{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b5d61745",
   "metadata": {},
   "source": [
    "# Numerically Controlled Oscillator\n",
    "\n",
    "In this tutorial we will demonstrate how to use the numerically controlled oscillator (NCO) during an experiment by [Changing the modulation frequency](#Frequency-sweeps), e.g. for rapid spectroscopy measurements\n",
    "\n",
    "We will show this by using a [QRM](https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/cluster/qrm.html) and directly connecting outputs\n",
    "$\\text{O}^{[1-2]}$ to inputs $\\text{I}^{[1-2]}$ respectively. We will then use the QRM's sequencers to sequence waveforms on\n",
    "the outputs and simultaneously acquire the resulting waveforms on the inputs.\n",
    "\n",
    "Whilst this tutorial is for the baseband modules, a similar procedure for spectroscopy with RF modules can be found in the [RF Control](https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/tutorials/q1asm_tutorials/basic/rf/rf_control.html) tutorial.\n",
    "\n",
    "## Getting Started\n",
    "\n",
    "### How the NCO works\n",
    "\n",
    "#### Modulation\n",
    "\n",
    "The NCO modulates the AWG output with a certain frequency, such that we only need to store the envelope of any waveform to create a wave with the correct frequency and phase. To enable IQ up- and down-conversion to RF frequencies, the device will generate a phase shifted signal on both signal paths.\n",
    "\n",
    "If modulation is enabled, the value of the NCO ($e^{i\\omega t}$) will be multiplied by the awg outputs ($z(t) = \\text{awg}_0 + i \\cdot \\text{awg}_1$), and also by a factor of $1 / \\sqrt{2}$ to prevent clipping, and forwarded to path 0/1 as follows:\n",
    "\n",
    "\\begin{equation*}\n",
    "\\text{path}_{0, \\text{out}} = \\frac{1}{\\sqrt{2}}(\\cos(\\omega t)\\text{awg}_0 - \\sin(\\omega t)\\text{awg}_1)\n",
    "\\end{equation*}\n",
    "\n",
    "\\begin{equation*}\n",
    "\\text{path}_{1, \\text{out}} =  \\frac{1}{\\sqrt{2}}(\\sin(\\omega t)\\text{awg}_0 + \\cos(\\omega t)\\text{awg}_1)\n",
    "\\end{equation*}\n",
    "\n",
    "These two outputs can then be used within a QxM-RF module or by an external IQ mixer to generate RF pulses. Each path, 0/1, will have two components (real and imaginary) referred to as I and Q. However, the NCO can also be operated in real mode which will create a direct modulated output.\n",
    "\n",
    "#### Demodulation\n",
    "\n",
    "Usually, we are interested in the envelope of the acquired signal instead of the oscillating values, in particular when integrating. Therefore, the NCO can also be used to demodulate the signal before integration.\n",
    "\n",
    "If demodulation is enabled, the signal is again multiplied with the NCO(effectively $e^{-i\\omega t}$ during demodulation), this time also with a factor of $1 / \\sqrt{2}$. Both in modulation and demodulation the mixed NCO $\\sin$ and $\\cos$ terms have a maximum amplitude of $1 / \\sqrt{2}$ to prevent clipping.\n",
    "\n",
    "This leads to an amplitude factor of $1 / \\sqrt{2}\\times 1 / \\sqrt{2}$ = 0.5 when demodulating your output signal. Internally, this is accounted for in the datapaths to allow maximum dynamic range:\n",
    "\n",
    "\\begin{equation*}\n",
    "\\text{path}_{0, \\text{in}} = \\frac{1}{\\sqrt{2}}(\\cos(\\omega t)\\, \\text{in}_0 + \\sin(\\omega t)\\, \\text{in}_1) = \\frac{1}{\\sqrt{2}}\\left(\\frac{1}{\\sqrt{2}}\\cos(\\omega t) \\cdot \\cos(\\omega t)\\, \\text{awg}_0 + \\frac{1}{\\sqrt{2}}\\sin(\\omega t) \\cdot \\sin(\\omega t)\\, \\text{awg}_0\\right) = \\frac{1}{2}\\text{awg}_0\n",
    "\\end{equation*}\n",
    "\n",
    "\\begin{equation*}\n",
    "\\text{path}_{1, \\text{in}} = \\frac{1}{\\sqrt{2}}(-\\sin(\\omega t)\\, \\text{in}_0 + \\cos(\\omega t)\\, \\text{in}_1) = \\frac{1}{\\sqrt{2}}\\left(\\frac{1}{\\sqrt{2}}\\sin(\\omega t) \\cdot \\sin(\\omega t)\\, \\text{awg}_1 + \\frac{1}{\\sqrt{2}}\\cos(\\omega t) \\cdot \\cos(\\omega t)\\, \\text{awg}_1\\right) = \\frac{1}{2}\\text{awg}_1\n",
    "\\end{equation*}\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64bf5764",
   "metadata": {},
   "source": [
    "### Setup\n",
    "\n",
    "First, we are going to import the required packages and connect to the instrument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "12a90f6b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:37:46.006887Z",
     "iopub.status.busy": "2024-09-02T01:37:46.006678Z",
     "iopub.status.idle": "2024-09-02T01:37:46.862587Z",
     "shell.execute_reply": "2024-09-02T01:37:46.861707Z"
    }
   },
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "import json\n",
    "from typing import TYPE_CHECKING, Callable\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from matplotlib import colors\n",
    "from qcodes.instrument import find_or_create_instrument\n",
    "from scipy.signal import spectrogram, welch\n",
    "\n",
    "from qblox_instruments import Cluster, ClusterType\n",
    "\n",
    "if TYPE_CHECKING:\n",
    "    from numpy.typing import NDArray\n",
    "\n",
    "    from qblox_instruments.qcodes_drivers.module import Module"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c008730e",
   "metadata": {},
   "source": [
    "### Scan For Clusters\n",
    "\n",
    "We scan for the available devices connected via ethernet using the Plug & Play functionality of the Qblox Instruments package (see [Plug & Play](https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/api_reference/tools.html#api-pnp) for more info)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a0e16606",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:37:46.866044Z",
     "iopub.status.busy": "2024-09-02T01:37:46.865672Z",
     "iopub.status.idle": "2024-09-02T01:37:48.860376Z",
     "shell.execute_reply": "2024-09-02T01:37:48.856554Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No devices found\r\n"
     ]
    }
   ],
   "source": [
    "!qblox-pnp list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "14491107",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:37:48.869280Z",
     "iopub.status.busy": "2024-09-02T01:37:48.869069Z",
     "iopub.status.idle": "2024-09-02T01:37:48.872293Z",
     "shell.execute_reply": "2024-09-02T01:37:48.871612Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster_ip = \"10.10.200.42\"\n",
    "cluster_name = \"cluster0\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "570d9305",
   "metadata": {},
   "source": [
    "### Connect to Cluster\n",
    "\n",
    "We now make a connection with the Cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9345aa8e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:37:48.874500Z",
     "iopub.status.busy": "2024-09-02T01:37:48.874329Z",
     "iopub.status.idle": "2024-09-02T01:37:49.584198Z",
     "shell.execute_reply": "2024-09-02T01:37:49.583029Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "cluster = find_or_create_instrument(\n",
    "    Cluster,\n",
    "    recreate=True,\n",
    "    name=cluster_name,\n",
    "    identifier=cluster_ip,\n",
    "    dummy_cfg=(\n",
    "        {\n",
    "            2: ClusterType.CLUSTER_QCM,\n",
    "            4: ClusterType.CLUSTER_QRM,\n",
    "            6: ClusterType.CLUSTER_QCM_RF,\n",
    "            8: ClusterType.CLUSTER_QRM_RF,\n",
    "        }\n",
    "        if cluster_ip is None\n",
    "        else None\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d27e62b",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "#### Get connected modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "89953761",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:37:49.587656Z",
     "iopub.status.busy": "2024-09-02T01:37:49.587320Z",
     "iopub.status.idle": "2024-09-02T01:37:49.594899Z",
     "shell.execute_reply": "2024-09-02T01:37:49.593573Z"
    }
   },
   "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": 6,
   "id": "6cfaa5cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:37:49.598396Z",
     "iopub.status.busy": "2024-09-02T01:37:49.598013Z",
     "iopub.status.idle": "2024-09-02T01:37:49.626699Z",
     "shell.execute_reply": "2024-09-02T01:37:49.625893Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{4: <Module: cluster0_module4 of Cluster: cluster0>}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# QRM baseband modules\n",
    "modules = get_connected_modules(cluster, lambda mod: mod.is_qrm_type and not mod.is_rf_type)\n",
    "modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7e78cbcd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:37:49.629595Z",
     "iopub.status.busy": "2024-09-02T01:37:49.629366Z",
     "iopub.status.idle": "2024-09-02T01:37:49.633012Z",
     "shell.execute_reply": "2024-09-02T01:37:49.632172Z"
    },
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "readout_module = modules[4]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "844ca929",
   "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": "2235b8b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:37:49.636020Z",
     "iopub.status.busy": "2024-09-02T01:37:49.635775Z",
     "iopub.status.idle": "2024-09-02T01:37:51.951431Z",
     "shell.execute_reply": "2024-09-02T01:37:51.950739Z"
    }
   },
   "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": "5f1ee3bb",
   "metadata": {},
   "source": [
    "Frequency sweeps  <a id='freq_sw'></a>\n",
    "------------------------------\n",
    "One of the most common experiments is to test the frequency response of the system, e.g. to find the resonance frequencies of a qubit or a resonator. For the purpose of this tutorial, we will sweep the full frequency range supported by the QRM. To improve accuracy we can use the maximum integration time and multiple averages. This does not change the overall measurement time much, as most of it is used for the setup."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2f9bc843",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:37:51.953808Z",
     "iopub.status.busy": "2024-09-02T01:37:51.953636Z",
     "iopub.status.idle": "2024-09-02T01:37:51.956833Z",
     "shell.execute_reply": "2024-09-02T01:37:51.956121Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "start_freq = -500e6\n",
    "stop_freq = 500e6\n",
    "\n",
    "n_averages = 10\n",
    "MAXIMUM_SCOPE_ACQUISITION_LENGTH = 16384"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2269165",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "In this tutorial, we will analyze the raw data measured by the scope acquisition of the QRM. For this we will define a simple helper function using [scipy.signal.spectrogram](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.spectrogram.html) and [scipy.signal.welch](https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.welch.html). The spectrogram shows the frequency spectrum of the QRM output as a function of time, to visualize the frequency sweeps we are doing. Welch's method is used to compute the input power as a function of frequency (power spectral density). This way we obtain the response of the system to find features of interest, e.g. a resonance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ad1a1e02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:37:51.959177Z",
