{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "81d4e9b5",
   "metadata": {},
   "source": [
    "Characterizing Input Offset\n",
    "==========================="
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5c3a65c",
   "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": "8fed4acf",
   "metadata": {},
   "source": [
    "Measuring the Offset\n",
    "--------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1837372",
   "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": "markdown",
   "id": "ab95a1f6",
   "metadata": {
    "tags": []
   },
   "source": [
    "Setup\n",
    "-----\n",
    "\n",
    "First, we are going to import the required packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "73796483",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:01.672791Z",
     "iopub.status.busy": "2025-05-07T16:49:01.672368Z",
     "iopub.status.idle": "2025-05-07T16:49:02.284743Z",
     "shell.execute_reply": "2025-05-07T16:49:02.284183Z"
    },
    "tags": [
     "imports"
    ]
   },
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "import json\n",
    "import warnings\n",
    "from typing import TYPE_CHECKING, Callable\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from qcodes.instrument import find_or_create_instrument\n",
    "\n",
    "from qblox_instruments import Cluster, ClusterType\n",
    "\n",
    "if TYPE_CHECKING:\n",
    "    from qblox_instruments.qcodes_drivers.module import Module"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01244196",
   "metadata": {},
   "source": [
    "### Scan For Clusters\n",
    "\n",
    "We scan for the available devices connected via ethernet using the Plug & Play functionality of the Qblox Instruments package (see [Plug & Play](https://docs.qblox.com/en/main/api_reference/tools.html#api-pnp) for more info)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "618bc865",
   "metadata": {},
   "source": [
    "`!qblox-pnp list`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "db7df7d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:02.288001Z",
     "iopub.status.busy": "2025-05-07T16:49:02.287734Z",
     "iopub.status.idle": "2025-05-07T16:49:02.290601Z",
     "shell.execute_reply": "2025-05-07T16:49:02.290232Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster_ip = \"10.10.200.42\"\n",
    "cluster_name = \"cluster0\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dc56cdf",
   "metadata": {},
   "source": [
    "### Connect to Cluster\n",
    "\n",
    "We now make a connection with the Cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "89bfe3a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:02.292867Z",
     "iopub.status.busy": "2025-05-07T16:49:02.292697Z",
     "iopub.status.idle": "2025-05-07T16:49:03.191338Z",
     "shell.execute_reply": "2025-05-07T16:49:03.189782Z"
    },
    "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",
    "            10: ClusterType.CLUSTER_QTM,\n",
    "        }\n",
    "        if cluster_ip is None\n",
    "        else None\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "683b1c4a",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "#### Get connected modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8b45d5bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:03.195474Z",
     "iopub.status.busy": "2025-05-07T16:49:03.195254Z",
     "iopub.status.idle": "2025-05-07T16:49:03.200608Z",
     "shell.execute_reply": "2025-05-07T16:49:03.199634Z"
    }
   },
   "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": "86e4d5cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:03.204039Z",
     "iopub.status.busy": "2025-05-07T16:49:03.203700Z",
     "iopub.status.idle": "2025-05-07T16:49:03.222592Z",
     "shell.execute_reply": "2025-05-07T16:49:03.221633Z"
    },
    "tags": [
     "module_select"
    ]
   },
   "outputs": [],
   "source": [
    "# QRM-RF modules\n",
    "modules = get_connected_modules(cluster, lambda mod: mod.is_qrm_type and mod.is_rf_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d9676c67",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:03.226592Z",
     "iopub.status.busy": "2025-05-07T16:49:03.225840Z",
     "iopub.status.idle": "2025-05-07T16:49:03.229729Z",
     "shell.execute_reply": "2025-05-07T16:49:03.228789Z"
    }
   },
   "outputs": [],
   "source": [
    "# This uses the module of the correct type with the lowest slot index\n",
    "module = list(modules.values())[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7db3a7a",
   "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": "d26f6449",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:03.233230Z",
