{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "28d2631a",
   "metadata": {},
   "source": [
    "# Rabi experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed313f23",
   "metadata": {
    "tags": []
   },
   "source": [
    "In this tutorial we will combine the techniques explained in the other tutorials and show how to perform a Rabi experiment. For this tutorial we will need one QCM to generate the Rabi pulses and one QRM to perform the readout, although the QCM could be replaced by another QRM if needed.\n",
    "\n",
    "Ports $\\text{O}^{[1-2]}$ of the QCM are used for the driving pulse, while $\\text{O}^{[1-2]}$ of the QRM are used for the readout pulse. Finally, ports $\\text{I}^{[1-2]}$ are used for the acquisition of the readout tone. In this tutorial it is assumed $\\text{O}^{1}$ of the QRM is connected to $\\text{I}^{1}$ of the QRM for time of flight calibration.\n",
    "Furthermore we assume that $\\text{O}^{1}$ of the QCM and $\\text{O}^{2}$ of the QRM are connected to an external oscilloscope to view the Rabi experiment pattern. The scope can be triggered of marker 1 of the QCM.\n",
    "\n",
    "The SYNQ technology synchronizes the programs in the two modules."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2d7ca36",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Setup\n",
    "\n",
    "\n",
    "First, we are going to import the required packages and connect to the instrument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "84ff459f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:47.061874Z",
     "iopub.status.busy": "2024-09-02T01:36:47.061132Z",
     "iopub.status.idle": "2024-09-02T01:36:48.113771Z",
     "shell.execute_reply": "2024-09-02T01:36:48.113027Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "from __future__ import annotations\n",
    "\n",
    "import json\n",
    "from typing import TYPE_CHECKING, Callable\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from 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": "3bb2a049",
   "metadata": {},
   "source": [
    "### Scan For Clusters\n",
    "\n",
    "We scan for the available devices connected via ethernet using the Plug & Play functionality of the Qblox Instruments package (see [Plug & Play](https://qblox-qblox-instruments.readthedocs-hosted.com/en/main/api_reference/tools.html#api-pnp) for more info)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "87d418be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:48.117533Z",
     "iopub.status.busy": "2024-09-02T01:36:48.116878Z",
     "iopub.status.idle": "2024-09-02T01:36:50.242663Z",
     "shell.execute_reply": "2024-09-02T01:36:50.241756Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No devices found\r\n"
     ]
    }
   ],
   "source": [
    "!qblox-pnp list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5e86db02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:50.245293Z",
     "iopub.status.busy": "2024-09-02T01:36:50.245094Z",
     "iopub.status.idle": "2024-09-02T01:36:50.248419Z",
     "shell.execute_reply": "2024-09-02T01:36:50.247824Z"
    }
   },
   "outputs": [],
   "source": [
    "cluster_ip = \"10.10.200.42\"\n",
    "cluster_name = \"cluster0\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ad12599",
   "metadata": {},
   "source": [
    "### Connect to Cluster\n",
    "\n",
    "We now make a connection with the Cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4f38aa86",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:50.250610Z",
     "iopub.status.busy": "2024-09-02T01:36:50.250440Z",
     "iopub.status.idle": "2024-09-02T01:36:53.169502Z",
     "shell.execute_reply": "2024-09-02T01:36:53.168648Z"
    },
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "cluster = find_or_create_instrument(\n",
    "    Cluster,\n",
    "    recreate=True,\n",
    "    name=cluster_name,\n",
    "    identifier=cluster_ip,\n",
    "    dummy_cfg=(\n",
    "        {\n",
    "            2: ClusterType.CLUSTER_QCM,\n",
    "            4: ClusterType.CLUSTER_QRM,\n",
    "            6: ClusterType.CLUSTER_QCM_RF,\n",
    "            8: ClusterType.CLUSTER_QRM_RF,\n",
    "        }\n",
    "        if cluster_ip is None\n",
    "        else None\n",
    "    ),\n",
    ")\n",
    "cluster.reset()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f616055f",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "source": [
    "#### Get connected modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "30f47ba9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:53.172333Z",
     "iopub.status.busy": "2024-09-02T01:36:53.172088Z",
     "iopub.status.idle": "2024-09-02T01:36:53.177079Z",
     "shell.execute_reply": "2024-09-02T01:36:53.176137Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_connected_modules(cluster: Cluster, filter_fn: Callable | None = None) -> dict[int, Module]:\n",
    "    def checked_filter_fn(mod: ClusterType) -> bool:\n",
    "        if filter_fn is not None:\n",
    "            return filter_fn(mod)\n",
    "        return True\n",
    "\n",
    "    return {\n",
    "        mod.slot_idx: mod for mod in cluster.modules if mod.present() and checked_filter_fn(mod)\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2bd18c4e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:53.179834Z",
     "iopub.status.busy": "2024-09-02T01:36:53.179512Z",
     "iopub.status.idle": "2024-09-02T01:36:53.204925Z",
     "shell.execute_reply": "2024-09-02T01:36:53.204151Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{4: <Module: cluster0_module4 of Cluster: cluster0>}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# QRM baseband modules\n",
    "readout_modules = get_connected_modules(cluster, lambda mod: mod.is_qrm_type and not mod.is_rf_type)\n",
    "readout_modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0cda1062",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:53.207902Z",
     "iopub.status.busy": "2024-09-02T01:36:53.207622Z",
     "iopub.status.idle": "2024-09-02T01:36:53.211557Z",
     "shell.execute_reply": "2024-09-02T01:36:53.210834Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module = readout_modules[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ee0d513e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:53.215872Z",
     "iopub.status.busy": "2024-09-02T01:36:53.215268Z",
     "iopub.status.idle": "2024-09-02T01:36:53.238714Z",
     "shell.execute_reply": "2024-09-02T01:36:53.237778Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2: <Module: cluster0_module2 of Cluster: cluster0>}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# QCM baseband modules\n",
    "control_modules = get_connected_modules(\n",
    "    cluster, lambda mod: not mod.is_qrm_type and not mod.is_rf_type\n",
    ")\n",
    "control_modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1ffacaae",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:53.242914Z",
     "iopub.status.busy": "2024-09-02T01:36:53.242545Z",
     "iopub.status.idle": "2024-09-02T01:36:53.248655Z",
     "shell.execute_reply": "2024-09-02T01:36:53.247698Z"
    }
   },
   "outputs": [],
   "source": [
    "control_module = control_modules[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13cf761a",
   "metadata": {},
   "source": [
    "## Sequencer Setup <a id='seq_setup'></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0da7d10",
   "metadata": {},
   "source": [
    "Set `sync_en` to synchronize across modules."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b4e57725",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:53.253525Z",
     "iopub.status.busy": "2024-09-02T01:36:53.252718Z",
     "iopub.status.idle": "2024-09-02T01:36:53.275527Z",
     "shell.execute_reply": "2024-09-02T01:36:53.274539Z"
    }
   },
   "outputs": [],
   "source": [
    "# Set sync_en\n",
    "readout_module.sequencer0.sync_en(True)\n",
    "control_module.sequencer0.sync_en(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a07eff1c",
   "metadata": {},
   "source": [
    "Configure the `NCO` of both the QRM and QCM to 100 MHz and enable the up- and down-conversion in the sequencers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e7c0b5c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:53.280811Z",
     "iopub.status.busy": "2024-09-02T01:36:53.280182Z",
     "iopub.status.idle": "2024-09-02T01:36:53.330332Z",
     "shell.execute_reply": "2024-09-02T01:36:53.329415Z"
    }
   },
   "outputs": [],
   "source": [
    "readout_module.sequencer0.nco_freq(100e6)\n",
    "readout_module.sequencer0.mod_en_awg(True)\n",
    "readout_module.sequencer0.demod_en_acq(True)\n",
    "\n",
    "control_module.sequencer0.nco_freq(100e6)\n",
    "control_module.sequencer0.mod_en_awg(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b8a61df",
   "metadata": {},
   "source": [
    "Configure the outputs of the QRM and QCM such that sequencer0 is the only enabled sequencer and maps to $\\text{O}^{[1-2]}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "296f0274",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:53.337819Z",
     "iopub.status.busy": "2024-09-02T01:36:53.334431Z",
     "iopub.status.idle": "2024-09-02T01:36:53.405485Z",
     "shell.execute_reply": "2024-09-02T01:36:53.404595Z"
    }
   },
   "outputs": [],
   "source": [
    "# Map sequencer of the QCM to specific outputs (but first disable all sequencer connections)\n",
    "control_module.disconnect_outputs()\n",
    "\n",
    "control_module.sequencer0.connect_sequencer(\"out0_1\")\n",
    "\n",
    "# Map sequencer of the QRM to specific outputs (but first disable all sequencer connections)\n",
    "readout_module.disconnect_outputs()\n",
    "readout_module.disconnect_inputs()\n",
    "\n",
    "readout_module.sequencer0.connect_sequencer(\"io0_1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9354cf52",
   "metadata": {},
   "source": [
    "## Define waveforms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "287ea213",
   "metadata": {},
   "source": [
    "To readout the systems we define constant pulses `one` and `zero` which will be up converted by the `NCO` to create the appropriate tones for an IQ mixer.\n",
    "Similarly for driving the qubit, we define a Gaussian pulse, together with a zero pulse of equal length to serve as inputs for an IQ mixer. In this tutorial we do not assume mixers to be connected to the inputs and outputs of the QCM/QRM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7d91b326",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:53.409983Z",
     "iopub.status.busy": "2024-09-02T01:36:53.409419Z",
     "iopub.status.idle": "2024-09-02T01:36:53.415622Z",
     "shell.execute_reply": "2024-09-02T01:36:53.414714Z"
    }
   },
   "outputs": [],
   "source": [
    "t = np.arange(-80, 81, 1)\n",
    "sigma = 20\n",
    "wfs = {\n",
    "    \"zero\": {\"index\": 0, \"data\": [0.0] * 1024},\n",
    "    \"one\": {\"index\": 1, \"data\": [1.0] * 1024},\n",
    "    \"gauss\": {\"index\": 2, \"data\": list(np.exp(-(0.5 * t**2 / sigma**2)))},\n",
    "    \"empty\": {\"index\": 3, \"data\": list(0.0 * t)},\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5869c3fc",
   "metadata": {},
   "source": [
    "Hence we obtain the following waveforms for readout:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8e315eaa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:53.419800Z",
     "iopub.status.busy": "2024-09-02T01:36:53.419248Z",
     "iopub.status.idle": "2024-09-02T01:36:53.589772Z",
     "shell.execute_reply": "2024-09-02T01:36:53.589030Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5879c6ddf0>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(wfs[\"zero\"][\"data\"])\n",
    "plt.plot(wfs[\"one\"][\"data\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42b41699",
   "metadata": {},
   "source": [
    "And for drive:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "aac532ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:53.592183Z",
     "iopub.status.busy": "2024-09-02T01:36:53.591956Z",
     "iopub.status.idle": "2024-09-02T01:36:53.759818Z",
     "shell.execute_reply": "2024-09-02T01:36:53.759171Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5879be89d0>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(wfs[\"gauss\"][\"data\"])\n",
    "plt.plot(wfs[\"empty\"][\"data\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9943bb9b",
   "metadata": {},
   "source": [
    "Finally, we define two acquisitions. A single readout to perform the calibration measurements. Secondly we create a `rabi` readout sequence that contains 50 different bins for saving the results, one for each of the different amplitudes used for the drive tone."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "84b2e535",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:53.762277Z",
     "iopub.status.busy": "2024-09-02T01:36:53.762083Z",
     "iopub.status.idle": "2024-09-02T01:36:53.765484Z",
     "shell.execute_reply": "2024-09-02T01:36:53.764942Z"
    }
   },
   "outputs": [],
   "source": [
    "num_bins = 50  # Number of amplitudes to be measured\n",
    "acquisitions = {\n",
    "    \"single\": {\"num_bins\": 1, \"index\": 0},\n",
    "    \"rabi\": {\"num_bins\": num_bins, \"index\": 1},\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "358a2ffb",
   "metadata": {},
   "source": [
    "## Calibration experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f336f2e6",
   "metadata": {},
   "source": [
    "### TOF calibration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9c28553",
   "metadata": {},
   "source": [
    "As a first step, we calibrate the time of flight (tof) for the QRM module. In order to do so, we play a readout pulse and analyze the obtained signal on the oscilloscope to find the travel time of the pulse through the system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "80dd8fc6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:53.767828Z",
     "iopub.status.busy": "2024-09-02T01:36:53.767653Z",
     "iopub.status.idle": "2024-09-02T01:36:53.771204Z",
     "shell.execute_reply": "2024-09-02T01:36:53.770465Z"
    }
   },
   "outputs": [],
   "source": [
    "qrm_prog = \"\"\"\n",
    "play    1, 0, 4     # start readout pulse\n",
    "acquire 0, 0, 16384 # start the 'single' acquisition sequence and wait for the length of the scope acquisition window\n",
    "stop\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95dda2e2",
   "metadata": {},
   "source": [
    "Upload the program, together with the waveforms and acquisitions to the QRM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "175c4e14",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:53.773776Z",
     "iopub.status.busy": "2024-09-02T01:36:53.773598Z",
     "iopub.status.idle": "2024-09-02T01:36:53.832704Z",
     "shell.execute_reply": "2024-09-02T01:36:53.831933Z"
    }
   },
   "outputs": [],
   "source": [
    "sequence = {\n",
    "    \"waveforms\": wfs,\n",
    "    \"weights\": {},\n",
    "    \"acquisitions\": acquisitions,\n",
    "    \"program\": qrm_prog,\n",
    "}\n",
    "with open(\"sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "    json.dump(sequence, file, indent=4)\n",
    "    file.close()\n",
    "# Upload sequence.\n",
    "readout_module.sequencer0.sequence(\"sequence.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ae5d937",
   "metadata": {},
   "source": [
    "Perform the calibration experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "da5b411b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:53.835749Z",
     "iopub.status.busy": "2024-09-02T01:36:53.835446Z",
     "iopub.status.idle": "2024-09-02T01:36:53.855299Z",
     "shell.execute_reply": "2024-09-02T01:36:53.854560Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Status: OKAY, State: STOPPED, Info Flags: FORCED_STOP, ACQ_SCOPE_DONE_PATH_0, ACQ_SCOPE_DONE_PATH_1, ACQ_BINNING_DONE, Warning Flags: NONE, Error Flags: NONE, Log: []\n"
     ]
    }
   ],
   "source": [
    "# Arm and start sequencer.\n",
    "readout_module.arm_sequencer(0)\n",
    "readout_module.start_sequencer()\n",
    "\n",
    "# Wait for the sequencer and acquisition to finish with a timeout period of one minute.\n",
    "readout_module.get_acquisition_status(0, 1)\n",
    "readout_module.store_scope_acquisition(0, \"single\")\n",
    "# Print status of sequencer.\n",
    "print(readout_module.get_sequencer_status(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8664a1e5",
   "metadata": {},
   "source": [
    "Analyze the resulting signal on the scope to find the tof"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "098a80be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:53.858076Z",
     "iopub.status.busy": "2024-09-02T01:36:53.857785Z",
     "iopub.status.idle": "2024-09-02T01:36:53.916996Z",
     "shell.execute_reply": "2024-09-02T01:36:53.916213Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "149.16666666666666\n"
     ]
    }
   ],
   "source": [
    "p0 = np.array(readout_module.get_acquisitions(0)[\"single\"][\"acquisition\"][\"scope\"][\"path0\"][\"data\"])\n",
    "p1 = np.array(readout_module.get_acquisitions(0)[\"single\"][\"acquisition\"][\"scope\"][\"path1\"][\"data\"])\n",
    "# Determine when the signal crosses half-max for the first time (in ns)\n",
    "t_halfmax = np.where(np.abs(p0) > np.max(p0) / 2)[0][0]\n",
    "\n",
    "# The time it takes for a sine wave to reach its half-max value is (in ns)\n",
    "correction = 1 / readout_module.sequencer0.nco_freq() * 1e9 / 12\n",
    "\n",
    "tof_measured = t_halfmax - correction\n",
    "print(tof_measured)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a209a3e",
   "metadata": {},
   "source": [
