{ "cells": [ { "cell_type": "markdown", "id": "da5198c6", "metadata": {}, "source": [ "(sec-dataset-advanced-examples)=\n", "# Quantify dataset - advanced examples\n", "\n", "```{seealso}\n", "The complete source code of this tutorial can be found in\n", "\n", "{nb-download}`Quantify dataset - advanced examples.ipynb`\n", "```\n", "\n", "Here we will explore a few advanced usages of the Quantify dataset and how it can\n", "accommodate them." ] }, { "cell_type": "code", "execution_count": 1, "id": "64643c33", "metadata": { "mystnb": { "code_prompt_show": "Imports and auxiliary utilities" }, "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "from pathlib import Path\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", "from rich import pretty\n", "\n", "from quantify_core.analysis.calibration import rotate_to_calibrated_axis\n", "from quantify_core.analysis.fitting_models import exp_decay_func\n", "from quantify_core.data import handling as dh\n", "from quantify_core.utilities import dataset_examples\n", "from quantify_core.utilities.dataset_examples import (\n", " mk_nested_mc_dataset,\n", " mk_shots_from_probabilities,\n", " mk_surface7_cyles_dataset,\n", ")\n", "from quantify_core.utilities.examples_support import (\n", " mk_iq_shots,\n", " round_trip_dataset,\n", ")\n", "from quantify_core.utilities.inspect_utils import display_source_code\n", "\n", "pretty.install()\n", "\n", "dh.set_datadir(Path.home() / \"quantify-data\") # change me!" ] }, { "cell_type": "markdown", "id": "b075ee74", "metadata": {}, "source": [ "## Dataset for an \"unstructured\" experiment\n", "\n", "Let's take consider a Surface Code experiment, in particular the one portrayed in\n", "Fig. 4b from one of the papers from DiCarlo Lab {cite}`marques_logical_qubit_2021`.\n", "\n", "For simplicity, we will not use exactly the same schedule, because what matters here\n", "are the measurements. It is difficult to deal with the results of these measurements\n", "because we have a few repeating cycles followed by a final measurement that leaves the\n", "overall dataset \"unstructured\".\n", "\n", "```{figure} /images/surface-7-sched.png\n", ":width: 100%\n", "```\n", "\n", "``````{admonition} Source code for generating this schedule and visualizing it\n", ":class: dropdown, info\n", "\n", "If you want to create and visualize the schedule above using `quantify-scheduler`,\n", "you can use this code:\n", "\n", "```{code-block} python\n", "from quantify_scheduler import Schedule\n", "from quantify_scheduler.operations.gate_library import CZ, Y90, Measure, Reset, X\n", "from quantify_scheduler.visualization.circuit_diagram import circuit_diagram_matplotlib\n", "\n", "def mk_surface7_sched(num_cycles: int = 3):\n", " \"\"\"Generates a schedule with some of the feature of a Surface 7 experiment as\n", " portrayed in Fig. 4b of :cite:`marques_logical_qubit_2021`.\n", "\n", " Parameters\n", " ----------\n", " num_cycles\n", " The number of times to repeat the main cycle.\n", "\n", " Returns\n", " -------\n", " :\n", " A schedule similar to a Surface 7 dance.\n", " \"\"\"\n", "\n", " sched = Schedule(\"S7 dance\")\n", "\n", " q_d1, q_d2, q_d3, q_d4 = [f\"D{i}\" for i in range(1, 5)]\n", " q_a1, q_a2, q_a3 = [f\"A{i}\" for i in range(1, 4)]\n", " all_qubits = q_d1, q_d2, q_d3, q_d4, q_a1, q_a2, q_a3\n", "\n", " sched.add(Reset(*all_qubits))\n", "\n", " for cycle in range(num_cycles):\n", " sched.add(Y90(q_d1))\n", " for qubit in [q_d2, q_d3, q_d4]:\n", " sched.add(Y90(qubit), ref_pt=\"start\", rel_time=0)\n", " sched.add(Y90(q_a2), ref_pt=\"start\", rel_time=0)\n", "\n", " for qubit in [q_d2, q_d1, q_d4, q_d3]:\n", " sched.add(CZ(qC=qubit, qT=q_a2))\n", "\n", " sched.add(Y90(q_d1))\n", " for qubit in [q_d2, q_d3, q_d4]:\n", " sched.add(Y90(qubit), ref_pt=\"start\", rel_time=0)\n", " sched.add(Y90(q_a2), ref_pt=\"start\", rel_time=0)\n", "\n", " sched.add(Y90(q_a1), ref_pt=\"end\", rel_time=0)\n", " sched.add(Y90(q_a3), ref_pt=\"start\", rel_time=0)\n", "\n", " sched.add(CZ(qC=q_d1, qT=q_a1))\n", " sched.add(CZ(qC=q_d2, qT=q_a3))\n", " sched.add(CZ(qC=q_d3, qT=q_a1))\n", " sched.add(CZ(qC=q_d4, qT=q_a3))\n", "\n", " sched.add(Y90(q_a1), ref_pt=\"end\", rel_time=0)\n", " sched.add(Y90(q_a3), ref_pt=\"start\", rel_time=0)\n", "\n", " sched.add(Measure(q_a2, acq_index=cycle))\n", " for qubit in (q_a1, q_a3):\n", " sched.add(Measure(qubit, acq_index=cycle), ref_pt=\"start\", rel_time=0)\n", "\n", " for qubit in [q_d1, q_d2, q_d3, q_d4]:\n", " sched.add(X(qubit), ref_pt=\"start\", rel_time=0)\n", "\n", " # final measurements\n", "\n", " sched.add(Measure(*all_qubits[:4], acq_index=0), ref_pt=\"end\", rel_time=0)\n", "\n", " return sched\n", "\n", "sched = mk_surface7_sched(num_cycles=3)\n", "f, ax = circuit_diagram_matplotlib(sched)\n", "f.set_figwidth(30)\n", "```\n", "``````\n", "\n", "How do we store all the shots for this measurement?\n", "We might want this because, e.g., we know we have an issue with leakage to the second\n", "excited state of a transmon and we would like to be able to store and inspect raw data.\n", "\n", "To support such use-cases we will have a dimension in the dataset for the repeating cycles\n", "and one extra dimension for the final measurement." ] }, { "cell_type": "code", "execution_count": 2, "id": "d2f2ddcf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
def mk_iq_shots(\n",
       "    num_shots: int = 128,\n",
       "    sigmas: Union[Tuple[float], NDArray[np.float64]] = (0.1, 0.1),\n",
       "    centers: Union[Tuple[complex], NDArray[np.complex128]] = (-0.2 + 0.65j, 0.7 + 4j),\n",
       "    probabilities: Union[Tuple[float], NDArray[np.float64]] = (0.4, 0.6),\n",
       "    seed: Union[int, None] = 112233,\n",
       ") -> NDArray:\n",
       "    """\n",
       "    Generate clusters of (I + 1j*Q) points with a Gaussian distribution.\n",
       "\n",
       "    Utility to mock the data coming from qubit readout experiments.\n",
       "    Clusters are centered around ``centers`` and data points are distributed between\n",
       "    them according to ``probabilities``.\n",
       "\n",
       "    .. seealso:: :ref:`howto-utilities-examples-ssro`\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_shots\n",
       "        The number of shot to generate.\n",
       "    sigma\n",
       "        The sigma of the Gaussian distribution used for both real and imaginary parts.\n",
       "    centers\n",
       "        The center of each cluster on the imaginary plane.\n",
       "    probabilities\n",
       "        The probabilities of each cluster being randomly selected for each shot.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    """\n",
       "    if not len(sigmas) == len(centers) == len(probabilities):\n",
       "        raise ValueError(\n",
       "            f"Incorrect input. sigmas={sigmas}, centers={centers} and "\n",
       "            f"probabilities={probabilities} must have the same length."\n",
       "        )\n",
       "\n",
       "    rng = np.random.default_rng(seed=seed)\n",
       "\n",
       "    cluster_indices = tuple(range(len(centers)))\n",
       "    choices = rng.choice(a=cluster_indices, size=num_shots, p=probabilities)\n",
       "\n",
       "    shots = []\n",
       "    for idx in cluster_indices:\n",
       "        num_shots_this_cluster = np.sum(choices == idx)\n",
       "        i_data = rng.normal(\n",
       "            loc=centers[idx].real,\n",
       "            scale=sigmas[idx],\n",
       "            size=num_shots_this_cluster,\n",
       "        )\n",
       "        q_data = rng.normal(\n",
       "            loc=centers[idx].imag,\n",
       "            scale=sigmas[idx],\n",
       "            size=num_shots_this_cluster,\n",
       "        )\n",
       "        shots.append(i_data + 1j * q_data)\n",
       "    return np.concatenate(shots)\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def}\\PY{+w}{ }\\PY{n+nf}{mk\\PYZus{}iq\\PYZus{}shots}\\PY{p}{(}\n",
       "    \\PY{n}{num\\PYZus{}shots}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{128}\\PY{p}{,}\n",
       "    \\PY{n}{sigmas}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{float}\\PY{p}{]}\\PY{p}{,} \\PY{n}{NDArray}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{float64}\\PY{p}{]}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{0.1}\\PY{p}{,} \\PY{l+m+mf}{0.1}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{centers}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{complex}\\PY{p}{]}\\PY{p}{,} \\PY{n}{NDArray}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{complex128}\\PY{p}{]}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{l+m+mf}{0.2} \\PY{o}{+} \\PY{l+m+mf}{0.65}\\PY{n}{j}\\PY{p}{,} \\PY{l+m+mf}{0.7} \\PY{o}{+} \\PY{l+m+mi}{4}\\PY{n}{j}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{float}\\PY{p}{]}\\PY{p}{,} \\PY{n}{NDArray}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{float64}\\PY{p}{]}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{0.4}\\PY{p}{,} \\PY{l+m+mf}{0.6}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{seed}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n+nb}{int}\\PY{p}{,} \\PY{k+kc}{None}\\PY{p}{]} \\PY{o}{=} \\PY{l+m+mi}{112233}\\PY{p}{,}\n",
       "\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{NDArray}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Generate clusters of (I + 1j*Q) points with a Gaussian distribution.}\n",
       "\n",
       "\\PY{l+s+sd}{    Utility to mock the data coming from qubit readout experiments.}\n",
       "\\PY{l+s+sd}{    Clusters are centered around ``centers`` and data points are distributed between}\n",
       "\\PY{l+s+sd}{    them according to ``probabilities``.}\n",
       "\n",
       "\\PY{l+s+sd}{    .. seealso:: :ref:`howto\\PYZhy{}utilities\\PYZhy{}examples\\PYZhy{}ssro`}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    num\\PYZus{}shots}\n",
       "\\PY{l+s+sd}{        The number of shot to generate.}\n",
       "\\PY{l+s+sd}{    sigma}\n",
       "\\PY{l+s+sd}{        The sigma of the Gaussian distribution used for both real and imaginary parts.}\n",
       "\\PY{l+s+sd}{    centers}\n",
       "\\PY{l+s+sd}{        The center of each cluster on the imaginary plane.}\n",
       "\\PY{l+s+sd}{    probabilities}\n",
       "\\PY{l+s+sd}{        The probabilities of each cluster being randomly selected for each shot.}\n",
       "\\PY{l+s+sd}{    seed}\n",
       "\\PY{l+s+sd}{        Random number generator seed passed to ``numpy.random.default\\PYZus{}rng``.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{k}{if} \\PY{o+ow}{not} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{sigmas}\\PY{p}{)} \\PY{o}{==} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{centers}\\PY{p}{)} \\PY{o}{==} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{probabilities}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{k}{raise} \\PY{n+ne}{ValueError}\\PY{p}{(}\n",
       "            \\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Incorrect input. sigmas=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{sigmas}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{, centers=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{centers}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{ and }\\PY{l+s+s2}{\\PYZdq{}}\n",
       "            \\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{probabilities=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{probabilities}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{ must have the same length.}\\PY{l+s+s2}{\\PYZdq{}}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{rng} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{default\\PYZus{}rng}\\PY{p}{(}\\PY{n}{seed}\\PY{o}{=}\\PY{n}{seed}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{cluster\\PYZus{}indices} \\PY{o}{=} \\PY{n+nb}{tuple}\\PY{p}{(}\\PY{n+nb}{range}\\PY{p}{(}\\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{centers}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\n",
       "    \\PY{n}{choices} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{choice}\\PY{p}{(}\\PY{n}{a}\\PY{o}{=}\\PY{n}{cluster\\PYZus{}indices}\\PY{p}{,} \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots}\\PY{p}{,} \\PY{n}{p}\\PY{o}{=}\\PY{n}{probabilities}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{shots} \\PY{o}{=} \\PY{p}{[}\\PY{p}{]}\n",
       "    \\PY{k}{for} \\PY{n}{idx} \\PY{o+ow}{in} \\PY{n}{cluster\\PYZus{}indices}\\PY{p}{:}\n",
       "        \\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{sum}\\PY{p}{(}\\PY{n}{choices} \\PY{o}{==} \\PY{n}{idx}\\PY{p}{)}\n",
       "        \\PY{n}{i\\PYZus{}data} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{normal}\\PY{p}{(}\n",
       "            \\PY{n}{loc}\\PY{o}{=}\\PY{n}{centers}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{o}{.}\\PY{n}{real}\\PY{p}{,}\n",
       "            \\PY{n}{scale}\\PY{o}{=}\\PY{n}{sigmas}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{p}{,}\n",
       "            \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "        \\PY{n}{q\\PYZus{}data} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{normal}\\PY{p}{(}\n",
       "            \\PY{n}{loc}\\PY{o}{=}\\PY{n}{centers}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{o}{.}\\PY{n}{imag}\\PY{p}{,}\n",
       "            \\PY{n}{scale}\\PY{o}{=}\\PY{n}{sigmas}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{p}{,}\n",
       "            \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "        \\PY{n}{shots}\\PY{o}{.}\\PY{n}{append}\\PY{p}{(}\\PY{n}{i\\PYZus{}data} \\PY{o}{+} \\PY{l+m+mi}{1}\\PY{n}{j} \\PY{o}{*} \\PY{n}{q\\PYZus{}data}\\PY{p}{)}\n",
       "    \\PY{k}{return} \\PY{n}{np}\\PY{o}{.}\\PY{n}{concatenate}\\PY{p}{(}\\PY{n}{shots}\\PY{p}{)}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_iq_shots\u001b[0m\u001b[1m(\u001b[0m\n",
       "    num_shots: int = \u001b[1;36m128\u001b[0m,\n",
       "    sigmas: Union\u001b[1m[\u001b[0mTuple\u001b[1m[\u001b[0mfloat\u001b[1m]\u001b[0m, NDArray\u001b[1m[\u001b[0mnp.float64\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m0.1\u001b[0m, \u001b[1;36m0.1\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    centers: Union\u001b[1m[\u001b[0mTuple\u001b[1m[\u001b[0mcomplex\u001b[1m]\u001b[0m, NDArray\u001b[1m[\u001b[0mnp.complex128\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m-0.2\u001b[0m + \u001b[1;36m0.65j\u001b[0m, \u001b[1;36m0.7\u001b[0m + \u001b[1;36m4j\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    probabilities: Union\u001b[1m[\u001b[0mTuple\u001b[1m[\u001b[0mfloat\u001b[1m]\u001b[0m, NDArray\u001b[1m[\u001b[0mnp.float64\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m0.4\u001b[0m, \u001b[1;36m0.6\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    seed: Union\u001b[1m[\u001b[0mint, \u001b[3;35mNone\u001b[0m\u001b[1m]\u001b[0m = \u001b[1;36m112233\u001b[0m,\n",
       "\u001b[1m)\u001b[0m -> NDArray:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generate clusters of \u001b[1m(\u001b[0mI + \u001b[1;36m1j\u001b[0m*Q\u001b[1m)\u001b[0m points with a Gaussian distribution.\n",
       "\n",
       "    Utility to mock the data coming from qubit readout experiments.\n",
       "    Clusters are centered around ``centers`` and data points are distributed between\n",
       "    them according to ``probabilities``.\n",
       "\n",
       "    .. seealso:: :ref:`howto-utilities-examples-ssro`\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_shots\n",
       "        The number of shot to generate.\n",
       "    sigma\n",
       "        The sigma of the Gaussian distribution used for both real and imaginary parts.\n",
       "    centers\n",
       "        The center of each cluster on the imaginary plane.\n",
       "    probabilities\n",
       "        The probabilities of each cluster being randomly selected for each shot.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    if not \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0msigmas\u001b[1m)\u001b[0m == \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mcenters\u001b[1m)\u001b[0m == \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mprobabilities\u001b[1m)\u001b[0m:\n",
       "        raise \u001b[1;35mValueError\u001b[0m\u001b[1m(\u001b[0m\n",
       "            f\"Incorrect input. \u001b[33msigmas\u001b[0m=\u001b[1m{\u001b[0msigmas\u001b[1m}\u001b[0m, \u001b[33mcenters\u001b[0m=\u001b[1m{\u001b[0mcenters\u001b[1m}\u001b[0m and \"\n",
       "            f\"\u001b[33mprobabilities\u001b[0m=\u001b[1m{\u001b[0mprobabilities\u001b[1m}\u001b[0m must have the same length.\"\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    rng = \u001b[1;35mnp.random.default_rng\u001b[0m\u001b[1m(\u001b[0m\u001b[33mseed\u001b[0m=\u001b[35mseed\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    cluster_indices = \u001b[1;35mtuple\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mcenters\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\n",
       "    choices = \u001b[1;35mrng.choice\u001b[0m\u001b[1m(\u001b[0m\u001b[33ma\u001b[0m=\u001b[35mcluster_indices\u001b[0m, \u001b[33msize\u001b[0m=\u001b[35mnum_shots\u001b[0m, \u001b[33mp\u001b[0m=\u001b[35mprobabilities\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    shots = \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
       "    for idx in cluster_indices:\n",
       "        num_shots_this_cluster = \u001b[1;35mnp.sum\u001b[0m\u001b[1m(\u001b[0mchoices == idx\u001b[1m)\u001b[0m\n",
       "        i_data = \u001b[1;35mrng.normal\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[33mloc\u001b[0m=\u001b[35mcenters\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m.real,\n",
       "            \u001b[33mscale\u001b[0m=\u001b[35msigmas\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m,\n",
       "            \u001b[33msize\u001b[0m=\u001b[35mnum_shots_this_cluster\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "        q_data = \u001b[1;35mrng.normal\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[33mloc\u001b[0m=\u001b[35mcenters\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m.imag,\n",
       "            \u001b[33mscale\u001b[0m=\u001b[35msigmas\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m,\n",
       "            \u001b[33msize\u001b[0m=\u001b[35mnum_shots_this_cluster\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "        \u001b[1;35mshots.append\u001b[0m\u001b[1m(\u001b[0mi_data + \u001b[1;36m1j\u001b[0m * q_data\u001b[1m)\u001b[0m\n",
       "    return \u001b[1;35mnp.concatenate\u001b[0m\u001b[1m(\u001b[0mshots\u001b[1m)\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
def mk_shots_from_probabilities(probabilities: Union[np.ndarray, list], **kwargs):\n",
       "    """Generates multiple shots for a list of probabilities assuming two states.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    probabilities\n",
       "        The list/array of the probabilities of one of the states.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to\n",
       "        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    :\n",
       "        Array containing the shots. Shape: (num_shots, len(probabilities)).\n",
       "    """\n",
       "\n",
       "    shots = np.array(\n",
       "        tuple(\n",
       "            mk_iq_shots(probabilities=[prob, 1 - prob], **kwargs)\n",
       "            for prob in probabilities\n",
       "        )\n",
       "    ).T\n",
       "\n",
       "    return shots\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def}\\PY{+w}{ }\\PY{n+nf}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{,} \\PY{n+nb}{list}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Generates multiple shots for a list of probabilities assuming two states.}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    probabilities}\n",
       "\\PY{l+s+sd}{        The list/array of the probabilities of one of the states.}\n",
       "\\PY{l+s+sd}{    **kwargs}\n",
       "\\PY{l+s+sd}{        Keyword arguments passed to}\n",
       "\\PY{l+s+sd}{        :func:`\\PYZti{}quantify\\PYZus{}core.utilities.examples\\PYZus{}support.mk\\PYZus{}iq\\PYZus{}shots`.}\n",
       "\n",
       "\\PY{l+s+sd}{    Returns}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    :}\n",
       "\\PY{l+s+sd}{        Array containing the shots. Shape: (num\\PYZus{}shots, len(probabilities)).}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "    \\PY{n}{shots} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{array}\\PY{p}{(}\n",
       "        \\PY{n+nb}{tuple}\\PY{p}{(}\n",
       "            \\PY{n}{mk\\PYZus{}iq\\PYZus{}shots}\\PY{p}{(}\\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{prob}\\PY{p}{,} \\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{prob}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "            \\PY{k}{for} \\PY{n}{prob} \\PY{o+ow}{in} \\PY{n}{probabilities}\n",
       "        \\PY{p}{)}\n",
       "    \\PY{p}{)}\\PY{o}{.}\\PY{n}{T}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{shots}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0mprobabilities: Union\u001b[1m[\u001b[0mnp.ndarray, list\u001b[1m]\u001b[0m, **kwargs\u001b[1m)\u001b[0m:\n",
       "    \u001b[32m\"\"\u001b[0m\"Generates multiple shots for a list of probabilities assuming two states.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    probabilities\n",
       "        The list/array of the probabilities of one of the states.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to\n",
       "        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    :\n",
       "        Array containing the shots. Shape: \u001b[1m(\u001b[0mnum_shots, \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mprobabilities\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "\n",
