{ "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.float_]] = (0.1, 0.1),\n",
       "    centers: Union[Tuple[complex], NDArray[np.complex_]] = (-0.2 + 0.65j, 0.7 + 4j),\n",
       "    probabilities: Union[Tuple[float], NDArray[np.float_]] = (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{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}{float\\PYZus{}}\\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}{complex\\PYZus{}}\\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}{float\\PYZus{}}\\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.float_\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.complex_\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.float_\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{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{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>\n",
       "Dimensions:     (repetitions: 128, dim_cycle: 3, dim_final: 1)\n",
       "Coordinates:\n",
       "    cycle       (dim_cycle) int64 0 1 2\n",
       "    final_msmt  (dim_final) int64 0\n",
       "Dimensions without coordinates: repetitions, dim_cycle, dim_final\n",
       "Data variables:\n",
       "    A0_shots    (repetitions, dim_cycle) complex128 (-0.23630343679164473+0.6...\n",
       "    A1_shots    (repetitions, dim_cycle) complex128 (-0.23630343679164473+0.6...\n",
       "    A2_shots    (repetitions, dim_cycle) complex128 (-0.23630343679164473+0.6...\n",
       "    D0_shots    (repetitions, dim_final) complex128 (-0.23630343679164473+0.5...\n",
       "    D1_shots    (repetitions, dim_final) complex128 (-0.23630343679164473+0.5...\n",
       "    D2_shots    (repetitions, dim_final) complex128 (-0.23630343679164473+0.5...\n",
       "    D3_shots    (repetitions, dim_final) complex128 (-0.23630343679164473+0.5...\n",
       "Attributes:\n",
       "    tuid:                      20231215-162519-547-e330b9\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\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 \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 \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 \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, dim_cycle\u001b[1m)\u001b[0m complex128 \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, dim_cycle\u001b[1m)\u001b[0m complex128 \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, dim_final\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0.5\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D1_shots    \u001b[1m(\u001b[0mrepetitions, dim_final\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0.5\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D2_shots    \u001b[1m(\u001b[0mrepetitions, dim_final\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0.5\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D3_shots    \u001b[1m(\u001b[0mrepetitions, dim_final\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0.5\u001b[0m\u001b[33m...\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20231215\u001b[0m-\u001b[1;36m162519\u001b[0m-\u001b[1;36m547\u001b[0m-e330b9\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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      ],
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     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "
<xarray.Dataset>\n",
       "Dimensions:     (repetitions: 128, cycle: 3, final_msmt: 1)\n",
       "Coordinates:\n",
       "  * cycle       (cycle) int64 0 1 2\n",
       "  * final_msmt  (final_msmt) int64 0\n",
       "Dimensions without coordinates: repetitions\n",
       "Data variables:\n",
       "    A0_shots    (repetitions, cycle) complex128 (-0.23630343679164473+0.61937...\n",
       "    A1_shots    (repetitions, cycle) complex128 (-0.23630343679164473+0.61937...\n",
       "    A2_shots    (repetitions, cycle) complex128 (-0.23630343679164473+0.61937...\n",
       "    D0_shots    (repetitions, final_msmt) complex128 (-0.23630343679164473+0....\n",
       "    D1_shots    (repetitions, final_msmt) complex128 (-0.23630343679164473+0....\n",
       "    D2_shots    (repetitions, final_msmt) complex128 (-0.23630343679164473+0....\n",
       "    D3_shots    (repetitions, final_msmt) complex128 (-0.23630343679164473+0....\n",
       "Attributes:\n",
       "    tuid:                      20231215-162519-547-e330b9\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:    []
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      ],
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       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m\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 \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 \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 \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0.61937\u001b[0m\u001b[33m...\u001b[0m\n",
       "    A1_shots    \u001b[1m(\u001b[0mrepetitions, cycle\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0.61937\u001b[0m\u001b[33m...\u001b[0m\n",
       "    A2_shots    \u001b[1m(\u001b[0mrepetitions, cycle\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0.61937\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D0_shots    \u001b[1m(\u001b[0mrepetitions, final_msmt\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0\u001b[0m\u001b[33m...\u001b[0m.\n",
       "    D1_shots    \u001b[1m(\u001b[0mrepetitions, final_msmt\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0\u001b[0m\u001b[33m...\u001b[0m.\n",
       "    D2_shots    \u001b[1m(\u001b[0mrepetitions, final_msmt\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0\u001b[0m\u001b[33m...\u001b[0m.\n",
       "    D3_shots    \u001b[1m(\u001b[0mrepetitions, final_msmt\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0\u001b[0m\u001b[33m...\u001b[0m.\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20231215\u001b[0m-\u001b[1;36m162519\u001b[0m-\u001b[1;36m547\u001b[0m-e330b9\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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YyDg9GhoaUoeoxb/iZfH+r8l34XK56NOnD/r06YM1a9YgODgYCxYsQERERJ3srgvGxsYAqvdweZ/79++jSZMmrFGez4W2tjZUVVVRVVVV57a+PwVJCMGjR48Y56hFixYAqkdZatN56NAheHp6siLQSktLJTaRrOn+ievS0dGRWZe4/6VNn0q7J41FffoOALZu3Yrw8HAsXboUy5Ytg4+PD44fPy4hJx51fbcfHz58CADMQu8WLVqgsLCwwZ75huJT2CXr+4DH48HFxQUuLi4QiUSYMmUKtm3bhoULFzI/0ihfHnRND+Wz07NnT3Tu3Blr165FaWkpdHR00LNnT2zbtg1ZWVkS8i9evGD+fn96RkVFBWZmZigrK2PSWrRogfv377PK3blzR2aklRixQ/H+y/T169cSsuJf2O/W/bHo6enBxsYGv//+O8uGxMREXLhwAd9//32D1VUf5OTkMGzYMBw+fBiJiYkS+e/2tZg//vgDb9++ZT4fOnQIWVlZGDBgAIDqfWpatGiBVatWobCwUKZOOTk5iV/gGzZskBi9q+n+OTk5QU1NDcHBwaioqKixrnf7/91ps/DwcGY7gS+B+vRdWloaZs+ejWHDhmH+/PlYtWoVTpw4gT/++EOi3PPnz1nh+QUFBfjjjz9gY2PDjAy5ubkhJiYG58+flyifl5eHysrKhmhivfkUdtX0PL3/PcTlchlnviG/DygNDx3poTQKs2fPxogRI7Br1y5MnjwZmzZtQrdu3WBtbY2JEyfC1NQUOTk5iImJwbNnz3Dnzh0AgKWlJXr27IkOHTpAU1MTt2/fxqFDhzB16lRG9/jx47FmzRo4OTnB29sbubm52Lp1K6ysrFBQUCDTrg4dOgAAFixYgFGjRkFBQQEuLi4ICgpCVFQUnJ2dYWxsjNzcXGzevBkGBgbo1q1bg/bNypUrMWDAANjb28Pb25sJWRcKhY26M+zy5csREREBOzs7TJw4EZaWlnj9+jXi4uJw8eJFCcdQU1MT3bp1g5eXF3JycrB27VqYmZlh4sSJAKpfFL/99hsGDBgAKysreHl5oVmzZsjMzERERATU1NRw8uRJAMDAgQOxe/duCIVCWFpaIiYmBhcvXoSWlharThsbG8jJyWHFihXIz88Hn89H7969oaOjgy1btuCHH35A+/btMWrUKGhrayMjIwOnT5+Gg4MDNm7cCABYtmwZnJ2d0a1bN4wfPx6vX7/Ghg0bYGVlJdXB+Bh++eUXAGD2Ctq9ezeuXr0KAAgICKixXF37jhCC8ePHQyAQYMuWLQAAHx8fHD58GNOnT0ffvn2hr6/P6G3VqhW8vb1x69YtNG3aFDt37kROTg5CQ0MZmdmzZ+PEiRMYOHAgxo0bhw4dOqCoqAj37t3DoUOHkJ6eLrGe7HPwKewSfx/4+fnByckJcnJyGDVqFCZMmIDXr1+jd+/eMDAwwJMnT7BhwwbY2NgwWyRQvlAaJWaM8k0gDll/PzSWkOow2hYtWpAWLVowYZ9///038fDwILq6ukRBQYE0a9aMDBw4kBw6dIgp98svv5DOnTsTdXV1IhAIiLm5OVm6dCkpLy9n6d+zZw8xNTUlPB6P2NjYkPPnz9cpZJ2Q6tDXZs2aES6Xy4SvX7p0iQwePJjo6+sTHo9H9PX1ibu7u0R4rDRkhayvXLlSapmLFy8SBwcHIhAIiJqaGnFxcSHJycksGXHI+vth9J6enkRZWblOdkgDAPH19ZWal5OTQ3x9fYmhoSFRUFAgurq6pE+fPmT79u2MjDhk/c8//yTz5s0jOjo6RCAQEGdnZ/LkyRMJnfHx8WTo0KFES0uL8Pl8YmxsTNzc3MilS5cYmTdv3hAvLy/SpEkToqKiQpycnMj9+/eJsbGxREj4jh07iKmpKZGTk5MIA4+IiCBOTk5EKBQSRUVF0qJFCzJu3Dhy+/Ztlo7Dhw8TCwsLwufziaWlJTly5IjU56c2agtZB1DjVRdq67t169ZJhKETQkhGRgZRU1Mj33//PZNmbGxMnJ2dyfnz50nbtm0Jn88n5ubm5ODBgxL1vn37lsybN4+YmZkRHo9HmjRpQrp27UpWrVrF/F+U9YzXFLJen+dWbG9D2vX+90FlZSWZNm0a0dbWJhwOh7kvhw4dIv369SM6OjqEx+MRIyMj4uPjQ7KysiR0Ur4sOITUcWUjhUKh1IHIyEj06tULBw8exPDhwxvbHEodad68Odq0aYNTp041tikUyieDrumhUCgUCoXyTUCdHgqFQqFQKN8E1OmhUCgUCoXyTUDX9FAoFAqFQvkmoCM9FAqFQqFQvgmo00OhUCgUCuWbgDo9FMoXzq+//gpzc/Naz5hKT08Hh8PBrl27mLTAwECJrfQrKysxZ84cGBoagsvlwtXVFQBQWFiICRMmQFdXFxwOBzNmzGjglnx5cDicRt3w8Utg69atMDIyojsJU74JqNNDoXzBFBQUYMWKFfD39/+o09zfZefOnVi5ciWGDx+O33//HTNnzgQABAcHY9euXfjxxx+xe/du/PDDDw1S36dg3759WLt2baPVn5ycjMDAQObE+MbmY/pj3LhxKC8vx7Zt2xrWKArlS6Rx90akUCiyCAkJIWpqaqSkpKRWWWm73FZUVEiUHTlyJGnWrJlEeTs7O+Lg4PDRNn8OnJ2d6707sjQgZUfuunDw4EGJ3Z4bk4/tjzlz5hBjY2MiEokazigK5QuEjvRQKF8woaGhGDRoEBQVFT+ovLy8vETZ3NxcqKurS8jWlP6hiEQilJaWNpg+yqfDzc0NT548QURERGObQqF8UqjTQ6F8oaSlpeHu3bvo27evRF5eXh7GjRsHoVAIdXV1eHp6SpwEDbDX9IjX/ERERCApKQkcDgccDgeRkZHgcDhIS0vD6dOnmXTx1E1ZWRkWLVoEMzMz8Pl8GBoaYs6cORJrQDgcDqZOnYq9e/fCysoKfD4f586dAwBkZmZi/PjxaNq0Kfh8PqysrLBz505WebEdBw4cwNKlS2FgYABFRUX06dMHjx49YuR69uyJ06dP48mTJ4ytzZs3l9mXZWVlmDlzJrS1taGqqopBgwbh2bNnEnJPnjzBlClT0Lp1awgEAmhpaWHEiBGsaaxdu3ZhxIgRAIBevXqx+hEAjh8/DmdnZ+jr64PP56NFixZYsmSJxInwqampGDZsGHR1daGoqAgDAwOMGjWKdbo7AOzZswcdOnSAQCCApqYmRo0ahadPn9a5P8SHpSopKUFDQwMdO3bEvn37WHWID/A9fvy4zH6kUL526CnrFMoXSnR0NACgffv2rHRCCAYPHoyrV69i8uTJsLCwwNGjR+Hp6SlTn7a2Nnbv3o2lS5eisLAQy5YtAwBYWFhg9+7dmDlzJgwMDPC///2PkReJRBg0aBCuXr2KSZMmwcLCAvfu3UNISAgePnyIY8eOseq4fPkyDhw4gKlTp6JJkyZo3rw5cnJy0KVLF8Yp0tbWxtmzZ+Ht7Y2CggKJBdPLly8Hl8vFTz/9hPz8fPz6668YM2YMbty4AQBYsGAB8vPz8ezZM4SEhAAAVFRUZLZ9woQJ2LNnD0aPHo2uXbvi8uXLcHZ2lpC7desWoqOjMWrUKBgYGCA9PR1btmxBz549kZycDCUlJfTo0QN+fn5Yv3495s+fz5yqLf53165dUFFRwaxZs6CiooLLly/j559/RkFBAVauXAkAKC8vh5OTE8rKyjBt2jTo6uoiMzMTp06dQl5eHoRCIQBg6dKlWLhwIdzc3DBhwgS8ePECGzZsQI8ePRAfHw91dXWZ/bFjxw74+flh+PDhmD59OkpLS3H37l3cuHEDo0ePZrW9ffv2uHbtmsx+pFC+ehp7fo1CoUgnICCAACBv375lpR87dowAIL/++iuTVllZSbp37y6xpkd8Evu71OfU6t27dxMul0v++usvVvrWrVsJAHLt2jUmDQDhcrkkKSmJJevt7U309PTIy5cvWemjRo0iQqGQFBcXE0L+PZ3dwsKClJWVMXLik8Lv3bvHpNVnDUtCQgIBQKZMmcJKHz16tMSaHrEt7xITE0MAkD/++INJk7WmR5oOHx8foqSkREpLSwkh1aejA5B6grmY9PR0IicnR5YuXcpKv3fvHpGXl2el19QfgwcPlnqvpTFp0iQiEAjqJEuhfK3Q6S0K5Qvl1atXkJeXlxjFOHPmDOTl5fHjjz8yaXJycpg2bVqD23Dw4EFYWFjA3NwcL1++ZK7evXsDgMQaEEdHR1haWjKfCSE4fPgwXFxcQAhh6XByckJ+fj7i4uJYOry8vMDj8ZjP3bt3BwA8fvz4g9pw5swZAICfnx8rXVpIvkAgYP6uqKjAq1evYGZmBnV1dQk7a+JdHW/fvsXLly/RvXt3FBcX4/79+wDAjOScP38excXFUvUcOXIEIpEIbm5urH7T1dVFy5Yt67T+Rl1dHc+ePcOtW7dqldXQ0EBJSUmN9lAo/wXo9BaF8pXx5MkT6OnpSThDrVu3bvC6UlNTkZKSAm1tban5ubm5rM8mJiaszy9evEBeXh62b9+O7du310mHkZER67OGhgYA4M2bN/WyXcyTJ0/A5XLRokULVrq0/iopKcGyZcsQGhqKzMxMkHdO6Xl/rU1NJCUlISAgAJcvX0ZBQQErT6zDxMQEs2bNwpo1a7B37150794dgwYNwtixYxmHKDU1FYQQtGzZUmo9CgoKtdri7++PixcvonPnzjAzM0O/fv0wevRoODg4SMiK2/r+vk4Uyn8J6vRQKF8oWlpaqKysxNu3b6GqqtooNohEIlhbW2PNmjVS8w0NDVmf3x3lEJcHgLFjx9a45qht27asz3JyclLlyGc4JnDatGkIDQ3FjBkzYG9vD6FQCA6Hg1GjRtW6OSRQvcDc0dERampqCAoKQosWLaCoqIi4uDj4+/uzdKxevRrjxo3D8ePHceHCBfj5+WHZsmW4fv06DAwMIBKJwOFwcPbsWal9Uts6JqB6ndGDBw9w6tQpnDt3DocPH8bmzZvx888/Y/HixSzZN2/eQElJSeIeUij/JajTQ6F8oZibmwOojuJ61zEwNjbGpUuXUFhYyHrxPXjwoMFtaNGiBe7cuYM+ffp80AiAOFqqqqpKahTah1IfW4yNjSESifD333+zRnek9dehQ4fg6emJ1atXM2mlpaUSkXE11R8ZGYlXr17hyJEj6NGjB5OelpYmVd7a2hrW1tYICAhAdHQ0HBwcsHXrVvzyyy9o0aIFCCEwMTFBq1atZLZRVn8oKytj5MiRGDlyJMrLyzF06FAsXboU8+bNY21nkJaWxizGplD+q9A1PRTKF4q9vT0A4Pbt26z077//HpWVldiyZQuTVlVVhQ0bNjS4DW5ubsjMzMSOHTsk8kpKSlBUVCSzvJycHIYNG4bDhw8jMTFRIv/FixcfZJeysnKdp5sGDBgAAFi/fj0rXdoOxnJychIjShs2bJAIN1dWVgYACWdIPCLzro7y8nJs3ryZJVdQUIDKykpWmrW1NbhcLrMVwNChQyEnJ4fFixdL2EQIwatXr1j2SOuPd2UAgMfjwdLSEoQQVFRUsPLi4uLQtWtXCR0Uyn8JOtJDoXyhmJqaok2bNrh48SLGjx/PpLu4uMDBwQFz585Feno6LC0tceTIkTo7AfXhhx9+wIEDBzB58mRERETAwcEBVVVVuH//Pg4cOIDz58+jY8eOMnUsX74cERERsLOzw8SJE2FpaYnXr18jLi4OFy9exOvXr+ttV4cOHbB//37MmjULnTp1goqKClxcXKTK2tjYwN3dHZs3b0Z+fj66du2KS5cusfb+ETNw4EDs3r0bQqEQlpaWiImJwcWLF6GlpSWhU05ODitWrEB+fj74fD569+6Nrl27QkNDA56envDz8wOHw8Hu3bslnJbLly9j6tSpGDFiBFq1aoXKykrs3r2bcRKB6lG2X375BfPmzUN6ejpcXV2hqqqKtLQ0HD16FJMmTcJPP/0ksz/69esHXV1dODg4oGnTpkhJScHGjRvh7OzMmjKNjY3F69evMXjw4HrfCwrlq6JRYsYoFEqdWLNmDVFRUZEIg3716hX54YcfiJqaGhEKheSHH35gwqAbMmSdEELKy8vJihUriJWVFeHz+URDQ4N06NCBLF68mOTn5zNyAIivr6/UduTk5BBfX19iaGhIFBQUiK6uLunTpw/Zvn07IyMOWX8/jFva8RqFhYVk9OjRRF1dnQCoNXy9pKSE+Pn5ES0tLaKsrExcXFzI06dPJULW37x5Q7y8vEiTJk2IiooKcXJyIvfv3yfGxsbE09OTpXPHjh3E1NSUyMnJscLXr127Rrp06UIEAgHR19cnc+bMIefPn2fJPH78mIwfP560aNGCKCoqEk1NTdKrVy9y8eJFCdsPHz5MunXrRpSVlYmysjIxNzcnvr6+5MGDB7X2x7Zt20iPHj2IlpYW4fP5pEWLFmT27Nms+0YIIf7+/sTIyIgeQ0H5z8Mh5DOsDqRQKB9Efn4+TE1N8euvv8Lb27uxzaH8BykrK0Pz5s0xd+5cTJ8+vbHNoVA+KXRND4XyBSMUCjFnzhysXLmyTtFDFEp9CQ0NhYKCAiZPntzYplAonxw60kOhUCgUCuWbgI70UCgUCoVC+SagTg+FQqFQKJRvAur0UCgUCoVC+SagTg+FQqFQKJRvgm9yc0KRSITnz59DVVWVHq5HoVAoFMpXAiEEb9++hb6+Prjc+o/bfJNOz/PnzyUOSqRQKBQKhfJ18PTpUxgYGNS73Dfp9Ii3X3/69CnU1NQa2RoKhUKhUCh1oaCgAIaGhqxjVOrDN+n0iKe01NTUqNNDoVAoFMpXxocuTaELmSkUCoVCoXwTUKeHQqFQKBTKNwF1eigUCoVCoXwTfJNreigUSuNSVVWFioqKxjaDQqF8YSgoKEBOTu6T6adOD4VC+WwQQpCdnY28vLzGNoVCoXyhqKurQ1dX95Pso0edHgqF8tkQOzw6OjpQUlKim4NSKBQGQgiKi4uRm5sLANDT02vwOqjTQ6FQPgtVVVWMw6OlpdXY5lAolC8QgUAAAMjNzYWOjk6DT3XRhcwUCuWzIF7Do6Sk1MiWUCiULxnxd8SnWPdHnR4KhfJZoVNaFApFFp/yO6LRnZ6oqCi4uLhAX18fHA4Hx44dq7VMZGQk2rdvDz6fDzMzM+zateuT20mhUCgUCuXrptGdnqKiIrRr1w6bNm2qk3xaWhqcnZ3Rq1cvJCQkYMaMGZgwYQLOnz//iS2lUCgUCqVxGTduHFxdXWXK9OzZEzNmzPgs9rxPYGAgbGxsGqXuutDoTs+AAQPwyy+/YMiQIXWS37p1K0xMTLB69WpYWFhg6tSpGD58OEJCQj6xpbVz/cBhnJr/K64fONzYplAolAZi2bJl6NSpE1RVVaGjowNXV1c8ePCAJVNaWgpfX19oaWlBRUUFw4YNQ05ODpN/584duLu7w9DQEAKBABYWFli3bh1Lx9WrV+Hg4AAtLS0IBAKYm5tLfK/VNjJeUVEBf39/WFtbQ1lZGfr6+vDw8MDz589ZckuXLkXXrl2hpKQEdXV1qe3mcDgSV1hYmMy+SkpKwrBhw9C8eXNwOBysXbtWqlxmZibGjh3LtNXa2hq3b9+uVxvEdbx7LV++nMmPjIzE4MGDoaenB2VlZdjY2GDv3r0Sthw8eBDm5uZQVFSEtbU1zpw5I7ONQO33GwD8/PzQoUMH8Pl8qU5AaWkpxo0bB2tra8jLy9fqyDQ0gYGBMDc3h7KyMjQ0NNC3b1/cuHHjs9rQGDS601NfYmJi0LdvX1aak5MTYmJiGsmias5MXYlmsU1gI7JHs9gmODN1ZaPaQ6FQGoYrV67A19cX169fR3h4OCoqKtCvXz8UFRUxMjNnzsTJkydx8OBBXLlyBc+fP8fQoUOZ/NjYWOjo6GDPnj1ISkrCggULMG/ePGzcuJGRUVZWxtSpUxEVFYWUlBQEBAQgICAA27dvZ2RqGxkvLi5GXFwcFi5ciLi4OBw5cgQPHjzAoEGDWHLl5eUYMWIEfvzxR5ltDw0NRVZWFnPV9mIuLi6Gqakpli9fDl1dXakyb968gYODAxQUFHD27FkkJydj9erV0NDQqFcbACAoKIhl37Rp05i86OhotG3bFocPH8bdu3fh5eUFDw8PnDp1iiXj7u4Ob29vxMfHw9XVFa6urkhMTJTZztrut5jx48dj5MiRUnVUVVVBIBDAz89P4p32OWjVqhU2btyIe/fu4erVq2jevDn69euHFy9efHZbPivkCwIAOXr0qEyZli1bkuDgYFba6dOnCQBSXFwstUxpaSnJz89nrqdPnxIAJD8/v0Hsjtl/iGTMiSRP/aOY68mcSBKz/1CD6KdQ/guUlJSQ5ORkUlJS8tG6nucVk2uPXpDnedL/z39KcnNzCQBy5coVQggheXl5REFBgRw8eJCRSUlJIQBITExMjXqmTJlCevXqJbOuIUOGkLFjx0rNq8v3JSGE3Lx5kwAgT548kcgLDQ0lQqHwo/TXhLGxMQkJCZFI9/f3J926dauXLmltqEm/LL7//nvi5eXFfHZzcyPOzs4sGTs7O+Lj41Ojjvre70WLFpF27drJtMvT05MMHjy4Tm0QywYGBpImTZoQVVVV4uPjQ8rKyhgZR0dH4uvrS3x9fYmamhrR0tIiAQEBRCQS1ag3Pz