{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0efa0c7e",
   "metadata": {},
   "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/master/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/master/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.\n",
    "\n",
    "To run this tutorial please make sure you have installed and enabled ipywidgets:\n",
    "```\n",
    "pip install ipywidgets\n",
    "jupyter nbextension enable --py widgetsnbextension\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "614ec7ab",
   "metadata": {
    "tags": []
   },
   "source": [
    "Setup\n",
    "-----\n",
    "\n",
    "First, we are going to import the required packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0bd48a11",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:21.130355Z",
     "iopub.status.busy": "2023-11-08T11:51:21.130355Z",
     "iopub.status.idle": "2023-11-08T11:51:22.418463Z",
     "shell.execute_reply": "2023-11-08T11:51:22.417456Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Import ipython widgets\n",
    "import contextlib\n",
    "import json\n",
    "import math\n",
    "\n",
    "import ipywidgets as widgets\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Set up the environment.\n",
    "import scipy.signal\n",
    "\n",
    "# from ipywidgets import fixed, interact, interact_manual, interactive\n",
    "from IPython.display import display\n",
    "from qcodes import Instrument\n",
    "\n",
    "from qblox_instruments import Cluster, PlugAndPlay"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdd18bf7",
   "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/master/api_reference/tools.html#api-pnp) for more info)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1f811273",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:22.422481Z",
     "iopub.status.busy": "2023-11-08T11:51:22.421474Z",
     "iopub.status.idle": "2023-11-08T11:51:24.312908Z",
     "shell.execute_reply": "2023-11-08T11:51:24.311934Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Devices:\n",
      " - 10.10.200.33: cluster_mm 0.4.0 with name \"PE-Cluster\" and serial number 00015_2321_004\n",
      " - 10.10.200.42: cluster_mm 0.6.1 with name \"QAE_1\" and serial number 00015_2320_004\n",
      " - 10.10.200.50: cluster_mm 0.6.1 with name \"cluster-mm\" and serial number 00015_2219_003\n",
      " - 10.10.200.51: pulsar_qcm 0.11.1 with name \"pulsar-qcm\" and serial number 00012_2039_005\n",
      " - 10.10.200.52: pulsar_qrm 0.11.1 with name \"pulsar-qrm\" and serial number 00013_2120_008\n",
      " - 10.10.200.53: cluster_mm 0.6.1 with name \"cluster-mm\" and serial number 00015_2320_003\n"
     ]
    }
   ],
   "source": [
    "!qblox-pnp list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "771558cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:24.317940Z",
     "iopub.status.busy": "2023-11-08T11:51:24.316940Z",
     "iopub.status.idle": "2023-11-08T11:51:24.327787Z",
     "shell.execute_reply": "2023-11-08T11:51:24.326753Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster_ip = \"10.10.200.42\"\n",
    "cluster_name = \"cluster0\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd473a1a",
   "metadata": {},
   "source": [
    "### Connect to Cluster\n",
    "\n",
    "We now make a connection with the Cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d0e6006d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:24.332773Z",
     "iopub.status.busy": "2023-11-08T11:51:24.331786Z",
     "iopub.status.idle": "2023-11-08T11:51:24.903966Z",
     "shell.execute_reply": "2023-11-08T11:51:24.902966Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "from qblox_instruments import Cluster, ClusterType\n",
    "\n",
    "try:  # Close the chosen QCodes instrument to prevent name clash\n",
    "    Cluster.find_instrument(cluster_name).close()\n",
    "except KeyError:\n",
    "    pass\n",
    "\n",
    "cluster = Cluster(\n",
    "    name=cluster_name,\n",
    "    identifier=cluster_ip,\n",
    "    dummy_cfg={\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",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79ad347c",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "#### Get connected modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e867cf26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:24.908985Z",
     "iopub.status.busy": "2023-11-08T11:51:24.908985Z",
     "iopub.status.idle": "2023-11-08T11:51:24.920010Z",
     "shell.execute_reply": "2023-11-08T11:51:24.917992Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_connected_modules(cluster, filter_fn=None):\n",
    "    def checked_filter_fn(mod):\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": "946dae34",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:24.924559Z",
     "iopub.status.busy": "2023-11-08T11:51:24.923554Z",
     "iopub.status.idle": "2023-11-08T11:51:25.244644Z",
     "shell.execute_reply": "2023-11-08T11:51:25.242601Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{4: <QcmQrm: 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": "65330db8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:25.248121Z",
     "iopub.status.busy": "2023-11-08T11:51:25.248121Z",
     "iopub.status.idle": "2023-11-08T11:51:25.258729Z",
     "shell.execute_reply": "2023-11-08T11:51:25.257724Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module = modules[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "397de41d",
   "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": "9207db7b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:25.264797Z",
