:34:59.673008Z",
     "shell.execute_reply": "2024-11-13T02:34:59.672081Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "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": "bf426bf0",
   "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://docs.qblox.com/en/main/api_reference/tools.html#api-pnp) for more info)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fec6f47",
   "metadata": {},
   "source": [
    "`!qblox-pnp list`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6fe27599",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:34:59.676076Z",
     "iopub.status.busy": "2024-11-13T02:34:59.675842Z",
     "iopub.status.idle": "2024-11-13T02:34:59.679501Z",
     "shell.execute_reply": "2024-11-13T02:34:59.678653Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster_ip = \"10.10.200.42\"\n",
    "cluster_name = \"cluster0\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bac8d13f",
   "metadata": {},
   "source": [
    "### Connect to Cluster\n",
    "\n",
    "We now make a connection with the Cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5a2849b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:34:59.683372Z",
     "iopub.status.busy": "2024-11-13T02:34:59.682890Z",
     "iopub.status.idle": "2024-11-13T02:35:03.040200Z",
     "shell.execute_reply": "2024-11-13T02:35:03.039327Z"
    },
    "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": "6f375342",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "#### Get connected modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "50c533f2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.043430Z",
     "iopub.status.busy": "2024-11-13T02:35:03.043221Z",
     "iopub.status.idle": "2024-11-13T02:35:03.047673Z",
     "shell.execute_reply": "2024-11-13T02:35:03.046888Z"
    }
   },
   "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": "21b32f50",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.050550Z",
     "iopub.status.busy": "2024-11-13T02:35:03.050354Z",
     "iopub.status.idle": "2024-11-13T02:35:03.074181Z",
     "shell.execute_reply": "2024-11-13T02:35:03.073357Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{4: <Module: cluster0_module4 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": "86dfbf60",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.077044Z",
     "iopub.status.busy": "2024-11-13T02:35:03.076841Z",
     "iopub.status.idle": "2024-11-13T02:35:03.080155Z",
     "shell.execute_reply": "2024-11-13T02:35:03.079444Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module = readout_modules[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a6b06c7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.082881Z",
     "iopub.status.busy": "2024-11-13T02:35:03.082677Z",
     "iopub.status.idle": "2024-11-13T02:35:03.104015Z",
     "shell.execute_reply": "2024-11-13T02:35:03.103251Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: <Module: cluster0_module2 of Cluster: cluster0>,\n",
       " 10: <Module: cluster0_module10 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": "0622c101",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.106887Z",
     "iopub.status.busy": "2024-11-13T02:35:03.106668Z",
     "iopub.status.idle": "2024-11-13T02:35:03.110008Z",
     "shell.execute_reply": "2024-11-13T02:35:03.109228Z"
    }
   },
   "outputs": [],
   "source": [
    "control_module = control_modules[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ed38832",
   "metadata": {},
   "source": [
    "## Sequencer Setup <a id='seq_setup'></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d51354e",
   "metadata": {},
   "source": [
    "Set `sync_en` to synchronize across modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f5cf453",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.113007Z",
     "iopub.status.busy": "2024-11-13T02:35:03.112801Z",
     "iopub.status.idle": "2024-11-13T02:35:03.133444Z",
     "shell.execute_reply": "2024-11-13T02:35:03.132737Z"
    }
   },
   "outputs": [],
   "source": [
    "# Set sync_en\n",
    "readout_module.sequencer0.sync_en(True)\n",
    "control_module.sequencer0.sync_en(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8794723f",
   "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": "41f02d6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.136738Z",
     "iopub.status.busy": "2024-11-13T02:35:03.136516Z",
     "iopub.status.idle": "2024-11-13T02:35:03.182956Z",
     "shell.execute_reply": "2024-11-13T02:35:03.182222Z"
    }
   },
   "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": "1805c2f1",
   "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": "073119d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.185336Z",
     "iopub.status.busy": "2024-11-13T02:35:03.185151Z",
     "iopub.status.idle": "2024-11-13T02:35:03.246454Z",
     "shell.execute_reply": "2024-11-13T02:35:03.245710Z"
    }
   },
   "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": "e15462a5",
   "metadata": {},
   "source": [
    "## Define waveforms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "329b007c",
   "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": "d76666a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.248767Z",
     "iopub.status.busy": "2024-11-13T02:35:03.248587Z",
     "iopub.status.idle": "2024-11-13T02:35:03.252769Z",
     "shell.execute_reply": "2024-11-13T02:35:03.252085Z"
    }
   },
   "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": "8e072b3f",
   "metadata": {},
   "source": [
    "Hence we obtain the following waveforms for readout:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8e325fd7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.255394Z",
     "iopub.status.busy": "2024-11-13T02:35:03.255223Z",
     "iopub.status.idle": "2024-11-13T02:35:03.451441Z",
     "shell.execute_reply": "2024-11-13T02:35:03.450725Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbf86d0c220>]"
      ]
     },
