{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6c5afd96",
   "metadata": {},
   "source": [
    "# Numerically Controlled Oscillator\n",
    "\n",
    "In this tutorial we will demonstrate how to use the numerically controlled oscillator (NCO) during an experiment by [Changing the modulation frequency](#Frequency-sweeps), e.g. for rapid spectroscopy measurements\n",
    "\n",
    "We will show this by using a [QRM](https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/cluster/qrm.html) and directly connecting outputs\n",
    "$\\text{O}^{[1-2]}$ to inputs $\\text{I}^{[1-2]}$ respectively. We will then use the QRM's sequencers to sequence waveforms on\n",
    "the outputs and simultaneously acquire the resulting waveforms on the inputs.\n",
    "\n",
    "Whilst this tutorial is for the baseband modules, a similar procedure for spectroscopy with RF modules can be found in the [RF Control](https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/tutorials/q1asm_tutorials/basic/rf/rf_control.html) tutorial.\n",
    "\n",
    "## Getting Started\n",
    "\n",
    "### How the NCO works\n",
    "\n",
    "#### Modulation\n",
    "\n",
    "The NCO modulates the AWG output with a certain frequency, such that we only need to store the envelope of any waveform to create a wave with the correct frequency and phase. To enable IQ up- and down-conversion to RF frequencies, the device will generate a phase shifted signal on both signal paths.\n",
    "\n",
    "If modulation is enabled, the value of the NCO ($e^{i\\omega t}$) will be multiplied by the awg outputs ($z(t) = \\text{awg}_0 + i \\cdot \\text{awg}_1$), and also by a factor of $1 / \\sqrt{2}$ to prevent clipping, and forwarded to path 0/1 as follows:\n",
    "\n",
    "\\begin{equation*}\n",
    "\\text{path}_{0, \\text{out}} = \\frac{1}{\\sqrt{2}}(\\cos(\\omega t)\\text{awg}_0 - \\sin(\\omega t)\\text{awg}_1)\n",
    "\\end{equation*}\n",
    "\n",
    "\\begin{equation*}\n",
    "\\text{path}_{1, \\text{out}} =  \\frac{1}{\\sqrt{2}}(\\sin(\\omega t)\\text{awg}_0 + \\cos(\\omega t)\\text{awg}_1)\n",
    "\\end{equation*}\n",
    "\n",
    "These two outputs can then be used within a QxM-RF module or by an external IQ mixer to generate RF pulses. Each path, 0/1, will have two components (real and imaginary) referred to as I and Q. However, the NCO can also be operated in real mode which will create a direct modulated output.\n",
    "\n",
    "#### Demodulation\n",
    "\n",
    "Usually, we are interested in the envelope of the acquired signal instead of the oscillating values, in particular when integrating. Therefore, the NCO can also be used to demodulate the signal before integration.\n",
    "\n",
    "If demodulation is enabled, the signal is again multiplied with the NCO(effectively $e^{-i\\omega t}$ during demodulation), this time also with a factor of $1 / \\sqrt{2}$. Both in modulation and demodulation the mixed NCO $\\sin$ and $\\cos$ terms have a maximum amplitude of $1 / \\sqrt{2}$ to prevent clipping.\n",
    "\n",
    "This leads to an amplitude factor of $1 / \\sqrt{2}\\times 1 / \\sqrt{2}$ = 0.5 when demodulating your output signal. Internally, this is accounted for in the datapaths to allow maximum dynamic range:\n",
    "\n",
    "\\begin{equation*}\n",
    "\\text{path}_{0, \\text{in}} = \\frac{1}{\\sqrt{2}}(\\cos(\\omega t)\\, \\text{in}_0 + \\sin(\\omega t)\\, \\text{in}_1) = \\frac{1}{\\sqrt{2}}\\left(\\frac{1}{\\sqrt{2}}\\cos(\\omega t) \\cdot \\cos(\\omega t)\\, \\text{awg}_0 + \\frac{1}{\\sqrt{2}}\\sin(\\omega t) \\cdot \\sin(\\omega t)\\, \\text{awg}_0\\right) = \\frac{1}{2}\\text{awg}_0\n",
    "\\end{equation*}\n",
    "\n",
    "\\begin{equation*}\n",
    "\\text{path}_{1, \\text{in}} = \\frac{1}{\\sqrt{2}}(-\\sin(\\omega t)\\, \\text{in}_0 + \\cos(\\omega t)\\, \\text{in}_1) = \\frac{1}{\\sqrt{2}}\\left(\\frac{1}{\\sqrt{2}}\\sin(\\omega t) \\cdot \\sin(\\omega t)\\, \\text{awg}_1 + \\frac{1}{\\sqrt{2}}\\cos(\\omega t) \\cdot \\cos(\\omega t)\\, \\text{awg}_1\\right) = \\frac{1}{2}\\text{awg}_1\n",
    "\\end{equation*}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95a5f353",
   "metadata": {},
   "source": [
    "### Setup\n",
    "\n",
    "First, we are going to import the required packages and connect to the instrument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "826c8424",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:35.426351Z",
     "iopub.status.busy": "2024-10-17T13:12:35.426144Z",
     "iopub.status.idle": "2024-10-17T13:12:36.284054Z",
     "shell.execute_reply": "2024-10-17T13:12:36.283098Z"
    }
   },
   "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 matplotlib import colors\n",
    "from qcodes.instrument import find_or_create_instrument\n",
    "from scipy.signal import spectrogram, welch\n",
    "\n",
    "from qblox_instruments import Cluster, ClusterType\n",