     "iopub.status.busy": "2025-05-07T16:49:03.232698Z",
     "iopub.status.idle": "2025-05-07T16:49:05.781187Z",
     "shell.execute_reply": "2025-05-07T16:49:05.779429Z"
    }
   },
   "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": "d1e9712c",
   "metadata": {},
   "source": [
    "Specify acquisitions\n",
    "--------------------\n",
    "\n",
    "We need to specify the acquisitions so that the instrument can allocate the required memory for its acquisition list. In this case we will create 5 acquisition specifications that each create one or multiple bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9d7e1135",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:05.785479Z",
     "iopub.status.busy": "2025-05-07T16:49:05.785042Z",
     "iopub.status.idle": "2025-05-07T16:49:05.792600Z",
     "shell.execute_reply": "2025-05-07T16:49:05.791285Z"
    },
    "lines_to_next_cell": 2,
    "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": "code",
   "execution_count": 9,
   "id": "3d9be81c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:05.796483Z",
     "iopub.status.busy": "2025-05-07T16:49:05.796073Z",
     "iopub.status.idle": "2025-05-07T16:49:05.844339Z",
     "shell.execute_reply": "2025-05-07T16:49:05.842894Z"
    },
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "# Create a helper function that creates an acquisition program, runs the sequencer and calculates the ADC offsets based on acquired results.\n",
    "\n",
    "\n",
    "def acquire_scope_and_calc_offsets() -> tuple[float, float]:\n",
    "    seq_prog = \"\"\"\n",
    "        move    1000, R0        #Loop iterator.\n",
    "\n",
    "    loop: acquire 0,1,20000   #Acquire bins and store them in \"non_weighed\" acquisition.\n",
    "        loop     R0, @loop #Run until number of iterations is done.\n",
    "\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",
    "    module.sequencer0.sequence(\"sequence.json\")\n",
    "\n",
    "    # Arm and start sequencer.\n",
    "    module.arm_sequencer(0)\n",
    "    module.start_sequencer()\n",
    "\n",
    "    # Wait for the acquisition to stop\n",
    "    module.get_acquisition_status(0)\n",
    "\n",
    "    # Retrieve results\n",
    "    module.store_scope_acquisition(0, \"single\")\n",
    "    single_acq = 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)\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",
    "    if np.isnan(I_offset):\n",
    "        warnings.warn(\"Determining offset failed, setting to 0.\")\n",
    "        I_offset = 0\n",
    "    if np.isnan(Q_offset):\n",
    "        Q_offset = 0\n",
    "        warnings.warn(\"Determining offset failed, setting to 0.\")\n",
    "\n",
    "    return I_offset, Q_offset\n",
    "\n",
    "\n",
    "# Map sequencer to specific outputs (but first disable all sequencer connections)\n",
    "module.disconnect_outputs()\n",
    "module.disconnect_inputs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "34b8bdd4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:05.848422Z",
     "iopub.status.busy": "2025-05-07T16:49:05.848016Z",
     "iopub.status.idle": "2025-05-07T16:49:05.863912Z",
     "shell.execute_reply": "2025-05-07T16:49:05.862638Z"
    },
    "tags": [
     "connect"
    ]
   },
   "outputs": [],
   "source": [
    "module.sequencer0.connect_sequencer(\"io0\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f238a67d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:05.868582Z",
     "iopub.status.busy": "2025-05-07T16:49:05.867555Z",
     "iopub.status.idle": "2025-05-07T16:49:05.873101Z",
     "shell.execute_reply": "2025-05-07T16:49:05.871823Z"
    },
    "tags": [
     "adc_offset"
    ]
   },
   "outputs": [],
   "source": [
    "adc_offset_path0 = module.in0_offset_path0\n",
    "adc_offset_path1 = module.in0_offset_path1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e01d99bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:05.876954Z",
     "iopub.status.busy": "2025-05-07T16:49:05.876344Z",
     "iopub.status.idle": "2025-05-07T16:49:05.947793Z",
     "shell.execute_reply": "2025-05-07T16:49:05.946534Z"
    }
   },
   "outputs": [],
   "source": [
    "# Ensure offset is reset to zero\n",
    "adc_offset_path0(0.0)\n",
    "adc_offset_path1(0.0)\n",
    "\n",
    "# Configure scope mode\n",
    "module.scope_acq_sequencer_select(0)\n",
    "module.scope_acq_trigger_mode_path0(\"sequencer\")\n",
    "module.scope_acq_trigger_mode_path1(\"sequencer\")\n",
    "module.scope_acq_avg_mode_en_path0(True)\n",
    "module.scope_acq_avg_mode_en_path1(True)\n",
    "\n",
    "# Configure integration\n",