    "Plot the signal on the scope, around the rising and falling edge of the acquisition signal, as determined by the tof analysis above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "90c304fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:53.919726Z",
     "iopub.status.busy": "2024-09-02T01:36:53.919407Z",
     "iopub.status.idle": "2024-09-02T01:36:54.201166Z",
     "shell.execute_reply": "2024-09-02T01:36:54.200436Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gzLQ66HreoG1bq7Ht5m8+FEsgv/ULd2P7fbv7QWMqMKvSLgC447EnYoWx9RFBke1nhbZl+lqszraw63bnvgMAgGeuWpawy/N9HGklJd7bO/6hsutA5mtpElD1mn3vyUP4/tNB+Vk687TQtn3p0cDXTl89gXroa3n9WJX72t6ADJ+2YmnCrp7v42h7sPevakLMYDAYDIYIJk8Ctt+3G2/9/NcABCfv2+/bDUDIBggBrXdgGq0/vBrwQ9LieWi9903W/qBvv283PnT/9wEA//Ct3dh+3+5YBhkIMgIDtnn2bZuemcVv3nZ3/7N94E237kKnF3y22LfgHZhG673V2AUEa/ayf/4PAMDRdhf/91t7AGRnnhbCtixfy2qWXwjb/iFcq//7rUew/b7dGGvUMVJzs22r6DrI97VA8ODgAvvamTd/Jn78mYf3AsjOPC2Ebb/1+WDdvn3gaeG+Nkg6F8K2z+wO1ur9d38n8LV6Dc0MXwOqJ3YMBoPBYKTB5CnE9Mwsrr51F6IiER9BYDY9M5tZHuft3dMPGOMf9uDte8SabREi2/YfncNE2I+VIAIV2rbn4Ay8VGVNz/cxE2YnxMyTt3dPPyizbFd6PwFhPzMyTwtpG8nXKrYtQsK2DHnrwNdSDrBAvvb0AvuaaFu0ZlmZp2HxtcxDhMptuyt+HNn2oyPHMjOwVRM7BoPBYDCywOQpRBCYJSOznu/jkYMzmX/I3ZM2AI6TeD7cGtwT1y+IbWJAW6VtG1ZNIPVJqDkOznrGSgBBxifKQrknbQDclMstwJplBY3DYluWSuHQ+dpcytfSHlCxr20OfU3sxxqW/VyVIX4wLLZlHSJU72vJn6Wvg4U6RGAwGAwGIw9MnkJsWDUBNxU01BwH6wVVu0SQsWYK9ctf33+y42DkupvhrpmyZFvyZ2nbEsH2minUnvOi/pNd15ptUxPj+KUzTknYdfPW83DG8Svin0Wn7u6aKYxc98HE60feeZPFNcvez6x+LHfNFJpvuSFpm9X9zPO1jIB2zRTq/88b+k9eKF/LyKK4a6bgbj5XsM2ur736jFMTdt289Tw8+/iAPPkADofrlulr1/5V9b42Oli2566ZQvOaP0nathC+lkXUh8XXsu65FRI7BoPBYDDywOQpxNTEOD649bz4sRsGZlMT47mqVLUNZ8X/dk47A80rt1mz7ffPP7v/uQnbstXZnNVr4n83rvoda7YBwFnPCJrjtz7zBDz21ldi25aNqLlu3JMlBmfNK7cB4xPxY/e0Z1mxaXA/Ea9ZTAJSjfL1F17af1Cro/Gy11RkW8Z+pmyrbervP9acaNXX3vWiHF/LUY5zVh4f/7t+2S9b9bUoyyT62ki9hiWNOoDkddC8chuwfFX82D3Frq9FYb0LwddyZNTr51/Wf7BQvpYjnCL6mnPqs6z62rXn9++htax7bvpQaMvP9t/A4qEQg8FgMBh5YPIk4NefsyFekK/92uXYtmUjgHz5aP/Iof6/H/0u/COHrdn2c6etAwCcsGxJHDQC/XlKaXU20Tbvu/dYswsAZjuBItb6VROYmhiPf5516u57HjB3NH7cu/fL1uzatmUjzg1nO/3Ny14grFn2HBlxzdDrovftu2ALv/6cDXGwnfS1bJXChG3798Lba69U6dJnngAAWDs+lvC1lTnKcRB97XvV+Nozc32tb5vv+8Dskfhx72tfsmbXti0b8aITgwOLP/+5cwf2My27PeBr99i7DvJ8LU+yX7yP+Y9+F97TT1qz7ZJT1gIATpwYT/ladB2kiPpxk/G/G798jVWizmAwGAxGFpg8CWj3PEQV9etDiWZAkLaeyydP8H30vnWnNduOhUHj8UtGU0Gjmtj1vn0X/Llj1myb7QQN+9Hpf4TMU/djRxN9C717dlqzCwC6YcB64vKl8c+iwEzsxwp+kCS/vW/cYc2uds+Lm/g3rOpn4vIyFQlfA9Dd9QVrts2G8tDHjad9LTtTkSDqP3gI3k+esGdb6Gvjub4m2DY/B3T7surdr/2HNbuAoF8HAE5c3l8zUXb7SFuQeE8dtNi0Lc/X8jJPaV/r3WPvOojI8DPSvhbtp+RQqHf/3WAwGAwGo2oweRIQ/SEHgPFGI/533om7f/RQ8I+w+bv3za9Yt20waFRnntBpo/ftr9qzrZ1jW8bw0oHA7IG74bcHh3Qasy0j2BYl3sU9HSAoFoldwteafduyRBlE25wVQSate5dF8pTja3nlq/G6jYwCsEuIj3V6UtsSmafUfnrf+cbAz8zaNrhuY406Ruu1QdvS945dX7RmV56v5WaeItvqwT2w97Xbrds2eO/IIXYzT8f/9h78WuIxg8FgMBhVgMmTgIgENFwXjVp/afJP3IPT49rZLwQAdO+1SJ4igtJMB43yUir3WecAqCgAStuWIczgh9kdZ+XxQV9Wax69B79uz7YMYldzXSwPJd7FPY0Ca/fMQADB++434B/rlxjasKtZc+OhpUD/xD1dthftZ/3iXwQQZAP8TtIfjdmWR56ysjvo72ntZ34usO3enVbsAvKvg6yMXWQXJlbCPWUT4HmVEOJ8Yjfoa7VzXgw4DrxHv2tNclvla+n9jLJitXMvBgB0v27v3pFFOJO25RB11w320+J9jcFgMBiMLDB5EnCsm0dQsslTHNBe8AoAQb+HPzdrxbbc0ris7I7vx6fHUbC9EAHQqozhwnHws2wFas+9AIDdTEUesctSKYwD2k3PgbP2ZKDbRe9bdjJ2eeVnqh6Z2nNfDGfVM4BjR631ZEXBdtrXsrJifrcb9xU1XnIlAKBrsdwxvg7qhGs0ytYtW9EndnfbK49T+VpWltM94TS4z35+YJulnqzcUkchW5fwtejeccErANeFv+8ReE/stWSb/J6bFieJ9rT2vIsAAD2L5asMBoPBYGSByZOAvPKz6BT0WKeLdq8X/zwKMtxNz4EzeWIQbFuqw8/PBmSUUs0fA0Ii2AjJk/e9e62VuOSXFEZZlJyANiJPFkUjcomdhDw5y1ag9vwwOLNEBKI1y+sT6/k+jrb75VaxbROrUDvvpcFzLJXu5RO7jGzA7Ez8z9rPvjwItvfugffjH1myTZ8MB+TpEgBA16JoxGxbTogzy1eXrUD9BcF+di2V7uX52kqFr7lrT+4TO0uHL7n33Axf830/tq1+6asBBL1/fnpIM4PBYDAYFsHkSUAeCYhKvIDUqbsYnJ3zYgD2iEChvqJaDc7JG4OSJd+3VrKUTwTy+1AC8nQhAKB3/11WStB6nodWKAiR378jfG5U5rV0BeqhbV1LJWh5+zlWr6EZlozmrVv9vEBS3ZZohCobkFXqiNElcFcdD3fTFgD2somqnqcs25xlK1B/3oUBsfvhbvtZFIpwingdvKCfFfPTQ2BN2CXxtRGJr2HZctQtl+6p7h15AiD1C38eaI7A378P3g8esmIbg8FgMBhZqIQ8HTp0CO94xzuwefNmrF27FpOTk5icnMQZZ5yBN7/5zThw4ID09d1uF+95z3uwYcOG+LWTk5OYmprCBRdcgJ07dxqxM680LtkjkxHQLhXIkyXRCFVfUaZdy1bAcRzUnv+SwLYFOj1ONsqHPU9Ll8M97VnBjKD5OfS+8w3zdgmN8nkZnmyCsjzOPHnfvQe+IHdt2rb0fjqOkynMkCh3DDNP3kP3oXP7p433yuRnYOUEBQBqz7sQgD3ylHeNZvqaeB0sWw732UEvm43yuHavh64XZEAGsmKy62DZCtTOegGwZCn8Q0/C2/1t47ZJfS2L2Im2heSp9/XbrWR48vvEBvuxEodCK4/v33O5dI/BYDAYFcI6eer1erj44osxPj6OnTt34vHHH8f+/fuxf/9+7Nq1C6effjrOP/98HDuWL6X953/+57jvvvtw1113xa/dv38/9u3bhz/5kz/BG97wBvzgBz8obWte0AgM/jH3e73+DBkx82RJPS5flSo/aEQU0Eanx1/+rJWm9PxSKnmw7TiO1YxdtJ8OECueRcjMVBzt2+auPQnO1GlAr4f2J/7aPEHJ2U9gUHbb97xEQOuuegacyZMAAPP/85WY3Xoy2ju2W7ctugYOzbfhhYG0SAIAoB6Szs6dt9rxtbwMbJavxZnE5YFtYYanfetHrRFOIKs8TnIdLF0Op9GMe3ja//K31fpaKnMtlsY5y1YEYjgjo/Cf3I/OZz9UHVEXhuTGvpa6d0QZ2M5t5veTwWAwGIw8WCdPO3bswCmnnILrr78eq1evhuM48e+WL1+Oa665Blu3bsXNN9+c+x5f+MIXcN111+H4449P/NxxHLzgBS/A61//enzuc58rbWseCQAyAiCh18NZthzuKZuCRv52C+1P3Vx5NqDV8zAXZVqEjBgA+GH/ib9/n/FAG1ALRkiJXdj31L39X+0Fjc16wu9UtkXr5h4XDCZu33SteYJCIOqxbceOAmEA6SxbAe/ANPwD+/ov8Dy03vsmY+unKtvzfB9HWkEGKN7PkKB4PwoPMX7yOGYvs+Br3V6ObfLSOADxoYb3jf+0RjjrroNmLUnUpb4W2uYsCeaQdXf8fcW+lspyzs0CYV+ns3Q5nJFROOtOBQC03v3r9oh6s5H4eXRf8wHMtMJ7bure4c8H4jzed+6xcl9jMBgMBiML1snTAw88gIsuukj6nEsuuQQPPPBA7u/f/va346yzzsr9/QknnKAs/aMgjwQAgwFQv9djDE6jCcdx4Kw5EQDQ/tNrKgsyljUbqIXEIG1bFGi3/uzt/RcYDrRltmXKbgtlewDgzxwMzPrevZVlUICcsj0hi+IdmE7OxrJGUBoDv0tnxWJfa47AGRmFt3dPTKb69vXg7XvEjG15PTLCzKI42E772p9e03+Bb8HXckQZ+vuZneX0Dkyj8+H3919gaz+zCEos2Z8tGOEdmEb3C5+wb1uGr6XXLc7W1WrA2HhA1B8TeooqWrfReh1jka9Fth0JBG+cpcGatW9+jzW7GAwGg8HIg3XyND09jbVr10qfs27dOuzdm9/E/fKXvxzNZjP399PT0zjnnHMK2xiBEgClA9ooS+EdmIb30Df7L6goyAj6FnJsW7YiCLTTTegGA23f9/vS1vX0ifug7HY6oG1/8A8Eu8yuWZ8MqwkKAOFke7l9gqJTtpfKUrgnbQBSmTS4NbgnrjdsG53YVeFrom25Muo5vX+2bevPn8ogKBmS/Ujb5lu0TXZfS61bujTOPlHPJsPA4Byqqn2NwWAwGIwsVNLzVEuVsaRRr9fR7Xalz8lCt9vFV7/6VXzhC1+QZrdarRZmZmYS/2Uhb74NIJQFRdmdVK/HQgYZeVkxZ9mKINB2U9tsMNBu9zz0/JxGeUEK+Ug7WeZVbUAryTxFQWOq18P2ukmDxtGkSuEAeVozhcav/q+EXSPX3Qx3zZQZ22Tlq2lfE3uxrPuaIMqQQ56OtrvohAqLCXES2/tJyXLmELuF9LWB6yBVGhcQ9QrWLdPX8q8D22vGYDAYDEYefuqkyj/wgQ/Eanvj4+O48MILccMNN2DlypW5r7nhhhuwfPny+L8TTzwx83mUP+QDpVRh+dmCBhk5mScsWwF3zRRGrvtgP1PhOEYD7Si7AwxmKsYa9QEp5CqD7X6WYpC8Dwx8FXs9onX733+XsKs6gpLKoqQCWgBovDSYc4PlqzF+62NoXrnNiF2AvHw1N/O0dHm1vpZatxWj/cx0OouSuA4iOK7h/VST4bj8rDUPhNL8TpZtC+Fr0XVwJFlW666ZQuMN77Bvm46vVXBfYzAYDAYjD5WTp4cffhinnXYadu/eXej111xzTay2d+TIEdx11134/d//fezatSv3Nddeey0OHz4c/7dv377M5+mUt6Sb0d01U2i+4Z39FxgOMvLm2wAZDekp25pXbkNz2+8DAGoXXmE00I7WrOG6aNQG3SmvBA1isB3BeECbX342cOIe9XrU68DoEgBA8xffCIyMAgDG/r+vGCYokv3MK1cKA1oAcMbGg3/0OsYDRlL56twgUQcs+1qYSaw5Dhop0i2OE4ivA0E9MbKtFiq0NX/rvVZs08ruOA4QCkU0r9wWr+Ho336xcl/rXweBbY5I1MOBtJhYaZyoU9YtiwwDwZo1fu33AAC1l/yCUbsYDAaDwchD5eRp06ZNePTRR7Fx48bS79VsNvH85z8fN954I/7oj/4o93kjIyOYmJhI/JcF+Ym7vA8FAOqXvTb4x7IV5oMM6cm22jYnDLCddG9FabvysztAhjBDBrGrvfBlwb9/8w8rC8zE8rNEyd7S5QllPmcsCG6d8WyfKWwbYT/zyvYAABF5mps1Pn9HVu44oGq3AL6WpZ4IqHvFAMBZeVzw/0Z+D2VZ29JIlxSKBwiOQAKd8WUAADf8vznbNLJiqV5OoH8NoGuRqBOy/bGvTfSrDNxnnBD8zKhVDAaDwWDkwzp5ovQzdbtd1OuDfzypOPvss3H//fcXfn0EqQJa+sQ91fMECNmATrvSICOacSMLtiPb/LlZs3ZJGuUBsW8hEI3IXLeVgQS9k5EhKoNjXdmpdmBX1/Mx2+kOnGrHEEiKSRQpw0ysWZixQK8Xl4CZsy17EC2QUUoluQ5M+5rscCOwLUWeUsqONm2TZesGSgqzyDD6JMU/dtSObZTev7BsD8v6a2aTqFMOrOT3tXDN5syuGYPBYDAYebBOnqamprB//37pcx5//HGcdNJJmb+7//778eu//uvS17uuCzfdO1MAtCG5qT/kSzOCjPljwWBTg+gr2hXLPGEBgkYgVXoj9npUEdBKMihLGnU0xX6sjBP3SmyTlGEOZAPE/Rwd7/+7wj3NVWfLuA6q9jVRYMBvt4D5ucC2xHVgJ9iW7WfNdWMCdXCulb2fgD2iTpnzFPU8RWV7icxTaJfvB9ewSdt0lB1nQqnynAwsg8FgMBhVwDp52rx5M26//Xbpc26//XZs3rw583crV67E1772NemJ53e+8x0885nPLGUnQBQYyBBliBBnAwBg/lhpeyJ4vo+5nOGgQH7mCVlZsYqzAWI/VmyX68a9HgDif9s6cc/KoDiOk9jTrAwKAOuZp8zsTjwXKNmHAjGgbTSAehBwmiQpovS8rnx0bJtlEpC1ZkCybC/uYQMAoeTSmm2S7A6QvH/kkSfbWbGsdcvrx8okKIZt63oe2qEyoiwrliWjHsHWmjEYDAaDkQfr5OmKK67A9PQ0rr/+ejz11FMJEjQzM4ObbroJt9xyC66++urM109NTWH16tV4//vfj1arlfid7/vYs2cP3v72t+Otb31raVt1TkEzS29Gxvq2GfxjnlS0K9iHYuvEXZUNEAaE5vZ6WAto83s9gGQ/VmYGBX1CbK2USpINODzfhuf7uWVeNjI8890eoiuUos6WTdQt+5qCoIiZRCydgCOOSoj201pWLLv0NOFrC0TUZfeOmVYHXc8TCIqQGa7VYuEUGNzTiAwDNEXM+BChgow6g8FgMBh5sE6earUavvSlL2Fubg4XXHAB1q1bF0uNn3vuuXjwwQdx5513Ynw8+CO4c+dOrF+/Pp7F5DgOPvGJT+Chhx7Cpk2b4tdOTk7ihBNOwGte8xq87W1vw8///M+XtpVy4n6s00W718vu9XBdKwGQGGSMSU+Po76iQwO22S4Jys0GiPOxMnpQAIGgVFiuBKREIyokKCrbIrt8BAQqL9i2QTpnNYi673nA7MygbQuV5RQyFf1+pxWJ58RrZpoMK31N6P1LjTlI21apr431+7EOib5WQflq5Guu42AkYxbgyvR8LFnvn+H9ZDAYDAYjD8VVGjSwfPly3HjjjbjxxhuVz73wwgvxyCPJIalr167F9u3bbZkX45jkZHv5SAMOgoD26bk2luZlKsbG4c/NGs1UiGU3bobKWP+EthWUC4ZCCZUIRkTZnZxsQILYHWkN2AWg379TcaZi1dhgKVVaMMKJbJu3VeY1mKkYqdewpFHHsU4XB+damKyQ2EWB9kjNRS2jjzCRgT12NB5yvNCiDAnb5trwjwS9OWIGxa5ttOtAJhhhvVcsw9fqrotlzQaOtDt4er6NsTzhlNFxAE9ZIerjjWz1xMR+poZYR+CyPQaDwWBUjZ+6Ibk2IQvOaq6L5UKzfJVN31RRhkRpXK2W6FWwpZiltk3dKL9Q6myJ+VgL1IeiJAKEHhmTp+6yQBtI9RVFvtZo9su6gH4/myVfU/U8HRSvz1QGJRKMqLI0DkiJWSjU9qwdIhCu0Sqvg9m2BuFMDbGOMdY/3DCtBMhgMBgMRhaYPAlQBWeZpTfpICMuQTPZGyDv3YnsOjTfhicoUiXmFUUBre/HKmQmIBvAGdiWVUqVzAbYFoxQ9WMtiGCEYk8zSUqetLXBrBiVBEh9TVRnM+hr1NK4p2XXZ0wCqi3bWzU6uJ+VEXXl/SPjGl2WfY1WeSgU9/61OugePhj8sN6Ih1gDwn2t2zUu2c9gMBgMRhaYPIXwfJ8+R2Zuvt+/k1OCZqM3ILdRPiQBnu/j6FM/CX44YFc/4DBrG7FsjxDQVqloB2QrAWaVYQJm16zneWhJVMYAgagfmxdk1FMBrZX+OkWgLfja7MEngx9W5GuyslpA6GFb0NI4DXGSCog6xdeyrtFcYmcyy0nsSQSAI+F9LU3UbUr2MxgMBoORBSZPIebERnlFQHvk0NP9Xo+czFOVJ7Sj9TrG6kHD9dEwoB1o+BYVs0xmKiTzpwBgVYYC2kDQuFCCEVllXjm22SiNk9oW+dphia9ZbOLPuwZG63WMxr72VLZdtnyto/C1rExijjiJccEIatmeKKNe4X5SbDt85Eg8x6mS/jqFrzVqbmzzkSezD4VsSfYzGAwGg5EHJk8hxCAjt2wvDM5mnw6CRtQbyV4PLMwJLdA/PT72dHZAG9hmvjxOnRUbiZ/XVfXuVC0YoVXmZZ5wOkBMRAZsC9dtTvS10bHkkyzYpirDBPrBduxr6YwYFsjXxExi1kBVLGB/XZZgRCWZRLqvzT59sP/DJcsSz7Gp7Kjlaxn3NZYrZzAYDEaVYPIUIgp+xuq1TEU7oB+czQt/yAdUoiwEGapypcC2IMhohQGQLMioMgCKVAoBoH0otC2dDbBQ6gjQA9qD8638MkwrBKW/n1kqYwDN1+wEtPKyPdG21iF1QGvlOlDsZ7vnoZtDnmwJDMQHHDlCGzTBiIXytfDeEe7nwGwswKqyI+VQaO7pMKOedSjEcuUMBoPBqBBMnkKoTrWBfpDRiZqXJX/IrZy4E4KM9uF88mQ1OMuxrea6WJFaN1lpnBUlQEVAK2ae8nqebJSfUU7cO4cDEpDlawtRSiXaVrWvqQbRLm3WUQsJQjtn3WwJDFAVMYOSwkOBLXnlqwt174jIcFqhEHaUAFX7CSycrzEYDAaDkQcmTyFUvTuAENDOHAKQc+JuYS6QTpDRDW2TBds2yoLySh0D24LgzFMJRvh+3HNRFr7vC3uqKFeaPQK05TOoqiQBom29vAwKLGWeKNdBWL7am8nu3QnewELvX+xr2fvpOI5g26HgZ+nSOEsCA1TydPTo0ViBMLd8dYHvHQNKe7CfFcu1LVy37uH868CWIiaDwWAwGFlg8hSCduIe/CHPy1IAolR5dUEj0A/OcoUPYDkrJlm3pc3A7tyAVphHZWrd2j0PvTCLpeqRcY7OBD9wHGC8gl6PNj2741H200Y2gHIdHA0D2syeJwu9fwTbVqWv0QoEBnqeh/luT2pbtJ9LWsf6PxyfSNpmg6gTfC26d3h5s7GABSn5BYTrQHIoFK+b4UHWDAaDwWBkgclTCNIf8vBUG0dy+mOAfilVxUFjFNDKegOslnnlrNv2+3bj/h8fAiAoAcrU2QwRgaTKWE7ZXrifE50woF26HI6bvCRsEk4ZGY5sk5UrxVmUBQponbw+McBur5gki7IkJOqyQwTT5XGRyAaQn4H95+/8AACwPPS19tjS3L4iG/sp9TVKOfIC3DsAIWMX3ztWDj6Je54YDAaDUSGYPIVQ9e4AfYLSnVHX39sJaPODxkcPHQEAzDwVBBlfPjg4nNSxUFIoW7fpmVlcfeuu+HEUOO53moO2GQ7OohP3huuiUct283/89iMAgBWhXbPN8cEnLdB+3rn3AIB+z9PDrcFmf5tKgBTyJCulsuFrKiKw/b7d+Nb+wKaeLGNneE8jkQ0HiEcGiJiemcWbwusgugZ+jCamZ5KfvxB9YgDwldjXDgEAvt/JuF5sHCIQSn6//3RwXzv4kx8DAHYdbg08h3ueGAwGg1ElmDyFUKllAcBX9u4HAHhh5umBOW/gOTbL9vIC2umZWfzbw3sB9IOz//OdxweDMxu2SUQZ9hycgReWztW9LsZ7QYP+o+0M1a+4Id2Mbce68mydSOyWt4M1e7TrVhPQEvbzhrseCGwL9/OW/UcGbLNK7CTXwcNPBWWO7bAf6z9+PPj5Vn1NQdRrXg/LukHv3BMY/B62iPqSRrainXgdLO8EhxqHGkvwyMGZ5BOF/TQlnELxtffe+e3Atm7ga1/88dHc68AsGZYP2Bbvays6wef+7cMHcq8DJk8MBoPBqAJMnkKoTrWnZ2bxx1+9H0A/oP3UvkP5Aa3RMi+5fPSegzOIQq3ItoONsfzgzMbpccaJ+4ZVE7Hs+0Snnwk7dWpq4Lmme2RUQWMyoA3WTBrQzh+D7w2S5UK2EfbTCzc0su3pDNsWopRqemYWn/huWIIWks4//vYPK7kOjhGJ+kS372t7pETdbIloHgkQr4NoP2eaS7B+VarnKSo/8/1YVKK8bRq+1pZcB1aFU+j3tacz7mssVc5gMBiMKsHkKQTlD3k6oD1Uz/hDbqF5mRKcRSFiZNvR5vhgcGY42PZ9X6qYNTUxjg9uPQ8O+qVxnZElmFo1KDBgOovSJ8PqgDYidpkBrSBmgZapgJYSbAf/jjIVRxtjA7bZmb1TgKhnXQcWfE1GiBMEJSQBs7Um1j9j9cBzjWeeFPeO6DoA+mt28tQJmJpIlYmOLon/adw2kq9JiJ1N4RTJNRrf19rBdXCkgvsag8FgMBgyMHkK0f9Dnl2ulBVkZP0hX4hZLVMT47jm3DMA348Dx2sv/dnB4MxwADTX7cWBdN66bduyEb/zgmfHBKW5YlXm80wLM6hO3LMC2mefcnI1Aa0iaJyaGMffXhbaFu7nLz7/7AHbrPbXqYi67/eJXdZ1YNg2UT0xKzssEvVoP51lKwb3EzAuo07pE9u2ZSNeOPWMeD9PPiEj+yoKpxg6fKH42t9tfSGA/iHCz5+zedDXFkCpM76voX/48vaLX5hxjZovKWQwGAwGIw9MnkJQ/pD/XRTQhkHGVeedU01ASwjOrtx0EsZ6bTT9QPnrteedM/Ac0wHQMUHRTqbmtWZ8rB/QZshaAzCeRaEGtC8+aU1s24aTTxp4juO6fQJljNipbbv6nE1w0ScCF21+1qBt8ZpVS9R/9wXPxlivjUboa3/48gutB9sU9cRtWzbidwWivnTVcZnPM14iSlDDBIAVo824ryhTBRDVZ8UA4I1bNqLhOrGv/ewZmwaftEBS5VduOgmO72EiXLdXnvucgedw5onBYDAYVYLJUwiK2t4bz9mEutMPMl569hkDz7ErypBv21ijHtuFWi0xOymG4ZLCiKCM1muoufmuNFavJbIBWXCiPhRTtmkEtCuUtlkKaBW2LWnWY9tUg2irEhgAgF88/eR4P/1aDb/ygucMPsmwr0XXp0w9EQAml/aJOqoi6gRFOyC8DtqKQwTD8vNkXxPuHzKFwqqVHccadSzttlAL/duZYKlyBoPBYCwsmDyFoJyCAkmSIp92X102AAgIjGhXluqXeYlmtdQwEASNEzISAPPZAAoZjmxTETvj/ViEoDGwjRbQotcDOm0jtsl62GK7xGtg6fJKfU1FAlRrFthWfdleYBvlEKH6DCwQ3D+ijJ2zTNKTaFQ4Ra3sKN7X0ByBE5U1CuDME4PBYDCqBJOnEHQi4PZPj2UBbbcL33hAmx9kiIFZLkExnBWjEs7Rei1u+FaV7RkPtrWCxhWZzzEdnMUy6qpMRc2VB9ujQnaxwnUjkQDTvkaYCQQE+0nPJFbTXyfaFpX85l2jprMoZF8TSefSQdtiRTsAmD9mxDaqr61oBz6kOtxg8sRgMBiMKsDkKYRKZSzCSqcX9xVJM0+AkQBIpTIWf2y9hhXt/OAnYZsxOXDamo016uqA1nSPDDHYFoNGVZmX8UyFIouyEh3U/eCUP5OoNxpAPQiKqyxBG6vX+76mytZV2CcW2LYAmURqVkyVtYbFzJPCtqU1YCKcjZVJ7EbG4n+asM0TlToNEXWT/VgMBoPBYOSByVMIagB0nBdMuPddt39KLMBpNI0GtKLKGLVsT1kaZyyDEpBI1ZqlSwozsWABbU2aSQQWLmMX+ZpXqydU/xIwuKddz0O7F5A1mQDIaL3Wn6U0nk04jfsauaxWyCTmkGFbWTFa5ol2HVTta6tCXwtsG1y3hHCKAdvmw3sHQC8R9XMOheLxEAZLpRkMBoPByAOTpxDHOiERUAW04elsd8myzF4PAH25cgNBhqgyJsuiBEFGEDR64xM5T1qo3h2h5ykvoB1boJLCGiGgNRyckclTL/K1iVxfM9lbFO0noApo+4TTW1qRr5HLauvKQ4SFzIpNEDNPVa/b6l5AnnrN0eAASGKbkftam3ZfE8sw/YoywwwGg8FgyMDkKQS1b2G1FxCUzpKcoBFCAGQgOIuCDJXKmHiq3c3LBsSkzmyvB6UPhX7iXrFgRKNO7nkyTTpV67aqG/nasvwnmQxowzVzHQcjtfzMk1hK1avK13SyO6pM4gKpJ44JPU+VZZ6I67a6q76vmezHiu4dY/VaPNw4C+J+dnMOhVgwgsFgMBhVgslTCHpAG2QDOmNq8mQmoA1JnSIwcx0nDra7ecF2JDDQmoff62U/R8s2nT6UigkKQWQDAJY4HpaGp+65ZV7Gg23anq4M97M1lk+eHKMBbX8/c7OqAGquK/haznVg2NcoKoAAtedpYYjdmONjWdRXpMjAmpPsN+hrFoi66n7rOg5W9QLbenm+ZkGyn8FgMBiMPDB5CkE9PY5IgCzIgEEpZCpBAcRMhfyEFoARxSxqqWOQ3ZHPt7GmzlbPz6AAwESoAgigOsEI4p72A9rB3roIjsF5SrEAiOIaAICVYUlhuyJf0xklUHkmkTjnKbILIBD1iksKV/TUvmY0y0kknACwOvI1RX8dfB+Yn8t8DoPBYDAYpsDkKQQ5yAhJwLwoE52CSSlknSAjDrZzM09jQJhRMBMA0YJtUl+RrT4UhW0ToQzyXHMMTj37uSZP3KnqiQDicqW5UXXZXtVEPfK1dl6wbdrXiJmKRIloZZlEPV+bb4wGSolZMLifOr62IjxEmB+VEPV41pNBX6MQ9U7ka3n3tb6YCpfuMRgMBsM2mDwhqTKmDGjDwGxOdkJrUDpXJ8joE7vsIMNxHKMkhdzE36gp5cDtKdrJswHjIUGZbeaTYZP7Od/tISosIhM7QkBbdTYg9rWc68C8rxFl8Qmqk6ZnKVFtGw/38+gI5eClWl9b1ol8TVa2Z14Ih0TUY1/Lua/VagFZB4yVOzIYDAaDkQcmT+j3UwCUgDb4Q35shBDQGu5DUWG5VlasugBo1HUw0QlKb3LLHU0r2hGJwFKdgNbgfurYNiuxzYZgBIWoL1cQdcCsr5FLRF1HqTppXkadZtt4K1izIzKibiGTSLFtWXSIMJIjiS/aZlAIxwRRB4TyVUOEmMFgMBiMPDB5Qv8PuUplDBBPj4evNyAmdpJMhdHgjKqA1p6DG56Bt3JsM92HQhYYmA+CrSNNSdBotK8osGuk5qLmyi8/HWJXddle1Ct2TELUbfiaKss5IvhaXqYizjzNH4PvecZso/rajIQ82cgk6vjaLOVQyChRl2eGAcT9klXd1xgMBoPBkIHJE8Tys5pUZQwAlrbCgLY5lvscx4ZgBCHIWEYKgMLSGwNE4FiXFjTWZ2cAAPNuHXO17O8Rq8a1W/C73czn6IBKBJbMHwEAHG7kkycrBIWwn+PhHlEyFVUT9TjYlhIBc75GVY0bDQlKy61j3s3xNeNiFrSyvcjXjjTy7x2wURqn5WuEzFPFRH1ZeM89VlG5I4PBYDAYMjB5gt4f8iVxpqIiwQiibb7vY7ylzlTAZAkaMdj2jx4GEBCUuU4OMRID2ioFBsKgkUKequ71WBJnKiS2Gezficmwok8MELNihAysCduIpXGO4Gvz3RyJ9JE+ealyT0fC+8EhCXnqE/VqS36XhPcOaVbM5PDvNo1wAkRfM9zHxmAwGAxGHpg8QSQB6qAxKr2RBdsmT2ipw14xP4eGFzz3SMVZFBVB8Y8cAgDMNJZgPm/mT3MECEuLygZnnu+T1615LMiKHarLsgELsJ/oZ1EO1QnEzmBJoSq747dbGOm2AQxfSWHka4cbSzCXk8F0XNfYniYU7RTrNjIX+NrTlP2s2NfGKFkxg32J1P463/MwRugV48wTg8FgMKoCkyfoNcqPtKLTY3U2wKhghCrICIPGHhwcqY/m27YQWRQxoO1kkyfHcQRVu3LrNqchANKcC4LGgxLyZPTEXcfXwoBWlqmIh9FWSVDC7I4HBzMyXzO6bkGmQkXUIWY58zJPMCcCIiraqWxrHFP7mpXSOIqvhdfc01WXr6r2c3Ym7mGTZcVMlq8yGAwGgyEDkyfondCOhAHQYUnQuBCiDBF5mq2PoPbk4/lPNFjeomvb4cZYfikVBMWskusmqowpA9rZYD+fqhH2s2KVsZGIpLRauc+xQYaVBCXczyP1Ucz1/PznLcC69bOccl8zdY3qKNpF5OnJ2ih8P3vdFkp6fiQ8RHiaQOyqzIpF+3ms1sSs5M9V3GfKUuUMBoPBsAwmT9DrDYjKvLz5+dznLMSJe/eWDwMAJrrz+OXrLkV7x/Zs22wEQIqGdO+JxwAAbbcuzQbA0LpFdo3Va3AVAiD1QwcAAD3Pg6cKaE0KHyj2s73jH9CcPQQAeNenrs3dz4Uk6vO1Bmo/ySfqVqTKFVkU7/EfAAgEI3L762DuGtVRtKsdDHwNvo+ul0M640G0x3IJFtk2arYOQGM2JOrzc7nPcUzOr6OWiM48HXyk25AeCpkcJ8BgMBgMhgxMnkCXQW5/6u9Q7wRZgPd+/HfyA1obvQES27wD02h/6E/ix67vofXeN8E7MJ1rm4kTWopt7R3b0f67dwMALt3/baz44kdyn+sYylRQMyjtHdtR+8Z/AgDe8+CnMPfpv89+YhzQzpWWtqbY5h2YRuu9b0JE+1zfz91Ps5knmqJd5/MfBwCsac3g1999mfI6MONratKZ9rWJL3w0/w0NXaNURbv2ju1wv3E7gNDX/jXb12KC4vuAhMho2UYg6o3wUOiGHb+vPnipcH5d57aPAQBWd2bxG+/eWskhAoPBYDAYMjB5Ai0w8w5Mo3XDb8WPKQFtVdkAb++eINhK/LAHb98jg7ZZ6N9Z0siejRWQgKtj21wAm/7+f2eTOpgjAqQ1i2wL4cJH74bflO8nUFramryfaZKWs59WhuQq1q3zsf8TP5YRdTu+lm1blq+t/+DvKX3NVNmerq/hfb+Vc7jR7zkqTezI1wGNqNvox1IdInQ+/GfxY6mvcc8Tg8FgMCoCkyfQGqt1AtqqBQbckzYA6fI0twb3xPWDTzZ0etzzvLinJE+lMGvNnDwSAAiCEYYCWs39zLXNoLQ1fT9Tl2bOfloppTJE1E35mu/7SqlyXV8zV7ZHOHjRsM1xXWA09LcFug5y72sV92Np+ZrB64DBYDAYDBmYPIE2Q0YnoDXaKE842XbXTKF+2S/Hj3uOi5Hrboa7ZmrguaZO3I8Jqnl5wZl70oZYfjyC57rZawZRMMJQKZVqP1O2+XkExaC0NXU/m//j3fFjyn5WJR+tQ9RN+Vqr14v70Uz5mqlrlLxmRF8DzAunmLuvmRNlsOVr3PPEYDAYDNtg8gRa2Z67ZgrNN14XP5YGtEaHSdJ6A2qnnwMA+M/jn4Vr3/ZxNK/clvk8U6fHx4Rm/LF6dtmeu2YKI9d9EAiLgjw4+Orrrs9cMwDGTo8pSl592wL04ODgNX+ea5up4Iy6n/WfvRwA8JPmUpx/xQdy91MkdeUFBohE/aW/FD+mETszZZhAfplXlq/dfdV71PtZERlO+9pP3vZn+deBKaJO8LWAqF8fP+5S9tPooVB+r5i7Zgr1S18TP5b5WtzDxmp7DAaDwbAMJk+gC0bUz78MAPDEyHJaQDt/zJjAgKoh3W8H6n/7xo/D40tW5T/RcGnckkY9mNGUg+aV2+D+zEsAAO8681W4/2euyH2ucYKiWLPmlduAkUCi/KUX/m88eckv5z/ZdOZJYRtawX4eaSzBYyMrcp8WZ3d6PaDTLmcbkdi5G58DAPjimjPx9t/8aP51YNjXmjUXdYmiXfPKbXDPvQhA4GsPvOCK/DcdM2Qbcc3Svvbji1+b+1zjxE7ha/XztwIIiPpzX/7n6vtauwU/b9i1rm0qX9v0HADAf6w5E9e8+SP5h0JctsdgMBiMisDkCRqDaFuB+tXh5jh+0Fye+zyjAgNUGfVQmWu+1swdRAtUWxoXf2aYENk/tkIuVW44aMwTsojg+z7QDtQTfzh+PG2oagWZCqDva3O1BuY63fys0qjga5URu8C2vePH40dj+UTdlK/pzGFzwsyTytdMlTuS7x1pX5Nco8Z7/1S+Fh68RPc11QwqI7ZFvWKqAb7hIcLeJcfJfY3L9hgMBoNREZg8QYMIhH/I52oNzHV7+QHtyFhcq19FQzrQD4Dm3UY1w0GJdgEAItvCdcuDqdNj8n52O3FDeou4blXtZ7RmrVoDPoB2LzuD6TQaQL1hxjbqurWj66CJuW7+LKUq+8TSts2HpFNlW2Vr1mknfU2SuTGXgSX6Wqt/7wCCHrNMjIzGvVuVrVtI1Fu1au5rDAaDwWCowOQJGsNBhYAWkAS0rtuXHC4RAPU8D63wM9QntP0gQxbQmurHog4tBfpZFBVBMZYNiPdTkUER5ujMqdYtLsW0r4AGAH6USQwDWgpJKbOngaIdNStGI+qmB9FShr1GvjZfa8oJSkTUjfWw0cowgX42MRfGy/Zo9475WjOwLycr5jiOEaGNdq8XDwmm3nPnVAcvLFXOYDAYjIpQCXk6dOgQ3vGOd2Dz5s1Yu3YtJicnMTk5iTPOOANvfvObceDAAeXr3/KWt+DUU0+NXzs5OYmpqSm85CUvwV133VXKvmNdvSBjzg2DDMskJaFoR86KNWmlcaZUxijZANE2adBYnVQ50A/MAKDt1uXljmOGSScxuxMFtBTSWWbdRJ+hB9vygNa4rxGIuphFke9ntQQlInUeHLTdunw/DQ0Xpvpa+lCIdMBRwjZRAIRattdy5YcbLFXOYDAYjKpAiEbKodfr4eKLL8YrXvEK7Ny5E6tWrYoFBg4fPowPfehDOP/883H//fdjyZIlme/x9re/HStWrMCDDz6I8fF+3b3nebjrrrvwute9Dl/96lexbt26Qjbqnh5HQcZcp4cVoznPNXBCG6kAOgBGcxTtIlDL9swNBw1sI2UDhOCMFJiVDLbJPTJhQNuuNwHHoZUFlSYCxDLM0Nc6dXk2AAiIuo9yRICiaJe2LSiNs+9rOj1PPjEDa66vSK8MsxP6WjX9dUTbwixn7GuWy1cjwll3HTRrivta3Pun6OU0KNnPYDAYDIYM1jNPO3bswCmnnILrr78eq1evTiizLV++HNdccw22bt2Km2++Ofc9Pv/5z+OP//iPE8QJAFzXxYte9CK85jWvwb/9278VtpEsMBD+Ie801EGGyRPa8aZc0Q6AIBhBK29BtwM/DK5K2UbJPM0LAVCFghFUgtKujwT2Ecr2TJWgUbM7nUZkm11iFwXao/UaXJWvCWVepFLHsr7WoZftiVlOSnbHXIkozde6FIJiitgRfS063GjXCRl1A9lhHTIsHljJyjBj4ZTWfGklQAaDwWAwZLBOnh544AFcdNFF0udccskleOCBB3J//453vANjY2O5vz/xxBOxb9++wjbqCkZ0w2BbnqkoX+alEzT61Eb5KDADSpUF6ZRSiVmxKgUjlOvWTga0lQhtaIoydCkBrYEZNzplmGRxEtO+ptPzpPA189LzemS4igws+ToIbdO5DsrYRlZ1FGwLyjAJRB0orXDKYDAYDIYM1snT9PQ01q5dK33OunXrsHfv3tzfv/3tb5e+/sEHH8T69YNT5yO0Wi3MzMwk/hNBnQvkp4IM0qm7iSBDQ2VMWRrXaAL14P3KBEB6p8d6ilnlswF65UpdQnbHdI8MtTSuRwi2TRCBIpnElirLuRC+1iaWFJoSs9A+eInKMIdHpTDOijVHQ9vslmLq+Jov9P5JyfCooHDKcuUMBoPBsAjr5KnX66GmqGuv1+voyvoTJPjsZz+Lz3/+87jyyitzn3PDDTdg+fLl8X8nnnhi/DsdlbE4GxAF25aDM60gY74vZiGVUQf6JS4GsmLKwMz3UxLvdkuCErYRy5V6hP00IWbR9bxYoZHaj0XyNYN9KCRRBkEBbb4KX6OWnwm+Nk8l6sbU9oiCETqZp6p8LboO6tUcIlDLpAEk9rPd89DLGTqeUAJk0QgGg8FgWMRPrVT5008/jTe84Q14+9vfjs985jNYuXJl7nOvvfZaHD58OP5PLPFr9zz0fKJsbviH3GtqZAOMlLfoZZ4AyawWGCJ21JKgTjv+Z3WCEURFu3R2hzJ7x5Aog1qdLfQ1jZ4nI+SJVBrXV0ADFESgSl8Lh9ACGv1YZcUsyEqdoa9F2R3b+6mjaBcevPSa6t4/E/1YfV9Tl+35grIjgHh0QxZYrpzBYDAYVaBy8vTwww/jtNNOw+7duwu93vd9fPzjH8fZZ5+NlStX4pvf/CbOOuss6WtGRkYwMTGR+C/CrFA+Q+0N6DXUAZDRbAAhyEAqyLDet0DNirXEWUpyxSzx5FiazVDZRla0C7MBUdBImr1Tngy7joMRRTY2WjefQNSrLqWCoICmss1ojwwxgwJoDGTuduALBF/bNqJSZ5Tl7JNhuzPFdHwtbRtNaKMiXxP66wDFNcpy5QwGg8GoANalytPYtGkTHn300UKv3bdvH97whjfA933cdtttePazn13anugPecN10agpuGR4euxTTmijMi8Tans62QBh0GWejLozNg4fMHN6TMyg+I6DjlMjZSkQlV+N5ouESG2jDlWNg8ZgoawTFIEEqNQT44A2zlSog0YzZXuEbIAou43gECEv72vC18jDe8MMiu+6Sl+DKDAwNwuECpq6oPc8hUR9hOJrBveT4GuxbYSeJzOHCOHhBkVsJpJRpyicjo6Hkv3c88RgMBgMe7CeeaL0M3W7XdTr8j+kX//61/HiF78Yr371q3H77bcbIU6A5h/yOBtAb6wud+JOFD4AYiLQJ3Z2s2K6s5T85mg430biC6P9OV9m1LxowTYqWrNYyEJj2KuOr1UmTjKfzIpRVNCqKdsL16wR+RpRzKLKQ4SqpOc1fC2yDSNqom76EEGJ+L4WHKTQBGc488RgMBgMe7CeeZqamsL+/fulz3n88cdx0kkn5f7+0KFD+IVf+AV8/OMfx4te9CKj9hX6Qz5SzR9yLTnwiAiQbKuubyEOzAi9Hk6tFgRwrfkwY3d8SduIwXa4ZralrbV8rZXcT9IMqlJZTjpR9+N1o/TvGPA1qiiDBqmLbTtyqJpyx2g/m/QsZ1U9bDq2mezH0pnz5IzQDzi4bI/BYDAYNmE987R582bcfvvt0ufcfvvt2Lx5c+7v/+zP/gy//Mu/bJw4ARriAhCJAKFvwURjdYHeAIdQ5uWYKL2JS+MUvTvt5Km2NDBD+ZIlPUW75H5aFz4oINHsUNbNpHqihjiJQ8gGmPC1Y1TbYl8jHCCgfHa40/PQ8fQU7ZywFJXa+1cURUp+KWTYyH1N51AozlxH66a+53LmicFgMBg2YZ08XXHFFZiensb111+Pp556KiEEMDMzg5tuugm33HILrr766tz3uPfee3H55ZdbsU+nbC86oXUImQozZXsacuCR0tiIusyr0qxYKiMmkxsWbStasnRMCK7UpVSp7I7l2TtaBKWVJsO2yzBphwi+78d7SrkOzCoB0uawOQIJkAqPlLQtuncAhLld88nsDu1woypfC9etqSZ2Vd7XAPRJJynLWb7ckcFgMBgMFayTp1qthi996UuYm5vDBRdcgHXr1mFychKTk5M499xz8eCDD+LOO+/E+Hjwh2/nzp1Yv359YpDtwYMH8cpXvjJ+XdZ/r3vd6wrZF2dQFD1XQP+E1q2oNI584h5lUAC4Yd8QSZjBSM8TTWXMHemrV0jlhkueHkf7SVK0i2wLswE0qfJqhr3GRCDcT/slhcSyPUGZLg5oCdkAE6RTmeWMS7z6QiMUyf6itkW+VnMcNFViM2lfo+zn/DH4soMGCYoMFqZcB1VmxfxuFwhtodxzWaqcwWAwGFWgErW95cuX48Ybb8SNN96ofO6FF16IRx55JPGzu+++25ZpxU5oR8eAOWo2oFqJ5oCkzA/PXKCMgHau080/qS95eiwG2kpFu1aS2EmzdVFpXGsefq8X9GcVtE2n16M2MgocqUCUgTiIVpSed8fGgMPtymYWUa8Dd1T0tR5Gcw5G+rLb5XxtvElQT0wdvJBK44BgvUVlQF3bNMr23JFR4JjC16I1K9Ff15+NpVB2HPC1jnWFUwaDwWAwVPipHZJrCkVKSFyNMq9KypWikiDXRaMZSfoSFLMMlN6osgFRBsUdHUPdDQJMm6fHRRrl3Ti7Q1gzAJg/VtI2+nDQGiVTUeEg2rjvz3HQ0BA/KONr1GDbF8oJXUfta2WzKGV8TWqXcNBQtDyuyIw4l5LlrLCX0xcy6rWKFE4ZDAaDwVCByZNOY3UYnNVHK2qs1hZlGMNY+D1IBMXIDCpFcBbZ1hzFWJgBoKizlS3bowWNUXaHQFBGx4AwIC9rm3I/gXjdamPqgLZS+ei4T2w09jX7SoB6mSdnZBSj9Vpomz0RkCICILUxQubJdfuy/RX4WmwbgahX2vMU+VqjidGmes4TS5UzGAwGowosevJE7isChIA2+CNdWZBBnCHjiAGtVDCiHLFLqIxRZymNjNEC2pgIlC+lUiGd3ZHa5TjmMhUaghF1im0G1RPJ2QDifpb1Nd/342uUmuUMiHpkGyE7XDa7Q9nP+OCFkLWGwQyshm0NArGrVGxGPBQK99P2IGsGg8FgMFRY9OSJPIAT/RNaSkAbl7fMz8oVvwi2KbMogmrcaE0dNJYldglFO41sACkAKpkNKNIoX19CKKWCyZJCetlePfxMqhJgaV8jBrSOmEm0WEo11+0h+kbq66AvB67ja+XL9ghZzvjeQfM1cyWFBF+LM09hlpPQX2cko64Q6UkcChHIMEuVMxgMBqMKMHnSCbbjE1p1QBsHGd1uQqFMB4fng9dFA0zz4IulcQ110FheojlUtAPw49k5+ZMF2+JMBYUIlMwGtLo9TM/Iv18UnDXioJEY0Ba07cdHg7XqStQGY4TrFmUDSCfuvV5hXzvUinxNMVg2yiSOjlXiayJR18o8ETKwZYldtFZaviaUYcqIblmiruVrrei+pqHU2e3AL3pfawVjFUSpd5ldiSwnZT4W9zwxGAwGwyIWPXn68dEgqFEFGX6vB3SDP/aNJZQeGUFgoEAAtP2+3dgbBmS/+Ok7sP2+3flPTpzQqvuKyp4ef+jbgRqiB+CUv/q01La4iZ8YbJcNGr/w/R8BAO47cBAn3/Qpxbolg8aOJ59BVaZ/Z/t9u/GJ7z0GAPjTux+Ur1m3G5BuAM2IqFv2tccOBQHnL/3rTrlticwTPRtQtnen4Tp44qiCqAvKjqSSwmjdCvZjffHRwNfu3f+Uhq/190omo15GmEHL13w/XrfmEoKvjZX3tR8dCdbiik/+p9y2rHJk2b3DgBIgg8FgMBgqLGry9E/ffgSffOgxAMCf7JIHGaIc+AghoHUaDaARNDnrEoHpmVlcfeuu+LHnA2+6dVf+6XbWCa0ldbbpmVm868vfFGzz5bYVFIwoEphNz8ziww98n2xbFJw1Q/IE0Ahx2f30odhPwdf6Aa2EoDQaQL1hxDayrzVpogxlM0//9EBA1DueryYocY+M/RLR6ZlZfPTBR+PHSl9rRwSF5mtF+9i0fa3bAcIMWD8rJvO1JhBex1X6GmU/TSgBMhgMBoOhwqImT2/74tfjfyuDDEE2lxJkACjckL7n4Ay8VElPz/fxyMGZzOfHJ7TEIKOMKENgW/JnUtsE+ehRSqaihPjBnoMzSBdCyWyLgm2RPNnI2BXdTwAYoUiVA4UJcWFfGx3T6nkq4mvTM7N491e+FT9Wk+G+r+kIRhQp89L2NUEwIpoIRZkrZns/4zJMACMRUVeVr45WdB20s8RJ7M47YzAYDAZDhUVNntI9B6Q/5PU6xkZGAKiDjKIlSxtWTcRzaiLUHAfrV01kvyAhH63uK3IKBmaxbamfSW2rMBuwYdUE0qNKKevmjo2j4brWbCu8n/UGxkaJvlaQ2GnbptlfV8bXdIl6fMAhSpVTep4q8LW43HF0idY4Adv7Gd/XHAejhJ4noOx1kPyZ/DrIEIwg9DzxnCcGg8Fg2MSiJk+OTtAoSG5Tgh+geHA2NTGOv7z03IRdN289D1MT45nPjwMzzbK9IupsUxPjeOvzn6Vtm26ZV5HSm6mJcVx62jq6bYmeCg3bNIOzqYlxfHDrefFj14HUrpgEjBL3E8XFD6YmxvFXl/5M/Fi5ZuJ1QMnulPA1XaLutzSv0RJEfWpiHC/V8DXEWTGxV8w8sdP3tf7ByyihrwhAYZIyNTGOD7yU7mvQ3E+WKmcwGAxGFVjU5OkvX9onKKogQ2yUpwa0/fIW/ZPQ15xxavzv7//WL2Dblo35T26J2R2NIMPzgHZL27YLTp4EAJx53Ao89tZXym0TBSM0bCt6erxx9XIAwOvOPE1tW1b/DkFBsUiwvW3LRmwKg/6PXvFiqV26ogwASpHO1zy772t7flPha7qDaEv42tTEOH7zeXSijozyVUqZV9GZYhvC/byK5Gt6JWhl9lPL11piTyLN18qQlFcL97UfvOUXSbZpi5OUUAJkMBgMBkOFRU2efuXs9diwMggy/vmKC2iBNvXEHeVK0CKC0XBdnLximfS5fqZtNMWsIiQlsm3N0rH8QDZCRpmXtPSmZNN3ZNum1cuVtokZO1JDesmT7V6YeTlRtWYZ+6nMcpZQGove23UcnLJiqYZtUc8TgQSgmK+9+KQ1AICznrGSToapxK5ESSHQX7fTj9PwNWpfYsmZRbq+RrULKHeIEL13s+bixOVUX9PMWgOcfWIwGAyGNSxq8gQIQcZyRfDT0uvdAVCqIT167yhokKKVkRWTZVBqNaAZ9NIUCTIi20YItolN/KMaYhZFBCPE99ZZN2hKvJcNtlW2ZUk0dz0fXYmMehliJ9qVLmWV20bYT0O+Nkkg6kkyrBazMEXUab7Wn49Vxcwism1CiSjd14qv23yPvma6+1lGCZDBYDAYDCoWPXlqUf+Yiye0xCCj1AltLyIohC0SRRmigFY2QwYls2IaARAySKc0Y1c2G0C0zRcHyhKJHUrOkSGvW0afGGBP/EBvP0X5aFqPjIkMrLavEa6DsiWiZF8T5naJ9w+Sbbavg4xyZIDqa9UcCunc18oQOwaDwWAwKFj05KnICS01yCjTkB7bVdPL7pBO3IFS/Vhatmn2ipUdqtqi2ib03yQFI+wNfG11PZJtoihDwtcIPTJFfK2lEdD6uoNogXK+ppOpELOcNXUGVszW9fbv07eN7Gt96XlQlePKEnWqbTm+Rilfreq+lihHlu0nWK6cwWAwGPbB5In4xzz3hFYWbIcBkPf9B+EdmC5ml+YJLakZHYgH+HqPP6Zll7Zt8/1yJS0xi3YLvR9ZtE0MaJujQrBtMVNBLXcURBlcx8FIjS6jXsbXos+h2JYkAfZ8jUyGRduaNPXE7u2fjv997PJT0N6xXdO2kAwTyzAj27RmFh2Y1t5PLdtyfI1iWxFf0yHqmeXICqLuR742/aj0eQwGg8FgFAWTJ2pAK5zQUgNab/r7AIDurR/F7NaTtYIzHYLiJ0qp1OVn7R3b4e97JHje771GO2jUsi1LqlxCUDpf+mT872OveGZh21TljvGa1etw6nWatHUU0P5YP6DteR46Hi2gFUvjAMS2yQLH2Nc+9+FqfG1kjNTzVKmvaRB178A0Wje+TfiBh9Z736S1r7pkGPUGnFqNdI327r8rMOvhb2nvp+hrStuE/QRAIinejwJiYtvXEvPrCCWi7R3bgR/9IPicd/6Stq8xGAwGg0HBoiZPXc+LBSN0TmjF5+f9MfcOTKN31+eFH+gFZ4UEI4R5KDK7Wu+9uv8D3y8cNOoEQA6hb8E7MI3WDb8l/KBAQEvuYeuTOgAklcLet74amLX7fu2gsdXr98aRBSNGUwFtTobHOzCN3q4vCj/Q9LUCPWwOIaCt3NdERTtF5snbuyeQT0/8sAcvJHok26jrJpA68fmydet84m+EH+jtZxFfw0iSqMtIpwlfI4nNiIIRCqJuwtcYDAaDwaBgUZMn8Q9x4WxAXkC7dw+QHgqqEZwVCRoTZXs52R0jQWOhkkJ134JR21RlmIIACND/LrLgrPOJvxZ+UIwMi5+Vi7YesavU17JmYw0DQRFtI/Q8uSdtANzU7c+twT1xPd22AiW/ANRZsb17AL/4uhW5rzlh5ol0jZrwNUoZZpbYjMV7B4PBYDAYFCxq8tQSggxVv4cvCEYAUJbeuCdtANKyzxrBWZFSKsq8IqNBo87pMSHYrtS2nP3M6/UoG9BGvlZzHNTT3zGFqPysH9DKg21jvqYjAJIiw346oMYC+JqGjLq7Zgoj131Q+IGLketuhrtmyrxtqdK4/rwzCbFziq/bvIavpYk66RpdgPuaaJctX2MwGAwGg4JFTZ7EQbQ1YpCRPj3ODTLWTKH+8l8RflDTCs6i0puicuAdz0MvQ0a9HzSGAZDjaAeNZHl3IDHfRkkCKgxo/bygMSdjZyqg1c0kAlCWoLlrplC//PUJu7R8jSguEDx5UD7aB+Iem7RdZX2tL2ah2E/f15ZRb165DQhtGf3zzwSPNVBkbpf4fNl+Nt/8HuEHevtZpE8sso1yjZbxtULXgbCfANDu2fE1BoPBYDAoYPIE/ZIg8TWy4Kz+M5cAANxnPRfjtz6mFZyVze7IbGteuQ21l74KANB4wzvtBY2p+TYUlbHmlduAtScH7/+nn9a2jUw60+VKitk77popNH/zD4QfaAaNGr0eA9kAgux2/dyLA7Oe/fzCvqY79FgMaPNIZ1W+hm43LttyxEG0CtVJN1QpdMYntOwCNA4RBsow1eIkjcuvCv/R1N5PrcONdr+sNrCNcF8r4Ws6tvUl+/tEPbAte0+bV25D7ZJfBAA0fu33tH2NwWAwGAwKFjV50vpDnjo9pihmRcGSM7pE+wRUrzdAVEDrB7Qy29zlqwPbmiNadmnZljPfRrpmEALapfoBrXbZXmo/ZUqAjejEvdGwS4ajbMBossxLum4jJXytp+Nr/XVr1tzonF9KUoz4mqb0PNXXoms08XpN21SkU5zbBdAOXmK7uh04zzjBil2AJCsmk58v42sFZPGdkTE0XBduWC4ou0b7vjaqZReDwWAwGFQscvKkU66k17cA9AMS32JgBiQVs1zHQTOa1SIbwhkFF63itunOt+mLbBAD2hK2Kcu8UmWYKsGI4E1DuzrFA1rdeUWAukQU6H+PMr6mK1XuOA5Jfr4aX5vrPxgp4GvC0GRt24iHCDoHL9Fz4ftx9ta4XUD+fW0IfE0k6glfo1yjBWxjMBgMBoOCxU2eCjaji6+Rnx6HJ+1lAjMdOfCBpm8JsQtt8zvFbVPNUkrPt4kJSk5pXGxbRDo1bfN9n75uKflolWAEADgNIXPSaWvZVqxRXsPXRuwTFACDvX+KckfAzCECXXp+hB5oQ7gONG3zfD/uvSksGCEjRaKvadpWdA4bQCsp7BMUu/c1P0cJUEo6I2JX4DpgMBgMBoOCRU2eiko0A7RsAMqc0BaYvUNVAgRQLtguON+GUhoHoE86NW1r68y3yck8kQhKAdv0RDZy+lBkpVRRsF2ADOscIkDoQ0naRsg82Qy20wSFWrZX8DoQlTq1BSNqlDXrkyddIqB1X0uXiFKug8jXbGeeciX71SWFRa4DBoPBYDAoWNTkKQpoKfX3fk5jNan0pkw2QEc+uipiV3C+DbWJv2gfSqm5XZQT90Yz/qfuupGzdcjIBhB6nuLsjk0yLNg2MBdItqcFszs6tuX1JKp8rWgJmokZcdL9dN2+v1nMPOVKlVPKkQv4mk6pdFoJkFQi2ih28MJgMBgMBhWLmjwVqb+PgzNCz1OZbIARJUBZAFRFSeEAQQme3/V8dDOkrfu2FQvOWkLpWFN3bhelh81xCmcqtPpQBrIBdDJcSvhAo4kfGgFtJYcIKV8bJfpa0axY5Guu46DuOtLn9omdhmAEULjsV08IJ1nySxInMeJrBZQAG4TevxIlogwGg8FgUMDkCfakyktlA3TkwMNgKU3spL1FBgIgcjZgNLlm4ntk21aMdIp2OelBnmnkCUYo+rHKZsVKZQNk/VhV+VokPa8R0Fbia+mMWEMtox7YVqwETSScVF9LlzqqSgrLZsVIZHig5FeHoNg7ePF9v08aCyiccuaJwWAwGLawqMmTznDQmAjolHlVEDSmJZpF2yjZAJvB9kDPAlFGHQVt0woa59M9MjR1tqKBo16jfNrXNPqKbGY5M33NbhN/Uel5KlEveh0UVSgE6CWFpbOcmjPixNdQyjCt3teE76xTIsqZJwaDwWDYxqImT3qiDDmN8qQgoxWcpOrYRj2hjUiAYJvWHBmLwXZ6vg1VRt2pILvTz1RorBlQuFm+kCz+aDKLQsrWteYr9TWt68Cmr7UGfS0i0XLbKsgk5s5SomU5dVUniwhGaInNCNkdW76WRdRpsvjFy5EZDAaDwaBgUZOnlsZw0HRZEKnXQxzUqCltTe5biIKMRjNoMgc1AAqb+CvIPMU9L9A92bYYNMbS1pqlVKWzYkUEQOglosFcoI6WbWRil+FrpIC2lMCAXqYi29fMl6CVKfmlyLsDQl9iBZmndHbYnq9pEnXXBcKscN8282SYwWAwGAwqFjV5KjqwESD2epSQti4q0RzYRpnzVCzI8H2fHmynmvgBzTIvm5mnVkqUgTJ7B6gmU5HKBtAIZxW+lrwGALFHxryv6dmWdR1QFBTL9TyVKo2Tic0AFWXFkrOUaDLqgq/ZIp2CWETUU8ZznhgMBoMxDFjU5Kmt1fNUYI6MKG1tqfQmLdEMEAOggv0UWrOUUoIR4mtsDHwtJcpQszsXqNzgUnomMXh9MV9TZcXSQ0sBojpbwTXreh66XlAWpju3CyDOxyo650mrDLOgYETBjF0p2yj7Kd7XLGVgs+5rJNXJeIAvkycGg8Fg2MGiJk9aPU+pEjRS87LjFB74Si7zSgXaAHGOTHjiXpTUAfqCEYCe0IZu5qnIDJkBGWSVYEQFWbEBXyPY5jiO/blAkv2UlnkV7N3RGUSbztaJryHZVljRTiPLmRInafU8eLKeoYJ9ieVsI9zXqphBlZFJ1CmVLqIEyGAwGAwGBYuaPFHV9nzfH1Bn0236thVk+KmZQACtR6ZsBsUB0HAVs5Tm8zMV8jKvkj1PGjNk0k38Pd9HpyeZCxSfbFvKJGb4Gmk/RdssZcWyfI0mTlLuAAFQKyhmZ54IRL2KLGdBJcCy66aVgdUVTrHta5mHQpQRDMWVABkMBoPBoGBRk6foj7DyhFYQe3BSjdXUpm9rDekZQYZWj0zRE3eNWUrIaOK3IfEu2qZEjmCEyraimQqy8EGGr5H2s4Rt5TJPhNK4knY1XBc1BVGX9ddJiUDJDGyhkt+GXV8rNLdLY8xBwjZb6xYfvFRThslgMBgMBhWLmzzplpAAgxLNtjJPVFWqVn5AS5pvo0vqNEod/cyA1t4MqjKCEeJrSEqAtmZQie+r2SPT9zXN8jiyr2XsJ6FHximZpRipq29Tsa9kZcUkwXZZ27TKMMN1q7su6q6jtM16Vky8J6WIndrXil0H2iqiYtZap0SUM08MBoPBsIRFTZ5a1BPaKEAQ+kqo2YCyA1+pxC5Rtkfp3ylY3lJovo1umVekgKZ7ql2ghy0iAo44F4iiNGYrGxARFMHXSMqOQF9+3lofyuB+kpr4LR8giO/taBK7orbpjDnol2HqZWDjvsSiZJgoyiDaRh7gW7pXTFGG2RrcTx1ZfC7bYzAYDIYtLGrypC+bOzogm6sKaK337xQs24uDkm43KN8xbVeObaQsSlnCSWqUHywppJRiWs+KZfgaVdq6dIanlK8RMk+eZ8/X5gcFBkiZiiqynKkZcVTbihIB7Yx61twuohKg9REMo4P9ktLrU7yvKUqqGQwGg8EogkVNnsiZp6zAjDJDBih0su35fiwJTm7iHxkUjCA1ygNaGZ5CpXFZilkW5gKVmW+TsI3Qv2NbAAQZ+6nOPFVjW5avSdcsyiRq2laMoOgRu8JZTo2Swqz5WDShjbK9f8QS0Qw5cOV9rcC66cyIKyw93xR9jRX3GAwGg2Eei5o8zVOlrYv+IRdeo3Oy3RZOTHUyFX3bCKfajWJBRhmVscA2DWKnW65UYs4T1Tbrs3ckqnFthbR1EZnmrueh59NmKWX6Gqk0TphBpbFuRchwFum0muUseIgwDH2JqrldvszXCqxbS+e+Jil1JBFOgEv3GAwGg2EFi5o8kZuXJTNk1LNa9E9odWYpFe0NcOp1ICyHshXQZgsMEAQjGgV7dyL1RI05T4X7d2zN3sksJ9SUti6Q3QEKBtuU0rhaDag3CttG87XBzFMsiy8TjKiwHytLaIOUsbNdIppx7xDfJxMF1i0aDUGxLb72M31Nsp8F72sMBoPBYFCxYOTp0KFDeMc73oHNmzdj7dq1mJycxOTkJM444wy8+c1vxoEDB0jvs2vXLpx88smYnZ3VtiHOVKgaqyXZAPF9MlHghDYKWlzHiVW5clGwNwBAoQDIlGCE1Lay/RSq/fT9wv07tmfvyEodAZo6m1Y2QGOWkrz8jCacYi/LKVF2tDnnieJrEiVAynVQWABEZVumHHj/vmY6Yye+n2pGHDIOhfRVJ5k8MRgMBsM8FoQ89Xo9XHzxxRgfH8fOnTvx+OOPY//+/di/fz927dqF008/Heeffz6OHTsmfZ+vfOUreM1rXoOnn34anU5H2w5dlbGsk2OAJp1bhKCM1FzlLKXMYZKaAgM6ZUGFBnBm2rZwinbodgEvOAXPmiOzkLN3snwtIW1tydcos5TkvqYQGCiQTSySeULB/jrdeUXUAdsiWcw6fJFmYKPn65aIkm0bXLNGzUXNIfhagZ4ncT+VM+IyD4WIM6gKljsyGAwGg0HBgpCnHTt24JRTTsH111+P1atXJ/6QLl++HNdccw22bt2Km2++Ofc9vvrVr+Kqq67CZz7zGaxataqQHbrzbcQMSt0VggxKA7PlviLtRnmgXxakU3pTYM6TvmCE5cHCibldmoIRRTMVVF/LED4AiI38JbI7JOGDLMEIqnBKgXUrNEtJt7/OciYxOUtJM2MXPd+SZH/WjLiEbYavA60ZcRnZfpv3NQaDwWAwqFgQ8vTAAw/goosukj7nkksuwQMPPJD7+2azidtuuw1btmwpbAe5RybjxB0Q+hYMZyrKN8pXENBS5KNLC0ZYKo0T31cQMyANfC2YeSKLWWTsp2gbJRug5WtFency9pOqBGjNtqzrgDIfK+5ha8P3vPzn5dimunfE6omu2+/7AjHLGe1n4eHCNF8TD4UAPUlwe/c1ma8RS0S554nBYDAYFlBXP8U8pqencdZZZ0mfs27dOuzduzf3989//vNL29Hq9gDHLSQfDQQk5Wi7SyMCtkQZJMNB1TOoRuHDYimVpFeMVH4WzmpxKEQNGlmx+MR9JJH1HIoemYygUXyd8WyA1mysYpnE4DWBr+nYFpefacztSmRg4zVTK9oBCDJ2KSKRhyJz2Ar7mqWep9xDoXodQIs2jLZAf52Or2mrJ6LYfY3BYDAYDCoWhDz1ej3UFH9A6/U6uhoDNWVotVpotfqlLzMzMwCA+a4HNOhBxmAplYZ0rqVZSplKgJRAG7BfUihRKSTPoGq3gLElZm3Lye5oSSEXngtEzAbkZJ4oCmgLkg1Q9KH0+9gsC0bkyG4r7QKCPdUlT9TZWKPZvkbaTw2C4vt+qbld4utMz8cqVI6sqdQJgDNPDAaDwbCKRSFVfsMNN2D58uXxfyeeeCIAoEOdvZOh5CW+Ti6FrF96U7Y3gDqrpUywTemRyRQY0JFoBgoLbUjtyshSAJqCERr7WWiWUmY2QJGxK5QNIF4DEDJPGQFtz/fRlZS99fvYqsxyErJi9ToQZoSsSParSn6lghH6BKXr+fHohCJzuwB78870BgvnH7z0fD++d2eioAgIg8FgMBgUDAV5evjhh3Haaadh9+7dVt7/2muvxeHDh+P/9u3bl/g9eZZSXm+A4Xko5bMBQUDrIxismociwXaRPpTsMi9JYGZ7BpWqr4gkAKJvF8W2XGLXIBCBItkAjdlYWepsY8LrTCsoavmaJMsp9TXHKVQepys2M1CGqSMYYWkQbVYZJkD0Ncv7KSPDgOqAo5gICIPBYDAYFCxI2V4amzZtwqOPPmrt/UdGRjAyMpL7+yqyATrKcXq9HoNEIDEXqNvND4zLlFLpzLfJIHakWS1zs1ZIZ1ZgJr6ONHun4Hwb9SylnJJCjf6dQoRTo69I3M+RlK8tG2mkX5Z8jQUy7Pd6QDccVZDRX0fytfk5S0Q9IsPpfkk74iQJX6P2/uUoO5J8zcK9I8+29H0t19d4zhODwWAwLGJBMk+UfqZut4t63T63owyiVfcGELIBtiWahaCxWXMRfSPKCa2VUqpOW/icwQBIrc6mL1euPUupSNBYIDCLGuVJs5RyM08EImA9yzl4HbiOExNCyrpZ2U/h+yakygmlcaJthSTeS4kyUAUj9O1quC5c1Yw4VTnyAt7XsrJijuMIpdJmDxEYDAaDwaBiQcjT1NQU9u/fL33O448/jpNOOsm6LaSBjYreAJIqVYVle47j0JqrbQbb4iyl0cEyL4pili3bsrJ14utMl1Jp9XrkBtsE0ml7llIe6YyInSxjV6LcUUlQxO+bSdSpwikWfE1x8CK9DiwPFi6TFStU6qjRX5d3HZD2lDNPDAaDwbCIBSFPmzdvxu233y59zu23347Nmzdbt0XnFHSwR0YdNBY6ce8Rg0bk9y2QMjxlSm+oAzgdJznfhtJPIdim1+9BFQDJye7YKqUqMFh4UBZfvW5OgUb5stmAhG1S4ZQSJYXUuV31etAvl7JLSdTLlDsWFWWgzO2qaLBwbgbW0nVA6a/zM3rYAtvoxE53yDaDwWAwGBQsCHm64oorMD09jeuvvx5PPfVUQhFuZmYGN910E2655RZcffXV1m3RO6EtkHmyeKot2lZIMctm6Y1AAsTMXtTvQM08FSICQ1pKpdPrkfY1SrkSCjTKk2djAYRswAJlYCVz2JR2AVYzsLmHG5RxApbLMJUHL6SMuqXrIKcvkUTsGvr3XAaDwWAwqFgQ8lSr1fClL30Jc3NzuOCCC7Bu3TpMTk5icnIS5557Lh588EHceeedGB8fBwDs3LkT69evj+czpfGMZzwDzWazkC3KBn7ASAmJlVNtELIBlBI0K0GjvMRLPReoOLFTnWyrgkZK5gndTiBUoGFX0XlFAK3nqYzAAC3LmXeIsLAlonkCILqS/TpEQHsgc07miSIHDs+DT5x5p0WGS9gWlxRWXCKqRey454nBYDAYFrBganvLly/HjTfeiBtvvFH53AsvvBCPPPJI7u+//vWvF7bDBEEhBUC2hkmqiB2lid9GaZxC0S6a1dLII6+aRKDneeh4emV7uaVUlOwOQB7gqzdLKe/EfRh6nuQS79LyVYsloqqhx0BAKkZzBGh0hVO6noeuZ39u18C8s/pSpW1VHbw4lns5y8ioFzlEYDAYDAaDiqGY87SQMNEoLw2ASmRQlCfuOXLgom2kJn4bxE7RsyC+VxZ0+z1awjyrwqVUOs3oADlwLDZLqUSjvIX99H2/n5nJLV81rM5GnqUk790JbDNHOluC3yoHMpcRjBCIOvWAgzooGkC5MswiqpMGSkS1xCw488RgMBgMC2DyVKY3gDJ7p0QplfpUux8k5zfxEwIgC3OBVLOUAKIUMjFToTOINv6+BQKzIgN89WYpqUQZCNLzFucVZdtG7xUrdB0o1fbCNUvtZ6Pmohb225kUP9CapaRS6pRlUGq1vtiKpm2k+1rcK5ZNOimHG1bEZjodICRaA4PJKb5WoF+SwWAwGAwqmDwVHNgIEHsDbJZSiQFVgUyFUyagLVhK5QhzgWjBmV42oOY4qKtmKZVQGQOgfepuVA58oeY8ie9ZokTUjjhJ+J6pQDtpGyFjp0mG6y7B18qIMgCC4IzeIYLWjLi80jjD4iTas7GAgr6mbxuDwWAwGFQwedKRj87tQ1FnUHROQanlLXly4KJttGC7gIw60ba0uABALXe0SVBKlFJB/9S9SK9HEV8rIrlN9rUoS+G6QKp3iNLzBIu9f3lZTrJtmuWrlc3GQvGsmBFRBooQjk2xGSDZYwjqfuofIjAYDAaDQQWTpzIntBTZ3DKZJ2K5UloOnGybZqN8wraCjfLia6XZAF2CotNXpBKMMJx5KqKAli5X0upDKUBQlD0ycbYuy9fU++nYVGfLkSoPbFMrAeqSTrJoCghkWJV5Ktj7V5kQjs2sdXMETiqzR/K1AocIDAaDwWBQweTJiGzuwsxqyWuUJ9tmkdj50oCWEJxpCgxoZXfy+rEos3cA7ZIlE+qJlD4U0S6pNHcB26SZRK35WBayYu1822yIHxSb21Usy6mbHS4yS6mQEqBgF9XXyIcIkoMXPduYPDEYDAbDPJg8GZDNlc/eCQNajVkt/XlFiu3JsYtqm26Q4fu+duapcECrGWxrBY15Q1WJc4GKllLRZinJfe3A7BymZ2az7YrWzPeBbkfLtqI9bOJrH3rqUK5tVuedSWyLBCP25dkF/UwFdZ4YAKVgRMfz0PO89Kv6tmlmh3Vsi5UA06IMoa/tnz2W72vCfQ2a9zVqiWjaLvG1Ul8rIGbBYDAYDAYVTJ5KBUDBKagsoC0jbV10XpFo20NPHVYGQNQgo+v58HzifBtCQJu7ZhAJioW+ohxiF62ZD+DRp4/kv4Fmxs5EH8rOx54AADz81AxOvulT2H7f7sHXir5m2DZfkg3Y/dRhAMDHv/NYrm26ZV6e76NNLEHLy8Buv283Hgptu+ozX8leM0C73NHEvCLxtY/IfK2gmEWZLOfOx/YDAB56kuhr2ocIxBLRor4WK3Vy5onBYDAY5rHoyZMqG+B3u/HJajoAuuOHQUD7kDSgLT6rpUyjfBQ0fuK7+UFG0XIlkm05ZV7b79uNB39yCADwq5+9UxLQ6gVAJoa9/vN3Ho3/vfFv/1UZbJtu4vd7vX7GSNjT6ZlZ/MXXvxs/9nwfb7p11yD5FH1Ns1eM2leUzgZMz8zitu//iGCbXvlZS8PXsjKw0zOzuPrWXfFjH8i2C9AvwyzQw5Ymdh/7zg/if5/xdztyfU1XdZI8G8vzhLldyXX7wDc0fc30fS3n3kH2NZ7zxGAwGAyLWPTkqahE8/TMLD5ACGiDWS2hOpnuybaGYISI6ZlZ3PbItNI2FCxXAjTm26TWjB7Q6jbK62QDBgPa6ZlZ/NbnvxY/9vx823RFQIrIgYskZc/BGXipKsKe7+ORgzNJuxwHaDQH3kuGVjfM7lDlo1NEfc/BGaQLHDNtK1h+BhTrkQnWLGlZll2BbQXJcMEyzOmZWfwP4TqQ+Zq1nqfEjLj0uiWfmrmfrtv3NdMZu5z7Gt3X9PaTwWAwGAwdMHnSkc0tEGQAsFZ6k3dCqxtk6AY/zZoLN6W4NmDb/GDQqBXQ6p64F5lvUzDY1i3zIhO7hERz37YNqybgppa75jhYv2pi8D0KEjuy9HyKPG1YNYG0J2TaVtAu0tyujOsgWLOkZabWrFWyNE7P1/QysGTbcg6FtHzNknBKXomoLV9jMBgMBkMHTJ6op6CNZkI2VyfIsDarRZCPFkEOMmyWK2X0iWkFtAWHgyr7KYDMk20d2+L91CUoCttiWet6I8hYhpiaGMd7LzgnYdfNW8/D1MR4vm2mfS2yLVW2NzUxjtdvfqbatqIiGwXLMKcmxvHBrefF14EDqNfMSs9TueugqK+R53bVanAa/RlxUxPj+ENLvkaWUc8pEdX1NR3JfgaDwWAwqGDyVFCUQSegLUoEyAFQc9A2SpChHfwUGfZaNqC1kHnKylREtkVwSftpmqBk+xoA/MpZzwztAh576yuxbcvG7PfQ7WPTlQPP6K+75NR1AIDnrV2da1tfMMJwLxbyM7DbtmzEK08/BQDwey/cLFmzYocIOsTOybgOIkh9TVd1kmqbRJQhunfUXUfua0WznAVLRIG+rz1f5mtRiShnnhgMBoNhAUyeVIIREjnw14cBbc0xG2SQT2glcuA/d1oQZDx3Mj/IKKwapzXfZjCgffUZpwAA/tcLzjQemJVRGdu2ZSNOnFgCANjxyguVtlUpABK91vOBtUsHfTGGpoKiiUG00WvHG/VsAgAAggIaZS6QCQEQAFi1pKl+nwrmPKUJ8bYtG3HKiqUAgE/+wgVKYqe9nyXua9FadT0fJyxbkv8mljJ2eSWiQH98wxKZr/GcJwaDwWBYBJOnEie00Wt7vo91kiBDJ4uiNUuJMHtHGmREAW23E6hvKWCCoADA6iXBz6Sn4zbloyVzZJY2gxKmiZFm7uudosG2gf0E+uQ60zZLhNjPKMOMEAW0UdZDahdxLpCWKAPhGpXOYrNEhoP3zCcpyxbU1/L3M+lrhBl2ptctp0RUfC3J13o98mw9BoPBYDCoYPJUUJQBSJaftCTBmU7Zns4sJZlt0WulwY/4OoJtpoJGUgCkWXpjithRbNNXAiw3rwhIEk1Tg49936eXYkqyARHBIdlFtE1rPwlDVUn7aYGoV+1r9OxOPlEXh3Ob8jXP99HxiNeBLAOr62vc98RgMBgMw2DyZCgbQPljTsk8JeTAS/QGjNgIaLV6PSQn26FtcsKpd+JOJig5821i2yLS2VVnd7TFD4j7mRU01l03Hi4szaJoCAx0PC9WZRQD5kxQsgGUAwSibcYISk29n9Ak6lT1RL/TAcLnZhI7wnUQ+5olZcesg5eG68Z9iaYydlpzuwglolRfY8U9BoPBYJgGkydqNiAzoHVi1SxKkEH5Q64zSylLDjwCKcio14FQQZDSU1F2vk2EEYptBUup1AQle75NBK2TbVt9RRmBtvh6yrpRTty1hh4T+rGkhFNzLpCpLCfF16yVxuXIgfdtc5W22ZIDlx0KOY5DJCl2fU1WUij1NWG2Hs96YjAYDIZpLHrypJS2buWXBDmOE79eVh4X9xZpBBmUWUpZcuARSL0ejqOlHKfXxF/SNo0107JNEdDqnGybVgKUERTx9VJf08iiaGU5JdmAmKDI7BJs0zlEqMLXiqvG0eZPAUhmQ3RsKyrxrrKN6GumiF30Pi5hbpesRJTua3p7ymAwGAwGFYuePJURjBBfb6q8RUuiOWfOU8IuYpBhupSK1MQvs82S2l7efJv4YzX203g2QEJQyLZpZFHEQNtRDT0uSdRF23TKV3V8TZYVk/fX6ZXGaWd3miOJGXFattnyNQnhFF8vFSextJ+lS0Q1bWMwGAwGQwdMnkqUxomvN9VYXahRXlreQgsyzGeeJCWFhNI464OF88hwjUDstPuxNDNPeQEtpaRQI9gmS+IDpfdT1zatgcySckdaGaal0jiqrxkqjdOxTVbqCBB7Ji3tZ2nBCNE2Ymktg8FgMBhUMHmiBkCKE1pamZfZviJKo7wyyNBQAjRdSiUldkWHqirn20QEpTgZ1hFlEN+rkiynRhbFRmmcdIaThm0tjeuAkuUkiZN02kYl+2WS+EnbZNkdS6qTkl5O8fWU0lqt+5oOGZYKuqh8TY8QMxgMBoNBBZOnEgMbgf4JLaW8xXR2R94oH/ViedIgQ6uk0LRUuSygjXueaAEtXXI7CsxKkOER+n6K76XsQzEhGFGgD0XZ7wRaaRyAWI46CzpzgUxlObXKMAESWdcdYp1HUEjCKbZUJyWlceLrTV0HRchw5niI8L7mQ+VrXLbHYDAYDDtg8lRiYKP4emOlVIazAYBKYMBS2Z6poaqA0ayYLEshvp7WxK+2S2vosRHBCEt9KFLBCPNzgcj76fu0rBjlGtC0TUU6ZXPYyLZpHG6IvlZGFj9hm+H9VErii++nIOok2zjzxGAwGAzDYPJUQbCtVd5iqjdAe6gq5cSdGNB2u0C3m2+b1RlUCpemllJRiB3BLnGWUtlSqihzZVoWX8vXFMOiKxdOSUjPF+x5qteBUDDDaAmaRA6capuO6mRbyH6XFSfRuQ6MC+FIMrDavsaZJwaDwWAYBpMnYpBRSjBCI9gulA3ICDLIgy41ZLfJ/Vjie5WZQWUjoDWSedIvP6PY5ktk8em20QUGCimg5cwFohA7K1nOhK8V63lyHMfKNUo9eCH1/pn2NYU4CWk/G5b6JSWHQonxENLBxywYwWAwGAw7WPTkiVrektcjQ5rzZFmiOSs4EwddSoMMC5mKxEl0mRlURQJaaimVCVEGUq9Hf+3JvlbKNrrAADWTmHi/EtLWOiIg2r7mOEA9S3qeQAIAO5L9xCynKV8T36epnF9n4BBBI7ujJYSjyNiRZj1pzK9jMBgMBkMHi5o8NTXm25RSZysQNFKa+KFQjqMEGVbU2aLgp9GUzreRBtqAnYBW0cNGOnEvQDhJvkYuKaScuNvpYVP6Gql8VaeviDbEGiNjmetLnXdmRdSFKhhBmXemSTiVvmYgy+lo9BVFhwg6RF3ZK2ZQEZPBYDAYDCoWNXkiNS/PG1Bna9hVGTMho64zq0W5bsQsBXXQpQ3BCBON8iQSoDOvSBVsm569o0XUo4GvOetmyTZy5qnMbCzx9SZ7/xSzlEi2NegZFJ1MopHy1QKZ4RETtun4GmeeGAwGg2EYi5w8lRNlAGjZAJ3m5UJN/CaGcJosjVPYRSIBgFbpDVk+Og62TWQSDe+nidk7luY8Gc0GmFw3FUEhE3V6uSM9K1b+EKHIKAGlXRBEGZSCEZL7WhEhHIpUOXk+FmeeGAwGg1E9FjV5MlJ/r0VQLDXxlym9sTHnSaUyRhyqasM230i2rr+f0kGdoBNOAH0iUPGcJ5ocOH1Pc2Gh54lKULqej55sXpgNGXXqwUvVIhvC+5Uhw9aEcEzMx4psIxwiMBgMBoOhg0VNnkjDQU2UoGlkUMiBmUIOXHwPaXCmIYWsW0qlCsyApLzyAIjKcVrzbaiCERSpcs+L9yD34wwGjQuW3el2g+8K9Z5KhVM0StB0ybCqFyuwTe1rKqKuNbeLmN0h+VqvF1zzso8zpJ4I6B0KGT3c8Lw+uS6xbjql0gwGg8Fg6GBRkyedIKOUYIROkEHtWxAJRUVDOE1nnsT3zAQxi9L1fHhhBqisYIRWDxugJAI6vR5U0iklKDoDmam+Fq0ZUCrYtjPniUY4qbapiLrO3K6YwJaRA28KvqawrfIyTI3sDvkQITG3q3g5Ms95YjAYDIYtMHlSgSzRbKaEJA4yVH1F80JAW2aoqo1yJUXvjiijLB3CScyiFJpvo+gTk8+Q6Qe0KiKgJRihzAZozFIymUkU118M5gXolTsa7K9TzGGruy5qofKcCaKu42tG+rFEol6hr9kSm9Ga22Wi3JEzTwwGg8EwjEVNnkwIRtDKW/Sb0clBRr0BJyfAXLA+FEU/hTiDyoQ6m/geyj010cTvukCjGTwgEjtSo7wBJUCrwgfNkVwJ7AXrr1Nkd8T3IAkMKPZTa26XCV+r14OB0VALM8Ry4JX5mn6JqMrX4kOhWg1OY3BuF0Cc3cWZJwaDwWBYwqImT6MUVapjRwEA3tGZ7PfQmYdiVGWsT568A9PFbSswe4d64u7PHcu1TScrplKOi7JXzZoLVzHfxjv0ZPCeOYEodS6QLrEjlVLNhr42W9zXrAgMRFmKWl3pa5Rh0TaG5PrHjub7GmVPiZkKnbld3uGD0vckzzsj9opplYgemw1sPHpYYVu10vPxd6wR7muErDXF1xgMBoPB0MGiJk+qIKO9Yzvw9E8AAPNvuSx4nILx7A6xvKVz20fDFxzD7NaTy9tmsPSm+7XbAQDed75ezjbNUiqVXe0d29H70qcAAJ3/+75Mu0hKXtAgdhq24eCB4LPferl0zUiy+AYD2s5t/xz8Y242dz+1+ncMErveN/4TAODdv8vMdaDqKyJeA+0d29G7Y0fw7394b3G7QN9TnesAT+0PXvO2l+fsp4ZghMES0c6t4X2tPZ+/nxrZfp7zxGAwGAzTWNTkSfaH3DswjdZ7r+7/wPfQeu+bBk5DKSe0RbI7svIW78A02jf/gfADlW0ElTHKLCVCWZB3YBrdiNgRbDORRaEo7Q3up59tF3kGFTFTQQi2qb6mJ4tvhgx7B6bR/vs/FH5QfD91yrwoc7u8A9PofuHjatsoAgPEckcKCaD6Gn3eGfE6MOhrOiWipjJP3oFptP/u3cIPDPga9zwxGAwGwzAWNXlqyoLtvXtieeb+D3vw9j2S+BHpxN1weYu3dw/gU2zTCIAMlVIFtqVmH2XYptUjo7CNbBdhP6kzqOI+NjKxk5DhArblQshyqmZQkcgw0deMZzkr9jVdoq5cM5O+Rsxc27ItF8KaKX2NQoY1fU0q6qIxg4rBYDAYDB0savIk+0PunrQBcFPL49bgnrg+8z1IQUa3E8wxkYASNLonbQAcw7YpAtqe56HjqQOgwLZUL4jENmlWTLPXw+R+er6Prqce4GuipNCkr8Vk2PeBbseMbRr7Scs80ed2mfW18mIWlDJM3f0E5PPOqJlrG7aZmndGyQ5T72s6PWz/XTNP0zOzuOOxJzA9M1v5+9v+7IXEf9fvxt9ruN77vzMWy54sbvIk+0O+Zgoj//P/CD+oYeS6m+GumUq+h05AC9BPj2UB0JopNF79m2ZsI6pSiSRHZVvtvEuN2EadQUVds5HrPtj/geNK7VLappupUNn2jr8UflBizUT1NOK6yXr/3DVTqL+K7mvyElEaGW7r+NrPXKK0jaTORpR4t+VrJoQZjPpaLNlP9DVD69Z4zVvItpmaQWULqj/06d+rHkfYft9unHzTp/CSj3wBJ9/0KWy/b3fp9xYf/929D+Hkmz6Z+f62P5uybjLorrkIyncz+dk6j8u8tsielfleur+XPb/sflV5jenC5gFFmT2x7WtF7Q7eO7ovfRLb79utfe8wSb7qpd/hpxhRUJWH+kt/Ca0b3wYAWHLLD1CbPHHgOTp9RQCCwDFnQCtAb6yunXMBOh//K7gbzsLYTbcMBBjie9BIAC34AdRCG+4pm9C76/Oob30dRt72vkzbaCWF5no9AKB55Ta0P/xn8H/wPYz+0YfRuOyXB+1KkadlI9lyydSeCup+1n/ul9B6XxA4Lvnco6itPWngOTRFO2EGVXseDibybSOuW/2cn0X3X/4a7sazMfaXnyu8n7qEk2Kbe/JG9O7+D9Rf/isYecsfZV8HOgIDxB421TXQvHIb2v94I/wf7sbo+/4ZjZf+0uBzhFLO+W4PE9kjtMh9iVTb6i99NdnXqAN8/dY8nCVL822j3teeewE6//yXcJ95Jsb++rYS9zV6P1ZRTM/MYs/BGWxYNYGpifHE4y98/0e4+tZd8HwfruPgg1vPw6XPPCH391edeSo+8uCj8HzAdRy8/szT8OEHHx14/V37foyrb70LUVLc83286dZdOOsZK3G00xXe+67wvYBXnn4yPvXQD+PHLzllLW7/wRPwATgAzpt6BnZN/xhZeXbx/X9w6Cjxs6Pvlf7swe+Vfvy6M0/FR4V1SK8bAGNrLv5+tO7ijbfcFa9B1ncr89mf/O5j+N0vfQM+ghPrreuncOv3p0nrEuXWfQT79/oznylZQ+AVG07Ev+3ep9zP6Hv928N78dYvfC18/6Tt9z7xFN75n/cWWtMNqybw77v34S2fvxteaPsHt74w8fv0On7++z/Cm0IfS37v5HsvbdSVvphYcwf45Wefho995wdKX6NfY/nfO/29dPxFteZZ7y1i+327CdfBXZnX2Nd+9BP8/s5vCveOU8LrN3ivKzaeiB0P79W+d0TrJlsX2fceqaWvT+A3brkLLhD6Vv49c8/BGaxfuQwffuBRXLfzm7E/pH0RAL71w/0Z3yQbi5o8KeehRIRiZDSTOAHEoLFeD0plPC8IMiQfqT1LafWazABDfA9KsE3t3ak5Durpsp80woDFPWmj0jaaYhZR0Y4w38bxveDCf0a2Xa7joFlz0e55tGwAMWOn3M/ohLzRzAxmASLhdJxg3dotIz0yAOL9dI5bW2o/dVXjHAANqq+dsqmcbZoDmSm+Bi94rrsm+97hOA5Gai5aPY+WRTG1n9F3lPkaIVvnuC5QbwTloQYysKJtpPuaIXESHST/0N8TB3k/c8Jx+NqPnswNIt54y11wHMTP91O//6cHHk08/scHvp/7+jR6vo9z/+8tmfZ6PvAv3/th4vGXfvBE/NgHcNf0j6Xfuef7+Jn/e0vmd9P77MHvlX784dQ6/MYtdw2sF0AL3GRrJntv8btlfW8HwJnHr8CDPzmU+9my9/YAfO6R6cTzZeuS9BUo1hD4zO59Od9I/r1Utke/y4Pye4cBb/R7B8Bz167GvU88lfn89DUi28/oe8l88SMPJn0ry9fyQFmzfiAPvPjENfjy3gNxoP66M0/DRx/8QUyGqd8t/p3w3lduOhk7Hu4fSIgkwXWcgUMAynWQt2b/8r3HEs/914f3xo917x2uA7xw6hn46r4fx4cIV2w6CZ/ZvTf/e0v2G+GaRM9PXxc6vhgjmjNIgHXydOjQIfzxH/8xbrvtNjz55JNxY/GqVatwwQUX4N3vfjfWrFkjfY97770X73nPe/Dggw/i2LFjAIBarYYTTjgBV199NX7t134NNUowk4JyHkocZOQdCRMDsyignZ9TlpFQg+04AM0ZcgkQg20b84o0BpcaDWgptkVEQGFbWxHQgti/Q+n1AKAcqBrZJb5nvm0heTK0p5EUtWrNlLbpqifWa8pZSqphr6JtJIEBg9eBOFxYZpuKPJns/dOxC1D0FQHBunU7ynVrEbOclOuA1POkIU4iQ95psQgfwN0/elL6Pj762iZyaQ316xcCC/jRuYG1KnCjrJnqe+V99gM/OaR45cKumQpFvrfp9/YB3PPEU1rvO8zXQD+QB3buPdD/uY8EUcvzqbzv5guv8Xzg0w8lDyRUhwDDsm6eD9y5r3/NekCCjGW9rqzdNv3cKnnq9Xq4+OKL8YpXvAI7d+7EqlWr4mDo8OHD+NCHPoTzzz8f999/P5YsWZL5Hvfeey9e9apX4a/+6q9w8cUXY2Qk+IPveR727NmDd77znXjooYfw/ve/X9s+KkEpHTQCQeA4P2eszCs+oa04oNUJGikBrYkAqB9sE1r4ou+psG2m1SFmAwwRlGg/SxJ1IFh3HzNGBAaCJ9LWDCCKMqgIJzXQFm2TBds1ta9R53ZpXQdEXztM9DVT9w4S4STKqDvNUfizR8jrpjpEiHyjtG0GMk83f/MhvPnWu4c6CI5Ou03CQXCi3fN96fvb+OyFhpuTHfxphrif/52+F9DfL5u++NPg58Oyp7Z9TdxvkVQuBKwKRuzYsQOnnHIKrr/+eqxevTpxirx8+XJcc8012Lp1K26++ebc97j++uvxgQ98AFu3bo2JEwC4rotNmzbhox/9KD75yU/iwIEDue+RB2U2gBj8AIrSOGj075BLb1pk20jzbQwIWfRtI2RRNBrSTQbbFEJM698xO8BXJ/Ok8jWTc4EAIQCVZCq01sxkJrFDCLYN9siQMyjQO3wh9Uwqs9YWfE2WrQPMXwcEX9MpEaWMYMjC9MxsKeLkIChxBoI/sun8qfj7muPgVzc/M/dx1utdAP/yCxfg7l+7HG4qO6vz3lmP//7yF+Kxt74Sd1x1aeb72/xs1feWQWfNs35fcxzc/YbLccdVl+JrGd/N1GfrrkMQkKLQa9P7mfW9VLZHKLKm1PfOer74vVX7VdYXy1xjun6q8/oi7w3I14362TZ8jWx/hl3ifv/wba/C31/+Qq39LLtnaVjNPD3wwAO46KKLpM+55JJL8JnPfKbwe4yPj+O5z30uHn74YWX5XxrqoFFdrkQfdEnr39E/PS6ZqbCQeSKdHuuoFJrsQ+mYIp1mSwp9AlGPZkV1PR9dz8vvPdOcj6UsX9XJJBJl8X3fzy3J09pPnRK0qjOwBF/TIZ3GMk8a10DH89DzPNTyfM30daDha9KDF6HUUeZrebhrX3YvTRZqjoOrzjwNH3nwUfR8HzXHwc1hH8IjB2ewPiz5e9Otu3J/PzUxjvdedE7u46zXv+qMUwEAH9x6Xqn3znoMIP5/1vvb/Oy87+0AcQ+EiTXP+v3zTzg+3lfxu5n+bN11AGB1P2W233DROXj+uuMKr6ns96p1FL+3zn5VfY2pvpeuv8jWXHyvrAxMzXGw6w1bMdvpFtoT8bNN+5pqXVS+Ju73ti0bpfur873j65t4zwcsk6fp6WmcddZZ0uesW7cOe/fm1z0ePnwYS5fmqzhR3iMPyjIvjZ6nds+LVT6y4IyMBk4uCYDEWUrUgLZ0Viw1VFUZ0BrKPI1oBtsykAMz3zfXW0S0TTcbQMlSAEHgWG9m+6/THAluptR1o2Zgy/awRa+PZlA1mnK7DJbGUW2jEhTZ0GMA8LvdePZR2QMO3ay1yrY+GZZkEhMy6h6W5JCn6L5mPPMkvXeEYhY6M6ga2aqZefik0Cgdvyf6JSmUIAPoBxFZf+jF30f/znuc93oT7531WETVny373oCcROisuex72f7soutS9LWy76WzLkVfK/u9bB2pdplYc9PfW4dk6H62iiSoSIbOupr2Ncq6UOwqup+ye8u3f/gjvPyPMr/KAKz3PKmEHOr1OrqKAYsqqN6j1Wqh1eqfws/MzACgBxmUoBEIAtqxRs6SEgIg6iwlwLwoAwCg0849wS9SGkcS2iD1yKh6d6iKdu3+e5e0DUTbyEEjJUuRklEfb+YEhARf83w/nqdEzsCayjwBQQbWAHnSK42rMPMkltiVJeqmxSw0CGf0vkuU97X8LGfX89ALO3/VthGyYjqy+EDwfTXI0z2PP4lPhU3ZruPAk5xki5AFEZTfqyB7fdn3/mn6bGrgZuP3Jj97IVHGdhtrSl2nYb7GypAM3c+mkj4Tn10WZdbF5n5m/X7i5Enyey8KqfIbbrgB73nPewZ+bjJoBILgLJ88qcv2dObbgGBbRA6ps1rQms8lTzpy4CQxC60eGUM9T2LwWdI2hxA0iu+hbJQnlCvVXRd110HX82klhZJgu6Xla4b6sQSy5Lfm4Ywvy3ya6cwT6TrQLHXU8rWyap2ayo4mhHDqrotamGWh9Uya8TVz5cipGVQ5vpbGvsNH8Wv/ficA4KozT8MNL3mu9CSbwWAwFhrDTNT/u8KqYEQaDz/8ME477TTs3q03dVjE+973PrziFa/Qes21116Lw4cPx//t2xfMQaALRuT/Ia+7Ttx4VrZHRmuWkqHMUyKgJdhWZSmVbj+FWslLDGizsx6ibdKeCsNqexRfS9gmExiIbJMIDIgkh0wEJCSAQlAc1+2vuyFf84VZbHkgERTDcuCxXfU6nHr+GZXW4OMKs5zi+5TtFUsM2KZm+0tKzyd8TSG0EeEfv70HJ9/0KTwYylCfvWYVpibGceEpazkwYTAYDEaMSjNPmzZtwqOPPqp+ogS/93u/p/2akZGRhFJfBBOzlBzHwWi9hrlur3TpTaFypbL9FI4T2NaaJ2XFjJdSVTmDSijDlDWQa6kUmurHIpSIAgFBPIpuaYGB6PWu46DuypvpfUomkSrZPzIalE8aug7iLGdZiXeir5GHHhMOEACqYASxfJVqG9HXRus1zHa6tJ5Jwn7WXSdfeCIEpRyZNOcJCK4Dha+JeMsXvgZf8KPf+8978Zpnn8rEicFgMBgJWM08UfqZut0u6pKTWQqKvgd1SK4saAT0TrZl81CsZXdUQQbh1F1r9o4p24gSzSZ7PbRtUwa0erbJyvYStpUUGBDFBZRKZBrZAMpcIMBgllNL4l2SqbCmGkfLJFY5yJoyUwygibpQSgr17muG+utA39MI6YGMPd/HIwdnSK9lMBgMxuKBVfI0NTWF/fv3S5/z+OOP46STTsr9/YoVK3D06NFS75GHUaXanl55C6mUynDmidJYrZrVQlEao5bGAVSJd0KZl+nsDsEuqm0g9BXp2KaTDVDaZtrXNDJP0msAIAkMxIST5Gtq0knK7hDnApGvA12iTiqNM2Qb1dcMze4qdvAiySRSx0MQSWceao4TqzAxGAwGgxHBKnnavHkzbr/9dulzbr/9dmzevLnwe8zOzuLee+/Fpk2btO0bccs3LwN6pTdytT39GTJSgkItpdLIVCxEz5P5viID2R3NnieltDVh2Ktom6yUKj5xN5bl1JtX5KeP8EUQBAYK2Va69y+8xjtt+F4+AVwIMkwRZdCxjUI46bapDxFiNUwKGdac8yT3Nb3M02uffWr870hdj0v2GAwGg5GGVfJ0xRVXYHp6Gtdffz2eeuqpxB+6mZkZ3HTTTbjllltw9dVX577HH/zBH+C3f/u3ccsttyTkxj3PwyOPPIKrrroKr3zlK7UH5AIaAZCRICMKgAwFtBrDQaNBl7m2EZTGCs1SKquYlZpBlQcq6TRZSmV6BhXV13SGqpruK6LsJ4BYAj0LlBI0sq95Xl9+vqyinUhyTFwHBsmw+UMENeEU34d2jRKy1jq+Rpjz5APxXLwsOMRsYoTTVwdZpktPW4fH3vpKbNuykfQ6BoPBYCwuWCVPtVoNX/rSlzA3N4cLLrgA69atw+TkJCYnJ3HuuefiwQcfxJ133onx8eB0b+fOnVi/fn08hwkAtmzZgn/913/FzTffjGc961nx66empvDa174Wl112GW688cZC9pmYpSS+jylVKq1SKrKMuqScitLzRLWt24mbB8oKDCR6MoQZTYVts5B5kmV3fN8nN/H7BIJCtY1y4k4WF4BeNoBqmxFfE4Li0mIW6blAZW0jHCAAejLqMhLg+X5MJMjXgaLnSY/YGRKbocx5ovoaUUExwpPHgnvM5mes5IwTg8FgMHJhXW1v+fLluPHGG0kE58ILL8Qjjzwy8PMtW7bgs5/9rHHb1IIRtNPj6CSUokplqq9Id4CvdNClyR4Z8T0opTeUPrHoffMG+Jru9TCUeSoiB25EnMRwzxMlG9AUyhLnuz0sz7ONIjCgKbIRGEDxNcl+1uuA6wKeB7/dQp6EBjnLaZKoN9S9f1qzlEz6GqUMs0DPkyw7LF7n890eJnKeSunlFPHkXPC845fI14XBYDAYixuVznkaNlDV9pTZAMpQ1fgU1FDZHmFWSzToElBIghtUzEoEUWXL9gzPoDLZ60Hp3RGFOqrMilFUCvUEQNTrFkn2A+VnFmnPUnLdgPzkgFLq6DiOnm1Eom6CoOiUxonvmQuyjDpdOIVE1A0dCjmOo2cbMfP01FzgT8cxeWIwGAyGBIuaPKkG0Zo8odUJgEzNeQKIohEGA1qxXEkmgU0OaAklS7aa+KXS1hrZHQBoGPY1uWAEfc6TKVEGQK8EjZaBJQ6KVvgaeQYVQfyA3L9jcBCtjry7SxiwTVWd1LmvyQ8RzJYjA/31J2WuiT1PffIkv6cudngHptH9xh3wDkwvtCnatgyT7cOMYVmnsnakX6/7uErbF/KzTWJYfMc2Kh2S+1MHk6U3lADI8Cyl6L2OdbqkmUXSoJFcSmUuaAQQ9C205s1kKjSb+E327ihnKRGDbVMn7sXESdTrdrjVMTaDilp+ZsrXnOYofMO20QUjypEAW/spvncW+vtJyHIaJOqj9RpmiL5GVdt78ljwvOPGzGSevAPT8PbugXvSBrhrppS/13k+AOlrdR9Tbet99x60P/B7gO8BrouR6z6I+gsvNWqL7LH4vbt3fQGt914NeDRbdJ+vWqeya6zjD6b3W/Y4a52aV24rZHeZx73v3ov2X75Tul8ytHdsT3yP+uWvR/eWD/cfX/Y6dG/7aO7vm297H2pnPM/YGspsT9tq+7Nt+dpCfraN+6AMTJ5kIBIB0iloU6NsT0fS12SPDOX0WGEbZSYQ1a7offyjh80QO2rQSMmKmSbDJomArTlPJsQsTNqmWYYpLScUbTPZj2VkzAFBPdHwLCXxvWj3NTO2xdeTSszC0LqJCDJPNRw/nu9P1D/0qiAi8XvHRe1nLkbva18KxHaynn/nbWj90f8ISEt0EBM+t/Zzr0Lvi/8SPHYc1C78efS+/Nn4s2sveCl6u74Q/9591nPhfe/e3M/q/Ps/ov0378p+fmIxPLT+4DfQcpy+LS98GXpfvS18rYvaS65A747P5AewWQHtrR+Nv6d71nnw7t/V/+zoswY2xkPrD94Y2hK+10tfg+4XPh48znr+H74RLQjPV9gmBrTp/R147Utfje4XPkEmmd27Po/WH74p+3un9jvxWY6L2gUvR+/Ln+t/j8uuQve2j+TaVnvR5ej91+fiPaq/9JfQ/Y9/CX6fuU5XwxlfhtrZL1T6uXJ/dR5n+RocAIHdI++6GfUXvixzTf2RMbT+8Or+3nseuv/+j4n3697y4eTj1O/b/+cdwb9dF7XzL0PvzluzrwnHRe0lV6J3x478NUz5pugP/rGjaP3BG4PvlfHZKtKXuL4zP1u4RtP7PeBPKV972WvRve2fQ7904Z7zs/C++ZX+e20VbHGc8Cv4OZ89uG71S1+N7hc/kfnZ6TWvPe8i9O7ZSffzn3sVel/8ZGx74jpJX2Ph9d096VmDa5gDJk8SkINGjTlPxkqpqGVBWoMuDZxsayp5kWdQSfp3yGVB1ID2p5ygUIidziBaimCEaJu03NHkDCqT5YQI1t6HmWvU1ywRNVW2Z7I0ztScpyI9T2bELPQyT0fbXaBZw3Fj2ddg4nRY/EPvuKj97OXo/dct+YG6GESk4Xvo3f0fGs/3E8/tfeETid/17vhM8vd3fT7xe++792h9VuL5WfD7wVLvzluT3+v2f0181kAAKwtofR/et+/K/qxsQxK2dD//MYLdwvMVtsUBbRpZr/38Pycep4Pn2oVXBMF23pqL3zu134nP8j30dn42+ftb/klqW+8r/554ffcLH8/+XsJz5t/56iDgfO6F8O7dmRuoK/dX53G2MbFNrT94o0CmFLaVgecF13ZsQuqa8D30bv+02m5fJBWCPyg+W0X6Etd37sf31y2x3wP+lPK1Wz8q/M6Dd++XE4/T16v8swfXLXGdpO9rqTXvfeM/k7ap/DxxX0xdJ+lrLLy+5xThqAgmTzKYDIAM9jxRZymJ70UTjKg+aOx4Hjzfh5tX1qbTv6PKipnsYYtJXRu+72eW5VGzdcGTzREUnTKvSCkyD363C3S7AAh9KBXPoNKd29X1fHQ9L78niDLImrqnJsVJDGaGgyfTfI20n4Z7OXVLpWX3NcoBRxo1x8Hy0b5QTXySHQZr/eBN+EPvpwLSPOgGdaaDwGH5rEWJZPDc+89/lT99GOH78O65Y6GtSEEIzIfONhl8vuZ+yrGoBSNUMKvOpg5oqTLI4swjsm2UWS1Gep5oayaqvFGIXV5wJs5SUhEB7R42mSgDYaiqjWwASWgjkrY2EdCKGSKDpNNIuaPmfgKKYFuRRdGZ26Xd+ycVAAnfo9uFn/O8IrOUzKpOls8M+91uXPZCFsKh3NckWc40Vi8ZiQ9y2ju2Y3bryZi7+iWYf9MliAM1RgDH7WfgFuTzHcCt0W3Rff5ihbhOw4Ki+xUdlLk11P+fX+1/L9Xj0vYW8DWF0I/VzzaFRXSNMXmSgVoWpKOAZlLRjmSbhgKaiVIqzQwK1bY8YqczS4mcedLJoAC5xI6a3QFgdgaV0b4iwScMlGKanEFFFYwYIfpabFvOdSD6mjJjp1nyKyd1oq/lEPWehq8ZHMhMEgAxPLcLIPYlas55AvoznrwD0/0SvSJQBRHi79OBm+r5jhM8J+u1uo91bXNraP72jRj7+zswftsPMfKuvy/+2dq2Jr/3yLv+HuO3Pka2Rff50mBasUbKNdZ5P5P7TXgcrdPon/xL/3OL2G3gca6vUdbUrWHsH+8OXn/rYxj7gw/191/xuPnbf2pkDcm2C7Za/WzNa+yn6bNLPS4ALtuTwWTzssmgUQzuhFlIRW1zNEqpVPLR1FPtuuvAdRx4vk/sW5BndwCNLIqJE/d6I25e9lvzcJZl2KbTxG/SNoMBbRx41mpwJLOUtG2jEHVVCVqknqggnHXXRd110PV8mvhBznWg5WvU0jidNQOCPR1bkmubjdI4U/c15b0jMWDbQM9kPFuPTp6ifidv755s4hQOUg6Cn6iHpYb65Vehe8tHAK8XBBHX3Rw0hO97BO6J68PG+jfl/t5dMwXvt95Lfj6A3NfqPta1TVSlal65Tflck4/T3xtA/H+KLbrPjx73vnMP2n/5e1prJFtjlb+ovrfNNY7X6aWvgj87U8puU7Zk7RdpTc98fvLyXTOVeM+8x/XnXYjGy15Tfg1zfC3zmgtttf3ZutfYT9NnF30cX98qUSkBTJ4ksNMjQxlcqjjtIc5S0rbNRFaMKC4QDVU91umWmkEVvdaBepaSUYnmaAbV/JzStoVSTyQNPSb27qjWTLStshlUxL4/ICh37HpyX1P1/un4mm+QoDj1OlCrAb0e/HbQIp1nmw1fk++nwax19B71BhzF9yD1chbIPEUDcgM1vbAZPYJbw9g/7gLmZ0l/6AF5ECH+Pvq37vOzXqv7WNe2NMp8dtHHeVC9tqjtWQFt2hbdNZb5i+p7217jCKbsNmFL+ndU24rCpN0U26v6bOrjn8bPLvo4ur473/s2cNHLc7+LCCZPMhhsXjYaNBYIaMuWt2iXUimyddF7lZ1B1er2e1BURFJX0U4pbd0MyBPFNiU0s5xS2wgn7lTbqIE2QBWMMEfUqX1i0XvNKom6PIuiNbdLdxCtytcaI0DvmJqo68xSMjJTzJwAiBYZjsqRKT1POpmncECuu2YKzvFr4f/k8eAXkpNs8d86QYQKus8vgyo/66cVZddIN+gcFgyz3cNsmwo/TbYuBrhrplAfmyA/n8mTBD4xADL1h1xbZYwQ0JLU2aJg24h8dAHbSvShFJqlZGoGVTxU1YBgBHFPKT1sOmRYObjUMFGnlHmRRRmImURd2/IyT2S7ALOlcZFt88eMEHWjs9hMKnXqkGEtJUC6YETU8+TPHoH/5BPBZ73/06ideS4HOgwGg8EAwORJDoM9T1qlVIZ6dwBNueGKs2ImTrbJJABmFe2CJ8oJcb+Jv+Jg22gTP000hWqb0ZlFHX3bKCWFRsmwCcEIiERd7ms2huSWFQDRJsOmfI2gOplGVLbX+943Ad+HM3kiGhf/Avn1DAaDwfjvD1bby0FilpKJE1pCKZW+yhitNE5lmyqg1ZFoLpIVM9GHYmOWUrsXzKDKt01eikmeP+X7ZJJCKvMSTtz9HPvJtkWEk1iGCShKChXZAF8QEDE15wkwU1LY70k0R4ajTGKr5+XuVfBE+bpRbUvOiDPga1q9nESirtNf1yXMO9PpeYoEI777DQBA7dnnkl/LYDAYjMUBJk95KDBLifqHPC9IOtwOPvNouyO3TbPXAyhX3iIGw0/NKUpgipR5lZhBZVNlDKDNBSrdh2LY12Iy4ftAN9uXTEvPA7RMoiob0PX8mLBa8bUS5Y5FMk86kv3SrJhi3ci2dTvxcEayYATF13q9YE5TCduoPYkAbc4TJcuZRpx5evDrAAD32c+XPZ3BYDAYixCLmjz907cfyf+lSCSocsOUQZdAZkC7/b7d+Nb+pwEAb/zcXdh+3+78D6y4vOXvBVvOvPkzUtt8w2Ve8RDOvJ6n8LUdz8P0zKz083SHg6ptkwfbP56di22TQmdul04GJf3eAmbmaUSdqhoHmOl5+uA3H47/fdYH/03ua4aznFQyTPG1WEbdlK8p1u3Hs/OxbVKI9zUDs5QGZNQzcLhFPBTS6WEzVI6cxvEp8lQ7kzNPDAaDwUhiUZOna7749dwgKEEkDMxSSpTYpf6YT8/M4upbd8WPPQBvunWX0jadzBOplCojyJiemcVvf/Hrfdt8uW1oadim0SuWFwD9+559AIDvP30EJ9/0KTnpJMqoRzOoyLZlBI3b79uNv77nIQDAh779iIJwCq+vN6S26RDOgfcWbLtn/1MAgr2kEPUqCMr0zCze9sWvxY+Vvmaa2MW2ZRN1LV8jltY2XDeWHpdmriVZlO337cbf3hv42v/3rT0kwhl8uPy+RptBJfhaxjW6/b7duG//QQDAG2+RHwpRhSwAc+Mh0jhuyQi8gz+G/8QPAcdB7VnPJb+WwWAwGIsDi5o8eb6PRw7OZP/S+Cyl/IB2z8GZgd6answ2jewOTZQhP6ANbEv+TG6bfj8WJWjMC7b/JiQoQLCfUtJJDGijGVQAcd1yyHC0bD5ohBMjo3Rfk2U5HUeYj5UMHNNEXWmbhjhJ2RlU+r5WoEeGMoPKhK8RDziovpYnZqHta7EIThOOYlYVyddqtT7h75T0tUI9T+XGQ6Rx3JJReN8J+p3cU06Hs5QuXctgMBiMxYFFTZ5cx8H6VTl/HIsMB1UNVc0JgDasmogzHRFqEtt0ZykBtMAsK6ANbEv+TGqbRlaMcrKtCrbT3WMUYmcqYxfblgoa9cmw4QwKkKtSqGubr5NJ1JHFzyAoxX2NIhhBOESQZDm1fU2nV6xE75+2rxku+Q1sM+NrOgcvWmtG7HkardewpFFHLyJPXLLHYDAYjAwsavL0gZeei6mJ8czfUWc8AcQ/5EBukDE1MY4Pbj0vfuw6Dm7eel6ubUVmKRXNPE1NjOO6F50dP66pbNMJzmqEdVME2+k8jSzYNh445gS0RckwaT81pK2BwSyKrm1aAa2OZH8GQZmaGMe7bPlaSWVHXV8z3SvWP0Qo6WumZynBnK9pEfWSvpaFSGkv7ndisQgGg8FgZGBRk6dfOXt9/i+JM54AvaGqQHZwtm3LRtTDQGPXG7Zi25aNatsqUhm79LQTAADrlo7hsbe+UmpblQIDUxPj+KUzTokfq4Jt44FjTplXRIajsNEF5CRAZ24XORuQTYh1iTpVNQ4wI8pw2fpgEOma8VGyr1WhOqnra9A4fNESZkjZpu1rhkvjAOReo0UPharytTRWLxmF7/vwvsNiEQwGg8HIx6ImTzJoBY3UoaqSP+Y9z0M3LHE5beUyuW06gRmllErRWD0XShCvHBvJD3wiFLCtzJyns9esAgBc9swTlMG2Vq9YnSC7LZFR37ZlIy44eRIA8KeXPE9OAgqIMvR8H12Zspqk3HHblo1xGdudv/oyElE3TlByJPvnOoGvrdLwNb0hucVKCgFdX9M/fJHL4uf3Y23bshE/e9IaAMCf/9y5xMMNemmc0tckQhvbtmxEw6UdClHVMAHimhHmnYlYPTYC//HH4B96Cmg04W44S/kaBoPBYCw+MHnKQ4Ggsa0adCnpLRKDOvLsHVNZMaGcMMv+6LVjlFlKOnLDOrYphoOeumKZNNj2e71YIl6LCJAGvspPtk9YpiIB+vsJEAUGMoidOPT4tBWKhvgC+ykXZRDeJ0OyP1pvrbldhHUrW1IIAHOd4LWnrVT4WrcLhN/D9CFCnupkdNmuW7ZE/mEFiLratnxi1/M8dEIVkFNXyA+F9HqewsMNggCIbN6ZiOOXjPTnO208m9RLx2AwGIzFByZPeSiQpQBoAgNZAVB04g7Qh0nS1PY05MBzgoy5mDzVlZ+n08SvE2znZZ5i2xqKYDsx38aU0IY82LY5iBYoPsBXXG/VukViGKTev5KS/UCfoJB8reo5T1RiJ76eVIqpJgIqoh69Vn0d0Mmw6GtFev+A5KGQ0jbTc54I885ErF4yEotFcMkeg8FgMPLA5CkHcWBmMBsgC86i19VdB3WFhLDWLCWdoBHIzPCQSQBQKDgrM+gyIp1RMKW0i2ib1hBORbmjKmOn07tTc13UXcIMKgmxiwgKoEHsDBFO1QwqW76mU1KYlRkG+r6mzMDqEnXKAUfTjG06pXE110XDJZSv5ohZiHYB9EMhc2RY7mtprB4dRe+b/wUAcE94pvL5DAaDwVicYPKUB43sjjjosqgkuE52Jw7WTQUZwrBMWbCtPDkWX1/RoMt5YuYptqtWg0NY4zJqexGidTOZeSLbJslURK9zHScOjvNgXDVOlOzP8jWNElG/QM+T1DZFz9MckdjFa1avB3OQDNjWL18tmeXUKI0T34+W5cy/r5EOhQqIWZTxtTSed+/nYrGI1l/8T7R3bFe+hsFgMBiLD0yecuBrlJCIgy6LDnzVOXHvS/pqZANkpXGu2ydQWafHYQZFJxtgKiumGnRJJp0aJEDbNkVAO9aQ2xaXxunaRphxIyfqNeVQ3iI9MmrJ/vxgu0jmSSc7LCsRjTNPR2fgHZjOtU3paxr3joRtlHtHyetA53ADIJYUSogdec1QTMxCKgACSH0tjed//I8FYzy03vumTD9gMBgMxuIGk6c8xAEt7YSWNOtJ0pCuc+JueoCvaFtWcFZIMMJw5sk/elga0CqzARokQHw/ikqht39fpm3kPdUojQOIJYVe8Dv/yScy7NInw0Z9LQyke088lmubaV+jlIh2v/zvwT+OHsbs1pMHMg/U/jqdMkyAdu+QlcaJtlEzT2SiTvK14BrxfpLla/SsdSGiXsLX0nDSYjleD96+R5SvYzAYDMbiApOnPNgopQqDI/+p/QO/KpJ5ovUVUYOMRmDi/h8O/IocmEEvi0IhKN3/+lzwj5mn5QGtyjaNWUqibbJ164UlPt59/5Vpm34plZlMRXvHdvTuvDX499/+v7l26ZTGUfaTQlDaO7YDh54KnvebLyu+ZjA758k7MI32X18n/GAw80Dur9O9d2gIbeQRdfKeGr6vtXdsR++uzwf//pvr8veTUL6oRdQJa6bytTT8dBLWrcE9UTILkMFgMBiLEkyecqB7eqz6Y97esR29r94W/Ptv3jVIAqKGb8oJrYYCGoUEJIKMN18qCbYJ/VgGM0/egWm0