{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d5945468",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "Binned acquisition\n",
    "==================\n",
    "\n",
    ".. Note::\n",
    "        A version of this tutorial suitable for RF modules can be found [here](https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/tutorials/q1asm_tutorials/basic/rf/binned_acquisition.html).\n",
    "\n",
    "In this tutorial, we will demonstrate the sequencer-based acquisition binning procedure. The binning process is applied on the input path after real-time demodulation, (weighed) integration, IQ rotation and discretization. It allows storing both the integration and discretization\n",
    "results on the fly without intervention of the host PC in up to 131072 bins. It also allows the averaging of those bins on the fly\n",
    "(see section [Sequencer Acquisition](https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/cluster/q1_sequence_processor.html#acquisitions)).\n",
    "We will show this by using a QRM and directly connecting outputs $\\text{O}^{[1-2]}$ to inputs $\\text{I}^{[1-2]}$, respectively. We will then use\n",
    "the QRM's sequencers to sequence waveforms on the outputs and, simultaneously, acquire the resulting waveforms on the inputs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ed037c6",
   "metadata": {
    "tags": []
   },
   "source": [
    "Setup\n",
    "-----\n",
    "\n",
    "First, we are going to import the required packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "addf96c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:11.067703Z",
     "iopub.status.busy": "2024-03-28T14:30:11.066120Z",
     "iopub.status.idle": "2024-03-28T14:30:12.642717Z",
     "shell.execute_reply": "2024-03-28T14:30:12.641743Z"
    },
    "lines_to_next_cell": 0,
    "tags": []
   },
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "import json\n",
    "import math\n",
    "from typing import TYPE_CHECKING, Callable\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import scipy.signal\n",
    "from qcodes.instrument import find_or_create_instrument\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 QcmQrm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c435ed0",
   "metadata": {},
   "source": [
    "### Scan For Clusters\n",
    "\n",
    "We scan for the available devices connected via ethernet using the Plug & Play functionality of the Qblox Instruments package (see [Plug & Play](https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/api_reference/tools.html#api-pnp) for more info)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b9f5a58f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:12.648289Z",
     "iopub.status.busy": "2024-03-28T14:30:12.647289Z",
     "iopub.status.idle": "2024-03-28T14:30:14.782538Z",
     "shell.execute_reply": "2024-03-28T14:30:14.781533Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Devices:\n",
      " - 10.10.200.13 via 192.168.207.146/24 (reconfiguration needed!): cluster_mm 0.6.2 with name \"QSE_1\" and serial number 00015_2321_005\n",
      " - 10.10.200.42 via 192.168.207.146/24 (reconfiguration needed!): cluster_mm 0.7.0 with name \"QAE-I\" and serial number 00015_2321_004\n",
      " - 10.10.200.43 via 192.168.207.146/24 (reconfiguration needed!): cluster_mm 0.6.2 with name \"QAE-2\" and serial number 00015_2206_003\n",
      " - 10.10.200.50 via 192.168.207.146/24 (reconfiguration needed!): cluster_mm 0.7.0 with name \"cluster-mm\" and serial number 00015_2219_003\n",
      " - 10.10.200.53 via 192.168.207.146/24 (reconfiguration needed!): cluster_mm 0.7.0 with name \"cluster-mm\" and serial number 00015_2320_004\n",
      " - 10.10.200.70 via 192.168.207.146/24 (reconfiguration needed!): cluster_mm 0.6.1 with name \"cluster-mm\" and serial number 123-456-789\n",
      " - 10.10.200.80 via 192.168.207.146/24 (reconfiguration needed!): cluster_mm 0.6.1 with name \"cluster-mm\" and serial number not_valid\n"
     ]
    }
   ],
   "source": [
    "!qblox-pnp list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ec7b4a51",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:14.791045Z",
     "iopub.status.busy": "2024-03-28T14:30:14.789983Z",
     "iopub.status.idle": "2024-03-28T14:30:14.798270Z",
     "shell.execute_reply": "2024-03-28T14:30:14.796976Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster_ip = \"10.10.200.42\"\n",
    "cluster_name = \"cluster0\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "181b7c05",
   "metadata": {},
   "source": [
    "### Connect to Cluster\n",
    "\n",
    "We now make a connection with the Cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "45e58ba2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:14.804641Z",
     "iopub.status.busy": "2024-03-28T14:30:14.803462Z",
     "iopub.status.idle": "2024-03-28T14:30:15.962189Z",
     "shell.execute_reply": "2024-03-28T14:30:15.961189Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "\n",
    "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": "695c3913",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "#### Get connected modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1a3677a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:15.967727Z",
     "iopub.status.busy": "2024-03-28T14:30:15.967727Z",
     "iopub.status.idle": "2024-03-28T14:30:15.978749Z",
     "shell.execute_reply": "2024-03-28T14:30:15.976748Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_connected_modules(cluster: Cluster, filter_fn: Callable | None = None) -> dict[int, QcmQrm]:\n",
    "    def checked_filter_fn(mod: ClusterType) -> bool:\n",
    "        if filter_fn is not None:\n",
    "            return filter_fn(mod)\n",
    "        return True\n",
    "\n",
    "    return {\n",
    "        mod.slot_idx: mod for mod in cluster.modules if mod.present() and checked_filter_fn(mod)\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1aadec50",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:15.984751Z",
     "iopub.status.busy": "2024-03-28T14:30:15.983734Z",
     "iopub.status.idle": "2024-03-28T14:30:16.133359Z",
     "shell.execute_reply": "2024-03-28T14:30:16.131369Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{4: <Module: cluster0_module4 of Cluster: cluster0>}"
      ]
     },
     "execution_count": 6,
     "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": 7,
   "id": "74d52279",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:16.137872Z",
     "iopub.status.busy": "2024-03-28T14:30:16.136886Z",
     "iopub.status.idle": "2024-03-28T14:30:16.148623Z",
     "shell.execute_reply": "2024-03-28T14:30:16.147465Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module = modules[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "946bda4d",
   "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": 8,
   "id": "06f538f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:16.154033Z",
     "iopub.status.busy": "2024-03-28T14:30:16.154033Z",
     "iopub.status.idle": "2024-03-28T14:30:19.576466Z",
     "shell.execute_reply": "2024-03-28T14:30:19.575324Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, Flags: NONE, Slot flags: NONE\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\work\\code\\qblox_instruments_install\\qblox_instruments\\native\\generic_func.py:1033: FutureWarning: \n",
      "        After June 2024, this feature is subject to removal in future releases.\n",
