{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f93d7a99",
   "metadata": {},
   "source": [
    "Characterizing Input Offset\n",
    "==========================="
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a74e27ef",
   "metadata": {},
   "source": [
    "The Qblox QRM and QRM-RF use analog-to-digital converters (ADCs) to digitize the incoming analog signals. Due to thermal, manufacturing effects or other factors, the digitized signal is not centered around 0V but has a small offset, which we will hereby refer to as input (ADC) offset. This input offset can get demodulated and integrated by the hardware along with the signal. The integrated offset can then show up as oscillations in the result, e.g. during a frequency sweep. In this section we show how to measure and calibrate away this offset to prevent such effects."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baa3c2d7",
   "metadata": {},
   "source": [
    "Measuring the Offset\n",
    "--------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f6b63d8",
   "metadata": {},
   "source": [
    "We will use a simple scope acquisition to determine the mean value of this offset. Before proceeding, please **make sure that there is no DC signal going into** to your QRM."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9187796",
   "metadata": {
    "tags": []
   },
   "source": [
    "Setup\n",
    "-----\n",
    "\n",
    "First, we are going to import the required packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a91e72ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:35.868905Z",
     "iopub.status.busy": "2025-05-07T16:48:35.868523Z",
     "iopub.status.idle": "2025-05-07T16:48:36.471857Z",
     "shell.execute_reply": "2025-05-07T16:48:36.471244Z"
    },
    "tags": [
     "imports"
    ]
   },
   "outputs": [],
   "source": [
    "from __future__ import annotations\n",
    "\n",
    "import json\n",
    "import warnings\n",
    "from typing import TYPE_CHECKING, Callable\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from qcodes.instrument import find_or_create_instrument\n",
    "\n",
    "from qblox_instruments import Cluster, ClusterType\n",
    "\n",
    "if TYPE_CHECKING:\n",
    "    from qblox_instruments.qcodes_drivers.module import Module"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7043693c",
   "metadata": {},
   "source": [
    "### Scan For Clusters\n",
    "\n",
    "We scan for the available devices connected via ethernet using the Plug & Play functionality of the Qblox Instruments package (see [Plug & Play](https://docs.qblox.com/en/main/api_reference/tools.html#api-pnp) for more info)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c2e3dc2",
   "metadata": {},
   "source": [
    "`!qblox-pnp list`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c106ad8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:36.474385Z",
     "iopub.status.busy": "2025-05-07T16:48:36.474237Z",
     "iopub.status.idle": "2025-05-07T16:48:36.476656Z",
     "shell.execute_reply": "2025-05-07T16:48:36.476262Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster_ip = \"10.10.200.42\"\n",
    "cluster_name = \"cluster0\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "031f7693",
   "metadata": {},
   "source": [
    "### Connect to Cluster\n",
    "\n",
    "We now make a connection with the Cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "25047f64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:36.478079Z",
     "iopub.status.busy": "2025-05-07T16:48:36.477944Z",
     "iopub.status.idle": "2025-05-07T16:48:37.287953Z",
     "shell.execute_reply": "2025-05-07T16:48:37.287377Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "cluster = find_or_create_instrument(\n",
    "    Cluster,\n",
    "    recreate=True,\n",
    "    name=cluster_name,\n",
    "    identifier=cluster_ip,\n",
    "    dummy_cfg=(\n",
    "        {\n",
    "            2: ClusterType.CLUSTER_QCM,\n",
    "            4: ClusterType.CLUSTER_QRM,\n",
    "            6: ClusterType.CLUSTER_QCM_RF,\n",
    "            8: ClusterType.CLUSTER_QRM_RF,\n",
    "            10: ClusterType.CLUSTER_QTM,\n",
    "        }\n",
    "        if cluster_ip is None\n",
    "        else None\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dec1b97e",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "#### Get connected modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8a5d1586",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:37.290994Z",
     "iopub.status.busy": "2025-05-07T16:48:37.290846Z",
     "iopub.status.idle": "2025-05-07T16:48:37.294097Z",
     "shell.execute_reply": "2025-05-07T16:48:37.293712Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_connected_modules(cluster: Cluster, filter_fn: Callable | None = None) -> dict[int, Module]:\n",
    "    def checked_filter_fn(mod: ClusterType) -> bool:\n",
    "        if filter_fn is not None:\n",
    "            return filter_fn(mod)\n",
    "        return True\n",
    "\n",
    "    return {\n",
    "        mod.slot_idx: mod for mod in cluster.modules if mod.present() and checked_filter_fn(mod)\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1ccabfaa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:37.296540Z",
     "iopub.status.busy": "2025-05-07T16:48:37.296401Z",
     "iopub.status.idle": "2025-05-07T16:48:37.314129Z",
     "shell.execute_reply": "2025-05-07T16:48:37.313750Z"
    },
    "tags": [
     "module_select"
    ]
   },
   "outputs": [],
   "source": [
    "# QRM modules\n",
    "modules = get_connected_modules(cluster, lambda mod: mod.is_qrm_type and not mod.is_rf_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dd7bda60",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:37.316927Z",
     "iopub.status.busy": "2025-05-07T16:48:37.316791Z",
     "iopub.status.idle": "2025-05-07T16:48:37.318998Z",
     "shell.execute_reply": "2025-05-07T16:48:37.318628Z"
    }
   },
   "outputs": [],
   "source": [
    "# This uses the module of the correct type with the lowest slot index\n",
    "module = list(modules.values())[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49684bcb",
   "metadata": {},
   "source": [
    "### Reset the Cluster\n",
    "\n",
    "We reset the Cluster to enter a well-defined state. Note that resetting will clear all stored parameters, so resetting between experiments is usually not desirable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c9eb707a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:37.321492Z",
     "iopub.status.busy": "2025-05-07T16:48:37.321346Z",
     "iopub.status.idle": "2025-05-07T16:48:39.876385Z",
     "shell.execute_reply": "2025-05-07T16:48:39.874997Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: BOOTING, Flags: NONE, Slot flags: NONE\n"
     ]
    }
   ],
   "source": [
    "cluster.reset()\n",
    "print(cluster.get_system_status())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b1ab874",
   "metadata": {},
   "source": [
    "Specify acquisitions\n",
    "--------------------\n",
    "\n",
    "We need to specify the acquisitions so that the instrument can allocate the required memory for its acquisition list. In this case we will create 5 acquisition specifications that each create one or multiple bins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "79efc738",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:39.880682Z",
     "iopub.status.busy": "2025-05-07T16:48:39.880269Z",
     "iopub.status.idle": "2025-05-07T16:48:39.887226Z",
     "shell.execute_reply": "2025-05-07T16:48:39.886117Z"
    },
    "lines_to_next_cell": 2,
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Acquisitions\n",
    "acquisitions = {\n",
    "    \"non_weighed\": {\"num_bins\": 10, \"index\": 0},\n",
    "    \"weighed\": {\"num_bins\": 10, \"index\": 1},\n",
    "    \"large\": {\"num_bins\": 131072, \"index\": 2},\n",
    "    \"avg\": {\"num_bins\": 10, \"index\": 3},\n",
    "    \"single\": {\"num_bins\": 1, \"index\": 4},\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9b73cc76",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:39.891078Z",
     "iopub.status.busy": "2025-05-07T16:48:39.890706Z",
     "iopub.status.idle": "2025-05-07T16:48:39.937819Z",
     "shell.execute_reply": "2025-05-07T16:48:39.936572Z"
    },
    "lines_to_next_cell": 0
   },
   "outputs": [],
   "source": [
    "# Create a helper function that creates an acquisition program, runs the sequencer and calculates the ADC offsets based on acquired results.\n",
    "\n",
    "\n",
    "def acquire_scope_and_calc_offsets() -> tuple[float, float]:\n",
    "    seq_prog = \"\"\"\n",
    "        move    1000, R0        #Loop iterator.\n",
    "\n",
    "    loop: acquire 0,1,20000   #Acquire bins and store them in \"non_weighed\" acquisition.\n",
    "        loop     R0, @loop #Run until number of iterations is done.\n",
    "\n",
    "        stop                #Stop.\n",
    "    \"\"\"\n",
    "\n",
    "    # Add sequence program, waveforms, weights and acquisitions to single dictionary and write to JSON file.\n",
    "    sequence = {\n",
    "        \"waveforms\": {},\n",
    "        \"weights\": {},\n",
    "        \"acquisitions\": acquisitions,\n",
    "        \"program\": seq_prog,\n",
    "    }\n",
    "    with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "        json.dump(sequence, file, indent=4)\n",
    "        file.close()\n",
    "\n",
    "    # Upload sequence.\n",
    "    module.sequencer0.sequence(\"sequence.json\")\n",
    "\n",
    "    # Arm and start sequencer.\n",
    "    module.arm_sequencer(0)\n",
    "    module.start_sequencer()\n",
    "\n",
    "    # Wait for the acquisition to stop\n",
    "    module.get_acquisition_status(0)\n",
    "\n",
    "    # Retrieve results\n",
    "    module.store_scope_acquisition(0, \"single\")\n",
    "    single_acq = module.get_acquisitions(0)\n",
    "    I_data = np.array(single_acq[\"single\"][\"acquisition\"][\"scope\"][\"path0\"][\"data\"])\n",
    "    Q_data = np.array(single_acq[\"single\"][\"acquisition\"][\"scope\"][\"path1\"][\"data\"])\n",
    "\n",
    "    # Plot results\n",
    "    fig, ax = plt.subplots(1, 1)\n",
    "    ax.plot(I_data, label=\"I\")\n",
    "    ax.plot(Q_data, label=\"Q\")\n",
    "    ax.set_xlabel(\"Time (ns)\", fontsize=20)\n",
    "    ax.set_ylabel(\"Relative amplitude\", fontsize=20)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "    # Print mean offset values\n",
    "    I_offset, Q_offset = np.mean(I_data), np.mean(Q_data)\n",
    "    print(f\"I Offset : {I_offset * 1e3:.3f} mV \\nQ Offset : {Q_offset * 1e3:.3f} mV\")\n",
    "\n",
    "    if np.isnan(I_offset):\n",
    "        warnings.warn(\"Determining offset failed, setting to 0.\")\n",
    "        I_offset = 0\n",
    "    if np.isnan(Q_offset):\n",
    "        Q_offset = 0\n",
    "        warnings.warn(\"Determining offset failed, setting to 0.\")\n",
    "\n",
    "    return I_offset, Q_offset\n",
    "\n",
    "\n",
    "# Map sequencer to specific outputs (but first disable all sequencer connections)\n",
    "module.disconnect_outputs()\n",
    "module.disconnect_inputs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "19999836",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:39.941467Z",
     "iopub.status.busy": "2025-05-07T16:48:39.941075Z",
     "iopub.status.idle": "2025-05-07T16:48:39.956812Z",
     "shell.execute_reply": "2025-05-07T16:48:39.955710Z"
    },
    "tags": [
     "connect"
    ]
   },
   "outputs": [],
   "source": [