    "\n",
    "if TYPE_CHECKING:\n",
    "    from numpy.typing import NDArray\n",
    "\n",
    "    from qblox_instruments.qcodes_drivers.module import Module"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81d28342",
   "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": "54224f3d",
   "metadata": {},
   "source": [
    "`!qblox-pnp list`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "70bd8be8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:36.286875Z",
     "iopub.status.busy": "2024-10-17T13:12:36.286468Z",
     "iopub.status.idle": "2024-10-17T13:12:36.290090Z",
     "shell.execute_reply": "2024-10-17T13:12:36.289449Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster_ip = \"10.10.200.42\"\n",
    "cluster_name = \"cluster0\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96d5bfc4",
   "metadata": {},
   "source": [
    "### Connect to Cluster\n",
    "\n",
    "We now make a connection with the Cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "47fdb508",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:36.292012Z",
     "iopub.status.busy": "2024-10-17T13:12:36.291825Z",
     "iopub.status.idle": "2024-10-17T13:12:37.101832Z",
     "shell.execute_reply": "2024-10-17T13:12:37.101069Z"
    },
    "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",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6514d778",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "#### Get connected modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7c4ebf0f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:37.104163Z",
     "iopub.status.busy": "2024-10-17T13:12:37.103965Z",
     "iopub.status.idle": "2024-10-17T13:12:37.107875Z",
     "shell.execute_reply": "2024-10-17T13:12:37.107143Z"
    }
   },
   "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": "e1dd5d05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:37.109747Z",
     "iopub.status.busy": "2024-10-17T13:12:37.109560Z",
     "iopub.status.idle": "2024-10-17T13:12:37.134565Z",
     "shell.execute_reply": "2024-10-17T13:12:37.133834Z"
    }
   },
   "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",
    "modules = get_connected_modules(cluster, lambda mod: mod.is_qrm_type and not mod.is_rf_type)\n",
    "modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "18a3cd00",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:37.136413Z",
     "iopub.status.busy": "2024-10-17T13:12:37.136248Z",
     "iopub.status.idle": "2024-10-17T13:12:37.139047Z",
     "shell.execute_reply": "2024-10-17T13:12:37.138330Z"
    },
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "readout_module = modules[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a319e43b",
   "metadata": {},
   "source": [
    "### Reset the Cluster\n",
    "\n",
    "We reset the Cluster to enter a well-defined state. Note that resetting will clear all stored parameters, so resetting between experiments is usually not desirable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "832fb695",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:37.140852Z",
     "iopub.status.busy": "2024-10-17T13:12:37.140692Z",
     "iopub.status.idle": "2024-10-17T13:12:39.589687Z",
     "shell.execute_reply": "2024-10-17T13:12:39.588984Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, Flags: NONE, Slot flags: NONE\n"
     ]
    }
   ],
   "source": [
    "cluster.reset()\n",
    "print(cluster.get_system_status())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e50cbd61",
   "metadata": {},
   "source": [
    "Frequency sweeps  <a id='freq_sw'></a>\n",
    "------------------------------\n",
    "One of the most common experiments is to test the frequency response of the system, e.g. to find the resonance frequencies of a qubit or a resonator. For the purpose of this tutorial, we will sweep the full frequency range supported by the QRM. To improve accuracy we can use the maximum integration time and multiple averages. This does not change the overall measurement time much, as most of it is used for the setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "028f8918",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:39.591812Z",
     "iopub.status.busy": "2024-10-17T13:12:39.591634Z",
     "iopub.status.idle": "2024-10-17T13:12:39.594831Z",
     "shell.execute_reply": "2024-10-17T13:12:39.594128Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "start_freq = -500e6\n",
    "stop_freq = 500e6\n",
    "\n",
    "n_averages = 10\n",