       "    shots = \u001b[1;35mnp.array\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[1;35mtuple\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[1;35mmk_iq_shots\u001b[0m\u001b[1m(\u001b[0m\u001b[33mprobabilities\u001b[0m=\u001b[1m[\u001b[0mprob, \u001b[1;36m1\u001b[0m - prob\u001b[1m]\u001b[0m, **kwargs\u001b[1m)\u001b[0m\n",
       "            for prob in probabilities\n",
       "        \u001b[1m)\u001b[0m\n",
       "    \u001b[1m)\u001b[0m.T\n",
       "\n",
       "    return shots"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
def mk_surface7_cyles_dataset(num_cycles: int = 3, **kwargs) -> xr.Dataset:\n",
       "    """\n",
       "    See also :func:`mk_surface7_sched` inlined in the documentation as an example in:\n",
       "        :ref:`sec-dataset-advanced-examples`\n",
       "\n",
       "\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_cycles\n",
       "        The number of repeating cycles before the final measurement.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to :func:`~.mk_shots_from_probabilities`.\n",
       "    """\n",
       "\n",
       "    cycles = range(num_cycles)\n",
       "\n",
       "    mock_data = mk_shots_from_probabilities(\n",
       "        probabilities=[np.random.random() for _ in cycles], **kwargs\n",
       "    )\n",
       "\n",
       "    mock_data_final = mk_shots_from_probabilities(\n",
       "        probabilities=[np.random.random()], **kwargs\n",
       "    )\n",
       "\n",
       "    # %%\n",
       "    data_vars = {}\n",
       "\n",
       "    # NB same random data is used for all qubits only for the simplicity of the mock!\n",
       "    for qubit in (f"A{i}" for i in range(3)):\n",
       "        data_vars[f"{qubit}_shots"] = (\n",
       "            ("repetitions", "dim_cycle"),\n",
       "            mock_data,\n",
       "            mk_main_var_attrs(\n",
       "                unit="V", long_name=f"IQ amplitude {qubit}", has_repetitions=True\n",
       "            ),\n",
       "        )\n",
       "\n",
       "    for qubit in (f"D{i}" for i in range(4)):\n",
       "        data_vars[f"{qubit}_shots"] = (\n",
       "            ("repetitions", "dim_final"),\n",
       "            mock_data_final,\n",
       "            mk_main_var_attrs(\n",
       "                unit="V", long_name=f"IQ amplitude {qubit}", has_repetitions=True\n",
       "            ),\n",
       "        )\n",
       "\n",
       "    cycle_attrs = mk_main_coord_attrs(long_name="Surface code cycle number")\n",
       "    final_msmt_attrs = mk_main_coord_attrs(long_name="Final measurement")\n",
       "    coords = dict(\n",
       "        cycle=("dim_cycle", cycles, cycle_attrs),\n",
       "        final_msmt=("dim_final", [0], final_msmt_attrs),\n",
       "    )\n",
       "\n",
       "    dataset = xr.Dataset(\n",
       "        data_vars=data_vars,\n",
       "        coords=coords,\n",
       "        attrs=mk_dataset_attrs(),\n",
       "    )\n",
       "\n",
       "    return dataset\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def}\\PY{+w}{ }\\PY{n+nf}{mk\\PYZus{}surface7\\PYZus{}cyles\\PYZus{}dataset}\\PY{p}{(}\\PY{n}{num\\PYZus{}cycles}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{3}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    See also :func:`mk\\PYZus{}surface7\\PYZus{}sched` inlined in the documentation as an example in:}\n",
       "\\PY{l+s+sd}{        :ref:`sec\\PYZhy{}dataset\\PYZhy{}advanced\\PYZhy{}examples`}\n",
       "\n",
       "\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    num\\PYZus{}cycles}\n",
       "\\PY{l+s+sd}{        The number of repeating cycles before the final measurement.}\n",
       "\\PY{l+s+sd}{    **kwargs}\n",
       "\\PY{l+s+sd}{        Keyword arguments passed to :func:`\\PYZti{}.mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities`.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "    \\PY{n}{cycles} \\PY{o}{=} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}cycles}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{mock\\PYZus{}data} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\n",
       "        \\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{random}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n}{cycles}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{mock\\PYZus{}data\\PYZus{}final} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\n",
       "        \\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{random}\\PY{p}{(}\\PY{p}{)}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} \\PYZpc{}\\PYZpc{}}\n",
       "    \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{p}{\\PYZob{}}\\PY{p}{\\PYZcb{}}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} NB same random data is used for all qubits only for the simplicity of the mock!}\n",
       "    \\PY{k}{for} \\PY{n}{qubit} \\PY{o+ow}{in} \\PY{p}{(}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{A}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{i}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}} \\PY{k}{for} \\PY{n}{i} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{l+m+mi}{3}\\PY{p}{)}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{n}{data\\PYZus{}vars}\\PY{p}{[}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\n",
       "            \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}cycle}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "            \\PY{n}{mock\\PYZus{}data}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{IQ amplitude }\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{has\\PYZus{}repetitions}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{for} \\PY{n}{qubit} \\PY{o+ow}{in} \\PY{p}{(}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{D}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{i}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}} \\PY{k}{for} \\PY{n}{i} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{l+m+mi}{4}\\PY{p}{)}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{n}{data\\PYZus{}vars}\\PY{p}{[}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\n",
       "            \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}final}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "            \\PY{n}{mock\\PYZus{}data\\PYZus{}final}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{IQ amplitude }\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{has\\PYZus{}repetitions}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{cycle\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Surface code cycle number}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{final\\PYZus{}msmt\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Final measurement}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{cycle}\\PY{o}{=}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}cycle}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{cycles}\\PY{p}{,} \\PY{n}{cycle\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{final\\PYZus{}msmt}\\PY{o}{=}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}final}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\\PY{p}{,} \\PY{n}{final\\PYZus{}msmt\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{(}\n",
       "        \\PY{n}{data\\PYZus{}vars}\\PY{o}{=}\\PY{n}{data\\PYZus{}vars}\\PY{p}{,}\n",
       "        \\PY{n}{coords}\\PY{o}{=}\\PY{n}{coords}\\PY{p}{,}\n",
       "        \\PY{n}{attrs}\\PY{o}{=}\\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{dataset}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_surface7_cyles_dataset\u001b[0m\u001b[1m(\u001b[0mnum_cycles: int = \u001b[1;36m3\u001b[0m, **kwargs\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    See also :func:`mk_surface7_sched` inlined in the documentation as an example in:\n",
       "        :ref:`sec-dataset-advanced-examples`\n",
       "\n",
       "\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_cycles\n",
       "        The number of repeating cycles before the final measurement.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to :func:`~.mk_shots_from_probabilities`.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "\n",
       "    cycles = \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_cycles\u001b[1m)\u001b[0m\n",
       "\n",
       "    mock_data = \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mprobabilities\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mnp.random.random\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in cycles\u001b[1m]\u001b[0m, **kwargs\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    mock_data_final = \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mprobabilities\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mnp.random.random\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m, **kwargs\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    # %%\n",
       "    data_vars = \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
       "\n",
       "    # NB same random data is used for all qubits only for the simplicity of the mock!\n",
       "    for qubit in \u001b[1m(\u001b[0mf\"A\u001b[1m{\u001b[0mi\u001b[1m}\u001b[0m\" for i in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m:\n",
       "        data_vars\u001b[1m[\u001b[0mf\"\u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m_shots\"\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\n",
       "            \u001b[1m(\u001b[0m\u001b[32m\"repetitions\"\u001b[0m, \u001b[32m\"dim_cycle\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "            mock_data,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33munit\u001b[0m=\u001b[32m\"V\"\u001b[0m, \u001b[33mlong_name\u001b[0m=\u001b[35mf\"\u001b[0mIQ amplitude \u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m\", \u001b[33mhas_repetitions\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    for qubit in \u001b[1m(\u001b[0mf\"D\u001b[1m{\u001b[0mi\u001b[1m}\u001b[0m\" for i in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m:\n",
       "        data_vars\u001b[1m[\u001b[0mf\"\u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m_shots\"\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\n",
       "            \u001b[1m(\u001b[0m\u001b[32m\"repetitions\"\u001b[0m, \u001b[32m\"dim_final\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "            mock_data_final,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33munit\u001b[0m=\u001b[32m\"V\"\u001b[0m, \u001b[33mlong_name\u001b[0m=\u001b[35mf\"\u001b[0mIQ amplitude \u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m\", \u001b[33mhas_repetitions\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    cycle_attrs = \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Surface\u001b[0m\u001b[32m code cycle number\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    final_msmt_attrs = \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Final\u001b[0m\u001b[32m measurement\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    coords = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mcycle\u001b[0m=\u001b[1m(\u001b[0m\u001b[32m\"dim_cycle\"\u001b[0m, cycles, cycle_attrs\u001b[1m)\u001b[0m,\n",
       "        \u001b[33mfinal_msmt\u001b[0m=\u001b[1m(\u001b[0m\u001b[32m\"dim_final\"\u001b[0m, \u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m, final_msmt_attrs\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    dataset = \u001b[1;35mxr.Dataset\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mdata_vars\u001b[0m=\u001b[35mdata_vars\u001b[0m,\n",
       "        \u001b[33mcoords\u001b[0m=\u001b[35mcoords\u001b[0m,\n",
       "        \u001b[33mattrs\u001b[0m=\u001b[1;35mmk_dataset_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    return dataset"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mock data parameters\n",
    "num_shots = 128  # NB usually >~1000 in real experiments\n",
    "ground = -0.2 + 0.65j\n",
    "excited = 0.7 + 4j\n",
    "centroids = ground, excited\n",
    "sigmas = [0.1] * 2\n",
    "\n",
    "display_source_code(mk_iq_shots)\n",
    "display_source_code(mk_shots_from_probabilities)\n",
    "display_source_code(mk_surface7_cyles_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12829358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.Dataset> Size: 27kB\n",
       "Dimensions:     (repetitions: 128, dim_cycle: 3, dim_final: 1)\n",
       "Coordinates:\n",
       "    cycle       (dim_cycle) int64 24B 0 1 2\n",
       "    final_msmt  (dim_final) int64 8B 0\n",
       "Dimensions without coordinates: repetitions, dim_cycle, dim_final\n",
       "Data variables:\n",
       "    A0_shots    (repetitions, dim_cycle) complex128 6kB (-0.23630343679164473...\n",
       "    A1_shots    (repetitions, dim_cycle) complex128 6kB (-0.23630343679164473...\n",
       "    A2_shots    (repetitions, dim_cycle) complex128 6kB (-0.23630343679164473...\n",
       "    D0_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "    D1_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "    D2_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "    D3_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "Attributes:\n",
       "    tuid:                      20250713-040851-495-530439\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
       "    json_serialize_exclude:    []
"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 27kB\n",
       "Dimensions:     \u001b[1m(\u001b[0mrepetitions: \u001b[1;36m128\u001b[0m, dim_cycle: \u001b[1;36m3\u001b[0m, dim_final: \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "    cycle       \u001b[1m(\u001b[0mdim_cycle\u001b[1m)\u001b[0m int64 24B \u001b[1;36m0\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;36m2\u001b[0m\n",
       "    final_msmt  \u001b[1m(\u001b[0mdim_final\u001b[1m)\u001b[0m int64 8B \u001b[1;36m0\u001b[0m\n",
       "Dimensions without coordinates: repetitions, dim_cycle, dim_final\n",
       "Data variables:\n",
       "    A0_shots    \u001b[1m(\u001b[0mrepetitions, dim_cycle\u001b[1m)\u001b[0m complex128 6kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "    A1_shots    \u001b[1m(\u001b[0mrepetitions, dim_cycle\u001b[1m)\u001b[0m complex128 6kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "    A2_shots    \u001b[1m(\u001b[0mrepetitions, dim_cycle\u001b[1m)\u001b[0m complex128 6kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D0_shots    \u001b[1m(\u001b[0mrepetitions, dim_final\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D1_shots    \u001b[1m(\u001b[0mrepetitions, dim_final\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D2_shots    \u001b[1m(\u001b[0mrepetitions, dim_final\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D3_shots    \u001b[1m(\u001b[0mrepetitions, dim_final\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20250713\u001b[0m-\u001b[1;36m040851\u001b[0m-\u001b[1;36m495\u001b[0m-\u001b[1;36m530439\u001b[0m\n",
       "    dataset_name:              \n",
       "    dataset_state:             \u001b[3;35mNone\u001b[0m\n",
       "    timestamp_start:           \u001b[3;35mNone\u001b[0m\n",
       "    timestamp_end:             \u001b[3;35mNone\u001b[0m\n",
       "    quantify_dataset_version:  \u001b[1;36m2.0\u001b[0m.\u001b[1;36m0\u001b[0m\n",
       "    software_versions:         \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
       "    relationships:             \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
       "    json_serialize_exclude:    \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = mk_surface7_cyles_dataset(\n",
    "    num_shots=num_shots, sigmas=sigmas, centers=centroids\n",
    ")\n",
    "\n",
    "assert dataset == round_trip_dataset(dataset)  # confirm read/write\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b71d9599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001b[1m(\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m128\u001b[0m, \u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m, \u001b[1m(\u001b[0m\u001b[1;36m128\u001b[0m, \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.A1_shots.shape, dataset.D1_shots.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4cf70976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "
<xarray.Dataset> Size: 27kB\n",
       "Dimensions:     (repetitions: 128, cycle: 3, final_msmt: 1)\n",
       "Coordinates:\n",
       "  * cycle       (cycle) int64 24B 0 1 2\n",
       "  * final_msmt  (final_msmt) int64 8B 0\n",
       "Dimensions without coordinates: repetitions\n",
       "Data variables:\n",
       "    A0_shots    (repetitions, cycle) complex128 6kB (-0.23630343679164473+0.6...\n",
       "    A1_shots    (repetitions, cycle) complex128 6kB (-0.23630343679164473+0.6...\n",
       "    A2_shots    (repetitions, cycle) complex128 6kB (-0.23630343679164473+0.6...\n",
       "    D0_shots    (repetitions, final_msmt) complex128 2kB (-0.2363034367916447...\n",
       "    D1_shots    (repetitions, final_msmt) complex128 2kB (-0.2363034367916447...\n",
       "    D2_shots    (repetitions, final_msmt) complex128 2kB (-0.2363034367916447...\n",
       "    D3_shots    (repetitions, final_msmt) complex128 2kB (-0.2363034367916447...\n",
       "Attributes:\n",
       "    tuid:                      20250713-040851-495-530439\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
       "    json_serialize_exclude:    []
"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 27kB\n",
       "Dimensions:     \u001b[1m(\u001b[0mrepetitions: \u001b[1;36m128\u001b[0m, cycle: \u001b[1;36m3\u001b[0m, final_msmt: \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "  * cycle       \u001b[1m(\u001b[0mcycle\u001b[1m)\u001b[0m int64 24B \u001b[1;36m0\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;36m2\u001b[0m\n",
       "  * final_msmt  \u001b[1m(\u001b[0mfinal_msmt\u001b[1m)\u001b[0m int64 8B \u001b[1;36m0\u001b[0m\n",
       "Dimensions without coordinates: repetitions\n",
       "Data variables:\n",
       "    A0_shots    \u001b[1m(\u001b[0mrepetitions, cycle\u001b[1m)\u001b[0m complex128 6kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0.6\u001b[0m\u001b[33m...\u001b[0m\n",
       "    A1_shots    \u001b[1m(\u001b[0mrepetitions, cycle\u001b[1m)\u001b[0m complex128 6kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0.6\u001b[0m\u001b[33m...\u001b[0m\n",
       "    A2_shots    \u001b[1m(\u001b[0mrepetitions, cycle\u001b[1m)\u001b[0m complex128 6kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0.6\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D0_shots    \u001b[1m(\u001b[0mrepetitions, final_msmt\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.2363034367916447\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D1_shots    \u001b[1m(\u001b[0mrepetitions, final_msmt\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.2363034367916447\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D2_shots    \u001b[1m(\u001b[0mrepetitions, final_msmt\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.2363034367916447\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D3_shots    \u001b[1m(\u001b[0mrepetitions, final_msmt\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.2363034367916447\u001b[0m\u001b[33m...\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20250713\u001b[0m-\u001b[1;36m040851\u001b[0m-\u001b[1;36m495\u001b[0m-\u001b[1;36m530439\u001b[0m\n",
       "    dataset_name:              \n",
       "    dataset_state:             \u001b[3;35mNone\u001b[0m\n",
       "    timestamp_start:           \u001b[3;35mNone\u001b[0m\n",
       "    timestamp_end:             \u001b[3;35mNone\u001b[0m\n",
       "    quantify_dataset_version:  \u001b[1;36m2.0\u001b[0m.\u001b[1;36m0\u001b[0m\n",
       "    software_versions:         \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
       "    relationships:             \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
       "    json_serialize_exclude:    \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset, dimension=\"dim_cycle\", coords_names=[\"cycle\"]\n",
    ")\n",
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset_gridded, dimension=\"dim_final\", coords_names=[\"final_msmt\"]\n",
    ")\n",
    "dataset_gridded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a216dc93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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      ],
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 64\u001b[0m\u001b[1;36m0x480\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_gridded.A0_shots.real.mean(\"repetitions\").plot(marker=\"o\", label=\"I-quadrature\")\n",
    "dataset_gridded.A0_shots.imag.mean(\"repetitions\").plot(marker=\"^\", label=\"Q-quadrature\")\n",
    "_ = plt.gca().legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41b4f6af",
   "metadata": {},
   "source": [
    "(sec-nested-mc-example)=\n",
    "## Dataset for a \"nested MeasurementControl\" experiment\n",
    "\n",
    "Now consider a dataset that has been constructed by an experiment involving the\n",
    "operation of two\n",
    "{class}`.MeasurementControl` objects. The second of\n",
    "them performs a \"meta\" outer loop in which we sweep a flux bias and then perform\n",
    "several experiments to characterize a transmon qubit, e.g. determining the frequency of\n",
    "a read-out resonator, the frequency of the transmon, and its T1 lifetime.\n",
    "\n",
    "Below we showcase what the data from the dataset containing the T1 experiment results\n",