      "        Transition to an alternative is advised.\n",
      "        See https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/getting_started/deprecated.html\n",
      "        \n",
      "  warnings.warn(\n",
      "c:\\work\\code\\qblox_instruments_install\\qblox_instruments\\native\\generic_func.py:77: FutureWarning: \n",
      "            After June 2024, this feature is subject to removal in future releases.\n",
      "            Transition to an alternative is advised.\n",
      "            See https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/getting_started/deprecated.html\n",
      "            \n",
      "  self._deprecation_warning()\n",
      "c:\\work\\code\\qblox_instruments_install\\qblox_instruments\\native\\generic_func.py:129: FutureWarning: \n",
      "            After June 2024, this feature is subject to removal in future releases.\n",
      "            Transition to an alternative is advised.\n",
      "            See https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/getting_started/deprecated.html\n",
      "            \n",
      "  self._deprecation_warning()\n"
     ]
    }
   ],
   "source": [
    "cluster.reset()\n",
    "print(cluster.get_system_status())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86fc88c0",
   "metadata": {},
   "source": [
    "Generate waveforms and weights<a id='gen_wf'></a>\n",
    "------------------------------\n",
    "\n",
    "Next, we need to create the waveforms used by the sequence for playback on the outputs as well as weights used by the sequence for weighed\n",
    "integrations. To keep it straightforward, we use the DC offset from the sequencers as our waveform and define waveform weights in the cell\n",
    "below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fab1d129",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:19.581642Z",
     "iopub.status.busy": "2024-03-28T14:30:19.580642Z",
     "iopub.status.idle": "2024-03-28T14:30:19.592343Z",
     "shell.execute_reply": "2024-03-28T14:30:19.590252Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Waveform and weight parameters\n",
    "waveform_weight_length = 600  # nanoseconds\n",
    "\n",
    "# These will be used as weights in the \"Weighed acquisition\" section\n",
    "waveforms_weights = {\n",
    "    \"gaussian\": {\n",
    "        \"data\": scipy.signal.windows.gaussian(\n",
    "            waveform_weight_length, std=0.12 * waveform_weight_length\n",
    "        ).tolist(),\n",
    "        \"index\": 0,\n",
    "    },\n",
    "    \"sine\": {\n",
    "        \"data\": [\n",
    "            math.sin((2 * math.pi / waveform_weight_length) * i)\n",
    "            for i in range(0, waveform_weight_length)\n",
    "        ],\n",
    "        \"index\": 1,\n",
    "    },\n",
    "    \"block\": {\"data\": [1.0 for _ in range(0, waveform_weight_length)], \"index\": 2},\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ca50b2a",
   "metadata": {},
   "source": [
    "Specify acquisitions\n",
    "--------------------\n",
    "\n",
    "We also need to specify the acquisitions so that the instrument can allocate the required memory for its acquisition list. In this case we will create 4 acquisition specifications that each create multiple bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "73234eb9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:19.596345Z",
     "iopub.status.busy": "2024-03-28T14:30:19.596345Z",
     "iopub.status.idle": "2024-03-28T14:30:19.607656Z",
     "shell.execute_reply": "2024-03-28T14:30:19.605561Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Acquisitions\n",
    "acquisitions = {\n",
    "    \"non_weighed\": {\"num_bins\": 10, \"index\": 0},\n",
    "    \"weighed\": {\"num_bins\": 10, \"index\": 1},\n",
    "    \"large\": {\"num_bins\": 131072, \"index\": 2},\n",
    "    \"avg\": {\"num_bins\": 10, \"index\": 3},\n",
    "    \"single\": {\"num_bins\": 1, \"index\": 4},\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3595eadc",
   "metadata": {},
   "source": [
    "Create Q1ASM program\n",
    "--------------------\n",
    "\n",
    "Now that we have the waveform and acquisition specifications for the sequence, we need a simple Q1ASM program that sequences the waveforms and triggers the acquisitions.\n",
    "In this case we will simply trigger 10 non-weighed acquisitions and store each acquisition in a separate bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4c08bb61",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:19.612566Z",
     "iopub.status.busy": "2024-03-28T14:30:19.611390Z",
     "iopub.status.idle": "2024-03-28T14:30:19.622163Z",
     "shell.execute_reply": "2024-03-28T14:30:19.620637Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Sequence program.\n",
    "seq_prog = \"\"\"\n",
    "      move    0,R0        #Loop iterator.\n",
    "      nop\n",
    "\n",
    "loop: acquire 0,R0,1200   #Acquire bins and store them in \"non_weighed\" acquisition.\n",
    "      add     R0,1,R0     #Increment iterator\n",
    "      nop                 #Wait a cycle for R0 to be available.\n",
    "      jlt     R0,10,@loop #Run until number of iterations is done.\n",
    "\n",
    "      stop                #Stop.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a85ec9b3",
   "metadata": {},
   "source": [
    "Upload sequence\n",
    "---------------\n",
    "\n",
    "Now that we have the waveform, weights and acquisition specifications and Q1ASM program, we can combine them in a sequence stored in a JSON file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "336c2611",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:19.629075Z",
     "iopub.status.busy": "2024-03-28T14:30:19.628075Z",
     "iopub.status.idle": "2024-03-28T14:30:19.653168Z",
     "shell.execute_reply": "2024-03-28T14:30:19.652179Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Add sequence program, waveforms, weights and acquisitions to single dictionary and write to JSON file.\n",
    "sequence = {\n",
    "    \"waveforms\": waveforms_weights,\n",
    "    \"weights\": waveforms_weights,\n",
    "    \"acquisitions\": acquisitions,\n",
    "    \"program\": seq_prog,\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": "329fe53f",
   "metadata": {},
   "source": [
    "Let's write the JSON file to the instruments. We will use sequencer 0, which will drive outputs $\\text{O}^{[1-2]}$ and acquire on inputs $\\text{I}^{[1-2]}$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3dad8392",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:19.656711Z",
     "iopub.status.busy": "2024-03-28T14:30:19.656711Z",
     "iopub.status.idle": "2024-03-28T14:30:19.836849Z",
     "shell.execute_reply": "2024-03-28T14:30:19.835242Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Upload sequence.\n",
    "readout_module.sequencer0.sequence(\"sequence.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9db361b5",
   "metadata": {},
   "source": [
    "Play sequence\n",
    "-------------\n",
    "\n",
    "The sequence has been uploaded to the instrument. Now we need to configure the sequencers. To keep it simple we will set a DC signal on the outputs of the instrument by enabling the sequencer offsets and disabling the modulation. These DC signals will then be acquired through the inputs. As such, we will also disable the demodulation on the input path. Furthermore, since we are running non-weighed integrations, we need to specify the integration length. This integration length will be used for every non-weighed integration moving forward. We will also put the integration result acquisition rotation to 0 degrees and acquisition threshold to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9cc18453",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:19.841853Z",