    "MAXIMUM_SCOPE_ACQUISITION_LENGTH = 16384"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "784c2eeb",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "In this tutorial, we will analyze the raw data measured by the scope acquisition of the QRM. For this we will define a simple helper function using [scipy.signal.spectrogram](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.spectrogram.html) and [scipy.signal.welch](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.welch.html). The spectrogram shows the frequency spectrum of the QRM output as a function of time, to visualize the frequency sweeps we are doing. Welch's method is used to compute the input power as a function of frequency (power spectral density). This way we obtain the response of the system to find features of interest, e.g. a resonance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "31c1687a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:39.596912Z",
     "iopub.status.busy": "2024-10-17T13:12:39.596732Z",
     "iopub.status.idle": "2024-10-17T13:12:39.603047Z",
     "shell.execute_reply": "2024-10-17T13:12:39.602287Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Power as function of frequency and time by chunking the data\n",
    "def plot_spectrogram(time_series: np.ndarray) -> None:\n",
    "    f_sample = 1e9  # All devices have 1 GSPS sample rate\n",
    "    fig, ax = plt.subplots(1, 2)\n",
    "\n",
    "    f, t, Sxx = spectrogram(time_series, f_sample, return_onesided=False, detrend=False)\n",
    "\n",
    "    idx = np.argsort(f)\n",
    "    f = f[idx] / 1e6\n",
    "    Sxx = Sxx[idx]\n",
    "\n",
    "    cmap = colors.LinearSegmentedColormap.from_list(\"qblox\", [\"#FFFFFF\", \"#00839F\"])\n",
    "    spec = ax[0].pcolormesh(t, f, Sxx, shading=\"auto\", cmap=cmap)\n",
    "    cb = fig.colorbar(spec)\n",
    "    cb.set_label(\"Power Spectral Density [V$^2$/Hz]\")\n",
    "    ax[0].set_ylabel(\"Frequency [MHz]\")\n",
    "    ax[0].set_xlabel(\"Time [s]\")\n",
    "\n",
    "    f, Pxx = welch(time_series, f_sample, return_onesided=False, detrend=False)\n",
    "\n",
    "    idx = np.argsort(f)\n",
    "    f = f[idx] / 1e6\n",
    "    Pxx = Pxx[idx]\n",
    "\n",
    "    ax[1].semilogy(f, Pxx)\n",
    "    ax[1].set_xlabel(\"Frequency [MHz]\")\n",
    "    ax[1].set_ylabel(\"Power Spectral Density [V$^2$/Hz]\")\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d61af035",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "And two more helper functions for plotting the amplitude of an array of I, Q values and a scope acquisition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "23eb25ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:39.605132Z",
     "iopub.status.busy": "2024-10-17T13:12:39.604939Z",
     "iopub.status.idle": "2024-10-17T13:12:39.610028Z",
     "shell.execute_reply": "2024-10-17T13:12:39.609277Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_amplitude(x: NDArray, I_data: NDArray, Q_data: NDArray) -> None:\n",
    "    amplitude = np.abs(I_data + 1j * Q_data)\n",
    "\n",
    "    plt.plot(x / 1e6, amplitude)\n",
    "    plt.xlabel(\"Frequency [MHz]\")\n",
    "    plt.ylabel(\"Integration [V]\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def plot_scope(trace: NDArray, t_min: int, t_max: int) -> None:\n",
    "    x = np.arange(t_min, t_max)\n",
    "    plt.plot(x, np.real(trace[t_min:t_max]))\n",
    "    plt.plot(x, np.imag(trace[t_min:t_max]))\n",
    "    plt.ylabel(\"Scope [V]\")\n",
    "    plt.xlabel(\"Time [ns]\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ff700f4",
   "metadata": {},
   "source": [
    "### Setting up the QRM\n",
    "We set up a modulated DC offset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b496f46c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:39.612130Z",
     "iopub.status.busy": "2024-10-17T13:12:39.611938Z",
     "iopub.status.idle": "2024-10-17T13:12:39.745426Z",
     "shell.execute_reply": "2024-10-17T13:12:39.742064Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.disconnect_outputs()\n",
    "readout_module.disconnect_inputs()\n",
    "\n",
    "# Configure channel map\n",
    "readout_module.sequencer0.connect_sequencer(\"io0_1\")\n",
    "\n",
    "# Set DC Offset\n",
    "readout_module.sequencer0.offset_awg_path0(1)\n",
    "readout_module.sequencer0.offset_awg_path1(1)\n",
    "\n",
    "# Enable modulation and demodulation. Note that the scope is not demodulated\n",
    "readout_module.sequencer0.mod_en_awg(True)\n",
    "readout_module.sequencer0.demod_en_acq(True)\n",
    "\n",
    "# Enable hardware averaging for the scope\n",
    "readout_module.scope_acq_avg_mode_en_path0(True)\n",
    "readout_module.scope_acq_avg_mode_en_path1(True)\n",
    "\n",
    "readout_module.sequencer0.integration_length_acq(MAXIMUM_SCOPE_ACQUISITION_LENGTH)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e1a4954",
   "metadata": {},
   "source": [
    "### NCO sweep controlled by the host computer\n",