    "module.sequencer0.integration_length_acq(16000)\n",
    "module.sequencer0.thresholded_acq_rotation(0)\n",
    "module.sequencer0.thresholded_acq_threshold(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "162bb5ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:05.952537Z",
     "iopub.status.busy": "2025-05-07T16:49:05.951318Z",
     "iopub.status.idle": "2025-05-07T16:49:05.975372Z",
     "shell.execute_reply": "2025-05-07T16:49:05.973961Z"
    },
    "tags": [
     "configure"
    ]
   },
   "outputs": [],
   "source": [
    "# Configure the sequencer\n",
    "module.sequencer0.mod_en_awg(True)\n",
    "module.sequencer0.demod_en_acq(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7bcda502",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:05.979485Z",
     "iopub.status.busy": "2025-05-07T16:49:05.978859Z",
     "iopub.status.idle": "2025-05-07T16:49:06.002214Z",
     "shell.execute_reply": "2025-05-07T16:49:06.000790Z"
    },
    "tags": [
     "configure"
    ]
   },
   "outputs": [],
   "source": [
    "# Enable marker switches to toggle the RF switch before output port\n",
    "module.sequencer0.marker_ovr_en(True)\n",
    "module.sequencer0.marker_ovr_value(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "05c43bb8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:06.007066Z",
     "iopub.status.busy": "2025-05-07T16:49:06.005881Z",
     "iopub.status.idle": "2025-05-07T16:49:06.020572Z",
     "shell.execute_reply": "2025-05-07T16:49:06.019291Z"
    },
    "tags": [
     "configure"
    ]
   },
   "outputs": [],
   "source": [
    "# Set the NCO frequency\n",
    "module.sequencer0.nco_freq(50e6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2304c733",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:06.024906Z",
     "iopub.status.busy": "2025-05-07T16:49:06.023833Z",
     "iopub.status.idle": "2025-05-07T16:49:06.044863Z",
     "shell.execute_reply": "2025-05-07T16:49:06.043566Z"
    },
    "tags": [
     "configure"
    ]
   },
   "outputs": [],
   "source": [
    "# Set the LO frequency. If this is commented out, the value is set to the default value\n",
    "module.out0_in0_lo_freq(3e9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9d9ad5ff",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:06.048514Z",
     "iopub.status.busy": "2025-05-07T16:49:06.048096Z",
     "iopub.status.idle": "2025-05-07T16:49:07.203472Z",
     "shell.execute_reply": "2025-05-07T16:49:07.201969Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I Offset : 2.069 mV \n",
      "Q Offset : -1.072 mV\n"
     ]
    }
   ],
   "source": [
    "I_offset, Q_offset = acquire_scope_and_calc_offsets()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5e7efc6",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "Correcting the Offsets\n",
    "----------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47c7ab21",
   "metadata": {
    "tags": [
     "correct_1"
    ]
   },
   "source": [
    "One can correct for these offsets by changing the reference Voltage of the ADC. This can be done using the parameters <code>[QRM_RF.in0_offset_path0](https://docs.qblox.com/en/main/api_reference/module.html#QRM_RF.in0_offset_path0)</code> and <code>[QRM_RF.in0_offset_path1](https://docs.qblox.com/en/main/api_reference/module.html#QRM_RF.in0_offset_path1)</code>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "24fddfcc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:07.206617Z",
     "iopub.status.busy": "2025-05-07T16:49:07.206378Z",
     "iopub.status.idle": "2025-05-07T16:49:07.216811Z",
     "shell.execute_reply": "2025-05-07T16:49:07.215867Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "adc_offset_path0(-I_offset)  # Negative sign to compensate for the offset\n",
    "adc_offset_path1(-Q_offset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd87d5ca",
   "metadata": {},
   "source": [
    "Repeating the offset measurement as before, we get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d2f774c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:07.218803Z",
     "iopub.status.busy": "2025-05-07T16:49:07.218635Z",
     "iopub.status.idle": "2025-05-07T16:49:08.261770Z",
     "shell.execute_reply": "2025-05-07T16:49:08.260513Z"
    },
    "lines_to_next_cell": 2,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I Offset : -0.902 mV \n",
      "Q Offset : 0.576 mV\n"
     ]
    }
   ],
   "source": [
    "I_offset, Q_offset = acquire_scope_and_calc_offsets()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b58416e5",
   "metadata": {},
   "source": [
    "Advanced ADC Offset Calibration : Curve Fitting Method\n",