     "iopub.status.busy": "2024-03-28T14:30:19.840853Z",
     "iopub.status.idle": "2024-03-28T14:30:20.068268Z",
     "shell.execute_reply": "2024-03-28T14:30:20.066876Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Configure scope mode\n",
    "readout_module.scope_acq_sequencer_select(0)\n",
    "readout_module.scope_acq_trigger_mode_path0(\"sequencer\")\n",
    "readout_module.scope_acq_trigger_mode_path1(\"sequencer\")\n",
    "\n",
    "# Configure the sequencer\n",
    "readout_module.sequencer0.offset_awg_path0(0.5)\n",
    "readout_module.sequencer0.offset_awg_path1(0.5)\n",
    "readout_module.sequencer0.mod_en_awg(False)\n",
    "readout_module.sequencer0.demod_en_acq(False)\n",
    "readout_module.sequencer0.integration_length_acq(1000)\n",
    "readout_module.sequencer0.thresholded_acq_rotation(0)\n",
    "readout_module.sequencer0.thresholded_acq_threshold(0)\n",
    "\n",
    "# Map sequencer 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": "15b3e4a1",
   "metadata": {},
   "source": [
    "Now let's start the sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5788f7d7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:20.074041Z",
     "iopub.status.busy": "2024-03-28T14:30:20.072527Z",
     "iopub.status.idle": "2024-03-28T14:30:20.114961Z",
     "shell.execute_reply": "2024-03-28T14:30:20.113840Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: STOPPED, Flags: ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_BINNING_DONE\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\work\\code\\qblox_instruments_install\\qblox_instruments\\native\\generic_func.py:2414: FutureWarning: \n",
      "        After June 2024, this feature is subject to removal in future releases.\n",
      "        Transition to an alternative is advised.\n",
      "        See https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/getting_started/deprecated.html\n",
      "        \n",
      "  warnings.warn(\n",
      "c:\\work\\code\\qblox_instruments_install\\qblox_instruments\\native\\generic_func.py:85: FutureWarning: \n",
      "            After June 2024, this feature is subject to removal in future releases.\n",
      "            Transition to an alternative is advised.\n",
      "            See https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/getting_started/deprecated.html\n",
      "            \n",
      "  self._deprecation_warning()\n"
     ]
    }
   ],
   "source": [
    "# Arm and start sequencer.\n",
    "readout_module.arm_sequencer(0)\n",
    "readout_module.start_sequencer()\n",
    "\n",
    "# Print status of sequencer.\n",
    "print(readout_module.get_sequencer_status(0, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2082d290",
   "metadata": {},
   "source": [
    "Retrieve acquisition\n",
    "--------------------\n",
    "\n",
    "Next, we will have a quick look at the input signal so that we can compare it to the integration results. Since we are integrating over a DC signal we are expecting the integration results to be roughly equal to the average DC value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "12daf622",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:20.119574Z",
     "iopub.status.busy": "2024-03-28T14:30:20.119574Z",
     "iopub.status.idle": "2024-03-28T14:30:21.079482Z",
     "shell.execute_reply": "2024-03-28T14:30:21.079482Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\work\\code\\qblox_instruments_install\\qblox_instruments\\native\\generic_func.py:3210: FutureWarning: \n",
      "        After June 2024, this feature is subject to removal in future releases.\n",
      "        Transition to an alternative is advised.\n",
      "        See https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/getting_started/deprecated.html\n",
      "        \n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x465.839 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Wait for the sequencer to stop with a timeout period of one minute.\n",
    "readout_module.get_acquisition_status(0, 1)\n",
    "\n",
    "# Move acquisition data from temporary memory to acquisition list.\n",
    "readout_module.store_scope_acquisition(0, \"non_weighed\")\n",
    "\n",
    "# Get acquisition list from instrument.\n",
    "non_weighed_acq = readout_module.get_acquisitions(0)[\"non_weighed\"]\n",
    "\n",
    "# Plot acquired signal on both inputs.\n",
    "fig, ax = plt.subplots(1, 1, figsize=(15, 15 / 2 / 1.61))\n",
    "ax.plot(non_weighed_acq[\"acquisition\"][\"scope\"][\"path0\"][\"data\"][0:1000])\n",
    "ax.plot(non_weighed_acq[\"acquisition\"][\"scope\"][\"path1\"][\"data\"][0:1000])\n",
    "ax.set_xlabel(\"Time (ns)\")\n",
    "ax.set_ylabel(\"Relative amplitude\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1888f918",
   "metadata": {},
   "source": [
    "To check if the integration results match what we expect, we need to divide the integration results by the integration length which was set through the\n",
    "corresponding QCoDeS parameter. Note that the 'valid' key of the dictionary indicates if the bin was actually set during the sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a3e78b73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:21.084143Z",
     "iopub.status.busy": "2024-03-28T14:30:21.084143Z",
     "iopub.status.idle": "2024-03-28T14:30:21.111939Z",
     "shell.execute_reply": "2024-03-28T14:30:21.110895Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'integration': {'path0': [0.27127308255984367,\n",
       "   0.2712550073277968,\n",
       "   0.27125598436736686,\n",
       "   0.2712877381533952,\n",
       "   0.27111577918905716,\n",
       "   0.27119491939423546,\n",
       "   0.27113971665852465,\n",
       "   0.2710879335613092,\n",
       "   0.2711172447484123,\n",
       "   0.2711265266243283],\n",
       "  'path1': [0.2632638006839277,\n",
       "   0.2631763556424035,\n",
       "   0.2632916463116756,\n",
       "   0.26313287738153396,\n",
       "   0.26318808011724476,\n",
       "   0.2631573033707865,\n",
       "   0.26326233512457253,\n",
       "   0.263161700048852,\n",
       "   0.26319394235466537,\n",
       "   0.26334147532975083]},\n",
       " 'threshold': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n",
       " 'avg_cnt': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_len = readout_module.sequencer0.integration_length_acq()\n",
    "bins = non_weighed_acq[\"acquisition\"][\"bins\"]\n",
    "bins[\"integration\"][\"path0\"] = [(val / int_len) for val in bins[\"integration\"][\"path0\"]]\n",
    "bins[\"integration\"][\"path1\"] = [(val / int_len) for val in bins[\"integration\"][\"path1\"]]\n",
    "bins"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa651b93",
   "metadata": {},
   "source": [
    "Weighed acquisition\n",
    "-------------------\n",
    "\n",
    "In the following, we look into weighed integrations. To do this, we will need to modify the sequence program slightly and reupload it. We will be using a gaussian weight to integrate over input path 0 and a sine weight to integrate over input path 1. The integration length of a weighed integration is determined by the weight length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ecf3afe7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:21.118924Z",
