{
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    "(sec-dataset-examples)=\n",
    "# Quantify dataset - examples\n",
    "\n",
    "```{seealso}\n",
    "The complete source code of this tutorial can be found in\n",
    "\n",
    "{nb-download}`Quantify dataset - examples.ipynb`\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a7db0795",
   "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",
    "import quantify_core.data.dataset_attrs as dattrs\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.examples_support import (\n",
    "    mk_iq_shots,\n",
    "    mk_trace_for_iq_shot,\n",
    "    mk_trace_time,\n",
    "    round_trip_dataset,\n",
    ")\n",
    "from quantify_core.utilities.inspect_utils import display_source_code\n",
    "from quantify_core.visualization.mpl_plotting import (\n",
    "    plot_complex_points,\n",
    "    plot_xr_complex,\n",
    "    plot_xr_complex_on_plane,\n",
    ")\n",
    "\n",
    "pretty.install()\n",
    "\n",
    "dh.set_datadir(Path.home() / \"quantify-data\")  # change me!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e2529b",
   "metadata": {},
   "source": [
    "In this page we explore a series of datasets that comply with the {ref}`Quantify dataset specification <dataset-spec>`.\n",
    "\n",
    "## 2D dataset example\n",
    "\n",
    "We use the {func}`~quantify_core.utilities.dataset_examples.mk_two_qubit_chevron_dataset`\n",
    "to generate our exemplary dataset. Its source code is conveniently displayed in the\n",
    "drop-down below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c6409a9",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Source code for generating mock Chevron dataset"
    },
    "tags": [
     "hide-cell"
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   "outputs": [
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">mk_two_qubit_chevron_dataset</span><span class=\"p\">(</span><span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    Generates a dataset that look similar to a two-qubit Chevron experiment.</span>\n",
       "\n",
       "<span class=\"sd\">    Parameters</span>\n",
       "<span class=\"sd\">    ----------</span>\n",
       "<span class=\"sd\">    **kwargs</span>\n",
       "<span class=\"sd\">        Keyword arguments passed to :func:`~.mk_two_qubit_chevron_data`.</span>\n",
       "\n",
       "<span class=\"sd\">    Returns</span>\n",
       "<span class=\"sd\">    -------</span>\n",
       "<span class=\"sd\">    :</span>\n",
       "<span class=\"sd\">        A mock Quantify dataset.</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    <span class=\"n\">amp_values</span><span class=\"p\">,</span> <span class=\"n\">time_values</span><span class=\"p\">,</span> <span class=\"n\">pop_q0</span><span class=\"p\">,</span> <span class=\"n\">pop_q1</span> <span class=\"o\">=</span> <span class=\"n\">mk_two_qubit_chevron_data</span><span class=\"p\">(</span><span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">dims_q0</span> <span class=\"o\">=</span> <span class=\"n\">dims_q1</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"s2\">&quot;repetitions&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;main_dim&quot;</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">pop_q0_attrs</span> <span class=\"o\">=</span> <span class=\"n\">mk_main_var_attrs</span><span class=\"p\">(</span>\n",
       "        <span class=\"n\">long_name</span><span class=\"o\">=</span><span class=\"s2\">&quot;Population Q0&quot;</span><span class=\"p\">,</span> <span class=\"n\">unit</span><span class=\"o\">=</span><span class=\"s2\">&quot;&quot;</span><span class=\"p\">,</span> <span class=\"n\">has_repetitions</span><span class=\"o\">=</span><span class=\"kc\">True</span>\n",
       "    <span class=\"p\">)</span>\n",
       "    <span class=\"n\">pop_q1_attrs</span> <span class=\"o\">=</span> <span class=\"n\">mk_main_var_attrs</span><span class=\"p\">(</span>\n",
       "        <span class=\"n\">long_name</span><span class=\"o\">=</span><span class=\"s2\">&quot;Population Q1&quot;</span><span class=\"p\">,</span> <span class=\"n\">unit</span><span class=\"o\">=</span><span class=\"s2\">&quot;&quot;</span><span class=\"p\">,</span> <span class=\"n\">has_repetitions</span><span class=\"o\">=</span><span class=\"kc\">True</span>\n",
       "    <span class=\"p\">)</span>\n",
       "    <span class=\"n\">data_vars</span> <span class=\"o\">=</span> <span class=\"nb\">dict</span><span class=\"p\">(</span>\n",
       "        <span class=\"n\">pop_q0</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">dims_q0</span><span class=\"p\">,</span> <span class=\"n\">pop_q0</span><span class=\"p\">,</span> <span class=\"n\">pop_q0_attrs</span><span class=\"p\">),</span>\n",
       "        <span class=\"n\">pop_q1</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">dims_q1</span><span class=\"p\">,</span> <span class=\"n\">pop_q1</span><span class=\"p\">,</span> <span class=\"n\">pop_q1_attrs</span><span class=\"p\">),</span>\n",
       "    <span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">dims_amp</span> <span class=\"o\">=</span> <span class=\"n\">dims_time</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"s2\">&quot;main_dim&quot;</span><span class=\"p\">,)</span>\n",
       "    <span class=\"n\">amp_attrs</span> <span class=\"o\">=</span> <span class=\"n\">mk_main_coord_attrs</span><span class=\"p\">(</span><span class=\"n\">long_name</span><span class=\"o\">=</span><span class=\"s2\">&quot;Amplitude&quot;</span><span class=\"p\">,</span> <span class=\"n\">unit</span><span class=\"o\">=</span><span class=\"s2\">&quot;V&quot;</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">time_attrs</span> <span class=\"o\">=</span> <span class=\"n\">mk_main_coord_attrs</span><span class=\"p\">(</span><span class=\"n\">long_name</span><span class=\"o\">=</span><span class=\"s2\">&quot;Time&quot;</span><span class=\"p\">,</span> <span class=\"n\">unit</span><span class=\"o\">=</span><span class=\"s2\">&quot;s&quot;</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">coords</span> <span class=\"o\">=</span> <span class=\"nb\">dict</span><span class=\"p\">(</span>\n",
       "        <span class=\"n\">amp</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">dims_amp</span><span class=\"p\">,</span> <span class=\"n\">amp_values</span><span class=\"p\">,</span> <span class=\"n\">amp_attrs</span><span class=\"p\">),</span>\n",
       "        <span class=\"n\">time</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">dims_time</span><span class=\"p\">,</span> <span class=\"n\">time_values</span><span class=\"p\">,</span> <span class=\"n\">time_attrs</span><span class=\"p\">),</span>\n",
       "    <span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">dataset_attrs</span> <span class=\"o\">=</span> <span class=\"n\">mk_dataset_attrs</span><span class=\"p\">()</span>\n",
       "    <span class=\"n\">dataset</span> <span class=\"o\">=</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">(</span><span class=\"n\">data_vars</span><span class=\"o\">=</span><span class=\"n\">data_vars</span><span class=\"p\">,</span> <span class=\"n\">coords</span><span class=\"o\">=</span><span class=\"n\">coords</span><span class=\"p\">,</span> <span class=\"n\">attrs</span><span class=\"o\">=</span><span class=\"n\">dataset_attrs</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"k\">return</span> <span class=\"n\">dataset</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}two\\PYZus{}qubit\\PYZus{}chevron\\PYZus{}dataset}\\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}{    Generates a dataset that look similar to a two\\PYZhy{}qubit Chevron experiment.}\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}{    **kwargs}\n",
       "\\PY{l+s+sd}{        Keyword arguments passed to :func:`\\PYZti{}.mk\\PYZus{}two\\PYZus{}qubit\\PYZus{}chevron\\PYZus{}data`.}\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}{        A mock Quantify dataset.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{n}{amp\\PYZus{}values}\\PY{p}{,} \\PY{n}{time\\PYZus{}values}\\PY{p}{,} \\PY{n}{pop\\PYZus{}q0}\\PY{p}{,} \\PY{n}{pop\\PYZus{}q1} \\PY{o}{=} \\PY{n}{mk\\PYZus{}two\\PYZus{}qubit\\PYZus{}chevron\\PYZus{}data}\\PY{p}{(}\\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{dims\\PYZus{}q0} \\PY{o}{=} \\PY{n}{dims\\PYZus{}q1} \\PY{o}{=} \\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}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{pop\\PYZus{}q0\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\n",
       "        \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Population Q0}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{has\\PYZus{}repetitions}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "    \\PY{p}{)}\n",
       "    \\PY{n}{pop\\PYZus{}q1\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\n",
       "        \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Population Q1}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{has\\PYZus{}repetitions}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "    \\PY{p}{)}\n",
       "    \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{pop\\PYZus{}q0}\\PY{o}{=}\\PY{p}{(}\\PY{n}{dims\\PYZus{}q0}\\PY{p}{,} \\PY{n}{pop\\PYZus{}q0}\\PY{p}{,} \\PY{n}{pop\\PYZus{}q0\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{pop\\PYZus{}q1}\\PY{o}{=}\\PY{p}{(}\\PY{n}{dims\\PYZus{}q1}\\PY{p}{,} \\PY{n}{pop\\PYZus{}q1}\\PY{p}{,} \\PY{n}{pop\\PYZus{}q1\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{dims\\PYZus{}amp} \\PY{o}{=} \\PY{n}{dims\\PYZus{}time} \\PY{o}{=} \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\\PY{p}{)}\n",
       "    \\PY{n}{amp\\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}{Amplitude}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{time\\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}{Time}\\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}{)}\n",
       "    \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{amp}\\PY{o}{=}\\PY{p}{(}\\PY{n}{dims\\PYZus{}amp}\\PY{p}{,} \\PY{n}{amp\\PYZus{}values}\\PY{p}{,} \\PY{n}{amp\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{time}\\PY{o}{=}\\PY{p}{(}\\PY{n}{dims\\PYZus{}time}\\PY{p}{,} \\PY{n}{time\\PYZus{}values}\\PY{p}{,} \\PY{n}{time\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{dataset\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{p}{)}\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_two_qubit_chevron_dataset\u001b[0m\u001b[1m(\u001b[0m**kwargs\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generates a dataset that look similar to a two-qubit Chevron experiment.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    **kwargs\n",
       "        Keyword arguments passed to :func:`~.mk_two_qubit_chevron_data`.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    :\n",
       "        A mock Quantify dataset.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    amp_values, time_values, pop_q0, pop_q1 = \u001b[1;35mmk_two_qubit_chevron_data\u001b[0m\u001b[1m(\u001b[0m**kwargs\u001b[1m)\u001b[0m\n",
       "\n",
       "    dims_q0 = dims_q1 = \u001b[1m(\u001b[0m\u001b[32m\"repetitions\"\u001b[0m, \u001b[32m\"main_dim\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    pop_q0_attrs = \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mlong_name\u001b[0m=\u001b[32m\"Population\u001b[0m\u001b[32m Q0\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"\"\u001b[0m, \u001b[33mhas_repetitions\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "    \u001b[1m)\u001b[0m\n",
       "    pop_q1_attrs = \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mlong_name\u001b[0m=\u001b[32m\"Population\u001b[0m\u001b[32m Q1\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"\"\u001b[0m, \u001b[33mhas_repetitions\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "    \u001b[1m)\u001b[0m\n",
       "    data_vars = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mpop_q0\u001b[0m=\u001b[1m(\u001b[0mdims_q0, pop_q0, pop_q0_attrs\u001b[1m)\u001b[0m,\n",
       "        \u001b[33mpop_q1\u001b[0m=\u001b[1m(\u001b[0mdims_q1, pop_q1, pop_q1_attrs\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    dims_amp = dims_time = \u001b[1m(\u001b[0m\u001b[32m\"main_dim\"\u001b[0m,\u001b[1m)\u001b[0m\n",
       "    amp_attrs = \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Amplitude\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"V\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    time_attrs = \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Time\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"s\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    coords = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mamp\u001b[0m=\u001b[1m(\u001b[0mdims_amp, amp_values, amp_attrs\u001b[1m)\u001b[0m,\n",
       "        \u001b[33mtime\u001b[0m=\u001b[1m(\u001b[0mdims_time, time_values, time_attrs\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    dataset_attrs = \u001b[1;35mmk_dataset_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\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(dataset_examples.mk_two_qubit_chevron_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d623919",
   "metadata": {},
   "outputs": [
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       "Attributes:\n",
       "    tuid:                      20231215-162525-279-c4ebbc\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:    []</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fc9d47d2-36ec-46be-80c9-b6ffccfcc15e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fc9d47d2-36ec-46be-80c9-b6ffccfcc15e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>repetitions</span>: 5</li><li><span>main_dim</span>: 1200</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-49ffcd3e-d8e7-48f5-9915-4ae8a2b0b511' class='xr-section-summary-in' type='checkbox'  checked><label for='section-49ffcd3e-d8e7-48f5-9915-4ae8a2b0b511' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(main_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.45 0.4534 0.4569 ... 0.5466 0.55</div><input id='attrs-f6aeb9c8-88fc-417f-be67-ceec45dd69a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6aeb9c8-88fc-417f-be67-ceec45dd69a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aca7220b-8309-4f99-af6d-1d576db6aa61' class='xr-var-data-in' type='checkbox'><label for='data-aca7220b-8309-4f99-af6d-1d576db6aa61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Amplitude</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.45      , 0.45344828, 0.45689655, ..., 0.54310345, 0.54655172,\n",