    "------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5ed4f2e",
   "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": 20,
   "id": "02a04428",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:08.265266Z",
     "iopub.status.busy": "2025-05-07T16:49:08.265076Z",
     "iopub.status.idle": "2025-05-07T16:49:08.270741Z",
     "shell.execute_reply": "2025-05-07T16:49:08.269805Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define helper functions\n",
    "def get_real_root(coeffs: np.array) -> np.array:\n",
    "    if all(np.isnan(coeffs)):\n",
    "        warnings.warn(\"All NaN array. Returning 0.\")\n",
    "        return 0\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: np.array, coeffs: np.array) -> np.array:\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": 21,
   "id": "71c928ef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:08.273838Z",
     "iopub.status.busy": "2025-05-07T16:49:08.273663Z",
     "iopub.status.idle": "2025-05-07T16:49:08.277602Z",
     "shell.execute_reply": "2025-05-07T16:49:08.276651Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define the program that we will run\n",
    "seq_prog = \"\"\"\n",
    "      move    0,R0        #Loop iterator.\n",
    "      nop\n",
    "\n",
    "loop:\n",
    "      move    100, R1     #Averages\n",
    "avg_loop:\n",
    "      acquire 0,R0,20000   #Acquire bins and store them in \"non_weighed\" acquisition.\n",
    "      loop    R1, @avg_loop\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": 22,
   "id": "480348cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:08.280981Z",
     "iopub.status.busy": "2025-05-07T16:49:08.280217Z",
     "iopub.status.idle": "2025-05-07T16:49:08.285412Z",
     "shell.execute_reply": "2025-05-07T16:49:08.284472Z"
    },
    "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": 23,
   "id": "358f0828",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:08.288467Z",
     "iopub.status.busy": "2025-05-07T16:49:08.287852Z",
     "iopub.status.idle": "2025-05-07T16:49:08.342699Z",
     "shell.execute_reply": "2025-05-07T16:49:08.341558Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Upload sequence.\n",
    "module.sequencer0.sequence(\"sequence.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "089d13b7",
   "metadata": {},
   "source": [
    "Let's start the sequence and retrieve the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "be31cc48",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:08.346144Z",
     "iopub.status.busy": "2025-05-07T16:49:08.345880Z",
     "iopub.status.idle": "2025-05-07T16:49:18.588174Z",
     "shell.execute_reply": "2025-05-07T16:49:18.586349Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set the domain (X) around these original offset values\n",
    "\n",
    "I_offset_setpoints = np.linspace(-10e-3 + adc_offset_path0(), +10e-3 + adc_offset_path0(), 12)\n",
    "Q_offset_setpoints = np.linspace(-10e-3 + adc_offset_path1(), +10e-3 + adc_offset_path1(), 12)\n",
    "\n",
    "int_len = 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",
    "    module.delete_acquisition_data(0, all=True)\n",
    "    adc_offset_path0(I_offset_i)\n",
    "    adc_offset_path1(Q_offset_i)\n",
    "    module.arm_sequencer(0)\n",
    "    module.start_sequencer()\n",
    "    module.get_acquisition_status(0, 1)\n",
    "    non_weighed_acq = 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",
    "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": 25,
   "id": "463cb616",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:18.592566Z",
     "iopub.status.busy": "2025-05-07T16:49:18.592312Z",
     "iopub.status.idle": "2025-05-07T16:49:18.744510Z",
     "shell.execute_reply": "2025-05-07T16:49:18.743331Z"
    },
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Offset setpoints corresponding to observed zero offset: \n",
      "I offset: 18.194615380875135\n",
      "Q offset: -23.29959052502039\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 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": "0c6a489c",
   "metadata": {},
   "source": [
    "Applying The Offset Corrections\n",
    "-------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50305ee1",
   "metadata": {},
   "source": [
    "Using the zeros obtained from the curve fitting, we attempt to correct for the input offsets again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0eb9ffd2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:18.748285Z",
     "iopub.status.busy": "2025-05-07T16:49:18.747668Z",
     "iopub.status.idle": "2025-05-07T16:49:19.606837Z",