    "could look like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d839806c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5c2f88MMPnNPTqFEjhUPUlb/ia+LlX5NV4fP56N69O7p3747ffvsNixYtwuzZsxEREVEnu+uChYUFgIo9XF7m9u3b0NXVlRnleVfo6elBQ0MDEomkzm19eQqSiHD37l3OObK2tgZQMcpSm869e/fC19dXJgKtuLhYbhPJ6u5fZV36+vo11lXZ/4qmTxXdk4aiPn0HAOvWrcOpU6ewcOFCBAUFYcyYMTh06JCcXOWoa9V+vHPnDgBwC72tra1RUFDwxp75N8XbsKum7wOhUIi+ffuib9++kEqlGDduHNavX4+5c+dyP9IY7x9sTQ/jndOlSxc4OzsjODgYxcXF0NfXR5cuXbB+/XpkZmbKyf/zzz/c/1+enlFXV4eNjQ1KSkq4NGtra9y+fVum3PXr12uMtKqk0qF4+WX69OlTOdnKX9hV635djIyM4OjoiK1bt8rYcPPmTZw8eRJ9+vR5Y3XVByUlJQwcOBD79u3DzZs35fKr9nUlv//+O54/f8593rt3LzIzM+Hp6QmgYp8aa2trLF26FAUFBTXqVFJSkvsFvnLlSrnRu+run4eHBzQ1NbFo0SKUlZVVW1fV/q86bXbq1CluO4H3gfr0XVpaGqZNm4aBAwfi+++/x9KlS3H48GH8/vvvcuUePXokE56fn5+P33//HY6OjtzI0KBBgxAdHY0TJ07Ilc/NzUV5efmbaGK9eRt2Vfc8vfw9xOfzOWf+TX4fMN48bKSH0SBMmzYNX3zxBUJDQzF27FisXr0aHTt2hIODA0aPHg0rKytkZ2cjOjoaDx48wPXr1wEA9vb26NKlC5ycnKCjo4Nr165h7969GD9+PKfbz88Pv/32Gzw8PDBq1Cjk5ORg3bp1aNGiBfLz82u0y8nJCQAwe/ZsDBkyBAKBAH379sWCBQtw7tw5eHl5wcLCAjk5OVizZg1MTU3RsWPHN9o3S5YsgaenJ1xcXDBq1CguZF1LS6tBd4b9+eefERERgfbt22P06NGwt7fH06dPERsbi9OnT8s5hjo6OujYsSNGjhyJ7OxsBAcHw8bGBqNHjwZQ8aLYtGkTPD090aJFC4wcORImJiZ4+PAhIiIioKmpiT///BMA8Omnn2Lbtm3Q0tKCvb09oqOjcfr0aTRu3FimTkdHRygpKWHx4sXIy8uDSCRCt27doK+vj7Vr1+Krr75C27ZtMWTIEOjp6eHevXs4evQo3NzcsGrVKgBAUFAQvLy80LFjR/j5+eHp06dYuXIlWrRoodDBeB1++uknAOD2Ctq2bRsuXLgAAJgzZ0615erad0QEPz8/iMVirF27FgAwZswY7Nu3DxMnTkSPHj1gbGzM6W3WrBlGjRqFq1evwsDAAFu2bEF2djZCQkI4mWnTpuHw4cP49NNPMWLECDg5OeHFixdISEjA3r17kZ6eLree7F3wNuyq/D6YMGECPDw8oKSkhCFDhuDrr7/G06dP0a1bN5iamiIjIwMrV66Eo6Mjt0UC4z2lQWLGGB8FlSHrL4fGElWE0VpbW5O1tTUX9pmamko+Pj5kaGhIAoGATExM6NNPP6W9e/dy5X766SdydnYmbW1tEovF1Lx5c1q4cCGVlpbK6N++fTtZWVmRUCgkR0dHOnHiRJ1C1okqQl9NTEyIz+dz4etnzpwhb29vMjY2JqFQSMbGxjR06FC58FhF1BSyvmTJEoVlTp8+TW5ubiQWi0lTU5P69u1LSUlJMjKVIesvh9H7+vqSmppanexQBAAKCAhQmJednU0BAQFkZmZGAoGADA0NqXv37rRhwwZOpjJk/Y8//qBZs2aRvr4+icVi8vLyooyMDDmdcXFxNGDAAGrcuDGJRCKysLCgQYMG0ZkzZziZZ8+e0ciRI0lXV5fU1dXJw8ODbt++TRYWFnIh4Rs3biQrKytSUlKSCwOPiIggDw8P0tLSIhUVFbK2tqYRI0bQtWvXZHTs27eP7OzsSCQSkb29Pe3fv1/h81MbtYWsA6j2qgu19d3y5cvlwtCJiO7du0eamprUp08fLs3CwoK8vLzoxIkT1KpVKxKJRNS8eXMKCwuTq/f58+c0a9YssrGxIaFQSLq6uuTq6kpLly7l/hZresarC1mvz3Nbae+btOvl74Py8nL69ttvSU9Pj3g8Hndf9u7dS7169SJ9fX0SCoVkbm5OY8aMoczMTDmdjPcLHlEdVzYyGAxGHYiMjETXrl0RFhaGzz//vKHNYdSRJk2aoGXLljhy5EhDm8JgvDXYmh4Gg8FgMBgfBczpYTAYDAaD8VHAnB4Gg8FgMBgfBWxND4PBYDAYjI8CNtLDYDAYDAbjo4A5PQwGg8FgMD4KmNPDYLzn/PLLL2jevHmtZ0ylp6eDx+MhNDSUS/vhhx/kttIvLy/H9OnTYWZmBj6fj/79+wMACgoK8PXXX8PQ0BA8Hg+TJk16wy15/+DxeA264eP7wLp162Bubs52EmZ8FDCnh8F4j8nPz8fixYsxY8aM1zrNvSpbtmzBkiVL8Pnnn2Pr1q2YPHkyAGDRokUIDQ3FN998g23btuGrr756I/W9DXbu3Ing4OAGqz8pKQk//PADd2J8Q/M6/TFixAiUlpZi/fr1b9YoBuN9pGH3RmQwGDWxbNky0tTUpKKiolplFe1yW1ZWJld28ODBZGJiIle+ffv25Obm9to2vwu8vLzqvTuyIqBgR+66EBYWJrfbc0Pyuv0xffp0srCwIKlU+uaMYjDeQ9hID4PxHhMSEoJ+/fpBRUXllcorKyvLlc3JyYG2tracbHXpr4pUKkVxcfEb08d4ewwaNAgZGRmIiIhoaFMYjLcKc3oYjPeUtLQ03LhxAz169JDLy83NxYgRI6ClpQVtbW34+vrKnQQNyK7pqVzzExERgcTERPB4PPB4PERGRoLH4yEtLQ1Hjx7l0iunbkpKShAYGAgbGxuIRCKYmZlh+vTpcmtAeDwexo8fjx07dqBFixYQiUQ4fvw4AODhw4fw8/ODgYEBRCIRWrRogS1btsiUr7Rjz549WLhwIUxNTaGiooLu3bvj7t27nFyXLl1w9OhRZGRkcLY2adKkxr4sKSnB5MmToaenBw0NDfTr1w8PHjyQk8vIyMC4ceNga2sLsViMxo0b44svvpCZxgoNDcUXX3wBAOjatatMPwLAoUOH4OXlBWNjY4hEIlhbW+PHH3+UOxE+JSUFAwcOhKGhIVRUVGBqaoohQ4bInO4OANu3b4eTkxPEYjF0dHQwZMgQ3L9/v879UXlYqqqqKho1aoR27dph586dMnVUHuB76NChGvuRwfjQYaesMxjvKVFRUQCAtm3byqQTEby9vXHhwgWMHTsWdnZ2OHDgAHx9fWvUp6enh23btmHhwoUoKChAUFAQAMDOzg7btm3D5MmTYWpqiqlTp3LyUqkU/fr1w4ULF+Dv7w87OzskJCRg2bJluHPnDg4ePChTR3h4OPbs2YPx48dDV1cXTZo0QXZ2Njp06MA5RXp6evjrr78watQo5Ofnyy2Y/vnnn8Hn8/Hdd98hLy8Pv/zyC4YPH47Lly8DAGbPno28vDw8ePAAy5YtAwCoq6vX2Pavv/4a27dvx7Bhw+Dq6orw8HB4eXnJyV29ehVRUVEYMmQITE1NkZ6ejrVr16JLly5ISkqCqqoqOnfujAkTJmDFihX4/vvvuVO1K/8NDQ2Furo6pkyZAnV1dYSHh2PevHnIz8/HkiVLAAClpaXw8PBASUkJvv32WxgaGuLhw4c4cuQIcnNzoaWlBQBYuHAh5s6di0GDBuHrr7/GP//8g5UrV6Jz586Ii4uDtrZ2jf2xceNGTJgwAZ9//jkmTpyI4uJi3LhxA5cvX8awYcNk2t62bVtcvHixxn5kMD54Gnp+jcFgKGbOnDkEgJ4/fy6TfvDgQQJAv/zyC5dWXl5OnTp1klvTU3kSe1Xqc2r1tm3biM/n0/nz52XS161bRwDo4sWLXBoA4vP5lJiYKCM7atQoMjIyosePH8ukDxkyhLS0tKiwsJCI/j2d3c7OjkpKSji5ypPCExISuLT6rGGJj48nADRu3DiZ9GHDhsmt6am0pSrR0dEEgH7//XcuraY1PYp0jBkzhlRVVam4uJiIKk5HB6DwBPNK0tPTSUlJiRYuXCiTnpCQQMrKyjLp1fWHt7e3wnutCH9/fxKLxXWSZTA+VNj0FoPxnvLkyRMoKyvLjWIcO3YMysrK+Oabb7g0JSUlfPvtt2/chrCwMNjZ2aF58+Z4/Pgxd3Xr1g0A5NaAuLu7w97envtMRNi3bx/69u0LIpLR4eHhgby8PMTGxsroGDlyJIRCIfe5U6dOAIC///77ldpw7NgxAMCECRNk0hWF5IvFYu7/ZWVlePLkCWxsbKCtrS1nZ3VU1fH8+XM8fvwYnTp1QmFhIW7fvg0A3EjOiRMnUFhYqFDP/v37IZVKMWjQIJl+MzQ0RNOmTeu0/kZbWxsPHjzA1atXa5Vt1KgRioqKqrWHwfgvwKa3GIwPjIyMDBgZGck5Q7a2tm+8rpSUFNy6dQt6enoK83NycmQ+W1paynz+559/kJubiw0bNmDDhg110mFubi7zuVGjRgCAZ8+e1cv2SjIyMsDn82FtbS2Trqi/ioqKEBQUhJCQEDx8+BBU5ZSel9faVEdiYiLmzJmD8PBw5Ofny+RV6rC0tMSUKVPw22+/YceOHejUqRP69euHL7/8knOIUlJSQERo2rSpwnoEAkGttsyYMQOnT5+Gs7MzbGxs0KtXLwwbNgxubm5yspVtfXlfJwbjvwRzehiM95TGjRujvLwcz58/h4aGRoPYIJVK4eDggN9++01hvpmZmcznqqMcleUB4Msvv6x2zVGrVq1kPispKSmUo3dwTOC3336LkJAQTJo0CS4uLtDS0gKPx8OQIUNq3RwSqFhg7u7uDk1NTSxYsADW1tZQUVFBbGwsZsyYIaPj119/xYgRI3Do0CGcPHkSEyZMQFBQEC5dugRTU1NIpVLweDz89ddfCvuktnVMQMU6o+TkZBw5cgTHjx/Hvn37sGbNGsybNw/z58+XkX327BlUVVXl7iGD8V+COT0MxntK8+bNAVREcVV1DCwsLHDmzBkUFBTIvPiSk5PfuA3W1ta4fv06unfv/kojAJXRUhKJRGEU2qtSH1ssLCwglUqRmpoqM7qjqL/27t0LX19f/Prrr1xacXGxXGRcdfVHRkbiyZMn2L9/Pzp37sylp6WlKZR3cHCAg4MD5syZg6ioKLi5uWHdunX46aefYG1tDSKCpaUlmjVrVmMba+oPNTU1DB48GIMHD0ZpaSkGDBiAhQsXYtasWTLbGaSlpXGLsRmM/ypsTQ+D8Z7i4uICALh27ZpMep8+fVBeXo61a9dyaRKJBCtXrnzjNgwaNAgPHz7Exo0b5fKKiorw4sWLGssrKSlh4MCB2LdvH27evCmX/88//7ySXWpqanWebvL09AQArFixQiZd0Q7GSkpKciNKK1eulAs3V1NTAwA5Z6hyRKaqjtLSUqxZs0ZGLj8/H+Xl5TJpDg4O4PP53FYAAwYMgJKSEubPny9nExHhyZMnMvYo6o+qMgAgFAphb28PIkJZWZlMXmxsLFxdXeV0MBj/JdhID4PxnmJlZYWWLVvi9OnT8PPz49L79u0LNzc3zJw5E+np6bC3t8f+/fvr7ATUh6+++gp79uzB2LFjERERATc3N0gkEty+fRt79uzBiRMn0K5duxp1/Pzzz4iIiED79u0xevRo2Nvb4+nTp4iNjcXp06fx9OnTetvl5OSE3bt3Y8qUKfjkk0+grq6Ovn37KpR1dHTE0KFDsWbNGuTl5cHV1RVnzpyR2funkk8//RTbtm2DlpYW7O3tER0djdOnT6Nx48ZyOpWUlLB48WLk5eVBJBKhW7ducHV1RaNGjeDr64sJEyaAx+Nh27Ztck5LeHg4xo8fjy+++ALNmjVDeXk5tm3bxjmJQMUo208//YRZs2YhPT0d/fv3h4aGBtLS0nDgwAH4+/vju+++q7E/evXqBUNDQ7i5ucHAwAC3bt3CqlWr4OXlJTNlGhMTg6dPn8Lb27ve94LB+KBokJgxBoNRJ3777TdSV1eXC4N+8uQJffXVV6SpqUlaWlr01VdfcWHQbzJknYiotLSUFi9eTC1atCCRSESNGjUiJycnmj9/PuXl5XFyACggIEBhO7KzsykgIIDMzMxIIBCQoaEhde/enTZs2MDJVIasvxzGreh4jYKCAho2bBhpa2sTgFrD14uKimjChAnUuHFjUlNTo759+9L9+/flQtafPXtGI0eOJF1dXVJXVycPDw+6ffs2WVhYkK+vr4zOjRs3kpWVFSkpKcmEr1+8eJE6dOhAYrGYjI2Nafr06XTixAkZmb///pv8/PzI2tqaVFRUSEdHh7p27UqnT5+Ws33fvn3UsWNHUlNTIzU1NWrevDkFBARQcnJyrf2xfv166ty5MzVu3JhEIhFZW1vTtGnTZO4bEdGMGTPI3NycHUPB+M/DI3oHqwMZDMYrkZeXBysrK/zyyy8YNWpUQ5vD+A9SUlKCJk2aYObMmZg4cWJDm8NgvFXYmh4G4z1GS0sL06dPx5IlS+oUPcRg1JeQkBAIBAKMHTu2oU1hMN46bKSHwWAwGAzGRwEb6WEwGAwGg/FRwJweBoPBYDAYHwXM6WEwGAwGg/FRwJweBoPBYDAYHwUf5eaEUqkUjx49goaGBjtcj8FgMBiMDwQiwvPnz2FsbAw+v/7jNh+l0/Po0SO5gxIZDAaDwWB8GNy/fx+mpqb1LvdROj2V26/fv38fmpqaDWwNg8FgMBiMupCfnw8zMzOZY1Tqw0fp9FROaWlqajKnh8FgMBiMD4xXXZrCFjIzGAwGg8H4KGBOD4PBYDAYjI8C5vQwGAwGg8H4KPgo1/QwGIyGRSKRoKysrKHNYDAY7xkCgQBKSkpvTT9zehgMxjuDiJCVlYXc3NyGNoXBYLynaGtrw9DQ8K3so8ecHgaD8c6odHj09fWhqqrKNgdlMBgcRITCwkLk5OQAAIyMjN54HczpYTAY7wSJRMI5PI0bN25ocxgMxnuIWCwGAOTk5EBfX/+NT3WxhcwMBuOdULmGR1VVtYEtYTAY7zOV3xFvY90fc3oYDMY7hU1pMRiMmnib3xEN7vScO3cOffv2hbGxMXg8Hg4ePFhrmcjISLRt2xYikQg2NjYIDQ1963YyGAwGg8H4sGlwp+fFixdo3bo1Vq9eXSf5tLQ0eHl5oWvXroiPj8ekSZPw9ddf48SJE2/ZUgaDwWAw/vtkZWWhZ8+eUFNTg7a2dkOb80ZpcKfH09MTP/30Ez777LM6ya9btw6Wlpb49ddfYWdnh/Hjx+Pzzz/HsmXL3rKltXNpzz4c+f4XXNqzr6FNYTAYb5CgoCB88skn0NDQgL6+Pvr374/k5GQZmeLiYgQEBKBx48ZQV1fHwIEDkZ2dzeVfv34dQ4cOhZmZGcRiMezs7LB8+XIZHZGRkeDxeHJXVlaWjNzq1avRpEkTqKiooH379rhy5QqXl56erlAHj8dDWFgYJzdhwgQ4OTlBJBLB0dFRrs3Jycno2rUrDAwMoKKiAisrK8yZM6dO6yxqsq8qRARPT0+Fo/z37t2Dl5cXVFVVoa+vj2nTpqG8vFxGZseOHWjdujVUVVVhZGQEPz8/PHnyhMsPDQ2V6wMVFRUuv6ysDDNmzICDgwPU1NRgbGwMHx8fPHr0qNY21mZfXe6lRCLB3LlzYWlpCbFYDGtra/z4448gohrrHjNmDKytrSEWi6Gnpwdvb2/cvn27VpvryrJly5CZmYn4+HjcuXMHALBhwwZ06dIFmpqa4PF4CredWLhwIVxdXaGqqvreOksN7vTUl+joaPTo0UMmzcPDA9HR0Q1kUQXHxi+BSYwuHKUuMInRxbHxSxrUHgaD8eY4e/YsAgICcOnSJZw6dQplZWXo1asXXrx4wclMnjwZf/75J8LCwnD27Fk8evQIAwYM4PJjYmKgr6+P7du3IzExEbNnz8asWbOwatUqufqSk5ORmZnJXfr6+lze7t27MWXKFAQGBiI2NhatW7eGh4cHF+ZrZmYmUzYzMxPz58+Huro6PD09Zerx8/PD4MGDFbZZIBDAx8cHJ0+eRHJyMoKDg7Fx40YEBgbW2Fe12VeV4OBghes3JBIJvLy8UFpaiqioKGzduhWhoaGYN28eJ3Px4kX4+Phg1KhRSExMRFhYGK5cuYLRo0fL6NLU1JTpi4yMDC6vsLAQsbGxmDt3LmJjY7F//34kJyejX79+NbaxLvZVUtO9XLx4MdauXYtVq1bh1q1bWLx4MX755ResXLmyxvqdnJwQEhKCW7du4cSJEyAi9OrVCxKJpMZydSU1NRVOTk5o2rQpZ29hYSF69+6N77//vtpypaWl+OKLL/DNN9+8ETveCvQeAYAOHDhQo0zTpk1p0aJFMmlHjx4lAFRYWKiwTHFxMeXl5XHX/fv3CQDl5eW9Ebujd++le9Mj6f6Mc9yVMT2SonfvfSP6GYz/AkVFRZSUlERFRUVvRN+j3EK6ePcfepSr+O/+bZKTk0MA6OzZs0RElJubSwKBgMLCwjiZW7duEQCKjo6uVs+4ceOoa9eu3OeIiAgCQM+ePau2jLOzMwUEBHCfJRIJGRsbU1BQULVlHB0dyc/PT2FeYGAgtW7dutqyVZk8eTJ17NixRpm62hcXF0cmJiaUmZkp991/7Ngx4vP5lJWVxaWtXbuWNDU1qaSkhIiIlixZQlZWVjI6V6xYQSYmJtznkJAQ0tLSqlPbKrly5QoBoIyMjGpl6mJfXe6ll5eX3H0ZMGAADR8+vF42X79+nQDQ3bt3ubSbN2+Sl5cXaWhokLq6OnXs2FEmf/PmzWRvb09CoZAMDQ25e2ZhYUEAuMvX11emrrq061X6vSo1fVfk5eW91vv7gxvpeRWCgoKgpaXFXWZmZm9U/+P4VPB4sl3J5/Hx+HrqG62HwfivQUQoLC2v97UtOh1uP4dj2MbLcPs5HNui0+utg2qZQqiJvLw8AICOjg6AilGcsrIymVHo5s2bw9zcvMZR6Ly8PE5HVRwdHWFkZISePXvi4sWLXHppaSliYmJk6uHz+ejRo0e19cTExCA+Ph6jRo2qXyNf4u7duzh+/Djc3d2rlamrfYWFhRg2bBhWr14NQ0NDOT3R0dFwcHCAgYEBl+bh4YH8/HwkJiYCAFxcXHD//n0cO3YMRITs7Gzs3bsXffr0kdFVUFAACwsLmJmZwdvbmytfHXl5eeDxeDVOz9TFvkqqu5cA4OrqijNnznBTSNevX8eFCxfkRuRq4sWLFwgJCYGlpSX3bnv48CE6d+4MkUiE8PBwxMTEwM/Pj5t+W7t2LQICAuDv74+EhAQcPnwYNjY2AICrV6+id+/eGDRoEDIzM+WmYD90PrjNCQ0NDWXmyQEgOzsbmpqa3KZGLzNr1ixMmTKF+5yfn/9GHR9dR2tQjFTG8ZGSFLqtrd9YHQzGf5GiMgns571eEIKUgLmHEjH3UM0vs5dJWuABVWH9vwKlUikmTZoENzc3tGzZEkDFwk+hUCj3ojQwMJBbj1NJVFQUdu/ejaNHj3JpRkZGWLduHdq1a4eSkhJs2rQJXbp0weXLl9G2bVs8fvwYEolE5mVbWU91azo2b94MOzs7uLq61rutQMWLOTY2FiUlJfD398eCBQuqla2rfZMnT4arqyu8vb0V6snKylKoozIPANzc3LBjxw4MHjwYxcXFKC8vR9++fWWCYmxtbbFlyxa0atUKeXl5WLp0KVxdXZGYmAhTU1O5eouLizFjxgwMHToUmpqa1bazLvbVdi8BYObMmcjPz0fz5s2hpKQEiUSChQsXYvjw4dXWXcmaNWswffp0vHjxAra2tjh16hSEQiGAijVVWlpa2LVrFwQCAQCgWbNmXNmffvoJU6dOxcSJE7m0Tz75BACgp6cHkUgEsVis0CH90PngRnpcXFxw5swZmbRTp07BxcWl2jIikQiampoy15ukw6CBSHhxBVKSAqj49Rr3LA62Ldu80XoYDEbDExAQgJs3b2LXrl2vrOPmzZvw9vZGYGAgevXqxaXb2tpizJgxcHJygqurK7Zs2QJXV9dXDtQoKirCzp07X2uUZ/fu3YiNjcXOnTtx9OhRLF26FABw/vx5qKurc9eOHTvqpO/w4cMIDw9HcHDwK9sEAElJSZg4cSLmzZuHmJgYHD9+HOnp6Rg7diwn4+LiAh8fHzg6OsLd3R379++Hnp4e1q9fL6evrKwMgwYNAhFh7dq1XLqnpyfXxhYtWtTZvrrcyz179mDHjh3YuXMnYmNjsXXrVixduhRbt24FACxatEimj+/du8eVHT58OOLi4nD27Fk0a9YMgwYNQnFxMQAgPj4enTp14hyequTk5ODRo0fo3r17ndvyX6LBR3oKCgpw9+5d7nNaWhri4+Oho6MDc3NzzJo1Cw8fPsTvv/8OABg7dixWrVqF6dOnw8/PD+Hh4dizZ4/Mr6WGoM+qabi0ex90YzSgwhcjv/QhIn89js82j2tQuxiM9xmxQAlJCzzqVSYrrxg9fjsLaZXZKT4POD3FHYZaKtUXVFB3fRk/fjyOHDmCc+fOyYwUGBoaorS0FLm5uTKjPdnZ2XK/lpOSktC9e3f4+/tjzpw5tdbp7OyMCxcuAAB0dXWhpKSkcLRb0a/yvXv3orCwED4+PvVppgyVo+L29vaQSCTw9/fH1KlT0a5dO8THx3NyBgYGEIlEtdoXHh6O1NRUuVGxgQMHolOnToiMjIShoaFcxFelzko9QUFBcHNzw7Rp0wAArVq1gpqaGjp16oSffvpJ4blNAoEAbdq0kXnnAP86PBkZGQgPD5f5Ybxp0yYUFRVx5SttqM0+RVS9lwAwbdo0zJw5E0OGDAEAODg4ICMjA0FBQfD19cXYsWMxaNAgTt7Y2Jj7f+VyjaZNm6JDhw5o1KgRDhw4gKFDh1Y76wGgxryPgQYf6bl27RratGmDNm0qRkWmTJmCNm3acKvgMzMzZbxbS0tLHD16FKdOnULr1q3x66+/YtOmTfDwqN8X59ugw+CBENtXrHTXFWnjPlSRui+iga1iMN5feDweVIXK9bqs9NQRNMABSv+P+lHi8RA0wAFWeur10lOfXV+JCOPHj8eBAwcQHh4OS0tLmXwnJycIBAKZUejk5GTcu3dPZhQ6MTERXbt2ha+vLxYuXFinuuPj47kXuFAohJOTk0w9UqkUZ86cUTjavXnzZvTr1w96enp1bmtNSKVSlJWVQSqVQiwWw8bGhrs0NDTqZN/MmTNx48YNxMfHcxdQESYdEhICoGKEJiEhQSbi69SpU9DU1IS9vT2AinVBfL7sK6zynKbq1mtJJBIkJCTIOESVDk9KSgpOnz4tdy6ciYkJ10YLC4s626eIqveypjZIpRWzBjo6OjJ9rKyseJyCiEBEKCkpAVDhAJ4/f17h9gIaGhpo0qSJ3IzJR8MrLq7+oHnd1d818fziQ7o/4xxd+XYr/TZ8CoWO2EHlxaVvvB4G40PjbURvRd19/E6it7755hvS0tKiyMhIyszM5K6qEaNjx44lc3NzCg8Pp2vXrpGLiwu5uLhw+QkJCaSnp0dffvmljI6cnBxOZtmyZXTw4EFKSUmhhIQEmjhxIvH5fDp9+jQns2vXLhKJRBQaGkpJSUnk7+9P2traMpFEREQpKSnE4/Hor7/+UtimlJQUiouLozFjxlCzZs0oLi6O4uLiuOij7du30+7duykpKYlSU1Np9+7dZGxsXGtkUV3tqwpeit4qLy+nli1bUq9evSg+Pp6OHz9Oenp6NGvWLE4mJCSElJWVac2aNZSamkoXLlygdu3akbOzMyczf/58OnHiBKWmplJMTAwNGTKEVFRUKDExkYiISktLqV+/fmRqakrx8fEy96WyHxRRF/vqci99fX3JxMSEjhw5QmlpabR//37S1dWl6dOnV1t3amoqLVq0iK5du0YZGRl08eJF6tu3L+no6FB2djYRET1+/JgaN25MAwYMoKtXr9KdO3fo999/p9u3bxMRUWhoKKmoqNDy5cvpzp07FBMTQytWrODq8Pb2lovayszMpLi4ONq4cSMBoHPnzlFcXBw9efKEk8nIyKC4uDiaP38+qaurc8/U8+fPq22PIt5m9BZzet4wJQ+f0/0Z5+jvaafo10H9aOXoIxT10843Xg+D8aHxpp2edwmqhPBWvUJCQjiZoqIiGjduHDVq1IhUVVXps88+o8zMTC4/MDBQoQ4LCwtOZvHixWRtbU0qKiqko6NDXbp0ofDwcDl7Vq5cSebm5iQUCsnZ2ZkuXbokJzNr1iwyMzMjiUSisE3u7u4K7UlLSyOiCuelbdu2pK6uTmpqamRvb0+LFi2q0/2ri31VednpISJKT08nT09PEovFpKurS1OnTqWysjIZmRUrVpC9vT2JxWIyMjKi4cOH04MHD7j8SZMmcXYYGBhQnz59KDY2lstPS0ur9t5GRETUaHNt9tXlXubn59PEiRPJ3NycVFRUyMrKimbPnl2jw/Xw4UPy9PQkfX19EggEZGpqSsOGDeMcmkquX79OvXr1IlVVVdLQ0KBOnTpRamoql79u3TqytbUlgUBARkZG9O2333J5ipye6p7fqn8Dvr6+r9SXL/M2nR4e0WvEbX6g5OfnQ0tLC3l5eW98UTNJCY8WRIOKJTj5MBTPlZpDRdkOw+a2g4bFf28lPINRV4qLi5GWlgZLS0uZXXEZDAajKjV9V7zu+7vB1/T81+DxeRBZVNwIPRUzSItjUaYkxtklfzWwZQwGg8FgfNwwp+ctILTUAgA0FptCSs9BkkfIKLdAxslrDWwZg8FgMBgfL8zpeQuImlSM9OiK/78B4ouKVfLnd92BtPzNnI3CYDAYDAajfjCn5y0gNNUAlHlQ5alCQ6CDIvoH/LJc5AkNEbOqYfcTYjAYDAbjY4U5PW8BnjIfQjMNAICalimUiAdh6T4AQFwCUJj5pCHNYzAYDAbjo4Q5PW8JUZOKdT2GuhVn89wXPIFa4UOUCdRxbilb1MxgMBgMxruGOT1vCdH/FzM3EzVBOZ+g8UIZemoVZ9OkFhji4fmEhjSPwWAwGIyPDub0vCWEFhoAD+DlS/DCuOJoimgVPowLrgA8Ps5tTeC2GmcwGAwGg/H2YU7PW4IvUobAWB0A4NKyLwBAPRtobX4AfEkJniob4saGEw1pIoPBYDAYHxXM6XmLVIaut9dqiVw1KQQSPvapW6NFySEAwLWrJSh6nN+QJjIYDAbjAyMrKws9e/aEmpqa3Gn1NXH79m106NABKioqcHR0fGv2vc8wp+ctUrmupzTjOcqb2wIActPy4NL6KlSLs1Ei0MSFpUca0kQGg1EHgoKC8Mknn0BDQwP6+vro378/kpOTZWSKi4sREBCAxo0bQ11dHQMHDkR2djaXf/36dQwdOhRmZmYQi8Wws7PD8uXLZXRERkaCx+PJXVlZWTJyq1evRpMmTaCiooL27dvjypUrXF56erpCHTweD2FhYZzchAkT4OTkBJFIpPAFmJycjK5du8LAwAAqKiqwsrLCnDlzFJ7c/TI12VcVIoKnpyd4PB4OHjwok3fv3j14eXlBVVUV+vr6mDZtGsrLy2VkduzYgdatW0NVVRVGRkbw8/PDkyf/RseGhobK9UHVYw3KysowY8YMODg4QE1NDcbGxvDx8cGjR49qbWNt/RcZGQlvb28YGRlBTU0Njo6O2LFjh4xMbfZVx7Jly5CZmYn4+HjcuXMHAJCamorPPvsMenp60NTUxKBBg2SePwAIDAyEmpoakpOT63XKelhYGJo3bw4VFRU4ODjg2LFjdS77vsGcnreI8P8jPeXZhfDp/w0kPIJOPg8njD9FB2EoACDlqS6yr95uQCsZDEZtnD17FgEBAbh06RJOnTqFsrIy9OrVCy9evOBkJk+ejD///BNhYWE4e/YsHj16hAEDBnD5MTEx0NfXx/bt25GYmIjZs2dj1qxZWLVqlVx9ycnJyMzM5C59fX0ub/fu3ZgyZQoCAwMRGxuL1q1bw8PDAzk5OQAAMzMzmbKZmZmYP38+1NXV4enpKVOPn58fBg8erLDNAoEAPj4+OHnyJJKTkxEcHIyNGzciMDCwxr6qzb6qBAcHg8fjyaVLJBJ4eXmhtLQUUVFR2Lp1K0JDQzFv3jxO5uLFi/Dx8cGoUaOQmJiIsLAwXLlyBaNHj5bRpampKdMXGRkZXF5hYSFiY2Mxd+5cxMbGYv/+/UhOTka/fv1qbGMlNfVfVFQUWrVqhX379uHGjRsYOXIkfHx8cOSI7A/dmuyrjtTUVDg5OaFp06bQ19fHixcv0KtXL/B4PISHh+PixYsoLS1F3759ZdaOpqamomPHjrCwsEDjxo3r1MaoqCgMHToUo0aNQlxcHPr374/+/fvj5s2bdSr/3vFKx5R+4LzNU9ZfJnPpVbo/4xwVJj6mKZMG0tJBXjR5+lCi9e4UNvxXWjXmDO0avb3ak5AZjP8Kb/yU9dwHRH+frfj3HZOTk0MA6OzZsxWm5OaSQCCgsLAwTubWrVsEgKKjo6vVM27cOOratSv3OSIiggDQs2fPqi3j7OxMAQEB3GeJRELGxsYUFBRUbRlHR0fy8/NTmBcYGEitW7eutmxVJk+eTB07dqxRpq72xcXFkYmJCWVmZsqdsn7s2DHi8/mUlZXFpa1du5Y0NTW5E8iXLFlCVlZWMjpXrFhBJiYm3OeQkBDS0tKqU9squXLlCgGgjIyMOsnXp//69OlDI0eOfC37LCwsZE4w9/X1pRMnThCfz5d5p+Xm5hKPx6NTp04REcmdfB4YGEhERPfu3aMvvviCtLS0qFGjRtSvXz9KS0vj9AwaNIi8vLxkbGjfvj2NGTOG+1xcXEzTp08nU1NTEgqFZG1tTZs2bapXu6ryNk9ZZyM9b5nK/XpK0vNh26kPAEDvfi6ynCeji95G8KRleMw3QtLvdR9qZDD+MxABpS/qf13ZCAS3BLb2rfj3ysb66yB6ZbPz8vIAADo6OgAqRnHKysrQo0cPTqZ58+YwNzdHdHR0jXoqdVTF0dERRkZG6NmzJy5evMill5aWIiYmRqYePp+PHj16VFtPTEwM4uPjMWrUqPo18iXu3r2L48ePw93dvVqZutpXWFiIYcOGYfXq1TA0NJTTEx0dDQcHBxgYGHBpHh4eyM/PR2JiIgDAxcUF9+/fx7Fjx0BEyM7Oxt69e9GnTx8ZXQUFBbCwsICZmRm8vb258tWRl5cHHo9Xr7UydUXR/a6vfVevXkXv3r0xaNAgZGZmYvny5SgpKQGPx4NIJOLkVFRUwOfzceHCBQBAZmYmWrRogalTpyIzMxPfffcdysrK4OHhAQ0NDZw/fx4XL16Euro6evfujdLSUgAV96Lq/QQq7kXV++nj44M//vgDK1aswK1bt7B+/Xqoq6u/Vl+9LZQb2oD/OsImmnhxNQul6XkYNcYXC/7cC81CPtZfuIbAts3R/OQx3FLzxuXzz9FswAsINdUa2mQG491RVggsMn49HSQFjn1XcdWH7x8Bwvr/vUmlUkyaNAlubm5o2bJi89GsrCwIhUK5F6WBgYHcepxKoqKisHv3bhw9+u/RNEZGRli3bh3atWuHkpISbNq0CV26dMHly5fRtm1bPH78GBKJRMYZqKzn9m3F0+SbN2+GnZ0dXF1d691WAHB1dUVsbCxKSkrg7++PBQsWVCtbV/smT54MV1dXeHt7K9STlZWlUEdlHgC4ublhx44dGDx4MIqLi1FeXo6+ffti9erVXBlbW1ts2bIFrVq1Ql5eHpYuXQpXV1ckJibC1NRUrt7i4mLMmDEDQ4cOhaamZi09Uz/27NmDq1evYv369a9sHwDo6elBJBJBLBZzDmOHDh2gpqaGGTNmYNGiRSAizJw5ExKJBJmZmQAAQ0NDKCsrQ11dnSu3fft2SKVSbNq0iZtmDAkJgba2NiIjI9GrV69q70Xlfbhz5w727NmDU6dOcc6RlZXVG+y5Nwsb6XnLcIuZHxSAJwFKmlkDAIpvxAE9foCb+S6olDxFsUAbET8exN0DUXiWfL8hTWYwGDUQEBCAmzdvYteuXa+s4+bNm/D29kZgYCB69erFpdva2mLMmDFwcnKCq6srtmzZAldXVyxbtuyV6ikqKsLOnTtfa5Rn9+7diI2Nxc6dO3H06FEsXboUAHD+/Hmoq6tz18uLdKvj8OHDCA8PR3Bw8CvbBABJSUmYOHEi5s2bh5iYGBw/fhzp6ekYO3YsJ+Pi4gIfHx84OjrC3d0d+/fvh56enozjUUlZWRkGDRoEIsLatWu5dE9PT66NLVq0eCVbIyIiMHLkSGzcuFFGR232LVq0SKaP7927p1C/np4ewsLC8Oeff0JdXR1aWlrIzc1F27ZtwedX/5q/fv067t69Cw0NDa4OHR0dFBcXIzU1tU5ti4+Ph5KSUo0jgO8TbKTnLaPUSAQlTSEk+aUovfccwwYF4MT1qdDN4+FQyiN4t/kUztlbcQ6TcTffEHdPFAPHk9HePg7tJtZtMR2D8cEiUK0YcakP+Y+A1c4VIzyV8JSAgMuAZj1GjQSq9asXwPjx43HkyBGcO3dO5pe4oaEhSktLkZubKzPak52dLTd9k5SUhO7du8Pf3x9z5syptU5nZ2duikJXVxdKSkpyUTmK6gGAvXv3orCwED4+PvVppgxmZmYAAHt7e0gkEvj7+2Pq1Klo164d4uPjOTkDAwOIRKJa7QsPD0dqaqrcqNjAgQPRqVMnREZGwtDQUC7iq1JnpZ6goCC4ublh2rRpAIBWrVpBTU0NnTp1wk8//QQjIyO5tggEArRp0wZ3796VSa90eDIyMhAeHi4zyrNp0yYUFRVx5evL2bNn0bdvXyxbtqzW+/CyfWPHjsWgQYO4fGPj6p/vXr16ITU1FY8fP4aysjK0tbVhaGhY46hLQUEBnJycFDqsenp6ACr6u6b7KRaLa2zT+wYb6XnL8Hg8CCtHe9Lz0Mq6ObIMKuZdzx3bAXSbAxPtlIr1BZVRDDw+LieJ2YgP478Pj1cxxVSfS7cp0Hd5haMDVPzbN7givT56FEQNVQcRYfz48Thw4ADCw8NhaWkpk+/k5ASBQCATBpycnIx79+7BxcWFS0tMTETXrl3h6+uLhQsX1qnu+Ph47gUuFArh5OQkU49UKsWZM2dk6qlk8+bN6NevH/cCe12kUinKysoglUohFothY2PDXRoaGnWyb+bMmbhx4wbi4+O5C6gIww4JCQFQMQKSkJAgE/F16tQpaGpqwt7eHkDFuqCXRzGUlCqeCapmvZZEIkFCQoKMQ1Tp8KSkpOD06dNyUU0mJiZcGy0sLOrVX5GRkfDy8sLixYvh7+9fq/zL9uno6Mj0sbJy7eMUurq60NbWRnh4OHJycmqMRGvbti1SUlKgr68vU4+NjQ20tCreWy4uLnLh7adOneLup4ODA6RSKc6ePVurbe8DbKTnHSBqoomi6/+gJL1iI0Irt14o3ncEje8/xWMlbeTqfAYUvPQFzFPCk1v30cjWrAEsZjDec9r6ANbdgad/AzpWgJbJW60uICAAO3fuxKFDh6ChocGtZ9DS0oJYLIaWlhZGjRqFKVOmQEdHB5qamvj222/h4uKCDh06AKiY0urWrRs8PDwwZcoUToeSkhLnlAQHB8PS0hItWrRAcXExNm3ahPDwcJw8eZKzZcqUKfD19UW7du3g7OyM4OBgvHjxAiNHjpSx+e7duzh37ly1e6rcvXsXBQUFyMrKQlFREed82NvbQygUYseOHRAIBHBwcIBIJMK1a9cwa9YsDB48uMYRj9rsMzQ0VDgqZW5uzjmTvXr1gr29Pb766iv88ssvyMrKwpw5cxAQEMAt1u3bty9Gjx6NtWvXwsPDA5mZmZg0aRKcnZ25EZEFCxagQ4cOsLGxQW5uLpYsWYKMjAx8/fXXACocns8//xyxsbE4cuQIJBIJd190dHQgFAqrbWdt/RcREYFPP/0UEydOxMCBAzm9QqGQW8xcm331ISQkBHZ2dtDT00N0dDQmTpyIyZMnw9bWttoyw4cPx5IlS+Dt7Y0FCxbA1NQUGRkZ2L9/P6ZPnw5TU1NMnDgR7u7u+PXXX+Hl5YVdu3bh2rVr2LBhAwCgSZMm8PX1hZ+fH1asWIHWrVsjIyMDOTk5MqNU7w2vHFP2AfMuQ9aJiEozC+j+jHP0YM4FkpZLqLS0lAJ9PGnpIC/6cc18ehp3nVb5n6JVY878e/mfpidJdQuZZDA+BN54yPo7BC+F+1ZeISEhnExRURGNGzeOGjVqRKqqqvTZZ59RZmYmlx8YGKhQh4WFBSezePFisra2JhUVFdLR0aEuXbpQeHi4nD0rV64kc3NzEgqF5OzsTJcuXZKTmTVrFpmZmVW7HYa7u7tCeyrDlXft2kVt27YldXV1UlNTI3t7e1q0aFGd7l9d7KsKXgpZJyJKT08nT09PEovFpKurS1OnTqWysjIZmRUrVpC9vT2JxWIyMjKi4cOH04MH/25hMGnSJM4OAwMD6tOnD8XGxnL5aWlp1d7biIiIGm2urf98fX0V5ru7u9fZvurw9vYmX19fmbQZM2aQgYEBCQQCatq0Kf36668klUplZFq3bs2FqleSmZlJPj4+pKurSyKRiKysrGj06NEy78c9e/ZQs2bNSCgUUosWLejo0aMyOoqKimjy5MlkZGREQqGQbGxsaMuWLbW2ozreZsg6j+g14jY/UPLz86GlpYW8vLw3vkJfESQlPPrxEqioHPoBjhCaaWD6wnEwuHEP/2jz8PMv63Bt0lRcFo6qGKr//1SXlYUEHjN6gM+v+zA8g/G+UlxcjLS0NFhaWtZp11kGg/FxUtN3xeu+v9manncAj8/jzuEqSavY32PwF99AyiPo5RLOXNiPdhZ/YZjGWHhgPlplhYInleDvDCWcWnMNUulH55cyGAwGg/HGYU7PO6LqJoUA4NTMAdl6FfPFxy9eAnh8NFJ/DBvDeLg5/Ik29zaARxLcvfkcZzbdYI4Pg8FgMBivCXN63hFCy4qRntL0PND/HRgzl24AAO17z5Df42cuGoWvTGjXIQYOD/eBRxLciX2C8NBE5vgwGAwGg/EaMKfnHSE0VgdPwIe0sBzl/xQCAMZ87o8XKlKolPGxOukJMCkBGLoH0DCCAE/wST8JWtzdCR5JkHwlBxHbbnEOE4PBYDAYjPrBnJ53BE+ZD6GZBoB/p7hUhCI8t6oISc+NvVQRdmvrAfSv2A1U5dEetP26LexvbQVIitvRWYjYcZs5PgwGg8FgvALM6XmHcJsU/n8xMwAM+NwfBIL+UylOXztfkWjdFWjnBwDQyN4Ah686wv5WKEBS3LqYiUjm+DAYDAaDUW+Y0/MO4SK4/j/SAwAuLZyQrVex0ddfh0L+Fe65ANAyB3LvQcfwNpq7W3EjPkkXMxH5RzJzfBgMBoPBqAfM6XmHCM01AT4gyS1B+bNiLt3wk84AgEZpWVi9ex2u3I4DRBqA9yoAAC8mBIZfdoaVlTLsb/8OECHp/COc3XWn2u3WGQwGg8FgyMKcnncIX6QEgbE6ANnRHv9BY1CiJIW4jI/i/UdwNnAOflg5B7ByBz6p2I6cd2wSTH/5Eeaqj2H3f8cn8dxDnGOOD4PBYDAYdYI5Pe+Yyv16qq7ruZVxB0LJv7su88GD2oW4ihGfHvMBbQsg7z6ULi+B2bq1MC1Oht3tbQAIN88+xPndKczxYTAYjI+YLl26YNKkSQ1tRp2IjIwEj8dDbm7uO6+bOT3vGJFl5bqef52e+KQ48CB71AQfPFy/FQ+I1AHv1RWJMaEQlt2F6aqVMHoai+a3twMgJEQ+QPjvt/Dg9lMUVJk2YzAYb4agoCB88skn0NDQgL6+Pvr374/k5GQZmeLiYgQEBKBx48ZQV1fHwIEDkZ2dzeVfv34dQ4cOhZmZGcRiMezs7LB8+XIZHZUvg5evysMqK1m9ejWaNGkCFRUVtG/fHleuXOHy0tPTFerg8XgICwvj5CZMmAAnJyeIRCI4OjrKtTk5ORldu3aFgYEBVFRUYGVlhTlz5qCsrKzW/qrJvqoQETw9PcHj8XDw4EGZvHv37sHLywuqqqrQ19fHtGnTUF5eLiOzY8cOtG7dGqqqqjAyMoKfnx+ePHnC5YeGhsr1QdVjDcrKyjBjxgw4ODhATU0NxsbG8PHxwaNHj2ptY239BwAnTpxAhw4doKGhAT09PQwcOBDp6elyfWVnZwexWAxbW1v8/vvvtdb9rigoKMD48eNhamoKsVgMe3t7rFu3TkamS5cucn08duzYBrK4dpjT844R/n+kpzynCJIXFV8ejvZtIIXsSI0UhNZ2jhUfLDsBzmMq/n94AlQdmsFowQIYZ11C89s7AAC3o7NwKDgev38fhaSLtf/BMhiMunP27FkEBATg0qVLOHXqFMrKytCrVy+8ePGCk5k8eTL+/PNPhIWF4ezZs3j06BEGDBjA5cfExEBfXx/bt29HYmIiZs+ejVmzZmHVqlVy9SUnJyMzM5O79PX1ubzdu3djypQpCAwMRGxsLFq3bg0PDw/k5OQAAMzMzGTKZmZmYv78+VBXV4enp6dMPX5+fhg8eLDCNgsEAvj4+ODkyZNITk5GcHAwNm7ciMDAwBr7qjb7qhIcHAweT/5sQYlEAi8vL5SWliIqKgpbt25FaGgo5s2bx8lcvHgRPj4+GDVqFBITExEWFoYrV65g9OjRMro0NTVl+iIjI4PLKywsRGxsLObOnYvY2Fjs378fycnJ6NevX41trKSm/ktLS4O3tze6deuG+Ph4nDhxAo8fP5Z5JtauXYtZs2bhhx9+QGJiIubPn4+AgAD8+eefdar/bTNlyhQcP34c27dvx61btzBp0iSMHz8ehw8flpEbPXq0TB//8ssvDWRxHXjlY1A/YN71Kesvk/nrNbo/4xwV3vyHSwtcMZuWDOpDSwd50ZJBfWjeiu9lC5UUEAW3JgrUJDoYQERE2b/+RrGtOtAq/9MyJ7Sv/uYMPX/64Z1kzfhv86ZPWc8syKTLjy5TZkFm7cJvmJycHAJAZ8+eJSKi3NxcEggEFBYWxsncunWLAFB0dHS1esaNG0ddu3blPkdERBAAevbsWbVlnJ2dKSAggPsskUjI2NiYgoKCqi3j6OhIfn5+CvMCAwOpdevW1ZatyuTJk6ljx441ytTVvri4ODIxMaHMzEy5U9aPHTtGfD6fsrKyuLS1a9eSpqYmlZSUEBHRkiVLyMrKSkbnihUryMTEhPscEhJCWlpadWpbJVeuXCEAlJGRUSf56vovLCyMlJWVZU65P3z4MPF4PCotLSUiIhcXF/ruu+9kyk2ZMoXc3NxqrLOgoIC++uorUlNTI0NDQ1q6dCm5u7vTxIkTOZni4mKaOnUqGRsbk6qqKjk7O8udHH/+/Hnq2LEjqaiokKmpKX377bdUUFDA5bdo0YIWLFggU6Zt27Y0e/Zs7vPL9Sri6NGj1LRpU1JRUaEuXbpQSEhIjc/52zxlnY30NADcFFfav4uZf/j2J5iPrBjNIQBtOnWQLSRUA/qvAcAD4rYBKaehN2ki0KkP8NIvJZICeTlFb7MJDMYbgYhQWFZY72vX7V3w2OuBUSdHwWOvB3bd3lVvHfQa6+Dy8iqmp3V0dABUjOKUlZWhR48enEzz5s1hbm6O6OjoGvVU6qiKo6MjjIyM0LNnT1y8eJFLLy0tRUxMjEw9fD4fPXr0qLaemJgYxMfHY9SoUfVr5EvcvXsXx48fh7u7e7UydbWvsLAQw4YNw+rVq2FoaCinJzo6Gg4ODjAwMODSPDw8kJ+fj8TERACAi4sL7t+/j2PHjoGIkJ2djb1796JPnz4yugoKCmBhYQEzMzN4e3tz5asjLy8PPB4P2traNcrVhpOTE/h8PkJCQiCRSJCXl4dt27ahR48eEAgqtikpKSmRO0VcLBbjypUrNU4jTps2DWfPnsWhQ4dw8uRJREZGIjY2VkZm/PjxiI6Oxq5du3Djxg188cUX6N27N1JSUgAAqamp6N27NwYOHIgbN25g9+7duHDhAsaPH8/pcHV1xeHDh/Hw4UMQESIiInDnzh306tVLpq4dO3ZAV1cXLVu2xKxZs1BYWMjl3b9/HwMGDEDfvn0RHx+Pr7/+GjNnzny1Tn0DKDdYzR8xoiZaeHE5S2ZdDwAM6t0Pc8JC0aigFCdO/YH+jn1lC1q4Au3HApfXAoe/BW9cNKxnf4uoH64BvKr+K0FLX/z2G8JgvCZF5UVov7P9a+mQQoqFlxdi4eWF9Sp3edhlqApU61+fVIpJkybBzc0NLVu2BABkZWVBKBTKvSgNDAzk1uNUEhUVhd27d+Po0aNcmpGREdatW4d27dqhpKQEmzZtQpcuXXD58mW0bdsWjx8/hkQikXEGKuu5ffu2wno2b94MOzs7uLq61rutQMWLLzY2FiUlJfD398eCBQuqla2rfZMnT4arqyu8vb0V6snKylKoozIPANzc3LBjxw4MHjwYxcXFKC8vR9++fbF69