    "module.sequencer0.connect_sequencer(\"io0_1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7d274c0b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:39.960466Z",
     "iopub.status.busy": "2025-05-07T16:48:39.960064Z",
     "iopub.status.idle": "2025-05-07T16:48:39.965145Z",
     "shell.execute_reply": "2025-05-07T16:48:39.964040Z"
    },
    "tags": [
     "adc_offset"
    ]
   },
   "outputs": [],
   "source": [
    "adc_offset_path0 = module.in0_offset\n",
    "adc_offset_path1 = module.in1_offset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "87692380",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:39.968880Z",
     "iopub.status.busy": "2025-05-07T16:48:39.968201Z",
     "iopub.status.idle": "2025-05-07T16:48:40.032153Z",
     "shell.execute_reply": "2025-05-07T16:48:40.030924Z"
    }
   },
   "outputs": [],
   "source": [
    "# Ensure offset is reset to zero\n",
    "adc_offset_path0(0.0)\n",
    "adc_offset_path1(0.0)\n",
    "\n",
    "# Configure scope mode\n",
    "module.scope_acq_sequencer_select(0)\n",
    "module.scope_acq_trigger_mode_path0(\"sequencer\")\n",
    "module.scope_acq_trigger_mode_path1(\"sequencer\")\n",
    "module.scope_acq_avg_mode_en_path0(True)\n",
    "module.scope_acq_avg_mode_en_path1(True)\n",
    "\n",
    "# Configure integration\n",
    "module.sequencer0.integration_length_acq(16000)\n",
    "module.sequencer0.thresholded_acq_rotation(0)\n",
    "module.sequencer0.thresholded_acq_threshold(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e84efc27",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:40.035980Z",
     "iopub.status.busy": "2025-05-07T16:48:40.035608Z",
     "iopub.status.idle": "2025-05-07T16:48:40.058546Z",
     "shell.execute_reply": "2025-05-07T16:48:40.057432Z"
    },
    "tags": [
     "configure"
    ]
   },
   "outputs": [],
   "source": [
    "# Configure the sequencer\n",
    "module.sequencer0.mod_en_awg(False)\n",
    "module.sequencer0.demod_en_acq(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "92d5c074",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:40.062234Z",
     "iopub.status.busy": "2025-05-07T16:48:40.061861Z",
     "iopub.status.idle": "2025-05-07T16:48:40.987254Z",
     "shell.execute_reply": "2025-05-07T16:48:40.986311Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I Offset : -14.911 mV \n",
      "Q Offset : 11.028 mV\n"
     ]
    }
   ],
   "source": [
    "I_offset, Q_offset = acquire_scope_and_calc_offsets()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cf4f060",
   "metadata": {
    "lines_to_next_cell": 0
   },
   "source": [
    "Correcting the Offsets\n",
    "----------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e6740c1",
   "metadata": {
    "tags": [
     "correct_1"
    ]
   },
   "source": [
    "One can correct for these offsets by changing the reference Voltage of the ADC. This can be done using the parameters <code>[QRM.in0_offset](https://docs.qblox.com/en/main/api_reference/module.html#QRM.in0_offset)</code> and <code>[QRM.in1_offset](https://docs.qblox.com/en/main/api_reference/module.html#QRM.in1_offset)</code>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7e334855",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:40.989614Z",
     "iopub.status.busy": "2025-05-07T16:48:40.989449Z",
     "iopub.status.idle": "2025-05-07T16:48:40.997891Z",
     "shell.execute_reply": "2025-05-07T16:48:40.997157Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "adc_offset_path0(-I_offset)  # Negative sign to compensate for the offset\n",
    "adc_offset_path1(-Q_offset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfc66e91",
   "metadata": {},
   "source": [
    "Repeating the offset measurement as before, we get:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7d4fa6a4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:40.999706Z",
     "iopub.status.busy": "2025-05-07T16:48:40.999558Z",
     "iopub.status.idle": "2025-05-07T16:48:41.835714Z",
     "shell.execute_reply": "2025-05-07T16:48:41.834638Z"
    },
    "lines_to_next_cell": 2,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I Offset : 2.878 mV \n",
      "Q Offset : -2.512 mV\n"
     ]
    }
   ],
   "source": [
    "I_offset, Q_offset = acquire_scope_and_calc_offsets()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76f8714b",
   "metadata": {},
   "source": [
    "Advanced ADC Offset Calibration : Curve Fitting Method\n",
    "------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12740d5b",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "As you may have noticed in the previous section, manually compensating for the offset does not entirely eliminate it, leaving some residual offset. This is because of the non-linear effects of these input offsets. To circumvent this, one can curve-fit the dependence of the set offset value on the actual offset value. In the following section, we do this by fitting 10 setpoints against the 10 measured offset values from the binned acquisition with a function. We then find the roots/zeros of this function to set the actual offset to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a7905839",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:41.838727Z",
     "iopub.status.busy": "2025-05-07T16:48:41.838562Z",
     "iopub.status.idle": "2025-05-07T16:48:41.843409Z",
     "shell.execute_reply": "2025-05-07T16:48:41.842648Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define helper functions\n",
    "def get_real_root(coeffs: np.array) -> np.array:\n",
    "    if all(np.isnan(coeffs)):\n",
    "        warnings.warn(\"All NaN array. Returning 0.\")\n",
    "        return 0\n",
    "    for root in np.roots(coeffs):\n",
    "        if root.imag == 0:\n",
    "            output = root\n",
    "    return np.real(output)\n",
    "\n",
    "\n",
    "def get_curve(x: np.array, coeffs: np.array) -> np.array:\n",
    "    y = 0\n",
    "    for i, coeff in enumerate(coeffs):\n",
    "        y += coeff * x ** (len(coeffs) - (i + 1))\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "199e24da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:41.846232Z",
     "iopub.status.busy": "2025-05-07T16:48:41.846086Z",
     "iopub.status.idle": "2025-05-07T16:48:41.849319Z",
     "shell.execute_reply": "2025-05-07T16:48:41.848570Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define the program that we will run\n",
    "seq_prog = \"\"\"\n",
    "      move    0,R0        #Loop iterator.\n",
    "      nop\n",
    "\n",
    "loop:\n",
    "      move    100, R1     #Averages\n",
    "avg_loop:\n",
    "      acquire 0,R0,20000   #Acquire bins and store them in \"non_weighed\" acquisition.\n",
    "      loop    R1, @avg_loop\n",
    "      add     R0,1,R0     #Increment iterator\n",
    "      nop                 #Wait a cycle for R0 to be available.\n",
    "      jlt     R0,100,@loop #Run until number of iterations is done.\n",
    "\n",
    "      stop                #Stop.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a687ca5e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:41.852200Z",
     "iopub.status.busy": "2025-05-07T16:48:41.852055Z",
     "iopub.status.idle": "2025-05-07T16:48:41.856116Z",
     "shell.execute_reply": "2025-05-07T16:48:41.855372Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Add sequence program, waveforms, weights and acquisitions to single dictionary and write to JSON file.\n",
    "sequence = {\n",
    "    \"waveforms\": {},\n",
    "    \"weights\": {},\n",
    "    \"acquisitions\": acquisitions,\n",
    "    \"program\": seq_prog,\n",
    "}\n",
    "with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "    json.dump(sequence, file, indent=4)\n",
    "    file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fda4206a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:41.858549Z",
     "iopub.status.busy": "2025-05-07T16:48:41.858401Z",
     "iopub.status.idle": "2025-05-07T16:48:41.913613Z",
     "shell.execute_reply": "2025-05-07T16:48:41.912331Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Upload sequence.\n",
    "module.sequencer0.sequence(\"sequence.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12832946",
   "metadata": {},
   "source": [
    "Let's start the sequence and retrieve the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7916cd8d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:41.917452Z",
     "iopub.status.busy": "2025-05-07T16:48:41.917051Z",
     "iopub.status.idle": "2025-05-07T16:48:52.225609Z",
     "shell.execute_reply": "2025-05-07T16:48:52.224502Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Set the domain (X) around these original offset values\n",
    "\n",
    "I_offset_setpoints = np.linspace(-10e-3 + adc_offset_path0(), +10e-3 + adc_offset_path0(), 12)\n",
    "Q_offset_setpoints = np.linspace(-10e-3 + adc_offset_path1(), +10e-3 + adc_offset_path1(), 12)\n",
    "\n",
    "int_len = module.sequencer0.integration_length_acq()\n",
    "I_offset_sum, Q_offset_sum = [], []\n",
    "\n",
    "for I_offset_i, Q_offset_i in zip(I_offset_setpoints, Q_offset_setpoints):\n",
    "    module.delete_acquisition_data(0, all=True)\n",
    "    adc_offset_path0(I_offset_i)\n",
    "    adc_offset_path1(Q_offset_i)\n",
    "    module.arm_sequencer(0)\n",
    "    module.start_sequencer()\n",
    "    module.get_acquisition_status(0, 1)\n",
    "    non_weighed_acq = module.get_acquisitions(0)[\"non_weighed\"]\n",
    "    I_offset_sum += [\n",
    "        np.mean(non_weighed_acq[\"acquisition\"][\"bins\"][\"integration\"][\"path0\"]) * 1e3 / int_len\n",
    "    ]\n",
    "    Q_offset_sum += [\n",
    "        np.mean(non_weighed_acq[\"acquisition\"][\"bins\"][\"integration\"][\"path1\"]) * 1e3 / int_len\n",
    "    ]\n",
    "\n",
    "output = {\n",
    "    \"offsets_I\": I_offset_setpoints,\n",
    "    \"offsets_Q\": Q_offset_setpoints,\n",
    "    \"I_m\": I_offset_sum,\n",
    "    \"Q_m\": Q_offset_sum,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8ec01712",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:52.229106Z",
     "iopub.status.busy": "2025-05-07T16:48:52.228922Z",
     "iopub.status.idle": "2025-05-07T16:48:52.367256Z",
     "shell.execute_reply": "2025-05-07T16:48:52.366288Z"
    },
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Offset setpoints corresponding to observed zero offset: \n",
      "I offset: 12.599599686312622\n",
      "Q offset: -8.894126538114762\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "\n",
    "## Fit I offset and find its root\n",
    "coeffs = np.polyfit(np.array(output[\"offsets_I\"]) * 1e3, np.array(output[\"I_m\"]), 3)\n",
    "new_I_offset = get_real_root(coeffs)\n",
    "\n",
    "plt.plot(\n",
    "    output[\"offsets_I\"] * 1e3,\n",
    "    get_curve(np.array(output[\"offsets_I\"]) * 1e3, coeffs),\n",
    "    c=\"b\",\n",
    "    label=\"I\",\n",
    ")\n",
    "plt.scatter(output[\"offsets_I\"] * 1e3, output[\"I_m\"], c=\"b\")\n",
    "\n",
    "# Fit Q offset and find its root\n",
    "coeffs = np.polyfit(np.array(output[\"offsets_Q\"]) * 1e3, np.array(output[\"Q_m\"]), 3)\n",
    "new_Q_offset = get_real_root(coeffs)\n",
    "\n",
    "plt.plot(\n",
    "    output[\"offsets_Q\"] * 1e3,\n",
    "    get_curve(np.array(output[\"offsets_Q\"]) * 1e3, coeffs),\n",
    "    c=\"r\",\n",
    "    label=\"Q\",\n",
    ")\n",
    "plt.scatter(output[\"offsets_Q\"] * 1e3, output[\"Q_m\"], c=\"r\")\n",
    "\n",
    "# Plot zeros on the plot\n",
    "plt.axvline(x=new_I_offset)\n",
    "plt.axvline(x=new_Q_offset)\n",
    "plt.axhline(y=0)\n",
    "\n",
    "plt.xlabel(\"Set Offset (mV)\")\n",