+fACAXL15k0ubMmUNatmxJBAIBMTExIQEBAaS8vJxVbtmyZURHR4eoqKiQ8ePHE39/f4n2/t///R+xtLQkPB6P6OrqEl9fX5ltlPVdIbbzQ9/fX91Iz4ewbNkyCIVC5jI0NGxQ/S8T/gaHw+5KLoeLl3f+btB6KJT/EoQQFJdX1vvaHZMOh+WXMXrHDTgsv4zdMen11kEI+WC78/PzAQCampoAqkdxKioqWL/Wzc3NYWRkJHMEOj8/n9Ehjfj4eERHR8PR0fGDbRXXw+FwapzGkoWvry+aNGmCzp07Y+fOnR/Vb2JOnDiBjh07YsSIEdDR0YGtrS127Nghs0xNbVi+fDm0tLRga2uLlStXorKyslY97/b5h8wcfOj9bkguXbqElJQUREZG4s8//8SRI0ewePFilszvv/8OeXl53Lx5E+vWrcOaNWvw22+/SdVXXl6O7du3QygUol27dky6qqoqdu3aheTkZKxbtw47duxgTbkeOHAAgYGBCA4Oxu3bt6Gnp4fNmzezdG/ZsgW+vr6YNGkS7t27hxMnTsDMzKwBe6N+fHWbE+rq6krMnebk5EBNTY3Z1Oh95s2bh1mzZjGfCwoKGtTxaWLTAiRWxHJ8RESEJu1aNFgdFMp/jZKKKlj+/HEBCCICLDyehIXHk+pVLjnICUq8+n/9iUQizJgxAw4ODmjTpg2A6l2meTyexAu5adOmyM7OlqonOjoa+/fvx+nTpyXyDAwM8OLFC1RWViIwMBATJkyot51iSktL4e/vD3d3d6ipqdWrbFBQEHr37g0lJSVcuHABU6ZMQWFhIfz8/D7YHgB4/PgxtmzZglmzZmH+/Pm4desW/Pz8wOPx4OnpWec2+Pn5oX379tDU1ER0dDTmzZuHrKwsrFmzRmq9Bw4cwK1bt7Bt2zYmLTs7G02bNmXJybpv4jL1vd8NDY/Hw86dO6GkpAQrKysEBQVh9uzZWLJkCbjc6veQoaEhQkJCwOFw0Lp1a9y7dw8hISGYOHEio+fUqVMYNWoUiouLoaenh/DwcDRp0oTJDwgIYP5u3rw5fvrpJ4SFhWHOnDkAgLVr18Lb2xve3t4AgF9++QUXL15EaWkpU+6XX37B//73P0yfPp1J69Sp06fpmDrw1Y302Nvb49KlS6y08PBw2Nvb11iGz+dDTU2NdTUkXdyG4V7RTYiICED1L9j4N/Fo3ca2QeuhUCiNi6+vLxITE2td0CuLxMREDB48GIsWLUK/fv0k8v/66y/cvn0bW7duxdq1a/Hnn39+UD0VFRVwc3MDIQRbtmypd/mFCxfCwcEBtra28Pf3x5w5c7ByZfVaxYyMDKioqDBXcHBwnfWKRCK0b98ewcHBsLW1xaRJkzBx4kRs3bq1Xm2YNWsWevbsibZt22Ly5MlYvXo1NmzYgLKyMgk9ERER8PLywo4dO2BlZVVnW4ODg1ntzMjIqHPZj0VWH7dr1461yae9vT0KCwvx9OlTJq1Lly6s/W7s7e2RmpqKqqoqJk0cBR0dHY3+/fvDzc2NOQICAPbv3w8HBwfo6upCRUUFAQEBrD5ISUmBnZ0dy+5338W5ubl4/vw5+vTp85G90XA0+khPYWEhHj16xHxOS0tDQkICNDU1YWRkhHnz5iEzMxN//PEHAGDy5MnYuHEj5syZg/Hjx+Py5cs4cOCA1F9Mn5PvN87G9f2H0SRWFYpcAQrKMxG5+hyG/N+URrWLQvlSESjIITnIqV5lsvNL0XfNFYjemWXhcoCLsxyhK1SsV931ZerUqTh16hSioqJgYGDApOvq6qK8vBx5eXmsX/85OTkSi3mTk5PRp08fTJo0ifUr+l1MTEwAANbW1sjJyUFgYCDc3d3rZavYWXjy5AkuX77cID/07OzssGTJEpSVlUFfXx8JCQlMnqxpuvfR09ODpaUlK83CwgKHD7OjXuvbBjs7O1RWViI9PR2tW7dm0q9cuQIXFxeEhITAw8ODVaammQPxfZs8eTLc3NyYPH19/Xrd74/hY/q4rigrK8PMzAxmZmbo0qULWrZsif/7v//DvHnzEBMTgzFjxmDx4sVwcnKCUChEWFgYVq9eXWf9Nc2+NCaNPtJz+/Zt2Nrawta2elRk1qxZsLW1xc8//wwAyMrKYnmWJiYmOH36NMLDw9GuXTusXr0av/32G5yc6vfl+SnoMnIYBJY6AIAmfHU8hRL+PhzRyFZRKF8mHA4HSjz5el2m2ipYNtQacv/8gpXjcLBsqDVMtVXqpac+O74SQjB16lQcPXoUly9fZpwSMR06dICCggJrBPrBgwfIyMhg/epNSkpCr1694OnpiaVLl9apbpFIJHXkQhZiZyE1NRUXL15ssHPOEhISoKGhAT6fD3l5eeZlaWZmVq8XsoODg0TI/8OHD2FsbPxRbUhISACXy4WOjg6TFhkZCWdnZ6xYsQKTJk2SKFPbzIGmpiarnfLy8nW+3x+LrD6+c+cOSkpKmM/Xr1+HiooKa9nG++Hn169fR8uWLWWeZfXu8xYdHQ1jY2MsWLAAHTt2RMuWLfHkyROWvIWFhdR6xKiqqqJ58+YSfdyYNPpIT8+ePWUujpO223LPnj0RHx//Ca36cIStdJB3/y2a8A1xv+gOrp7iofnACsjxFRrbNArlP8HITkbo0Uob6S+L0byJEvSEn/bXpK+vL/bt24fjx49DVVWVWbchFAohEAggFArh7e2NWbNmQVNTE2pqapg2bRrs7e3RpUsXANVTWr1794aTkxNmzZrF6JCTk4O2tjYAYNOmTTAyMoK5uTmA6j15Vq1axVpDU9vIeEVFBYYPH464uDicOnUKVVVVTF2amprg8XgAqqdOXr9+jYyMDFRVVTEjCmZmZlBRUcHJkyeRk5ODLl26QFFREeHh4QgODsZPP/0ks6/Ky8uRnJzM/J2ZmYmEhASoqKgwi1dnzpyJrl27Ijg4GG5ubrh58ya2b9/OhObXpQ0xMTG4ceMGevXqBVVVVcTExGDmzJkYO3YsE/oeERGBgQMHYvr06Rg2bBijg8fjMQ7E9OnT4ejoiNWrV8PZ2RlhYWG4ffs2a5uA96nL/QaAR48eobCwENnZ2SgpKWH62NLSkrkPycnJKC8vx+vXr/H27VtGprbN/crLy+Ht7Y2AgACkp6dj0aJFmDp1KrOeR3yPZ82aBR8fH8TFxWHDhg3MKE1RURGWLl2KQYMGQU9PDy9fvsSmTZuQmZmJESNGAABatmyJjIwMhIWFoVOnTjh9+jSOHj3KsmP69OkYN24cOnbsCAcHB+zduxdJSUkwNTVlZAIDAzF58mTo6OhgwIABePv2La5du8baXuCz8kExX185HxvyJouyzLfkqX8UeTw7nKx2G0Q2TDxFon/Z1+D1UChfGw0Zsv45ASD1Cg0NZWRKSkrIlClTiIaGBlFSUiJDhgwhWVlZTP6iRYuk6jA2NmZk1q9fT6ysrIiSkhJRU1Mjtra2ZPPmzaSqqoqRiYiIkKrH09OTEEJIWlpajfZGREQwejw9PWXKnD17ltjY2BAVFRWirKxM2rVrR7Zu3cqyRRo11e/o6MiSO3nyJGnTpg3h8/nE3NycbN++vVYd79oXGxtL7OzsiFAoJIqKisTCwoIEBweT0tLSWtv4vi0HDhwgrVq1Ijwej1hZWZHTp0/LbCMhtd9vQqrDxqXVn5aWxsgYGxtLlZGFOGT9559/JlpaWkRFRYVMnDiR1XZHR0cyZcoUMnnyZKKmpkY0NDTI/PnzmZD1kpISMmTIEKKvr094PB7R09MjgwYNIjdv3mTVNXv2bKaOkSNHkpCQEIktDpYuXUqaNGlCVFRUiKenJ5kzZ45EyPrWrVtJ69atiYKCAtHT0yPTpk2rtX8/Vcg6h5AGiEH8yigoKIBQKER+fn6DL2omIoLnQTEgpVW4kLkLb+XMoShvgdELO0LVuOHmeymUr43S0lKkpaXBxMQEiop1X39DoVC+LWR9V3zs+7vR1/T81+BwOeAbV98IbUVDiErjUCEnwJWVZxvZMgqFQqFQvm2o0/MJ4JkIAQBaAgOIyFuQqud4UmmMJxduN7JlFAqFQqF8u1Cn5xPAb1490tNE8M9K+qLqlet/hT2EqLKqpmIUCoVCoVA+IdTp+QTwDFQBeQ6UOEpQVdBECXkBbkUe8nm6iN3YuPsJUSgUCoXyrUKdnk8AR54LnqEqAEBZaAA5wgGvvHrjrfh7QHHWq8Y0j0KhUCiUbxLq9Hwi+M2r1/XoNqk+n+epwisoF2eiQkEFUavoomYKhUKhUD431On5RPD/Wczcit8clVwC1SJ5aCvvBQD8XaiLzL/uNaZ5FAqFQqF8c1Cn5xPBM1YFOACnoApF+tXboscocqFfeBPgcBH1+z2IRKJGtpJCoVAolG8H6vR8Irh8eSjoqwAA7Nu4AABUcoB2RkfBrSrDa3ld3N1+vjFNpFAoFArlm4I6PZ8Qcei6nbAN8pRFUKji4rBKC1iVHQcA3L5VhpKXBY1pIoVCoVAoHwQhBJMmTYKmpiY4HA4SEhLQs2dPzJgxo7FNqxHq9HxCxOt6yp+8RaV5awBAXlo+7NvdglJpDsoU1HB11anGNJFCodTCsmXL0KlTJ6iqqkJHRweurq4Sp4SXlpbC19cXWlpaUFFRwbBhw5CTk8Pk37lzB+7u7jA0NIRAIICFhQXWrVvH0nH16lU4ODhAS0sLAoEA5ubmCAkJYclERUXBxcUF+vr64HA4OHbsGCu/oqIC/v7+sLa2hrKyMvT19eHh4YHnz5+z5JYuXYquXbtCSUkJ6urqUtvN4XAkrrCwMJl9lZSUhGHDhqF58+bgcDhYu3atVLnMzEyMHTuWaau1tTVu375drzaI63j3Wr58OZMfGRmJwYMHQ09PD8rKyrCxscHevXslbDl48CDMzc2hqKgIa2trnDlzRmYbgdrvd137b+/evWjXrh2UlJSgp6eH8ePH49Wrukf3Ll++HBwOR8LJ8PHxQYsWLSAQCKCtrY3Bgwfj/v37LJmMjAw4OztDSUkJOjo6mD17NiorK1kykZGRaN++Pfh8PszMzCQOAD937hx27dqFU6dOISsrC23atKmz7Y0FdXo+Ibx/Rnoqc4rh4fojqjgEmgUcnNcfiC68XQCA1NdNkHPrvgwtFAqlMbly5Qp8fX1x/fp1hIeHo6KiAv369UNRUREjM3PmTJw8eRIHDx7ElStX8Pz5cwwdOpTJj42NhY6ODvbs2YOkpCQsWLAA8+bNw8aNGxkZZWVlTJ06FVFRUUhJSUFAQAACAgJYJ34XFRWhXbt22LRpk1Rbi4uLERcXh4ULFyIuLg5HjhzBgwcPMGjQIJZceXk5RowYgR9//FFm20NDQ5GVlcVcrq6uMuWLi4thamqK5cuXQ1dX+lmDb968gYODAxQUFHD27FkkJydj9erVzOnodW0DAAQFBbHse/fk7ujoaLRt2xaHDx/G3bt34eXlBQ8PD5w6dYol4+7uDm9vb8THx8PV1RWurq5ITEyU2c7a7ndd+u/atWvw8PCAt7c3kpKScPDgQdy8eRMTJ06UWbeYW7duYdu2bWjbtq1EXocOHRAaGoqUlBScP38ehBD069cPVVXVm+NWVVXB2dkZ5eXliI6Oxu+//45du3bh559/ZnSkpaXB2dkZvXr1QkJCAmbMmIEJEybg/Pl/l2X8/fff0NPTQ9euXaGrqwt5efk62d6ofNAxpV85n/KU9ffJWnWLPPWPIsVJL8msGcPIKjdnMnOOOyHbHMnBMavJRp9LJGzinlpPL6ZQvnYa9JT1vGeEPL5S/e9nJjc3lwAgV65cqTYlL48oKCiQgwcPMjIpKSkEAImJialRz5QpU0ivXr1k1jVkyBAyduxYqXkAyNGjR2u19+bNmwQAefLkiUReaGioxKnZ9dVfE8bGxiQkJEQi3d/fn3Tr1q1euqS1oSb9svj++++Jl5cX89nNzY04OzuzZOzs7IiPj0+NOup6v2vrv5UrVxJTU1NW2vr160mzZs1qbcfbt29Jy5YtSXh4OHF0dCTTp0+XKX/nzh0CgDx69IgQQsiZM2cIl8sl2dnZjMyWLVuImpoaKSsrI4QQMmfOHGJlZcXSM3LkSOLk5EQIkTzF3tjYmBBSfcK7r68v8fX1JWpqakRLS4sEBAQwJ7zXhU95yjod6fnEiPfrKUsvQOvu3wMAtJ/mIbvzTPTU3gGOqAIvuXpI/uNSY5pJoXx+CAHKi+p/3dwBrG0D/O5S/e/NHfXXQcgHm52fnw8A0NTUBFA9ilNRUYG+ffsyMubm5jAyMkJMTIxMPWId0oiPj0d0dDQcHR0/2FZxPRwOp8ZpLFn4+vqiSZMm6Ny5M3bu3AnyEf0m5sSJE+jYsSNGjBgBHR0d2NraYseOHTLL1NSG5cuXQ0tLC7a2tli5cqXE9Iw0Pe/2eUxMDOu+AYCTk5PM+1af+y2r/+zt7fH06VOcOXMGhBDk5OTg0KFD+P7772W2QazX2dlZwnZpFBUVITQ0FCYmJjA0NGTabW1tjaZNm7LaXVBQgKSkJEZGVt+sW7cOQUFBMDAwQFZWFm7dusXI/f7775CXl8fNmzexbt06rFmzBr/99luttn4OvoKxqK8bXnM1FN3KRnl6Prx9PBF08hDUirnYdvU2FrU3h/mFM0hRHowbf71Fq6FF4KkpN7bJFMrnoaIYCNb/OB1EBJz5qfqqD/OfA7z6/18TiUSYMWMGHBwcmPUL2dnZ4PF4Ei/kpk2bIjs7W6qe6Oho7N+/H6dPSx5LY2BggBcvXqCyshKBgYGYMGFCve0UU1paCn9/f7i7u0NNTa1eZYOCgtC7d28oKSnhwoULmDJlCgoLC+Hn5/fB9gDA48ePsWXLFsyaNQvz58/HrVu34OfnBx6PB09Pzzq3wc/PD+3bt4empiaio6Mxb948ZGVlYc2aNVLrPXDgADMlJCY7O5v14gdk3zdxmbrc79r6z8HBAXv37sXIkSNRWlqKyspKuLi41Dh1KSYsLAxxcXEsJ0Mamzdvxpw5c1BUVITWrVsjPDwcPB5PZrvFebJkCgoKUFJSAqFQCFVVVcjJyUlMZRoaGiIkJAQcDgetW7fGvXv3EBISUuepu08JHen5xDCLmZ8VglMFlLVqAQAovRsP9A2Eg1EYFMteo1RBHRFLjuHR0Wi8efC0MU2mUCg14Ovri8TExFoX9MoiMTERgwcPxqJFi9CvXz+J/L/++gu3b9/G1q1bsXbtWvz5558fVE9FRQXc3NxACMGWLVvqXX7hwoVwcHCAra0t/P39MWfOHKxcuRJA9SJYFRUV5goODq6zXpFIhPbt2yM4OBi2traYNGkSJk6ciK1bt9arDbNmzULPnj3Rtm1bTJ48GatXr8aGDRtQVlYmoSciIgJeXl7YsWMHrKys6mxrcHAwq50ZGRl1Liur/wAgOTkZ06dPx88//4zY2FicO3cO6enpmDx5MoDq5+Dduvfu3YunT59i+vTp2Lt3LxQVFWXWP2bMGMTHx+PKlSto1aoV3NzcUFpaWmf7P4YuXbqAw+Ewn+3t7ZGamsqsKWpM6EjPJ0ZOgw85NR6qCspRnvEWo918cf7O/9Akn4Pjqc8x2HYgOuf8jijMxKMCXTw6XwqcewA7y3h0nC65cI9C+c+goFQ94lIfCp4DmzpXj/CI4cgBvjcAtXqMGiko1a9eAFOnTsWpU6cQFRUFAwMDJl1XVxfl5eXIy8tj/frPycmR+AWcnJyMPn36YNKkSQgICJBaj4mJCQDA2toaOTk5CAwMhLu7e71sFTsLT548weXLl+s9yiMNOzs7LFmyBGVlZdDX10dCQgKTJ2ua7n309PRgaWnJSrOwsMDhw4dZafVtg52dHSorK5Geno7WrVsz6VeuXIGLiwtCQkLg4eHBKqOrqysRdfXufZs8eTLc3NyYPH19/Xrd7/ftE/cfn8/HsmXL4ODggNmzZwMA2rZtC2VlZXTv3h2//PILOnbsyOrjpk2b4tKlS8jNzUX79u2Z9KqqKkRFRWHjxo0oKyuDnJwcAEAoFEIoFKJly5bo0qULNDQ0cPToUbi7u0NXVxc3b96UaLe4T2T1jZqaGgQCQY3t/NKhIz2fGA6HA554tCc9H21bmCO7KR8AEHVmL9A7AM3UU6vXGIg9Yw4XN5IFdMSH8t+Gw6meYqrP1aQl4LKu2tEBqv91WVudXh897/wKrQ1CCKZOnYqjR4/i8uXLjFMipkOHDlBQUMClS/+uy3vw4AEyMjJgb2/PpCUlJaFXr17w9PTE0qVL61S3SCSSOnIhC7GzkJqaiosXL0JLS6te5WsiISEBGhoa4PP5kJeXh5mZGXPVx+lxcHCQCPl/+PAhjI2NP6oNCQkJ4HK50NHRYdIiIyPh7OyMFStWYNKkSRJl7O3tWfcNAMLDw5n7pqmpyWqnvLx8ne+3NPvE/QdUR6lxuexXsNhhIYRAIBCw6lZVVUWfPn1w7949JCQkMFfHjh0xZswYJCQkMOXfhxACQgjzLNnb2+PevXvIzc1ltVtNTY1xSGvrG1ncuHGD9fn69eto2bJljfZ9TuhIz2eA31wNJXdeoCy9eiNCU4d+KD18ClpPX+OlnDryNIcAhe99CXPk8CrlKTRaGzaCxRTKF0x7D6BFH+D1Y0DTFBA2+6TV+fr6Yt++fTh+/DhUVVWZNQ9CoRACgQBCoRDe3t6YNWsWNDU1oaamhmnTpsHe3h5dunQBUD2l1bt3bzg5OWHWrFmMDjk5OWhrawMANm3aBCMjI5ibmwOo3pNn1apVrDU0hYWFePToEfM5LS0NCQkJ0NTUhJGRESoqKjB8+HDExcXh1KlTqKqqYurS1NRk1nRkZGTg9evXyMjIQFVVFTOiYGZmBhUVFZw8eRI5OTno0qULFBUVER4ejuDgYPz0k+y1U+Xl5UhOTmb+zszMREJCAlRUVGBmZgagOty7a9euCA4OhpubG27evInt27czofl1aUNMTAxu3LiBXr16QVVVFTExMZg5cybGjh3LhL5HRERg4MCBmD59OoYNG8bo4PF4jJM2ffp0ODo6YvXq1XB2dkZYWBhu377N2ibgfepyv+vSfy4uLpg4cSK2bNkCJycnZGVlYcaMGejcuTP09aWPWqqqqkrshaOsrAwtLS0m/fHjx9i/fz/69esHbW1tPHv2DMuXL4dAIGAWSffr1w+Wlpb44Ycf8OuvvyI7OxsBAQHw9fVlnLLJkydj48aNmDNnDsaPH4/Lly/jwIEDUtehvU9GRgZmzZoFHx8fxMXFYcOGDVi9enWt5T4LHxTz9ZXzOUPWCSGkPKuQPPWPIs8CrhJRZRUpLy8nizwGkFVuzmTJ5sXkdfwdsnFSONnoc+nfa9JF8ipZMsSUQvlaadCQ9c8I3gnLffcKDQ1lZEpKSsiUKVOIhoYGUVJSIkOGDCFZWVlM/qJFi6TqEIf5ElIdrmxlZUWUlJSImpoasbW1JZs3b2ZtZxERESFVj6enJyGEkLS0tBrtjYiIYPS8H278vszZs2eJjY0NUVFRIcrKyqRdu3Zk69attW6tUVP9jo6OLLmTJ0+SNm3aED6fT8zNzcn27dtr1fGufbGxscTOzo4IhUKiqKhILCwsSHBwMCktLa21je/bcuDAAdKqVSvC4/GIlZUVOX36tMw2ElL7/a5r/61fv55YWloSgUBA9PT0yJgxY8izZ/XbhuH9kPXMzEwyYMAAoqOjQxQUFIiBgQEZPXo0uX//Pqtceno6GTBgABEIBKRJkybkf//7H6moqGDJREREEBsbG8Lj8YipqSnrmSeEkJCQENYzLLZnypQpZPLkyURNTY1oaGiQ+fPnfzEh6xxCGiAG8SujoKAAQqEQ+fn5DTLXXRtERPB8yXWQkkro+NqAZ6iKOUunoOndDLxQ52D5r1txe8b/cIPnXT1c/89Ul6lxFZz8+4LLrftQPIXypVJaWoq0tDSYmJjUugiTQqF8u8j6rvjY9zdd0/MZ4HA5zDlcZWnVe3yMHPEjRBwC7TyCS1ePoKPxWYxWnQwnLEbb7F3giKrw+Ikcwjffhkj0zfmlFAqFQqE0ONTp+Uy8u0khAHRoZY0c7er59XPXrgMcLjRUXsJMNwEO1idhm7EdHFKFR4lvcem3u9TxoVAoFArlI6FOz2eCZ1I90lOeng/yjwNjaN8bAKCe8QYFfZczESlceYKOXWJhnXkYHFKFh3GvcHlXEnV8KBQKhUL5CKjT85ng6auAo8CFqLgSlS+KAQA+wyehSFEExQouNiW/AmbcA9wPAKp6UMArdBpUBatH+8AhVXhwMxcRu1MYh4lCoVAoFEr9oE7PZ4IjzwXPUBXAv1Ncijw+3ppWh6TnxV2vDr1t7QS4Vu88qvj8ANpPaA/LlN8BIsL9mGxE7L1PHR8KhUKhUD4A6vR8RphNCv9ZzAwAQ4dPAgGBzmsRLt7+qzqxRS+g43gAgGrOdlj/0A2WKbsAIkLKtSxEUseHQqFQKJR6Q52ezwgTwfXPSA8A2Ft1QI62AgDg7PHQf4W/CwKERkBeBjR178Pc0ZQZ8Um+loXIPx9Qx4dCoVAolHpAnZ7PCM9IDeACVXllqHzz78Fvup16AAA00rKxaf9W3LwfD/BVgcEbAQCc2FDoju0BU1N5WN7/AyAEyX89x5Wwh/gGt1miUCgUCuWDoE7PZ4TLl4OCvgoA9mjPJDcflMmJIKjgovTIKVxZFIDADQGAqSPQaQIAgHNmBgx+XQIjpZew+MfxSYrKRBR1fCgUCoVCqRPU6fnMiPfreXddT8qTh+BV/bvrMhccKF+Nrx7x6bsYUDcG8p9C7sZKGG7dAoPSB7C4vxsAQeKVTPy1P5U6PhQKhUKh1AJ1ej4zfBPxup5/nZ6E5HhwwD5qggsO7qQkAHwVYPCm6sTYXeBVPILBxg3Qex0H8/t7ABDci3yGy3+k4Nn91yh8Z9qMQqF8PMuWLUOnTp2gqqoKHR0duLq6SpwSXlpaCl9fX2hpaUFFRQXDhg1DTk4Ok3/nzh24u7vD0NAQAoEAFhYWWLduHUvH1atX4eDgAC0tLQgEApibmyMkJIQlExUVBRcXF+jr64PD4eDYsWOs/IqKCvj7+8Pa2hrKysrQ19eHh4cHnj9/zpJbunQpunbtCiUlJairq0ttN4fDkbjCwsJk9lVSUhKGDRuG5s2bg8PhYO3atVLlMjMzMXbsWKat1tbWuH37dr3aIK7j3Wv58uVMfmRkJAYPHgw9PT0oKyvDxsYGe/fulbDl4MGDMDc3h6KiIqytrXHmzBmZbQSA7du3o2fPnlBTUwOHw0FeXp6EzOvXrzFmzBioqalBXV0d3t7eKCwslKrv0aNHUFVVrfFevM+mTZvQvHlzKCoqws7ODjdv3pSQiYmJQe/evaGsrAw1NTX06NEDJSUlEnJlZWWwsbEBh8NhDp4Vc/fuXXTv3h2KioowNDTEr7/+KlE+Ly8Pvr6+0NPTA5/PR6tWrVh9KO0+cTgc+Pr61qmtDQ11ej4zvH9GeipzS1BVVAEAsLG0hQjskRoRCNpZ2FR/MOkOdPap/vuEH5SsW0EvKAj62ddhfr/6P/H9mGwcX5uAP+ZHI/ka+8uBQqF8OFeuXIGvry+uX7+O8PBwVFRUoF+/figqKmJkZs6ciZMnT+LgwYO4cuUKnj9/jqFDhzL5sbGx0NHRwZ49e5CUlIQFCxZg3rx52LhxIyOjrKyMqVOnIioqCikpKQgICEBAQADrxO+ioiK0a9cOmzZtkmprcXEx4uLisHDhQsTFxeHIkSN48OABBg0axJIrLy/HiBEj8OOPP8pse2hoKLKyspjL1dVVpnxxcTFMTU2xfPly6OrqSpV58+YNHBwcoKCggLNnzyI5ORmrV69mTkevaxsAICgoiGXftGnTmLzo6Gi0bdsWhw8fxt27d+Hl5QUPDw+cOnWKJePu7g5vb2/Ex8fD1dUVrq6uSExMrLWd/fv3x/z582uUGTNmDJKSkhAeHo5Tp04hKioKkyZNkpCrqKiAu7s7unfvLrNOMfv378esWbOwaNEixMXFoV27dnByckJubi4jExMTg/79+6Nfv364efMmbt26halTp4LLlXzlz5kzR+qp7gUFBejXrx+MjY0RGxuLlStXIjAwkPU8lpeX47vvvkN6ejoOHTqEBw8eYMeOHWjWrBkjc+vWLdY9Cg8PBwCMGDGiTu1tcD7omNKvnM99yvr7ZK2+TZ76R5HixBdM2qL1C8hKt+/JKjdnstLte/Lz+vnsQmWFhKxtR8giNUKO+RJCCMlZvYbEte1CNk66yDqhfdOPl8jb11/XSdaU/z4Necp6VmEWufH8BskqzKpduIHJzc0lAMiVK1cIIYTk5eURBQUFcvDgQUYmJSWFACAxMTE16pkyZQrp1auXzLqGDBlCxo4dKzUPADl69Git9t68eZMAIE+ePJHICw0NJUKh8KP014SxsTEJCQmRSPf39yfdunWrly5pbahJvyy+//574uXlxXx2c3Mjzs7OLBk7Ozvi4+NTJ33iU+/fvHnDSk9OTiYAyK1bt5i0s2fPEg6HQzIzM1myc+bMIWPHjpV5L96lc+fOxNfXl/lcVVVF9PX1ybJly1htCAgIqFXXmTNniLm5OUlKSiIASHx8PJO3efNmoqGhQcrKypg0f39/0rp1a+bzli1biKmpKSkvL6+1LjHTp08nLVq0kHnq+qc8ZZ2O9DQCzBRX2r+LmQOn/QIjr+rRHALAtnsXdiGeMuC6GQAHiN8NpF6E9ozpQPfvAQ57aoyIgPxcyWFMCuVLghCC4oriel9h98PgdMgJ3he84XTICWH3w+qtg3zEGrj8/OqpaU1NTQDVozgVFRXo27cvI2Nubg4jIyPExMTI1CPWIY34+HhER0fD0dHxg20V18PhcOo8dfIuvr6+aNKkCTp37oydO3c2yNrBEydOoGPHjhgxYgR0dHRga2uLHTt2yCxTUxuWL18OLS0t2NraYuXKlaisrKxVz7t9HhMTw7pvAODk5CTzvtWFmJgYqKuro2PHjkxa3759weVycePGDSbt8uXLOHjwYI0jd+9TXl6O2NhYls1cLhd9+/ZlbM7NzcWNGzego6ODrl27omnTpnB0dMTVq1dZunJycjBx4kTs3r0bSkpKUtvQo0cP8Hg8Js3JyQkPHjzAmzdvAFTfS3t7e/j6+qJp06Zo06YNgoODUVVVVaP9e/bswfjx48F57731uZBvlFq/cfjNhSi6kc1a1wMAbv0HIeDgLmgUluN8+J9wtXFhFzTuCthNBm5sAU5MA2dKDFosmIbowNsA513/lUCoI/j0DaFQPoKSyhLY7bP7KB0iiLD0xlIsvbG0XuVujL4BJQXJL/pa6xOJMGPGDDg4OKBNmzYAgOzsbPB4PIkXctOmTZGdnS1VT3R0NPbv34/Tp09L5BkYGODFixeorKxEYGAgJkyYUG87xZSWlsLf3x/u7u5QU1OrV9mgoCD07t0bSkpKuHDhAqZMmYLCwkL4+fl9sD0A8PjxY2zZsgWzZs3C/PnzcevWLfj5+YHH48HT07PObfDz80P79u2hqamJ6OhozJs3D1lZWVizZo3Ueg8cOIBbt25h27ZtTFp2djaaNm3KkpN13+pKdnY2dHR0WGny8vLQ1NRkdL969Qrjxo3Dnj176nxvXr58iaqqKqk2379/H0B1/wJAYGAgVq1aBRsbG/zxxx/o06cPEhMT0bJlSxBCMG7cOEyePBkdO3ZEenq61DaYmJhI1CPO09DQwOPHj3H58mWMGTMGZ86cwaNHjzBlyhRUVFRg0aJFEjqPHTuGvLw8jBs3rk7t/RTQkZ5GQHz4aMXzQojK2B6xcqtOAADF9BzkFudKlEWfnwFNU+Dtc+D8Agg4pTBP/RMg/+pRLHkJXumbT9cACuUbxdfXF4mJibUu6JVFYmIiBg8ejEWLFqFfv34S+X/99Rdu376NrVu3Yu3atfjzzz8/qJ6Kigq4ubmBEIItW7bUu/zChQvh4OAAW1tb+Pv7Y86cOVi5ciUAICMjAyoqKswVHBxcZ70ikQjt27dHcHAwbG1tMWnSJEycOBFbt26tVxtmzZqFnj17om3btpg8eTJWr16NDRs2oKysTEJPREQEvLy8sGPHDlhZWdXZ1uDgYFY7MzIy6ly2NiZOnIjRo0ejR48eUvP/+usvVt3SFmFLQyQSAQB8fHzg5eUFW1tbhISEoHXr1ti5cycAYMOGDXj79i3mzZv3UW0QiUTQ0dHB9u3b0aFDB4wcORILFiyQei8B4P/+7/8wYMAAqWuIPhd0pKcRkFdXhJw6H1V5ZSjPKIBiSw0mb4DzMFyKuwb9V4r4Mz4M0x3e+1XFU6o+m2tnfyBhD8orLaD/PBqar5JRoGKMJCsvlAq08exWOkwH6X3mllEodUcgL8CN0TdqF3yHnOIcuB5zhQgiJo3L4eLY4GNoqtRURknJuuvL1KlTmQWpBgYGTLquri7Ky8uRl5fHGu3JycmRWMybnJyMPn36YNKkSQgICJBaj/jXtbW1NXJychAYGAh3d/d62Sp2Fp48eYLLly/Xe5RHGnZ2dliyZAnKysqgr6/PivSRNU33Pnp6erC0tGSlWVhY4PDhw6y0+rbBzs4OlZWVSE9PR+vWrZn0K1euwMXFBSEhIfDw8GCV0dXVZUXZAez7NnnyZLi5uTF5dX1Z6+rqshYWA0BlZSVev37N6L58+TJOnDiBVatWAaie7hWJRJCXl8f27dvh7u7O6uOmTZuCz+dDTk5Ops16etXf+9L6WOy0Xb58GTExMeDz+SyZjh07YsyYMfj9999r7Btx+8R1KSgoQE5OjlVPdnY2ysvLWVNjT548wcWLF3HkyJHauu+TQkd6GglpR1IAgE2bVshXUgKXcHDjr9MQEZFkYaMugH11uB8vaSPA5UKxLA86r+5AL7v6JZL8N/VnKV82HA4HSgpK9bpMhCZY1HURuP9M53I5XCyyXwQToUm99NRnPQEhBFOnTsXRo0dx+fJliSH/Dh06QEFBAZcuXWLSHjx4gIyMDNjb2zNpSUlJ6NWrFzw9PbF0ad2m40QikdSRC1mInYXU1FRcvHgRWlpa9SpfEwkJCdDQ0ACfz4e8vDzMzMyYqz5Oj4ODg0TI/8OHD2FsbPxRbUhISACXy2VNK0VGRsLZ2RkrVqyQGjllb2/Pum8AEB4eztw3TU1NVjvl5ev2vWpvb4+8vDzExsYyaZcvX4ZIJIKdXfWUbkxMDBISEpgrKCgIqqqqSEhIwJAhQyAQCFh1q6qqgsfjoUOHDiybRSIRLl26xNjcvHlz6Ovry+zj9evX486dO0zd4hDz/fv3M8+mvb09oqKiUFFRweqb1q1bM5F2Dg4OePToETO6JK5HT0+P5fAA1ZGAOjo6cHZ2rlMffjI+aPnzV05jR28RQsjb68/JU/8okrvtjkTequUhZJWbM5k+tReJzoyWrqC8mJD17QlZpEbezBtEki0sSXJrc3LLpgfZOCmcbJx8ibzOKvzEraBQ6k5DR2/dzLr5WaK3fvzxRyIUCklkZCTJyspiruLiYkZm8uTJxMjIiFy+fJncvn2b2NvbE3t7eyb/3r17RFtbm4wdO5alIzc3l5HZuHEjOXHiBHn48CF5+PAh+e2334iqqipZsGABI/P27VsSHx9P4uPjCQCyZs0aEh8fz0Q1lZeXk0GDBhEDAwOSkJDAquvdKJwnT56Q+Ph4snjxYqKiosLofPv2LSGEkBMnTpAdO3aQe/fukdTUVLJ582aipKREfv75Z5l9VVZWxujS09MjP/30E4mPjyepqamMzM2bN4m8vDxZunQpSU1NJXv37iVKSkpkz549dW5DdHQ0CQkJIQkJCeTvv/8me/bsIdra2sTDw4Op5/Lly0RJSYnMmzePpePVq1eMzLVr14i8vDxZtWoVSUlJIYsWLSIKCgrk3r17MtuZlZVF4uPjyY4dOwgAEhUVReLj41m6+/fvT2xtbcmNGzfI1atXScuWLYm7u3uNOusavRUWFkb4fD7ZtWsXSU5OJpMmTSLq6uokOzubkQkJCSFqamrk4MGDJDU1lQQEBBBFRUXy6NEjqTrT0tIkorfy8vJI06ZNyQ8//EASExNJWFgYUVJSItu2bWNkMjIyiKqqKpk6dSp58OABOXXqFNHR0SG//PILS39VVRUxMjIi/v7+tbaPkE8bvUWdnkaiPLuQPPWPIs8CrhJRRRUrLzY+maxycyYrRn1Ppp/xq1lJxg1CAtUJWaRGyq/uI2+vXyd/D3YlYcPWkI0+l8jl3SmfuBUUSt1pSKfnc4LqgEqJKzQ0lJEpKSkhU6ZMIRoaGkRJSYkMGTKEZGX965AtWrRIqg5jY2NGZv369cTKyoooKSkRNTU1YmtrSzZv3kyqqv79fhCHSL9/eXp6EkL+fXlJuyIiIhg9np6eMmXOnj1LbGxsiIqKClFWVibt2rUjW7duZdkijZrqd3R0ZMmdPHmStGnThvD5fGJubk62b99eq4537YuNjSV2dnZEKBQSRUVFYmFhQYKDg0lpaWmtbXzflgMHDpBWrVoRHo9HrKysyOnTp2W2kZCa7+e7z8SrV6+Iu7s7UVFRIWpqasTLy4txKqVRV6eHEEI2bNhAjIyMCI/HI507dybXr1+XkFm2bBkxMDAgSkpKxN7envz111816pPm9BBCyJ07d0i3bt0In88nzZo1I8uXL5coGx0dTezs7AifzyempqZk6dKlpLKykiVz/vx5AoA8ePCgTu37lE4Ph5Bv7/yCgoICCIVC5OfnN8hc94dACEHWkusQFVdCe0o78I3YdgSOGw3VkgJctXmN/5t5CpqKNQwfXwgAojcASk0Al/UoTCvDvcU7EWc7C3LyHHgEO0BJjSe9LIXyGSktLUVaWhpMTEygqKjY2OZQKJQvFFnfFR/7/qZrehoJDofD7M5cnlYgka/aunre1yhLESf/Plmzol4LAJWmQPFLYP9oKN/0RlOtIqjlP0ZVJcHdiKefxH4KhUKhUL42qNPTiDCLmdPyJfK69esDAGj2QoAjSYdq3hSs+DVQ9IL5yIEI2gYpMHp6EQCQGPkMFWXSN4qiUCgUCuVbgjo9jQjfpHqkp+xJAYiI7dR0srVEIV8IOREH5O9cxOfGS1fy+u/qLZjfQUWvBIbqeRAU56KspAop0fQsLgqFQqFQqNPTiCjoK4OjwAUpqURlbjErj8vlQql1BwCAcbYSDqcelqYC0Gzx3m7MAIfLhfak8TB6Vh3WmHDhCURVUkLfKRQKhUL5hqBOTyPCkeOCZ1zzFJdD3+opLoNcAS4+uoCCcsm1PxA2A1zWAZx/N4eCrjVUXEbAWJgHhfK3ePumHH/Hv5AsS6FQKBTKNwR1ehqZmjYpBIAunaxRxBNCXsSFdjYXpx9LntMDAGjvAcy4Bwz+59C67HvgvElD0ymTYJAZBQCIP5vWIIcFUigUCoXytUKdnkaGZyKO4MqXcEq4XC4UW9kCAJpnK+Hww8M1Oy7CZoDtWMCsb/Uan+tboNqnD0wFmeBWleNFZjEyH+Z9yqZQKBQKhfJFQ52eRoZnqArIcVBVUI6qN5LbzXfp3QtA9RTXo5cPkfwqWbbCrtOq/43fA05pHvR/HA+97JjqpLN/N6jtFAqFQqF8TVCnp5Hh8uTAa6YCoIbQdXtbFCuoQqGKi2YvFHEo9ZBshSaOQFNroKIYuL0Tqt/1RQu5xwARIeN+AV49L/wUzaBQKBTKF0Lz5s2xdu3axjYDADBu3Di4uro2thkM1On5AmA2KZSyrofL5ULBzAYAYJytjDOPz6C4olhCjoHD+Xe05+Z2cEQVMPYZDe2XdwAA8WfoaA+FUh+WLVuGTp06QVVVFTo6OnB1dZU4zLG0tBS+vr7Q0tKCiooKhg0bxjqh+s6dO3B3d4ehoSEEAgEsLCywbt06lo6rV6/CwcEBWlpaEAgEMDc3R0hICEsmKioKLi4u0NfXB4fDwbFjx1j5FRUV8Pf3h7W1NZSVlaGvrw8PDw88f87etmLp0qXo2rUrlJSUWCfDvwuHw5G4wsLCZPZVUlIShg0bhubNm4PD4dT44s3MzMTYsWOZtlpbW+P27dv1aoO4jnev5cuXM/mRkZEYPHgw9PT0oKysDBsbG+zdu1fCloMHD8Lc3ByKioqwtrZmDt+Uxfbt29GzZ0+oqamBw+EgLy9PQqY2+x48eIBevXqhadOmUFRUhKmpKQICAlgHfEqjqqoKCxcuhImJCQQCAVq0aIElS5bUe82mj48PWrRoAYFAAG1tbQwePBj3799nyfj5+aFDhw7g8/mwsbGR0FHXPv6SoE7PF8C/i5klR3oAoPM/U1yGOUooKyvGufRzshW2GQqo6gOFOcC9g1B1ckILkgIASI19iUIp02gUCkU6V65cga+vL65fv47w8HBUVFSgX79+KCoqYmRmzpyJkydP4uDBg7hy5QqeP3+OoUOHMvmxsbHQ0dHBnj17kJSUhAULFmDevHnYuHEjI6OsrIypU6ciKioKKSkpCAgIQEBAALZv387IFBUVoV27dti0aZNUW4uLixEXF4eFCxciLi4OR44cwYMHDzBo0CCWXHl5OUaMGIEff/xRZttDQ0ORlZXFXLX9Yi8uLoapqSmWL18OXV1dqTJv3ryBg4MDFBQUcPbsWSQnJ2P16tXMyd11bQMABAUFseybNm0akxcdHY22bdvi8OHDuHv3Lry8vODh4YFTp06xZNzd3eHt7Y34+Hi4urrC1dUViYmJtbazf//+mD9/vkw5WfYpKCjAw8MDFy5cwIMHD7B27Vrs2LEDixYtkqlzxYoV2LJlCzZu3IiUlBSsWLECv/76KzZs2CCz3Pt06NABoaGhSElJwfnz50EIQb9+/VBVxd7Mdvz48Rg5cqRUHXXp4y+ODzqx6yvnSzhw9F2qisrJU/8o8tQ/ilS+LZPIr6yoJEFjRpJVbs6k/4pOZPTp0bUrvbqWkEVqhGy0I0QkInmnTpHdI7eRjT6XyNU/kz5BKygU2XytB46+T25uLgFArly5QgipPo1aQUGBHDx4kJFJSUkhAEhMTEyNeqZMmUJ69eols64hQ4aQsWPHSs0DQI4ePVqrvTdv3iQAmJPY30XWIZd11V8TxsbGJCQkRCLd39+fdOvWrV66pLWhJv2y+P7774mXlxfz2c3NjTg7O7Nk7OzsiI+PT530iQ+AffPmjUTeh9g3c+bMWvvG2dmZjB8/npU2dOhQMmbMGFbdQUFBZNSoUURJSYno6+uTjRs3ytR7584dAkDqSeyLFi0i7dq1q1Mb3u9jT09PMnjwYBIYGEiaNGlCVFVViY+PDykrk3zXifmUB47SkZ4vAK6SAuSbKgEAiq5noTKfPRIjJy8H+RbtAADNs5Vx98VdpL5Jla20vSfAUwFepACPLkGtf3+0qLgHAEiKykR5SWXDN4RC+UxUZGej6PoNVGRnf/a68/OrR2Q1NasPAY6NjUVFRQX69u3LyJibm8PIyAgxMTEy9Yh1SCM+Ph7R0dFwdHT8aHs5HE6N01iy8PX1RZMmTdC5c2fs3LmzQba9OHHiBDp27IgRI0ZAR0cHtra22LFjh8wyNbVh+fLl0NLSgq2tLVauXInKStnfa+/3eUxMDOu+AYCTk5PM+1Yf6mPfo0ePcO7cuVrvd9euXXHp0iU8fPgQQPXU6dWrVzFgwACW3MqVK9GuXTvEx8dj7ty5mD59OsLDw6XqLCoqQmhoKExMTGBoaFjPVrKR9lxfunQJKSkpiIyMxJ9//okjR45g8eLFH1XPh/JFOD2bNm1C8+bNoaioCDs7O9y8eVOm/Nq1a9G6dWsIBAIYGhpi5syZKC0t/UzWfhq4ygoAgIKLGchefhNFt9hf5p169gQAGGUrgytCzTs0ixGoVzs+ABCzARw5OVh4OkGpKBsVIjkkXk5v2AZQKPWEEAJRcXG9r9f79uFR7z7IGDcOj3r3wet9++qt40Nf3iKRCDNmzICDgwPatGkDAMjOzgaPx5N4ITdt2hTZNThl0dHR2L9/PyZNmiSRZ2BgAD6fj44dO8LX1xcTJkz4IFuB6rVG/v7+cHd3r/eJ1EFBQThw4ADCw8MxbNgwTJkypd5TKNJ4/PgxtmzZgpYtW+L8+fP48ccf4efnh99//71ebfDz80NYWBgiIiLg4+OD4OBgzJkzp8Z6Dxw4gFu3bsHLy4tJy87ORtOmTVlysu5bfairfV27doWioiJatmyJ7t27IygoSKbeuXPnYtSoUTA3N4eCggJsbW0xY8YMjBkzhiXn4OCAuXPnolWrVpg2bRqGDx8usUZs8+bNUFFRgYqKCs6ePYvw8HDweLwPbrO0PgYAHo+HnTt3wsrKCs7OzggKCsL69eshEn3+kwLkP3uN77F//37MmjULW7duhZ2dHdauXQsnJyc8ePAAOjo6EvL79u3D3LlzsXPnTnTt2hUPHz7EuHHjwOFwsGbNmkZowcdTmV+G8sfvrOchwJsjqeC30oC8kA8A6NW9M67/pgxBZRH0XlafvD6zw0zw5fg1K+4yGbixFXgcCWTdhdB5AEz+mIck5f64c+5vtOtvCjm5L8LvpXyDkJISPGjf4eOUiETICVqCnKAl9SrWOi4WHCWlelfn6+uLxMREXL16td5lxSQmJmLw4MFYtGgR+vXrJ5H/119/obCwENevX8fcuXNhZmYGd3f3etdTUVEBNzc3EEKwZcuWepdfuHAh87etrS2KioqwcuVK+Pn5ISMjA5aWlkz+/Pnza13fIkYkEqFjx44IDg5mdCcmJmLr1q3w9PSscxtmzZrF/N22bVvweDz4+Phg2bJl4PPZ34sRERHw8vLCjh07YGVlVbcOABAcHMzYCQDJyckwMjKqU9m62rd//368ffsWd+7cwezZs7Fq1SrMmTMHf/31F2v0Ztu2bRgzZgwOHDiAvXv3Yt++fbCyskJCQgJmzJgBfX19Vv/Z29uz7LG3t5dYWD5mzBh89913yMrKwqpVq+Dm5oZr165BUVGxTm18F1l93K5dOyi98//N3t4ehYWFePr0KYyNjetd18fQ6E7PmjVrMHHiRMYz3Lp1K06fPo2dO3di7ty5EvLR0dFwcHDA6NGjAVSvkHd3d8eNGzc+q90NSeXLEslEUp0udnrk5OXANWkLpMagRbYmonSeI/xJOAaaDqxZsboRYOUKJB4GYjaBM3Qb2o52QOqpAhRDDanXnsK8x+d94CiUr5WpU6fi1KlTiIqKgoGBAZOuq6uL8vJy5OXlsUZ7cnJyJBbzJicno0+fPpg0aRICAgKk1mNiYgIAsLa2Rk5ODgIDA+vt9IidhSdPnuDy5cv1HuWRhp2dHZYsWYKysjLo6+sjISGByZM1Tfc+enp6LIcJACwsLHD4MHv0ur5tsLOzQ2VlJdLT09G6dWsm/cqVK3BxcUFISAg8PDxYZXR1dVlRdgD7vk2ePBlubm5Mnr6+fp3bWVf7xNNJlpaWqKqqwqRJk/C///0PHTt2ZPWxeERq9uzZzGgPUP2cPHnyBMuWLZNwGmtDKBRCKBSiZcuW6NKlCzQ0NHD06NF6P2+y+vhLo1GdnvLycsTGxmLevHlMGpfLRd++fWucU+3atSv27NmDmzdvonPnznj8+DHOnDmDH374ocZ6ysrKUFb27zqZggIpZ1g1IvJNBAAHwLsj7px/0t+hvaMjklJjoJ/NB6cNcCT1iGynBwDsp1Y7PYmHgD4/Q2OQM4zDliBV0xG3jyajdXcjcDicBm8ThVIbHIEAreNi61WmIicHj50HAu8Oi3O5MD19CgrvTVPUVnddIYRg2rRpOHr0KCIjIxmnREyHDh2goKCAS5cuYdiwYQCqw5EzMjJYv7aTkpLQu3dveHp6YunSpXWqWyQSsb676oLYWUhNTUVERAS0tLTqVb4mEhISoKGhwYxSmJmZfZAeBwcHiZD/hw8fsn7xf0gbEhISwOVyWTMEkZGRGDhwIFasWCF1KtHe3h6XLl3CjBkzmLTw8HDmvmlqatbLoauvfe8jEolQUVEBkUgEgUAgtY+Li4vB5bJH6OXk5CSmiq5fvy7x2cLCosa6CSEghNT7eautj4HqdUclJSUQ/PP/7vr161BRUfno9UMfQqM6PS9fvkRVVZXUOdX39wsQM3r0aLx8+RLdunUDIQSVlZWYPHmyzKHVZcuWNdqiqbogL+RDY2hLvDn87+JkjaEtmVEeMX16dUFsqACKlSXQeyXALe4tPCl4AmM1GaM1zdoDxt2AJ1eBm9vA+S4I7YbZ4vHFMuSX8JERnwXj9h/+64VC+VA4HE69p5j4JibQC1qMrJ8XVTs+XC70ghaD/54j0pD4+vpi3759OH78OFRVVZn1HkKhEAKBAEKhEN7e3pg1axY0NTWhpqaGadOmwd7eHl26dAFQPaXVu3dvODk5YdasWYwOOTk5aGtrA6he22hkZARzc3MA1XvyrFq1Cn5+fowthYWFePToEfM5LS0NCQkJ0NTUhJGRESoqKjB8+HDExcXh1KlTqKqqYurS1NRk1mtkZGTg9evXyMjIQFVVFTOiYGZmBhUVFZw8eRI5OTno0qULFBUVER4ejuDgYPz0008y+6q8vBzJycnM35mZmUhISICKigrzAp85cya6du2K4OBguLm54ebNm9i+fTsTml+XNsTExODGjRvo1asXVFVVERMTg5kzZ2Ls2LFM6HtERAQGDhyI6dOnY9iwYYwOHo/HODLTp0+Ho6MjVq9eDWdnZ4SFheH27dusbQKkkZ2djezsbOZe3Lt3D6qqqjAyMoKmpmad7Nu7dy8UFBRgbW0NPp+P27dvY968eRg5ciQUFBRqrNvFxQVLly6FkZERrKysEB8fjzVr1mD8+PEsuWvXruHXX3+Fq6srwsPDcfDgQZw+XX1+4+PHj7F//37069cP2traePbsGZYvXw6BQIDvv/+e0fHo0SMUFhYiOzsbJSUlzHNiaWkJHo9Xpz4WPwve3t4ICAhAeno6Fi1ahKlTp0o4b5+FD4r5aiAyMzMJABIdHc1Knz17NuncubPUMhEREaRp06Zkx44d5O7du+TIkSPE0NCQBAUF1VhPaWkpyc/PZ66nT59+USHrYt7ezCJP/aNI5rIbNcr8Mm8xWeXmTPx+GkLa7GpDVt9eXbvi+2eqw9eDDQkpLSCiigpyYtQKstHnEjk4+1QDtoBCqZmGDFkvz8oihddvkPKsrAawTDaoHoOVuEJDQxmZkpISMmXKFKKhoUGUlJTIkCFDSNY7ti1atEiqDmNjY0Zm/fr1xMrKiigpKRE1NTVia2tLNm/eTKqqqhgZcYj0+5enpychhJC0tLQa7Y2IiGD0eHp6ypQ5e/YssbGxISoqKkRZWZm0a9eObN26lWWLNGqq39HRkSV38uRJ0qZNG8Ln84m5uTnZvn17rTretS82NpbY2dkRoVBIFBUViYWFBQkODialpaW1tvF9Ww4cOEBatWpFeDwesbKyIqdPn5bZRkJqvp/iZ6Iu9oWFhZH27dszfWxpaUmCg4Nr/f9RUFBApk+fToyMjIiioiIxNTUlCxYsYIWAGxsbk8WLF5MRI0YQJSUloqurS9atW8fkZ2ZmkgEDBhAdHR2ioKBADAwMyOjRo8n9+/dZdTk6OkptZ1paWp37WByy/vPPPxMtLS2ioqJCJk6cyOqL9/mUIescQhrv6O3y8nIoKSnh0KFDrE2vPD09kZeXh+PHj0uU6d69O7p06YKVK1cyaXv27MGkSZNQWFhYJ8+xoKAAQqEQ+fn5DTLX3VCIyirxfHEMIAJ053SCvKbkYrIz56OQsvNXlMorYH/fR9BQ0sTF4RehIFfzLwOIRMCmzsCrVMBpGWA/Bc/2HMOJv5RBOHIY8T9r6LTU/oQto1Cqo3DS0tJgYmLyQQslKRTKt4Gs74qPfX83augOj8dDhw4dcOnSJSZNJBLh0qVLEivPxdQ0nwmgQfaQaEy4fHnwDKtvYumjN1Jl+vayR6mcIhQrK2Ba0ASvS18j8llkLYq5QNep1X9f3wxUVaLZSGc0LayeV7+567qMwhQKhUKh/Ddo9HjlWbNmYceOHfj999+RkpKCH3/8EUVFRUw0l4eHB2uhs4uLC7Zs2YKwsDCkpaUhPDwcCxcuhIuLC+P8fM0otlQHAJSl5knN5/EUQIyq9wcxy6oOnax1zx4AaDsKUGoC5D8Fko+Bo6AAmz7NAAAZLxWR/1x6fRQKhUKh/FdodKdn5MiRWLVqFX7++WfY2NggISEB586dYxY3Z2RkICsri5EPCAjA//73PwQEBMDS0hLe3t5wcnLCtm3bGqsJDQq/ZfUit7K/80BE0keu2nbvAQBQf1oEDgGiM6PxvPC5VFkGBUWg8z8r66M3AITAdKwzNIvTQThyuLXjSoO1gUKhUCiUL5FGXdPTWHypa3oAgFSJ8DzoOkhZFXSm2oBnoCohU1pWjjXj3MEXlSGpjxpu8e9hcrvJ8LXxla286CUQYgVUlgLjTgPNuyFp81FE3hWCW1WGnr0F0G3fAhqtP38YIeW/D13TQ6FQ6sJ/dk0PRRKOHBd8UyEAoPRRnlQZRT4PVYbVO17qPqnev+Jo6lFUiaqkyjMoNwFsqjd1RHT16c4W3t+DV54PkRwfl6+IsG/NA9xed+LjG0KhUCgUyhcGdXq+QBTN1AEAZTU4PQDQpls3AIAgPRdCBSFyinNw7fm12pV38QXAAR6eBV48RH56LsoV3hlN4nBxI1mANw+efngDKBQKhUL5AqFOzxcIs64nPR+kQvrojdN3PVDO5UGpsgjdSHWk2293f0N2US0H5TUxA1r/s/nU9U14lfwU4Lz3GHDk8CqFOj0UCoVC+W9BnZ4vEHltAeSEPKCSoCxd+pEZSgJFVBpUn19TlVwd3h7/Ih5Oh5xwJPWI7ArE4esJf0LLRAUg7510SwgEBrqS5SgUCoVC+YqhTs8XCIfDAd+serSntIbQdQCw6NodAKDy7BlzbpcIIiyOWSx7xMfIHmjWAagqg8ab07CzLAbIPyNKhAAcDi4ffIrCN/U7g4VCoVAolC8Z6vR8oTD79dSwSSEA9O/XAxVcOaiUyqNJPo9JFxERnr6VMT3F4VQfRAoAt3ag45TvMHqWOZz6K8JBdAGKJS9RkF+FY2tiUZRPHR8KhUL5momMjASHw0FeXh6TduzYMZiZmUFOTo514Oq77Nq1C+rq6vWqKzs7G9999x2UlZXrXfZzQJ2eLxR+C3UAQMXzIlQVlkuVUVEWoESv+hC/5ln/HtzIAQeGqrWEnVsMAtSNgOJXwJ0/odHaEGauXWG9aj46Z4WBX/oa+S9KcTwkHsUF0uunUL4Fli1bhk6dOkFVVRU6OjpwdXWVOCW8tLQUvr6+0NLSgoqKCoYNG4acnBwm/86dO3B3d4ehoSEEAgEsLCywbt06lo6rV6/CwcEBWlpaEAgEMDc3R0hICEsmKioKLi4u0NfXB4fDwbFjx1j5FRUV8Pf3h7W1NZSVlaGvrw8PDw88f87ex2vp0qXo2rUrlJSUanwxcTgciSssLExmXyUlJWHYsGFo3rw5OBwO1q5dK1UuMzMTY8eOZdpqbW2N27dv16sN4jrevZYvX87kR0ZGYvDgwdDT04OysjJsbGywd+9eCVsOHjwIc3NzKCoqwtraGmfOnJHZxtevX2PatGlo3bo1BAIBjIyM4Ofnh/z8fJZcRkYGnJ2doaSkBB0dHcyePRuVlZVM/pEjR/Ddd99BW1sbampqsLe3x/nz52XW3ZD4+Phg+PDhePr0KZYsWdJgekNCQpCVlYWEhAQ8fPiwwfQ2FNTp+UKRU+VBQVcZQPVGhTVhZd8bAGCcrcxMcXHAQVlVLSM0cvJAlynVf8dsqj6fC4CcUAizX3+GbeJG8Mve4E12MU6si0dJDY4XhfJf58qVK/D19cX169cRHh6OiooK9OvXD0VFRYzMzJkzcfLkSRw8eBBXrlzB8+fPMXToUCY/NjYWOjo62LNnD5KSkrBgwQLMmzcPGzduZGSUlZUxdepUREVFISUlBQEBAQgICGCd+F1UVIR27dph06ZNUm0tLi5GXFwcFi5ciLi4OBw5cgQPHjzAoEGDWHLl5eUYMWIEfvzxR5ltDw0NRVZWFnO9e0ZiTfWbmppi+fLl0NWVvi7wzZs3cHBwgIKCAs6ePYvk5GSsXr2aOX28rm0AgKCgIJZ906ZNY/Kio6PRtm1bHD58GHfv3oWXlxc8PDxw6tQploy7uzu8vb0RHx8PV1dXuLq6IjExscY2Pn/+HM+fP8eqVauQmJiIXbt24dy5c/D29mZkqqqq4OzsjPLyckRHR+P333/Hrl278PPPPzMyUVFR+O6773DmzBnExsaiV69ecHFxQXx8vMw+bggKCwuRm5sLJycn6OvrQ1VVcj+4D+Xvv/9Ghw4d0LJlS+jo6DSY3gbjg44p/cr52FNaPxdvTv5NnvpHkVcHH9Qok19QRJaNHExWuTmTP87/ScafG0/a7GpD/hf5v9orKC0gZJlh9QnsKeyThV/u2EFu23Qn272OkI0+l0jYLzdISWH5xzaJ8g3TkKesNya5ubkEALly5QohhJC8vDyioKBADh48yMikpKQQACQmJqZGPVOmTCG9evWSWdeQIUPI2LFjpeYBIEePHq3V3ps3bxIA5MmTJxJ5oaGhRCgUfpT+mjA2NiYhISES6f7+/qRbt2710iWtDTXpl8X3339PvLy8mM9ubm7E2dmZJWNnZ0d8fHzqpffAgQOEx+ORiooKQgghZ86cIVwul2RnZzMyW7ZsIWpqaqzT0N/H0tKSLF68WGZdf/zxB+nQoQNRUVEhTZs2Je7u7iQnJ4clc/r0adKyZUuiqKhIevbsSUJDQwkA8ubNGxIREVHj6fWhoaHE0NCQCAQC4urqSlatWiXxfBw7dozY2toSPp9PTExMSGBgINNuY2Njll5PT8869iCbT3nKOh3p+YL5d11PXo2HqaqpKqFMrzUA4PG5OxijNw4ccHA+/TySXibJroCvCnSoPuMMUauAtCggPxMAoDl+PLQ7tIZtwjrwqorw8mkhTqxLQFlxRYO0jUL5GArflOLZgzcofFP62esWT2NoamoCqB7FqaioQN++fRkZc3NzGBkZISYmRqYesQ5pxMfHIzo6Go6Ojh9tL4fD+aD1Fb6+vmjSpAk6d+6MnTt3NsihzidOnEDHjh0xYsQI6OjowNbWFjt27JBZpqY2LF++HFpaWrC1tcXKlStZ00c16Xm3z2NiYlj3DQCcnJxk3rea9KqpqUFeXp7Ra21tzRynJNZbUFCApCTp38sikQhv376V+UwA1dN/S5YswZ07d3Ds2DGkp6dj3LhxTP7Tp08xdOhQuLi4ICEhARMmTMDcuXOZ/K5duzLTs4cPH0ZWVha6du2KGzduwNvbG1OnTkVCQgJ69eqFX375hVX3X3/9BQ8PD0yfPh3JycnYtm0bdu3ahaVLlwIAbt26hf79+8PNzQ1ZWVkSU7hfAvKNbQClZngmQkCOg6q8MlS+KoVCE4FUOXmBCgBAJfMeYpcmwrFzB0Q2uY2QuBD81u832ZXY+VSfxfU8FvjdpXrPHpd14LT3gP6K5ShzHQKb2BAk2M3Bi4y3OLH+DgZNtwFfQB8dysdBCEFluah2wfe4H5OFv/Y/FAcaovvIVjC316uXDnkeFxwOp951i0QizJgxAw4ODmjTpvrg3+zsbPB4PIkXctOmTZGdLT2KMjo6Gvv378fp06cl8gwMDPDixQtUVlYiMDAQEyZMqLedYkpLS+Hv7w93d/d6b9kfFBSE3r17Q0lJCRcuXMCUKVNQWFgIPz+/D7YHAB4/fowtW7Zg1qxZmD9/Pm7dugU/Pz/weDx4enrWuQ1+fn5o3749NDU1ER0djXnz5iErKwtr1qyRWu+BAwdw69Yt1jmN2dnZLMcEkH3fpPHy5UssWbIEkyZNqlWvOE8aq1atQmFhIdzc3GTWN378eOZvU1NTrF+/Hp06dUJhYSFUVFSwZcsWtGjRAqtXrwYAtG7dGvfu3cOKFSsAADwej5l20tTUZKYh161bh/79+2POnDkAgFatWiE6Ohrnzp1j6lu8eDHmzp3L3CdTU1MsWbIEc+bMwaJFi6CtrQ0+nw+BQFDj9GZjQ99cXzBcnhz4xmooe5yPstQ3Up2ev9Ofgff3zX/LgMDo5kuo9eHjRtYNRD+PRlf9rjVXQsi/4epA9Z49J2cALfpAXqsZ9FeuRKWXF2xurkRC1/nITS/AqQ0JcPGzAU+RPj6UD6eyXITt0z/uoFtCgKiwh4gKq9+CyUnrHKHAl6t3fb6+vkhMTMTVq1frXVZMYmIiBg8ejEWLFqFfv34S+X/99RcKCwtx/fp1zJ07F2ZmZnB3d693PRUVFXBzcwMhBFu2bKl3+YULFzJ/29raoqioCCtXroSfnx8yMjJgaWnJ5M+fPx/z58+vk16RSISOHTsiODiY0Z2YmIitW7dKOD2y2jBr1izm77Zt24LH48HHxwfLli0Dn89nyUZERMDLyws7duyAlZVV3ToAQHBwMGMnACQnJ8PIyIj5XFBQAGdnZ1haWiIwMLDOet9n3759WLx4MY4fP844JHv37oWPjw8jc/bsWXTv3h2xsbEIDAzEnTt38ObNG4j+WY8pvicpKSmws7Nj6be3t6/VhpSUFAwZMkSi3LtOz507d3Dt2jVmZAeoXr9UWlqK4uJiKCkp4UuHTm994fD/meKqab+ehw/TwAV7yJkLgm786iHxtbFrIXp/88F3ef23ZBqpAl4/BgAod7FDkx9/hErRc9jErwWPz0X24wKc2ngHFWW1nPVFofyHmDp1Kk6dOoWIiAgYGBgw6bq6uigvL2eFAwNATk6OxK/d5ORk9OnTB5MmTUJAQIDUekxMTGBtbY2JEydi5syZH/QyFTsLT548QXh4eIMcrGxnZ4dnz56hrKwM+vr6SEhIYK7JkyfXWY+enh7LYQIACwsLZGRkfFQb7OzsUFlZifT0dFb6lStX4OLigpCQEHh4eLDydHV1WVF2APu+TZ48mdVOfX19Ru7t27fo378/VFVVcfToUSgoKNSqV5z3LmFhYZgwYQIOHDjAmmobNGgQq+6OHTuiqKgITk5OUFNTw969e3Hr1i0cPXoUQPXi9E9NYWEhFi9ezLLr3r17SE1N/WoOEaY/1b9wFM00UHD+Ccoe54FUEXDk2EPyrVqZIBEcluMjAgejuozHldhrSHmdgvPp5zHAZID0CjRbVE9pvesYcbiApinzsYnvFBTfugXcuoWOWWG43cwdWY/ycXrTHThPbQcFXv1/MVMo8jwuJq2r33qVwrwy/Bl4He8uLeFwAPfALlBR59dcUErddYUQgmnTpuHo0aOIjIyEiYkJK79Dhw5QUFDApUuXMGzYMADAgwcPkJGRwfqFnZSUhN69e8PT05P1S1kWIpEIZWX12ytL7CykpqYiIiICWlpa9SpfEwkJCdDQ0GBGUczMzD5Ij4ODg0TI/8OHD2FsbMx8/pA2JCQkgMvlsiKGIiMjMXDgQKxYsYI1/STG3t4ely5dYu1TEx4eztw