     "iopub.status.busy": "2024-09-02T01:37:51.959001Z",
     "iopub.status.idle": "2024-09-02T01:37:51.965370Z",
     "shell.execute_reply": "2024-09-02T01:37:51.964653Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Power as function of frequency and time by chunking the data\n",
    "def plot_spectrogram(time_series: np.ndarray) -> None:\n",
    "    f_sample = 1e9  # All devices have 1 GSPS sample rate\n",
    "    fig, ax = plt.subplots(1, 2)\n",
    "\n",
    "    f, t, Sxx = spectrogram(time_series, f_sample, return_onesided=False, detrend=False)\n",
    "\n",
    "    idx = np.argsort(f)\n",
    "    f = f[idx] / 1e6\n",
    "    Sxx = Sxx[idx]\n",
    "\n",
    "    cmap = colors.LinearSegmentedColormap.from_list(\"qblox\", [\"#FFFFFF\", \"#00839F\"])\n",
    "    spec = ax[0].pcolormesh(t, f, Sxx, shading=\"auto\", cmap=cmap)\n",
    "    cb = fig.colorbar(spec)\n",
    "    cb.set_label(\"Power Spectral Density [V$^2$/Hz]\")\n",
    "    ax[0].set_ylabel(\"Frequency [MHz]\")\n",
    "    ax[0].set_xlabel(\"Time [s]\")\n",
    "\n",
    "    f, Pxx = welch(time_series, f_sample, return_onesided=False, detrend=False)\n",
    "\n",
    "    idx = np.argsort(f)\n",
    "    f = f[idx] / 1e6\n",
    "    Pxx = Pxx[idx]\n",
    "\n",
    "    ax[1].semilogy(f, Pxx)\n",
    "    ax[1].set_xlabel(\"Frequency [MHz]\")\n",
    "    ax[1].set_ylabel(\"Power Spectral Density [V$^2$/Hz]\")\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caa18000",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "And two more helper functions for plotting the amplitude of an array of I, Q values and a scope acquisition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9f828782",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:37:51.967946Z",
     "iopub.status.busy": "2024-09-02T01:37:51.967752Z",
     "iopub.status.idle": "2024-09-02T01:37:51.972767Z",
     "shell.execute_reply": "2024-09-02T01:37:51.971883Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_amplitude(x: NDArray, I_data: NDArray, Q_data: NDArray) -> None:\n",
    "    amplitude = np.abs(I_data + 1j * Q_data)\n",
    "\n",
    "    plt.plot(x / 1e6, amplitude)\n",
    "    plt.xlabel(\"Frequency [MHz]\")\n",
    "    plt.ylabel(\"Integration [V]\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def plot_scope(trace: NDArray, t_min: int, t_max: int) -> None:\n",
    "    x = np.arange(t_min, t_max)\n",
    "    plt.plot(x, np.real(trace[t_min:t_max]))\n",
    "    plt.plot(x, np.imag(trace[t_min:t_max]))\n",
    "    plt.ylabel(\"Scope [V]\")\n",
    "    plt.xlabel(\"Time [ns]\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4124b25f",
   "metadata": {},
   "source": [
    "### Setting up the QRM\n",
    "We set up a modulated DC offset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8dfaffaf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:37:51.975199Z",
     "iopub.status.busy": "2024-09-02T01:37:51.975009Z",
     "iopub.status.idle": "2024-09-02T01:37:52.106410Z",
     "shell.execute_reply": "2024-09-02T01:37:52.103137Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.disconnect_outputs()\n",
    "readout_module.disconnect_inputs()\n",
    "\n",
    "# Configure channel map\n",
    "readout_module.sequencer0.connect_sequencer(\"io0_1\")\n",
    "\n",
    "# Set DC Offset\n",
    "readout_module.sequencer0.offset_awg_path0(1)\n",
    "readout_module.sequencer0.offset_awg_path1(1)\n",
    "\n",
    "# Enable modulation and demodulation. Note that the scope is not demodulated\n",
    "readout_module.sequencer0.mod_en_awg(True)\n",
    "readout_module.sequencer0.demod_en_acq(True)\n",
    "\n",
    "# Enable hardware averaging for the scope\n",
    "readout_module.scope_acq_avg_mode_en_path0(True)\n",
    "readout_module.scope_acq_avg_mode_en_path1(True)\n",
    "\n",
    "readout_module.sequencer0.integration_length_acq(MAXIMUM_SCOPE_ACQUISITION_LENGTH)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11d4723b",
   "metadata": {},
   "source": [
    "### NCO sweep controlled by the host computer\n",
    "As a baseline, we will run a simple frequency sweep controlled by the host computer using QCoDeS. We do this by setting a DC offset and modulating it with the NCO. This is quite slow, so we will only measure 200 steps to keep the measurement time reasonable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "302e2894",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:37:52.114345Z",
     "iopub.status.busy": "2024-09-02T01:37:52.114177Z",
     "iopub.status.idle": "2024-09-02T01:37:52.117614Z",
     "shell.execute_reply": "2024-09-02T01:37:52.116972Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step size 5.0 MHz\n"
     ]
    }
   ],
   "source": [
    "n_steps = 200\n",
    "\n",
    "step_freq = (stop_freq - start_freq) / n_steps\n",
    "print(f\"Step size {step_freq/1e6} MHz\")\n",
    "\n",
    "nco_sweep_range = np.arange(start_freq, stop_freq, step_freq)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39eec34f",
   "metadata": {},
   "source": [
    "Now we set up a simple program that acquires data and averages. The frequency will be set later with the QCoDeS interface."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "729ca18c",
   "metadata": {},
   "source": [
    "Note: While most operations and parameters are executed/updated on a 1 ns timegrid, the instructions that operate on the sequencer’s NCOs (e.g. set_freq, reset_ph, set_ph, set_ph_delta) are updated on a 4 ns timegrid. The minimum time between frequency changes is 8 ns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f45728e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:37:52.119885Z",
     "iopub.status.busy": "2024-09-02T01:37:52.119720Z",
     "iopub.status.idle": "2024-09-02T01:37:52.123791Z",
     "shell.execute_reply": "2024-09-02T01:37:52.123190Z"
    }
   },
   "outputs": [],
   "source": [
    "acquisitions = {\"acq\": {\"num_bins\": 1, \"index\": 0}}\n",
    "\n",
    "# Sequence program.\n",
    "slow_sweep = f\"\"\"\n",
    "    move    {n_averages}, R0            # Average iterator.\n",
    "\n",
    "avg_loop:\n",
    "    reset_ph\n",
    "    upd_param 200\n",
    "    acquire 0, 0, {MAXIMUM_SCOPE_ACQUISITION_LENGTH}\n",
    "\n",
    "    loop    R0, @avg_loop\n",
    "\n",
    "    stop\n",
    "\"\"\"\n",
    "\n",
    "# Add sequence to single dictionary and write to JSON file.\n",
    "sequence = {\n",
    "    \"waveforms\": {},\n",
    "    \"weights\": {},\n",
    "    \"acquisitions\": acquisitions,\n",
    "    \"program\": slow_sweep,\n",
    "}\n",
    "with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "    json.dump(sequence, file, indent=4)\n",
    "    file.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc33788d",
   "metadata": {},
   "source": [
    "Next, we prepare the QRM for the measurement:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a2e99922",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:37:52.126057Z",
     "iopub.status.busy": "2024-09-02T01:37:52.125762Z",
     "iopub.status.idle": "2024-09-02T01:37:52.149049Z",
     "shell.execute_reply": "2024-09-02T01:37:52.148300Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.sequence(\"sequence.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffc4e9fb",
   "metadata": {},
   "source": [
    "Now we can run the frequency sweep. This is simply a loop where we set the frequency with QCoDeS and then run the program defined above. We measure the run time using the `%%time` Jupyter magic command."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7275c259",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:37:52.151911Z",
     "iopub.status.busy": "2024-09-02T01:37:52.151716Z",
     "iopub.status.idle": "2024-09-02T01:38:04.555309Z",
     "shell.execute_reply": "2024-09-02T01:38:04.554331Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.35 s, sys: 1.04 s, total: 4.39 s\n",
      "Wall time: 12.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "data = []\n",
    "for nco_val in nco_sweep_range:\n",
    "    # Set the frequency\n",
    "    readout_module.sequencer0.nco_freq(nco_val)\n",
    "\n",
    "    # Run the program\n",
    "    readout_module.arm_sequencer(0)\n",
    "    readout_module.start_sequencer()\n",
    "\n",
    "    # Wait for the sequencer to stop with a timeout period of one minute.\n",
    "    readout_module.get_acquisition_status(0)\n",
    "\n",
    "    # Move acquisition data from temporary memory to acquisition list.\n",
    "    readout_module.store_scope_acquisition(0, \"acq\")\n",
    "\n",
    "    # Get acquisition list from instrument.\n",
    "    data.append(readout_module.get_acquisitions(0)[\"acq\"])\n",
    "\n",
    "    # Clear acquisition data so we do not average the results from different frequencies\n",
    "    readout_module.sequencer0.delete_acquisition_data(\"acq\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8a3dc43",
   "metadata": {},
   "source": [
    "Plotting the acquired integration data, we can see the frequency behavior of the QRM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e37a3130",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:38:04.559148Z",
     "iopub.status.busy": "2024-09-02T01:38:04.558844Z",
     "iopub.status.idle": "2024-09-02T01:38:04.771473Z",
     "shell.execute_reply": "2024-09-02T01:38:04.770781Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "I_data = (\n",
    "    np.asarray([d[\"acquisition\"][\"bins\"][\"integration\"][\"path0\"][0] for d in data])\n",
    "    / MAXIMUM_SCOPE_ACQUISITION_LENGTH\n",
    ")\n",
    "Q_data = (\n",
    "    np.asarray([d[\"acquisition\"][\"bins\"][\"integration\"][\"path1\"][0] for d in data])\n",
    "    / MAXIMUM_SCOPE_ACQUISITION_LENGTH\n",
    ")\n",
    "plot_amplitude(nco_sweep_range, I_data, Q_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b774ba5",
   "metadata": {},
   "source": [
    "We can see that the output amplitude decreases with frequency, this is expected due to the analog filters. We can also analyze the accumulated scope data with a spectrogram. This takes a few seconds, as there are 16384 data points per frequency step. Note that the time axis of the spectrogram refers to measurement time (16.4us * 200 steps $\\approx$ 3.3ms) and not the wall clock time, which is significantly longer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7b78ce40",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:38:04.773907Z",
     "iopub.status.busy": "2024-09-02T01:38:04.773707Z",
     "iopub.status.idle": "2024-09-02T01:38:09.058287Z",
     "shell.execute_reply": "2024-09-02T01:38:09.057598Z"
    },