    "plt.ylabel(\"Observed Offset (mV)\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "print(\n",
    "    f\"Offset setpoints corresponding to observed zero offset: \\nI offset: {new_I_offset}\\nQ offset: {new_Q_offset}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22313395",
   "metadata": {},
   "source": [
    "Applying The Offset Corrections\n",
    "-------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7560c2e1",
   "metadata": {},
   "source": [
    "Using the zeros obtained from the curve fitting, we attempt to correct for the input offsets again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b18bd006",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:52.369482Z",
     "iopub.status.busy": "2025-05-07T16:48:52.369313Z",
     "iopub.status.idle": "2025-05-07T16:48:53.695445Z",
     "shell.execute_reply": "2025-05-07T16:48:53.694598Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hQlrZx7KvuL3dEuYrF2HY8CX8jAbXhV3xwmRfQPF7Lre1H3Lb+MOw3vV2Q0JBXvF5SsVN7I3b18F85qhq9anFfPJfmC/Zf0+bTvwL/QsPip7OL5w/B8Pmb8oG7ipNPy56fW5xdR+/pKgeQV8Iw4YvIWR57n2mWN6N16LUPa8NP3+B3G7VfD77b9FnrwEATDs3l3lOUJD53Hg9MVPhY2Nk11ERMKAkr6R/7j4I1y5D/2LZO43G338qnvZVat2W+bT9rKiuSbvIcfThao9w7ZJHTtL6l2dCuHYJRa89Zvd5c9pJ5E/tA+OfG4qnb9hb8+ahEUoh55rDD+r8qb1ROOs26MWOwJS6QHXr9D8VLoYLF92HwpdmOi1j2r7O5nvL9B3rKUSClyVvMCnYUkWN150awY4XES6c07oLFvnjO6Hw0VvReeP7WnfFbaxv5BXOGe1y6nbh0xNROOs2FKi0Ltr4x88omNYfRa/MUqU+teXf1sLu48I1admaDWveR+Ejw2H0wJYQ5owzsvcz1C97DIWP3or8ST2VdcJHZv0UzroNyLkGo5uTVampcMnDKHz+AY+3a75y0eNt+goGlOSVTOYbJ+LzubZT6AruuwWmPzdoMyKhOXkfUIVP3AXTnxtQMLUPCueMRt6AOjCsK5Xd1gMffoLJhNyuVZHbvbrdUciSkTk508SMJQmL7DDt+wP6N+eJngpk/me35PbFMHzxBgwfLnE6Amva95vN93lDG8B0cKfNY0XvPuP2dUKCIIhuQ2xWUnuvMeGS+skbxEzZEgwG6N+cB8O6T6B/dY5mSSQEsxn6dxbCuGPTjQf1hTAd2KFJf0oTzh4HADQ8sFnyOcKr1rJJ6Etez5oQchyvtS05P5VsZq9Uwb1lp+h5E5czZ0rKiT1f7NzkupADPxw5gxf+OOiyrbz+iSiY1l9WG8afiv++5iP7ZB2vlPnqJeiXPeZw67CKwtHfWMjNhuHjl2D49FWYr7823XWuKZ2fwOkMHCe86lzoJgwoyStZv/WMjlKwy3xj+zSZJyWz1UW78Xq2v6IV7lv35ZD19N7L522ekrN+yWS1t1qBk7vJ+eNvQtHbT8PwqW2gYW+k1Hze/uiQs0Qvkjn7O5Z6Tkg7ifw7Otg8VvTOAvFBnAyCICB/QhcUTOmt7gehhz5UDd++B5OLv5f+mSkoevtpFD42FkUrnkNen1i7yRzUpis1I8K44UsUvfEECqb0snnc8MMqmM8cs0104WZCYQEMG1fbDaZqZR7HgfUzEWYQt0ZWyMlC3oA6andRNaVv0pSW2zUSpqPce1asoo9fRl5qFEwn/nVbG8K1y0ge3xJFL83AtjPnXZY3/fGz2/riLobN36Bw3ngUvb8Iebe3dFHa/vnUuHWN6v1yN+P2dWWCt7yetVD0zXtlC5utRp5NJhj/3IjcHjVUu8FTwrDpa+QNTFZcj1BYgLxhjVDw5HjlnfJiDCjJpwlGo8MAQDQ7UxmNW9cg//7+MF92/aHlURIvyIu+fgcFDw+zWePmuGrP3kETsmyz0ckZlTT9+gMKHh8nOhg1n7LdlN3efllCrv2RicLHxsB8+ijy7+0Lw9qPlP2+HEyflVKnkHFafvvXFX25HAWPjIBgKELR6rdR8MhwCEV6CBlnYN73O0w7NwHXtz1wJ7nv4cL590D/1tN2nxNcTFs0rCk7hdPwxeuy+qGEs+yBeUPqO3zO8J3609P0Sx5C4cyRKHioeC1y6SmDifmXMe7Ur6LqMny3osxr1BNTHcUqmNjdsqWHI4ULJkIwm1Hw+DgUrXzBQz2zJeRkQci+CqEgT9U14nKngzqiX/IQhKsXoX/uPlXrtVb02TLEF1zF9KM/IT0nH4KhCMLFdLe1pwXTtu9h+vWH4m/0hTBuW4v8+/pJmkGhXzjZZRnBbIY544zcbipmvUe08c+NKJjWv0zwJly7BP3T/0PRl8ud1lUwtTfghvWuYvJkiGHc9j2E00dg/H6l68I+jAEl+bSCaf2Rd0sCjH9uVLVe4fJ5mH5bB/1LM2Qc7D1TG/QLJsG45VsIoi7Yrfrt8GcQsY7Q0TSVUo/nj2kDg9UFplHm3WTjjx8XBz5ySPxbFcwcCdMfP6PwiTtR9OoceW06YT60S/brx7RtreRj9M9OhXHz1zCsWQH9wskwbv4Ghm9L3RFWM5GKg5/N+j0sJag2rHkfRcvnKWrbmuj1zpa63PdeN3z+mtPnjT9+bPl/0YdL1GnzepBq2vNL8X6KbQPkV+ah6c2mgzuhXz7fZYZse1wm1REEmP7cAOOPH0P/8qPIaaVD3l03yeypdMVLBCKR260aij543u7rU/aNLXeNfLvz86/0Z8i49iiYPtB97XmBggcGwfT7etX3L81t44+8/olu259U/87TMP39u8Pn8/rVtmQmL73Mo0xdz05VtW8eZ+9cKGFJia9gQEk+zfTnBgDFd6jccWIUu3akRMGs21D05pOu6825BsOWNdLTe3v5Cch8+TwOrZuBuYdst1TRv/Ekclv7wVxqClnh3DukN2InwBEK8u0UtM+4czPM19eFSWW2SpZS9MFi2enyHY5QumHLDXPGGehff8LpVDTrUVlna8fcyfDF69C/Ogd5/ZMsiQ/MVy/BuPU7xVNRBaMRv2x+Ch/+WTwCWfDYWMX9tcd8+iiM17PuepLeRaInWXUumKh6nXIJ2Vcdni/z7+iAoreekpQRXFLbpfbTM+//wy3tlFbw6K02ibjMJ8u+f/WvP4G8/omazaRxxw0CR0zHDsK0zzZA0WqNoxaEK+r9ja0/Z9xx7gCKZyPkT+jstEzRp6+Irq/o45clte9qSrs1j8/OEgQUTE5FweTUchVUMqAkn1UmNbbV96b/9iL//v4wHRbxgaPTFU8NnTlKeh9KTZkyigww8if3QuHDQ1H0huvg07ZBqz0uc6SlBpdSt1T6V2ah8MUZKFrxHBIKruDR/2yzrRa9swAAXH7AiKJgxMz07x4UTE5F3uB6yvsBiE6XX5rTkREVPmCst5rI65+IoncXIn9EE8X1KudiT7sVz0HIPGNJ+59/R3sUPDTEcpPGtP9PWa2a/9mNVtdOY2hacbIlY+mEVBbiXlvCxXS7UwbzhjZAwcQeMJXeV8/LLhoEsxlmO9uReAPzxYziTNRWibHyJ6cWny/fdDwabT6uYC9DBaRk/ZZyvHHDlw6n35coenchhMyzlvXwxj83IH/6QFFTyAseGKg4EVXRe886fV4QBHHT2UWc0/NHpcC0Q/5MJPP5c4ov3oWiQhQ8MgIGDbKhmvZuh2GD472SBbOEKczWN2YKxd+M1ZJ+yUOSylu/tkvfiBJMJpgvqD9VumQNqCAITs+vwtVLMO3aAtOuLcXTxFcsRsH8u30+uGRAST6rdFIXa/kTOsP02zrk/6+rqLr0CyY53djeuG2tZY8ja1LvmpUw/7sHAGBY97HjQq4+ZPNynD7t+uRU6nnr8lZt29ZjZ+pGbjaKPngeho+WOv2bqEZBQGn+b6+KHXHBST/zUmvB+Iv7tjgpeGiIy3UnNtzwQWb4biUM6z+DIAjQf/A8CuaMgXHT164PtOqPcP0Duuj9RRAK8mE6KDPzqco/n/6lGcVrkx0oPRJf9O4zN7rixmmyNn3IPIvC56ahcMEkmE8fsXmucO4dyOsb7/QC1RMMW761eZ0a//4NeX1ikds10qZcyfvWOjO1t1x85fWOsZzP5R7v8Ln+iTe+cfrzFj9XMLUPTL/+gLxbEmDY4jwxi+mPn5VPo3QRxBS9vQB5tyRIr1fl/UqLPnsNebckQP/yo4rqEc6dgHHz1yicf7fN4566OVP4qOOb3s7OR04p2C9ZMxLbzr+1uc33BQ8PQ17fOCfVy0x+eP08q39mSvH59fsPRRylQ9Grs2FcswJF77vYs9zLMaAk3yD1A6ZkSoeYjadF1F3wwCDoF0+DqdSG81I3PjZuWwvh2o1kNOLWNtq06LKEOfMscvQGNHzTeRZQ89ED0L9dPHJoTj8F066ymwEXzL+7zMm4bEXWGdfcnyXT7Zx9mKi48L/gwcGOOqBK/fqXJUxlsvqZz2Tn4thVZdNezVcvoXDeeBTOGQ3jz1+g6JVZMK7/FPrnp8uu0/jnzy6TqCgm4TwjZc/TMtkH3X4xpkPBvX1g+Pw1GL5+B3ml9hEsGaEteu8Zewe7qFk9hQ8Pg/7ZqTCdLE6WZfjqenBZapppaea0k8i7JcFm7ajjUWfHTL+tU2e7GJUT3Njj7IanPYUPD3VZxqxgyqpgKIK59Eh8KaLXN7uZfmnx6JZBpbXGZZQaATP9/TvMpw67/3xlxbzP8XpFZ6TuJQoUz8zKG9YIhc/dL+EgN53zRJyzS2+/4uzcbdy9FXm9Y2BQkEXdsPptAID+jSckHedoH3FfoWDFPZE7qXfyMV/MgK5GNHQy73par08pXjMiv28FDwyCLr5UJjO9hHVzIk7KQtZlfHguF0evuA6mi958EkGTnnCY3l+rjY4FZ4GpynevfZVQkGfZg0ss0yHX+2su33MYX174GYo2Scm/MXoudi8145ZvbxxzIQ35D9gG3IVO7sCbju6X1j83EDv1UQedR8YozSetMho72fdUyLkGBFSCrnKI+zvlqA9XLwJ1Gokur39pJoQLaQ7Xf5mvXIAuojp0/v5O6yl86h4gsBJC19nPdmmWsA7LGdO/f7n3JoKDNcZytmISy9GWHJLzAvgoQV8IXVCw3eccLe0wX7kAv2q1btRhNELIvmLzmCQyf9dmha9Fw7pPIJw+AsPpIwiebT1zS2a9Ivtj2PwNAnvKHIkVqWBiDwBA4SPDFdclZJ5BwbwJCOjYR3FdvoAjlOT15AaCJfL6xMqe6mI68CfyekUrar80odQ2AXmpURIOdn3iLZjSC2bvmA0GADB8W3Z7BpecjQR6YK9A6ZxPC5ZSj9jpNkVvPCHttQPAdGiXg2Zt29Q6ZDf+8JGkEcCCu28u+6CHbzw4m7rocS7W3pUQ8nOR27Uqcm8Ov/6AF504nHEy1dL071/IS41Cwf+6iavLUGRJ7mb36U9elti5sgrn3w3j7z8prscRRxmBC2XkBRDNwWtF7kiZrzF8+abkY/JSo2zyOuTf1an4MRcjvY7opYwQWrnrO3Fb/1gznzkG/VtPF9+AKnWjwvTvX9C/9ywgI9OyFGoEec64Y+qy8bsPYNzh+PxSnjCgpArB8OESmB3tS+fswlPMlFkPEQryYSzZn8pZuetTahftk76vYwnDZ8uQ0ylU9vHWCl98RJV6PMX0l/QPW2uyM79eZz5gf52gUe7WKKI4DiTEjGra0CLgVysQsnMuMB13Mlbrol3Tkf3iEoOprNDJ1OKiz2/stSmcPVb8Hw9M2bS0ee0yjL+tV30fxBIl296Y/v5N9P6mzrJNm/ZuV9wn85F9orJ/K2G9lKKEcbPINcti6jcYYPz1R9dbrXiKwtHXM1m5SHz1Syz5oziYM6efgvGvX6F/fS4EEbMNDC72uXV43Nob6+pKEk9ZbwHkSukZH44yYJeeMaF/cx5yB9WF+cpFfH/0rN1jnMm7rTmKls9D4bP32rZfkIf8MW1Q9NrjsnNKuItQkCdyHWMxR6PuNpxcLxY84mD01GoKv2HjatH98TUMKKnCyOtZU+suKFLwwMCya7Gus54uWOK+Y/L2dbTwkexvAFA4Q507l8bf1kO/6F7XBa3o31tk80FkPnO0+D8yghzz4b9h+mub/XZEbFbtDoUzpW3uXPSRm9YpySQIAgqfEf+7E0wmy7YlQl4O8kc2k9euvhD5t7VA/u0tXZWUVb8z1hlSS3M0qpH/wCDAaFCn/cvnHe85OroVCu7vZ1ln5E6yEsL4qLyRTRUdb75yEQWPDLfsDVha0fJ5KJg+APn391PUjiNSbzDE/SFyz1gH5mz+C2ey8zBzU/F7JW9AHRTc09UmgZYz+V+/q6h9aw5njtiRN8x2anhJRuwySs1SKHr7aQjnTsAg4/xcMOt2S14K09+2e0YWPj7O8n+znaUHhp8+L94Cx4PrSYsJKHxsLAqfvEvVWnenO7mRUFhg93VstNrSrnDmSFX7400YUJJ38ujUK2lT40rvhaUqB3e/zFcuwLRri1ualDUl1duotFaoQMbFUtFrj6HggUE3HlAw1VJqanRXRK+xdPJ+EyQGGYb18kfG3cG0d3uZrKsOy/65AQVTeiEvtRZMh3Yht0u48wOc/d5cZGEuUfTa46LKWZO6dlYM07a1qtRj+P5D5PWKht7BzAQhs3h0RM3RM3Ke9VwM/cszYdz8DQqmD7D7vOG74vX0JVNana2VLPr6XYeBqV06ne3rQcTnf9ReZTM2jIKyz4zga1bvQYXXKyYFs08MP30mqbzUdbVCkR7Gnz93+Ly9m9nWCmffbhNQ2ZDxWZlvED8DxrjVeabjG90o7oeY3+WejLIzAawVzhsvqs0ShtVvSSrvzZiUh0giw2fLbvx/1Yvwb9QKgQPGOTlCOXcFk8D1xBRuoBMZqJsO7wMEM0y/rXNDJzy/GtD4/UqYJ0rL7uYuovfJdHFBZL58Hubjh+DfrgdmbtqNdcfSsONu+xeerraz8bhSGUPjM444KFjMtHsrAIgaqXAUFBU+NgahP6u/z1kJLdep6SBAKNIjt3cM/KJrI/Tzv4s36p4+EDCbYf5H/GiL9Mblv5+Fi+77e/iygnkTYD5+CLpg50mZrKeBmjPPOh1p0S+Y6LxRO8GokHPN+TEq+ffSNfT7dANOZ+WhXk4GQo3uXfdXmmHVixAyz6DyCw6CLG8jJQCV+/6UEJAfvZKN5kFW5R3c8HSWxM1uF3KyRE15bbvReaJC4w+rJLVbJDETrDdjQEkkQcEjw8t8GBbOvcOtAaU57SQKZ9/utvrlEEwmIDcLuohqDsu8vvNtGL51fTfR9ZRANzK452Ki8PnpCOjh3mx0YpQER0rl9asNGIpQ+aU1eG/bWeQEBGPFvmMY76B8gZuTJ4hhProfBZ+8DL/a9W0en/3W/0QdL+budun9Ha25dSaDxozb1gLZV2HOvor8+/rBr34KTNsljEiVZjKi4Mnxki/GxBKcZLmt6IzffSD5mLx+tZW1+dNnwHPazGT439rfcTqrePrlXz8Xb9NgviovuY1cUreAccZcaiuz8q7Wc5Ng6NjD8n3R8vmq1CuIXOLTeLfrPBYVFQNK8g3ekn1Qg5ToBTKCyUp5yvYRdKVgYneY9m5HyOp/4FfDfhbc20//hsKnfgPCIt3aFyXErpeRTOR+nPo33JuooywH7yNX76/rr/uib97FmW3f41B4HHbc4vhurvnw35b/G9dL3x9QDYo3bVfK2e/U17e+sZouaPp9vcO13WIZf/ocxu9XKu2VQ7ndq7utbtJG7A55NzCK7KxxE85LT1LjTdy5PYy3qbJ1NfRbbyS2MazxvSU7pl/VWV7gbbiGksiL2BtRKr3NiBhjHuqkQm8cK8l8WPj0/2yzyXlL4H+d+cQ/0L+9wOPtih2dKnrHjX1zsTm8jVJ7oTraKbFkK4+m2eLTq4vdh7LccXRTwceDyWCTAYWP3qpqnUK2k22CJJTLnz7Q/jnIhxKMeVS+l0xPt/M3K1w4BXlj20EwGNy+t6Vp52ZZx+W00tlkTdaMhHOK+cg+1MsRt2euu5mub6VjTjupcU9Q5jPQnQrn3+2xtjyJI5TkdfSvz4Vur7KtG7QgSLmAd8Amfb2XBWf2mPf9jvwRTZwX0vDncBawCYVu/ADJz4X5kDqbonuCYcONrU7mH1qNcyHWIzq+HQBpRe/mbSKkEvSFMKxxvv5HjBFn7W9r4xF5OU63rTD9+gPQPtWDHfJt3jwVuCRZSW7XSASkSss07Yyf2YQ3dr+HgxE3MgDrX5ohuz79c/cj4Pvjko+Tu/ekWEL2VQhmM3R+tuNGpj83YCu8a19E4w8fad0FFD45Xusu+DwGlOR1Sq9LcvvlrEojBkUfPK9KPdaMv3wP4Zq8/a7cw8XvyodGX4qskiu5g+Hrd9xav6pK7WX27i73b+lQ3gkl28eUYvj2PQR0H+Lh3gC5N0eoMmW/ilGdIMT0n7y1X7k3R8C/1c2O63Xrfq3kcYX5ygMOq5uavQ9vxbjTyvcWVSp/VIp767+rE/w79ELIcmXBY9HKF1TqkXcz+eAghrfhlFfyfjnXIGSe0boXThm+W6n69EXjT5+h4MHBqtaplHHz1zBscZysxG568Fz3rueUqyJeeD697W+7jwsZpyXXNfzJgQp7U3GJTWevKpWmDSblq3SDK0vcNFd7ePFXzpS6EanmtjjzftlrM0km9chW1eq20Hg2kXD1ot3HTTs2Kq67aPk80WUdbg9CFQIDSvJ6usJ86F940H0NqJCuXOreQ77K8OWbKHx4qNbdUIWYFOHlzazf1Bs1jTzvBeteqFzghah3ySr0fPI5a4YPl6hW19O/7oPJKuBre3afanWXEAryVK9TirzUKM82qGIAXfj8A6rVRdpiQElex1iBMpYRke8yXk9SRN6raDWnbzvkYIlC5BL3Z2Yu+ujFG98IAgxfvumWdiKL3B/s5d/Wwu1tlFeGT1/VugukknIbUF67dg2PPvooUlJSEBMTg+joaERHR6NJkyaYOnUqzp8/77KOPXv2YPDgwUhOTrYcHxcXh/bt2+Pdd9+FyU766aNHj2LMmDGoX7++5ZjY2Fi0bt0aixcvhl5fdt+7jIwMTJ48GY0bN7b0NSYmBs2bN8fs2bORnV02CcGqVavQsmVLm58tJiYGTZo0wYsvvgijUdy2Bd7owIVrWneBiCy8PzmUZrw4qQkV0y+crHUXfE5svvzpyGLpX3zE8v/8ST1h/m+vW9rpeMn+WmaSz3xE/VFeLVXYTOQqK5cBpclkQmpqKkJDQ7F161akp6cjMzMTmZmZ+OOPP9CoUSN07twZ+fmOU4nv2bMHo0aNwpQpU/Dvv/9ajj979iw++ugjrF27FrNmzbI55ty5c0hNTcXAgQOxb98+yzFpaWlYs2YNjh8/jrFjx9ock5ubiy5duiAlJQU7d+5ERkYGMjMzkZ6ejk2bNiEwMBC9evWC2WrUbuvWrXjuuefwxRdfWMpnZmYiIyMDW7ZswdatW/Hmm+652+cJF/NtL9L8d3pXRjKiCkXj6VxE5B5CqQR4Jf5b94jdx93GzevsAwTOelKLkHkGhq+Wa92Ncsvw3QfIaaWD8U/fu+7VCYIP7E0g0VdffYVPP/0Uq1evdlhm+vTpqFOnDh566CG7zw8cOBCTJ0/GoEGD7D6fl5eHJk2aYOfOnYiKKp6/fv/996Nhw4aYNs3+htpmsxmtW7fGihUr0KpVKwDAkiVLkJGRgaVLlzrs6+DBgzFhwgQMGzYMADBr1iwkJydj8mT7d14PHz6MO++8Ezt2iE/vnp2djYiICGRlZSE8PFz0ce6wZmQX9Dz+m6Z9ICIiIt82tc3deHPP+1p3QzNhe4sv8XNa+U4GdipW8rfTkpTYoFyOUB44cAA9evRwWqZXr144cOCA7DpCQ0PRpk0bHD58WPQxfn5+6Nmzp027cvraqVMn9O7d22H5hIQEnD171mmd3ozBJBERESlVkYNJoDiQLFwwSetuUAVQLgPKc+fOISYmxmmZ2NhYnDnjeCuKrKwsVKlSRVIdctqVc8zQoUORnJzssPzBgwdRr149p3USERERUfnmU3sik88qlwGlyWSCv7+/0zIBAQGKE9eUrkNOu2r3NS0tDZMnT8b06dNFlSciIiIiIpKrXAaUFZEgCPjggw/QsmVLjB8/HiNHjnRaXq/XIzs72+aLiIiIiIhIigoRUB4+fBjJyck4csR+RjMxnnvuOQwePFjSMatWrbIk3xFr69atqFevnqQA78iRI+jTpw/eeust/PTTT3jgAdcbxS5atAgRERGWr4SEBEn9JCIiIiIiqhABZcOGDXHixAk0aNBAdh2zZ8/Gd999J+mYcePGYe9eaXsrde/eHceOHROVadVkMuHpp59Gr169MHbsWPz2229o3bq1qHbmzJmDrKwsy5cvJ/EhIiIiIiJtBGjdAXcQs+bQaDQiIEDZj1+6Djntyu1rbm4uxowZg4CAAOzfvx+RkZGS+h4UFISgoCBJxxAREREREVkrlyOU8fHxyMzMdFomPT0dtWvXdvh8ZGQkcnNzJdUhp125fZ0xYwbi4+OxevVqycEkERERERGRGsplQJmSkoJNmzY5LbNp0yakpKTIriMvLw979uxBw4YNRR9jNpuxZcsWm3bl9PXo0aNYv349li5dCp2Om9USEREREZE2ymVAOXToUJw7dw7z5s3D5cuXIQiC5bns7GwsW7YMP/zwAyZNcrzZ69NPP40HH3wQP/zwA/R6veVxs9mMY8eOYdy4cRg5ciSioqIsz82ePRtLly7Fxx9/jPz8fMvjgiAgLS0NU6ZMQXJysk2inilTpuC7777DsmXLkJOTY3PM5cuX8eSTTyI9PR1DhgyxPLd371706NEDlStXlv9LIiIiIiIiUqhcBpT+/v7YuHEjCgoK0K1bN8TGxiI6OhrR0dFo3749Dh48iO3btyM0NBSA/cyqrVq1wtdff4233noLjRs3thwfHx+P0aNHo1+/fnj++edt2o2Li8PmzZuxdu1atGjRwnJMbGwsBgwYgOTkZHz88cc2x4SGhuK3337DwYMH0a5dO8TExFiO6datG/R6PTZt2gQ/vxt/qsuXL+PLL7+01G/vKyEhAenp6W78LRMRERERUUWnE6yH76jCys7ORkREBLKyskRlmHWnnFacxktEREREFVPYXu3DMymxQbkcoSQiIiIiIiL3Y0BJREREREREsjCgJCIiIiIiIlkYUBIREREREZEsDCiJiIiIiIhIFgaUREREREREJAsDSiIiIiIiIpKFASURERERERHJwoCSiIiIiIiIZGFASURERERERLIwoCQiIiIiIiJZGFASERERERGRLAwoiYiIiIiISBYGlERERERERCQLA0oiIiIiIiKShQElERERERERycKAkoiIiIiIiGRhQElERERERESyMKAkIiIiIiIiWRhQEhERERERkSwMKImIiIiIiEgWBpREREREREQkCwNKIiIiIiIikoUBJREREREREcnCgJKIiIiIiIhkYUBJREREREREsjCgJCIiIiIiIlkC1Khk69atWL9+PU6ePImLFy+icuXKWL58ORISEjBt2jTExMRgwoQJiImJUaM5IiIiIiIi8gKKRigFQcDtt9+O1NRUfPzxx8jIyEBgYCDWr1+PAwcOWMo888wzuOmmm5CWlqZKp4mIiIiIiEh7igLKFStW4IsvvsD777+PM2fOYNu2bXjvvfcgCIKlzGuvvYZ///0Xubm5ePXVVxV3mIiIiIiIiLyDooDygw8+wODBg3HXXXdBp9MBgOVfa7Vr18aIESPwww8/KGmOiIiIiIiIvIiigPL48eNo2bKlqLJ16tRBRkaGkuaIiIiIiIjIiygKKDMyMlCzZk1RZUNDQ3Ht2jUlzREREREREZEXUbxtiL0prkrKERERERERkW9QFFD6+/vDaDSKKmsymeDv76+kOSIiIiIiIvIiigLK2rVrIz09XVTZCxcuID4+XklzRERERERE5EUUBZT169fH5s2bRZXdtm0bGjRooKQ5IiIiIiIi8iKKAsoZM2Zg9+7duOeee3D06FEYDAbLcyVrJjMzMzF79mz89ttvmDFjhrLeEhERERERkdfQCYIgKKlgyZIlmD9/PgoKCgAAlStXRn5+PoKDg2EymWA0GhEcHIz58+dj5syZqnSa1JednY2IiAhkZWUhPDxc077ktGICJyIiIiKqmML2KgrPVCElNlAcUALA+fPnsWPHDpw4cQJZWVmWx8PDw5GcnIz27dsjJiZGaTPkRgwoiYiIiIi052sBZYAaDUZFRWHw4MFqVEVEREREREQ+QvE+lERERERERFQxMaAkIiIiIiIiWRhQEhERERERkSyi1lCmpqbi5MmTihtLTk7Gxo0bFddDRERERERE2hMVUA4ePBinTp2yeUwQBLz66qvo1q0bWrZsafc4QRDw999/49dff0WvXr3Qr18/pf0lIiIiIiIiL6Fo2xA/Pz+89tpruPfee52We+mll7Bw4UIcOHAAsbGxcpsjN+K2IURERERE2vO1bUM8sobywQcfRGhoKJYsWeKJ5oiIiIiIiMgDPBJQ6nQ69O/fHxs2bPBEc0REREREROQBHsvyGhcXh7Nnz3qqOSIiIiIiInIzjwWUkZGRyMnJ8VRzRERERERE5GaKAsrAwEAUFRWJKmsymRAYGKikOSIiIiIiIvIiigLKpKQkpKeniyqbkZGBxMREJc0RERERERGRF1EUUDZu3Bjr1q2D2Wx2Ws5kMuGHH35AkyZNlDRHREREREREXiRAycFz585Fhw4dMHz4cDzyyCNITExElSpVLM/n5OTg1KlTWLp0Kf777z+sXLlScYeJiIiIiIjIOygKKNu2bYtPP/0UDz/8MLp16wadruyG9IIgIDY2Fp9++inatGmjpDkiIiIiIiLyIooCSgC49dZbMWTIEBw+fBgnTpxAVlaW5bmIiAgkJyejYcOGCAoKUtoUEREREREReRHFASUABAUFoXnz5mjevLka1REREREREZEP8Ng+lERERERERFS+MKAkIiIiIiIiWRhQEhERERERkSyK1lBOmDABp0+fFl2+Tp06eO+995Q0SURERERERF5CUUAZGRmJa9euOXzeaDTijz/+wNWrV5Gamorw8HAlzREREREREZEX0QmCILizAYPBgIkTJ+Lo0aP45ZdfEBCgSmJZUll2djYiIiKQlZWleeCf06rsfqZERERERBVB2F63hmeiSIkN3L6GMjAwEG+99Rb+/fdfvPPOO+5ujoiIiIiIiDzEI0l5goKCMGjQIHz++eeeaI6IiIiIiIg8wGNZXuvUqYNDhw55qjkiIiIiIiJyM48FlNWqVcPVq1c91RwRERERERG5mccCSoPBgKCgIE81R0RERERERG7msYAyPT0dSUlJnmqOiIiIiIiI3MwjAaVer8fq1avRsmVLTzRHREREREREHqBoU8izZ8/i4sWLDp/Pzc3FqVOnsGzZMmRmZuKpp55S0hwRERERERF5EUUBZZs2bXD58mWHzwtC8aacrVq1wqZNm1CvXj0lzREREREREZEXURRQbtu2DefPn3f4fEREBOrUqYOIiAglzRAREREREZEXUhRQNmrUCI0aNVKrL0RERERERORDPJbllYiIiIiIiMoXRQFlfn6+W8sTERERERGR91IUUMbFxeGTTz4RVfajjz5CYmKikuaIiIiIiIjIiygKKLOysnDt2jVRZY8cOYLKlSsraY6IiIiIiIi8iMfWUG7fvh1169b1VHNERERERETkZoqyvAKATqdz+nxGRgZeeeUVbNu2DV9++aXS5oiIiIiIiMhL6ARBEMQW/uyzzzB58mSUHJKbm4tKlSqhUqVKdssXFhbCZDIhICAAM2fOxMKFC9XpNakuOzsbERERyMrKQnh4uKZ9yWnl/CYFEREREVF5FbZXdHjmNlJiA0kjlO3atcMjjzwCQRAgCAKefvpppKamon379nbLV6lSBUlJSWjXrh1q164tpSkiIiIiIiLycpJGKEvz8/PDa6+9hnvvvVfNPpEGOEJJRERERKQ9XxuhVJSUJzU1FU2bNlVShdtcu3YNjz76KFJSUhATE4Po6GhER0ejSZMmmDp1Ks6fP++yjj179mDw4MFITk62HB8XF4f27dvj3XffhclkKnPM0aNHMWbMGNSvX99yTGxsLFq3bo3FixdDr9eXOSYjIwOTJ09G48aNLX2NiYlB8+bNMXv2bGRnZ7vl5yMiIiIiIlJCUUC5YcMGdOvWTa2+qMZkMiE1NRWhoaHYunUr0tPTkZmZiczMTPzxxx9o1KgROnfujPz8fId17NmzB6NGjcKUKVPw77//Wo4/e/YsPvroI6xduxazZs2yOebcuXNITU3FwIEDsW/fPssxaWlpWLNmDY4fP46xY8faHJObm4suXbogJSUFO3fuREZGBjIzM5Geno5NmzYhMDAQvXr1gtlsVvXnIyIiIiIiUkrRlFdv9dVXX+HTTz/F6tWrHZaZPn066tSpg4ceesju8wMHDsTkyZMxaNAgu8/n5eWhSZMm2LlzJ6KiogAA999/Pxo2bIhp06bZPcZsNqN169ZYsWIFWrVqBQBYsmQJMjIysHTpUod9HTx4MCZMmIBhw4ap9vOVximvRERERETaK7dTXn/++WcsXLgQu3fvVtxBdztw4AB69OjhtEyvXr1w4MAB2XWEhoaiTZs2OHz4sOhj/Pz80LNnT5t25fRVjZ+PiIiIiIhIKdEB5ZAhQzBv3jxMmjTJ8lj9+vXh7+8v+qtBgwZu+SFKO3fuHGJiYpyWiY2NxZkzZxw+n5WVhSpVqkiqQ067njqGiIiIiIhIbaK3DenSpQs2b96Mzp07Wx5bvHgxzp49K7qxhIQEab2TyWQywd/f32mZgIAAGI1GRe2UrkNOu546pjS9Xm+TIMhe4h8iIiIiIiJnRAeUGzZsKPPY8OHDVe0Mec6iRYvw1FNPad0NIiIiIiLyYYqyvPqKw4cPIzk5GUeOHJFdx3PPPYfBgwdLOmbVqlWW5Dtibd26FfXq1ZM0Yijn55szZw6ysrIsX1JGmomIiIiIiAAJI5RqEAQBOp3nM3g2bNgQJ06cUFTH7NmzJR8zbtw4jBs3TtIx3bt3x7FjxyQdI+fnCwoKQlBQkKRjiIiIiIiIrHlshPKrr77y2BpKMesjjUYjAgKUxdOl65DTrqeOISIiIiIiUpvHAsqDBw96bHQyPj4emZmZTsukp6ejdu3aDp+PjIxEbm6upDrktOupY4iIiIiIiNTmkYDy8OHD+Oijj9C+fXtPNIeUlBRs2rTJaZlNmzYhJSVFdh15eXnYs2cPGjZsKPoYs9mMLVu22LQrp69q/HxERERERERK6QRBEFwVatWqFY4fPy6rgaKiIhgMBsTExGDz5s0e2YvSZDKhY8eO6N+/P6ZPn45q1apZRkezs7OxcuVKvPLKK9i3bx9CQ0Pt1rF3714MHz4cr732Gnr16mVZb2g2m3HixAnMnDkTycnJWLp0qeWYtLQ0dOrUCYsWLcKwYcMQEhICoHjtaHp6Op566ilcvnwZq1evthyTl5eH5s2b48EHH8T48eMRFhZmOebKlSt45ZVXsG7dOuzYsQN+fn6q/XylZWdnIyIiAllZWQgPD5fy61ZdTivPr7MlIiIiIvIGYXtdhmduJyU2EBVQfvDBBzh9+rSszlSuXBl16tRBv379UKVKFVl1yJGVlYVnnnkGP/74Iy5fvoySHzMyMhLdunXDU089hejoaADFmVX/97//4a+//rL5he3duxfz5s3DwYMHkZ+fDwDw8/NDXFwcJk6ciHvuuafMfpDHjh3DE088gd27dyMnJwcAoNPpEBUVhdtvvx0PPvgggoODbY7JzMzEvHnz8MsvvyArK8uSvKh69eoYMGAAHn/88TJ/SCk/nxgMKImIiIiItFcuA0oq/xhQEhERERFpz9cCygqxDyURERERERGpjwElERERERERycKAkoiIiIiIiGQJUKOSffv2Ydu2bThx4gSysrIsj0dERKBu3bro2rUrmjdvrkZTRERERERE5CUUB5RTp07F22+/jcDAQMTHx1u2vQCKF3OmpaXBaDRi8uTJeP3115U2R0RERERERF5CUUD52Wef4a233sLzzz+PBx54AIGBgWXKGAwGvPTSS5gzZw5uvvlm3H777UqaJCIiIiIiIi+haNuQ7t27o1KlSvj5559dlu3bty8KCwvxyy+/yG2O3IjbhhARERERaa9CbRty4sQJdOjQQVTZDh064MSJE0qaIyIiIiIiIi+iKKBMS0tDVFSUqLI1a9ZEenq6kuaIiIiIiIjIiygKKHU6HUwmk7iG/LhDCRERERERUXmiKMqrXr06Ll26pFZfiIiIiIiIyIcoCiibNGmCo0ePqtUXIiIiIiIi8iGKAspRo0bhhx9+QF5enlr9ISIiIiIiIh+hKKCcNGkS2rVrh1tvvRVXrlxRq09ERERERETkAwKUHDx8+HCcPXsWx48fR1RUFBo0aAB/f3+7ZS9fvgxBENC8eXObxxMTE/H9998r6QYRERERERFpQFFA2bp1a1SvXh1dunSRXUdSUpKSLhAREREREZFGFAWU8+fPV6kbRERERERE5Gu4OSQRERERERHJwoCSiIiIiIiIZGFASURERERERLIoWkNZ4vLlyzh8+DDS0tJQUFDgsFx8fDx69uypRpNERERERESkMUUBpclkwgMPPID33nsPRUVFEATBafn4+HicOXNGSZNERERERETkJRQFlC+88ALeeOMNTJ48GXfffTfi4+MREhLisLyz54iIiIiIiMi3KAooP/vsMwwYMABvvvmmWv0hIiIiIiIiH6EoKc+xY8fQpUsXtfpCREREREREPkRRQJmfn48qVaqo1RciIiIiIiLyIYoCyrCwMFy5ckWtvhAREREREZEPURRQpqSk4O+//1apK0RERERERORLFAWUs2fPxpo1a/DFF1+o1R8iIiIiIiLyETrB1eaRLnz99de47bbbEB8fj+TkZISGhjosm5ycjJdffllJc+Qm2dnZiIiIQFZWFsLDwzXtS04rnabtExERERFpJWyvovBMFVJiA0Xbhly4cAFPPvkkTCYTioqKkJubC7PZ7LRjREREREREVD4oCihnzZqFEydOYN26dejbt69afSIiIiIiIiIfoGgN5U8//YTx48czmCQiIiIiIqqAFAWU58+fR8OGDdXqCxEREREREfkQRQGlTqeDn5+iKoiIiIiIiMhHKYoGY2JikJGRoVZfiIiIiIiIyIcoCigHDx6MzZs3q9UXIiIiIiIi8iGKAspnn30WOp0OU6ZMwenTp6FwS0siIiIiIiLyITpBQRTYqlUrHD58GIWFhdDpdPD390dwcLDD8nXr1sXevXvlNkduJGXzUnfLaaXTtH0iIiIiIq2E7dV+kE5KbKBoH8rp06fjzJkzossnJiYqaY6IiIiIiIi8iKKAcsKECWr1g4iIiIiIiHwM9/wgIiIiIiIiWRhQEhERERERkSwMKImIiIiIiEgWRWsoSxw9ehTbt2/HiRMnkJWV5bBcUlISHn74YTWaJCIiIiIiIo0pDihnzpyJV155BUajEcHBwdDr9ahRowZCQkJgNBqRlpYGnU6H+Ph41K9fnwElERERERFROaFoyus333yDpUuX4sEHH8SFCxdw+PBhCIKAFStW4OTJkzh79iwOHTqEdu3aITU1FRs3blSr30RERERERKQxRQHlsmXL0KVLFzz//POoUaMG/PzKVte4cWOsWbMGn3zyCb799lslzREREREREZEXURRQnjhxAjfffLPLclFRUejXrx/ef/99Jc0RERERERGRF1EUUJ49exbR0dGiyjZp0gS7d+9W0hwRERERERF5EUUBpSAI8Pf3F1W2Zs2auHTpkpLmiIiIiIiIyIsoCigrV66MgoICUWUDAwNhMpmUNEdEREREREReRFFAGRUVhQsXLli+DwkJAQC7QWZhYSGCg4OVNEdEREREREReRFFA2bp1a6xfvx5msxkAULVqVYSHh+Pw4cNlyh4+fBhJSUlKmiMiIiIiIiIvoiignDhxIg4cOIBBgwZh586dAIBhw4bhpZdewldffYUzZ87g5MmTWLFiBT766CMMGzZMlU4TERERERGR9nSCIAhKKnjvvfewcOFCJCUlYcuWLTh//jxuu+02bNu2DTqdDkBx8p4hQ4Zg1apVCA0NVaXjpK7s7GxEREQgKysL4eHhmvYlp5VO0/aJiIiIiLQStldReKYKKbGB4oCyhMlkssn4euDAARw/fhz+/v5o0KABGjZsqEYz5CYMKImIiIiItOdrAWWAWo2W3j4kJSUFKSkpalVPREREREREXkbRGkoiIiIiIiKquBhQEhERERERkSwMKImIiIiIiEgWBpREREREREQkCwNKIiIiIiIikoUBJREREREREcnCgJKIiIiIiIhkYUBJREREREREsjCgJCIiIiIiIlkC1KjEaDTi2LFjOHnyJC5evIjg4GAMHToUlSpVwm+//YbExETEx8er0RQRERERERF5CcUB5dq1a/HQQw/hxIkTEAQBAKDT6bB+/Xr07t0bd955J06dOoX//e9/ePPNN+Hnx0FRIiIiIiKi8kBRdHfgwAEMGzYMderUwfr163Hs2DEcOHAAgiDAYDAAAPbu3YvFixfj3XffxQcffKBGn4mIiIiIiMgLKAoon3rqKdSrVw8//vgjevfujeTkZERGRtqUCQ8Px4wZM9CvXz+sXLlSSXNERERERETkRRQFlIcOHcLAgQMREOB65myHDh1w/PhxJc0RERERERGRF1EUUJ44cQJxcXGiykZERODChQtKmiMiIiIiIiIvoiigNBgMqFSpkqiy/v7+MJlMSpojIiIiIiIiL6I45WpJZle1yhEREREREZFvUBRQ1qhRA1euXBFVNisrC9WrV1fSHBEREREREXkRRQFlcnIy/vvvP1Fljx07hjp16ihpjoiIiIiIqNwy6Py17oJkigLKQYMG4YsvvsAvv/zitNz+/fvx2WefYfDgwUqaIyIiIiIiKrfuaT9Z6y5I5nq/DydmzJiBr776Cj179kSHDh1Qt25dyxYib775Jr799lucOXMGmzdvRpMmTfDII4+o0mkiIiIiIqLy5myI7y0RVDRCGRQUhF27duHll19GdHQ0Dhw4gK1btyIxMREHDx7Ejh07ULlyZSxZsgS7du1CcHCwWv2mcmxrzcZad4GIiIiIyON8MY2pohFKAAgICMC0adMwbdo0NfpDRERERERUIVW4EUoiIiIiIiJSx8XgCK27IJmigPLixYsoLCxUqy9ERERERETkQxQFlAMGDEBSUhKWLFmC3NxctfqkWEFBARYtWoRWrVohNjYW0dHRiI6ORoMGDTB27FgcP35cVD1Hjx7FmDFjUL9+fUsdsbGxaN26NRYvXgy9Xl/mmIyMDEyePBmNGzdGTEwMoqOjERMTg+bNm2P27NnIzs4uc8y1a9fw6KOPIiUlxXJMdHQ0mjRpgqlTp+L8+fNljlm3bh06deqE2NhYREVFWdpp0KABHn/8ceTn50v/xREREREREUmgKKD86KOP0KNHD8yaNQtJSUlYuHAhrl27plLX5Bs9ejTOnDmDtWvXIi0tDZmZmcjMzMTevXvRt29f9OjRA+np6U7rOHfuHFJTUzFw4EDs27fPUkdaWhrWrFmD48ePY+zYsTbH5ObmokuXLkhJScHOnTuRkZGBzMxMpKenY9OmTQgMDESvXr1gNpstx5hMJqSmpiI0NBRbt25Fenq6pa0//vgDjRo1QufOnW0CxCNHjmDKlCl4/fXXkZaWhvPnzyMzMxMZGRnYvXs3Ll68iCeeeELdXyoREREREVEpOkEQFCcTOnLkCBYtWoRVq1YhNDQU06ZNw4MPPojq1T2/qHT37t2YMmUKdu7cCT8/+/Hyiy++iNOnT+OVV15xWM/999+Phg0bOkw2ZDab0bp1a6xYsQKtWrUCACxZsgQZGRlYunSpw3oHDx6MCRMmYNiwYQCAr776Cp9++ilWr17t8Jjp06ejTp06eOihhwAUb8ly5swZLFq0yG75vLw81K9f32XQbC07OxsRERHIyspCeHi46OPc4fs+TdD94r+a9oGIiIiIyNPCR6yAMHe81t2QFBuokpSnQYMGWLFiBY4ePYrRo0fjhRdeQGJiImbOnInMzEw1mhDtwIED6N69u8NgEgB69eqFAwcOuKynR48eDp/38/NDz549bepxdYy9tuUc07RpU4wYMcJh+dDQUJhMJrtTcomIiIiIiNSiapbXpKQkvPnmmzhx4gT+97//4Y033kCdOnUwbdo0nDlzRs2mHDp37hxiYmKclomNjXXZHzn1eOqYrl27om3btg7Lnz9/HsHBwQgKCnJar7fS+eQOPEREREREFY9btg2JjY3Fyy+/jJMnT+KBBx7AypUrUb9+fcyaNcsdzdkwmUzw9/d3WiYgIABGo1H1ejx1jDPXrl3DHXfc4XJfUL1ej+zsbJsvb6HTugNERERERCSKW/ehrFWrFiZOnIgBAwbAYDBg1apVsupZtWoVIiMjnX7Jrbs8Wbt2LZo1a4aWLVvi4Ycfdlp20aJFiIiIsHwlJCR4qJdERERERFReuC2g3LFjB0aOHImGDRvixx9/xCOPPIJdu3bJqmvcuHG4du2a069x48bZPTYrKwv16tXD1q1bFfw0xUFtSfIdsbZu3Yp69epJGv07fPgwkpOTceTIEdHHpKenY/To0Xj00UexcuVKPP/8807XkALAnDlzkJWVZfk6e/as6PbcjRNeiYiIiIh8Q4CalQmCgO+//x4vvPACfv/9d8TGxuK5557DpEmTNMscGhERgWPHjimuZ9y4cQ6DVke6d+8uue2GDRvixIkTosoKgoC33noLCxcuxEMPPYQPP/wQgYGBoo4NCgry2TWWRERERETkHVQJKPV6PVauXIkXX3wRR44cQUpKCj744APcfvvtogMctYhZb2g0GhEQ4PxHl1OPp44BAIPBgPvvvx///PMPduzYgbi4OKd1+BKuoSQiIiIi8g2KprxeuXIFCxYsQO3atTFlyhQkJCRg/fr12LdvH+644w6PB5MAEB8f73KrkvT0dNSuXVv1ejx1DAAsXboU6enp2Lx5c7kKJgFAp3xrVCIicoPwESu07gIREXkZRQHlgAED8PTTT6N3797466+/sGHDBvTp00etvsmSkpKCLVu2wGw2OyyzadMmpKSkuKxn06ZNDp83m83YsmWLTT2ujrHXtpxjsrOzsXTpUrz//vuaBO1ERERERESAwoDy7bffxokTJ7Bq1Sq0bNlSpS4p06ZNGyQmJmLq1KlIS0uDYDXalZ+fj1WrVuGll15yuYXJ7NmzsXTpUnz88cfIz8+3PC4IAtLS0jBlyhQkJyfbJOqZMmUKvvvuOyxbtgw5OTk2x1y+fBlPPvkk0tPTMWTIEMtzQ4cOxblz5zBv3jxcvnzZpr/Z2dlYtmwZfvjhB0yaNMny+H///YdmzZqhZs2a8n5JXk7QcdIrEREREZEvUBRQpqSkeOV2E5988gmSkpIwYMAAxMbGIjo6GtHR0WjRogV+/PFHbNmyBbGxsZby9jKrxsXFYfPmzVi7di1atGhhqSM2NhYDBgxAcnIyPv74Y5t2Q0ND8dtvv+HgwYNo164dYmJiLMd069YNer0emzZtssnA6u/vj40bN6KgoADdunWz6W/79u1x8OBBbN++HaGhoZZjLl++jD///NNSzt5XTEwM9uzZ48bfsvtwwisRERERkW/QCQIXrFHxaGhERASysrI0y8hb4oc+jdH14n+a9oGIiMoKH7EC2asnaN0NIqJyK3zECghzx2vdDUmxgegsrz179sS2bdswadIkvPHGGwCADz74AGfOnBHdsdq1a2P8+PGiyxMREREREZH3Eh1Q7ty5E2azGTt37rQ89tprr+H48eOiG6tXrx4DSiIiUt3hsBg0zMnQuhtEREQVjuiA8ptvvsGff/6J/v37Wx7bvXu3WzpFFRu3DSEiqX6t0YgBJZGHTWo7EW/vfkfrbpRbu6smo+3VE5KP+6VmI3Tj0iHyINFJeXr37o0nnngCbdq0cWd/iMiDfo5yvn0OOTeu431ad4HKuXvaTXJdiEgjJ0PLZ7Z5b/Fecnetu0AkiqIsr0Tk2y4ER8AMbtMi13dxbbXuApVzX9buhGuBlbXuBlG5VnXYu1p3gcinKQoojx8/DoPBIKpsUVGRpPWWREREYuUEBmvdBbfp2eMJvFm3F/ZHeMc2Xc83GqR1F4jKnd+r1y/zmCDzhi9vE5OnKQoomzdvjlWrVokqu3LlSk6XJfIyv9co+wFG5GuuBobgxYYDsa1mI0xrPd5t7Zg0ukw7FhaDWS3HIqNyVaflDoXHeaQ/W2o18Ug7VNaJ0Fpad8Htvo5rJ/vYtTGtVOyJ5xyrEoVBXR/Fhgq8DKVd74Wq1bWnah3Jx3yQ1FW19isiRQFlQUEBCgoKRJU9efIkqlWrpqQ5qiB4Z80z7mk3CR8ndtG6G0SKTGo7ER17L0RWpRAM7DoLK+t007pLbjOv2Uitu0Aa+zsy0SPtrI6XH9QppWS2wd6qSep1xIMOh8XA4Cc6T6bbLGk4QLO2z4TUUK0uOe+T6W2U7a+7Or695GN+iGmJP6vXU9Sut/DIGkpBEPDzzz+jQYMGnmiOfJwO4rK8cu2fMl/W7gRB553LqHnhXLEcqRIt+9jPEm9yOXLnyrc+shb2n4gEWRctrrzQcKAq9bzcoJ8q9ZBjYj8f5Xizbi+31S3F8SpRso+Ve1Ug6LzjesKdf18xrgWGokvqfE37oIYiLwjOKxrJV5NXr17FlStXcOXKFQBAXl6e5fvSX+fOncMff/yBMWPGYO/evZgzZ47qPwAR+Y7vYlu7LPNunR5IVxggeNLcZre6LFOk8/dAT3zXySraTuMz+tDfx+DnuK9y11staDZCbnc8LiuACYpKG9fxPrTqs0hxPSesAjmdhLhmS83yMQVazjRJOZyNSJXZNk2DOHd/ZCLWRbfwfMMAMoMjNGmXlJMUUL7zzjuoXr06atasiZo1i1NFz5492/J96a/ExER07twZGzZswPLly9GtW/mdikTqEXtR5KfinbzHU1wHBaSc2UtHRJV4taHrUZlDEfEe6Il3uBAU7tHj1HCyime2PvgqvoNH2inPOvReiFpD3yrz+I5qdTXojeeV/nx8r053fBfXFsfD5I/yK/VfeKxqdZ0Jqa5aXVL17DEXk9pOVCU4d6ZP98fLPCb3ZpC73Nv2bq274DNy/YOQExCMdTEtte6KpiSNCQ8ZMgRBQUEQBAGCIODuu+/G2LFjkZqaard8lSpVkJSUhEaNGiE0NFSVDhO5w24fvRhJ7T4XH+54HXEFV91S/4qkbphw6he31E3q+D62NW7J2IdAwaR1VyzmNx2B+YdWa9b+X1WT0PrqKdHllzYciDBDIaYc3+i0nKDTAaVHECS4u/1kjDy3Q/bxYtzTbhIW7/sENYpy7T5/NTAE22s2wqD0v9zaD3dJDynOxVD6NXZXh3vx37pHtOqWS9/FtsZgN/zOtQ5EjoZFY2HT4fCDgI6XjqJF1hlF9Q2+eSYGp+1WqXfAwfB4NMs+J6qsoPPDZ4k3qda2Gv60k/lVjDIjnRK5Ws+Z6x+EKia9ojZK85Zpx1KN7PwQdlavi9Tzh7TuiqYkDRfUqlULd955J+666y6MHz8eANCxY0fcdddddr9GjBiBNm3aMJgkSbReQ+BLdlWvixktxqle79qYVrgp9SmsSrpZ9bpd2V092eNteisxoy5FTqZAauVFGYkd1HzfS73Izg8IwqMtx6rWvkMqXDC5ulD8snYnPNNkmMPnZ7Ych2X1+yruh9Y2RttmwywJNO3x1JTzxEHLHD7nLdOqW8sYfevfdZbdx1NueR5XgsKQE1gZM1uOw04JyUUcZasVoMOBiNqS+3jj+BteaDQQc5vfJrsurf0dWdtmGrKahnaxf/NFuH6KcjXl2dnU+4pG0Olg9AuQ9Qlm77PqvtbKkgNpRdH8s3vuuQcdO3ZUqy9EVIonLvzeS+5R5jG9fwAORta2fLh4ypQ29+DT2s7vEC9uNBhLG/b3UI+0peTCSisCoErgpERFvSkl9g6/u/46SkdFXJE7ZXh4l4dV7ol9VytV8Ug7ShwTOTX2+7g20PsFYFOtpthes5HdMqdDxU0VP2dnTfzVSo4HGjZFNcM97SaJqvvvSMfnyH2RiTCJXGbhLWcM6/fmcQXJyhzpnPoUJrSfgs1RzVSvWympiRYXOLlxdlbi1GlX1x1iqHVe/aiOb25foiigfOedd9C6teskG0TucDUwROsulAtPpozSugsWnyR18drMs0rYC9p9xUseyNz5SWJnt7fhyLVAJnnxFXe3n2z5v1HCeeJSUJg7umPj9Xp9nD6/yQsv4J25FhiC2CFvYpiDkSylnM4i0OnwZe1OGNvxPpf17JA5JdTb3LgZdCO0dcd05gORtbE6wTvXchf5B0oq/5mTIPDtuvaX4ll732qLqTVxbSS1be2IhuuXvUn5u3Ijnyf2QmFMp2lu7oljR1W+c3g1ULtp4XkBwV53Ue3uLWF+im7u1vpL+zCpK27uOQ8T2030aLtq8MQarW8UbGSuVLN+S2Qfu1KDKeFqm9P8dsnHpFXWaE9pqxHYf8K9I9FVrn8QuvV8Eo+7mFp5ILK2qhu3e4LBL8DhbIOkga8qrv+YCtM5XZ2f1Bo1z/UPwqMtxqhSV3mg9iwQNfc9PVolWlRw+mDr8WUea9P7WUkJ4lbHt8dlDRPKeRNVAkqz2YyTJ09i06ZN+Prrrx1+/fnnn2o0R+XckTBxGeP+EZE58+PEzrhXxma140TcGS2xXoX02ltrNZZ9rDum96m5wbDa1JiGm+cfpLwSKx16LXBeQKfDvqpJKPSTdgdWbdNb3aVp+/YYdP6ATqfqdEkpQXC2hJkOp0u9L2a18MC6SxX85WSz99dlTKs/pfE2LwA0n1Zdwujnh71V64jKYH04PM6tffHk9h1XPDDqK4e7Erv07vE4ltfr7Za6bXnH67qiOhoeg3oDXxFd/slm4md4pVWuijWxZUdC55aTXQYUB5Tff/896tevj3r16qFPnz4YNWoURo0ahZEjR2LkyJE23w8b5ni+M1GJpxzsiWa9nnBC+ymi6jpaJRqrknxzPrqWnm882C31qhEyaJ3V0J5/ndzcGNxlhuX/Ui92vhM1Dce2zn+cXLR+UKdibN30cWIXt9T7QuNBNt/nB6h7Y6K0t5N7Sipf8v7S+9/I0PhU0xHo0eMJm3Jip2i5e6aAGN42e0Kp1O5zFR1vLJ0MxepPdCQ8xumxXXvOU9S2K/n+lSSV98ZzuSNSP7vkz2JS58aamN+sq2na3k7Nqez7IxMlH3M4LAZnQ+3ffH+66XDMKHXDUQBwZ8d78Vqp3/vO6vUxzc5oqa9RFFD+/fffGDp0KGJiYvD5559jzZo1EAQB7777Lk6ePImjR4/itddeQ40aNTB37lz8999/avWbyjFHIwaPW03NOhQRL2njZalO2slAl2YnsYAYSi7K/i51kksa6DiLIAC06LsYDfu/KLs9AMgOCEZeQLCiOqjYn9XrYWtUU9nHyzm2Z48nZF84bq4lvj1H04alBs0/xLaSVF6Mz2t3Ur1OwP7Itqv3pBKzZE6x+zLhxs+v9wsosy75clA4WvVZhPr9X3JaT99uc1y2tbvqjazM7pgtcUfH+7E/IgG33vSA6nUDwIaoFNeFVLSrel0kDHodN6U+hUIXWzOUtrVmY/wsc7r+v2Gx+NvJSLUaXmw0AAVunIXxuw+tl5zRchx69nB882B8qZvi9t45O6uru53Z6vj2Nt9/aGfKfkmQ7wuJzQpK3dC7eD3A3Hz9c1NMno26A15Gm97P4pyM/U+dzTha0mgQ3q7Xq8zjgs4Pj7UYXeZxvcazl9SgKKBcsGAB6tSpgy1btmDkyJFo1ar4wiAqKgqJiYmoW7cupk6diu+//x6LFi3CqVOn1OgzkUs/X79IcLZo25kDdjLHyb2487NzYp7dvOwJxZ6vEjrg2cZDABRPrb0S5DyL4MkqtZAhM/At4cm7xkOsRu+kWFFHfJKb5xsNcl1IAoOH0v9vriVv+lp+QBD+rpqEAxEJko99MmUUHmp5h6iycuq3tiKpGx5ueQfulzEl3RVPXgpdCaritotok53U/GLenXqr9UOO3s/Hw6JxvnKk03p21HB9AX/awR16tRyMiEeXXk9jvZs2DR/hoQyw1rIqheCgk+ykjgzu+ih+im4h6iblJ7VvwrDON362zOBIye1Z21O1jssyVytVQfyQN8o8LnYZCwAUWL12lX4S6QTbV/8SJ9sZSf3cu+wio69R5+d0f2tHn9PWN8rfkThDwZU3PTJl17mpbe4GID9jszNde87DQy3vsExDFbO/+MXgCBx1MbJfYp8bsq776t6b9igKKA8dOoShQ4ciMND5h2n79u3RsWNHvPzyy0qaIxJtZOeHUGvoW073JnOlcb8l+Mhq6pyaF6lv1O+DqCHLreq2f1IRdH54rslQ1Br6lqQ79KWnVLjiaLTXW/ZOsyYAOBNaA7WGviWq/MKmw7GwVHpxueswN9VqipUemEJda+hbGCoz2C4xutN0rJI49TPfvxLeq2t7EeOuO9UXg8Pxbt2elhkJ1n+SPIlT5+RylnZeCl+4m1/Cm/ePu+JkKwlncjwwo+ITkTcn1UwuYk9WpRBEOTj3FVi9b6a0m4hN0eqNvvbsMVdUAieDnVHXyW3/Z/NZ6khGcCQyJd4QLf3OcxYYKr3ZChSPLC6v2wvfxrUVVf6X61uuiE30UmgVUNu7oVSaq8DW2vFSa5+VnLesrxnuaTcJr9frA72IEfePk25G1JDlWOlga4ycANsp7lL2kE0LqY736vYsM3JJnqEooDx16hTi4sQtMu/UqRN++eUXJc0RWVxxdRLV6VAo8qJ00fURwNLSQqrjvrb3SO2aaK5OetbZbgv9K0lKQvFEyq04YrWGQ2zwVdo+GXfRAeCxFGWbSYv5ScX+fYHiNaE/qzC1LT8gyO3Bw5zmt9v8vUuvwxDrTGgN3OvG1683eFfCSLU9SxoNlFTe2+8mO7uYfrFBf/xWowHWxnrvVl9Sk1YN6TID/4THYcjNym6+iDHNwUh66d/5pHaTkNp9LuoOeBkdez0tq61OLo7TO8hg+VLD/tgbmWg38LuscL2ZoPOTnVTsfOVIUZ+lUt+PWvg6oQMebTm2TBKmFxvY7o1cEryO6vwQevaYi1Z9n8OeqnUwv6n9HBElXpOYJKvAwWuh9KfUbZ2muy0b6Ze1O2FOi9Gil/c4u/a5s+O9Nt/XH/iykq6RBykKKIuKihAUJO5OQM2aNZGZmamkOSIM6TIDYzrejwvBEarVuajJUEXHW5+4j5dae1ngF4jDYeKmU5T2e40Gospds3NX3+Tnj53V61m+lxJ8WRN0fpYpt1KcCq2JzqlPlXlc6mbD3mBFkucS2Qzv/HCZrJv21mFYkzRVywMB0RGZr3epPk/oiIdb36moDrX2PPXuMLPY/JRR6Ndtjt0RJGslN7L+J3JjeTVMaXtPmcRBJa46uXm4JaopOvZeKGpqm1Kufm/W5XZVr4uLwRH4JyIBp0JqQO8XgH/tJMty9N69VEl88Gddx9VKVdAtdb7dzL1ycwCoqVWfRU6fL51kxfqz9UsP753Ys8dcTG77P6u+OH+X/1XNdkrwseuJrwr9K2F3tbrICayMHj2fxIsOguaS+rMqSdtf+2iVsudbe7+royITcV3SeAuMQxEJNtnIS7//t9aUnxFfC5us8hLYew19lOj7W0+VUPRpGhgYCIPBIKpsSEgI9Hq9kuaogvqk9k2Wi40tUU2xVsEGtO7SvccT+KT2TZhkdRH2d2RtJAx+HUUiLkTKZO6TYHuNhlhWvy/ua+18LZrY5BN5Kk0XsbcO9YVGg20SAZi8fMQHAM5XvnHzQurC+dJZD/fZySQ3vv0UvJPcE3GD38BGFaeoaWVSW/fvtbmgyTBM9GDAU5G8Xq8Poocsxxd2Ehu5mvYp9938SWIX7KmWbPc5MdtxSPFp7ZvwYCvHNyJGdH5I1fZa9X0O8YPfcDiqWJEcdxHUrHEyjfSedpPtPu6u3+vuanXxaWJnt9QNACaVXtdT2v3P5vvoIctxT7vJot6L9gKcL2p3VKVfSqxK6oL36nSXtH2bu6ea2yNm6cywLo84fd7da9A9SdErulatWrh69arl+4CA4gtne0Gm0WiEv7/3rt0g7zWl3USHFxtS7XTTney/qiVjSruJSLdKcmHS+bvcXPeFRgOxq1qy7ORBAACdDo83vx0fOViTUELsiOdtpdZqHhKx36dYOYHBuL/N3Xg7uSe21myM7dfXl7hinaDGegSsf9dZ+C8sFj9HpWCvi7TfcqcrCtBhequ7cDA8XvJ+UQ+V2vfxdGhNdOz1NExWH+RfJ3TAI63uQI6PbY9g72IkbvAbstctS5lKnB8Q5DX7EKrl27i22CHz/FRy48Lu+0nGr8nd26FYk7IGTA2T2010uqXDBhFZVJ1tzVOayc/fYdBj/ZpPueV57I9IwCMtx4muWwpH579e3R9zS3slxGZmXRfdwvnNA50Ov9VoaPn2sZTbsC+iNl5qaDvV1PosIuh0Nr9jNfe6tZYtYw3vLpWuaUqvC3V0bhSbFV/sDZyS98nJkJriKpbA6BeAh1rfhe/s3GD4Q+R1jDv8KvJ6xUY5+5xyRlrO6lLq1auHP/74w/J9rVq1EBQUhPT09DJl09PTkZCgLCsgkStjO97ndMuLsyHV0f7KcQ/2yLkFTUdgQdMR6HzR/VvqvNrgFsw7tNruc9PbjMfKHW9iQZNh2Fcqtfz3sW0wvdVd2Fs1Cb9uLjuNVY4ZrcRlEgWAWc1HY0N0Cv76ufjCZ6vVFJLtNRuhfZ9nLN9nr1Y/YygAfJDcHR8kd5d83Ek7G8D/E5GAjMqRiC+4aucI3/JXZBJaXztl+V6toHhamwl4f6e8db9qyAkIxswWY7F8z3uYk3I7Fh34DIDjxFzXAkMQpc9W1Ob9bSYgOzAED/+3FvMdvE8d6dB7Ifpl/O2RhFGlZQfI/5vPaj4a31vNODkaFo3YwmsAgHvbTMC/4erdzFLThaBwNEGaqnWeD45Al+trJ6MKrqlatzM73bwVx9hO98s+tvSl+PONB+NCcAQ2RKfgRJUovNbgFmWds2lM/oV/p14L0MrqPChG6en2L18PjN0V9Erl6gbfg63vxK5qyfjWamTwvrZ34/2db2F+0xE4EhaDT/58TfV+LW04AJcrVcGmqGZlelza4saD0fv8AdXaXtB0OGrps3Hr2T9Vq7PEz9dnJ7m6Me7NFI1Qjh49Ghs2bMBzzz2HnJwc6HQ6dOrUCW+88QYyMjIs5Y4dO4YPP/wQnTu7b/oAVURlT3jfx7XF5jInGvcoWS/5dUJ7u89/meCeqSNv1U2VdZzBLwBfOOjTN/HtETv4DbzQeHDZJ3U6fJDcvUyg6Slv1u9TJvObHLYf1PIvHrTO6Dm4ywx8bvV31PsH4PX6xVl9v3NDwhVnIzY9e9pf96bUVyq/d6Tu9wcAnyR1QezgN/B6A9dJMrbXbOiyTAn5G54XW2Nnyv/p0JpYXq+3KtkNxY7kP9zyDrxavy/2Ojgv3NHhXsQOLruFhLU36/ex2f9tctuJ+DyhI3r0eAKrkrpKnplyzYOj/OujWwAA3pF5Pq4I1sS2UTURjN4/EG/V64UTVaIU13U1UF5G4RLrrm9jczUwBGdDa2C/gi0lvo5rp2gWltx1hUo+y7IDQ/Bm/T42I6RfJXRE7OA38GKjgZKXJhWJXE5S5B+It+v1cjl9GgB2Vq9neZ+WNvDmmZL6BxQnE/pfe/tTr+UqOd9eCQpD9JDl6NHzSVXr9yRFI5T/+9//sH37djz22GP48ccfsW3bNixZsgR9+vRBYmIi4uPjYTKZkJaWhpiYGCxYsECtfhO5zVMOsrCVnuLXLXUeGmelYYdV8htrq5KkbdlQtj3PyhVxMZbrH4QqJq6F1tLWqKbYGtUUO6vXwz0ntuCppiNwMSgc66NbqDY13Nr2mo3Qo8cTeG3P+2iabTsqo/b6NneZ2vZ/WLHzxjY9nXo9jWsiNr0u/Z5QY4/Wec1GOr1zby95i7XvY9ugV/fHsHHrs4r7osS717eXueuk/ezta+LaSh71SQ+phokKLtha3PI86uWcx6L9n9rMRLksIcmNGDur18OSRgPR+spJh+d/T9kvMxN3Rad0/f7GqGZI7T4Xx8KKg9tTdmajiJUbKG/bGylbasjdKksqMdcRpf0THof5zUa6oTfAL7Ua45bMfTaPrYltg20i93n+q2oSWl895YaeleXJpQbuoOhqwM/PDx9++CFOnDiB5cuLP6xbt26Nw4cPY8mSJRg8eDBGjhyJ119/Hf/++y8SE313KJc8a0qb4hTjT1zfoNY+95whl4pIXX4qtCayA0OKN/528MFUkkLbW6awAMCy69n/vo6Tt4C9U+8FNtuRuMvG66PMhySsVZLGe/4mcr1TNxUdey9EZuWqMPn54/eaDd2WoGJPtWTF2w644mjkriSJ00armQdSA4TSmZYPRSQgTaOMw/b2ds31D0Lu9an662Ja4sFWd6K7g6yn0OncPk1RFaXOi8vr9sL0VneV2V5BTVcrVcGu6nXLJLo4XzkSd3a4V3HCndZ9FmF289F4odEgFPpXwu81G4raK1CqS0FhKPALRK6/4wvMm1KfwowWY/FRkvdmiUz3UGbZ9+vcyMRdenaDWp+/ZUbudTrsql7XJgvp3ddvhsjJjC7HoK6PqlbX7Oaj0dpFFl6pzlYWt56+Y++FquwRas9bdVPxaIsxaN97oaTj2vZ+BrOaj8aCJsMdlpF6g9Hbt51SStEIZYmkpCSb76tXr47p06erUTVVUJ8kdcHauNaWTc+9Ra/uj2HS8c2Sk7O4IiXj20/RLTD5+CanFxuO7KuahLjBb8jeCPx0aE1MazMBP/2i7gdPaVeCwhAz5E3LRt2eOhE/13gwZv/7nUfa0srv1evjpstHRZX1ZMKUCR2m4qmDX+L1en1sHj9WJcrymr317J/ofPGwx7cQUFPpJFe3d5qGjVEpN0Z7dTq8n6xsf01rYpNxyWW9X+6GqBRMO/qT3Q3OBRSvRR6Ytset/QGAR1uMwaizOwDAMj38WxWyQB4Li7ZsB6EGR2c1k58/ag9+HQKAS9/az2Z8MLI2DkoYnfTkLbThnR/GqLN/4pmmwzzS3oOt7sK8ZrfCpNPJGiFTy1cJHfFzdHPJ1y1FOnmX4uevb59m70ai1Omsb9Tv47qQRENunoFfNj+NMGOh6nWLZfQLwPJ6vQEA5ypXRXzBVXwnYjrukfBYHAmPBQQBy+v2wr/hse7uqs9TJaAkcgepJ2VPXPzurF7fLaMDO6vXw/YaDXG8Si3cdepXp2U3RjVDr+6P4ZjMkUIts4lK+YizTq50ISgcP8S0hEnnhyt29t1Ui73Ro9L2+/CieQAY3uURtLp6Eq/8tRINcp3vDWw9/XJHtXroapU86j+FH7Clp3aeC6mOe9pPsVu25DX7Re1OdrezUMvpkOpIzL+MzVaJn5S6FlgZkYYCy/dnQ2ugc+pT+G3TPADFW9G4ygYtR70BLyMh/zL2Vq3jurAVqYHH6oQOmHh8M36t2Qhbo5qiV/fHcVzB9D81XA4KR/Vh76DjpaN2p6RqvQ7antKjHaWDhG+cbKkhVaFfAILNRtXqK21jdIrLLZD+qFEft2Tsc1pG9OiiTid5/0Z3kXLdMifldtx1ahuea3JjRNPZrdN/wuPQJLtsMqiZLcYiOfcCXlUzSZEKjoXF4J72k/HF76+oUt8vTrKsihkt7NRrIRpnn8OfUq7hdDo82nKs+PIVmMcCykuXLuHPP//EwIGupxMSyfHm9btQvsis80P/brMBwGVAKXbK2/J6vTDu9Hb8cD15gLvti6iNMGMhkvMu4DcHSUpkbySv02F0qe1MJFdh9f/V8R0w4twum+efbDYSlcymMseVvqRZmdQVwSaDvBTiXiA/IAi/1WyE41WiygSUzvbZfKHxIFytFIoLwRGoWpSHr1yMEt7fejxe++sDrI5vjxHndloezwqojIVNh+NrDfYNc+WWbnMw4uxOfFhqC56TITVRJ/+izWifmFHS+1pPwKqkLph8fJPNRYy9PVrVdiE4AheCI1wXVKjQvxK6p86zfL9T4zWFJQx+Afi1lrxkJZ8ndMRtZ//EK/VvwQNH16vcM3nOhKi3X13pi++SbM3fx7bGC/s+Vq0de1r1WYR+GX/jveQeaJSdjvoubmp5wqibHtSk3dcb9BWV9KtL6nx0vngYAYIJzxz4wvJ4yd/xVJVaaNPX+awhsduGeDOlN3OzKoXgTw23HSnvPBZQfvrpp3jssceQk5PjqSapgtnmoxf47rI/MhEJg15HlhtHJMe3n4IPrJKdtO3zDIJNBk2nHYlxOrTs3lkvNxyAR0VMdzX5+btlepCnTW89Hs/t/xTvJPdEhyvHEGYowFknmywX+leSlKb/wzrd8F1cW8QVXLEJKP+qWgdv1eulqO/ukhZSHa827Ffm8TZ9n0WwyYA8q6QJixvdyIi8NrZNmRsUJQSdn2XKlZpOhtZEnbyLqtdLwMR2kzCz5Th0vHzUawJKd+rZ8wmEGvU2s1fclXDreFg0XgsrPo/c12YCjvz4sFvaKWE9k8LRGNZPMfYzgVrLDI5Up0My7I9MxP7IRNx39CdV63U2plceAlB3ORCRgJSss/hc4v7hRXaWAmj5ulKbxwLKQ4cOIT7eO/eUIl9144w3puP9ou48nbQTSLibltOr3D0N6OuEDjYBpdEvALkytmggzztfORITOkwFAPwuYdsLKa5VCkVcwRW31O1JJa9rnWC2+/zq+PY2WWSlyKgcqaBn5BY6Ha5VCsXGqGbYVS0Z+yJ8e5q7K2adnyWYXJHUDU2yz3nkBm2mB5L2pIVUR5fU+cgKDHE5xdaeVn0WIchswDU3LrXwBE9leS3bsGea+duDS1Fu6TYHra6exPZS75E0B6/n8e2nYN6h1birw72Wx8rL68qaqCu/HTt2IC1N3ia+er0e+/fvxwcffIA5c+bIqoPIFVdz4m/pOhuNctJxOqQGZhz+QVLdZ0Kqo3b+Zfxewz0X3Y6osUVBRfZ4yq1O91DU0uPNb8fKHW/itXq+P9I5o8VYLNn3sYuMzJ4lJcmVKnQ6ZAVURoSxwHXZ64Z0mYHYgiv4JyJBVpOnQjw/Qvl2Bdtz0egXgFRH2XZVcCakhtunfP5aoyFuvnQYHyeK28bqgTbj3dqf0lbHt8OIc7vwSoOyMwPUYm+q5MkqtVDjSq7LY8Xsd1geaRaAStSx19Poef6QR2e95ARWtrvtyMHI2ri3zQScq2ybQfzrhA74utQSifL4uhIVUA4ZMgQXLlyQ3UhISAimTJmCWbNmya6DSInfazbE7zUbIjXzgORjW/Z9DpVNBlHJbBgEeo9lTi5QtE7K8U18e/xSs4noBEMm6ODvhYlEAODter3wVUJHXAmynxRLi21zCgKC8EyToXj8n2/d1obS9/qWKGWJf6a2vQeL9n+q+nRaRz/XFwkd8ZeCfU7Le8p8Oaa1Ho/1255zaxsDuz6KCEOB146ETGg/FY+0vNPh+cOd7S488DmW1feuRDZmCe+TS062cvKm7cqUKvn822InWdo/EQmyb8q5w6qkrq4LlVOiAsojR47g6tWrshqoXLkyatXSNuMbkRJGvwDkiJzGmV65KjZGNYPeL8Dr1xF6wl0dpmLljjedlvmlZiN0u/ifR/a3dKenmo7A2NPbUS/3vKjyUi6g9kfWRqtrp+V2ze08fTEoxuLGQ9waUGotPaQa7up4n8fau+qlAYkc3hLanrdKmuSuy39B5+e1wSQAQKdzfDPKjc2eCa2BOz34/hHrQEQCttdo6HIq/Pt1unk8Y/vGqGaom3MeS0Ts1a2mhgNeQv2cDPzGPBleTdRVcnh4OMLDw93dFyLfp9NheJdHtO6F1/gmvj2+jWuLe49twK5qde2WGd9hKiac/EX0lCy55I5KXhG5Hc3SRgOxtNFAZK+eAKA4cyJVHLNajMHyPe9p3Q2vp+W2RaTMupiWeOnvj/BfGPfkcxfBKuO7M55eggMAh8NiNbm+8VTGalKG2TOI3OyL2p3w1MGvtO6GZgSdH16v7zg1+uWgcCxpNMiDPRJnUtuJ6HX+AD6o003W8c6mI0ll9HO9P6av8JW1OVJ9ktQFP8U0x8m1yra38VZqTef/pWZjrEjqhn8imKTP16SHVEPtQa8h1yrbMWljdXx7xXU4mxbLLK8kFQNKIjd7tf4t2B9RG/sja+Pb7UvtbrRN6pCaGAUAend7DOnXs7MdtVoo/1niTfgsUVpacGtqrhm7t83dWP3bSzZbVZD3uRzk+zN53B7w63QeT/xSHhwOj9G6