     "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": "f1da2ee6",
   "metadata": {},
   "source": [
    "And for drive:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "79e71a7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.455994Z",
     "iopub.status.busy": "2024-11-13T02:35:03.455782Z",
     "iopub.status.idle": "2024-11-13T02:35:03.567604Z",
     "shell.execute_reply": "2024-11-13T02:35:03.566722Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbf86830b50>]"
      ]
     },
     "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": "b5f3d3fb",
   "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": "8364c2d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.570394Z",
     "iopub.status.busy": "2024-11-13T02:35:03.570208Z",
     "iopub.status.idle": "2024-11-13T02:35:03.573876Z",
     "shell.execute_reply": "2024-11-13T02:35:03.573212Z"
    }
   },
   "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": "a5808f92",
   "metadata": {},
   "source": [
    "## Calibration experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4859687",
   "metadata": {},
   "source": [
    "### TOF calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c735c0e",
   "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": "1551fc45",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.576755Z",
     "iopub.status.busy": "2024-11-13T02:35:03.576585Z",
     "iopub.status.idle": "2024-11-13T02:35:03.579841Z",
     "shell.execute_reply": "2024-11-13T02:35:03.579115Z"
    }
   },
   "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": "c90cccba",
   "metadata": {},
   "source": [
    "Upload the program, together with the waveforms and acquisitions to the QRM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "977208ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.582463Z",
     "iopub.status.busy": "2024-11-13T02:35:03.582298Z",
     "iopub.status.idle": "2024-11-13T02:35:03.640605Z",
     "shell.execute_reply": "2024-11-13T02:35:03.639897Z"
    }
   },
   "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": "06ef2449",
   "metadata": {},
   "source": [
    "Perform the calibration experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "02aa9692",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.643892Z",
     "iopub.status.busy": "2024-11-13T02:35:03.643665Z",
     "iopub.status.idle": "2024-11-13T02:35:03.663773Z",
     "shell.execute_reply": "2024-11-13T02:35:03.662920Z"
    }
   },
   "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": "f98e4be5",
   "metadata": {},
   "source": [
    "Analyze the resulting signal on the scope to find the tof"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ae8c8cfc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.666096Z",
     "iopub.status.busy": "2024-11-13T02:35:03.665889Z",
     "iopub.status.idle": "2024-11-13T02:35:03.718592Z",
     "shell.execute_reply": "2024-11-13T02:35:03.717785Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "148.16666666666666\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",
    "\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] if np.any(p0) else 16\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": "0e8d96e1",
   "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": "a95ea478",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.720963Z",
     "iopub.status.busy": "2024-11-13T02:35:03.720743Z",
     "iopub.status.idle": "2024-11-13T02:35:03.972457Z",
     "shell.execute_reply": "2024-11-13T02:35:03.971599Z"
    }
   },
   "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": "f3d1ec82",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "699cd98d",
   "metadata": {},
   "source": [
    "Set the parameters for the Rabi experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "001d100d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.975442Z",
     "iopub.status.busy": "2024-11-13T02:35:03.975255Z",
     "iopub.status.idle": "2024-11-13T02:35:03.978872Z",
     "shell.execute_reply": "2024-11-13T02:35:03.978221Z"
    },
    "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": "f3750612",
   "metadata": {},
   "source": [
    "## Rabi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81f4ed96",
   "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": "f96e95b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.981646Z",
     "iopub.status.busy": "2024-11-13T02:35:03.981477Z",
     "iopub.status.idle": "2024-11-13T02:35:03.985970Z",
     "shell.execute_reply": "2024-11-13T02:35:03.985289Z"
    }
   },
   "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": "06d1e454",
   "metadata": {},
   "source": [
    "Upload programs and waveforms to QRM and QCM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "522bf2a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:03.988648Z",
     "iopub.status.busy": "2024-11-13T02:35:03.988478Z",
     "iopub.status.idle": "2024-11-13T02:35:04.089155Z",
     "shell.execute_reply": "2024-11-13T02:35:04.088357Z"
    }
   },
   "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": "87c223f5",
   "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": "60ab674e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:04.092207Z",
     "iopub.status.busy": "2024-11-13T02:35:04.092028Z",
     "iopub.status.idle": "2024-11-13T02:35:04.107520Z",
     "shell.execute_reply": "2024-11-13T02:35:04.106871Z"
    }
   },
   "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": "b6231b96",
   "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": "41533fed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-11-13T02:35:04.110335Z",
     "iopub.status.busy": "2024-11-13T02:35:04.110160Z",
     "iopub.status.idle": "2024-11-13T02:35:11.429548Z",
     "shell.execute_reply": "2024-11-13T02:35:11.428496Z"
    }
   },
   "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": "6ba37888",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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