    "As a baseline, we will run a simple frequency sweep controlled by the host computer using QCoDeS. We do this by setting a DC offset and modulating it with the NCO. This is quite slow, so we will only measure 200 steps to keep the measurement time reasonable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4bfa19df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:39.753625Z",
     "iopub.status.busy": "2024-10-17T13:12:39.753456Z",
     "iopub.status.idle": "2024-10-17T13:12:39.757464Z",
     "shell.execute_reply": "2024-10-17T13:12:39.756814Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step size 5.0 MHz\n"
     ]
    }
   ],
   "source": [
    "n_steps = 200\n",
    "\n",
    "step_freq = (stop_freq - start_freq) / n_steps\n",
    "print(f\"Step size {step_freq/1e6} MHz\")\n",
    "\n",
    "nco_sweep_range = np.arange(start_freq, stop_freq, step_freq)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d95f1b7",
   "metadata": {},
   "source": [
    "Now we set up a simple program that acquires data and averages. The frequency will be set later with the QCoDeS interface."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40adec37",
   "metadata": {},
   "source": [
    "Note: While most operations and parameters are executed/updated on a 1 ns timegrid, the instructions that operate on the sequencer’s NCOs (e.g. set_freq, reset_ph, set_ph, set_ph_delta) are updated on a 4 ns timegrid. The minimum time between frequency changes is 8 ns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a46d8477",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:39.759342Z",
     "iopub.status.busy": "2024-10-17T13:12:39.759182Z",
     "iopub.status.idle": "2024-10-17T13:12:39.763386Z",
     "shell.execute_reply": "2024-10-17T13:12:39.762718Z"
    }
   },
   "outputs": [],
   "source": [
    "acquisitions = {\"acq\": {\"num_bins\": 1, \"index\": 0}}\n",
    "\n",
    "# Sequence program.\n",
    "slow_sweep = f\"\"\"\n",
    "    move    {n_averages}, R0            # Average iterator.\n",
    "\n",
    "avg_loop:\n",
    "    reset_ph\n",
    "    upd_param 200\n",
    "    acquire 0, 0, {MAXIMUM_SCOPE_ACQUISITION_LENGTH}\n",
    "\n",
    "    loop    R0, @avg_loop\n",
    "\n",
    "    stop\n",
    "\"\"\"\n",
    "\n",
    "# Add sequence to single dictionary and write to JSON file.\n",
    "sequence = {\n",
    "    \"waveforms\": {},\n",
    "    \"weights\": {},\n",
    "    \"acquisitions\": acquisitions,\n",
    "    \"program\": slow_sweep,\n",
    "}\n",
    "with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "    json.dump(sequence, file, indent=4)\n",
    "    file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bba811d",
   "metadata": {},
   "source": [
    "Next, we prepare the QRM for the measurement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8f668cdf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:39.765230Z",
     "iopub.status.busy": "2024-10-17T13:12:39.765068Z",
     "iopub.status.idle": "2024-10-17T13:12:39.795480Z",
     "shell.execute_reply": "2024-10-17T13:12:39.792388Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.sequence(\"sequence.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3e137c8",
   "metadata": {},
   "source": [
    "Now we can run the frequency sweep. This is simply a loop where we set the frequency with QCoDeS and then run the program defined above. We measure the run time using the `%%time` Jupyter magic command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7ef46c7c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:39.804402Z",
     "iopub.status.busy": "2024-10-17T13:12:39.803561Z",
     "iopub.status.idle": "2024-10-17T13:12:52.630280Z",
     "shell.execute_reply": "2024-10-17T13:12:52.627105Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.51 s, sys: 1.21 s, total: 4.72 s\n",
      "Wall time: 12.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data = []\n",
    "for nco_val in nco_sweep_range:\n",
    "    # Set the frequency\n",
    "    readout_module.sequencer0.nco_freq(nco_val)\n",
    "\n",
    "    # Run the program\n",
    "    readout_module.arm_sequencer(0)\n",
    "    readout_module.start_sequencer()\n",
    "\n",
    "    # Wait for the sequencer to stop with a timeout period of one minute.\n",
    "    readout_module.get_acquisition_status(0)\n",
    "\n",
    "    # Move acquisition data from temporary memory to acquisition list.\n",
    "    readout_module.store_scope_acquisition(0, \"acq\")\n",
    "\n",
    "    # Get acquisition list from instrument.\n",
    "    data.append(readout_module.get_acquisitions(0)[\"acq\"])\n",
    "\n",
    "    # Clear acquisition data so we do not average the results from different frequencies\n",
    "    readout_module.sequencer0.delete_acquisition_data(\"acq\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08882832",
   "metadata": {},
   "source": [
    "Plotting the acquired integration data, we can see the frequency behavior of the QRM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "93d3333e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:52.639859Z",