CwDg3dNnXdAiy7u7mCTcZPy1RkMc8/ElJeT9GFASuZnJzx+bolMAAJ17Pe2y/Ndx7TA8zb1Z79S0KrELxp3ejsWNtQ92ljW4BXP/+UbSMTtq3MgQ/G1cWzyecit2VvOuoP9weBya9Vsi69gLQeGopc9WuUfilacByfL0s5BvSO0+FzddOoxP3LwkoCL4Obo5Zjcfjf2RtbXuikcNEDGFlkgpxbnV8/PzMX/+fHTs2BFRUVHw9/dHaGgo/v77bwBAq1at0KdPH2zdulVpU0Q25KyJczRq5E13Lid0mIK6A162BKHe7t42d6PugJexNq6N1l1RTND5YVmDfjZBpq/L96+kdRfKpfI6dZc852LwjRFtR9Pad1Wvi1ca9ofZ01vheAEpGU9F0enwRv0+ZfYPLI++jW8HADgZou21zeFwrretKBSNUObl5aFFixY4d+4cRowYgT59+kCn02HBggVIT09Hy5YtMXPmTLz44ou45ZZb8Ouvv6Jdu3Zq9Z3IQuzF3baajbA3MrFMxszMylXRs8dcZAWEuKF30gg6P1z0pQXoOp1P9Nd6vYjYRDsV3d6qSVp3QZE1sW0Qpc/CIa7X0xy3VCorOzAEN/ecB4Ofv0cDxu01G6H3+QPI9PLz9vroFjgYHo/dCrarqajerpuK41WisKeqtr+7k1VqYV9EbbTIOqNpP8j9FAWUzz77LDIyMrB//340aNAAAJCWloYFCxZYyowZMwbDhw9Hy5Yt8eqrr+Kjjz5S1mMiBYx+AeiWOh+z//kWj/27xua53Q6ykFL5cy6kOqa2uRvZzDhpV4deCzAo/S+85iSZkrv9GxaLxjnp+D5W/sj3HZ3uV7FH5VvJjbYvEzrafb485ejYd32j+7zro/eZwRGILsyCSYOgd58GN20mtpuIScc3ef002iL/QNzUe4Hrgg7k+Vfc5EFmnR82RDfXuhsAgJ3V6zGgrAAUBZQbNmzA6NGjLcGkI8HBwRg0aBC+/fZbJc0RqaYiTh8iWx8n3ax1F7zWvxHx+NdNo3piR6o693oKEYb8cpHoxhf07PEEqhbl4VJw+f99X6sUitqDXkOhfyAAYEiXGXj64Jd4pskwjXvmGVeCwvBck6Fad8Nt5jS/HSnXzmBzVFOtu+LV1EwcJ7ltzVomd1EUUB47dgzDhok7AUdHRyMzM1NJc0SqebtuKsad+tWyzoBIbfsjKlbiB0es1zqnB0diZstxoo4z+gUwmPQgk59/hQgmS1hnK/03Ih6jOj+kYW9ITc62qaIb/gmPw65qyTgfZG/qc9mQ70iYeplif6/RAPsiauNomHdkLyblFAWU165dQ0SEuDn4wcHByM/PV9IckWquVQpF81ueBzS8Q0flU5fU+eifvhcvN+yvdVcAAM82GYa3d7+DD71gRLZR/xd9+j1nthpdPRdS3W6ZzbWaoueFQ57qksdc9eHtIoioLEHnh9Tuc12ek8d2vA+NstPxYVJXWe2sSuqCiSc2Y7fVek6jXwBuTp3v058HZIvbhlDFxRNZhfJjbCukXjiES25OyLM/MhH7r6/R8gafJd6EbTUbWfba1JSvv+d0OsQOfgMBghmFDrLnDu/yMK59fY+HO+Y+E9tNxJBzu7GswS1ad4WoQtvljuREIs7JO6rXx/dxbWU3sbdqHdTv/xIuBYVJbpt8h6KAMjg4GAUF4jYR1+v1CA4OVtIcEZFs7yX3wNnK1StkxsD0kGpad6HcyHWRyKm8rc/+vPZN+Lz2TVp3g6jC21m9PoZ0meFV25yJdb5ypNZdIDdT9MmXnJyMc+fOiSqbkZGBxETvuWtPvi8rMMTu/4nsMev8sD62ZYVaJ0bauHB97efWWk007on2/r6+ifzntTtp3BMi37clqilOVamldTeIylA0QtmiRQt89dVXePbZZ1G5suO7tkajEd988w06dOigpDkiG0a/ACQMet3yfyIib9Cs3wsINxTggpfv8+cJPXs8gZr6HGR4w5RrIiJyC0VX4fPnz0ezZs3Qvn17TJ8+HcnJySgqKgJQnAH2999/x5kzZ/D666/j7NmzWLdunSqdJiqRVYkjk5522c1rEKl8ORiRgL2RiTgfHKl1Vzym0L+SwzWWFY3RL4DBJJGP0XFjD5JIUUBZv359bNmyBdOmTcPkyZNtnnvwwQct/2/ZsiU2b96M+vXrK2mOiLzAkfBYzGwxFuc5+kIimHV+6NZzntbdICIiGRhakhiK5wnedNNN2LNnD7Kzs3HixAlkZWVZngsPD0dycrLorUWIyDe8Va+X1l0gX8JsfkREROWWagvPwsPD0bJlS7WqIyIiIiIiIi9XvvKbExERERERkcd4NKA0mUyebI6IKpiKuMckERERkZYkT3ndv38/tm7diiNHjkCv1yMhIQGtW7fGwIEDHR6za9cuTJw4EU2aNMEnn3yiqMNERI5sjmqG2zpNx5GwGK27QkRERFQhSAooH3roISxbtgwBAQFITExEUFAQVq9ejWvXrqFly5bYsGEDqlevbimfl5eHuXPnYtmyZWjUqBFmzJih+g9ARGRtXWwrrbtARETks7ICb2zJdq1SqIY9IV8hesrrl19+iVdeeQULFixATk4ODh8+jP379+PKlSvYtGkT0tPTcffdd1vKr1u3Dk2bNsUbb7yBJ598Env37kXr1q3d8kMQEREREZFyRr8A1Bn4CuoMfAUGP9Xyd1I5JjqgfOONN5Camoo5c+YgMDDQ5rkePXpg3rx5+PHHH/Hff/9hzJgxGDBgAOLi4vD333/jySefLHMMERERERF5n8tB4bgcFK51N8hHiA4ojxw5gptuusnh8506dYLJZELz5s2xdu1avPbaa9i+fTsaN26sSkeJiIjkWla/LwBgacP+GveEiIiofBE9jp2RkYFatWo5fL5GjRoAgNTUVLzzzjuIj49X3jsiIiIVzE25FR8m3YzDYbFad4WIiKhckTQxWqfTuXxu2rRpDCaJiMirCDo/HA6P07obRERE5Y5H96EkIiIiIiKi8oMBJREREREREcnCgJKIiIiIiIhkkbSGMi8vD1euXLH7XMnj2dnZDstUqVIFlSpVkthFIiIiIiIi8kY6QRAEMQUDAwNhNpudlhEEwWninri4OJw5c0ZaD8kjsrOzERERgaysLISHa7vvkG7hB5q2T0RERESkFWHueK27ICk2ED1CuX79epw7d05Rx5j9lYiIiIiIqPwQHVCmpqa6sx9ERERERETkY5iUh4iIiIiIiGRhQElERERERESyMKAkIiIiIiIiWRhQEhERERERkSwMKImIiIiIiEgWBpREREREREQkCwNKIiIiIiIikoUBJREREREREcnCgJKIiIiIiIhkYUBJREREREREsjCgJCIiIiIiIlkYUBIREREREZEs5TagLCgowKJFi9CqVSvExsYiOjoa0dHRaNCgAcaOHYvjx4+Lqufo0aMYM2YM6tevb6kjNjYWrVu3xuLFi6HX68sck5GRgcmTJ6Nx48aIiYlBdHQ0YmJi0Lx5c8yePRvZ2dlljrl27RoeffRRpKSkWI6Jjo5GkyZNMHXqVJw/f15UfwVBwAMPPIAlS5aIKk9ERERERCRXuQ0oR48ejTNnzmDt2rVIS0tDZmYmMjMzsXfvXvTt2xc9evRAenq60zrOnTuH1NRUDBw4EPv27bPUkZaWhjVr1uD48eMYO3aszTG5ubno0qULUlJSsHPnTmRkZCAzMxPp6enYtGkTAgMD0atXL5jNZssxJpMJqampCA0NxdatW5Genm5p648//kCjRo3QuXNn5Ofnu/y53333XSxfvhy5ubnyfnFEREREREQilcuAcvfu3Th37hxef/11xMXFQafTWZ4LDQ3FnXfeiQcffBCLFy92Ws9zzz2HmTNnYsyYMQgJCbE8rtPpkJCQgOXLl+PYsWPYu3ev5bnly5dj6NChuP/++xEWFmZzTM2aNbFgwQJER0djzZo1lue++eYbJCUlYd68eahevbpNfyMiIvDAAw+gf//+eOutt5z2d9euXVi8eDFmzJjh+pdERERERESkULkMKA8cOIDu3bvDz8/xj9erVy8cOHDAZT09evRw+Lyfnx969uxpU4+rY+y1LeeY0i5duoQxY8bgo48+Qs2aNZ3WRUREREREpIZyGVCeO3cOMTExTsvExsbizJkzqtfjqWOsmUwmjB49GtOnT0enTp2c1kNERERERKSWAK074A4mkwn+/v5OywQEBMBoNKpej6eOsfbkk0+iRo0auP/++53WYU2v19skFLKXKIiIiIiIiMgZnxmhXLVqFSIjI51+rVq1SutuetyaNWuwZs0avPPOOzZrL11ZtGgRIiIiLF8JCQlu7CUREREREZVHPhNQjhs3DteuXXP6NW7cOLvHZmVloV69eti6dauiPqxatQqtWrWSdMzWrVtRr149SSOAhw8fRnJyMo4cOeK03NGjR3Hffffhyy+/RJUqVST1a86cOcjKyrJ8nT17VtLxRERERERE5XLKa2kRERE4duyY4nrGjRvnMGh1pHv37pLbbtiwIU6cOOG0TF5eHkaMGIGlS5eicePGkuoHgKCgIAQFBUk+jqg82H3PQLR9b63W3SAiIoX6JsfipxPOt4EjIvfymRFKKcSsjzQajQgIcB5Py6nHE8cIgoBJkyahZ8+euO2225we54tqhgRr3QUq56oF82YKEVF58N1tqVp3wavUiZQ2Y41IDeUyoIyPj0dmZqbTMunp6ahdu7bq9XjimJMnT+L777/HJ598gujo6DJfc+fOxZIlS9CiRQsIguC0Xm/0eJfmWneBiIiINDS8UaKocpVcJDWsaL4d1VPrLlAFVC4DypSUFGzZsgVms9lhmU2bNiElJcVlPZs2bXL4vNlsxpYtW2zqcXWMvbalHpOcnIzs7GxcuHABmZmZZb5mzJiBhQsXYt++fZIS9XiLIP9y+bIkH9AhrobWXSAi8jmjGid5tL0aIZxl4khYpUCtu0AVULm8cm/Tpg0SExMxdepUpKWl2YzS5efnY9WqVXjppZcwa9Ysp/XMnj0bS5cuxccff4z8/HzL44IgIC0tDVOmTEFycrJNop4pU6bgu+++w7Jly5CTk2NzzOXLl/Hkk08iPT0dQ4YMsTw3dOhQnDt3DvPmzcPly5dt+pudnY1ly5bhhx9+wKRJkxT9XojIuU+HdtO6CxXS95yyVmFceWQ0No7to3U3iIhIReUyoASATz75BElJSRgwYABiY2Mt00FbtGiBH3/8EVu2bEFsbKylvL3MqnFxcdi8eTPWrl2LFi1aWOqIjY3FgAEDkJycjI8//tim3dDQUPz22284ePAg2rVrh5iYGMsx3bp1g16vx6ZNm+Dnd+NX7+/vj40bN6KgoADdunWz6W/79u1x8OBBbN++HaGhoaJ+9vDwcISFhSn8DRJVLA91aKJ1FyqsQD/1P4oW9Witep2kXNXKQUitE+u6IPmUB9vz/ElUkZXbLK/BwcGYM2cO5syZI6q8o8yq9erVw6effiqp7ejoaLz11luSjomIiMDzzz+P559/XtJx9jz88MOK6yD36JJQC9vPXtC6G2RHXFiI1l2osASov9Z7dufmmLPlL9XrJaKybkqohQsP3Y5aL33m1naGN0rEpFYNsPlUBp7/4yC6JNQCAGwa2xepH//k1raJyLFyO0JJvsv30giJF1NFm6DliS4tNGnXW9zTsj6+GtHdZTlfeu35S1wfXXLhJVWr6GqyjpNCSe6wzeP6OnyuSqVye89UsVubJGndBUXW3NoTa25l8hFvUjPU/RnaV4/sgb5147Cwe2v8eHsv/Hh7bwBAzzoxbm+byvrljlu07oLXGNssWesuaIoBJZUr3j7tJsoDH7il7bp7oMfb9DYhgQHoXy9e626oZnHPNrgjpa6kY+pXC5fVlpTj5CbmUBLI90iKkfy7IN83uEFtDG7gPFO71rbyYltV1rfQAv390K9ePMKCmIBGS/z93+Dng0kw1cSAksqVRzs107oLTj1xs+dHCtvGMnOpvdP8wPq+G2A+elNKufrwUrq90cjGN7YXuKdlfeTNGne9XkXVEinSLTEaq4bcjKY1I7XuSrkQzuBFFJ72tKHmNn3DGnr3zTJ7GFASeVDVYG1SnSdX5UbHpX06TN2Mri+ktlW1Plfcse5QK2r+JBFBlRASqP1U11gV1uQOqp9g+f+xe4crrs9a+bkdIc0jHZt6tL2xKXXxpYjp9u70zagemrbvKXO7NEdSBD/ryPfVifS9xJoMKIk8KFCjPTbL03RPtVSxs1eXmncYvY0OOjSsLm/aq7uFekEAqLaPh9ysuI7xLepZ/l9X5pRlR8bIXO+jViAqd02vUi+kthX9epvbpbmbe+MZQxsmui7kBeROyy+xoHtrnLh/BL4dpWxtrfnxuxQdX561jamuWdtq3KRTan7XlnYf9/Q+rN6IASWVG5U0CtZ8gU6ly8DycoFVfsNG5/ZPGuKVU327J0ZjfPN6Dp8/N32U0+PVvg8w7+YWeMrBhYNY3ZNicG+bRup0yA2a1oh0OVo3w42jeVqtL9TpdBA7W7xR9QjV2y+9PVHp13ZFv/nXrXaUouN1Oh2GKJwuqPPh5QTNa1V1a/1aJRa0R62cFBkP3iq67Dw7nwufDuuKLzSeheANeAVOXkfuxaGv3yFaPdJ905KCA/xVqWdB99aoEaLNtF2lggP80Tc5Fl0SaqFOZMWcFlXJ3x89Er0vG6JOp8OKwV0cPh8XLm4PXrXM79YKTVRY97a0d1t8O6onEjzcf2vDGtZGh7iy66jrVA1DVGhlp8f2TCr7WlErdvd3w96jJbztxpf1Z9pzPdvYPGf92m4fWwM/3N7LU91Szc0JyoJAa9/dlurwOSmBXnsXuQN89XPMlVVDu7qt7pRaVdEjKdpt9Uv154QBqtQTrTBIjgvT7vzuTRhQkk95vLN3XSio5dW+HTC8kfJpSREOkhaEBQVixaDOiut3p1Q7F69yRAZXsvu4TqfDutG9se3OfnYvTOxljNx+Vz883a2VKv1S2wiZr5f72zWStODfd+/Ve17ppCHBAQEY0rC2pslEbq4dhfWje+OJLi3QuEbxiNu3o3q6LanTtHaN3VKvFAu6t1alHnvT4pVSa7aIVuyN2iZGqHNBrQMQHlQJva5vAXKfghF+d/ztAGDj2D4237fRcApoad0To5FSq6qqSzeszxP7Jg5GoBtvBIlhfSMgKTIMb/XvpGFvbHli2xxvxoCSfMrCHupcKHgbtS4xMh68DSfuG2H3ObW2VnD2WTW6aR1Zdc6+KQU/jekts0fiFU93s//btrd+p3NClOjMvJ6eJSV3Ol4lf398LWGNkS9PD/Z036e3a4yhDWuXuXmj9dLcyOAgPN29FQ5OHoqz00cpnhLozKt9O7itbk/zxunhUilZl2hvzfXC7q1QudSMlz7JcbLbsFZyDl0/ujfOTR+FnnVisGms431mtZBaJ9ZmeY033R5Qqy9fDO+OLgm1cHraSPhZVarT6RSdU0tuaMmlA9Cilu2+yJNaN1RUpxylZziVfP+knWsF/Zw7PNInb8CAkryOq+lh0VXsT9PyphM74N7+jGlqP6FG5cAAn7xLFhcW4tYpcGJ5c/C09rZU1AgJwlonU8JIPdZBoNgNq0MCA/DNqJ4Y36K+Kn1Qe99aP50O8RpOv1UyC0OLPXylnJNaRlVzXcgFtW5KWf+uol1May5RvXLZKaBiblp9OPhmjE1Rd0N3fz8/y1TgnnXkzVxxZ4I1rW8QuduoJkn49a7+qK1SxtxX+3aAMHe87ORrEUGBqBES5HBZ0BNdPLsd206rvb07xde0nFMj7WTxr+SvznIjX1D+UuuRz0uV+QHizF3N62Ll/uOiyvauE4sNJ9NV74MYoxon4ct/T2nSthqkJEZ68uYWePrXfar3wZ2BfI2QIFzK17uxBfuW9mqHAfUTcOGh2706YYScxFj1qnp/evRVQ7vi44MnZB8fFuCHxNBKkPrqbFk9DG/1aYuYKpVRWFiIxFDp674qw4zCwkK7z4XA7LROf5PR5vm1t6Xi1zOZWPzHIYfHOOvn5JQ6Nn1xVC4yOBDXCg02j9WoHIhgq1s+JfUU1yEg22DC1SKTw37J1Tc5Fj+dcP15sKhna/T7dKPd5zz9lrU+R4htu21MdfStG4eHN+yS1NYdzZ3PfGlSIxL/XLomqU5PWNitNaas+0NxPY5ucJcXz6e2xcMbdmG6ClPZH+2Uglu/3irpmJmdmuGJLi1QpVKAwxHSqCqevdlUI+RGe2quH/Z1DCjJ67j6/HP2vKMbh+8P6iI6oBSrbUx17M64rGqdz6e28ZqA8vDUYei88kfRAVSf5FiXd7T9dDqYr9/eHVg/wRJQyr3g6lUnBhtPZsg7WIIvR3THe38fxfCGiZj04+9ub6+0m+JrAlCWfVDuoVIOS5a4d9bQhrXxmYv9QFtHe3aNkr0ENnJlZ2fj0qVLeKpZLMwQJN/sOHP6NG4O8wdQhJMnT2J5J8eZcIHikf68IiOu6Yssj1UNEnDy5Em75dtUFpzWWUOfY/O8f9ZldI8IRJc+zXE+rxBGs7nMMc76WUOfg5MnCyzff9q9CbKu9zUiqBKKTGaEBgYgJNAfZkHAuZx8S1nrc0dJOwCwvFM9CACMJjMOZ+Xjq9P2z8l3Na9rmY57Z0o9vLHnP4c/t7UG1SNEBZQ3J0Th8+HdcNvXv5R5zldGtB7q0FRyQOnK3omDELToI1XrVMPkNg1tAso5N6Vg0e8HJN8Ue7NfJ6w9+qXa3XO7FlFVse/8VZflHurQFMMa1kaiCqOVo5ok4VTsSMzYtAtf/Xta1DELurVCkIKkgs/1bIPZm/cAAEY2TnTYrqeuJcozBpTkdYID/JGaFINNp+y/uVeP7IGBn2/ClQLxI0XuSEDhjpEisQkbnCX5UOvipUH1CATYmfLlqPq3+9+Ez/6xf+FqOVblK6sNY/tCt/ADl+WU/qlGNk7CyMZJ+PawuA9BAJjYqgHe2XtEWcMS3dokCV/8c8qjbZZ4uU97jG2WjPf2HUWh0WR5f2694xZ0/2i93WPiwkJcXizc2iRJ7a469OWI7qolh8rOzkZaWhqqVKmCmNhYFAmQ/EKsU2oLgLwLzi8A69eqigt5BQjIuzEKmBAeiggHiaou5hUiIK+gzOOBfjqEBQUirFIgzFl5dvsjXM5Ckak4oAwNDECewWgp46ifCRGhCAu60Rej2YzjV7IRFhSIWDuZEgut6gnw08FovnH+KOlL3oWrxSc9swnR1fLRICIE2dnZZeqqXjkY4dfbXtq7bZmAsmNcTfyZdtFuv8XQ6XSisvlavwSUrilzN0efcWI++x5o3xjT2jb26il/Z6ePwp6MyxjUIAEA0D6uBtrFSLuhpGQK+dPdWuHJX/bKPl6OI/cOx4mrOfj6v9OiAkqgOPmNWhIjq8Dfg0P21lO+nb0/1cqEX5Fpv2iJqBSdTocNY/tgahv7i607xdfCpYdvx6n7R+LhDuruk7bSydYFpblaVyUldJJyfn2zX0dNNvg9MGmI2+qWE2fGu+F34KobUvr59oCbbL7/2o3bwpT4fHh30WX/GN9f9fZrhATj3PRRWNqrreWxbomO08yLedmHlcrW2LtObJkyHeNqWv7vKNOxGCMbJ6Hq9bVkoZUc328N8LPf8yFWmYIvXbqEKlWqID4+HnE1qgGBlYCAQElfwcHBNl+OysVEhqNxdE0EBwcjMCjI8nhs1QjUiggrU0/Jl3VZP6v+NY+LQnLN6oisUsVhf3SBNx6vZNU3Z/2sFGTbfpWQEEtb9voHqzZ0pX5/Nr+TwEpAUGXowqshplokLl265PTvHBwQgJmdmtk81kBB8hq53uznPRkqpRBzY7Bf3XjU1eB3KkV8eCiGNKwNP50OfjodhjZMFLVFkeCG1fZVnJxvxCoJ9J1dH9SvFo6+deUnUXJw6vMJ1jfstcy8XVrJzK75Cvc+1hoDSvJKOp3O6WidTqdDYmQVLE69sadXx/iaDstbeyG1rcPnSl9UOLuj5WqfK2vtYmvgRxH7i1WtbH8kocQtdeMwpU0jWSN9Sj8Cm4nYMFlKv9wREHqzWxR8iMuRHBlmdx/BEh3ja4mqR+rrRu2R+3ta2ia4sVd9VJXKeKZ7a3StHYWHO964yTTu+k0fOXvO9U2Oxa1NkrDITmbp0nsJlijZu9JgMECv1yMiIgI6nQ7VQ4IRKGN9qRg6nQ5x4aF2A+DYsBBZf4+SY9zVZ3ttqSEqLAT1Y6Kg1+tRtZL3jzhUdTBy7A6fDeuGPsmxWDm4i2VbDmslfwctt0ny9S1VrPWzc75PTYrButH2rwOe7dEGfZLL3iyTo3JgAC4+fDu6JIg7x0uqO8CzExuVnh6a1Yq0+/jjnZujb3IsPhx8s7IGVPDOgJtw7N7hNlli3fG3czcGlOS1pN4FtDdyYW1y6waIDQspc4FqrfTFjb0AaUC9eDSpEYm2LqbGWNe08+6B6FfPdQr68KBK2H5XP4fPN1Vhs3WlOkgIpJ2JsQooS37tWmRy9AVyLrqb1YrEpnHapNxXa2Zzq+hqotfPPNalOX65s5/N/nMv92mPz4Z1w7rbpW9J4+/nh8+Hd8fs63vf/jNlqOhjTabi5DCBgTf6EuTFU//EcLS/qxSORnbVEujnh6Cg4psHYaVeN3VdJH9yFEjJnab/Zr+ON+rQMH908+s3Am9rWgc/jemDO5vXwwYnW3E8cXMLLOzeCtUqB+F5Ozdf5ZyLTt0/EqtH9kBnkTex1KD1stWHOjTFF8O722whtHFcX9xS1/51QI3KQfhpzI09Lu9ykezIlRohwaq8Z0tzZ/Zcd2jhIPtyZHAlrB/Tx25SqSHXp0B7ik4H1K0WDp1OhxP3jcA3o3pgqBu3dnIXBpTk06Sc25b3vwlnp4+yTGlzWKeLj6Lvb0vFgclD3Hb3vrODrGENq4fjKQ9PibD34fHB4C6yR9u61naeEe23u9SfhuluJUHwABE3DMqD0lMF3Wn2TSmKjq8cGIDbmtZR5UZM4xrS63BnRt6a1zMNxnlopF9OBt8SyVXDEB8eilA3bTZvreR3Xvp3P6l1A5vvS5/bEiOr4NDkoar1Y0qbRvb7Z/3/Un1Uc4Ru9z0D8WinZni2h/3RdGce79ICFx++XdEeltYSI6tgeKNEl6NN09srzyTqLQL8dBjVJEl2FthB9dUPau5UaS9qa3LOC54MSR295Eq/16y3zZGy7leNU3wTq8+WOlXDMLRholdnc3eEASWVG2LegGok59FdX2/hTqOb1inz2HM924i6IFNyNzxExD5RtUIr271z7Ur3xGiXCQy8ac3NYgfTGq092qkZTtw/EqenjUQjGQGHOwRd/4B3NWLvSI/EaHwzyvF6Tzl/+0+GdpU0RfzDwTdjx4QBuLXJjfdByVpKOTczKsvc/8yb1Y4IRbNaVb1yVL9VdDXEh98IdKtVDtJ8ewV7CcZKa+IFM0DU0iamBhantkWYg7Vij3du7jQPgLs/4+wZ1TjJ423KIeVGtrsG9KT+ro7cOxwrJOSIEOOu5nVtgiFftrhnW6QmxeDz4d1Qy0Pn1P2ThiDjwVtdDnL4CgaUVK5tGNMHf/1vkCZtS/kcKT3KUDqhi5K6LceIOMidewL6wprJ5MgqaF6rKjrH17I7Emf9KyyYPQ6LU9siJDBAtQ2gpaji4ObCsftG4PPh3coktRJ7ebj5jlvQN1nd9Z6jmyVjw9g+rgteF12lMtrH2a6J/m/qMHw+vBumqbAfmiezDLoiZ30nUHxjKzjAX7M72c7OJ/5+fggUEcB5yohGiW5vQ82/wsHJQ8qsuX8+tS1uUnHK6MIerbFqaFfL997zjqiYpL6NvxjRHbNusv2MerlPe4fl61cLV/0mwQeDb/b4+aeZm276RFWpjI3j+uLWJnVsRivdKaVWVURX8f7rIrG854xPpILS05h6JceilRv3sbMe0QuVOQry5M0tbLJDAsXBQunpRo6mQyndVmF5v06Y2amZKtNNncWsdzV3voeeWpR8wPn7+WHvxMH49a5+LusJFpGcoG1M8WvPHWvHoqpUxmu3dCjzeHx4KG5tUgf+Kl3Qa3Gh2dLOupfYsBDc2qSOqJEmVw5MHoKHOjSRfbyaUxOrBgdpkrUZuJERN8BPZ+mDvADXPcMwak3nbe4ioZijRClSudqHV6ymNauWWXM/s1Mzh1MW99wzCI90VDfjuTO+tYpOG0rXTIv5HHuup+1skRQRifO0oFayIQBY2L01ZnRsip13D7Q8JvbaS+ylwbBS6xe96QakN2NASV7LF9Z+/3LHLeiSUAt/jO+Pj4bYZgsTMzWvkr8fnurWyu6Hh6cSGIRWCii++20nq5iadx97OflQkXOBPqKxe0Yd/HQ61X7unXcPxL9ThqJw9h2q1FfafW1vjNZJmVJa4o/x/V1mk7P3NnQVAIl963Yu1fbVGaNxdvoo1HTzlKPGNSLxYm/bu/n2srl6SkyVypok3KocGICUWlWRcn3qbNOakapsYK4W6z3krFm/PZNFzKpw9X52lCgFkBY8Va0chLQHbsWVR0bb1iHzs2xM0+IpqTc7WFdfonVMdSzp1U5eIx7U38U68/Jw2f5Y5+a4rUkSOonMOq9Ut+t5CTy5X68zpW8GPtW1JRoquNFS+togLCgQL/Rqh3ZWn3djmiXbzarrTIqD7K8A0DK6Ok7cN8Lyfce4mritSRIeu56gTaqG1b1nKY87lb+FJUQKSP1AaxtbA79eH9k7YLUJ98dDu6J65SCsP54muy/OpqeomWnNFz/EG1QLxyIJySa0Skev0+ksayuF65vAq+2OlLr44dg5rBstPZNpx/ha+PWu/uj8wY/4/dwFRXs4ynGb1RpJAIgMDkJksOsRsl13D8SX/57CSJXWXH08tCvGuNhX1pU3rLJ6SqXT6TRb52mdRdfb1pqWDgSrVQ7ClQK9TYKkapWDcOJqDoCSBCHizo11VNys3Zqrmy1Sbla9NaATbqkbh4H1y0fCrxkdmyExogqCA/zxxT8n8c3hM1gxqAvGfrtN6645FBIYgHpVw7Df6vPd2SvsGQ/fmPpmVE+sPXoOwxrJzwpapVIAcouMqvSn9HVLUqT7b1BV8vfHD7f3gt8zK12WPTh5CI5fzXG5bVYdqxtVOh3wmYM9nsVcW2y7sx9+PpGOO9b86rKsL/OuTw8iJ6pJnNfubHsQZ6a0boiNJzPQJaEWbmtSB9N+2iG5jjHNkrHpZLrLcvNubunwuYhgeRf33jSyO6h+Ar4/etbyvVp9G5dS1+sufrXy4ZCbYTKbFU1x/XJEdzz/xwFMdZCZUg32/vZyR4LbxtZAW5W2r1HLrS6CW38Vpj1HV6mMzNwCjyWNAIoDTr3RJPn8K0aLFi2wb98+0eWTq4ahTmSVMq+betXCkVdkQNXgStDr9aLqmtiqAU5n5cpOXqWmXnVisPFkBu5ra/v+q1Ip0GZbg1Q7+0eqNTLlibVwgf5+lhs3wxslwmQ240JeoVvamtqmIbaezlRcT4fYGmgRVc0moHQHuTc9q1YOsrv1hSvhbrp5qOUWOfaUDnCb1qyKpjU9OzW4VmhljEupy4CSyBvcFF/L7noxZ16QkY0SAEY0TsK/U4YiuWoYAv380DMpBk3f+lZWXc68O+Am3O0k6J3bpQX+yriMX86cV6U9NS5oS4jNLDmnc4pNQClGJX8/FLlpNK+0iKBAZOkNostrtQeXq7+c0vWSsWEheLmPtPcX2XJ1QZ4YHor9hUWK2ogLC0H1ykEIFrk3pxqa1ohEkdnsljZPnz4t+Rh7v+fI4EqS99wL9PfDYiefEVLrkxOP1bq+9cuPt/fGsavZLremqVctHIt6tMacLX8BAA5NHooGbp5O9+PtvdD/s42W79UMO9Va523PrU3qoPn/27vz8CiqdA3gb2VfCWvS2UMSCEnIxioYJBAIIjuCrGpEQZBFAUEQBBkFdGYQBcFBcQYuOK4gm7hdTBRH5io7OhoCCCErICYhCwnpPvcPTE+aTq/pdHV33t/z8Dyk6pzqL3VSXfVV1TnHvw1yr9/AyA8ONdvn2KtlqYn4679/kuWzm+M02tj3wtCoYLucfsNesQ8l2ayGd7r+lXlfo4PruDhJ6KZoh6g/7lw3ZMyIZo8kRePHx0dpLe/SvjXcnG+PoNhcQ8n3DfXX+2XX1tMd2Q8NVf+sq2hnI6facJIkrX1kLn9vT3wxOQNfTDZ+5E59Gv5uuU+MxfYmDm9ubH+0i3PHYWTnUBxusJ8dzZO9YuHm7NTkOR0NMfW0/VQv8wfFsTRzLjnOz74f60zot+ZmgYSs/tVYa14kOTlJjSaT9a+MhhqYCshe/W1on2bb9neZ9+GLyRkI+GMqFVdnJ6PnOW34qm5ch9YWGaRKnzsHB7K0hn9bpsz/Z4zb53F5L3Pbe+m++RrayvhBp+b3jlOP0/DR/WlNDUura0H9VFGGzr3jYiPQ2sMNo2PMf8VWl/4G5qk2lb59L7f3x/aXOwSL4xNKsmuSJOGHR4dDCGHW3c77u4Rb7PWHCCMHs9g2IhXFldVmTZRer+ENvqV3J6Kitg5jjRga/+4Qf/xaWmH25zY0ODIIlbWaT/cscecxzM8HUxOi8PC+b3WWMXQ5/cmEQfjLv3/EpqO/6C3X2sMdex9INyNK+/FqRm/8dVBPky48G+YrjY1wZ+hJrTFPctfrGeLe1knS7dcvJ3ftiIX/+4PJ9YUQuFmnBABU1tapRwGuvqXZj+nO48sYVbfq1Nsxp74hXn8ktL7uruge2A6SJKHiuvbn6JrWxlymjpTcO6g9LuXe7sNuTh9EH7fbl0eW+E67cxN9rDTgmilM6T9tyTc12ni6Y+OQ3pAk6Jwz01Lm9ozFxh9+xuJGpoQyhb+3B4orqo0q2zOoPV7on6IxeNQnEwch+1IxHkyIwiP7/2VwG3lzxyPU7/aNm4nxlhnp+k6jY8Jx69mHDG7bz8MNVxdM1DnyqQQJ2Q/ei7Qdn5kcw/i4CIu9kXU7Ftv1wB3jBzgCJpRk95wkybz3jaA9QmBT7vz7urvi8rzxBu+IPpxk2ekzvFxdmu3ifHTnMPzteI7RT0FtRXhrH7x+710GE8qWwtQLEA8XF8zqHoPK2jqNCen/Osi818jvFO7nmE+1jHWzTol7zLjgsgUVi6fA+49kUd/3pbuLMxL82zT5VftObVuhsKLK6Bt29d4a3hcLSqvQ1tNdawomOdnaRe7OUf3wxrEck562W9ocC8wta4xXM3rhseRO6Gri9Bp33TEn7qcTB2HWp//GGiMH4FneL0nj5/uiQ7RGvNV36dHwZkpzPpE2dtt3lnsxLQXLs0+of+4frrBYTJZ8GcPcTenr32rKNvuEdMCR/KsY10wj1MuNCSU5FIWPJ6L+6Puo727nD9OGo6iiaU8JGxPS4PUvuQbHaUpn+/gOrTUGH1g3uCd6BbU3ONy7sZqjw/6EuAjsyclDzR/9LpNsdC6uxqxO64Zl2cflDqNRm/945e+ZQ0fVyxbe1bQ7+/Uc/akw3eZugdd8/Tzc4Gdif0bg9uuTvYOtM3WDPZuSEIUpOua3tBUbh/TG3M//D++OuadJ23GSJCQ2MsetPpIk4YG4CCiFUM8rnKxohyOPDGtSLI5kWWqSRkJpLrlGY7eW/Q+kY+/ZyxhvodHJbQ0TSnIozk5OyJk1BoD+PpS2NkKkMYz9spUkCe+P7Y8Ju7/WWmconds4pDfaerojM/H2U1QvVxc8YuZouaZoymnk3TH9UacSyPmtDJuP/YLnUpMMVzKTJS6QG+prpbnKbEHDmwltjZgaxNLm9OiC14/+ggW9tSeAt9accfU8XJzxzYP3Arg9H5rrH33Hjhf9plGuW6B2v3FjqISAhOYZudOrhYyu3Fif/Xpd2vkhNdQfWZeKcf6PKUsc/WJYTnN6xuLxbjFwNaM/pCX6UEqS1ORphYjaeXnoHYjR3rWMMwPZJXOf8DXHyHEDIwLx1cUiDImUf4h5wPC+MWZAonoNLzrbeXng9XvNn0/PWhr+epIkwdVZQlf/NuqnanfqZaEbCPdGBWNoVLDei00yTI6B917N6IVpyZ2Q1OAJxW8LJ+F6dQ0immlOQl0kScJdIf5QCaF3HkhvC/dDJMOuLZiIsppbeueTDPL1wlvD78b56+WI3rzbitHJY1h0CD45l29SHUsf4uYkk8Dt1y9HdApFl/Z+Fo6IiBriKK9ERvjo/jS8eV9fvGcnI3PJ9bptc7zS2saM193q/TxzNLaPTMXEeMt0gHdxcsLBSYPNmrza1IEs/j78bvX/bW3oc0O/SUCDfpe2wtnJCSmKdho3W9p6uiNapv51rs5OFn/iTU3XzstDo2/9wrtuP9E2d15jwLKD2AyNDoa3qwsGWLCfmiGxdpyMOUkS9k1Ix5/NnEbMGPWjpzdlxOPmeML9Ytrt81Rjb2VYi5xP7i01qj0Zh08oiRrQNZJgG093TO/W2crRaHJ1tq2kwpAPmzC0ecMLsMMPD0XXLXvN2k6X9q3RxcL9ZK3lkeROmHbA8AiAzc2cVxyHRYdgcZ+u6G7mK5u2rv5INDdPaCmvjTqCyDa+qFn6INycnfH2ydwmb6+pN4daubvh+tOT4NrM04XoIvc0HLboy8kZ+PORH7GoiaPHNuTb4O2ENp7m3VRNCmir/tvVp42VuyDouvFsyb+t2T26YHEzT5UF2N7NXjnxrEaE28OJXy6vRLKJHfb1sdQ96Wf6dsWpkt8xuON/X7dtjieBljYuNgJH8q9oLNN1Aa7vLqYpr+/aG3s4Gc3vHYcvLxSaNDKdJEl6J413RKa0ZDsv6/chbU62/23UNPUX5M6SBKUQ6qeDbTz/245NHc3WnHjkMCQq2OHb21RRbVthy7C+Ft2mm7MTLs8bDyFuj7pt/nZ0/618Pmkwns0+jrcbvA0jp8amIjHnqOrctlWjXXescYOzfgCplogJJRGADUN6yx2CTi8NbFkX5o15876+mHHwO/XPgT7GTwht64Z3CsGBXNP6J1lTK3c3fJt5n8FyhpJjuV7DJrKUc7PH4ssLRXg46faoqG093XFw4iC4Ozs363QOcpvdowv++u+fMDGueeZAtGXWuk3QzlP7JlNIE16hNUZGVDAyooItuk0575H+qX8KVnx9ApuHaiaTPz0+Gt8XXsUEK8z9OKdnLEoqq/Hit6eb/bNsDRNKslnjYiPw5omzCNEzOAKZxtIX9Q2TiOZ8kji9W2c8mBiJz84XIOtiMR5KtO1h7k2x74F07D2bhzEfZskditXY/nNZIm0RrX0xvZvmAE5DjZhSyd7vpUS09kX1kqlwl/HJqKPaMaoffrz6OwZEWK9PrKN6rl8SFvWJ13qiG9ehNeI6tDZ7u3bwIpFNYEJJNmtwZBBOPDZCY4AEsi1eri7ITIxGdV0dQlp5aYygaWkeLi4YHROO0TGONSmwJEm8UCMtLeF7LzzcsY5luYX7eeNSWSXui7LMvMENNeW1S9Jtqo3PAWptc3p0waajv5h9E4Z/p/JpWe8ukN1JVrRDK3fzR/mUU+smjE5qiL4h7QHr9rH8x8hUvDc2DZIkYVh0CLaNSMWJx0aYtI077wA6SRIi/HzQ2sMNUS3gwtrebP2jv9CLaSkyR+KY2nt5oG0jr8A5mlOnTskdgkP5LnMYNg+9C68N6SV3KERm2XjvXfj5j7nEbdmYmDAAwPQU7dGfDV2fOSqm8kTNpGdQezx7dyIi/Cw/dPVjyZ3x45VSDI4MbPK2LPk2hyRJeDgpWud6Y0e3lCQJubPHQiWErANQNLf6QQgMjdgYaWPDnw/rFIqqZ6bC09UFy7NPyB2O1dnDYErU8gT5emFW9y5yh0HUJEENxkiw1UH5do0bgJt1Sq25gwHLXp/ZEyaURM3InPkKjeHq7IRNQ7VHMbN1qwek4ETJb5iRYngKFkce/GFEp1DUKpXoG+oPABgQocCgjoH431+LNMpdnjceVbfq0M7LQ44w9WrsRErG8XSw+Sd93FxRerNW7jDISkZ2DsVHP1/SOz8lb7oQAHRqq/2GUd8Qf3z08yUAwJHM+9Bn20GN9b7ursh9YizcnJ1s9u9IkiSd50B7vT5rKl4RELUg4X7ao8ZZc8AIhY8Xjj820oqfaDs8XV2g8PFE9a067B4/QCNhdnZywpdThuCL8wUY8u6XePbuRADNP8qfNfl5uMHf2wO3lCoE+HjKHY5FuDdIDI25ARLb3g/VdUq7fY1fF39vDzhJQCt3V8OFWyAfB7v5snXY3egfpjBpKiF7ZaP5jM07knkfzv1+A3eF+Gutm9MjFp4uzhgQEYiYdo3flIhu26q5QyQLc6xvOSLS69nURFypummRCbotSd88lPaosd/GSZKQN3c8BITO5CMjKhgVi6fA283xLsydJAn58x7Q+/vbm7ae7lg7oBucJAm+RiRT3m6uDtu2/t6OcZOgOUS1bYWV/ZIcpl+sn4cb5vSMlTsMhxbTzr4TqrtC/BtNJoHbT/BmNvJqtquzY5wXWiomlEQtiI+bK7YOvxvJAW1b7OS7cjLmhOmICUc9R7xgWPLH02RLcqzbKwQAz/fnAFZk2OV543Gj9hYUDjTXsiFL+ybg67wSjI+NkDuURvULDZA7BLvAhJLIARmab3JOz1gmlGQR/UIDcPhyCcZ1cfzX3+p1sME+rURk/+y9m4M5N8PWDOxu8Tgs4dwTY3HwfEGjI7mSNiaURCS7vqEd5A6BzJT90L2ovlXn0E9W670z+h4czivBhLgIuUMhIqJmFNW2FeayL6fRmFASkWyKnnoARRXViO/QRu5QyExOktQikkkAmNw1EpO7RsodBhERkU1xvA4tRGQSOftrKXy8kKJoJ2MEREREmh5Nvv2a44p+yfIGQmQn+ISSiMjCbHXuLCIiMuytYX3xyuCeDjfFD1Fz4RNKIiIiG+Lp6my4EJENC/a171FKJUliMklkAj6hJHJAgzoGAgC66Jg0mIhsT1yH1rhRe4ujyJLd+t8pGSi4UYW4Dq3lDoWIrIgJJZEDauflgfJFU/ikg8iOeLm6wMuVp2WyX+kdg+QOgYhkwDMXkYPydW8ZI28SERGR/O4O9Zc7BJIJE0oiIgvjkDxERNRSnH1iLPbk5OGJ7jFyh0Iy4aA8REREJAshBHbt2oXU1FSEhoZCoVCo/wUHB6NHjx7YuHEjbt26JXeoRKRDp7atsKhP1xYzJzFp4xNKohaOM1wQkVw2btyIXbt2YdOmTYiPj4eLy38vS1QqFS5evIjly5fj1KlT2Lp1q4yREhGRLkwoiYiIyOrq6urw8ssv4+jRowgMDNRa7+TkhMjISGzbtg1du3bF+fPnERUVJUOkRESkD195JSIiIqvLz89HUFBQo8lkQ25ubkhNTcVPP/1kpciIiMgUfEJJRGRhfI2Y9BFCADer5A7DPB5ekCz0B56fn4+AgACjygYFBSEvL88in0tERJbFhJKIiMiablahoq+P3FGYxee7CsDT2yLbqqur0+gzqY+Liwvq6uos8rlERGRZfOWViJpNx9b2edFMRERERMbhE0oiajYKHy98P204fN34VUOk5uF1+0mfPfLwkjsCIiKyMbzKI6Jm1TOovdwhENkUSZIs9tooERGR3PjKK1ELJ4EjyFhKRmQQAGB6SmeZIyGyfc7Ozkb3izSlvyUREVkXv52JiCzko/sHIPtSMQZH6p8GgYiAkJAQFBcXG1W2sLAQvXr1auaIiIjIHHxCSURkIb7urhjRORQefJJCZFBoaCgKCwtRVFTU6PqLFy9i5MiR+P333/Gvf/0LcXFxVo6QiIiMwYSSiIiIrM7FxQXPPPMMJkyYgFOnTkGpVGqsDw0NRbt27RAbG4t77rkH0dHRMkVKRET68DY6ERERyWLevHkIDg7GE088gUuXLqn7VAohUFtbi6CgICQlJeHixYuoqamBu7u7zBETEdGdmFASERGRLCRJwrhx4zBu3DidZVQqFaZPn47s7GwMGTLEitEREZExmFASERGRzXJycsLbb78tdxhERKQD+1AStVAPJ0ahc9tWGNk5VO5QiIiIiMhO8QklUQu1bWQ/CCFuT7JORERERGQGPqEkasGYTBIRERFRUzChJCIiIiIiIrMwoSQiIiIiIiKzMKEkIiIiIiIiszChJCIiagZCCLlDaHG4z4mIrI8JJRERkQU5Od0+tSqVSpkjaXnq93l9GxARUfPjNy4REZEFubq6wtnZGdXV1XKH0uJUV1fD2dkZrq6ucodCRNRiMKEkIiKyIEmS4OXlhbKyMj6ltCKlUomysjJ4eXlxSiQiIitykTsAIiIiR+Pv74+LFy/i0qVLaNu2Ldzd3ZnkNBMhBGpqanD9+nWoVCr4+/vLHRIRUYvChJKIiMjC3NzcEBISgmvXrqGoqEjucFoEb29vKBQKuLm5yR0KEVGLwoSSiIioGXh5eSEsLAx1dXWoq6uTOxyH5uLiAhcXXtIQEcmB375ERETNiMkOERE5Mg7KQ0RERERERGZxyISyuroaa9euRUpKCoKCgqBQKKBQKNC5c2dMmTIF58+fN2o7ubm5mDx5Mjp16qTeRlBQELp164aXX34ZNTU1WnWKiorw+OOPIzY2FoGBgVAoFAgMDERiYiKWLFmC8vJyrTqlpaVYvHgxEhIS1HUUCgXi4uIwa9YslJSUaNU5duwYMjIyEBISoi4fEBCA4OBgDBw4EEeOHDF9xxEREREREZnAIRPKSZMmIS8vDwcOHEBBQQGKi4tRXFyMEydOYMiQIRgwYAAKCwv1biM/Px/p6ekYPnw4Tp06pd5GQUEB9u7di/Pnz2PKlCkadSoqKpCamoqEhAR8//33KCoqQnFxMQoLC3Ho0CG4urpi0KBBUKlU6jpKpRLp6enw9vZGdnY2CgsL1Z915MgRdOnSBXfffTeqqqrUda5fv44xY8Zg4cKFyMvLU5cvKSlBfn4+VqxYgQkTJuDq1auW3bFEREREREQNSEIIIXcQlnT06FHMnDkT33//PZycGs+XX3nlFVy6dAmvvfaazu3MmTMHMTExmDt3bqPrVSoVunXrhn/84x9ISUkBAPz1r39FUVER1q1bp3O7I0eOxCOPPIIxY8YAAD766CO8++672LVrl8468+bNQ8eOHTF//nwAwD//+U9kZ2fjzTff1Fnn0UcfxbBhwzB27FidZRoqLy+Hn58fysrK0KpVK6PqEBERERGR4zElN3C4J5RnzpxBWlqazmQSAAYNGoQzZ84Y3M6AAQN0rndycsLAgQM1tmOoTmOfbU6dixcvIioqSm+de++9F15eXnrLEBERERERNYXDDTuXn5+PwMBAvWWCgoKQl5dn8e0YW+f06dMadRITE036nNraWrRu3VpvnfHjx+tdT0RERERE1FQO94RSqVTC2dlZbxkXFxeDc4KZsx1r1QGAmpoaPPvss+jdu7fGQD4xMTGYPHkyzp49q3ebNTU1KC8v1/hHRERERERkCrt4Qrlz507MmTNHb5nXX38dU6dOtVJE8tu4cSPWrFmDpUuXwtfXV738xo0b2L9/PzIyMnDq1Cn4+fk1Wn/t2rVYtWqV1nImlkRERERELVt9TmDMcDt2kVBOnTrV7GSxrKwM3bt3x9atW5GWlmZ2DDt37sS6detw4sQJo+tkZ2fjsccew/Hjx40e6CYnJwdDhw7FZ599hs6dO+ssl56ejoceekhrua+vLyZPnoxvv/0Wu3btwrRp0xqtv3TpUixYsED9c0FBAeLi4hAaGmpUnERERERE5Nhu3Lih8wFVPbtIKJvCz88P586da/J2zElq09LSTP7smJgYXLhwwWC5Ll266F3ftWtXvfNturu7w93dXf2zj48PLl++DF9fX0iSZHzAzaC8vByhoaG4fPkyR5y1A2wv+8G2si9sL/vBtrIvbC/7wbaSjxACN27cQFBQkMGyDpdQGtM/sq6uDi4u+n91c7ZjrToA4OnpqbeOl5cXKisr9ZZpyMnJCSEhIUaXt4ZWrVrxy8OOsL3sB9vKvrC97Afbyr6wvewH20oehp5M1nO4QXlCQkJQXFyst0xhYSHCwsIsvh1r1fH29sbNmzf11qmurua0IURERERE1KwcLqFMSEhAVlYWVCqVzjKHDh1CQkKCwe0cOnRI53qVSoWsrCyN7Riq09hnm1MnMjISubm5euv8/PPPiIyM1FuGiIiIiIioKRwuoezevTvCw8Mxa9YsFBQUaIxMVFVVhZ07d2L9+vV45pln9G5nyZIlWLduHd555x1UVVWplwshUFBQgJkzZyIyMhIpKSnqdTNnzsS+ffuwceNG3LhxQ6POb7/9hhUrVqCwsBCjRo1Srxs9ejTy8/OxcuVK/PbbbxrxlpeXY+PGjfjkk08wY8YM9fKhQ4fi8OHD2L17N5RKpUbcSqUSBw4cwJ49ezBmzBgT9pztcHd3x8qVKzX6eJLtYnvZD7aVfWF72Q+2lX1he9kPtpV9kIQxY8HamZs3b2L9+vV4//33UVJSok7SfH190bNnT7zwwguIiopSl9c1suq5c+fw3HPP4ejRo+oEUZIkBAQEYOLEiXjqqafg4eGh8dnFxcVYuXIlvv76a5SVlUEIAUmS0K5dOwwbNgzLli3Tege8rKwMq1evxsGDBzWSytatW6N///5YtWoVFAqFRp3c3FwsXboUP/zwA2pqatTLXV1dkZiYiNWrVyM5ObnpO5OIiIiIiEgHh0woiYiIiIiIqPk53CuvREREREREZB1MKImIiIiIiMgsTCiJiIiIiIjILEwoySaUlpZi8eLFSEhIQGBgIBQKBRQKBeLi4jBr1iyUlJTIHaLd27lzJ5KTkzX2b2BgIOLi4vDKK6+grq5OXbZ///7o0KGDulxj/5YsWaKx/erqaqxduxYpKSkICgpSl+vcuTOmTJmC8+fPa8VUV1eHN998Ez179kRwcLC6TmRkJEaNGoWTJ082926xOV999RW8vLz07vvQ0FAUFhaq6xw7dgwjR45EZGSkukxwcDB69eqFrVu3ao0GDdwe2Gvy5Mno1KmTuk5QUBC6deuGl19+WWOwr3pFRUV4/PHHERsbq/47CgwMRGJiIpYsWYLy8vJm3Te2KDs7W29beXt7Y/v27cjNzYWnp6fesoGBgTh27JjG9tlOTVdRUYFhw4bhrbfe0lhuy8dNSz4nNtZepaWlmDNnDjp27KhxzISEhGDgwIH47rvvNLbB4806Gmurhx9+GG3bttW776dMmaKxHXOuBYQQ+Oijj5CamoqwsDB1nfDwcAwePBjZ2dmNxnzo0CEMGjQIERERGufUfv36Yffu3eDQMmYSRDKrq6sT3bp1E88//7y4du2aUKlU6nWlpaXi1VdfFVFRUaKyslLGKO