     "iopub.status.busy": "2023-11-08T11:51:25.264797Z",
     "iopub.status.idle": "2023-11-08T11:51:28.538229Z",
     "shell.execute_reply": "2023-11-08T11:51:28.537228Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, Flags: NONE, Slot flags: NONE\n"
     ]
    }
   ],
   "source": [
    "cluster.reset()\n",
    "print(cluster.get_system_state())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feff0689",
   "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": "297ed681",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:28.542224Z",
     "iopub.status.busy": "2023-11-08T11:51:28.542224Z",
     "iopub.status.idle": "2023-11-08T11:51:28.554787Z",
     "shell.execute_reply": "2023-11-08T11:51:28.552759Z"
    },
    "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": "01ae14d6",
   "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": "c1d791b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:28.559869Z",
     "iopub.status.busy": "2023-11-08T11:51:28.559351Z",
     "iopub.status.idle": "2023-11-08T11:51:28.569937Z",
     "shell.execute_reply": "2023-11-08T11:51:28.567918Z"
    },
    "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": "02162c24",
   "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": "216a79cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:28.573952Z",
     "iopub.status.busy": "2023-11-08T11:51:28.573952Z",
     "iopub.status.idle": "2023-11-08T11:51:28.585675Z",
     "shell.execute_reply": "2023-11-08T11:51:28.584106Z"
    },
    "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": "1550131c",
   "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": "a7d5a538",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:28.590032Z",
     "iopub.status.busy": "2023-11-08T11:51:28.589035Z",
     "iopub.status.idle": "2023-11-08T11:51:28.616072Z",
     "shell.execute_reply": "2023-11-08T11:51:28.614816Z"
    },
    "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": "21abc1c7",
   "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": "7486d957",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:28.620742Z",
     "iopub.status.busy": "2023-11-08T11:51:28.620237Z",
     "iopub.status.idle": "2023-11-08T11:51:28.783757Z",
     "shell.execute_reply": "2023-11-08T11:51:28.782745Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Upload sequence.\n",
    "readout_module.sequencer0.sequence(\"sequence.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f78041e7",
   "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": "100460c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:28.788621Z",
     "iopub.status.busy": "2023-11-08T11:51:28.787616Z",
     "iopub.status.idle": "2023-11-08T11:51:29.001507Z",
     "shell.execute_reply": "2023-11-08T11:51:29.000423Z"
    },
    "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": "7d397a73",
   "metadata": {},
   "source": [
    "Now let's start the sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2f243625",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:29.005973Z",
     "iopub.status.busy": "2023-11-08T11:51:29.005973Z",
     "iopub.status.idle": "2023-11-08T11:51:29.033111Z",
     "shell.execute_reply": "2023-11-08T11:51:29.031049Z"
    },
    "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"
     ]
    }
   ],
   "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_state(0, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7607041",
   "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": "c4e95fa4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:29.037617Z",
     "iopub.status.busy": "2023-11-08T11:51:29.037617Z",
     "iopub.status.idle": "2023-11-08T11:51:30.002054Z",
     "shell.execute_reply": "2023-11-08T11:51:30.001041Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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k6cf1R/TS9S0dGgDHcwr00i/bdX1spObtSFGXhqGaujpRy/cZPZNWH8hQXL0g9W0ZoR/WHdbUNYmy2iSrzWZv3O04liV31/KV2SGng6eyHv16vbo0DFHPxrU0e1uyZmw0GqrJWfkK9XXXb1scz4SWVGqbRfjpjk7RDo89PnWjJOmH9cbBeNbWY8o93ZjffDhTmw9nakPiCT1xRRP7PCV6vrlQN7WvqymrEsuVUZK9HHO2Jatnk1q6LS5KV7QoDTPzCov16swdCvf31NA+jbXpcGlYN3HRPi3YkaLFp9eZGRuOal9qru7uXE/T1iZpQKcodW0Yan9vJpP06bKDp9/LEVmspWe53p67R8v3HtcrN7TSntQcTVi4V4XFFj2Z0ESzt6Uo5XRgkZ1frDs/WqmuDUP16GWN5FImyJq19Zhmb0tRv5bhmrHhqAZ2raevVh7SoC4x+nLlIR05ffnfvrQcFRZb5Wo26Y3Zu+TmYiwjI7dQ9UN9tD89V50bhGju9hS9emMr+3q07tAJe++B5Kx8pWTla+720u9xY9JJNazlqxE/bpEk9WpSS10bhtofv/vjVTqZV6SZp3uuzdxyTG3qBmhzmc80v8iiXcnZ9v83H85UkLe7hk3bqNxCi1xMJuUVWfRC/xaKCPCs8Ds9U36RRcNPlync39Ne0dl6JFMfLN6nAR2j9fmKg9qQeFLpOQVasS9di5+5TD5lwvCfNpYGLTM2Hqmw4rvuUIYmLt6vIZc3UlSQt/4zfYt+31r6+exPy1XbqEC9v3CvjmWe0vNXtzjrSYVdKaW9NR/5ep26NQrVXfH1lF9k0Zjfd8rfy61cz5jl+47rtokr5OXuolduaCWbTXrttx3KL7Zo0a40/bzpqJY+e5nenrdb7aICdU+XGElGZX3B6X13ZKCXQk7vj1zMJlmsNu1Py9U9H6/WgfRcbTl8UoueuazScpeEVZLKnRX/eOkBe0/Qg8fzNG7ubiVl5NkbiPlFVv3rszVqEu6n565sVuH+pjKLdqXpm9WJ+mZ1on5cf0Q9G9cqdxw506YkY91Lyy7QnZNX2veHkpR0Ik8B3gEqKLaU62k3fcMRNQj10ZA+jbVgZ4o+WnpAZpNJL1zTQk0j/JR4PE93f7xKiRl59qClMj9tPOpQWV1zMEMfLz2ggV3rqXmEv579YbNO5hXqjo7R2p/mGLR1k/TtmiRtOZKpjvWDtWBHil48fQLptd92aMHOVP26+ZiubBmhe7vFqEEtH702c4diowI1qGuMTCbj+31n3m4t23dc9UN99EL/FvaA8X+L9mnu9hT7Nrv1SKamPtBZI37cohN5hborvp5uaGeU/f2Fe2Wz2fTY5Y2VkpWvR6cYwZari7lcz+H8Ioue+najFu4yjle3x0UpNjpQA07v+9+dv0d5hRbFhHhr0+GTGnVtS3m6uWjV/uP6ZNkBPd7HODaWnASSpMemrFf76CBtP5alUF8Pe8+IlKx8HT5RGpDM3Z6sG9pF2v/fnVK6v5GM9eiH9Yf1w/rDOnwiT8OvbKbx8/aooNiq565sqmJraWjn6mLW6JtKw4OtR0v3ZWce4w6k5zqc7Nmdkl0uaFt36ITGzt4lVxeTPbRZc/CElg2/3OGze33WTrm5mOXl5qI/9qarXrC3Rt/cutKTCCVOFVp00wfLtC8tV9+vO6xrWkfq69WHlF9k1fsL98pitWnqmiT1b11bz/dvrl0p2fpo6X6ZZNJ9Perr+3WH9a9u9dWhXpCKLFa99Ms2ta4ToNs7RispI8/eiDWZpLKd2kN8PfTaja3tn8PIn7Yqr