     "iopub.status.busy": "2024-10-17T13:12:52.638949Z",
     "iopub.status.idle": "2024-10-17T13:12:52.840752Z",
     "shell.execute_reply": "2024-10-17T13:12:52.840079Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "I_data = (\n",
    "    np.asarray([d[\"acquisition\"][\"bins\"][\"integration\"][\"path0\"][0] for d in data])\n",
    "    / MAXIMUM_SCOPE_ACQUISITION_LENGTH\n",
    ")\n",
    "Q_data = (\n",
    "    np.asarray([d[\"acquisition\"][\"bins\"][\"integration\"][\"path1\"][0] for d in data])\n",
    "    / MAXIMUM_SCOPE_ACQUISITION_LENGTH\n",
    ")\n",
    "plot_amplitude(nco_sweep_range, I_data, Q_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6c587c9",
   "metadata": {},
   "source": [
    "We can see that the output amplitude decreases with frequency, this is expected due to the analog filters. We can also analyze the accumulated scope data with a spectrogram. This takes a few seconds, as there are 16384 data points per frequency step. Note that the time axis of the spectrogram refers to measurement time (16.4us * 200 steps $\\approx$ 3.3ms) and not the wall clock time, which is significantly longer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "5c02c431",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:52.842739Z",
     "iopub.status.busy": "2024-10-17T13:12:52.842552Z",
     "iopub.status.idle": "2024-10-17T13:12:55.086353Z",
     "shell.execute_reply": "2024-10-17T13:12:55.085682Z"
    },
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_scope = (\n",
    "    np.asarray([d[\"acquisition\"][\"scope\"][\"path0\"][\"data\"] for d in data]).flatten()\n",
    "    + 1j * np.asarray([d[\"acquisition\"][\"scope\"][\"path1\"][\"data\"] for d in data]).flatten()\n",
    ")\n",
    "\n",
    "plot_spectrogram(data_scope)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bd5be3a",
   "metadata": {},
   "source": [
    "### Spectroscopy using Q1ASM\n",
    "Now we will run the same spectroscopy experiment using Q1ASM to change the NCO frequency in real time. First, we set up the QRM for continuous wave output and binned acquisition with many bins. This is significantly faster than using QCoDeS. The maximum number of points that can be measured this way is 131072 per sequencer. This corresponds to the number of bins available for acquisition per sequencer.\n",
    "\n",
    "The sequencer program can fundamentally only support integer values. However, the NCO has a frequency resolution of 0.25 Hz and supports $10^9$ phase values. Therefore, frequencies in the sequencer program must be given as integer multiple of $1/4$ Hz, and phases as integer multiple of $360/10^9$ degree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7cdf8f8b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:55.088411Z",
     "iopub.status.busy": "2024-10-17T13:12:55.088243Z",
     "iopub.status.idle": "2024-10-17T13:12:55.091672Z",
     "shell.execute_reply": "2024-10-17T13:12:55.091105Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 steps with step size 5.0 MHz\n"
     ]
    }
   ],
   "source": [
    "n_steps = 200\n",
    "\n",
    "step_freq = (stop_freq - start_freq) / n_steps\n",
    "print(f\"{n_steps} steps with step size {step_freq/1e6} MHz\")\n",
    "\n",
    "# Convert frequencies to multiples of 0.25 Hz\n",
    "nco_int_start_freq = int(4 * start_freq)\n",
    "nco_int_step_freq = int(4 * step_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "63c5a717",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:55.093427Z",
     "iopub.status.busy": "2024-10-17T13:12:55.093265Z",
     "iopub.status.idle": "2024-10-17T13:12:55.098084Z",
     "shell.execute_reply": "2024-10-17T13:12:55.097485Z"
    }
   },
   "outputs": [],
   "source": [
    "acquisitions = {\"acq\": {\"num_bins\": n_steps, \"index\": 0}}\n",
    "\n",
    "setup = f\"\"\"\n",
    "    move {n_averages}, R2\n",
    "\n",
    "avg_loop:\n",
    "    move    0, R0          # frequency\n",
    "    move    0, R1          # step counter\n",
    "\"\"\"\n",
    "\n",
    "# To get a negative starting frequency, we subtract a positive number from 0\n",
    "if start_freq <= 0:\n",
    "    setup += f\"\"\"\n",
    "    sub R0, {-nco_int_start_freq}, R0\n",
    "    \"\"\"\n",
    "else:\n",
    "    setup += f\"\"\"\n",
    "    add R0, {nco_int_start_freq}, R0\n",
    "    \"\"\"\n",
    "\n",
    "spectroscopy = (\n",
    "    setup\n",
    "    + f\"\"\"\n",
    "    reset_ph\n",
    "    set_freq        R0\n",
    "    upd_param       200\n",
    "\n",
    "nco_set:\n",
    "    set_freq        R0                           # Set the frequency\n",
    "    add             R0, {nco_int_step_freq}, R0  # Update the frequency register\n",
    "    upd_param       200                          # Wait for time of flight\n",