       "       0.55      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(main_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 1e-07 1e-07 1e-07</div><input id='attrs-83b61b1a-e452-4467-ba48-a5ddabfee89c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83b61b1a-e452-4467-ba48-a5ddabfee89c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3b4443d-7962-41d3-b531-32e40af8ebb6' class='xr-var-data-in' type='checkbox'><label for='data-c3b4443d-7962-41d3-b531-32e40af8ebb6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.e+00, 0.e+00, 0.e+00, ..., 1.e-07, 1.e-07, 1.e-07])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c891d809-94a4-4027-b0b3-ed95fc4557c8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c891d809-94a4-4027-b0b3-ed95fc4557c8' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>pop_q0</span></div><div class='xr-var-dims'>(repetitions, main_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 0.5 0.5 ... 0.4886 0.4818 0.5</div><input id='attrs-1bdd5f8e-bda8-4c8a-b675-c535523aebb3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1bdd5f8e-bda8-4c8a-b675-c535523aebb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-230b1ebd-8ff3-4b8d-b05b-e6b92a4e49b2' class='xr-var-data-in' type='checkbox'><label for='data-230b1ebd-8ff3-4b8d-b05b-e6b92a4e49b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Population Q0</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[0.5       , 0.5       , 0.5       , ..., 0.47934742, 0.45911157,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.48484689, 0.46978367,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.47697503, 0.48068839,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.4903811 , 0.45931355,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.48855559, 0.48175285,\n",
       "        0.5       ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pop_q1</span></div><div class='xr-var-dims'>(repetitions, main_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 0.5 0.5 ... 0.5243 0.5371 0.5</div><input id='attrs-15734b5a-0a3f-488d-a46d-c7af4e46f5a6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15734b5a-0a3f-488d-a46d-c7af4e46f5a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57f10902-a6ad-4b55-bbb5-435e1087a85b' class='xr-var-data-in' type='checkbox'><label for='data-57f10902-a6ad-4b55-bbb5-435e1087a85b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Population Q1</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[0.5       , 0.5       , 0.5       , ..., 0.51325446, 0.53973285,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.50964979, 0.52643865,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.51995816, 0.54094941,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.51040954, 0.53633277,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.52434067, 0.53709612,\n",
       "        0.5       ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fd9b157c-c8a7-4a63-87c4-f7989ebcaa39' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fd9b157c-c8a7-4a63-87c4-f7989ebcaa39' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-2977e475-8d8e-4c09-93c8-954f8162081e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2977e475-8d8e-4c09-93c8-954f8162081e' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20231215-162525-279-c4ebbc</dd><dt><span>dataset_name :</span></dt><dd></dd><dt><span>dataset_state :</span></dt><dd>None</dd><dt><span>timestamp_start :</span></dt><dd>None</dd><dt><span>timestamp_end :</span></dt><dd>None</dd><dt><span>quantify_dataset_version :</span></dt><dd>2.0.0</dd><dt><span>software_versions :</span></dt><dd>{}</dd><dt><span>relationships :</span></dt><dd>[]</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div></li></ul></div></div>"
      ],
      "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;36m5\u001b[0m, main_dim: \u001b[1;36m1200\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "    amp      \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 \u001b[1;36m0.45\u001b[0m \u001b[1;36m0.4534\u001b[0m \u001b[1;36m0.4569\u001b[0m \u001b[1;36m0.4603\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.5431\u001b[0m \u001b[1;36m0.5466\u001b[0m \u001b[1;36m0.55\u001b[0m\n",
       "    time     \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 \u001b[1;36m0.0\u001b[0m \u001b[1;36m0.0\u001b[0m \u001b[1;36m0.0\u001b[0m \u001b[1;36m0.0\u001b[0m \u001b[1;36m0.0\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m1e-07\u001b[0m \u001b[1;36m1e-07\u001b[0m \u001b[1;36m1e-07\u001b[0m \u001b[1;36m1e-07\u001b[0m\n",
       "Dimensions without coordinates: repetitions, main_dim\n",
       "Data variables:\n",
       "    pop_q0   \u001b[1m(\u001b[0mrepetitions, main_dim\u001b[1m)\u001b[0m float64 \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.4886\u001b[0m \u001b[1;36m0.4818\u001b[0m \u001b[1;36m0.5\u001b[0m\n",
       "    pop_q1   \u001b[1m(\u001b[0mrepetitions, main_dim\u001b[1m)\u001b[0m float64 \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.5243\u001b[0m \u001b[1;36m0.5371\u001b[0m \u001b[1;36m0.5\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20231215\u001b[0m-\u001b[1;36m162525\u001b[0m-\u001b[1;36m279\u001b[0m-c4ebbc\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 = dataset_examples.mk_two_qubit_chevron_dataset()\n",
    "\n",
    "assert dataset == round_trip_dataset(dataset)  # confirm read/write\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b866501",
   "metadata": {},
   "source": [
    "The data within this dataset can be easily visualized using xarray facilities,\n",
    "however, we first need to convert the Quantify dataset to a \"gridded\" version with the {func}`~quantify_core.data.handling.to_gridded_dataset` function as \n",
    "shown below.\n",
    "\n",
    "Since our dataset contains multiple repetitions of the same experiment, it is convenient\n",
    "to visualize them on different plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "95035601",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 160\u001b[0m\u001b[1;36m0x300\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m6\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 160\u001b[0m\u001b[1;36m0x300\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m6\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset,\n",
    "    dimension=\"main_dim\",\n",
    "    coords_names=dattrs.get_main_coords(dataset),\n",
    ")\n",
    "dataset_gridded.pop_q0.plot.pcolormesh(x=\"amp\", col=\"repetitions\")\n",
    "_ = dataset_gridded.pop_q1.plot.pcolormesh(x=\"amp\", col=\"repetitions\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8143b28c",
   "metadata": {},
   "source": [
    "In xarray, among other features, it is possible to average along a dimension which can\n",
    "be very convenient to average out some of the noise:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "26ce1ae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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GG29skX1uCKeWiIiIDECHCZoXp1DrP2VLS0s97rfb7bDb7fUef/z4cbhcLsTGxnrcHxsbix07djS4jTvvvBPHjx/H5ZdfDl3XUVdXh8mTJ+P//u//lPfbWz4fkfH3RUZERERNoammlpKSkhAREeG+zZ07t8n2MScnB08//TReffVVbNmyBe+//z6ysrLw5JNPNtk2pHw6ItMaFhkREREZSUFBAcLDw93/3dBoDABER0fDYrGgqKjI4/6ioiLExcU1mHn88ccxZswY3HPPPQCAPn36oKKiAvfeey8ee+wxmM0tPz7i00Lml4uMAGDx4sXIyspCZmYmpk+fXu/xv1xkBADJyckYNWoUNmzY0CL7a65TaL6kcITrourEmWCbPFNyrI04AwCRR+SZumD5sTvZS76dK9sp7ByAbZXyZmah2+WNyUyhIeKMyyH/Yrim425xBgBO1sr3Ly+yhzhjK5I3SAzZpdYITrtU/t7rFnFMnPlvhwRxJjxf/rN1qPV8RGmivBOcxSL/OdVFyE8CNh+ziDMAYFL4TkaQQpfSFqLpJmi6+tTSmWx4eLhHIXM2NpsNKSkpyM7OxogRI04/h6YhOzsb06ZNazBTWVlZr1ixWE7//HTdN8fWZ1NLrWWRERERUVNw/XT1a29uUhkZGViyZAneeOMNbN++HVOmTEFFRYV7gGHs2LGYMWOG+/HDhg3DokWLsGLFCuzfvx+ffvopHn/8cQwbNsxd0LQ0n43ItNQio5qaGtTU/Nz/+teLoIiIiALVyJEjcezYMcycOROFhYXo168fVq1a5f7dnJ+f7zEC85e//AUmkwl/+ctfcOjQIbRv3x7Dhg3DX//6V1+9BP86a+mXi4xSU1OxZ88ePPjgg3jyySfx+OOPN5iZO3cuZs+e3cJ7SkREJNNUU0tS06ZNO+tUUk5Ojsd/W61WzJo1C7NmzVLaVnPwWSHTUouMZsyYgYyMDPd/l5aWIikpqQlfCRERkfc0mKF5seLDm6w/89mr/uUiozPOLDJKS0trMKOyyMhut7sXPjV2ARQRERH5B59OLWVkZGDcuHEYOHAgBg0ahPnz59dbZJSYmOg+B37YsGGYN28e+vfv755a8vUiIyIioqbg0k1weTG15E3Wn/m0kGkNi4yIiIiagq/WyPg7ny/29fdFRkRERE1B9/Lq17oXWX/m80LGn6gUu64weYMgS5ta+YYU2A7JG2QBgLVS/pqc4fKD57iwRJyZ0P4LcQYAHtl5mzhjk+8etFPF4kxxl87izPC2W8QZAKjW5e+JvyTKOxeGb3WKM2EH1Zpt/X3vFeJMSkyBOGNKrhRnyveHijP2YrXjYDsobyiodZf/nEyh8u+vumq1X8CWqsAcgSBPLGSIiIgMwAUTXF5cNNKbrD9jIUNERGQAmu7dOhfNuFdfaFaBOaFGRERErQJHZIiIiAxA83KxrzdZf8ZChoiIyAA0mKB5sc7Fm6w/C8zyjYiIiFoFjsgQEREZADv7qmEhQ0REZABcI6MmMF81ERERtQockRHQrfKT9F1t6sQZh02eqToq7xAaWiGOAADMLnmmIl6euSoxT5yZf/g6+YYAFO2OFmd6fFYoztRcKu+CW95ZE2dURZrl3WlLu8r3L6GySpwJ3yfPAMDeve3Eme7Jm8SZ/THy7ezrKP/cRv0ojgAAgo/JMyVRweKMLUb+c6oJVftVZHK1rosFa/DyWksButiXhQwREZEB6F6etaSzkCEiIiJf4dWv1XCNDBEREfktjsgQEREZAM9aUsNChoiIyAA4taQmMMs3IiIiahU4IkNERGQAvNaSGhYyREREBsCpJTUsZARcDnlDPItD3tyuptQuztiL5I2h7KfEEQBAdZT8wxLUrVSc6R16WJz5f0cHiTMAELZPYZZVoambbo0SZ9okl4gzhXUR4gwADHLkizOJPYvEmcoBHcWZ4IIycQYAQgpCxJk1x3rKt2OtFWfQVd6VUqX5JQBYFfoJBh+Rf69U2uTfX6YgtaaPmk3+uTXVBeYv+9aMhQwREZEBcERGDQsZIiIiA2Aho4ZnLREREZHf4ogMERGRAXBERg0LGSIiIgPQ4d0p1PLTUVoHFjJEREQGwBEZNVwjQ0RERH6LIzJEREQGwBEZNSxkJCzyGUhXlfwQW0vkmeBj4gjq5H3CAAAVHeTHISlc3sxsU2knceZEYbg4AwAd8l3ijCupvThTfIFNnOkfu0ucuVmhmSAAaJA3QLu0/QFx5tOu8eJMcJ78ZwQA4fnyZmvb9yWIM5d0yxNnoiPKxZmjHdQ+uBG7FRpZyncP1mL591ddhNrPFkHy7yIjN8RjIaOGU0tERETktzgiQ0REZAAckVHDQoaIiMgAdN0E3YtixJusP+PUEhEREfktjsgQEREZgAaTVw3xvMn6MxYyREREBsA1Mmo4tURERER+iyMyREREBsDFvmpYyDQzc6W8wVhQqfzNaJL3/IKzjTwDAEioFkfs1jpxptoVJM6Eb5NnACAsr0ScMVXVijPFPeTNzCbEfCHOVOry463qxoit4sx7XQeJM9HfhYkzABB8XP5zCt5rF2eKkuT753TJvx8snSrEGQCoLJHvX/Bx+XbsJ+XfX5pNbXJAb2VzCpxaUsNChoiIyAA4IqOmldWzREREFEg4IkNERGQAupdTS4E6IsNChoiIyAB0ALr8Opge+UDEqSUiIiLyWxyRISIiMgANJpjY2VeMhQwREZEB8KwlNZxaIiIiIr/FERkBU6282g0qk9eKZoVeZjWR8kx1olrTtD6JR8SZxJBicWbVpovFmaS9aq+ptq1DnKnsES7OhHc+Jc60McsbEM47fpk4AwC9Qw6JM0lBJ8SZuO7HxJmypBhxBgCiPvhenImIuUicyVfYv4t7HRBnjlpc4gwAHIkLFmcsNfKGfdYqcQTWCrW/qetCFTqBGpimm2BiQzwxFjJEREQGoOtenrUUoKctcWqJiIiI/BZHZIiIiAyAi33VsJAhIiIyABYyaljIEBERGQAX+6rhGhkiIiLyWxyRISIiMgCetaSGhQwREZEBnC5kvFkj04Q740c4tURERER+iyMyAmanvFK2yJuywqTQnLY6Rl6KB7evlG8IQFhQjTjz3/3dxJmQA/KuopaaWnEGAIJOyX9QJ6+yizNDE/aJM0EmeffS70sSxBkA0BT+thncLk+c+W3iD+LMG92vEWcAIKLvBeJMmzx5e9rSTqHizK727cWZjlHy7tAAUNGhVJ6pihRn7CcUOqCXiyMAAC1Ivi1d/rXSYnjWkhoWMkRERAag/3TzJh+IOLVEREREfosjMkRERAbAqSU1LGSIiIiMgHNLSnw+tbRw4UIkJyfD4XAgNTUVGzduPOfji4uLMXXqVMTHx8Nut6Nbt2745JNPWmhviYiImslPIzKqNyiOyPj772GfjsisXLkSGRkZWLx4MVJTUzF//nwMHToUO3fuRExMTL3HO51OXHvttYiJicG//vUvJCYm4sCBA4iMjGz5nSciIvJzreH3sE8LmXnz5mHSpEkYP348AGDx4sXIyspCZmYmpk+fXu/xmZmZOHnyJNavX4+goCAAQHJyckvuMhERUbPwRWff1vB72GdTS06nE5s3b0Z6evrPO2M2Iz09Hbm5uQ1mPv74Y6SlpWHq1KmIjY3FRRddhKeffhoul6uldpuIiKhZeDOt9MuFwqWlpR63mpqGe3+1lt/DPhuROX78OFwuF2JjYz3uj42NxY4dOxrM7Nu3D//9738xevRofPLJJ9izZw/uu+8+1NbWYtasWQ1mampqPH6IpaXyplBnqDTEU2m+VNNWnnFFyxvBRYbKm34BwN7iaHGm7qC8WVjUAXkjOM2mNkd8fECEOKN3qxBnro74UZx54+Rl4sz+4+3EGQAorXGIMxeHHBBn7m37jTiT2TVNnAGAkz1DxJmIPHnTx4g8+fu1KKaNOFPRRv6+A4DIEPnnvSw2WJxxuuSNIq0Vap9bU53Kd3LrXxGblJTk8d+zZs3CE088Ue9xLfV7uLn51VlLmqYhJiYGr732GiwWC1JSUnDo0CH87W9/O+sBnDt3LmbPnt3Ce0pERCTkxYJddx5AQUEBwsPD3Xfb7fLi8mxUfg83N58VMtHR0bBYLCgqKvK4v6ioCHFxcQ1m4uPjERQUBIvl52GOnj17orCwEE6nEzabrV5mxowZyMjIcP93aWlpvWqViIjI15pqjUx4eLhHIXM2LfV7uLn5bI2MzWZDSkoKsrOz3fdpmobs7GykpTU8hHzZZZdhz5490LSfh3B37dqF+Pj4sx48u93u/qE29odLRETU2rXU7+Hm5tM+MhkZGViyZAneeOMNbN++HVOmTEFFRYV79fTYsWMxY8YM9+OnTJmCkydP4sEHH8SuXbuQlZWFp59+GlOnTvXVSyAiImoaehPchFrD72GfrpEZOXIkjh07hpkzZ6KwsBD9+vXDqlWr3AuP8vPzYTb/XGslJSVh9erVePjhh9G3b18kJibiwQcfxKOPPuqrl0BERNQkfHGJgtbwe9jni32nTZuGadOmNfhvOTk59e5LS0vD119/3cx7RUREFBj8/fewzwsZIiIi+knrPzu8ybGQISIiMgBe/VoNCxkBXWFpdJ28nxRqo+rEmXbRZeKM06XQrQ/AiYOR4kzEfvkHLPi4vMmfblb7IJd3kGfio0rEmVpd/pErr5OfCVBzSN6AEAAOm+S5I4nyDo5lmryJ3uDk/eIMAGzs2FucsZcGiTO2Ynln09CD8u0UtJE3pASADh1PiDPBofLGgJXh8tcETe27yCTvQWhsvPq1kkZ9q3788cfiJ7722msRHKzwW5yIiIiokRpVyIwYMUL0pCaTCbt370aXLl1U9omIiCgAmX66eZMPPI2eLCksLISmaY26hYTIr21CREQU0HzQR6Y1aFQhM27cONE00V133cUOukRERNTsGjW1tGzZMtGTLlq0SGlniIiIAhYX+yrx+hIFpaWl+PDDD7F9+/am2B8iIqLAdObq197cDO7HH3/Efffdh/79+yM+Ph7x8fHo378/7rvvPvz4449Kzyk+F/T222/HlVdeiWnTpqGqqgoDBw5EXl4edF3HihUrcOuttyrtCBEREbVe//nPfzBixAgMGDAAN998s/syCEVFRfj0008xYMAAfPTRRxg6dKjoecWFzOeff47HHnsMAPDBBx9A13UUFxfjjTfewFNPPcVChoiISIGun755kzey6dOn49FHH8WcOXPq/dsTTzyBJ554Ao888kjzFzIlJSWIiooCAKxatQq33norQkJCcNNNN+GRRx6RPp1f0YPk7xKXQ96xyRIqbwRXVSNvQlVV6hBnAMBxUN7ULeyQvFmYyyGf+SyPV+vxWHthlThzd1KuOFNQGyXOnHLKm9SF71GbNdYUepltKO4szpgVOpnd3n6jOAMAX10gbwNRUyDvgRVcJG8eF35A3gjOZVf4IQE4EhwhzsRGl4ozlSHy7xVXrdqUiLna+FMpIq18jcyuXbswevTos/77qFGj8Oyzz4qfV/xtl5SUhNzcXFRUVGDVqlW47rrrAACnTp2Cw6H2i5GIiIhat+TkZGRlZZ3137OystCpUyfx84r/fH3ooYcwevRohIWFoVOnTrjqqqsAnJ5y6tOnj3gHiIiICN4v2DX4Yt85c+bgzjvvRE5ODtLT0z3WyGRnZ2PVqlV46623xM8rLmTuu+8+pKamIj8/H9deey3M5tODOl26dMFTTz0l3gEiIiICTPrpmzd5I7vtttuQmJiIl156CS+88AIKCwsBAHFxcUhLS0NOTg7S0tLEz6u0oCAlJQUpKSke9910000qT0VERERAq18jAwCDBw/G4MGDm/Q5G7VGJiMjAxUVFY1+0hkzZuDkyZPKO0VERETUGI0akVmwYAFmzJiB0NDGnT2xcOFCTJo0yX12ExEREZ1HK18jc0b//v1hMjVuX7ds2XLexzSqkNF1Hd26dWv0hiWjN0RERISAmFoCgOuvvx6vvvoqevXq5V4T8/XXX2Pbtm2YMmWK6NqOQDNdawmAezUyERER0RnHjh3DAw88gCeffNLj/lmzZqGgoACZmZmi52tUITNu3DjRkxIREZFQgIzIvPvuu9i0aVO9+++66y4MHDiweQoZOk1T6Oyr2+UdTFVUldnFGWuhTWlbjhPyjG6Wz91Wtpd3py3urvZJ7t3hiDhzoa1QnNlSnSzObMpPEmc67nCKMwBg0uTHb+slieJMn/BD4ky3oOPiDACMvuh/4sybhVeIM9YqeUNQlePtUDsMcCp0Kz7ilHcetoXJ33tOxV/Ami7vcmxyGXgdSYAUMsHBwfjqq69w4YUXetz/1VdfKTXWZSFDRERELeahhx7ClClTsGXLFgwaNAgAsGHDBmRmZuLxxx8XPx8LGSIiIiMIkLOWpk+fji5dumDBggX45z//CQDo2bMnli1bhttvv138fCxkiIiIDKC1d/b9pdtvv/28Rcvbb7+N4cOHn7f1i9olcgHs2bMHq1evRlXV6asG60a/fjgRERH5jT/84Q8oKio67+PEhcyJEyeQnp6Obt264cYbb8SRI6cXSU6cOBF//OMf5XtKREREPy/29ebWijR2gERcyDz88MOwWq3Iz89HSEiI+/6RI0di1apV0qcjIiIiUiZeI7NmzRqsXr0aHTp08Lj/wgsvxIEDB5psx4iIiAKJCV6ukWmyPfEv4hGZiooKj5GYM06ePAm7Xd7LhIiIiEiVeETmiiuuwD/+8Q93a2GTyQRN0/Dcc8/h6quvbvIdNBJdoSGeSWE5tatSfjKZtUSeCZX3gAMAtDnkEmcqo+WNtaoUrnLR5kK1q66Pit8ozhRr9Qv681l/6gJxJmiHfDuObfvFGQCo6xQjzlh3Ne5isr90sGtbcaY6XP4eAgCLSd6UMv6i8y8w/LUTpXHiTNQ2+XdKUKXan+yhR+R/r5cHyRvOOVtwWEC3KnwnG7ohXmCcft3UxL/9nnvuOVxzzTXYtGkTnE4n/vznP2Pbtm04efIkvvrqq+bYRyIiotYvQDr7NlanTp0Q1IhiWlzIXHTRRdi1axdeeeUVtGnTBuXl5bjlllswdepUxMfHK+0sERERBZZNmzZh+/btAE43xBs4cKDHv//www+Neh6lhngRERF47LHHVKJERETUkAAZkTl48CBGjRqFr776CpGRkQCA4uJiDB48GCtWrKh3MtH5KBUy1dXV+O6773D06FFomuf88/Dhw1WekoiIKKAFSmffe+65B7W1tdi+fTu6d+8OANi5cyfGjx+Pe+65R9zKRVzIrFq1CmPHjsXx4/UvwWoymeByyReCEhERUWBYt24d1q9f7y5iAKB79+54+eWXccUV8ivPi8+puf/++3HbbbfhyJEj0DTN48YihoiISFGAdPZNSkpCbW1tvftdLhcSEhLEzycuZIqKipCRkYHYWIVzY4mIiKhhAVLI/O1vf8P999+PTZs2ue/btGkTHnzwQTz//PPi5xNPLf3+979HTk4OunbtKt4YERERBba7774blZWVSE1NhdV6ugypq6uD1WrFhAkTMGHCBPdjT548f28wcSHzyiuv4LbbbsMXX3yBPn361DvH+4EHHpA+ZevmlHfEs1bIM45j8kZIQeVq5XudQ76t6ij5diq7OcWZPhHF8g0B6Gc/KM78t6L7+R/0Kxv3dRJnog/If0413eXDswBgWbtZnAnrNVic+Wx7D3Em2lYuzgDANW22iTM728hHnA91ljf5Ky2Xd0N3nFL73AaVKTTfK5V/1nWTvImeFqa4LEGh4aiRBcpi3/nz5zfp84kLmbfffhtr1qyBw+FATk4OTKaf3+gmk4mFDBERkYoA6ew7bty4Jn0+cSHz2GOPYfbs2Zg+fTrM5lZWDhMREflKgPSRAU4v7P3www/dDfF69+6N4cOHw2KRX4pEXMg4nU6MHDmSRQwRERGJ7dmzBzfeeCMOHTrkPgV77ty5SEpKQlZWlngNrrgaGTduHFauXCmNERER0TmcWSPjzc0fPPDAA+jatSsKCgqwZcsWbNmyBfn5+ejcubPS8hTxiIzL5cJzzz2H1atXo2/fvvUW+86bN0+8E0RERAEvQKaW1q1bh6+//hpRUT+fBdKuXTs888wzuOyyy8TPJy5kvv/+e/Tv3x9A/Qs6/XLhLxEREdGv2e12lJWV1bu/vLwcNptN/HziQmbt2rXijRAREdF5eDs95CcjMr/97W9x7733YunSpRg0aBAAYMOGDZg8ebLS9Rq5YpeIiMgIAqSz70svvYSuXbsiLS0NDocDDocDgwcPxgUXXIAFCxaIn69RIzK33HILli9fjvDwcNxyyy3nfOz7778v3gl/YaqTT52ZnQqN6krkGVupOAJnG7WpwOooea6is7zhVVq3veLMNVHbxRkA2OGMEWf+kXepOGPf7RBnQorqX5PkfIK++l6cAQBd4dTH8APyxoUV++SN4NbFXiDOAMBxZ5g40z2sSJzJj5c3xDtU0V6c0Q/If0YAYC+WZ8zyHy3sJ+XfD05d7TVpQX7ym5s8REZG4qOPPsKePXvw448/AgB69eqFCy5Q+4w3qpCJiIhwr3+JiIhQ2hARERGdQ4As9gWApUuX4sUXX8Tu3bsBABdeeCEeeugh3HPPPeLnalQhs2zZMsyZMwd/+tOfsGzZMvFGiIiI6NwC5RIFM2fOxLx583D//fcjLS0NAJCbm4uHH34Y+fn5mDNnjuj5Gr3Yd/bs2Zg8eTJCQkJke0xERET0k0WLFmHJkiUYNWqU+77hw4ejb9++uP/++8WFTKMX++q6n5R6REREZFi1tbUYOHBgvftTUlJQV1cnfj7RWUvsE0NERNRMAuSspTFjxmDRokX17n/ttdcwevRo8fOJ+sh069btvMXMyZMnxTtBREQU6AJljQxwerHvmjVrcOmlp8/+3LBhA/Lz8zF27FhkZGS4H9eYqwWICpnZs2fzrCUiIiJS9sMPP2DAgAEAgL17T7fZiI6ORnR0tMcVAxo7CyQqZO644w7ExMj7bRAREVEj+NGoiqqmvkJAowsZro8hIiJqRgHUR6YpNbqQ4VlLal16rRUKnX3LxRG45I1S4WwjzwBATXtNnIlMKhFn+oYfFGf2VMeKMwDwbfEAcebogajzP+hX2h4VRxCy5YA8FKk2BayVV4gzQaU14kz0d/JOrgfbR4szABDeu1qcqXIFiTMXtzskzpgUFjUc1NTe47pFfkUalc6+JoWMZlP7Q7kuVJ7ReWGeVqfRhYymyX95ERERUeME0mLfpiS++jURERE1A04tKeEgGxEREfktQxQyCxcuRHJyMhwOB1JTU7Fx48ZG5VasWAGTyYQRI0Y07w4SERE1szNTS97cApHPC5mVK1ciIyMDs2bNwpYtW3DxxRdj6NChOHr03Ksi8/Ly8Kc//QlXXHFFC+0pERFRMwqQzr5NzeeFzLx58zBp0iSMHz8evXr1wuLFixESEoLMzMyzZlwuF0aPHo3Zs2ejS5cuLbi3REREZCQ+LWScTic2b96M9PR0931msxnp6enIzc09a27OnDmIiYnBxIkTz7uNmpoalJaWetyIiIgMx0cjMv6+vMOnhczx48fhcrkQG+vZFyE2NhaFhYUNZr788kssXboUS5YsadQ25s6di4iICPctKSnJ6/0mIiJqar5YI9Malnf41enXZWVlGDNmDJYsWYLo6MY1x5oxY4bHBahKS0uVixlTrbxpk0l+RXJoCj+VWoXmds62ar2B2iTJR7W6tj0hzgSZXOJMUY1al78dBXHiTJud8qZucdlF4kxd1wRxxrqzQJwBAPToLI5YTlWKMyHiBBBaoPaz3RUhbyDXq/e3StsSbydS/n44Fh+mtK1KyLvHOY4r/K2r8LVikfdUPL2pIPl3ssth4IUkPjj9+pfLOwBg8eLFyMrKQmZmJqZPn95g5pfLO7744gsUFxd7sdPe82khEx0dDYvFgqIizw9zUVER4uLq/2LZu3cv8vLyMGzYMPd9Zxr1Wa1W7Ny5E127dvXI2O122O0KbW+JiIhasTPLO2bMmOG+T7q844svvmiJXT0nnxYyNpsNKSkpyM7Ods+xaZqG7OxsTJs2rd7je/Toge+//97jvr/85S8oKyvDggULOG1ERET+q4lGZH69FvRsf9Cfa3nHjh07GtzEmeUdW7du9WJHm5bPp5YyMjIwbtw4DBw4EIMGDcL8+fNRUVHhHuYaO3YsEhMTMXfuXDgcDlx00UUe+cjISACodz8REZE/aapLFPz6j/pZs2bhiSeeUH/in6gs72gJPi9kRo4ciWPHjmHmzJkoLCxEv379sGrVKneFmJ+fD7PZ52eJExER+YWCggKEh4e7//tsyytaYnlHS/B5IQMA06ZNa3AqCQBycnLOmV2+fHnT7xAREVFLa6KppfDwcI9C5mxay/IOQxQyREREgc4XV79uDcs7WMgQEREFqNawvIOFDBERkRH4oI8M4P/LO1jICJgUGj3pCke4JkqeqWsjbx5nja6WbwhAmEPevapD8Clx5uPDfcWZgkKFgwfAttchzoQWqTUUlLLuOSQPRag1jzOXyJvboUr+PjLb5R+MqB3y9zgA1AXLf7YfW/uIMzFty8SZMJvCZ6ltsTgDAAcVMtUWeevCoGJ5o0hN7UcLhZ6ZxuajQsbfGXu8iIiIiOgcOCJDRERkAKafbt7kAxELGSIiIiPg1JISFjJEREQG4IvTr1sDrpEhIiIiv8URGSIiIiPg1JISFjJERERGEaDFiDc4tURERER+iyMyAlqQvFR2yXtxwRVeJ87Ywp3iTGJUsTijauOxTuJM4dEIcSbogMIBBxC5W/6zbbux6PwP+jWF5nFwNHzl2nNu5oJo+Xag1vTRUVAs3051rTgTukfeVBEA2kS1E2dKrfJGcEeS5I3gOsSfFGfa2qvEGQAItsuPuStK/n6tNck/g+ZKtb+pzbWt64RjLvZVw0KGiIjICLhGRgmnloiIiMhvcUSGiIjIADi1pIaFDBERkRFwakkJp5aIiIjIb3FEhoiIyAA4taSGhQwREZERcGpJCQsZIiIiI2Aho4RrZIiIiMhvcURGQLfJy13NIW+VarLJM5FhleJMZa1NnAGA8mp5rrI4WJwJOiLfTliBOAIACN8rP356qEIXYZWMyyWOnOyl9rO1yg8DLDVtxJmgw8XijKlK3r0aAKK2lYszLof8NekmeQfmg5q863Bl+zJxBgCCg+SdfausQeKM1kb+c9J0tferbpV39jW5jNsNmGtk1LCQISIiMgJOLSnh1BIRERH5LY7IEBERGYBJ12HS1YdVvMn6MxYyRERERsCpJSWcWiIiIiK/xREZIiIiA+BZS2pYyBARERkBp5aUcGqJiIiI/BZHZAQ0hYZ4CJY3MzNb5Q3xSirlDeecpfIGXgCAKnn96zhmEWci9siPd1CF/NgBio21FBq0aWHyY37s0mhxpjxJ7U8zpaFpXf7ea1+hcOxsal9XlooacSZqW5V8O9XyZocqzSVPlrcVZwDAEl0tzyh8F+m6/LOkKzQBPR2UfxexIV7rw0KGiIjICDi1pISFDBERkQFwREYN18gQERGR3+KIDBERkRFwakkJCxkiIiKDCNTpIW9waomIiIj8FkdkiIiIjEDXT9+8yQcgFjJEREQGwLOW1LCQkVBqFiZvvqTVypvH1VQEiTPWEvl2AMBxVP6arPL+Ygg9LG+apjpZaj1VKc6cGthevp0q+ZuoPEkcgTWpQh4C4HLJD2BFRYjCliLEifabihW2A6W/Ui2VdeJM6BF5RrfIP7cWp9qbvLpaoWlmuLyhJ4Lkze2Um9QF6C9u8sRChoiIyAh41pISFjJEREQGYNJO37zJByKetURERER+iyMyRERERsCpJSUsZIiIiAyAZy2pYSFDRERkBOwjo4RrZIiIiMhvcUSGiIjIADi1pIaFjITKm6Rc3nTOrNDwKqhM3lAqqFQcOZ1T6LUWfEJ+XqAWpNJ4T6GBF4Dybm3Fmar28p9TlbyHHlxd5d0EB3UokG8IQLVL3qBta20HcUa3OMQZkx4pzgBAaJG8UV3I/hJxpi5E3hjQXiz/XLhsagPp5lr558lZIf8VURsu/6LUbAF63vCvcbGvEk4tERERkd/iiAwREZEBcGpJDQsZIiIiI+BZS0o4tURERER+iyMyREREBsCpJTUsZIiIiIyAZy0p4dQSERER+S2OyBARERkAp5bUsJAhIiIyAk0/ffMmH4BYyAiYa+QzcZYaeTdNi7yRK4LK5Jngk2pv+jqH/DVVxMqPnW6Rvz0rbfLOtABQFSV/TZWJ8uNnSqoUZ4ZesEOcibEpvCEApIXuFmdyIy4UZ/6fNVWcKbbJuwEDQHVb+fvIZZN3eg4qk3cQdkbI9y2oUu1zq/J5r6yVf25VOgjXtpF3QAcALUjhWMh3r+VwjYwSrpEhIiIiv2WIQmbhwoVITk6Gw+FAamoqNm7ceNbHLlmyBFdccQXatm2Ltm3bIj09/ZyPJyIi8gcm/LxORunm6xfgIz4vZFauXImMjAzMmjULW7ZswcUXX4yhQ4fi6NGjDT4+JycHo0aNwtq1a5Gbm4ukpCRcd911OHToUAvvORERURM609nXm1sA8nkhM2/ePEyaNAnjx49Hr169sHjxYoSEhCAzM7PBx7/55pu477770K9fP/To0QOvv/46NE1DdnZ2C+85ERER+ZpPCxmn04nNmzcjPT3dfZ/ZbEZ6ejpyc3Mb9RyVlZWora1FVFRUg/9eU1OD0tJSjxsREZHReDWt5OWp2/7Mp4XM8ePH4XK5EBsb63F/bGwsCgsLG/Ucjz76KBISEjyKoV+aO3cuIiIi3LekpCSv95uIiKjJ6U1wC0A+n1ryxjPPPIMVK1bggw8+gMPR8KmZM2bMQElJiftWUFDQwntJREREzcWnfWSio6NhsVhQVFTkcX9RURHi4uLOmX3++efxzDPP4LPPPkPfvn3P+ji73Q673d4k+0tERNRcTLoOkxcLdr3J+jOfFjI2mw0pKSnIzs7GiBEjAMC9cHfatGlnzT333HP461//itWrV2PgwIEttLdqjZ6UmttVyDNmTZ6pbqt2sp6tVP5h0c3ybZUnyAcMayLFEQCAM8olzlja1Ygzl3TMF2fGtftKnAkyyV/P6Zz8jRRidooz3RKKzv+gX8mzN7wO7nwqwkLFmboweYO2oDKbOGOW99CD44TaL6vKaIWmlApj9gpvB1jkH6XTdPn3imY38C977aebN/kA5POppYyMDCxZsgRvvPEGtm/fjilTpqCiogLjx48HAIwdOxYzZsxwP/7ZZ5/F448/jszMTCQnJ6OwsBCFhYUoLy/31UsgIiLyW/7ey83nhczIkSPx/PPPY+bMmejXrx+2bt2KVatWuRcA5+fn48iRI+7HL1q0CE6nE7///e8RHx/vvj3//PO+eglEREReOzO15M1NqjX0cjPEtZamTZt21qmknJwcj//Oy8tr/h0iIiJqaT641tIve7kBwOLFi5GVlYXMzExMnz693uPffPNNj/9+/fXX8d577yE7Oxtjx45V2m1v+XxEhoiIiNBknX1/3TutpqbhRUgt0cutJbCQISIiakWSkpI8+qfNnTu3wce1RC+3lmCIqSUiIqJA52133jPZgoIChIeHu+9vrhYkZ3q55eTknLWXW0tgIUNERGQE3l748adseHi4RyFzNi3Ry60lcGqJiIgoAP2yl9sZZ3q5paWlnTX33HPP4cknn8SqVatatJfb2XBERkClx5hKwyuXvK+WEoXeZwCAsk4KTaiC5NupjZQf8KDoavmGACRHFcu3ZZHv392xX4ozTl3enC3EXCvOAMC+2mhxpqdDftqlPUa+f9tCEsUZANho6yjOlLWVD5M7j8kz1jL535LOCLVGlhaVj4bCd4RLYYZB9bsIaofCsEyaF8cCatmMjAyMGzcOAwcOxKBBgzB//vx6vdwSExPd62yeffZZzJw5E2+99Za7lxsAhIWFISwsTH3nvcBChoiIyAiaaGpJYuTIkTh27BhmzpyJwsJC9OvXr14vN7P554L7l73cfmnWrFl44okn1PfdCyxkiIiIApi/93JjIUNERGQEPmiI1xqwkCEiIjIAXv1aDc9aIiIiIr/FERkiIiIj8MFi39aAhQwREZER6FA65d0jH4BYyBARERkA18ioYSEjoFvlb5LaNi3Tsclpke+bZld707vC5I3gbG2c4kywVb6dAQkHxRkAiHeUijNtrRXijFOXf+R62U6JM4mWNuIMAJRp8uNwzHX+Vui/1s+RL84k246LMwDQKfiEOPNjebw4syM8Rpw5cVR+7Jwute8Uc4W8saKlSr6M0puGbvKNBeYvbvLEQoaIiMgIdHi5RqbJ9sSvsJAhIiIyAi72VcLTr4mIiMhvcUSGiIjICDR4dyHMllyfZCAsZIiIiAyAZy2p4dQSERER+S2OyBARERkBF/sqYSFDRERkBCxklHBqiYiIiPwWR2QENJs8o1vky8j1IIWq2ibfTmhUpXw7ii5oJ+/KmhhSLM7E2MrEGQAYFLpXnNldEyfOnHSFiTNba8QRFFrlHXoBYH1ld3HmnogfxRm7Sf7Vk1tTJc4AQEeFjsA/Qt7Z96J2heLMLou8e7Wuq53WUlZtF2cqSoLlG6pW6AZco/Y3tUlTOBZGHrTgiIwSFjJERERGwNOvlbCQISIiMgCefq2Ga2SIiIjIb3FEhoiIyAi4RkYJCxkiIiIj0HTA5EUxogVmIcOpJSIiIvJbHJEhIiIyAk4tKWEhQ0REZAheFjKGbpLTfFjICGhW+ZtED5Y3vLKG1IkzISHyrmntQivEGQCID5U3W2tjle/fiRp58zjVhng7qxPEme0V8qZpCY5icaa9wvG2Qf6+A4CkoJPizPvlncQZTaFZRpBJ7TXV6hZxpn94vjjzTWlHcSbK0XJNKS1meZMRlUyZQhM9zSr/GQFqjfTMis33yLhYyBARERkBp5aUsJAhIiIyAk2HV9NDPGuJiIiIyL9wRIaIiMgIdO30zZt8AGI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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;36m2\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = dataset_gridded.pop_q0.mean(dim=\"repetitions\").plot(x=\"amp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "365794d7",
   "metadata": {},
   "source": [
    "A repetitions dimension can be indexed by a coordinate such that we can have some\n",
    "specific label for each of our repetitions. To showcase this, we will modify the previous\n",
    "dataset by merging it with a dataset containing the relevant extra information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "42912255",
   "metadata": {},
   "outputs": [
    {
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       "  color: var(--xr-font-color3);\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "  font-weight: normal;\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
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       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (repetitions: 5)\n",
       "Coordinates:\n",
       "  * repetitions  (repetitions) &lt;U1 &#x27;A&#x27; &#x27;B&#x27; &#x27;C&#x27; &#x27;D&#x27; &#x27;E&#x27;\n",
       "Data variables:\n",
       "    *empty*</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7cc30ae3-9733-42a4-b128-bea6f307a694' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7cc30ae3-9733-42a4-b128-bea6f307a694' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>repetitions</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6e9cfe8d-e77f-4d82-8680-70263c1ecfb2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e9cfe8d-e77f-4d82-8680-70263c1ecfb2' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>repetitions</span></div><div class='xr-var-dims'>(repetitions)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;A&#x27; &#x27;B&#x27; &#x27;C&#x27; &#x27;D&#x27; &#x27;E&#x27;</div><input id='attrs-cba7d20d-90aa-493f-b8e8-1284e4c45d3a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cba7d20d-90aa-493f-b8e8-1284e4c45d3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4aca4ac-89b6-4e2d-917f-163bb5c8b9a1' class='xr-var-data-in' type='checkbox'><label for='data-f4aca4ac-89b6-4e2d-917f-163bb5c8b9a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;A&#x27;, &#x27;B&#x27;, &#x27;C&#x27;, &#x27;D&#x27;, &#x27;E&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0382aa72-a550-4c0a-b27e-045b13110aa1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0382aa72-a550-4c0a-b27e-045b13110aa1' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-bacb9de3-48e7-454e-90bb-73471b2e48db' class='xr-section-summary-in' type='checkbox'  ><label for='section-bacb9de3-48e7-454e-90bb-73471b2e48db' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>repetitions</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1c9a1a94-f45e-47ea-8178-6707770df2df' class='xr-index-data-in' type='checkbox'/><label for='index-1c9a1a94-f45e-47ea-8178-6707770df2df' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;A&#x27;, &#x27;B&#x27;, &#x27;C&#x27;, &#x27;D&#x27;, &#x27;E&#x27;], dtype=&#x27;object&#x27;, name=&#x27;repetitions&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-930b7db2-d384-437e-9d8d-a5d48dd6b5f5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-930b7db2-d384-437e-9d8d-a5d48dd6b5f5' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "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;36m5\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "  * repetitions  \u001b[1m(\u001b[0mrepetitions\u001b[1m)\u001b[0m <U1 \u001b[32m'A'\u001b[0m \u001b[32m'B'\u001b[0m \u001b[32m'C'\u001b[0m \u001b[32m'D'\u001b[0m \u001b[32m'E'\u001b[0m\n",
       "Data variables:\n",
       "    *empty*"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coord_dims = (\"repetitions\",)\n",
    "coord_values = [\"A\", \"B\", \"C\", \"D\", \"E\"]\n",
    "dataset_indexed_rep = xr.Dataset(coords=dict(repetitions=(coord_dims, coord_values)))\n",
    "\n",
    "dataset_indexed_rep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "239aed59",
   "metadata": {},
   "outputs": [
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       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (amp: 30, time: 40, repetitions: 5)\n",
       "Coordinates:\n",
       "  * amp          (amp) float64 0.45 0.4534 0.4569 0.4603 ... 0.5431 0.5466 0.55\n",
       "  * time         (time) float64 0.0 2.564e-09 5.128e-09 ... 9.744e-08 1e-07\n",
       "  * repetitions  (repetitions) &lt;U1 &#x27;A&#x27; &#x27;B&#x27; &#x27;C&#x27; &#x27;D&#x27; &#x27;E&#x27;\n",
       "Data variables:\n",
       "    pop_q0       (repetitions, amp, time) float64 0.5 0.5 0.5 ... 0.5 0.5 0.5\n",
       "    pop_q1       (repetitions, amp, time) float64 0.5 0.5 0.5 ... 0.5 0.5 0.5\n",
       "Attributes:\n",
       "    tuid:                      20231215-162525-279-c4ebbc\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:    []</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-aeacc5db-fbf0-4031-9b8e-c5183e3cf281' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-aeacc5db-fbf0-4031-9b8e-c5183e3cf281' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>amp</span>: 30</li><li><span class='xr-has-index'>time</span>: 40</li><li><span class='xr-has-index'>repetitions</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3aa00944-2cdf-443c-8d7a-41907a34b80a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3aa00944-2cdf-443c-8d7a-41907a34b80a' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>amp</span></div><div class='xr-var-dims'>(amp)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.45 0.4534 0.4569 ... 0.5466 0.55</div><input id='attrs-89bbced8-a597-4d98-8977-8f8adc511d4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89bbced8-a597-4d98-8977-8f8adc511d4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b98e09c6-d69f-4345-8002-7109d0f0ccfd' class='xr-var-data-in' type='checkbox'><label for='data-b98e09c6-d69f-4345-8002-7109d0f0ccfd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Amplitude</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.45    , 0.453448, 0.456897, 0.460345, 0.463793, 0.467241, 0.47069 ,\n",
       "       0.474138, 0.477586, 0.481034, 0.484483, 0.487931, 0.491379, 0.494828,\n",
       "       0.498276, 0.501724, 0.505172, 0.508621, 0.512069, 0.515517, 0.518966,\n",
       "       0.522414, 0.525862, 0.52931 , 0.532759, 0.536207, 0.539655, 0.543103,\n",
       "       0.546552, 0.55    ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 2.564e-09 ... 9.744e-08 1e-07</div><input id='attrs-d5bdaad0-029d-4761-aa06-ca7438dac00c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5bdaad0-029d-4761-aa06-ca7438dac00c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed70fbd8-9391-40ed-813e-b0b6a6cc93f4' class='xr-var-data-in' type='checkbox'><label for='data-ed70fbd8-9391-40ed-813e-b0b6a6cc93f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.000000e+00, 2.564103e-09, 5.128205e-09, 7.692308e-09, 1.025641e-08,\n",
       "       1.282051e-08, 1.538462e-08, 1.794872e-08, 2.051282e-08, 2.307692e-08,\n",
       "       2.564103e-08, 2.820513e-08, 3.076923e-08, 3.333333e-08, 3.589744e-08,\n",
       "       3.846154e-08, 4.102564e-08, 4.358974e-08, 4.615385e-08, 4.871795e-08,\n",
       "       5.128205e-08, 5.384615e-08, 5.641026e-08, 5.897436e-08, 6.153846e-08,\n",
       "       6.410256e-08, 6.666667e-08, 6.923077e-08, 7.179487e-08, 7.435897e-08,\n",
       "       7.692308e-08, 7.948718e-08, 8.205128e-08, 8.461538e-08, 8.717949e-08,\n",