/+b/FX4wGNDaULSj2tb993+U2kYWjNDMiskG5XoHptF679XCm/sZJIBWTghAq0dGJMNZkveDtmURFB3bzPU8eXv3AH6KjKYyD9T+Op3eHYptANB7MFCByyLqvu+T7x+FS0QL+xr9vlZkztN8t1fY1wAk5ld1X/Hr/V+4NYxcdzPcNVNKWxgMBoOxuMDkKQ+F+xaKBRla820KKKDlDbokBbRUEuB5QKcdPDAwe4cS0Ma2UUupCHbRbcsv8+l6HrrhfBtbmaesMi9v7564jCrLLqBgllNzLlBWbxHFNrIog6avqbKc7kkbgLSgQSrzQO4r0pDrF22TEvXP5RP1juch8kST6oni+2Wtm8n9BFCIqAPB9y9iGwA8PdeO/73kuS8CEMx4Gr/1MTSv3Ka2mcFgMBiLDkyecuDHZV7Unqf8kiVSkKEz36ZAuVIZ2+jlZ/2mbBNZMVJAu0CKdu5JG4C02IJgm/g6pWCEbj+WhNjpkQA7AiBAdkkhxTZy+Zmmr6nIsLtmCiP/+28TdqUzD+ResaIHLzk9TyqiLkrPU4mdiayY3n7S53bpztYrahsAPHms30NWawdEyjl+HWecGAwGg5ELJk95KCgwkJUN0AkytDJPBoaq6hAUZSmV7iwlFUFZM4WRa/86YVc6oKUGZyaDxsi2xut/N9c28XXKPS3Yj5W5n2umMHLdB/vEznFySYBpUQZR9jwzoI1si+C4xQmKYV8DgOYvXg2EJGvsn3YNZB6oxE5nXhFAIHYKoh6tmQOgWSPOiDNgm3Ff0yDqIzX5oRDF1wDgqbk+Cdc9xGAwGAzG4gSTpzzoBttVBhkawbZqqCotoNVTtIPrxipXMlAC2sYvvin+99hHvzEQ0JLXrSgZltl28S8G/zhucqDMJyKczZoLlygHTs9yym1rXrkNjV/9XwCA+ktfnUsCTAuAiJL9Mtvc514AABj5nT8rQVDCa8CgrwVGjQVvO7Fq4Ff0QwSzPU9Uoj5KkZ43LJzSvHJbbFv90tcMXp/UzDCg1cPmOI5SoCTwtQuDt3z7+zPL8ETy1J/tN6a2lcFgMBiLFkyechA3L+tKW+cooDWv3IbGr/xPANlBhq05T+J7SoOMc14MABj53T8vHNCKhFMZxEFdrgSEgy5Ho4B25cDvbSvayQfRBnY5wMCJto4cuLY4CUHa2l21JvhHhsqZlihD0XJHmW3RPo4OBqm6kttojmj5mmoGVbQHWZLgRWyjgETUX/ILwT+OWztI1InzxADz9w4AcFdHvjb4+dF6k2zT7EukqHW6EyuCf4wtyfz9U3P9fe6vDWeeGAwGg5EPJk95iAIgqsAAJciQBbQ6inYGBQZi25atCP6REWSQBSM0To4TdqmyAVFAK5uPpeor0lDyAvqZRCmxk8ztsjUbS7SNQuwwPzfwq5jYEeSjfc3rQGd2V5mhqjpy/aJdyhlUo/nrRs6KaQbhpDUbzSfqRQYL2zhEyByw3bF3HVDuubFtrcH9BIAnj2Vlnpg8MRgMBiMfTJ7yYOHEnRKYaWWeKgq2dbM7phTtIjiSACguC1IRAQvZABkJ0CHDhTNPEttkQ1XJktuAfrljjUBSZMOiO3o9T+RBtNR5Z9LME7FEVDMIp2RgpXZZJChatmVcn0XmdpkolY4h8TUg3fMU2s9lewwGg8GQgMlTDkzPeRLfS1YSRApoDQ9VBdAPMqS2KYiAjcHC4vulAqCe58UyxUqpchu2RWS4NT8wa8aWAIj4nnKCIiGc1OyO72v7mkx1MoKMDPfLvKpVT+zblu1rOtLzupknynBh6QGCxRJRCunMWzPRNppgRLFS6aLrBgBPCWp7up/PYDAYjMUJJk950CxBGykZ0MbBtiKDEgS0ejLqekFG/sm20raCGZS8GVRp29In2wk5cNOZiogEyMr2xPcSpLMB+vwpANpN/BTbpNkdKrHrdmMZe5MBbfw9Jb6mVk+MyjD1rgGVr+VdozrS8/F+mlwzgaDkEXUdgmLWNjVR1yop1O15KpgdBoAnWTCCwWAwGJpg8pSDos3LRUtIonIlZbDd6Q91pBMBQklhHJxJej0sZQPEz8hETrCtJQdesEeGFDQCucG2FQEQUjZATdSVwXZHCCwN9tc5o9lkWLTNlgCI+BmZtuVkh8VZSmRfM3jvSFzrwj0AKFgiasE2ee+fggx3u0DoM2QZdR1fyyhHBpLkiQUjGAwGg0EBk6c8WOh5cgiN8uTATMe20iWFtLIg3Z4F1Qyq2LacksIoaGy4LmrpWVU5thntE6vX4xlZ6cBRZ+ixlSZ+WT8WkagnvpMmsZOWiJJ6xYi2GfY1sRRTRLTWFOl5bV/TKREFShF1K+WOo+qeROXBi+bcLoB4jSoyTweOHus/iNYmQwWSwWAwGIwITJ7yYKN5WSIYQZ6lJAYBjSbNNp0sSgmVMd2egZrrxoNVi5QF6ZUEmZ1vA4Qy6jnBmY5tRfuxZARFpgSoLbldb8BRkNMIFAU0ikohWTBCw9dk885i23LEDwoNFtbtSZRIbqPeiOfE5RJ1m/2ShEOhMgcvibJXC+XIWVnO7fftxhNH+zbv+8lTwWs488RgMBgMCZg85UB3zlPUhyLNoEiCDGp5i9izQJlvA2iWFJZRjtMMGgGawEBeuaOOalz8vUwLDOSUBen1oej2Y5XrQyH7miapA0z0yFAV7fTIMNm2nBJRLTKs2fNEEmVwHMkhgo5ghOZ9jSBmkSfoIr5OPeYgIup1OAQJfYAqnJJt2/TMLK6+9a7Ezx458JPkaxgMBoPByMCCkKe5uTnccMMN2LJlC9atW4fJyUlMTk5i48aNeN3rXofvf//75PfavXs3Tj/9dDz88MNmjbQx38ZEY7WmXeJ7Uk6P00GG7/vkbEBfMELfNmm5Y17Q2KHPK4JuX5HmDKrBzBM9oC0856ngDCpyaZwmqQOIkv2EHpmF8jXk9GORCSdgz9dUhwgWRRko97Ws7E4lBy+Ee0fatj0HZ+AldTcw4oW9ZCwYwWAwGAwJCJGAebz2ta/F2rVr8bnPfQ7r1q2LMyizs7P49Kc/jYsuugh333031q1bJ32f7373u7j88ssxNzeHubnshuCi0J6lRCIBlMZqogyyxukoacZNTt+CSLjIfQs6wXaJvgWteUWFy5UkpVRA7sm2TkAbZSqMDlWVzsbSLKXSyTzpSFun9lOUAyfL4hfwNWm5Yx4Zpgq6iK81uZ+hbX6WbTqCEUXvawX2U3wdnQwbvnfkXJ8bVk3AdQDRE0Z7HW0bGAwGg7H4UHnm6Z577sH09DT++q//GieccEKi9Gx8fBy/8iu/gt/+7d/Gn/zJn0jf5+GHH8Zll12Gv/u7v8Ppp59u3lAbPU9xdqfErJYiBEVHMauEyphu7w7ZtpzSuEJBY4GANi0NnbAt52S734dCz1RYGeDbacNPSXNTZyn5ZbKcBYidjnqibhkmQC13NFC2V8LX5E/M7kvUEoywQeyEfsn0tUKW7Nccv6BrW/r6nJoYx3UvOjvxs1PGGuEbc+aJwWAwGPmonDw98MADuPDCC+FKGtAvueQSPPDAA9L3cV0XH/vYx3DppZeaNjE5S0l7zpMkUxH9UW63Cs9q0e1ZAPTnyGTZ5TpOLO6QixJlXkXU2QoFjZon7p7vx9kQLdt6xBP3Xg/odpLvRbRNWoYpfs/0DCqqEmCRLGcJwQgtOfC2XrZOfM8iAgNaZXsFfa3jefBkRD3vgIPYJ+b7fvGBzJIMrFRGnTgjzlZ/nUzh9OdOTVY2LHd6ydcwGAwGg5GBysnT9PQ01q5dK33OunXrsHfvXulzNmzYgPPPP9+kaX10O0AYxJjsW5ANVdWeb1MgG1AkaBT7Y5QCFWUCoALljjoKaPrS1sS5QCM5WbEOjQyLfqArTmJ9BlWZLKd0ppg880SRA+/bZrjnSSEYQfI1TTEL0deKZOxigqIcetyJ/0nOiumU/GbY1i+tVQ091j94GSEQO5nC6WxYihmjQDaTwWAwGIsPlfc89Xo91BSnkPV6Hd1uV/ocHbRaLbRa/SB1ZmZG8QIhcDI4HHQgoBX+SM8Rg4wyBEWWqTCR3fELZANI/Vhlg0YgHviqO4Azsm3ZSCPniSXXzdJ8G6deB2o1oNeD35qHSEXImYoCmacyM8WKkGHTtuWpTpL7xAAhA6t38BLZlncPyDtEoBKU5H2NZhup1DGSUff9wNeW9X9FJp0letiKzq9Lk6e+T3HZHoPBYDDyYSTz9JGPfAQrVqyQ/veRj3zExEcVwg033IDly5fH/5144onS5yf+0GoOBy06VNVmY7WO3HCZ7E6Rk1ud/p10ANTv3dHpK6LZ5joOmpR1y1GOIxOUKEtRqwWEhwC6OlvJ+VgdvQyK+J5F5KNt9u4AYmmtvrIjtU8seK3edVB3nTjTVmbeGVkOHDA7S8lxcg8RyAO2O3Z7nrL6TGfbOZknLttjMBgMhgRGyNNVV12FQ4cOSf+76qqrMl97+PBhrF+/Hjt37jRhSiauvfZaHD58OP5v37598hcIzcvUWUqkwEw2q6WjOd/GdG+AgexOGYEBimBEuvSGXBoH2JNRz1Ep1JaeL0JQFEqAeafu2pkKrZ4neklhfokopa/I0pynPMEIap8Y9A84HMfRO0QoSjoFYRLtGXGyrDXQ39Oi886KZBJLDmSe7XSSP4iqE1gwgsFgMBgSLIhUuYjly5fjkUcesfoZIyMjGBmhB8266meAhtzwyCj8udnEH3Pf9+NAmKxoZ5igKCW3NbI7xsUsTAhGFCx3nGl1ysmoK4Jtv4CvkUqpgNx+DyrpjElAEV+jiFmUyDxZI8O5JYU6WbFiBxzHOl2aCEjRckdbhBMSGXWq6mSpQ4SCmad0z1MIFoxgMBgMhgyVC0ZQ+pm63S7qxBImG7A2HBTIDLbFwER5st22Q1Bi4YNSQaOdAChfzEKjR8bSDKp85TiqRHMBX6OUiAKZ5Y49z0PHoxH1QoRTc26XqDpJlrW2adtINuHUmttVIANLK60tlx0uRDjj2Vj6RL3reeiF+6u2zVa/ZORrgwqnA2V78Ws488RgMBiMfFROnqamprB//37pcx5//HGcdNJJFVmUgTIkgNiHIhIBnfk2tuY85c2g0goabWWeDMzeKUM6C80sIkqVl5Vols+gGly3JFE331+nVYbpeQkFuCIlosVk8QlkOC+7o0HsqipBI/f+FeoT65M6qa9lEHVRel6dFbOT7U+IP6TWLTPz5LpBbyqDwWAwGDmonDxt3rwZd9xxBzwvv1/j9ttvx+bNmyu0Kgm/QNkN6eQY2QFtRAJqjoNGTb4lZeY8SWe1RO/X68EXMoNaghEFBAYop8dlZ+8k5nbZGi5csEemDAlQzqDKIHZzOkS9jLIjZW6X+BnQnaVU4BrVkXjPKxFVKIUGvmbpgEOReaKqJxbZTx+IM5aZyJB4nxPuIyMWbSMdCgEDe5pJnpqj5H4wBoPBYCxOVE6envvc5+Lkk0/Gm9/8ZvzoRz9KnGYeO3YMH/nIR/AXf/EXeOc731m1aX1EJ8cFTkFbPU96QpsVABXq3dEqCdIIzErYZi0AUjTxq+fbdIMsBwoOfJWVYuYIRtClyosLgACKckdJNqDhuqiphh4XIcOkWUp93/UziJ1WiajhfqxYdTKvT0yV3Snoa1r9WDnKjtSsdRGiDtCyiVkZ9RGNuV2FMomyNWs0Asl+DO7p0XZn8PksFsFgMBgMBSonTwDwsY99DKeccgouv/xyrFu3DpOTk5icnMTZZ5+NW2+9FXfccQfWretPf3/44Ydx2mmnYffu3Znvt3LlSixZssScgSVOjgFiQJtxQkspCYoDYeMntGJAO5gVo2UDSggMFJEqj8UsNGYp6dim0b+T1ytGlo8uQFCUtmX0oehIbvtFDhEomURR2joz87Rwc54coR9LBJmgRIQTKFj2K8nuKGaK2VDqpA6LzurlJM+uE20zPYIhxzYgP/PEYDAYDIYMC1LcPTo6imuvvRbXXnst6fmbNm3Co48+mvv7HTt2mDINgBg0FiRPXQ+jeSsbB40Z2R1FSVDwOkt9KK4LNJpAp50MgHSGg1rKPOXKqGsOewVgoacirx+LuG4FCIrrOGi4Ljqepz0glCyJD9grP4veszWfCrb1B9FWVRpHJShFfU0nO5wn8U5V6tQ53HAcByM1F62epyB2g1LlOveOUuqJylLpsVDhNLlumYIRLBbBYDAYDAUWJPM09Ojon9A2hBIoXVU7cklQQdvKDFUlZ3dQsomfUEpVWNo6LvFqBiRR0zabwXbRwZxFRUB0SuPKCICohFOy+tiKZJ70ep7U2Z1c9URqBjbaz3rDuK+pJN7JtmmsGUArxcySUdfZT1sHL4n3JAhG6Pg6g8FgMBYnmDxloMjsHfKgS0m5klZAW6gfiyZmUbhsz3LmqfBQ1QJ2kW3LK9ujykcXICji+0r3NO5DyQpo6WWYxnuegMzrQEdtLzpEsFUimjevyIbIBtm2DKLu+z75gKN/X6uWqOsMFrYxLDovY8dlewwGg8EoAiZPWSiQQQGoDemDf8ip/TGibVX1BhQZXGrcNsVwUPp8GxsB7SAJEOfb2JCPBmjrltW/oyW5XULMgjJUFUiSTrLkNlBuzpMsgxL1ifV68Dt9QQFqr5gfkTpdX6PYlrVmOuqJkW26xI7SKyYh6jqDhQsJuhRQOAWyy/YcLttjMBgMhgJMnrJQ8vRYOkcmQ9K3kKKdhT6UOACaL0rsisuok3qeut2EjLp2o3xB8qQ7wFdnvk0RWXzRNt2Br4VmKZkW2QAyxSxi2wi9f7ZmUOVJW5N7xQpkrUXbpOWOWQIgOgO2S9pWWDDClnoi1ddySmszM09ctsdgMBgMBZg8ZaDo6TEpi5Ihba01S6nEsFfVoMvMAEhHMML2AN8B24ilcQV7PcqWYQLq+Tals5yaA1+1yvZKlGG2ex48gq9l2qYxiLbYDKriqpNkX9O9dxBmUGUKgITPdx0HdVcuB142AysfLlxSMKLkYGHdAb5AXs8TZ54YDAaDIQeTpyxYPKGVld7olLcUUY2jDrpMlN5Qy5W6XSB8biHFLIpgRNo2qhRygT6xhG2aCmgRGSbNt7GZDZAMySWJMpScC9QmZOyyDhGUBMXzgG5YUqcz7ywiKDK7XLe/F5liFkRfq5ioj9Vr6uGuZXueKNdoUTJccjyE9L6WkbEDgNmMOU+ceWIwGAyGCkyeMlD09LhoAKSlgFYyoJUqjWVlxailVOKpro5tNYICWiSjnrbNsqJd8b4iu/spvresWT5bAMSu9HxEUIByRECK6AAB5ktEAShmFllWT9Tul7Q7Iw4oLqOuJxhRYAYV0deyDqx6npd97XDmicFgMBgKMHnKQmkFNMI8lCKn2oJtOkFGs8SgS2p2p/B8mxJyw+RMRYHZWOL7FiYBloQPAGJAK9tPS2WYDddFlP8oWoJGltzWtI3aI5NJOqMSNMUhgl+g749qWyZR1+gTi3sSLZaIFj0UKjJgmzzANyPLmdnvBP21YTAYDMbiA5OnLESzlDT+kAO0gDZLMKLIMEmdoNF1HDQpWZSY2BVo+hbn21ACuRA6gy4D2wrMBbLY85QtGEELtAHbWc6sPhSdrJg+ESBL9kvED6hkGK4LUA4coo8sOO9MRw68iPCBaJt0EK1kzUhEPfa1agQjbM95igb4Km3LIMOZA3I1P5/BYDAYixNMnjJQdvaOrmAEta8IQCFFu4RtpGG0+gFt6fk2qrlAqeCs63noej7JttLzbaz2epTcT0o2oEAmMfG6gkRAP/NELEGLDjeao+o+HwEjlMwwBjNP7Z6HSI7A1kwxSgZWlq2zJcogvnfRQwRbA7Z1bcvKPI03a9nPZTAYDAYjB0yesmAxG5AZAGmV3lQQ0GaUxiklt4vOtykoo56QaFYRAZvzbaLvK8ioFylXsiGjXlowomCwTSIpGUNVY6KuKo2zKbIh2hbuzZwgj299pljBww1bZZiibXJfyyhf7RUQwrHqa0LmKSJPaTLMmScGg8FgKMDkKQtl5zxRgoyCpTeFT48p/R4ywQhb822os1rSAa0wS8mabTqKdoJtOj1s/X4sCyWiZQUjigbbGr6W7CsiXgdFCYrmXKAoixI93wHi8td82wrO7SLtpzDANybqOmMOis07IwmnSOadkXo5ix4KaQ3wFTJPYdnekkYj+WTOPDEYDAZDASZPGYizKDZ6nqIgo2DpTenTY0LfQkJGvUcLaMv27rSIM6jSAW2TIAdefr6NTACk/54RESg0HNRGxk6S3VFmEn2/fCmmpvhBZSWiRMGI6HPEAwRVmWBMhgv3PBH2Exg4RNBTdrRxiJDRy6lB1K2WSmf4Wr9sL3lfY8EIBoPBYKjA5CkLNoOzEo3Vvu+XLm/RVszqEE+2C5d4BS6omkHVl1FPllJpNaNbKKXKklHXmdvl2yQoWUSdmt3p9mfg2FEpHBQ/oJJOv6RqnOf76MrmAqXKHfsy5RoZFAvXZ4Kop8pXbSo76tzXCgm6AHZLpVMlvwBwNJzxtCS9bly2x2AwGAwFmDxlwGbzMjL+kJOD7U47/mfRk23KHJlCghElgx/xszIx0Ieif+JuRQ5cfN80sdMYDlqVjHqcDVD11xWUnhdtk5WvxiWFRTJPBVXjRoi+lu79q5QEUIl6qh9LR9nRqihDAen5xIDtwj1PBTNPKdtYMILBYDAYKjB5yoLFnqfsoap62Z1CtmmIH0RBluf7cSO2MgAqmnkizmpxRrIFI3RmY1kTs0itW5HZO3b66yTy7gpiJ5IabZJCkY/OkuynipMUzCSO1DQH+KYJis3yM81+rHSWk0TUS9/XCOWrGT1s6kOh/tBjKz2TGddB3POUUtvjzBODwWAwVGDylIXCc54oM0eKZ3cSAW10Ak1EkUyF+FxyAKQZ0OrOaun3emgIH3TKEZTiAS1BMKJgNoDWxJ9FUIikUxDZ0JEDBzRLqcI105Gej7N1mvtZc100XH3xA73MU7HrQLcfa7C/jnCIUPAa1erlLEDUE4dCNlREM66DOPPU4MwTg8FgMPTA5CkDduehJIMfQKOnQshSFA1oZQ3peUFjYJudJn7RNq2AVmM2ll8y8zTTamN6ZlZiW3ZASyJ2JZUAf3j4aL5tAqmLxDjIthUsPxPfW6cMU0t6viBBIds2UIapT4YLlzpqy6gXmPNU0La9Ul/Lz6iTD4XqdTgaQ4/F99Y9sIrI00DPEwtGMBgMBkMBJk9ZqEIBLavXQ1XmVVDOFwB6flBy88TssfwnDaiMBQFGzXFQd+WuEmdQCtgWZQN+eDifoAzaVkAGWTNo/Pfd+wAAh1sdnHzTp7D9vt3ZT0yp2ulkKoqqFN7zxJMAgJ0/PJBrW0z+fT8WgCCLkxQc3gsE5Z4AsP/oXP6TIsIZlmHqSM8XVY0DgLobHDr88PDR/CeVKcMsSNQj4ZSZdkdO1HPmndnsx/rG44GvfXmvzNdCgiLMOyPbVoIMx74mua9lza+b7QTXw9JmSqp8lDNPDAaDwZCDyVMGigaOUXnLvpnZ3AAoVo3r9eCHf8D7Cmi0Xg9du7bftxu3PvIjAMD/u/NbahKQnldksZ9i+3278eRcsN4v++cv5tuWyjwVOXHXCc6mZ2Zx3Ze/GT/2fB9vunVX9r7GtiV7ZHSUAHX2dHpmFh954FG1beL3TRFi5Z4WDLS337cbn9szDQB491fyfS0tMKAjPV9UPXH7fbvx9HwguvLSj+X7mpNSAtS6DgqKk3z24YCoz1CJeoHy1SL3j+mZWXz4ge/Hj0m+lsqKkfslNcnw9vt245ZHAl+T3tcy5tfFPU9pwQjOPDEYDAZDASZPWSiYqaCc0GYFGVEJmo1SqumZWVx96674sQ/kkoB0aZxOP0WR3p20bZ4vsS0lMFDkxF0naNxzcAZeauxUz/fxyMGZQdtSJUs6UuVF9nTPwRmkJ2Jl2ib4ry7p9AuUE0b7Gdkm87XB8jMNElCAoOj4Wp5gBC2TqC+jXoyoFyB2BXqeyL4mmXe24Pe1jPl1/TlPLBjBYDAYDD0weUohMRxU84856YQ2EWREJUtElbECGbGABCTDnzwSkCcYodW7Y922iARoCEYU2M8NqybgphIgNcfB+lUTGbal5gIRZyn53S4QzhvS2dMNqyaQzs1k2eY4Tu6eKglxATKss5/lys/0CYqWbemyvUJkmE46CxF1TcGI5H2NbhvZ1yTzzmz0S2rdO0aTmUQgv+eJBSMYDAaDoQKTpzS6naBHBPokhXJC6zhOP3jRJSkFep4CEpAMf/JIQF7QaKt3R8u21MDXIn1FOvs5NTGOm7e+MGHXzVvPw9TEeIZtyZ4K8uydhHoifU+nJsbxuy94Nsm2wuWOBQJanf0sI3xQhKBo2TaaU7ZHycDaJuqp/jryuokz4jRsm5oYx/96wZkJu/J9Ld2XSDwUCjNiOv2SRXwtkXlqZ6vtsWAEg8FgMFRg8pRG1LwMaAdAlBPaxPuGKmja8tGaJOCDW8+LbXOA/OAnFTRqyYEXyIpFtkVwHYlteYIRBAW0og3pv7FlI5aEp+Y7X38ptm3ZmP3Egr1iYgO7bonoq551CgBgzfgoHnvrK3NtE+eK+b6vkeXUzzzp+FqacBYhKLZ8bXBuV4Ghx9aIerK/jnyIUGJG3C89+xQAwHFjI3JfE4i6eF+zQdT1fI2utuewYASDwWAwFGDylEIioNWYpTQ1MY7/dR7thLY/42YebWHwJF1lTC/42bZlI971s2cDAH5+40lKEuCnerG0GuU11fa2bdmIl522DgDw3gu2kAIzQK+ksKh8NNBX45oYyfeF9ABf7V6PegMORclNwHgzCPp6vp9NNiNEAen8HDqeF2dH1YIRxWZjbduyEe9+8XMAAJdvmMr3tXSpo04ZZkExi21bNuLnTl0LAPjjC8+R2JZdGmfTtt/YshFLwz29Q0bUCw7wLTMjLsrOKH1NIOrifU1ZUlhiP6P72is2nkjaz0iyvz/nKaW2x5knBoPBYCjA5CmNmAQ0gzp+Dbz6jFMBAKsVJ7RikBEFP4DdWUrrli4BAPgDxYV9pE9odSSaiyoBAsBxS4LXNGUBYE6Zly1FuwhR4His081/Uo5ghI1MYoRIJSwqP8qDWO6oJQce2VZAev6EZaGv5btafz87bfieV0zZsUCgS/K13CG5FOGU4hLvy0Kiviwtny0gd4CvBhnWnREXZWdmZdcAhGzi/FzivkbNPBVZs9jXpHYJ2aRwHWK1vYGyPX1/ZzAYDMbiApOnNEoEtFE2oOt50hNaMQCKgh8H/XlHeYgCsyIBbWSbNABKndD2AzOdWUoFiECDQARG09kd+5kKoE+epOsWz5HRVLQrmEkE+vs51+0NNM4nIOyp6GsjxH6sQoST4GuJ923N6xH1qEemQKA7TvA1xwBRLxKEk0hK0XlnJWbERWvW7nnoel7+EwX5edHXmjXFn5lOifuazuFGaBvQn/OUUNtr6h+YMRgMBmPxgf9SpNAPzCwF2kAiAIqDxnpNfSJcJqCNg4z8Ab6J79xpF5pXVIZ0SoPtvL4iHcGIMsROZls8R0bTtoKlcUCy0X1OSlL6RF0knFRfK0Y4g8yJPKAVswHzekTdsq+V6f0rWoIG0IjdoAAIsWyvTPa12d8TqW3CMFqxJ1HlayZsk+5nvQGEpCg6fIl7nkTSySV7DAaDwSCAyVMaJU6Oo+Cn6/lo9/JJihgA9fuK7GZ3+sSuk/8ksVm6Nac3iLZMFkXrxD1VUmib2FFOtnMGlyoFI0pkA0R/oWTFghJR/cHCZYi6lHDW60CYZfJbmraV8rWG2jYDvX/WMnZF552VuHeM1Gqxsp3sOnCEYbRV9IkBtPLVQOE0eY1mqe05o0yeGAwGg6EGk6cUSp0cE09onZzMk9K2EgEtqTSu3gDCIMkXyrwovR4msijyNUupjPVotvm+35dpLrGnNNvmErbZ7PVwHScOmKnljnplmMUUCgHECoWqfixRNEJrzpMBMizP7mSXxqlKCn3fL7VuRfrr5oh7WqYXy3Gc/p4SibpWZth2JhHJuWI9z4vXbYlwz0aDyRODwWAw1GDylEaJgLZZq8V9SzaCjFKBGeVUWxyq2tYkdqX6d8Iyr6757I4oPV+KdNrsQylYLqRb7ti3y65CYbSfyoBWyPBo9bB1TFwH+RnYNBmmEhSIQ4/L9GMRyzABQXWSOlOsQJZT2zZBMIJ0Xysw50nHLgCJ+4dYupzIPBW4PzAYDAZj8YHJUxolAjOA2iyf3YeiQqmAlnKqDfRLCufn9MqVDJQ7SrM7A4IRmnLgJW2jlO350SwlqmBECaJOtk3McuoQlFYJMlyg969IpsIWQcmd26Xla/aJes/zYklwm0qdgFDuSMnYaR68lOthI9gFJEqlReKcIMRMnhgMBoNBAJOnFMoEZkA/ODvalvQWFQwaqwhok5kKjdPjUmp7eiVBAF0+Oi4JcpygLFETpBN3QWCg6/mx+p3NPpTANkL/Toayo9Z+luwTkysBiocI1fTI6JbtBWRYT5QhsM3SIULGfpJsK5EZBvqHQtKeJ0EwQqeX01S/pC/xNbFUWpQpd0UxCy7bYzAYDAYBTJ7SKB3Q6gVAhYLGEuVnajGLQWJnX2CAUOYVyyC34As9C+TMU4H5NgAxG5Azt0ttW/E+scA2PdI5p9HDZkJGHVApAWYdIhBss63smJoLpJ15KjAjjmpbVskvybbSmScNlcLWnJb0vIl+Sc/30SKI9KA1JwzITfqaIwrmMBgMBoORAyZPKZQJzADNAEg3aIyavq2KWQwSO5pghN1sQIKUaQS08X6WJMO0E/eC2YCyfSjEcketUqoSAa04eJR2HcwJ0tYaPTK2lR2BZK+YKotSkgzTDhFCX5vvX58N10WNOCOuaOZJ5xDBT0jPa5Qjl5hfB9Bmd/mt+fg7LG2m9pMH5DIYDAaDACZPaZQIzAC94Mxv6Qa0xfuKmrUa6q5ablgkdpVJWxNKghJzgVpzGoIRZsqVKIp2onriSM1VZ7pKE3XCPKUscRJKQFuCdFKVAGOi3tbLcpror5OuWUp1kly2Z4gMS4m6MIi2WKmjRaI+0i9fLTIjrsi9o+668RBe2Qw7MSuWJVMefD5nnhgMBoOhBpOnNEoEZoBmkNGe1xJlMJVFkZcsZZwe21ZAKzAXiCwHXro0jq5o57er6/UA9MQPRHESnZ6nwr1/ESGWKShmiFnYHnpMUQIMVCcLyKibyloT+7F0yLCpcmTSfhYmdmWvA4mConCNxmV7A5kn7nliMBgMhhpMnlIo31hNL70JMhXhqTapN6D4CS1AVfMS+xaIJ+6+XypwJNkFZJd5EQUjyhIUaaZCEIzQEtkoGWxTep4cMStWKLtTTe9focyTrTlPwnuLA3yVvlaSBMQDX6lKnRr3jtLKjpR5Zxnlq1rlyFXc1+bFnqekgEzRgwIGg8FgLC4weUqjgsbqTAU00ulxSRl1DaWxpGKWwrZoCC3KzWpRqbNF6+bpCG2YKlci9npolTqaUtuT7WfmTDGNrJjNgDajRFRJUDwP6HaSr9eA6GtSdbbwvb35Y7EcODXLWZqgWCjDLHvvIO1nonyVWFYLmJtBRbmvtedzy/bAZXsMBoPBIIDJUxpl5zzFf8glUuUZghF6g2hLllJReio0sjsx4QRKKaABcnW26L1783NkOfCypY6ksr2RYgSlimA7a3BptaVUtICWfB2IQ48LDfAN7PLRnxWWiZAItOdm4x9R++vKD6KVlJ+JAiAFyHD5QwRaaVyRfkmbIj1OQm0v+A4D5IkzTwwGg8EggMlTCmXnPC2l/CEfFUpvdIJtU4MuNWW3qY3yAIBGU9susjpbGAC1Zo/GP6JmnuwOoh0s29MiKBYFBrIGl9L6isoqx2n0/un0Y5UcRCu+P6UErXXsWPyzqoYeUzOJVQ09Fm2TijJk9onRlTqrEOlBK7/niQUjGAwGg0EBk6c0qjhxzyi9qaLMi9QjI5btFWiULzJLSVRnk2fFAtvac/SAtmzvzhIKCRDeuzU/B4BIUEoG27SsWFaJKJ2oF89y6vX+Ua+DOEvhugAlME+h5rrxZ8iyKJG/tI8Fmae666Cumt1U9t7RpBCU6N7RwnyRIdYViFn4uv11FRJ1X6K2V/TzGQwGg7G4wOQphfKN1UHQeJQSZOhK+pYt8yL1BojZAN2+ouLBB6nfI7QtCmhH6zUlWSvbu0Ozq//e7dm+bUpUWBqnLRhRgW2iwAB5lpJQVluEqJNtC7PDnZCo2x7em7RLUhonZEeirJheyW9ZMQuCbS094ZTSEu8Uoi6USucOyeWyPQaDwWAQwOQpjegUtHTfAlGqvEhAW9i2cC6QRG44M/OkCGjLqmUBQnBGyPBEmSetU22banuNZjwXqDsfBtuURnlTwTalv65dnRw42bYRQWBAN/NUItDVKY/rRJlEy8N7AU2RDYi+Ru+vszr8W8wkaoxgqESkRyiVzpcq57I9BoPBYKhhnTzNzc3hhhtuwJYtW7Bu3TpMTk5icnISGzduxOte9zp8//vfV75Hq9XCu971Lqxfvz5+/eTkJNatW4cXvOAF+NznPmfO4LLlLdoBrUbTdwVywwnbqD0yJUldwjZCcNaNswE6pY7FbIsC2lbPQ8/zMp8TzAWKiF1UtlfBnCdCmZcoMKA1t6uCJv4swQiqrxVdM7ptwbp1oywnZZSAwRlxeUqATqMRzzvrHNO4DlrlrlFKz1NWJlFnBIPVAw7hvpY/JJfL9hgMBoOhhn7TgCZe+9rXYu3atfjc5z6HdevWxaU2s7Oz+PSnP42LLroId999N9atW5f7Hu973/uwZ88efP3rX8eqVavin/u+jwcffBCvetWrMDU1hec85zml7TUV0JJOQef///bOPEyK6tz/3+rume5ZmGERGYYZGJFFQERQvCF6E1yuJhJFcItKjIlbNBK3a9RoxKvegIk+XIOaqJd4RVwSFZQYNAuC+nMNCIoaWQSEYRaBWXq2Xuv8/qilq7q7qk51V1U3zPt5Hh5mept3zjlV837Puxyb59t4UPOkOI1ipBfxEr4WzfmKOsBexE7acS/3tFBesa0qaNAQI1gGRPqQzCUq5kU7cI0Y5jq3S2k/72KDAV2aV4iz+UGeok5rG0/tXyLSB2AQ3yHWDt07GIBIImkcUSoNAX09iEfsrDWHmlmY1YlliajzHRbtQYt3TdfJbrXbXknaa0g8EQRBENa4Gnlav349Ghsb8cgjj2DEiBG6GoWKigpceumluOGGG3D//febfs7q1avxX//1XzrhBEg7/pMnT8aNN96I5557zhmjPUghyRrdsXDOGGMOFFZb1wYoDkxSTlcC+KMBLJGA2NqYo238UbFkxEZ0p/1r6f+kidNnQijgh7JqTZtZlGqdbb5ogNjTKX3R05WTbXZqZGw1jNC0A2cd+12zTVnHYrQPCZGz9bwTkacc1hpXXdEBZa1ZHMBrYRfA1zglaScC2yWtNdbbY/HK7HA1Tsl6sLCdTaEco8M8Kb+arpNq5CkjbY/EE0EQBGGNq+Jp8+bNmDlzJnwmXapOO+00bN682fRzLrnkEhx55JGGz9fX12PPnj0526lF7AkDAFi+Di2Xk9Gn1gZYOmeag2hZe34OLU96i1Y8WdmWePPPkl2NX6LnzFGIrVxq3za56NvUNjlil+SsQ4mtXIr4Hx+VbHzlyZzsEgSBy3FMnUHFFw2IrVwK9sUmAEDknitzGzMbdsGGQxtf8YT6de/5R+dkm50uhWIkoj7Ge84TS8RyF+ocDQYEda3xNYyIrVyK+PO/BQAk/rwspzHz+3wI+n2WtilRlGSUT9jFVi6F+NmHAIDowmtyvD75ozva1vOWUc5EApBfyzrbbNult42nvq7PuGEEtSonCIIgOHBVPDU2NmL48OGmr6mtrcXu3btNX/Ozn/0MARPn5dNPP8WYMWMMn49GowiHw7p/2ZAc2o3Se+7N0aG15WREuLtSxV96XP2694LJ+dnG0dJXlAVKic8Hv4n4FVsbEf/TI5oHRETvu9q2Y2unO5tim1k9hdjaiOh9V0FKggLAWE52AfYO4RSj1sIuZZsMy3HMlJons8Neld30RBxRWXiYObRiayOiD9ygeSA/23hTRBWsnO24ItT35CHUbUSHWdQ6kqjOp1qnlPtas5OKKXJEYDPXWm622TtTjL+NenyF5r72/WNdm09tVMxIPNEhuQRBEAQProqnZDIJv0XBcCAQQMKk+5sV77//PpYsWYLLLrvM8DULFy5EdXW1+q++vj7jNU47GVxpe/EYovJuqRcObardsLUDJMrpi1bOrLh7m8ZpVB5MQtyz3Z5tNuuxAAuBsnsbkN7gIQe7AHsROy5h55BtPM1JlAgKkKpNMxUCTtvG4dAqa41HqCdeeFTzQJ5C3WzcbFwHbqw10w2OkH6Dw4vrwM7mhtY2y/va/fM1D+Q7n3xRMaO0vXxSQQmCIIj+g23xtHz5cgwcOND03/Lly92wVUdfXx9uueUWzJkzB08//TRGjx5t+Nrbb78dnZ2d6r9sKX5OORmVSkoQh/MDeOuccUUDlB33GF9qnG/kWLVNd+pBP3z1xpHArLaVcIybZvcYMB8z38ix0kGqedoFcB5Gq2mFDJjXFTllG08nQG0dh7LWLG0T8rfNjkPLOKJ1gHNC3VaDAQ/nk9s2RahzpO05vdYSIkMsaRDpDGrva9bj5uV9Te0EGEtFnpR7tQo1jCAIgiA4sC2e5s2bh46ODtN/8+bNy/rezs5OjBkzBuvWrcvL6L/97W849thj0draio0bN2LmzJmmrw8Gg6iqqtL9S8cpJ0NxGuOiiHjS2qFNRQO8c2hNi9EVYcfZ+MA3rA7+U+bq7Are+Rh8w+rs2ZaLQ2s2ZsPqELwzlRIEwZeTXQBf57j0NC9ThzbdtlzHLK0TYDYEvx8IlMi2Wddj+YbVofSyn+dvm81zgazsApwU6jaiwxzRutR8yrbltdZ4mrqk0n75bVMeyM02bZTGUBAHAql7aIzDNqeFOke9JCJmNU8kngiCIAhrPD0kt7q6Gtu3b7cUO0a0t7fjwgsvxK233oo//OEPWLZsGWpqahyxzTGHVutkGBQwC4GA5GgAAKezXXrZrfnbZrMToJVdCv7RE6X/Z85GxepdKJ1zuS27tLbxdBkDR+0OAJTOuRy+SScAAIK3P5KTXVrbeDrHKbZZic6S712qfl3+/MacbOPtBKjYJkT5bPOfcIr0+hFH5DGfHCIgba1xCfWT52geyPM64OzOBnCutWO/CQAI3rI477XGM5+M07aSc36sipqyZe/nZFup34+ATzC1TTrvTC/sLO9rV9yhecC9+YQm8tRrVPNEDSMIgiAIDlwVTzz1TIlEwrQZhMK2bdtw4okn4uijj8b69etx4oknOmWmSslZP1S/ztWh1ToZ3VyH0dp0aGsbcnZo7bQbFjgiYgqsT2p/7Bs1LqfddsCesBM4ivhVROnzfDUjc7IL4B23VE2FZJvFuEVSLaN9o8blZJcgCLaagPjifM62Mp/C4GHuzqdsl8DZNQ4A/KMnSP+fPCd3YWcjyilwCjsAgJzOJuSx1rjmU74OfLxrLR5T0+P8I8fmbpuNOjZfXLmvmdvm/8Z/SF8Mq8tjPq07daZH1KX3BcC0aYN5HPJNEARB9B9cFU91dXVoaWkxfU1TUxNGjjR3NuLxOObMmYMFCxbgl7/8pWUTipzpy9+hBewVVwcTfFEU1aEdkodDa6OlrxDnswuAOm5CWWVOdgGcXcbSHVqOg0vVOS2ryNk2O13tBM6omDKf8PuBEoODdzmw050tJJ91ZelsK/NZnvt8qmPG4WgLnI42ALDebgCAv2G8q8JOSJtPnsNeFUHs1XUA3utAc19DKPfrwI5tZcpas7JNnk/foKGebLywWEStmysvCejONKO0PYIgCIIHV8XT5MmTsXbtWohGxewA1qxZg8mTJ5t+zrJlyzBx4kRceOGFTpuog/VJf8jzdWgrOJpGKDn4IZHTyXDAMeOp3Uk5jfw77sq4CfkIFBsd0OwIO+aEEMjBNm6HtqxCd3h0zrZxpHmFOB1aRaA4M58ckUROwQnAGTHM0ZxEGTO/khrHsWHj6LjxpGEq42ZhmyrUAyUQSkpMX8tlG8ecBpPWXUS1tuUzZjydOpXoq8AYSsUEygJ++AQhdUAvQGl7BEEQBBeuiqfjjjsOo0aNwjXXXIO9e/eCaTpl9fb2Yvny5Vi8eDFuvfVWk08BNmzYgFmzZrlpqoSXDm2akxG0coAcdsxYetcyBVnU+e3suDsY3eHZ1VZts5FSmI9tdnbc/VHrVuVau4Q8IgEAb61YSqj7BQEBk3bgAByZT2XMzBqnKKlUPjGJgOzQWuGIGLZxbpePVwwDDgk7HqFu7xpVr4E8xgywF00sS8YQ8NlZa/lsbkiCMJJIGned1ESVQmJc/V2Y5oBmgeN+QhAEQRCuN4x49tln0dDQgFmzZqG2thY1NTWoqanBlClTsHr1aqxduxa1tbXq67ds2YLRo0dj69at6mNtbW248cYb1fdm+/etb30rb1uZA+lnAF+DAUGT3hIK+K3FmoMOrcgYokbthpV6imQcPibai+64vOOeXk/BFalwUHSaRuxU22IAOKI7DswnYK+rXTAZ5xIBTs6nZJvBdaDpOhmSrwNL2+T5dESgcDQA8asphR6NG0+KqN3rwIFrAOBrAgKNUOeaz4hzYwaYXKMlpWqnxlAynlqf8Uj21xMEQRCEAa5vtYVCIdx+++24/fbbuV4/fvx47NixQ/fY888/74ZpGTjm0HKdcZNyaD1LP0trNxzKstOqzfsPJuO2nEYnHFrzLmOSYxaI86UUsngcSEgOsjPCzrpWLMDZlMGJuiJAc/AxR8OIsmSMK1rnhFAv9fvgFwQkGUNPLIGBoSzF+EG9ePKqroir9bw6n5w1bKIIyIc35xPh4eoEKItO7uvAAVEHcKbHaSLqdmrY8llrZXLXSQbpGh0QzExNlDoBhoBInyyelPb9fRmvJQiCIAgzPG1VXvQ4tUNr49DGMjHG18nLAYc24POh1O8zt00TDSjjFHZONhjgcxql6I6lbZqOdo6k7XHUevgTcuTJwnF0yqG1c6gqdzTAAdt0nQDNWluXBlXbuIS6E5FErrQ9ea1xzieifWojgvzqd/iFemqt8dfX5QPXplAoFVG3c1/Ld61xXaNKVCwZS20kRaPGrycIgiCILJB40uBEdAdInVzPc2ij3XSl/FNvzCM82jOogrwObV/+u8d8dUWKQ8tX8+RURzueVCrV2Y5zOrQO7LgD9s7HCvFGEiP516FobePtBOhdwwjruiLFLkU8cXdPBIBQef628VwHvFExJVqXd32d3BLc5PgJNfJkU6g7VY/Fs2EVElNpeyxGkSeCIAjCHiSetHi4Q2s7vUVxaPN0gOy2trbXqtztLmOS4CxJ8NUVaYvRHWkAYuZsy7aV2m0972UdCmfNk3M1MhzXQSgVDfCsYQRXfZ08n3a7J4bKIVg1SeCxjSMNM5hUrgO+1vOOCRSOdOSyZKwwa41rwyqWqnmKUs0TQRAEYQ8STxrUltsOdUDjOw8l5llqnM42DucsxFkj40TnOMWuWFJEwqBjlhJBKeGNBjjkmPEITiGteyJv6/l8hbpah8JTX+dh2h7gTkqhExE7xa6EyBAzapwij1kp59lYjkWGbTQACfG2A3fsOrCueYIq7OxtCrkdUQc0kadkqtue9pwngiAIguCBxJMWhzugdZtGKlIOrVe1HlrbuJwz0TpSwRhztFU5YOIAKY5ZIgYwxl0o71xqnHUHtFDSXppX3kLdRtttLxtGAJo0L+6UQhvOdh6bCOWaSI2h6JQd7RIxwdd10vG1xlEvafMsJafmkzc1zl4apvtpe9oDfNW0PWoYQRAEQdiExJMGp2qe7KQF2W3K4IVzZittLxYF5EhRPuMW9MuHVsLYoVXbgYOhhCX5O9o5lhJk3XpeOfTY8twuDwUKdCmiHkaebERgedYaSyaBiOzs5iE6S/1+BHyCqW1KOqFim1cd7eyMWVCMI+j3qdeNIQ7bZrrWck3DzDcqxtV1MrVhpabtUeSJIAiCsAmJJw1OtM0FOLtSaR1aj87eAThT0LQ1MpwpQQDyGjepY5ZFWlAw5dDyRFEcHzMbred5z+3ywtkWQoUR6lwphXauA6UVODwYt5JUa3WuWjGH6op41pr2IFqvjjkA7HXEDCUTnp3bpbONM1VaaepDkSeCIAjCLiSetHhY62HXAXKs6NtGNy+uM6gUu0qDECyiLXnbpumYx+VsO5yGaX5wqb0WzU5FnrjqUOw2J3EsAsuR5qUIO9H6OlDHTBDUCEfOtikdMY2inGldJ0OWkUQv02o1dUU8Z2OpzSwciu5wRRLtpYh6ErHTXAfUMIIgCILIFRJPGrxM29Om3vA5tB52QNOdQcXZDjxPuyTbLBxa5aBLyFEUztS4fA5Uleyy7rYn2GzK4JzTaD5mWtvKRL4OaGrreQ/qsWylFGqiFPl0TwTsN3WxEinMIYFiZ3MjxHENSLYVZ72kZJtTm0Ic14EmAqv8LixOaXsEQRCEPUg8aXHKabTh0HKf8+SUc6ZEUTjPoOLtGpevQNHaxr/rbhV5ciYlSEmliiZFJA06AWrrxDyN7vDMp2bMvBV2HLViNurrnErD5LXNlrBzOFoXSSQhyofupqOtr+NqB16AZhYh7oi6h8Iua+SJ0vYIgiAIe5B40uBpi2YbdUUAHHPO7Lbd5o/uOOfQ8jQ/sJPm5ZQIkGzLnrqna+9eAIeW99wuyxq2ZDJVRO9F7Z9WqFuJToeiFLy2sVL+1Fqn7x2AyXWQq1D39Cwlm81JHIvYWTd1KRPpnCeCIAgid0g8aXG8rsh6V5unpS9LJFSH1stuXjzpSk6cu2PHNmajtbVTKYWhgB9KkpjhnNqOJDpzphiXGA7xd7RTrwF4VPtn5zpwaMx4bVPEU1DkaH7g0HWg/TlWXSd5oztOpcZxNbOwIeycOuYA4BTqmlpONW0vRuKJIAiCsAeJJw1OnaVUydOVyo4DpHFoPXUyxDg6ozHTz3My8sTjnLGSlHNW6rdYvg7ZJnUCtLBNdhpLWRIVPFdVISJPHPV1uqYM8jpw1TZN4xTuc7ucjDyZ2CbKHfcqWQJ7u3oNX6e1Ld+15tOuNYuuk6FkHGVW1wDgQiMcs/PrUimFXh1zoLXN7Cw2QXfOk5QeSZEngiAIwi4knjQ4faiqU2l7ql0+H1AaNH2tpW1yl7HehLFtX4Qjqm2z/7QGSzduNf5ARxtGWDtnSVkI2HFoHbHNouOe9lygATCoi8pim1MOLW8dSjRp0jEQ0EVfnWrKwFuPZSXU4dDmhtY2M6G+Lyn9/oF4FKOWvMh5Hbgv7JSNFz8Y+iIRNIZ7sr5OgTlU+8dzWLS2ltNqrTl1zIHWNnNhl4o8Kc1fKPJEEARB2IXEkxaHi767eSJPYhwRsxbYGrucdGiNnMbGcA/+urddsi0Zh8iAq1e/Z+igOdX4AOBLpdqXkIro/Ql+h9YJ21KHcBo4Z6WpKE28r5fDofUuXWn1nv0ApPm89+2PTcfMqc5sAN+5QB+3SeMQEuM454U3LGxzsL7OYq01hnvwZU9MtU1kzOI6cEPYGaWIpoT65sZm/uvAwU0hZtDM4rXdylqLWa41J4854Elf/ecBaY7KxBjOfWmtZBtFngiCIAibkHjS4FRNheKYxUUR8WT2KMRrGof2Hl6H1oFaD6tzgba1hdHnk+wPiZLzlmQM29vCBra5EHkyEXZbuiWHtixpw6H1oEZG8PuRkMftUw8dWsVpNFprjeEe/Gr9NgDSfDLwiWEn5tPKoW0M9+DFnV9LtiVjlkLd2YYR5mdQbWsLI+IvkW3z+DqwSEHbG0nZHOS6DpxpyqDMp8hY1qhSY7gHCzdsByCNGe9ac2TjhWOtPbu1WbVNWWt9veabHARBEASRDoknGTeKl4HsaSSN4R786p+yQ2vDyXD0LCUDJ2Ps4CrEZKcxKDuNfkHAmMFV2T/QSYGipAUZpBRuawsj4tPbZubQupFSaOTQNoZ70KsRnWYOLUskgLgkAp3sBJhtrW1rC6PXxy8C4JCjrbXNXKjbESjOrzUjoT52cBX6/KWybdJcmV8HbrRRNxi39i5E5LVWJttmNG66+5pDdUVA9nHTbrwEOTZenDoaArCez21tYfSqYjg1ZpHu7qyvJwiCIAgjSDwpOFi8XOr3wS+n11k5GSEL5weAo+lnVnUodVUVmDnuCACSY+YXBDx25gzUVWV3cFxJpTJxaKOyA1TG4dC60szCoKZiW1tYtc1S2DnYAES71rIJO60YDnGIYS8jiWMHVyEWkMdMTPDb5miKaPb5rKuqwIghgwBI48Z9HTiRImqx1sYOrkJEFnaKSDEct2gkdV/Lc04DPp/apMVorUUDkl1lNtaaF2mYRtdBGTOpxyMIgiCILJB4knGyeFkQBNM/5pKTITV+CFk5PxrbvKpDGVNzOACgShCxa/55uHzqOOMP9DDNq66qArWHDQEgOY28Dq1XQkCJVFgJOycbgFittbqqCiw47RsAJKHusxgzRyMopeZRzrqqCsyaNAYAn1B3pWGESY3MgMoBAIAJVSHr68CNlv0Ga62uqgJJuRNgmYWwYw4KdZ1tWYRdXVUF7jrl3wBIGwi8a82LYw7qqiowe/J4ANI9Vxmz0qRFkxKCIAiCSMP6hMX+goPFy4D0xzwcjRs7tKeeAPxN2gX10sngKazuhh+HA6hG0tgmGS/rFgCgaoDk0E4aEMSu+eeZ2+fCzrZRt726qgqES0NArxRFMRUCmvnMtwEIIM1pOBo3bDBwwdQJ6AVQJsbx7g+/g3+rG2b4WV7P58QRNQCASpa0nE8vxTAA9MgphbWlAv914EEUBQASsni6fEI9zj3fZNwcvq+VlwTQHokZ2na+staSMW/XmiLUTeZzcl3mWvtaORCaIAiCIDgh8STjpGMGWDtn502diD5I4umDy87E8SOGWtrm1Y57lyC9poInpcXDHXcA6JZtqyn1eSrsLLvtAUiWSJGny8bX4k+XGDu0LKLMZ/52AdZzqqSIAsDEqnLzD3NhPnvjCYiMwZdFKHaxAGoADEDCcj4dTV/lECg9giQ2KplFN0wAiHjXqhyA2sxiyqAyzwSnzjaDa1RZa34wTBw0wPSzmEsHbDPGsm5KdMKPegCVLK6OGaNuewRBEIRNKG1PwWmH1sI564HkmPnAMHGQhfPggmPWa9JuuEteFuWitXhyY8fd7AyqHlk8VfIIO4fOt9HaZubQKo0Zjq62cGgddBoB62YWHSwVcaiA+bi5MZ8A0GcwbmFZoPCtNRcaRphGYJW1ZnJ2kGKbCymFZudjKWutykrYuXRfM7pGO1jqT0qFaDFuLmwKiYwhZtDhtFO2rUy71qJ9ef9sgiAIon9B4knGSecHACotutq1iamhV4qrvbBN59AapKB1ys52mZXzA5c6AZpEnrrAJ55YPAbIDp6zDq2xs9orC7sqge/cLqfWmlXzg/YEgwh5J94qTUkR6o60xU+tNaNx6xQVh9Z6rbnTPdF4HSnCrsJCoLBkMjWuXgl1ea0NsFhrTt/XrCJP7Zr7GuLmay0l1J1rsgEYj5uyiRDS1jlZ2EgQBEEQ6ZB4UnA6vcWiMUO7xuexOuXeSYFSFkhFIYxsU3aPQzwObcQ5IWB1BhUAdCLL7nE2HC6Ut+qABqRSCgdYOdsOr7VUSqHBWovE0CeneVnttDsZefIJgrrejMatTV5rQY7Cfa9rZLg3ETRrzZHrwGI+GWP8EVin15pFxK49ElPbqFseQBtxzrYSvw8lPmktGY1bW1LaQCjVbFaxCEWeCIIgCHuQeJJx0jEDzLtSAUBbJKaecWOZOuKgQ+v3+RCycGiV3ePShA2H1qNaDyX1ptzCoVUFSiAAQa5FcsI2owiKyBh/rZhrkSdjh1Zpo25V4+G4sLNytpnk0AYtoq+AezUyRihrLWQVGdZ2TwyG8rfNYj574wk1bc+qHstJMQxYj5sk1OXrzUqoOx0Vs4jAHpDns1SONjHGAIuNK4IgCIJIh8STjFt1KN0m0QDlrBZLh1ZxMhwWdkZC4IAN8eRogwGOaEC7HA2wjFT0OZd+Jtlm7jR2RmKIyg6tVf2OWw6tUQpaeySqHi7MLdSd3kQwmNMDCUk8lfCsNY87ASqptZZrTXMNONE9kUegRNQzxTivA6/S9iJRbqHumm0G47ZffjiQiEnCKZEAxOx1nwRBEARhBIknBY+jAW19UdUBstz99NjJOCBrqoCtyJPzHbOy0S7KkQoL25yOJFp122vri6qpcX6rOgqP51Mr1K1SqdyLBhg4tPJ8lnDUnqgNIxysK4okkkiK2RsMtMkPl1gIFOZg6iqQqv0zFsMp8WQ5n06L4VIroR7jFuquRfsNhN0+OW3Px0SIe3ci/tJjuudjq55yxA6CIAji0IZalcs4mX4GaJofmEUDFAfIIu9edc4ciqKoqVQGQmC/7GQErAq+GQMivdI3LnTMCgb059IwxnAgKel9S4fW6UhiqXm0TpsaZyWGnY48lVs4jVqhblVf5300QFprfjkaYBS5YYy50jACkOZ0QFC/j9QXT6jd9krifELdiW6YgHW9ZFtfKpJoWS/p8HVgmYbZp4moe2yblVCf/PHf1a97zzoy4/no/fMhnnI2fMPqHLGHIAiCODShyJOCg22tAetd0DbNDi23Q+vw7rGRk/G17ND64hHDCBAASfTJzzvTMMK8Y1ZXLI5euRg90LEfYmuj8Yd5XOuhdWjFXV+Y2+ZgRzse29ojMSRlUcJa9ph+lms1MgbXQWsitb7EPduNPygeA5JJ2bb8r4NQwK/0H8xaI6ON1vk69pnPp8PROqvGKdrUOMs0TIc3XqzXWhTy3ovlWnOy2YyVbWJrI65Z+6j5B4ii+RokCIIgCJB4UnHcabRqH90XTTm0zRYOrUvtho2iKK1xWRCJIsS9u4zt0na0C1kcvsqBVces9r4YTtq3Rfpm1xfoOXMUYiuXmtrmeHTHaD4jMYzpagEAJP76R09ts0qlmvDuShzdKTn/kbt/ZGiX9AKnu06aR2C/8dla9eveOUcZ26ZEKRyyTRAE04hdW18U/77vXwAA9tVWrvl0buPFImrdl2rKkPxqq6mw81oMH/XuSkwMNwEAInf90HStMafrEk1sE3dvg99sIwgAfD746sc4YgtBEARx6ELiScHp6I7FDu3R77+MSeG9AIDI3ZfxORmO79BmCgGRMZy2/W31+97ZY4xtU6J1oTIIfn/219i1zUR0du7ZiWu3/01jrIjofVdndx5dGjMjwRlp+gqnt37CZRvzcK2JrY2Y9+oDapQFjBmPGdxztrMJO7G1EXe++4TmAY4xKw1CCDiTbWwWge1u3IWfbrO31rzsaDdeFijJv79gKuycP4JBrsfKckiu3bXmeHMSE9t8I8ciibSUUEGQOiTKBG9dQil7BEEQhCUknmS8PExSbG3EZasftO9keFC3EN6zAw9sWq4x1tqhdSolCDB3HKM7t8CPtN1jMZk11cbpOjHLg0v3bM+8mAxscz7Ny0Q87d4GH0triGBkF1yo3zGxLbHLxnw6XB9jZVvMjm0uNWUwqkmMt+zGGS0fa+ziuEY9aMpge615eM+NDanBz6ZdhoQgX6U+P4K/fAIVK/6lvqb07B86YgdBEARxaEMNI2Scju5UmqQrSU5Gdscs286nlw5Q9/YvUG3gNGbY5vCYaW3LFuFpGTwCYyHonVqfP2uqjepse9Rtb1dVDUQI8PHY5mEqlW/kWCQFQZ+yZGAXAMeFnVnzg65ho+DnnE+n62MA83FrGTQCSV7bXKqXNIpyluzdYSjUM65RxxtGGNdj2V5rjp8pZmxbe18MTx/xLbwx7GjsnDMV/pFj4RtWh3hPT8ZrCYIgCMIMijwpqM6Z+00ZFCdD/6CBo82Y886ZSY3MgaH1mektHokArW3Z0vZayofg9ikX6ewK3vlY9lQbl+qKokkxa2vrr4ID8b+jZ9qyzfH5zJauNKwOd37jSqgWC4KhXSyRkBozwJuoWFvV4fjZtMtS8sTnM7bN4W6YgPk1urd8MH4+5eLUAybz6XQEVhFPcVFEPJm51r6sHAaxQNeoWT2WnbWms82xTSFj29oj0rruHVyDkuknU3oeQRAEkTMknmS8TCHxDavDz6f/mM/J0HUZc7+mYl/FYdwOrdN1YoA2wpPNAYriidGnqN+XPfcRSudcnvVz3KoTA7JHBNojMfx5xPEAAKG2ARWrd1na5rhDm2XMGGN4vO5EPDLmdABA4DsXG9qlzifgSX1de18UTx/xLXxymOT0B297xNg2V9L2TJztvhj+98hT1Gu0bPmHnq01fdfJzHHbUVqNx448NfUAh7BzPipmvNZ+f+RpAIDA9y41HjNRdPSYA61tRvcOABhcFnTkZxEEQRD9FxJPCi45Gd1Z0rziSRFP1J9k36F1+pwngx3ap4/4Fr4cOhoAELzzcWMHyGHBCZhHA9oiUSR9fkRLpc5+PrMOfw4LFKvW1m19UXSWyPYk4uY72w53tDNLV+qNJxAXRTSWD5EeSBrUbEHjaPt8QKkzTqaZQ9smO7RdlYOkB0yajjgdpbCyrT0SBRN8iJUNkH4uz1pzyLZSvw9+OTJtZNuKuhOkb4bWmgp1x9uBm6RhKmttb/lg6QExe9qhZFev+qUXHU7b+qTI06BQqSM/iyAIgui/kHiSca9VufEuKJdDq+y4l5RCkHfK87aNY4e2p1JygAS/cVmc07vaOtsMogEAECuXHFrW1WFpm1PzqW1tnS3y1NYXRbikTPrZ3Z2mn+VazZNJulKXLDhNbdNEEo0Oq7Vtm9l1IM9nvKxKesDENncbRmQ/50mybYDntgmCYDlunfJ8CvGYqVB3Ot3RauMFALpKpXHguT4BAMEyR2wzvw4o8kQQBEE4A4knBYcbDPA4tKoI6O4w/BxXBIpJjYxiW6K8ytI2pw/gBPgiFSnbOISAR1Gx9kgMHUrkqbdbqh8ywPHzbZQWzSZrjVVUS/+bObRuzqdBJBEAkhXWYtjpSCJgEeXsU2yrKoxtJulx7ZEYwgFFqHeYH2TtsLDjua8l5bUGjjFDWQUEnzN/hkzvHfJ8UuSJIAiCyBcSTzLMpdPuY0kRibQGA8ofcrFyoPSzC+SYmUWekpUczraLDm1Wp1GOVIgcDi3rU1IK3ajHyp62p0SeAJhGKlw7FyiWyHCkU2utwNEdg0OPAUDkEXZuNIwwvQ5k0WnrOnDBtrTrgDGG9khUjTwhkdClwBnb5nzNU/paa5fXGiqsNze8TvlV5nNQiCJPBEEQRH6QeIJSvNwnfeNw2h6Q6Zwpf8ghiyezHVo3aj1MU2/6FNusnUY3GkbwdMzCgIH8trki7PRpe5FEAn2JJBK+AFjIPD2OJZNALOqobUrNU5IxxNK6syli2DeAfz5dEcMmUU6Baz69PedJGTfVNlMx7Lxt5QbCrieeQEJk6PEH1Roxo3FjjDnflEFzX+tL6K8D9fqssnHv8DBaB1DaHkEQBJE/JJ6AtOJlZ4RA0O+HTyn6TvtjrkQD/FUDAXA6jR6lUilOho/DNjd2j82aH6SPm2kUxc2IXZptiuD0CQJ8VkJAU+vhdItmM9v8VVINm2k0wBWH1rgToDKfXGvN47Q95TrguUa9tE0ZsxK/33oTIdIHyNEhp2wrC6SaemRuCkm2BQZIDUB41poX1ydAaXsEQRCEc5B4giZdSRCAkDPFy4IgGDvbsmMWqJKdDK8dM9MdWtk5q5Y7ZhVo99gspbDExri5Eg1IazCgzOfAUKllFEVdaw52tCvx+1Ai140YRTlLFcEZ6QXL0iABgLspoiZrrZRnrTlck6izLZaZGqc426XyWuMR6l5ExdT0szKOtabr1GnSLdAGfp8PoYCywZH9OiipVsYsLEVas9qmROtciKibpGFS5IkgCILIFxJPQEoEhMod6zIGaNv66p0MxTELDpKdxlgULBoxt83RXW3jaIDqbMsOrfc77tnT9pKiiM5oXLbNWjy5W8SvdwiV+RwcKk2lOxo529pCeRfWWnp6nCJQQopAMbHNnRRRs0iitNaCA/mjYs7OZ/a1pqTG6WzjWWseCLt2TQRFUNN+jdaaErUug2DSBj5X24yug6BmraG3y8C2wjQAocgTQRAEkS+ui6e+vj4sXLgQU6dORW1tLWpqalBTU4Nx48bhkksuwZdffmn5GYwxPPzww5g0aRKGDx+ufkZtbS1OOOEErFixIi8b3XAaAaDScPdY+kNeVjVIikDAOlLhlUOrpHmFBg4xtQvwdse9Q6mnAFA26DBu25xNKTRIpdK0QVaiAUZRFKcbk2TYZhB5qq4oT82TobBz8SBak0iinfl0MlJh5Gwr10CJz5cS6l43P5DHLVMMpxofCEodm0FHTPUacDDlV7LNPCpWVVkJBEOSDRZRMa+jnBR5IgiCIPLF+BAfh7joooswfPhwvPrqq6itrVV323t6evDSSy/h5JNPxvvvv4/a2lrDz3jhhRfw7LPP4rXXXsPIkSPVxxlj+Pzzz3H++edj+PDhmDFjRm5GutBlDDA+UFLZcR9cUSZFKsLtkpNxWE3GZ3h+lpLsZFQMthZPqQM4nd9xT08pbJMds8rSAPxVA5EADEUAYywlBFyJBqSlK2kO4BQsIk9uzKfONgMhoKR5sb4ea4fWyfo6+RqIiyLiSREl/tR+jeJsVw7mie64IVCM0jDlKEVZKYQBHFFOhw89BozvHdr0M8GqW6dLa63c6jook64DFo2AGUbF3Gs2E0kkkRRF+DUt0OmQXIIgCMIpXI08rV+/Ho2NjXjkkUcwYsQIXZpSRUUFLr30Utxwww24//77TT/nr3/9K2666SadcAKkuqJJkybh5ptvxosvvpiznW5FA4zSglTnTFMjY1jv4WK6UiSRhKhpNywypkZ4Kg8bCsD7hhHG0QAlNS5o3Z0tHgPkWgt32qinpStliTxZObTuRQMMhEAoWBBhp9iVzTYllapqyOHSAybnY7kaqcjY3NCmxslnFhmNWTwmtQt32Daj6HC2e4eX0R3A5BrVrjXO2j83rk9Af40q7d0V2wiCIAgiH1wVT5s3b8bMmTPhMzkE8bTTTsPmzZtNP2fevHk45ZRTDJ8fMWIEWltbc7bTjZQgIOWcdRtFnjicbVfOkNE5GSnbwtEYFClVrTi03Z1SK/dsuNGUIWC+485TKA9dobz7aXupyBP/fLoVeUoXdqk0r1Lr7mwuONulfh/8SrRZM6dKe3cAGHjYYak3WNWKORmpMBDD+tS4gQBMxkyJWgPupDtmdNuzMZ8u0GEV4wAAOIxJREFUNNmQbHNurTk5ZmUBP5TtOa1Q19awDS6jyBNBEASRH66Kp8bGRgwfPtz0NbW1tdi9e7fpa04++WQMHjzY8PnGxkZMmzYtJxsBqE6Gezu0RtEAHiHgfEphyKDdsCICygJ+tVAejAE92Yu+3W3RnD1KYSuCEiiBoGnjnbdtBtGdVOSJv2GEa2vNoH00VxTFBYdWEISskQplrQkAqioq1J/paaTConuinc0Naa0555gbNozIEt0xrK9za61ZRjn515qTtgmCkLX2T23v7vOpzxMEQRBErrgqnpLJJPwWXZ4CgQASBqk6VoiiiE8++QRPPPEEzjnnHMPXRaNRhMNh3T8t7jsZ2dOC7EWenLPNp3UyNLa1qbUeQQjBkGXRt6utyg2K0W05tB5Fd7LNp9cOrVUb9UF2Ugpdi1RoxJPGLp8g8EcT3ahhM0uNK1QNW2nmmAGZNWyA99eBYQTWxlpjEZei/WrXydQ1mrp3lDra4ZIgCILon9gWT8uXL8fAgQNN/y1fvtwNWwEAK1asULvtVVZWYsqUKbj22msxevRow/csXLgQ1dXV6r/6+nr9C1xwzICUc/bp1+1oDEs/Q8q/z5LeEm7P/iGuC4HMaIBSVJ1ygLLbppzV4kaLZiOBoh0z9ISzpxS6JAIsnUauSKJ3DSMYY/aaWbgVgc0m1NNaR6vjZihS3KuR6Y4lsKczlX6XLTUOPV3ZzyxyWXBy1TwZRTmVWk636utiua8116L9WZpZaKPWBEEQBJEvtsXTvHnz0NHRYfpv3rx5Wd/b2dmJMWPGYN26dTkbPHfuXLS0tKClpQXhcBiff/45nn76abz00kuG77n99tvR2dmp/tuzZ4/uebecxp0dUrrb/33yJUYteRFLN25FbzyBWFJy+LUds2DUbtiFVuVAdudMJ+oAUyHARBGI9EnfuNKUIYHdGodW32VMTgkySCl0XQQYnNultc3yLCUPomI98QTisrjkqt9xSdgpHfZ2dWSfTwCpdMdsay2ZBJRz0BwUAi/96yv164aHpetTsk2bGledekOPPmINeCs4Jdsyr4NCNYz4bF+HuinUm7bWrGqe3LJNOSx6l8G9gyAIgiDyxdNDcqurq7F9+3bMnDnTkc8LBAKYMGECli5digULFhi+LhgMoqqqSvdPh5JC4qBj1hjuwT92Nqvfi4zh6tXv4fN9HZLtPgEVJYGCpd6oTobOoU3boTVrhRzpVb900gF64V+71K+P0Di0ug5owRBQGjS2zeVUqqauPtVpBGw2GFBr2NxJV/p8fyrKqUQCAj4BlaUBy3os5kKkYunGrfjXfunn/eCVt7MIFL1Qz5ruqF1rDm0iNIZ78LO/fah+LzLg6tXvoTHco47b4LJSqY4pVAbA27WmRDmbunsM11qhGkbsaJc2LJZtTm0KKXYpa82q5smNaP/SjVuxtU0SuBevfCu11uiAXIIgCMJBXBVPPPVMiUQCgUB+RbyjRo1CU1MT4mn1Hry4sQu6rS0MlvZYkjF88rWUAje4LAiBp9bDhTbqSzduxbZ2ycm4aOWbGoc2e+Qpm0OrijoACJY5YldjuAc/+6uBQ2vgbGeNVLiUrvT2bqmj4+Z97arTCGgjT6WmERTAvR33rQek+Xz+s10ah1YTQdGsNau2+E4JgcZwD65a/Z76PUNqPg3T9rLNpyICBEGtw8uXbW1hXZt+QLo+t7eFMzYRzKKJbq21t3a3AAC+2B/OutZ4jjlwY601hnvwtx1N6vfKppAikNPXmlcNQEzXWtp9jSAIgiDywVXxVFdXh5aWFtPXNDU1ZZzfpKWtrQ2nn3665c8qyaOrGnNhh3bs4Cqklyb7BQFVQekPOI/TCDjfqtzMydAeDmppm+Joh8ohmLSit4O5Q6tPvTGt93AhGtAY7sHD//yX+r3iNO7u7M4aeUJ3p3RYbxpuRBIbwz1Yvb0xw7YtsqBS15rHKYU5zadFdMepgv+xg6vgS/ssvyBgzOCqlLNt5zpw8N7RGO7B/3z4ufq9Mp97OrvVc9jS6+uyrTU3rgOjTaHP97WrdgHWa83pg4VN15omrZYgCIIg8sVV8TR58mSsXbsWotE5QQDWrFmDyZMnGz5fVVWFTZs2mUawmpubUVlZmbuAcmGHtq6qAr859Xj1e78g4LEzZyDgkxw2LqcRSKXeeOJkyA5tyNo2N2qxTB3a9KJvJS3IJCrmdCQxfRUnGcMnre3qeOqiAaKoPwNIwcMo55YDHZJdPCIAGmHn0JzamU/TteZCY5K6qgo8fuYMdYNDAPDYmTNQV1WhSfNKr8fKEnlyoeZJukb1jylR66Sy1rSdHeOxVE2Y1jYX1trYwVUZfzT8goDK0hLZLrubQs7YZi6GKW2PIAiCcA5XxdNxxx2HUaNG4ZprrsHevXt1u6O9vb1Yvnw5Fi9ejFtvvdXwMwKBAM444wz853/+J7q6MpsD7N27F1dddRXmz5+fs51upVLd9I1JqhB5+YJTcPnUcfqaBcDzwmozJ0PbahjgjwY4halDq2k3bGWb0yIAMB43xZ6ygB9lJQGpPkZOQ82a5uWWQ5sWkPELAspkO3jSzwA43gFNmU/tuP3eaj5NxszpuqLLp47Dr2ZK58N9a+QwXD51HAAY2maavur4fGautSHydVnq96Es4JfWtxz1NR83Z0Xn4tNP0Nn12Jkz4JcXoN37mlO2pdZa6rHMewdFngiCIIj8cb1hxLPPPouGhgbMmjULtbW1apvxKVOmYPXq1Vi7di1qa2vV12/ZsgWjR4/G1q1b1ccefvhh+P1+HHvsser7a2pqMHz4cJxxxhk4/fTT8xJPbhVWC4KA44YPAQA0d0md6dT6GJ66IsZSxfKOO7Spx1JORtoOLYdAcVpwXj51HP5Hds6OHz5EdWjtRCrcaIOsjJuCT5DGLaQIFEVwCoLaQdFL0fnYmd/U2CY5tEqKG9d8xuNAQqoZdHLcLp86Dl/85Bz1RnPmmDoAyNxEMIkkwuHUVS0nHyEd4v3FAUl8iLqjBNJFp4ltHkTFlMOt1XpJnw+okJrfZF9r7nQC/NkJEzFigFTn+Nycb+PyqeP07d0B64YRLlyjl08dh81XnaN+f854KR2cWpUTBEEQTuK6eAqFQrj99tuxadMmNDc3q23Gt23bhmeffRZHHnmk7vXjx4/Hjh07MG7cOPWx6upqPPjgg/jyyy/V97e0tKC5uRmffvop5s+fb3kYrxluFX0DwLE1gwEAm1rbAMA0upNRtxDpk9pxw1nn7PKp4/DJVbPV72fLTkZODSMcdswA4LQjJDH9+f5OiIwhmkiiTz70Uo0GmBXxuxipuOLYsQCASycfKTuNejEMABhg0nbbJdF5xdRxOKn+cADAwpOnyVHOdMGZcmgz15qmAYjDto0dUo0JQwcCADa2HACQ1mQDnA0jXFhrkw8fBJ8goLUnguauXnRF47o0TL1t3qTtAdJa+89vTAIgiQB91Dq11niuUadtA4DptUMBAE3d0uZO5lqT7Mp2X2OJhJRq6IJtE4cOxJGDBgDIds+ltD2CIAgifzxtVV60uCgEjh2mF0+GkacsdQtKrQcAIFTuqF2Thg7C2MHSrvWGZsmhTR1yyRHdibgXDRg/pAplAT964gls03Q/EwC14YZpWpCLTuPJDUqkQmrGkBFBATRnd3nTzEJhRp0knr7q7EmzLW2tJZMpO2RUwen3AyXOO5lTh6VvItiIJLo4n+UlAYwfUqXaptgV9PukNEwr21w45kBhZoM+KpZ1rXmcWqug3NcUMZyx1pTIUyKeWY/V555QB4BpNVK0/6MWea2l1XISBEEQRD6QeIJmh9bhtD0AmDJsEADg49Y2OSVI6Wgn/yHX1i2kO0BqR7syxzraaTleTilc37wfAGx123Nrxx0A/D4fpsjO2UfNB9SUoIGhUrUWxHTH3YX27grKmG1qbUM8KWZEULS2eRl5AlICZWNrmhhWbAuVS+IIWSJ2LnS006JEYDe2KJsINg5kdumgaIWpw1JzmrU+xiQFzYv53HIgjN54IqO9O1C4tZa+KZSx1kzua+qmkN+vntfmJNNqUvcOAKmGERR5IgiCIByAxBPcTQsaP6QaQb8PPfEEdrR3qU6j2tHO5zM8G0h1flzY1QaA44cfBgBY33wAImO6NshA4Xa1Ae3u8QFV1GkdWtPmB33uRQPGDK5CVbAEkUQSn+/vUB2zrLaZRSrccLZlp/Hj1nYkRTEzumNy/o7TLfHT0TrbTLOJkNl6viPzzS6OGaAXdunRV51tHrUqVxg+oBzDKkIQGcMnGmHHK57ctE1Za5/t60A0kcxca9p6LI+F+lTNvSMpiuiMUOSJIAiCcA4ST4CraUEBnw+TD5eiT9q0IO0uqFAlPZ8RRXGpkYWCGnlq2o9wNKa2u+ZrH+3emAGp3eONLW0ZUQpu21wYN58g4Lia1LiZplJli1SoETvnbRs7uArlJQH0xhPYqjlLSdeiWW3MoLfNzQ0EICWevmzvQnN3HxKivq5IScNETxdY2tEGXgq7bG2tTevrXIzAAikhsLG1LdVCXRsVM+1S6J5t9VUVGBQqRUJk+Hx/h616LLc3hRRht7UtjMZwb+q+RpEngiAIwgH6vXhi8Viqy5hLIkV1zjRCYDDHzrbbAmVqzRAIABq7evGv/ZLzVRbwIyh39Uo5tOEMh9b1yNPw1O5xW5ZDLgvVYAAAjq9NReyype0Z1WOxZBKIRV2zTUp3lIS4JDptpHm5vNaGlIdQXyV99rqvpIOzS/0+lCt1RYqoYwzoCeve65Ww29YWxm65XmyQzTRMt2xTUzFbDCJPBl0KmShKDWdcsk0QBFXYbWppy+zUCWPR6cZRAloOryhD3QCpRlRZaxUlAZTm0VSIIAiCIBT6vXhyu3gZgFq/Yxh5snBo3bJrQLAERx0mOTh/39Ek25UpULI6tC5GdwBg0tCBKPH50B6JqTU8ugiKyY6720JAWyuWvWGEQY2MZq25Jojl+p2NLQcyOjtqbTNyaN2yC0hFBN7Y1SzZFSpV07aE0iAQDEm2eCzshlaEMEJ2tt/cLTnbuhQvjhRR1yNPRumrRvcO5YgDF21Tm0ZoUwp5bHM5WgekNl/+sVO+r9EBuQRBEIRD9HvxpO6CBgIQXOgyBqScjI9aDqh1RYOzdGczjDy5JFCAVN3T37M4GUJpUDr0FQALt6fZJkcDXEq9KfX7cfThAwEAa3YqznYWgVKAaIAyZh+3tqNZbtXM0zBCtUsQVKHgNFO19TuKs81Tv+PymAGp6yAlnvQ1KIbj5nLDCCA1bm/KkYpsmxvZ15p7aZhauzZ/3Y6ve6SudTyRRKbdFAqWuWKbtuNeto52RrV/Xgh1pWZyjbLW6IBcgiAIwiH6vXhKOY3uOWbHyKlUzd19WfPvjR0gdwUKkIqivL93n2RX2g6t4YGvHjpAn+6TfnbWHffuLOdjuWzbEQMrMShUirgoYn2TFBXjcRrdLpQHUmP2/t59qbqissyap0JEnhRne2eHtK4Hl/GJJy+FXWdUSuHNJtTN1ppbto0eNABVwRJEkyL+KXfF5GpmoV1rLnTqBFLCbn3zAcTltN5B2dJXTRpGuIViW3O3lLo4mCJPBEEQhEP0e/HkdsE3IJ1NNHrgAPX7jPx7ZWc7vdOYBw6tEkVJFfBzRgM8cIAUIaCQtRg9kUjVdii2udiqHJDqPZRxSzmN1kX8XgiUSUMHIuAT0BNPAAACPgEVcl0RYBJFibi/iaA4tAoZqVRGEVgXzxRTUMSTAvdac3lOfYKg2tYdS2TYZlhf58F9TekkGpEPsOZda25H64As9w6KPBEEQRAOQeLJAxEApNohA1miOwVqGKHY5dNEQQand6QqUIMBINVxT0EXqSirSJ1ZpLGNMZbqUuiic6ZE7FTbOLqMebHjHgz4MWnoQPX7QaGgLsplWPPkgbM9qroS1cES9XujyFN6J0C43DACSNUWKehsMziziDHmajvwlG1pwi5bSmEBojsl/lQnUYB/rXlh24gB5RhankqNpTblBEEQhFP0e/HkhQgA9DvbhulKaXVFbrcqB4DykkCGs53NNsN2wy7adswwvbDTRQMEIfv5WLEoIEeDXI3YyR33VNt0KYUGtR4uR8QUlKYRQLY0TPM6FDcdWkETRTG1LS0C68UmQsPASlRphJ3hWtMKgUif1EzFZdu08ynZVhx1RQDM57OAm0KCIOg2X6hNOUEQBOEU/V48eZFCAhSvkwHooyi8tnkRDSgvCeCoIdXq98aRCo1tHnRPBPRjJgC6iIrROU+qQHGxhg3QRyrSnUbDM6g8WmvaCA9vzZPbra0BfXocwLeJoGvKECp3zTazdEejw4ULElFPFygFalWuoE3do8gTQRAE4RT9Xjx5kUICQD1/B5DOUtJSyEJ5IFX3JBmjf67Qwk67exyTaytU27I4Z6pjVlIKIRCAW9RXVahpQRUlAbUwHdA42tEIWDSSelMBBEq6CMgarYOHzrZGoKS3zChka2tAb