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_scope = (\n",
    "    np.asarray([d[\"acquisition\"][\"scope\"][\"path0\"][\"data\"] for d in data]).flatten()\n",
    "    + 1j * np.asarray([d[\"acquisition\"][\"scope\"][\"path1\"][\"data\"] for d in data]).flatten()\n",
    ")\n",
    "\n",
    "plot_spectrogram(data_scope)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3622493",
   "metadata": {},
   "source": [
    "### Spectroscopy using Q1ASM\n",
    "Now we will run the same spectroscopy experiment using Q1ASM to change the NCO frequency in real time. First, we set up the QRM for continuous wave output and binned acquisition with many bins. This is significantly faster than using QCoDeS. The maximum number of points that can be measured this way is 131072 per sequencer. This corresponds to the number of bins available for acquisition per sequencer.\n",
    "\n",
    "The sequencer program can fundamentally only support integer values. However, the NCO has a frequency resolution of 0.25 Hz and supports $10^9$ phase values. Therefore, frequencies in the sequencer program must be given as integer multiple of $1/4$ Hz, and phases as integer multiple of $360/10^9$ degree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "15dbffbc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:38:09.060701Z",
     "iopub.status.busy": "2024-09-02T01:38:09.060529Z",
     "iopub.status.idle": "2024-09-02T01:38:09.064216Z",
     "shell.execute_reply": "2024-09-02T01:38:09.063553Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200 steps with step size 5.0 MHz\n"
     ]
    }
   ],
   "source": [
    "n_steps = 200\n",
    "\n",
    "step_freq = (stop_freq - start_freq) / n_steps\n",
    "print(f\"{n_steps} steps with step size {step_freq/1e6} MHz\")\n",
    "\n",
    "# Convert frequencies to multiples of 0.25 Hz\n",
    "nco_int_start_freq = int(4 * start_freq)\n",
    "nco_int_step_freq = int(4 * step_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "07c05161",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:38:09.066365Z",
     "iopub.status.busy": "2024-09-02T01:38:09.066199Z",
     "iopub.status.idle": "2024-09-02T01:38:09.071099Z",
     "shell.execute_reply": "2024-09-02T01:38:09.070453Z"
    }
   },
   "outputs": [],
   "source": [
    "acquisitions = {\"acq\": {\"num_bins\": n_steps, \"index\": 0}}\n",
    "\n",
    "setup = f\"\"\"\n",
    "    move {n_averages}, R2\n",
    "\n",
    "avg_loop:\n",
    "    move    0, R0          # frequency\n",
    "    move    0, R1          # step counter\n",
    "\"\"\"\n",
    "\n",
    "# To get a negative starting frequency, we subtract a positive number from 0\n",
    "if start_freq <= 0:\n",
    "    setup += f\"\"\"\n",
    "    sub R0, {-nco_int_start_freq}, R0\n",
    "    \"\"\"\n",
    "else:\n",
    "    setup += f\"\"\"\n",
    "    add R0, {nco_int_start_freq}, R0\n",
    "    \"\"\"\n",
    "\n",
    "spectroscopy = (\n",
    "    setup\n",
    "    + f\"\"\"\n",
    "    reset_ph\n",
    "    set_freq        R0\n",
    "    upd_param       200\n",
    "\n",
    "nco_set:\n",
    "    set_freq        R0                           # Set the frequency\n",
    "    add             R0, {nco_int_step_freq}, R0  # Update the frequency register\n",
    "    upd_param       200                          # Wait for time of flight\n",
    "    acquire         0, R1, {MAXIMUM_SCOPE_ACQUISITION_LENGTH}\n",
    "    add             R1, 1, R1\n",
    "    nop\n",
    "    jlt             R1, {n_steps}, @nco_set      # Loop over all frequencies\n",
    "\n",
    "    loop            R2, @avg_loop\n",
    "\n",
    "    stop                                  # Stop\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "# Add sequence to single dictionary and write to JSON file.\n",
    "sequence = {\n",
    "    \"waveforms\": {},\n",
    "    \"weights\": {},\n",
    "    \"acquisitions\": acquisitions,\n",
    "    \"program\": spectroscopy,\n",
    "}\n",
    "with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "    json.dump(sequence, file, indent=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e7858e2",
   "metadata": {},
   "source": [
    "Now we prepare the QRM for measurement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5a7f28f2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:38:09.073352Z",
     "iopub.status.busy": "2024-09-02T01:38:09.073188Z",
     "iopub.status.idle": "2024-09-02T01:38:09.095744Z",
     "shell.execute_reply": "2024-09-02T01:38:09.095131Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.sequence(\"sequence.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "76f5990b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:38:09.097994Z",
     "iopub.status.busy": "2024-09-02T01:38:09.097716Z",
     "iopub.status.idle": "2024-09-02T01:38:09.194131Z",
     "shell.execute_reply": "2024-09-02T01:38:09.193488Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status:\n",
      "Status: OKAY, State: RUNNING, Info Flags: ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_OVERWRITTEN_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_SCOPE_OVERWRITTEN_PATH_1, ACQ_BINNING_DONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n",
      "CPU times: user 57.1 ms, sys: 20.6 ms, total: 77.7 ms\n",
      "Wall time: 92.6 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "readout_module.arm_sequencer(0)\n",
    "readout_module.start_sequencer()\n",
    "\n",
    "print(\"Status:\")\n",
    "print(readout_module.get_sequencer_status(0))\n",
    "\n",
    "# Wait for the sequencer to stop with a timeout period of one minute.\n",
    "readout_module.get_acquisition_status(0)\n",
    "\n",
    "data = readout_module.get_acquisitions(0)[\"acq\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89470b9f",
   "metadata": {},
   "source": [
    "Note that the same measurement as before is now two orders of magnitude faster. If we plot the integrated data, we get the same results as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9e40223e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:38:09.196543Z",
     "iopub.status.busy": "2024-09-02T01:38:09.196364Z",
     "iopub.status.idle": "2024-09-02T01:38:09.345433Z",
     "shell.execute_reply": "2024-09-02T01:38:09.344862Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For plotting, convert the NCO integer values back to frequencies\n",
    "nco_sweep_range = (np.arange(n_steps) * nco_int_step_freq + nco_int_start_freq) / 4.0\n",
    "\n",
    "I_data = (\n",
    "    np.asarray(data[\"acquisition\"][\"bins\"][\"integration\"][\"path0\"])\n",
    "    / MAXIMUM_SCOPE_ACQUISITION_LENGTH\n",
    ")\n",
    "Q_data = (\n",
    "    np.asarray(data[\"acquisition\"][\"bins\"][\"integration\"][\"path1\"])\n",
    "    / MAXIMUM_SCOPE_ACQUISITION_LENGTH\n",
    ")\n",
    "plot_amplitude(nco_sweep_range, I_data, Q_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e85df264",
   "metadata": {},
   "source": [
    "#### NCO input delay compensation\n",
    "By default, the input and output of the QRM are multiplied with the same NCO value. As the output path has a time of flight of about 149 ns between the NCO and playback (provided no RTP filters are activated), this means that there is a short time window after frequency/phase updates where demodulation is updated, but playback is still using the old value. There is also always a (fixed) relative phase between playback and demodulation. We can showcase this by using a similar program as before, but with less points, so that the frequency steps are more clearly visible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "95a5010b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:38:09.347784Z",
     "iopub.status.busy": "2024-09-02T01:38:09.347608Z",
     "iopub.status.idle": "2024-09-02T01:38:09.351575Z",
     "shell.execute_reply": "2024-09-02T01:38:09.350928Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20 steps with step size 50.0 MHz\n"
     ]
    }
   ],
   "source": [
    "n_steps = 20\n",
    "n_averages = 1000\n",
    "\n",
    "step_freq = (stop_freq - start_freq) / n_steps\n",
    "print(f\"{n_steps} steps with step size {step_freq/1e6} MHz\")\n",
    "\n",
    "# Convert frequencies to multiples of 0.25 Hz\n",
    "nco_int_start_freq = int(4 * start_freq)\n",
    "nco_int_step_freq = int(4 * step_freq)\n",
    "\n",
    "# For plotting, convert the NCO integer values back to frequencies\n",
    "nco_sweep_range = np.arange(nco_int_start_freq, 4 * stop_freq, nco_int_step_freq) / 4.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e209d855",
   "metadata": {},
   "source": [
    "To make the effect of NCO delay compensation more apparent, we modify the spectroscopy program for short integration time and acquire immediately after the frequency update, without waiting for time of flight. This means that the output at the new frequency only arrives at the input AFTER integration in the current loop iteration has finished. Without further modifications of the program this leads to an off-by-one error. Therefore, we increase the frequency as the first step in the loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b728e075",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:38:09.353875Z",
     "iopub.status.busy": "2024-09-02T01:38:09.353596Z",
     "iopub.status.idle": "2024-09-02T01:38:09.358542Z",
     "shell.execute_reply": "2024-09-02T01:38:09.357933Z"
    }
   },
   "outputs": [],
   "source": [
    "acquisitions = {\"acq\": {\"num_bins\": n_steps, \"index\": 0}}\n",
    "\n",
    "setup = f\"\"\"\n",
    "    move {n_averages}, R2\n",
    "\n",
    "avg_loop:\n",
    "    move    0, R0          # frequency\n",
    "    move    0, R1          # step counter\n",
    "\"\"\"\n",
    "\n",
    "# To get a negative starting frequency, we subtract a positive number from 0\n",
    "if start_freq <= 0:\n",
    "    setup += f\"\"\"\n",
    "    sub R0, {-nco_int_start_freq}, R0\n",
    "    \"\"\"\n",
    "else:\n",
    "    setup += f\"\"\"\n",
    "    add R0, {nco_int_start_freq}, R0\n",
    "    \"\"\"\n",
    "\n",
    "spectroscopy = (\n",
    "    setup\n",
    "    + f\"\"\"\n",
    "    reset_ph\n",
    "    set_freq        R0\n",
    "    upd_param       200\n",
    "\n",
    "nco_set:\n",
    "    # Due to time of flight, the new frequency will only arrive at the input AFTER integration is done\n",
    "    # Therefore, we already increase the frequency before the first measurement.\n",
    "    add             R0, {nco_int_step_freq}, R0\n",
    "    nop\n",
    "    set_freq        R0\n",
    "\n",
    "    # we removed upd_param, so that acquisition starts the moment the frequency is updated\n",
    "    acquire         0, R1, 1200                  # Update the NCO and immediately acquire\n",
    "    add             R1, 1, R1\n",
    "    nop\n",
    "    jlt             R1, {n_steps}, @nco_set      # Loop over all frequencies\n",
    "\n",
    "    loop            R2, @avg_loop\n",