9AiF7NJt8dFycfDVZPO6MW2bG+6Q11g6Z40fbv2sG5sF6nPlx+yH3cl6bt1h+3rjsVq08iftmrn6eNIu6hANQ731fajWWpbpq6ZdCJPK/cf18d/HFCxxaqFu9K0OyVHjcN91bdFhDJPFemz5QclSZ3qB6tN3UBtSCo9cXIg3bE3fWU9xbPyizTm950K8/PQ0MsbS5ImLNyrxbvTFB3srcbhvnpjlmPvs53HHNfJktClS4MQHTyeq1p+HvYwasqqRG05clLtooP07Peb7c9JySrQzM3HNHPzMfVvXVu3dKirmVuOKaF5uJbvS1ezCH/dGW98Zp8vN07YPnelccL24a/WK6egWD+uP6K+LcL1fP/mDiciy+7jU7LyFebnobFzdiktu+B03aZ8r++vViYqObNAz13ZVI3LBP/Hcwr0yswd8vd01ecrjHDiuraRDg30wWf0gl26J11FFqtO5BXq5V936O74aMU3CLGvk7kFFk3fYOx3b+kQpWAfd327Nkkr9x/X/13fSsv2puunjUfO2o7YfDjTvk7G1QtyWHdKrNp/XF+tStRN7etoyqpEPdCzgTrGnD0Uf2/+Hi3anaZGtXz1yo2t9NPGo1qwM0W3dojSd+uSdFO7upq2Nknbj2bpyMlTmr7hiG7tUFeXNwvTVa1LT/z/b9FeJWXk6Yd1pceXnceyywVth47nqtebi0r/PkvQVjY8nrMtWc9d2Uyvz9qpeTtSZbHaNPGeDnr6+02auz3F4Xn70nJUN8jbfhJh7cETWl1BL+PfthxTUkae/thr7NuW7zsu19N12Ed6N9SyvenadDhTP286qkBvNx1Iy9WIq5ur7HnlknrKh0v26edNR/Xr5qOy2qSFu9K05NnL5O/pqjdm71K4n4fu6RKjV2eWXl1Vsn8qea+bD5/UdROWSTLCm2KLTUdOntK6xBOatyNFB9Jz1bdFuMN3f++naxRXL0hZZXpsrTmUofWJJzRp8X71alpL7843jhclnp+xVWnZRh2+XXSg1h86oaSMU/pPf+OkxuLdaZq6OlEjr22h2gFe+nz5QXtbetm+dL15S1td0SJcr83coahgLz3Su5H9JPZnyw7ol83HFOHvqdE3t5a/p5tsNpsWlmmLj5u7W9e0qa1pZ/Ri+3atUa9ZfSBDOfnF9n1muL+H/nN1c9UNMsLOh75ap21Hs7Qh8YR+HdJd/cYvUUZuoeZsT9GqAxkK9XHX4wlNtOZghr333pYjmbr1wxX66dFu5U4wp2bl64Wftqp7o1CtPXRCcfWCdHOHuhr92075ebpq2BVNKj1xsmhXqh76ap3yi6yauy1FHw2K09I9pXW/pIw8DbuiSbU7nlzqTLZzXUf2D5SVlaWAgABlZmbK379qZ4j/ytYnntAdH65UYZlxY7zcXLTmvwlaezBD365N0kvXtTJ6M/28zV6BqcyjlzXUH3uNRk+xxaptR8uf4evcIFiP92miWn7uGjt7t/q1ClevJmH6v1+26cDxPPVtEa6YEB97hf9M9UK8tejp3vrP9C32hP9MH9zVXiv3H1eAt7usVpuW7k1XYbFVO45VfMaxQ70gff9QFz317Sb9ePpA3+B0eHGmkkZs4zBf3d25nn7ZdFQebmYdzyk83QPCX78O6a4xs3baD/gLn+6t+qFGhWfW1mP2S9KeTGiixxMaK2b4TElSx5ggBXm767/9Wyg6xFtDvtlQ4aUOXm4uCvR207HMfDWs5aNJA+PU/92lyi/687o673/tas3almxvvNzbNUYmk+Tj7qrU7Hz7weBc/D1dlZV/fuOulNW8tr8GdamnP/ama8nuNPsyb4+LKnegOpeDY/pr1tZkPfTVuirN37VhiNYeOuHQ1byk0eLpZnb4Xq5uHaFxt8Vq0pL9Ong8t9IG/ZmNHkma9UQP7UrOtoeYlenTLEwmk9SraZje+H2nss9yOcFN7eromra17WdEm0X4ycvdRVabFHi6MnEulzcL067kbHsoOLBLPbmYTfaAskSIj7va1A3QU32bKszfQ6/O3KEO9YK06kCGjpw4pYTmYfbKyB970nX3x6skSd7uLmoc7qdgb7ezjmf2YK8GGnFVc1msNr3w01aHoNbf01Wrn0/Qwp2pmr7hiP5zdXOl5xToltNnXHs2qaW4ekEaN3e3wzLfvr2tLmsaptj/mytJ8vNw1WXNwnR5szB9seKgLKc/p2f6NVWrOgG66X/LtL5MkCBJLWr7K/NUkf3zOZsrWoTL18PV3uA4k8kkbRzZV9n5Rer++sIK53n2yqblGoGStPo/ffTfGVuVml2g69pGanC3GL322w6tPnhCKZn59ktZA73d1CDURxab5OPuYg+yEpqHa96OlHLLLSvYx119W4TrtRtbO/SMXJ94Qv9buFf/ubq5GtQqDSomL9mvV3/b4bCMB3s1kMViU2Sgl/akZiu+fohmb0vW8/2bq3aAl1qNmq1TRRVfbhPo7abhVzbTvB0pmrcjVd7uLvrxka6atHi/ftxwRJ5uZjUN99OWI5kqaVf2b11bQ/s01j0fr1JqdoFCfNx1vJLL0+/uHK2vVibKxWzSGze30TerE1VktWnnsSwVFFvl7mJWl4Yh9u3G082sUF8Ph9DogZ4NygUUHq5mdWsUam+QlGgbFaiTeYX2xtSQyxsp61SRftuabG8ESFKEv6fCAzwr3UZaRvrbj8dhfh66unVtebiZ9eHpnpX396ivyUsPVPieS5Qc+87UpUGI8gqLHU5oSNJjlzXS0/2a6vYPV2jV6eEGOtUPPq+hB0wm6bYOpfvy2gGeWjGij07mFer5GVuVkpmvtYdKA5UuDUK04vSl8i1q++to5imdzDMaWa5mk2Y90VO5BcV6e95uLTrLPmXcbW21Yt9xe++NQG83xYT4KDLQ0+jp7euhK99ZWuFwCNfHRqpviwj1alpL//58jVbuL/++L28WphN5hQr2dteIq5s7hHgncgv13xlb5e5qdtgfDL28kd5dUL1L9AO93bRxZF/9sumo/STbgE5R5epQ0cHeGtwtxt4TuF10oMbfHqv/zthqDxFLuLuYHeqOJW7tUFf/vaaF/D1ddflbix16bJXVLMJPs57oKUlasjtNAz9Z7fB4yTGwlp+Hw7ru7mqu9LIuV7NJxafX0e6NQtWlYYi951CJa9tGqlP9YC3ZnabFu9IqfA9D+zS2nxBpWMuou+1Lq/h9lNW2boDu79lA3687XG69MpmMbT8jp9ChN1B1bRx5hTzdXNTshVmSjO8xp8BSrq54f4/6er5/C+1JydYbs3dp0a5UFVmMz2bi3R3k5mLSfZ+vrdJr3tqhrt68ta0k42Rx3/GL7ScSSzQK89XlzcLUINRHv2w+qmV7j5dbzif3xunTZQft69K1bSNVWGzR7G2Ox5VeTWqpsNhq34Y7xgRp7aET9jpR5wbBGtqnscL8PDV+3m5d1aq25u9I0Z7UHIdgfGCXevrP1c3l6eainzYe0bwdqeU+Jw9Xsz64u706RAfr2R826bKmYbqjU7QsVpte/nW7sk4V2dsFkuO+paqfXWSglwZ0ilbn0fPLPe7pZtZlTcP04nUtFe5vnIh84Iu1mlMmGLu6dYSOnsxXn2Zhur9nA/s6HR1cGpSV+PK+Thr86Rr7dtAsws8expTl7+mq+NMnZ8tqVcdfW4+cu3dm89NtnR/WH9az329W/VAf+7bev01tZeQUOnxOlzcL06JdqTrzEPJI74a6unVtXfPeH8ZzW9fWzC3H5GI2KcLf06HeFBsVqFp+HuXKfCazSXqoV0P9b1HFl7RKxvHM3cXsUJdoFOar569uXi4kLsvXw1Ud6gXZj/H929TW+3e215Xjlzh8zn6erqoT6GWfdkfHKI25uY32pmar79tL7J9DnUAvtarjrxvb1Sk3XMnVrSPKnfw5m9oBnvrpsW7y9XBVi5Gz7dOf7ttEY+fsLjd/mJ+HUk/vW7s1CtHe1BylZBXo9Ztb6/aO0dp6JFPj5+3W0/2aavr6I/rwLMMDhPp66IoW4aob5KW521NkkxTs7aarW9fWf6ZvUZHF5lC3KtvGCff30G9De1yUnnQ1oapZEUFbBf5uQZtknNF78EsjUAjydteRk6f0dN8m+n7dYR08nqeHejXU8Kuaqc9bi6pU0ThTqK+H0nMKylWWKmMySb2b1Dprg7pOoFeVGq3VUXaHUyLEx129mtRyOMhWxTt3xGru9hT9url0/KrH+zTW430a64WfturrMgFAj8ah5Sqwnm5m9Wxcy+FAW1afZmEad3usLhu7yGGcgdioQN0ZH+1whvRMnm5mDb+ymV76dbuig731xb86aV9ajj1oqar20YE6dDyv0gZpRYJ93HVf9/p6c/YuNazl47A+vXx9S3VrFKpr3/vD3vOtrNvi6iqhebge+LJq4deFuLxZWLkG7z2d66lJuK/enrenwrEdpPIVlO6NQpXQPMzeO9PZ3F3M8nQznzOojK8frPxiq71nW+0A47KKV2buUP1QH/VuWqtcKFaZB3s1sDfOKxPk7aacgmJ7pf5MzWv7q0m4r0OPsxI3t6+r129urTfn7Drn65RoEu6r3ad7kg2/qpnC/Dw07NtNkozKjo+7q5Kz8vXf/s01cfF+pecUyNVsUp0gL4ezwZWFvu2jA8uFZxUFoRXNdzbuLmY1jTBCn3bRgdpQxeeOuam1jucW6s3Zu9Q2KlAJzcL01umAsGm4n34Z0l0tR80q9/k3r+1vP9lgNklv3x57ztC2bFk3jrpCb8zadc4TLpJRqTyZV6S4mCD9sTfd3ujs0ThUX94Xr/ScAv13+latTzxRbr97NhH+nvZAsCpl/vjeOPVoXEtWq003/G+ZQ0/MkuOIu6vRyyjzVJGaRfjpi391UkZeoZ77frNDeOTr4aqFT/fWoE9Wa/uxrHLrQKivu9JzLs5g5gFebuXGuXF3MevO+Gj9uvlYhWMpfXN/Z21IOlFh6Ppn6VAvyH5pnbNtHHmFxs7Zpa9Wlh5P4+sHa/XBjHLb5plCfd3l6+FqH9fP082s7x/qqkGfrK7yMa1OoJeubRtp74UXGxVY4WXgJdN9Tp/AKD7dK/rM7TPI200vXNNCX69KlM1mU0FxxScrz1Q3yEvDrmiid+fvsb+fukFeDuGuJPVtEa41BzN0Iq/ycXieSGisJxKa6P4v1tobsyUhrZuLSW/e0lZbj2Tqoz/OHsq2qO2vF65poQGTV5Z77N6uMZqyKlGFFqt+eLirpqxKtPdqv7JlhFKy86u8L6xIZICnjlZjLERXs0kvXd9Sz0/fap9mNqlcIOBqNunO+GjN2Zai5Kx83dExqtrjYFbEz9NVkwfG6Y5J5T+rylRUbzyTr4erWtT2r7CX0tkM6BSte7vG6JvVifZ9fS0/D63+Tx+ZTCa98uv2c37/lbktrm6VT8JWpKrtiLL8PV3VvLa/PeyviKvZpC4NQ+yf6dDLG6leiI+e+m7TeZe1otcoLrNSVRTY9WtpDDVztjr8xVRyfH74q3X6fWuyWtcJsAeXg7oYl/w9/PV6xYR468v74hUV7K2s/CK1/7+5Du+tOjzdzHoioYnG/L7TPs1kkt4b0E5t6gTqro9XOlz+XXLCZ1CXevp8xSG1jPTXidzCctv8mdvwgE7RWrEvvdxYriXrVJu6AfpscCcF+7jrX5+tKdcGcDGb5O/pWuH+870B7TTkmw0ymaQ1zyfo7o9W2QO2IG83ZeUXy2K1VSnE7N4oVMVWa4UnZiSjrXpHp2j7iYBGYb566oomemP2Lh1Iz9VlTWtp6Z70an8f79wRq31puXp3/h75eriqU/1grTmYoez8Yvl5uMrbw8Uerpe06yVj/1VQbD3nmHbXtKmtV29srT5vLbY/t0uDEF0fG6n29YLOe6iMv6JLKmh7//339eabbyo5OVlt27bVe++9p06dOlU47+TJk/XFF19o61bjYNmhQwe99tprDvPn5ORo+PDhmjFjho4fP6769etr6NCheuihh6pUnr9j0CYZA3MXFlu1MelkhQ2vRmG+2puaI7NJeuu2tnpymnHw6d20lv7bv7lu+3ClPXwI8XHXG6eve68T5KV6wT7an56jxmF+Wrn/uCYv3W8/mJVtNFXWgHJ3MeubB+IV6O2uiYv2OZwJdDWbNOX+zhr27cZylcqyro+N1HVtI+Xl5qJ7Plkti9Wmvi3CNbRPY83amqwJlQzg/ky/puoYE6zbPlzhML1ukJdczSYdPJ5X4Rn6qGAvebq6aM8ZZ7nrhXgrNaug0t4YVfXUFU00pE9jbUo6qXs/Xa0TeUUymaTvHuyiuJhgDf9hs6auSVLbugHlehlc2zZS7w1op72pOQrwclMtPw/ZbDZd8faSCs/KO9PLN7TS3fHR2nQ4U81r++na9/7Q7pQcubuatXlUX3m6uSjxeJ4Kii265+PVDuvD8Kua6cGeDVR/xG/llvvDw100+NM1Dr3YGof7OnQ5r6jiXB0/P9ZNbeoGKiO3UOk5BXp77m6HSwwl6ZfHumvb0UwN/3GLWtcJ0GeDOyqv0KI+4xarTqCX/tu/uR6bsuGCv//aAZ766t/x8nA1y9vdVSfyCvXMd5sqDHkSmodpwp3t9casXfpkmVE5/mxwR8VGBarjq/MqDMNqB3iqf+vaFVamFz3dWweP52rwZ2vO2Zgt24vmbKKCvdSnWbi+XHlIFqtNV7QI16HjudqdkqOR17RQTKi3Fu9Ks1+iUsLFbNK3D3ZWbFSQPli0t9wZu8ubhenFa1tq+b50+2WoFQn2cbfvvzzdzPrqvngt2ZNe7hLPHo1DlZiRZw/nXM0mvX9Xe3267ECllaKpD3S2X4r5+qyd9kCwbZTRUyTU111JGafUItJfI37com9Wl79cevojXXUyr0hrDmbof4v2KcDLTRarTTkFxXrj5ja6Na6ubpm4QusTT+jTezuqd9MwdX5tvn37qeyM9rm8cXMbPfuDUeGPDPDU8hF9ZLPZtONYtsbN3aV5OxwroqNvam0f6+Nstr3UTyN+3OIwhtqTCU206sBxh8tAz6ZlpL9euaGVbvzf8krnia8frGkPdrH/fzKvUBsST8pqsynIx13togId9nvtowP16b2d7Jdgvj13t334gGf6NdV1bSMVFeytUT9tdVgX37yljaKDvRUV7K2uYxbYp79yQyv7GDHVEe7vof/d1d4+loxknJ1ftCvV/j3WDfLS1Ac6q26QtzJPFWndoQxtTDxp7+nUMtJfM4f2UH6RRZePXaSjmflydzWrlq/HWU9S3dqhbqW9beLqBenBXg31+9ZjDj1xA73ddKrQ4nDZzZki/D11R6cojZ9XfjD7wd1itO7QCYcQtKwnE5ro181HtSc1R/d2jdHc7SlGQFpBj6qv7ovXjI1H9P0Z9YQALzedyCvUF/+K12u/7XAYy1AyeonvePnKcj1JKlMvxNshpP9v/+b6V7f62pWSrRd/3lauUe/n6aqv/x2vcH9PFRZbVVBsVcK4xfbHq7KNvnFzG42YvqVcr8JbOtTV2Fvb6lShRasPZsjXw1XtowN11TtLq7TdB3i5KcTHXUczT2nOE70UHeKtvMJijfppm8O6cHfnaL1yQ2vZbLZKl+3mYpKvR8WN0XbRgRp5TQu1qhOgx6asL9eLSTKO5esPnSzXyzXEx11XtY7QVysTdUuHug7fb1m1Azz15X3xeuDLtQ6XbLu5mMod60rqnCXh4o5jWZq7PcWhV/MTCY3Vuo4xbm3jMD9Fh3grO79IR0/mq0m4b4X1kRJPXdFEP286Wq4eWJbZJH3173h1bRhqv7LhQlzeLEw3t69b6RUhZwr2cZer2aS8QosiAz11ID1XPz/WXc1r+yu/yKKNSSd1z8erVGSxKSrYSwM7x+jN2btUaLHKz9NV2VW8GqEkFKkud1ez6of4aFdKth69rKEe79OkXDuiIi9d11I2m01vzdl91h79LSP91bCWb5VulnNf9/r6+HSdqKJ9T4va/uX2K5XxcnPR1Ac66/r3l1U6z+1xUdp6NNOhDlVZr2JJmnJ/vEb/ttMejJUM8REbFaiHezfUN6sTz9p7193VrBmPdFOLSH8VFFu0OzlHTSP8tOZghqw2m+Lrh8jd1ay9qTmqG+QlT7fSS96veW9plXrBlRXq66GYEG+Hnsgl2kYF6qdHu0mScgqKtS81R7d9uMJ+jGlR21+/Pd5Du1OyFRPio2e+32Q/cXtmp4n372yvphG+ahTmp7zCYu1NzdHMLcf04eL96t+mtsbd1lZ7Tl9+XnIZ/56UbF3z3h+qHeCpzwYbnREah/kpwMtNi3an6u25u3UoI09Nwvy0q8wwBq3q+OvXIT20ZHeaBn+2xqir3xevj/84oC/LjP/m7mLWr0O766VftmnZ3uMO7d//u76lPFzNeu4Hxzpr5wbBeuPmtvJ0Mys9p1BXv2uMV/pMv6Z69LJG+mzZgbOe1Hd3MevfPerbe/m9f2d7pWbna9yc3bLYbFr67GXan55rH1evIv6ervrivng1DvPV0ZOnlJiRp9ioQBUUWzV/R4rGztmt7PwiPXdlM/l6utpPXtQO8NTCp3vL081F365J0rM/bFbLSH99/q9OCv2b9GIrq6pZUY2P0TZt2jQNGzZMEydOVHx8vMaPH69+/fpp165dCgsrP4bYokWLNGDAAHXt2lWenp56/fXX1bdvX23btk116hjjkQwbNkwLFizQV199pZiYGM2ZM0ePPPKIIiMjdd111/3Zb/Evo3aAlyQpKshbHy094ND1WpK9IdI0wl83tqurBqG+OnmqSO2jA+Xn6aZ2UYH2a9Mbh/uWG/y/ZID9nk1qqXujUG1IOqGCIqviYoK1Ly1HqdkF6lAvSD9tPGLfMP08XfX74z1ktUrRIcY15y/f0Eod6wfbz/YEerurU/1gRQaUnr1969a2evaHzaoT6KWs/CLZbNJ/+xuD+UvGGaPftybr3q4xalUnQM1r+6tDTJAGf1q+q/D1sZEK8fEodxYtrl6QRt/URkkn8tQk3E8v/bJNny47qP9c3UyTlx5wOPsiGWdMTuSWXv7j7+mqMH/P8w62YqMDJRkHo4VP99aGpJOqHeCpZhHGBv3qja11T5d6igr2VpsX59ifN/+pXqp9eoysspeomEwmfftgFyVn5uua95bKajO6c1/bNtI++HRV/Lt7fSWdyCtXgb6mTW09elkj+7gaJeP7jbi6uf712Rpd3jTMftAu+a5/GdJdBcUWDZtm3MHxypYRFV6//3ifxupQL1gT7+mguz9apTZ1AzX6ptY6VWTRxMX7VGSx6buHusjNxaxXZ+7Qwl2pio0K1LpDJ+xnhePrB1d4trNJuK+SM/MV5ONuX4eDfdwV7OOusbe21cO9G9rHipCMSlvrugHqWD9Y0cHecnMxK0TSvCd7yc/TVUE+7lr8bG+j4mQrHcNkcLcY3RYXVekdWcsa0ClKT17RRGF+pWOdBfu46/N/ddKOY9n2UNjdxazpj3ZV8wh/mc0mXd06Qp8sO6CeTWqpV5NaMplMuiu+XoW9k7o3CtXALjHlgjZ3F7PqBnkpJtRH84b10kdLD1QYDElG4/b1m9vYLwcoUfaM3g2xkXr0skaqE+Qlb3dXdWsUqkenrLf3pPBwNQbZD/X1UFSQd7mKeofoIHWoZ4yt8tjljbVyf4Z9HJHaAZ56/8728nJ3UWRgXX38xwF7g6dsL5vIAGMcv5Ig7to2kYqLCa6wAdGnWZhqB3rZewC3jw5Sv5YR6tMsTN+vO1wuzLshNlKdy4wLt3BXamnQVjfAfjl5i0gj2Bl1bQulZOVrwc5UxdUzLpX5d/f6and6oO9mtf308R8H7D2b/D1d1a+VsV18NrijkjPz7ePopGSXhtRP9W2q+78weqx2axSiF69tqaveWWo/41lR74g6gV66rWOU9qRma/LSA3rk9Bh5JpNJLSL99Uy/ZlqyO13Na/vp4PE8+Xu56pYOdZWWXVDu8tszeyC3HDVbZ7qsWS25u5orDdrqBHrpoV4N9MJP206/dze1iw6qsCdyiZL9ZIlAb/dyAy/f0TFKr8zcoR6NQ/XhPR0cbpRQ9vn9WpYO8h4bHWhfF4O83XRLh7r2fZOHq9neGLgrPlquZpP+M32LXF0qvtzN1WzSDw93lZe7i/qeHgNVkjrUC1ZUsJf9WBIbFaggbzeNPn3Wf8xNbexjsQR4uenyZuHq3CDEHrT5nH4fnm4uevbKZnpi2kZd1zZSrmUGwz7TZU1rKaFFeLmgLTrYW/+7q72aRfjJ1cVsXJ6y8ai6NwrVqze2UoCXmwqLrdqVnK07PzIu9/5v/+bKzi+2B5XtogP16GWNKgzarmkTKW93F4egLcjbOAmUmJGnW+Pq6sFeDXTweK6ahvtpX1qOjpw8VeElf81q+2lYrSaavTVZPh6umvFoN5lNkoebi1KzjO1j6oOd9e/P1mr1wQy1jQrUpqSTGtKnkf2zLOHuapaPu4ue7tdUsVGB6v+usS97pHdD/at7fQ36ZLW2Hc3SoC719K9u9WU2m9S8tn+F41Be3aq22tQNdJgWXz9Yaw+d0JR/xyvM31NXjFt81l4I18VGakPSiXKXezaLMLZ5L3cXh7Gc7oqPtm8vFYkK9tLngzvJ081Fnm4uyi0otq/j3u6u+s/VzR3WhTs7GTdhMJlMGn1Ta9324QqH8OqdO2LVPjpIG5JO2m8+JMneW3dQlxj7vuy2uKhy9YQm4b5qHx1U4c1KujYK1X/7t9CdneqpeW0/+02UujYM0fJ9x3VT+zp6ok8T1fLzkJe7i2KjAu1BW/dGoXr5hlYa8/sOzd2eokn3xKlOkJeahPtpf1qOvS7UvLa/rKdvflRiQKdo++V8Jfw83dQ0wlhPSi4D79WklhbvTrP3ALqubaSG9Gms47mFFQZt3u4umv5IN7mYTfbXL7nk/7UbW6tN3QB9vepQpcOjVCYmxEdXtYpwOLE4/Kpm+nJF6XivrmaTgnzc9XTfJurZpJZczWYVFFvk52GE0TGnj0uebi7q3CBE7aKCtPpghpIyTtkD0G6NQnR5s3C9/GvlDXsXs0nfP9RFvh6uqhPkpSmrEyvt5R5XL0jj74jVgp2pGnl6nb2lQ1093qexavl56Fhmvv14eWY7Irug2H5MLnF9bKQCvd11bdtILdiZqrfm7K7wRP6gLjHq0zzMIWgb2KWevlx5SDabUT84VWSR2WTSk1c00bpDJ7TtaKamPthZ93++VsdzC3VTuzq6t1uMWtcJ0I5j2crILbQPe1Ei0NvNfun6ewPaKTYq8Kw3DfFwNeuJKxrrzdm77EHbF//qpPqhPtqblqMf1x+xXwLbNipQY29po8bhfvrh4a5aezBDbq5mtYsK1I5j2WpW209uLmaH8Qi93FwcTvj+5+pmurJlbXv928PVRa3rGnXebo1Kx+yVVOHNYGKjAisN2gZ0itJd8fU0f0eq3p63277NXtOmtq5uXbtcRwZJ6lHmNX09XNU2KlBh/h724+GtccaNk0p6QUUFlX6Wz/RrqmdOtxH9PF11WbNa9n2Kt7ur2tQNVNMIP13bJlItahv141Z1Sm8CJ0mNw/00b1gvebu7KMTXw75NSNL1sXV0ZSvjUt5wfw89+OU6e/2pXwtjrL2eTWppwVO9FO7vKU83Fw3p00i/bj6q/CKrnurbRJc1C1PDWr765N6OOnLilOoGeWvtoQy5uZjVPjpIOQXFeuGn0pOWM4d2V90gb/vxKdTXQw1q+Sg1q0A3tzc+i+6NHcfxe+eOWFltNnvHmKtaR+iOjtH6+I8DahLup6tbG3XHa9pE6lShRSG+HvI/xw11bmpf195+axzu5zBu4z1dYnRNm0hl5RfZx4Zctjddv21J1rNXNrW38W7rGKUOMUH2NtI/WY0HbePGjdP999+vwYMHS5ImTpyomTNn6pNPPtHw4cPLzf/11187/P/RRx/phx9+0Pz58zVw4EBJ0vLlyzVo0CD17t1bkvTAAw/oww8/1OrVq//RQVsJs9mkyQPjdM/Hq7QnNcfeIC25DOH6WGMw4jMHGC17wGhYq/xO+MzXKGkcS0blpvnpsUL7t66t13/fKZtN+nhQR3tDooSnm4tui4tS4vE8TVi4V0NPV46fu6qpbp24Qje0q6ObO9RVjyah8nB1UUGRRTbJHrJJ0lu3xurpvqfs4wS5mE26rGmY3r7d6KnXvVGoVuw/rl5Natlf/7ehPbR8X7q9t1+z2v7ycnex7+Rf6N9Ct8VFqVmEn7zdXe29GNxcTPptaA/VDvRS5qki7U/LsX9GPu6uyjxVpBHTN2vFvuPlels9mdBEfVuGK+tUkQZMXunweNkKe6C3e7mbV7iYTfZg6OUbWumFGVs15PJGZ/1uSgKky5qGacmeNF3bNrJKN64o68b2dbTtSFa5CnSbugEVLuuypmFa+FRvhfmXP6NR8p19fG+csvKLVSfQCINLxhp4545YNYvwV+PTB/2uDUM1/6neCvV1l9lsko+Hq357vIdkk8JOV5TH3xGrjJxChQcYYybVC/bWgfRcNQrz1Z7UHKXnFOjOyauU0DxMz5++2cWpQovcXc0ONzCQJB8PV4fvwdPNbB+T6szPuaTyIklhfp4Ka2qUp0uDEK07dEJ3xUerUZhx4H973m71blpLi3al2QOQHo1DtXzfcbWpG6BXb2hd4d1L/TzdHO5yF+jt5nAH2biYYM1/qpfqBHrZQ4EhlxsH/7xCiybdE6cJC/doY9JJPdiroUOZS9QL8bYPdtqwlm+5OxnWCfTS9Ee7aldytmoHeKlRmK99TKsv/tVJYf4eahLmpw1JJ1RYbFO76ECHs6JXtAjX54M76f4v1qrYatXEuzvYz3ZVVLm7tq3j3YWHX9VMt0xcrkAvd33xr072mxa4upj1yb0ddc/Hq3QoI0//7d9cP6w/rK9WJuqpvk11U/s6ahrhp/wiq9qdDlda1w2Qt7uL8spcxtwuOkj1a/nYGzHdG4fal39Hp2iHoG3BU70UeXqdLdEgtPQ9RFdQyfZ0c9FHA+O0IzlLzSL8dSA912G+2gFemvtkLx3KyLV/ByUVLz9PN4fBa0dd00Iv/rJdL1zTQgnNw/TLY92VlV+k2KhA+Xi46n93tddj32xQqI8xJmS/8UscylLyuiOuaq6bO9RVkzPugNc0wk8Lnu6lAC835RVa5OZilpuL2eEmOT0ah+rF61oqJsRHSRl5mrUt2eHykLLqh/oot6D0s554dwcVFFvs+9z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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_state(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": "2ab294f2",
   "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": "0491481c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:30.005579Z",
     "iopub.status.busy": "2023-11-08T11:51:30.005579Z",
     "iopub.status.idle": "2023-11-08T11:51:30.033984Z",
     "shell.execute_reply": "2023-11-08T11:51:30.032828Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'integration': {'path0': [0.28240302882266727,\n",
       "   0.2824792379091353,\n",
       "   0.28240009770395696,\n",
       "   0.2824020517830972,\n",
       "   0.2824015632633121,\n",
       "   0.2822261846604787,\n",
       "   0.28231851489985343,\n",
       "   0.2822735710796287,\n",
       "   0.2821763556424035,\n",
       "   0.2822115290669272],\n",
       "  'path1': [0.12596042989741085,\n",
       "   0.12608451392281386,\n",
       "   0.1260630190522716,\n",
       "   0.12594088910600879,\n",
       "   0.12586321446018564,\n",
       "   0.125857352222765,\n",
       "   0.12573131411822178,\n",
       "   0.1258495359062042,\n",
       "   0.1258876404494382,\n",
       "   0.1259819247679531]},\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': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}"
      ]
     },
     "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": "b6c785f9",
   "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": "a6b7b36d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:30.038076Z",
     "iopub.status.busy": "2023-11-08T11:51:30.038076Z",
     "iopub.status.idle": "2023-11-08T11:51:30.048047Z",
     "shell.execute_reply": "2023-11-08T11:51:30.048047Z"
    }
   },
   "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": "6a99ff52",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:30.052592Z",
     "iopub.status.busy": "2023-11-08T11:51:30.052592Z",
     "iopub.status.idle": "2023-11-08T11:51:30.079769Z",
     "shell.execute_reply": "2023-11-08T11:51:30.078681Z"
    }
   },
   "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": "513555f2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:30.084747Z",
     "iopub.status.busy": "2023-11-08T11:51:30.083320Z",
     "iopub.status.idle": "2023-11-08T11:51:30.264778Z",
     "shell.execute_reply": "2023-11-08T11:51:30.263758Z"
    }
   },
   "outputs": [],
   "source": [
    "# Upload sequence.\n",
    "readout_module.sequencer0.sequence(\"sequence.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20dd7ad0",
   "metadata": {},
   "source": [
    "Let's start the sequence and retrieve the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c3c335b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:30.268782Z",
     "iopub.status.busy": "2023-11-08T11:51:30.268782Z",
     "iopub.status.idle": "2023-11-08T11:51:30.310630Z",
     "shell.execute_reply": "2023-11-08T11:51:30.309529Z"
    }
   },
   "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_state(0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f24975fe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:30.316363Z",
     "iopub.status.busy": "2023-11-08T11:51:30.315340Z",
     "iopub.status.idle": "2023-11-08T11:51:31.084921Z",
     "shell.execute_reply": "2023-11-08T11:51:31.084419Z"
    }
   },
   "outputs": [],
   "source": [
    "# Wait for the sequencer to stop with a timeout period of one minute.\n",
    "readout_module.get_acquisition_state(0, 1)\n",
    "\n",
    "# Get acquisition list from instrument.\n",
    "weighed_acq = readout_module.get_acquisitions(0)[\"weighed\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb316aea",