     "iopub.status.busy": "2024-03-28T14:30:21.117924Z",
     "iopub.status.idle": "2024-03-28T14:30:21.127542Z",
     "shell.execute_reply": "2024-03-28T14:30:21.126520Z"
    }
   },
   "outputs": [],
   "source": [
    "# Sequence program.\n",
    "seq_prog = \"\"\"\n",
    "      move            0,R0            #Loop iterator.\n",
    "      move            0,R1            #Weight for path 0.\n",
    "      move            1,R2            #Weight for path 1.\n",
    "      nop\n",
    "\n",
    "loop: acquire_weighed 1,R0,R1,R2,1200 #Acquire bins and store them in \"weighed\" acquisition.\n",
    "      add             R0,1,R0         #Increment iterator\n",
    "      nop                             #Wait a cycle for R0 to be available.\n",
    "      jlt             R0,10,@loop     #Run until number of iterations is done.\n",
    "\n",
    "      stop                            #Stop.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "8e96e60c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:21.132546Z",
     "iopub.status.busy": "2024-03-28T14:30:21.131539Z",
     "iopub.status.idle": "2024-03-28T14:30:21.158333Z",
     "shell.execute_reply": "2024-03-28T14:30:21.156819Z"
    }
   },
   "outputs": [],
   "source": [
    "# Add sequence program, waveforms, weights and acquisitions to single dictionary and write to JSON file.\n",
    "sequence = {\n",
    "    \"waveforms\": waveforms_weights,\n",
    "    \"weights\": waveforms_weights,\n",
    "    \"acquisitions\": acquisitions,\n",
    "    \"program\": seq_prog,\n",
    "}\n",
    "with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "    json.dump(sequence, file, indent=4)\n",
    "    file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "769f6762",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:21.161954Z",
     "iopub.status.busy": "2024-03-28T14:30:21.161954Z",
     "iopub.status.idle": "2024-03-28T14:30:21.340623Z",
     "shell.execute_reply": "2024-03-28T14:30:21.339601Z"
    }
   },
   "outputs": [],
   "source": [
    "# Upload sequence.\n",
    "readout_module.sequencer0.sequence(\"sequence.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99c74e50",
   "metadata": {},
   "source": [
    "Let's start the sequence and retrieve the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "21f51f6b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:21.346622Z",
     "iopub.status.busy": "2024-03-28T14:30:21.346622Z",
     "iopub.status.idle": "2024-03-28T14:30:21.372582Z",
     "shell.execute_reply": "2024-03-28T14:30:21.371579Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: STOPPED, Flags: ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_BINNING_DONE\n"
     ]
    }
   ],
   "source": [
    "# Arm and start sequencer.\n",
    "readout_module.arm_sequencer(0)\n",
    "readout_module.start_sequencer()\n",
    "\n",
    "# Print status of sequencer.\n",
    "print(readout_module.get_sequencer_status(0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1cda16a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:21.376742Z",
     "iopub.status.busy": "2024-03-28T14:30:21.376742Z",
     "iopub.status.idle": "2024-03-28T14:30:22.063436Z",
     "shell.execute_reply": "2024-03-28T14:30:22.062452Z"
    }
   },
   "outputs": [],
   "source": [
    "# Wait for the sequencer to stop with a timeout period of one minute.\n",
    "readout_module.get_acquisition_status(0, 1)\n",
    "\n",
    "# Get acquisition list from instrument.\n",
    "weighed_acq = readout_module.get_acquisitions(0)[\"weighed\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cde112ba",
   "metadata": {},
   "source": [
    "To check if the integration results match what we expect we need to divide the integration results by the integration length again. In this case the integration\n",
    "length is determined by the length of the weights."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1e18166c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:22.069000Z",
     "iopub.status.busy": "2024-03-28T14:30:22.069000Z",
     "iopub.status.idle": "2024-03-28T14:30:22.079571Z",
     "shell.execute_reply": "2024-03-28T14:30:22.078329Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'integration': {'path0': [0.08151050500504073,\n",
       "   0.08152540481870951,\n",
       "   0.08147226745332592,\n",
       "   0.08154728916863098,\n",
       "   0.08150382912483203,\n",
       "   0.08154228106521656,\n",
       "   0.08152959752922427,\n",
       "   0.08153823552317638,\n",
       "   0.08152653523169529,\n",
       "   0.0815166856832201],\n",
       "  'path1': [-9.745854363509473e-05,\n",
       "   -6.418693355550205e-05,\n",
       "   -0.00010425016986026236,\n",
       "   1.75281630741251e-05,\n",
       "   -5.773260156394744e-05,\n",
       "   -5.191586704465895e-05,\n",
       "   -0.00011765443362530763,\n",
       "   -4.96256073782159e-05,\n",
       "   -5.645502010361138e-05,\n",
       "   -2.6292244611187654e-05]},\n",
       " 'threshold': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n",
       " 'avg_cnt': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_len = waveform_weight_length\n",
    "bins = weighed_acq[\"acquisition\"][\"bins\"]\n",
    "bins[\"integration\"][\"path0\"] = [(val / int_len) for val in bins[\"integration\"][\"path0\"]]\n",
    "bins[\"integration\"][\"path1\"] = [(val / int_len) for val in bins[\"integration\"][\"path1\"]]\n",
    "bins"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "326d845b",
   "metadata": {},
   "source": [
    "Large number of bins\n",
    "--------------------\n",
    "\n",
    "The QRM supports up to 131072 bins. To show that we need to change the program slightly. We will use the non-weighed acquisition program,\n",
    "however, we will now loop over the maximum number of acquisitions while storing each result in a separate bin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f5ef8792",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:22.083594Z",
     "iopub.status.busy": "2024-03-28T14:30:22.083594Z",
     "iopub.status.idle": "2024-03-28T14:30:22.094766Z",
     "shell.execute_reply": "2024-03-28T14:30:22.093772Z"
    }
   },
   "outputs": [],
   "source": [
    "# Sequence program.\n",
    "seq_prog = \"\"\"\n",
    "      move    0,R0            #Loop iterator.\n",
    "      nop\n",
    "\n",
    "loop: acquire 2,R0,1200       #Acquire bins and store them in \"large\" acquisition.\n",
    "      add     R0,1,R0         #Increment iterator\n",
    "      nop                     #Wait a cycle for R0 to be available.\n",
    "      jlt     R0,131072,@loop #Run until number of iterations is done.\n",
    "\n",
    "      stop                    #Stop.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "346d953a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:22.099310Z",
     "iopub.status.busy": "2024-03-28T14:30:22.098291Z",
     "iopub.status.idle": "2024-03-28T14:30:22.127314Z",
     "shell.execute_reply": "2024-03-28T14:30:22.125289Z"
    }
   },
   "outputs": [],
   "source": [
    "# Add sequence program, waveforms, weights and acquisitions to single dictionary and write to JSON file.\n",
    "sequence = {\n",
    "    \"waveforms\": waveforms_weights,\n",