    "\n",
    "    stop                                  # Stop\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "# Add sequence to single dictionary and write to JSON file.\n",
    "sequence = {\n",
    "    \"waveforms\": {},\n",
    "    \"weights\": {},\n",
    "    \"acquisitions\": acquisitions,\n",
    "    \"program\": spectroscopy,\n",
    "}\n",
    "with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "    json.dump(sequence, file, indent=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a218bc8",
   "metadata": {},
   "source": [
    "As a baseline, we will do a measurement with delay compensation disabled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9490e88f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:38:09.360770Z",
     "iopub.status.busy": "2024-09-02T01:38:09.360603Z",
     "iopub.status.idle": "2024-09-02T01:38:09.404379Z",
     "shell.execute_reply": "2024-09-02T01:38:09.403624Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.sequence(\"sequence.json\")\n",
    "readout_module.sequencer0.integration_length_acq(140)\n",
    "readout_module.sequencer0.nco_prop_delay_comp_en(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "519e7a3b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:38:09.407023Z",
     "iopub.status.busy": "2024-09-02T01:38:09.406845Z",
     "iopub.status.idle": "2024-09-02T01:38:09.522028Z",
     "shell.execute_reply": "2024-09-02T01:38:09.521404Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status:\n",
      "Status: OKAY, State: RUNNING, Info Flags: ACQ_BINNING_DONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "readout_module.arm_sequencer(0)\n",
    "readout_module.start_sequencer()\n",
    "\n",
    "print(\"Status:\")\n",
    "print(readout_module.get_sequencer_status(0))\n",
    "\n",
    "# Wait for the sequencer to stop with a timeout period of one minute.\n",
    "readout_module.get_acquisition_status(0)\n",
    "\n",
    "data = readout_module.get_acquisitions(0)[\"acq\"]\n",
    "I_data = np.asarray(data[\"acquisition\"][\"bins\"][\"integration\"][\"path0\"]) / 140\n",
    "Q_data = np.asarray(data[\"acquisition\"][\"bins\"][\"integration\"][\"path1\"]) / 140\n",
    "plot_amplitude(nco_sweep_range, I_data, Q_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b0ca358",
   "metadata": {},
   "source": [
    "Even though we only measured a small number of points, we can see that this is not compatible with the previous spectroscopy measurements. What happened is that `set_freq` updates the NCO frequency immediately. However, there is a time of flight of about 149 ns between the NCO and the output of the device. Thus, the signal will be demodulated at $f_0 + 100$ MHz immediately after `set_freq`, but the incoming signal is still modulated at $f_0$ for another 149 ns - longer than the integration time chosen above. The integrated signal will therefore be approximately zero.\n",
    "\n",
    "Now we run the same experiment again, with delay compensation enabled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5c6ce34d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:38:09.524468Z",
     "iopub.status.busy": "2024-09-02T01:38:09.524289Z",
     "iopub.status.idle": "2024-09-02T01:38:09.561132Z",
     "shell.execute_reply": "2024-09-02T01:38:09.559521Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.sequence(\"sequence.json\")\n",
    "readout_module.sequencer0.nco_prop_delay_comp_en(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87bb47c2",
   "metadata": {},
   "source": [
    "This ensures that, for demodulation, the NCO only updates after the time of flight, i.e. that frequency and phase of modulation and demodulation always match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "233d6941",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:38:09.566615Z",
     "iopub.status.busy": "2024-09-02T01:38:09.566217Z",
     "iopub.status.idle": "2024-09-02T01:38:09.696075Z",
     "shell.execute_reply": "2024-09-02T01:38:09.695409Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status:\n",
      "Status: OKAY, State: RUNNING, Info Flags: ACQ_BINNING_DONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n"
     ]
    },
    {
     "data": {
      "image/png": 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U4eT08x+YW9YKIHrUYIJ9vc2OJU6m6bniHurrze5TKfSZsUADc4iIU1IBExGRMpNtt3PtT0tJSs8ionY1lt42mMCqVcyOJU6qUXU//rh9CHX9fNh7OpVRvy4n3zDMjiUicllUwEREpMyMW7iOjUdPUaOKJ3Nu7E8tHy+zI4mTa1DNl19v6IeXm415+xJ4acU2syOJiFwWFTARESkTn23Zy8eb92ABvh7RSwNuSKlpG1SDaYO7APCfZZtZdCDR5EQiIkWnAiYiIqVuS9IpHliwBoDnerdjUMM6JicSV3NX28bc27YxBnDzrGXEpaSbHUlEpEicsoAlJyczceJEIiIiCA4OJigoiKCgIFq0aMHYsWM5duxYgduOHTv2/PoXe/n4+JCTo4d6RUSK60xWDtf8uJRsu4NhjUJ5qkdrsyOJi5o6uDPtg2pwKiuH63+KJsfuMDuSiMglOV0Bczgc9O/fHx8fH6Kjo0lMTCQpKYmkpCRWr15Ns2bN6N69O5mZFx+e9oMPPji//sVevr6+eHh4lPO7EhFxDfmGwahfl3MwOZ0GAVWZMbwnVovF7Fjiorzc3Pjx2j5U8/JgXeJJHl203uxIIiKX5HQFbNasWdSvX5/JkydTo0YNLH/5i93f35/x48czdOhQpk+fftn7TktLw9vb+4J9iohI0b20Yhtz9yXg5Wbjp+v6Uq2Kp9mRxMU1qObLVyN6ATBt426+itlvciIRkcI5XQGLiYmhb9++ha4zYMAAYmJiLnvfv/76K4MGDSpuNBGRSu33/Uf4z7LNAEwb3IV2QTVMTiSVxdBGofy7RxsARs9dRczxMyYnEhEpmNMVsISEBIKDgwtdJyQkhLi4uMva75EjR3j22Wd57LHHShJPRKRSOpyczi2//IEB3NeuCXe1bWx2JKlkJvdqw6DwELLsDq79cSmpOblmRxIRuSinK2AOhwObzVboOm5ubtjt9iLvc9OmTfTp04dXXnmFxo0L/tCQk5NDamrqBS8Rkcoux+7g+p+jOZWVQ4fgGrx7RaTZkaQSslmtzBzRi7BzkzTfNXslhiZpFpEKyOkKWGlbsGABN9xwAzNnzuTaa68tdN0pU6bg7+9//hUWFlZOKUVEKq5Hfl/H+sSTVK/iyY/X9sHLzc3sSFJJ1fT24sdr++ButfLz7sO8sWaH2ZFERP7B6QtYbGws4eHh7Nmz57K3PXjwIGPHjmXx4sVERl76G9tJkyaRkpJy/hUfH1+cyCIiLuPLbfv4cFMsFmDmiJ7UD/A1O5JUcpF1avHOuauwTy7ZyLLDSSYnEhG5kNMXsKZNm3LgwAGaNGly2ds+9dRTvPDCC9SvX79I63t6euLn53fBS0Skstp67DRj5q0GYHKvtgxuGGpyIpGz7m/flNtaheMwDG78OZqjaRefmkZExAxOV8CK8nyX3W7H7RK3wNjtdpYsWcINN9xQmvFERCqF5Owcrj032fKQhnX4d882ZkcSOc9isfDh0K60qhXAsYxsbvg5mjxHvtmxREQAJyxgoaGhJCUVfjtBYmIidevWLXSdHTt20KxZM9zd3UsznoiIy8s3DG7/dQX7z6RR378qX43opcmWpcLx8XDnp+v64ufpzor440xautHsSCIigBMWsIiICKKiogpdJyoqioiIiELXOXLkCHXq1CnNaCIilcLLK2OYvTceT5uVH6/rQ3VNtiwVVJMa/nx+VQ8A3lizgx93HTI3kIgITljARowYQUJCApMnT+bUqVMXDDGbmprK1KlTmTt3LqNHjy50P+np6fj4+JR1XBERl7L4QCL/PjfZ8nuDu9AhuKbJiUQKN7JZPf7VtRUAd81eQeypFJMTiUhl53QFzGazsXjxYrKysujduzchISEEBQURFBREZGQk27dvZ8WKFefLVXR0NI0aNfrHnF3u7u4EBQWZ8RZERJxSfEoGN/+yjHzD4O42jbm33eUPfiRihpf6tqdX3UDSc+1c88NS0nPzzI4kIpWYxdAshcWWmpqKv78/KSkpGhFRRFxajt1B7xnzWXvkJO2CqrPyjqFUcdd8X+I8ktIzaffJbJLSs7i5ZQNmjuiFRc8uikgpKmo3cLorYCIiUv4eXbSetUdOUs3Lg5+u7avyJU4nqKo331/TB5vFwjc7DjJt426zI4lIJaUCJiIihfoqZj/TNu7GAnw1ohcNqmmyZXFOPesG8mr/jgBM+H09axKOm5xIRCojFTARESnQtmOnGT13FQD/7tmGoY002bI4twmdW3Bd83rk5edz/U/RnMjINjuSiFQyKmAiInJRKdm5XPvjUrLsDgaFh/AfTbYsLsBisfDpld1pWsOPhLRMbp61DEe+JmkWkfKjAiYiIv9gGAZ3zl7BvjNp1PXz4esRvbBZ9VeGuAY/Tw9+uq4v3u5uRB06yuRlW8yOJCKViP42FRGRf3h19XZ+iY3Dw2blx+v6UsPby+xIIqWqZa1qfDysGwAvrtzGnL3xJicSkcpCBUxERC6w5OBRnlq6CYCpV3SmU4gmWxbXdEurcB7q2AyAUb8u58CZNJMTiUhloAImIiLnJaRmcNOss5Mt39m6EfdpsmVxcW8M7ESXOrVIzs7lup+WkpVnNzuSiLg4FTAREQEg1+E4OypcZjZtAqsxbUgXTVQrLs/DZuOHa/tQ09uTzUmneWjBWrMjiYiLUwETEREAHlu0njVHTuDv6a7JlqVSCfXz4duRvbFaLPxv614+3bzH7Egi4sJUwEREhK+3H+C9DbuBs5MtN6zuZ3IikfLVv0EIz/duB8CDC9aw6egpkxOJiKtSARMRqeS2Hz/DfecmW366e2uubBxmciIRczzZPYIrG4eS48jn2h+Xcjorx+xIIuKCVMBERCqx1Jyzky1n5tkZ2CCE//Zua3YkEdNYLRa+vLon4QG+HEpJ5/Zfl5NvGGbHEhEXowImIlJJGYbBXbNXsud0KmF+Pnw9UpMti1Sr4slP1/XBy83G3H0JvLRim9mRRMTF6G9aEZFK6vU1O/h592HcrVZ+vLYPNTXZsggAbYNqMG1wFwD+s2wziw4kmpxIRFyJCpiISCUUfegoTy7ZCMA7V0QSWaeWyYlEKpa72jbm3raNMYCbZy0jLiXd7Egi4iJUwEREKpkjqRnceG6y5VERDbm/fVOzI4lUSFMHd6Z9UA1OZeVw/U/R5NgdZkcSERegAiYiUonkOfK54edlHM/IpnXtanw4tKsmWxYpgJebGz9e24cALw/WJZ7kscXrzY4kIi5ABUxEpBL5V9R6ViUcPzvZ8nV98dZkyyKFalDNl6+G9wTg/Q27mRmz3+REIuLsVMBERCqJb3cc4J11uwD4cnhPGmmyZZEiGdY4jH/3aAPA6Hmr2X78jMmJRMSZqYCJiFQCO08kc++cs5MtT+oWwdVN6pqcSMS5TO7VhoENQsjMs3PNj0tJzck1O5KIOCkVMBERF5eak8s1Py4hI89O//rBPN+nndmRRJyOzWrl65G9CPPzYe/pVO6avRJDkzSLSDGogImIuDDDMLh79kpiT6VSx9dbky2LlEBNby9+uLYP7lYrP+8+zJtrd5gdSUSckP4WFhFxYW+u3cFPf5lsubZPFbMjiTi1znVq8fagSACeiNrIH4eTTE4kIs5GBUxExEX9cTiJJ6LOTrb81qBOdAmtbXIiEdcwtkNTbm0VjsMwuHHWMo6mZZodSUSciAqYiIgLOpqWyY2zluEwDG5tFc4DHZqZHUnEZVgsFqYP7UqrWgEkpWdx48/LyHPkmx1LRJyECpiIiIs5O9lyNEnpWbSqFcB0TbYsUup8PM7Opefr4c7y+GNMWrrR7Egi4iRUwEREXMwTSzawIv44fp7u/Hx9P3w83M2OJOKSmtTw5/OrewDwxpod/LjrkLmBRMQpqICJiLiQ2XvieWvtTgA+v6oHjTXZskiZuqZZPR7v0hKAu2ev5MCZNJMTiUhFpwImIuIizmTlMGbe2cmWH+3ckpHN6pmcSKRymNKvAz3CapOWm8e9czQ/mIgUTgVMRMRFTFi0jqPpWTSt4ceLfTXZskh5cbNa+fyqHlRxs7H0cBIfb95jdiQRqcBUwEREXMD8fQl8sW0/FuB/V/bAy83N7EgilUrD6n681Lc9AI8v3kB8SobJiUSkolIBExFxcinZudw39+yth490bkG3MM33JWKGhzs1p2toLdJy8xg9b5VuRRSRi1IBExFxco8vXs+RtEwaVfPlhT7tzY4jUmnZrFb+d2V3PG1WFuw/wpfb9psdSUQqIBUwEREntuhAIp9s2QvAp1d2x9tdtx6KmKlZzQD+2/vsM5iPLFrH0bRMkxOJSEWjAiYi4qTScs6OuAbwUMdm9KoXZHIiEQF4rEtLOgTXIDk7l7HzV+tWRBG5gAqYiIiTemLJBuJSM2gQUJUp/TqYHUdEznGzWvnsqh64W638uiee73YeNDuSiFQgKmAiIk5o6aGjfLAxFoBPhnWnqoe7yYlE5K8ialfj6R6tAXh44VpOZGSbnEhEKgoVMBERJ5ORm8e9c86OejimfRP6NQg2OZGIXMyk7hG0rl2Nk5k5PLxwjdlxRKSCUAETEXEyTy3dxIHkNML8fHi1f0ez44hIATxsNv53VXdsFgvf7TzErN2HzY4kIhWACpiIiBNZEXeMqet3AfDxsG74eXqYnEhECtMhuCYTu7YCYOz81ZzOyjE5kYiYTQVMRMRJZObZuXvOSgzg7jaNuaJhHbMjiUgR/KdXG5rV8OdYRjYTfl9ndhwRMZkKmIiIk/jPss3sPZ1KiK83bwzUrYcizsLLzY3/XdUdC/BlzH7m7UswO5KImEgFTETECaxJOM5ba3cC8NHQrgR4eZqcSEQuR9fQ2kzo3BKAMXNXkZKda3IiETGLCpiISAWXbbdz1+yV5BsGoyIaMqxxmNmRRKQYnu/TjobVfElIy2Ri1Aaz44iISVTAREQquP/+sZXdp1IIqlqFtwdFmh1HRIrJ292NT6/sDsBHm/cQdTDR5EQiYgYVMBGRCmxD4kleW70dgA+GdKF6Fd16KOLMetcL4oEOzQC4d84q0nPzTE4kIuVNBUxEpILKsTu4a/YKHIbBTS0aMKJpPbMjiUgpeLlfB+r5+3AoJZ2nlm4yO46IlDMVMBGRCurFFdvYfiKZWt5eTB3c2ew4IlJKfD3d+XjY2VsRp67fxfK4YyYnEpHypAImIlIBbUk6xZRV2wB4f3AXanp7mZxIRErTwPAQ7mnbGIB75qwkM89uciIRKS8qYCIiFUyeI5+7Zq/Enm9wbbN6XN+ivtmRRKQMvD6gIyG+3uw9ncrkZZvNjiMi5UQFTESkgnl5VQxbjp2mRhVP3h/cxew4IlJGArw8mT60KwBvrt3J2iMnTE4kIuXBaQpYcnIyEydOJCIiguDgYIKCgggKCqJFixaMHTuWY8cKv386JyeHF154gaZNm57f9s9Xo0aNeOaZZ8jOzi6ndyMicnExx8/w/PKtALx7RWcCq1YxOZGIlKUrG4dxW6tw8g2Du2evJMfuMDuSiJQxpyhgDoeD/v374+PjQ3R0NImJiSQlJZGUlMTq1atp1qwZ3bt3JzMzs8B9vPLKK2zdupWVK1ee3/bP1/r16zl48CAvvPBCOb4rEZEL2fPzuWv2CvLy87m6SRg3t2xgdiQRKQdvD4qkto8XO08m88KKrWbHEZEy5hQFbNasWdSvX5/JkydTo0YNLBbL+WX+/v6MHz+eoUOHMn369AL38dVXX/HOO+9Qs2bNfyyrVq0aL7/8Mr/99luZ5BcRKYrXV29n49FTBHh58OGQrhf8rhMR11XD24tp5243nrIyhs1Jp0xOJCJlySkKWExMDH379i10nQEDBhATE1Pg8mPHjhEcHFzg8jp16tC5s4Z5FhFz7DqZzOQ/tgDw9sBIgn29zQ0kIuXq2ub1ua55PRyGcfZKuCPf7EgiUkacooAlJCQUWp4AQkJCiIuLK3C5xWIp9Ntkq9XKxx9/XOyMIiLF5cjP5+7ZK8l15DOkYR1ub93Q7EgiYoL3ruhCjSqebD12hldWFfylsog4N6coYA6HA5vNVug6bm5u2O2Fz6GxcuVKRowYQcOGDc8PwPHnla8PPvjgktvn5OSQmpp6wUtEpKTeXreTNUdO4OfpzkfDuunWQ5FKKrBqFd694uzdOM8t38qOE2dMTiQiZcEpClhpyMrK4q233uLll19mz5495wfgiIuLY8aMGcyePZs33nij0H1MmTIFf3//86+wsLBySi8irmrPqRSeiT47/88bAzoR6udjciIRMdPNLRtwVeMw8vL/nA9QtyKKuBqnLGCxsbGEh4ezZ8+eIm+Tm5vLa6+9RrNmzS64mmaz2WjSpAmfffYZ06ZNK3QfkyZNIiUl5fwrPj6+2O9BROTPWw+z7Q4GNgjhnraNzY4kIiazWCx8MKQL/p7urE88yVtrd5odSURKmVMWsKZNm3LgwAGaNGlS5G08PT2pX79+gcsDAwPJyckhJyen0H34+fld8BIRKa73NuxmZcJxqnq48bFuPRSRc+r4+fDmwEgA/h29idhTKSYnEpHS5BQFrCjPd9ntdtzc3Apc7uXldckPN15eXmRlZRUro4jI5dh/OpVJSzYC8Gr/jtQLqGpyIhGpSO5q04hB4SHkOPK5Z/ZK8g3D7EgiUkqcooCFhoaSlJRU6DqJiYnUrVu3wOVubm4Yl/jllZ2djZeXV7EyiogUVb5hcM+clWTZHfSpF8SY9k3NjiQiFYzFYuGjod2o6uHGyoTjvLd+l9mRRKSUOEUBi4iIICoqqtB1oqKiiIiIKHB5cHAwCQkJBS4/deoU7u7uKmAiUuY+3BjLsrhjeLu78emV3bHq1kMRuYh6AVV5tX9HACYt3cSBM2kmJxKR0uAUBWzEiBEkJCQwefJkTp06dcGVrNTUVKZOncrcuXMZPXp0gfsYO3YsDzzwAEeOHPnHsqSkJEaPHs29995bJvlFRP50KDmNiVEbAJjStz3h1XxNTiQiFdmY9k3pXTeQzDw7985Zecm7eUSk4nOKAmaz2Vi8eDFZWVn07t2bkJCQ8/N4RUZGsn37dlasWIGPz9nhm6Ojo2nUqNEF83SNGTOGIUOGMHjw4Au2Dw4Opm/fvkRGRjJp0iSz3qKIVAKGYXDf3FVk5NnpEVabhzo1NzuSiFRwVouFT67sThU3G0sPJ/Hx5qKPAC0iFZPF0FcpxZaamoq/vz8pKSkaEVFELunjTXsYPW8VXm42to0eTuPq+r0hIkXz9todTFi0Hl8Pd3aMGUGYv+YMFKloitoNnOIKmIiIs4tPyeCxxesBeKFPO5UvEbksD3dqTtfQWqTl5jF63irdiijixFTARETKmGEYjJ63irTcPLrUqcUjkS3MjiQiTsZmtfK/K7vjabOyYP8Rvty23+xIIlJMKmAiImXsi237WLD/CJ42K/+7qjs2q371isjla1YzgGd7tQXgkUXrOJqWaW4gESkWfQoQESlDiWmZTFh09tbD//ZuR/OaAeYGEhGn9njXVnQIrkFydi5j56/WrYgiTkgFTESkjBiGwf3zVpOcnUunkJo81qWl2ZFExMm5Wa18dlUP3K1Wft0Tz3c7D5odSUQukwqYiEgZ+Xr7AWbvjcf93LMbbrr1UERKQUTtajzdozUADy9cy4mMbJMTicjl0KcBEZEykJSeybjf1wHwn55taFW7msmJRMSVTOoeQeva1TiZmcPDC9eYHUdELoMKmIhIKTMMgwcXrOV0Vg7tgqrzRLcIsyOJiIvxsNnODupjsfDdzkPM2n3Y7EgiUkRFKmB/Tipms9lK9eXl5cX69evL+j2KiJSrH3Yd4ufdh3GzWs4+q2HTd10iUvo6BNdkYtdWAIydv5rTWTkmJxKRonArykppaWmkpaXx0ksv0bVr11I58NGjR7nllls4ceJEqexPRKQiOJGRzYMLzt4O9FT31rQJrG5yIhFxZf/p1YZZsXHsPpXChN/X8cXwnmZHEpFLKFIBs1gsALRp04bevXuXyoHj4uIu2LeIiCt4eOEaTmbmXPCQvIhIWfFyc+N/V3Wn++fz+DJmPze2bMDQRqFmxxKRQhTpvpiQkBA+/fRTevXqVWoHrlu3Lp988kmp7lNExEyzdh/mu52HsFksfHZVdzxsNrMjiUgl0DW0No90bgHAmLmrSMnONTmRiBSmyA8mBAQEcMcdd/D777+X2sHvvvtufHx8Sm1/IiJmOZWZzdj5qwGY2LUVHYJrmpxIRCqTF/q0p2E1XxLSMpkYtcHsOCJSiCIXsHr16nH06FEGDx5Ms2bNmDZtGunp6WWZTUTEaTzy+zqOZWTTvKY//+nVxuw4IlLJeLu78emV3QH4aPMeog4mmpxIRApS5ALWvn17Vq5cyfr16+ncuTOPPvoooaGhPPbYY+zfv78sM4qIVGhz9sbz1fYDWC1nRz30civS47UiIqWqd70gHujQDIB756wiPTfP5EQicjGXPTZyhw4d+OKLL4iLi+Oxxx7j22+/pUmTJgwfPpyoqKiyyCgiUmElZ+cwZt7ZWw8f7dyCznVqmZxIRCqzl/t1oJ6/D4dS0nlq6Saz44jIRRR7cpratWvz73//m8OHDzNz5kxOnjzJwIEDadmyJdOnTyczM7M0c4qIVEiPLlpPYlomTar78VzvdmbHEZFKztfTnY+Hnb0Vcer6XSyPO2ZyIhH5uxLPDurm5sZNN93EypUrWbt2LR06dGDcuHHUqVOHf/3rXxw8eLA0coqIVDi/7z/CZ1v3YQH+d1V3qrjr1kMRMd/A8BDuadsYgHvmrCQrz25yIhH5qxIXsL/q1KkTX375JfHx8UyYMIGZM2fSuHFjRo4cSXR0dGkeSkTEVFl5dsbOPzvh8sOdmtM9LNDkRCIi/+/1AR0J8fVm7+lUnl++1ew4IvIXpVrA/lS7dm3+85//cPjwYWbMmEFcXBz9+/enZ0/Nzi4iruHFFds4kJxGHV9vXujT3uw4IiIXCPDy5L0rOgPw2prt7DhxxuREIvKnMilgf4qPj2f9+vXs27cPm81Gy5Yty/JwIiLlYtfJZF5dvR2Ad6/ojK+nu8mJRET+aUTTulzVOAx7vsH981aTbxhmRxIRyqiARUVFMXz4cJo0acLMmTMZP348hw4d4sMPPyyLw4mIlBvDOPtBJi8/nysbhzKyaV2zI4mIXJTFYmHqFZ3xdndjRfxxPt+6z+xIIkIpFrCsrCw++ugjWrVqxaBBg0hISODTTz8lLi6O5557jpCQkNI6lIiIab7Yto8/4o7h7e7Ge1d0wWKxmB1JRKRA9QKq8t9ebQH4V9QGTmRkmxtIREpewOLi4njiiScIDQ3lwQcfpEWLFixbtoyNGzdyxx134OnpWRo5RURMdzIzm8cXbwBgcs821AuoanIiEZFLGx/Zgta1q3E6K4d/Ra03O45IpVfsAvbHH39w3XXX0bBhQz799FNGjx7NgQMH+P777+nRo0dpZhQRqRCeiNrIqawcImpXY0JnPdMqIs7B3WZl+tCuWIAvtu0n+tBRsyOJVGqXVcBycnL47LPPaNeuHX379mXPnj18+OGHxMfHM2XKFMLCwsoqp4iIqZbHHeN/W/cC8OGQrrjbynQMIxGRUtUltDZj2jcF4P75q8mxO0xOJFJ5FfkTxLx58wgNDeXee++lQYMGREVFsW3bNu655x6qVKlSlhlFREyV63AwZt4qAEa3a0K3sNomJxIRuXxT+rUn0MeL2FOp50dyFZHyV+QCVr16dR544AH279/Pzz//TJ8+fcowlohIxfH66h3sOplCLW8vpvTrYHYcEZFiCfDy5K2BkQC8uGIre0+nmpxIpHIqUgHLzc3lww8/5N5776V+/fqlcuCcnBzuuOMO4uPjS2V/IiJl4cCZNJ5fsRWANwd2onoVDSwkIs7rppYNGNgghBxHPg/MX42hucFEyl2RCtiJEyeYMWMG27eX3uXqpKQkvvrqK2JiYkptnyIipckwDB5csIZsu4N+9YO5tVW42ZFERErEYrEwbUgXPG1WFh88yjc7DpodSaTScSvqioZhMHPmTNavL53hS5OTk0tlPyIiZeWHXYdYsP8IHjYr04Zozi8RcQ2NqvvxTI82/HvZZiYsWseQhnWopqv7IuWmSAWsZs2aDBs2jCNHjnDkyJFSO3j//v1p1qxZqe1PRKS0pGTn8sjv6wCY1C2CpjX8TU4kIlJ6/tW1FTO3H2D3qRQmLd3Ih0O7mR1JpNKwGLr5t9hSU1Px9/cnJSUFPz8/s+OISCl6eMEa3tuwm8bV/dg2+mq83Ip8w4CIiFNYdjiJPjMWALDqzqF0DdUIryIlUdRuoIlsRET+Zn3iSd7fsBuAaYO7qHyJiEvqXS+IO1s3AmDMvNXkOfJNTiRSOaiAiYj8hT0/nzHzVmEAt7YKZ0B4iNmRRETKzGsDOlKjiicxx8/w9rqdZscRqRRUwERE/uL9DbvZnHSaAC8P3hjQyew4IiJlqqa3F6/17wjAs39s4XByusmJRFyfCpiIyDkJqRk8E70JgJf7diCwahWTE4mIlL072zSiV91AMvPsPLRwjeYGEyljKmAiIuc88vs60nPtdA2txX3tm5gdR0SkXFgsFj4c2hV3q5U5exOYFRtndiQRl6YCJiICzN0bz0+7D2OzWPhwSFesmvNLRCqR5jUDmNi1FQDjFq4lLSfP5EQirksFTEQqvYzcPB5csAaAR7u0pHVgdZMTiYiUv6d7tKZhNV+OpGXy72WbzI4j4rJUwESk0ntu+VYOp2RQ18+HyT3bmB1HRMQUVdzdmDa4CwBT1+9m09FTJicScU2lXsDy8/MLfImIVDQxx8/w5todALw3uAs+Hu4mJxIRMc+ghnW4qUUD8g2DMfNW4dDnN5FSV+ICdvLkScaNG0fr1q3x9fXF3d29wFejRo1KI7OISKnINwzun7cae77ByKZ1uapJmNmRRERM9+bATvh5urPh6CmmbYw1O46Iy3ErycYnTpygZcuWANxxxx3Uq1cPX1/fAtcPCdGEpiJScXy6eS+rEo5T1cONdwZ1NjuOiEiFEOzrzZS+HXhwwRqeXrqJa5rWpY6fj9mxRFxGiQrYf/7zHwzDYPfu3dSoUaO0MomIlLnjGVk8sWQDAM/1bkeYvz5ciIj8aUz7JnyxbR/rEk/yyKJ1/HBtX7MjibiMEt2CuHLlSm6++WaVLxFxOo8v3sCZ7FzaBlbn4U7NzY4jIlKh2KxWpg/tis1i4cddh5m3L8HsSCIuo0QFbP/+/dSrV6+0soiIlIslB48yI2Y/FmD60K64WTUgrIjI37UNqsH4yBYAPDh/DZl5dpMTibiGEn3qyM7OxsPDo7SyiIiUuRy7g7HzVwMwtkMzIuvUMjmRiEjF9d/ebQnz8+FQSjrPL99qdhwRl1CiAlazZk1OnDhRWllERMrcK6ti2HM6laCqVXipb3uz44iIVGhVPdyZesXZQYpeX7Od7cfPmJxIxPmVqIC1bt2avXv3llYWEZEytfd0Ki+t3AbAWwM74e+lK/giIpcyvGldhjcJw55/duqOfMMwO5KIUytRAbvjjjuYPXs2aWlppZVHRKRMGIbBA/NXk+PIZ1B4CDe2aGB2JBERpzH1ii74uLuxMuE4/9uiL99FSqJEBey2225j5MiRjBgxgri4uNLKJCJS6r7ZcZDFB4/iabMybXAXLBaL2ZFERJxGmL8Pz/VuB8DEqA0cz8gyOZGI8yrRPGC9e/dm3759HD16lAYNGhAUFITNZitw/fDwcKKjo4t1rOTkZF566SXmz5/PyZMnMc5d/q5evTq9e/fm2WefJTAwsMDt/7xaV9igIf3792fmzJnFyiciFdeZrBwmLFoHwDM92tCwup/JiUREnM+4yObMiNnPlmOneXzxBr4c3tPsSCJOqUQF7M4777ysK19hYWHFOo7D4aB///5cffXVREdHU7169fPfXqekpPD555/TvXt3tm3bhre390X3kZKSws8//0yfPn2KlUFEnNekpRs5npFNsxr+/KtrK7PjiIg4Jbdzc4N1+WwuM2L2c2frRvRrEGx2LBGnU6ICdtddd5VWjkLNmjWL+vXrM3ny5H8s8/f3Z/z48ezfv5/p06czYcKEcskkIs5hdcJxpm/aA8CHQ7vi6VbwVXoRESlcZJ1ajO3QjGkbdzN2/mq2jR6u36sil8kpZh+NiYmhb9++ha4zYMAAYmJiyimRiDiDPEc+9887O+fXna0b0btekMmJRESc30t92xNUtQp7Tqfy8ip99hK5XE5RwBISEggOLvwSd0hIiAYCEZELvLNuJ9uOn6F6FU9eG9DR7DgiIi7B38uDtwdGAvDSym3sOZViciIR5+IUBczhcBQ6uAeAm5sbdru90HVmzZrFoEGDCA8PJzAwkKCgIMLCwujRowfffPPN+YE9CpKTk0NqauoFLxGpmOJS0pn8xxYAXuvfkZreXuYGEhFxITe0qM8V4SHkOvIZO3/NJT9Dicj/K9EzYHC2lLz33nssW7aMgwcPkpLy/9+C+Pv706BBA3r37s1DDz2Ep6dnSQ9XbC1atGDHjh08++yztGrVCj+/s6Og2e12du7cyZgxYwC4+eabC9zHlClT+O9//1sueUWkZB5euJbMPDs9wwK5s00js+OIiLgUi8XCtCFdaTn9F5YcOsrM7Qe4LaKh2bFEnILFKMFXFmlpaXTo0IHDhw9z5ZVX0qBBA3x9fS9YfvDgQebMmUO9evXYuHHjBcuL6s4772TEiBGMGDECgNjYWIYMGcKCBQto0qQJAFu2bOGRRx4p9jD3Gzdu5JFHHmH58uUFrpOTk0NOTs75P6emphIWFkZKSsr5Qici5vsl9jAjf1iKm9XClvuupmWtamZHEhFxSS+t2MbT0Zuo5e3F7rEjqV7FvC/bRcyWmpqKv7//JbtBia6AvfDCCyQmJrJt2zaaNm1a4HqxsbG0b9+eF154gVdeeaUkhwSgadOmHDhwoMT7+avmzZuzf//+Qtfx9PQ09SqeiFxaem4eDy9YC8C/urRS+RIRKUOPd23JzO0H2HkymSeXbOSjYd3MjiRS4ZXoGbBFixZx8803F1q+4GxhuvXWW1m0aFGxjlOU57vsdjtubsXvk97e3mRmZhZ7exGpGCYv20JCWiYNAqryTM82ZscREXFpHjYbHw7tCsDHm/ewMv6YyYlEKr4SFbD9+/fTqFHRnq1o2LBhsa9ahYaGkpSUVOg6iYmJ1K1bt8DljzzySKHbZ2VlUaVKleLEE5EKYkvSKd5ZtxOA9wd3wdu9xI+5iojIJfSsG8jdbRoDcP+81eQ58k1OJFKxlaiApaWlFfmZrqpVq5KWllas40RERBAVFVXoOlFRUURERBS4/Keffip01MI9e/YQHh5erHwiYj5Hfj5j5q3GYRhc37w+QxqFmh1JRKTSeLV/B2p6e7L9RDJvrt1hdhyRCs0phqEfMWIECQkJTJ48mVOnTl0w1GlqaipTp05l7ty5jB49usB9jBw5kkceeYQzZ85c8HPDMDh48CCPPPJIoduLSMX20eY9rEs8ia+HO28PijQ7johIpVLD24vX+3cC4L9/bOHgmeJ96S5SGZSogPn6+hb5qlZ6enqxRkAEsNlsLF68mKysLHr37k1ISAhBQUEEBQURGRnJ9u3bWbFiBT4+PgBER0fTqFGjC654vfzyywQFBdGxY8fz2wYFBVGnTh1GjhzJrbfeyu23316sfCJirqT0TCYt2QjAi33bE+LrbXIiEZHK5/bWDeldN5Asu4OHFq7V3GAiBSjRMPTt27enQ4cOfPzxx5dcd/To0WzYsIFNmzYV93AVTlGHmhSRsnXzz8v4dudBOgbXYM1dw7BZneLivoiIy9l9MpnWH/1GXn4+P1zbh+ua1zc7kki5KWo3KNGnlAEDBvD1118TGxtb6HqxsbHMnDmTAQMGlORwIiL/8Pv+I3y78yBWi4XpQ7upfImImKhZzQCe6NYKgPG/ryM1J9fkRCIVT6lNxDx06FDq169/wW2G6enpHDp0iHnz5hEWFsamTZuKfRtiRaQrYCLmysqzE/HRr+w/k8b4yOa8Paiz2ZFERCq9v/5uHtepOe9cod/NUjkUtRuUqIAB5OTk8N577/HHH39w+PBhUlJSzi/z9fWlfv369OzZk4cffhgvL6+SHKrCUQETMde/ozfxwopthPh6s+v+Efh5epgdSUREgEUHEhn09e9YLRbW3T2MDsE1zY4kUubKrYBVZipgIub563MGP17bh2v1nIGISIVyy6xlfLPjIB2Ca7BWz+dKJVAuz4CJiJjBMIyzk33m5zOsUSjXNKtndiQREfmbNwd2wt/TnY1HT/H+ht1mxxGpMFTARMTpfLltP8vijlHFzcZ7gztjsVjMjiQiIn8TVNWbl/t1AOCZ6M0cSc0wOZFIxaACJiJO5VRmNo9HrQdgcq+21A9wnYF9RERczej2TelSpxZpuXmM/32d2XFEKgS3oq7Yu3dvVq5cyd13381HH30EwKeffsrhw4eLfLB69epxzz33XH5KEZFznliykZOZObSsFcCjnVuaHUdERApxdoqQrrT/ZDY/7T7M3L3xDGscZnYsEVMVuYBt2bKF/Px8YmJizv/s66+/5sCBA0U+WHh4uAqYiBTb8rhjfLplLwDTh3bF3aaL+CIiFV3rwOpM6NyC19fs4MEFa+hTLwgfD3ezY4mYpsgFbMWKFWzZsoWePXue/1lUVFSZhBIR+btch4P7560G4N62jekeFmhyIhERKapne7Xl+52HOJySwXPLt/JK/45mRxIxTZG/Po6IiGDUqFHUr1+/DOOIiFzcm2t2sPNkMjW9PfUXt4iIk/HxcOe9wV0AeHPtDmKOnzE5kYh5dP+OiFR4B86k8d/lWwF4Y0AnqlfxNDmRiIhcrquahDGyaV3s+QZj5q0iX1PRSiVVogI2c+ZMjh49WqR1ExMT+eqrr0pyOBGphAzD4IH5q8m2O+hXP5hREQ3NjiQiIsX07hWdqerhxuqEE3y8aY/ZcURMUaICdvvtt/PLL78Uad3vvvuOsWPHluRwIlIJfb/zEAsPJOJhs/LBkC6a80tExImF+vnwYp/2ADyxZANJ6ZkmJxIpfyUqYIZhYBTx8nFcXBz16tUryeFEpJJJzs5h/O9rAXi6e2ua1PA3OZGIiJTUgx2b0SG4Bik5eUz4fb3ZcUTKXbk8A5aSksI333xDly5dyuNwIuIiJi3ZxLGMbJrW8OOJbhFmxxERkVJgs1r5aGg3rBYL3+48yIL9CWZHEilXRR6GHuDYsWPMmzfvgqteq1atwsvL66LrZ2ZmEh8fz1dffUVubi7PP/98ydKKSKWxOuE40zfFAvDhkK54utlMTiQiIqWlfXANxnVqztvrdvLA/DVsHzMCb/fL+lgq4rQsRlHvIQS++OIL7rrrrv/f2GIp9BZEb29v6tevT/fu3Xn22WcJDg4uWdoKJjU1FX9/f1JSUvDz8zM7jojLyHPk0+HT2cQcP8OdrRvx2dU9zI4kIiKlLC0njxYfziIhLZNJ3SJ4qV8HsyOJlEhRu8Fl3YJ4xx13kJ+ff/5lGAbvvffeBT/76ys9PZ3t27czffp0lytfIlJ23jo3R0yNKp68NkBzfomIuCJfz/+fG+y1NdvZrrnBpJIo0TNgnp6eBd5+KCJSHAfPpPHsH1uAs3N+1fTW7xgREVc1vGldRpyfG2y15gaTSqFEBWzbtm3ceuutpZVFRCo5wzB4aOFasuwO+tQL4vbWmvNLRMTVvTvo7NxgqxKO88lmzQ0mrq9EBaxx48Z4enqWVhYRqeR+3HWYefsSNOeXiEglEubvwwvn5wbbqLnBxOWVyzD0IiKXkpKdy7hzc35N6hZBs5oB5gYSEZFy89C5ucGSs3N5dJHmBhPXVuLxPnNycnj//fdZvnw5hw4dIiUlpcB1GzRoQFRUVEkPKSIu6OnoTSSlZ9G4uh9PdtecXyIilYnNamX60K5E/m8u3+w4yJ2tGzGoYR2zY4mUiRJdAUtLSyMiIoInn3wSh8NBmzZtOHToEJGRkdx6663ceOON1KhRg0OHDtGzZ09uuumm0sotIi5k7ZETTNuwGzg755eXm+aCERGpbDoE1+ThTs0AGDt/DVl5dpMTiZSNy5oH7O8mTpzItGnTWL9+Pc2bNychIYG6desyZ84chg4den69KVOmMG3aNGJiYggICCiN3BWC5gETKbk8Rz4dP53NtuNnuD2iIV8M72l2JBERMUlaTh7NP5zFkbRMnuremhf7tjc7kkiRlck8YH+3ePFibrnlFpo3bw5Q4APzTz75JO7u7rz++uslOZyIuKB31u1k2/EzVK/iyesDOpkdR0RETHR2brDOALy6OoYdJzQ3mLieEhWw/fv307DhpYeJtlgsDBs2jN9++60khxMRF3M4OZ3J5+b8eq1/R2r5aM4vEZHKbkTTegxvEnZ2brC5mhtMXE+JnwHz9fUt0rohISEcPny4JIcTERdiGAYPLlhDZp6dXnUDuatNI7MjiYhIBTH1ii74uLuxMuE4n27ea3YckVJVogJmtVpxOBxFWtfX15f09PSSHE5EXMjPuw8zd18C7lYrHw7tqjm/RETkvDB/H57v0w6AiUs2cCw9y+REIqWnRAWsdu3aJCcn///OrGd3d7FSZhiGPmCJCACpObmM+30dAE92i6C55vwSEZG/ebhTc9oFVSc5O5fHFmtuMHEdJSpgDRs2ZMOGDef/HBgYiLu7O8eOHfvHuseOHaNOHc3nICLwTPRmEtMyaVTNl6d6aM4vERH5JzerlY+GdsNqsTBz+wEWHUg0O5JIqShRAbv55puZM2cOU6dOJTMzE6vVSocOHfj4449JTU09v96xY8f4+uuv6dy5c4kDi4hzW594kvfW7wLgA835JSIihegYUpOHOv45N9hqzQ0mLqFEBWzMmDHcdtttjB8/nsGDBwPw1ltvsWfPHkJDQ2nfvj1t27alQYMGZGVl8dJLL5VKaBFxTvb8fMbMW4UB3NYqnAHhIWZHEhGRCu75Pu2o4+vN/jNpvLhim9lxREqsRBMx/+nIkSNkZGTQpEkTAE6ePMnMmTM5ePAgNpuNRo0aceutt7rcZMWaiFnk8ry1dgePLlpPNS8Pdo8dSW2fKmZHEhERJzBr92Gu+XEp7lYrW+67mha1AsyOJPIPRe0GpVLAKisVMJGii0tJp8WHv5CRZ+fjYd24t10TsyOJiIiTMAyDET8s4bc98fQIq82y24dg1eBuUsEUtRuU6BZEEZGienjhWjLy7PQIq83dbRubHUdERJyIxWJh6hWd8XF3Y0X8cT7bornBxHmVqIB17dqV33//vbSyiIiL+iX2ML/ticfdamX6uRGtRERELkdd/6o81/vs3GD/itrA8QzNDSbOqUQFbP/+/fzxxx+llUVEXFBaTh4PLVgLwL+6ttJ9+yIiUmzjIpvTNrA6Z7JzeWyR5gYT51SiAta0aVP2799fWllExAX9e9kmjqRlEh7gyzM9WpsdR0REnJib1cpHw7phAb7afoDFmhtMnFCJCtioUaOYN28eJ0+eLK08IuJCNh49ydT1uwH4YEgXqrhrzi8RESmZTiE1eahTc+Ds3GDZds0NJs6lRAXsnnvuoUOHDjz44INkZ2eXViYRcQH2/HxGz11NvmFwS8twBjWsY3YkERFxES/0aUeIrzf7zqTx0ooYs+OIXJYSDUN//PhxDh48yDPPPENiYiJ33HEHLVq0KHDYxcDAQJo2bVrssBWNhqEXKdg763byyO/rCPDyYPf9Iwmsqjm/RESk9Py06xDX/RSNu9XK1tFX07xmgNmRpJIrl3nAAgMDOXnyJH/fheUiI5wZhkHNmjU5fvx4cQ9X4aiAiVxcQmoGzT+cRXqunelDuzK6vet88SIiIhWDYRhc/X0Uc/Ym0DMskOjbB2uUXTFVUbtBiR7IWLZsGceOHSvy+kFBQSU5nIg4iXEL15Kea6dbaG1NuCwiImXCYrHw3hVdWHLoF5bHH+Pzrfs0z6Q4hRIVsGbNmtGsWbPSyiIiLuDX2DhmxcbhZrUwfWhXfRspIiJlpl5AVZ7r3ZbHF2/g8cXrubJxKLV9dMu7VGwlGoRDROSv0nPzeHjh2Tm/Hu/Sila1q5mcSEREXN34yBa0CazGmexcHl+8wew4IpekAiYipWbysi3Ep2bQIKAq/+7Zxuw4IiJSCbhZrXw09OzcYDNi9rPk4FGzI4kUqsQFLD8//7JfJRj3Q0QqqM1Jp3h73U4Apg3pgrfm/BIRkXISWacWD3Y8+1jM/ZobTCq4EhWwhg0b4u7uftkvNzc36tWrxyOPPEJGRkZpvRcRMYkjP5/Rc1eRbxjc2KI+gxuGmh1JREQqmRf6tCe4ahX2nk5lykrNDSYVV4m+on733Xe555578PPz47bbbiMsLAyr9f87nWEYbNu2jU8//ZSrr76aAQMGAGevmh06dIi33noLi8XCW2+9VbJ3ISKmmrYxlg1HT+Hv6c5bAyPNjiMiIpWQv5cH717Rmet/imbKyhhubtmAZpobTCqgEhWwdevWYbPZ2Lx5Mz4+PgWud+WVVzJ06FCefvrpC0ZNzMzM5IcfflABE3FiR1IzeHrpJgBe7teBYF9vkxOJiEhldW2zegxrFMrcfQmMmbea6FGDLzo/rYiZSnQL4u+//871119faPkC6NevH23atOH111+/4Od169YlISGhyMdLTk5m4sSJREREEBwcTFBQEEFBQbRo0YKxY8de1pxkf7Vz504aN9a8ESLFMf73daTl5tGlTi1NuCwiIqayWCy8N7gz3u5u/BF3dm4wkYqmRAVsx44dhIWFFWndnj17snTp0gsPbrUWeUAOh8NB//798fHxITo6msTERJKSkkhKSmL16tU0a9aM7t27k5mZeVnvITU1leuvv54DBw5c1nYiAnP2xvPT7sPYLJrzS0REKob6Ab78t1dbAP4VtYGTmdnmBhL5mxIVsPT0dKpUKdpkd7Vr1yYpKanYx5o1axb169dn8uTJ1KhR44LLyf7+/owfP56hQ4cyffr0Iu/TMAzuuusuRo0aha+vb7GziVRGGbl5PDh/DQCPdWlJ68DqJicSERE568+5wU5l5fD44vVmxxG5QImHoS/qFSxvb2+ys4v/DURMTAx9+/YtdJ0BAwYQE1P0UW9ef/118vLymDhxYrFziVRWz/6xhbjUDOr7V+U/mvNLREQqEHeblenn5gb7Ytt+lh7S3GBScZSogFWvXp2UlJQirZuamkq1atWKfayEhASCg4MLXSckJIS4uLgi7W/p0qV88sknfPnllxeM3Cgil7b12GneWvv/c375eLibnEhERORCnevUYmyHc3ODzdPcYFJxlKh5NGrUiNjY2CKtGxsbS3h4+AU/y8zMxM2taAMxOhwObDZboeu4ublhL8J/XAkJCdx55518++23BAQEFOn4ADk5OaSmpl7wEqlsHPn5jJm3CodhcH3z+gxppDm/RESkYnqpb3uCqlZhz+lUXtbcYFJBlKiAjRw5km+//Zbly5cXut6KFSv45ptvuOaaay74+ebNm6lbt25JIly23Nxcrr/+eiZPnky7du0ua9spU6bg7+9//lXUAUhEXMn0TXtYe+Qkfp7uvD1Ic36JiEjF5e/lwbuDOgMwZVUMsaeKdueWSFkq0Txgjz76KAsXLqRPnz60bduWunXrXjCYRUZGBnFxcWzatIkePXrw2GOPAXDy5EnuvPNO5s+fz0svvVSsY8fGxjJkyBAWLFhAkyZNirzdhAkTaNWqFXffffdlH3PSpEk8+uij5/+cmpqqEiaVSmJaJpOWbgRgSt8OhGjOLxERqeCua16PoY1CmbcvgfvnrWbJbVdobjAxVYkKmLu7O0uWLGHWrFksW7aMQ4cOER8ff365r68v3bp144knnuC66647//OcnBx8fX355JNPuOuuu4p17KZNm1720PFffvklq1evZtWqVcU6pqenJ56ensXaVsQVPPL7OlJz8uhcpyZj2hf9iw8RERGzWCwW3h/cmRYfHiX6cBJfbtvPHW0amR1LKrESFbA/jRw5kpEjRxZ5/Tp16vDNN99c1jGK8nyX3W4v8JmyrVu3MmnSJJYvX46Xl9dlHVtEYN6+BH7YdejcnF/dsGnwGhERcRL1A3z5b+92TIzawGOL1zOscSg1vfV5UMzhNJ+gQkNDLzmPWGJiYoHPlE2bNo2MjAy6detGUFDQP14pKSkEBQUxY8aMsogv4tQycvN4YP5qACZ0bkEbzfklIiJO5pHIFkTUPjs32L8WbzA7jlRiTlPAIiIiiIqKKnSdqKgoIiIiLrps+vTpJCcnk5SUdNGXv78/SUlJjBo1qiziizi155Zv5XBKBnX9fHi2V1uz44iIiFy2s3ODdcUCfL5tH9GaG0xMUuoFLD8/v8iTM1+OESNGkJCQwOTJkzl16tQFx0hNTWXq1KnMnTuX0aNHl/qxRSqzmONneHPtDgDe15xfIiLixLqG1ub+Dk0BGDNvNTl2h8mJpDIqcQFLSUlh0qRJdOjQgRo1auDu7o63tzebNm0CoF27dvTr14/ff/+9RMex2WwsXryYrKwsevfuTUhIyPnbByMjI9m+fTsrVqzAx8cHgOjoaBo1alTkubrq1KlTonwirijfMBg9dxX2fINrm9XjysYa9VNERJzbX+cGe2WV5gaT8mcxSnC5Kjk5mVatWpGens6oUaMIDw/HMAwef/xx5syZw9ChQ5kzZw5vvfUWy5cvZ8mSJfTo0aM085sqNTUVf39/UlJS8PPzMzuOSKn7cONuxs5fg6+HO7vuH0EdPx+zI4mIiJTY9zsPcuPPy/CwWYkZPZwmNfzNjiQuoKjdoERXwCZPnkxWVhaxsbFMnTqVCRMmcOONN16wzpVXXsmiRYto1aoVb7/9dkkOJyLlKCk9kyeXnJ3z66W+7VW+RETEZVzfvD5DGtYh15HP/fNWl8njMyIFKVEBW7ZsGbfccguBgYGFH8RqZejQoWzcuLEkhxORcjTh9/Wk5OTRMbgGY8/dLy8iIuIKzs4N1oUqbjaWHk5iRsx+syNJJVKiArZv3z7q1atXpHVr1qx5yWHkRaRiWLj/CN/uPIjVYuGjYZrzS0REXE+Dar5MPjey76OL1nMyM9vcQFJplOhTVWZmJt7e3kVa193dndzc3JIcTkTKQUZuHvfPOzvn1/jI5rQLqmFyIhERkbLxaOeWtKoVwKmsHB5fvN7sOFJJlPhr7aLeM6t7a0Wcw3+Xb+VQSjp1/Xx4rnc7s+OIiIiUGXeblY+GdcMCfLFtP0sOam4wKXslKmDVq1cnOTm5SOumpaVRrVq1khxORMrYlqRTvLnm7Jxf04Z0oarm/BIRERfXNbQ2D3RsBsCYeavIyrObnEhcXYkKWKNGjdi5c2eR1t2zZw8NGzYsyeFEpAw58vO5b+4qHIbBDS3qM0xzfomISCXxUt/21PH1Zt+ZNJ5fvtXsOOLiSlTARo4cyffff8+CBQsKXW/9+vV88803XHvttSU5nIiUofc27GbD0VP4e7rzzqBIs+OIiIiUGz9PD94f3AWA19ZsZ9ux0yYnEldWoomY8/LyuOKKK1i2bBnNmzcnPDwcd3d3Zs2aRZ8+fahZsybx8fGsX7+eHj16sGjRItzdXeeWJk3ELK4iLiWdFh/+QkaenelDuzK6vYadFxGRyufaH5fy8+7DdK5Tk5V3DNUowHJZymUiZnd3d5YsWcKPP/7IgAEDsFqtnD59mt69e2MYBtnZ2URGRvLdd98RHR3tUuVLxFUYhsFDC9aSkWenR1ht7m3XxOxIIiIipnh3UCR+nu6sPXKSDzbGmh1HXFSJroBVdroCJq7gp12HuO6naNytVraOvprmNQPMjiQiImKaDzbu5oH5a6jq4cbOMSMJ8/cxO5I4iXK5AiYizi05O4eHF64FYFL3CJUvERGp9Ma0b0q30Nqk59p5cMEaTaUkpU4FTKQSm7RkE0fTs2hS3Y9J3SPMjiMiImI6q8XCR8O64m61MntvPD/vPmx2JHExJSpgTZs2Zfbs2UVa99dff6VpUz3YL1JRrIw/xoebzt7f/tGwbni5uZmcSEREpGJoWasaT3Y7+8XkQwvXkpydY3IicSUlKmB79+4lPj6+SOtu3ryZ9PT0khxOREpJrsPB6LmrAbinbWN61wsyOZGIiEjF8lSPCJrW8CMpPYsnl2w0O464kHK7BXHDhg2Eh4eX1+FEpBCvrd7OzpPJ1Pbx4tX+Hc2OIyIiUuF4ubkxfWg3AKZv2sOKuGMmJxJXUeICZrFYCl2elZXFa6+9xvz583nwwQdLejgRKaE9p1J4fvlWAN4eGEn1Kp4mJxIREamYetcL4t62jQEYPW8VOXaHyYnEFVzWMPQ///wzEyZMOD8aTEJCAgEBAVStWvWi62dmZnLmzBlsNhsTJkzglVdeKZ3UFYSGoRdnYxgG/b9ayNLDSVwRHsL8mwde8ksUERGRyuxMVg7NP5zFsYxsnu3Vlsm92podSSqoonaDy3rqPiIignvuuQfDMDAMg+eee46uXbsSGRl50fV9fHyoX78+nTp1ol69epf3DkSk1H2xbR9LDydRxc3GB0O6qnyJiIhcQrUqnrwzqDM3zVrGSyu3cUOL+pq2RUqkRBMxW61W3nvvPR544IHSzOQ0dAVMnMmJjGyafTiL01k5vNa/I493bWV2JBEREadgGAZXfRfF3H0J9AwLJPr2wVj1Jab8TblMxHz33XfTuXPnkuxCRMrJo4vWcTorh7aB1Xmkcwuz44iIiDgNi8XC+4O74OPuxvL4Y3y6ea/ZkcSJlaiAffLJJ3To0KG0sohIGfl9/xG+2n7g3OSS3XCzag52ERGRy1EvoCov9GkHwL+i1nM0LdPkROKs9ClMxMVl5tm5f/7ZOb8e7tSMTiE1TU4kIiLinB7u1JyOwTVIyclj/O/rzI4jTuqyBuEoSFZWFrt27eLgwYOkpKQUuF5oaCiDBg0qjUOKSBE9t3wLB5PTCfPz4fne7c2OIyIi4rRsVisfD+tGx0/n8MOuQ8zeE89VTcLMjiVOpsQF7LPPPmPSpEmcOHGCS43n0bBhQ/bu1T2zIuVl67HTvL56BwDvD+6Cr6e7yYlEREScW9ugGjzWpSWvrt7OgwvW0KdekP5+lctSolsQly1bxj333MOwYcPYtm0bqamp5OfnF/hS+RIpP478fO6buwqHYXBd83r6hk5ERKSUTO7VlgYBVYlPzeCZ6E1mxxEnU6ICNmXKFDp37synn35Ky5YtC5yQWUTK37SNsaxPPIm/pzvvDtJopSIiIqXF292N6UO7ATB1/S7WHTlhciJxJiUqYLGxsfTr16+0sohIKYlPyeCppRsBeLlfB4J9vU1OJCIi4loGhocwKqIhBnDf3FXkOfLNjiROokQFLD4+nqCgoNLKIiKlwDAMHlq4hvRcO91CazO6fVOzI4mIiLikNwZ0okYVT7YdP8Mba7abHUecRIkKmLu7O7m5uaWVRURKwazYOH7bE4+71cpHw7pitVjMjiQiIuKSavl48ebATgD8d/lW9p1ONTmROIMSFbCgoCCSkpJKK4uIlFBKdi4PLVgDwBPdWtGyVjWTE4mIiLi2URENGdAgmGy7g/vnrb7kqOAiJSpgffr0YdWqVaWVRURK6KmlmziankXj6n483aO12XFERERcnsVi4cMhXfFysxF16CgzYvabHUkquBIVsMcff5wNGzawYsWK0sojIsW0OuE4H2zcDcD0oV3xciuVedZFRETkEhpW9+PZXm0BeHTRek5kZJsbSCq0En1Cs1gsPPTQQ1x11VU8+uijdOnSBZvNVuD6QUFBtGjRoiSHFJGLyHU4GD13FQZwV5tG9K0fbHYkERGRSuXRzi35evsBth0/w6OL1jFjRC+zI0kFZTFKcKNqUFAQx48fL/L6gYGBHD16tLiHq3BSU1Px9/cnJSUFPz8/s+NIJfbSim08Hb2Jmt6e7L5/JDW8vcyOJCIiUumsO3KCLp/NxQAW3jyQQQ3rmB1JylFRu0GJroDFxsaSnJxc5PUDAgJKcjgRuYi9p1N5bvkWAN4eGKnyJSIiYpLIOrUYF9mcd9bt4v75q9k+ZgTe7nokQC5UojPC398ff3//0soiIpfJMAzun7eaHEc+g8JDuKVVuNmRREREKrXne7fn591xHExO59k/tvBq/45mR5IKpkSDcIiIub7ctp8lh45Sxc3GB0O6YtGcXyIiIqby9XTn/cFdAHhzzQ42J50yOZFUNCpgIk7qREY2jy1eD8CzvdoSXs3X5EQiIiICcFWTMK5vXh+HYXDf3FU48vPNjiQViAqYiJN6bPF6TmXl0Lp2NSZ0bml2HBEREfmLd6+IxN/TnY1HTzF1/S6z40gFUuRnwPr168e+fftKdLCGDRuydOnSEu1DRGDRgURmxOzHAnw8rBvuNn2XIiIiUpEEVfXmtf6dGD1vFc9Eb2Zk03rUC6hqdiypAIpcwO68804OHjxYooPVq1evRNuLCGTm2bl/3moAHu7UnMg6tUxOJCIiIhdzT7vGzIjZz/L4Y4ydv5q5Nw3Q89pS9AJ2++23l2UOESmi55dv5UByGqG+3rzQp73ZcURERKQAVouFj4Z1pc3HvzF//xG+33mIG1s2MDuWmEz3LYk4kW3HTvP6mu0AvD+kC76e7iYnEhERkcI0qxnA091bAzDu97WcycoxOZGYTQVMxEk48vMZPW8V9nyDa5rV4+omdc2OJCIiIkXwRLcImtf053hGNv+K2mB2HDGZCpiIk/hgYyxrj5zEz9OddwdFmh1HREREisjTzcZHQ7sB8OmWvUQfOmpyIjGTCpiIE0hIzeCppZsAeLlfB+r4+ZicSERERC5Hj7qBjGnfBIAx81aTbbebnEjMogIm4gQeXriWtNw8uobWYkz7pmbHERERkWJ4uV8HgqtWYc/pVF5csc3sOGISFTCRCm7W7sP8EhuHm9XCR0O7YdXwtSIiIk4pwMuTqVd0BuDlVTFsP37G5ERiBhUwkQosNSeXhxauBWBi1wha1a5mciIREREpibMDaYVhzzcYPW8V+YZhdiQpZypgIhXYU0s3kZiWSaNqvjzTo7XZcURERKSELBYL713RhaoebqxOOMH0TbFmR5JypgImUkGtSTjOtA27AfhwaFequBd53nQRERGpwML8fZjStwMAT0Rt5EhqhsmJpDw5TQFLTk5m4sSJREREEBwcTFBQEEFBQbRo0YKxY8dy7NixQre32+3897//pXHjxue3DQoKIjQ0lN69exMdHV0+b0SkCPIc+dw3dxUGcEfrhvRvEGJ2JBERESlFYzs0pXOdmqTl5vHwuccNpHJwigLmcDjo378/Pj4+REdHk5iYSFJSEklJSaxevZpmzZrRvXt3MjMzC9zHm2++yebNm1m1atX5bZOSkoiPj+eVV17hzjvv5ODBg+X4rkQK9vqa7Ww/kUxNb09eH9DJ7DgiIiJSymxWKx8N7Yab1cKs2Dhm7T5sdiQpJ05RwGbNmkX9+vWZPHkyNWrUwPKXUeD8/f0ZP348Q4cOZfr06QXuY+HChTzzzDPUqlXrgp9bLBa6dOnCqFGjmDNnTpm9B5Gi2nc6leeWbwXgzQGR1PT2MjmRiIiIlIXWgdX5V5dWADy0cC2pObkmJ5Ly4BQFLCYmhr59+xa6zoABA4iJiSlw+YQJE2jduuBBDOrUqXPJ2xhFypphGNw/bzXZdgcDGgRzW0S42ZFERESkDP27ZxsaVfMlMS2TSUs2mR1HyoFTFLCEhASCg4MLXSckJIS4uLgCl1955ZV4eHgUeoz27dsXO6NIafgq5gBRh47i5WbjwyFdL7jaKyIiIq6nirsb04d2A+CDjbtZFX/c5ERS1pyigDkcDmw2W6HruLm5YbfbL3vfdrudlStXsnDhwkteZcvJySE1NfWCl0hpOZmZzYRF6wCY3LMNDav7mZxIREREykO/BsHc2boRBjB63ipyHQ6zI0kZcooCVtreeeed86Mg+vj40KdPH6ZMmUK1aoVPcjtlyhT8/f3Pv8LCwsopsVQGjy1az6msHCJqV+Oxc/eDi4iISOXw+oCO1PL2YseJZF5bvd3sOFKGnLKAxcbGEh4ezp49e4q1/fjx48+PgpiWlsaqVat4+umnWb16daHbTZo0iZSUlPOv+Pj4Yh1f5O+iDibyZcx+LMDHw7rhbnPK/zRFRESkmGp4e/H2oEgAnl++lT2nUkxOJGXFKT/lNW3alAMHDtCkSZMS78vDw4NOnTrx6quv8uKLLxa6rqenJ35+fhe8REoqK8/OmHlny/+DHZvRuU6tS2whIiIirujmlg24IjyEHEc+o+euwjAMsyNJGXCKAlaU57vsdjtubm7FPkabNm3Ytm1bsbcXKa7nlm9l/5k06vh682JfDQQjIiJSWVksFj4Y0pUqbjaWxR3jk817zY4kZcApClhoaChJSUmFrpOYmEjdunUvumzbtm3cfffdhW5vtVqxWp3iX4e4kJXxx3j13H3e7w3ujJ9nwSN1ioiIiOtrUM2X5/u0A+DRxes4cCbN5ERS2pyicURERBAVFVXoOlFRUURERFx0WbVq1Vi7dm2hl3F37NhBw4YNS5RT5HKk5eRx+68ryDcMbo9oyIim9cyOJCIiIhXAI5Et6BkWSHqunTt/W4EjP9/sSFKKnKKAjRgxgoSEBCZPnsypU6cuKFKpqalMnTqVuXPnMnr06ItuHxoaSo0aNXj99dfJycm5YJlhGOzdu5cJEybw8MMPl+n7EPmrxxav50ByGnX9fHj3is5mxxEREZEKwma18vnVPajq4cby+GO8uXan2ZGkFDlFAbPZbCxevJisrCx69+5NSEjI+WHkIyMj2b59OytWrMDHxweA6OhoGjVqdH6eLovFwnfffcfu3btp2rTp+W2DgoKoU6cON910E+PGjWP48OFmvk2pRGbviefjzXuwAF9c3QN/L916KCIiIv8vvJovbw08OyriM9Gb2HbstMmJpLRYDA2vUmypqan4+/uTkpKiERGlyE5kZNPqo184npHNY11a8vqATmZHEhERkQrIMAyGf7+E2XvjaV27GuvuvhJPN5vZsaQARe0GTnEFTMRVGIbB6HmrOJ6RTataAbxw7iFbERERkb+zWCx8PKwbNb092Xb8DJP/2Gx2JCkFKmAi5eiLbfv4JTYOd6uVGcN74lWCqRNERETE9QVWrcJHQ7sB8Oqq7ayIO2ZyIikpFTCRcnIoOY1xC9cB8FzvtrQNqmFyIhEREXEGI5vV447WDTGA239bTlpOntmRpARUwETKgSM/nzt+W0Fabh7dQ2vzr66tzI4kIiIiTuSdQZ2p6+fDweR0Hl20zuw4UgIqYCLl4K21O/kj7hhVPdz4cnhPbJr0W0RERC6Dv5cHX1zdAwvwyZa9zN4Tb3YkKSZ9ChQpYzHHz/B09CYA3hoYSXg1X5MTiYiIiDPqUz+YCZ1bAnDv3JWcyMg2OZEUhwqYSBnKsTu47Zc/yHXkc1XjMO5p29jsSCIiIuLEXuzbjpa1Ajiekc2YeavQjFLORwVMpAxN/mMz246foaa3Jx8P64bFYjE7koiIiDgxLzc3ZgzvibvVyqzYOGbE7Dc7klwmFTCRMrI87hivrtoOwMfDuhFYtYrJiURERMQVtAuqwbO92gLw8MK1xKWkmxtILosKmEgZSMvJ447flmMAd7VpxIim9cyOJCIiIi5kYrdWdKlTi9ScPO78bQX5uhXRaaiAiZSBCYvWcTA5nfr+VXl7UKTZcURERMTFuFmtzBjeE293N5YeTuKddTvNjiRFpAImUsp+jY3j0y17sQBfXN0DP08PsyOJiIiIC2pU3Y83BnQEYNKSjew4ccbkRFIUKmAipeh4Rhb3zV0FwONdW9GrXpDJiURERMSVjWnflCEN65DjyGfUr8vJdTjMjiSXoAImUkoMw+C+uas4kZlNRO1qPN+7ndmRRERExMVZLBY+vbI71at4sjnpNM/9sdXsSHIJKmAipeSzrfv4bU88HjYrXw3viaebzexIIiIiUgkE+3rz4ZCuAExZFcPqhOMmJ5LCqICJlIKDZ9IY//taAJ7v3Y7WgdVNTiQiIiKVyfUt6nNrq3DyDYPbf11ORm6e2ZGkACpgIiXkyM/n9t+Wk55rp2dYII91aWl2JBEREamE3hvcmVBfb/adSeNfURvMjiMFUAETKaE31uxgRfxxqnq48cXVPbBZ9Z+ViIiIlL8AL08+v7oHAB9sjGXB/gSTE8nF6JOiSAlsPXaaZ6I3A/DOoM40qOZrciIRERGpzPo3CGFcp+YA3D17Jacys01OJH+nAiZSTDl2B6N+XU5efj7Dm4RxV5tGZkcSERERYUq/DjSt4cfR9CweWLAGwzDMjiR/oQImUkz/XraZmONnqO3jxUfDumGxWMyOJCIiIoK3uxszhvfCZrHw/c5DfLPjoNmR5C9UwESKYdnhJF5fvR2Aj4d1o7ZPFZMTiYiIiPy/TiE1+XfPNgA8uGANCakZJieSP6mAiVym1Jxc7vhtOQZwT9vGXN2krtmRRERERP7hqe6t6RRSk+TsXO6avYJ83YpYIaiAiVym8QvXcTglgwYBVXlrYKTZcUREREQuyt1mZcbwnlRxs7H44FHe37Db7EiCCpjIZfkl9jCfb9uHBfji6p74erqbHUlERESkQE1r+PNq/44ATIzawO6TyeYGEhUwkaI6lp7FfXNXATCxWyt61g00OZGIiIjIpT3QsRkDG4SQ/ecIzo58syNVaipgIkVgGAb3zl3JycwcWteuxn97tTM7koiIiEiRWC0WPruqOwFeHmw4eoqXVm4zO1KlpgImUgSfbtnLnL0JeNisfDWiF55uNrMjiYiIiBRZHT8fpg3uAsDzy7eyPvGkyYkqLxUwkUvYfzqVR35fB8CLfdoTUbuayYlERERELt/NrcK5sUV9HIbBqF//IDPPbnakSkkFTKQQjvx8bv9tBRl5dnrVDWRC5xZmRxIREREptmlDuhJctQqxp1J5cslGs+NUSipgIoV4dfV2ViUcx9fDnS+u7oHNqv9kRERExHlVr+LJ/67qDsDU9btYdCDR5ESVjz5NihRgS9IpJi/bAsC7V0RSP8DX3EAiIiIipWBww1DGdmgKwF2zV3AmK8fkRJWLCpjIRWTb7dz263Ly8vMZ2bQud7RuZHYkERERkVLzWv+ONK7ux5G0TB5asNbsOJWKCpjIRTy9dDM7TiQT6OPF9KHdsFgsZkcSERERKTU+Hu58eXUPrBYLX+84wPc7D5odqdJQARP5m6WHjvLW2h0AfHJld2r5eJmcSERERKT0dQmtzVPdIwC4f95qEtMyTU5UOaiAifxFSnYud/y2AgO4r10TrmwcZnYkERERkTLzn55taR9UgzPZudwzZyWGYZgdyeWpgIn8xbiFa4lPzSA8wJc3B3YyO46IiIhImXK3WZkxvCeeNisL9h9h+qZYsyO5PBUwkXN+2nWIL2P2Y7VY+HJ4D6p6uJsdSURERKTMtagVwMv9OgDw2OIN7D2danIi16YCJgIcTctkzLzVADzRtRXdwwJNTiQiIiJSfsZFtqBvvSAy8+zc/uty7Pn5ZkdyWSpgUukZhsG9c1dxKiuHtoHVebZ3W7MjiYiIiJQrq8XC51f3wM/TnTVHTvDKqhizI7ksFTCp9D7atId5+xLwPHcPtIfNZnYkERERkXJX178qU6/oDMCzf2xh09FTJidyTSpgUqntO53Ko4vXA/BS3w60ql3N5EQiIiIi5hkV0ZBrmtXDnm8w6tflZNvtZkdyOSpgUmnZ8/O5/bflZObZ6VsviEc6tzA7koiIiIipLBYL04d2JdDHi50nk3lq6SazI7kcFTCptF5ZFcPqhBP4ebrz+bmZ4EVEREQqu5reXnx6ZXcA3lq7k6WHjpqcyLWogEmltOnoKZ79YwsAU6/oTF3/quYGEhEREalAhjUO4752TQC447cVpGTnmpzIdaiASaWTlWfntl//wJ5vcG2zeoyKaGh2JBEREZEK582BnQgP8CU+NYPxv681O47LUAGTSueppZvYdTKFQB8vPhzaFYtuPRQRERH5h6oe7nw5/OxjGl9s28+s3YfNjuQSVMCkUlly8Chvr9sJwKdXdqemt5fJiUREREQqru5hgUzs2gqA0fNWkZSeaXIi56cCJpXGwTNp3DRrGQBj2jdhWOMwkxOJiIiIVHz/7d2WNoHVOJmZw82z/iDX4TA7klNTAZNKISU7lyu/i+JEZjZtA6vzxoBOZkcSERERcQoeNhszR/TC18Od6MNJjJm7GsMwzI7ltFTAxOXlOfK5/qdodp5MJsTXm9k39sfHw93sWCIiIiJOo2Wtanx3TW+sFgufb9vHlJUxZkdyWipg4tIMw+DhhWtYdDARb3c3Zt/Qn1A/H7NjiYiIiDidIY1CmXpFZwCejt7EdzsOmpzIOTlNAUtOTmbixIlEREQQHBxMUFAQQUFBtGjRgrFjx3Ls2LFLbv/QQw/RoEGD89sGBQURGhpKv379WLVqVTm9EylPb63dyfRNe7AAX4/oRfvgGmZHEhEREXFaD3RsxiORLQC447flrE44bnIi5+MUBczhcNC/f398fHyIjo4mMTGRpKQkkpKSWL16Nc2aNaN79+5kZhY8KsuECRNwd3dn+/bt57dNSkoiLi6O5557jltvvZXExMRyfFdS1n6NjePxxesBeGNgJ4Y3rWtyIhERERHn9/qAjlzdJIwcRz7Dv1/CgTNpZkdyKhbDCZ6g+/HHH/nmm2/46aefClxn3LhxNGjQgAkTJlx0eXBwMAcOHKBKlSoXXT5p0iTq1q3L2LFji5wrNTUVf39/UlJS8PPzK/J2UvY2HT1Fzy/nk5ln5/72TZk2pIvm+xIREREpJem5efT6cj6bk07TrIY/q+8aSoCXp9mxTFXUbuAUV8BiYmLo27dvoesMGDCAmJiCHwacOHFigeULICwsjPj4+GJnlIojITWDq76PIjPPzqDwEN69orPKl4iIiEgpqurhzuwb+lPH15vdp1K47sdo8hz5ZsdyCk5RwBISEggODi50nZCQEOLi4gpcXtCVsT9t376dRo0aFSufVBzpuXlc9V0UiWmZtKgZwPfX9MHd5hSnuYiIiIhTqePnw5wb++Pj7kbUoaOMna/h6YvCKT6ZOhwObDZboeu4ublht9uLtf/ffvuNBQsWMHLkyELXy8nJITU19YKXVByO/HxumfUHW46dpraPF3Nv6o+/l4fZsURERERcVtugGueHp/90y15eXb3d7EgVnlMUsLJy5swZ7rzzTiZMmMAvv/xCtWrVCl1/ypQp+Pv7n3+FhYWVU1Ipin9FbWD23ng8bVZ+vb4f9QN8zY4kIiIi4vKGNQ7j7UGRADy5ZCM/7jpkbqAKzikLWGxsLOHh4ezZs6dY2xuGwbfffkubNm2oVq0amzZtonXr1pfcbtKkSaSkpJx/6ZmxiuODjbt5a+1OAL4c3pMuobVNTiQiIiJSeTzcqTkPd2oOwKhfl7P2yAmTE1VcbmYHKI6mTZty4MCBYm0bHx/PnXfeiWEYzJ8/n5YtWxZ5W09PTzw9K/foLhXRwv1HeHjBWgBe7NOeG1o0MDmRiIiISOXz1sBOHDiTxtx9CVz9fRRr7xqmO5IuwimugBXl+S673Y6bW+F9ct26dfTq1Ysbb7yRqKioyypfUjFtP36G63+KxmEY3NG6IZO6R5gdSURERKRSslmtfDOyN20Cq3E8I5th30aRkp1rdqwKxykKWGhoKElJSYWuk5iYSN26BU+0m5yczDXXXMOMGTMYPXq0hiV3AUnpmQz7djFpuXn0rhvIR8O66f9XERERERP5eroz58YBhPh6s/NkMtf/pOHp/84pClhERARRUVGFrhMVFUVERMFXP9544w1uueUWevToUdrxxARZeXaGf7+EuNQMGlf346fr+uJxiZEyRURERKTshfr5MPuG/ni7u7HoYCIPLVij4en/wikK2IgRI0hISGDy5MmcOnXqgv8DU1NTmTp1KnPnzmX06NEF7mPjxo0MGzasPOJKGcs3DO74bQXrEk9SvYonc28aQA1vL7NjiYiIiMg57YNr8M3IXliAjzbv4Y01O8yOVGE4RQGz2WwsXryYrKwsevfuTUhICEFBQQQFBREZGcn27dtZsWIFPj4+AERHR9OoUaML5uk6ffo011133fntLva69dZbzXqLchn+Hb2ZH3Ydwt1q5efr+tK4up/ZkURERETkb65uUpc3B3YCYGLUBmbtPmxyoorBYuh6YLGlpqbi7+9PSkoKfn4qAeXh8617uWv2yrP/fFUP7mjTyOREIiIiIlIQwzB4aMFapm3cTRU3G8tuH0KnkJpmxyoTRe0GTnEFTAQg+tBRRs9dDcDT3VurfImIiIhUcBaLhXeuiGRIwzpk2R1c/X0UcSnpZscylQqYOIU9p1K45sel5OXnc2OL+jzXp53ZkURERESkCNysVr69pjcRtauRlJ7FsG8Xk5pTeYenVwGTCu9UZjbDvl3MmexcutSpxWdX9cCq4eZFREREnIafpwdzbuxPUNUqbD+RzI0/L8OeXzmHp1cBkwotx+5g5A9L2Xcmjfr+Vfnl+n5UcS98wm0RERERqXjq+ldl9g39qeJmY8H+I4xbuLZSDk+vAiYVlmEY3Dd3Fcvjj+Hn6c6cm/oTWLWK2bFEREREpJg6htTk63PD03+wMZa31+00O1K5UwGTCuvFFduYEbMfm8XCD9f0oWWtamZHEhEREZESGtG0Hq8N6AjAY4vW82tsnMmJypcKmFRI3+44wL+XbQbg/cFdGNSwjsmJRERERKS0PNq5JWPaN8EAbvnlDzYePWl2pHKjAiYVzuqE49z52wrg3H+cHZqanEhERERESpPFYmHqFV0YFB5CZp6dq76LIj4lw+xY5UIFTCqUg2fSGP79EnIc+QxvEsar/TuYHUlEREREyoC7zcr31/ShZa0AjqZnceV3i0nLyTM7VplTAZMKIzk7h2HfLeZEZjbtgqozc0QvbFadoiIiIiKuyt/Lg7k3DiDQx4ttx89w0yzXH55en26lQshz5HPDT8vYdTKFOr7ezL6hPz4e7mbHEhEREZEyVi+gKr+dG55+3r4EJvy+zuxIZUoFTExnGAYPL1zDooOJ+Li7MfvG/tTx8zE7loiIiIiUk8g6tZgxvCcA723YzbsuPDy9CpiY7q21O5m+aQ8W4JuRvWkXVMPsSCIiIiJSzq5tXp9X+p19/n/CovXM2RtvcqKyoQImpvol9jCPL14PwJsDO3FVkzCTE4mIiIiIWf7VtRX3tm1MvmFw08/L2Jx0yuxIpU4FTEyz8ehJbv1lOQYwtkNTxke2MDuSiIiIiJjIYrEwbUhXBjQIJiPPzpXfRXEk1bWGp1cBE1MkpGZw1XdRZObZuSI8hHev6IzFYjE7loiIiIiYzN1m5Ydr+9CiZgCJaZlc9X0U6bmuMzy9CpiUu/TcPK78bjFH07NoVSuA76/tg5uGmxcRERGRcwK8PJlzY39qeXuxOek0t8z6A4eLDE+vT71Srhz5+dw8axlbj52hto8Xc24cgJ+nh9mxRERERKSCaVDNl99u6IeXm43Ze+N57Ny4Ac5OBUzK1eOLNzBnbwJebjZ+u6E/9QKqmh1JRERERCqoLqG1+fLqs8PTv7NuF+9v2GVyopJTAZNyM23Dbt4+N6fDl1f3pHOdWiYnEhEREZGK7voW9Xmpb3sAxi1cx7x9CSYnKhkVMCkXC/YnMG7hWgBe6tue61vUNzeQiIiIiDiNJ7tFcHebs8PT3/hzNFuPnTY7UrGpgEmZ2378DDf8tAyHYXBn60Y82S3C7EgiIiIi4kQsFgsfDO1C33pBpOfaufLbxSSmZZodq1hUwKRMJaVnMuzbxaTl5tG7biDTh3XVcPMiIiIictk8bDZ+uq4vzWr4k5CWyVXfRZHhhMPTq4BJmcnKszP8+yXEpWbQpLofP1/fDw+bzexYIiIiIuKkqlXxZO5NA6jp7cmmpFPc+ovzDU+vAialzp6fz4+7DtF7xgLWJZ6k+rn/UKpX8TQ7moiIiIg4ufBqvvx6fX88bVZ+3RPPxKiNZke6LG5mBxDXcTIzm48372Haht0knLsn19vdjVnX9aVRdT+T04mIiIiIq+gWVpvPr+7BzbP+YNvx0+Q6HE5zp5UKmJTYlqRTTF2/m5nb95PjOHsJuLaPF2PaNeX+Dk0J8fU2OaGIiIiIuJqbWobj5+nBwAYhuNuc58Y+FTApFnt+Pr/GxvHu+l38EXfs/M87BNdgfKcW3NCiPp5uzvEthIiIiIg4p6GNQs2OcNlUwOSynMrM5pMte3l/w27iUzMAcLNauK5ZfcZFNqdLnVoa5VBEREREpAAqYFIkW4+dZur6XczcfoBsuwOAWt5ejGnfhPvbN6WOn4/JCUVEREREKj4VMCmQPT+f3/bE8+66nSz7y22G7YNqMD6yOTe0qI+Xm04hEREREZGi0qdn+YfTWTl8snkP72/YTdy52wxtFgvXNa/HuE4t6Bqq2wxFRERERIpDBUzO2/aX2wyzzt1mWNPb8/xohqG6zVBEREREpERUwCo5x5+3Ga7fRfThpPM/bxdUnXGdmnNTywa6zVBEREREpJTok3UldTorh0+37OX9Dbs4nPL/txle27weD3dsTvew2rrNUERERESklKmAVTLbj59h6vpdzIjZf/42wxpVPBnTviljdZuhiIiIiEiZUgGrBBz5+czeG8+763ax9C+3GbYN/P/bDKu461QQERERESlr+tTtws6cv81wN4dS0oGztxle06weD3dqTg/dZigiIiIiUq5UwFzQjhN/3mZ4gMw8O3D2NsPR7ZowtkMzwvx1m6GIiIiIiBlUwFyEIz+fOXsTeHf9LpYcOnr+520CqzGuUwtu1m2GIiIiIiKm0ydyF3AiI5vI/805f5uh1WJhZNO6jOvUnJ51A3WboYiIiIhIBaEC5gJq+XhR28eL1Nw87mvXmAc6NKOuf1WzY4mIiIiIyN+ogLmImSN6UcfXW7cZioiIiIhUYPq07iIaVfczO4KIiIiIiFyC1ewAIiIiIiIilYUKmIiIiIiISDlRARMRERERESknKmAiIiIiIiLlRAVMRERERESknKiAiYiIiIiIlBMVMBERERERkXKiAiYiIiIiIlJOnLqAJScnM3HiRCIiIggODiYoKIigoCBatGjB2LFjOXbsWJH2s3r1aurVq0dGRkYZJxYRERERkcrMaQuYw+Ggf//++Pj4EB0dTWJiIklJSSQlJbF69WqaNWtG9+7dyczMLHQ/f/zxBzfddBNnzpwhLy+vnNKLiIiIiEhl5LQFbNasWdSvX5/JkydTo0YNLBbL+WX+/v6MHz+eoUOHMn369AL3sXLlSm677TZ++eUXqlevXh6xRURERESkEnPaAhYTE0Pfvn0LXWfAgAHExMQUuNzDw4P58+fTrl270o4nIiIiIiLyD25mByiuhIQEWrduXeg6ISEhxMXFFbi8U6dOpR1LRERERESkQE5bwBwOBzabrdB13NzcsNvtpXbMnJwccnJyzv85NTW11PYtIiIiIiKuz2lvQTTDlClT8Pf3P/8KCwszO5KIiIiIiDgRlylgsbGxhIeHs2fPnjI7xqRJk0hJSTn/io+PL7NjiYiIiIiI63HaWxD/rmnTphw4cKBMj+Hp6Ymnp2eZHkNERERERFyX014BK8rzXXa7HTc3l+mYIiIiIiLi5Jy2nYSGhpKUlFToOomJidStW7fMMhiGAWgwDhERERGRyu7PTvBnRyiI0xawiIgIvvrqKx566KEC14mKiiIiIqLMMqSlpQFoMA4REREREQHOdgR/f/8Cl1uMS1W0CsrhcNClSxeGDh3KuHHjqF69OhaLBTjbPr/44gveeecdtm7dio+PzyX3V79+fbZs2UJAQECRM+Tn55OYmIivr+/5Y0vFlpqaSlhYGPHx8fj5+ZkdR1yAzikpTTqfpDTpfJLSpnOqcIZhkJaWRkhICFZrwU96Oe0VMJvNxuLFi3nxxRfp3bs3p06dOn+5LyAggN69e7NixYrz5Ss6Opp7772XTZs2XfSEqV27Nh4eHpeVwWq1EhoaWvI3I+XOz89PvzikVOmcktKk80lKk84nKW06pwpW2JWvPzntFTCR4khNTcXf35+UlBT94pBSoXNKSpPOJylNOp+ktOmcKh1OOwqiiIiIiIiIs1EBk0rF09OTyZMnaz43KTU6p6Q06XyS0qTzSUqbzqnSoVsQRUREREREyomugImIiIiIiJQTFTAREREREZFyogImIiIiIiJSTlTAREREREREyokKmLgswzAYP348r7/++j+Wbdy4kauvvprw8HCCgoIICgqiTp06REZG8sknn+BwOP6xzd69e7nlllto3Ljx+W1CQkJo3749r7zyCjk5OeXxtqQczJ8/n65duxISEkJgYCBBQUEEBwfTpEkTnn76aTIzM/+xjc4pKa7k5GQmTpxIREQEwcHB58+FFi1aMHbsWI4dO2Z2RDHJV199Rdu2bS84L4KDg2nRogVvvvkmdrv9gvWjoqIYMGAA9evXP79+WFgYPXv25Oeff+Zi464V53eXuBaHw8E111zDDz/8cMHPdT6VIUPERX300UeGh4eHMXny5At+vmHDBqNBgwbG3Llzjezs7PM/dzgcxu7du43hw4cbjz322AXbxMfHG2FhYcbMmTONjIyM8z/Pz8834uLijPvuu8+49tpry/T9SPmIjY016tata2zcuNHIz8+/YFlKSopx3333GY8++ugFP9c5JcVlt9uN9u3bG88++6xx8uTJC8655ORk4+233zYaNmx4wTkilcPSpUuNli1bGrGxsf9YlpSUZFx11VXGu+++e/5ns2bNMiIiIozly5cbubm553+el5dnbNy40ejRo4cxderUC/ZTnN9d4nqeeuopw8PDw/jss8/O/0znU9lSAROXtG7dOqNhw4bGU0899Y8CNmzYMOO3334rcNv09HSjbt26RlJS0vmfPfjggxf8Rfd3DofDaNOmjbFp06YSZxdzTZs2zXjyyScLXJ6enm4EBwdf8DOdU1JcP/zwg3HNNdcUus7DDz9svPnmm+WUSCqKiRMnGh9++GGBy3fv3m1ERkae/3PLli2NrVu3Frh+QkKCERISYtjt9vM/K87vLnEtv/zyi9G2bVtj9OjRFxQwnU9lS7cgiss5efIkt9xyCzNmzKBWrVr/WB4TE0Pfvn0L3N7Hx4cOHToQGxtb5G2sViv9+vUjJiamZOHFdC1btuTaa68tcLmPjw8Oh+OC2wN1TklxXeo8ABgwYIDOg0qoa9euDBw4sMDlYWFhxMfHA5CXl8fJkydp3bp1gevXqVOHwMBAjhw5cv5nxfndJa5j7969jB8/nh9//JEqVaqc/7nOp7KnAiYuxeFwcPPNNzNu3Di6du160XVSUlKoWrVqofsJCQkhLi7u/J8TEhIIDg6+rG3EOfXq1YuOHTsWuPzYsWN4eXnh6el5/mc6p6S4dB5IQUaMGEF4eHiBy7dv306jRo0ASEpKuugXjn/393OpOL+7xDVkZGRw7bXX8u6779KwYcMLlul8KnsqYOJS/vOf/1CzZk0eeuihEu3Hzc3tgoebHQ4HNpvtsrYR15OcnMyoUaN4+OGHL3tbnVNyMToPpDiOHDnCmDFjGDduHFC08wiKdy7p/HM9hmEwevRohg0bxtVXX/2P5Tqfyp4KmFRIX331FQEBAYW+vvrqqwu2+fXXX/n111/5+OOPsVgsJiWXiqg459PfzZkzh1atWtG2bVseffTRckouIvL/DMPg888/p23bttx5551cd911ZkcSJ/Tee+9x9OhRnn/+ebOjVFoqYFIh3XbbbSQnJxf6uu22286vv3fvXh588EF++OGHS17+/ruXX375ot8AFearr76iXbt2l7WNmOdyz6e/SkxM5Oabb2bixIl88cUXvPrqq1ithf/q1DklxRUbG0t4eDh79uwxO4pUMHv27GHQoEFMnz6dhQsXMn78+ELXf/DBB3nwwQcv6xjF+d0lzmXlypW88cYbfPPNN7i5uRV5O51Ppavo/+ZFKqg/72N+4403aN68+WVv/+STT172NrfddluBH9jFNRiGwfTp03nhhReYMGECX375Je7u7kXaVueUFFfTpk05cOCA2TGkAnE4HLz44ot88sknPPfcc9x+++2X/BII4P3337/sYxXnd5c4j6SkJG655Ra+/vprAgMDL2tbnU+lSwVMnNqf9zH369ePG2+8sdT2a7fbL/hmqCj3LP99G3FeeXl5PPTQQ+zcuZO1a9dSp06dEu9T55RcjM4DKUx6ejq33HILbm5ubNu2jYCAgIuuV9TnaopzLun8cw12u50bb7yRCRMm0L1790LX1flU9vRvQJzawYMHmT17Nl5eXnz77bf/WJ6eng7ArFmz2LJlCxaLhYCAANLT0wu9VTExMZG6deue/3NoaChJSUnUrFmz0G3atm1b/DcjFcYbb7xBYmIiS5YsKdJVL51TUlx/ngeF+fu5I5XH448/TmhoKO+//36hzzYHBQVx8uTJS+7v7+dScX53iXNauXIlGzduZPfu3bz88sv/WJ6SkoKHhwc//vgjv/zyi86nMqZnwMSphYeHk5qayvHjx0lKSvq/9u49KMrqjQP4dxcXhQlQQER0YUHNK2LqTJPE7RdqmiZgSCGmSYqWeMErloEINWJoDCZJCGKayjCM4g0cIUqryUTRUUdl5SKIYejGRS4S7O8Pxx233WUXhAX0+5nhn3Pb54V3lveZc95zVH7WrFmDyMhIXL58WfHPy9HREdnZ2RrHfPToEfLy8jB8+HBFmbY+LS0t+Omnn+Do6NhxF0ddorq6GjExMUhKStJ5ySHvKWovbfcBAGRnZ/M+eAkVFBQgMzMTMTExWjeW6tWrF/r374/8/HyNbcrLy1FRUaE0o9+e7y7qmdzc3FBbW4uKigq1z0t+fn5ISUnB8ePHeT/pARMweulERERg5cqVOHHihNJhui0tLZBKpQgICMB7772ntD56w4YNiImJwYEDB1BXV6col8vluHv3LpYsWQIHBwduovACuHHjBsaMGaPTGShP8Z6i9vLy8kJZWRnCwsLw4MEDyOVyRV11dTXi4uJw4sQJLF68uAujpK5w6dIleHh4KB2Q25qoqCjMmzcP586dQ1NTk6K8ubkZ+fn58PPzw4YNG5S2F2/Pdxe9HHg/dS6B/Nlve6IXzPbt22FmZobAwECl8kuXLiEsLAxXr15VPPwKhUIMGjQIixYtQmBgoMoZGFKpFJs2bcKFCxdQU1MDABAIBBgwYADef/99rFy5En369NHPhVGnOXXqFHx8fGBmZqaxjUAgwPHjxzFhwgRFGe8paq+qqipERUXh5MmTSklY37594ebmhs2bN8Pa2rqLoyR9i4+Px9q1a1tdziUSifDHH3/AxsYGAJCTk4Mvv/wSBQUFigdgkUgEOzs7rFq1Cj4+Piqzae357qIXz4oVKzBz5kx4enoqyng/dR4mYERERERERHrCJYhERERERER6wgSMiIiIiIhIT5iAERERERER6QkTMCIiIiIiIj1hAkZERERERKQnTMCIiIiIiIj0hAkYERERERGRnjABIyIiIiIi0pNeXR0AERERdb6GhgZUVlYCAPr374/evXt3cUSdQyaT4dGjRzAwMIC1tTUEAkFXh0REpIQzYEREPVBycjIEAoFOP/v27evqcKkb8PX