     "shell.execute_reply": "2025-05-07T16:49:19.605446Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I Offset : 23.371 mV \n",
      "Q Offset : -29.505 mV\n"
     ]
    }
   ],
   "source": [
    "adc_offset_path0(new_I_offset * 1e-3)  # Multiplying by 1e-3 to convert to mV\n",
    "adc_offset_path1(new_Q_offset * 1e-3)\n",
    "\n",
    "I_offset_calib, Q_offset_calib = acquire_scope_and_calc_offsets()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7fc832c",
   "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": "b7976c78",
   "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": "ef15a1ef",
   "metadata": {},
   "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": 27,
   "id": "17238e16",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:49:19.610600Z",
     "iopub.status.busy": "2025-05-07T16:49:19.610412Z",
     "iopub.status.idle": "2025-05-07T16:49:25.489201Z",
     "shell.execute_reply": "2025-05-07T16:49:25.487384Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, State: STOPPED, Info Flags: FORCED_STOP, ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_OVERWRITTEN_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_SCOPE_OVERWRITTEN_PATH_1, ACQ_BINNING_DONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n",
      "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_module8:\n",
      "\tparameter                        value\n",
      "--------------------------------------------------------------------------------\n",
      "connected                         :\tTrue \n",
      "in0_att                           :\t0 (dB)\n",
      "in0_offset_path0                  :\t0.018195 (V)\n",
      "in0_offset_path1                  :\t-0.0233 (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",
      "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",
      "out0_att                          :\t0 (dB)\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_in0_lo_en                    :\tTrue \n",
      "out0_in0_lo_freq                  :\t3000000000 (Hz)\n",
      "out0_in0_lo_freq_cal_type_default :\toff (Hz)\n",
      "out0_latency                      :\t0 (s)\n",
      "out0_offset_path0                 :\t7.625 (mV)\n",
      "out0_offset_path1                 :\t7.625 (mV)\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_module8_sequencer0:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq                      :\tin0 \n",
      "connect_out0                     :\tIQ \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           :\t16000 \n",
      "marker_ovr_en                    :\tTrue \n",
      "marker_ovr_value                 :\t3 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tTrue \n",
      "nco_freq                         :\t5e+07 (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",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module8_sequencer1:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq                      :\toff \n",
      "connect_out0                     :\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",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module8_sequencer2:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq                      :\toff \n",
      "connect_out0                     :\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",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module8_sequencer3:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq                      :\toff \n",
      "connect_out0                     :\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",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module8_sequencer4:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq                      :\toff \n",
      "connect_out0                     :\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",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module8_sequencer5:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq                      :\toff \n",
      "connect_out0                     :\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",
      "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",
    "module.stop_sequencer()\n",
    "\n",
    "# Print status of both sequencers (should now say it is stopped).\n",
    "print(module.get_sequencer_status(0))\n",
    "print(module.get_sequencer_status(1))\n",
    "print()\n",
    "\n",
    "# Print an overview of the instrument parameters.\n",
    "print(\"Snapshot:\")\n",
    "module.print_readable_snapshot(update=True)\n",
    "\n",
    "# Reset the cluster\n",
    "cluster.reset()\n",
    "print(cluster.get_system_status())"
   ]
  }
 ],
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