3TU1NqWtsCgoK4OTkBD6fjxMnTki88O3t7bF06VLk5uYy9ogdt3cdvj///BPjx49HWFgYnJ2dWTpUVVUloqpiY2Px6tUrLF++HIaG1duSiEP9xVhYWODECfah0devX5dow/tYWFjgxo0bMsu1b98eDx48+OB7/yVAnZ4vHIVmKuAI5EFKKlGe+RZ8I/avnRbNDaDlNBqvzu9jHJ9ik46wbW0Fr1IvbEzYiA3xG9DXqC8U5BQkKxA2A1zWVU9piae5eKqs87g4cnLQX7USaa5DoHj3ChxaW+AqsUHmwzyc2XwXzr5tIa9AHR9K/eBwOPWeYtJoqoSeY80Rufc+iKj6Me05xhwaTT/dsLqvry/27duH48ePQ1VVlVmTIRQKIRAIIBQK4e3tjVmzZkFTUxNqamqYNm0a7O3t0aVLFwDVU1q9e/eGk5MTZs2axeiQk5ODtrY2AGDTpk0wMjKCubk5gOqQ5lWrVrHW0BQWFuLRo0fM57S0NCQkJEBTUxNGRkaoqKjA8OHDERcXh1OnTqGqqoqpS1NTEzxe9SamGRkZeP36NTIyMlBVVYWEhAQA1Y6MiooKTp48iZycHHTp0gWKiooIDw9HcHAwfvrpJ5l9VV5ejuTkZObvzMxMJCQkQEVFhXlRzpw5E127dkVwcDDc3Nxw8+ZNbN++nQnNr0sbYmJicOPGDfTq1QuqqqqIiYnBzJkzMXbsWCb0PSIiAgMHDsT06dMxbNgwRgePx2McmenTp8PR0RGrV6+Gs7MzwsLCcPv2bdY2Ae9TUFCAfv36obi4GHv27EFBQQEKCqqPC9LW1oacnBz69esHS0tL/PDDD/j111+RnZ2NgIAA+Pr6Mk7jvn374OnpiXXr1sHOzo6xT/xMScPIyAg8Hg8bNmzA5MmTkZiYKLHHzuTJk7F69WrMnj0bEyZMQGxsLHbt2iXzvgHVa6QcHBywatUqDB48GOfPn2dNbQHAzz//jIEDB8LIyAjDhw8Hl8vFnTt3kJiYKLHo+Yvlg2K+vnK+lpB1MS93J5Gn/lEk/6JkyKmYR2lPSfCcALLKzZnMnehLRCIRKSovIo5hjqTNrjZkX8o+2ZXkPSPk/jlC1tlWh7BvcySkrIgl8jYykiS3NifJrc1J6r4LZKtfJNnoc4mcWB9P8nKKyNP7r8nb1193ODLl09GQIetvX5eQZ5/pecN74b3iKzQ0lJEpKSkhU6ZMIRoaGkRJSYkMGTKEZGVlMfmLFi2SqsPY2JiRWb9+PbGysiJKSkpETU2N2Nraks2bN5OqqipGRlq4Md4JDU5LS6vRXnFYMiGEeHp6ypQ5e/YssbGxISoqKkRZWZm0a9eObN26lWWLNGqq39HRkSV38uRJ0qZNG8Ln84m5uTnZvn17rTretS82NpbY2dkRoVBIFBUViYWFBQkODialpaW1tvF9Ww4cOEBatWpFeDwesbKyIqdPs7fveJ+a7gEAkpaWxsilp6eTAQMGEIFAQJo0aUL+97//MaHdhBDi6Ogo817WxL59+0jz5s0Jn88n9vb25MSJEwQAiY+PZ/WvmZkZ4fP5pHv37mTnzp1MyDohhLx580bimSCEkP/7v/8jBgYGRCAQEBcXF6kh6+fOnSNdu3YlAoGAqKmpkc6dO7Pu3+DBgz84VF3MpwxZ5xDSADGIXxkFBQUQCoVMmOGXTuH1LOQdewSeiRp0fNrVKPcsIxNhs33AAdBh9kr07GiB/ff345cbv0BTURNnh56FkkItv4hfpwE7egMlrwGrIcDw0Or5g3/I+XUlXu/cCa6aGhTX7sG5PzNRWfHv1BiHA/Qcaw5LB31p2infMKWlpUhLS4OJiclXM/9PoVA+P7K+Kz72/U0XMn8FiPfrKc94C5GMxcMGRs1QodsSABB+tHpOd2iroTBSNcLr0tf4Pfn32ivTNAFG7gG4CkDSUeDKCla2zozpUGzbFqKCApD1P6PXmFasfEKAyL33G2X/FAqFQqFQZEGdnq8AOU1FyGnwgSqCsrR8mbL2A6oXw/HTYvHs1VsocBUwrX311uy7Enfhdenr2its7gAM/Gevi8hlQOIRJovD46HZmtXgqqqiJCEB5eEnJYoTEZCXU1zH1lEoFAqF8nmgTs9XAIfDgWLL6sV5Zak1n7oOAN379kQFTxnKVcX483D14XX9jPvBUssSxZXF2H635gV6LNp7/HsS+7EfgcxYJotnYAC9fxbPVRzaBWlbvF0/9hj5L0rqVheFQqFQKJ8B6vR8JfDN1AEApY/yZMrJyctDr2N3AEDWjQiUVlSBy+FiZoeZAID9D/bj2dtndav0uyCgZT+gshT4czRQ8O8px2r9naDuPgqKZXmweHr032U/HIArz0FOegHCfrmJpL8yG2TregqFQqFQPhbq9Hwl8FuoAxygMqcYVQWyN6FyHu4KANAvfIJjV6vPeemi1wX2evaoFFViY8JGGaXfgSsHDPs/QNsCKMwG/nQHyv+dtmo6dy74rVtD9++LsI9eANuEtegasxADO7+BnpkQlWVViNz7AKc23kVRXv32GqFQKBQKpaGhTs9XgpyyAhT0q8/YKv07T6Zsk2YG4DZrCQ6Aa6dPMyMtMzrMAACceXwGD14/qFnBuyiqAe5/AkpaQFZC9VTXP9uec/l8NF1QvfW8YlkeNPJSoVj6GoXLF8J5tD4chptBTp6LjKRX+DPoBh7ezKajPhQKhUJpNKjT8xWh+M8UV23regCgu4sLAEDreQJi018BACy1LDGg+QAQEKyNW1v3it+N6Eo+xo7oEklxYkQiVD59Cpu+RnCb3wnaRqooK65E+M5knN+RiJLCT79dOoVCoVAo70Odnq+Id8/hqm3ExKZbd4h4SlCtKsLBYxeZ9Gm20yDPkcfVzKu4lX2r7pUbdwUGhlT/fWU5cO8QAIDX3BjgSj5GHF717s+a+soY5t8BnV1MwOVy8HfcC/wZdBNpd1/WvW4KhUKhUBoA6vR8Rfw/e+cZHkXVBeB3Nsmm95BKCj2U0CEU6QhIkyaK8oGiIh1EpRcBaYKiAjaUooAUKQJKM/TeQg89IYE00nvbne/HkoUlm0pCErzv88yT7J1bzsxuMmfPPcXY0xoMFagTM8iKzDsk3NDIiMot2gKQfOUYkYmavDnuVu70q94PgCXnlxRuu6nh/55EdP01Eh6cx8jZGZfZs3IoPg9GjiLlgj8ABgYKmnSrRN+JjbB1MSc1IYN/vr+M35rrpKdmkRSbxoObsSK3j0AgEAhKFKH0lCMkIwXGlTQZKHOruv40bXtqtrg8k++z/sAVbftH9T7C1NCUK1FX+Df439yG6+fV2VCtsyaia8PbEP8Qm379qHrAD481a/DavAnjWjVRxcQQ/O67xO/6WzvU0dOK/lMa0+BVD5Dgxslwfp92gjVTTvDXEn9+m3KC68dD81hcIBAIBOWBoKAgJEnS1nUrKwilp5xhUvVxvp58QtcB7N3cMXGvigKZi377yFRpHJAdTB0YXHswAN9d+I4sdVbBBVAYQN9fnkR0bRgAGckYOTtj7tsUUx8fvH7/HYv27ZEzMgj99FMeLV+utSgZGhnQom9Ven/SEAtbY9KTs8guEC+yOQvKIvPnz6dJkyZYWlri6OhIr169clQJT0tLY+TIkdjb22NhYUHfvn2JiIjQnr906RIDBgzA3d0dU1NTatasybfffqszx7Fjx2jZsiX29vaYmpri7e3NkiVLdPocOXKEHj164OrqiiRJbN++Xed8ZmYmEydOxMfHB3Nzc1xdXRk0aBChobpfJubOnUuLFi0wMzPDxsZG73VLkpTj2LBhQ5736tq1a/Tt2xcvLy8kSeKbb77R2+/hw4cMHDhQe60+Pj7aauEFvYbsNZ4+FixYoD1/6NAhXn/9dVxcXDA3N6d+/fqsW7cuhyybN2/G29sbExMTfHx8+Oeff/K8xpiYGEaPHk2NGjUwNTXFw8ODMWPGEB+vmzg2v/sXFhbG22+/TfXq1VEoFDqV3vNCpVIxffp0KlWqhKmpKVWqVGHOnDna/7EFvX/Pi77rkySJRYsWFes6xY1QesoZ2fl60u/FI6vUeXcGWnV/bO2JvsruK08+9INrDcbW2JaghCC23dlWOCFMrODtDY8jui5pIrriQiDwCMQ/RGFuTsWl32H33nsARC1dRujEiagznjgwu1a1oc3bNXJMLashPlIkNRSUHQ4fPszIkSM5deoU+/fvJzMzk06dOpGcnKzt8/HHH7Nz5042b97M4cOHCQ0NpU+fPtrz58+fx9HRkbVr13Lt2jWmTp3K5MmTWbbsSfoIc3NzRo0axZEjRwgICGDatGlMmzZNp+J3cnIy9erVY/ny5XplTUlJ4cKFC0yfPp0LFy6wdetWbt68Sc+ePXX6ZWRk8MYbbzB8+PA8r33VqlWEhYVpj169euXZPyUlhcqVK7NgwQKcnZ319omNjaVly5YYGRmxe/durl+/zldffaWtjl7QawCYPXu2jnyjR4/Wnjtx4gR169Zly5YtXL58mffee49Bgwaxa9cunT4DBgzg/fffx9/fn169etGrVy+uXr2a6zWGhoYSGhrK4sWLuXr1KqtXr2bPnj28//77hbp/6enpVKhQgWnTplGvXu41FZ9l4cKF/PDDDyxbtoyAgAAWLlzIl19+ydKlSwt9/56Hp68rLCyMlStXIkkSffv2LdZ1ip0iFkEt15S3KutPo1ap5YezT8ghE4/IaYFx+fbPSE+TFw/sJy/u300e8sXvOufWXl8r11ldR263sZ2ckplSeGGCTsjyLHtNVfaZ1pqfn9vI8vk12i4xGzbK12vVlq/X8JYD335HzoyJ0Z5LjEmVlw/zk5d99ORYPsxPVGp/SSnOKusJUY/k+1cuyQlRj4pBssIRGRkpA/Lhw4dlWZbluLg42cjISN68ebO2T0BAgAzIJ0+ezHWeESNGyO3atctzrd69e8sDBw7Uew6Qt23blq+8Z86ckQH5/v37Oc6tWrUqRxXtws6fG56envKSJUtytE+cOFF+5ZVXCjWXvmvIbf686Nq1q/zee+9pX/fv31/u1q2bTh9fX1/5o48+KtS8mzZtkpVKpU4V9cLcvzZt2shjx44tUN9u3brJQ4YM0Wnr06eP/M477+Q6Rt/9CwkJkd966y3Z1tZWNjMzkxs1aiSfOnVKlmVZvnPnjtyzZ0/Z0dFRNjc3lxs3bizv378/T7lef/11uX379trXgYGBMiD/8ccfcvPmzWVjY2O5du3a8qFDh/K9xpKssi4sPeUMSSFpEhVSML8eI6Ux3q3aAaC8c5qrD5+YYN+o/gZuFm48Sn3EuoCcZt988WwOHT9//CJ7j0oNO8dB/EMAbN/sj/vPP2lqdZ0/T9Cbb5F+7x4AFrYmtB3o/XQRd9xr2WFhKypw/xeQZZnMtLRCHxf3/s2Kke+xec4UVox8j4t7/y70HPJz5IvK3saws7MDNFaczMxMOnbsqO3j7e2Nh4cHJ0+ezHOe7Dn04e/vz4kTJ2jTpk2RZc1eR5KkXLex8mLkyJE4ODjQtGlTVq5cWSx5tnbs2EHjxo154403cHR0pEGDBqxYsSLPMbldw4IFC7C3t6dBgwYsWrSIrKy8t+qfvecnT57Ued8AOnfunOf7ltu8VlZWGBoa6rSXxP1r0aIFfn5+3Lp1C9BsnR47dozXXnstT/mevn9JSUm0adOGhw8fsmPHDi5dusSECRNQP87BlpSURNeuXfHz88Pf358uXbrQo0cPgoOD9c4fERHB33//rdfa9dlnn/HJJ5/g7+9P8+bN6dGjB9HR0c95F4qOYf5dBGUNk2q2pF6O0uTredUz3/6+Xbpyw+8fKqXcZ+3BKywY+AoASgMloxqMYvLRyfxy+ReqWFehpn1NnM31m6X14lI3Z5usgph7YO0GgEXLlnj9sZ6QYcPJDA4m6K0BVPzuW8ybNaNWS1c8atlx41Q4p/+6x4MbscSGJ2PrbF5wGQTlkqz0dL4b3O+55pBlGb+VP+C38odCjRuz5k+MTAqvXKvVasaNG0fLli2pU6cOAOHh4SiVyhwPZCcnJ8LDw/XOc+LECTZu3Mjff/+d41zFihV59OgRWVlZfP7553zwwQeFljObtLQ0Jk6cyIABA7CysirU2NmzZ9O+fXvMzMzYt28fI0aMICkpiTFjxhRZHoB79+7xww8/MH78eKZMmcLZs2cZM2YMSqWSwYMHF/gaxowZQ8OGDbGzs+PEiRNMnjyZsLAwvv76a73rbtq0ibNnz/LTTz9p28LDw3FyctLpl9f7po+oqCjmzJnD0KFDddpL6v5NmjSJhIQEvL29MTAwQKVSMXfuXN555x29/fXdv/Xr1/Po0SPOnj2rVQKrVq2qHVOvXj2dLbc5c+awbds2duzYwahRo3KssWbNGiwtLXW2dLMZNWqUdsvrhx9+YM+ePfz6669MmDCh6DfhORCWnnJItl9PxoNE1Gn5OyE7eHhh5VkVA9TcO3GI2OQnvjVdK3XFycyJ5KxkxhwcQ+ctndl6e2sesz2DXRWQnvkYSQZgV/kZmavitXEDpvXro05IIPiDD4n7U5Prx8LWhMaveeHpY49aJXN0022RuVlQJhk5ciRXr17N16E3L65evcrrr7/OzJkz6dSpU47zR48e5dy5c/z444988803/PHHH0VaJzMzk/79+yPLMj/8UDilEGD69Om0bNmSBg0aMHHiRCZMmKB1Ug0ODsbCwkJ7zJs3r8DzqtVqGjZsyLx582jQoAFDhw7lww8/5McffyzUNYwfP562bdtSt25dhg0bxldffcXSpUtJT89Z8ubgwYO89957rFixgtq1axdY1nnz5ulc57OWjoSEBLp160atWrX4/PPPdc7ldf8KwtGjR3XWznbC3rRpE+vWrWP9+vVcuHCBNWvWsHjxYtasWZNjjtzu38WLF2nQoEGulsakpCQ+/fRTatasiY2NDRYWFgQEBORq6Vm5ciXvvPMOJnq+SDRv3lz7u6GhIY0bNyYgIKDA96G4EZaecoihrQmGDqZkRaWSfjce09r2+Y5p9lo39v34LTXir7Ph7H2Gt60GQGRKJJEpkdp+alnNrJOzaOHaomAWH2s36PEt7Byr2doCaDNJa+XRkdveHo81qwmbMpWEv/8mbNp0MoKCsHn7bTKDQ2jW1oWQgBhCrscQeDGKyg0qFOyGCMolhsbGjFnzZ6HGJMZEs3r8MB2lWFIoePerH7C0y//v4Om1C8uoUaPYtWsXR44coWLFitp2Z2dnMjIyiIuL07H2RERE5HDmvX79Oh06dGDo0KFMmzZN7zqVKlUCwMfHh4iICD7//HMGDBhQKFmzH3b379/nwIEDhbby6MPX15c5c+aQnp6Oq6urTihyXtt0z+Li4kKtWrV02mrWrMmWLVt02gp7Db6+vmRlZREUFESNGk+CJA4fPkyPHj1YsmQJgwYN0hnj7OysE2UHuu/bsGHD6N+/v/acq6ur9vfExES6dOmCpaUl27Ztw8jIKF/5su+fcQE+f40bN9a5x9kWqc8++4xJkybx1ltvAZrPyf3795k/f76OpSyv+2dqaprn2p9++in79+9n8eLFVK1aFVNTU/r160dGRs5s+kePHuXmzZts3Lgx32sqCwhLTznlSdX1/EtSAHg3b4WkNMEmK4G9+4+jelw+IjghGBldq4paVhOSGFJwYRoOgnFXwd1X8zroiCb+XA8KY2NcFy/CYeRIAKJ/+ZW77TsQ/O67PHrjNWq5a5IuHt18i8wMVcFlEJQ7JEnCyMSkUIedqxuvDh2N9DgZpqRQ8OqHo7BzdSvUPNLTjmT5IMsyo0aNYtu2bRw4cECrlGTTqFEjjIyM8PPz07bdvHmT4OBgnW+5165do127dgwePJi5c+cWaG21Wq3XcpEX2Q+727dv8++//2JvX3BlMC8uXryIra0txsbGGBoaUrVqVe1RGKWnZcuWOUL+b926hafnk636olzDxYsXUSgUODo6atsOHTpEt27dWLhwYY7tJ9BYIZ5+3wD279+vfd/s7Ox0rjPbZychIYFOnTqhVCrZsWOHXguHPvmy719BMDU11Vnb0tIS0ERnKZ5JBmtgYKD1x4H871/dunW5ePEiMTExetc+fvw47777Lr1798bHxwdnZ2eCgoL09v31119p1KhRrhFop06d0v6elZXF+fPnqVmzZr7XX1IIS085xaSqDcmnwgqUrwfAyMSE2q3acdVvN05hF/k3oAudazvjYeWBQlKglp/8wShQ4G7pXjiBrN2gz8+w3BeCjsK1rVBHf+iiJElUGD0KAxtrIuY+ZRZXq7H/fSoW3ZaSFJPOhT338e1ZWe8cgv8uPu074VWvIXHhodg4u2Jp71Ci640cOZL169fz119/YWlpqfX3sLa2xtTUFGtra95//33Gjx+PnZ0dVlZWjB49mubNm9OsWTNAs6XVvn17OnfuzPjx47VzGBgYUKGCxqK5fPlyPDw88Pb2BjQ5eRYvXqzjA5KUlMSdO3e0rwMDA7l48SJ2dnZ4eHiQmZlJv379uHDhArt27UKlUmnXsrOzQ6lUAprtqZiYGIKDg1GpVFqLQtWqVbGwsGDnzp1ERETQrFkzTExM2L9/P/PmzePTTz/N815lZGRw/fp17e8PHz7k4sWLWFhYaH1GPv74Y1q0aMG8efPo378/Z86c4eeff9aG5hfkGk6ePMnp06dp164dlpaWnDx5ko8//piBAwdqQ98PHjxI9+7dGTt2LH379tXOoVQqtUra2LFjadOmDV999RXdunVjw4YNnDt3TidNwLNkKzwpKSmsXbuWhIQEEhISAKhQoQIGBgYFvn/Z9z0pKYlHjx5x8eJFlEplDkvY0/To0YO5c+fi4eFB7dq18ff35+uvv2bIkCEFvn8DBgxg3rx59OrVi/nz5+Pi4oK/vz+urq40b96catWqsXXrVnr06IEkSUyfPl1HqXr6XmzevJmvvvoqV3mXL19OtWrVqFmzJkuWLCE2NlYra6lQpJivck55DlnPRpWSKYdMOiKHTDwiZ8amFWhMROBdeXH/bvLC/j3kQcv/1bZvubVFrrumrlxndR25zuo6cqs/WslpWQWbMwcH52tC1xd7y3JaYp5dk06ekq/X8M5xXNtwXF72kZ/8w8iDclxkctHkEJQ5ijNk/UWCJjQxx7Fq1Sptn9TUVHnEiBHa8N/evXvLYWFh2vMzZ87UO4enp6e2z3fffSfXrl1bNjMzk62srOQGDRrI33//vaxSqbR9Dh48qHeewYMHy7L8JExY33Hw4EHtPIMHD86zz+7du+X69evLFhYWsrm5uVyvXj35xx9/1JFFH7mt36ZNG51+O3fulOvUqSMbGxvL3t7e8s8//5zvHE/Ld/78ednX11e2traWTUxM5Jo1a8rz5s2T09Ke/N/K7RqflWXTpk1y9erVZaVSKdeuXVv++++/87zG3N4DQA4MDCzU/cvvM6GPhIQEeezYsbKHh4dsYmIiV65cWZ46daqcnp5e4Psny7IcFBQk9+3bV7ayspLNzMzkxo0by6dPn9bO0a5dO9nU1FR2d3eXly1bpjes/qeffpJNTU3luLic6VOy5Vi/fr3ctGlTWalUyrVq1ZIPHDiQ5/XJcsmGrEuyXDCP0YYNGxakmxZJktixYwdubjl9O0qbhIQErK2ttWGG5ZXI5RfJCEnEtl81zBsXLOJq5YSxxN6/yzG75nz5+RiqOWlMpuHJ4dyOvc3049OJTotmZP2RDKs3rPBCZaZqrD1x96HlWE3Zity6hodzp30HePobhCRR5YAfezaFExIQi6ePPd1HFjxxl6DskpaWRmBgIJUqVSrQdoBAIPhvktf/iud9fhfYp+fixYt06NCB119/Pd+jZ8+eXLt2rdB70YLC8XTV9YLSuJMml0PtxADWnAjUtjubO9OqYismNp0IwC9XfuFh0sPCC2VkCq8t1Px+cjk8upV7Vz3FSiUjI0hPp9Wb1VEYSNy/Ek2QqMguEAgEgmKgUD49n332mY6TWF7ktccnKB5MqtqSeCCE9DtxyGoZSZG/c6Z3y9b4rVmBbUYcfx8/S8JrNbEyeRJ10MWrC5tvbeZs+FkWnV3EN+2+KbxgNV7TFCW9vRd2fwb/2w65OI7a9OuH+SuvkB4YyKNvviXt0iUejB+P14YN1Ovgjv++YI5uukXFmrYYGhkUXhaBQCAQCB5TYEtPYGCg1uGuIFy/fl3HG19Q/Cg9LJGUCtTJmWSGJ+c/AFCamlHrFU2G1yox1/jz3AOd85IkMbnpZAwkA/yC/Tj+8HjRhHttARgYw71DcP2vPLsaOTtj0bw5Fb/7FgNbW9KvBxD55SIad/XC3FpJQlQa/vv054cQCAQCgaCgFFjp8fT0LFSYp7u7OwYG4pt5SSIZKjCuZA0UrOp6NvU6dAGgaspd/jgagFqt69ZVzbYab9d8G4AFZxaQocqZmyFf7CprfHoA9k6FjPyVMiMnJ1wXzAcgdu1a0o4epGU/TT6h83vukxAlCpEKBAKBoOgUWOm5fPmy3pC13Lh27Vq+dVAEz49xVU14ZlohlB6nKtWw9/DCUFZhGnyJI7cf5egzvN5w7E3sCUoI4vfrvxdNuFc+BmsPSHgARxYXaIhFmzbYva8JZwybOg0P50zcqtugylRz/M87+YwWCAQCgSB3Cqz0NGjQoFBFwpo3b55rympB8WHy2Jk5IzAeObNgSqkkSdTvqHForpN4nTXHA3P0sVRa8knjTwD46fJPhCcXvBaNFqUZdNFYbjixFKIKprQ4jhuHab16qBMSCPv0U17pWxlJIXHv4iOCr5VeoTqBQCAQlG8K7MgsyzLTp0/HzMysQP31pasWFD+GTmYoLI1QJ2aSHpyAyeMK7PlRs1VbDv3+K/aZsRy8cpWgqDp4OegW+exeuTubb23GP9Kfr859xaI2Ba8bo8W7G1TtCHf+hd0TYOCWXJ2as5GMjHD96isC+/Qh9dIlzDb/Qt12r3PJL4QjG28xYLovBkYimbhAIBAICkeBnxytW7fm5s2b+Pv7F+ho3rx5vvU9BM+PJEmYPN7iSi9E6LqxmTneLVoDUCvhOr+fuq937im+U1BICvYE7eFM2JmiCAivfQkGSrjrBzd2FWiYsqIbLl/MATSlKmrZhmJqpSQ+MpWLfsKCKBAIBILCU2BLz6FDh0pQDMHzYFzVhhT/SFKuPsK8uQuG1gWr7eLToTPXDv9LteS7bDh1m5ZV7anpYoWL9RNl1dvOm/7V+7Ph5gbmnZ7H5p6bMVLkXVgvB/ZVoMUYOLoY9kyGKh00W1/5YNWpEynvvEPsunVETZ+M7+erOLTtIef+CaKGrzMWtiLBnUAgEAgKjtgjeAlQJ2cCoIpKI3zBGZLPFsz/xrW6N/YVPTCSs3CPucGQ1edoueAAG8/qWlJGNRiFrbEtd+Pv8kfAH0UTstUnYO0O8SFw7OsCD3Oc8BnGNWuiio3FfO1cnCtbkZUhnJoFAoEgN7y8vPjmm29KfJ3Vq1djY2NT4usUJ0LpKedkxacTv/spR2QZYrfeJis+/2zYkiTh2bI9ALUTr4Mso5ZhytarhMU/CQ+3NrZmXKNxAHx/6XsepeSM9soXpRl0flxc9Pi3EH23QMMUxsa4ff0VCjMzUs+exQd/JAnunI/kwQ39FYIFguJk/vz5NGnSBEtLSxwdHenVq1eOKuFpaWmMHDkSe3t7LCws6Nu3LxEREdrzly5dYsCAAbi7u2NqakrNmjX59ttvdeY4duwYLVu2xN7eHlNTU7y9vVmyZIlOnyNHjtCjRw9cXV2RJInt27frnM/MzGTixIn4+Phgbm6Oq6srgwYNIjQ0VKff3LlzadGiBWZmZrk+tCRJynFs2LAhz3t17do1+vbti5eXF5Ik5frgffjwIQMHDtReq4+PD+fOnSvUNWSv8fSxYMEC7flDhw7x+uuv4+Ligrm5OfXr12fdunU5ZNm8eTPe3t6YmJjg4+PDP//8k+c1xsTEMHr0aGrUqIGpqSkeHh6MGTOG+Ph4nX5+fn60aNECS0tLnJ2dmThxok5E8+eff673Hpubmz+7pA4qlYrp06dTqVIlTE1NqVKlCnPmzKGAFaVyIMsyr732mt7PU37XUB4RSk85JysqVVNK7mnkx+0FQFGtMVmSARUyoqmTeA3zrCRUskxQVIpOv15Ve+Hj4ENyZjJLzi/JZbZ8qNkDqrQHVQbsmQQF/CM1rlQJ51mzAFCtWkKNGppd2SMbb6NSFTyNgkBQFA4fPszIkSM5deoU+/fvJzMzk06dOpGc/CT31Mcff8zOnTvZvHkzhw8fJjQ0lD59+mjPnz9/HkdHR9auXcu1a9eYOnUqkydPZtmyZdo+5ubmjBo1iiNHjhAQEMC0adOYNm2aTsXv5ORk6tWrx/Lly/XKmpKSwoULF5g+fToXLlxg69at3Lx5k549e+r0y8jI4I033mD48OF5XvuqVasICwvTHr169cqzf0pKCpUrV2bBggU4O+uvBxgbG0vLli0xMjJi9+7dXL9+na+++kpbHb2g1wAwe/ZsHflGjx6tPXfixAnq1q3Lli1buHz5Mu+99x6DBg1i165dOn0GDBjA+++/j7+/P7169aJXr15cvXo112sMDQ0lNDSUxYsXc/XqVVavXs2ePXt4//33tX0uXbpE165d6dKlC/7+/mzcuJEdO3YwadIkbZ9PP/1UR/awsDBq1arFG2+8kec9XrhwIT/88APLli0jICCAhQsX8uWXX7J06dI8x+XGN998ozcHX0GuoVxSpDKl5ZyXocp6Nplxadpq69pj0hE5M65gVdJD41LkT//3nry4fzd5cf9u8pf9u8uvjVgkh8al5Oh75dEV2We1j1xndR35fPj5ogn86LYsz7LXVGIPyLua8bM8nDJFU4W99avyLx8fkpd95Cf7779fNDkEL5zirLKeGZcmp96JLfDnvDiJjIyUAfnw4cOyLMtyXFycbGRkJG/evFnbJyAgQAbkkydP5jrPiBEj5Hbt2uW5Vu/eveWBAwfqPQfI27Zty1feM2fOyIB8/37Ov5VVq1bJ1tbWzzV/bnh6espLlizJ0T5x4kT5lVdeKdRc+q4ht/nzomvXrvJ7772nfd2/f3+5W7duOn18fX3ljz76qFDzbtq0SVYqlXJmZqYsy7I8efJkuXHjxjp9duzYIZuYmMgJCQl657h48aIMyEeOHMlzrW7duslDhgzRaevTp4/8zjvvaF97enrKs2fPlt966y3ZzMxMdnV1lZctW5ZjLn9/f9nNzU0OCwvL8X4X5BqyPz/btm2Tq1atKhsbG8udOnWSg4ODdcZt375dbtCggWxsbCxXqlRJ/vzzz7X3Sh8lWWVdWHrKOYbWxtj2qQZPKeqm9SoU2JnZIisZ5/Qn21UKZNpFHebM1Xs5+tZxqEOfappvr3NPzyVLXQQzp0NVaDFK8/ueiZqq7AXEeepUlFWrIEWEUCPxBABndgUSeT+BBzdjSYpNK7w8glJDlmXUGapCH4knQwlfcIaoFVcIX3CGxJOhhZ5DLuJWAKDdxrCzswM0VpzMzEw6duyo7ePt7Y2HhwcnT57Mc57sOfTh7+/PiRMnaNOmTZFlzV5HkqQi+V6MHDkSBwcHmjZtysqVK5/rvmWzY8cOGjduzBtvvIGjoyMNGjRgxYoVeY7J7RoWLFiAvb09DRo0YNGiRfluvTx7z0+ePKnzvgF07tw5z/ctt3mtrKwwNNRYodPT03NUBzc1NSUtLY3z58/rneOXX36hevXqtGrVKs+1WrRogZ+fH7duaYo5X7p0iWPHjvHaa6/p9Fu0aBH16tXD39+fSZMmMXbsWPbv3689n5KSwttvv83y5cv1WuUKeg0pKSnMnTuX3377jePHjxMXF8dbb72lPX/06FEGDRrE2LFjuX79Oj/99BOrV69m7ty5eV5nSVGogqNPc/v2bQ4ePEhkZGSOTM0zZsx4bsEEBce8iTPG1W1JPBhC8qkwsqIL/vCPDQvl2f0xBTLfbD2Jl4cb9dxtdM6NbTiW/ff3cyv2FptubtKWqygUrT+Dy5sgLhiOfQPtJhdomMLMjIpLlhD4Rn/sjv6OXdd6xKSYsHm+xhdAkqDtQG9qtXQtvEyCF46cqSZ0xonnnATi/7pL/F8F8xHLxnV2CyRl4cvkqNVqxo0bR8uWLalTpw4A4eHhKJXKHA9kJycnwsP1BxWcOHGCjRs38vfff+c4V7FiRR49ekRWVhaff/45H3zwQaHlzCYtLY2JEycyYMAArKysCjV29uzZtG/fHjMzM/bt28eIESNISkpizJgxRZYH4N69e/zwww+MHz+eKVOmcPbsWcaMGYNSqWTw4MEFvoYxY8bQsGFD7OzsOHHiBJMnTyYsLIyvv9YfKLFp0ybOnj3LTz/9pG0LDw/HyclJp19e75s+oqKimDNnDkOHDtW2de7cmW+++YY//viD/v37Ex4ezuzZswEICwvTe43r1q0r0NbRpEmTSEhIwNvbGwMDA1QqFXPnzuWdd97R6deyZUvtfNWrV+f48eMsWbKEV199FdBsybZo0YLXX39d7zoFvYbMzEyWLVuGr68vAGvWrKFmzZqcOXOGpk2bMmvWLCZNmqR9bytXrsycOXOYMGECM2fOzPd6i5siWXpWrFhBzZo1mTFjBn/++Sfbtm3THs86QgleDIbWxlh19ACFRGZIIpmRKfkPAmxdXHPs58pAtGzKB7+dIzRO1xJja2LLmAaaf3rLLi4jOrUIGZKV5tD5sZZ/bAnE5MwInRvG