       "       8.974359e-08, 9.230769e-08, 9.487179e-08, 9.743590e-08, 1.000000e-07])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>repetitions</span></div><div class='xr-var-dims'>(repetitions)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;A&#x27; &#x27;B&#x27; &#x27;C&#x27; &#x27;D&#x27; &#x27;E&#x27;</div><input id='attrs-4cde4186-f75b-477c-b028-80fefd8c9984' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4cde4186-f75b-477c-b028-80fefd8c9984' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26d54d7d-705b-46c6-94fc-0e067e42f15c' class='xr-var-data-in' type='checkbox'><label for='data-26d54d7d-705b-46c6-94fc-0e067e42f15c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;A&#x27;, &#x27;B&#x27;, &#x27;C&#x27;, &#x27;D&#x27;, &#x27;E&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ceb1aff3-151a-4c58-90a3-04495f4f834c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ceb1aff3-151a-4c58-90a3-04495f4f834c' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>pop_q0</span></div><div class='xr-var-dims'>(repetitions, amp, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5</div><input id='attrs-113b4b32-eb24-4f7f-9427-285d975fadf7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-113b4b32-eb24-4f7f-9427-285d975fadf7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2a08831-f6b3-4e98-9633-c6403b893887' class='xr-var-data-in' type='checkbox'><label for='data-d2a08831-f6b3-4e98-9633-c6403b893887' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Population Q0</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[[0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ],\n",
       "        [0.5       , 0.52462974, 0.54556513, ..., 0.49369154,\n",
       "         0.46593909, 0.47829501],\n",
       "        [0.5       , 0.53096479, 0.54671207, ..., 0.58439532,\n",
       "         0.52840192, 0.48526448],\n",
       "        ...,\n",
       "        [0.5       , 0.53554992, 0.54556213, ..., 0.53421524,\n",
       "         0.52683234, 0.47934742],\n",
       "        [0.5       , 0.52263221, 0.53324799, ..., 0.49520272,\n",
       "         0.48292625, 0.45911157],\n",
       "        [0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ]],\n",
       "\n",
       "       [[0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ],\n",
       "        [0.5       , 0.511993  , 0.54040107, ..., 0.49323746,\n",
       "         0.46472548, 0.4717735 ],\n",
       "        [0.5       , 0.53839618, 0.59404155, ..., 0.57712699,\n",
       "         0.52809145, 0.47479363],\n",
       "...\n",
       "        [0.5       , 0.5543903 , 0.56225336, ..., 0.55717664,\n",
       "         0.54072803, 0.4903811 ],\n",
       "        [0.5       , 0.51269098, 0.51747944, ..., 0.49306189,\n",
       "         0.46207336, 0.45931355],\n",
       "        [0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ]],\n",
       "\n",
       "       [[0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ],\n",
       "        [0.5       , 0.53282701, 0.53331208, ..., 0.49573131,\n",
       "         0.46313136, 0.45196851],\n",
       "        [0.5       , 0.52376577, 0.55784942, ..., 0.58932903,\n",
       "         0.52031397, 0.47580372],\n",
       "        ...,\n",
       "        [0.5       , 0.53875118, 0.57789726, ..., 0.53942745,\n",
       "         0.52059331, 0.48855559],\n",
       "        [0.5       , 0.52515217, 0.54606107, ..., 0.49641976,\n",
       "         0.46010524, 0.48175285],\n",
       "        [0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pop_q1</span></div><div class='xr-var-dims'>(repetitions, amp, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5</div><input id='attrs-41243e13-a0e0-45b7-acbe-df15ad83adf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41243e13-a0e0-45b7-acbe-df15ad83adf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cf68d9a-2daf-412d-b2e5-86f212af3037' class='xr-var-data-in' type='checkbox'><label for='data-8cf68d9a-2daf-412d-b2e5-86f212af3037' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Population Q1</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[[0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ],\n",
       "        [0.5       , 0.47119973, 0.47901959, ..., 0.50297437,\n",
       "         0.51760087, 0.54529387],\n",
       "        [0.5       , 0.44878307, 0.40519978, ..., 0.42239416,\n",
       "         0.46766323, 0.52530065],\n",
       "        ...,\n",
       "        [0.5       , 0.44208814, 0.40902762, ..., 0.44348506,\n",
       "         0.45882752, 0.51325446],\n",
       "        [0.5       , 0.48384946, 0.47746526, ..., 0.50707099,\n",
       "         0.52288233, 0.53973285],\n",
       "        [0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ]],\n",
       "\n",
       "       [[0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ],\n",
       "        [0.5       , 0.47468685, 0.48200051, ..., 0.50796779,\n",
       "         0.53158208, 0.53673514],\n",
       "        [0.5       , 0.43322213, 0.41173447, ..., 0.42695649,\n",
       "         0.45665907, 0.5238828 ],\n",
       "...\n",
       "        [0.5       , 0.44793137, 0.40302014, ..., 0.46628379,\n",
       "         0.45595214, 0.51040954],\n",
       "        [0.5       , 0.48137118, 0.46009508, ..., 0.50666135,\n",
       "         0.51858203, 0.53633277],\n",
       "        [0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ]],\n",
       "\n",
       "       [[0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ],\n",
       "        [0.5       , 0.46862904, 0.47483531, ..., 0.50652217,\n",
       "         0.51719523, 0.51827645],\n",
       "        [0.5       , 0.45521752, 0.46430465, ..., 0.43180196,\n",
       "         0.46456878, 0.51473245],\n",
       "        ...,\n",
       "        [0.5       , 0.45105997, 0.39821704, ..., 0.44310153,\n",
       "         0.46092092, 0.52434067],\n",
       "        [0.5       , 0.4840124 , 0.47170094, ..., 0.50421072,\n",
       "         0.54069819, 0.53709612],\n",
       "        [0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cb358bef-9379-4c89-aa69-db086e9402c3' class='xr-section-summary-in' type='checkbox'  ><label for='section-cb358bef-9379-4c89-aa69-db086e9402c3' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>amp</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2b5bfd61-1a0c-4659-b856-50334acb4094' class='xr-index-data-in' type='checkbox'/><label for='index-2b5bfd61-1a0c-4659-b856-50334acb4094' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.45,   0.453448275862069, 0.45689655172413796,\n",
       "       0.46034482758620693,  0.4637931034482759,  0.4672413793103448,\n",
       "        0.4706896551724138, 0.47413793103448276, 0.47758620689655173,\n",
       "        0.4810344827586207,  0.4844827586206897, 0.48793103448275865,\n",
       "        0.4913793103448276,  0.4948275862068966, 0.49827586206896557,\n",
       "        0.5017241379310345,  0.5051724137931035,  0.5086206896551725,\n",
       "        0.5120689655172415,  0.5155172413793103,  0.5189655172413794,\n",
       "        0.5224137931034483,  0.5258620689655172,  0.5293103448275862,\n",
       "        0.5327586206896552,  0.5362068965517242,  0.5396551724137931,\n",
       "        0.5431034482758621,  0.5465517241379311,                0.55],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;amp&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-85e53e91-fe20-4976-aa27-af0e7481e705' class='xr-index-data-in' type='checkbox'/><label for='index-85e53e91-fe20-4976-aa27-af0e7481e705' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                   0.0,  2.564102564102564e-09,  5.128205128205128e-09,\n",
       "        7.692307692307693e-09, 1.0256410256410256e-08,  1.282051282051282e-08,\n",
       "       1.5384615384615385e-08,  1.794871794871795e-08, 2.0512820512820512e-08,\n",
       "       2.3076923076923076e-08,  2.564102564102564e-08, 2.8205128205128203e-08,\n",
       "        3.076923076923077e-08, 3.3333333333333334e-08,   3.58974358974359e-08,\n",
       "        3.846153846153846e-08, 4.1025641025641025e-08,  4.358974358974359e-08,\n",
       "        4.615384615384615e-08, 4.8717948717948716e-08,  5.128205128205128e-08,\n",
       "        5.384615384615384e-08,  5.641025641025641e-08,  5.897435897435897e-08,\n",
       "        6.153846153846154e-08,   6.41025641025641e-08,  6.666666666666667e-08,\n",
       "        6.923076923076923e-08,   7.17948717948718e-08,  7.435897435897436e-08,\n",
       "        7.692307692307692e-08,  7.948717948717949e-08,  8.205128205128205e-08,\n",
       "        8.461538461538461e-08,  8.717948717948718e-08,  8.974358974358974e-08,\n",
       "         9.23076923076923e-08,  9.487179487179487e-08,  9.743589743589743e-08,\n",
       "                        1e-07],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>repetitions</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cf28244f-5985-44d1-8eb2-6b936bce92f1' class='xr-index-data-in' type='checkbox'/><label for='index-cf28244f-5985-44d1-8eb2-6b936bce92f1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;A&#x27;, &#x27;B&#x27;, &#x27;C&#x27;, &#x27;D&#x27;, &#x27;E&#x27;], dtype=&#x27;object&#x27;, name=&#x27;repetitions&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7e1685c9-813c-4146-89f0-2f9a4bd8f987' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7e1685c9-813c-4146-89f0-2f9a4bd8f987' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20231215-162525-279-c4ebbc</dd><dt><span>dataset_name :</span></dt><dd></dd><dt><span>dataset_state :</span></dt><dd>None</dd><dt><span>timestamp_start :</span></dt><dd>None</dd><dt><span>timestamp_end :</span></dt><dd>None</dd><dt><span>quantify_dataset_version :</span></dt><dd>2.0.0</dd><dt><span>software_versions :</span></dt><dd>{}</dd><dt><span>relationships :</span></dt><dd>[]</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m\n",
       "Dimensions:      \u001b[1m(\u001b[0mamp: \u001b[1;36m30\u001b[0m, time: \u001b[1;36m40\u001b[0m, repetitions: \u001b[1;36m5\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "  * amp          \u001b[1m(\u001b[0mamp\u001b[1m)\u001b[0m float64 \u001b[1;36m0.45\u001b[0m \u001b[1;36m0.4534\u001b[0m \u001b[1;36m0.4569\u001b[0m \u001b[1;36m0.4603\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.5431\u001b[0m \u001b[1;36m0.5466\u001b[0m \u001b[1;36m0.55\u001b[0m\n",
       "  * time         \u001b[1m(\u001b[0mtime\u001b[1m)\u001b[0m float64 \u001b[1;36m0.0\u001b[0m \u001b[1;36m2.564e-09\u001b[0m \u001b[1;36m5.128e-09\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m9.744e-08\u001b[0m \u001b[1;36m1e-07\u001b[0m\n",
       "  * repetitions  \u001b[1m(\u001b[0mrepetitions\u001b[1m)\u001b[0m <U1 \u001b[32m'A'\u001b[0m \u001b[32m'B'\u001b[0m \u001b[32m'C'\u001b[0m \u001b[32m'D'\u001b[0m \u001b[32m'E'\u001b[0m\n",
       "Data variables:\n",
       "    pop_q0       \u001b[1m(\u001b[0mrepetitions, amp, time\u001b[1m)\u001b[0m float64 \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m\n",
       "    pop_q1       \u001b[1m(\u001b[0mrepetitions, amp, time\u001b[1m)\u001b[0m float64 \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20231215\u001b[0m-\u001b[1;36m162525\u001b[0m-\u001b[1;36m279\u001b[0m-c4ebbc\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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge with the previous dataset\n",
    "dataset_rep = dataset_gridded.merge(dataset_indexed_rep, combine_attrs=\"drop_conflicts\")\n",
    "\n",
    "assert dataset_rep == round_trip_dataset(dataset_rep)  # confirm read/write\n",
    "\n",
    "dataset_rep"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e0d82c9",
   "metadata": {},
   "source": [
    "Now we can select a specific repetition by its coordinate, in this case a string label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "65a14e83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 64\u001b[0m\u001b[1;36m0x480\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m2\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = dataset_rep.pop_q0.sel(repetitions=\"E\").plot(x=\"amp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "012ce4e3",
   "metadata": {},
   "source": [
    "## T1 dataset examples\n",
    "\n",
    "The T1 experiment is one of the most common quantum computing experiments.\n",
    "Here we explore how the datasets for such an experiment, for a transmon qubit, can be\n",
    "stored using the Quantify dataset with increasing levels of data detail.\n",
    "\n",
    "We start with the most simple format that contains only processed (averaged) measurements\n",
    "and finish with a dataset containing the raw digitized signals from the transmon readout\n",
    "during a T1 experiment.\n",
    "\n",
    "We use a few auxiliary functions to generate, manipulate and plot the data of the\n",
    "examples that follow:\n",
    "\n",
    "- {func}`quantify_core.utilities.examples_support.mk_iq_shots`\n",
    "- {func}`quantify_core.utilities.examples_support.mk_trace_time`\n",
    "- {func}`quantify_core.utilities.examples_support.mk_trace_for_iq_shot`\n",
    "- {func}`quantify_core.analysis.fitting_models.exp_decay_func`\n",
    "\n",
    "Below you can find the source-code of the most important ones and a few usage\n",
    "examples in order to gain some intuition for the mock data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bb93d8f4",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Source code for generating mock data"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">mk_iq_shots</span><span class=\"p\">(</span>\n",
       "    <span class=\"n\">num_shots</span><span class=\"p\">:</span> <span class=\"nb\">int</span> <span class=\"o\">=</span> <span class=\"mi\">128</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">sigmas</span><span class=\"p\">:</span> <span class=\"n\">Union</span><span class=\"p\">[</span><span class=\"n\">Tuple</span><span class=\"p\">[</span><span class=\"nb\">float</span><span class=\"p\">],</span> <span class=\"n\">NDArray</span><span class=\"p\">[</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">float_</span><span class=\"p\">]]</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"mf\">0.1</span><span class=\"p\">,</span> <span class=\"mf\">0.1</span><span class=\"p\">),</span>\n",
       "    <span class=\"n\">centers</span><span class=\"p\">:</span> <span class=\"n\">Union</span><span class=\"p\">[</span><span class=\"n\">Tuple</span><span class=\"p\">[</span><span class=\"nb\">complex</span><span class=\"p\">],</span> <span class=\"n\">NDArray</span><span class=\"p\">[</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">complex_</span><span class=\"p\">]]</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"o\">-</span><span class=\"mf\">0.2</span> <span class=\"o\">+</span> <span class=\"mf\">0.65</span><span class=\"n\">j</span><span class=\"p\">,</span> <span class=\"mf\">0.7</span> <span class=\"o\">+</span> <span class=\"mi\">4</span><span class=\"n\">j</span><span class=\"p\">),</span>\n",