    "    acquire         0, R1, {MAXIMUM_SCOPE_ACQUISITION_LENGTH}\n",
    "    add             R1, 1, R1\n",
    "    nop\n",
    "    jlt             R1, {n_steps}, @nco_set      # Loop over all frequencies\n",
    "\n",
    "    loop            R2, @avg_loop\n",
    "\n",
    "    stop                                  # Stop\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "# Add sequence to single dictionary and write to JSON file.\n",
    "sequence = {\n",
    "    \"waveforms\": {},\n",
    "    \"weights\": {},\n",
    "    \"acquisitions\": acquisitions,\n",
    "    \"program\": spectroscopy,\n",
    "}\n",
    "with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "    json.dump(sequence, file, indent=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "850397df",
   "metadata": {},
   "source": [
    "Now we prepare the QRM for measurement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "985460df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:55.099987Z",
     "iopub.status.busy": "2024-10-17T13:12:55.099822Z",
     "iopub.status.idle": "2024-10-17T13:12:55.123123Z",
     "shell.execute_reply": "2024-10-17T13:12:55.122443Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.sequence(\"sequence.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "69cfa27e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:55.125244Z",
     "iopub.status.busy": "2024-10-17T13:12:55.125076Z",
     "iopub.status.idle": "2024-10-17T13:12:55.242169Z",
     "shell.execute_reply": "2024-10-17T13:12:55.241542Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status:\n",
      "Status: OKAY, State: RUNNING, Info Flags: 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",
      "CPU times: user 69.5 ms, sys: 28.3 ms, total: 97.7 ms\n",
      "Wall time: 113 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "readout_module.arm_sequencer(0)\n",
    "readout_module.start_sequencer()\n",
    "\n",
    "print(\"Status:\")\n",
    "print(readout_module.get_sequencer_status(0))\n",
    "\n",
    "# Wait for the sequencer to stop with a timeout period of one minute.\n",
    "readout_module.get_acquisition_status(0)\n",
    "\n",
    "data = readout_module.get_acquisitions(0)[\"acq\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2468d117",
   "metadata": {},
   "source": [
    "Note that the same measurement as before is now two orders of magnitude faster. If we plot the integrated data, we get the same results as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cf035c8b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:55.244215Z",
     "iopub.status.busy": "2024-10-17T13:12:55.244038Z",
     "iopub.status.idle": "2024-10-17T13:12:55.374764Z",
     "shell.execute_reply": "2024-10-17T13:12:55.374140Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For plotting, convert the NCO integer values back to frequencies\n",
    "nco_sweep_range = (np.arange(n_steps) * nco_int_step_freq + nco_int_start_freq) / 4.0\n",
    "\n",
    "I_data = (\n",
    "    np.asarray(data[\"acquisition\"][\"bins\"][\"integration\"][\"path0\"])\n",
    "    / MAXIMUM_SCOPE_ACQUISITION_LENGTH\n",
    ")\n",
    "Q_data = (\n",
    "    np.asarray(data[\"acquisition\"][\"bins\"][\"integration\"][\"path1\"])\n",
    "    / MAXIMUM_SCOPE_ACQUISITION_LENGTH\n",
    ")\n",
    "plot_amplitude(nco_sweep_range, I_data, Q_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e8a694",
   "metadata": {},
   "source": [
    "#### NCO input delay compensation\n",
    "By default, the input and output of the QRM are multiplied with the same NCO value. As the output path has a time of flight of about 149 ns between the NCO and playback (provided no RTP filters are activated), this means that there is a short time window after frequency/phase updates where demodulation is updated, but playback is still using the old value. There is also always a (fixed) relative phase between playback and demodulation. We can showcase this by using a similar program as before, but with less points, so that the frequency steps are more clearly visible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "90dbb90d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:55.376716Z",
     "iopub.status.busy": "2024-10-17T13:12:55.376544Z",
     "iopub.status.idle": "2024-10-17T13:12:55.380496Z",
     "shell.execute_reply": "2024-10-17T13:12:55.379912Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 steps with step size 50.0 MHz\n"
     ]
    }
   ],
   "source": [
    "n_steps = 20\n",
    "n_averages = 1000\n",
    "\n",
    "step_freq = (stop_freq - start_freq) / n_steps\n",
    "print(f\"{n_steps} steps with step size {step_freq/1e6} MHz\")\n",
    "\n",
    "# Convert frequencies to multiples of 0.25 Hz\n",
    "nco_int_start_freq = int(4 * start_freq)\n",
    "nco_int_step_freq = int(4 * step_freq)\n",
    "\n",