    "    \"weights\": waveforms_weights,\n",
    "    \"acquisitions\": acquisitions,\n",
    "    \"program\": seq_prog,\n",
    "}\n",
    "with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "    json.dump(sequence, file, indent=4)\n",
    "    file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c27684cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:22.132880Z",
     "iopub.status.busy": "2024-03-28T14:30:22.131880Z",
     "iopub.status.idle": "2024-03-28T14:30:22.311397Z",
     "shell.execute_reply": "2024-03-28T14:30:22.310391Z"
    }
   },
   "outputs": [],
   "source": [
    "# Upload sequence.\n",
    "readout_module.sequencer0.sequence(\"sequence.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8399e31f",
   "metadata": {},
   "source": [
    "Let's start the sequence and retrieve the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "273911a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:22.317611Z",
     "iopub.status.busy": "2024-03-28T14:30:22.317611Z",
     "iopub.status.idle": "2024-03-28T14:30:22.525510Z",
     "shell.execute_reply": "2024-03-28T14:30:22.523649Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: STOPPED, Flags: ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_BINNING_DONE\n"
     ]
    }
   ],
   "source": [
    "# Arm and start sequencer.\n",
    "readout_module.arm_sequencer(0)\n",
    "readout_module.start_sequencer()\n",
    "\n",
    "# Print status of sequencer.\n",
    "print(readout_module.get_sequencer_status(0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4906a340",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:22.529639Z",
     "iopub.status.busy": "2024-03-28T14:30:22.529639Z",
     "iopub.status.idle": "2024-03-28T14:30:23.220322Z",
     "shell.execute_reply": "2024-03-28T14:30:23.218748Z"
    }
   },
   "outputs": [],
   "source": [
    "# Wait for the sequencer to stop with a timeout period of one minute.\n",
    "readout_module.get_acquisition_status(0, 1)\n",
    "\n",
    "# Get acquisition list from instrument.\n",
    "large_acq = readout_module.get_acquisitions(0)[\"large\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09554a22",
   "metadata": {},
   "source": [
    "Since the number of bins is now to large to simply print, we will check the number of bins and we will check the bins for NaN values which indicate that a bin is not written."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "27a383de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:23.223924Z",
     "iopub.status.busy": "2024-03-28T14:30:23.223924Z",
     "iopub.status.idle": "2024-03-28T14:30:23.327876Z",
     "shell.execute_reply": "2024-03-28T14:30:23.326875Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of bins: 131072\n",
      "All values are valid.\n"
     ]
    }
   ],
   "source": [
    "int_len = readout_module.sequencer0.integration_length_acq()\n",
    "bins = large_acq[\"acquisition\"][\"bins\"]\n",
    "bins[\"integration\"][\"path0\"] = [(val / int_len) for val in bins[\"integration\"][\"path0\"]]\n",
    "bins[\"integration\"][\"path1\"] = [(val / int_len) for val in bins[\"integration\"][\"path1\"]]\n",
    "\n",
    "print(\"Number of bins: {}\".format(len(bins[\"avg_cnt\"])))\n",
    "for it, val in enumerate(bins[\"integration\"][\"path0\"]):\n",
    "    if math.isnan(val):\n",
    "        raise Exception(f\"NaN found at index {it}.\")\n",
    "for it, val in enumerate(bins[\"integration\"][\"path1\"]):\n",
    "    if math.isnan(val):\n",
    "        raise Exception(f\"NaN found at index {it}.\")\n",
    "print(\"All values are valid.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dbea256",
   "metadata": {},
   "source": [
    "We will also plot the integration results in every bin to visualize the contents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "20446f0b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:23.333047Z",
     "iopub.status.busy": "2024-03-28T14:30:23.331997Z",
     "iopub.status.idle": "2024-03-28T14:30:23.592761Z",
     "shell.execute_reply": "2024-03-28T14:30:23.590569Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x465.839 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot bins\n",
    "fig, ax = plt.subplots(1, 1, figsize=(15, 15 / 2 / 1.61))\n",
    "ax.plot(bins[\"integration\"][\"path0\"])\n",
    "ax.plot(bins[\"integration\"][\"path1\"])\n",
    "ax.set_xlabel(\"Bin index\")\n",
    "ax.set_ylabel(\"Relative amplitude\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f4522bb",
   "metadata": {},
   "source": [
    "Averaging\n",
    "--------------------\n",
    "\n",
    "As you may have noticed, the acquisition results also contain an average counter. This average counter reflects the number of times a bin has been averaged during the sequence.\n",
    "Each time the sequencer writes to the same bin the results are automatically accumulated and the average counter is increased. Upon retrieval of the acquisition results, each\n",
    "result is divided by the average counter and therefore automatically averaged. To show this we will change the sequence one last time. This time we will average 10 bins a 1000\n",
    "times each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4f3e49b0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:23.597310Z",
     "iopub.status.busy": "2024-03-28T14:30:23.597310Z",
     "iopub.status.idle": "2024-03-28T14:30:23.607114Z",
     "shell.execute_reply": "2024-03-28T14:30:23.606126Z"
    }
   },
   "outputs": [],
   "source": [
    "# Sequence program.\n",
    "seq_prog = \"\"\"\n",
    "      move    0,R1         #Average iterator.\n",
    "\n",
    "avg:  move    0,R0         #Bin iterator.\n",
    "      nop\n",
    "\n",
    "loop: acquire 3,R0,1200    #Acquire bins and store them in \"avg\" acquisition.\n",
    "      add     R0,1,R0      #Increment bin iterator\n",
    "      nop                  #Wait a cycle for R0 to be available.\n",
    "      jlt     R0,10,@loop  #Run until number of avg iterations is done.\n",
    "      add     R1,1,R1      #Increment avg iterator\n",
    "      nop                  #Wait a cycle for R1 to be available.\n",
    "      jlt     R1,1000,@avg #Run until number of average iterations is done.\n",
    "\n",
    "      stop                 #Stop.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a0211307",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:23.612999Z",
     "iopub.status.busy": "2024-03-28T14:30:23.611745Z",
     "iopub.status.idle": "2024-03-28T14:30:23.638270Z",
     "shell.execute_reply": "2024-03-28T14:30:23.638270Z"
    }
   },
   "outputs": [],
   "source": [
    "# Add sequence program, waveforms, weights and acquisitions to single dictionary and write to JSON file.\n",
    "sequence = {\n",
    "    \"waveforms\": waveforms_weights,\n",
    "    \"weights\": waveforms_weights,\n",
    "    \"acquisitions\": acquisitions,\n",
    "    \"program\": seq_prog,\n",
    "}\n",
    "with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "    json.dump(sequence, file, indent=4)\n",
    "    file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c3486893",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:23.642276Z",
     "iopub.status.busy": "2024-03-28T14:30:23.642276Z",
     "iopub.status.idle": "2024-03-28T14:30:23.811422Z",
     "shell.execute_reply": "2024-03-28T14:30:23.810104Z"
    }
   },
   "outputs": [],
   "source": [
    "# Upload sequence.\n",