WqujK2tLbZs2YJWrVohLy8PS5cuhaurKxITE2FqaipXb3FxMWbMmIGhQ4dCU1Ozlp6pGUtLS5w8eRKDBg3CmDFjIJFI4OLigmPHjnEyHh4e2LRpE/r374+2bdsiJiYGmzZtQllZGR4/fgwjIyM5vQUFBdi8eTO2b9+O7t27AwC2bt0q05579+4hJCQE9+7dg7GxMQDgu+++w/HjxxESEoJFixYhKCgIw4cP5xY/N23aFCtWrIC7uzvWrl0LFRUVrFy5Ev7+/jA1NYWysjL4fD42btyIzp07c3UNGzYMFhYWMDY2xo0bNzBjxgwkJydj//79ACqm8KytrfHrr79y9yQhIQGLFy9+rf59VdhITwNQefho2aMCSEskMnlqzT4BAKikZyOnUH4OHN3nATpWwPNHwInZEPOK0TzlD4D+1aNS9BjC4mdvrwEMxkdMQEAAbt68iV27dr2yjps3b8Lb2xuBgYEyv5ptbW0xZswYODk5wdXVFVu2bIGrqyuWLVv2SvUUFRVh586drzXKs3v3bsTGxmLnzp04evQoli5dCgA4f/481NXVuWvHjh110nf48GGEh4cjODj4lW0CgKSkJEycOBHz5s1DTEwMjh8/jvT0dJlFtC4uLvDx8YGjoyPc3d2xf/9+6OnpYf369XL6ysrKMGjQIBAR1q5dy6V7enpybWzRokWd7cvKysLo0aPh6+uLq1ev4uzZsxAKhfj888+5Uca5c+fC09MTHTp0gEAggLe3N3x9fQFUjJAp6uPU1FSUlpaifft/fyzo6OjA1taW+5yQkACJRIJmzZrJlD979ixSU1MBVCysDw0Nlcn38PCAVCpFWloaAGDlypW4dOkSDh8+jJiYGPz6668ICAjA6dOnubr8/f3h4eEBBwcHDB8+HL///jsOHDjA1XPr1i0ZWyvvS0PBRnoaAGVtFShpiyDJLUHpvXyoNG3E5Xl6DcSZ2IswfqKCP+J2YaLbBNnCQtWKs7m29Abit6O03A7Gj6Kg8yQJ+eoWSGwxEsViPTy4mg6rfvK/EhiM9wmxshiXh12uV5nswmz0P9gfUki5ND6Pj4PeB2GgalBDSfm668v48eNx5MgRnDt3TuaXtaGhIUpLS5Gbmysz2pOdnS03fZOUlITu3bvD398fc+bMqbVOZ2dnXLhwAQCgq6sLJSUlmaiw6uoBgL1796KwsBA+Pj71aaYMZmYV5wPa29tDIpHA398fU6dORbt27RAfH8/JGRgYQCQS1WpfeHg4UlNT5UbFBg4ciE6dOiEyMhKGhoZyEV+VOiv1BAUFwc3NDdOmTQMAtGrVCmpqaujUqRN++uknhaMkAoEAbdq0wd27d2XSKx2ejIwMhIeHy4zybNq0CUVFRVz5urJ69WpoaWnJLOrdvn07zMzMcPnyZXTo0AFisRhbtmzB+vXrkZ2dDSMjI2zYsIGL9tLU1JTr47///rvWugsKCqCkpISYmBgoKSnJ5Kmrq3MyY8aMwYQJE+TKm5ubo6ioCN9//z0OHDgALy8vABV9HB8fj6VLl8pMYVal0sG5e/curK2ta7X1XcNGehoIRUdSAIBjy2bIU1UFn3i4fP4opCSVL2zeAXAJAAAIE1cBfD5USnKh/+Q6jLIqXiBJqcyfZbz/8Hg8qApU63VZalki0DUQ/P9P6fJ5fAS6BMJSy7JeehRFDVUHEWH8+PE4cOAAwsPDYWlpKZPv5OQEgUCAM2fOcGnJycm4d++ezK/axMREdO3aFb6+vli4sG7TcfHx8dwLXCgUwsnJSaYeqVSKM2fOKPz1vHnzZvTr1w96enp1bmtNSKVSlJWVQSqVQiwWw8bGhrs0NDTqZN/MmTNx48YNxMfHcxcALFu2DCEhFUfxuLi4ICEhQSbi69SpU9DU1IS9vT2AinVBfL7sK6zyBV/dei2JRIKEhAQZh6jS4UlJScHp06fRuHFjmTImJiZcGy0sLOrcVzXZJ5XKfq8LBAKYmppCSUkJu3btwqeffgo+n6+wj62trSEQCHD58r8/Fp49e4Y7d+5wn9u0aQOJRIKcnByZ8jY2NpzT2LZtWyQlJcnl29jYQCgUoqysDGVlZQrb8LL9Vam8n5V9bGdnJ+fAXrp0qS5d+HZ4peXPHzgNHb1FRPT80iO6P+Mc5ay/Lpe39OdltHSQF00c35WiHkYpVlBaSLSiLVGgJj2b1Y+S7OwpybY5XXXsTKv8T9GqsWfoaWaB4rIMRgPwNqK3rmReeSfRW9988w1paWlRZGQkZWZmcldhYSEnM3bsWDI3N6fw8HC6du0aubi4kIuLC5efkJBAenp69OWXX8royMnJ4WSWLVtGBw8epJSUFEpISKCJEycSn8+n06dPczK7du0ikUhEoaGhlJSURP7+/qStrS0T6URElJKSQjwej/766y+FbUpJSaG4uDgaM2YMNWvWjOLi4iguLo6Ljtq+fTvt3r2bkpKSKDU1lXbv3k3GxsY0fPjwGvuqrvZVBS9Fb5WXl1PLli2pV69eFB8fT8ePHyc9PT2aNWsWJxMSEkLKysq0Zs0aSk1NpQsXLlC7du3I2dmZk5k/fz6dOHGCUlNTKSYmhoYMGUIqKiqUmJhIRESlpaXUr18/MjU1pfj4eJn7UtkP1VFb/505c4Z4PB7Nnz+f7ty5QzExMeTh4UEWFhbcc5OcnEzbtm2jO3fu0OXLl2nw4MGko6NDaWlpNdY9duxYsrCwoDNnzlBCQgL169eP1NXVZaKohg8fTk2aNKF9+/bR33//TZcvX6ZFixbRkSNHiIjo+vXrJBaLKSAggOLi4ujOnTt08OBBmcg7d3d3atGiBUVERNDff/9NISEhpKKiQmvWrCEiort379KCBQvo2rVrlJaWRocOHSIrKyvq3LkzpyMjI4OEQiF99913dPv2bdqxYwcZGho2WPQWc3oaiNKsAro/4xw9mHOBpGUSmbyYuCRaOsiLFg/pQxOPTaheyb3LRD9oEwVqUumFnfT80iVK9e5Puwb+RqvGnKHwbbfecisYjLrzpp2edwkqgirlrpCQEE6mqKiIxo0bR40aNSJVVVX67LPPKDPzX4csMDBQoQ4LCwtOZvHixWRtbU0qKiqko6NDXbp0ofDwcDl7Vq5cSebm5iQUCsnZ2ZkuXbokJzNr1iwyMzOTCZmuiru7u0J7Kl+4u3btorZt25K6ujqpqamRvb09LVq0qE73ry72VeVlp4eIKD09nTw9PUksFpOuri5NnTqVysrKZGRWrFhB9vb2JBaLycjIiIYPH04PHjzg8idNmsTZYWBgQH369KHY2FguPy0trdp7+3J4d337j4jojz/+oDZt2pCamhrp6elRv3796Natf7+Xk5KSyNHRkcRiMWlqapK3tzfdvn27xnqJiJ4/f05ffvklqaqqkoGBAf3yyy9yoeOlpaU0b948atKkCQkEAjIyMqLPPvuMbty4wclcuXKFevbsyd3jVq1a0cKFC7n8zMxMGjFiBBkbG5OKigrZ2trSr7/+SlKplIiI7t27R507dyYdHR0SiURkY2ND06ZNk3u3/vnnn2RjY0MikYg6depEW7ZsaTCnh0f08Z1fkJ+fDy0tLeTl5b32Cv1XhYiQ+eMlSAvLoTeuNUTmsnb8MGIYNIryccHxKTZPPgIdFfmwVgDAyTlA1EpAVRfouwIFaSVImL8FsW2mQEmZB59FblDVFL6DFjEYNVNcXIy0tDRYWlrKhekyGAxGJTV9V7zu+5ut6WkgeDwetztzaVq+XL6GbcViMPNMFfyZWsPunF1nA+oGQOFjYPcwqF0ZBYPGL6CZ9zck5YQbEfffiv0MBoPBYHxoMKenAeEWM6flyeV17FWx/4LJP2LsT9xb/UZqhU+BF/9wH3mQQs/0FszvV4QU3ox8gLKXwuIZDAaDwfgYYU5PAyKyrBjpKcnIB0llnZpP2tijQKQFJSkPlJqDuJw4xUqeplZswVwFdaMimGnnQlyYg5IiCW5FsbO4GAwGg8FgTk8DIjBWA0/ABxWVozynUCaPz+dD1dYJAGCRpYp9KfsUK9Gxfmk3ZoDH50PP3w/mDyrCRuNPZkAqqT7EkMFgMBiMjwHm9DQgPCU+hBbVT3G59aiY4jLNEeP03ZPIL5Vf+wMtE6DvcoBXZQMqQweo9/0CFlq5EJQ+x/NnpUiN+0e+LIPBYDAYHxHM6WlgqtukEAA6fOKAF0ItKEv50Mvi4+jfR+VkAABtfYBJCYD3/8+cyUoA71kaDMb5w/ThOQBA3F9pr3XAIoPBYDAYHzrM6WlghJaVEVx5ck4Jn8+HSrM2AIAmWarYd2df9Y6LlgnQ5kvApkfFGp9La6HRvTusxA/Bl5Tin4eFeHgn9202hcFgMBiM9xrm9DQwQjMNQIkHSX4pJM9K5PI7dOsKoGKK6+7jO0h6klSzQtdvK/6N2w5ecS6Mv/GDUVZ0RdJfqW/UdgaDwWAwPiSY09PA8IVKEJpUHACnMHTdpQ0KBRoQSPgw+UcFe1P21qzQ0h0wcADKCoFrW6DRsweslf4GSIp7t/Px5FHB22gGg8FgMP5jXLx4EQ4ODhAIBOjfv39Dm/NGYE7PewC3SaGCdT18Ph8CG0cAgEWWGo79fQyFZYVychw83r+jPVc2gCctg8WYYdB7fB0AEHeMjfYwGPUlKCgIn3zyCTQ0NKCvr4/+/fsjOTlZRqa4uBgBAQFo3Lgx1NXVMXDgQJnTxq9fv46hQ4fCzMwMYrEYdnZ2WL58uYyOyMhI8Hg8uSsrK0tGbvXq1WjSpAlUVFTQvn17mQMd09PTFerg8XgICwvj5CZMmAAnJyeIRCI4OjrKtTk5ORldu3aFgYEBVFRUYGVlhTlz5qCsrKzW/qrJvqoQETw9PcHj8XDw4EGZvHv37sHLywuqqqrQ19fHtGnTUF5eLiOzY8cOtG7dGqqqqjAyMoKfnx+ePHnC5YeGhsr1QdUdfsvKyjBjxgw4ODhATU0NxsbG8PHxwaNHNW/z8abu5dq1a9GqVStoampCU1MTLi4u+Ouvv2qs+01T0z2YMmUKHB0dkZaWhtDQUJm8J0+ewNTUFDweD7m5uVz6/v370bNnT+6UeBcXF5w4ceLtN6SOMKfnPeDfxczyIz0A4Pz/KS6zbFWUlBTiePrxmhW2HABoGAMF2UBCGDQ8PGBNtwAAKTGPUaBgGo3BYFTP2bNnERAQgEuXLuHUqVMoKytDr1698OLFC05m8uTJ+PPPPxEWFoazZ8/i0aNHGDBgAJcfExMDfX19bN++HYmJiZg9ezZmzZqFVatWydWXnJyMzMxM7tLX1+fydu/ejSlTpiAwMBCxsbFo3bo1PDw8uBPJzczMZMpmZmZi/vz5UFdXh6enp0w9fn5+GDx4sMI2CwQC+Pj44OTJk0hOTkZwcDA2btyIwMDAGvuqNvuqEhwcrPC0e4lEAi8vL5SWliIqKgpbt25FaGgo5s2bx8lcvHgRPj4+GDVqFBITExEWFoYrV65g9OjRMro0NTVl+iIjI4PLKywsRGxsLObOnYvY2Fjs378fycnJ6NevX41tfFP30tTUFD///DNiYmJw7do1dOvWDd7e3khMTKyx/jdJdfcAAFJTU9GtWzeYmppCW1tbJm/UqFFo1aqVXJlz586hZ8+eOHbsGGJiYtC1a1f07dsXcXHV7DX3rnmlE7s+cN6HA0erInlRSvdnnKP7M85R+XP5k33Ly8ppwfDBtHSQF/Ve/AkNOzqsdqUXgokCNYlWtSeSSin3yBHaNng9rRpzhi78kfgWWsFg1MyHfODoy+Tk5BAAOnv2LBER5ebmkkAgoLCwME7m1q1bBICio6Or1TNu3Djq2rUr9zkiIqLGgxiJiJydnWVOwpZIJGRsbExBQUHVlnF0dCQ/Pz+FeYGBgdS6detqy1Zl8uTJ1LFjxxpl6mpfXFwcmZiYUGZmptyBo8eOHSM+ny9zMvvatWtJU1OTO8V8yZIlZGVlJaNzxYoVZGJiwn0OCQkhLS2tOrWtkitXrhAAysjIqFe5V7mXimjUqBFt2rSpRpnp06dT06ZNSSwWk6WlJc2ZM4dKS0tlZA4ePEht2rQhkUhElpaW9MMPP8gd2FrdPVB0EGvVw3XXrFlD7u7udObMmTq10d7enubPn1/nPnibB46ykZ73AL6qAMoGqgCAF5cyUZ4nOxKjpKwEZevWAIAmWWq48c8NpDxLqVlpW19AqA78cwu4ewaavXvDuiwBAJB47iFKi8prLs9gvOeUZWXhxaXLKHtp6uddkJdXMSqro1NxEHBMTAzKysrQo0cPTqZ58+YwNzdHdHR0jXoqdVTF0dERRkZG6NmzJy5evMill5aWIiYmRqYePp+PHj16VFtPTEwM4uPjMWrUqPo18iXu3r2L48ePw93dvVqZutpXWFiIYcOGYfXq1TA0NJTTEx0dDQcHBxgYGHBpHh4eyM/P50ZBXFxccP/+fRw7dgxEhOzsbOzduxd9+vSR0VVQUAALCwuYmZnVaRQlLy8PPB5PbmSjNup7L19GIpFg165dePHiBVxcXGqsS0NDA6GhoUhKSsLy5cuxceNGLFu2jMs/f/48fHx8MHHiRCQlJWH9+vUIDQ3FwoULOZma7kHlaKGmpiaCg4ORmZnJjQgmJSVhwYIF+P3338Hn1+5CSKVSPH/+XGHfNATvhdNT1/nfSoKDg2FrawuxWAwzMzNMnjwZxcXF78jatwNfTQAAyD99D1k/X8GLq7Jf5J906QIAMM9SA1+K6ndorkSsXeH4AED0SvCUlGDn6wHVF1kokyrhZnj6m20Ag/EKEBGkhYX1vp7u3Im73brj3ogRuNutO57u3FlvHfSK+1ZJpVJMmjQJbm5uaNmyJQAgKysLQqFQ7kVpYGAgtx6nkqioKOzevRv+/v5cmpGREdatW4d9+/Zh3759MDMzQ5cuXRAbGwsAePz4MSQSiYwzUFs9mzdvhp2dHVxdXV+pva6urlBRUUHTpk3RqVMnLFiwoFrZuto3efJkuLq6wtvbW6GerKwshToq8wDAzc0NO3bswODBgyEUCmFoaAgtLS2sXr2aK2Nra4stW7bg0KFD2L59O6RSKVxdXfHgwQOF9RYXF2PGjBkYOnRovU7wfpV7WUlCQgLU1dUhEokwduxYHDhwAPb29jXWN2fOHLi6uqJJkybo27cvvvvuO+zZs4fLnz9/PmbOnAlfX19YWVmhZ8+e+PHHH7F+/XpOpqZ7oKSkBENDQ/B4PGhpacHQ0BBisRglJSUYOnQolixZAnNz8zr1zdKlS1FQUIBBgwbVSf5to9zQBlTO/65btw7t27dHcHAwPDw8kJycLDP3WcnOnTsxc+ZMbNmyBa6urrhz5w5GjBgBHo+H3377rQFa8PqU55Wg9O8q63kIeLY/BaJmjaCsJQIAdO3kjEub1CAufwGjxxUnr092mgyRkqh6xR3GApfXAX9HApk3oOXlCcvfZyFRrTeuH09F695WUFJ6L/xexkcKFRUhua3T6ymRSpG94EdkL/ixXsVsY2PAU1Wtd3UBAQG4efMmLly4UO+yldy8eRPe3t4IDAxEr169/rXJ1ha2trbcZ1dXV6SmpmLZsmXYtm1bvespKirCzp07MXfu3Fe2dffu3Xj+/DmuX7+OadOmYenSpZg+fTrOnz8vs0Zo/fr16Nq1a636Dh8+jPDw8Nde45GUlISJEydi3rx58PDwQGZmJqZNm4axY8di8+bNACpGg6qOmri6usLOzg7r16/Hjz/KPi9lZWUYNGgQiAhr167l0j09PXH+/HkAgIWFhdxI0eveS1tbW8THxyMvLw979+6Fr68vzp49C3t7e4wdOxbbt2/nZAsKKqJvd+/ejRUrViA1NRUFBQUoLy+XcdKuX7+OixcvyozsSCQSFBcXo7CwEKdPn36lezBr1izY2dnhyy+/rJP8zp07MX/+fBw6dEjh+7whaPA33m+//YbRo0dj5MiRsLe3x7p166CqqootW7YolI+KioKbmxuGDRuGJk2aoFevXhg6dGito0PvM+WPi+QTSTZdSVkJfMuKRWPWWTrIL83HqYxTNSvWNgda9K/4f/Rq8JSU0GqYG4Sl+SgsEyDl4v031AIG4+Ng/PjxOHLkCCIiImBqasqlGxoaorS0VCaKBQCys7Plpg6SkpLQvXt3+Pv7Y86cObXW6ezsjLt37wIAdHV1oaSkJBMVVl09ALB3714UFhbCx8enrk2Uw8zMDPb29hg6dCh+/vln/PDDD5BIJGjXrh3i4+O5q1+/fnWyLzw8HKmpqdDW1oaysjKUlSt+ew8cOBBd/j+ibWhoqFBHZR5QEVHn5uaGadOmoVWrVvDw8MCaNWuwZcsWZGZmKmyLQCBAmzZtuP6spNLhycjIwKlTp2QciE2bNnFtPHbsmEy517mXlQiFQtjY2MDJyQlBQUFo3bo1Fwm2YMECmT4GKqb+hg8fjj59+uDIkSOIi4vD7NmzUVpayuksKCjA/PnzZcomJCQgJSUFKioqdboHiggPD0dYWBhXpnv3iqOSdHV15Ra479q1C19//TX27NkjM93Z0DToSE/l/O+sWbO4tNrmp11dXbF9+3ZcuXIFzs7O+Pvvv3Hs2DF89dVX1dZTUlKCkpJ/18nk5ys4w6oBUdYVAzxULBerhPf/9Cq0dXdHYko0jLNE4LUE9qfsx6dWn9as3GU8cHMfcHMv0H0eGvXzgsWuH5Gi445rB5Jg28m82pX7DMbbhicWwzY2pl5lyrKz8bfXp4C0yiG6fD6sjh6B4KUpkdrqritEhG+//RYHDhxAZGQkLC0tZfKdnJwgEAhw5swZDBw4EEBF1M69e/dkRhoSExPRrVs3+Pr6yvwKr4n4+HgYGRkBqHhBOjk54cyZM9y+KVKpFGfOnMH48ePlym7evBn9+vWDnp5endtaE1KpFGVlZZBKpRCLxbCxsZGTqc2+mTNn4uuvv5Yp4+DggGXLlqFv374AKkZoFi5ciJycHG6EoNIZqZz6KSws5F7WlSgpVZxBWN3UpUQiQUJCgsy6n0qHJyUlBREREWjcuLFMGRMTE4W6XvdeVodUKuXeV/r6+nIjJFFRUbCwsMDs2bO5tKoRaQDQtm1bJCcnK7w/QN3ugSL27duHoqJ/f4xfvXoVfn5+OH/+PKytrbn0P/74A35+fti1axe8vLxqbO8755WWP78hHj58SAAoKipKJn3atGnk7Oxcbbnly5eTQCAgZWVlAkBjx46tsZ7AwEC5leh4j6K3iIgKrmRyEVz3Z5yjgiuZcjKlZWX009DPaekgL+q15BNqGdqS0vPSa1e+pU9FJNfJuURElLXrAK39+hitGnOG0mMevummMBgKeZPRW8/CwijJzp6SbJtTkp09PasSNfU2+Oabb0hLS4siIyMpMzOTuwoLCzmZsWPHkrm5OYWHh9O1a9fIxcWFXFxcuPyEhATS09OjL7/8UkZHTk4OJ7Ns2TI6ePAgpaSkUEJCAk2cOJH4fD6dPn2ak9m1axeJRCIKDQ2lpKQk8vf3J21tbZlIJyKilJQU4vF49NdffylsU0pKCsXFxdGYMWOoWbNmFBcXR3FxcVx01Pbt22n37t2UlJREqamptHv3bjI2Nqbhw4fX2Fd1ta8qeCl6q7y8nFq2bEm9evWi+Ph4On78OOnp6dGsWbM4mZCQEFJWVqY1a9ZQamoqXbhwgdq1ayfz7pg/fz6dOHGCUlNTKSYmhoYMGUIqKiqUmFgRwVpaWkr9+vUjU1NTio+Pl7kvlf2giDd1L2fOnElnz56ltLQ0unHjBs2cOZN4PB6dPHmy2roPHTpEysrK9Mcff9Ddu3dp+fLlpKOjIxOldvz4cVJWVqYffviBbt68SUlJSfTHH3/Q7Nmz63wPiIi0tLRkorZeRlGE2o4dO0hZWZlWr14t0ze5ubnV6nmZtxm99cE5PREREWRgYEAbN26kGzdu0P79+8nMzIwWLFhQbT3FxcWUl5fHXffv33/vnB4iouf/d3weBl2uVuanWfNp6SAvmvDdZ9QytCX9eu3X2hXfPlbh9CwyIyrOJ2lZGR0esphWjTlDYdOOvMEWMBjV86ZD1kszM6ng0mUqzZT/gfCmUfSjCS+F8RYVFdG4ceOoUaNGpKqqSp999hllVrGtuh9fFhYWnMzixYvJ2tqaVFRUSEdHh7p06ULh4eFy9qxcuZLMzc1JKBSSs7MzXbp0SU5m1qxZZGZmRhKJRGGb3N3dFdqTlpZGRBXOS9u2bUldXZ3U1NTI3t6eFi1aVKf7Vxf7qqLohZuenk6enp4kFotJV1eXpk6dKhdyvWLFCrK3tyexWExGRkY0fPhwevDgAZc/adIkzg4DAwPq06cPxcbGcvmKQrMrr4iIiGrtfVP30s/PjywsLEgoFJKenh517969RoenkmnTplHjxo1JXV2dBg8eTMuWLZMLzT9+/Di5urqSWCwmTU1NcnZ2pg0bNlSr8005PdU9V76+vrW2q5K36fTwiBru6O3S0lKoqqpi7969Mltc+/r6Ijc3F4cOHZIr06lTJ3To0AFLlizh0rZv3w5/f38UFBTUKYQuPz8fWlpayMvLq9cK/beNtKQcj+ZHA1LAcPonUNZRkZM5duIcbm35BcXKAuzucReNVHVw+vPTECgJalAsBVY7A09SAI8gwGUcHmw/iMPn1UA8JXwx1QH6Td/M8DeDUR3FxcVIS0uDpaWlzK64DAaDUZWavite9/3doAuZq85PV1I5/1vdPgWFhYVyjk1t87gfCnyRMoRmFTex+O4zhTI9urqgWEkFKuVlsMrXxdPip4h8EFmLYj7g+v/5/ktrAEk5TAZ7waCgYhv9K6GX3lQTGAwGg8F4b2nw6K0pU6Zg48aN2Lp1K27duoVvvvkGL168wMiRIwEAPj4+Mgud+/bti7Vr12LXrl1IS0vDqVOnMHfuXPTt25dzfj5kVJpqAwBKUnIV5guFApB5xd4gNpkV+yTUumcPALQaAqjqAnn3gaSD4AkEcOxesUDv3mMV5D1SXB+DwWAwGP8VGtzpGTx4MJYuXYp58+bB0dER8fHxOH78OLcR1b1792TCD+fMmYOpU6dizpw5sLe3x6hRo+Dh4SGz6dKHjKhpIwBASWouSKp45KpVp84AAO37L8AjIOphFB4V1HxAHgQqgPP/N86KWgkQwepLL+gUpoN4Sri68ewbawODwWAwGO8jDbqmp6F4X9f0AABJpHi04BKoRAL98Y4QmmrIyRSXlOK3EUMhkpYgsbsmrooSMLb1WAQ4BtSs/MVjYFkLoLwYGHEUaNIRiWsOIPKGFviSEnTpJoZhW2s0sjV7S61jfMywNT0MBqMu/GfX9DDk4SnxIbLSAgAU381VKKMiEkJi1gIAYJhRsafEgZQDkEglNStX0wUch1X8P6riNGC7UX0gLM2DVEmE8LNS7PwtGdeWH379hjAYDAaD8Z7BnJ73EBUbbQBASTVODwC07NgRACBOz4GWQAvZhdm4+Kj6w+w4OgQA4AF3/gL+uYO89ByUCqqMJvH4uJwkxrNktlszg8FgMP5bMKfnPYRb15OeBypTPHrj0bMzSvlCqJa/QEeqiHTbdGMTsl7UcuK0rg1g+//dSC+txpOk+wDvpceAp4Qnt5jTw2AwGIz/FszpeQ9R1hNDSUsIlBNK0hUfmaEqVkG5acV27JKkivD2uH/i4LHXA/tT9tdcQWX4evwfaGypDpBUNp8IYlP5c3wYDAaDwfiQYU7PewiPx4PIpmK0p7ia0HUAsHPtBABQf/CAO7dLCinmR8+vecTH3AUwcQIkJWj07Cja2xcC9P8RJSKAx0N42H0UPCupXgeDwWAwGB8YzOl5T+H266lmk0IA6N2rM8r4SlAvVoZunpBLl5IU95/XMD3F41UcRAoAVzei3bieGDalOTx6q8BNehIqRY+RnyfBwd9i8CKPOT4MBoPxPlFYWIiBAwdCU1MTPB4Pubm5CuWaNGmC4ODgN17/xYsX4eDgAIFAIHOawocAc3reU0TW2gCAskcvICkoVSijriZGkVHFKbpNMlW5dB54MNOoJezcrh+gbQ4UPgGu/4FGtmaw6e8Kh6XfwzlzF0TFT5H3TzEOLYtDYb7i+hmMj4WgoCB88skn0NDQgL6+Pvr374/k5GQZmeLiYgQEBKBx48ZQV1fHwIEDkZ2dzeVfv34dQ4cOhZmZGcRiMezs7LB8+XIZHZGRkeDxeHJXVpbsyO3q1avRpEkTqKiooH379rhy5QqXl56erlAHj8dDWFgYJzdhwgQ4OTlBJBLB0dFRrs3Jycno2rUrDAwMoKKiAisrK8yZMwdlZWW19ldN9lWFiODp6Qkej4eDBw/K5N27dw9eXl5QVVWFvr4+pk2bhvLychmZHTt2oHXr1lBVVYWRkRH8/Pzw5MkTLj80NFSuD6qGQJeVlWHGjBlwcHCAmpoajI2N4ePjg0ePat737MmTJ+jduzeMjY0hEolgZmaG8ePHIz//3+UImZmZGDZsGJo1awY+n49JkyYp1JWbm4uAgAAYGRlBJBKhWbNmOHbsWI31b926FefPn0dUVBQyMzOhpaVVo3wlGzZsQJcuXWp0lhYuXAhXV1eoqqpCW1tboZ4pU6bA0dERaWlpCA0NBVD78/S+wJye9xQlDSEEhmoAKjYqrI4WLt0AABZZatwUFw88lEhqGaFRUgY6jKv4f/TqivO5AChpacHml3loc3MVRCXP8CyrEIeXx6GoGseLwfgYOHv2LAICAnDp0iWcOnUKZWVl6NWrF168eMHJTJ48GX/++SfCwsJw9uxZPHr0CAMGDODyY2JioK+vj+3btyMxMRGzZ8/GrFmzsGrVKrn6kpOTkZmZyV36+vpc3u7duzFlyhQEBgYiNjYWrVu3hoeHB3JycgAAZmZmMmUzMzMxf/58qKurw9PTU6YePz8/DB48WGGbBQIBfHx8cPLkSSQnJyM4OBgbN25EYGBgjX1Vm31VCQ4OBo/Hk0uXSCTw8vJCaWkpoqKisHXrVoSGhmLevHmczMWLF+Hj44NRo0YhMTERYWFhuHLlCkaPHi2jS1NTU6YvMjIyuLzCwkLExsZi7ty5iI2Nxf79+5GcnIx+/frV2EY+nw9vb28cPnwYd+7cQWhoKE6fPo2xY8dyMiUlJdDT08OcOXPQunVrhXpKS0vRs2dPpKenY+/evUhOTsbGjRthYmJSY/2pqamws7NDy5YtYWhoqLAPFVFYWIjevXvj+++/r1amtLQUX3zxBb755psa6+/WrRtMTU1lHKOanqf3hlc6pvQD53VPaX1XPPszle7POEdPwpKrlcnLf0FBg71p6SAv+v3EH+R33I9ahrakqZFTa6+gOJ8oyKziBPZbR2WyHm/cSNccO9GGkftp1ZgztOuny1RUUPq6TWJ8xLzpU9YbkpycHAJAZ8+eJSKi3NxcEggEFBYWxsncunWLAFB0dHS1esaNG0ddu3blPis6tfplnJ2dKSAggPsskUjI2NiYgoKCqi3j6OhIfn5+CvMCAwOpdevW1ZatyuTJk6ljx441ytTVvri4ODIxMaHMzEy5E76PHTtGfD6fsrKyuLS1a9eSpqYmlZSUEBHRkiVLyMrKSkbnihUryMTEhPscEhIid/p4bVy5coUAUEZGRr3KLV++nExNTRXmubu708SJE+XS165dS1ZWVlRaWvfv1pdPMXd3dyciouzsbPr0009JRUWFmjRpQtu3bycLCwtatmyZnI66PGeK+k7RqfQvn8Jen+epOt7mKetspOc95t91PbnVHqaqqaGKEiNbAMDfx69juNEI8MDDifQTSHycWHMFIg3AqeKMM5xbCqSdA/IeAgB0/Pyg52SLNvHLIZS8wOP7BTi8PB4lhbUPbTMY74KCZ8V4kPwMBc+K33ndeXl5AAAdHR0AFaM4ZWVl6NGjByfTvHlzmJubIzo6ukY9lTqq4ujoCCMjI/Ts2RMXL/67/1ZpaSliYmJk6uHz+ejRo0e19cTExCA+Ph6jRo2qXyNf4u7duzh+/Djc3d2rlamrfYWFhRg2bBhWr14NQ0P5SNHo6Gg4ODhwxxEBgIeHB/Lz85GYWPG95uLigvv37+PYsWMgImRnZ2Pv3r3o06ePjK6CggJYWFjAzMwM3t7eXPnqyMvLA4/Hq3ZqRxGPHj3C/v37a+wbRRw+fBguLi4ICAiAgYEBWrZsiUWLFkEiqX6j2f3792P06NFwcXFBZmYm9u+viNYdMWIE7t+/j4iICOzduxdr1qxROLr2OlSOImpqaiI4OBiZmZnv/8jOSzCn5z1GaKkFKPEgyS1B+ZPqv9iVxeoAAPWHCYhZ+AvcHzsBAJbFLqu9kvZjAJ4S8CgG2NoXCG4JxP4OHp8P48U/Q0tNCseYZRDySvHPvec4vOI6SorKa9fLYNQBIkJZiaTeV0LkA/z+fRQOLYvD799HISHyQb11VPdDojakUikmTZoENzc3tGxZcfhvVlYWhEKh3IvSwMBAbj1OJVFRUdi9ezf8/f25NCMjI6xbtw779u3Dvn37YGZmhi5duiA2NhYA8PjxY0gkEhlnoLZ6Nm/eDDs7O7i6ur5Se11dXaGiooKmTZuiU6dOWLBgQbWydbVv8uTJcHV1hbe3t0I9WVlZCnVU5gGAm5sbduzYgcGDB0MoFMLQ0BBaWlpYvXo1V8bW1hZbtmzBoUOHsH37dkilUri6uuLBgwcK6y0uLsaMGTMwdOjQOh1xMHToUKiqqsLExASamprYtGlTrWWq8vfff2Pv3r2QSCQ4duwY5s6di19//RU//fRTtWV0dHSgqqrKtVlHRwd37tzBX3/9hY0bN6JDhw5wcnLC5s2bUVRUVC97akNJSYmbTtPS0oKhoSHEYvEbreNto9zQBjCqhy9UgshCEyV/56Ek5RkEuvIPV2r6AwhT/10kyAfB/MpjaHYX4XLmZUQ9ioKrcQ1fdkT/hqsDFXv2/DkJsO4O5cYmMF6yBOUjR8LxyhLEu36PnPR8HFkZj74THCFUYY8P4/UoL5Viw8TXO+yWCDi36w7O7bpTr3L+y90hECnVu76AgADcvHkTFy5cqHfZSm7evAlvb28EBgaiV69eXLqtrS1sbW25z66urkhNTcWyZcuwbdu2etdTVFSEnTt3Yu7cua9s6+7du/H8+XNcv34d06ZNw9KlSzF9+nScP39eZo3Q+vXr0bVr11r1HT58GOHh4YiLi3tlmwAgKSkJEydOxLx58+Dh4YHMzExMmzYNY8eOxebNmwFUjAa5uLhwZVxdXWFnZ4f169fjxx9/lNFXVlaGQYMGgYiwdu1aLt3T0xPnz58HAFhYWMiMFC1btgyBgYG4c+cOZs2ahSlTpmDNmjV1boNUKoW+vj42bNgAJSUlODk54eHDh1iyZAkCAwOxY8cOjBkzhpP/66+/0KlTJzk9t27dgrKyMpycnLi05s2b12u06mOBvbXec0RNtVHydx6KU3Kh7mIsl3/nThr4kP3Fygeho8gdx3ASwTHB6GDUAfyXd12u5GmqfBpJgKd/A1omUOvQHrrffAOsWQPHuGDEO01F1t/5OLLqOvp+6/hKLw0G40Nl/PjxOHLkCM6dOwdTU1Mu3dDQEKWlpcjNzZV50WRnZ8tN3yQlJaF79+7w9/fHnDlzaq3T2dmZc7B0dXWhpKQkExVWXT0AsHfvXhQWFsLHx6c+zZTBzKwiEtTe3h4SiQT+/v6YOnUq2rVrh/j4eE7OwMAAIpGoVvvCw8ORmpoq90IeOHAgOnXqhMjISBgaGspFfFXqrNQTFBQENzc3TJs2DQDQqlUrqKmpoVOnTvjpp59gZGQk1xaBQIA2bdrg7t27MumVDk9GRgbCw8NlRnk2bdrEjZgIBAKZcoaGhjA0NETz5s2ho6ODTp06Ye7cuQrrVoSRkREEAgGUlP79HrWzs0NWVhZKS0vRr18/tG/fnsurbYEzo3aY0/Oeo2LTCPknMlDydy5IQuApya7Sb9bMEjfBk3F8pOBhSAc/nI25iFtPb+FE+gl4Wnq+rLoCHeuKYyiq7srM4wM6VtxH3YBxKLx6Fbh6Fe0yd+GayVBk3s3D0dXX4TW+NQRC5vgwXg1lIR/+y+u3DqIgtwR//HAJVWeneDxg6A8doK4tqlfddYWI8O233+LAgQOIjIyEpaWlTL6TkxMEAgHOnDmDgQMHAqiIwLp3757MSENiYiK6desGX19fLFy4sE51x8fHcy9RoVAIJycnnDlzhtsfRSqV4syZMxg/frxc2c2bN6Nfv37Q09Orc1trQiqVoqysDFKpFGKxGDY2NnIytdk3c+ZMfP311zJlHBwcsGzZMvTt2xdAxQjNwoULkZOTw0WunTp1CpqamrC3r9iJvrCwEMrKsq+wSuehuqlLiUSChIQEmXU/lQ5PSkoKIiIi0LhxY5kydXU0pP+PgC0pqfveZm5ubti5cyekUin4/Irn8c6dOzAyMoJQKIRQKISGhkYtWipGdcrLyxETE4NPPvkEQMXzV93+PR8zzOl5zxGYqIMnVgYVlaP04XOIzGXnma2bmKKxxzA8ObGTc3wKLduhjW0LjCweiVXxq7AybiV6mPeAQEkgX4GWCdB3ecWUVuU0l1BD5jwunpISjJcuQVr/z6By4yzcbO1wgRzx8E4ujq25Aa+AVlAWMMeHUX94PF69RwsbGaiiy5fNEbnjNkha8ah2Gd4cjQxUay/8igQEBGDnzp04dOgQNDQ0uHUlWlpaEIvF0NLSwqhRozBlyhTo6OhAU1MT3377LVxcXNChQwcAFVNa3bp1g4eHB6ZMmcLpUFJS4pyS4OBgWFpaokWLFiguLsamTZsQHh6OkydPcrZMmTIFvr6+aNeuHZydnREcHIwXL15g5MiRMjbfvXsX586dq3bPl7t376KgoABZWVkoKiriRm3s7e0hFAqxY8cOCAQCODg4QCQS4dq1a5g1axYGDx4sN+JRldrsqxwdeRlzc3POmezVqxfs7e3x1Vdf4ZdffkFWVhbmzJmDgIAAiEQVjm3fvn0xevRorF27lpvemjRpEpydnWFsXDEqvmDBAnTo0AE2NjbIzc3FkiVLkJGRwTldZWVl+PzzzxEbG4sjR45AIpFw90VHRwdCoVDOTgA4duwYsrOz8cknn0BdXR2JiYmYNm0a3Nzc0KRJE06usk8LCgrwzz//ID4+HkKhkHPcvvnmG6xatQoTJ07Et99+i5SUFCxatAgTJkyotn8VYWtri969e2PMmDFYu3YtlJWVMWnSJLn1NllZWcjKyuJGuhISEqChoQFzc3NuQf29e/fw9OlT3Lt3DxKJhGuDjY0N1NXVq7WhtufpveGVY8o+YD6UkPVKHm9LpPszzlHe6epDKO+m3adF0+fQ0kFeNHN0AEmlUnpR+oLcd7lTy9CWtPPWzporyX1AdPs40fI2FSHs692JSl7IiDyPjKQk2+aUZNucUnaepHUTImnVmDN0eEUc5Wa/oPu3n9Lzpx9+ODLj7fCmQ9afPy2iB+/omcNLYbpQEK5bVFRE48aNo0aNGpGqqip99tlnlJmZyeUHBgYq1GFhYcHJLF68mKytrUlFRYV0dHSoS5cuFB4eLmfPypUrydzcnIRCITk7O9OlS5fkZGbNmkVmZmYkkUgUtunl0OfKKy0tjYiIdu3aRW3btiV1dXVSU1Mje3t7WrRoUZ3uX13sqwpeClknIkpPTydPT08Si8Wkq6tLU6dOpbKyMhmZFStWkL29PYnFYjIyMqLhw4fTgwcPuPxJkyZxdhgYGFCfPn0oNjaWy1cUgl15RUREVGtveHg4ubi4kJaWFqmoqFDTpk1pxowZciHgtd1vIqKoqChq3749iUQisrKyooULF1J5eXmN/TVx4kQuVL2SzMxM8vLyIpFIRObm5vT777/LhaxX9wxWfY59fX1r7Q8tLS25UPXanqf68DZD1nlErxjC8AGTn58PLS0t5OXl1WmFfkNTcCkTuQfvQmipCf0xije5AoAH9x5i17Qx4AFwmrYEXdrZYfft3fjp8k/QUdHBXwP+gqqgll/DT9OAjd2AoqdAi8+Az0Mq5g7+T/YvS/B0yxbwNTWhErwdx/94iPKyf6fGeDygy5fNYe8mv/6I8XFTXFyMtLQ0WFpayuyKy2AwGFWp6bvidd/fLGT9A6Byv57Se88hLal+/wZTcxOUGTYFAJw6cBgAMKDZAJhrmONp8VNsTdpae2U6lsDg7QBfACQeAM4ulsnWnzQRKq1aQZqfD1oxD12HN5PJJwIid9xukL1TGAwGg8GoCeb0fAAo6ahAqZEIkBBK0vJqlHXx9AIAiNJi8ODJcwj4Anzb9lsAQOjNUDwtflp7hU3cgE9/q/h/ZBBwcz+XxRMKYfLbr+BraKAoPh6lp/6UK05SIDe7sI6tYzAYDAbj3cCcng8AHo8HlaaNAAAlKdWfug4AnXp0QZlQDWqSQvyx7wQAoJdFL9j/j73zDo+i+hrwO5veK6mkUEKTXgWkCVIFFPiBoKKCIgoK6KeAKAgiqKCCgo2OoDQBUZDeO6RAaCGEhARIIb233fv9sWRh2XQSEvS+zzNPsnduOTO7yZw99xSnRmTmZ/LL+V9Kt2jLkfcqsW99C275606Z1qyJ+90cF3mbVlJY1ZeTW6+TcqdiE2NJJBKJRPIwSKXnMcGsrj0A2deSi+1nZGyMe2tt8qroUwfIzlOjUlRMajUJgPUh67mZVng2UgOemQV+PSE/G34fAan3Kg/b9u6F/fAXMM9JpmHUlntuPwqojBViI1JZN/s0F4/cKnfmW4lEIpFIKhKp9DwmmNWxBwXyYzNRpxZf8bzfkOcA8Ei/wdaj2uyhT7o/SXv39uRr8lkUZFjVuVBURjB4GdRoCOkx8PtwyL23beU6ZQpm9evjFraX9sen0SJoAR1OfMKzbZNwr2tHfo6ag2tD+HvReTKSS5+7QiKRSCSSykAqPY8JRlYmmHhocyRkhyUX29fZsyYqTz8U4Nj27TpLy8RWEwHYcX0HIYkhpVvY3BaG/w6WThAdpN3qupuES2Vmhuu0j7TdcpJxSA7FPDuR9C8+od8IDzoOqYuRsYrIiwn8PusUV0/HSKuPRCKRSKoMqfQ8Rpjf3eIqya8HoNPdzKZOt4Pwj0gAoJFTI/r49kEgWBCwoPQL3x/RdWmrfkSXphAlRqMhPyqK5j28GfpRG2p425CTmc+e5ZfYteQCWenFW6okEolEIqkMpNLzGGF2N3Q9OzS5RItJ86c6oTG1xEadwcate3Xt77R4B2PFmKO3jnIm5kzpF/fpAM/erdp+6AsI3gSAqa8PqAw/RoqpNmOro4cVgye3om3/WqhUCmEBd/h91mnCz8eXfm2JRCKRSCoAqfQ8Rpj52IGxCk1aLvlxxYeEG5uYULtDVwAygo8Sl6bNm+Nl68WQekMA+Nb/27JtN7V8+V5E15/j4KY/Jm5uuM+aaaD43Bw3nswAbRVlIyMVbfrVYvDkVji4W5GVmsuOH86zb9UlcrLySU/K5mZIksztI5FIJJJKRSo9jxGKiQqzWtoMlNmhySX27zpAu8Xlk3GD3/YH69rfbPYmFsYWBMcHszdyb1HDC+eZWeDXSxvRtW4EpNzCfsgQ6u7fh/eqVfhu3IBZo4aoExOJfPVVUv7erhvq4mPL0I9a0+IZb1DgyokYfv34OKs+Os6f3way+qPjXDp2u5jFJRKJRFIRrFy50qDSfWn49NNPad68eYXL86iQSs9jhnndu/l6SghdB3Dy9MLcqy4qBEH7dpOn1jogO1s488oTrwDwXcB35GvySy+AyggGL70X0bVuOORmYOLmhlW7tlg0aYLvr79i/fTTiNxcbv/f/3Fn8WKdRcnYxIgOg+vy/PstsXYwIycjn4IC8TKbs6S6MnfuXNq0aYONjQ0uLi4899xzhIToBwNkZ2czbtw4nJycsLa2ZvDgwcTGxurOnzt3juHDh+Pl5YWFhQUNGzZk4cKFenMcPHgQRVEMjoIimAUsXrwYX19fzM3NadeuHadPn9adi4iIKHQORVHYuHGjrt+7775Lq1atMDMzK/QhFhISQrdu3XB1dcXc3JzatWvz8ccfk5eXV+L9Kk6++xFC0KdPHxRFYevWrXrnIiMj6devH5aWlri4uPDBBx+Qn6//v2rt2rU0a9YMS0tL3N3dGTVqFAkJCbrzK1euNLgH95c1yMvLY/LkyTRp0gQrKys8PDwYOXIkt28X/+UrISGB3r174+HhgZmZGV5eXowfP57U1NQyyQewceNGGjRogLm5OU2aNCmyQOz9dO3a1eC6xo4dq9dn3759dOjQARsbG9zc3Jg8ebLB/fsvIpWex4yCfD0511MQak3xnYFOz9619iRc4J/ge3/IrzR6BQczByJSI9hybUvZhDC3hRHr7kZ0ndNGdCVHQfhhSLmFysqKmt9/h+Pdqsrx3y/i9uTJaHLvOTB71LWny4j6BlMLDaTEyaSGkurFoUOHGDduHCdPnmTPnj3k5eXRs2dPMjIydH0mTZrEX3/9xcaNGzl06BC3b99m0KBBuvP+/v64uLiwZs0aLl68yLRp05g6dSqLFhmmkAgJCSE6Olp3uLi46M6tX7+e9957jxkzZhAQEECzZs3o1asXcXFxAHh5eemNjY6OZubMmVhbW9OnTx+9dUaNGsWwYcMKvWYTExNGjhzJ7t27CQkJYcGCBSxZsoQZM2YUe69Kku9+FixYgKIYpjdVq9X069eP3Nxcjh8/zqpVq1i5ciXTp0/X9Tl27BgjR45k9OjRXLx4kY0bN3L69GneeOMNvblsbW317sWNGzd05zIzMwkICOCTTz4hICCAzZs3ExISwoABA4q9RpVKxcCBA9m2bRtXr15l5cqV7N27V0/xKI18x48fZ/jw4YwePZrAwECee+45nnvuOS5cuFDs+gBvvPGG3nV99dVXunPnzp2jb9++9O7dm8DAQNavX8+2bduYMmVKifP+6ylXmdLHnMetyvr9aNQacWvWcRE1+bDIDk8usX9uTraY/9IQMX9oPzFq9q9659ZcWiMar2wsuq3vJjLzMssuTMRxIWY6aauyz7DT/vzUXgj/VbouievWi0uNnhCX6jcQ4SNeFHmJibpzaYlZYvHYfWLRm/eOxWP3yUrt/1Iqusp6avwdcSP4nEiNv1Mh85WFuLg4AYhDhw4JIYRITk4WJiYmYuPGjbo+ly9fFoA4ceJEkfO8/fbbolu3brrXBw4cEIBBte77adu2rRg3bpzutVqtFh4eHmLu3LlFjmnevLkYNWpUoedmzJghmjVrVuTY+5k0aZJ46qmniu1TWvkCAwOFp6eniI6ONqiyvmPHDqFSqURMTIyu7ccffxS2trYiJydHCCHEvHnzRO3atfXm/O6774Snp6fu9YoVK4SdnV2prq2A06dPC0DcuHGjTOMWLlwoatasqXtdGvmGDh0q+vXrp9enXbt24s033yx2rS5duogJEyYUeX7q1KmidevWem3btm0T5ubmIjU1VQhx795s2bJF1K1bV5iZmYmePXuKyMhIvXFz584VLi4uwtraWowaNUpMnjzZ4POyZMkS0aBBA2FmZibq168vFi9erHc+MjJS/O9//xN2dnbCwcFBDBgwoNjq65VZZV1aeh4zFJWiTVRI6fx6TEzNaNCpGwCm105x4da92l3/q/c/PK09uZN1h7WX15ZdGJ/20OPTuy8K9qg08NdESLkFgMOwoXj98rO2Vpe/PxHDXiDn+nUArB3M6fpSg/uLuOPVyBFrB1mB+7+CEIK87OwyH0G7trNk3Gts/Owjlox7jaBd28s8h3iInFEpKdq/I0dHR0BrxcnLy6NHjx66Pg0aNMDb25sTJ04UO0/BHPfTvHlz3N3deeaZZzh27JiuPTc3F39/f711VCoVPXr0KHIdf39/goKCGD16dNku8gGuXbvGzp076dKlS5F9SitfZmYmI0aMYPHixbi5uRnMc+LECZo0aYKrq6uurVevXqSmpnLxojbhavv27YmKimLHjh0IIYiNjWXTpk307dtXb6709HR8fHzw8vJi4MCBuvFFkZKSgqIoZfJ3uX37Nps3b9a7N6WR78SJE3r3quA6i/vMFLB27VqcnZ1p3LgxU6dOJTPzXnBLTk6OQXVyCwsLsrOz8fe/V1IoMzOTzz//nNWrV3Ps2DGSk5N54YUXdOc3bNjAp59+ypw5czh79izu7u788MMPBnJMnz6dzz//nMuXLzNnzhw++eQTVq3SFrjOy8ujV69e2NjYcOTIEY4dO4a1tTW9e/cmN/fRpy8xfuQrSh4acz8Hss7Ha/P1PONTYv92vftyZd8OamXeYM2BYL546SkATI1MGd9iPFOPTGXp+aXUsatDQ6eGuFkZ/hMqEvemhm1CDYnXwc4TAOuOHfH9/Teixr5FXmQkES8Mp+Z3C7F68kkadfTAu5EjV07GcOrP69y8kkRSTAYOblall0Hy2JKfk8N3rwx5qDmEEOxb/iP7lv9YpnHvrtqEiXnZFWyNRsPEiRPp2LEjjRs3BiAmJgZTU1ODB6Wrq6uBP04Bx48fZ/369Wzffs/Z393dnZ9++onWrVuTk5PD0qVL6dq1K6dOnaJly5bEx8ejVqv1lIGCda5cuVLoOsuWLaNhw4Z06NChzNcK0KFDBwICAsjJyWHMmDHMmjWryL6llW/SpEl06NCBgQMHFjpPTExMoXMUnAPo2LEja9euZdiwYWRnZ5Ofn0///v1ZvHixbkz9+vVZvnw5TZs2JSUlhfnz59OhQwcuXrxIzZo1DdbNzs5m8uTJDB8+HFtb2xLuDAwfPpw///yTrKws+vfvz9KlS3XnSiNfUddZ1GemgBEjRuDj44OHhwfnz59n8uTJhISEsHmztjh0r169WLBgAb///jtDhw4lJiZG975FR0fr5snLy2PRokW0a9cOgFWrVtGwYUNOnz5N27ZtWbBgAaNHj9YpzLNnz2bv3r1kZ9/zu5wxYwZff/21biu3Vq1aXLp0iZ9//plXXnmF9evXo9FoWLp0qW4rc8WKFdjb23Pw4EF69uxZ4n2uSKSl5zGkwK8n92YamuySHdOcvX2x9amLERquHz9IUsY97bpvrb64WrqSkZ/BuwfepdcfvdgcurmY2R7AsQ4oD3yMFCNwrP2AzHXxXb8Oi+bN0aSmEvn6GyRv0ub6sXYwp3UfX3yaOKFRC45sCJWZmyXVlnHjxnHhwgXWrVtX7jkuXLjAwIEDmTFjht4//fr16/Pmm2/SqlUrOnTowPLly+nQoQPffvttudbJysrit99+eygrz/r16wkICOC3335j+/btzJ8/H4AjR45gbW2tO9auLZ21eNu2bezfv58FCxaUWyaAS5cuMWHCBKZPn46/vz87d+4kIiJCz6+mffv2jBw5kubNm9OlSxc2b95MjRo1+Pnnnw3my8vLY+jQoQgh+PHHewp0nz59dNf4xBNP6I359ttvCQgI4M8//yQsLIz33nuvTPKVxJw5c/TucWRkJABjxoyhV69eNGnShBdffJHVq1ezZcsWwsLCAOjZsyfz5s1j7NixmJmZUa9ePZ2FSXVfehFjY2PatGmje92gQQPs7e25fPkyAJcvX9YpRPff0wIyMjIICwtj9OjRenLOnj1bJ8u5c+e4du0aNjY2uvOOjo5kZ2fr+jxKpKXnMcTYwRxjZwvy47PICUvB4gmnEsc82acfu39aSP2US6w7c4O3uvoBEJcZR1zmPQdDjdAw88RMOnh0KJ3Fx84T+i+EvyZot7YAukzRWXn05HZywnvVSqI/mkbq9u1Ef/wJuRER2I8YQV5kFE92dSfqciJRlxIJD4qndosapbshkscWYzMz3l21qUxj0hITWPneWD3FWFGpePXrH7FxLPlv4f61y8r48eP5+++/OXz4sJ6lwM3NjdzcXJKTk/WsPbGxsQbbN5cuXaJ79+6MGTOGjz/+uMQ127Zty9GjRwFwdnbGyMhILyqsqHUANm3aRGZmJiNHjizLZerh5eUFQKNGjVCr1YwZM4b333+f1q1bExQUpOvn6uqKmZlZifLt37+fsLAwA6vY4MGD6dSpEwcPHsTNzc0g4qtgzoJ55s6dS8eOHfnggw8AaNq0KVZWVnTq1InZs2fj7u5ucC0mJia0aNGCa9eu6bUXKDw3btxg//79elaepUuXkpWVpRt/P25ubri5udGgQQMcHR3p1KkTn3zyCe7u7qWSz83Nrdh7NXbsWIYOHao75+HhYXBNgE4xuXbtGnXq1AHgvffeY9KkSURHR+Pg4EBERARTp06ldu3ahc5RHtLT0wFYsmSJgXJkZGSk69OqVatCleIaNR79/3hp6XlMuVd1veSSFAAN2ndCMTXHPj+VXXuOob5bPiIyNRKBvlVFIzREpUWVXpiWI2HiBfC6+6GPOKyNPy8ElZkZHvPn4TxuHAAJS5cR9nR3Il99lTv/60MjL+2+9JGNV8nLVZdeBsljiaIomJibl+lw9PDkmTHvoNz9xqqoVDzzxngcPTzLNE9hUUNFIYRg/PjxbNmyhf3791OrVi29861atcLExIR9+/bp2kJCQoiMjNT7Znzx4kW6devGK6+8wueff16qtYOCgnQPcFNTU1q1aqW3jkajYd++fXrrFLBs2TIGDBhQYQ8XjUZDXl4eGo0GCwsL6tatqztsbGxKJd+UKVM4f/48QUFBugO0VpMVK1YAWmtCcHCwXsTXnj17sLW1pVGjRoDWH0X1QFLUggdtUZZitVpNcHCwnkJUoPCEhoayd+9enJz0FWdPT0/dNfr4FO1OoLlbkzAnJ6fU8rVv317vXhVcZ8G9cnR01LvHxsaF2ykK7uGDip6iKHh4eGBhYcHvv/+Ol5cXLVu21J3Pz8/n7NmzutchISEkJyfTsGFDABo2bMipU6f05jx58qTud1dXVzw8PLh+/bqenHXr1tX9jbRs2ZLQ0FBcXFwM+tjZ2RV6PZVKudyfH3Me5+itAjKD74ioyYdF9PwzpR6z8+dFYv7QfmLMaxPEzgvRQgghotOjRdNVTUXjlY11R9OVTUV0enTZhUoMF+IzF20UV/CmErsnrF4tLtVvoHcEP9FMrPzwsFj05j5x8s+wsssgqbZURvRW5IVHE7311ltvCTs7O3Hw4EERHR2tOzIz70U9jh07Vnh7e4v9+/eLs2fPivbt24v27dvrzgcHB4saNWqIl156SW+OuLg4XZ9vv/1WbN26VYSGhorg4GAxYcIEoVKpxN69e3V91q1bJ8zMzMTKlSvFpUuXxJgxY4S9vb1epJMQQoSGhgpFUcQ///xT6DWFhoaKwMBA8eabb4p69eqJwMBAERgYqIuOWrNmjVi/fr24dOmSCAsLE+vXrxceHh7ixRdfLPZelVa+++GB6K38/HzRuHFj0bNnTxEUFCR27twpatSoIaZOnarrs2LFCmFsbCx++OEHERYWJo4ePSpat24t2rZtq+szc+ZMsWvXLhEWFib8/f3FCy+8IMzNzcXFixeFEELk5uaKAQMGiJo1a4qgoCC996XgPhTG9u3bxfLly0VwcLAIDw8Xf//9t2jYsKHo2LFjmeQ7duyYMDY2FvPnzxeXL18WM2bMECYmJiI4OLjIta9duyZmzZolzp49K8LDw8Wff/4pateuLTp37qzX76uvvhLnz58XFy5cELNmzRImJiZ693jFihXCxMREtG3bVpw8eVKcPXtWPPnkk+LJJ5/U9Vm3bp0wNzcXy5cvFyEhIWL69OnCxsZGL3pryZIlwsLCQixcuFCEhISI8+fPi+XLl4uvv/5aCCFERkaG8PPzE127dhWHDx8W169fFwcOHBDvvPOOiIqKKvQaKzN6Syo9jynqzDwRNeWwiJp8WOQlZZdqTGx4mJg/tJ/4cmh/MXLxvX+if1z9Q0/x6fR7J5GdX7o5DTgwV6v0zG8gRHZasV3TT5w0UHou1W8gLq47Jha9uU/8OO6ASI7LKJ8ckmpHRSs9jxK04YkGx4oVK3R9srKyxNtvvy0cHByEpaWleP7550V09L0vDzNmzCh0Dh8fH12fL7/8UtSpU0eYm5sLR0dH0bVrV7F//34Deb7//nvh7e0tTE1NdQ+tB5k6darw8vISarW60Gvq0qVLofIUhBKvW7dOtGzZUlhbWwsrKyvRqFEjMWfOnFK9f6WR734eVHqEECIiIkL06dNHWFhYCGdnZ/H++++LvLw8vT7fffedaNSokbCwsBDu7u7ixRdfFDdv3tSdnzhxok4OV1dX0bdvXxEQEKA7Hx4eXuR7e+DAgSLl3b9/v2jfvr2ws7MT5ubmws/PT0yePNkg1UBJ8gkhxIYNG0S9evWEqampeOKJJ8T27duLvVeRkZGic+fOwtHRUZiZmYm6deuKDz74wOB51q1bN5187dq1Ezt27NA7XxCy/scff4jatWsLMzMz0aNHD4NQ/c8//1w4OzsLa2tr8corr4gPP/zQIGR97dq1onnz5sLU1FQ4ODiIzp07i82bN+vOR0dHi5EjRwpnZ2dhZmYmateuLd54440in8GVqfQoQpTOY/R+k1hpUBSFbdu24elp6NtR1aSmpmJnZ0dKSkqpPPSrK3GLg8iNSsNhiB9WrUsXcbX8wwkk3QjjqGN7vvr0XfxcbQCIyYghNCmUT459QkJ2AuOaj2Nss9I73OnIy4LF7SD5BnScoC1bUVTXmBiuPd0dNPclWVQU6uzfx84NMURdTsKniRPPjmtWdjkk1Y7s7GzCw8OpVauWQTitRCKRFFDc/4qHfX6X2qcnKCiI7t27M3DgwBKPAQMGcPHiRd3epqRyuL/qemlp3VObkfWJtMusOh6ua3ezcqNTzU5MbjsZgKXBS7mVfqvsQplYQJ8vtb+fWAx3rhbdtZBipYqJCeTk0GlYPVRGCjeCE4iQFdklEolEUgGUKXrrgw8+0EuHXhxff/11uQSSlB7zug6k7Y8i51oyQiNQVCU7Zjbo2Jl9q5bgkJvM9mNnSO3TEFvzexEJvX17s/HqRs7EnGHemXks6Lag7ILV76MtShq6C/75AF7eCkU4jdoPGYLVU0+REx7OnQULyT53jpvvvYfvunU06+5F4O5Ijmy4Ss2GDhibGJVdFolEIpFI7lJqS094eHiZIgAuXbpUrKe75OEx9bZBMVWhycgjLyaj5AGAqYUljZ7SZg2tk3iRTWdv6p1XFIWpbadipBixL3Ifx24dK2yakunzBRiZwfWDcOnPYruauLlh3b49Nb9biJGDAzmXLhP31Txa9/XFys6U1PhsAndHlk8OiUQikUjuUmqlx8fHp0whnl5eXrrwPEnloBirMKulDfkrTdX1App17w1A3cwwfj9yGY1G363Lz8GPEQ1HAPDF6S/IVZcjVbhjba1PD8CuaZBbslJm4uqKxxdzAUhas4bsIwfoOESbT8h/5w1S42UhUolEIpGUn1IrPefPn9flISgNFy9elGXsHwFmdR0AyC6D0uNaxw8nb1+MhRqLyHMcDr1j0OetZm/hZO5ERGoEv176tXzCPTUJ7Lwh9SYcnl+qIdZduuA4ehQA0dM+xtstD8969qjzNBzbdK2E0RKJRCKRFE2plZ4WLVqQkJBQ6onbt2+vS5ktqTzM7zoz54anIPJKp5QqikLzHlqH5sZpl1h1LNygj42pDe+3fh+An8//TExG8bVgCsXUEnprLTcc/x7iS6e0uEyciEWzZmhSU4n+v//jqcG1UVQK14PuEHmx9J9BiUQikUjup9SOzEIIPvnkEywtLUvVvyqqp/4XMXa1RGVjgiYtj5zIVMzvVmAviYadunLw12U45SVxIPgCEfGN8XXWL/L5bO1n2Xh1I4FxgXx99mvmdZlXdgEb9IO6PeDaXvjnQ3jpjyKdmgtQTEzw+PprwgcNIuvcOSw3LqVpt4Gc2xfF4fVXGf5JO4xMZDJxiUQikZSNUj85OnfuTEhICIGBgaU62rdvj4WFRWXKLkFrtTG/u8WVU4bQdTNLKxp06AxAo9RL/HryRqFzf9TuI1SKip0ROzkdfdqgTykEhD5fgZEphO2DK3+XaphpTU/cZ38GaEtVNHK4jYWtKSlxWQTtkxZEiUQikZSdUlt6Dh48WIliSB4Gs7r2ZAbGkXnhDlbt3TG2K10hxSbde3Hx0F78MsJYdzKUjnWdaOhui7vdPWW1gWMDhtYbyrqQdcw5NYeNAzZiojIpZtZCcKoDHd6FI/Nh51So01279VUCtj17kvniiyStXUv8J1Np9+kKDm65xdkdEdRv54a1g0xwJ5FIJJLSI/cI/gVoMvIAUMdnE/PFaTLOlM7/xqNeA5xqemMi8vFKvMKolWfp+MV+1p/Rt6SMbzEeBzMHwlLC+P3y7+UTstP7YOcFKVFw9JtSD3P58APMGjZEnZSE1ZrPcattS36udGqWSCT/XWJiYnjmmWewsrIyqFb/sGzdupW6detiZGTExIkTK3Tu6oBUeh5z8lNySPnnPkdkAUmbQ8lPKTkbtqIo+HR8GoAn0i6BEGgEfLT5AtEp98LD7czsmNhqIgA/nPuBO5mG0V4lYmoJveZofz+2EBLCSjVMZWaG5zdfo7K0JOvMGZoQiKLANf84bl5JLLscEkk5mDt3Lm3atMHGxgYXFxeee+45QkJC9PpkZ2czbtw4nJycsLa2ZvDgwcTGxurOnzt3juHDh+Pl5YWFhQUNGzZk4cKFenMcPHgQRVEMjpgY/S8yixcvxtfXF3Nzc9q1a8fp0/e2niMiIgqdQ1EUNm7cqOv37rvv0qpVK8zMzGjevLnBNYeEhNCtWzdcXV0xNzendu3afPzxx+Tl5ZV4v4qT736EEPTp0wdFUdi6daveucjISPr164elpSUuLi588MEHBhHBa9eupVmzZlhaWuLu7s6oUaP0Am5WrlxpcA/uL2uQl5fH5MmTadKkCVZWVnh4eDBy5Ehu375d4jUWkJCQQM2aNVEUheTk5EL7HDt2DGNjY4P7fPjwYfr374+Hh0eh96Aovv32W6KjowkKCuLq1aulfs8L7knTpk0xNzfHxcWFcePG6Z1/8803GTJkCFFRUXz2mdbF4Pz583Tq1Alzc3O8vLz46quv9Mbk5eUxa9Ys6tSpg7m5Oc2aNWPnzp2lupZHjVR6HnPy47O05fHuR9xtLwUqv9bkK0bUyE2gcdpFrPLTUQtBRHymXr/n6j5HE+cmZORl8K3/t+UTtmF/qPM0qHNh5xQoXdk3zGrVwm3mTADUK76lfn3truzh9aGo1aVPoyCRlJdDhw4xbtw4Tp48yZ49e8jLy6Nnz55kZNzLPzVp0iT++usvNm7cyKFDh7h9+zaDBg3Snff398fFxYU1a9Zw8eJFpk2bxtSpU1m0aJHBeiEhIURHR+uO+zPhr1+/nvfee48ZM2YQEBBAs2bN6NWrF3FxcYA2R9r9Y6Ojo5k5cybW1tb06dNHb51Ro0YxbNiwQq/ZxMSEkSNHsnv3bkJCQliwYAFLlixhxowZxd6rkuS7nwULFhSa/02tVtOvXz9yc3M5fvw4q1atYuXKlUyfPl3X59ixY4wcOZLRo0dz8eJFNm7cyOnTp3njjTf05rK1tdW7Fzdu3PNfzMzMJCAggE8++YSAgAA2b95MSEgIAwYMKPYa72f06NE0bdq0yPPJycmMHDmS7t27G5zLyMigWbNmLF68uNTrAYSFhdGqVSv8/PxwcXEp9Xv+zTffMG3aNKZMmcLFixfZu3cvvXr10p1PT08nLi6OXr164eHhgY2NDampqfTs2RMfHx/8/f2ZN28en376Kb/88otu3Mcff8zPP//M999/z6VLlxg7dizPP/88gYGBZbquR0K5ypQ+5vwbqqwXkJecrau2rjumHBZ5yaWrkn47OVP838uviflD+4n5Q/uJr4Y+K/q8PU/cTs406Bt8J1g0WdlENF7ZWPjH+JdP4DuhQsx00lZiv1x8NeEHufXRR9oq7J2fEUsnHRSL3twnAvfcKHmgpFpQ0VXW85KzRda1pFJ/1iuSuLg4AYhDhw4JIYRITk4WJiYmYuPGjbo+ly9fFoA4ceJEkfO8/fbbolu3brrXBw4cEIBBte77adu2rRg3bpzutVqtFh4eHmLu3LlFjmnevLkYNWpUoedmzJhhUDW7KCZNmiSeeuqpYvuUVr7AwEDh6ekpoqOjDaqs79ixQ6hUKhETE6Nr+/HHH4Wtra3IyckRQggxb948Ubt2bb05v/vuO+Hp6al7XVBJvCycPn1aAAbVxgvjhx9+EF26dBH79u0r8n0bNmyY+Pjjj0u8zw/eg6Lw8fHRqwb/yiuvFNrvwfc8MTFRWFhYiL179xbav+CzxwOV5n/44Qfh4OCgu+9CCDF58mRRv3593Wt3d3exaNEivfkGDRokXnzxxRKvpzAqs8q6tPQ85hjbmeEwyA/u+7Jk0axGqZ2ZrfMzcMu5t12lQtAt/hCnL1w36NvYuTGD/LTfXD8/9Tn5mnIkn3SuCx3Ga3/fOVlblb2UuE2bhmndOiixUdRPOw7A6b/DibuRys2QJNKTsssuj6RKEUKgyVWX+Ug7cZuYL04TvySYmC9Ok3bidpnnEKW0NBZGSkoKAI6OjoDWipOXl0ePHj10fRo0aIC3tzcnTpwodp6COe6nefPmuLu788wzz3Ds2L1SMLm5ufj7++uto1Kp6NGjR5Hr+Pv7ExQUxOjRo8t2kQ9w7do1du7cSZcuXYrsU1r5MjMzGTFiBIsXL8bNzc1gnhMnTtCkSRNcXV11bb169SI1NZWLFy8C2lxwUVFR7NixAyEEsbGxbNq0ib59++rNlZ6ejo+PD15eXgwcOFA3vihSUlJQFKVEX5lLly4xa9YsVq9ejUpV+KN0xYoVXL9+vUTrWFk4c+YMvXv3ZujQoURHRxtskULh7/mePXvQaDTcunWLhg0bUrNmTYYOHUpUVBQAHTp00G3Z/vHHH0RHR9OhQwdOnDhB586dMTU11c3Vq1cvQkJCSEpKAiAnJ8egGrqFhQVHjx6tsOuuKMpUcPR+QkNDOXDgAHFxcQaZmu83QUoqH6s2bpjVcyDtQBQZJ6PJTyj9wz8p+jYP7o+pECzYfAJfb0+aednrnZvQcgJ7buzhatJVNoRs0JWrKBOdP4DzGyA5Eo4ugG5TSzVMZWlJzW+/Jfx/Q3E88iuOfZuRmGnOxrlnAW10fNeXGtCoo0fZZZJUCSJPw+3pxx9yEkj5M4yUP0vnJ1aAx6wOKKZlL5Wj0WiYOHEiHTt2pHHjxoDWsdTU1NTgQenq6mrgj1PA8ePHWb9+Pdu3b9e1ubu789NPP9G6dWtycnJYunQpXbt25dSpU7Rs2ZL4+HjUarWeMlCwzpUrVwpdZ9myZTRs2JAOHTqU+VpB+zAMCAggJyeHMWPGMGvWrCL7lla+SZMm0aFDBwYOHFjoPDExMYXOUXAOoGPHjqxdu5Zhw4aRnZ1Nfn4+/fv319sqql+/PsuXL6dp06akpKQwf/58OnTowMWLF6lZs6bButnZ2UyePJnhw4dja2tb5HXm5OQwfPhw5s2bh7e3N9evG35JDA0NZcqUKRw5cgRj43I/ag2oUaMGZmZmWFhYFKowQuHv+fXr19FoNMyZM4eFCxdiZ2fHxx9/zDPPPMP58+cxNTXVbaM6Ojrq5o6JiaFWrVp689//Xjg4ONCrVy+++eYbOnfuTJ06ddi3bx+bN29GrVZX2HVXFOWy9CxZsoSGDRsyffp0Nm3axJYtW3RHaR2xJBWLsZ0Ztj28QaWQF5VGXlxmyYMAB3cPgz11ASQIC15ffZbbyfqWGAdzB95t8S4Ai4IWkZBVjgzJplbQ63Pt70e/hUTDjNBFYebnh+u0j1AQ1Dy9Ul9uAQfXXpEWH0mlMm7cOC5cuMC6devKPceFCxcYOHAgM2bMoGfPnrr2+vXr8+abb9KqVSs6dOjA8uXL6dChA99+Wz4/uqysLH777beHsvKsX7+egIAAfvvtN7Zv3878+dqSMkeOHMHa2lp3rF27tlTzbdu2jf3797NgwYJyywRaS8uECROYPn06/v7+7Ny5k4iICMaOHavr0759e0aOHEnz5s3p0qULmzdvpkaNGvz8888G8+Xl5TF06FCEEPz444+69j59+uiu8YknngBg6tSpNGzYkJdeeqlQ2dRqNSNGjGDmzJnUq1ev3Nc4Z84cvXtcmioHRb3nGo2GvLw8vvvuO3r16sWTTz7J77//rjNgPAwLFy7Ez8+PBg0aYGpqyvjx43nttdeKtIBVJeVSP2fPns3nn3/O5MmTK1oeyUNgZG2KeX0Hsi8nkukfi12fWiWOsXFy5pkx77BnySLEXYudAvTMDmRTaldeX3WWjWPbY2V276MypN4Q/gj9g8uJl1kYsJBZHYv+5lckjZ6DWl0g/BD8NRE6vw+OdcDOs8Sh9kOGkHnyFEnHrhqcExpIicuSOXweExQTFR6zymaBUKfkEPuNv76BUgHX91phVMpt3YK1y8r48eP5+++/OXz4sJ6lwM3NjdzcXJKTk/WsPbGxsQbfxi9dukT37t0ZM2YMH3/8cYlrtm3bVrdN4OzsjJGRkV5UWFHrAGzatInMzExGjhxZlsvUw8vLC4BGjRqhVqsZM2YM77//Pq1btyYoKEjXz9XVFTMzsxLl279/P2FhYQZWscGDB9OpUycOHjyIm5ubQcRXwZwF88ydO5eOHTvywQcfANC0aVOsrKzo1KkTs2fPxt3d3eBaTExMaNGiBdeu6ae8KFB4bty4wf79+/WsPEuXLiUrK0s3vuAagoOD2bRpE4Buq9TZ2Zlp06YxadIkzp49S2BgIOPHa7fzNRoNQgiMjY3ZvXs3Tz/9dJH3vICxY8cydOhQ3WsPj5Kt2EW95wX3o1GjRrq2GjVq4OzsXKwy5ebmVuj7WXCuYJ6tW7eSnZ1NQkICHh4eTJkyhdq1a5co76OmXGpYUlIS//vf/ypaFkkFYNVKa3bMCIxDaErns9Dk6Z68sWg5Q6fP4dmJk1EZGeGecIUumUFcik5lwrog1PfNZaQy4qN2HwGw5doWzt85X3ZBFQX6zgNFBeEHYVV/WNAYAlaXYqiC28xPsXU01Wo5D3Bmh9bPR1L9URQFlalRmQ6TGpb6fmwKOAzyw6SGZZnmKSxqqCiEEIwfP54tW7awf/9+A3N/q1atMDExYd++fbq2kJAQIiMjad++va7t4sWLdOvWjVdeeYXPP/+8VGsHBQXpHlimpqa0atVKbx2NRsO+ffv01ilg2bJlDBgwgBo1apT6WoujwFqg0WiwsLCgbt26usPGxqZU8k2ZMoXz588TFBSkO0Abhr1ixQpAa6EJDg7Wi/jas2cPtra2uod2ZmamgSXByEi7XVmUv5ZarSY4OFhPISpQeEJDQ9m7dy9OTk56Yzw9PXXX6OPjA2h9Xs6dO6eTf+nSpYDW+jVu3DhsbW0JDg7Wu8axY8dSv359goKCaNeuXanut6Ojo949Ls02WVHveceOHQH0Ui0kJiYSHx+vu67CaN++PYcPH9ZLVbBnzx7q16+Pg4ODXl9zc3M8PT3Jz8/njz/+KHL7skopj/fzqFGjxI8//lguz+nCWLRokfDx8RFmZmaibdu24tSpU8X2T0pKEm+//bZwc3MTpqamws/PT2zfXvpIoH9T9NaDaPLU4tbM4yJq8mGRFZJYrjnO7d2pi+bq9e5C4TP5bzFn+yWDfh8d+Ug0XtlYDP1rqMhX55d9oeSbQsyw00ZyFRyfOmjbS0HqoUNib5fXxKIxu8WiN/eJRWP2ikVj9mh/f3Of+HvxORF3I7Xsckkqhcc5euutt94SdnZ24uDBgyI6Olp3ZGbei3IcO3as8Pb2Fvv37xdnz54V7du3F+3bt9edDw4OFjVq1BAvvfSS3hxxcXG6Pt9++63YunWrCA0NFcHBwWLChAlCpVLpRdysW7dOmJmZiZUrV4pLly6JMWPGCHt7e71IJyGECA0NFYqiiH/++afQawoNDRWBgYHizTffFPXq1ROBgYEiMDBQF6WzZs0asX79enHp0iURFhYm1q9fLzw8PEqMyCmtfPfDA5FL+fn5onHjxqJnz54iKChI7Ny5U9SoUUNMnTpV12fFihXC2NhY/PDDDyIsLEwcPXpUtG7dWrRt21bXZ+bMmWLXrl0iLCxM+Pv7ixdeeEGYm5uLixcvCiGEyM3NFQMGDBA1a9YUQUFBeu/L/dFKJVGaqLvCorfS0tJ09x0Q33zzjQgMDCwxcmzgwIGFRm2V9J4PHDhQPPHEE+LYsWMiODhYPPvss6JRo0YiNzdXCKF9tnI3aquA5ORk4erqKl5++WVx4cIFsW7dOmFpaSl+/vlnXZ+TJ0+KP/74Q4SFhYnDhw+Lp59+WtSqVavY+1EclRm9VS6lZ86cOcLZ2Vm88sorYv78+WLhwoV6R1lYt26dMDU1FcuXLxcXL14Ub7zxhrC3txexsbGF9s/JyRGtW7cWffv2FUePHhXh4eHi4MGDIigoqNRr/puVHiGESNwaKqImHxbxv10u9xwHVi0R84f2E1+PeE60mbhc+Ez+W6w7rf+HeCfzjnhy7ZOi8crGYun5peLU7VMiOj269ItcP6Sv8BQc1w+Xanj6iZPiUv0GIqDpk+JYu/4ioOmT4mzzTmLH53vE4rH7dMrP9h/OiTtRUvmpaipa6XmU8EAob8GxYsUKXZ+srCzx9ttvCwcHB2FpaSmef/55ER197+9hxowZhc7h4+Oj6/Pll1+KOnXqCHNzc+Ho6Ci6du0q9u/fbyDP999/L7y9vYWpqalo27atOHnypEGfqVOnCi8vL6FWqwu9pi5duhQqT3h4uBBC+7+5ZcuWwtraWlhZWYlGjRqJOXPmlOr9K4189/Og0iOEEBEREaJPnz7CwsJCODs7i/fff1/k5eXp9fnuu+9Eo0aNhIWFhXB3dxcvvviiuHnz3pemiRMn6uRwdXUVffv2FQEBAbrz4eHhRb639z/4S6K8Sk9hYeIUE4ZeQFFKT0nveUpKihg1apSwt7cXjo6O4vnnnxeRkZG684UpPUIIce7cOfHUU08JMzMz4enpKb744gu98wcPHhQNGzYUZmZmwsnJSbz88svi1q1bxV5DcVSm0qMIUfa4zQdNu/ejKEqhnuxF0a5dO9q0aaNL0KXRaPDy8uKdd95hypQpBv1/+ukn5s2bx5UrV3T7q2UlNTUVOzs7UlJSivXQf1zJvZlG3KIgMFbhMa0dKouyu25pNGr+nDeb6wFnwNKW5U4DyTG14dfR7Whf5575d82lNXx55kvda5WiYkb7GbrQ9mJJuaXd0npwi+qt4+D6RInD82JiuPZ0d3ggehBzc0yHjuKa41NcO5ek8/2o3aIGbZ+thZOndcmySSqc7OxswsPDqVWrlkF4q0QikRRQ3P+Kh31+l8unJzw8vMijLApPeXJObNu2jfbt2zNu3DhcXV1p3Lgxc+bMKTY0Licnh9TUVL3j34yJpzXGrpaQryHzfDlKRgAqlRF93/kAZy8fyEzlxdS9kJ/L2DX+hMffy0Lbzaub3jiN0DDzxExiMkpR/8vOE/ovBOWBsOGtb0NWUonDTdzccJ81Ewr29VUqjD09ITub3NU/4PvL6/TyDaFuMwdQ4HrgHdZ9dpqdv1wg4XY6AOlJ2TLHj0QikfxHeOjkAQWGorI4BRZQnpwT169fZ//+/bz44ovs2LGDa9eu8fbbb5OXl1dkAqi5c+cy824Zg/8CiqJg1cqVlB3hZPrHYt3OMIqhNJhZWvLch9NZO+09SInmfyaH+V15mtErz7D57Q7YW5pyK/2WwTiN0BCVFoWbVeE5JPRoOVJbdT3xOmjU8MdoiA6CX5+Hl7eChX2xw+2HDMHqqafIvRGJqY83xq6upB88yJ3vvyfn0mU0K77D13o5dV4YwzWr1oSdSyIsII6wwDhcvG2Ii0wDIXP8SCQSyX+BcgfRr169miZNmmBhYYGFhQVNmzbl119/rUjZCkWj0eDi4sIvv/xCq1atGDZsGNOmTeOnn34qcszUqVNJSUnRHQUZKP/NWDZ3ARXkRqaRd6d0OXsKw87FlYHvT8PI2Bjn+Kv0zArgenwGb60JIE+twdvWG5Wi/zFSKSq8bLzKsIgn1OoEdbrCK9vA0gluB8KaQZCdUuJwEzc3rNq1xcTNDUVRsOnWjVp//IHn999hVq8emvR08pZ+Q60VY+lVP4LaTRxAQNyNNN3Wl8zxI5FIJP9+yqX0fPPNN7z11lv07duXDRs2sGHDBnr37s3YsWPLlESrrDknQJtroF69errQRICGDRsSExNDbm5uoWPMzMywtbXVO/7tGNmaYu6nDSfMDDAs9FcWPBs0oueb2oSE9WNP0ywrlBPXE/hk6wVcLV2Z0X4Gqvs+SpNaTSqdlacwXJ+AkdvAwhFu+cOvgyC77NuRiqJg+8wz1Nq6Bc8F32Japw6a1FTyfp5HnV/fprWP4bZfQY4fiUQikfw7KZfS8/333/Pjjz/y5ZdfMmDAAAYMGMBXX33FDz/8wHfffVfqecqacwK0uQauXbumV/ri6tWruLu769UGkYDl3Zw9mQGxpc7ZUxSNOj9N2+e0uZm63DmIR040685EsexoOIP8BrFz8E7q2tcFKJ0/T3G4NYaRf4KFA9w6C2sGl0vxAVBUKmx796b2tj/xmDcPUx8f1MnJmK5bUGiOn6D9UWSn5xlOJJFIJJLHnnIpPQWFyB6kQ4cOREdHl2mu9957jyVLlrBq1SouX77MW2+9RUZGBq+99hoAI0eOZOrUe7WZ3nrrLRITE5kwYQJXr15l+/btzJkzh3HjxpXnUv7VWDR0QjE3Rp2SS05Y8kPP99Swl6nbpj1Cnc+QpL3Y5KXy+Y7L7L0Ui7u1Ox+2+RCAjSEbic2ILWG2EnBvqlV8zO3h5mlYOwRy0so9nWJkhF3/Z6m9/W/c587F2tGCBiG/gbjrAC8ECA0R5+L5fdYpws/HP5z8kiIpR8CoRCL5D1GZ/yPKpfTUrVuXDRs2GLSvX78ePz+/Ms01bNgw5s+fz/Tp02nevDlBQUHs3LlT59wcGRmpp0h5eXmxa9cuzpw5Q9OmTXn33XeZMGFCoeHt/3UUExWWzbVZOTP9H1IJQWs16Tv+fVx86yCy0nkpbS/G6lzeXRfI4ZA7aDLr8oRjM3I1uSy/sPyh18O92V3Fxw6iTsHa/0FO+sNdg7Ex9s8/h/vsz/CIOUGHk9NpEbSADic/pnXAPOzsFDJTc9nxw3n2rbxETqa0+lQUBSkmMjPL72MmkUj+/RT8jyhvWpriKFeenj/++INhw4bRo0cPXWrrY8eOsW/fPjZs2MDzzz9f4YJWJP/2PD33kxOZyp0fzqGYqHCf1g6V+cNX+01LiGftR5PISE4ixbkuv1p3R9x1Zja2uoaF91JMVabsGLQDVyvXEmYrBbcCYPVzkJMC3h3gxY1g9nC5dgrN8aNS4btrD4GnMwncGwkCrOzN6PZyA3yecCp6MkmpiY6OJjk5GRcXFywtLcsV9SmRSP6dCCHIzMwkLi4Oe3v7QuunPezzu1xKD4C/vz/ffvstly9fBrTOxO+//z4tWrQoz3SPlP+S0iOEIPYbf/LvZOEw2A+rNuV0MH6AmGtXWf/pFPLzcgm0bUqgXTPs81JINrFF1FmLkWUEIxqMYGq7qSVPVhpu+d9VfFLB5yl4cYO2WvtDkLxpE9HTZ+gUHyOXGvjt24diYkJ0WAr7Vl3SOTY36uhOxyF+mJYj0aPkHkIIYmJiSE5OrmpRJBJJNcXe3h63u9G4D1JlSs/jzH9J6QFIPRhF6s4ITH1tcRnbrMLmDTlxhL8XaLMxC7T1HzUoHPJoyI3m/1SstQfg5llt/p6cVPDtBCM2gKnlQ02ZFxND9qVL3P5oGprkZFw++D+cRo/WnstVc2rrdc4diAIB1o5mPD2yIV4NHCviav7TqNVqvQKGEolEAtotrfujsx/kkSk9qampugVKymhc3RWJ/5rSo07JIfqL0yDA7YPWGDtZVNjc25f+xJU9f+u1aVAIHmRDYHZwxVp7AKJOa8PYc9OgVmcYvv6hFR+A5M1biP7oIxRLS+rs2I7JfSkTbocmsW/VZVLjtTl8GnfxpP3zdTCtgK1CiUQikZSeR1aGwsHBgbg4bb4Xe3t7HBwcDI6Cdkn1wsjODLO7OXsyHjJnz4M0ftIwtYAKwYvuAwDYdHXTw0dy3Y9XW3jpDzC1hvDDsG44JIRpf08xzA5dWuyeG4hFixaIzExiv/hS75yHnwPDPm5L4y6eAFw4dIv1s09zOzRJlrGQSCSSx4hSf1Xdv38/jo5as/6BAwcqTSBJ5WDV0oWcq0lk+sdi290bRVUxDqSO7p4oiqIfYqioaN+oOy0z/iEgLoBlF5bxUbuPKmQ9ALzbwYubtPl7rh+E71vq1qX/Qm1pizKiqFS4zZhO+KDBpO3cSfrRIVg/1VF33tTcmC7D61O7eQ32/6q1+mz5OvDeeFnGQiKRSKo9pbb0dOnSBWNjrY5Uq1YtOnfuTJcuXfSOzp07F1uBXVJ1WDzhhGJmhDo5h5zwkks7lBYbJ2eeGfOO7rUArtfrg61zDd5u/jZQCdYeAJ/28PyP+m1CA39NLLfFx7xBAxxeehGA2M8+Q1NIhm+vho4M/6Qdfq1d9JeWZSwkEomk2lOuPD21atXizh3DNP6JiYlS6ammKCZGWDaruJw999Pk6Z4MnTFXuw5wPN2ai7dTaOvWlpYuLcnT5LHswrIKXRPQZmx+EKHWFi8tJzXeeQejGs7k3rhB4vLCcw2ZWhjTqJOn4dKyjIVEIpFUa8ql9AghCg0lS09Px9zc/KGFklQOBWUpsi7Eo8lRV+jcXo2aULeN1r+nZco5Vh2PQFGUyrX2ONbRbmndj6ICx9rlntLIxgbXD7WZpeN/+pncm4VbjexdLHjwT0BRwM6l4pzEJRKJRFKxlCn85L333gO0xRw/+eQTLC3vRc2o1WpOnTpF8+bNK1RAScVh6m2DsbMF+fFZZF2Ix6pVBYWS36XNgMFcO3OC+ulX+e3sVab0aaiz9lSKb4+dp9aH56+J98pJWDg8dOJC22efJXnjJjJPnyZ27ly8Fi8y6GPtYE7XlxpwcM0VCtyZvJ9wxNpBKv0SiURSXSmTpScwMJDAwECEEAQHB+teBwYGcuXKFZo1a8bKlSsrSVTJw6IoCpYttb4oFb3FBeBRrwGeDZ7ACA2NEs+x7kwkiqIwrrm2Ltqmq5sevhjpg7QcCROD4YXfwcYDMhNg2zvwEOmnFEXBbfonYGxM+r59pB08WGi/Rh09GDmnA237a7d0o64kkRwrSyxIJBJJdaVMSs+BAwc4cOAAr7zyCv/884/u9YEDB9i1axc///xzmWtvSR4tli1dQIGc6ynkJ1a8022bAYMBaJJ6kfVHQshXa2jj1oZWrq20vj3BleDbY+cJDfrCsF9BZQKX/oQzSx9qSrO6dXF8RRsFFjv7czTZhd8rawdzWvf1xbuRI5p8wZENV2VBTYlEIqmmlMunZ8WKFf+JpH7/RoztzTGrYw9AZkDFW3tqt2iNY01vTEUeTrcC2XMpVuvb00zr2/NH6B8Vb+0poGZreGaW9vddH8HtwOL7l0CNt9/G2NWVvJs3SVhStBKlKAqdhtVDZawQeTGR8HOyQrtEIpFUR8ql9ACcPXuWDz/8kBdeeIFBgwbpHZLqTYFDc0ZAXIVbJRSVirZ3rT3NU8+z6ug1gMq39hTw5FvQ4FlQ58KGVyArudxTqayscJ06BYCEJUvIjYwssq+9qyUtengDcHRDKHm5FesoLpFIJJKHp1xKz7p16+jQoQOXL19my5Yt5OXlcfHiRfbv34+dnV1FyyipYHQ5exKzyY0ovqRIeWjQsTMW9o5YqTNJvXCKy9Gpj87aoygwcBHYe0PyDdg2/qH8e2x69cKqQwdEbi4xs2cXqyS26uOLtYMZaYnZBOy8Ue41JRKJRFI5lEvpmTNnDt9++y1//fUXpqamLFy4kCtXrjB06FC8vb0rWkZJBaMyNcKiiTMAGZXg0GxkbELbZ58DoGVKEKuOafPmtHVvS2vX1uRp8lga/HA+N8Vi4QBDVmr9ey7/Bad/KfdUiqLg+snHYGJCxuEjpO3dW2RfEzMjnvqf1qctYPcNkuOkU7NEIpFUJ8ql9ISFhdGvXz8ATE1NycjIQFEUJk2axC+/lP8BI3l0FISrZwXHo6mErZgm3XtjZG6JQ14yAUePkZShzW5ckLdnc+jmyrP2ANRsBT0/0/6+axrcCij3VGa1auE0ahQAsXPnosksWpmp3aIGXg0d0OQLjm4IlU7NEolEUo0ol9Lj4OBAWloaAJ6enly4cAGA5ORkMot5IEiqD6a+thg5miNy1GRdTKjw+c0sLWnVS6sYN00MYN0ZrT9MG7c2j8baA9BurNa/R5MHG199KP8e57FvYuLhQf7taOJ/+rnIfjqnZiOFGxcSiDgvnZolEomkulAupadz587s2bMHgP/9739MmDCBN954g+HDh9O9e/cKFVBSOSiKglUl5uwBaNl3ABgZ454Tyz97T5Cv1gCP0NqjKDBwMdj7aP17/hxXbv8elYUFrtO0iRUTVqwg53rRpS4c3Kxoftep+ciGUPKlU7NEIpFUC8ql9CxatIgXXngBgGnTpvHee+8RGxvL4MGDWbasEiNzJBWKZUvtFldOWDL5yRWfs8fK3oFGnZ8GwOvWSfZejgMesbXHwh7+t1Lr33PlbzhVtJWmJKyffhrrLl0gL4+Yzz4rduuqdd+7Ts0J2QTskk7NEolEUh0ol9Lj6OiIh4eHdgKViilTprBt2za+/vprHBwKKQIpqZYYO5pjVtsOBGQGxFXKGu0GDEagUDvzBut3n9G1PzJrD4BnS+j1ufb33R/DTf9yTaMoCq4fT0MxNSXzxEnSdu4ssq+JmREdh9x1at4VScodue0rkUgkVU2plZ7U1NRSH5LHh4KcPZmVkLMHwNHDE68WbQFQXTzIlRjt56ONWxvauLV5NNYegLZjoOEArX/PplchK6lc05h6eeE0ZgwAsXO/QJ2eUWTfOi1rULOBA+p8DUc3hJZrPYlEIpFUHKVWeuzt7XFwcCj2KOgjeXywaOyMYqoiPz6L9BPR5KfkVPganQcPBaB+eii/7j2va3+r2VuAtibXzvCdj8C/ZxE4+EJyJGwtv3+P0xuvY+LtTX5cHHFffUXGyVPkxRjKrigKnV/QOjVHBEunZolEIqlqFFHKr/eHDh0q9aRdunQpt0CPgtTUVOzs7EhJSZHlNIC4H4LIjdRG46GAwyA/rNq4VegaS6a8T2p4COccmvPdN9OxtzQFoP+W/kSkRgCgUlTMaD+DQX6VmNX7diAs66nN2NxrLrR/u1zTpB8+TNSYN+81qFS4z5qJ/ZAhBn2Pb75G4O5IbJ3NGT6jHcYmRuWVXiKRSP7TPOzzu9RKz78JqfTcIz8lh5gvTsP9nwIF3Ka0xdjOrMLWCfM/zdavZpGrmOA4ehZvPtOEmIwYem7qibhvcZWiYtfgXbhZVazSpcfpJbDj/7TOzaN2aXP6lJG8mBiude2m36hSUXf/Pkzc9GXPzc7nt09PkZGcQ9v+tWjTr9bDSC+RSCT/WR72+W1cnkUPHz5c7PnOnTuXZ1pJFZAfn6Wv8AAIbXtFKj21W7bB2NkD4m9zasd2Xu/emMjUSD2FB0AjNESlRVWu0tPmdYg4Cpe2wvqXoO988GiurdZeSnIjConI0mjIvRFpoPSYmhvTcUhddi+9iP/OG9Rv54ats8XDXYNEIpFIyky5lJ6uXbsatCmKovtdrZZ5SR4XjJ0tQMHA0mNcwQ9lRVHoMvh/7Pt5Ib6xAewJvknzut6oFBUaodHrG5dROZFk9wkDA77TKj5pt2H9CFBU0H8htBxZqilMfX1ApQKNvuwq+8Jrz9Vt5cLFI7e5FZLEkQ2h9Hu76UNfhkQikUjKRrlC1pOSkvSOuLg4du7cSZs2bdi9e3dFyyipRIztzHAY5KdVfO5i6m1boVaeApp07oqwtMNKncmOLX/jZuXGjPYzUCn6H8MZJ2Zw/PbxCl9fj5x0yEq891po4K+JkHKrVMNN3NxwnzVTq/jcR/SUqaiTkw36K4pC52H1UKkUIs7HExEsnZolEonkUVMupcfOzk7vcHZ25plnnuHLL7/kww8/rGgZJZWMVRs33Ka0xW5gHQByI1PJiyk6FLu8GBmb0KLvQABsrx0lJDqFQX6D2DV4F8t7Leef5/+ha82u5KhzeHf/u5yMPlnhMuhIDNMqOvcj1HDjWKmnsB8yhLr79+G9ahXeK1dg5OREzuXL3Bg1qlDFx9HDiqbdvYC7mZrzpEVUIpFIHiXlUnqKwtXVlZCQkIqcUvKIMLYzw6a9BxZPOIGAlF0RlbJOp2efRW1ijkNeCr9v+gcANys32ri1oaZtTb7u+jVdanYhR53DO/ve4VT0qUqRA8c62i2tB9nxAYQfKfU0Jm5uWLVri9WTT+KzcgVGjo7kXLpM5OjXUaekGPRv088XKztTUu9kEbQn8mGuQCKRSCRlpFxKz/nz5/WOc+fOsXPnTsaOHUvz5s0rWETJo8S2ty+oIPtyIjnhhg/th8XUwhLvjs8AkHV2D8l3q6/rzhuZ8k3Xb+hcszPZ6mzG7xvP6ejTFS4Hdp5aHx7lbvi4ogI7b8hOhl+fA/+VZZ7SzM9Pa/FxdCT74kWt4vNAsk5Tc2M6DKkLwJkdEVw9HUN6UsWXAJFIJBKJIeUKWVepVCiKYpDB98knn2T58uU0aNCgwgSsDGTIevEkbQkl41QMpt421HirmZ6TekWQnpTID2+9hpFQYzVoAmOHPWPQJ1edy8QDEzly6wjmRub80OMH2ri1qVA5AK0PT+J1cKwNFg7aoqQXN2vPtRsLPT8Ho7L5+2eHXCXy1VdRJyVh3qQJ3suWYnTf50wIwdoZJ0mJywK0ftVdX2pAo44eFXZZEolE8m/kYZ/f5bL0hIeHc/36dcLDwwkPD+fGjRtkZmZy/Pjxaq/wSErGtrsPiomK3Mg0si8mVPj81g6O2DRpD0DYvr9Qawz1blMjU77t9i0dPTuSrc5m3L5xnI05W+GyYOcJtTppf5pawpDl0G2a9typn+C3oZCVXKYpzevX01p87O3JDg4m8vU3UKel6c5nJOeQcidL91oIOLjmirT4SCQSSSVTLqXHx8dH7/Dy8sLc3LyiZZNUEUa2plg/pc1Zk7IrAqGu+PyVz738IgJwS7nOjsMBhfYxMzJjYbeFdPToSFZ+Fm/vexv/2PIVCy01igJdPoShq8HEEsL2wdIekBBWpmnM69fXKj52dmSfP0/k66+jTk8HIDnOMDeSEPD3onNEXUmslBpoEolEInkIR+Z9+/bx7LPPUqdOHerUqcOzzz7L3r17K1I2SRVi06UmKktj8u9kkeFf8TWx3L29ED5NADj55x9F9jMzMmPh0wvp4NGBrPws3tr7FgGxhStJFUqjgTBqJ9h6QkIoLHkarh8s0xTmDRrcU3zOnSdqtFbxsXexoLAdw4RbGWxbEMTmeQHcuJgglR+JRCKpYMql9Pzwww/07t0bGxsbJkyYwIQJE7C1taVv374sXry4omWUVAEqc2NsnvYGIHVPJJrcig+vfmbYCwDY3Q5m1ZothEXcLLRfgcXnSfcndYpPYFxghctjgHszeGM/eLa+6+A8SFvCogyYN2yI94rlqOzsyDp3jqjX38DCRE3XlxrogscUFbQfVIcmXWtiZKwi5noKf39/jk1fnCX8fLxUfiQSiaSCKJcjc82aNZkyZQrjx4/Xa1+8eDFz5szh1q3SJXirKqQjc+kQ+Rpivj6LOikH216+2HbzqvA1Zr3yMlbZSQBoUHDqNYJRo4YX2jcrP4t39mvD2C2NLfn5mZ9p7tK8wmUyIC8btr0DwRu0r9u8Dr2/ACOTUk+RdfEika+NQpOaikXLlnj98gtZeUakxGVh52KBtYN2ezgjJYfAPZFcPHSL/DxtHiFnL2ta9/WldrMaKKqKdSqXSCSSx4kqcWROTk6md+/eBu09e/YkpZDcJJLHE8VYhV1PXwDSDkahzsir0PnDIm5ieVfhAVAhSNj1W5EWHwtjC75/+nvaubUjMz+TsXvHcu7OOWIyYjgdfZqYjIrfhgPAxBwG/QLdZwAKnFkKawZDZqI2+iv8cImZnC2eeALv5ctR2dqSFRBA1JtvYpJ0G/ukq5jlJOv6WdmZ8dQQP17+vAMte3ljbGZEfFQ6O3++wLrZpwk9G4vmruN3elI2N0OSpAO0RCKRlJJyWXpGjBhBixYt+OCDD/Ta58+fz9mzZ1m3bl2FCVgZSEtP6REaQdz3geRFZ2D9lCf2z9ausLn/2X2ES8u+NGh/4vUp9H7mqSLHZeVnafP3xJzGzMiMXHUuAoFKUTGj/QwG+Q2qMBkNuLId/ngD8jLAylmr+AhNqWt3ZQUHEzlqNJr7orlQqXCfNRP7IUMM+men53FufxTn90eRm63dYnRws8Stjh1XjkcjhAx5l0gk/x0e9vldLqVn9uzZzJ8/n44dO9K+vTb0+OTJkxw7doz3339fT5B33323zEJVNlLpKRvZV5OIX34BjBTc3m+NsWPFROqFRdxk8+S3UN0XyiSA5pO+oseTjYodm5mXyRu73+B8/Hm9dpWiYtfgXZVbpT3mAqwdAmnR+u2KEUwMLrFae9qBA9x86239RpWKuvv3GVRoLyAnM4/zB25ybl8UOZn5BucVFYz8vINum0wikUj+jVSJ0lOrVq3STa4oXL9+vcxCVTZS6SkbQgjil10g51oyli1ccBxWv8LmXr78dxJ2/YYKgUBb9/S6fUPenfkJ9d2Kf28O3zzMuH3jDOfstbxyEhnez6VtsOFlw/ZX/tbm/SmGjJOniHz1VYN2j6/nY9evX7Fjc7PyOboplMvHog3OPTepBZ71HYodL5FIJI8zD/v8Lluq2buEh4eXZ5jkMUVRFOx6+xK3KIjMoDisO3li6mFdIXOPGjWcsKc7ERoagUl2CgFrfqJ28mU+n/M9n0x7Fz9XmyLH1nOohwoVGu4VDlUpKrxsKt7h2gDPVlrzyv1FSxWVNrNzCZj6+mirs2v0C57enjyFzNNncH5zDCYehW9VmVoY0/bZWrqtrftJjs2USo9EIpEUw0MXHBVCyJDa/wCmNW2waOqsLUa6M6JC567jW5PezzxF9/79eOrlNwBoFnuCyfNWcy0urchxblZuzOgwA9V9hUN7+vSs3K2tAh6s3QVapSchtMShJm5uuM+aqVV8AFQqTGvXhvx8ktev51qv3kTPnEletKE1B8DawVwv5L2Ag7+FsHvZRbLTK9bhXCKRSP4tlGt7C2D16tXMmzeP0FDtP/l69erxwQcf8PLLhZj8qxlye6t85MdnEfONP2gEzq83wbyufaWss3PZz1zc/Rf5ihEHag1m0aRB1HUp2rIUkxHDqourWHN5DcYqY37t8yuNnRtXimwGpNyCOyFwdAFEHAJjC3hxA9TqXOLQvJgYcm9EYurjjYmbG5lnz3Jn0WIyT54EQDExwf5/Q3AaM6ZQX5/0pGxS4rKwdjTj8vFoAnZFIjQCS1tTur7UgFpNnSv6aiUSiaRKqRKfnm+++YZPPvmE8ePH07FjRwCOHj3K4sWLmT17NpMmTSqzII8SqfSUn6Q/r5FxIhqTmta4jGte4cVIATQaNZu+nE1U0BkyVebs8xvOsnd6UadG0YqPEIL3D73Pnht78LT2ZGP/jdiYFr01VuHk58D6lyB0t1bxeWkT+BYdgVYcGadPE79oMZmntdXlFRMT7IcO1So/ri5FjouNSGXfykskxWQC0OBJN54a6oeZZenzCUkkEkl1psocmWfOnMnIkfrhuatWreLTTz+t9j4/UukpP+q0XGLmnUXkqnEc0QDLpjUqZZ287GzWTv+QhBvXSTSx52C9F/j1ra7ULkbxSc1NZehfQ7mVfotevr2Y13lepShlxQgN61+Ea3u1dbte3AS+Hcs9Xcap08R//z2ZZ7WFVhVTU+yHDcPpjdcxcXHRWooibmDq66OzBOXnqTm1LZygvZEgwNrBjG4vNcD7CacKuUSJRCKpSqpE6TE3N+fChQvUrVtXrz00NJQmTZqQnV29k6VJpefhSN17g9S9kRg7meP6XisUo4d2DSuU9MQEfv3oPTKTEogy9+RkvUH8/uZT1HK2KnLM+TvneeWfV8gX+Xzy5CcMrT+0UmQrkrxsWDccwvaDiRW89Af4tC/3dEIIMk+d4s73i8jy1xZbVczMsGjZgsxTp7XO0IXk+Ym+lsy+VZd11dwbdfKg4+C6mJqXK3ZBIpFIqgVVkpG5bt26bNiwwaB9/fr1+Pn5lWdKyWOEdSdPVNYm5Cdkk3GmkrIgA9aOTgyeMgNjM3O8sm/R6Poehv98goj4jCLHNK3RlImtJgLw5ekvCUkMqTT5CsXEHF74DWp30yYwXDsEIk+WezpFUbB68kl81vyK9/JlWLRogcjJIfPEyXvRXxoN0dNnkBdz771wr2vPsI/b0qRbTQAuHbnNus9OczMkqbBlJBKJ5D9BuSw9f/zxB8OGDaNHjx46n55jx46xb98+NmzYwPPPP1/hglYk0tLz8KSfuE3yn2GorE1w+6ANKjOjkgeVk+uBZ9j65WcIoeGYw5Pc9m7PujFP4luExUcjNLyz/x0O3zyMr60v659dj6WJZaXJVyi5mfD7CxB+CExt4OXN4NX2oacVQpCwfDl35s03OOe9ahVW7QzXuBmSxP7Vl0lL0Fpgm3StSZNuNclIzsH+vrpfEolEUt2pEkvP4MGDOX36NM7OzmzdupWtW7fi7OzM6dOnq73CI6kYrNq4YeRkjiY9j/QjhdfKqihqt2hDt1e1oewdk05iFX2R4UtOciOhcIuPSlExu+NsXCxdiEiN4PNTn1eqfIViagnD14FvJ8hN01Zojzrz0NMqiqJNYKgy/NNN3bkTTW6uQXvN+g688ElbGnXS5v4JPniT32ac5M9vA1n90XEuHbv90HJJJBLJ40CZLT2pqamcOnWK3Nxc2rZtS40alePIWplIS0/FkHnuDom/X0ExNcLtw9YYWZtW6nr7V/xM4M6/UCvG/OE2ACNXH9aNaY+3U+FWHP9Yf0btGoVGaJjdcTYD6w6sVPkKJTcDfhsGEUfAzBZe3go1Wz30tMmbNhE9fYZ2i0tRKMhUaFq3Dh5z52LRpEmh40JORbN3xWW9NkWBkXNkCQuJRFL9eaSWnqCgIBo0aEDv3r3p378/devWZdeuXWVeVPLvwKKJMyY1rRG5alL+iSA7LJn8lJxKW6/rK69Tu2UbjEQ+A+/sJC0+juFLTuJ/I5HjYfFEp2Tp9W/l2oq3m2lrXH1+6nOup1RBSRRTKxixHnw6Qk4q/Po83Ap46Gnthwyh7v59eK9aRd0D+/H8/juMnJzIvRZGxAvDifvm20KtPlb2hoqNEBB+Pv6hZZJIJJLqTpksPb169SI9PZ358+djbm7OZ599RnBwsC5B4eOCtPRUHNnXkolfGnyvQQGHQX5YtamcrMi5WZmsmzGZOzfCSTN34jfXgeSqzABQKTB3UBOGtfHW9Vdr1Ly5901ORZ/Cz8GP3/r+hrlxFVg0ctLvOjWfAHM7GPkneLSo0CXyk5KInf05qdu3A4VbfdKTsln90XGDEhYALXv50PbZWhiZVE40nkQikTwsjzRk3dnZmd27d9OyZUsAkpOTcXR0JDk5+bFSHqTSU3Hkp+QQM/e0fqMCblPaYmxnVilrpiXE89u090hPSiTSvCb7nTtjm59Osokd2SY2HJ3SDXc7C13/+Kx4Bm8bTGJ2IkPrDeWT9p9UilwlkpMGa4ZA1Ekwt4dBS7TRXo51SqzMXhZS9+wh5tOZqBMSwMgIp9dfx3nc26hMtduPl47d5uDaK4i7O2MuvrbEhqcC4OhhRfdXGuLiI/8uJBJJ9eORKj0qlYqYmBhcXO5lhbWxseH8+fOlrrxeHZBKT8WRHZZM/JJgg3bnN5pgXse+0taNvX6N36Z/iCYvV1edXYPCAecufPLea7Svo5+M7/jt44zdMxaBYF6XefT27V1pshVLThqsGQxRp+61KSptHa+WI4seV0byk5KI/Ww2qTt2AGDmVxf3OXOxaKItz1FQwsLubvTW9cA7HPztCllpeSgqhVZ9fGjdxxcjY2n1kUgk1YdHHr116dIlzp8/rzuEEFy+fFmvTfLfwdjZQqtx3I9yt70Sca1dlzbDR+sUHgAVgm7xh3AxyjLo38GjA683eR2AmcdnEpUaVanyFYmZDfT/Xr9NaOCvido6XhWEsYMDnt98jed3CzFyciIn9BoRL7xA3LcL0OTmYu1gjmd9B53zcu0WNRg+vR11WrogNIKz2yPY9OVZ4m8WXfBVIpFIHjfKbOlRFKXQquoF7YqioFarK1TIikZaeiqWjDMxJG0OhbsfC5Oa1riOr1h/lcKIvHCejZ99ZND+9Huf0qJda4P2fE0+o3aNIjAukEZOjfi1z6+YGlVuxFmhhB+GVf0N21/5G2p1qvDlDK0+ftR4/31U5uZ6JSwKCD0by+Hfr5KdkYfKSKFNP19a9PLBqJIyb0skEklpeaSWnvDwcK5fv054eLjBUdB+/XrZI2QWL16Mr68v5ubmtGvXjtOnT5c8CFi3bh2KovDcc8+VeU1JxWHVxg23KW2xf74uKJB3M52si5UfDeTg7mFQW0uDwvwT8eSrNQb9jVXGfNX5K+zM7LiUcIlv/b+tdBkLxbGOdkvrQa7tpVAP44fE0OoTys2xY4l89VWuPd2d5E2b9Pr7tXZl+Ix21GrmjEYtOLUtnD++9CfhdnqFyyaRSCSPknJlZK5I1q9fz8iRI/npp59o164dCxYsYOPGjYSEhOj5Dj1IREQETz31FLVr18bR0ZGtW7eWek1p6ak8UnaGk3bwJiobE9wmtUJVyRW+g/fvZs+SRYi7JRmyjcz51fMFxjzThPd61i90zMGog7yz/x0AZrafSU2bmnjbeuNmVTkRZ4USsFq7pSXUaDfo7v4ZtngJ+n0LxpVjgcoKCSFi4HP6jSoVdffvM7D4CCG4ejqWI+uvkpOZj8pYoV3/2jR/xpvMlByS47JkRmeJRPJIqZKCoxVJu3btaNOmDYsWLQJAo9Hg5eXFO++8w5QpUwodo1ar6dy5M6NGjeLIkSMkJydLpaeaIPI0xH4XQP6dLCxbueL4v3qVvmZaQjyx169xYOUvpMbHcdvMja0eA/j1jQ50qONc6JivznzFr5d+1b1WKSpmtJ/BIL9BlS6vjpRbkHgdHGtDyA7450Otf49vJxj2K1g4VPiSGSdPEfnqqwbt9sOH4/bJxyiFZHrOSM7hwNor3AhOAMDW2ZzUhGwQ2uivri81oFFHjwqXVSKRSB6kSspQVBS5ubn4+/vTo0cPXZtKpaJHjx6cOHGiyHGzZs3CxcWF0aNHPwoxJWVAMVHhMNgPFMj0jyX7auUXuLRxcqZumycZ9NFMzCyt8MiJoeudw0z8PZCE9MKTJY6oP0LvtUZomHliJjEZlVdA1QA7T60Pj50ntH0DRmwAU2tt9uZlPSExvMKXNPX1KbSERfLvvxP56mvk3TJ0prayN6Pf2015emRDTMxUpMZn6wxTQsDBtVdIT8qucFklEomkoqlSpSc+Ph61Wo2rq6teu6urKzExhT98jh49yrJly1iyZEmp18nJySE1NVXvkFQeZr52WLfXfvNP2hyKJufROLY7eXrx7MTJKIqKRulX8Lx1mvc3nkOjMTRm3s4wrDelERqi0qooqgvA7xkYtRNsPSH+KiztDpGnSh5XBkzc3HCfNfOe4qNSYTtgAIqlJZmnT3N94HMkb91qEKygKAoNO7jT7aWGBnMKDaTEGUbMSSQSSXXjsQrHSEtL4+WXX2bJkiU4Oxe+bVEYc+fOxc7OTnd4eXlVopQSANtevhjZm6FOziFlZ8VbLIrCt1lLur5ytzhp4gkiAs+w/Jjh+t623qgKcSau4t1ecGsCr+8D92aQmaCN8greVPK4MqBXwmL/Pjy/+pLaWzZj0bw5mvR0oqdM5daEieQnGVrp3OvaoTyYogCICU+p+nsnkUgkJVClSo+zszNGRkbExsbqtcfGxuLmZuhUGhYWRkREBP3798fY2BhjY2NWr17Ntm3bMDY2JiwsrNB1pk6dSkpKiu6IiqrCb/P/EVRmRjgM8gMg42Q0OREpj2ztFr2fpWmP3ihAr7i9LPnzGOeikvX6uFm5MaP9DAPFZ+KBiZyKrljrSpmxdYfX/oH6/UCdA3+MhsPzKjSyy8TNDat2bXXOy6Y+Pvis+ZUaEyeCsTFpu3dzfcAA0g8f1htn7WBO15caGASfndx6nb++Pye3uSQSSbXmoRyZIyIieO+995g7dy716xceKVMS7dq1o23btnz/vTZhm0ajwdvbm/Hjxxs4MmdnZ3Pt2jW9to8//pi0tDQWLlxIvXr1MDUtOepFOjI/OhI3XiXTPxbjGha4vtsS5RHVdVLn5/PHnOlEXTxPirENxxu9xJb3e2Jjrh9NFpMRQ1RaFDamNnx+8nOC7gRhrBgzvf10nvd7/pHIWiQaNeyZDie0Tv40G6HN3FxJkV0FZF28yO0PJ5N790uE/QvDcP3wQ1SW96rZF2R0tnU253pQPCe2hqHO02BqYUynYX7Ub+dmkE5AIpFIHpYqdWRetWoVW7duZdmyZeWe47333mPJkiWsWrWKy5cv89Zbb5GRkcFrr70GwMiRI5k6dSoA5ubmNG7cWO+wt7fHxsaGxo0bl0rhkTxa7PvVQmVjQv6dLFL33Xhk6xoZG9P/vanYurhjl59Gs5AtfLQpyGALxs3KjTZubWjg2IClvZbS27c3+SKf6cen813Ad2iEYb6fR4bKCHp9Dv2+AcUIzv2mrdKemVipy1o88QS1/tiE4yvashjJ69YT/vwgss6d0/UpyOhs42RBs+5eDJvWBtdatuRm5bNv5WX++SmYzFTDKu8SiURSlTyU0rN69WpGjBjBmjVr0GjK93AYNmwY8+fPZ/r06TRv3pygoCB27typc26OjIwkOjr6YcSUVCEqSxMcnqsLQNrhm+TeenQJ7iysbRg0ZTpG5pZ45MSQeWAdG84UvbVpZmTGl52/5I0mWp+gJcFLmHx4MjnqwiPAHhltRsOLG8DUBm4chWXPQEKYNuQ9/HCFlq8oQGVujuvUqXivWI6xmxu5N24QMeJF7nz3HSIvj7yYGDJOniLvbsCBg5sVg/6vJU8+VxuVkUL4uXh+n3mKa/5xFS6bRCKRlJdyb28dPnyYAQMGEBUVhZ+fHytWrKBPnz4VLV+lILe3Hj0Jv10m63w8Ju5WuIxvjvIISxpEnAvgj7mfgtBwyrk9c2a8S10Xm2LHbL22lZnHZ5Iv8mleozkLn16Io7njoxG4KGIvwm/DICUKTKwgP1Pr51MJBUvvR52SQsxns0n9+28AjD08yI+O1q6tUuE+ayb2Q4bo+sffTGfvyksk3NQquH5tXOn8Qj3MrSo3UaVEIvn3U2XbW6tWrWLw4MHY2NjwwgsvsHLlyvJOJfkPYD+gDipLY/KiM0g7ePORru3brCVdR2qLjbaNP8GnizaSnVd8GP1zdZ/jp2d+wsbUhqA7Qby4/UXCUx5dFFqhuD6hjexybQx5GfccmyuhYOn9GNnZ4Tl/Hp7ffI1iY0P+7dv31tZoiJ4+Q2fxAXCuac3/prSmdV9fFJVC6JlYfp91iojgyi9NIpFIJMVRLqUnKyuLTZs2MXKk9pvlyJEj+euvv0hJeXQROpLHCyNrU+z71wEgdX8kebEZj3T9ln36U69LTxTgiat/MWft/hLHtHNvx5o+a/C09uRm+k1e2vESZ2LOVL6wxWHjCt0/NWwXaki4Zthegdj27Yv7rFmGJzQaEpYsIS/23laWkbGKdgNqM/iDVji4WZKZksv2xefZ/+tlEqMzuBmSJCO9JBLJI6dcSs8ff/yBo6MjXbp0AaBly5bUrl2bdevWVahwkn8XFs1rYN7AEdSCpE2hiEKSBlYWiqLQd8zb2NRuiKnIQ9mznL9OhpQ4rrZ9bdb2XUvTGk1JzU1lzJ4xbAvb9ggkLgbXRoUXLP17ElzcCuX0rysNli2aF5rROWntb1zr2pUbL48kad068hO1ztautWwZ+lEbmvXwAgUuH4vm95mn+PPbQFZ/dJxLxwyTREokEkllUS6lZ/Xq1bz44ot6bS+99JLc4pIUi6Io2D9fF8XMiNyoNNIf8QPPyNiYl6dNR9g4Y5ufxpGf5hMRW7J10snCiWU9l9HTpyf5mnymHZ3G4qDFVZeMz85T68OjGN1tUMDYAhLDYOMr8EtnCPmnUiq2F5rRuV8/LFq2BCHIPHOGmE9nEtqpM5Gvv0Hy5i0o2Rk8NcSP3m801ptLCDjw6xUO/naFkFMxJNxKR51fhdFyEonkX0+ZHZmjoqKoVasWly9fxs/PT9d+8+ZNfH19uXTpEvXqVX6RyYdBOjJXLemnoknecg3FRIXrxJYYO1k80vVjIyNZOWUSxuocYh3r03ngABo2qEMd35rFjtMIDd8Hfs/S4KUA9K3Vl/HNxxOdEf3oq7SDfsFSUys4+QOc+AFy07TnPVvB0x9D7W4Umkb5IciLiSH3RiSmPt66BId5t2+T+s9OUnfsIPviRV1fxcQEq06dyGz7LHtPmhU7r8pIwcHNCqeaVjh5WONU0xpnT2ss7Ux1eX/Sk7JlhXeJ5D/KY19lvSqQSk/VIoQgfkkwOddTMKtth/MbTR55Irszx05y8LvZOlOnBgWnXiMYNWp4iWO3hG5h1olZ5It8XVuVVGkvjMxEOLYQTv8CeZnaNp+O0G0a+HZ8ZGLk3rhB6j//kLp9BzmhoQBkm9lz/MnPHtiaE9R/0p3U+CwSbqaTm124g7m5lQlONa1QFIWbV7TlMWSFd4nkv4dUesqBVHqqnvyELGIXBCDyNNj29sXUywZjZwuM7Yq3BFQUYRE32TJ5LPerWhoUBn35Y4kWH4Ad4TuYfHiyXptKUbFr8K5Hb/EpjPQ4OPotnFmmLWUBWovP0x9DzdZ3rURh4FhHu11WiWRfvUrqP/+Qsm0bkWofrtQfrt2aE2oaXF1HpzWzMPX0RAhBWmI2CbcySLiZTsLtdBJuppMcm1nkTp2iwMg5HaTFRyL5j1CtlJ6MjAz8/f3p3LlzRU1ZKUilp3qQduQWKduv32tQwGGQH1ZtKl9p+Gf3ES4t+9KgPazOM4wb/ypPeNgVO/509GlG7x5t0L6813LauLWpMDkfmpRbcORrCFgNmjxtm2tjiLukDXWv5Bw/95N+8iRRr75Gtpk9WRY1sMi6g3lOMsYeHrh++CE2PZ9BKcRJOj9XTVJMJqFnYwncHWlw3ruxI12G18f2EW+TSiSSR0+VlqF4kGvXrtGtW7eKnFLyL8aisZN+g4CkzaHkp1R+BuR69WqhwXBLrVbYHmZ/Op+3V58iNDatyPFFVWkPSSw5IuyRYucJz34D75yF5i8BKoi9oFV4oNJz/NyPma8vqFSY5yTjkByKeU4yAPm3b3Nr4kTCBw0mbf9+AwdxY1Mjanjb0LRbzUJdkyIvJLJ2+kkO/RZCelIVZ8+WSCTVmiqtsi75b5OfWEieFgH58VmVvnYd35o49RqhU3w0KCguPqiA1imB2O35geFfbmbiukDC4w1zChVVpf3LM1/yQ9APVRfZVRQOvvDcYnjuB8NzQq3d6qpkCov8cv14Gs7jxqGysiLnyhVuvj2OiKHDSD9y1OAePljhXVFBy94+1GzggEYtuHD4Fms+OcHRDaGy7pdEIimUMm1vOToWn4ZfrVaTnp6OWl18ttuqRm5vVQ/yU3KI+eI03P8JVMBtSttH6tsTGhqBn58vdXxrEnr6ODt/XkRueipqVJx2aE2QQ0ueb+nFu9398HK01BtfUKW9pnVN1oWsY/mF5QA8W/tZZnaYialRNSuCm3ILFjS+Z+kpoE53GPQLWDlXugiFRX7lJyWRuHwFiWvWILK0Sq9Fy5bUmDABq3Zt9cYXVHi3uy9669bVJE5tu070NW0KAmNTFU261qRlTx/MrWX5C4nk38Ij9emxsrLirbfeokmTJoWev3HjBjNnzpRKj6TUZJyJIWlzqE7xUdma4v5BaxQTo+IHViKZKcnsWbKIa2dOAhBt5sreGk+TYe7A0NZejH+6Lu52hfuPbLq6idknZ6MWalq6tGRht4XYm9s/QulLQcBq7ZaWUAOK1mQi1GBVQ+vf06BflYmWn5BAwpKlJP3+OyJHu1Vl+eST1Hj3XSxbtih2rBCCm5eTOPXXdWLDUwEwMTei2dNeNO/hhZmlVH4kksedR6r0dOzYkaFDhzJhwoRCz587d46WLVtKpUdSJvJTcsiNSiNpcygiMx+rdm44PO9X8sBKRAjBpcP72b/iZ3KzMtGojDni8CTnbRpjamLEi+28eatrHdQaQXh8BrWcrXSK0PHbx3n/4Puk56XjY+vD4u6L8bH1qdLrMeD+HD+ZCbDlTa1zM2h9f3rPBfOq+9vIi40j4eefSdq4EfK0DthWnTpR4913Ma7hTG7EDUx9fXSWovsRQnAjOIFTf10nPkpb9NTM0pjmPbxp+nRNcrPyZZ4fieQx5ZEqPXPmzCEvL48ZM2YUej4qKorp06ezYsWKMgvyKJFKT/UkOzSJ+GUXAHB6qSEWjSt/q6UkUuPj2PXjQiIvnAMgxcGXzdadSDe2xthIQa0WCEClwNxBTRjWxhuAa0nXGLdvHLczbmNnZsfCbgtp5dqqCq+kBPJz4MDncOw7QICdt9b/p1anKhUr79Yt4n/6ieTNW+DBL1OFVHi/H6ERXD93h9N/hZN4W+uXZWxqRH6udh6Z50ciefyoViHrjwtS6am+JP8TTvqhmyjmxrhObIGxfdV/ExcaDUG7t3N47Uryc3MwMrMg2Lsb+/K8sVJnYJ+XQrKJHdkmNhyd0k1n8YnPiuedfe9wIeECJioTZnWcxbO1n63iqymBGye0Vp/kG9rXT74N3aeDSdWGg+dGRhI3/2vSdu/WP6FSUXf/vkItPgVoNIJr/rGc+vM6qfH6zvOKCkZ+LvP8SCSPC9UqZF0ieVjsnvHBpKY1IjufxHUhCHXV6+SKSkWL3v15+cvvcPerjzoni0ahO3jh9kZejVrDoJhtvBq1hvqpl4iIz9SNc7ZwZnnv5fTw7kGeJo+pR6by47kfq19k1/34tIe3jkGrV7WvT/4AP3eGWwFVKpaptzcOI0YYntBoyDx1utixKpVCvTZudH2xvsE5oYGkGMPoPIlE8u9EKj2SaoVirMJpeANtUdKIVFL3GyajqyocPTx5YeZXPPXCSBSVihq5CajuemCrEHSLP4Rlnn5uHwtjC77u+jWvPfEaAD8E/cC0o9PIVVfjkGozG61D84iNYO0G8VdhaQ84+AWo86pMLFNfn0IrvN+eMYOkdetLVCYd3KwKzfNzYksYKXcyDU9IJJJ/HaXe3qpVq1a56iNNnDiRd999t8zjKhO5vVX9yQyKI3FdCChQ442mmNUuPkPyo+b8vp3s+WWRYXvTESz+v6FYmxkbnNt4dSOfn/wctVDTyrUV09pNIyk7qWqKlZaWzETY/h5c3KJ97dECnv8ZTK0fWRmL+0netIno6TNAowGVClMfH3LDwwGw6tIZj9mzMa5Ro8jxl47d5uDaK9qIfQWMjBTU+QJjMyOeGlKXRk95PPI6cBKJpPQ8Mp+eQ4cOlXlyAF9fX3x8qlfkilR6Hg8SN4SQGRCHkZ0prhNaoqpGIcdpCfEsGfeannVBACtrvkgjP19WjmqDpamh4nP81nHeO/QeGXn3tlSqTbHS4gjeBNvfh+xkUBmDRg2IR1rGooD78/wYu7iQuHo1d775FpGbi5G9PW6zZmLbs2eR4+/P86PRCPavusytq8kA+DZxotvLDbG0rWb5lSQSCSAdmcuFVHoeDzQ5auK+DyQ/PgvzRk44vdywWn0LD96/mz1LFiE09xL9RVrXYodTd9r4ubH81TaYF5Jv6MTtE4zZM0avrVoVKy2K1Nvwx+tw45h+u2IEE4MfqcXnQXJCQ7n14WRyLl8GwO6553Cd9hFGNjYljhUawbn9UZzYGoYmX2BubUK3lxpQu3nRFiOJRFI1SEdmyb8WlZkRjsMbgJFC9qUEMk5FV7VIejR5uidvLFrO0Olz6D76bYxMTPBOD2dIzJ+cC4lk7Bp/cvINc1YZKYaKkEZoql/drgex9YAukw3bH1EZi+Iw8/Oj1vp1OL3xBigKKVu3Ej7wOTLPnClxrKJSaN7Dm6FT2+DkaU12eh7//BTM/tWXyc3KfwTSSySSR4VUeiTVGlNPa+x61wIg+e/r5FWzSBsbJ2e8nmhK8559GTp9Dha2djjn3GFo9GaCg68w/rdA8tT6JR+KKlY668QsTkcXH4lU5TjVhUJk58i3kH7n0ctzH4qpKS7vv4fPml8xqVmTvNu3uTHyFWLnzUOTW7LjuJOnNf+b0pqWvbxBgcvHo1k3+zS3Q5MrX3iJRPJIkEqPpNpj3dED8/oOkC9I+O0KmtzqmfHbo15DRsz+GkePmljnpzMkegshZ88wcV0Q+fcpPg8WK1Whwt7MnrisOEbvHs1XZ74iO7+QYqzVATtPrQ+PzlqlaH+/vh9+eBIu/12l4gFYtmpFra1bsRsyGIQgcdlyIob8j+yQki1pRiYq2j9fl+ffa4mNkzlpCdls+SaAE1uuoc7TlDheIpFUb6RPj/TpeSxQp+cSuzAATVpetShTURzZ6els+/pzoi4Fo0HhoFMn6nXpyfz/NcNIdc8nqaBYqZeNFzamNsw/O59NVzcBUNuuNnOemsMTzk9U1WUUz/1lLLISYfObEHdRe67ZCOjzBZhXfcRd2r59RH8yHXViIoqJCTUmTsSmdy/yom4WWcaigNysfI5sDOXKce22qlNNazoOrouiUmQJC4mkipCOzOVAKj2PJ9mhScQvvwACHF9siGWTqi9TURTq/Dx2//w9lw7vB8DfrjnePYcwZ1AzVKqinbEP3zzMjOMziM+Kx0gx4s2mb/J609cxUVWfyLVCyc+BA3Pg+HfajH92XnfLWHSuasnIj48n+pPppB84oH+ihDIWBVwPvMOBtVfITr+Xo0iWsJBIqgap9JQDqfQ8vqT8E05aNStTURRCCE5uXsfxDWsBuGZZmxrPvsbMQc2LjUJLzk7ms5OfsfuGtuRCY6fGfN7pc2rb1X4kcj8UkSe1ZSySIrSv270FPWZUeRkLIQQJy5ZxZ/7X+icUBc/vv8Pm6adRCkl8WEDcjVQ2zj1r0P7kc7Vp2MFDhrhLJI8IqfSUA6n0PL4ItYa4n86TF5WGqY8tNcY0RTGqPmHshXH5yAF2/LgQ1PnEmLlg138s04a0KVbxEULwT/g/zD41m7TcNMyMzJjUahLDGwwv1Am6WpGTDrs/Bv+7hYed68HzP4Fn1RZczTh5ishXXy30nLG7O7a9e2PbpzfmTZoYvDc3Q5L489vAQscqCnjWd6BuKxdqt6iBhbVUgCSSykIqPeVAKj2PN/kJWcR+F4jIUWPT3Ru7Z6pX8svCuHnpAhu+/AyRnUGKsQ22A97i/4aVvPUTmxHL9OPTOX77OADt3NrxWcfPcLd2r2yRH57QPfDneEiP0To7d/4AOv8fGFXNVl1eTAzXnu6uzeZcgKKgmJkhsu85jpt4emLbpzc2ffpg3qgRiqKQnpTN6o+Oo/ffUgEnDysSbt2LKFRUCl4NHKjTyoXazWtgblXNtyUlkscMqfSUA6n0PP7cX6bCcXgDVFYmGDtbYGxnVtWiFUni7Vus+vRjNCl3yFaZonpqCM39vKhXrxZ1fGsWOU4IwYaQDXzt/zVZ+VlYm1gztd1U2ri2ISot6jEoY/E+XNysfe3eHHp+pv39EZewAMMyFu6zZmL77LOkHzlC2j87STt4EJF5rw6XiY83tr37YNunN2HxNhxaG4IQ+j49KXcyueYfxzX/OOKj0nVjVUYKXo0cqdvKhVrNamBmYUx6UjbJcVnSEVoiKSdS6SkHUun5d5C48SqZ/rH3GhRwGOSHVZtqqgAAmakp/DL9E9TR1xGAAmhQcOo1glGjhhc79kbqDT46+hHn75zXa3/sylgUUAUlLEC/jMWD0VuarCzSDx0m9Z9/SD90SM8CZOTsTEaamiwLZyyyE6j18SQDJ+jk2AIFKFbPAqQyVnBwsyLhVrq2eod0hJZIyoVUesqBVHr+HeTFZRL7jb9+owJuU9pWa4vP1dDrbPv4Xe73GtGgMOjLH4u1+ADka/L5PvB7ll9Yrtf+WJSxuBUAS7oZtnu2Bvdm4NIQajTQ/rQqJjIv5dYjKXaqycgg7eBB0nbuJO3gIch7oMK8ouC9aiVWbdsWOj4xOkOrAJ2NJSmm8CrudVq54Opri6OHFY7uVlg7mBXp6yWtRBLJwz+/DSsiSiSPCeq0QrLsCsiPz6rWSk9Y+C0efKypEFy6cr1EpcdYZUxHj44GSo9GaIhKi6reSk9ueuHtt85qj/uxdL6rADW4pwjVaAgh2+GvCdqw+Eq2FKmsrLDr1w+7fv1IO3iQm2Pf0u8gBJEjX8Gsfn1s+2i3wEzvK67s6G5F22dr0fbZWlw6dpsDv14xWCPMP44w/zjdaxNzIxzdtQpQgSLk4G5F5KWEQrfWJBJJ2ZBKj+SxxdjZQrs/9IBzqbFz1YZHl0S9erW4gIIKfSPrjj3HaNuhLa62xX+LLyhjoRH6GYLNjar5t3/HOlpF5X65FRX0mgNpMXAnBO5chqQbkBkPN45qj6IQGvhrItTpXum+QeYNGoBKpe8EDWBkRE5ICHdCQrizYAHmjRph27cPNr37YFrznkzejRxRFAwcoZs97UV6Ug6J0RmkxGaSl60mNjyV2PDUImURAg6uvYJ3I0dp8ZFIyojc3pLbW481GWdiSNocqlN8TGpa4zq+RdUKVQqWL/+dhF2/oULofHsAAjy7MfX912nsWXw2482hm5l5Yqae4uNl48XyXsurt7UnYLVWURFqbURX/wWGlprcDIi/CnFXtErQnRCIuwzJNwqf85W/oVanypa8UCdo6+7dSd+3j9Qd/5Bx6hSo75VIMW/aVGsB6t0LE3d3Lh27zcE1V4q01qjzNSTHZZIUnUni7XQSozNIvJ1Bcmwmhf2Xfm5SCzzrO1T6dUsk1Qnp01MOpNLz7yI/JYfsywkkb9VW+nZ6uSEWT1TfbM0FhEXcJDQ0grp1fQg5sIsru7YCcMylM2PeHEnvxsUrLwVlLMyNzPnw8IfcTL/5eCg+95ewKIuFJj4UFrfVtxQBjN4DXoX71VQ0xTlB5ycmkrZ7D6n//EPm6dN6Zh2Lli0xcXcnbt8JssydinSELoyUO5msmX6SBwyD9B7bhDrNa1TIdUkkjwtS6SkHUun5d1KQrVllbYLbe61QWT4+OVKEEOz7dQXntmtDuw86daLv0CGM7VK72CSGBUSnR/Parte4lX4LbxtvlvdajquVa2WL/ei531JUgFUNGLYWvNtVmVgPkn/nDqm7dpO68x+y/AMo1FSjUlF3/75i638VcOnYbQ6uvaKn76mMFZ5+qQH1n3wMcjZJJBWEVHrKgVR6/p2IPA2x3wWQfycLyxYuOA6rX9UilQkhBIfWrsT/rz8AOODUiQbdejPn+SaYGpechfk/o/gUWIpMLODviRATDEamMOB7aPZCVUtnQF5sLAk//0zSb78bnPNetQqrdqWzUqUnZZMSl4WlvRknt4RxPegOAC17+/DkgNooxdR0k0j+LTzs87ua57OXSEqPYqLCYUg9UCAzMI6sK4lVLVKZUBSFLi++SpsBgwHolnCEKwd28dKyUyRmFBKp9gDu1u4s77UcT2tPItMiGb17NLEZsSWOe+yw89T68NRsDaN2QYNnQZ2rrfm1d6ahs3EVY+LqitOYMVpH6AdI+u031CkppZrH2sEcz/oOOLha0ntMY1r11kaKBey8wc5fLpCXoy5hBolEIpUeyb8KMx9brDtq/USSNoeiycqvYonKhqIodBrxKq37axMNdks4TMa5Izz/wzGuxRUR8n0fHtYeOsXnRuoNRu8eTVxmXInjHltMrWDor9Dpfe3ro9/Ahpe1ztDVCBM3N9xnzbyn+NzdskzbtYvr/QeQdvBgmeZTVApPPleHHq82RGWscD3oDpvn+5OWmF3yYInkP4zc3pLbW/86NLlq4r4LJD8+C8vWrjgOqVfVIpUZIQSH1izH/+8tAOx36kKkS1N+fLEVT/mV7KR9K/0Wo3aO0C2GOwAATs1JREFU4nbGbXxtfVnWaxkuli6VLXbVcm4dbHtHa/VxawLD14Fd8XmPHjX3O0Ln3Y4m+qOPyI2IAMBu4EBcP5qKkV3xkXsPEh2Wwj8/nScrLQ9LW1P6vtUU11ry/5rk34nc3pJIHkBlaoTDED/tNtfZWLKvJlW1SGVGURS6vDSKVv2eA+DphEN4x53nlRWnWXOyiNDt+/C09mR57+W4W7kTkRrB6F2juZN5p5KlrmKavaANX7d01vr5LHkabvqXPO4RYuLmhlW7tpi4uWHZsgW1tm7BcdQoUBRS/vyT68/2J+3AgTLN6V7HjiFTWuPkaUVmai5bvgkg9My/cFtTIqkApKVHWnr+tSRvCyP9+G2M7MxwndQSlfnjl4tTCMGhX5fiv/1PQGvxuWjbiFc7+DL6KV+ikrKo5WyFu13hCRlvpt1k1K5RRGdE42vry/Jey6lh+S8Pc06OhN9egLiLYGwOz/0AjQdXtVTFkhkYSPRH08gNDwfAbuAAXD/6qExWn9zsfPYsu0hEcAIArfv50rZfLengLPlXIaO3yoFUev4baHLVxC4IQJ2YjVU7Nxye96tqkcqFEIKDq5cSsEOr+Oxz7sIlm0a68yoF5g5qwrA23oWOj0qLYtSuUcRkxFDLrhbLey3H2aL65zF6KHLS4I/X4epO7esuU6DrFJ0vTXVEk53Nne+/J3HFStBoMK5RA7eZM7F5upB6ZUXNoRGc3BJG4J5IAOq2cuHpVxpiYmpUSVJLJI8WqfSUA6n0/HfIDksmfkkwAM6vN8G8rn3VClROhBAcXLWEgH+2AbDPuSs3LLywz0sh2cSObBMbjk7pVqTF537Fp7Zdbb7o9AVpuWl423pX70SGD4NGDXs/hePfaV8/8Tw89yNkJj6SgqXlJSsoiNtTP9JZfWwH9Mfto4/QZGeTG3EDU1+fEnP7XDp2m0O/haBRC1x8bOj7VlOEELJgqeSxRyo95UAqPf8tkrZeI+NkNEYOZrhObIXK7PH81nu/4lPwR6ugrdB+wLkLn7z3Gu3rOBU5Pio1itd2vUZs5j1/D5WiYkb7GQzyG1S5wlclAb/C35NAkwd23pB685EULH0YNNnZxC9aRMLyFaDRoFhbIzIytEkO75bAKCmb8+3QJP756QLZGXmYWhiRm60GWbBU8pgjHZklkhKw6+OLkb0Z6qQcUnaGV7U45UZRFLq+8ga1OnRF4V69LhWCbvGHcDHKKna8l60X8zrP02vTCA0zT8wkJiOmcoSuDrR8GUb+Ceb2kBJ5r4xFQcHSlFtVKV2hqMzNcfm//8P3998w8fZGpKffy+qs0RA9fQZ5McW/Zx5+DgyZ0gq7GubkZql1ZSwKCpamJ8nwdsl/D6n0SP71qMyMcRis9efJOBFNzvXkqhXoIVAUhVbdnzFoVyE4FhBS4vg8TZ5Bm0ZoiEqLqhD5qi2+HaHPPMN2odZmd66mWDRrhtsnnxie0GjIvRFZ4ni7GpZ0/J9hygahgZS44pVkieTfiFR6JP8JzP0csGqr9YNI/CMUTe7jm73W0d3ToB6XBoVvTiVw8npCsWO9bb1RKYZ/9vZm9hUpYvXEt6N2S+t+FJW28Gk1xsyvbqHZnI3dS1dzq4aXdaH+29ZOZg8rmkTy2CGVHsl/Bru+tTCyM0WdkE3qroiqFqfc2Dg588yYd1DuexBqTC3J1hgxdo0/4fFFZyN2s3JjRvsZBorPtKPTSMkpXTmExxY7T60Pj3KfT5eJVbXL3vwgBtmc7xL35ZeI3JLLk1g7mNP1pQYG+t6BX0PIzjC0/Ekk/2akI7N0ZP5PkR2SSPyKi6BAjTebYuZbtuy31Ym0hHhiroeyf/nPpCfGk2znxVqHPvjUsGXL2x2wtzQtcmxMRgxRaVFohIYPD39IYnYijZ0a80vPX7AxtXmEV1EFpNyC6HOwbybcuQLWbvDqdnCuW9WSFUtBNuf8O3FEfzQNkZuLzTPP4PnN1ygmJiWOLyhYmpqYxZF1oeTlqHFws6TfuGbY1Sg86k8iqW7I6K1yIJWe/zaJG6+S6R+LsbMFrhNaoJg8ntFcBcRH3eD3T/6P3KwsIhyf4C/bTjxZx4nVo9qVqjp7aFIoo3aNIjknmaY1mvJzj5+xNrV+BJJXMRkJsKq/NomhjQe8tr3ab3UVkH7kCDffHofIy8OmT288581DMS598s34m2lsX3ye9KQczK1N6PtWU9zrPL5fACT/HWT0lkRSRuz71UJlY0p+fBZJf18nOyyZ/JScqhar3Dh7+fDsxCkoigrfxIu0yzjPyeuJfLw1mNJ8p/Fz8GNpz6XYmdlx/s553t73Npl5mY9A8irGykkb1VWjAaTdhpX9ISmiqqUqFdadOuH5/XdgYkLaPzu5PWUqQl16PzXnmjYMmdyaGt42ZKfn8ee3gVw98y+O4JNI7iKVHsl/DpWlCQ7Pa7cyMk/FEL8kmJgvTpPxGP/Tr9W8Fd1efQOAtvEnqJMZzoazN/n5cOkik+o71ueXZ7RbW4Fxgf8dxce6BozcBk5+2vw9q/pD8uMRyWbTtSs1F3wLxsak/v23dsurDIqPlb0Zz7/fklrNnFHna9iz7BJntoeXSlGWSB5XpNIj+U9i4vnA9o2ApM2hj7XFp0Xv/jTv1Q+EoG/Cfpxz7vDlzivsvFA6Za6RUyN+eeYXrE2s8Y/1593975KV/x8Ia7ZxhVf+0mZoTo6EVc9Wy9w9hWHTvTueX38NRkak/Pkn0dOnIzSaUo83MTOi95tNaN7DC4DTf4Wzb+Vl1Hmln0MieZyQSo/kP0l+fCEPc1FE+2NEt1fG4NO0BeTnMixpN5Z5GUxcH0jwzdJFZjV2bsyPPX7E0tiSUzGnmLB/Ajnqx1cRLDW27lrFx8FXu8W1qj+kRle1VKXCtldPPOd9BSoVKX9sJmbmrDJZa1QqhY5D/Ogyoj6KSiHkVAx/LgwkO11Gdkn+fUilR/KfxNjZ4l5K4wKUu+2PMSojI/pPmoKjpxeqrFReSNlLfk4Or68+Q0xK6TLwNndpzo89fsTC2IIT0SeYeGAiueqSQ6Mfe+w8tYqPnbe2NtfqAZAWW/K4aoBt3754fPkFKArJ69cT+9nsMm9TNe7sybPjm2JqbkT0tRQ2fXmW5Nj/wBan5D9FtVB6Fi9ejK+vL+bm5rRr147Tp08X2XfJkiV06tQJBwcHHBwc6NGjR7H9JZLCMLYzw2GQn57iY2RjipFVyaG/1R0zSyuenzwDcxtbLFNv83zaYWJTshm96gwZOfmlmqOla0sWd1+MuZE5R28d5f2D75On/g9887f3hlf/AtuaEH9Vq/ik36lqqUqFXf/+uM+ZA4pC0m+/EffFF2VWfLwbOTHog1bYOJqTcieLTV+d5dbVpEqSWCJ59FS50rN+/Xree+89ZsyYQUBAAM2aNaNXr17ExcUV2v/gwYMMHz6cAwcOcOLECby8vOjZsye3bj0ee/CS6oNVGzfcprTF8aWGKBbGqFNzST3weDixloS9qxsD/28aRsbGuCVepVuGPxdvpzJxfRBqTekehG3c2rCo+yLMjMw4ePMgHxz+oNAyFv86HHzhlW1g467N47N6oDa8/THA/vnncP9sFgCJq1YTN29+mRUfJ09rhkxpjWstW3Iy8tm2MIigfZHcDEmS9bokjz1VnqenXbt2tGnThkWLFgGg0Wjw8vLinXfeYcqUKSWOV6vVODg4sGjRIkaOLF21ZJmnR/IgmefvkPjbFVApuIxvjqnHvyNPzcVD+9j5w7cA7HftwUVLP8Z0rs1HfRuWeo7jt47zzv53yNXk0tOnJ192/hJjVelzwjy2xF+DlX0hPRbcmmijvCwdq1qqUpG0bh0xn84EwGnMGGpMmmhQuqQk8nPV/H979x0fVZU//v91p6ZOKimENJLQexGCKCBgQLChq6t+LKxtLbvu+vu6lkURy4Jld/msdUVd3f0orFhQUSmi9EjvJSShJJBMQspkkkmbZM7vjyETJpkkk0lC2nk+HvMI3nvOvXeOkHnPKe/z40fHyNxb/wVU7tAudbZunaenurqaPXv2MGPGDMcxlUrFjBkzSE1Ndesa5eXlWK1WgoO7xy8jqWvyHh6K99AQsAmKV55A1PaM1StDp0xnwo23AHBVwUYiK3N5b/NJ/rkxk+2ZBeSWtDxxe1LUJP4+7e9oVVrWnVnHM1ufIac0h525O3v27uyhiXD3avDtA8ZD8J8bIe8YnNrc5Vd3Bf3614QvWABA4XvvUfDGm1iNRiy/7Ghxd/Y6Gp2ay29KcDomBPz8n+Mc/Dmb6kr3hkolqSvp1J6enJwcoqKi2L59O8nJyY7jf/rTn9i0aRM7duxo8RoPP/wwa9eu5ciRI3h5ebksU1VVRVVV/QoUs9lMdHS07OmRnNSWVpP39z3YymswzIzFMD2msx+pXQibjW+XLiF9x3bw8uXj0Bswa+1/71UKLJ43nFvHt/xeN2Zv5I8b/0iNrf7DTqWoWJi8kHlJ8zrq8Ttf/jH4aA6UXzTEpajs+3iNca93ubMUffwxeYuX2P9DUexRi0pF5AuLCLz55hbrn00r5uu/73N5Tq1RETM0mIQxYcSPCEXn3Qt6/6RO1617etpqyZIlrFixgq+++qrJgAdg8eLFBAQEOF7R0dGX8Cml7kLtryPwOvs3W/NPWViNXXsjSncpKhWzH36c8P6JUGlhbt736Gz2LwE2Ac98editHp+p0VNZcNkCp2M2YWNR6qKe3eMTNhjmLXM+Jmzw7R+6fI9P8N13E/LbB+3/Uff91mYj97mFbvX4BIZ5u9yh3RDqRW2NjVMHCvjxX0f58ImtfP/OQdJ2GKmukD1AUtfVqUFPaGgoarWavDznZaF5eXlEREQ0W/f1119nyZIlrFu3jhEjRjRb9umnn6akpMTxys7uGZNVpfbnPbIPXoODoVZQtPIEorZnZKfVenlxwxPPojMEEWItZk7eD/Qrz8a3poxaIThd4N7S5GhD4y8MNmEju7SH/5tSu1jVJ2rh2LeX/llayXdicuODNhvlBw60WLfhDu2KCqbdOYj/eTGZWxdcxrhr4ggM92kUAH33tnMAVFZcKSdCS11Cp/ZH6nQ6xo4dy4YNG7jhhhsA+0TmDRs28OijjzZZ79VXX+Xll19m7dq1jBs3rsX76PV69Hp9ez221IMpikLQjUkYT+3Beq6M0i1nMUztGT2DfsEhXPW7p1j98tP0q8ylX+VqbCj8HDoFISa4dY0YQwwqRYVNOM95+ir9K4aGDMVH69MRj975ghPsn/gN3jdrnoTjq2HKnyDuClx2i3QyXVwsqFTQIFNzzv97goqduwi5/z60zXzJHHJ5X2KGBFOSX0FAmDd+QfZe9dB+foT28+Oya+MpPGchc28+GXvyMeWVc/pgAacPFqDWqAiM8KHwXBkIORFa6nydPrz1+OOPs2zZMj7++GOOHTvGQw89hMViYf78+QDcddddPP30047yr7zyCs8++ywffvghcXFxGI1GjEYjZWVlnfUWpB5GbdAReK19t23z+jNY83tOgraYqHA01PdeqRBMK9jEk//ZQkZ+aYv1I3wjWJi8ENWFr/7KhURH3578lnnfzGOXcVfHPHhnC4iyz+FR1Pb/VlQQezmotHB6iz2D879mQ+ZP9cNIXYQ2IoLIFxbZAx8AlQptTAxYrRR/8gmZM68m9/nnsTaT9sMvyIuogUGOgOdiiqIQ2s+PCdf15/bnJ/DrZ517gArP2gMesDfNxv87Lnt8pE7T6UvWAd58801ee+01jEYjo0aN4h//+AcTJti/eU6dOpW4uDg++ugjAOLi4jhz5kyjayxcuJDnn3/erfvJJetSS4QQFH50hMq0YnTR/vR5aCSKqut9i2+trMMHWfniM42OfxN+DZawAax4YAKJYf4tXsdoMZJdmk20fzSnzad5bttz5Frs2zbcMfgOHhvzGN6a7p3d2qWSc1B0EoL72wOhkrOwdSns/TfUbdcRNQ6mPAlJM7tUz4/VaKT6TBa62Bg04eGU79hBwVtvU77rQqCq0RB44w2EPPAAunaY9yiE4Nj2XH7+z/FG54ZM7svlNyei85KTn6XWaevnd5cIei41GfRI7qgpqSLvb3sQVbUEXBOP/5X9OvuR2qy0sIBlj8xvlLDO4hXEytBr0Af1YcUDE0kMa12eorLqMv665698fuJzAGL8Y3hp8kuMDhvdbs/epZlzYfs/YPeHUHOhFyNylD34GTi7SwU/DVl27qTgnXcoT/3FfkCtJuC66wh98AF0cXFtunZZcSX/fma7y84vLz8tY66OZdiUKLR6dZvuI/UeMujxgAx6JHdZdhkp/iIdNCrCHxuNtk/3n7Ny6Kd1rF/2JsJmQ1EUtF7eVFeUU63x4Zs+V1PTJ47l97c+8AHYdm4bC7cvJK88DwWFu4bcxaOjH8VL0/Tqyh6lNA9S34BdH4D1wrBo+HCY8gQMuhZKc+37egUn2HuKupDyvfsoeOcdLFu22A+oVBjmzCH0tw+i8vWl+vQZdHGxzc7/ceXothw2fnIcYbOPCg6aGMm5dBPm8/YVg97+WsakxDL0yii0Ohn8SM2TQY8HZNAjuUsIQcGHh6lKN6GLNdDnwRE9YpirtLAAkzGHwAj7hNJVr75I/ulMbIqaH0OmUNR3BCsemEhCn9YHPuZqM6/teo1VGasAiDPE8fLklxnRp/lVlj2KpQBS34Kd70H1hfmGfpFQZsQ+o7fr5vmpOHiQgrffoWzjxsYnW5Hj52JlxZVOE6FttTbSduSx+/tTmAvsPWM+Bp09+LmiLxoZ/EhNkEGPB2TQI7VGjamSvL/tRVTXEjC3P/6Tu9Y39PZgrazkh7f+RvrO7QDsDhhNZsyVLH8w2aPAB2Dz2c08v/15zlecR6WouGfoPTw86mH06l60krK8CH55B355uz74qaOo4Q+HulyPT52Kw0fIX/p3yrducz6hKMSt/AzvYcPafI/aWhtpvxjZ/d1pSosuBD8BOsbOimPI5Eg0Whn8SM5k0OMBGfRIrVX2Sy6mVRkoWhXhj41BE9rzJukKm41tn/0fO776DIBMn3j2x8/m/x66kv4eBj4lVSUs2bmE1SdXA5AQkMDjYx/HS+NFjCGGCN/WDZV0W8e/hxW3NT4+7Vm48v/rsnN+LL/sIOueexqfUBR8kydimDMX/6tnovZvefJ7c2prbBxPzWX396cpK7ZPCPcN1DNudizRg4MpLa4i8KLl8lLvJYMeD8igR2otYRMUvH+IqpMl6OIN9Lm/ZwxzuXJ0y8+sffcf2GqsnNeFsCPhej58NIX4UF+Pr7khawMvpL5AUWWR45gKFQsn9fAtLOqUnIOlwxrn+QHoOwam/RkSp3e54MdqNJJx1fRGOX4upuh0+E2ZgmHuXPymTkHVhpxotVYbx7bnsGfNGUfw47iPzPEjIYMej8igR/JETVEleUv3IKptBF6fgF9yz/3lm3PiGF+99hKV5hIsam9S46/j7T/Oa1Pgk1aUxs3fOs8FUVBYfeNqYgw9Y5+zZu39t33rClFrH9pKuArObKuf8Bw9Ea76M8Rf2amP2ZDp88/JfW6hPfC5MKfHZ8IEzN99T8nqb6nOyHSUVfn54T9zJoa5c/CdMAFFY1+SbjUaWzURusZay751Wez89lSjc5NvSWJwcqTc66uXkkGPB2TQI3mqbHsOpm8yUXQqwv8wFk1wz+1uN5/P5/Mliyg+e4YaRc3u6Kt57cl7iPMw8NmZu5N7193b6HigLpCHRj3ETQNu6vnzfRrm+Sk7D9uWwq7365e6x10BVy2AmImd+qgXuzjHz8VBixCCqhMnMK9eTcl331GTk+s4pw4NxTB7NiofbwqXve8UNLV1s1OVRiFmSAiJY/oQN7IPehkA9Roy6PGADHokTwmb4Pyyg1SfMqNPDCT03mEoXWxIoj1VV1aw6u+vkr3fnsDuaPgEFiz4A/FuJDBsyGgxkvJFSqMtLOqE+YRx//D7mZc0D51a16bn7nbMubDlr7DnI7BZ7ccSptt7fqLGduqjuUvYbFTs20fJ6tWU/rCGWpPJdUGVisSfNrTY49NUjp+APl6UnK/P6KxSK0QPCSZxTBjxI0PR+7jYJ03qMWTQ4wEZ9EhtUVNQQd7/7kVYbRhmxaGL9kcT6o0moGf2Uthstaz7+F8cWbMKgLMBSdxw/32UFxcxYEA8CXHuJ238Mv1LFqUuwiZsqBQVf57wZ4QQLDu0jLxy+8bDEb4R3D/8fm5IvKH3BT+mbNj8Guz/BGwXdisfeA1Mewa8g7tsjp+GhNWKZft2Cj/6mPLU1EbnDddeS8hv5qMfNKjZLw0Nc/xMvWMQgydFUpRjIWNvPpl78ik21m8To1IrRA8OJmFMH+JH9sHLV0tZcSWm/Ao5EbqHkEGPB2TQI7VV6ZZzlHx3sv6AAkHzkvAd33NXI6Wu+YGtH72DStgQgALYUAhJuZ3f/MbFyqQmXLyFRd3qreraar5I/4L3D75PfkU+UB/83Jh4I1pXu5z3ZEWnYNOrcHBF48nPXTjHT0MtTYTWxsZgSJmFYVYK+sGDXQZADXP8NOQIgPbmU5RjcRxXqRQCI3wcx+RE6J5BBj0ekEGP1FY1xZUYX2m8uabPZRHoYwxoI3zQhPmg6mFJ1n78YT37P/pfLv5oEkBFzCjCEwfRf0ACI4YNIqxPkEfXr6qt4vMTn/PBoQ84X3EegL6+fbl/xP1cn3B97wt+CtJh/bOQ9kPjc4Ovh35jIWwIhA0GQ1SXW/0FjSdCB9x8E7ZiE2WbNyOq6ldoaWNjMFydgmH2rCYDoJYU5dp3e8/cm0/hOYvLMgMnRBAebyAkypfgvn54+fayv1PdnAx6PCCDHqmtKjNNFCw71HwhBdTBXmjDfdGG+6CN8EEb7osm1BtFo6KmpIqagopuNTT2w7otHP3glRbLlWv9qDGE4xMeRVhsHIkDExkxdAABBj8yT5/lxIlTzQ6N1QU/7x96n4KKAgCi/KK4f/j9TIicQE5ZTu/J83Nqs30X95boDfbgJ2ywPRDqM8j+06+P/XzJuU4bHnM1EdpmsVC2aRPmNWvtAVBl/TwdbUwMhpQU/GeloA4Oxnomq9VbYBxPzWHDx403O23IJ0BHSF97ABTc15eQvn4ERfqg89K0eWhMDq21Pxn0eEAGPVJb1ZRUYVyy097NUUcBnzHh1JoqseaVYyuzuq6sUlD5abGZqx31usvQWObps3z55EOoLnrjAqhMnEBNSSFqUx4+1lKXdQVQpfZGX1uBcuG/A2fewX33NT00VllTycoTK/ng0AcUVhY6nes1eX5c5fhRVDDxUTCfhfxjUJhePweoIZ9Q8A6CwgzsW2AokLIYJj50SR7fHTaLhbLNm+0B0KZNTgGQg6LgN2UKXkMGu3XNcquG1ekDoUG/5NApUViKqijMsVBa6OI+F3j5aagsq2/T2OEh9Il2fwL/+exSzhwqrHt0ObTWTmTQ4wEZ9EjtwbLLSPGX6dRNcGkYuNSWVWPNK6fGaMGaV25/GS2IqlqX1wv/4xi04Z7nwblUPvxwOYVrP0WFcDmnp6DIxMHDJzh5IoPzWWeozD+HrjQPr5qKRtcSQFXiRKbfcD2XjR2KSqVyec+Kmgo+PPQh7x58t9G5J8Y9wY1JN+Kva1tW4C6tYY6fa5c6z+mpqbb34uQftQdB+cfsfy46hXNkfhHvYAgfau8R6jPwws9B4BvaeJjsEvYS2crLKdu0CdOqr7Fs2tSma+VEJHN84G32NhO1DEpbTlTpQbySktAlJqCKS6IiJI4yXR9MFg1FORaKciyU130haWfJ8xIYcnlfOaTWBjLo8YAMeqT20tohKiEE5QcLKF7euNtd0aswXBWLb3Jkl58LlHn6LOnpp0lKinN79dZnK78j+/N3mjxf5hWM3+BxXHVNCqNHDGx0vqk8PwBalZbJUZOZHT+bKf2m4KP1ce+NdCcNc/y4o7rcPhl69R/dv493cH0gFDYYTFn2vcPqllBdoknUTW2B4TdjBtrw8BbrW41GyjZsoFIfSIV3H7wrzuNVZWqyvMrHB11CAvqEBM6HDGdrZuOe1/iRoW4NU5UVV3LqQIHr+6gVYoYEkzgunPiRoei8ZI6h1pBBjwdk0CN1JpdDYxdR+WsxTI3Gd0IkisZ1z0d31NTQWGlIf3yKzqAR9T1gpT5hBA0bz8y5sxgyMB5wnedHQSHaP5qs0izHMW+NN1f2u5LZcbOZ3G9yz0942BKXw2Nq+PVyqCiC88fhfJr9Z/FpmvyL6airgscOQmB0Rz6165Vfbub4aa5+9PvLsJWUUJWRSVVmJtWZGVSdPgPW+uHoSn0g2ye+aH+vdUQtt/0ugeBh/Vu8t8scQwoEhntjMtb3eKq1KuKGhZA4LpzY4SFou/iXna5ABj0ekEGP1NkaDo0F3pCIolZh3nCG2gt7DqkD9PhPj8Z3bDiKumcEP00NjRUWl7D62/Wc2rkVn/MnUVP/QWX2jyRs5ERmXZvCAetOXvvpJfwsKsp8bTxx1QJuTLyRdFM6a06tYc3pNWSXZjvq+mp9uSr6KmbFzyI5MhmtWovRYiTLnNV7JkJDy8NjdawVUHCiPgg6vQ2yf2lczicURt0Ow38FEcM7bNWYqy0w3Mnm3Nr6wmqlOjubqvQMqjIzKN+xk4wzqsZDY+YDGK6ZTcDcufiMH4+ibjpIcZVjaMjlfSnKsZC+J4+M3fmY8upzDGn0auJHhJI0PpyYwcGotSo5EdoFGfR4QAY9UlfgamhM1Niw7M7D/FOWY6KzOtgLw4wYfEaF9YhNTlsaGsvNK+T7b9eRtWcbfkVnnHqGyvRB+FYVX8gRBCEpdzjNJxJCcLTwKGtO2wMgo8XoOBegDyAhIIF9+fsQCFSKioXJvWAidB1Phsea2yi1TuhAGH4zDLsJQhLa51kv0tQWGB1Zv66XqFJraHJoTNOnD4ZrrsEwdy5ew4a2OseQEIKCs2Vk7M4jfVc+pUX1k6r1PhqCIn0xnixxzD2XE6HtZNDjARn0SF2dsNZStsNI6cZsxyowTZg3hhmxeA8L7RHBjzuyzubxw+o15Oz9BUNJdqPzAijvN5KwxAHEJSYwcsRgIsNDALAJGwfOH2DNqTWsPb220eqvOrcNuo3RYaNJDEwkLiAOrUpOMnXSsJfomtfALwwOrYS0NVB70W7oUWPtvT9D54F/y/NuurKGvUQRzy9EFxuHefVqzOvWYSspcZTVxcZimDsXw5w56PvHt/peQgjyTpvJ2JVPxp48LCWuJ1IPmxpFZP8AQqL8CAz3Qd2Dhr/dJYMeD8igR+oubFW1lKXmULrpLKLCvnxWG+mLYWYs2r6+1BRWdqs8P23xyfKvMa5a1mK5Co0PVkM43uH9CIuJI2FgIsOGJPBdzje8tvs1fCrUGMo1mH1qKPd2XkmnUWmIM8SRFJhEYlCi42eUXxQqRdXmobFuO7TWVC9RpRmOr7YHQCc31vcIKSr7bvHDfwWDr4WqMs9Xf7V15Vgb6jfVS2SrrsaydSvm1d9R+tNPTkvsvYYOtQdA18wGIVq1uzzY9/c7+HM2W1dmNFtOpVIICPchJMrXkWcoJMoXQ4g3ikqhOC2bwqPZhAyJJmhg6+dftWVorSOH5WTQ4wEZ9Ejdja2yhtIt5yjbeq7xkvdulOenLZqaCF2dNJHqkiJUpjx8q0uarF+m96dKV0BwqQ4FBRuC1OFFJF15JefKzpFhysBidZ3F11vjTbBXMOfKzgH2CdRToqcwJGSI289/tPAom7I39dyhtbJ8OLLKHgCd3Vl/XNGAqMt3o8DA2RA5yr1r5u6/kI1atL5uw/odtPLMZrFQ+tNPlKxejWXrNqh1kZJCUfCbNg2voe79fSm3alh9PJGGOYZiAksor9ZRUqnHanM9n0it2NCJciqEr31cTAj6BlUQNXmI21muz2eVOq0+ix8ZSp8Y91JCONVVYFo7D8vJoMcDMuiRuqtaixXz+tNYfjE6n1Ag4qnLenyPT0s5gkwlpRw4lEbmiXTyz9hzBGnMeXjXlLu8ngB2DrqFqCEjGBDuR3hwBWp9PgXVp8kwZZBhyuCk6STVtvbP26JSVKy9aW336vFxV9EpOPwF7P/U3svSVSgq+MMhCHB/k9zWqCkqonTtWkxffkXloRYytrfAVY6hvkb75q0CqNIHYvHtS5lvXyy+kVh8+2LxicDWxTbpVVRw18uT2q3HRwY9HpBBj9SdNbUFhravL4HXJ6KP7dl/pz3JEXQuN59VK7+lcttXLs8X6EJI800i3TeRUq0/Ad5aBkb4MzjCn6RwXwpI5YO0xY3qTe03lTCfsBbvn1+ez8azGxsdHx8+ngXJC+gf0PIy6G7p5Cb493WNjw+YDYbI5uuac+GEiz3H3KnbXP3gBJi1GJKu7rBVZ03lGPKdNg1teMt/X6x5eVh+3tgox1BL9YWAU3l69jOh0bnQ8wfRN+wJVUDl54c6MAB1QCDqwEAqVL5kZzZOJBo3IhTfwOa/VFlMVZw+2Dg/0Q1/HE3UQM/242tIBj0ekEGP1J21lOdHPyCIgJmx6FqRMr83aGp4TFFUTquTcrwiSfNNJMM3gUq1NwCKpgTfxCUoykV1hcKns75hRERci/d2lWOojkpRMSd+Dr8d+VtiDDGev8GuqKkcQX841PL8mrbUbar+xaLGwVV/hv7T2j346agcQ+7UL07L5tO/pTXKMXTTDTq8ygupOnGCqvQTVJ5Ip7agcYDSVI6i2x8f1OLcIFf5ibpaT0/vm/otSd2cJkBP0Lyk+uF+BQyz4vAZFw4qqDpRTP5b+yn4+AjVOWWd+qxdSUJcP0JSbsd2oeFsKASn3MHD73/CzAceJXqIPd9M38pcphVu4f6z/+bh6p+5zs9IqNpAVe48vMs1RBTq8S7XUJU7j8wc97LpRvhGsDB5IaoLHyQqRcVDIx9iWvQ0bMLGtye/5bpV17Fw+0JyynI6rA0uuYAo+zwa5cL8k7ocQe4ELW2p21T9lMUw6feg8YZzu+E/N8K/roFTW1r7zpqljYgg8oVFULetyoUcQe5OZm5L/aCB0UwYUm5fbQcgapkwpIKI2VMIvGke4U8/RcyHHzJg6xaStm0l5l8fEv70UwTcNA+vYcPwslkYlPapU/1Bacspf+9/qThyhOb6SfyCvJj6P4Mc8VJdfqKulGNI9vTInh6pm3KV56emoALzT1mU78t39AR5Dwuxr/bqBvt6XQrNDY+VFhaQtn0zx7ZtIv9U/VwUtU7POQIIr8535Aj6OXQqxw2DuWVcNL+bnkRUoHeL9zZajGSXZhPtH+2Yy3Ok4Ahv7n+Tree2AvYVZPMS53H/iPt7znwfT3IEtUfdpuqX5sG2pbDrg/ol9/FXwrQFENN4aMhTnZFjqE5xWjaFx7IJGdy61VvV586ROWMmlboAlzmKtDExGGbNwjArBf3gwa3OT9RWcnjLAzLokXo6a3455g1ZVBw871j44j2iD4YZMWj7+LR6z7DeqPBcNse3beb4to2YjLmNzgsUPup3O2VaAzq1itsui+aRaYmEGTz7Jb8/fz9v7n+THbk7ANCpdPxq4K+4b/h9hHqHdt/l7l2ZOQe2/BX2fAy2C9tQJM6Aac/Ycw5dwo1Wu5KGOYoCf3UztcUmyjZvdlqer42NwTBrtj0AGjTIEQBZjcZWL9V3lwx6PCCDHqm3sBotmNefoeLIhcR8Cuii/anOLm1yd3jJmRCCA+u/Z8MHjTdLVXv5kBs6mC01/cjxikSvVXP3pDh+OyWBYF/PVtHsMu7izX1vsjd/LwBeai/GhI/hl5xfsGHrmcvdO5spCza/Bvs+qR/WCR8O+Ucu+UarXYWrXiabxULZpk2Yf1hjD4Cq6hNT6mJj8Z89C0WtoeCddzzeOqQlMujxgAx6pN6m+lwZ5vVnqDxe1PikAhFPjEMT3PLwTG9VWljAskfmNzufoUrnxzGvePsKMP++/OaK/tx3RX8CvFuf4VkIwS+5v/Dm/jc5eP5go/MqRcUPN/5AX3+5LUG7KjoJm16FAytotFJAUcGjuztkq43uyGaxULpxI6Vr1lC2eYtTAOSkFRO43SGDHg/IoEfqrUpTcyj52kXeFBVow33RRviijax/qf1c91b0xuGxQz+tY/2yNxE2G4pKxfR7HyIgNIzjqVvI2JlKVXl9YkOz2o90v0RyggZy09UTmD+5P356DZmnz3LixCkGDIh3a7m9EIIPDn3A/+7730bndCodA4MHMiBoAAOCBjAweCBJQUkYdK5/p8nhsVbY9yl8/ZCLEwqEDoDwoRAxDMIvvAx9Xa8A68Rs0pdSbZmFso0bKfr0Eyr37mt0Pubjj/GdcFm73EsGPR6QQY/UW7W03L0hlZ+2PgiK8EUb6Uf1GTOmrzN65fBYaWEBJmMOgRF98Q8JdRyvsVo5c3Avadu3kLF7B9bK+jwnJk0AZ4MG0DfYj8C0jU0mVmyK0WLk6s+vRrj5P62vb18GBA0gKSjJERTtMe7hxR0vYhNyeMwt7my0ejHvoAsB0ND6nzn74Pv/5/nw2N5/w7ePdavhtbYu1XeHDHo8IIMeqTez7DJS/GW6I2gJvDERr8QgrLkWrMYLr1wLNYUV7gVHvSQbtLus1VWc2reb49s2k7FnJ6LG6rKcDYUDyY+g+DeftK3KWsu+4nXoI79EUQRCKFQbb+Q///NrimvOcKLoBCeK7a9cS+MJ16706GzQ7aXhRqtz/25PaJh32P4yHoa8I1Bwon4eUEtiJ4HGjYnuNZVwZrvzsdbkKOpEDSdByzk9XYAMeqTezp3hKVt1rVMQZM21UH2uDKyNv/0G3z4InxF9Ovqxu53qygrSd+3gu/9+jvr86Ubnj/gOYm/QaEzawBavpWhKUOkKsFWHImoCeOH6odyVHOdUpqSqhPTidEcQdKL4BMeLjmO1NQ685sTPYf6w+QwIGuD2nky9jjvL5Wuq4PxxewBkvBAQ5eyDKnP7P8+Yu+GKxyEorv2v3Y7aulS/OTLo8YAMeiTJMzWmSoyv7GrcA6QC38si8Z8ajaaFVPW9kats0BfThUXjO3gsfoPGoA1y3mbAVF7NC98ec1nziqRQHpuexLi44CbvnVOaw6wvZzU5PBZniCMlLoVZcbNIDEp0+z1JzSg5C0uHN8gmrbInR/R2YzuGimJY8xRNdrX2HQNDb7S/Alu/g3p3JoMeD8igR5I813B4TB3sRW3hhdwdagXfceH4T4lGE9x1srB2BQ03S/UbNoEwjZWsQ/uxXbQzd1h8AgOTr2Bg8mQCwuzfkv+7K4tnvjxMrRCoFBgTE8S+bBO1Nvuv70kJIfx+ehIT+4e4vPeX6V+yKHWRY07PvMR5FFcVs+XsFqfNVBMCEkiJSyElPqXn7gd2qTQcHrt2qQdzeurqq2D4LVCaC6e3OAdT/cbbg58h13fYRqpdiQx6PCCDHklqm4uHx9QGHVUnSyjdkEXVyQsbGqoUfMaEYZgWjSZELoWv4yobdEWpmYxdv5CWuoWswwcQF00CjUhIYsCFAOh8WTXpJ06TNMBeN7uonLc3ZrBy91lqLgQ/l8UH84fpSSQnhDQasnKVDbqsuoyNZzey9vRatp3b5jQMlhSUREpsCilxKXhpvOTKL090RDbpsvNw7Gs4sgpOb8WpNyh6Yn0AJGzdYuVXa8mgxwMy6JGkjlF1sgTzT1lUZZjsB1TgMzrcHvyEyuCnJeXmEjJ2ppKWuoXsI4cQrlYPKQqTb72T8dffhEql5mxxOe9szOSz3dlYa+2/zsfFBvH76UlckRTq9nyd0upSfs7+mbWn17I9Zzs1tprGt0bhkVGP8Jvhv0Gran3+IamdlebBsW/g8JeQlYrr4TAFpj8Hlz8GKvWlfsJ2J4MeD8igR5I6VtUZM+YNWVSdKLYfUMBnVBj+V0Wj6NS9LsePJ8pLTJzYsZ2jm38iN/14o/MqtZrgvv3sr6h+KIHhrM+x8d/0Kspt9o1QR8cE8vvpSUwd0IeTZ865nSOopKqEn7N/5uvMr9lt3N3ovBo1MQExxBviiQ+IJy4gjvgA+59d5QmSOYIuAXMOHP3Gnlgxt3GuHNR6CE268Bpw4ZUEIYmgc7EvX1tyBHVgfiEZ9HhABj2SdGlUZZkp/Sm7yUzQvSnHj6eyDh9k5YvPtKqOzSeAHOFPkSaQYl0gfdXlJOTvRQWtyhG0M3cn9667t1X3DvEKcQRA8QHx5JTl8MmxTxAImSPoUji1GT6+tnV1AqIvCoaSoPg0pL5VnyNo1isw8tfuXevACljzZIflF5JBjwdk0CNJl1b12VJK1pyuH/aqo0DYo6PQRfl3ynN1B662wFBUKn79/CtUVZRTdO4sRTnZFOWcpejcWcpLTC1e04aC5rYFTByeyMAI/yb3CTNajKR8kYLtomE2laLi/2b/H2XWMk6VnLK/zPaf+eX5Ld5bQeHBEQ8yOnw0SYFJhHq7PwQnucFVYkVFDfd8Z19GX3Diwivd/rO8sGOfp53zC8mgxwMy6JGkS68y00TBskMuz+kTAvAe2QfvoaGofeVckYYaboEx8/5HGX7V1S7LVpaV2QOgC6/De/ZRcbbx1iNmtR/H/AeR7puIOjiCQRH+DAj3Z2CEHwPC7X/21WsarfxqrqemrLqMM+YznCw5yamSU+zJ2+PYOLUpgfpAEgMTSQxMJCkoiaSgJBICExzDZHJozAOtWTlmKYTC9PpgKOsXOLurfZ/n7tUQf0W7XEoGPR6QQY8kXXpubYGhUtAnBuIzog/eQ0NQeWsu2fN1dU1tgdESVzmCLmQbcDivCyHdN5F030TM2vrfidHB3vjpNaSdP4uiK0BYQ3lhzuX8z8RYt+7tqqdIQeHyvpdztuwsWaVZTucuFuEbgZ/Wj0xTJgKBgsIzE57h14PcHGbp7TxdOdZUT9Hv9tj3GGuOOQfeGNO4ruzp6Vwy6JGkztEwx0/QvCT0/QMoP1RAxYHzWHPrN+1EreA1IAifkX3wGhyCSm9fedLWzU5742apDXMEBV31K5IHRZGWuoUzB/c55QmyGCI54ZvIfk0cZRo/AHxrygi0lmDSBmDR+BET5M3gvgYGhPuTFO7PgHA/+of6odOoGt37y/Qvee2nl/CzqCjztfHEVQscPUWVNZWcLDlJhimDjOIMTphOkFGcQV55nqO+T4UaQ7kGs08N5d61RPhEMCh4EAmBCSQEJpAYmEh8QDxeTWzv0Jaeol7by9SWHENtzU/UAhn0eEAGPZLUeZoLOqzny6k4WED5gfPU5JfXn9Co8B4cjMpHg2Wn0ePNTl0FXb1lIrWrHEEAFWWlZOxM5fj2zWQfPui0TF4dEc/RUjVJlkxHwPRz6BSO+g9udH21SiE+1JcB4X4khflfGCLzY9O3qyle/8mFSdQQknJHi5OozdVmvs38li++eJPkQ8GoULAhSB1eRHp0WaPyKkVFP79+jiCo7uf+8/v5y46/eLTRamuG9XqktuQYamt+ombIoMcDMuiRpK7ParRQfvA8FQcLqCmoaLKcNtoPxUUPQ0OixoY1u8EHptws1YnFVEz6ju2kpW7h7PEj4OLjQQBekXHUKBoqrDVUVNdSUV1LrYuyKlFLRFW+01CaAEr9I0Hd/NwtUVuJoTQP5aLaNgSRf7gDVSBkmjLJMGWQWZJJSVWJ2+9xeOhwdGrXE7frVNdWc6jAef6ZgsIrV77C+IjxhHg1Tv4oXRoy6PGADHokqfsQQmDNsWDelE3lwYJ2v77P+HAMU2Xm6IZKiwrY+dVK9q/7rrMfxUmpdyiRE6cxe+4sYvqFI4SgsLLQHgDVBUKmTI4XHqeitulguS2C9EGO3qTEwEQSg+w/A/QBTuV67fBYB5JBjwdk0CNJ3Y/LidAKBN6QgMqn5RVftnIrpq8ar2Kqo+3nZ59APSIUTaDcNwyaWC6vKEy/9yG8/Vv+3ZmbV8iuT5c16ukJmP5r/AMDm7+3yUTJhhU01Z9iQ6EsOJbosZOYc20KkeHO+47lluUy64tZ2HCeRL1g4gIC9c3f21Rl4qVfXmq0SWtf377kWnKb3Ly1j3cfRzBkrjLz7clvZX6idiaDHg/IoEeSuqe2zslpWN9ndBi15mqqMk1OwZQu1oD3iFB8hvdBbWh+KKSna81yeVcaTqJ2NzGiq7peE69FpVaRf3AHhtJcR7laVFhC44kbfzlz5l5NWGgg0LZ5OU3Vraip4FTJKTJNmaSb0u29S8UZ5FhyWrzmLQNuYWTYSBICE+gf0B9vjexdbC0Z9HhABj2S1H11xOqt2tJqKg4XUH7wPNWnzfUBkAK6uAB8RobiPSwUUSt63cov8Hy5fJ2mJlG3pe6xE6dZ/91aig7twN9SnxSxFhXlYYkkTryCa+bMIMOYwf6j+xk1ZBSXDRrdqnvvPL7P7boWq4VMUyaZpky2nNvC+jPrmy2voBDtH91omCzeEI/2wnwnufKsMRn0eEAGPZIkNaW2pMq+hP7geaqzSl0X6mUrv7q6A4dP8NMP6yk5sgv/ivp5X7UoqBAo2OPYssghBMclunXNotMZ+OUeddQtGjid6GlzUVQtT5ovqyng47P303As9rr+15Jbnkt6cTqmKpPLuhpFQ4whBi+1F8eKjjlyFM2IncGw0GFuPfvhgsP8eOZH+9AaKhZO6jlDazLo8YAMeiRJckdNcSUVBwuw7Mujxlje6LxhZiy+48JR96Jen65uz/6jbPxhPZajO/Gtdn9VlztqUFGsC6JIG0zRRT9LNAaE4hwMaQN2ERT4NQEVakq8ayk2Xc/jyXdy89h+hPrpKKwsdEy8Ti9Od/y5zNp4SX57eHjUw9yYeGO37/WRQY8HZNAjSVJrNLeFBoAuxh/voaF4DwuRq8C6iO/XbubYh682Om4OSUDjF+CiRr2ashIMhU1Pem9IqDTU+IdSYwijxhBGgTYI4+nTTDDtcuQn+jl0Kkf9B6MoMD4umFlDI5g1LIK+gfV/X4QQ5JXn8d3J71i6d2mj+1ze93JCvZsfXjxfcZ7tOdubPD88dDjTY6YzM3YmMYYYt99jVyGDHg/IoEeSpNZoagsNbZQf1nPO38y1kb54D7MHQNpw30v4lNLFXG2/YUNh3ivvtDivqKm6Vz+xCD/FSkF2FkVnsyg4m0XRubPUVFe1+DwCOB43nQPVQZi0gVhV9nk7I/sFMGtYJLOGRRAfav/70tRGr2tvWttiT01TW38MDRnKkcIjTivPkoKSmBkzk+mx00kKTOoWuYdk0OMBGfRIktRaTa0cqzVXUXGkkIrDBVSdLHEKjDR9vC8EQKFo+/pSa672eCJ0Z26/0V237mjPlWNN1RU2GyXn8yk8m+V45aYfpzi3+dVcVXoDeSoDxdogirWBFGsDCYnqx5RRiVwzIpIj5vW8/vPLLrfvaElTW38UVBTwU9ZP/HjmR3Yad1Ir6rcfiTXEOnqAhoYMZVfafvYf3ceoIaM9nADuWd2WyKDHAzLokSTJEy19+NdarFQetQdAlRkmqL0ov42PBlFe4/hv7+Gh6KL93bpvdXYpFYfqJ+i2pm5b6zvVVSBgTn/8J7fv1gIdqSNWjrXEVX4jgIiEAZScz6PC3PRco2pFS7E2CJVaRWiF0TGR2hI5lJD4JLfuX3gqHd/cI83WrRFVFFVnUWA9Q7H1HOKiAKiPyZdYow4FBYEgN9ILXWS0W/euzs0mMrfSUbds8mie/91LbtV1hwx6PCCDHkmSOpqtsobK40VUHCqgIq0IanrOr1rFV4suyg9thC/aCB/7zzCfJrcD6cxeps6696Gf1rH1w4/wUwdSVmti8m/uceQ3qig1U5RzjqKcbIrOnaUo5ywFZ7Mx5+c57VDurfbHXxtEqbWYitomVhJ2cTYEUxa91G49Pm39/Na0y1O00VtvvcVrr72G0Whk5MiRvPHGG1x22WVNll+5ciXPPvssp0+fJikpiVdeeYVrrrnmEj6xJElS81ReGnxGheEzKoyK40UUfnSkURl9YiBq/+aTH9aWVlOVYfKoblvrN1VXWKxUnSim6kRx/UGVgqaPtz0AivS9EBD5UnmiCNNXGR4llGzvZJSX8t79/UcQFP1QfX3/+p4Wb38DUQMNRA103ri1xmqlJC+XNd+uxXv3KcaHpqAoKoSwsatgLQd0pah9m/+gr7WYMRSdanTcHBzfYl2AKss5gosab/diDFFR69V8pnJ1ZSURhTanYyoUDhzb3+7DXJ7q9J6e//73v9x11128++67TJgwgaVLl7Jy5UrS0tIICwtrVH779u1ceeWVLF68mLlz5/Lpp5/yyiuvsHfvXoYNcy+HgezpkSTpUmpqCw13NjttS92Ounfw/wzGVmbFarRgzbVgNVoQlbVNXqchrwGBKFp1s2WEtZbKEyaP6ra1fmfeG6DMbEGVVeE0sVgIG9W/jiZhdP9m67ZlAjfY5+NsWrgAVYONXt3prWlLXXd1++GtCRMmMH78eN58800AbDYb0dHR/O53v+Opp55qVP7WW2/FYrGwevVqx7GJEycyatQo3n33XbfuKYMeSZIutc7sdejoewshqC2ptgdBFwVCNfnljVa8SW2jDtChCfNBG24fUtSE+6Dt4+20/9yHHy6n/MdvCNAGUWItxmfGdW5P4AZ4/o0FhKZmEKANpsRaREFyotvzctpS1x3denirurqaPXv28PTTTzuOqVQqZsyYQWpqqss6qampPP74407HUlJSWLVqVUc+qiRJUpv4jo9APyDIozkibal7Ke6tKAqaQD2aQD3eg4Idx2sKKzC+vrtRT5Hh6jhU3s1//NgqajCvPd3gRu7VbWv9zrx3k/UvqC2pprakmqp0k9Nxlb8ObbgP2jAfrvcZQ3lMP8dE5oD+cdQUV7Z43zqPD3+QknOn6+sPd79+w7rBE92bfH2pdGrQU1BQQG1tLeHh4U7Hw8PDOX78uMs6RqPRZXmj0djkfaqqqqiqqs+jYDab2/DUkiRJntEE6D1e8t2Wup11b02IN0HzkjzuZVL7advUw9WW+p1576bqew8NwXq+gpq8cqz59ldNXjm1JVXYSqupumgOVt0AkwKY15zGvOa02/dua/2L6xZ/mY5+QFCXSXXQJSYyd7TFixezaNGizn4MSZKkXqcn93B1Rn19rBZ9rPOwjq2yxh4A5ZdTcaKYyoONJyKjUuyvltiE/eVJfVd1Bfb3IIMeCA0NRa1Wk5eX53Q8Ly+PiAjXEXFERESrygM8/fTTTkNiZrOZ6Gj3cg5IkiRJbdPbergudX2VlwZ9jMH+SgrCeKig8cT1J8e3beK7G/WbqqsJ7Tpbs7S8XWwH0ul0jB07lg0bNjiO2Ww2NmzYQHJysss6ycnJTuUB1q9f32R5AL1ej8FgcHpJkiRJUk+jCdATNC/JaYwpaF6S28FXW+q39d6XQqcPbz3++OPcfffdjBs3jssuu4ylS5disViYP38+AHfddRdRUVEsXrwYgMcee4wpU6bw17/+lTlz5rBixQp2797Ne++915lvQ5IkSZK6hO48LNjROj3oufXWWzl//jzPPfccRqORUaNGsWbNGsdk5aysLFSq+g6pSZMm8emnn7JgwQKeeeYZkpKSWLVqlds5eiRJkiSpp+vOw4IdqdPz9HQGmadHkiRJkrqftn5+d+qcHkmSJEmSpEtFBj2SJEmSJPUKMuiRJEmSJKlXkEGPJEmSJEm9ggx6JEmSJEnqFWTQI0mSJElSryCDHkmSJEmSegUZ9EiSJEmS1CvIoEeSJEmSpF5BBj2SJEmSJPUKnb73Vmeo23nDbDZ38pNIkiRJkuSuus9tT3fQ6pVBT2lpKQDR0dGd/CSSJEmSJLVWaWkpAQEBra7XKzcctdls5OTk4O/vj6Io7XZds9lMdHQ02dnZciPTFsi2ah3ZXu6TbeU+2Vbuk23lvo5sKyEEpaWl9O3bF5Wq9TN0emVPj0qlol+/fh12fYPBIP9RuEm2VevI9nKfbCv3ybZyn2wr93VUW3nSw1NHTmSWJEmSJKlXkEGPJEmSJEm9ggx62pFer2fhwoXo9frOfpQuT7ZV68j2cp9sK/fJtnKfbCv3deW26pUTmSVJkiRJ6n1kT48kSZIkSb2CDHokSZIkSeoVZNAjSZIkSVKv0KuCnrfeeou4uDi8vLyYMGECO3fubLb8ypUrGTRoEF5eXgwfPpzvv//e6bwQgueee47IyEi8vb2ZMWMG6enpTmWKioq44447MBgMBAYGcu+991JWVuZU5uDBg1xxxRV4eXkRHR3Nq6++2upnaW/dta2WLVvGFVdcQVBQEEFBQcyYMaPFZ2+r7tpWF1uxYgWKonDDDTe07s23UnduK5PJxCOPPEJkZCR6vZ4BAwZ06L/D7txWS5cuZeDAgXh7exMdHc0f//hHKisrPWyJlnXFtqqsrOSee+5h+PDhaDSaJv9tbdy4kTFjxqDX60lMTOSjjz7yqA1ao7u215dffsnMmTPp06cPBoOB5ORk1q5d27o3L3qJFStWCJ1OJz788ENx5MgRcf/994vAwECRl5fnsvy2bduEWq0Wr776qjh69KhYsGCB0Gq14tChQ44yS5YsEQEBAWLVqlXiwIED4rrrrhPx8fGioqLCUWbWrFli5MiR4pdffhFbtmwRiYmJ4rbbbnOcLykpEeHh4eKOO+4Qhw8fFsuXLxfe3t7in//8Z6uepT1157a6/fbbxVtvvSX27dsnjh07Ju655x4REBAgzp492wEt1b3bqs6pU6dEVFSUuOKKK8T111/ffo3TQHduq6qqKjFu3DhxzTXXiK1bt4pTp06JjRs3iv3793dAS3Xvtvrkk0+EXq8Xn3zyiTh16pRYu3atiIyMFH/84x87oKW6bluVlZWJ3/72t+K9994TKSkpLv9tnTx5Uvj4+IjHH39cHD16VLzxxhtCrVaLNWvWtF8DNdCd2+uxxx4Tr7zyiti5c6c4ceKEePrpp4VWqxV79+51+/33mqDnsssuE4888ojjv2tra0Xfvn3F4sWLXZa/5ZZbxJw5c5yOTZgwQTz44INCCCFsNpuIiIgQr732muO8yWQSer1eLF++XAghxNGjRwUgdu3a5Sjzww8/CEVRxLlz54QQQrz99tsiKChIVFVVOco8+eSTYuDAgW4/S3vrzm3VUE1NjfD39xcff/yxu2+/Vbp7W9XU1IhJkyaJ999/X9x9990dGvR057Z65513RP/+/UV1dbWnb79VunNbPfLII+Kqq65yepbHH39cXH755a1qA3d11ba6WFP/tv70pz+JoUOHOh279dZbRUpKSgvv2nPdub1cGTJkiFi0aJFbZYUQolcMb1VXV7Nnzx5mzJjhOKZSqZgxYwapqaku66SmpjqVB0hJSXGUP3XqFEaj0alMQEAAEyZMcJRJTU0lMDCQcePGOcrMmDEDlUrFjh07HGWuvPJKdDqd033S0tIoLi5261naU3dvq4bKy8uxWq0EBwe3phnc0hPa6oUXXiAsLIx7773X02ZwS3dvq2+++Ybk5GQeeeQRwsPDGTZsGH/5y1+ora1tS7O41N3batKkSezZs8cxZHLy5Em+//57rrnmGo/bpCldua3ccSl/t0P3b6+GbDYbpaWlrfr93iuCnoKCAmprawkPD3c6Hh4ejtFodFnHaDQ2W77uZ0tlwsLCnM5rNBqCg4Odyri6xsX3aOlZ2lN3b6uGnnzySfr27dvoH2176O5ttXXrVj744AOWLVvm3htug+7eVidPnuTzzz+ntraW77//nmeffZa//vWvvPTSS+41QCt097a6/fbbeeGFF5g8eTJarZaEhASmTp3KM888414DtEJXbit3NPUsZrOZiooKt6/jru7eXg29/vrrlJWVccstt7hdp1cEPVLvtGTJElasWMFXX32Fl5dXZz9Ol1JaWsqdd97JsmXLCA0N7ezH6fJsNhthYWG89957jB07lltvvZU///nPvPvuu539aF3Oxo0b+ctf/sLbb7/N3r17+fLLL/nuu+948cUXO/vRpB7k008/ZdGiRXz22WeNAqrm9IqgJzQ0FLVaTV5entPxvLw8IiIiXNaJiIhotnzdz5bK5OfnO52vqamhqKjIqYyra1x8j5aepT1197aq8/rrr7NkyRLWrVvHiBEjmn/THurObZWZmcnp06e59tpr0Wg0aDQa/v3vf/PNN9+g0WjIzMx0ux3c0Z3bCiAyMpIBAwagVqsdZQYPHozRaKS6urqFd9863b2tnn32We68807uu+8+hg8fzo033shf/vIXFi9ejM1mc68R3NSV28odTT2LwWDA29vb7eu4q7u3V50VK1Zw33338dlnn7W6F79XBD06nY6xY8eyYcMGxzGbzcaGDRtITk52WSc5OdmpPMD69esd5ePj44mIiHAqYzab2bFjh6NMcnIyJpOJPXv2OMr89NNP2Gw2JkyY4CizefNmrFar030GDhxIUFCQW8/Snrp7WwG8+uqrvPjii6xZs8ZpDLm9dee2GjRoEIcOHWL//v2O13XXXce0adPYv38/0dHRbWwdZ925rQAuv/xyMjIynD60T5w4QWRkpNP8lvbQ3duqvLwclcr5o6UuWBTtvOtRV24rd1zK3+3Q/dsLYPny5cyfP5/ly5czZ86cVtUFeteSdb1eLz766CNx9OhR8cADD4jAwEBhNBqFEELceeed4qmnnnKU37Ztm9BoNOL1118Xx44dEwsXLnS5TC8wMFB8/fXX4uDBg+L66693uUxv9OjRYseOHWLr1q0iKSnJaZmeyWQS4eHh4s477xSHDx8WK1asED4+Po2WrLf0LLKt6u+j0+nE559/LnJzcx2v0tJS2VYN2qqhjl691Z3bKisrS/j7+4tHH31UpKWlidWrV4uwsDDx0ksvybZq0FYLFy4U/v7+Yvny5eLkyZNi3bp1IiEhQdxyyy29qq2EEOLIkSNi37594tprrxVTp04V+/btE/v27XOcr1uy/sQTT4hjx46Jt95665IsWe+u7fXJJ58IjUYj3nrrLaff7yaTye3332uCHiGEeOONN0RMTIzQ6XTisssuE7/88ovj3JQpU8Tdd9/tVP6zzz4TAwYMEDqdTgwdOlR89913TudtNpt49tlnRXh4uNDr9WL69OkiLS3NqUxhYaG47bbbhJ+fnzAYDGL+/PmNPoAPHDggJk+eLPR6vYiKihJLlixp9OwtPUt7665tFRsbK4BGr4ULF7a9UZrQXduqoY4OeoTo3m21fft2MWHCBKHX60X//v3Fyy+/LGpqatrYIk3rrm1ltVrF888/LxISEoSXl5eIjo4WDz/8sCguLm57ozShq7ZVU7+PLvbzzz+LUaNGCZ1OJ/r37y/+9a9/tb1BWtBd22vKlCkuzzd83ubIXdYlSZIkSeoVesWcHkmSJEmSJBn0SJIkSZLUK8igR5IkSZKkXkEGPZIkSZIk9Qoy6JEkSZIkqVeQQY8kSZIkSb2CDHokSZIkSeoVZNAjSZIkSVKvIIMeSZK6nbi4OJYuXXrJ73vPPfegKAqKorBq1Sq36sTFxTnqmEymDn0+SZKaJ4MeSZI8UvdB3tTr+eefB+D3v/89Y8eORa/XM2rUqGavuXHjxhavu3HjRnbt2sUDDzzQ8W/ShVmzZpGbm8vs2bPdKr9r1y6++OKLDn4qSZLcoensB5AkqXvKzc11/Pm///0vzz33HGlpaY5jfn5+jj//5je/YceOHRw8eLDZa06aNMnpuo899hhms5l//etfjmPBwcHtvrN5a+j1eiIiItwu36dPH4KDgzvwiSRJcpfs6ZEkySMRERGOV0BAAIqiOB2rC3r+8Y9/8Mgjj9C/f/8Wr6nT6Zyu4e3t7Qgy6l46na7R8JaiKPzzn/9k7ty5+Pj4MHjwYFJTU8nIyGDq1Kn4+voyadIkMjMzne739ddfM2bMGLy8vOjfvz+LFi2ipqamVe1QXV3No48+SmRkJF5eXsTGxrJ48eJWXUOSpEtDBj2SJPUIL774InfddRf79+9n0KBB3H777Tz44IM8/fTT7N69GyEEjz76qKP8li1buOuuu3jsscc4evQo//znP/noo494+eWXW3Xff/zjH3zzzTd89tlnpKWl8cknnxAXF9fO706SpPYgh7ckSeoR5s+fzy233ALAk08+SXJyMs8++ywpKSmAfahs/vz5jvKLFi3iqaee4u677wagf//+vPjii/zpT39i4cKFbt83KyuLpKQkJk+ejKIoxMbGtuO7kiSpPcmgR5KkHmHEiBGOP4eHhwMwfPhwp2OVlZWYzWYMBgMHDhxg27ZtTj07tbW1VFZWUl5ejo+Pj1v3veeee5g5cyYDBw5k1qxZzJ07l6uvvrqd3pUkSe1JBj2SJPUIWq3W8WdFUZo8ZrPZACgrK2PRokXMmzev0bW8vLzcvu+YMWM4deoUP/zwAz/++CO33HILM2bM4PPPP/fofUiS1HFk0CNJUq80ZswY0tLSSExMbPO1DAYDt956K7feeis333wzs2bNoqioSK7akqQuRgY9kiR1qIyMDMrKyjAajVRUVLB//34AhgwZ0qlLz5977jnmzp1LTEwMN998MyqVigMHDnD48GFeeuklt6/zt7/9jcjISEaPHo1KpWLlypVEREQQGBjYcQ8vSZJHZNAjSVKHuu+++9i0aZPjv0ePHg3AqVOnOnWVU0pKCqtXr+aFF17glVdeQavVMmjQIO67775WXcff359XX32V9PR01Go148eP5/vvv0elkotjJamrUYQQorMfQpIkqTu45557MJlMbm9BUWfjxo1MmzaN4uJi2QMkSZ1IfhWRJElqhdWrV+Pn58fq1avdKj906FC3t6yQJKljyZ4eSZIkN+Xn52M2mwGIjIzE19e3xTpnzpzBarUC9lxActhLkjqPDHokSZIkSeoV5FcOSZIkSZJ6BRn0SJIkSZLUK8igR5IkSZKkXkEGPZIkSZIk9Qoy6JEkSZIkqVeQQY8kSZIkSb2CDHokSZIkSeoVZNAjSZIkSVKvIIMeSZIkSZJ6hf8fwUYri+JTEl8AAAAASUVORK5CYII=",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 64\u001b[0m\u001b[1;36m0x480\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "rng = np.random.default_rng(seed=112244)  # random number generator\n",
    "\n",
    "num_t1_datasets = 7\n",
    "t1_times = np.linspace(0, 120e-6, 30)\n",
    "\n",
    "for tau in rng.uniform(10e-6, 50e-6, num_t1_datasets):\n",
    "    probabilities = exp_decay_func(\n",
    "        t=t1_times, tau=tau, offset=0, n_factor=1, amplitude=1\n",
    "    )\n",
    "    dataset = dataset_examples.mk_t1_av_with_cal_dataset(t1_times, probabilities)\n",
    "\n",
    "    round_trip_dataset(dataset)  # confirm read/write\n",
    "    dataset_g = dh.to_gridded_dataset(\n",
    "        dataset, dimension=\"main_dim\", coords_names=[\"t1_time\"]\n",
    "    )\n",
    "    # rotate the iq data\n",
    "    rotated_and_normalized = rotate_to_calibrated_axis(\n",
    "        dataset_g.q0_iq_av.values, *dataset_g.q0_iq_av_cal.values\n",
    "    )\n",
    "    rotated_and_normalized_da = xr.DataArray(dataset_g.q0_iq_av)\n",
    "    rotated_and_normalized_da.values = rotated_and_normalized\n",
    "    rotated_and_normalized_da.attrs[\"long_name\"] = \"|1> Population\"\n",
    "    rotated_and_normalized_da.attrs[\"units\"] = \"\"\n",