1arhNHUKEjIVz6zWlVuGQ+tuCIuPoMQYOXIkV69ezdehNy+uXr3K66+/zsyZM+nUqVOO80ePHuXcuXP8+OOP2odOUcjMzKR///7IsswPP/xQ6PHTp0+nZcuWNGjQgIkTJzJhwgQWLdIkKA0ODsbCwkJ7zJs3r8DzqtVqGjZsyLx582jQoAFDhw7lww8/5McffyzUNYwfP562bdtSt25dhg0bxldffcXSpUtJT88ZxHHw4EHee+89VqxYQe3atQss67x583Su89kqAwkJCXTr1o1atWrx+eefa9s7derEokWLGDZsGMbGxlSvXp2uXbsCoFDkfOxu27aNxMREHaXv6NGjOmtnO2Fv2rSJdevWsX79ei5cuMCaNWtYvHgxa9as0ZmzefPmOV4HBAQAGmvbgQMH8ozwKug1GBoa0qRJE+1rb29vbGxstGtdunSJ2bNn61zLhx9+SFhYGCkpBXtOFSdFsvR88cUXzJ07l4kTJxa3PILnwMBCiUkNW9ICYkg5H4H1a5XyHWNp78CrQ0ezf8Uy5McWOwnolObPnwlt+WDNOTYPa4658ZOPSr/q/dhyewsBMQF8e+FbZrecXXhha/WCSm0g8DDsHAetPwG7KmDtlu9Qm379SDl1mtjjt3Kck9UQH5kqcviUAyQjBa6zWxRqjCo+nYivz+saJyVwGt8IgwJu6WavXVhGjRrFrl27OHLkCBUrVtS2Ozs7k5GRQVxcnI61JyIiIse2wfXr1+nQoQNDhw5l2rRpetepVEnzd+vj40NERASff/45AwYMKJSs2crC/fv3OXDgQKGtPPrw9fVlzpw5pKen4+rqysWLF7Xn8tqmexYXFxdq1aql01azZk22bNmi01bYa/D19SUrK4ugoCBq1KihbT98+DA9evRgyZIlDBo0SGeMs7OzTpQd6L5vw4YNo3///tpzrq5PrMiJiYl06dIFS0tLtm3bhpGRbv6y8ePH8/HHHxMWFoatrS1BQUFMnjyZypUr55D9l19+oXv37jpWp8aNG+vc4+xzn332GZMmTdJuIfn4+HD//n3mz5+v11KmjwMHDnD37t0c1sm+ffvSqlUrbV6+wlxDbiQlJTFr1iwdx/5snt0+exEUSemJjY3N18NcUDqYN3IiLSCGZP9IrDp7ISnyLvkA4NO+E171GhIXHkpKQjz/LF2MS/QN2hjYcDisAWM3XOSn/zXC4PFcBgoDpvhO4X+7/8e2O9voV70fdSvULZygkgRdF8H3zSDwkOaQFNDjW2g4KJ+hEs6zPie2/7saLUfSfYCd/ScQIxMDHD2f/x+9oOSQJKnQW0yKCmbY9qlG7NbbGsVHAts+1TCqULDyOEVBlmVGjx7Ntm3bOHTokFYpyaZRo0YYGRnh5+dH3759Abh58ybBwcE637avXbtG+/btGTx4cIH9GdRqtV7LRV5kKwvZLgj29vaFGp8bFy9exNbWFmNjjXJZtWrVIs3TsmXLHCH/t27dwtPTU/u6KNdw8eJFFAoFjo6O2rZDhw7RvXt3Fi5cqLP9lE3z5s3x8/Nj3Lhx2rb9+/dr3zc7Ozu9Cl1CQgKdO3fG2NiYHTt25PrwliRJqyj98ccfuLu707BhQ50+gYGBHDx4kB07dui0m5qa6r3HKSkpOaxFBgYGOdxMTp06leN1zZo1Ac0W2bPbpj4+PixZsoQePXoU6hqysrI4d+4cTZs2BTSf/bi4OO1aDRs25ObNm0X+vBQ7RfF+HjJkiPzDDz8UyXNaH8uWLZM9PT1lY2NjuWnTpvLp06fz7B8bGyuPGDFCdnZ2lpVKpVytWjX5778LHgn0MkVvPYs6UyU/nHVCDpl4RE69GVOkOS79u0cbzdV5zLey58Rd8ry/r+foN+XoFLnO6jpy/5395SxVVuEXinsgyzOtNZFc2cfntpr2ApBw+LD8b5v35GVD98nLPvKTlw39V142dL/m94/85F3LL8mR9/VHSghePOU1emv48OGytbW1fOjQITksLEx7pKQ8iXAcNmyY7OHhIR84cEA+d+6c3Lx5c7l58+ba81euXJErVKggDxw4UGeOyMhIbZ9ly5bJO3bskG/duiXfunVL/uWXX2RLS0t56tSp2j6JiYmyv7+/7O/vLwPy119/Lfv7+2ujmjIyMuSePXvKFStWlC9evKizVnp6unae+/fvy/7+/vKsWbNkCwsL7ZyJiYmyLGuidFasWCFfuXJFvn37tvz999/LZmZm8owZM/K8V+np6dq5XFxc5E8//VT29/eXb9++re1z5swZ2dDQUJ47d658+/Zted26dbKZmZm8du3aAl/DiRMn5CVLlsgXL16U7969K69du1auUKGCPGjQIO06Bw4ckM3MzOTJkyfrzBEdHa3tc/z4cdnQ0FBevHixHBAQIM+cOVM2MjKSr1y5kus1xsfHy76+vrKPj498584dnbmzsp78H/zyyy/ly5cvy1evXpVnz54tGxkZ6Y2GmzZtmuzq6qozNi8GDx4su7m5ybt27ZIDAwPlrVu3yg4ODvKECRO0fTw9PWUrKyt54cKF8s2bN+Vly5bJBgYG8p49e3KdFz3Revldw6pVq2QjIyO5adOm8qlTp+Rz587JzZo1k5s1a6bts2fPHtnQ0FD+/PPP5atXr8rXr1+X//jjD53P9bOUZPRWkZSeefPmyQ4ODvLgwYPlxYsXy99++63OURg2bNggK5VKeeXKlfK1a9fkDz/8ULaxsZEjIiL09k9PT5cbN24sd+3aVT527JgcGBgoHzp0SL548WKB13yZlR5ZluWY7bflkIlH5Kj1AUWe4+CaFfLi/t3kr97uJTcZt1L2nLhL3nBGN+T1Ucojudm6ZnKd1XXkXy7/Ip8OPS2HJYUVfJF7h3UVnuzjXt4hm9kknTwlX6/hLV+o20w+7ttDvlC3mXyufiv5n7n75eXD/LTKz9/fX5IfhQjlp7QpTqXnRYLGppTjWLVqlbZPamqqPGLECNnW1lY2MzOTe/fuLYeFPflbmDlzpt45PD09tX2+++47uXbt2rKZmZlsZWUlN2jQQP7+++9llUql7XPw4EG98wwePFiWZVkODAzMVd6DBw9q5xk8eHCefXbv3i3Xr19ftrCwkM3NzeV69erJP/74o44s+sht/TZt2uj027lzp1ynTh3Z2NhY9vb2ln/++ed853havvPnz8u+vr6ytbW1bGJiItesWVOeN2+enJb2RAnO7RqflWXTpk1y9erVZaVSKdeuXTvfL9C5vQeAHBgYqO3Xrl07rXy+vr7yP//8k2MulUolV6xYUZ4yZUqeaz5NQkKCPHbsWNnDw0M2MTGRK1euLE+dOlVHqfX09JRnzZolv/HGG7KZmZns7Oyc77NZn9KT3zVkh6xv2bJFrly5smxsbCx37NgxR3qEPXv2yC1atJBNTU1lKysruWnTpjrv+bOUpNIjPb7YQvGsefdpJEni3r2c4c654evrS5MmTbRJutRqNe7u7owePVqvJ/uPP/7IokWLuHHjRo491IKSkJCAtbW1NszwZSPjQSKRyy6CoQLXqb4oTAu/i6lWq/hr0Rfcu3AWzKxYaf866UpLfn/fl+ZVnpia115fy8KzC7WvFZKCmc1nakPb8yT+IXxTR7NF9TTDT4BT/s6GmeHh3GnfAZ4x62JigrL/EO7YvcKdS7Fa/4/KDSrQtHsl7N0s8pdNUOykpaURGBhIpUqVSmUvXyAQlA/y+l/xvM/vIkVvBQYG5noURuHJyMjg/PnzOnkSFAoFHTt2zDVPwo4dO2jevDkjR47EycmJOnXqMG/ePFQqVa7rpKenk5CQoHO8zBi5WWDoZAZZalIuF6FkBKBQGNB19Gc4uHtCSgLvJPwLWRkMW3uewKgnmWjbubfTGaeW1cw6OYvw5AKEfFq7aXx4pGf8OraPgNTYfIcbOTvjMnsWZO9vKxQYurlBWhoZv32P188f0NnrJlXr2YIE9/wfsWHOGfb8fJXo0CQAkmLTRI4fgUAg+I9Q5Dw92WQbivSlsc6PqKgoVCqV3jwJN27c0Dvm3r17HDhwgHfeeYd//vmHO3fuMGLECDIzM3ON+Z8/fz6zHpcx+C8gSRLmjZyI/yeQlPMRWPi6FGkeYzMzek2Ywbqp4yE+jDeMjvCH1J73V59l64gW2JgpeZj0MMc4tawmJDEEZ3P9aeh1aDhIU3U95h6oVbDlfQi7CL/3hv9tB1ObPIfb9OuH+SuvkHE/GKWnB4ZOTiQdOsSjpUtJvx6AetV3eFmspMpbQ7lj3pi7l2K5eyGSu/6ROHpYEhmcCLLI8SMQCAT/BYqckfm3337Dx8cHU1NTTE1NqVu3Lr///ntxyqYXtVqNo6MjP//8M40aNeLNN99k6tSpenM8ZDN58mTi4+O1R0hISInLWdqY1XcEBWQEJ5L5qOi5EKwdnXj9k6kYGBriEHWLTqkXuBeVzPC1F8hUqfGw8kDxTPSUQlLgbuleiEXcoFIrqNIWBu8AM3sI9Ye1fSAtPt/hRs7OmPs2xcjZGUmSsGzXjkpbtuC29DuMq1dHnZRE5i9fU2nVMDrXCKKyjy3IEHk/Ubv1JXL8CAQCwctPkZSer7/+muHDh9O1a1c2bdrEpk2b6NKlC8OGDctRFTgvHBwcMDAwyDNPwrO4uLhQvXp1DAyebInUrFmT8PBwMjIy9I4xNjbGyspK53jZMbBSYlLtcQG/C5HPNZebdy06faRJSFgj4gz1Um9z8l4007dfxcnMiZnNZ6J46qP0caOPC2bl0YdTbRi0A0zt4OF5+L0PpBV+O1KSJKxefZVK27fh9s0SlFWqoE5IIPOnRVT5fQSNPXNu+2Xn+BEIBALBy0mRlJ6lS5fyww8/sHDhQnr27EnPnj358ssv+f777/nuu+8KPI9SqaRRo0b4+flp29RqNX5+fjmySWbTsmVL7ty5o5OT4NatW7i4uKBUKotyOS8tZo0024YpFyKQ1c9XM6dW6/Y07aXJzdTm0SFc08PYcDaEX48F0qdaH/b03UNVG00ehgL58+SFcx0Y9BeY2sLDc7C2b5EUHwBJocCqSxcq7/gL10WLUHp6ooqLQ7nhm5wO1MDFAyGkJWU+n/wCgUAgKJMUSekJCwujRYucmVRbtGiht65IXowfP54VK1awZs0aAgICGD58OMnJybz33nsADBo0iMmTn9RmGj58ODExMYwdO5Zbt27x999/M2/ePEaOHFmUS3mpMa1pj2RiiCo+g/S7cc893ytv/o+qTZojq7LoF/svlpkJzP0ngH+vR+Bi4cKEJhMA2HxzMxHJEfnMlg8udTWKj4kNPDgD6/pBemKRp5MMDLDu0Z3Kf+/CZf58LOxM8b65HuTHDvCyDLKaoEtR/DH7NIGXo55PfkGuFCFgVCAQ/Icoyf8RRVJ6qlatyqZNm3K0b9y4kWrVqhVqrjfffJPFixczY8YM6tevz8WLF9mzZ4/WuTk4OFhHkXJ3d2fv3r2cPXuWunXrMmbMGMaOHVugQm3/NSQjBWb1KwCQcv45lRA0VpOuoz7B0asKcmoSAxP/xVCVwZgN/hy5+Qh1SlVq29UjQ53Byqsrn3s9XOo9VnysIeQ0rHsD0pOe7xoMDbHp3QuXL+bgGn6SFqdm0ODiN7Q4NY3GFxZhbS2RkpDBP99fxm/1ddJThNWnuMhOMVEa9XYEAkH5Ift/RFHT0uRFkfL0bNmyhTfffJOOHTvSsmVLAI4fP46fnx+bNm2id+/exS5ocfKy5+l5mvTgBB59fwnJSIHLVF8UJs8dsEdidBTrpnxMclws8Q5V+d2iA/JjZ2ZD8zuYevyCUqHknz7/4GTulM9sBeDhBfitF6THg0cLeGczGD9frh29OX4UCrz27sf/TAr+/waDDOY2xrT7nzeetYsnlf9/nbCwMOLi4nB0dMTMzKxIUZ8CgeDlRJZlUlJSiIyMxMbGBheXnJHHz/v8LpLSA3D+/HmWLFmiraRas2ZNPvnkExo0aFCU6V4o/yWlR5ZlIr4+T9ajVGz7VsO8SREdjJ8h/M4tNn4+iazMDPyt6uJvXQ+bzHjijKyQq6zDwCyIt73fZrLv5PwnKwgPzz9WfBLA8xV4Z5OmWvtzEPfnn4TNmKlVfAwcK1DNzw/JyIiwu/H4rbmudWyu1dKFlv2qoSxCokfBE2RZJjw8nLi4uNIWRSAQlFFsbGxwfhyN+yylpvSUZ/5LSg9AwqEQEvYEofSywnFYvWKb9+bJo+z6RpON+XHtR9RIHHatyf36u4vX2gPw4Jwmf096Ani1grc3gfL5Ck1mhoeTdv06oVOmoo6Lw/GzT7F//33NuQwVp7ff49LBEJDBws6Y9oNq4u5d8IrSAv2oVCoyM8XWoUAg0MXIyEgnOvtZXpjSk5CQoF0gv4zGZV2R+K8pPar4dMIWnAEZnD9rjKG9abHN/fcvP3Jj/y6dNjUSV/pY4p92pXitPQAhZzRh7BmJUKk1DNj43IoPQNzWbYRNmYJkZkaVf/7G6KmUCaG3Y/FbE0BClCaHT502bjTvXQVlMWwVCgQCgaDgvLAyFLa2tkRGavK92NjYYGtrm+PIbheULQysjTF+nLMn+Tlz9jxLnWY5UwsokHnHpScAf9768/kjuZ7GvSkM3AJKCwg8AhsGQPRdze/xObNDFxTrXq9j2qABckoKEQsW6pxzrWbLm9OaUqeNGwBXDz9k4xdnCL0dK8pYCAQCQTmiwF9VDxw4gJ2dxqx/8ODBEhNIUDKYN3Qk/VYsKecjsOrggaQoHgdSOxc3JEnSDTGUFDSv1YGGybu5EHmBX6/+yhTfKcWyHgAevvDOn5r8PfcOwdKG2nXp8a2mtEUhkRQKnGfOILBPXxL37CHpWD8sXmmpPa80MaTNgBpUrl+BA79rrD7bvvJ/Ml6UsRAIBIIyT4EtPW3atMHQUKMjVapUidatW9OmTRudo3Xr1nlWYBeUHqa17ZGMDVDFpZMemH9ph4Jiae/Aq0NHa1/LwL3qr2HlUIER9UcAJWDtAfBsDr1/0G2T1bBzXJEtPibe3tgOfAeAiDlzUOvJ8O1e044B032p1thRd2lRxkIgEAjKPEXK01OpUiUePcqZxj8mJkYoPWUUycgAs3rFl7PnaXzad6L/zPmadYATSRZcC42nqXNTGjo2JFOdya9Xfy3WNQFNxuZnkVWa4qVFpMLo0RhUcCDj/n1iVurPNaQ0NaRWK7ecS4syFgKBQFCmKZLSI8uy3lCypKQkTExMnlsoQcmQXZYi9WoU6nRVsc7tXsuHqk00/j0N4y+x5kQQkiSVrLXHropmS+tpJAXYVS7ylAaWljhN0GSWjvrxJzIe6Lca2Tia8uyfgCSBtWPxOYkLBAKBoHgpVPjJ+PHjAU0xx+nTp2Nm9iRqRqVScfr0aerXr1+sAgqKD6WHJYYOpmRFpZJ6NQrzRsUUSv6YJj37cufsSWok3WL9uVtMeq2m1tpTIr491m4aH56d456UkzC1fe7EhVbduxO3+U9SzpwhYv583Jcvy9HHwtaEtgO9ObT2BtnuTB617bCwFUq/QCAQlFUKZenx9/fH398fWZa5cuWK9rW/vz83btygXr16rF69uoREFTwvkiRh1lDji1LcW1wArtW9cfOujQFqasVcYsPZYCRJYmR9TV20P2/9+fzFSJ+l4SAYdwXe+gMsXSElGnaMhudIPyVJEs4zpoOhIUl+fiQeOqS3X62Wrgya14KmPTRbuiE3YomLECUWBAKBoKxSKKXn4MGDHDx4kMGDB7N7927t64MHD7J3715++umnQtfeErxYzBo6ggTp9+LJiil+p9smPfsC4JNwjY1Hb5KlUtPEuQmNnBppfHuulIBvj7UbeHeFN38HhRFc/wvO/vJcUxpXrYrdYE0UWMQXc1Gn6b9XFrYmNO7qhUctO9RZMkc33RIFNQUCgaCMUiSfnlWrVv0nkvq9jBjamGBcxQaAlAvFb+2p3KAxdhU9UMqZ2D/0Z//1CI1vTz2Nb8+W21uK39qTTcXG8Opsze97p0Cof97986HCiBEYOjmR+eAB0StyV6IkSaLVm9VRGEoEX4sh8JKo0C4QCARlkSIpPQDnzp1jwoQJvPXWW/Tp00fnEJRtsh2aky9EFrtVQlIoaPrY2lM/4TJrjt0BKHlrTzbNhoN3d1BlwKbBkBpX5KkU5uY4TZ4EQPSKFWQEB+fa18bJjAYdPQA4tuk2mRnF6yguEAgEguenSErPhg0baNGiBQEBAWzbto3MzEyuXbvGgQMHsLa2Lm4ZBcWMNmdPTBoZQXmXFCkK3i1bY2pjh7kqhYSrpwkIS3hx1h5JgteXgY0HxN2HHaOey7/HsnNnzFu0QM7IIPyLL/JUEhu95oWFrTGJMWlc2HO/yGsKBAKBoGQoktIzb948lixZws6dO1EqlXz77bfcuHGD/v374+HhUdwyCooZhdIAUx8HAJJLwKHZwNCIpt17AdAw/iJrjmvy5jR1aUpjp8ZkqjP55crz+dzkiakt9Fut8e8J2Alnfi7yVJIk4TR9GhgZkXzkKIn//ptrXyNjA155Q+PTdmHffeIihVOzQCAQlCWKpPTcvXuXbt26AaBUKklOTkaSJD7++GN+/rnoDxjBiyM7XD31ShTqEtiK8enQBQMTM2wz47hw7DixyZrsxtl5e7be3lpy1h6Aio2g0xzN73unwsMLRZ7KuFIl7IcMASBi/nzUKbkrM5UbVMC9pi3qLJljm24Lp2aBQCAoQxRJ6bG1tSUxMREANzc3rl69CkBcXBwpeTwQBGUHpZcVBnYmyOkqUq9FF/v8xmZmNOqsUYzrxlxgw1mNP0wT5yYvxtoD4DtM49+jzoTN7z6Xf4/DsI8wcnUlKzSMqB9/yrWf1qnZQOL+1WiCLgunZoFAICgrFEnpad26Nfv37wfgjTfeYOzYsXz44YcMGDCADh06FKuAgpJBkiTMSzBnD0DDrj3BwBCX9Ah2/3uSLJUaeIHWHkmC15eDjafGv+evkUX271GYmuI0VZNYMXrVKtLv5V7qwtbZnPqPnZqPbrpNlnBqFggEgjJBkZSeZcuW8dZbbwEwdepUxo8fT0REBH379uXXX0swMkdQrJg11Gxxpd+NIyuu+HP2mNvYUqt1ewDcH57i34BI4AVbe0xt4I3VGv+eG7vgdO5WmvywaN8eizZtIDOT8Dlz8ty6atz1sVNzdBoX9gqnZoFAICgLFEnpsbOzw9XVVTOBQsGkSZPYsWMHX331Fba2eopACsokhnYmGFe2BhlSLkSWyBq+PfsiI1E55T4b953Vtr8waw+AW0PoPFfz+75p8OB8kaaRJAmnaVORlEpSTp4icc+eXPsaGRvQst9jp+a9wcQ/Etu+AoFAUNoUWOlJSEgo8CEoP2Tn7EkpgZw9AHaubrg3aAqA4tohboRrPh9NnJvQxLnJi7H2ADQdCjV7avx7/nwXUmOLNI3S3R37oUMBiJi/AFVScq59qzSsQEVvW1RZao5tul2k9QQCgUBQfBRY6bGxscHW1jbPI7uPoPxgWscBSakgKyqVpJNhZMWnF/sarfv2B6BG0m1+//eytn14veGApibXnsA9L8C/ZxnYekFcMGwvun+P/YcfYOThQVZkJJFffknyqdNkhueUXZIkWr+lcWoOuiKcmgUCgaC0keQCfr0/fPhwgSdt06ZNkQV6ESQkJGBtbU18fLwopwFEfn+RjGBNNB4S2PaphnkT52JdY8WkT0gIvMkl2/p89/UMbMyUAPTY1oOghCAAFJKCmc1n0qdaCWb1DvWHXztpMjZ3ng/NRxRpmqQjRwgZ+tGTBoUCl9mzsOnXL0ffE1vv4L8vGCsHEwbM9MXQyKCo0gsEAsF/mud9fhdY6XmZEErPE7Li0wlfcAae/hRI4DypKYbWxsW2zt3zZ9j+5WwyJCPs3p/NR6/6EJ4cTqc/OyE/tbhCUrC3716czYtX6dLhzAr451ONc/OQvZqcPoUkMzycO23b6TYqFFQ94IeRs67sGWlZrP/8NMlx6TTtUYkm3So9j/QCgUDwn+V5n9+GRVn0yJEjeZ5v3bp1UaYVlAJZUam6Cg+ArGkvTqWncsMmGDq4QlQop//5mw861CE4IVhH4QFQy2pCEkNKVulp8gEEHYPr22HjQOi6GFzra6q1F5CMID0RWWo1GfeDcyg9ShNDWvaryr5frnF+z31q+Dpj5WD6fNcgEAgEgkJTJKWnbdu2OdokSdL+rlKJvCTlBUMHU5DIYekxLOaHsiRJtOn7Bn4/fYtXxAX2X3lA/aoeKCQFalmt0zcyuWQiyZ4SBnp+p1F8EkNh49sgKaDHt9BwUIGmUHp5gkIBal3ZFTb6a89VbeTItaOhPLwZy9FNt+k2ou5zX4ZAIBAICkeRQtZjY2N1jsjISPbs2UOTJk3Yt29fccsoKEEMrY2x7VNNo/g8RulhVaxWnmx8WrdFNrPGXJXCP9t24WzuzMzmM1FIuh/DmSdnciL0RLGvr0N6EqTGPHktq2HnOIh/WKDhRs7OuMyepVF8niJs0mRUcXE5+kuSROs3q6NQSARdjiLoinBqFggEghdNkZQea2trncPBwYFXX32VhQsXMmHChOKWUVDCmDdxxnlSU6xfrwJARnACmeG5h2IXFQNDIxp0fR0AqzvHuBkWT59qfdjbdy8rO69kd+/dtK3YlnRVOmMOjOFU2Klil0FLzF2NovM0sgruHy/wFDb9+lH1gB8ea9bgsXoVBvb2pAcEcH/IEL2Kj52rOXU7uAOPMzVnCouoQCAQvEiKpPTkhpOTEzdv3izOKQUvCENrYyybu2Ja2x5kiN8bVCLrtOreHZWRCbaZ8fzx524AnM2daeLchIpWFfmq7Ve0qdiGdFU6o/1GczrsdInIgV0VzZbWs/zzGQQeLfA0Rs7OmPs2xbxZMzxXr8LAzo706wEEv/8Bqvj4HP2bdPPC3FpJwqNULu4Pfp4rEAgEAkEhKZLSc/nyZZ3j0qVL7Nmzh2HDhlG/fv1iFlHwIrHq4gUKSAuIIT0w50P7eVGamuHR8lUAUs/tJ+5x9XXteQMlX7f9mtYVW5OmSmOU3yjOhJ0pdjmwdtP48EiPw8clBVh7QFoc/N4Lzq8u9JTG1appLD52dqRdu6ZRfJ5J1qk0MaRFv6oAnP0niFtnwkmKLf4SIAKBQCDISZFC1hUKBZIk5cjg26xZM1auXIm3t3exCVgSiJD1vInddpvk0+EoPSypMLyejpN6cZAUG8P3w9/DQFZh3mcsw958NUefDFUG4w6O4+jDo5gYmPB9x+9p4tykWOUAND48MffArjKY2mqKkl7bqjnnOww6zQWDwvn7p928RfC776KKjcXExwePX3/B4KnPmSzLrJt5ivjIVEDjV912oDe1WroW22UJBALBy8jzPr+LZOkJDAzk3r17BAYGEhgYyP3790lJSeHEiRNlXuER5I9VB08kIwUZwYmkXYsu9vktbO2w9GkOwF2/najUOfVupYGSJe2W0NKtJWmqNEb6jeRc+LlilwVrN6jUSvNTaQb9VkK7qZpzp3+E9f0hNa5QU5rUqK6x+NjYkHblCsEffIgqMVF7PjkunfhHqdrXsgyH1t4QFh+BQCAoYYqk9Hh6euoc7u7umJiYFLdsglLCwEqJxSuanDXxe4OQVcWfv7LX/95BBpzj7/HPkQt6+xgbGPNtu29p6dqS1KxURviN4HxE0YqFFhhJgjYToP9vYGQGd/3gl44QfbdQ05jUqKFRfKytSbt8meAPPkCVlARAXGTO3EiyDLuWXSLkRkyJ1EATCAQCwXM4Mvv5+dG9e3eqVKlClSpV6N69O//++29xyiYoRSzbVERhZkjWo1SSzxd/TSwXD3dkTx8ATv21Jdd+xgbGfNv+W1q4tiA1K5Xh/w7nQoR+JalYqfU6DNkDVm4QfRtWtId7hwo1hYm39xPF59JlQt7XKD42jqbo2zGMfpjMjm8usnXRBe5fixbKj0AgEBQzRVJ6vv/+e7p06YKlpSVjx45l7NixWFlZ0bVrV5YvX17cMgpKAYWJIZbtPQBI2B+MOqP4w6tfffMtAKxDr7Bm7TbuBj3Q2y/b4tPMpZlW8fGP9C92eXLgUg8+PABujR87OPfRlLAoBCY1a+KxaiUKa2tSL10i5IMPMTVS0XagtzZ4TFJA8z5V8GlbEQNDBeH34tm19BJ/LjhH4OUoofwIBAJBMVEkR+aKFSsyadIkRo0apdO+fPly5s2bx8OHBUvwVloIR+aCIWepCf/qHKrYdKw6e2HVzr3Y15g9+H+Yp8UCoEbCvvPbDBkyQG/f1KxURh/QhLGbGZrx06s/Ud+xfrHLlIPMNNgxGq5s0rxu8gF0WQAGRgWeIvXaNYLfG4I6IQHThg1x//lnUjMNiI9MxdrRFAtbzfZwcnw6/vuDuXb4IVmZmjxCDu4WNO7qReV6FZAUxetULhAIBOWJUnFkjouLo0uXLjnaO3XqRLye3CSC8olkqMC6kxcAiYdCUCVnFuv8d4MeYPZY4QFQIBO9d32uFh9TQ1OWtl+Kr7MvKVkpDPt3GJceXSI8OZwzYWcITy7+bTgAjEygz8/QYSYgwdlfYG1fSInRRH8FHsk3k7Np7dp4rFyJwsqK1AsXCPnoI4xiQ7GJvYVxepy2n7m1Ma/0q8b/5ragYWcPDI0NiApJYs9PV9nwxRlun4tA/djxOyk2jQc3Y4UDtEAgEBSQIll63n77bRo0aMBnn32m07548WLOnTvHhg0bik3AkkBYegqOrJaJXOpPZlgyFq+4YdO9crHNvXvfUa7/ujBHe+0PJtHl1VdyHZealarJ3xN+BmMDYzJUGcjIKCQFM5vPpE+1PsUmYw5u/A1bPoTMZDB30Cg+srrAtbtSr1wheMj7qJ+K5kKhwGX2LGz69cvRPy0pk0sHQrh8IISMNM0Wo62zGc5VrLlxIgxZFiHvAoHgv8PzPr+LpPR88cUXLF68mJYtW9K8uSb0+NSpUxw/fpxPPvlER5AxY8YUWqiSRig9hSPtVixRK6+CgYTzJ40xtCueSL27QQ/YOnE4iqdCmWSg/sdf0rFZrTzHpmSm8OG+D7kcdVmnXSEp2Nt3b8lWaQ+/Cuv6QWKYbrtkAOOu5FutPfHgQR4MH6HbqFBQ9YBfjgrt2aSnZHL54AMu+YWQnpKV47ykgEFzW2i3yQQCgeBlpFSUnkqVKhVsckni3r17hRaqpBFKT+GQZZmoX6+SficOswaO2L1Zo9jmXrnyD6L3rkeBjIym7uk9m5qMmTWdGs55vzdHHhxhpN/InHN2XlkyiQyf5voO2PS/nO2Dd2ny/uRB8qnTBL/7bo52168WY92tW55jM1KzOPbnbQKOh+U41+vjBrjVsM1zvEAgEJRnnvf5XbhUs48JDAwsyjBBOUWSJKy7eBG57CIpFyOxaOWG0tWiWOYeMmQAd9u34vbtIIzS4rmw9kcqxwUwd95Spk8dQzUny1zHVretjgIFap4UDlVICtwti9/hOgdujTTmlaeLlkoKTWbnfFB6eWqqs6t1C56GTpxEypmzOHw0FCNX/VtVSlNDmnavpN3aepq4iBSh9AgEAkEePHfBUVmWRUjtfwBlRUtM6zpoipHuCSrWuat4VaTLq6/QoUc3XvnfhwDUizjJxEW/cScyMddxzubOzGwxE8VThUM7eXYq2a2tbJ6t3QUapSf6dr5DjZydcZk9S6P4ACgUKCtXhqws4jZu5E7nLoTNmkVmWE5rDoCFrYlOyHs2h9bfZN+v10hLKl6Hc4FAIHhZKNL2FsBvv/3GokWLuH1b80++evXqfPbZZ/zvf3pM/mUMsb1VNLKiUgn/+jyoZRw+8MGkqk2JrLPn15+4tm8nWZIBByv1ZdnHfajqmLtlKTw5nDXX1rA2YC2GCkN+f+136jjUKRHZchD/EB7dhGPfQNBhMDSFdzZBpdb5Ds0MDyfjfjBKTw+MnJ1JOXeOR8uWk3LqFACSkRE2b/TDfuhQvb4+SbFpxEemYmFnTMCJMC7sDUZWy5hZKWk70JtKdR2K+2oFAoGgVCkVn56vv/6a6dOnM2rUKFq2bAnAsWPHWL58OV988QUff/xxoQV5kQilp+jE/nWH5JNhGFW0wHFk/WIvRgqgVqv4c+EXhFw8S4rCBL9qA/h1dGeqVMhd8ZFlmU8Of8L++/txs3Bjc4/NWCpz3xordrLSYeNAuL1Po/gM/BO8co9Ay4vkM2eIWraclDOa6vKSkRE2/ftrlB8nx1zHRQQl4Lf6OrHhKQB4N3Pmlf7VMDYreD4hgUAgKMuUmiPzrFmzGDRINzx3zZo1fP7552Xe50coPUVHlZhB+KJzyBkq7N72xqxuhRJZJzMtjXUzJhB9/x4xRjYcqv4Wvw9vS+U8FJ+EjAT67+zPw6SHdPbqzKLWi0pEKctDaNj4Dtz5V1O3650/watlkadLPn2GqKVLSTmnKbQqKZXYvPkm9h9+gJGjo8ZSFHQfpZen1hKUlani9I5ALv4bDDJY2BrTbqA3HrXti+USBQKBoDQpFaXHxMSEq1evUrVqVZ3227dv4+PjQ1pa2U6WJpSe5yPh3/sk/BuMob0JTuMbIRk8t2uYXpJiovl9ynhSYqMJMXHjVPU+/PHRK1RyMM91zOVHlxm8ezBZchbTm02nf43+JSJbrmSmwYYBcPcAGJnDwC3g2bzI08myTMrp0zxauozU85piq5KxMaYNG5By+ozGGVpPnp+wO3H4rQnQVnOv1cqVln2rojQpUuyCQCAQlAlKJSNz1apV2bRpU472jRs3Uq1ataJMKShHWLRyQ2FhRFZ0GslnSygLMmBhZ0/fSTMxNDbBPe0hte7tZ8BPJwmKSs51TN0KdRnXaBwAC88s5GbMzRKTTy9GJvDWeqjcTpPAcF0/CD5V5OkkScK8WTM81/6Ox8pfMW3QADk9nZSTp55Ef6nVhM2YSWb4k/fCpaoNb05rik+7igBcPxrKhjlneHAzVt8yAoFA8J+gSJaeLVu28Oabb9KxY0etT8/x48fx8/Nj06ZN9O7du9gFLU6Epef5SToZStxfd1FYGOH8WRMUxgb5Dyoi9/zPsn3hHGRZzXHbZoR6NGfD0GZ45WLxUctqRh8YzZEHR/Cy8mJj942YGZmVmHx6yUiBP96CwMOgtIT/bQX3ps89rSzLRK9cyaNFi3Oc81izBnPfnGs8uBnLgd8CSIzWWGB92lbEp11FkuPSsXmq7pdAIBCUdUrF0tO3b1/OnDmDg4MD27dvZ/v27Tg4OHDmzJkyr/AIigfzJs4Y2JugTsok6aj+WlnFReUGTWj3riaUvWXsKczDrjFgxSnuR+u3+CgkBV+0/AJHM0eCEoKYe3puicqnF6UZDNgAXq0gI1FToT3k7HNPK0mSJoGhIuefbsKePagzMnK0V6xhy1vTm1KrlSb3z5VDD1g/8xR/LfHntyknuH489LnlEggEgvJAoS09CQkJnD59moyMDJo2bUqFCiXjyFqSCEtP8ZBy6RExf9xAUhrgPKExBhbKEl3vwKqf8N+zE5VkyBbnnhg4ebJhaHM87PVbcc5HnGfI3iGoZTVftPyC16u+XqLy6SUjGda/CUFHwdgK/rcdKjZ67mnj/vyTsBkzNVtckkR2pkJl1Sq4zp+PqY+P3nE3T4fx76oAnTZJgkHzRAkLgUBQ9nmhlp6LFy/i7e1Nly5d6NGjB1WrVmXv3r2FXlTwcmDq44BRRQvkDBXxu4NIuxtHVnx6ia3XdvAHVG7YBAM5i9cf7SExKpIBK05x/n4MJ+5GERafqtO/kVMjRtTT1Liae3ou9+JLoSSK0hze3gieLSE9AX7vDQ8vPPe0Nv36UfWAHx5r1lD14AHcln6Hgb09GXfuEvTWACK/XqLX6mNuk1OxkWUIvBz13DIJBAJBWadQlp7OnTuTlJTE4sWLMTExYc6cOVy5ckWboLC8ICw9xUfanTiifrnypEEC2z7VMG9SMlmRM1JT2DBzIo/uB5JoYs96p9fJUBgDoJBgfh8f3mzioe2vUqv46N+POB12mmq21VjfdT0mhqVg0UhPeuzUfBJMrGHQX+DaoFiXyIqNJeKLuST8/Teg3+qTFJvGb1NO5ChhAdCwsydNu1fCwKhkovEEAoHgeXmhIesODg7s27ePhg0bAhAXF4ednR1xcXHlSnkQSk/xkRWfTvj8M7qNEjhPaoqhtXGJrJkYHcX6qeNJio0h2KQiBxxaY5WVRJyRNWlGlhyb1A4Xa1Nt/6jUKPru6EtMWgz9q/dnevPpJSJXvqQnwtp+EHIKTGygzwpNtJddlXwrsxeGhP37Cf98FqroaDAwwP6DD3AYOQKFUrP9eP14KIfW3UB+vDPm6GVFRGACAHau5nQYXBNHT/F3IRAIyh4vVOlRKBSEh4fj6PgkK6ylpSWXL18ucOX1soBQeoqPtLtxRK24kqPd4UMfTKrYlNi6EffusH7GBNSZGdrq7GokDjq0Yfr492heRTcZ34nQEwzbPwwZmUVtFtHFq0uJyZYn6Ymwti+EnH7SJik0dbwaDsp9XCHJio0lYs4XJPzzDwDG1ariMm8+pj6a8hzZJSysH0dv3fN/xKH1N0hNzERSSDR6zZPGr3lhYCisPgKBoOzwwqO3rl+/zuXLl7WHLMsEBATotAn+Oxg6mGo0jqeRHreXIE6Vq9JkwPtahQdAgUy7qMM4GqTm6N/CtQUf+HwAwKwTswhJCClR+XLF2BJ6LNVtk9Wwc5ymjlcxYWhri9vXX+H23bcY2NuTfvsOQW+9ReSSb1BnZGBha4JbDVut83LlBhUYMMOXKg0dkdUy5/4O4s+F54h6kHvBV4FAIChvFNrSI0mS3qrq2e2SJKFSqYpVyOJGWHqKl+Sz4cRuvQ2PPxZGFS1wGlW8/ir6CL56mc1zpuRobz/+cxr4Ns7RnqXOYsjeIfhH+lPLvha/v/Y7SoOSjTjTS+ARWNMjZ/vgXVCpVbEvl9PqU40Kn3yCwsREp4RFNrfPRXDkj1ukJWeiMJBo0s2LBp09MSihzNsCgUBQUF6opScwMJB79+4RGBiY48huv3ev8BEyy5cvx8vLCxMTE3x9fTlz5kz+g4ANGzYgSRK9evUq9JqC4sO8iTPOk5pi07sqSJD5IInUayUfDWTr4pqjtpYaicUno8hSqXP0N1QY8mXrL7E2tuZ69HWWnF9S4jLqxa6KZkvrWe78i14P4+ckp9XnNg+GDSP43Xe5074DcX/+qdO/WmMnBsz0pVI9B9QqmdM7Atmy8DzRoUnFLptAIBC8SIqUkbk42bhxI4MGDeLHH3/E19eXb775hs2bN3Pz5k0d36FnCQoK4pVXXqFy5crY2dmxffv2Aq8pLD0lR/yeQBIPPUBhaYTzx41QlHCF7ysH9rF/xTLkxyUZ0gxM+N3tLYa+6sP4TjX0jjkUcojRB0YDMKv5LCpaVsTDygNn85KJONPLhd80W1qyCs0G3eM/wwYDodsSMCwZC1TqzZsEvd5Lt1GhoOoBvxwWH1mWuXUmgqMbb5GekoXCUMK3R2Xqv+pBSnw6cZGpIqOzQCB4oZRKwdHixNfXlyZNmrBs2TIA1Go17u7ujB49mkmTJukdo1KpaN26NUOGDOHo0aPExcUJpaeMIGeqifjuAlmPUjFr5ITdG9VLfM3E6Cgi7t3h4OqfSYiKJNTYme2uPfn9wxa0qOKgd8yXZ7/k9+u/a18rJAUzm8+kT7U+JS6vlviHEHMP7CrDzX9g9wSNf49XK3jzdzC1LfYlk0+dJvjdd3O02wwYgPP0aUh6Mj0nx6VzcN0N7l+JBsDKwYSE6DSQNdFfbQd6U6ula7HLKhAIBM9SKmUoiouMjAzOnz9Px44dtW0KhYKOHTty8uTJXMfNnj0bR0dH3n///RchpqAQSEYKbPtWAwlSzkeQdqvkC1xa2jtQtUkz+kyZhbGZOa7p4bR9dIRxf/gTnaQ/WeLbNd7Wea2W1cw6OYvw5JIroJoDazeND4+1GzT9EN7eBEoLTfbmXztBTGCxL6n08tRbwiLujz8Ifvc9Mh/mdKY2tzGm24i6tB9UEyNjBQlRaVrDlCzDoXU3SIpNK3ZZBQKBoLgpVaUnKioKlUqFk5OTTruTkxPh4fofPseOHePXX39lxYoVBV4nPT2dhIQEnUNQchh7WWPRXPPNP3brbdTpL8ax3d7Nne7jJiJJCmol3cDt4Rk+2XwJtTqnMTM0OWe9KbWsJiSxlKK6AKq9CkP2gJUbRN2CXzpA8On8xxUCI2dnXGbPeqL4KBRY9eyJZGZGypkz3Hu9F3Hbt+cIVpAkiZotXGg3sGaOOWU1xEfmjJgTCASCska5CsdITEzkf//7HytWrMDBQf+2hT7mz5+PtbW19nB3dy9BKQUAVp29MLAxRhWXTvye4rdY5IZXvYa0Hfy4OGnMSYL8z7LyeM71Paw8UOhxJi7l3V5w9oEP/MClHqREa6K8rvyZ/7hCoFPC4oAfbl8upPK2rZjWr486KYmwSZN5OHYcWbE5rXQuVa2Rnk1RAIQHxpf+vRMIBIJ8KFWlx8HBAQMDAyIiInTaIyIicHbO6VR69+5dgoKC6NGjB4aGhhgaGvLbb7+xY8cODA0NuXv3rt51Jk+eTHx8vPYICSnFb/P/ERTGBtj2qQZA8qkw0oPiX9jaDbp0p27HLkhA58h/WfHXcS6FxOn0cTZ3ZmbzmTkUn3EHx3E6rHitK4XGygXe2w01uoEqHba8D0cWFWtkl5GzM+a+TbXOy0pPTzzX/k6FcePA0JDEffu417MnSUeO6IyzsDWh7UDvHMFnp7bfY+fSS2KbSyAQlGmey5E5KCiI8ePHM3/+fGrU0B8pkx++vr40bdqUpUs1CdvUajUeHh6MGjUqhyNzWload+7c0WmbNm0aiYmJfPvtt1SvXh2lMv+oF+HI/OKI2XyLlPMRGFYwxWlMQ6QXVNdJlZXFlnkzCLl2mXhDS07UGsi2TzphaaIbTRaeHE5IYgiWSkvmnprLxUcXMZQMmdF8Br2r9X4hsuaKWgX7Z8BJjZM/9d7WZG4uociubFKvXSN0wkQyHn+JsHnrTZwmTEBh9qSafXZGZysHE+5djOLk9ruoMtUoTQ1p9WY1avg650gnIBAIBM9LqToyr1mzhu3bt/Prr78WeY7x48ezYsUK1qxZQ0BAAMOHDyc5OZn33nsPgEGDBjF58mQATExMqFOnjs5hY2ODpaUlderUKZDCI3ix2HSrhMLSiKxHqST43X9h6xoYGtJj/GSsHF2wzkqk3s1tTPnzYo4tGGdzZ5o4N8HbzptfOv9CF68uZMlZzDgxg+8ufIdazpnv54WhMIDOc6Hb1yAZwKX1mirtKTEluqxp7dpU2vIndoM1ZTHiNmwksHcfUi9d0vbJzuhsaW9KvQ7uvDm1CU6VrMhIzcJvdQC7f7xCSkLOKu8CgUBQmjyX0vPbb7/x9ttvs3btWtTqoj0c3nzzTRYvXsyMGTOoX78+Fy9eZM+ePVrn5uDgYMLCwp5HTEEpojAzwrZXVQASjzwg4+GLS3BnamFJn0kzMDAxwzU9nJSDG9h0NvetTWMDYxa2XsiHPhqfoBVXVjDxyETSVfojwF4YTd6HdzaB0hLuH4NfX4Xou5qQ98AjxVq+IhuFiQlOkyfjsWolhs7OZNy/T9Db7/Dou++QMzPJDA8n+dRpMh8HHNg6m9Pn04Y061UZhYFE4KUo/ph1mjvnI4tdNoFAICgqRd7eOnLkCD179iQkJIRq1aqxatUqXnvtteKWr0QQ21svnuj1AaRejsLIxRzHUfWRXmBJg6BLF9gy/3OQ1Zx2aM68mWOo6miZ55jtd7Yz68QssuQs6leoz7ftv8XOxO7FCJwbEddg/ZsQHwJG5pCVovHzKYGCpU+jio8nfM4XJOzaBYChqytZYWGatRUKXGbPwqZfP23/qAdJ/Lv6OtEPNAputSZOtH6rOibmJZuoUiAQvPyU2vbWmjVr6Nu3L5aWlrz11lusXr26qFMJ/gPY9KyCwsyQzLBkEg89eKFre9VrSNtBmmKjTaNO8vmyzaRl5h1G36tqL3589UcslZZcfHSRd/5+h8D4FxeFphen2prILqc6kJn8xLG5BAqWPo2BtTVuixfh9vVXSJaWZIWGPllbrSZsxkytxQfAoaIFb0xqTOOuXkgKidtnI/hj9mmCrpR8aRKBQCDIiyIpPampqfz5558MGqT5Zjlo0CB27txJfPyLi9ARlC8MLJTY9KgCQMKBYDIjkl/o+g1f60H1Np2QgNq3djJv3YF8x/i6+LL2tbW4WbjxIOkBA/8ZyNnwsyUvbF5YOkGHz3O2yyqIvpOzvRix6toVl9mzc55Qq4lesYLMiCdbWQaGCnx7VqbvZ42wdTYjJT6Dv5df5sDvAcSEJfPgZqyI9BIIBC+cIik9W7Zswc7OjjZt2gDQsGFDKleuzIYNG4pVOMHLhWn9Cph424FKJvbP28h6kgaWFJIk0XXoCCwr10QpZyLtX8nOUzfzHVfZpjLruq6jboW6JGQkMHT/UHbc3fECJM4Dp1r6C5bu+hiubYci+tcVBLMG9fVmdI5dt547bdty/3+DiN2wgawYjbO1UyUr+k9pQr2O7iBBwPEw/ph1mr+W+PPblBNcP54zSaRAIBCUFEVSen777TfeeecdnbaBAweKLS5BnkiShE3vqkjGBmSEJJL0gh94BoaG/G/qDGRLB6yyEjn642KCIvK3Ttqb2vNrp1/p5NmJLHUWU49NZfnF5aWXjM/aTePDIxk8bpDA0BRi7sLmwfBza7i5u0QqtuvN6NytG6YNG4Isk3L2LOGfz+J2q9YEf/AhcVu3IaUl80q/anT5sI7OXLIMB3+/waH1N7h5Opzoh0moskoxWk4gELz0FNqROSQkhEqVKhEQEEC1atW07Q8ePMDLy4vr169TvXrJF5l8HoQjc+mSdDqMuG13kIwUOI1riKG96QtdPyI4mNWTPsZQlU6EXQ1av96Tmt5VqOJVMc9xalnNUv+l/HLlFwC6VurKqPqjCEsOe/FV2kG3YKnSHE59Dye/h4xEzXm3RtB+GlRuh940ys9BZng4GfeDUXp6aBMcZoaGkrB7Dwn//EPatWvavpKREeatWpHStDv/njLOc16FgYStszn2Fc2xd7XAvqIFDm4WmFkrtXl/kmLTRIV3geA/Srmvsl4aCKWndJFlmagVV0i/F49xZWscPvR54Ynszh4/xaHvvtCaOtVI2Hd+myFDBuQ7dtvtbcw+OZssOUvbVipV2vWREgPHv4UzP0NmiqbNsyW0mwpeLV+YGBn375OwezcJf/9D+u3bAKQZ23Ci2ZxntuZkajRzISEqlegHSWSk6XcwNzE3wr6iOZIk8eCGpjyGqPAuEPz3EEpPERBKT+mTFZ1KxDcXkDPVWHXxQuluiaGDKYbWeVsCiou7QQ/YNnEYT6taaiT6LPwhX4sPwD+B/zDxyESdNoWkYG/fvS/e4qOPpEg4tgTO/qopZQEai0/7aVCx8WMr0V2wq6LZLitB0m7dImH3buJ37CBY5cmNGgM0W3OyCu9bG2i1djZKNzdkWSYxJo3oh8lEP0giOjSJ6AdJxEWk5LpTJ0kwaF4LYfERCP4jlCmlJzk5mfPnz9O6devimrJEEEpP2SDx6EPi/773pEEC2z7VMG9S8krD7n1Huf7rwhztd6u8yshR71Lb1TrP8WfCzvD+vvdztK/svJImzk2KTc7nJv4hHP0KLvwG6kxNm1MdiLyuCXUv4Rw/T5N06hQh775HmrENqaYVME19hEl6HIaurjhNmIBlp1eR9DhJZ2WoiA1P4fa5CPz3Bec471HHjjYDamD1grdJBQLBi6dUy1A8y507d2jXrl1xTil4iTGtY6/bIEPs1ttkxZd8BuTq1SuhJueWWqW7+/ni88WM+O00tyMScx2fW5X2mzH5R4S9UKzdoPvXMPoc1B8IKCDiqkbhgRLP8fM0xl5eoFBgkh6HbdxtTNLjAMgKDeXhuHEE9ulL4oEDORzEDZUGVPCwpG67inpdk4KvxrBuxikOr79JUmwpZ88WCARlmlKtsi74b5MVoydPiwxZUaklvnYVr4rYd35bq/iokZAcPVEAjeP9sd7/PQMWbmXcBn8Co3LmFMqtSvvCswv5/uL3pRfZlRu2XtBrOfT6Puc5WaXZ6iph9EV+OU2bisPIkSjMzUm/cYMHI0YS1P9Nko4ey3EPn63wLimgYRdPKnrbolbJXD3ykLXTT3Js021R90sgEOilUNtbdnZ5p+FXqVQkJSWhUuWd7ba0EdtbZYOs+HTCF5yBpz+BEjhPavpCfXtu3w6iWjUvqnhV5PaZE+z5aRkZSQmoUHDGtjEXbRvSu6E7YzpUw93OTGd8dpX2ihYV2XBzAyuvrgSge+XuzGoxC6VBGSuCG/8QvqnzxNKTTZUO0OdnMHcocRH0RX5lxcYSs3IVMWvXIqdqlF7Thg2pMHYs5r5NdcZnV3i3fip66+GtWE7vuEfYHU0KAkOlAp+2FWnYyRMTC1H+QiB4WXihPj3m5uYMHz4cHx8fvefv37/PrFmzhNIjKDDJZ8OJ3Xpbq/gorJS4fNYYycgg74ElSEp8HPtXLOPO2VMAhBk78W+F9iSb2NK/sTuj2lfFxVq//8ift/7ki1NfoJJVNHRsyLftvsXGxOYFSl8ALvym2dKSVYCkMZnIKjCvoPHv8e5WaqJlRUcTveIXYv/4Azlds1Vl1qwZFcaMwaxhgzzHyrLMg4BYTu+8R0RgAgBGJgbUa+9O/Y7uGJsJ5UcgKO+8UKWnZcuW9O/fn7Fjx+o9f+nSJRo2bCiUHkGhyIpPJyMkkditt5FTsjD3dca2d7X8B5Ygsixz/cgBDqz6iYzUFNQKQ47aNuOyZR2URga84+vB8LZVUKllAqOSqeRgrlWEToSe4JNDn5CUmYSnlSfLOyzH08qzVK8nB0/n+EmJhm0faZybQeP702U+mJTe30ZmRCTRP/1E7ObNkKlxwDZv1YoKY8ZgWMGBjKD7KL08tZaip5FlmftXojm98x5RIZqip8ZmhtTv6EHd9hXJSM0SeX4EgnLKC1V65s2bR2ZmJjNnztR7PiQkhBkzZrBq1apCC/IiEUpP2STtdixRv14FwH5gTUzrlPxWS34kREWy94dvCb56CYB4Wy+2WrQiydACQwMJlUpGBhQSzO/jw5tNPAC4E3uHkX4jCU0OxdrYmm/bfUsjp0aleCX5kJUOB+fC8e8AGaw9NP4/lVqVqliZDx8S9eOPxG3dBs9+mdJT4f1pZLXMvUuPOLMzkJhQjV+WodKArAzNPCLPj0BQ/ihTIevlBaH0lF3idgeSdPgBkokhTuMaYGhT+t/EZbWai/v+5si61WRlpGNgbMoVj3b4ZXpgrkrGJjOeOCNr0owsOTapndbiE5UaxWi/0VyNvoqRwojZLWfTvXL3Ur6afLh/UmP1ibuved1sBHSYAUalGw6eERxM5OKvSNy3T/eEQkHVA356LT7ZqNUyd85HcPqveyRE6TrPSwoYNFfk+REIygtlKmRdIHherF/1xKiiBXJaFjEbbiKrSl8nlxQKGnTpwf8WfodLtRqo0lOpdfsf3grdzLsha+kTvoN3Q9ZSI+E6QVEp2nEOpg6s7LKSjh4dyVRnMvnoZH649EPZi+x6Gs/mMPw4NHpX8/rU9/BTa3h4oVTFUnp4YPv22zlPqNWknD6T51iFQqJ6E2favlMjxzlZDbHhOaPzBALBy4lQegRlCslQgf0Ab01R0qAEEg7kTEZXWti5uvHWrC955a1BSAoFFTKiUTz2wFYg0y7qMGaZurl9TA1N+artV7xX+z0Avr/4PVOPTSVDVYZDqo0tNQ7Nb28GC2eIugW/dIRDC0CVWWpiKb089VZ4D505k9gNG/NVJm2dzfXm+Tm57S7xj1JynhAIBC8dBd7eqlSpUpHqI40bN44xY8YUelxJIra3yj4pFyOJ2XATJKjwYV2MK+edIflFc9lvD/t/Xpazve7bLP+0PxbGhjnObb61mbmn5qKSVTRyasRU36nEpsWWTrHSgpISA3+Ph2vbNK9dG0Dvn0Bp8cLKWDxN3J9/EjZjJqjVoFCg9PQkIzAQAPM2rXH94gsMK1TIdfz146EcWndDE7EvgYGBhCpLxtDYgFf6VaXWK64vvA6cQCAoOC/Mp+fw4cOFnhzAy8sLT8+yFbkilJ7yQcymm6RciMTAWonT2IYoylDIcWJ0FCtGvqdjXZCB1RXfoVY1L1YPaYKZMqfic+LhCcYfHk9y5pMtlTJTrDQvrvwJf38CaXGgMAS1CpBfaBmLbJ7O82Po6EjMb7/x6OslyBkZGNjY4Dx7FladOuU6/uk8P2q1zIE1ATy8FQeAl4897f5XEzOrMpZfSSAQAMKRuUgIpad8oE5XEbnUn6yoVExq2WP/v5pl6lv4lQP72L9iGbL6SaK/YItK/GPfgSbVnFn5bhNM9OQbOhl6kqH7h+q0lalipbmREApbPoD7x3XbJQMYd+WFWnyeJf32bR5OmEh6QAAA1r164TR1CgaWlvmOldUylw6EcHL7XdRZMiYWRrQb6E3l+rlbjAQCQekgHJkFLy0KYwPsBniDgUTa9WiST4eVtkg6+LTvxIfLVtJ/xjw6vD8CAyMjPJIC6Rf+F5duBjNs7XnSs3LmrDKQcipCalld9up2PYuVK7SZmLP9BZWxyAvjatWotHED9h9+CJJE/PbtBL7ei5SzZ/MdKykk6nf0oP/kJti7WZCWlMnuH69w4LcAMlKzXoD0AoHgRSGUHkGZRulmgXWXSgDE7bpHZhmLtLG0d8C9dl3qd+pK/xnzMLWyxiH9Ef3DtnLlyg1GrfcnU6Vb8iG3YqWzT87mTFjekUiljn1V0CM7R5dA0qMXL89TSEoljp+Mx3Pt7xhVrEhmaCj3Bw0mYtEi1Bn5O47bu1nwxqTGNOzsARIEnAhjwxdnCL0dV/LCCwSCF4JQegRlHouWrpjUsIUsmej1N1BnlM2M367Va/L2F19h51oRi6wk+oVt4+a5s4zbcJGspxSfZ4uVKlBgY2xDZGok7+97ny/Pfklalp5irGUBazeND4/WWiVpfr93AL5vBgG7SlU8ALNGjai0fTvW/fqCLBPz60qC+r1B2s38LWkGRgqa965K7/ENsbQ3ITE6jW1fX+DktjuoMtX5jhcIBGUb4dMjfHrKBaqkDCK+vYA6MbNMlKnIi7SkJHZ8NZeQ61dQI3HIvhXV23Ri8Rv1MFA88UnKLlbqbumOpdKSxecW8+etPwGobF2Zea/Mo7ZD7dK6jLx5uoxFagxs/Qgir2nO1XsbXlsAJqUfcZfo50fY9BmoYmKQjIyoMG4cll06kxnyINcyFtlkpGZxdPNtbpzQbKvaV7SgZd+qSApJlLAQCEoJ4chcBITSUz5Jux1L1MqrIIPdOzUx8yn9MhW5ocrKZN9PS7l+5AAA563r49GpH/P61EOhyN0Z+8iDI8w8MZOo1CgMJAM+qvsRH9T9ACNF2Ylc00tWOhycBye+02T8s3Z/XMaidWlLRlZUFGHTZ5B08KDuiXzKWGRzz/8RB9fdIC3pSY4iUcJCICgdhNJTBITSU36J3x1IYhkrU5EbsixzausGTmxaB8Ads8pU6P4es/rUzzMKLS4tjjmn5rDvvqbkQh37OsxtNZfK1pVfiNzPRfApTRmL2CDNa9/h0HFmqZexkGWZ6F9/5dHir3RPSBJuS7/Dsn17JD2JD7OJvJ/A5vnncrQ361WZmi1cRYi7QPCCEEpPERBKT/lFVqmJ/PEymSGJKD2tqDC0LpJB2Qlj10fA0YP888O3oMoi3NgR6x7DmNqvSZ6KjyzL7A7czRenvyAxIxFjA2M+bvQxA7wH6HWCLlOkJ8G+aXD+ceFhh+rQ+0dwK92Cq8mnThP87rt6zxm6uGDVpQtWr3XBxMcnx3vz4GYsfy3x1ztWksCthi1VGzlSuUEFTC2EAiQQlBRC6SkCQukp32RFpxLxnT9yugrLDh5Yv1q2kl/q48H1q2xaOAc5LZl4Q0useg7n0zfz3/qJSI5gxokZnAg9AYCvsy9zWs7BxcKlpEV+fm7vh79GQVK4xtm59WfQ+lMwKJ2tuszwcO6076DJ5pyNJCEZGyOnPXEcN3Jzw+q1Lli+9homtWohSRJJsWn8NuUEOv8tJbB3NSf64ZOIQkkh4e5tS5VGjlSuXwET8zK+LSkQlDOE0lMEhNJT/nm6TIXdAG8U5kYYOphiaG1c2qLlSkzoQ9Z8Pg11/CPSFEoUr/SjfjV3qlevRBWvirmOk2WZTTc38dX5r0jNSsXCyILJvpNp4tSEkMSQclDG4hO4tlXz2qU+dJqj+f0Fl7CAnGUsXGbPwqp7d5KOHiVx9x4SDx1CTnlSh8vI0wOrLq9h9VoX7kZZcnjdTWRZ16cn/lEKd85Hcud8JFEhSdqxCgMJ91p2VG3kSKV6FTA2NSQpNo24yFThCC0QFBGh9BQBofS8HMRsvkXK+YgnDRLY9qmGeZMyqgAAKQnx/DxjOqqwe8iABKiRsO/8NkOGDMhz7P2E+0w5NoXLjy7rtJe7MhbZlEIJC9AtY/Fs9JY6NZWkw0dI2L2bpMOHdSxABg4OJCeqSDV1wDQtmkrTPs7hBB0Xka0ARehYgBSGErbO5kQ/TNJU7xCO0AJBkRBKTxEQSs/LQWZkChFfn9dtlMB5UtMybfG5dfseO6aN4WmvETUSfRb+kKfFByBLncVS/6WsvLpSp71clLF4eAFWtMvZ7tYYXOqBY02o4K35aZ5HZF78wxdS7FSdnEzioUMk7tlD4qHDkPlMhXlJwmPNasybNtU7PiYsWaMAnYsgNlx/FfcqjRxx8rLCztUcOxdzLGyNc/X1ElYigeD5n985KyIKBOUEVaKeLLsyZEWllmml527gQ559rCmQuX7jXr5Kj6HCkJauLXMoPWpZTUhiSNlWejKS9Lc/PKc5nsbM4bEC5P1EEapQE27+DTvHasLiS9hSpDA3x7pbN6y7dSPx0CEeDBuu20GWCR40GOMaNbB6TbMFpnyquLKdizlNu1eiafdKXD8eysHfb+RY4+75SO6ej9S+NjIxwM5FowBlK0K2LuYEX4/Wu7UmEAgKh1B6BOUWQwdTzf7QM86lhg6lGx6dH9WrV+IqEgp0jaz/7D9O0xZNcbLK+1t8dhkLtaybIdjEoIx/+7erolFUnpZbUkDneZAYDo9uwqMAiL0PKVFw/5jmyA1ZDTvHQZUOJe4bZOLtDQqFrhM0gIEB6Tdv8ujmTR598w0mtWph1fU1LLu8hrLiE5k8atkhSeRwhK7X3p2k2HRiwpKJj0ghM01FRGACEYEJucoiy3Bo3Q08atkJi49AUEjE9pbY3irXJJ8NJ3brba3iY1TRAqdRDUpXqAKwcuUfRO9djwJZ69sDcMGtHZM/+YA6bnlnM956eyuzTs7SUXzcLd1Z2Xll2bb2XPhNo6jIKk1EV49vclpqMpIh6hZE3tAoQY9uQmQAxN3XP+fgXVCpVUlLrtcJ2qJDB5L8/Ej4ZzfJp0+D6kmJFJO6dTUWoC6dMXJx4frxUA6tvZGrtUaVpSYuMoXYsBRiQpOICUsmJjSZuIgU9P2X7vVxA9xq2Jb4dQsEZQnh01MEhNLzcpEVn05aQDRx2zWVvu3/VxPT2mU3W3M2d4MecPt2EFWrenLz4F5u7N0OwHHH1gz9aBBd6uStvGSXsTAxMGHCkQk8SHpQPhSfp0tYFMZCE3UbljfVtRQBvL8f3PX71RQ3eTlBZ8XEkLhvPwm7d5Ny5oyOWce0YUOMXFyI9DtJqol9ro7Q+oh/lMLaGad4xjBIl2E+VKlfoViuSyAoLwilpwgIpeflJDtbs8LCCOfxjVCYlZ8cKbIs4/f7Ki79rQntPmTfiq79+zGsTeU8kxhmE5YUxnt73+Nh0kM8LD1Y2XklTuZOJS32i+dpS1E25hXgzXXg4VtqYj1L1qNHJOzdR8Ke3aSev4BeU41CQdUDfnnW/8rm+vFQDq27oaPvKQwl2g/0pkazcpCzSSAoJoTSUwSE0vNyImeqifjuAlmPUjFr4IjdmzVKW6RCIcsyh9et5vzOLQActG+Fd7suzOvtg9Iw/yzM/xnFJ9tSZGQKu8ZB+BUwUELPpVDvrdKWLgeZERFE//QTsev/yHHOY80azH0LZqVKik0jPjIVMxtjTm27y72LjwBo2MWTZj0rI+VR000geFl43ud3Gc9nLxAUHMlIgW2/6iBBin8kqTdiSlukQiFJEm3eeZcmPfsC0C76KDcO7mXgr6eJSdYTqfYMLhYurOy8EjcLN4ITg3l/3/tEJEfkO67cYe2m8eGp2BiG7AXv7qDK0NT8+ndWTmfjUsbIyQn7oUM1jtDPELt+Par4+ALNY2FrglsNW2ydzOgytA6NumgixS7suc+en6+Sma7KZwaBQCCUHsFLhbGnFRYtNX4isVtvo07NKmWJCockSbR6+10a99AkGmwXfYTkS0fp/f1x7kTmEvL9FK4WrlrF537Cfd7f9z6RKZH5jiu3KM2h/+/Q6hPN62Nfw6b/aZyhyxBGzs64zJ71RPF5vGWZuHcv93r0JPHQoULNJykkmvWqQsd3a6IwlLh38RFbF58nMSYt/8ECwX8Ysb0ltrdeOtQZKiK/8ycrKhWzxk7Y9ate2iIVGlmWObx2Jed3bQPggH0bgh3r8sM7jXilWv5O2g+THjJkzxBCk0PxsvLi186/4mjmWNJily6XNsCO0Rqrj7MPDNgA1nnnPXrRPO0InRkaRtiUKWQEBQFg/frrOE2ZjIF13pF7zxJ2N57dP14mNTETMyslXYfXxamS+L8meDkR21sCwTMolAbY9qum2eY6F0HardjSFqnQSJJEm4FDaNStFwDtow/jEXmZwavOsPZULqHbT+Fm4cbKLitxMXchKCGI9/e+z6OURyUsdSlT7y1N+LqZg8bPZ0V7eHA+/3EvECNnZ8x9m2Lk7IxZwwZU2r4NuyFDQJKI/+sv7nXvQeLBg4Wa06WKNf0mNcbezZyUhAy2fX2B22dfwm1NgaAYEJYeYel5aYnbcZekE6EYWBvj9HFDFCblLxenLMsc/v0Xzv/9F6Cx+FyzqsW7Lbx4/xUvQmJTqeRgjou1/oSMDxIfMGTvEMKSw/Cy8mJl55VUMHvJw5zjgmH9WxB5DQxNoNf3UKdvaUuVJyn+/oRNmUpGYCAA1q/3xGnKlEJZfTLSstj/6zWCrkQD0LibF027VRIOzoKXChG9VQSE0vPfQJ2hIuKbC6hi0jD3dca2d7XSFqlIyLLMod9+4cI/GsXHz6EN1y1rac8rJJjfx4c3m3joHR+SGMKQvUMITw6nknUlVnZeiYNp2c9j9FykJ8KWD+DWHs3rNpOg7SStL01ZRJ2WxqOlS4lZtRrUagwrVMB51iws2+upV5bbHGqZU9vu4r8/GICqjRxpP7gmRkqDEpJaIHixCKWnCAil579D2t04olZcAcDhAx9MqtqUrkBFRJZlDq1ZwYXdOwDwc2jLfVN3bDLjiTOyJs3IkmOT2uVq8