    "readout_module.sequencer0.sequence(\"sequence.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fac120d7",
   "metadata": {},
   "source": [
    "Let's start the sequence and retrieve the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "554ed5d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:23.816068Z",
     "iopub.status.busy": "2024-03-28T14:30:23.816068Z",
     "iopub.status.idle": "2024-03-28T14:30:23.871999Z",
     "shell.execute_reply": "2024-03-28T14:30:23.871156Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: STOPPED, Flags: ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_BINNING_DONE\n"
     ]
    }
   ],
   "source": [
    "# Arm and start sequencer.\n",
    "readout_module.arm_sequencer(0)\n",
    "readout_module.start_sequencer()\n",
    "\n",
    "# Print status of sequencer.\n",
    "print(readout_module.get_sequencer_status(0, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9330140f",
   "metadata": {},
   "source": [
    "Note that the average count of each bin is now set to a 1000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0e55161d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:23.877002Z",
     "iopub.status.busy": "2024-03-28T14:30:23.875996Z",
     "iopub.status.idle": "2024-03-28T14:30:24.551950Z",
     "shell.execute_reply": "2024-03-28T14:30:24.550362Z"
    }
   },
   "outputs": [],
   "source": [
    "# Wait for the sequencer to stop with a timeout period of one minute.\n",
    "readout_module.get_acquisition_status(0, 1)\n",
    "\n",
    "# Get acquisition list from instrument.\n",
    "avg_acq = readout_module.get_acquisitions(0)[\"avg\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7eebd230",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:24.557612Z",
     "iopub.status.busy": "2024-03-28T14:30:24.556611Z",
     "iopub.status.idle": "2024-03-28T14:30:24.583852Z",
     "shell.execute_reply": "2024-03-28T14:30:24.582844Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'integration': {'path0': [0.2706078280410357,\n",
       "   0.2706029037616023,\n",
       "   0.27060217098192474,\n",
       "   0.27060254518808013,\n",
       "   0.2706001856375183,\n",
       "   0.2706040092818759,\n",
       "   0.2706049306301905,\n",
       "   0.270606143136297,\n",
       "   0.2706071993160723,\n",
       "   0.2706015031753786],\n",
       "  'path1': [0.262927550073278,\n",
       "   0.2629266038104543,\n",
       "   0.26292529213483146,\n",
       "   0.26292771421592576,\n",
       "   0.2629262388861749,\n",
       "   0.2629293878847093,\n",
       "   0.2629244489496825,\n",
       "   0.26292817782120176,\n",
       "   0.26293092574499266,\n",
       "   0.2629261592574499]},\n",
       " 'threshold': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n",
       " 'avg_cnt': [1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000, 1000]}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_len = readout_module.sequencer0.integration_length_acq()\n",
    "bins = avg_acq[\"acquisition\"][\"bins\"]\n",
    "bins[\"integration\"][\"path0\"] = [(val / int_len) for val in bins[\"integration\"][\"path0\"]]\n",
    "bins[\"integration\"][\"path1\"] = [(val / int_len) for val in bins[\"integration\"][\"path1\"]]\n",
    "bins"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08b51089",
   "metadata": {},
   "source": [
    "Characterizing Input Offset\n",
    "============================="
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56529567",
   "metadata": {},
   "source": [
    "The Qblox QRM and QRM-RF use analog-to-digital converters (ADCs) to digitize the incoming analog signals. Due to thermal, manufacturing effects or other factors, the digitized signal is not centered around 0V but has a small offset, which we will hereby refer to as input (ADC) offset. This input offset can get demodulated and integrated by the hardware along with the signal. The integrated offset can then show up as oscillations in the result, e.g. during a frequency sweep. In this section we show how to measure and calibrate away this offset to prevent such effects."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "670cd29c",
   "metadata": {},
   "source": [
    "Measuring the Offset\n",
    "--------------------\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cc54535",
   "metadata": {},
   "source": [
    "We will use a simple scope acquisition to determine the mean value of this offset. Before proceeding, please **make sure that there is no DC signal going into** to your QRM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "25b92b62",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:24.589900Z",
     "iopub.status.busy": "2024-03-28T14:30:24.588585Z",
     "iopub.status.idle": "2024-03-28T14:30:27.999721Z",
     "shell.execute_reply": "2024-03-28T14:30:27.998669Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cluster.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8340457f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:28.003818Z",
     "iopub.status.busy": "2024-03-28T14:30:28.003818Z",
     "iopub.status.idle": "2024-03-28T14:30:28.015972Z",
     "shell.execute_reply": "2024-03-28T14:30:28.014636Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def acquire_scope_and_calc_offsets() -> tuple[float, float]:\n",
    "    seq_prog = \"\"\"\n",
    "    acquire 4,0,16380 #Acquire waveforms and wait remaining duration of scope acquisition.\n",
    "    stop              #Stop.\n",
    "    \"\"\"\n",
    "\n",
    "    # Add sequence program, waveforms, weights and acquisitions to single dictionary and write to JSON file.\n",
    "    sequence = {\n",
    "        \"waveforms\": {},\n",
    "        \"weights\": {},\n",
    "        \"acquisitions\": acquisitions,\n",
    "        \"program\": seq_prog,\n",
    "    }\n",
    "    with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "        json.dump(sequence, file, indent=4)\n",
    "        file.close()\n",
    "\n",
    "    # Upload sequence.\n",
    "    readout_module.sequencer0.sequence(\"sequence.json\")\n",
    "\n",
    "    # Arm and start sequencer.\n",
    "    readout_module.arm_sequencer(0)\n",
    "    readout_module.start_sequencer()\n",
    "\n",
    "    # Wait for the acquisition to stop\n",
    "    readout_module.get_acquisition_status(0, 1)\n",
    "\n",
    "    # Retrieve results\n",
    "    readout_module.store_scope_acquisition(0, \"single\")\n",
    "    single_acq = readout_module.get_acquisitions(0)\n",
    "    I_data = np.array(single_acq[\"single\"][\"acquisition\"][\"scope\"][\"path0\"][\"data\"])\n",
    "    Q_data = np.array(single_acq[\"single\"][\"acquisition\"][\"scope\"][\"path1\"][\"data\"])\n",
    "\n",
    "    # Plot results\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(15, 15 / 2 / 1.61))\n",
    "    ax.plot(I_data, label=\"I\")\n",
    "    ax.plot(Q_data, label=\"Q\")\n",
    "    ax.set_xlabel(\"Time (ns)\", fontsize=20)\n",
    "    ax.set_ylabel(\"Relative amplitude\", fontsize=20)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    # Print mean offset values\n",
    "    I_offset, Q_offset = np.mean(I_data), np.mean(Q_data)\n",
    "    print(f\"I Offset : {I_offset*1e3:.3f} mV \\nQ Offset : {Q_offset*1e3:.3f} mV\")\n",
    "\n",
    "    return I_offset, Q_offset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "93a5b7ad",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:28.021219Z",