1s0ab3W1DqxYAiCi2cITThsIIJ+6VYdCvhRlq2uKNIHppwhBgAebQrpo5x8UWuv5lOfiskMX0cQBEEQduj34skrEfA3+RwlAPjrjiYs3bg19WSBBUpTV2oH/e43N+lsMz5HxhtnO6Y5nPc7z//d0javRIAgCBhWIYmn7ngCo5a8mLKtPNUcRCfsPGi5DeiFuuJ0K1jVyLi91nZ2dKlf3/3WJr7rwKN0x7DcaQ8ATnvmr9ZrrdcbgVLi9+FoubaoPOBHY1gT8aqoStmTZRPB7TE7xmytFTilcHtb6my6O9dt1K81giAIgsgREk8e1BU1hntw9er3Uj8TwNWr31OdIKuuVG42PmgM92Dhu5sNbSuksGsM9+DFz3ep34ss+7ixLGl7bjtmjeEefLYv9XNFxlTbBL8fqJSdWq1I8UjYvaAZs1Vb9+hFgKVD6+5au/f/fZz6mbC+DrxqANIY7sGyzdvV7zPWWrbIk0fzCUiiCQDaIjGdUNettWzpqy7b9sfPdqpfZ6y1ArYqbwz34M43N6rfZ9zXCIIgCCJH+r148sLZ3tYWhph2PkySMXVnVCsCdOfIeGab/rFstukc2mQSUNLRXHRot7WFIaY9prUtW02FWsPmcnRnW1s4IxFIN25ZRIpXgvMqDqGe6dC6X8PGu9YK0QDEyrZsmwheRVAawz34f3u+Vr/XCnUg+1lsRbHWDIW6+xE7q3suQRAEQeRKvxdPXqR5jR1cpesaBwB+QcCYwdKOceocmbjuHBkvHCBe27JFd4rFtqw77i43ZeAeN48jFZZOo+zQoq8HLJ5KUyuKtWYW3QEKe40q41aA6I6lUB9QmKiY5VpTrs/ebrBEwlPbrOaTIAiCIHKFxNOBFumLtAJxJ6mrqsDjZ86AX/5j7hcEPHbmDNRVyc6DwTkyXrQqt7LNtK4IgNixvyhtczsaYGlbgaIB3CIAAHpSu/CibKeug5zD5DSfyjUQKEldq4W0LUsNGxJxiK2NrtlmPaeybdnqsVy8d1hvIGRfa15cB5b3XIIgCILIEffakR0ExP68DMJrzwIA4s8shm/0RJTOudyVn3X51HE448gR2N4WxpjBVbo/4oIgSLu0nW2SA3R4LQBAlB01tfbJJUxty+LQxlf9n/p176wGBO98vDDjZhLdYdEIxNZG+IbVuWKXlW3IctaT2C6lXul24R1GcRqvXv0ekoxlioCSEqmtdqQXrKsDwsAhiK1cCuyValcit14I1l2Y+cyWGhd/dZn0RSKOnjNHFX6taWxLvPs6AEDcsslV26zmNNu4FcdaKwVCZVInwO5OCNVSN0MvhDpgsdYIgiAIIkf6tXiK/uZnCCobp4whet/VCHzzDNcc7rqqCsM/4MKAgWCdbWpaUGzlUmCf1KEvcsM5YL90z2k0sy39HBmxtRGxR+5MvUAUCzZu2WoqEu//QzLrsw9dd7ZNbUvrahdbuRTJv/0JABB/6tfwjRxbGIEi28YivWDdnRBbGxG976rUk6yA86kZMyaKYPuaEPv93akXFMNak+dTbG1E4i9Pe2Ybj7DT3juKZq1VDgSL9KnXaGzlUqBpFwAg8vMLCnZfIwiCIIhc6d9pe+kV4mIS4p7t2V/rMtqdbSOH1s3UIENUhzYMlkxC3L0NYGltHAo1bmk77mJrIxJ/fU5jV+HGTZtKlTmfzHW76qoqMLNhuKXoFHdvUxsyqBRoPlURIIpAb3dxr7Xd2wDm7f3DaE7N7x0FXmuaeqyiuq8RBEEQRI70b/HkSzum0+eHr35MQUwRBkjnpbDuInNo02pkfCPHAkJxjFsqUtEBoDAOrRHCgOIUKABSQqC7U57PtNtAoa6DYAgoKZVs6+qQbEunUGtNuQ7kyFNR2aYVT8W21rSbCMVmG0EQBEHkQL8WTyVzNbugPj+Cdz7mao2MGVoHqKgESmlQqlsAwMLt8A2rg1B7hM6uQo1bKoIiObTC8IbMFxXaoS02gQJ95zjfsDr4TzpTZ1fB5lMQdOlxwuEj9M1SCmlbWuRJqBqkNnkpuG2aMSu6tTZAs9aK6L5GEARBELnSr8WTEAwBAPwnnomK1btczb23tEUrnobVQRg5LvVkoYWdZveY9fWAte4GAIQWPlfQcUuvx0Jbq/4FhRw3TWqcb1gdArPmFYddyNJoIyIV7pfMu6ng14E2PU7c+YXUcbI0iNCjfy3sWkureUpu/kCKogypQejxNwo7bmn3jsB3L0o9V+i1ptw7ujsloX78yUVjG0EQBEHkQr9uGJH46P8BAEq+8/3C/wHXHpQb6QNrkrqfhe59Cv7ppxTUPqFqENj+ZrCuDiQ/eR9IJCDU1CNwxoVSp8BC2aWk7UX6wOIxJDa8CQDwzzgDpT++Db76MQUXKOq5QEmp61nge5cieN1/F3Y+NcKOxeNIbn4fAFByzuUFvw6EAQOlM426OpDc8TkAwH/MDJTMOL2gdqWfWZTc+DYAIDD9ZJRMP9n4fR6QsdYS0vldgbN/hOC19xR2raV1nWRyF8CSn9yN0iJYbwRBEARhl34tnsStGwEA/uO+XWBL9A5Q8pP3gHgMwtBaBGb9oKACBYA+GrD1YwCAf9q3Cm9XRerAS9bViaQsngInfgeB42cWyCgJbXSHMabaVnLWDwvuMGptE7dslA5mrh4M3xFHFdQuQB+BTX70FoAiuT61tX/dnUh+JIkn/9R/L5BFKbRjxhhDcv06AEDJ7B8Vz1rr6oTYvh/i9k8BAKXnXwvf4KEFtIwgCIIgcqNfp+0hKUIYcQR8w0cW2hK90/jPNwAA/hNOKbxAQZqwU6I7075VOINkBL8fqBgAAGAd+9VogL/AwglIi+407gD7ei9QUgr/5G8U2DLoUgqTG6Xoq3/KiRB8hb8dqNdBuL241ppyPhbktfbJewAA/7TiEk/ijn+BtX0NhMrgP/qEwhoGpBpGdHeo16dv9EQSTgRBEMRBS+G9pQJTDLvagN4BSnwoiafA8YVNB1JQbBMPtKopXoEiG7fk+nVSfcyAgfCNmVxQmwB9dEcVAUefAKGsvIBWSWjPU0puksXT1JMKZ5AGdT63bpIEZyBQHIITKdsS69cBkV6gahB8oycW1CYgbeNl/VoAgP+Yb0rNXgpMqmFEpxoRK4bNDYIgCILIlX6dtgcUnwhgrXuklr6QIk/FgOrQfvB3IBaFMPhwCKPGmb/JI4TKajDsQeLNVwBIaVSC319gq6A7Hyvx4RoAxSTU5chTuB3il58BAALHFod4UqJiybdeBQD4Jk4vCsEJyGttXxMSb/0ZAOA/9qSiiNYpY4a+HiQ/kA6J9he4DktB29RFbPwSQPFcBwRBEASRC/1ePBXNLqgS3dn5BQBI6YS1DYWzR4MqnpQalGKod5JRbVOjdTMLZ4wGbY1M4p3XAAD+42YWyBo9Sge05JZNUpOB0iB8E48rpEkqqrPdvg9A8WxuAFCv0aQshgNFkLIHpK219/4KoHjua4pt4t6dYE27AJB4IgiCIA5uimDbtHAIw+qKTqAoBKYXR9QJ0NiWTAIojhoUFcU2ucNYsThmQmlQOvQVAMLtUvrZlBmFNUpBc/YOAPgnnVAUKV5A5nVQTGtNFSnRCIDiaBYByPVYZRXSN5E+IFQO/6TphTVKQRHDe3cCjMF3xFHwDRlWWJsIgiAIIg/6tXjyH3tioU1QyXAaiyTtBkBKoMgUi0AB0satsgq+8ccWypQMtBEBKf2sooDWpMgUKMUhAoA023w++Kd8s2C2pKOzLVQG34RpBbMlHa1t/mNPhFBSWjhjNOi6FALwTyueewdBEARB5EK/Fk+BItk5BrI4tEXSLAJIs23AQPjGHF0wW9LROmdFU+8kox23YkknBLI4tMVS7wT9mPnGH5tqOFAE6NbaMTOKRqAAaeKpWO8dKJ50QoIgCILIlX4tnoop8oSyCkAuPhfqj4Tv8NoCG5RC59BOPL44iuRldE7juCmFMyQbWtuKNVonCMWTTgjoxsx31NTC2ZEF3Voroo0XAECwTP2yqIV6EV0HBEEQBJELxeMFF4CEfO5IMRB/+Q+AKAIA2J4diK1cWmCLUiittgFA/HBNUdkmfrVV/Tr2h0VFZRtK5DqiIks/0wn1UeMzogOFJPnBGvXrxMt/KK751KZhNhT+QGGF2MqlED9fr36f3La5gNakUV4JKM1lahvgGzq8sPYQBEEQRJ70a/EU/c0NEFsbC20GxNZGRO+7SvMIQ/S+q4vGttjShakHWHHZlvjHi6kHmFg0tsVWLoUodyeEKCL+tz8V1iANOqG+a0vRCBSxtRGxx/8r9UARrTUAEHd8pn4dueOSohi3zHsHEF14bdGMWfzlPwCMSd80fVUUY0YQBEEQ+dCvxRNEEeKe7YW2QjrXSXZmUw8mi8c2Vsy2sbQHC29bVoe2SERAUQv1Yr4OWhuReO1ZzQPFIdSLfcyKda0RBEEQRK70b/Hk88FXP6bQVsA3cqyaRpV60E+2WVCsthW1Q1vEthXrfALFK9SLfsyKdK0RBEEQRK70a/EUvOUh+IbVFdoM+IbVIXjn44BP7hTn8yN452NkmwXFalsxO7RFbVuRzidQvONGY0YQBEEQ3iIwlr6deugTDodRXV2Nzs5OVFVVFdocFbG1EeKe7fDVjykK50cL2WaP2MqliN53NSAmVYe2dM7lhTYLQHHbBhTnfALFPW40ZrnR09ODyspKAEB3dzcqKorjLDaCIAjCW+xoAxJPRSSeiEOLYnVogeK2rZihcbNPMY8ZiSeCIAgCsKcNAh7ZRBD9Dt+wuqJzFhWK2bZihsbNPjRmBEEQxKFEv655IgiCIAiCIAiC4MUT8dTX14eFCxdi6tSpqK2tRU1NDWpqajBu3Dhccskl+PLLL3P+7JaWFkyaNAktLS0OWkwQBEEQBEEQBKHHk7S9iy66CMOHD8err76K2tpaCPKJ8z09PXjppZdw8skn4/3330dtba2tz43H47jwwguxbds2RCIRN0wnCIIgCIIgCIIA4IF4Wr9+PRobG7FixQr40trWVlRU4NJLL8X+/ftx//3346GHHrL12bfddhsmTJiAzs5OJ00mCIIgCIIgCILIwPW0vc2bN2PmzJkZwknLaaedhs2bN9v63D/96U9466238D//8z95WkgQBEEQBEEQBGGN65GnxsZGDB8+3PQ1tbW12L17N/dnfv7557jlllvw5ptvIhQK5WsiQRAEQRAEQRCEJa6Lp2QyCb/fb25EIIBEIsH1eeFwGOeddx5+97vfoaGhges90WgU0WhU9xkEQRAEQRAEQRB2yCltb/ny5Rg4cKDpv+XLlzttKxhj+NGPfoTzzz8fZ555Jvf7Fi5ciOrqavVffX2947YRBEEQBEEQBHFok5N4mjdvHjo6Okz/zZs3L+t7Ozs7MWbMGKxbt872z33ggQfQ3d2Nu+66y9b7br/9dnR2dqr/9uzZY/tnEwRBEARBEATRv/GkVbmW6upqbN++3fb71q5di0cffRT//Oc/LdMA0wkGgwgGg7Z/JkEQBEEQBEEQhILr4omnnimRSCAQMDalsbERl156KV588UUcdthhTptIEARBEARBEARhieviqa6uDp9++qnpa5qamjBy5EjD55966il0dnZi9uzZWZ/ft28fpk+fjhtvvBG/+MUv8rKXIAiCIAiCIAgiG66Lp8mTJ2PJkiUQRdHwrKc1a9Zg8uTJhp9xxx134I477jB8vqGhAZs2bcLAgQPzNZcgCIIgCIIgCCIrrh+Se9xxx2HUqFG45pprsHfvXjDG1Od6e3uxfPlyLF68GLfeeqvbphAEQRAEQRAEQeSM6+IJAJ599lk0NDRg1qxZqK2tRU1NDWpqajBlyhSsXr0aa9euRW1trfr6LVu2YPTo0di6dSvX5w8dOhQlJSVumU8QBEEQBEEQBAGBaUNB/YRwOIzq6mp0dnaiqqqq0OYQBEEQBaCnpweVlZUAgO7ublRUVBTYIoIgCKIQ2NEGnkSeCIIgCIIgCIIgDnY8P+epGFCCbeFwuMCWEARBEIWip6dH/TocDiOZTBbQGoIgCKJQKJqAJyGvX4qnrq4uAEB9fX2BLSEIgiCKAW3dLUEQBNE/6erqQnV1telr+mXNkyiKaGpqwoABAyAIgu65cDiM+vp67Nmzh+qhCgTNQeGhOSg8NAeFh+ag8NAcFBYa/8JDc+ANjDF0dXWhtrbW8GglhX4ZefL5fKirqzN9TVVVFS3SAkNzUHhoDgoPzUHhoTkoPDQHhYXGv/DQHLiPVcRJgRpGEARBEARBEARBcEDiiSAIgiAIgiAIggMST2kEg0EsWLAAwWCw0Kb0W2gOCg/NQeGhOSg8NAeFh+agsND4Fx6ag+KjXzaMIAiCIAiCIAiCsAtFngiCIAiCIAiCIDgg8UQQBEEQBEEQBMEBiSeCIAiCIAiCIAgOSDwRBEEQBEEQBEFwQOKJIAiCIAiCIAiCg0NKPHV3d2PWrFl44okndI9v2LABZ599NkaPHo2amhrU1NRgxIgROOGEE/C///u/SCaT6mt/9atfqa/J9q+srAw7duwAADz55JOorKw0ff3YsWMRj8c9HYdCYzQPAPDee+9h1KhR6Onp0T2eSCTw+OOPY/r06RgxYoQ6fqNHj8bs2bOxadOmjM9ijOHFF1/ESSedhJEjR6rvGTVqFP7jP/4D69atc+k3PDjIZR4++OADnHrqqRgxYgSGDRumjumRRx6Ja6+9Fm1tbbrXL1iwANXV1abXwLe+9S1Xf89ixe749/b2Wt5PampqsGrVKt1nNTc34+qrr8aECRMwfPhw1NTUYPjw4TjmmGNw2223IRwOu/67FjN25qGzsxNHHHGE4dgPGjQIP/rRj9T3jxo1ynK+HnnkEc9+12Ih25h3dHTguuuuyxjfuro6nHLKKXj33XczPqevrw8LFy7E1KlTUVtbq75n3LhxuOSSS/Dll19mvCeXvyWHKk7Nw2uvvYYZM2agtrZW/bswfPhwjBs3DnfccQd6e3t1r//hD3+IwYMHm14Xl1xyieu/f6FxYvy3bduGsrIy07EcPnw4NmzYkPG+iy++GGPHjlVfV1tbi2nTpuH+++9HNBr1ZAwOWdghQmdnJzvppJPYqFGj2OLFi9XH169fz4444gj2l7/8hUUiEfXxZDLJvvjiCzZ79mx28803c/+cadOmsT179jDGGFu8eDFbsGCBU7/CIYHRPDDG2JtvvslGjhzJBgwYwNrb23XPXX/99Wzu3Lls27ZtLJlMqo/39fWxVatWsYaGBrZx40bdexYvXsy+/e1vs02bNrFEIqE+Ho1G2bp169ikSZPYqlWrnP4VDwpymYf9+/ez+vp69o9//IOJoqh7T19fH1uwYAGbO3eu7vHrr7+ePfnkky79FgcvuYx/e3s7GzVqlK2f09XVxUaPHs2WLFnCwuGw+rgoiuzrr79md955J5s+fbrumupP5Ho/MmLVqlXs8ssvV7+vrq52zthDBKMxv+yyy9gNN9zAuru7da9PJpPs7bffZg0NDWzv3r2652bPns1+8pOfsMbGRt09qbu7mz311FOsvr4+4z25/C05FHFqHrZs2cJGjhzJNmzYkPF3obOzk1155ZXspptu0j0+e/ZstnbtWsd/p4MJp8Z/48aN7Nvf/ratn71nzx5WX1/PnnnmGdbT06M+Looi2717N7vyyivZueeem9PvRUgECi3enKCvrw+nn346zjzzTIiiqHtuwYIFeOihh3DmmWfqHvf5fBg/fjyeeeYZTJw4EbfccguGDRtm+nMYY9i3bx+GDh3q+O9wKGA2D++88w7mzZuHV155BXPmzNE919zcjJdffhn/+te/UFZWpnsuFArhrLPOQjwex4IFC/DKK68AkHYXf/3rX+Ojjz5CTU2N7j2lpaX49re/jWXLluHHP/4xzjrrLBd+2+Il13n48MMPceKJJ+LUU0/N+MxQKIS77roL9fX16O3tRXl5uau/w8FMruOfC7///e9xzjnn4LrrrtM9LggChg4dinvvvRcff/yxYz/vYMKNedi9ezfq6uqcNvWQwWzMX3/9dezYsSPjHu/z+XDSSSfh+9//Pl555RVcc801AID169ejsbERK1asgM+nT5KpqKjApZdeiv379+P+++/HQw89BCC3vyWHIk7Ow5o1a3DxxRdj2rRpGT+nqqoKixcvxtixY/Hggw+69wsdZDg5/rmwaNEi3HLLLbj44ot1jwuCgPr6evz+97/HtGnTsHHjRkydOjXnn9OfOSTS9hhjuOmmm3DXXXdlPLd582acfPLJhu+tqKjAcccdhy1btlj+nHfeeQcTJ06kU54NMJuH0tJSvPbaa1kv1C+++ALHH398xs1Ey6mnnorNmzer3+/Zs0dN4zBi2rRpaG5uRiKRsPmbHNzkOg8jR47UpSSl4/P5MGLECDQ2Njpq76FGruOfC1b3NwA47bTTdNdOf8GNeVi5ciW+853vOGXiIYfZmP/85z83vcfX19djz5496vebN2/GzJkzM4STlvS1ncvfkkMRJ+dh0qRJOPfccw1fX1FRgWQySWlgGpwc/1yw+rvg8/lwyimnHPLXgZscEpGn8vJyXHDBBVmf6+zsRGVlpen7a2trsXv3btPXhMNhXHfddViyZEnOdh7qmM3D9OnTDd/X2NiI4cOHm352dXW1rt6G5z0AMHToULS0tPSr3eJc52HSpEmYNGmS4fORSARfffUVRo4cmbeNhzK5jn8u8FwHtbW1+OSTTxz9uQcDTs/Dk08+Cb/fj3/7t3/L17RDFrMxv/HGG03f++mnn+L4449Xv+dd29q/3bn8LTkUcXIerGpWW1tbEQqFaFNZg5Pjnwu5XDuEPQ6JyFO+BAIB0+jEjh07cNJJJ+GKK67Av//7v+ue27RpEy666CIcddRRuuK9adOm4d57780opCQySSaT8Pv9rrzHam4JPiKRCK6++mqcf/75CIVCuufWrVuH2bNnY+zYsWoxsdKQ5ZFHHqHxt0Fvby9++ctfYtq0abqmHePGjcP3v/99fPHFF7rX81wHdA3kB2MMixYtwpIlS/B///d/EARB9/yiRYvwjW98A/X19ep8HXnkkTjrrLPw4YcfFsjqg4tVq1bh9ddf16VQ5rK2c/lbQqTINg9mdHR04Ac/+AHmz5+f8dzKlStx+umnY/To0ep1UV9fj5NOOgnPPfccGGNOm3/QYzT++/btw4033ohjjjlG1zhl4sSJuOKKKzKyQejvgvuQeLJg/fr1OP3007Fw4cKMuoIxY8agubkZ5513Ht5++200NzejpaUFTU1N+POf/4xdu3ZZ7jIQRLHz3nvv4dhjj4UoinjggQd0z40fPx67d+/GT3/6U3z44YdoaWlBS0sLdu/ejeeeew6vv/46fvOb3xTI8oOLUCiE+vp6MMawYsUK7Ny5E62trWhpacHGjRsxd+5cfOc730FHR0ehTe03JBIJXHPNNXjnnXfw1ltvZezmTp8+HY2NjXjqqaewbds2db4+/fRTXHfddbjgggvU7qxEJu3t7bjssstw44034uWXX8agQYMKbVK/JJd5ePXVV3H00Ufj2GOPxU033aR7buLEidi1axfuvvtubNq0Sb0udu7ciUcffRS//e1v8fzzz7v16xx0mI3/4YcfDr/fj7q6OqxevRq7d+9W/86+9957mDp1Kr773e/2u67OhabfiadFixbh7LPP5nptZ2cnzj//fLzwwguYNWtWxvPf+9738MEHH+Dcc8/F0KFD1R1JQRAwYsQIPProo1i5ciVisZijv0N/YOrUqXjmmWdsvefss8/GokWLXLKo/9HR0YH58+fjoosuwj333INly5ZlRJ2uueYavPHGGzj99NMxaNAg9Rrw+/048sgj8eSTT+J3v/tdIcw/6AiFQtiwYQPuu+8+NDQ0oLS0VH2uoqICF1xwAc466yy89NJLhp+xbt06jBkzpt+3J3eKBx54AN3d3Xj55Zezpn///e9/x8MPP4zx48frro2ysjKcccYZmD9/Pp588kkvTT4oYIzh+eefx5QpUzBo0CB89NFHOOaYY0zf09nZiTFjxtg+giKXvyX9hVzmoampCRdddBF+/vOf46mnnsKvf/3rjLq0X/3qV3jllVfwzW9+E1VVVerjgUAAxxxzDB5++GE8+uijrvxOBxM846+kXd98882oq6tDIJCqtqmursZPf/pTNDQ04K233jL8OcuXL6fGEA5zSNQ82eG2227jfu2iRYtwxRVX5LzogsEgGhoasGvXLowbNy6nz+ivbNy40fZ70s+/IXJnxYoVuPHGG3HJJZfgs88+Q0VFRU6fc9hhhyEWiyESiWQIL8I+EyZMyHq2jcLMmTOxfft2Dy06dNm/fz+eeOIJrF+/PudUsAkTJmD58uUOW3Zws2fPHlx22WVgjOG1114zrbPUUl1dndPazuVvSX/A7jwwxvDYY4/hvvvuw4033ohly5ahpKQkp59tdR/rD+R6HWRDGc9snXIBYN68eZg3b17On09k0u/EUzYSiYROzSu8/PLLWLNmTV6fXV5eTnVPFuSSe8v7HqO5JbLDGMO9996LP/3pT3j99dcxYcKEvD8zFAqReHKI9PsJz3VA10BurFmzBqeeempeqWR0/9fz4Ycf4sILL8Ttt9+OK6+8MqN+TEsua5vqOPiwMw8AEI/Hcd111+Hzzz/HBx98gBEjRuT18/v7dWF3/K2gvwvec8in7Q0cOBDd3d2mr2lqasroINbR0YG+vj7U1tYavm/RokVobm42/ey+vj7TtpQEUFdXh5aWFtPXhMNhDB482NZ7AKnQ0qydOaHnj3/8I1atWoUPPviASzjdeuutli1qo9EoCScOPvjgAzz77LOmr0m/n/BcB9nub4Q177//PmbMmGH4/M6dO9XzhYyg+3+Kjo4OzJ07F08//TSuuuoqS4cxl7Wdy9+S/obdeQCABx98EE1NTXjjjTe4hNMNN9xg+nx/vi7sjv+f//xny018+rvgPYe8eJo8ebLpwuvp6cGGDRswfvx43eNNTU2mwgkAPvvsM6xfv97w+Xg8jq+++goNDQ22bO5vHHXUUVi/fr3pTtSaNWswefJk9fu6ujo0NzebiteNGzeipqaGdlc4YYzhlltuwVNPPcWdpvfuu+9i69aths+3t7cjEAiQeOKgu7vbtJ4JALZu3YrRo0er31vd34DMa4fgY+/evaaOoiiKWLZsmelnpM9Xf+bBBx/ExRdfjJNOOonr9ZMnT8batWszDhnVkr62c/lb0t+wOw/hcBgPPvgg/vCHP3Cn6b300kumdZf9+bqwO/5ff/01Vq9ebfoau38XRFHE2rVr+/V1kC+HvHi65557cMMNN+Avf/mLbodcFEVs374d8+bNw3nnnYdhw4bp3tfd3W3pQM6ZMwf33HNP1jzszs5O3Hzzzfjud79L5x9YUFNTg7lz5+KSSy7B9u3bdX8so9EoXn31Vdx00024++671ccDgQBuvfVWXHjhhfj444+RTCbV5+LxON5++2384Ac/wH333eflr3JQs2/fPgQCAVu513PmzMFtt92GpqamjOdaW1tx9dVX44orrnDSzEOW6dOnY+PGjVixYkWGw5hMJrF69WqsXLkSc+fOVR//yU9+glWrVmHJkiXo6upSH2eM4cCBA7jrrrvQ1NSE2bNne/Z7HCpY/Q1oaGiAKIp49NFHMzpdiaKId999Fw899BB++MMfum3qQcGGDRuyNl4y4rjjjsOoUaNwzTXXYO/evbrW1r29vVi+fDkWL16MW2+9VX08l78l/Q278/DFF1/g6KOPxtChQ7nfM2fOHNxwww1ob2/XPc4Yw86dO3HDDTfgqquu4v68Qwm743/aaafhhRdewJtvvpnR3j0ej2PZsmXYsmWL7jyu2267DQ8++CCeeeYZ3UYCYwx79+7FT37yE4wePZqaSOTBIbclX1VVhQEDBqjfT506FStWrMCCBQswf/58dSH5fD6MGDECV155JS6//PKMz/H7/ZaRp7lz56KjowPnnnsu9u3bp96oBUFAZWUlzj33XPz3f/+3g7/dwUP6PGg5/PDDdZ3EAOA3v/kNli5diosuugh79+5Vx7K8vBxHH300VqxYkXGhz58/H7W1tbj22mvx1VdfqTm+wWAQY8eOxZIlS0xP2e4P2JmHffv2obm52TLN8fHHH1c7Vl5//fVIJBI45ZRT0NnZqd7cBUHAoEGDcNlll1mmcBzK2Bn/qqoqvPLKK/jFL36B66+/XueQl5SUYMqUKXjllVcwZMgQ9fGKigq88847WLBgAaZPn67OgSAIGDJkCGbNmoU1a9ZkdMPqb9i9HwFSx7zDDjvM8DP9fj9WrlyJO+64A4sWLdJ1VfX7/Rg7diyefPLJftssKH3M29racN5555k23zj11FN1nfGeffZZLF68GLNmzUJra6t6fxkwYACmT5+OtWvXZvydzuVvyaFMvvNw4MABvP/++6Z/FwRBwKuvvorjjjsOgFTScN999+H4449HT0+P+jqfz4fDDz8c1113HS699NJ8f7WDgnzHf9SoUXjmmWewYMECbNmyRbdRHAqFMGPGDPzlL3/RbdKPGDECb7zxBn75y1/i7rvvVjfWBEHAsGHD8P3vfx+//e1vnf5V+xUCo5PKCIIgCIIgCIIgLOnf25EEQRAEQRAEQRCckHgiCIIgCIIgCILggMQTQRAEQRAEQRAEBySeCIIgCIIgCIIgOCDxRBAEQRAEQRAEwQGJJ4IgCIIgCIIgCA5IPBEEQRAEQRAEQXBA4okgCIIgCIIgCIIDEk8EQRAEQRAEQRAckHgiCIIgCIIgCILggMQTQRAEQRAEQRAEBySeCIIgCIIgCIIgOPj/0AxXPlAMHd8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x621 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "r = readout_module.get_acquisitions(0)[\"single\"][\"acquisition\"][\"scope\"]\n",
    "plt.plot(r[\"path0\"][\"data\"], \".-\")\n",
    "plt.plot(r[\"path1\"][\"data\"], \".-\")\n",
    "plt.axvline(tof_measured, c=\"k\")\n",
    "plt.xlim(\n",
    "    tof_measured - 10 / readout_module.sequencer0.nco_freq() * 1e9,\n",
    "    tof_measured + 10 / readout_module.sequencer0.nco_freq() * 1e9,\n",
    ")\n",
    "plt.show()\n",
    "\n",
    "plt.plot(r[\"path0\"][\"data\"], \".-\")\n",
    "plt.plot(r[\"path1\"][\"data\"], \".-\")\n",
    "plt.axvline(1024 + tof_measured, c=\"k\")\n",
    "plt.xlim(\n",
    "    1024 + tof_measured - 10 / readout_module.sequencer0.nco_freq() * 1e9,\n",
    "    1024 + tof_measured + 10 / readout_module.sequencer0.nco_freq() * 1e9,\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0810a38b",
   "metadata": {},
   "source": [
    "## Parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba33a384",
   "metadata": {},
   "source": [
    "Set the parameters for the Rabi experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bbe6d540",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:54.203682Z",
     "iopub.status.busy": "2024-09-02T01:36:54.203461Z",
     "iopub.status.idle": "2024-09-02T01:36:54.206830Z",
     "shell.execute_reply": "2024-09-02T01:36:54.206209Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# all times must be divisible by 4\n",
    "reset_time = 200  # reset time for the qubit in microseconds\n",
    "tof = int(tof_measured / 4) * 4  # time of flight must be divisible by 4\n",
    "readout_delay = 164  # time to delay the readout pulse after the start of the rotation pulse\n",
    "\n",
    "navg = 1000  # number of averages\n",
    "stepsize = int(65535 / 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21027f4d",
   "metadata": {},
   "source": [
    "## Rabi"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c84cca99",
   "metadata": {},
   "source": [
    "Normally, a Rabi experiment would be performed by changing the amplitude in the inner loop, and averaging in the outer loop. To make the resulting experiment visible on an oscilloscope however, in this tutorial we swapped these two loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "95113d42",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:54.209150Z",
     "iopub.status.busy": "2024-09-02T01:36:54.208958Z",
     "iopub.status.idle": "2024-09-02T01:36:54.213370Z",
     "shell.execute_reply": "2024-09-02T01:36:54.212714Z"
    }
   },
   "outputs": [],
   "source": [
    "# QCM sequence program.\n",
    "qcm_seq_prog = f\"\"\"\n",
    "# Registers used:\n",
    "# R0 loops over the different awg amplitudes used for the rabi driving pulse\n",
    "# R2 is used to count the averages needed for a single amplitude\n",
    "# R3 contains the qubit reset time in microseconds\n",
    "\n",
    "           move          0, R0                       # start with awg amplitude 0\n",
    "           wait_sync     4                           # Synchronize the QRM with the QCM\n",
    "\n",
    "\n",
    "ampl_loop: add           R0, {stepsize}, R0          # increase the pulse amplitude by the stepsize\n",
    "           move          {navg}, R2                  # reset the number of averages and save in the R2 register\n",
    "\n",
    "           # let the qubit relax to its groundstate\n",
    "navg_loop: move          {reset_time}, R3            # reset the number of microseconds to wait and save in the R3 register\n",
    "rst_loop:  wait          1000                        # wait 1 microsecond\n",
    "           loop          R3,@rst_loop                # repeat the 1 microsecond wait as much as needed to let the qubit relax\n",
    "\n",
    "           set_awg_gain  R0, R0                      # Set the new amplitude used for the drive pulse\n",
    "           set_mrk       1                           # Set marker 1 high for to enable synchronization with external oscilloscope\n",
    "           wait_sync     4                           # Synchronize with the qrm to signify a measurement is coming\n",
    "           play          2,3,16384                   # Play waveforms and wait remaining duration of scope acquisition\n",
    "\n",
    "           set_mrk       0                           # Reset marker 1\n",
    "           upd_param     4\n",
    "\n",
    "           loop          R2,@navg_loop                        # Repeat the experiment to average, until R2 becomes 0\n",
    "           jlt           R0,{num_bins*stepsize},@ampl_loop    # Repeat the experiment for different pulse amplitudes from 0 to num_bins\n",
    "           stop                                               # Stop.\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "# QRM sequence program.\n",
    "qrm_seq_prog = f\"\"\"\n",
    "# Registers used:\n",
    "# R0 counts which bin to acquire into, a new bin for every new amplitude\n",