   "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": "4ba62963",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:31.088924Z",
     "iopub.status.busy": "2023-11-08T11:51:31.088924Z",
     "iopub.status.idle": "2023-11-08T11:51:31.102432Z",
     "shell.execute_reply": "2023-11-08T11:51:31.100432Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'integration': {'path0': [0.0848665679126427,\n",
       "   0.08481123256620374,\n",
       "   0.0848211509258846,\n",
       "   0.08481648687849859,\n",
       "   0.0848056306181068,\n",
       "   0.08482006744770965,\n",
       "   0.08482484405909109,\n",
       "   0.08480108112347937,\n",
       "   0.08486924041259039,\n",
       "   0.08483652088316676],\n",
       "  'path1': [-6.431660091417874e-07,\n",
       "   6.14810223865524e-05,\n",
       "   -2.9722463326610515e-05,\n",
       "   9.33883409316019e-06,\n",
       "   1.2995374057316314e-05,\n",
       "   1.685715438060488e-05,\n",
       "   -2.202276783807204e-05,\n",
       "   5.14015728889259e-06,\n",
       "   -6.03211757058737e-05,\n",
       "   2.0086508015869056e-07]},\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': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]}"
      ]
     },
     "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": "5c338315",
   "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": "f44aa349",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:31.106433Z",
     "iopub.status.busy": "2023-11-08T11:51:31.105433Z",
     "iopub.status.idle": "2023-11-08T11:51:31.118436Z",
     "shell.execute_reply": "2023-11-08T11:51:31.116435Z"
    }
   },
   "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": "17b19987",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:31.122437Z",
     "iopub.status.busy": "2023-11-08T11:51:31.121436Z",
     "iopub.status.idle": "2023-11-08T11:51:31.150128Z",
     "shell.execute_reply": "2023-11-08T11:51:31.148444Z"
    }
   },
   "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": "7ca88404",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:31.154113Z",
     "iopub.status.busy": "2023-11-08T11:51:31.154113Z",
     "iopub.status.idle": "2023-11-08T11:51:31.318290Z",
     "shell.execute_reply": "2023-11-08T11:51:31.317270Z"
    }
   },
   "outputs": [],
   "source": [
    "# Upload sequence.\n",
    "readout_module.sequencer0.sequence(\"sequence.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09b9d2bb",
   "metadata": {},
   "source": [
    "Let's start the sequence and retrieve the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "099388b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:31.323537Z",
     "iopub.status.busy": "2023-11-08T11:51:31.322515Z",
     "iopub.status.idle": "2023-11-08T11:51:31.533531Z",
     "shell.execute_reply": "2023-11-08T11:51:31.532520Z"
    }
   },
   "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_state(0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8223cf3a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:31.538444Z",
     "iopub.status.busy": "2023-11-08T11:51:31.538444Z",
     "iopub.status.idle": "2023-11-08T11:51:32.309956Z",
     "shell.execute_reply": "2023-11-08T11:51:32.308700Z"
    }
   },
   "outputs": [],
   "source": [
    "# Wait for the sequencer to stop with a timeout period of one minute.\n",
    "readout_module.get_acquisition_state(0, 1)\n",
    "\n",
    "# Get acquisition list from instrument.\n",
    "large_acq = readout_module.get_acquisitions(0)[\"large\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "208ac158",
   "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": "280dd282",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:32.315941Z",
     "iopub.status.busy": "2023-11-08T11:51:32.314912Z",
     "iopub.status.idle": "2023-11-08T11:51:32.402874Z",
     "shell.execute_reply": "2023-11-08T11:51:32.400819Z"
    }
   },
   "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": "43456767",
   "metadata": {},
   "source": [
    "We will also plot the integration results in every bin to visualize the contents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e7ac910c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:32.406866Z",
     "iopub.status.busy": "2023-11-08T11:51:32.405862Z",
     "iopub.status.idle": "2023-11-08T11:51:32.588941Z",
     "shell.execute_reply": "2023-11-08T11:51:32.587941Z"
    }
   },
   "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": "01abee7f",
   "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": "b4979f56",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:32.592944Z",
     "iopub.status.busy": "2023-11-08T11:51:32.591946Z",
     "iopub.status.idle": "2023-11-08T11:51:32.604353Z",
     "shell.execute_reply": "2023-11-08T11:51:32.603305Z"
    }
   },
   "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": "c3fe3a1e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:32.609828Z",
     "iopub.status.busy": "2023-11-08T11:51:32.608810Z",
     "iopub.status.idle": "2023-11-08T11:51:32.635958Z",
     "shell.execute_reply": "2023-11-08T11:51:32.634893Z"
    }
   },
   "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": "34b2b8f9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:32.640018Z",
     "iopub.status.busy": "2023-11-08T11:51:32.640018Z",
     "iopub.status.idle": "2023-11-08T11:51:32.788849Z",
     "shell.execute_reply": "2023-11-08T11:51:32.787720Z"
    }
   },
   "outputs": [],
   "source": [
    "# Upload sequence.\n",
    "readout_module.sequencer0.sequence(\"sequence.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76b7f834",