     "iopub.status.busy": "2024-03-28T14:30:28.020210Z",
     "iopub.status.idle": "2024-03-28T14:30:28.060595Z",
     "shell.execute_reply": "2024-03-28T14:30:28.059480Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "readout_module.scope_acq_sequencer_select(0)\n",
    "readout_module.scope_acq_trigger_mode_path0(\"sequencer\")\n",
    "readout_module.scope_acq_trigger_mode_path1(\"sequencer\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f81bc8cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:28.064790Z",
     "iopub.status.busy": "2024-03-28T14:30:28.063781Z",
     "iopub.status.idle": "2024-03-28T14:30:29.342833Z",
     "shell.execute_reply": "2024-03-28T14:30:29.341833Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x465.839 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I Offset : 0.325 mV \n",
      "Q Offset : -0.507 mV\n"
     ]
    }
   ],
   "source": [
    "I_offset, Q_offset = acquire_scope_and_calc_offsets()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb685549",
   "metadata": {},
   "source": [
    "Correcting the Offsets\n",
    "-------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e534a09c",
   "metadata": {},
   "source": [
    "One can correct for these offsets by changing the reference Voltage of the ADC. This can be done using the parameters <code>[QRM.in0_offset](https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/api_reference/module.html#QRM.in0_offset)</code> and <code>[QRM.in1_offset](https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/api_reference/module.html#QRM.in1_offset)</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b61e539d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:29.349905Z",
     "iopub.status.busy": "2024-03-28T14:30:29.348399Z",
     "iopub.status.idle": "2024-03-28T14:30:29.405577Z",
     "shell.execute_reply": "2024-03-28T14:30:29.403556Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "readout_module.in0_offset(-I_offset)  # Negative sign to compensate for the offset\n",
    "readout_module.in1_offset(-Q_offset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ae206d6",
   "metadata": {},
   "source": [
    "Repeating the offset measurement as before, we get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c2078cb5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:29.413113Z",
     "iopub.status.busy": "2024-03-28T14:30:29.411114Z",
     "iopub.status.idle": "2024-03-28T14:30:30.592237Z",
     "shell.execute_reply": "2024-03-28T14:30:30.591239Z"
    },
    "lines_to_next_cell": 2,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x465.839 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I Offset : -1.619 mV \n",
      "Q Offset : 10.095 mV\n"
     ]
    }
   ],
   "source": [
    "I_offset, Q_offset = acquire_scope_and_calc_offsets()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55813f61",
   "metadata": {},
   "source": [
    "Advanced ADC Offset Calibration : Curve Fitting Method\n",
    "----------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c718840d",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "As you may have noticed in the previous section, manually compensating for the offset does not entirely eliminate it, leaving some residual offset. This is because of the non-linear effects of these input offsets. To circumvent this, one can curve-fit the dependence of the set offset value on the actual offset value. In the following section, we do this by fitting 10 setpoints against the 10 measured offset values from the binned acquisition with a function. We then find the roots/zeros of this function to set the actual offset to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a55f2c1d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:30.596253Z",
     "iopub.status.busy": "2024-03-28T14:30:30.596253Z",
     "iopub.status.idle": "2024-03-28T14:30:30.608115Z",
     "shell.execute_reply": "2024-03-28T14:30:30.606291Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define helper functions\n",
    "def get_real_root(coeffs: NDArray) -> NDArray:\n",
    "    for root in np.roots(coeffs):\n",
    "        if root.imag == 0:\n",
    "            output = root\n",
    "    return np.real(output)\n",
    "\n",
    "\n",
    "def get_curve(x: NDArray, coeffs: NDArray) -> NDArray:\n",
    "    y = 0\n",
    "    for i, coeff in enumerate(coeffs):\n",
    "        y += coeff * x ** (len(coeffs) - (i + 1))\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "864450aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:30.612811Z",
     "iopub.status.busy": "2024-03-28T14:30:30.611799Z",
     "iopub.status.idle": "2024-03-28T14:30:30.624184Z",
     "shell.execute_reply": "2024-03-28T14:30:30.622083Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "seq_prog = \"\"\"\n",
    "      move    0,R0        #Loop iterator.\n",
    "      nop\n",
    "\n",
    "loop: acquire 0,R0,16000   #Acquire bins and store them in \"non_weighed\" acquisition.\n",
    "      add     R0,1,R0     #Increment iterator\n",
    "      nop                 #Wait a cycle for R0 to be available.\n",
    "      jlt     R0,100,@loop #Run until number of iterations is done.\n",
    "\n",
    "      stop                #Stop.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "9ee3c650",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:30.628303Z",
     "iopub.status.busy": "2024-03-28T14:30:30.628303Z",
     "iopub.status.idle": "2024-03-28T14:30:30.638793Z",
     "shell.execute_reply": "2024-03-28T14:30:30.638282Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Add sequence program, waveforms, weights and acquisitions to single dictionary and write to JSON file.\n",
    "sequence = {\n",
    "    \"waveforms\": {},\n",
    "    \"weights\": {},\n",
    "    \"acquisitions\": acquisitions,\n",
    "    \"program\": seq_prog,\n",
    "}\n",
    "with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "    json.dump(sequence, file, indent=4)\n",
    "    file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "023bba34",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:30.642353Z",
     "iopub.status.busy": "2024-03-28T14:30:30.642353Z",
     "iopub.status.idle": "2024-03-28T14:30:30.732888Z",
     "shell.execute_reply": "2024-03-28T14:30:30.730458Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Upload sequence.\n",
    "readout_module.sequencer0.sequence(\"sequence.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4396d5ac",
   "metadata": {},
   "source": [
    "Let's start the sequence and retrieve the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "65f0c225",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:30.737519Z",
     "iopub.status.busy": "2024-03-28T14:30:30.737519Z",
     "iopub.status.idle": "2024-03-28T14:30:39.112151Z",
     "shell.execute_reply": "2024-03-28T14:30:39.111146Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Start out by setting the original offset values obtained from inspecting the scope\n",
    "readout_module.in0_offset(-np.sign(I_offset) * I_offset)\n",
    "readout_module.in1_offset(-np.sign(Q_offset) * Q_offset)\n",
    "\n",
    "# Set the domain (X) around these original offset values\n",
    "I_offset_setpoints = np.linspace(\n",
    "    -10e-3 + readout_module.in0_offset(), +10e-3 + readout_module.in0_offset(), 12\n",
    ")\n",
    "Q_offset_setpoints = np.linspace(\n",
    "    -10e-3 + readout_module.in1_offset(), +10e-3 + readout_module.in1_offset(), 12\n",
    ")\n",