    "# For plotting, convert the NCO integer values back to frequencies\n",
    "nco_sweep_range = np.arange(nco_int_start_freq, 4 * stop_freq, nco_int_step_freq) / 4.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96d7cd2e",
   "metadata": {},
   "source": [
    "To make the effect of NCO delay compensation more apparent, we modify the spectroscopy program for short integration time and acquire immediately after the frequency update, without waiting for time of flight. This means that the output at the new frequency only arrives at the input AFTER integration in the current loop iteration has finished. Without further modifications of the program this leads to an off-by-one error. Therefore, we increase the frequency as the first step in the loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4668d952",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:55.382283Z",
     "iopub.status.busy": "2024-10-17T13:12:55.382122Z",
     "iopub.status.idle": "2024-10-17T13:12:55.387083Z",
     "shell.execute_reply": "2024-10-17T13:12:55.386482Z"
    }
   },
   "outputs": [],
   "source": [
    "acquisitions = {\"acq\": {\"num_bins\": n_steps, \"index\": 0}}\n",
    "\n",
    "setup = f\"\"\"\n",
    "    move {n_averages}, R2\n",
    "\n",
    "avg_loop:\n",
    "    move    0, R0          # frequency\n",
    "    move    0, R1          # step counter\n",
    "\"\"\"\n",
    "\n",
    "# To get a negative starting frequency, we subtract a positive number from 0\n",
    "if start_freq <= 0:\n",
    "    setup += f\"\"\"\n",
    "    sub R0, {-nco_int_start_freq}, R0\n",
    "    \"\"\"\n",
    "else:\n",
    "    setup += f\"\"\"\n",
    "    add R0, {nco_int_start_freq}, R0\n",
    "    \"\"\"\n",
    "\n",
    "spectroscopy = (\n",
    "    setup\n",
    "    + f\"\"\"\n",
    "    reset_ph\n",
    "    set_freq        R0\n",
    "    upd_param       200\n",
    "\n",
    "nco_set:\n",
    "    # Due to time of flight, the new frequency will only arrive at the input AFTER integration is done\n",
    "    # Therefore, we already increase the frequency before the first measurement.\n",
    "    add             R0, {nco_int_step_freq}, R0\n",
    "    nop\n",
    "    set_freq        R0\n",
    "\n",
    "    # we removed upd_param, so that acquisition starts the moment the frequency is updated\n",
    "    acquire         0, R1, 1200                  # Update the NCO and immediately acquire\n",
    "    add             R1, 1, R1\n",
    "    nop\n",
    "    jlt             R1, {n_steps}, @nco_set      # Loop over all frequencies\n",
    "\n",
    "    loop            R2, @avg_loop\n",
    "\n",
    "    stop                                  # Stop\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "# Add sequence to single dictionary and write to JSON file.\n",
    "sequence = {\n",
    "    \"waveforms\": {},\n",
    "    \"weights\": {},\n",
    "    \"acquisitions\": acquisitions,\n",
    "    \"program\": spectroscopy,\n",
    "}\n",
    "with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "    json.dump(sequence, file, indent=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3236c4c7",
   "metadata": {},
   "source": [
    "As a baseline, we will do a measurement with delay compensation disabled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "af7c994b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:55.388880Z",
     "iopub.status.busy": "2024-10-17T13:12:55.388718Z",
     "iopub.status.idle": "2024-10-17T13:12:55.431749Z",
     "shell.execute_reply": "2024-10-17T13:12:55.431000Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.sequence(\"sequence.json\")\n",
    "readout_module.sequencer0.integration_length_acq(140)\n",
    "readout_module.sequencer0.nco_prop_delay_comp_en(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "67371c9f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:55.433870Z",
     "iopub.status.busy": "2024-10-17T13:12:55.433702Z",
     "iopub.status.idle": "2024-10-17T13:12:55.609352Z",
     "shell.execute_reply": "2024-10-17T13:12:55.608728Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status:\n",
      "Status: OKAY, State: RUNNING, Info Flags: ACQ_BINNING_DONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "readout_module.arm_sequencer(0)\n",
    "readout_module.start_sequencer()\n",
    "\n",
    "print(\"Status:\")\n",
    "print(readout_module.get_sequencer_status(0))\n",
    "\n",
    "# Wait for the sequencer to stop with a timeout period of one minute.\n",
    "readout_module.get_acquisition_status(0)\n",
    "\n",
    "data = readout_module.get_acquisitions(0)[\"acq\"]\n",
    "I_data = np.asarray(data[\"acquisition\"][\"bins\"][\"integration\"][\"path0\"]) / 140\n",
    "Q_data = np.asarray(data[\"acquisition\"][\"bins\"][\"integration\"][\"path1\"]) / 140\n",