       "    <span class=\"n\">probabilities</span><span class=\"p\">:</span> <span class=\"n\">Union</span><span class=\"p\">[</span><span class=\"n\">Tuple</span><span class=\"p\">[</span><span class=\"nb\">float</span><span class=\"p\">],</span> <span class=\"n\">NDArray</span><span class=\"p\">[</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">float_</span><span class=\"p\">]]</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"mf\">0.4</span><span class=\"p\">,</span> <span class=\"mf\">0.6</span><span class=\"p\">),</span>\n",
       "    <span class=\"n\">seed</span><span class=\"p\">:</span> <span class=\"n\">Union</span><span class=\"p\">[</span><span class=\"nb\">int</span><span class=\"p\">,</span> <span class=\"kc\">None</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"mi\">112233</span><span class=\"p\">,</span>\n",
       "<span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"n\">NDArray</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    Generate clusters of (I + 1j*Q) points with a Gaussian distribution.</span>\n",
       "\n",
       "<span class=\"sd\">    Utility to mock the data coming from qubit readout experiments.</span>\n",
       "<span class=\"sd\">    Clusters are centered around ``centers`` and data points are distributed between</span>\n",
       "<span class=\"sd\">    them according to ``probabilities``.</span>\n",
       "\n",
       "<span class=\"sd\">    .. seealso:: :ref:`howto-utilities-examples-ssro`</span>\n",
       "\n",
       "<span class=\"sd\">    Parameters</span>\n",
       "<span class=\"sd\">    ----------</span>\n",
       "<span class=\"sd\">    num_shots</span>\n",
       "<span class=\"sd\">        The number of shot to generate.</span>\n",
       "<span class=\"sd\">    sigma</span>\n",
       "<span class=\"sd\">        The sigma of the Gaussian distribution used for both real and imaginary parts.</span>\n",
       "<span class=\"sd\">    centers</span>\n",
       "<span class=\"sd\">        The center of each cluster on the imaginary plane.</span>\n",
       "<span class=\"sd\">    probabilities</span>\n",
       "<span class=\"sd\">        The probabilities of each cluster being randomly selected for each shot.</span>\n",
       "<span class=\"sd\">    seed</span>\n",
       "<span class=\"sd\">        Random number generator seed passed to ``numpy.random.default_rng``.</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    <span class=\"k\">if</span> <span class=\"ow\">not</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">sigmas</span><span class=\"p\">)</span> <span class=\"o\">==</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">centers</span><span class=\"p\">)</span> <span class=\"o\">==</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">probabilities</span><span class=\"p\">):</span>\n",
       "        <span class=\"k\">raise</span> <span class=\"ne\">ValueError</span><span class=\"p\">(</span>\n",
       "            <span class=\"sa\">f</span><span class=\"s2\">&quot;Incorrect input. sigmas=</span><span class=\"si\">{</span><span class=\"n\">sigmas</span><span class=\"si\">}</span><span class=\"s2\">, centers=</span><span class=\"si\">{</span><span class=\"n\">centers</span><span class=\"si\">}</span><span class=\"s2\"> and &quot;</span>\n",
       "            <span class=\"sa\">f</span><span class=\"s2\">&quot;probabilities=</span><span class=\"si\">{</span><span class=\"n\">probabilities</span><span class=\"si\">}</span><span class=\"s2\"> must have the same length.&quot;</span>\n",
       "        <span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">rng</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">random</span><span class=\"o\">.</span><span class=\"n\">default_rng</span><span class=\"p\">(</span><span class=\"n\">seed</span><span class=\"o\">=</span><span class=\"n\">seed</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">cluster_indices</span> <span class=\"o\">=</span> <span class=\"nb\">tuple</span><span class=\"p\">(</span><span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">centers</span><span class=\"p\">)))</span>\n",
       "    <span class=\"n\">choices</span> <span class=\"o\">=</span> <span class=\"n\">rng</span><span class=\"o\">.</span><span class=\"n\">choice</span><span class=\"p\">(</span><span class=\"n\">a</span><span class=\"o\">=</span><span class=\"n\">cluster_indices</span><span class=\"p\">,</span> <span class=\"n\">size</span><span class=\"o\">=</span><span class=\"n\">num_shots</span><span class=\"p\">,</span> <span class=\"n\">p</span><span class=\"o\">=</span><span class=\"n\">probabilities</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">shots</span> <span class=\"o\">=</span> <span class=\"p\">[]</span>\n",
       "    <span class=\"k\">for</span> <span class=\"n\">idx</span> <span class=\"ow\">in</span> <span class=\"n\">cluster_indices</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">num_shots_this_cluster</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">sum</span><span class=\"p\">(</span><span class=\"n\">choices</span> <span class=\"o\">==</span> <span class=\"n\">idx</span><span class=\"p\">)</span>\n",
       "        <span class=\"n\">i_data</span> <span class=\"o\">=</span> <span class=\"n\">rng</span><span class=\"o\">.</span><span class=\"n\">normal</span><span class=\"p\">(</span>\n",
       "            <span class=\"n\">loc</span><span class=\"o\">=</span><span class=\"n\">centers</span><span class=\"p\">[</span><span class=\"n\">idx</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">real</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">scale</span><span class=\"o\">=</span><span class=\"n\">sigmas</span><span class=\"p\">[</span><span class=\"n\">idx</span><span class=\"p\">],</span>\n",
       "            <span class=\"n\">size</span><span class=\"o\">=</span><span class=\"n\">num_shots_this_cluster</span><span class=\"p\">,</span>\n",
       "        <span class=\"p\">)</span>\n",
       "        <span class=\"n\">q_data</span> <span class=\"o\">=</span> <span class=\"n\">rng</span><span class=\"o\">.</span><span class=\"n\">normal</span><span class=\"p\">(</span>\n",
       "            <span class=\"n\">loc</span><span class=\"o\">=</span><span class=\"n\">centers</span><span class=\"p\">[</span><span class=\"n\">idx</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">imag</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">scale</span><span class=\"o\">=</span><span class=\"n\">sigmas</span><span class=\"p\">[</span><span class=\"n\">idx</span><span class=\"p\">],</span>\n",
       "            <span class=\"n\">size</span><span class=\"o\">=</span><span class=\"n\">num_shots_this_cluster</span><span class=\"p\">,</span>\n",
       "        <span class=\"p\">)</span>\n",
       "        <span class=\"n\">shots</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">i_data</span> <span class=\"o\">+</span> <span class=\"mi\">1</span><span class=\"n\">j</span> <span class=\"o\">*</span> <span class=\"n\">q_data</span><span class=\"p\">)</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">concatenate</span><span class=\"p\">(</span><span class=\"n\">shots</span><span class=\"p\">)</span>\n",
       "</pre></div>\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"
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">mk_trace_time</span><span class=\"p\">(</span><span class=\"n\">sampling_rate</span><span class=\"p\">:</span> <span class=\"nb\">float</span> <span class=\"o\">=</span> <span class=\"mf\">1e9</span><span class=\"p\">,</span> <span class=\"n\">duration</span><span class=\"p\">:</span> <span class=\"nb\">float</span> <span class=\"o\">=</span> <span class=\"mf\">0.3e-6</span><span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"n\">NDArray</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    Generates a :obj:`~numpy.arange` in which the entries correspond to time instants</span>\n",
       "<span class=\"sd\">    up to ``duration`` seconds sampled according to ``sampling_rate`` in Hz.</span>\n",
       "\n",
       "<span class=\"sd\">    See :func:`~.mk_trace_for_iq_shot` for an usage example.</span>\n",
       "\n",
       "<span class=\"sd\">    Parameters</span>\n",
       "<span class=\"sd\">    ----------</span>\n",
       "<span class=\"sd\">    sampling_rate</span>\n",
       "<span class=\"sd\">        The sampling rate in Hz.</span>\n",
       "<span class=\"sd\">    duration</span>\n",
       "<span class=\"sd\">        Total duration in seconds.</span>\n",
       "\n",
       "<span class=\"sd\">    Returns</span>\n",
       "<span class=\"sd\">    -------</span>\n",
       "<span class=\"sd\">    :</span>\n",
       "<span class=\"sd\">        An array with the time instants.</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    <span class=\"n\">trace_length</span> <span class=\"o\">=</span> <span class=\"n\">sampling_rate</span> <span class=\"o\">*</span> <span class=\"n\">duration</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"n\">trace_length</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"n\">sampling_rate</span>\n",
       "</pre></div>\n"
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      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}trace\\PYZus{}time}\\PY{p}{(}\\PY{n}{sampling\\PYZus{}rate}\\PY{p}{:} \\PY{n+nb}{float} \\PY{o}{=} \\PY{l+m+mf}{1e9}\\PY{p}{,} \\PY{n}{duration}\\PY{p}{:} \\PY{n+nb}{float} \\PY{o}{=} \\PY{l+m+mf}{0.3e\\PYZhy{}6}\\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}{    Generates a :obj:`\\PYZti{}numpy.arange` in which the entries correspond to time instants}\n",
       "\\PY{l+s+sd}{    up to ``duration`` seconds sampled according to ``sampling\\PYZus{}rate`` in Hz.}\n",
       "\n",
       "\\PY{l+s+sd}{    See :func:`\\PYZti{}.mk\\PYZus{}trace\\PYZus{}for\\PYZus{}iq\\PYZus{}shot` for an usage example.}\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}{    sampling\\PYZus{}rate}\n",
       "\\PY{l+s+sd}{        The sampling rate in Hz.}\n",
       "\\PY{l+s+sd}{    duration}\n",
       "\\PY{l+s+sd}{        Total duration in seconds.}\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}{        An array with the time instants.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{n}{trace\\PYZus{}length} \\PY{o}{=} \\PY{n}{sampling\\PYZus{}rate} \\PY{o}{*} \\PY{n}{duration}\n",
       "    \\PY{k}{return} \\PY{n}{np}\\PY{o}{.}\\PY{n}{arange}\\PY{p}{(}\\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{trace\\PYZus{}length}\\PY{p}{,} \\PY{l+m+mi}{1}\\PY{p}{)} \\PY{o}{/} \\PY{n}{sampling\\PYZus{}rate}\n",
       "\\end{Verbatim}\n"
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      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_trace_time\u001b[0m\u001b[1m(\u001b[0msampling_rate: float = \u001b[1;36m1e9\u001b[0m, duration: float = \u001b[1;36m0.3e-6\u001b[0m\u001b[1m)\u001b[0m -> NDArray:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generates a :obj:`~numpy.arange` in which the entries correspond to time instants\n",
       "    up to ``duration`` seconds sampled according to ``sampling_rate`` in Hz.\n",
       "\n",
       "    See :func:`~.mk_trace_for_iq_shot` for an usage example.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    sampling_rate\n",
       "        The sampling rate in Hz.\n",
       "    duration\n",
       "        Total duration in seconds.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    :\n",
       "        An array with the time instants.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    trace_length = sampling_rate * duration\n",
       "    return \u001b[1;35mnp.arange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m0\u001b[0m, trace_length, \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m \u001b[35m/\u001b[0m sampling_rate"
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">mk_trace_for_iq_shot</span><span class=\"p\">(</span>\n",
       "    <span class=\"n\">iq_point</span><span class=\"p\">:</span> <span class=\"nb\">complex</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">time_values</span><span class=\"p\">:</span> <span class=\"n\">Optional</span><span class=\"p\">[</span><span class=\"n\">NDArray</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"kc\">None</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">intermediate_freq</span><span class=\"p\">:</span> <span class=\"nb\">float</span> <span class=\"o\">=</span> <span class=\"mf\">50e6</span><span class=\"p\">,</span>\n",
       "<span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"n\">NDArray</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    Generates mock &quot;traces&quot; that a physical instrument would digitize for the readout of</span>\n",
       "<span class=\"sd\">    a transmon qubit when using a down-converting IQ mixer.</span>\n",
       "\n",
       "<span class=\"sd\">    .. seealso:: :ref:`howto-utilities-examples-trace`</span>\n",
       "\n",
       "<span class=\"sd\">    Parameters</span>\n",
       "<span class=\"sd\">    ----------</span>\n",
       "<span class=\"sd\">    iq_point</span>\n",
       "<span class=\"sd\">        A complex number representing a point on the IQ-plane.</span>\n",
       "<span class=\"sd\">    time_values</span>\n",
       "<span class=\"sd\">        The time instants at which the mock intermediate-frequency signal is sampled.</span>\n",
       "<span class=\"sd\">    intermediate_freq</span>\n",
       "<span class=\"sd\">        The intermediate frequency used in the down-conversion scheme.</span>\n",
       "\n",
       "<span class=\"sd\">    Returns</span>\n",
       "<span class=\"sd\">    -------</span>\n",
       "<span class=\"sd\">    :</span>\n",
       "<span class=\"sd\">        An array of complex numbers.</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>  <span class=\"c1\"># pylint: disable=line-too-long</span>\n",
       "    <span class=\"k\">if</span> <span class=\"n\">time_values</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">time_values</span> <span class=\"o\">=</span> <span class=\"n\">mk_trace_time</span><span class=\"p\">()</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">iq_point</span> <span class=\"o\">*</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">exp</span><span class=\"p\">(</span><span class=\"mf\">2.0</span><span class=\"n\">j</span> <span class=\"o\">*</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">pi</span> <span class=\"o\">*</span> <span class=\"n\">intermediate_freq</span> <span class=\"o\">*</span> <span class=\"n\">time_values</span><span class=\"p\">)</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}trace\\PYZus{}for\\PYZus{}iq\\PYZus{}shot}\\PY{p}{(}\n",
       "    \\PY{n}{iq\\PYZus{}point}\\PY{p}{:} \\PY{n+nb}{complex}\\PY{p}{,}\n",