    "\n",
    "int_len = readout_module.sequencer0.integration_length_acq()\n",
    "I_offset_sum, Q_offset_sum = [], []\n",
    "\n",
    "for I_offset_i, Q_offset_i in zip(I_offset_setpoints, Q_offset_setpoints):\n",
    "    readout_module.delete_acquisition_data(0, all=True)\n",
    "    readout_module.in0_offset(I_offset_i)\n",
    "    readout_module.in1_offset(Q_offset_i)\n",
    "    readout_module.arm_sequencer(0)\n",
    "    readout_module.start_sequencer()\n",
    "    readout_module.get_acquisition_status(0, 1)\n",
    "    non_weighed_acq = readout_module.get_acquisitions(0)[\"non_weighed\"]\n",
    "    I_offset_sum += [\n",
    "        np.mean(non_weighed_acq[\"acquisition\"][\"bins\"][\"integration\"][\"path0\"]) * 1e3 / int_len\n",
    "    ]\n",
    "    Q_offset_sum += [\n",
    "        np.mean(non_weighed_acq[\"acquisition\"][\"bins\"][\"integration\"][\"path1\"]) * 1e3 / int_len\n",
    "    ]\n",
    "\n",
    "\n",
    "output = {\n",
    "    \"offsets_I\": I_offset_setpoints,\n",
    "    \"offsets_Q\": Q_offset_setpoints,\n",
    "    \"I_m\": I_offset_sum,\n",
    "    \"Q_m\": Q_offset_sum,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ac4e9a10",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:39.117422Z",
     "iopub.status.busy": "2024-03-28T14:30:39.116437Z",
     "iopub.status.idle": "2024-03-28T14:30:39.377069Z",
     "shell.execute_reply": "2024-03-28T14:30:39.375543Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Offset setpoints corresponding to observed zero offset: \n",
      "I offset: 0.55775333739167\n",
      "Q offset: -7.535537474603615\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "## Fit I offset and find its root\n",
    "coeffs = np.polyfit(np.array(output[\"offsets_I\"]) * 1e3, np.array(output[\"I_m\"]), 3)\n",
    "new_I_offset = get_real_root(coeffs)\n",
    "\n",
    "plt.plot(\n",
    "    output[\"offsets_I\"] * 1e3,\n",
    "    get_curve(np.array(output[\"offsets_I\"]) * 1e3, coeffs),\n",
    "    c=\"b\",\n",
    "    label=\"I\",\n",
    ")\n",
    "plt.scatter(output[\"offsets_I\"] * 1e3, output[\"I_m\"], c=\"b\")\n",
    "\n",
    "# Fit Q offset and find its root\n",
    "coeffs = np.polyfit(np.array(output[\"offsets_Q\"]) * 1e3, np.array(output[\"Q_m\"]), 3)\n",
    "new_Q_offset = get_real_root(coeffs)\n",
    "\n",
    "plt.plot(\n",
    "    output[\"offsets_Q\"] * 1e3,\n",
    "    get_curve(np.array(output[\"offsets_Q\"]) * 1e3, coeffs),\n",
    "    c=\"r\",\n",
    "    label=\"Q\",\n",
    ")\n",
    "plt.scatter(output[\"offsets_Q\"] * 1e3, output[\"Q_m\"], c=\"r\")\n",
    "\n",
    "# Plot zeros on the plot\n",
    "plt.axvline(x=new_I_offset)\n",
    "plt.axvline(x=new_Q_offset)\n",
    "plt.axhline(y=0)\n",
    "\n",
    "plt.xlabel(\"Set Offset (mV)\")\n",
    "plt.ylabel(\"Observed Offset (mV)\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "print(\n",
    "    f\"Offset setpoints corresponding to observed zero offset: \\nI offset: {new_I_offset}\\nQ offset: {new_Q_offset}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5a81b37",
   "metadata": {},
   "source": [
    "Applying The Offset Corrections\n",
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4aa29b2",
   "metadata": {},
   "source": [
    "Using the zeros obtained from the curve fitting, we attempt to correct for the input offsets again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7e82f701",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:39.382137Z",
     "iopub.status.busy": "2024-03-28T14:30:39.382137Z",
     "iopub.status.idle": "2024-03-28T14:30:39.407324Z",
     "shell.execute_reply": "2024-03-28T14:30:39.406249Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "readout_module.in0_offset(new_I_offset * 1e-3)  # Multiplying by 1e-3 to convert to mV\n",
    "readout_module.in1_offset(new_Q_offset * 1e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "87b0e541",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:39.414032Z",
     "iopub.status.busy": "2024-03-28T14:30:39.413032Z",
     "iopub.status.idle": "2024-03-28T14:30:40.596997Z",
     "shell.execute_reply": "2024-03-28T14:30:40.595350Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x465.839 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I Offset : 0.425 mV \n",
      "Q Offset : -0.039 mV\n"
     ]
    }
   ],
   "source": [
    "I_offset_calib, Q_offset_calib = acquire_scope_and_calc_offsets()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef64ffdb",
   "metadata": {},
   "source": [
    "As you can see, you have calibrated away the offsets to sub-millivolt range, which is at the limit of the ADC resolution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87b8cc35",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "We advise you to check/calibrate these offset values every few days if they are vital for your experiments, especially when you are dealing with low input signals. </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2b9728e",
   "metadata": {
    "tags": []
   },
   "source": [
    "Stop\n",
    "----\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": 51,
   "id": "a34e56e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-28T14:30:40.602022Z",
     "iopub.status.busy": "2024-03-28T14:30:40.602022Z",
     "iopub.status.idle": "2024-03-28T14:30:50.359941Z",
     "shell.execute_reply": "2024-03-28T14:30:50.357933Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: STOPPED, Flags: FORCED_STOP, ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_BINNING_DONE\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                    :\t0.00055775 (V)\n",
      "in1_gain                      :\t-6 (dB)\n",
      "in1_offset                    :\t-0.0075355 (V)\n",
      "marker0_inv_en                :\tFalse \n",
      "marker1_inv_en                :\tFalse \n",
      "marker2_inv_en                :\tFalse \n",
      "marker3_inv_en                :\tFalse \n",
      "out0_offset                   :\t0 (V)\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                     :\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_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_sequencer1:\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                     :\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_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                    :\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                     :\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_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                    :\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                     :\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_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                    :\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                     :\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_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                    :\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                     :\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_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 sequencer.\n",
    "readout_module.stop_sequencer()\n",
    "\n",
    "# Print status of sequencer.\n",
    "print(readout_module.get_sequencer_status(0))\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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