    "plot_amplitude(nco_sweep_range, I_data, Q_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02581e62",
   "metadata": {},
   "source": [
    "Even though we only measured a small number of points, we can see that this is not compatible with the previous spectroscopy measurements. What happened is that `set_freq` updates the NCO frequency immediately. However, there is a time of flight of about 149 ns between the NCO and the output of the device. Thus, the signal will be demodulated at $f_0 + 100$ MHz immediately after `set_freq`, but the incoming signal is still modulated at $f_0$ for another 149 ns - longer than the integration time chosen above. The integrated signal will therefore be approximately zero.\n",
    "\n",
    "Now we run the same experiment again, with delay compensation enabled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0bd4f21a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:55.611346Z",
     "iopub.status.busy": "2024-10-17T13:12:55.611173Z",
     "iopub.status.idle": "2024-10-17T13:12:55.651729Z",
     "shell.execute_reply": "2024-10-17T13:12:55.648625Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.sequence(\"sequence.json\")\n",
    "readout_module.sequencer0.nco_prop_delay_comp_en(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d067ba3a",
   "metadata": {},
   "source": [
    "This ensures that, for demodulation, the NCO only updates after the time of flight, i.e. that frequency and phase of modulation and demodulation always match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ad1a260f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:55.658588Z",
     "iopub.status.busy": "2024-10-17T13:12:55.658420Z",
     "iopub.status.idle": "2024-10-17T13:12:55.764679Z",
     "shell.execute_reply": "2024-10-17T13:12:55.763933Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status:\n",
      "Status: OKAY, State: RUNNING, Info Flags: ACQ_BINNING_DONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "readout_module.arm_sequencer(0)\n",
    "readout_module.start_sequencer()\n",
    "\n",
    "print(\"Status:\")\n",
    "print(readout_module.get_sequencer_status(0))\n",
    "\n",
    "# Wait for the sequencer to stop with a timeout period of one minute.\n",
    "readout_module.get_acquisition_status(0)\n",
    "\n",
    "data = readout_module.get_acquisitions(0)[\"acq\"]\n",
    "I_data = np.asarray(data[\"acquisition\"][\"bins\"][\"integration\"][\"path0\"]) / 140\n",
    "Q_data = np.asarray(data[\"acquisition\"][\"bins\"][\"integration\"][\"path1\"]) / 140\n",
    "amplitude = np.abs(I_data + 1j * Q_data)\n",
    "\n",
    "plot_amplitude(nco_sweep_range, I_data, Q_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57e04636",
   "metadata": {},
   "source": [
    "We can see that modulation and demodulation frequency now match, producing similar results as the spectroscopy measurements before."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a815cba",
   "metadata": {},
   "source": [
    "## Stop\n",
    "\n",
    "Finally, let's stop the playback 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": 29,
   "id": "61a78947",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-17T13:12:55.766657Z",
     "iopub.status.busy": "2024-10-17T13:12:55.766472Z",
     "iopub.status.idle": "2024-10-17T13:13:03.164876Z",
     "shell.execute_reply": "2024-10-17T13:13:03.164141Z"
    }
   },
   "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",
      "Status: OKAY, State: STOPPED, Info Flags: FORCED_STOP, Warning Flags: NONE, Error Flags: NONE, Log: []\n",
      "\n",
      "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   :\tTrue \n",
      "scope_acq_avg_mode_en_path1   :\tTrue \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           :\t140 \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                         :\t4.95e+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           :\tTrue (ns)\n",
      "offset_awg_path0                 :\t1 \n",
      "offset_awg_path1                 :\t1 \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_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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, Flags: NONE, Slot flags: NONE\n"
     ]
    }
   ],
   "source": [
    "# Stop both sequencers.\n",
    "readout_module.stop_sequencer()\n",
    "\n",
    "# Print status of both sequencers (should now say it is stopped).\n",
    "print(readout_module.get_sequencer_status(0))\n",
    "print(readout_module.get_sequencer_status(1))\n",
    "print()\n",
    "\n",
    "# Print an overview of the instrument parameters.\n",
    "print(\"Snapshot:\")\n",
    "readout_module.print_readable_snapshot(update=True)\n",
    "\n",
    "# Reset the cluster\n",
    "cluster.reset()\n",
    "print(cluster.get_system_status())"
   ]
  }
 ],
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