Xla8alsXZkFrRaQmJGIh5VH2U5k+DyoVfDv53DiO83r2r2h1w+QEvNCCpYWldSLFwmdPEVr9bHq2QPnKVNQp6WREXQfpZdnvrl9rh8P5fD6m6hVMo6elnQdXhdZlkXBUkG5Ryg9RUAoPf8tYrffIflUGAa2xjiNa4TCuHx+631a8cn+o5XQVGg/6NCG6ePfo3kV+1zHhySE8N7e94hIeeLvoZAUzGw+kz7V+pSs8KXJhd9h18egzgRrD0h48EIKlj4P6rQ0opYtI3rlKlCrkSwskJOTNUkOEOV3/gAAS29JREFUH5fAyC+bc+jtWHb/eJW05EyUpgZkpKlAFCwVlHOEI7NAkA/Wr3lhYGOMKjad+D2BpS1OkZEkibaDP6RSi7ZIPKnXpUCmXdRhHA1S8xzvbuXOotaLdNrUsppZJ2cRnhxeMkKXBRr+Dwb9BSY2EB/8pIxFdsHS+IelKZ1eFCYmOH76KV5/rMfIwwM5KelJVme1mrAZM8kMz/s9c61mS79JjbCuYEJGqkpbxiK7YGlSrAhvF/z3EEqP4KVHYWyIbV+NP0/yyTDS78WVrkDPgSRJNOrwao52BTLHL9zMd3ymOjNHm1pWE5IYUizylVm8WsJri3K2yypNducyimm9ejhPn57zhFpNxv3gfMdbVzCj5Rs5UzbIaoiPzFtJFgheRoTSI/hPYFLNFvOmGj+ImC23UWeU3+y1di5uOepxqZH4+nQ0p+5F5znWw8oDhZTzz97G2KY4RSybeLXUbGk9jaTQFD4twxhXq6o3m7OhS8FqblVwt9Drv21hb/y8ogkE5Q6h9Aj+M1h3rYSBtRJVdBoJe4NKW5wiY2nvwKtDRyM99SBUK81IUxswbO15AqNyz0bsbO7MzOYzcyg+U49NJT69YOUQyi3WbhofHukpny4j8zKXvflZcmRzfkzkwoXIGfmXJ7GwNaHtQO8c+t7B32+SlpzT8icQvMwIR2bhyPyfIu1mDFGrroEEFT6qi7FX4bLfliUSo6MIv3ebAyt/Iikmijhrd9bZvoZnBSu2jWiBjZky17HhyeGEJIagltVMODKBmLQY6tjX4edOP2OptHyBV1EKxD+EsEvgNwse3QALZ3j3b3CoWtqS5Ul2NuesR5GETZmKnJGB5auv4vb1V0hGRvmOzy5YmhCTytENt8lMV2HrbEa3kfWwrqA/6k8gKGuI6K0iIJSe/zYxm2+Rcj4CQwdTnMY2QDIqn9Fc2USF3OeP6Z+SkZpKkF1tdlq1olkVe34b4lug6uy3Y28zZO8Q4tLjqFuhLj91/AkLpcULkLyUSY6GNT00SQwtXeG9v8v8Vlc2SUeP8mDESOTMTCxf64LbokVIhgVPvhn1IJG/l18mKTYdEwsjug6vi0uV8vsFQPDfQURvCQSFxKZbJRSWSrKiUonddY+0u3FkxaeXtlhFxsHdk+7jJiFJCrxiruGbfJlT92KYtv0KBflOU822Gr90+gVrY2suP7rMCL8RpGSmvADJSxlze01UVwVvSAyF1T0gNqi0pSoQFq1a4bb0OzAyInH3HkInTUZWFdxPzaGiJf0mNqaChyVpSZn8tcSfW2df4gg+geAxQukR/OdQmBlh21uzlZFyOpyoFVcIX3CG5HL8T79S/Ua0e/dDAJpGnaRKSiCbzj3gpyMFi0yqYVeDn1/VbG35R/r/dxQfiwowaAfYV9Pk71nTA+LKRySbZdu2VPxmCRgakrBrl2bLqxCKj7mNMb0/aUileg6ostTs//U6Z/8OLJCiLBCUV4TSI/hPYuT2zPaNDLFbb5dri0+DLj2o37kbyDJdow/gkP6IhXtusOdqwZS5Wva1+PnVn7EwsuB8xHnGHBhDatZ/IKzZ0gkG79RkaI4LhjXdy2TuHn1YduiA21dfgYEB8X/9RdiMGchqdYHHGxkb0OUjH+p3dAfgzM5A/FYHoMos+BwCQXlCKD2C/yRZUXoe5nIu7eWIdoOH4lm3AWRl8GbsPswykxm30Z8rDwoWmVXHoQ4/dPwBM0MzToefZuyBsaSryq8iWGCsXDSKj62XZotrTQ9ICCttqQqEVedOuC36EhQK4rdsJXzW7EJZaxQKiZb9qtHm7RpICombp8P561t/0pJEZJfg5UMoPYL/JIYOpk9SGmcjPW4vxygMDOjx8STs3NxRpCbwVvy/ZKWn88FvZwmPL1gG3vqO9fmh4w+YGppyMuwk4w6OI0OVf2h0ucfaTaP4WHtoanP91hMSI/IfVwaw6toV14ULQJKI27iRiDlfFHqbqk5rN7qPqovSxICwO/H8ufAccRH/gS1OwX+KMqH0LF++HC8vL0xMTPD19eXMmTO59l2xYgWtWrXC1tYWW1tbOnbsmGd/gUAfhtbG2PappqP4GFgqMTDPP/S3rGNsZk7viTMxsbTCLCGU3olHiIhP4/01Z0lOzyrQHA2dGrK8w3JMDEw49vAYnxz6hEzVf+Cbv40HvLsTrCpC1C2N4pP0qLSlKhDWPXrgMm8eSBKx69cTuWBBoRUfj1r29PmsEZZ2JsQ/SuXPL8/x8FZsCUksELx4Sl3p2bhxI+PHj2fmzJlcuHCBevXq0blzZyIjI/X2P3ToEAMGDODgwYOcPHkSd3d3OnXqxMOH5WMPXlB2MG/ijPOkptgNrIlkaogqIYOEg+XDiTU/bJycef3TqRgYGuIcc4t2yee5FprAuI0XUakL9iBs4tyEZR2WYWxgzKEHh/jsyGd6y1i8dNh6weAdYOmiyePz2+ua8PZygE3vXrjMmQ1AzJrfiFy0uNCKj72bBf0mNcapkhXpyVns+PYiF/2CeXAzVtTrEpR7Sj1Pj6+vL02aNGHZsmUAqNVq3N3dGT16NJMmTcp3vEqlwtbWlmXLljFoUMGqJYs8PYJnSbn8iJj1N0Ah4TiqPkrXlyNPzbXDfuz5fgkAB5w6cs2sGkNbV2ZK15oFnuPEwxOMPjCaDHUGnTw7sbD1QgwVBc8JU26JugOru0JSBDj7aKK8zOxKW6oCEbthA+GfzwLAfuhQKnw8LkfpkvzIylDx7+oA7l548gVUVGgXlDblOk9PRkYG58+fp2PHjto2hUJBx44dOXnyZIHmSElJITMzEzu78vHPSFA2MfVxwLS2PahlYjffQla9HNErtdt0wLd3fwDaRx3CJS2Mn4/c46dDdzlxN4qw+Pwdt1u4tWBJuyUYKYzYd38fU45NITQxlDNhZ17u6uwOVWHwLjCvAOFX4PfeEBEAgUfKfHSX7Vtv4TRtGgDRP/9M1NJlZIaHk3zqdL7V2bMxVBrQsm8VnTZZhoO/3+DywRAy0gq2VSoQlCVK1dITGhqKm5sbJ06coHnz5tr2CRMmcPjwYU6fPp3vHCNGjGDv3r1cu3YNExMTvX3S09NJT38SgZKQkIC7u7uw9Ah0UCVmELHkPOqULKxe9cSqg0dpi1QsyGo1O79ZwO3TJ8DEnDUOvUgw0nzuFRLM7+PDm03yv9ZDIYf4+NDHZKmfPOwUkoKZzWfSp1qfkhK/9IkMgNXdIOWpLS5Joanj1bBg1uXSImbNGiLmL9C8kCSN1qJQ4DJ7Fjb9+uU7/sHNWP5a4q/3nIGhAo/adlRp6Eilug4oTf8D1j9BqVOuLT3Py4IFC9iwYQPbtm3LVeEBmD9/PtbW1trD3d39BUopKC8YWCqx6an5ZptwIJjM8LJdiLKgSAoFr40Yj1PlqpCWTPeIf1CqNV8C1DJM2Xq1QBaftu5tmdZ0mk6bWlYz6+Ssl9vi41gT+qzQbZPVsHNcmbf42A0ejP2wjzQvsr/fqtWEzZhZIIuPjaOp3grtVg4mqLLUBF6K4t9V11n52TH++eEyN0+Hk5EqLECCskupKj0ODg4YGBgQEaEbFhoREYGzs3OeYxcvXsyCBQvYt28fdevWzbPv5MmTiY+P1x4hIS+Hs6qg+DGtVwGTmnagkonZfAtZ9XJkpzUyMaHXZ9NRWtlinxlLt4jdVEwJwTwrCZUsExRVsNBkd6ucXxjUspqQxJf8b8pAT1SfrIKAnS9elkJi3qx5zka1mpRLl/Id+2yFdkkB7f7nzcA5zXlzWlMad/XCxskshwL09/e6ClBSbJpwhBaUCUrVHqlUKmnUqBF+fn706tUL0Dgy+/n5MWrUqFzHffnll8ydO5e9e/fSuHHjfNcxNjbG2Ni4uMQWvMRIkoRt72qEB54n82ESiUcfYNX25bAMWtjZ0370JHbNnUzFtDAqpu1CjcRBhzbIsm+B5vCw8kAhKVDLuj5P225vo7Z9bcyMzEpC9NLHrormif/MdbNnItzYBW0mgFcr9JpFShmllycoFPBMpubQTz8j9cxZ7D/8AKM8vmTWaumKRy074iNTsXY0xcJWY1V3qGiBQ0ULmvaoRPTDZO5eiOTO+UjiIlIIuhxF0OUoDAwV2DibEf0wCWThCC0ofUp9e2v8+PGsWLGCNWvWEBAQwPDhw0lOTua9994DYNCgQUyePFnbf+HChUyfPp2VK1fi5eVFeHg44f9v787jm6ry//G/7s3WNG2arulC95V9txRRQMCWxQ0dHPWjgus4OOPo7zfjMirihqgzH8Z9RB2dzyCMKIOKCjIge9n3rRstbWnTPc3SNOv5/hGakiZt07SlTfN+Ph55tN57zr03x9K8e5b3Uamg0+kG6i2QIUYgF0Nxk323bc3WizDXDp0EbQlxSgjR3nvFg2Fm/U48/X+7UVyr7bZ+tCway3KWgb/8pz93OdHR9xe+x8LvFuKQ6lD/PPhAC4mzz+HhBPb/5ngg8VqAFwFlu+0ZnP8xFyjZ3j6MNEiIoqMR8/Jye+ADADwPUUICYDajac0alMy5EdUvvQRzF2k/gkIDEJcZ6gh4rsRxHCKGBSH75hTc/VI2fv2Ccw9QQ6U94AHsTbPjX+epx4cMmAFfsg4A7733Ht566y2oVCqMGzcO77zzDrKz7X95zpgxA0lJSfj8888BAElJSbh48aLLNZYtW4aXXnrJo/vRknXSHcYYGj4/g9aCJojjgxH52Fhw/OD7K76nyk+fxPpXnnM5/p1yHvRRGVj3SDbSooK7vY5Kr0KFtgLxwfEo05Thxb0volpv37bhnuH34IkJT0Aq9O3s1m41XwIaLwBhKfZAqLkS2LMKOPpPoG27jrhJwPSngfQ5g6rnx6xSwXSxHOLEBAiVSrQcOID69z9Ay6HLgapQCMVttyL8kUcg7oN5j4wxnNtXjV/+77zLuRHTYnHtHWkQB9DkZ9Izvf38HhRBz9VGQQ/xhKXZiJq/HgEzWhEyLxnB1w8b6EfqNW1DPVYvXeKSsE4fEIr1EfMgCY3EukemIC2qZ3mKdCYd/nLkL/i68GsAQEJwAl6d9irGR43vs2cf1DTVwL53gMOfAZbLvRgx4+zBT+bcQRX8dKQ/eBD1H36Ilvz99gMCAUJuvhkRjz4CcVJSr66ta2rFP5/b57bzKyBIhAk3JmLU9DiIJIJe3Yf4Dwp6vEBBD/GU/pAKTd8UAUIeyifGQxTp+3NWTm3/GVtXvwdms4HjOIgCpDAZWmASBuK7yBthiUzC2od7HvgAwN5Le7Fs3zLUtNSAA4f7RtyHx8c/jgBh56srhxRtDZD/LnDoU8B8eVhUORqY/kcg6yZAW23f1yss1d5TNIi0HD2G+g8/hH73bvsBnod8/nxE/OZR8DIZTGUXIU5K7HL+jztn91Zhx5rzYDb7qGDWlBhcKlJDU2dfMSgNFmFCbiJGXh8HkZiCH9I1Cnq8QEEP8RRjDPWfnYaxSA1xohyRj44ZEsNc2oZ6qFVVUETbJ5RufPMV1JaVwMYJ8N/w6WiMHYN1j0xBamTPAx+NSYO3Dr2FjcUbAQBJ8iS8Nu01jInsepXlkKKvB/LfBw5+DJguzzcMigF0Kthn9A7ePD+GkydR/8GH0O3Y4XqyBzl+rqRranWaCG2z2lBwoAaHfyyFpt7eMxYoF9uDn+tiIaTgh3SCgh4vUNBDesKibkXNX4+CmawIWZCC4GmD6y/0vmBubcVP7/8VRQf3AQAOh4xHScL1WPtojleBDwDsqtyFl/a9hDpDHXiOx+KRi/Hbcb+FROBHKylbGoH9HwL7P2gPftpwAuAPpwZdj08bw+kzqF31v2jZs9f5BMchaf1XkI4a1et7WK02FOxX4fAPZdA2Xg5+QsSYmJeEEdNiIBRR8EOcUdDjBQp6SE/p9ldDvbEYnIiH8okJEEYMvUm6zGbD3q/+hQP/+QoAUBKYjOPJc/Gvx65HipeBT7OxGW8cfAObLmwCAKSGpOKpiU8hQBiABHkComU9GyrxWed/BNbd5Xp85gvA9f/foJ3zo99/AOWLF7ue4DjIcqZAPn8Bgm+cA0Fw95Pfu2K12HA+vxqHfyyDrsk+IVymkGDS3ETEDw+DtskIxRXL5Yn/oqDHCxT0kJ5iNob6T07BeKEZ4mQ5Ih8eGsNc7pzd/Qu2fPQObBYz6sThOJB6Cz57PBfJETKvr7mtfBtezn8Zja2NjmM8eCybOsS3sGjTfAlYNco1zw8AxE4AZv4ZSJs16IIfs0qF4htmueT4uRInFiNo+nTIFyxA0Izp4HuRE81qtuHcvioc2XzREfw47kM5fggo6PEKBT3EG5bGVtSsOgJmskFxSyqCcobuL9+qwnP4z1uvolXTDL1Aivzkm/HBkwt7FfgUNBbgju+d54Jw4LDptk1IkA+Nfc66dPSf9q0rmNU+tJV6A3Bxb/uE5/gpwA1/BpKvH9DH7Ej99deofnGZPfC5PKcnMDsbmh9+RPOm72EqLnGU5YOCEDxnDuQL5kOWnQ1OaF+SblapejQR2mK24tjP5Tj4fanLuWmL0jE8J4b2+vJTFPR4gYIe4i3dviqovysBJ+ah/MNECMOGbne7pq4WX7+xHE2VF2HhBDgcfyPeenoxkrwMfA5WH8SDPz/oclwhVuCxcY/h9ozbh/58n455fnR1wN5VwKFP2pe6J10H3PA8kDBlQB/1Slfm+LkyaGGMwVhYCM2mTWj+4QdYqqod5wQREZDPnQs+UIqG1Z84BU293eyUF3JIGBGOtAmRSBobCQkFQH6Dgh4vUNBDvMVsDHWrT8JUqoEkTYGIB0eBG2RDEn3J1GrAxv99ExXH7Qnsziqz8fzzf0CyBwkMO1LpVcj9JtdlC4s2UYFReHj0w1iYvhBigbhXz+1zNNXA7r8ARz4HbGb7sdRZ9p6fuIkD+mieYjYbDMeOoXnTJmh/2gyrWu2+IM8jbfu2bnt8OsvxExIZgOa69ozOvIBD/IgwpE2IQvLYCEgC3eyTRoYMCnq8QEEP6Q1LvQE1fzsKZrZBnpcEcXwwhBFSCEOGZi+FzWbFz1/8A2c2bwQAVIak49aHH0JLUyMyMpKRmuR50sYNRRuwPH85bMwGnuPx5+w/gzGG1adWo6bFvvFwtCwaD49+GLem3ep/wY+6Atj1FnB8DWC7vFt55jxg5nOANGzQ5vjpiJnN0O/bh4bPv0BLfr7LeflNNyH8gSWQZGV1+UdDxxw/M+7JwvCpMWis0qP4aC1KjtSiSdW+TQwv4BA/PAypEyKRPDYSATIRdE2tUNcaaCL0EEFBjxco6CG9pd19Cc0/XGg/wAGhC9Mhmzx0VyPlb/4Jez7/EDyzgQHgANjAITz3bjzwgJuVSZ24cguLttVbJqsJ3xR9g09OfoJaQy2A9uDntrTbIHK3y/lQ1lgK7HwTOLnOdfLzIM7x01F3E6FFiQmQ5+ZBnpcLyfDhbgOgjjl+OnIEQEdr0VildxzneQ6K6EDHMZoIPTRQ0OMFCnpIb1maWqFa6bq5ZuA10ZAkyCGKDoQwKhD8EEuy9t+ftuL453/DlR9NDIAhYRyUaVlIyUjFmFFZiIoM9er6RqsRXxd+jU9PfYo6Qx0AIFYWi4fHPIxbUm/xv+CnvgjY+gJQ8JPrueG3AMMmAlEjgKjhgDxu0K3+AlwnQofccTtsTWrodu0CM7av0BIlJkB+Yy7kc/M6DYC601ht3+295GgtGi7p3ZbJzI6GMlmO8DgZwmKDECDzs58pH0dBjxco6CG91VqiRv3qU10X4gBBWABEShlEykCIogMhUsogjJCCE/KwNBthqTf41NDYTz/vxtlPV3ZbrkUUBItciUBlHKISk5CWmYYxIzMQIg9CSVklCgtLuxwaawt+Pjn1CeoN9QCAuKA4PDz6YWTHZKNKV+U/eX5Kd9l3ce+ORG4PfqKG2wOhyCz716BI+/nmSwM2POZuIrRNr4du505oNm+xB0Ct7fN0RAkJkOfmIjgvF4KwMJgvlvd4C4zz+VXY9oXrZqcdBYaIER5rD4DCYmUIjw1CaEwgxAHCXg+N0dBa36OgxwsU9JDesjQboXrjoL2bow0HBE5QwqpuhbmmBTad2X1lngMfJIJNY3LU85WhsZKySmx4+jHwV7xxBqA1LRuW5gYI1DUINGvd1mUAjAIpJFYDuMv/rZhzDx56qPOhsVZLK9YXrsenpz5FQ2uD0zm/yfPjLscPxwNTHgc0lUDtOaChqH0OUEeBEYA0FGgohn0LDA7IXQFMeeyqPL4nbHo9dLt22QOgnTudAiAHjkPQ9OkIGDHco2u2mIXYVJQJdOiXHDk9DvpGIxqq9NA2uLnPZQFBQrTq2ts0cXQ4IuM9n8BfV6HFxVMNbY9OQ2t9hIIeL1DQQ/qC/pAKTRuK0DbBpWPgYtWZYK5pgUWlh7mmxf5S6cGMVrfXUz45ASKl93lwrpbPPluLhi1fggdzO6envlGNk6cLcaGwGHXlF9FaewlibQ0CLAaXazEAxrQpmHXrLbhm4kjwPO/2ngaLAZ+d+gwfnfzI5dwfJ/0Rt6XfhmBx77ICD2odc/zctMp5To/FZO/FqT1rD4Jqz9m/byyFc2R+BWkYoBxp7xGKzLz8NQuQRbgOk13FXiJbSwt0O3dCvfFb6Hfu7NW1qqJzcD7zLnubMSuyCtYiTnsSAenpEKelgk9KhyE8CTpxJNR6IRqr9Gis0qOl7Q+SPpazMBUjro2lIbVeoKDHCxT0kL7S0yEqxhhaTtajaa1rtzsn4SG/IRGynJhBPxeopKwSRUVlSE9P8nj11lfrf0DF1x92el4XEIag4ZNww7xcjB+T6XK+szw/ACDiRZgWNw1zk+di+rDpCBQFevZGfEnHHD+eMLXYJ0NvetLz+0jD2gOhqOGAuty+d1jbEqqrNIm6sy0wgmbPhkip7La+WaWCbts2tEoUMEgjITXUIcCo7rQ8HxgIcWoqJKmpqAsfjT0lrj2vyWMjPBqm0jW1ovREvfv7CDgkjAhD2iQlksdGQBxAOYZ6goIeL1DQQwaS26GxK/DBIshnxEOWHQNO6L7nwxd1NjSmDU9BYONFCFl7D5g2MAqhoyZjzoI8jMhMBuA+zw8HDvHB8SjXljuOSYVSXD/sesxNmotpw6YN/YSH3XE7PCYAfr0WMDQCdeeBugL716YydPqD6ajLA0+cBBTx/fnU7ld+eZjjp6v68Z+shq25GcbiEhhLSmAqKYax7CJgbh+ObpUosG/KK/b32oZZcdfvUhE2KqXbe7vNMcQBCqUUalV7j6dAxCNpVDjSJimRODocokH+x85gQEGPFyjoIQOt49CY4tY0cAIemm0XYb2855AgRILgWfGQTVSCEwyN4KezobGGpmZs+n4rSg/uQWDdBQjQ/kGlCY5B1NgpyLspFyfMB/HW9lcRpOehk9nwxxuex21pt6FIXYTNpZuxuWwzKrQVjroykQw3xN+AvOQ85MTkQCQQQaVXoVxT7j8ToYHuh8famA1AfWF7EFS2F6jY71ouMAIYdzcw+ldA9Oh+WzXmbgsMT7I597Q+M5thqqiAsagYxpJitBw4iOKLvOvQmOYE5PPmImTBAgROngxO0HmQ4i7H0IhrY9FYpUfRkRoUH66FuqY9x5BQIkDymAikT1YiYXgYBCKeJkK7QUGPFyjoIYOBu6ExZrFBf7gGmu3ljonOgrAAyGcnIHBc1JDY5LS7obHqmgb8+P3PKD+yF0GNF516hnSSUMiMTZdzBAHhufc4zSdijOFsw1lsLrMHQCq9ynEuRBKC1JBUHKs9BgYGnuOxLMcPJkK38WZ4rKuNUttEZAKj7wBG3Q6Ep/bNs16hsy0w+rN+Wy9Rq0je6dCYMDIS8nnzIF+wAAGjRvY4xxBjDPWVOhQfrkHRoVpoG9snVUsChQiNkUF1odkx95wmQttR0OMFCnrIYMfMVugOqKDdUeFYBSaMkkI+OxHSURFDIvjxRHllDX7atBlVR/dD3lzhcp4BaBk2FlFpGUhKS8XYMcMRowwHANiYDSfqTmBz6WZsKdvisvqrzV1Zd2F81HikKdKQFJIEEU+TTJ107CWa9xYQFAWcWg8UbAasV+yGHjfR3vszciEQ3P28m8GsYy9R9EvLIE5MgmbTJmh+/hm25mZHWXFiIuQLFkA+fz4kKck9vhdjDDVlGhQfqkXxkRrom91PpB41Iw4xKSEIjwuCQhkIwRAa/vYUBT1eoKCH+Aqb0QpdfhW0OyvBDPbls6IYGeRzEiGKlcHS0OpTeX56Y83ab6HauLrbcgZhIMxyJaTKYYhKSEJqZhpGjUjFD1Xf4a3DbyHQIIC8RQhNoAUtUueVdEJeiCR5EtIV6UgLTXN8jQuKA8/xvR4a89mhtc56iVo1wPlN9gDowo72HiGOt+8WP/pXwPCbAKPO+9VfvV051ov6nfUS2Uwm6PfsgWbTD9Bu3+60xD5g5Eh7ADRvLsBYj3aXB+z7+538pQJ71hd3WY7nOYQoAxEeJ3PkGQqPk0EeLgXHc2gqqEDD2QqEj4hHaGbP51/1ZmitP4flKOjxAgU9xNfYWi3Q7r4E3Z5LrkvefSjPT290NhHalD4FpuZG8OoayEzNndbXSYJhFNcjTCsGBw42MOSPbkT69dfjku4SitXF0JvdZ/GVCqUICwjDJd0lAPYJ1NPjp2NE+AiPn/9sw1nsrNg5dIfWdLXAmY32AKjyYPtxTgiwtnw3HJA5F4gZ59k1q49fzkbNel63Y/1+Wnlm0+uh3b4dzZs2Qb9nL2B1k5KC4xA0cyYCRnr289JiFmLT+TR0zDGUoGhGi0mM5lYJzDb384kEnA1i1gIDk9nHxRhDbKgBcdNGeJzluq5c67T6LHlsBCITPEsJ4VSXA2b28bAcBT1eoKCH+Cqr3gzN1jLo96ucT3BA9DPXDPken+5yBKmbtThxqgAlhUWovWjPESTU1EBqaXF7PQbgYNYixI0YgwxlEJRhBggktag3laFYXYxidTEuqC/AZOv7vC08x2PL7Vt8q8fHU42lwOlvgONf2ntZBguOB/5wCgjxfJPcnrA0NkK7ZQvUG/6D1lPdZGzvhrscQ7Eq++atDIBRooBeFgudLBZ6WQz0sljoA6NhG2Sb9HI8cN9rU/usx4eCHi9Q0EN8WWdbYIhiZVDckgZJ4tD+mfYmR9Cl6lpsXP89Wvf+x+35enE4CmTpKJKlQSsKRohUhMzoYAyPDka6UoZ65OPTghUu9WYMm4GowKhu71/bUosdlTtcjk9WTsbzOc8jJaT7ZdA+6cJO4J83ux7PmAvIY7quq6kGCt3sOeZJ3a7qh6UCeSuA9Bv7bdVZZzmGZDNnQqTs/ufFXFMD/S87XHIMdVefMaC0RoLjyHY5F1F3EpKOPaEcwAcFQaAIgSBEAYFCAQMvQ0WJayLRpDERkCm6/qNKrzai7KRrfqJbnxyPuEzv9uPriIIeL1DQQ3xZd3l+JBmhCJmTCHEPUub7g86GxziOd1qdVBUQgwJZGoplqWgVSAEAnLAZsrQ3wHFX1GUcvsz7DmOik7q9t7scQ214jsf85Pn4zdjfIEGe4P0bHIw6yxH0h1Pdz6/pTd3O6l8pbhJww5+BlJl9Hvz0V44hT+o3FVTgy78WuOQYuv1WMQJaGmAsLISxqBCthUWw1rsGKJ3lKLr7qaxu5wa5y0802Hp6/G/qNyE+ThgiQejC9Pbhfg6Q5yUhcJIS4AFjYRNq3z+O+i/OwFSlG9BnHUxSk4YhPPdu2C43nA0cwnLvwW8/WYM5jzyO+BH2fDOxrdWY2bAbD1f+E781/YKbg1SIEMhhrF4IaYsQ0Q0SSFuEMFYvREmVZ9l0o2XRWJazDPzlDxKe4/HY2McwM34mbMyG7y98j5s33oxl+5ahSlfVb21w1YXE2efRcJfnn7TlCPIkaOlN3c7q564Apv4eEEqBS4eB/7sN+Mc8oHR3T99Zl0TR0Yh5eTnQtq3K5RxBnk5m7k390Mx4ZI9osa+2AwBmRfYIA6LnTofi9oVQPvsMEj77DBl7diN97x4k/OMzKJ99BiG3L0TAqFEIsOmRVfClU/2sgrVo+fhvMJw5g676SYJCAzDjf7Ic8VJbfqLBlGOIenqop4f4KHd5fiz1Bmi2l6PlWK2jJ0g6Kty+2ssH9vW6GroaHtM21KNg3y6c27sTtaXtc1EEYgkuIQRKU60jR9AvETNwXj4ciybF43ez0hGnkHZ7b5VehQptBeKD4x1zec7Un8F7x9/Dnkt7ANhXkC1MW4iHxzw8dOb7eJMjqC/qdlZfWwPsXQUc+rR9yX3y9cDM54EE16Ehbw1EjqE2TQUVaDhXgfDhPVu9Zbp0CSWz56BVHOI2R5EoIQHyvDzI83IhGT68x/mJeouGt7xAQQ8Z6sy1LdBsK4fhZJ1j4Yt0TCTksxMgigzs8Z5h/qjhUgXO792F83t3QK2qdjnPwOHzYXdDJ5JDLOBx1zXxWDozDVFy737JH689jveOv4cD1QcAAGJejF9l/goPjX4IEdII313uPphpqoDdfwGOfAHYLm9DkTYbmPmcPefQVdxodTDpmKNI8as7YG1SQ7drl9PyfFFiAuR5c+0BUFaWIwAyq1Q9XqrvKQp6vEBBD/EXZpUemq0XYThzOTEfB4jjg2Gq0Ha6OzxxxhjDia0/YtunrpulCgICUR0xHLstw1AVEAOJSID7pybhN9NTESbzbhXNIdUhvHfsPRytPQoACBAEYIJyAvZX7YcNtqG53H2gqcuBXW8Bx9a0D+soRwO1Z676RquDhbteJpteD93OndD8tNkeABnbE1OKExMRPDcPnECI+g8/9HrrkO5Q0OMFCnqIvzFd0kGz9SJazze6nuSA6D9OgjCs++EZf6VtqMfqpUu6nM9gFAfhXECyfQVYcCweuC4FD12XghBpzzM8M8awv3o/3jv+Hk7WnXQ5z3M8frrtJ8QG07YEfarxArDzTeDEOrisFOB44PHD/bLVhi+y6fXQ7tgB7ebN0O3a7RQAOenBBG5PUNDjBQp6iL/S5leh+Vs3eVN4QKSUQRQtgyim/SUIct9b4Y/DY6e2/4ytq98Ds9nA8TxmPfgYQiKicD5/N4oP5sPY0p7YUCMIQlFQGqpCM3H7jdlYMi0FQRIhSsoqUVhYioyMZI+W2zPG8OmpT/G3Y39zOSfmxcgMy0RGaAYyQjOQGZaJ9NB0yMXuf6fR8FgPHPsS+PYxNyc4ICIDUI4EokcByssveaz7FWADmE36arLq9NDt2IHGL9eg9egxl/MJX3wBWfY1fXIvCnq8QEEP8VfdLXfviA8StQdB0TKIYoJguqiB+ttivxwe0zbUQ62qgiI6FsHhEY7jFrMZF08eRcG+3Sg+fADm1vY8J2phCCpDMxAbFgRFwY5OEyt2RqVX4cavbwTz8H9arCwWGaEZSA9NdwRFR1RH8MqBV2BjNDzmEU82Wr2SNPRyADSy/WvVMeDH/9/74bGj/wS+f8Knhtd6u1TfExT0eIGCHuLP9IdUaNpQ5AhaFLelISAtFOZqPcyqy69qPSwNBs+CIz/JBu0ps8mI0mOHcX7vLhQfOQhmMbstZwOHEzlLwQV3nbTNaLbiWNPPkMRsAMcxMMbBpLoN//c/v0aT5SIKGwtR2GR/VetdJ1y7M6SzQfeVjhutLvhfe0LDmtP2l+o0UHMGqC9snwfUncSpgNCDie6WVuDiPudjPclRNIA6ToKmOT2DAAU9xN95MjxlM1mdgiBztR6mSzrA7PrXb9jdWQgcE9nfj+1zTK0GFB06gB/+/TUEdWUu58/IsnA0dDzUIkW31+KEzeDF9bCZIsAsIXj5lpG4LyfJqUyzsRlFTUWOIKiwqRDnG8/DbHMNvOYnz8eSUUuQEZrh8Z5MfseT5fIWI1B33h4AqS4HRFXHAKOm759nwv3AdU8BoUl9f+0+1Nul+l2hoMcLFPQQ4h2LuhWqlYdce4B4QHZNDIJnxEPYTap6f+QuG/SVxFHxkA2fiKCsCRCFOm8zoG4x4eXvz7mteV16BJ6YlY5JSWGd3rtKW4W8DXmdDo8lyZOQm5SLvKQ8pIWmefyeSBeaK4FVoztkk+btyRGlHmzHYGgCNj+DTrtaYycAI2+zvxQ930Hdl1HQ4wUKegjxXsfhMUFYAKwNl3N3CDjIJikRPD0ewrDBk4V1MOi4WWrQqGxECc0oP3Uctit25o5KTkVmznXIzJmGkCj7X8n/PlSO5zachpUx8BwwISEUxyrUsNrsv76npobj97PSMSUl3O29NxRtwPL85Y45PQvTFqLJ2ITdlbudNlNNDUlFblIucpNzh+5+YFdLx+Gxm1Z5MaenrT4PjF4EaKuBst3OwdSwyfbgZ8Qt/baR6mBCQY8XKOghpHeuHB4TyMUwXmiGdls5jBcub2jIcwicEAX5zHgIw2kpfBt32aANWg2KD+1HQf5ulJ8+AXbFJNDo1HRkXA6A6nQmFBWWIT3DXreisQUf7CjG+sOVsFwOfq5JDsMfZqUjJzXcZcjKXTZonUmHHZU7sKVsC/Ze2us0DJYemo7cxFzkJuUiQBhAK7+80R/ZpHV1wLlvgTMbgbI9cOoNip/SHgAxm0+s/OopCnq8QEEPIf3DeKEZmu3lMBar7Qd4IHC80h78RFDw050WTTOKD+ajIH83Ks6cAnO3eojjMO3OezH5ltvB8wJUNrXgwx0l+OpwBcxW+6/zSYmh+P2sdFyXHuHxfB2tSYtfKn7BlrIt2Fe1DxabxfXW4LB03FI8MPoBiPie5x8ifUxbA5z7Dji9ASjPh/vhMA6Y9SJw7RMAL7jaT9jnKOjxAgU9hPQv40UNNNvKYSxssh/ggMBxUQi+IR6cWOB3OX680dKsRuGBfTi7azuqi867nOcFAoTFDrO/4oaBUyixtcqGfxcZ0WKzb4Q6PkGB389Kx4yMSFy4eMnjHEHNxmb8UvELvi35FodVh13OCyBAQkgCkuXJSA5JRlJIEpJD7N+7yxNEOYKuAk0VcPY7e2LFatdcORBIgIj0y6+My690IDwNELvZl683OYL6Mb8QBT1eoKCHkKvDWK6BdntFp5mg/SnHj7fKT5/E+lee61EdW2AIqlgwGoUKNIkViBW0ILX2KHigRzmCDlYfxIM/P9ije4cHhDsCoOSQZFTpqrDm3BowMMoRdDWU7gK+uKlndULirwiG0oGmMiD//fYcQXkrgbG/9uxaJ9YBm5/ut/xCFPR4gYIeQq4uU6UWzZvL2oe92nBA1OPjII4LHpDn8gXutsDgeB6/fmkljIYWNF6qRGNVBRqrKtF4qRItzepur2kDB+Fdz2PK6DRkRgd3uk+YSq9C7je5sF0xzMZzPP4191/QmXUobS61vzT2r7Uttd3emwOHR8c8ivHK8UhXpCNC6vkQHPGAu8SKnABY/IN9GX194eVXkf1rS0P/Pk8f5xeioMcLFPQQcvW1lqhRv/qU23OS1BBIx0ZCOjICAhnNFemo4xYYcx5+HKNvuNFt2Vadzh4AXX6dPnIMhkrXrUc0giCcC85CkSwNgrBoZEUHI0MZjMzoIGQo7d/LJEKXlV9d9dToTDpc1FzEheYLKG0uxZGaI46NUzujkCiQpkhDmiIN6aHpSA9NR6oi1TFMRkNjXujJyjF9A9BQ1B4Mle8HKg/17fPcvwlIvq5PLkVBjxco6CHk6vNoCwyegyRNgcAxkZCODAcvFV615xvsOtsCozvucgRdzjbgUCcOR5EsDUWyNGhE7b8T48OkCJIIUVBXCU5cD2aOwMvzr8X/TEn06N7ueoo4cLg29lpU6ipRri13OnelaFk0gkRBKFGXgIGBA4fnsp/Dr7M8HGbxd96uHOusp+h3R+x7jHVFUwW8O8G1LvX0DCwKeggZGB1z/IQuTIckJQQtp+phOFEHc3X7pp0QcAjICEXg2EgEDA8HL7GvPOntZqf+uFlqxxxBoTf8CjlZcSjI342LJ4855QnSy2NQKEvDcWESdMIgAIDMooPC3Ay1KAR6YRASQqUYHitHhjIY6cpgZCiDkBIRBLGQd7n3hqINeGv7qwjS89DJbPjjDc87eopaLa240HwBxepiFDcVo1BdiOKmYtS01DjqBxoEkLcIoQm0oEVqRXRgNLLCspCqSEWqIhVpijQkhyQjoJPtHXrTU+S3vUy9yTHU2/xE3aCgxwsU9BAycLoKOsx1LTCcrEfLiTpYalvaTwh5SIeHgQ8UQn9Q5fVmp+6CLn+ZSO0uRxAAGHRaFB/Mx/l9u1Bx+qTTMnlBdDLOagVI15c4AqZfIqbjbPBwl+sLeA7JETJkKIOQHhV8eYgsCDu/34SmrWsuT6IGwnPv6XYStcakwfcl3+Obb95Dzqkw8OBgA0P+6EYUxetcyvMcj2FBwxxBUNvX43XH8fqB173aaLUnw3pDUm9yDPU2P1EXKOjxAgU9hAx+ZpUeLSfrYDhZD0u9odNyovggcG56GDpiFhvMFR0+MGmzVCd6dROKDuxDQf5uVJ4/A7j5eGAAAmKSYOGEMJgtMJisMJissLopyzMroo21TkNpDIA2OAYQdD13i1lbIdfWgLuitg0MMX+4B7wCKFGXoFhdjJLmEjQbmz1+j6MjRkMscD9xu43JasKpeuf5Zxw4rLx+JSZHT0Z4gGvyR3J1UNDjBQp6CPEdjDGYq/TQ7KxA68n6Pr9+4GQl5DMoc3RH2sZ6HPzPehz/+YeBfhQnWmkEYqbMxNwFeUgYpgRjDA2tDfYAqC0QUpfgfMN5GKydB8u9ESoJdfQmpSnSkBZq/xoiCXEq57fDY/2Igh4vUNBDiO9xOxGaAxS3poIP7H7Fl63FDPV/XFcxtRENC7JPoB4TAaGC9g0DOlkuz3GY9eBjkAZ3/7uzuqYBh75c7dLTEzLr1whWKLq+t1qN5m3r0Fl/ig0cdGGJiJ84FfNvykWM0nnfsWpdNfK+yYMNzpOon5/yPBSSru+tNqrx6v5XXTZpjZXFolpf3enmrZHSSEcwpDFq8P2F7yk/UR+joMcLFPQQ4pt6OyenY/3A8VGwakwwlqidgilxohzSMREIHB0JgbzroZChrifL5d3pOIna08SI7uoGTLkJvIBH7ckDkGurHeWs4KGPSEbS5Gsxf8GNiIpQAOjdvJzO6hosBpQ2l6JEXYIidZG9d6mpGFX6qm6vuShjEcZGjUWqIhUpISmQCql3saco6PECBT2E+K7+WL1l1ZpgOF2PlpN1MJVp2gMgDhAnhSBwbASkoyLArMzvVn4B3i+Xb9PZJOre1D1XWIatP2xB46kDCNa3J0W0gkdLVBrSplyHefNno1hVjONnj2PciHG4Jmt8j+598Pwxj+vqzXqUqEtQoi7B7ku7sfXi1i7Lc+AQHxzvMkyWLE+G6PJ8J1p55oqCHi9Q0EMI6Yy12WhfQn+yDqZyrftCfrbya7A7cboQ23/aiuYzhxBsaJ/3ZQUHHgwc7HGsLmYEwpLSPLpmY1kxgqrPOuo2Zs5C/MwF4PjuJ83rLPX4ovJhdByLvTnlJlS3VKOoqQhqo9ptXSEnRII8AQGCAJxrPOfIUTQ7cTZGRYzy6NlP15/Gfy/+1z60Bh7Lpg6doTUKerxAQQ8hxBOWplYYTtZDf6wGFlWLy3n5nETIJikh8KNen8HuyPGz2PHTVujPHoTM5PmqLk9YwKNJHIpGURgar/jaLJSDcc7BkCjkEEIV3yLEIECz1Iom9S14Kude3DFxGCKCxGhobXBMvC5qKnJ8rzO7LsnvC78d91vclnabz/f6UNDjBQp6CCE90dUWGgAgTgiGdGQEpKPCaRXYIPHjll0499mbLsc14akQBoW4qdHOomuGvKHzSe8dMV4IS3AELPIoWORRqBeFQlVWhmz1IUd+ol8iZuBs8HBwHDA5KQx5I6ORNyoasYr2nxfGGGpaavDDhR+w6ugql/tcG3stIqRdDy/WGeqwr2pfp+dHR4zGrIRZmJM4BwnyBI/f42BBQY8XKOghhPREZ1toiOKCYL7k/Je5KEYG6Sh7ACRSyq7iU5Irudt+wwYOC1d+2O28os7q3vjH5QjizKivKEdjZTnqK8vReKkSFpOx2+dhAM4nzcIJUyjUIgXMvH3ezthhIcgbFYO8UdFIjrD/vHS20euW27d021PT2dYfI8NH4kzDGaeVZ+mh6ZiTMAezEmchXZHuE7mHKOjxAgU9hJCe6mzlmFVjhOFMAwyn62G80OwUGAkjpZcDoAiIYmWwakxeT4QeyO03fHXrjr5cOdZZXWazobmuFg2V5Y5XddF5NFV3vZrLKJGjhpejSRSKJpECTSIFwuOGYfq4NMwbE4Mzmq14+5fX3G7f0Z3Otv6oN9Rje/l2/Pfif3FQdRBW1r79SKI80dEDNDJ8JA4VHMfxs8cwbsR4LyeAe1e3OxT0eIGCHkKIN7r78LfqzWg9aw+AWovVgPWK/DaBQrAWi+O/paMjII4P9ui+pgotDKfaJ+j2pG5v6zvV5YCQ+SkInta3Wwv0p/5YOdYdd/mNACA6NQPNdTUwaDqfa2TiRGgShYIX8IgwqBwTqfUxIxGenO7R/RtKiyCrPtNlXQszotFUjnrzRTSZL4FdEQBFqmVIVInBgQMDQ3VMAMQx8R7d21RdgZjqVkdd3bTxeOl3r3pU1xMU9HiBgh5CSH+ztVrQer4RhlP1MBQ0Apah86uWk4kgjguCKFoGUXSg/WtUYKfbgQxkL9NA3fvU9p+x57PPESRQQGdVY9oDix35jQxaDRqrLqGxqgKNlyrRWFWJ+soKaGprnHYolwqCESwKhdbcBIO1k5WEg5wNDNOXv9pnPT69/fwW9slT9NL777+Pt956CyqVCmPHjsW7776La665ptPy69evxwsvvICysjKkp6dj5cqVmDdv3lV8YkII6RofIETguCgEjouC4XwjGj4/41JGkqaAILjr5IdWrQnGYrVXdXtbv7O6TG+GsbAJxsKm9oM8B2Gk1B4AxcguB0QytBY2Qv2fYq8SSvZ1Msqree+U4DEIjX+svX5we0+LNFiOuEw54jKdN261mM1orqnG5u+3QHq4FJMjcsFxPBiz4VD9FpwQayGQdf1Bb9VrIG8sdTmuCUvuti4AGPWXENbout2LKpyHNaDrTOWC1lZEN9icjvHgcOLc8T4f5vLWgPf0/Pvf/8Z9992Hjz76CNnZ2Vi1ahXWr1+PgoICREVFuZTft28frr/+eqxYsQILFizAl19+iZUrV+Lo0aMYNcqzHAbU00MIuZo620LDk81Oe1O3v+4d9j/DYdOZYVbpYa7Ww6zSg7VaO71ORwEZCnAiQZdlmNmK1kK1V3V7W38g7w0AOo0efLnBaWIxYzaYfh2P1PEpXdbtzQRuwD4fZ+ey58F32OjVk96a3tT1lM8Pb2VnZ2Py5Ml47733AAA2mw3x8fH43e9+h2eeecal/J133gm9Xo9NmzY5jk2ZMgXjxo3DRx995NE9KeghhFxtA9nr0N/3ZozB2myyB0FXBEKW2haXFW+kdwQhYgijAiFS2ocUhcpAiCKlTvvPffbZWrT89zuEiELRbG5C4OybPZ7ADQAvvfs8IvKLESIKQ7O5EfU5aR7Py+lNXU/49PCWyWTCkSNH8OyzzzqO8TyP2bNnIz8/322d/Px8PPXUU07HcnNzsXHjxv58VEII6RXZ5GhIMkK9miPSm7pX494cx0GokECokECaFeY4bmkwQPX2YZeeIvmNSeClXX/82AwWaLaUdbiRZ3V7W38g791p/cuszSZYm00wFqmdjvPBYoiUgRBFBeKWwAloSRjmmMgckpIES1Nrt/dt89ToR9F8qay9/mjP63esGzbFs8nXV8uABj319fWwWq1QKpVOx5VKJc6fP++2jkqlcltepVJ1eh+j0QijsT2Pgkaj6cVTE0KId4QhEq+XfPem7kDdWxguRejCdK97mQRBol71cPWm/kDeu7P60pHhMNcZYKlpgbnW/rLUtMDabIRNa4LxijlYbQNMHADN5jJoNpd5fO/e1r+ybtOGIkgyQgdNqoNBMZG5v61YsQLLly8f6McghBC/M5R7uAaiviRRBEmi87COrdViD4BqW2AobELrSdeJyOA5+6s7NmZ/eVPfXV0G+3ugoAeIiIiAQCBATU2N0/GamhpER7uPiKOjo3tUHgCeffZZpyExjUaD+HjPcg4QQgjpHX/r4bra9fkAISQJcvsrPRSqU/WuE9efnty7ie8e1O+srjBi8GzN0v12sf1ILBZj4sSJ2LZtm+OYzWbDtm3bkJOT47ZOTk6OU3kA2Lp1a6flAUAikUAulzu9CCGEkKFGGCJB6MJ0pzGm0IXpHgdfvanf23tfDQM+vPXUU0/h/vvvx6RJk3DNNddg1apV0Ov1WLJkCQDgvvvuQ1xcHFasWAEAeOKJJzB9+nT85S9/wfz587Fu3TocPnwYH3/88UC+DUIIIWRQ8OVhwf424EHPnXfeibq6Orz44otQqVQYN24cNm/e7JisXF5eDp5v75CaOnUqvvzySzz//PN47rnnkJ6ejo0bN3qco4cQQggZ6nx5WLA/DXienoFAeXoIIYQQ39Pbz+8BndNDCCGEEHK1UNBDCCGEEL9AQQ8hhBBC/AIFPYQQQgjxCxT0EEIIIcQvUNBDCCGEEL9AQQ8hhBBC/AIFPYQQQgjxCxT0EEIIIcQvUNBDCCGEEL8w4HtvDYS2nTc0Gs0APwkhhBBCPNX2ue3tDlp+GfRotVoAQHx8/AA/CSGEEEJ6SqvVIiQkpMf1/HLDUZvNhqqqKgQHB4PjuD67rkajQXx8PCoqKmgj025QW/UMtZfnqK08R23lOWorz/VnWzHGoNVqERsbC57v+Qwdv+zp4Xkew4YN67fry+Vy+kfhIWqrnqH28hy1leeorTxHbeW5/morb3p42tBEZkIIIYT4BQp6CCGEEOIXKOjpQxKJBMuWLYNEIhnoRxn0qK16htrLc9RWnqO28hy1lecGc1v55URmQgghhPgf6ukhhBBCiF+goIcQQgghfoGCHkIIIYT4Bb8Ket5//30kJSUhICAA2dnZOHjwYJfl169fj6ysLAQEBGD06NH48ccfnc4zxvDiiy8iJiYGUqkUs2fPRlFRkVOZxsZG3HPPPZDL5VAoFHjwwQeh0+mcypw8eRLXXXcdAgICEB8fjzfffLPHz9LXfLWtVq9ejeuuuw6hoaEIDQ3F7Nmzu3323vLVtrrSunXrwHEcbr311p69+R7y5bZSq9VYunQpYmJiIJFIkJGR0a//Dn25rVatWoXMzExIpVLEx8fjySefRGtrq5ct0b3B2Fatra1YvHgxRo8eDaFQ2Om/rR07dmDChAmQSCRIS0vD559/7lUb9ISvtteGDRswZ84cREZGQi6XIycnB1u2bOnZm2d+Yt26dUwsFrPPPvuMnTlzhj388MNMoVCwmpoat+X37t3LBAIBe/PNN9nZs2fZ888/z0QiETt16pSjzBtvvMFCQkLYxo0b2YkTJ9jNN9/MkpOTmcFgcJTJy8tjY8eOZfv372e7d+9maWlp7K677nKcb25uZkqlkt1zzz3s9OnTbO3atUwqlbK///3vPXqWvuTLbXX33Xez999/nx07doydO3eOLV68mIWEhLDKysp+aCnfbqs2paWlLC4ujl133XXslltu6bvG6cCX28poNLJJkyaxefPmsT179rDS0lK2Y8cOdvz48X5oKd9uqzVr1jCJRMLWrFnDSktL2ZYtW1hMTAx78skn+6GlBm9b6XQ69pvf/IZ9/PHHLDc31+2/rQsXLrDAwED21FNPsbNnz7J3332XCQQCtnnz5r5roA58ub2eeOIJtnLlSnbw4EFWWFjInn32WSYSidjRo0c9fv9+E/Rcc801bOnSpY7/tlqtLDY2lq1YscJt+UWLFrH58+c7HcvOzmaPPvooY4wxm83GoqOj2VtvveU4r1armUQiYWvXrmWMMXb27FkGgB06dMhR5qeffmIcx7FLly4xxhj74IMPWGhoKDMajY4yTz/9NMvMzPT4WfqaL7dVRxaLhQUHB7MvvvjC07ffI77eVhaLhU2dOpV98skn7P777+/XoMeX2+rDDz9kKSkpzGQyefv2e8SX22rp0qXshhtucHqWp556il177bU9agNPDda2ulJn/7b+9Kc/sZEjRzodu/POO1lubm4379p7vtxe7owYMYItX77co7KMMeYXw1smkwlHjhzB7NmzHcd4nsfs2bORn5/vtk5+fr5TeQDIzc11lC8tLYVKpXIqExISguzsbEeZ/Px8KBQKTJo0yVFm9uzZ4HkeBw4ccJS5/vrrIRaLne5TUFCApqYmj56lL/l6W3XU0tICs9mMsLCwnjSDR4ZCW7388suIiorCgw8+6G0zeMTX2+q7775DTk4Oli5dCqVSiVGjRuH111+H1WrtTbO45ettNXXqVBw5csQxZHLhwgX8+OOPmDdvntdt0pnB3FaeuJq/2wHfb6+ObDYbtFptj36/+0XQU19fD6vVCqVS6XRcqVRCpVK5raNSqbos3/a1uzJRUVFO54VCIcLCwpzKuLvGlffo7ln6kq+3VUdPP/00YmNjXf7R9gVfb6s9e/bg008/xerVqz17w73g62114cIFfP3117Barfjxxx/xwgsv4C9/+QteffVVzxqgB3y9re6++268/PLLmDZtGkQiEVJTUzFjxgw899xznjVADwzmtvJEZ8+i0WhgMBg8vo6nfL29Onr77beh0+mwaNEij+v4RdBD/NMbb7yBdevW4T//+Q8CAgIG+nEGFa1Wi3vvvRerV69GRETEQD/OoGez2RAVFYWPP/4YEydOxJ133ok///nP+Oijjwb60QadHTt24PXXX8cHH3yAo0ePYsOGDfjhhx/wyiuvDPSjkSHkyy+/xPLly/HVV1+5BFRd8YugJyIiAgKBADU1NU7Ha2pqEB0d7bZOdHR0l+XbvnZXpra21um8xWJBY2OjUxl317jyHt09S1/y9bZq8/bbb+ONN97Azz//jDFjxnT9pr3ky21VUlKCsrIy3HTTTRAKhRAKhfjnP/+J7777DkKhECUlJR63gyd8ua0AICYmBhkZGRAIBI4yw4cPh0qlgslk6ubd94yvt9ULL7yAe++9Fw899BBGjx6N2267Da+//jpWrFgBm83mWSN4aDC3lSc6exa5XA6pVOrxdTzl6+3VZt26dXjooYfw1Vdf9bgX3y+CHrFYjIkTJ2Lbtm2OYzabDdu2bUNOTo7bOjk5OU7lAWDr1q2O8snJyYiOjnYqo9FocODAAUeZnJwcqNVqHDlyxFFm+/btsNlsyM7OdpTZtWsXzGaz030yMzMRGhrq0bP0JV9vKwB488038corr2Dz5s1OY8h9zZfbKisrC6dOncLx48cdr5tvvhkzZ87E8ePHER8f38vWcebLbQUA1157LYqLi50+tAsLCxETE+M0v6Uv+HpbtbS0gOedP1ragkXWx7seDea28sTV/N0O+H57AcDatWuxZMkSrF27FvPnz+9RXQD+tWRdIpGwzz//nJ09e5Y98sgjTKFQMJVKxRhj7N5772XPPPOMo/zevXuZUChkb7/9Njt37hxbtmyZ22V6CoWCffvtt+zkyZPslltucbtMb/z48ezAgQNsz549LD093WmZnlqtZkqlkt17773s9OnTbN26dSwwMNBlyXp3z0Jt1X4fsVjMvv76a1ZdXe14abVaaqsObdVRf6/e8uW2Ki8vZ8HBwezxxx9nBQUFbNOmTSwqKoq9+uqr1FYd2mrZsmUsODiYrV27ll24cIH9/PPPLDU1lS1atMiv2ooxxs6cOcOOHTvGbrrpJjZjxgx27NgxduzYMcf5tiXrf/zjH9m5c+fY+++/f1WWrPtqe61Zs4YJhUL2/vvvO/1+V6vVHr9/vwl6GGPs3XffZQkJCUwsFrNrrrmG7d+/33Fu+vTp7P7773cq/9VXX7GMjAwmFovZyJEj2Q8//OB03mazsRdeeIEplUomkUjYrFmzWEFBgVOZhoYGdtddd7GgoCAml8vZkiVLXD6AT5w4waZNm8YkEgmLi4tjb7zxhsuzd/csfc1X2yoxMZEBcHktW7as943SCV9tq476O+hhzLfbat++fSw7O5tJJBKWkpLCXnvtNWaxWHrZIp3z1bYym83spZdeYqmpqSwgIIDFx8ez3/72t6ypqan3jdKJwdpWnf0+utIvv/zCxo0bx8RiMUtJSWH/+Mc/et8g3fDV9po+fbrb8x2ftyu0yzohhBBC/IJfzOkhhBBCCKGghxBCCCF+gYIeQgghhPgFCnoIIYQQ4hco6CGEEEKIX6CghxBCCCF+gYIeQgghhPgFCnoIIYQQ4hco6CGE+JykpCSsWrXqqt938eLF4DgOHMdh48aNHtVJSkpy1FGr1f36fISQrlHQQwjxStsHeWevl156CQDw+9//HhMnToREIsG4ceO6vOaOHTu6ve6OHTtw6NAhPPLII/3/Jt3Iy8tDdXU15s6d61H5Q4cO4ZtvvunnpyKEeEI40A9ACPFN1dXVju///e9/48UXX0RBQYHjWFBQkOP7Bx54AAcOHMDJkye7vObUqVOdrvvEE09Ao9HgH//4h+NYWFhYn+9s3hMSiQTR0dEel4+MjERYWFg/PhEhxFPU00MI8Up0dLTjFRISAo7jnI61BT3vvPMOli5dipSUlG6vKRaLna4hlUodQUbbSywWuwxvcRyHv//971iwYAECAwMxfPhw5Ofno7i4GDNmzIBMJsPUqVNRUlLidL9vv/0WEyZMQEBAAFJSUrB8+XJYLJYetYPJZMLjjz+OmJgYBAQEIDExEStWrOjRNQghVwcFPYSQIeGVV17Bfffdh+PHjyMrKwt33303Hn30UTz77LM4fPgwGGN4/PHHHeV3796N++67D0888QTOnj2Lv//97/j888/x2muv9ei+77zzDr777jt89dVXKCgowJo1a5CUlNTH744Q0hdoeIsQMiQsWbIEixYtAgA8/fTTyMnJwQsvvIDc3FwA9qGyJUuWOMovX74czzzzDO6//34AQEpKCl555RX86U9/wrJlyzy+b3l5OdLT0zFt2jRwHIfExMQ+fFeEkL5EQQ8hZEgYM2aM43ulUgkAGD16tNOx1tZWaDQayOVynDhxAnv37nXq2bFarWhtbUVLSwsCAwM9uu/ixYsxZ84cZGZmIi8vDwsWLMCNN97YR++KENKXKOghhAwJIpHI8T3HcZ0es9lsAACdTofly5dj4cKFLtcKCAjw+L4TJkxAaWkpfvrpJ/z3v//FokWLMHv2bHz99ddevQ9CSP+hoIcQ4pcmTJiAgoICpKWl9fpacrkcd955J+68807ccccdyMvLQ2NjI63aImSQoaCHENKviouLodPpoFKpYDAYcPz4cQDAiBEjBnTp+YsvvogFCxYgISEBd9xxB3iex4kTJ3D69Gm8+uqrHl/nr3/9K2JiYjB+/HjwPI/169cjOjoaCoWi/x6eEOIVCnoIIf3qoYcews6dOx3/PX78eABAaWnpgK5yys3NxaZNm/Dyyy9j5cqVEIlEyMrKwkMPPdSj6wQHB+PNN99EUVERBAIBJk+ejB9//BE8T4tjCRlsOMYYG+iHIIQQX7B48WKo1WqPt6Bos2PHDsycORNNTU3UA0TIAKI/RQghpAc2bdqEoKAgbNq0yaPyI0eO9HjLCkJI/6KeHkII8VBtbS00Gg0AICYmBjKZrNs6Fy9ehNlsBmDPBUTDXoQMHAp6CCGEEOIX6E8OQgghhPgFCnoIIYQQ4hco6CGEEEKIX6CghxBCCCF+gYIeQgghhPgFCnoIIYQQ4hco6CGEEEKIX6CghxBCCCF+gYIeQgghhPiF/we04aykSRrG1AAAAABJRU5ErkJggg==",
      "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: Optional[int] = 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{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}{Optional}\\PY{p}{[}\\PY{n+nb}{int}\\PY{p}{]} \\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: Optional\u001b[1m[\u001b[0mint\u001b[1m]\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>\n",
       "Dimensions:               (main_dim: 7)\n",
       "Coordinates:\n",
       "    flux_bias             (main_dim) float64 -0.04 -0.02667 ... 0.02667 0.04\n",
       "    resonator_freq_tuids  (main_dim) <U26 '20231215-162520-646-76db1f' ... '2...\n",
       "    qubit_freq_tuids      (main_dim) <U26 '20231215-162520-647-d0d11b' ... '2...\n",
       "    t1_tuids              (main_dim) <U26 '20231215-162520-647-de987d' ... '2...\n",
       "Dimensions without coordinates: main_dim\n",
       "Data variables:\n",
       "    resonator_freq        (main_dim) float64 7e+09 7.05e+09 ... 7.25e+09 7.3e+09\n",
       "    qubit_freq            (main_dim) float64 4.5e+09 4.583e+09 ... 5e+09\n",
       "    t1                    (main_dim) float64 4.238e-05 3.867e-05 ... 4.154e-05\n",
       "Attributes:\n",
       "    tuid:                      20231215-162520-648-3ce995\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\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 \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": {
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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": [
       "
\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.Dataset>\n",
       "Dimensions:               (main_dim: 7)\n",
       "Coordinates:\n",
       "  * main_dim              (main_dim) object MultiIndex\n",
       "  * flux_bias             (main_dim) float64 -0.04 -0.02667 ... 0.02667 0.04\n",
       "  * resonator_freq_tuids  (main_dim) <U26 '20231215-162520-646-76db1f' ... '2...\n",
       "  * qubit_freq_tuids      (main_dim) <U26 '20231215-162520-647-d0d11b' ... '2...\n",
       "  * t1_tuids              (main_dim) <U26 '20231215-162520-647-de987d' ... '2...\n",
       "Data variables:\n",
       "    resonator_freq        (main_dim) float64 7e+09 7.05e+09 ... 7.25e+09 7.3e+09\n",
       "    qubit_freq            (main_dim) float64 4.5e+09 4.583e+09 ... 5e+09\n",
       "    t1                    (main_dim) float64 4.238e-05 3.867e-05 ... 4.154e-05\n",
       "Attributes:\n",
       "    tuid:                      20231215-162520-648-3ce995\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\n",
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       "    t1                    (main_dim) float64 4.238e-05 3.867e-05 ... 4.154e-05\n",
       "Attributes:\n",
       "    tuid:                      20231215-162520-648-3ce995\n",
       "    dataset_name:              \n",
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       "Coordinates:\n",
       "    flux_bias             \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 \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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   "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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     "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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