1bVlaWiI+PFzk5OVrriouLxYgRI8SGDRvUy5KSksSvv/5q0meMGjVKzJw5U+Tn52u0YUVFhdi+fbsIDQ0VBQUFGnWefPJJMXbsWJGbmyuUSqV6eXV1tdi3b5+IiIgQJ06cMCkOe/fxxx+Lhx9+2OjyR48eFR07dhSffPKJuHnzpnq5UqkUv/zyixg1apRYuHChRp3Lly+L0NBQ8c4772gcVyqVSuTl5Ynp06eL+++/X6POjRs3RGRkpNi4caMoLy/XqHPlyhWxfPly0bNnT412JCEWLFggduzYIU6cOCH69+9vUl22U9OVlZWJ1NRUER4eLtavX69ebsvHTUs+J+pqr8zMTPHUU0+JiooKjfJKpVIcPnxYREREaJxfeLw1P11tNWrUKJGVlWXStsy5Fli/fr3o37+/OHnypKirq1Mvr6mpEdnZ2SI+Pl7s27dPo87HH38sEhISxOHDh0Vtba16+a1bt8SxY8dEamqq2Lhxo0mx021MKEl2H374oRg7dqzeMnPnzhWvvPKKlSJyPIsXLxZ/+9vfdK7/5ZdfRK9evdQ/m5pQ/vDDD6J79+56T5rr1q0T8+bNU/9cWFgowsPDRVVVlc46u3btEiNHjjQ6DkdgakI5bNgwrZNmQxUVFSIsLEwUFxerl82ePVvjBsKdlEqlSEpKEsePH1cv+8tf/iIWLFigN5YRI0aI3bt3Gx17SzBu3DiRlZVl1gUu26lpqqqqRO/evcWqVavEypUrNS56bfm4aannRH3tpVAo9J4rlixZIjZv3qz+mcdb89LXVqYmlOZcC9y6dUsEBgaKoqIinXWOHTsmkpKSNJbFx8eLU6dO6ayTn58vgoKCNBJUMg5feSXZnTlzBgMGDNBbZtCgQThz5oyVInI8ffr0weDBg3WuDw0NxeXLl83e/pkzZ5CWlgYnJ91fKXe24S+//IIePXrA09NTZ5309HS2uwGGjh9vb290794dOTk5RtdxcnLCwIEDNfY9j1PTVVZW4v/+7//Qt29fs+qznZpGCIEFCxZgxYoVWuts+bhpqW2or70WL16s91zR1HMYwOPNFPraylTmXAtcvnxZ3bVGl27duqGoqEjdnefWrVu4du0aEhMTddYJDg5GQEAACgoKzPhNWjYmlCS7/Px8BAYG6i0TFBSEvLw8K0XkeEaPHo3IyEid63/88UdER0ebvX1z2tCYOn5+frh+/brZcbUEZWVl8PHx0VvGnH1viTotmUqlwpw5czBz5ky4ubmZtQ22U9N4eXnhgQceaHSdLR83LbUN9bXX/Pnz9dZt6jkMYFuZQl9bmcqcawFj6gBAhw4dUFxcDAAoLi5Ghw4dDNZxxPayBiaUJDulUglnZ2e9ZVxcXDQGjSHLKSgowOOPP4558+ZpLN+6dSvuuecehIeHIyAgAAEBAYiIiMCgQYPw+eefa5Q1pw2NqdNSXbhwAdOnT0d8fLzGgByJiYlYuHChyUm2OfveEnVaqqqqKowfPx61tbVYvHixevnVq1cxf/58JCYmagxcFRcXh8ceewz5+fka22E7yUuu44ZtaJp9+/bhs88+w5gxYzSW83iTz8cff4yMjAxERkYiICBAPfBNamoq3n33XY2Bb8y5FjC2TsN9b04dMh4TSqIWSgiBbdu2ITk5GZmZmRg3bpx6XVJSEi5duoQNGzbgP//5D0pKSlBSUoKzZ8/ihRdewJNPPqk1qh5ZRkREBMrKytCnTx8cOnQIBQUFKC4uRlFREb766it4enpi0qRJHInORl29ehXp6elITEzEzp074eLiAgDw9/eHs7MzQkJCcPDgQeTl5aG4uBjFxcU4cuQIUlJSMHToUNy6dUvm34DIPvz+++/IzMzE/PnzsWfPHrRp00a9jsebfOLi4nDx4kU8//zzOHnyJEpKSlBcXIxff/0VmzdvxoYNG/Dee+/JHSZZGBNKsjk5OTmIjIzE2bNn5Q7FYZ09exYZGRnYsmULPv/8czz55JMa67dv344dO3YgOTkZ3t7e6uVubm7o06cPVq9ejTfeeEPn9svKyhAdHa1z2G5dUlJS8M4775hUx9EkJyfj1KlTmDZtGhQKhbpfqiRJaN++PV544QUUFhbiwoULjdZ/6aWXMHLkSJM+c+fOnUhJSTGpTnZ2NqKjox16SHxTCSHw4IMPIjMzEytXroQkSep1QUFBOH36NBYuXIiQkBB1ogncfp1r9uzZiIiIwDfffKNz+2yn5mPLxw3PiZqEEHjvvfeQlJSENm3a4Pjx41r94ni8yWfNmjXYu3cv+vbti1atWqmXu7i4IDExEa+//jo2b96sdxvmXAuMHDkSL730kkl1Zs+ejdmzZ5tUhxrnYrgIkXXFxMTovFimplEqlVi9ejW2bt2KP/3pT3jooYf0DqSjS2xsLNatW6dzvZ+fH86dO2fydk+cOGFynZZGkiTExsbi/PnziIqK0lp/5/ygxpg6dSqmTp1qUp20tDSz2tiRffHFF1AqlZgxY4ZZ9evbNT09vdH1bKfmY8vHDc+J/3X58mVkZmZCCIFPP/0U8fHxZm+Lx5s86ve7PuZcC+zbt8/kOps2bTK5DjWOCSXJzpj31evq6jTuMJLpKioqMHnyZLi4uOD06dNo3bq12dvy8vJCVVWV+mdz2pD9FMx35/43xJx9b4k6Lc2ePXvwyCOPaDyZNIW1jquW3k7Gkuu4YRs27vvvv8eECROwdOlSTJ8+3ezjrB6PN3mYs9/vZGydhvvenDpkPL7ySrILCQlRj8KlS2FhIcLCwqwUkWN6+umnERISgl27dulNJsvLy/Hcc8/p3VZ1dbXGEN/mtKExdcrLy9G2bVu9ZRzN22+/bXCI+Yb7v3Xr1qioqNBb3px9b4k6Lc2///1v9OnTp9F1+/fvx6FDh/TWt9Zx1dLbCbDt44ZtqK20tBRjx47Fjh07MGPGDIPJJI83+Tz11FN615uz3++8FjCmDnC7T3v91CIKhQLXrl0zWKeltZelMKEk2SUkJBj84j906BASEhKsFJHjyc3NxWeffYZ169YZPBG7u7vjzTff1Dvoy9mzZzWmIUlISEBWVhZUKpXOOne2YZcuXXD06FG9T9paYrtfvHhRb99TIYTG/jd0/FRWVuLYsWOIiYlRLzNUR6VSISsrS2Pf8zg1rKCgAMHBwY2uu3LlCg4ePKi3fmPHFdupedjyccM21LZu3TpMnjwZqampRpXn8SafXbt26e1Deud+N+daICQkBEVFRSgqKtJZ58SJE1AoFBpPKDt06ICTJ0/qrFNYWIiSkhKd3+OkhyCSWV1dnejRo4dYsWKFuHbtmlCpVOp1ZWVlYsOGDSIqKkpUVFTIGKV9e//990VmZqbR5UeMGCGWLVsmKisrNZarVCpx+vRpkZCQILKysjTWjRkzRsyYMUPk5+drtGFlZaXYsWOHCA0NFQUFBRp15s+fL0aPHi1yc3OFUqlUL79586bYv3+/iIiIEMePHzfhN7V/33//vYiOjhYnTpzQ2I9CCFFVVSXWrl0r0tLS1OuOHz8uIiIixIEDB8TNmzfVZZVKpcjNzRWjR48WCxYs0NhOfn6+CA0NFTt37tRoY5VKJfLz88X06dPF2LFjNepUVFSIyMhIsWHDBlFeXq5R59q1a+K5554TPXr00GjHlsbPz0/nuosXL4rQ0FCRnZ2t1a61tbVi+/btIjY2VqMN2U6Ws3LlSrF+/Xr1z7Z83PCcqN1eQ4cOFdnZ2UbX5/FmPXe21dy5c8Ujjzwirl+/rlFOpVKJCxcuiLS0NLFt2zaNdeZcC7z22muiX79+4uTJk6Kurk69vLa2VnzzzTciPj5e7Nu3T6PO3r17RdeuXcXhw4dFbW2tenldXZ04ceKESE1NFRs2bDB7X7RkTCjJJpSWlopFixaJ+Ph4oVAoREBAgAgICBAxMTFixowZoqioSO4Q7drmzZuFt7e3er829i8kJESd8F29elXMmjVLhIeHa5QJDAwUvXr1Ert379b6jOrqarFmzRqRlJSk0YbR0dFi0qRJ4ty5c1p16urqxJYtW0SPHj1EYGCguk7Hjh3FiBEjWlwyWW/Pnj2id+/eGvukfr88/vjjoqSkRKP88ePHxYgRI0THjh012qpHjx5iy5YtGifberm5uWLixIkiOjpaXUehUIikpCSxdu1aUV1drVWnqKhIzJgxQ8TExKjbWKFQiPj4eLF48WJRVlbWbPvEHnTt2lXv+m+++UYMGDBABAUFabRreHi4mDhxorhw4YJWHbaTZaxbt05s3bpVY5ktHzct/Zx4Z3v17t1btG/fXu85bPLkyRrb4PFmHXe2VWVlpVi6dKmIjIzUun5ISkoSb731llaSb861gEqlEh9++KHo27evCA4OVtcJCwsT6enp4quvvmo03kOHDon09HQRFhamcf1z9913i48++kgrNjKOJAQnMyMiIiIiIiLTsQ8lERERERERmYUJJREREREREZmFCSURERERERGZhQklERERERERmYUJJREREREREZmFCSURERERERGZhQklERERERERmYUJJREREREREZmFCSURERERERGZhQklERERWZRSqURlZaXcYQAAKisroVQq5Q6DiMhhMaEkIiIywj//+U84OTlBkiST/nl5eeH8+fMAgKtXryIwMBD+/v4oKSmR+TeyLCEENm3ahOTkZPj4+KBz585yhwQA6Ny5M7y9vZGcnIxNmzZBCCF3SEREDsVF7gCIiIjswcCBA/H6669DpVJprVuxYgW6du2KBx54QGudl5cXQkNDAQA///wziouLAQA5OTkICAho3qCtaO3atVi2bBnuu+8+TJs2DX369JE7JADA3r17ceTIEXz66aeYM2cOysvLsXTpUrnDIiJyGJLgrToiIqImiYiIwPDhw/H666/rLXfjxg2MHDkSSqUSBw4cQKtWrawUYfMqLy9HeHg4HnroIbz66quQJEnukLQIIfDkk09i586duHTpEnx9feUOiYjIIfCVVyIiIivx9fVFVlYWvvnmG4dJJgFg//79KC8vx+LFi20ymQQASZKwePFilJWV4cCBA3KHQ0TkMJhQEhERUZNcvnwZwcHBCA4OljsUvUJCQhAYGIi8vDy5QyEichhMKImIiKxo/vz5yMzM1FpeWlqKgIAAHDlyBNu2bUNKSgq8vLwgSRL8/PzQr18/fP755wCAwsJCPPjgg1AoFHBycoKzszM6duyICRMm6E2WhBD44IMPkJaWhrCwMPUgQ05OTggPD0d6ejr27Nlj8sA1BQUF8Pf317m+tLQU/v7++O6773Dp0iXMnTsXXbp0gY+PDyRJgr+/P+666y6sWbMGN2/e1LmdXbt2oUePHup6Df95e3ujR48e2L17t95Y/f39UVBQYNLvR0REunFQHiIiIiv69ddfUVpaqrW8tLQUV65cwbJly3Dy5ElMnDgRkydPhoeHB4qLi7Fr1y7cd9992Lt3L+bNmwdPT088+OCD6qeC58+fxz/+8Q9kZWXhwoUL8PHx0fqMuXPnYtOmTUhLS8PUqVPVCalKpUJxcTGOHDmCMWPGYNGiRfjzn/9s9O9048YNeHt761xfWlqKq1ev4oMPPsCbb76JqKgoZGRkQKFQwNfXF7///jtycnKwcuVKfPDBB/jhhx/g6uqqsY3vvvsO48aNQ0ZGBlasWAEvLy+N9VevXsUXX3yB8ePH49tvv9U5KJCvry/Ky8uN/t2IiEg/JpREREQ25D//+Q/Onj2L9u3bayxftmwZ4uLiMGrUKAwZMgR79+7VSrqmTJmCPn364O9//zvmzZunse7UqVPYvHkzVqxYgeeff77Rvo5CCKxatQovvvgiHn30UcTExBgdtzF9J7ds2YJ3330Xo0ePbnT9vHnz0KdPH2zZsgVz5szRWLd9+3aEh4fj4MGDcHZ2brT+8uXLERYWhnfffVdnQmmrfTyJiOwVX3klIiKyITNmzNBKJoHb049MmjQJKpUKixYt0komAeCuu+5CbGwsTp8+rbUuOzsbkiRh0aJFOpOqhusPHTrU9F/mDjNmzNCZTAJA7969kZaWhq+++kprXV5eHjp16qQzmQQAV1dXrF+/HhkZGZYIl4iIjMAnlERERDYkKipK57r6+SwjIyN1lgkJCWm0j+CFCxegUCgafRW2IW9vbygUCvz6669GRmy8vn37GizTsWNHHD16VGt5u3btkJOTY7D+xIkTzYqNiIjMw4SSiIjIhujri1jfb9BQmcb6aJaVlcHb2xv5+fkGY/D19cXVq1cNB2sifU8XG352WVmZ1vL+/fvjnXfewRtvvIFBgwbB09NTY72HhwfatWvHV1qJiKyMCSUREVELkZubq37Kacg999zTzNGYZtq0aTh8+DCeeOIJnWV8fHyQmZmJtWvXGnwSS0RElsGEkoiIqIUIDQ3F5s2bjSrbq1evZo7GNM7Ozvif//kfrFmzBrm5uaisrNRYX1NTgzNnzuCll16Cm5sb1q1bJ1OkREQtCxNKIiKiFqBVq1Zwc3PD8OHD5Q6lSUJCQhASEtLouvvvvx9XrlzBxx9/zISSiMhKOMorERFRCxAZGYn8/HwolUqDZUeMGIGXX37ZClEZb8uWLUYNyhMWFoaioiIrRERERAATSiIiohahf//+qK2txZYtW/SWO3r0KA4cOICgoCArRWacV155BatXrzZYLicnB2FhYVaIiIiIAL7ySkRE1CKkpKRg5syZmD17Nj755BN0794d7du3h5OTE4QQuHHjBnJycrB7924kJSXpnS/yTi4uLrh161bzBQ9g0qRJWLVqFaqqqtC9e3f4+vpqrK+pqcFPP/2EHTt2YOXKlTq3U1tbCxcXXv4QEVkKv1GJiIiaKCYmBtHR0UaV7dixY6PTerRp0wb+/v56nwwGBwcjKChI7wimurYPAJs2bUK/fv3wxhtv4O9//zsKCwshhABwe57H4OBgPP3005gzZ45WwqZPYGAgvv76a53rW7dubfB3qxceHt7oPJvLly+HJEnYsWMH9u/fj9raWo317u7uCA8Px5/+9Cc8/fTTOrd/5coVDB482GAcRERkHEnUn0mIiIiIzLBlyxbMnz8fv/32m9b8kLakqqoK7du3x2uvvYbp06fLHQ4RkUNgH0oiIiJqkoEDB6Kmpgbbt2+XOxS9tm/fjpqaGgwYMEDuUIiIHAZfeSUiIqIm6dSpE6ZMmYK5c+eisLAQgwYNQteuXdG2bVu5Q8P169fx008/4csvv8TatWsxdepUo19PJiIiw/jKKxERETXZzZs3sXz5cqxfvx4qlQqBgYEoLCyUOywEBQWhqKgITk5OmD9/Pl588UV4eHjIHRYRkcNgQklEREQWc/PmTZw7dw51dXVITk6WOxycPHkSzs7O6NSpExNJIqJmwISSiIiIiIiIzMJBeYiIiIiIiMgsTCiJiIiIiIjILEwoiYiIiIiIyCxMKImIiIiIiMgsTCiJiIiIiIjILEwoiYiIiIiIyCxMKImIiIiIiMgsTCiJiIiIiIjILP8PRYQvUweuafEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I Offset : -0.114 mV \n",
      "Q Offset : 0.026 mV\n"
     ]
    }
   ],
   "source": [
    "adc_offset_path0(new_I_offset * 1e-3)  # Multiplying by 1e-3 to convert to mV\n",
    "adc_offset_path1(new_Q_offset * 1e-3)\n",
    "\n",
    "I_offset_calib, Q_offset_calib = acquire_scope_and_calc_offsets()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c710ca6",
   "metadata": {},
   "source": [
    "As you can see, you have calibrated away the offsets to sub-millivolt range, which is at the limit of the ADC resolution."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "875e7a7d",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\">\n",
    "We advise you to check/calibrate these offset values every few days if they are vital for your experiments, especially when you are dealing with low input signals. </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "818263ea",
   "metadata": {},
   "source": [
    "Stop\n",
    "----\n",
    "\n",
    "Finally, let's stop the sequencers if they haven't already and close the instrument connection. One can also display a detailed snapshot containing the instrument parameters before\n",
    "closing the connection by uncommenting the corresponding lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8bfbe9f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-07T16:48:53.698447Z",
     "iopub.status.busy": "2025-05-07T16:48:53.698263Z",
     "iopub.status.idle": "2025-05-07T16:48:59.838988Z",
     "shell.execute_reply": "2025-05-07T16:48:59.837564Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, State: STOPPED, Info Flags: FORCED_STOP, ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_OVERWRITTEN_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_SCOPE_OVERWRITTEN_PATH_1, ACQ_BINNING_DONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n",
      "Status: OKAY, State: STOPPED, Info Flags: FORCED_STOP, Warning Flags: NONE, Error Flags: NONE, Log: []\n",
      "\n",
      "Snapshot:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4:\n",
      "\tparameter                    value\n",
      "--------------------------------------------------------------------------------\n",
      "connected                     :\tTrue \n",
      "in0_gain                      :\t-6 (dB)\n",
      "in0_offset                    :\t0.0126 (V)\n",
      "in1_gain                      :\t-6 (dB)\n",
      "in1_offset                    :\t-0.0088941 (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                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t16000 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_freq_cal_type_default        :\toff (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer1:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_freq_cal_type_default        :\toff (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer2:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_freq_cal_type_default        :\toff (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer3:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_freq_cal_type_default        :\toff (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer4:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_freq_cal_type_default        :\toff (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer5:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_freq_cal_type_default        :\toff (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, Flags: NONE, Slot flags: NONE\n"
     ]
    }
   ],
   "source": [
    "# Stop both sequencers.\n",
    "module.stop_sequencer()\n",
    "\n",
    "# Print status of both sequencers (should now say it is stopped).\n",
    "print(module.get_sequencer_status(0))\n",
    "print(module.get_sequencer_status(1))\n",
    "print()\n",
    "\n",
    "# Print an overview of the instrument parameters.\n",
    "print(\"Snapshot:\")\n",
    "module.print_readable_snapshot(update=True)\n",
    "\n",
    "# Reset the cluster\n",
    "cluster.reset()\n",
    "print(cluster.get_system_status())"
   ]
  }
 ],
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