       "    \\PY{n}{time\\PYZus{}values}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n}{NDArray}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
       "    \\PY{n}{intermediate\\PYZus{}freq}\\PY{p}{:} \\PY{n+nb}{float} \\PY{o}{=} \\PY{l+m+mf}{50e6}\\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}{    Generates mock \\PYZdq{}traces\\PYZdq{} that a physical instrument would digitize for the readout of}\n",
       "\\PY{l+s+sd}{    a transmon qubit when using a down\\PYZhy{}converting IQ mixer.}\n",
       "\n",
       "\\PY{l+s+sd}{    .. seealso:: :ref:`howto\\PYZhy{}utilities\\PYZhy{}examples\\PYZhy{}trace`}\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}{    iq\\PYZus{}point}\n",
       "\\PY{l+s+sd}{        A complex number representing a point on the IQ\\PYZhy{}plane.}\n",
       "\\PY{l+s+sd}{    time\\PYZus{}values}\n",
       "\\PY{l+s+sd}{        The time instants at which the mock intermediate\\PYZhy{}frequency signal is sampled.}\n",
       "\\PY{l+s+sd}{    intermediate\\PYZus{}freq}\n",
       "\\PY{l+s+sd}{        The intermediate frequency used in the down\\PYZhy{}conversion scheme.}\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}{        An array of complex numbers.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}  \\PY{c+c1}{\\PYZsh{} pylint: disable=line\\PYZhy{}too\\PYZhy{}long}\n",
       "    \\PY{k}{if} \\PY{n}{time\\PYZus{}values} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
       "        \\PY{n}{time\\PYZus{}values} \\PY{o}{=} \\PY{n}{mk\\PYZus{}trace\\PYZus{}time}\\PY{p}{(}\\PY{p}{)}\n",
       "    \\PY{k}{return} \\PY{n}{iq\\PYZus{}point} \\PY{o}{*} \\PY{n}{np}\\PY{o}{.}\\PY{n}{exp}\\PY{p}{(}\\PY{l+m+mf}{2.0}\\PY{n}{j} \\PY{o}{*} \\PY{n}{np}\\PY{o}{.}\\PY{n}{pi} \\PY{o}{*} \\PY{n}{intermediate\\PYZus{}freq} \\PY{o}{*} \\PY{n}{time\\PYZus{}values}\\PY{p}{)}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_trace_for_iq_shot\u001b[0m\u001b[1m(\u001b[0m\n",
       "    iq_point: complex,\n",
       "    time_values: Optional\u001b[1m[\u001b[0mNDArray\u001b[1m]\u001b[0m = \u001b[3;35mNone\u001b[0m,\n",
       "    intermediate_freq: float = \u001b[1;36m50e6\u001b[0m,\n",
       "\u001b[1m)\u001b[0m -> NDArray:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generates mock \u001b[32m\"traces\"\u001b[0m that a physical instrument would digitize for the readout of\n",
       "    a transmon qubit when using a down-converting IQ mixer.\n",
       "\n",
       "    .. seealso:: :ref:`howto-utilities-examples-trace`\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    iq_point\n",
       "        A complex number representing a point on the IQ-plane.\n",
       "    time_values\n",
       "        The time instants at which the mock intermediate-frequency signal is sampled.\n",
       "    intermediate_freq\n",
       "        The intermediate frequency used in the down-conversion scheme.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    :\n",
       "        An array of complex numbers.\n",
       "    \u001b[32m\"\"\u001b[0m\"  # pylint: \u001b[33mdisable\u001b[0m=\u001b[35mline\u001b[0m-too-long\n",
       "    if time_values is \u001b[3;35mNone\u001b[0m:\n",
       "        time_values = \u001b[1;35mmk_trace_time\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
       "    return iq_point * \u001b[1;35mnp.exp\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m2.0j\u001b[0m * np.pi * intermediate_freq * time_values\u001b[1m)\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for func in (mk_iq_shots, mk_trace_time, mk_trace_for_iq_shot):\n",
    "    display_source_code(func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e796f261",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\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 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": [
    "ground = -0.2 + 0.65j\n",
    "excited = 0.7 - 0.4j\n",
    "centers = ground, excited\n",
    "sigmas = [0.1] * 2\n",
    "\n",
    "shots = mk_iq_shots(\n",
    "    num_shots=256,\n",
    "    sigmas=sigmas,\n",
    "    centers=centers,\n",
    "    probabilities=[0.4, 1 - 0.4],\n",
    ")\n",
    "\n",
    "plt.hexbin(shots.real, shots.imag)\n",
    "plt.xlabel(\"I\")\n",
    "plt.ylabel(\"Q\")\n",
    "_ = plot_complex_points(centers, ax=plt.gca())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c448495a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 120\u001b[0m\u001b[1;36m0x372\u001b[0m\u001b[39m.\u001b[0m\u001b[1;36m671\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": [
    "time = mk_trace_time()\n",
    "trace = mk_trace_for_iq_shot(shots[0])\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(12, 12 / 1.61 / 2))\n",
    "ax.plot(time * 1e6, trace.imag, \".-\", label=\"I-quadrature\")\n",
    "ax.plot(time * 1e6, trace.real, \".-\", label=\"Q-quadrature\")\n",
    "ax.set_xlabel(\"Time [µs]\")\n",
    "ax.set_ylabel(\"Amplitude [V]\")\n",
    "_ = ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e6fed2e",
   "metadata": {},
   "source": [
    "First, we define a few parameters of our mock qubit and mock data acquisition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a0873c0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 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": [
    "# parameters of our qubit model\n",
    "tau = 30e-6\n",
    "ground = -0.2 + 0.65j  # ground state on the IQ-plane\n",
    "excited = 0.7 - 0.4j  # excited state on the IQ-plane\n",
    "centers = ground, excited\n",
    "sigmas = [0.1] * 2  # sigma, NB in general not the same for both state\n",
    "\n",
    "# mock of data acquisition configuration\n",
    "# NB usually at least 1000+ shots are taken, here we use less for faster code execution\n",
    "num_shots = 256\n",
    "# time delays between exciting the qubit and measuring its state\n",
    "t1_times = np.linspace(0, 120e-6, 30)\n",
    "\n",
    "# NB this are the ideal probabilities from repeating the measurement many times for a\n",
    "# qubit with a lifetime given by tau\n",
    "probabilities = exp_decay_func(t=t1_times, tau=tau, offset=0, n_factor=1, amplitude=1)\n",
    "\n",
    "# Ideal experiment result\n",
    "plt.ylabel(\"|1> probability\")\n",
    "plt.suptitle(\"Typical processed data of a T1 experiment\")\n",
    "plt.plot(t1_times * 1e6, probabilities, \".-\")\n",
    "_ = plt.xlabel(\"Time [µs]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7ff3baf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convenience dict with the mock parameters\n",
    "mock_conf = dict(\n",
    "    num_shots=num_shots,\n",
    "    centers=centers,\n",
    "    sigmas=sigmas,\n",
    "    t1_times=t1_times,\n",
    "    probabilities=probabilities,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2d4ead4",
   "metadata": {},
   "source": [
    "### T1 experiment averaged\n",
    "\n",
    "In this first example, we generate the individual measurement shots and average them,\n",
    "similar to what some instruments are capable of doing directly in the hardware.\n",
    "\n",
    "Here is how we store this data in the dataset along with the coordinates of these\n",
    "datapoints:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f3501d05",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Source code for generating the dataset below"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">mk_t1_av_dataset</span><span class=\"p\">(</span>\n",
       "    <span class=\"n\">t1_times</span><span class=\"p\">:</span> <span class=\"n\">Optional</span><span class=\"p\">[</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">ndarray</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"kc\">None</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">probabilities</span><span class=\"p\">:</span> <span class=\"n\">Optional</span><span class=\"p\">[</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">ndarray</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"kc\">None</span><span class=\"p\">,</span>\n",
       "    <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">,</span>\n",
       "<span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    Generates a dataset with mock data of a T1 experiment for a single qubit.</span>\n",
       "\n",
       "<span class=\"sd\">    Parameters</span>\n",
       "<span class=\"sd\">    ----------</span>\n",
       "<span class=\"sd\">    t1_times</span>\n",
       "<span class=\"sd\">        Array with the T1 times corresponding to each probability in ``probabilities``.</span>\n",
       "<span class=\"sd\">    probabilities</span>\n",
       "<span class=\"sd\">        The probabilities of finding the qubit in the excited state.</span>\n",
       "<span class=\"sd\">    **kwargs</span>\n",
       "<span class=\"sd\">        Keyword arguments passed to</span>\n",
       "<span class=\"sd\">        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    <span class=\"k\">if</span> <span class=\"n\">t1_times</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">t1_times</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">linspace</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mf\">120e-6</span><span class=\"p\">,</span> <span class=\"mi\">30</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"k\">if</span> <span class=\"n\">probabilities</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">probabilities</span> <span class=\"o\">=</span> <span class=\"n\">exp_decay_func</span><span class=\"p\">(</span>\n",
       "            <span class=\"n\">t</span><span class=\"o\">=</span><span class=\"n\">t1_times</span><span class=\"p\">,</span> <span class=\"n\">tau</span><span class=\"o\">=</span><span class=\"mf\">50e-6</span><span class=\"p\">,</span> <span class=\"n\">offset</span><span class=\"o\">=</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"n\">n_factor</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">,</span> <span class=\"n\">amplitude</span><span class=\"o\">=</span><span class=\"mi\">1</span>\n",
       "        <span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">q0_iq_av</span> <span class=\"o\">=</span> <span class=\"n\">mk_shots_from_probabilities</span><span class=\"p\">(</span><span class=\"n\">probabilities</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">(</span><span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">0</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">main_dims</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"s2\">&quot;main_dim&quot;</span><span class=\"p\">,)</span>\n",
       "    <span class=\"n\">q0_attrs</span> <span class=\"o\">=</span> <span class=\"n\">mk_main_var_attrs</span><span class=\"p\">(</span><span class=\"n\">unit</span><span class=\"o\">=</span><span class=\"s2\">&quot;V&quot;</span><span class=\"p\">,</span> <span class=\"n\">long_name</span><span class=\"o\">=</span><span class=\"s2\">&quot;Q0 IQ amplitude&quot;</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">t1_time_attrs</span> <span class=\"o\">=</span> <span class=\"n\">mk_main_coord_attrs</span><span class=\"p\">(</span><span class=\"n\">unit</span><span class=\"o\">=</span><span class=\"s2\">&quot;s&quot;</span><span class=\"p\">,</span> <span class=\"n\">long_name</span><span class=\"o\">=</span><span class=\"s2\">&quot;T1 Time&quot;</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">data_vars</span> <span class=\"o\">=</span> <span class=\"nb\">dict</span><span class=\"p\">(</span><span class=\"n\">q0_iq_av</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">main_dims</span><span class=\"p\">,</span> <span class=\"n\">q0_iq_av</span><span class=\"p\">,</span> <span class=\"n\">q0_attrs</span><span class=\"p\">))</span>\n",
       "    <span class=\"n\">coords</span> <span class=\"o\">=</span> <span class=\"nb\">dict</span><span class=\"p\">(</span><span class=\"n\">t1_time</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">main_dims</span><span class=\"p\">,</span> <span class=\"n\">t1_times</span><span class=\"p\">,</span> <span class=\"n\">t1_time_attrs</span><span class=\"p\">))</span>\n",
       "\n",
       "    <span class=\"n\">dataset</span> <span class=\"o\">=</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">(</span>\n",
       "        <span class=\"n\">data_vars</span><span class=\"o\">=</span><span class=\"n\">data_vars</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">coords</span><span class=\"o\">=</span><span class=\"n\">coords</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">attrs</span><span class=\"o\">=</span><span class=\"n\">mk_dataset_attrs</span><span class=\"p\">(),</span>\n",
       "    <span class=\"p\">)</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">dataset</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}t1\\PYZus{}av\\PYZus{}dataset}\\PY{p}{(}\n",
       "    \\PY{n}{t1\\PYZus{}times}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
       "    \\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
       "    \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\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 mock data of a T1 experiment for a single qubit.}\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}{    t1\\PYZus{}times}\n",
       "\\PY{l+s+sd}{        Array with the T1 times corresponding to each probability in ``probabilities``.}\n",
       "\\PY{l+s+sd}{    probabilities}\n",
       "\\PY{l+s+sd}{        The probabilities of finding the qubit in the excited state.}\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",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{k}{if} \\PY{n}{t1\\PYZus{}times} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
       "        \\PY{n}{t1\\PYZus{}times} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{l+m+mi}{0}\\PY{p}{,} \\PY{l+m+mf}{120e\\PYZhy{}6}\\PY{p}{,} \\PY{l+m+mi}{30}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{if} \\PY{n}{probabilities} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
       "        \\PY{n}{probabilities} \\PY{o}{=} \\PY{n}{exp\\PYZus{}decay\\PYZus{}func}\\PY{p}{(}\n",
       "            \\PY{n}{t}\\PY{o}{=}\\PY{n}{t1\\PYZus{}times}\\PY{p}{,} \\PY{n}{tau}\\PY{o}{=}\\PY{l+m+mf}{50e\\PYZhy{}6}\\PY{p}{,} \\PY{n}{offset}\\PY{o}{=}\\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n}{n\\PYZus{}factor}\\PY{o}{=}\\PY{l+m+mi}{1}\\PY{p}{,} \\PY{n}{amplitude}\\PY{o}{=}\\PY{l+m+mi}{1}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{q0\\PYZus{}iq\\PYZus{}av} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\\PY{n}{probabilities}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\\PY{o}{.}\\PY{n}{mean}\\PY{p}{(}\\PY{n}{axis}\\PY{o}{=}\\PY{l+m+mi}{0}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{main\\PYZus{}dims} \\PY{o}{=} \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\\PY{p}{)}\n",