    "    rotated_and_normalized_da.real.plot(ax=ax, label=dataset.tuid, marker=\".\")\n",
    "ax.set_title(\"Results from repeated T1 experiments\\n(different datasets)\")\n",
    "_ = ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae491ce5",
   "metadata": {},
   "source": [
    "Since the raw data is now split among several datasets, we would like to keep a\n",
    "reference to all these datasets in our \"combined\" datasets. Below we showcase how this\n",
    "can be achieved, along with some useful xarray features and known limitations.\n",
    "\n",
    "We start by generating a mock dataset that combines all the information that would have\n",
    "been obtained from analyzing a series of other datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f7149f12",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Source code for mk_nested_mc_dataset function"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
def mk_nested_mc_dataset(\n",
       "    num_points: int = 12,\n",
       "    flux_bias_min_max: tuple = (-0.04, 0.04),\n",
       "    resonator_freqs_min_max: tuple = (7e9, 7.3e9),\n",
       "    qubit_freqs_min_max: tuple = (4.5e9, 5.0e9),\n",
       "    t1_values_min_max: tuple = (20e-6, 50e-6),\n",
       "    seed: int | None = 112233,\n",
       ") -> xr.Dataset:\n",
       "    """\n",
       "    Generates a dataset with dataset references and several coordinates that serve to\n",
       "    index the same variables.\n",
       "\n",
       "    Note that the each value for ``resonator_freqs``, ``qubit_freqs`` and ``t1_values``\n",
       "    would have been extracted from other dataset corresponding to individual experiments\n",
       "    with their own dataset.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_points\n",
       "        Number of datapoints to generate (used for all variables/coordinates).\n",
       "    flux_bias_min_max\n",
       "        Range for mock values.\n",
       "    resonator_freqs_min_max\n",
       "        Range for mock values.\n",
       "    qubit_freqs_min_max\n",
       "        Range for mock values.\n",
       "    t1_values_min_max\n",
       "        Range for mock random values.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    """\n",
       "    rng = np.random.default_rng(seed=seed)  # random number generator\n",
       "\n",
       "    flux_bias_vals = np.linspace(*flux_bias_min_max, num_points)\n",
       "    resonator_freqs = np.linspace(*resonator_freqs_min_max, num_points)\n",
       "    qubit_freqs = np.linspace(*qubit_freqs_min_max, num_points)\n",
       "    t1_values = rng.uniform(*t1_values_min_max, num_points)\n",
       "\n",
       "    resonator_freq_tuids = [dh.gen_tuid() for _ in range(num_points)]\n",
       "    qubit_freq_tuids = [dh.gen_tuid() for _ in range(num_points)]\n",
       "    t1_tuids = [dh.gen_tuid() for _ in range(num_points)]\n",
       "\n",
       "    coords = dict(\n",
       "        flux_bias=(\n",
       "            "main_dim",\n",
       "            flux_bias_vals,\n",
       "            mk_main_coord_attrs(long_name="Flux bias", unit="A"),\n",
       "        ),\n",
       "        resonator_freq_tuids=(\n",
       "            "main_dim",\n",
       "            resonator_freq_tuids,\n",
       "            mk_main_coord_attrs(\n",
       "                long_name="Dataset TUID resonator frequency", is_dataset_ref=True\n",
       "            ),\n",
       "        ),\n",
       "        qubit_freq_tuids=(\n",
       "            "main_dim",\n",
       "            qubit_freq_tuids,\n",
       "            mk_main_coord_attrs(\n",
       "                long_name="Dataset TUID qubit frequency", is_dataset_ref=True\n",
       "            ),\n",
       "        ),\n",
       "        t1_tuids=(\n",
       "            "main_dim",\n",
       "            t1_tuids,\n",
       "            mk_main_coord_attrs(long_name="Dataset TUID T1", is_dataset_ref=True),\n",
       "        ),\n",
       "    )\n",
       "\n",
       "    data_vars = dict(\n",
       "        resonator_freq=(\n",
       "            "main_dim",\n",
       "            resonator_freqs,\n",
       "            mk_main_var_attrs(long_name="Resonator frequency", unit="Hz"),\n",
       "        ),\n",
       "        qubit_freq=(\n",
       "            "main_dim",\n",
       "            qubit_freqs,\n",
       "            mk_main_var_attrs(long_name="Qubit frequency", unit="Hz"),\n",
       "        ),\n",
       "        t1=(\n",
       "            "main_dim",\n",
       "            t1_values,\n",
       "            mk_main_var_attrs(long_name="T1", unit="s"),\n",
       "        ),\n",
       "    )\n",
       "    dataset_attrs = mk_dataset_attrs()\n",
       "\n",
       "    dataset = xr.Dataset(data_vars=data_vars, coords=coords, attrs=dataset_attrs)\n",
       "\n",
       "    return dataset\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def}\\PY{+w}{ }\\PY{n+nf}{mk\\PYZus{}nested\\PYZus{}mc\\PYZus{}dataset}\\PY{p}{(}\n",
       "    \\PY{n}{num\\PYZus{}points}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{12}\\PY{p}{,}\n",
       "    \\PY{n}{flux\\PYZus{}bias\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{l+m+mf}{0.04}\\PY{p}{,} \\PY{l+m+mf}{0.04}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{resonator\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{7e9}\\PY{p}{,} \\PY{l+m+mf}{7.3e9}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{qubit\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{4.5e9}\\PY{p}{,} \\PY{l+m+mf}{5.0e9}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{t1\\PYZus{}values\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{20e\\PYZhy{}6}\\PY{p}{,} \\PY{l+m+mf}{50e\\PYZhy{}6}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{seed}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{|} \\PY{k+kc}{None} \\PY{o}{=} \\PY{l+m+mi}{112233}\\PY{p}{,}\n",
       "\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Generates a dataset with dataset references and several coordinates that serve to}\n",
       "\\PY{l+s+sd}{    index the same variables.}\n",
       "\n",
       "\\PY{l+s+sd}{    Note that the each value for ``resonator\\PYZus{}freqs``, ``qubit\\PYZus{}freqs`` and ``t1\\PYZus{}values``}\n",
       "\\PY{l+s+sd}{    would have been extracted from other dataset corresponding to individual experiments}\n",
       "\\PY{l+s+sd}{    with their own dataset.}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    num\\PYZus{}points}\n",
       "\\PY{l+s+sd}{        Number of datapoints to generate (used for all variables/coordinates).}\n",
       "\\PY{l+s+sd}{    flux\\PYZus{}bias\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock values.}\n",
       "\\PY{l+s+sd}{    resonator\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock values.}\n",
       "\\PY{l+s+sd}{    qubit\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock values.}\n",
       "\\PY{l+s+sd}{    t1\\PYZus{}values\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock random values.}\n",
       "\\PY{l+s+sd}{    seed}\n",
       "\\PY{l+s+sd}{        Random number generator seed passed to ``numpy.random.default\\PYZus{}rng``.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{n}{rng} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{default\\PYZus{}rng}\\PY{p}{(}\\PY{n}{seed}\\PY{o}{=}\\PY{n}{seed}\\PY{p}{)}  \\PY{c+c1}{\\PYZsh{} random number generator}\n",
       "\n",
       "    \\PY{n}{flux\\PYZus{}bias\\PYZus{}vals} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{o}{*}\\PY{n}{flux\\PYZus{}bias\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "    \\PY{n}{resonator\\PYZus{}freqs} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{o}{*}\\PY{n}{resonator\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "    \\PY{n}{qubit\\PYZus{}freqs} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{o}{*}\\PY{n}{qubit\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "    \\PY{n}{t1\\PYZus{}values} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{uniform}\\PY{p}{(}\\PY{o}{*}\\PY{n}{t1\\PYZus{}values\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{resonator\\PYZus{}freq\\PYZus{}tuids} \\PY{o}{=} \\PY{p}{[}\\PY{n}{dh}\\PY{o}{.}\\PY{n}{gen\\PYZus{}tuid}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}points}\\PY{p}{)}\\PY{p}{]}\n",
       "    \\PY{n}{qubit\\PYZus{}freq\\PYZus{}tuids} \\PY{o}{=} \\PY{p}{[}\\PY{n}{dh}\\PY{o}{.}\\PY{n}{gen\\PYZus{}tuid}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}points}\\PY{p}{)}\\PY{p}{]}\n",
       "    \\PY{n}{t1\\PYZus{}tuids} \\PY{o}{=} \\PY{p}{[}\\PY{n}{dh}\\PY{o}{.}\\PY{n}{gen\\PYZus{}tuid}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}points}\\PY{p}{)}\\PY{p}{]}\n",
       "\n",
       "    \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{flux\\PYZus{}bias}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{flux\\PYZus{}bias\\PYZus{}vals}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Flux bias}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{A}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{resonator\\PYZus{}freq\\PYZus{}tuids}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{resonator\\PYZus{}freq\\PYZus{}tuids}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Dataset TUID resonator frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{is\\PYZus{}dataset\\PYZus{}ref}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{qubit\\PYZus{}freq\\PYZus{}tuids}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{qubit\\PYZus{}freq\\PYZus{}tuids}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Dataset TUID qubit frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{is\\PYZus{}dataset\\PYZus{}ref}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{t1\\PYZus{}tuids}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{t1\\PYZus{}tuids}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Dataset TUID T1}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{is\\PYZus{}dataset\\PYZus{}ref}\\PY{o}{=}\\PY{k+kc}{True}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{resonator\\PYZus{}freq}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{resonator\\PYZus{}freqs}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Resonator frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Hz}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{qubit\\PYZus{}freq}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{qubit\\PYZus{}freqs}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Qubit frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Hz}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{t1}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{t1\\PYZus{}values}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{T1}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{s}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "    \\PY{n}{dataset\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{(}\\PY{n}{data\\PYZus{}vars}\\PY{o}{=}\\PY{n}{data\\PYZus{}vars}\\PY{p}{,} \\PY{n}{coords}\\PY{o}{=}\\PY{n}{coords}\\PY{p}{,} \\PY{n}{attrs}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}attrs}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{dataset}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_nested_mc_dataset\u001b[0m\u001b[1m(\u001b[0m\n",
       "    num_points: int = \u001b[1;36m12\u001b[0m,\n",
       "    flux_bias_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m-0.04\u001b[0m, \u001b[1;36m0.04\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    resonator_freqs_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m7e9\u001b[0m, \u001b[1;36m7.3e9\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    qubit_freqs_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m4.5e9\u001b[0m, \u001b[1;36m5.0e9\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    t1_values_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m20e-6\u001b[0m, \u001b[1;36m50e-6\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    seed: int | \u001b[3;35mNone\u001b[0m = \u001b[1;36m112233\u001b[0m,\n",
       "\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generates a dataset with dataset references and several coordinates that serve to\n",
       "    index the same variables.\n",
       "\n",
       "    Note that the each value for ``resonator_freqs``, ``qubit_freqs`` and ``t1_values``\n",
       "    would have been extracted from other dataset corresponding to individual experiments\n",
       "    with their own dataset.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_points\n",
       "        Number of datapoints to generate \u001b[1m(\u001b[0mused for all variables/coordinates\u001b[1m)\u001b[0m.\n",
       "    flux_bias_min_max\n",
       "        Range for mock values.\n",
       "    resonator_freqs_min_max\n",
       "        Range for mock values.\n",
       "    qubit_freqs_min_max\n",
       "        Range for mock values.\n",
       "    t1_values_min_max\n",
       "        Range for mock random values.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    rng = \u001b[1;35mnp.random.default_rng\u001b[0m\u001b[1m(\u001b[0m\u001b[33mseed\u001b[0m=\u001b[35mseed\u001b[0m\u001b[1m)\u001b[0m  # random number generator\n",
       "\n",
       "    flux_bias_vals = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m*flux_bias_min_max, num_points\u001b[1m)\u001b[0m\n",
       "    resonator_freqs = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m*resonator_freqs_min_max, num_points\u001b[1m)\u001b[0m\n",
       "    qubit_freqs = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m*qubit_freqs_min_max, num_points\u001b[1m)\u001b[0m\n",
       "    t1_values = \u001b[1;35mrng.uniform\u001b[0m\u001b[1m(\u001b[0m*t1_values_min_max, num_points\u001b[1m)\u001b[0m\n",
       "\n",
       "    resonator_freq_tuids = \u001b[1m[\u001b[0m\u001b[1;35mdh.gen_tuid\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_points\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n",
       "    qubit_freq_tuids = \u001b[1m[\u001b[0m\u001b[1;35mdh.gen_tuid\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_points\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n",
       "    t1_tuids = \u001b[1m[\u001b[0m\u001b[1;35mdh.gen_tuid\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_points\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n",
       "\n",
       "    coords = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mflux_bias\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            flux_bias_vals,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Flux\u001b[0m\u001b[32m bias\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"A\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mresonator_freq_tuids\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            resonator_freq_tuids,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33mlong_name\u001b[0m=\u001b[32m\"Dataset\u001b[0m\u001b[32m TUID resonator frequency\"\u001b[0m, \u001b[33mis_dataset_ref\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mqubit_freq_tuids\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            qubit_freq_tuids,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33mlong_name\u001b[0m=\u001b[32m\"Dataset\u001b[0m\u001b[32m TUID qubit frequency\"\u001b[0m, \u001b[33mis_dataset_ref\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mt1_tuids\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            t1_tuids,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Dataset\u001b[0m\u001b[32m TUID T1\"\u001b[0m, \u001b[33mis_dataset_ref\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    data_vars = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mresonator_freq\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            resonator_freqs,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Resonator\u001b[0m\u001b[32m frequency\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"Hz\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mqubit_freq\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            qubit_freqs,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Qubit\u001b[0m\u001b[32m frequency\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"Hz\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mt1\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            t1_values,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"T1\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"s\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "    dataset_attrs = \u001b[1;35mmk_dataset_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    dataset = \u001b[1;35mxr.Dataset\u001b[0m\u001b[1m(\u001b[0m\u001b[33mdata_vars\u001b[0m=\u001b[35mdata_vars\u001b[0m, \u001b[33mcoords\u001b[0m=\u001b[35mcoords\u001b[0m, \u001b[33mattrs\u001b[0m=\u001b[35mdataset_attrs\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    return dataset"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_source_code(mk_nested_mc_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "09512820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.Dataset> Size: 2kB\n",
       "Dimensions:               (main_dim: 7)\n",
       "Coordinates:\n",
       "    flux_bias             (main_dim) float64 56B -0.04 -0.02667 ... 0.02667 0.04\n",
       "    resonator_freq_tuids  (main_dim) <U26 728B '20250713-040852-807-ae83af' ....\n",
       "    qubit_freq_tuids      (main_dim) <U26 728B '20250713-040852-808-26e804' ....\n",
       "    t1_tuids              (main_dim) <U26 728B '20250713-040852-808-444d6c' ....\n",
       "Dimensions without coordinates: main_dim\n",
       "Data variables:\n",
       "    resonator_freq        (main_dim) float64 56B 7e+09 7.05e+09 ... 7.3e+09\n",
       "    qubit_freq            (main_dim) float64 56B 4.5e+09 4.583e+09 ... 5e+09\n",
       "    t1                    (main_dim) float64 56B 4.238e-05 ... 4.154e-05\n",
       "Attributes:\n",
       "    tuid:                      20250713-040852-809-f8f152\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
       "    json_serialize_exclude:    []
"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 2kB\n",
       "Dimensions:               \u001b[1m(\u001b[0mmain_dim: \u001b[1;36m7\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "    flux_bias             \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 56B \u001b[1;36m-0.04\u001b[0m \u001b[1;36m-0.02667\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.02667\u001b[0m \u001b[1;36m0.04\u001b[0m\n",
       "    resonator_freq_tuids  \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m \n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 100\u001b[0m\u001b[1;36m0x1000\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m4\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 1, figsize=(10, 10), sharex=True)\n",
    "\n",
    "_ = dataset.t1.plot(x=\"flux_bias\", marker=\"o\", ax=axs[0].twiny(), color=\"C0\")\n",
    "x = \"t1_tuids\"\n",
    "_ = dataset.t1.plot(x=x, marker=\"o\", ax=axs[0], color=\"C0\")\n",
    "_ = dataset.resonator_freq.plot(x=x, marker=\"o\", ax=axs[1], color=\"C1\")\n",
    "_ = dataset.qubit_freq.plot(x=x, marker=\"o\", ax=axs[2], color=\"C2\")\n",
    "for tick in axs[2].get_xticklabels():\n",
    "    tick.set_rotation(15)  # avoid tuid labels overlapping"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c30a1b",
   "metadata": {},
   "source": [
    "It is possible to work with an explicit MultiIndex within a (python) xarray object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3d1df2bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
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       "  * resonator_freq_tuids  (main_dim) <U26 728B '20250713-040852-807-ae83af' ....\n",
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       "  * t1_tuids              (main_dim) <U26 728B '20250713-040852-808-444d6c' ....\n",
       "Data variables:\n",
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<xarray.Dataset> Size: 2kB\n",
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       "Coordinates:\n",
       "    flux_bias             (main_dim) float64 56B -0.04 -0.02667 ... 0.02667 0.04\n",
       "    resonator_freq_tuids  (main_dim) <U26 728B '20250713-040852-807-ae83af' ....\n",
       "    qubit_freq_tuids      (main_dim) <U26 728B '20250713-040852-808-26e804' ....\n",
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       "    t1                    (main_dim) float64 56B 4.238e-05 ... 4.154e-05\n",
       "Attributes:\n",
       "    tuid:                      20250713-040852-809-f8f152\n",
       "    dataset_name:              \n",
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       "    flux_bias             \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 56B \u001b[1;36m-0.04\u001b[0m \u001b[1;36m-0.02667\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.02667\u001b[0m \u001b[1;36m0.04\u001b[0m\n",
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     "execution_count": 16,
     "metadata": {},
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   ],
   "source": [
    "all(dataset_multi_indexed.reset_index(\"main_dim\").t1_tuids == dataset.t1_tuids)"
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  {
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   "id": "27741c5c",
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    "But, for example, the `dtype` has been changed to `object`\n",
    "(from fixed-length string):"
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  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "62cb3085",
   "metadata": {},
   "outputs": [
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       "\u001b[3;92mTrue\u001b[0m"
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "dataset.t1_tuids.dtype == dataset_multi_indexed.reset_index(\"main_dim\").t1_tuids.dtype"
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