1hVgshlgsxqJFixTlycnJGDp0qCI501VZWRlsbW1x6tQplbqIiAgEBgbqPJZcLseIESOQk5PTphj+q76+HtbW1hCLxbCxsUF6evpzjUdE1Bk4A0ZE1AOVlJTAzMwMkZGRrbYTCAT43//+p6eoqDurqanBBx98AH9/fzg6OirKS0pKcPv2baxbtw5JSUk6jxccHIzS0lLcvXtXpa6wsBBSqVTnsZqbm3Hz5k3cuXNH5z7qGBkZIScnBzKZDAsXLkRNTc1zjUdE1BmYgBER9VCvvPIKli1b1tVhUA/y6quvYsaMGWrrkpOTMX/+fLi5uWkdJyMjA0eOHOng6DqGs7MzAMDY2LiLIyEiUo9LEImIiF5yAwcOxJtvvomgoCA0Nja22ra2thbLli3DzJkz9RQdEdGLhQkYERHRS04oFGL37t0oLCzE1q1bW20bHh6Ohw8fYufOnXqKjojoxcIEjIjoJTJs2DAcPXoUAJCVlYVZs2Zh8ODBMDMzw/3795XaXrlyBd7e3hg2bBhEIpFiUw+xWIy33noLJ0+ebPWz8vPz4eXlhaFDh0IkEkEoFMLS0hLOzs6IjY1FU1MTPDw8kJCQoNJ36NChyMzM1Ho9Hh4e+P7779XW1dbWIjQ0FOPHj4epqakifjMzM0ycOBGbNm1CfX292r5JSUlwdXWFXC7H/v374enpCSsrKwiFQhgbG8PR0RFLlixR+Z09q6WlBYmJiZg0aZJiNz6BQACRSASxWAw/Pz9cuXJFpV9YWBjmzZun9dp1baerUaNGYf369YiKisKtW7fUtrl8+TK++eYbREZGwtbWtsM+W5PHjx9j7dq1sLW1hYGBgcaNZtatW9fpsRARdRS+A0ZE9BKRSqUoLS3F+vXrsW3bNkyZMgW+vr4YPXo0LCwsFO2ys7MxdepUODg4YNasWRg0aBBEIhFaWlpQUVGB3377De+88w62bt2q9uH39OnTmDZtGoYPHw5vb2/Y2NhAKBSisrISUqkUn3/+OTIyMnDr1i21Gy/cvn0bZWVlWq+nqKgIJSUlKuWNjY2YOHEiiouL4e/vD19fX5iYmAB4shmFVCrF119/jaNHjyIvLw8ikUip/507dyCVSjFv3jykp6dj5syZWLx4Mfr374/Hjx+jpKQEBw8eRFpaGq5fvw4rKyuVGBYuXIiUlBR4e3tjypQpsLCwgEAgQH19PW7evInc3Fy4u7vj8uXLEIvFin4lJSUoKirSeu26tmuLjRs34tChQ1i6dCnOnDmjtIV7S0sLgoKC4OTkpLd3Dzdv3ozY2Fj4+/tj7NixMDQ0VKo/cuQIsrOzFe99ERH1BEzAiIheMkeOHEFFRQXy8/MxduxYtW1CQkLw+uuvIycnR+15UXK5HKGhodiyZQsWLFiglIDI5XKsXr0aHh4eyMzMRK9eqv9q6urqMHfuXJSXl3fchT1j165dKCgowMWLF+Hk5KS2zapVq+Dk5ITExEQsXbpUpf7evXsoKytDeXk5+vbtq1IfHh6OkSNHIiIiQmU5Xl5eHlJSUrB3717Mnz9f7edXV1fD1tYWqampWL16ddsvshMYGRkhPj4ekydPxv79+5Vm2Hbv3o0///wT58+fV/s37Qzp6en49NNPsWPHDpW6S5cuYe3atVi+fDlmzZqll3iIiDoCEzAioh7q33//1ThLZG1trfEh+ddff8XFixcxcuRItfUymQxXrlzB4cOHNR7WKxAIsGbNGmzduhW///670gOwTCbD1atXERUVpTEGY2Nj7NmzR7EcsqPl5uZi6tSpGpMvABgzZgymTZuG06dPq03AgCc7A6pLvgDA0tIS8+fPV7tU8pdffoGFhUWrSwRNTU2RkJCgNPvVHXh6eiIgIAAhISGYPn06LCwscO/ePYSGhmLFihWYMGGCTuM0NjbqNIsJPLmX1QkNDYWrq6tK+cOHD+Hj44Px48dj27ZtOn0GEVF3wQSMiKiHqqio0PjwHhQUhO+++05tnaurq8bkCwCKi4sBAA4ODq1+vqWlJUxMTBTt/9tf2ztC5ubmnZZ8FBcXw8XFRWs7BwcHnD17Vm2dtbU17O3tW+1vZ2eHoqIiyOVypeV6RUVFGDx4MITC1l+1njNnjtYYu0JMTAxOnDiBdevWYc+ePVi1ahVMTU0RERGh8xgXLlx47r/vhx9+qFLW0tKCgIAA1NXVITU1VWVZIhFRd8cEjIiohzI3N0dKSoraunHjxmnsN3r06FbHraqqAgDU19drncEwNTXF33//rVRWXV0N4Mk5Zdo8m7R0pKqqKgiFQq3xGxgYqMT/bJ02JiYmqKurQ3Nzs9JsX3V1tU7X311ZWVkhOjoaixYtgo2NDQ4fPoyMjIw2XdPIkSMRHR2tU9vm5mZ4eXnp1HbLli04ffo0zpw5g0GDBukcDxFRd8EEjIiohzIyMtJ4qG5rTE1NdWqnbumXOn369GlzDPoQFxeHuLg4re2GDBmih2h6nqebiERGRmL27NltPvfL3Nxc5/tT0xLE/zp16hQ2b96M6OhouLu7tykeIqLuggkYERGptXv3btjY2LTaRiAQdNsd6ObMmaPTNu3alhm+rIRCIVJSUrBt2zaEh4d3dTgoLCzE3Llz4e3t3W02LSEiag8mYEREpOTpdu2TJk3CmDFj2tzfzMwMwJNzuJ4nBl36a5o5MTExgVgsbtcMYUcwNTVt9/UbGxtrPJ/sWTKZrF3jt4WDgwPi4+M7/XO0qa+vx+zZs2FlZYXk5OROW7pKRKQPPIiZiIiUPJ0RKi0tbbWdXC6Hq6srfvzxR6VyiUQCAGrP99KVRCLR2r+oqAh3795VW2dvb681fgCIj4/vlCTN3t4ed+7cQUtLS6vt0tLScP78eaUyiUSCsrIyyOVyjf2ampqQl5fXIbF2d3K5HJ988gkKCgqQnp6udgnt2bNnce7cuS6Ijoio7ZiAERGRkn79+mH06NHYtWtXqwnEzz//jLNnz6osU+zbty8cHR0RFxen87s9/+Xm5oZ9+/ahsrJSbX1VVRUCAwM19nd1dcWxY8dUdmh8Vl1dHeLi4mBpadmuGFvj6uoKmUymcZMU4MlGHR9//LHKLoxvvPEG7t+/j/T0dLX9GhoaEBwcrDH5fNEkJCRg7969SExMxKhRo1TqnyZohw4d6oLoiIjajksQiYhIiUAgwI4dO/D222/jtddew/Tp0zFgwAD06tULcrkctbW1uHHjBtLS0uDq6qryDphAIMD27dsV53DNmDED1tbWEIlEKp/1dMfE/9q0aRMOHjyIESNGwN/fH2KxGIaGhnjw4AEKCgpw7NgxvPvuuxq3OV+2bBkSEhIwduxYzJ07F3Z2dood/BobG/HXX3/h6NGjKC8vx/79+5/zN6ZqwoQJ+Oijj7Bw4UIcO3YM48aNQ79+/SAQCFBfX49bt24hNzcXAoEAvr6+Sn1dXFzg5eWFOXPmwM/PD6NGjYKZmRlkMhlKS0uRkZGB3r17w83Nrd0Jbk9x7do1LF++HMOHD0dlZaXKgdfV1dXIycnBtWvXsGvXri6KkoiobZiAERH1QLa2toqlfm0xZMgQ2NnZaW03efJk5OXlISwsDKmpqSgpKUFzczOAJzNkYrEYGzduRHBwsNrEytPTExcvXkRYWBjS0tKU+uvCysoK169fxxdffIGcnBwUFhaioaEB5ubmcHJyQnx8PAICAuDh4aH2evr06YO8vDxERkYiKysLBw4cQE1NjaJu4MCBcHZ2xurVq9Vu2S8Wi7WegwYANjY2sLe3V3veV2JiIiZNmoSkpCR8++23uH//PoAn29sPGDAAzs7O+Oyzz9Sel5aamoq4uDgcPHgQWVlZePjwIWxsbDBixAgEBgYiJCQEa9asgVQq1RqjNra2tu3eCXLIkCEYPHiwSrlEIkFTU5PO4wiFQkgkEpWE+sKFC3j8+DFu3ryJ4OBglX6GhoYYNmwYDhw4oNO5b0RE3YFA3toicyIiok4kkUgQEBCAyMjIrg6lx1mwYAGkUqnO7z65u7vD3d29W+xoqA8SiQTh4eFYsGBBV4dCRKSE74ARERF1I9HR0QgKCtLarrm5WafDoomIqHthAkZERNSN/PPPP8jMzNS6g2JxcbHa5Yvaxi4uLkZDQ8PzhNitVVZWori4+IV/P46Iei6+A0ZERNSNTJs2DV999RV8fHzg4uKC3r17K9U3NTUhPz8f586dQ0hIiM7jikQixMbGIjY2FgEBAfjhhx86OvQuV19fj4EDByqSL3XvJxIRdTUmYERE1GVsbW112hTkZeLi4oKUlBRER0cjKytLZbbK0NAQ9vb22LlzJ7y8vHQeNz4+Hjdu3AAAODo6dmTI3YaRkRFyc3Mhk8lgYGAANze3rg6JiEgFN+EgIiIiIiLSE74DRkREREREpCdMwIiIiIiIiPSECRgREREREZGeMAEjIiIiIiLSEyZgREREREREesIEjIiIiIiISE+YgBEREREREekJEzAiIiIiIiI9YQJGRERERESkJ/8HTpGO7Z8lOC8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "readout_module.arm_sequencer(0)\n",
    "readout_module.start_sequencer()\n",
    "\n",
    "print(\"Status:\")\n",
    "print(readout_module.get_sequencer_status(0))\n",
    "\n",
    "# Wait for the sequencer to stop with a timeout period of one minute.\n",
    "readout_module.get_acquisition_status(0)\n",
    "\n",
    "data = readout_module.get_acquisitions(0)[\"acq\"]\n",
    "I_data = np.asarray(data[\"acquisition\"][\"bins\"][\"integration\"][\"path0\"]) / 140\n",
    "Q_data = np.asarray(data[\"acquisition\"][\"bins\"][\"integration\"][\"path1\"]) / 140\n",
    "amplitude = np.abs(I_data + 1j * Q_data)\n",
    "\n",
    "plot_amplitude(nco_sweep_range, I_data, Q_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f7033f0",
   "metadata": {},
   "source": [
    "We can see that modulation and demodulation frequency now match, producing similar results as the spectroscopy measurements before."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "163bdd09",
   "metadata": {},
   "source": [
    "## Stop\n",
    "\n",
    "Finally, let's stop the playback and close the instrument connection. One can also display a detailed snapshot containing the instrument parameters before\n",
    "closing the connection by uncommenting the corresponding lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "61a03bea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:38:09.698533Z",
     "iopub.status.busy": "2024-09-02T01:38:09.698362Z",
     "iopub.status.idle": "2024-09-02T01:38:16.999206Z",
     "shell.execute_reply": "2024-09-02T01:38:16.998477Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, State: STOPPED, Info Flags: FORCED_STOP, ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_BINNING_DONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n",
      "Status: OKAY, State: STOPPED, Info Flags: FORCED_STOP, Warning Flags: NONE, Error Flags: NONE, Log: []\n",
      "\n",
      "Snapshot:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4:\n",
      "\tparameter                    value\n",
      "--------------------------------------------------------------------------------\n",
      "in0_gain                      :\t-6 (dB)\n",
      "in0_offset                    :\t0 (V)\n",
      "in1_gain                      :\t-6 (dB)\n",
      "in1_offset                    :\t0 (V)\n",
      "marker0_exp0_config           :\tbypassed \n",
      "marker0_exp1_config           :\tbypassed \n",
      "marker0_exp2_config           :\tbypassed \n",
      "marker0_exp3_config           :\tbypassed \n",
      "marker0_fir_config            :\tbypassed \n",
      "marker0_inv_en                :\tFalse \n",
      "marker1_exp0_config           :\tbypassed \n",
      "marker1_exp1_config           :\tbypassed \n",
      "marker1_exp2_config           :\tbypassed \n",
      "marker1_exp3_config           :\tbypassed \n",
      "marker1_fir_config            :\tbypassed \n",
      "marker1_inv_en                :\tFalse \n",
      "marker2_exp0_config           :\tbypassed \n",
      "marker2_exp1_config           :\tbypassed \n",
      "marker2_exp2_config           :\tbypassed \n",
      "marker2_exp3_config           :\tbypassed \n",
      "marker2_fir_config            :\tbypassed \n",
      "marker2_inv_en                :\tFalse \n",
      "marker3_exp0_config           :\tbypassed \n",
      "marker3_exp1_config           :\tbypassed \n",
      "marker3_exp2_config           :\tbypassed \n",
      "marker3_exp3_config           :\tbypassed \n",
      "marker3_fir_config            :\tbypassed \n",
      "marker3_inv_en                :\tFalse \n",
      "out0_exp0_config              :\tbypassed \n",
      "out0_exp1_config              :\tbypassed \n",
      "out0_exp2_config              :\tbypassed \n",
      "out0_exp3_config              :\tbypassed \n",
      "out0_fir_config               :\tbypassed \n",
      "out0_latency                  :\t0 (s)\n",
      "out0_offset                   :\t0 (V)\n",
      "out1_exp0_config              :\tbypassed \n",
      "out1_exp1_config              :\tbypassed \n",
      "out1_exp2_config              :\tbypassed \n",
      "out1_exp3_config              :\tbypassed \n",
      "out1_fir_config               :\tbypassed \n",
      "out1_latency                  :\t0 (s)\n",
      "out1_offset                   :\t0 (V)\n",
      "present                       :\tTrue \n",
      "scope_acq_avg_mode_en_path0   :\tTrue \n",
      "scope_acq_avg_mode_en_path1   :\tTrue \n",
      "scope_acq_sequencer_select    :\t0 \n",
      "scope_acq_trigger_level_path0 :\t0 \n",
      "scope_acq_trigger_level_path1 :\t0 \n",
      "scope_acq_trigger_mode_path0  :\tsequencer \n",
      "scope_acq_trigger_mode_path1  :\tsequencer \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer0:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\tin0 \n",
      "connect_acq_Q                    :\tin1 \n",
      "connect_out0                     :\tI \n",
      "connect_out1                     :\tQ \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tTrue \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t140 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tTrue \n",
      "nco_freq                         :\t4.95e+08 (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tTrue (ns)\n",
      "offset_awg_path0                 :\t1 \n",
      "offset_awg_path1                 :\t1 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer1:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer2:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer3:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer4:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer5:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, Flags: NONE, Slot flags: NONE\n"
     ]
    }
   ],
   "source": [
    "# Stop both sequencers.\n",
    "readout_module.stop_sequencer()\n",
    "\n",
    "# Print status of both sequencers (should now say it is stopped).\n",
    "print(readout_module.get_sequencer_status(0))\n",
    "print(readout_module.get_sequencer_status(1))\n",
    "print()\n",
    "\n",
    "# Print an overview of the instrument parameters.\n",
    "print(\"Snapshot:\")\n",
    "readout_module.print_readable_snapshot(update=True)\n",
    "\n",
    "# Reset the cluster\n",
    "cluster.reset()\n",
    "print(cluster.get_system_status())"
   ]
  }
 ],
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