       "    \\PY{n}{q0\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\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+s2}{\\PYZdq{}}\\PY{l+s+s2}{Q0 IQ amplitude}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{t1\\PYZus{}time\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\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{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{T1 Time}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{o}{=}\\PY{p}{(}\\PY{n}{main\\PYZus{}dims}\\PY{p}{,} \\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{p}{,} \\PY{n}{q0\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{)}\n",
       "    \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\\PY{n}{t1\\PYZus{}time}\\PY{o}{=}\\PY{p}{(}\\PY{n}{main\\PYZus{}dims}\\PY{p}{,} \\PY{n}{t1\\PYZus{}times}\\PY{p}{,} \\PY{n}{t1\\PYZus{}time\\PYZus{}attrs}\\PY{p}{)}\\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",
       "    \\PY{k}{return} \\PY{n}{dataset}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_t1_av_dataset\u001b[0m\u001b[1m(\u001b[0m\n",
       "    t1_times: Optional\u001b[1m[\u001b[0mnp.ndarray\u001b[1m]\u001b[0m = \u001b[3;35mNone\u001b[0m,\n",
       "    probabilities: Optional\u001b[1m[\u001b[0mnp.ndarray\u001b[1m]\u001b[0m = \u001b[3;35mNone\u001b[0m,\n",
       "    **kwargs,\n",
       "\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generates a dataset with mock data of a T1 experiment for a single qubit.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    t1_times\n",
       "        Array with the T1 times corresponding to each probability in ``probabilities``.\n",
       "    probabilities\n",
       "        The probabilities of finding the qubit in the excited state.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to\n",
       "        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    if t1_times is \u001b[3;35mNone\u001b[0m:\n",
       "        t1_times = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m0\u001b[0m, \u001b[1;36m120e-6\u001b[0m, \u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    if probabilities is \u001b[3;35mNone\u001b[0m:\n",
       "        probabilities = \u001b[1;35mexp_decay_func\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[33mt\u001b[0m=\u001b[35mt1_times\u001b[0m, \u001b[33mtau\u001b[0m=\u001b[1;36m50e\u001b[0m\u001b[1;36m-6\u001b[0m, \u001b[33moffset\u001b[0m=\u001b[1;36m0\u001b[0m, \u001b[33mn_factor\u001b[0m=\u001b[1;36m1\u001b[0m, \u001b[33mamplitude\u001b[0m=\u001b[1;36m1\u001b[0m\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    q0_iq_av = \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0mprobabilities, **kwargs\u001b[1m)\u001b[0m\u001b[1;35m.mean\u001b[0m\u001b[1m(\u001b[0m\u001b[33maxis\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    main_dims = \u001b[1m(\u001b[0m\u001b[32m\"main_dim\"\u001b[0m,\u001b[1m)\u001b[0m\n",
       "    q0_attrs = \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33munit\u001b[0m=\u001b[32m\"V\"\u001b[0m, \u001b[33mlong_name\u001b[0m=\u001b[32m\"Q0\u001b[0m\u001b[32m IQ amplitude\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    t1_time_attrs = \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33munit\u001b[0m=\u001b[32m\"s\"\u001b[0m, \u001b[33mlong_name\u001b[0m=\u001b[32m\"T1\u001b[0m\u001b[32m Time\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    data_vars = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\u001b[33mq0_iq_av\u001b[0m=\u001b[1m(\u001b[0mmain_dims, q0_iq_av, q0_attrs\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\n",
       "    coords = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\u001b[33mt1_time\u001b[0m=\u001b[1m(\u001b[0mmain_dims, t1_times, t1_time_attrs\u001b[1m)\u001b[0m\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",
       "    return dataset"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_source_code(dataset_examples.mk_t1_av_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c8c55061",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (main_dim: 30)\n",
       "Coordinates:\n",
       "    t1_time   (main_dim) float64 0.0 4.138e-06 8.276e-06 ... 0.0001159 0.00012\n",
       "Dimensions without coordinates: main_dim\n",
       "Data variables:\n",
       "    q0_iq_av  (main_dim) complex128 (-0.19894114958423859+0.6515500138845804j...\n",
       "Attributes:\n",
       "    tuid:                      20231215-162528-613-751a9b\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:    []</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-92f81891-65fa-4bda-89a2-3348642ca67f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-92f81891-65fa-4bda-89a2-3348642ca67f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>main_dim</span>: 30</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-29165788-786c-41a8-8901-b70ef63ab634' class='xr-section-summary-in' type='checkbox'  checked><label for='section-29165788-786c-41a8-8901-b70ef63ab634' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t1_time</span></div><div class='xr-var-dims'>(main_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 4.138e-06 ... 0.0001159 0.00012</div><input id='attrs-ceea6070-372e-42ec-a6b4-60d2f990eb2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ceea6070-372e-42ec-a6b4-60d2f990eb2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a51dd02-395a-4e99-9824-9c0ffe6f1f36' class='xr-var-data-in' type='checkbox'><label for='data-6a51dd02-395a-4e99-9824-9c0ffe6f1f36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>T1 Time</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.00000000e+00, 4.13793103e-06, 8.27586207e-06, 1.24137931e-05,\n",
       "       1.65517241e-05, 2.06896552e-05, 2.48275862e-05, 2.89655172e-05,\n",
       "       3.31034483e-05, 3.72413793e-05, 4.13793103e-05, 4.55172414e-05,\n",
       "       4.96551724e-05, 5.37931034e-05, 5.79310345e-05, 6.20689655e-05,\n",
       "       6.62068966e-05, 7.03448276e-05, 7.44827586e-05, 7.86206897e-05,\n",
       "       8.27586207e-05, 8.68965517e-05, 9.10344828e-05, 9.51724138e-05,\n",
       "       9.93103448e-05, 1.03448276e-04, 1.07586207e-04, 1.11724138e-04,\n",
       "       1.15862069e-04, 1.20000000e-04])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7533bfc5-f9e7-4275-a73e-ad8a2b9da4de' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7533bfc5-f9e7-4275-a73e-ad8a2b9da4de' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_av</span></div><div class='xr-var-dims'>(main_dim)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(-0.19894114958423859+0.65155001...</div><input id='attrs-8eecd0d3-5dc3-4a71-9fa5-ec343a1642d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8eecd0d3-5dc3-4a71-9fa5-ec343a1642d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc38ebce-8fbd-49da-908d-8e83a5abdcee' class='xr-var-data-in' type='checkbox'><label for='data-fc38ebce-8fbd-49da-908d-8e83a5abdcee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ amplitude</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([-0.19894115+0.65155001j, -0.11155212+0.54951255j,\n",
       "       -0.01216713+0.43313537j,  0.05193768+0.35848368j,\n",
       "        0.15504951+0.2389656j ,  0.20661408+0.17861197j,\n",
       "        0.2833043 +0.08961707j,  0.33361813+0.03110011j,\n",
       "        0.37345395-0.01576696j,  0.40534772-0.05352011j,\n",
       "        0.42151034-0.07261241j,  0.47040441-0.12912367j,\n",
       "        0.49717119-0.16057795j,  0.53565518-0.20550725j,\n",
       "        0.56438276-0.23892233j,  0.58075078-0.25822004j,\n",
       "        0.59794213-0.27834108j,  0.60292187-0.28390675j,\n",
       "        0.60292187-0.28390675j,  0.61706292-0.30039156j,\n",
       "        0.6204547 -0.30436927j,  0.62775114-0.31283759j,\n",
       "        0.64350021-0.33151635j,  0.64683289-0.33543496j,\n",
       "        0.65442395-0.34419789j,  0.65442395-0.34419789j,\n",
       "        0.65650831-0.34686819j,  0.66449532-0.35602708j,\n",
       "        0.66901604-0.36113374j,  0.66901604-0.36113374j])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f894d269-9e14-4236-a01a-587726bd937e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f894d269-9e14-4236-a01a-587726bd937e' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-d2d8b489-7199-441b-930b-dcc83c712e7b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d2d8b489-7199-441b-930b-dcc83c712e7b' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20231215-162528-613-751a9b</dd><dt><span>dataset_name :</span></dt><dd></dd><dt><span>dataset_state :</span></dt><dd>None</dd><dt><span>timestamp_start :</span></dt><dd>None</dd><dt><span>timestamp_end :</span></dt><dd>None</dd><dt><span>quantify_dataset_version :</span></dt><dd>2.0.0</dd><dt><span>software_versions :</span></dt><dd>{}</dd><dt><span>relationships :</span></dt><dd>[]</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div></li></ul></div></div>"
      ],
      "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;36m30\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "    t1_time   \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 \u001b[1;36m0.0\u001b[0m \u001b[1;36m4.138e-06\u001b[0m \u001b[1;36m8.276e-06\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.0001159\u001b[0m \u001b[1;36m0.00012\u001b[0m\n",
       "Dimensions without coordinates: main_dim\n",
       "Data variables:\n",
       "    q0_iq_av  \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.19894114958423859+0.6515500138845804j\u001b[0m\u001b[33m...\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20231215\u001b[0m-\u001b[1;36m162528\u001b[0m-\u001b[1;36m613\u001b[0m-751a9b\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": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset_examples.mk_t1_av_dataset(**mock_conf)\n",
    "assert dataset == round_trip_dataset(dataset)  # confirm read/write\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9a2dbf79",
   "metadata": {},
   "outputs": [
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       "\u001b[1m(\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m30\u001b[0m,\u001b[1m)\u001b[0m, \u001b[1;35mdtype\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'complex128'\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m"
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     "execution_count": 16,
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   "source": [
    "dataset.q0_iq_av.shape, dataset.q0_iq_av.dtype"
   ]
  },
  {
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   "execution_count": 17,
   "id": "a3f589a9",
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       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:   (t1_time: 30)\n",
       "Coordinates:\n",
       "  * t1_time   (t1_time) float64 0.0 4.138e-06 8.276e-06 ... 0.0001159 0.00012\n",
       "Data variables:\n",
       "    q0_iq_av  (t1_time) complex128 (-0.19894114958423859+0.6515500138845804j)...\n",
       "Attributes:\n",
       "    tuid:                      20231215-162528-613-751a9b\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:    []</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-eefac42c-cd51-4370-97bf-22db9391ffd1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eefac42c-cd51-4370-97bf-22db9391ffd1' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>t1_time</span>: 30</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e1ab4727-e7c2-4c88-8c76-93b3febf2f82' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e1ab4727-e7c2-4c88-8c76-93b3febf2f82' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t1_time</span></div><div class='xr-var-dims'>(t1_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 4.138e-06 ... 0.0001159 0.00012</div><input id='attrs-1286f660-df4d-43ac-8ac2-9d573229f4d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1286f660-df4d-43ac-8ac2-9d573229f4d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f785c2e1-d5d7-4364-b611-86407786fdfd' class='xr-var-data-in' type='checkbox'><label for='data-f785c2e1-d5d7-4364-b611-86407786fdfd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>T1 Time</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.000000e+00, 4.137931e-06, 8.275862e-06, 1.241379e-05, 1.655172e-05,\n",
       "       2.068966e-05, 2.482759e-05, 2.896552e-05, 3.310345e-05, 3.724138e-05,\n",
       "       4.137931e-05, 4.551724e-05, 4.965517e-05, 5.379310e-05, 5.793103e-05,\n",
       "       6.206897e-05, 6.620690e-05, 7.034483e-05, 7.448276e-05, 7.862069e-05,\n",
       "       8.275862e-05, 8.689655e-05, 9.103448e-05, 9.517241e-05, 9.931034e-05,\n",
       "       1.034483e-04, 1.075862e-04, 1.117241e-04, 1.158621e-04, 1.200000e-04])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-71733e70-847f-4aa4-9120-218fd142c44d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-71733e70-847f-4aa4-9120-218fd142c44d' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_av</span></div><div class='xr-var-dims'>(t1_time)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(-0.19894114958423859+0.65155001...</div><input id='attrs-7f298f15-82a5-42df-a753-84052ec761e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f298f15-82a5-42df-a753-84052ec761e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55edc9a1-d992-4df2-91c7-daafc72949b7' class='xr-var-data-in' type='checkbox'><label for='data-55edc9a1-d992-4df2-91c7-daafc72949b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ amplitude</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([-0.19894115+0.65155001j, -0.11155212+0.54951255j,\n",
       "       -0.01216713+0.43313537j,  0.05193768+0.35848368j,\n",
       "        0.15504951+0.2389656j ,  0.20661408+0.17861197j,\n",
       "        0.2833043 +0.08961707j,  0.33361813+0.03110011j,\n",
       "        0.37345395-0.01576696j,  0.40534772-0.05352011j,\n",
       "        0.42151034-0.07261241j,  0.47040441-0.12912367j,\n",
       "        0.49717119-0.16057795j,  0.53565518-0.20550725j,\n",
       "        0.56438276-0.23892233j,  0.58075078-0.25822004j,\n",
       "        0.59794213-0.27834108j,  0.60292187-0.28390675j,\n",
       "        0.60292187-0.28390675j,  0.61706292-0.30039156j,\n",
       "        0.6204547 -0.30436927j,  0.62775114-0.31283759j,\n",
       "        0.64350021-0.33151635j,  0.64683289-0.33543496j,\n",
       "        0.65442395-0.34419789j,  0.65442395-0.34419789j,\n",
       "        0.65650831-0.34686819j,  0.66449532-0.35602708j,\n",
       "        0.66901604-0.36113374j,  0.66901604-0.36113374j])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b8e709f8-f2b9-4780-8dac-d29586f83997' class='xr-section-summary-in' type='checkbox'  ><label for='section-b8e709f8-f2b9-4780-8dac-d29586f83997' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>t1_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4636af12-bc05-4851-bbb4-68611bfbef19' class='xr-index-data-in' type='checkbox'/><label for='index-4636af12-bc05-4851-bbb4-68611bfbef19' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                   0.0,  4.137931034482758e-06,  8.275862068965517e-06,\n",