   "metadata": {},
   "source": [
    "Let's start the sequence and retrieve the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e5653de3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:32.793705Z",
     "iopub.status.busy": "2023-11-08T11:51:32.793705Z",
     "iopub.status.idle": "2023-11-08T11:51:32.850882Z",
     "shell.execute_reply": "2023-11-08T11:51:32.849857Z"
    }
   },
   "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_state(0, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88d263a6",
   "metadata": {},
   "source": [
    "Note that the average count of each bin is now set to a 1000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c4752719",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:32.856853Z",
     "iopub.status.busy": "2023-11-08T11:51:32.855699Z",
     "iopub.status.idle": "2023-11-08T11:51:33.611944Z",
     "shell.execute_reply": "2023-11-08T11:51:33.610963Z"
    }
   },
   "outputs": [],
   "source": [
    "# Wait for the sequencer to stop with a timeout period of one minute.\n",
    "readout_module.get_acquisition_state(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": "be09aede",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:33.615964Z",
     "iopub.status.busy": "2023-11-08T11:51:33.615964Z",
     "iopub.status.idle": "2023-11-08T11:51:33.642330Z",
     "shell.execute_reply": "2023-11-08T11:51:33.641323Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'integration': {'path0': [0.2817064665363947,\n",
       "   0.28170248754274546,\n",
       "   0.2817043038593063,\n",
       "   0.2817053722520762,\n",
       "   0.2817041563263313,\n",
       "   0.28170038641915,\n",
       "   0.2816994255007328,\n",
       "   0.28170050317537865,\n",
       "   0.2816968270639961,\n",
       "   0.2816986731802638],\n",
       "  'path1': [0.12522469369809477,\n",
       "   0.12522286858817783,\n",
       "   0.12522691597459698,\n",
       "   0.12522821201758672,\n",
       "   0.12522614167073767,\n",
       "   0.12522977625793846,\n",
       "   0.12523100928187592,\n",
       "   0.1252252325354177,\n",
       "   0.12523119345383488,\n",
       "   0.12522672252076208]},\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': [1000.0,\n",
       "  1000.0,\n",
       "  1000.0,\n",
       "  1000.0,\n",
       "  1000.0,\n",
       "  1000.0,\n",
       "  1000.0,\n",
       "  1000.0,\n",
       "  1000.0,\n",
       "  1000.0]}"
      ]
     },
     "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": "a81ddc61",
   "metadata": {},
   "source": [
    "Characterizing Input Offset\n",
    "============================="
   ]
  },
  {
   "cell_type": "markdown",
   "id": "316266d3",
   "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": "52b0c190",
   "metadata": {},
   "source": [
    "Measuring the Offset\n",
    "--------------------\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17f539d7",
   "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": "986badf1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:33.647937Z",
     "iopub.status.busy": "2023-11-08T11:51:33.646725Z",
     "iopub.status.idle": "2023-11-08T11:51:36.905536Z",
     "shell.execute_reply": "2023-11-08T11:51:36.904536Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "cluster.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "684c6f00",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:36.909537Z",
     "iopub.status.busy": "2023-11-08T11:51:36.909537Z",
     "iopub.status.idle": "2023-11-08T11:51:36.921531Z",
     "shell.execute_reply": "2023-11-08T11:51:36.920545Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def acquire_scope_and_calc_offsets():\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_state(0, 1)\n",
    "\n",
    "    # Retrieve results\n",
    "    readout_module.store_scope_acquisition(0, \"single\")\n",
    "    single_acq = readout_module.get_acquisitions(0)\n",
    "    I = np.array(single_acq[\"single\"][\"acquisition\"][\"scope\"][\"path0\"][\"data\"])\n",
    "    Q = 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, label=\"I\")\n",
    "    ax.plot(Q, 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), np.mean(Q)\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": "3ec7b727",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:36.925541Z",
     "iopub.status.busy": "2023-11-08T11:51:36.925541Z",
     "iopub.status.idle": "2023-11-08T11:51:36.967242Z",
     "shell.execute_reply": "2023-11-08T11:51:36.966715Z"
    },
    "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": "5135ef98",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:36.972267Z",
     "iopub.status.busy": "2023-11-08T11:51:36.971262Z",
     "iopub.status.idle": "2023-11-08T11:51:38.058331Z",
     "shell.execute_reply": "2023-11-08T11:51:38.056304Z"
    },
    "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 : 13.115 mV \n",
      "Q Offset : -6.629 mV\n"
     ]
    }
   ],
   "source": [
    "I_offset, Q_offset = acquire_scope_and_calc_offsets()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ce43a79",
   "metadata": {},
   "source": [
    "Correcting the Offsets\n",
    "-------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c82d71eb",
   "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/master/api_reference/qcm_qrm.html#QRM.in0_offset)</code> and <code>[QRM.in1_offset](https://qblox-qblox-instruments.readthedocs-hosted.com/en/master/api_reference/qcm_qrm.html#QRM.in1_offset)</code>.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "68b258c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:38.062337Z",
     "iopub.status.busy": "2023-11-08T11:51:38.061336Z",
     "iopub.status.idle": "2023-11-08T11:51:38.088417Z",
     "shell.execute_reply": "2023-11-08T11:51:38.087402Z"
    },
    "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": "9ffdc5ad",
   "metadata": {},
   "source": [
    "Repeating the offset measurement as before, we get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a1a8614e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:38.094429Z",
     "iopub.status.busy": "2023-11-08T11:51:38.094429Z",
     "iopub.status.idle": "2023-11-08T11:51:39.205863Z",
     "shell.execute_reply": "2023-11-08T11:51:39.204753Z"
    },
    "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 : -3.337 mV \n",
      "Q Offset : 1.467 mV\n"
     ]
    }
   ],
   "source": [
    "I_offset, Q_offset = acquire_scope_and_calc_offsets()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62f131a9",
   "metadata": {},
   "source": [
    "Advanced ADC Offset Calibration : Curve Fitting Method\n",
    "----------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1fd111e",
   "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": "ab8c7eee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:39.211517Z",
     "iopub.status.busy": "2023-11-08T11:51:39.211517Z",
     "iopub.status.idle": "2023-11-08T11:51:39.221742Z",
     "shell.execute_reply": "2023-11-08T11:51:39.220733Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define helper functions\n",
    "def get_real_root(coeffs):\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, coeffs):\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": "c90ba71e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:39.227131Z",
     "iopub.status.busy": "2023-11-08T11:51:39.227131Z",
     "iopub.status.idle": "2023-11-08T11:51:39.236182Z",
     "shell.execute_reply": "2023-11-08T11:51:39.235663Z"
    },
    "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": "94b8b7c1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:39.239256Z",
     "iopub.status.busy": "2023-11-08T11:51:39.239256Z",
     "iopub.status.idle": "2023-11-08T11:51:39.251714Z",
     "shell.execute_reply": "2023-11-08T11:51:39.250708Z"
    },
    "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": "73d93d94",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:39.256246Z",
     "iopub.status.busy": "2023-11-08T11:51:39.255228Z",
     "iopub.status.idle": "2023-11-08T11:51:39.345295Z",
     "shell.execute_reply": "2023-11-08T11:51:39.344289Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Upload sequence.\n",
    "readout_module.sequencer0.sequence(\"sequence.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b8cec39",
   "metadata": {},
   "source": [
    "Let's start the sequence and retrieve the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cc847e77",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:39.349911Z",
     "iopub.status.busy": "2023-11-08T11:51:39.349911Z",
     "iopub.status.idle": "2023-11-08T11:51:48.393189Z",
     "shell.execute_reply": "2023-11-08T11:51:48.392126Z"
    },
    "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_state(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": "e3251291",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:48.397207Z",
     "iopub.status.busy": "2023-11-08T11:51:48.396199Z",
     "iopub.status.idle": "2023-11-08T11:51:48.534737Z",
     "shell.execute_reply": "2023-11-08T11:51:48.533741Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Offset setpoints corresponding to observed zero offset: \n",
      "I offset: -9.97996872877298\n",
      "Q offset: 5.274293468076739\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": "cdb1dc17",
   "metadata": {},
   "source": [
    "Applying The Offset Corrections\n",
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a70e91d4",
   "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": "1ff1399e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:48.539744Z",
     "iopub.status.busy": "2023-11-08T11:51:48.538722Z",
     "iopub.status.idle": "2023-11-08T11:51:48.565478Z",
     "shell.execute_reply": "2023-11-08T11:51:48.564477Z"
    },
    "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": "597bcdfb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:48.569201Z",
     "iopub.status.busy": "2023-11-08T11:51:48.569201Z",
     "iopub.status.idle": "2023-11-08T11:51:49.767175Z",
     "shell.execute_reply": "2023-11-08T11:51:49.766174Z"
    },
    "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 : -0.346 mV \n",
      "Q Offset : 0.085 mV\n"
     ]
    }
   ],
   "source": [
    "I_offset_calib, Q_offset_calib = acquire_scope_and_calc_offsets()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac100e80",
   "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": "0343db7f",
   "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": "360fff22",
   "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": "0fde15ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-11-08T11:51:49.773175Z",
     "iopub.status.busy": "2023-11-08T11:51:49.772176Z",
     "iopub.status.idle": "2023-11-08T11:51:53.054892Z",
     "shell.execute_reply": "2023-11-08T11:51:53.052744Z"
    }
   },
   "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"
     ]
    },
    {
     "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_state(0))\n",
    "print()\n",
    "\n",
    "# Uncomment the following to print an overview of the instrument parameters.\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_state())"
   ]
  }
 ],
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