    "# R2 is used to count the averages needed for a single amplitude\n",
    "\n",
    "           wait_sync     4                           # Synchronize the QRM with the QCM.\n",
    "           move          0, R0                       # the first acquisition uses bin 0\n",
    "ampl_loop: move          {navg}, R2                  # reset the amount of averages to be taken to the initial value\n",
    "\n",
    "navg_loop: wait_sync     {readout_delay}             # wait for the QCM to signal a pulse is coming and wait the readout_delay\n",
    "           play          1,0,{tof}                   # play readout pulse and wait for the tof\n",
    "           acquire       1,R0,16384                  # Acquire waveforms and wait remaining duration of scope acquisition.\n",
    "\n",
    "           loop          R2, @navg_loop              # Repeat this measurement for every average\n",
    "           add           R0,1,R0                     # Increment the bin into which we are measuring\n",
    "           jmp           @ampl_loop                  # repeat\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bc88ffa",
   "metadata": {},
   "source": [
    "Upload programs and waveforms to QRM and QCM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d359e133",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:54.215544Z",
     "iopub.status.busy": "2024-09-02T01:36:54.215355Z",
     "iopub.status.idle": "2024-09-02T01:36:54.317234Z",
     "shell.execute_reply": "2024-09-02T01:36:54.316486Z"
    }
   },
   "outputs": [],
   "source": [
    "# Add QCM sequence to single dictionary and write to JSON file.\n",
    "sequence = {\n",
    "    \"waveforms\": wfs,\n",
    "    \"weights\": {},\n",
    "    \"acquisitions\": acquisitions,\n",
    "    \"program\": qcm_seq_prog,\n",
    "}\n",
    "with open(\"qcm_sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "    json.dump(sequence, file, indent=4)\n",
    "    file.close()\n",
    "\n",
    "# Add QRM sequence to single dictionary and write to JSON file.\n",
    "sequence = {\n",
    "    \"waveforms\": wfs,\n",
    "    \"weights\": {},\n",
    "    \"acquisitions\": acquisitions,\n",
    "    \"program\": qrm_seq_prog,\n",
    "}\n",
    "with open(\"qrm_sequence.json\", \"w\", encoding=\"utf-8\") as file:\n",
    "    json.dump(sequence, file, indent=4)\n",
    "    file.close()\n",
    "\n",
    "# Upload sequence to QCM.\n",
    "control_module.sequencer0.sequence(\"qcm_sequence.json\")\n",
    "\n",
    "# Upload sequence to QRM.\n",
    "readout_module.sequencer0.sequence(\"qrm_sequence.json\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "252f2472",
   "metadata": {},
   "source": [
    "Arm and start sequencer0 of both the QCM and QRM. The `wait_sync` command together with the SYNQ technology ensures both modules start simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "bfbb3021",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:54.320663Z",
     "iopub.status.busy": "2024-09-02T01:36:54.320459Z",
     "iopub.status.idle": "2024-09-02T01:36:54.336343Z",
     "shell.execute_reply": "2024-09-02T01:36:54.335622Z"
    }
   },
   "outputs": [],
   "source": [
    "# Arm and start sequencer of the QCM (only sequencer 0).\n",
    "control_module.arm_sequencer(0)\n",
    "control_module.start_sequencer(0)\n",
    "\n",
    "# Arm and start sequencer of the QRM (only sequencer 0).\n",
    "readout_module.arm_sequencer(0)\n",
    "readout_module.start_sequencer(0)\n",
    "\n",
    "# We didn't specify how many amplitudes would be measured, so we stop manually here.\n",
    "cluster.stop_sequencer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c7fe600",
   "metadata": {},
   "source": [
    "## Stop\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": 26,
   "id": "59659a64",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-09-02T01:36:54.339689Z",
     "iopub.status.busy": "2024-09-02T01:36:54.339482Z",
     "iopub.status.idle": "2024-09-02T01:37:00.829404Z",
     "shell.execute_reply": "2024-09-02T01:37:00.828686Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "QCM :\n",
      "Status: OKAY, State: STOPPED, Info Flags: FORCED_STOP, Warning Flags: NONE, Error Flags: NONE, Log: []\n",
      "\n",
      "QRM :\n",
      "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",
      "\n",
      "QCM snapshot:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module2:\n",
      "\tparameter              value\n",
      "--------------------------------------------------------------------------------\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_amplitude     :\t0 \n",
      "out0_exp0_config        :\tbypassed \n",
      "out0_exp0_time_constant :\t0 \n",
      "out0_exp1_amplitude     :\t0 \n",
      "out0_exp1_config        :\tbypassed \n",
      "out0_exp1_time_constant :\t0 \n",
      "out0_exp2_amplitude     :\t0 \n",
      "out0_exp2_config        :\tbypassed \n",
      "out0_exp2_time_constant :\t0 \n",
      "out0_exp3_amplitude     :\t0 \n",
      "out0_exp3_config        :\tbypassed \n",
      "out0_exp3_time_constant :\t0 \n",
      "out0_fir_coeffs         :\t[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
      "out0_fir_config         :\tbypassed \n",
      "out0_latency            :\t0 (s)\n",
      "out0_offset             :\t0 (V)\n",
      "out1_exp0_amplitude     :\t0 \n",
      "out1_exp0_config        :\tbypassed \n",
      "out1_exp0_time_constant :\t0 \n",
      "out1_exp1_amplitude     :\t0 \n",
      "out1_exp1_config        :\tbypassed \n",
      "out1_exp1_time_constant :\t0 \n",
      "out1_exp2_amplitude     :\t0 \n",
      "out1_exp2_config        :\tbypassed \n",
      "out1_exp2_time_constant :\t0 \n",
      "out1_exp3_amplitude     :\t0 \n",
      "out1_exp3_config        :\tbypassed \n",
      "out1_exp3_time_constant :\t0 \n",
      "out1_fir_coeffs         :\t[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
      "out1_fir_config         :\tbypassed \n",
      "out1_latency            :\t0 (s)\n",
      "out1_offset             :\t0 (V)\n",
      "out2_exp0_amplitude     :\t0 \n",
      "out2_exp0_config        :\tbypassed \n",
      "out2_exp0_time_constant :\t0 \n",
      "out2_exp1_amplitude     :\t0 \n",
      "out2_exp1_config        :\tbypassed \n",
      "out2_exp1_time_constant :\t0 \n",
      "out2_exp2_amplitude     :\t0 \n",
      "out2_exp2_config        :\tbypassed \n",
      "out2_exp2_time_constant :\t0 \n",
      "out2_exp3_amplitude     :\t0 \n",
      "out2_exp3_config        :\tbypassed \n",
      "out2_exp3_time_constant :\t0 \n",
      "out2_fir_coeffs         :\t[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
      "out2_fir_config         :\tbypassed \n",
      "out2_latency            :\t0 (s)\n",
      "out2_offset             :\t0 (V)\n",
      "out3_exp0_amplitude     :\t0 \n",
      "out3_exp0_config        :\tbypassed \n",
      "out3_exp0_time_constant :\t0 \n",
      "out3_exp1_amplitude     :\t0 \n",
      "out3_exp1_config        :\tbypassed \n",
      "out3_exp1_time_constant :\t0 \n",
      "out3_exp2_amplitude     :\t0 \n",
      "out3_exp2_config        :\tbypassed \n",
      "out3_exp2_time_constant :\t0 \n",
      "out3_exp3_amplitude     :\t0 \n",
      "out3_exp3_config        :\tbypassed \n",
      "out3_exp3_time_constant :\t0 \n",
      "out3_fir_coeffs         :\t[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...\n",
      "out3_fir_config         :\tbypassed \n",
      "out3_latency            :\t0 (s)\n",
      "out3_offset             :\t0 (V)\n",
      "present                 :\tTrue \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module2_sequencer0:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_out0                     :\tI \n",
      "connect_out1                     :\tQ \n",
      "connect_out2                     :\toff \n",
      "connect_out3                     :\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",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tTrue \n",
      "nco_freq                         :\t1e+08 (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                          :\tTrue \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module2_sequencer1:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "connect_out2                     :\toff \n",
      "connect_out3                     :\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",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n",
      "cluster0_module2_sequencer2:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "connect_out2                     :\toff \n",
      "connect_out3                     :\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",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module2_sequencer3:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "connect_out2                     :\toff \n",
      "connect_out3                     :\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",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n",
      "cluster0_module2_sequencer4:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "connect_out2                     :\toff \n",
      "connect_out3                     :\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",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module2_sequencer5:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "connect_out2                     :\toff \n",
      "connect_out3                     :\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",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n",
      "\n",
      "QRM snapshot:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4:\n",
      "\tparameter                    value\n",
      "--------------------------------------------------------------------------------\n",
      "in0_gain                      :\t-6 (dB)\n",
      "in0_offset                    :\t0 (V)\n",
      "in1_gain                      :\t-6 (dB)\n",
      "in1_offset                    :\t0 (V)\n",
      "marker0_exp0_config           :\tbypassed \n",
      "marker0_exp1_config           :\tbypassed \n",
      "marker0_exp2_config           :\tbypassed \n",
      "marker0_exp3_config           :\tbypassed \n",
      "marker0_fir_config            :\tbypassed \n",
      "marker0_inv_en                :\tFalse \n",
      "marker1_exp0_config           :\tbypassed \n",
      "marker1_exp1_config           :\tbypassed \n",
      "marker1_exp2_config           :\tbypassed \n",
      "marker1_exp3_config           :\tbypassed \n",
      "marker1_fir_config            :\tbypassed \n",
      "marker1_inv_en                :\tFalse \n",
      "marker2_exp0_config           :\tbypassed \n",
      "marker2_exp1_config           :\tbypassed \n",
      "marker2_exp2_config           :\tbypassed \n",
      "marker2_exp3_config           :\tbypassed \n",
      "marker2_fir_config            :\tbypassed \n",
      "marker2_inv_en                :\tFalse \n",
      "marker3_exp0_config           :\tbypassed \n",
      "marker3_exp1_config           :\tbypassed \n",
      "marker3_exp2_config           :\tbypassed \n",
      "marker3_exp3_config           :\tbypassed \n",
      "marker3_fir_config            :\tbypassed \n",
      "marker3_inv_en                :\tFalse \n",
      "out0_exp0_config              :\tbypassed \n",
      "out0_exp1_config              :\tbypassed \n",
      "out0_exp2_config              :\tbypassed \n",
      "out0_exp3_config              :\tbypassed \n",
      "out0_fir_config               :\tbypassed \n",
      "out0_latency                  :\t0 (s)\n",
      "out0_offset                   :\t0 (V)\n",
      "out1_exp0_config              :\tbypassed \n",
      "out1_exp1_config              :\tbypassed \n",
      "out1_exp2_config              :\tbypassed \n",
      "out1_exp3_config              :\tbypassed \n",
      "out1_fir_config               :\tbypassed \n",
      "out1_latency                  :\t0 (s)\n",
      "out1_offset                   :\t0 (V)\n",
      "present                       :\tTrue \n",
      "scope_acq_avg_mode_en_path0   :\tFalse \n",
      "scope_acq_avg_mode_en_path1   :\tFalse \n",
      "scope_acq_sequencer_select    :\t0 \n",
      "scope_acq_trigger_level_path0 :\t0 \n",
      "scope_acq_trigger_level_path1 :\t0 \n",
      "scope_acq_trigger_mode_path0  :\tsequencer \n",
      "scope_acq_trigger_mode_path1  :\tsequencer \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer0:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\tin0 \n",
      "connect_acq_Q                    :\tin1 \n",
      "connect_out0                     :\tI \n",
      "connect_out1                     :\tQ \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tTrue \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                       :\tTrue \n",
      "nco_freq                         :\t1e+08 (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                          :\tTrue \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer1:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer2:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer3:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer4:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster0_module4_sequencer5:\n",
      "\tparameter                       value\n",
      "--------------------------------------------------------------------------------\n",
      "connect_acq_I                    :\toff \n",
      "connect_acq_Q                    :\toff \n",
      "connect_out0                     :\toff \n",
      "connect_out1                     :\toff \n",
      "cont_mode_en_awg_path0           :\tFalse \n",
      "cont_mode_en_awg_path1           :\tFalse \n",
      "cont_mode_waveform_idx_awg_path0 :\t0 \n",
      "cont_mode_waveform_idx_awg_path1 :\t0 \n",
      "demod_en_acq                     :\tFalse \n",
      "gain_awg_path0                   :\t1 \n",
      "gain_awg_path1                   :\t1 \n",
      "integration_length_acq           :\t1024 \n",
      "marker_ovr_en                    :\tFalse \n",
      "marker_ovr_value                 :\t0 \n",
      "mixer_corr_gain_ratio            :\t1 \n",
      "mixer_corr_phase_offset_degree   :\t-0 \n",
      "mod_en_awg                       :\tFalse \n",
      "nco_freq                         :\t0 (Hz)\n",
      "nco_phase_offs                   :\t0 (Degrees)\n",
      "nco_prop_delay_comp              :\t0 (ns)\n",
      "nco_prop_delay_comp_en           :\tFalse (ns)\n",
      "offset_awg_path0                 :\t0 \n",
      "offset_awg_path1                 :\t0 \n",
      "sync_en                          :\tFalse \n",
      "thresholded_acq_marker_address   :\t1 \n",
      "thresholded_acq_marker_en        :\tFalse \n",
      "thresholded_acq_marker_invert    :\tFalse \n",
      "thresholded_acq_rotation         :\t0 (Degrees)\n",
      "thresholded_acq_threshold        :\t0 \n",
      "thresholded_acq_trigger_address  :\t1 \n",
      "thresholded_acq_trigger_en       :\tFalse \n",
      "thresholded_acq_trigger_invert   :\tFalse \n",
      "trigger10_count_threshold        :\t1 \n",
      "trigger10_threshold_invert       :\tFalse \n",
      "trigger11_count_threshold        :\t1 \n",
      "trigger11_threshold_invert       :\tFalse \n",
      "trigger12_count_threshold        :\t1 \n",
      "trigger12_threshold_invert       :\tFalse \n",
      "trigger13_count_threshold        :\t1 \n",
      "trigger13_threshold_invert       :\tFalse \n",
      "trigger14_count_threshold        :\t1 \n",
      "trigger14_threshold_invert       :\tFalse \n",
      "trigger15_count_threshold        :\t1 \n",
      "trigger15_threshold_invert       :\tFalse \n",
      "trigger1_count_threshold         :\t1 \n",
      "trigger1_threshold_invert        :\tFalse \n",
      "trigger2_count_threshold         :\t1 \n",
      "trigger2_threshold_invert        :\tFalse \n",
      "trigger3_count_threshold         :\t1 \n",
      "trigger3_threshold_invert        :\tFalse \n",
      "trigger4_count_threshold         :\t1 \n",
      "trigger4_threshold_invert        :\tFalse \n",
      "trigger5_count_threshold         :\t1 \n",
      "trigger5_threshold_invert        :\tFalse \n",
      "trigger6_count_threshold         :\t1 \n",
      "trigger6_threshold_invert        :\tFalse \n",
      "trigger7_count_threshold         :\t1 \n",
      "trigger7_threshold_invert        :\tFalse \n",
      "trigger8_count_threshold         :\t1 \n",
      "trigger8_threshold_invert        :\tFalse \n",
      "trigger9_count_threshold         :\t1 \n",
      "trigger9_threshold_invert        :\tFalse \n",
      "ttl_acq_auto_bin_incr_en         :\tFalse \n",
      "ttl_acq_input_select             :\t0 \n",
      "ttl_acq_threshold                :\t0 \n",
      "upsample_rate_awg_path0          :\t0 \n",
      "upsample_rate_awg_path1          :\t0 \n"
     ]
    }
   ],
   "source": [
    "# Print status of sequencers.\n",
    "print(\"QCM :\")\n",
    "print(control_module.get_sequencer_status(0))\n",
    "print()\n",
    "\n",
    "print(\"QRM :\")\n",
    "print(readout_module.get_sequencer_status(0))\n",
    "print()\n",
    "\n",
    "# Uncomment the following to print an overview of the instrument parameters.\n",
    "# Print an overview of instrument parameters.\n",
    "print(\"QCM snapshot:\")\n",
    "control_module.print_readable_snapshot(update=True)\n",
    "print()\n",
    "\n",
    "print(\"QRM snapshot:\")\n",
    "readout_module.print_readable_snapshot(update=True)\n",
    "\n",
    "# Close the instrument connections.\n",
    "cluster.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81f2508b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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