       "       1.2413793103448275e-05, 1.6551724137931033e-05,  2.068965517241379e-05,\n",
       "        2.482758620689655e-05,  2.896551724137931e-05, 3.3103448275862067e-05,\n",
       "        3.724137931034482e-05,  4.137931034482758e-05,  4.551724137931034e-05,\n",
       "         4.96551724137931e-05, 5.3793103448275856e-05,  5.793103448275862e-05,\n",
       "        6.206896551724137e-05,  6.620689655172413e-05,   7.03448275862069e-05,\n",
       "        7.448275862068965e-05,  7.862068965517241e-05,  8.275862068965516e-05,\n",
       "        8.689655172413792e-05,  9.103448275862069e-05,  9.517241379310344e-05,\n",
       "         9.93103448275862e-05, 0.00010344827586206896, 0.00010758620689655171,\n",
       "       0.00011172413793103448, 0.00011586206896551724,                0.00012],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;t1_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c556f0c6-79ee-41e0-89ab-9b4da9ca0116' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c556f0c6-79ee-41e0-89ab-9b4da9ca0116' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20231215-162528-613-751a9b</dd><dt><span>dataset_name :</span></dt><dd></dd><dt><span>dataset_state :</span></dt><dd>None</dd><dt><span>timestamp_start :</span></dt><dd>None</dd><dt><span>timestamp_end :</span></dt><dd>None</dd><dt><span>quantify_dataset_version :</span></dt><dd>2.0.0</dd><dt><span>software_versions :</span></dt><dd>{}</dd><dt><span>relationships :</span></dt><dd>[]</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m\n",
       "Dimensions:   \u001b[1m(\u001b[0mt1_time: \u001b[1;36m30\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "  * t1_time   \u001b[1m(\u001b[0mt1_time\u001b[1m)\u001b[0m float64 \u001b[1;36m0.0\u001b[0m \u001b[1;36m4.138e-06\u001b[0m \u001b[1;36m8.276e-06\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.0001159\u001b[0m \u001b[1;36m0.00012\u001b[0m\n",
       "Data variables:\n",
       "    q0_iq_av  \u001b[1m(\u001b[0mt1_time\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.19894114958423859+0.6515500138845804j\u001b[0m\u001b[1m)\u001b[0m\u001b[33m...\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20231215\u001b[0m-\u001b[1;36m162528\u001b[0m-\u001b[1;36m613\u001b[0m-751a9b\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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset,\n",
    "    dimension=\"main_dim\",\n",
    "    coords_names=dattrs.get_main_coords(dataset),\n",
    ")\n",
    "dataset_gridded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "45038c53",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Source code for plotting utilities"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">plot_xr_complex</span><span class=\"p\">(</span>\n",
       "    <span class=\"n\">var</span><span class=\"p\">:</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">DataArray</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">marker_scatter</span><span class=\"p\">:</span> <span class=\"nb\">str</span> <span class=\"o\">=</span> <span class=\"s2\">&quot;o&quot;</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">label_real</span><span class=\"p\">:</span> <span class=\"nb\">str</span> <span class=\"o\">=</span> <span class=\"s2\">&quot;Real&quot;</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">label_imag</span><span class=\"p\">:</span> <span class=\"nb\">str</span> <span class=\"o\">=</span> <span class=\"s2\">&quot;Imag&quot;</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">cmap</span><span class=\"p\">:</span> <span class=\"nb\">str</span> <span class=\"o\">=</span> <span class=\"s2\">&quot;viridis&quot;</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">c</span><span class=\"p\">:</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">ndarray</span> <span class=\"o\">|</span> <span class=\"kc\">None</span> <span class=\"o\">=</span> <span class=\"kc\">None</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">kwargs_line</span><span class=\"p\">:</span> <span class=\"nb\">dict</span> <span class=\"o\">|</span> <span class=\"kc\">None</span> <span class=\"o\">=</span> <span class=\"kc\">None</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">kwargs_scatter</span><span class=\"p\">:</span> <span class=\"nb\">dict</span> <span class=\"o\">|</span> <span class=\"kc\">None</span> <span class=\"o\">=</span> <span class=\"kc\">None</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">title</span><span class=\"p\">:</span> <span class=\"nb\">str</span> <span class=\"o\">=</span> <span class=\"s2\">&quot;</span><span class=\"si\">{}</span><span class=\"s2\"> [</span><span class=\"si\">{}</span><span class=\"s2\">]; shape = </span><span class=\"si\">{}</span><span class=\"s2\">&quot;</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">legend</span><span class=\"p\">:</span> <span class=\"nb\">bool</span> <span class=\"o\">=</span> <span class=\"kc\">True</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">ax</span><span class=\"p\">:</span> <span class=\"nb\">object</span> <span class=\"o\">=</span> <span class=\"kc\">None</span><span class=\"p\">,</span>\n",
       "<span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"nb\">tuple</span><span class=\"p\">[</span><span class=\"n\">Figure</span><span class=\"p\">,</span> <span class=\"n\">Axes</span><span class=\"p\">]:</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;Plots the real and imaginary parts of complex data. Points are colored by default</span>\n",
       "<span class=\"sd\">    according to their order in the array.</span>\n",
       "\n",
       "<span class=\"sd\">    Parameters</span>\n",
       "<span class=\"sd\">    ----------</span>\n",
       "<span class=\"sd\">    var</span>\n",
       "<span class=\"sd\">        1D array of complex data.</span>\n",
       "<span class=\"sd\">    marker_scatter</span>\n",
       "<span class=\"sd\">        Marker used for the scatter plot.</span>\n",
       "<span class=\"sd\">    label_real</span>\n",
       "<span class=\"sd\">        Label for legend.</span>\n",
       "<span class=\"sd\">    label_imag</span>\n",
       "<span class=\"sd\">        Label for legend.</span>\n",
       "<span class=\"sd\">    cmap</span>\n",
       "<span class=\"sd\">        The colormap to use for coloring the points.</span>\n",
       "<span class=\"sd\">    c</span>\n",
       "<span class=\"sd\">        Color of the points. Defaults to an array of integers.</span>\n",
       "<span class=\"sd\">    kwargs_line</span>\n",
       "<span class=\"sd\">        Keyword arguments passed to :meth:`matplotlib.axes.Axes.plot`.</span>\n",
       "<span class=\"sd\">    kwargs_scatter</span>\n",
       "<span class=\"sd\">        Keyword arguments passed to :meth:`matplotlib.axes.Axes.scatter`.</span>\n",
       "<span class=\"sd\">    title</span>\n",
       "<span class=\"sd\">        Axes title. By default gets formatted with ``var.long_name``, ``var.name`` and</span>\n",
       "<span class=\"sd\">        var.shape``.</span>\n",
       "<span class=\"sd\">    legend</span>\n",
       "<span class=\"sd\">        Calls :meth:`~matplotlib.axes.Axes.legend` if ``True``.</span>\n",
       "<span class=\"sd\">    ax</span>\n",
       "<span class=\"sd\">        The matplotlib axes. If ``None`` a new axes (and figure) is created.</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "\n",
       "    <span class=\"k\">if</span> <span class=\"n\">ax</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">_</span><span class=\"p\">,</span> <span class=\"n\">ax</span> <span class=\"o\">=</span> <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">subplots</span><span class=\"p\">()</span>\n",
       "\n",
       "    <span class=\"k\">if</span> <span class=\"n\">c</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">c</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">))</span>\n",
       "\n",
       "    <span class=\"k\">if</span> <span class=\"n\">kwargs_line</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">kwargs_line</span> <span class=\"o\">=</span> <span class=\"p\">{}</span>\n",
       "\n",
       "    <span class=\"k\">if</span> <span class=\"n\">kwargs_scatter</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">kwargs_scatter</span> <span class=\"o\">=</span> <span class=\"p\">{}</span>\n",
       "\n",
       "    <span class=\"k\">if</span> <span class=\"s2\">&quot;marker&quot;</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"n\">kwargs_line</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">kwargs_line</span><span class=\"p\">[</span><span class=\"s2\">&quot;marker&quot;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"s2\">&quot;&quot;</span>\n",
       "\n",
       "    <span class=\"n\">var</span><span class=\"o\">.</span><span class=\"n\">real</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">ax</span><span class=\"o\">=</span><span class=\"n\">ax</span><span class=\"p\">,</span> <span class=\"n\">label</span><span class=\"o\">=</span><span class=\"n\">label_real</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">kwargs_line</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">var</span><span class=\"o\">.</span><span class=\"n\">imag</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"p\">(</span><span class=\"n\">ax</span><span class=\"o\">=</span><span class=\"n\">ax</span><span class=\"p\">,</span> <span class=\"n\">label</span><span class=\"o\">=</span><span class=\"n\">label_imag</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">kwargs_line</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"k\">for</span> <span class=\"n\">vals</span> <span class=\"ow\">in</span> <span class=\"p\">(</span><span class=\"n\">var</span><span class=\"o\">.</span><span class=\"n\">real</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"o\">.</span><span class=\"n\">imag</span><span class=\"p\">):</span>\n",
       "        <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">scatter</span><span class=\"p\">(</span>\n",
       "            <span class=\"nb\">next</span><span class=\"p\">(</span><span class=\"nb\">iter</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"o\">.</span><span class=\"n\">coords</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">()))</span><span class=\"o\">.</span><span class=\"n\">values</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">vals</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">marker</span><span class=\"o\">=</span><span class=\"n\">marker_scatter</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">c</span><span class=\"o\">=</span><span class=\"n\">c</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">cmap</span><span class=\"o\">=</span><span class=\"n\">cmap</span><span class=\"p\">,</span>\n",
       "            <span class=\"o\">**</span><span class=\"n\">kwargs_scatter</span><span class=\"p\">,</span>\n",
       "        <span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_title</span><span class=\"p\">(</span><span class=\"n\">title</span><span class=\"o\">.</span><span class=\"n\">format</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"o\">.</span><span class=\"n\">long_name</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">))</span>\n",
       "\n",
       "    <span class=\"k\">if</span> <span class=\"n\">legend</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">legend</span><span class=\"p\">()</span>\n",
       "\n",
       "    <span class=\"k\">return</span> <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">get_figure</span><span class=\"p\">(),</span> <span class=\"n\">ax</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{plot\\PYZus{}xr\\PYZus{}complex}\\PY{p}{(}\n",
       "    \\PY{n}{var}\\PY{p}{:} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{DataArray}\\PY{p}{,}\n",
       "    \\PY{n}{marker\\PYZus{}scatter}\\PY{p}{:} \\PY{n+nb}{str} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{o}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "    \\PY{n}{label\\PYZus{}real}\\PY{p}{:} \\PY{n+nb}{str} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Real}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "    \\PY{n}{label\\PYZus{}imag}\\PY{p}{:} \\PY{n+nb}{str} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Imag}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "    \\PY{n}{cmap}\\PY{p}{:} \\PY{n+nb}{str} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{viridis}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "    \\PY{n}{c}\\PY{p}{:} \\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray} \\PY{o}{|} \\PY{k+kc}{None} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
       "    \\PY{n}{kwargs\\PYZus{}line}\\PY{p}{:} \\PY{n+nb}{dict} \\PY{o}{|} \\PY{k+kc}{None} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
       "    \\PY{n}{kwargs\\PYZus{}scatter}\\PY{p}{:} \\PY{n+nb}{dict} \\PY{o}{|} \\PY{k+kc}{None} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
       "    \\PY{n}{title}\\PY{p}{:} \\PY{n+nb}{str} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+si}{\\PYZob{}\\PYZcb{}}\\PY{l+s+s2}{ [}\\PY{l+s+si}{\\PYZob{}\\PYZcb{}}\\PY{l+s+s2}{]; shape = }\\PY{l+s+si}{\\PYZob{}\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "    \\PY{n}{legend}\\PY{p}{:} \\PY{n+nb}{bool} \\PY{o}{=} \\PY{k+kc}{True}\\PY{p}{,}\n",
       "    \\PY{n}{ax}\\PY{p}{:} \\PY{n+nb}{object} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
       "\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n+nb}{tuple}\\PY{p}{[}\\PY{n}{Figure}\\PY{p}{,} \\PY{n}{Axes}\\PY{p}{]}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Plots the real and imaginary parts of complex data. Points are colored by default}\n",
       "\\PY{l+s+sd}{    according to their order in the array.}\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}{    var}\n",
       "\\PY{l+s+sd}{        1D array of complex data.}\n",
       "\\PY{l+s+sd}{    marker\\PYZus{}scatter}\n",
       "\\PY{l+s+sd}{        Marker used for the scatter plot.}\n",
       "\\PY{l+s+sd}{    label\\PYZus{}real}\n",
       "\\PY{l+s+sd}{        Label for legend.}\n",
       "\\PY{l+s+sd}{    label\\PYZus{}imag}\n",
       "\\PY{l+s+sd}{        Label for legend.}\n",
       "\\PY{l+s+sd}{    cmap}\n",
       "\\PY{l+s+sd}{        The colormap to use for coloring the points.}\n",
       "\\PY{l+s+sd}{    c}\n",
       "\\PY{l+s+sd}{        Color of the points. Defaults to an array of integers.}\n",
       "\\PY{l+s+sd}{    kwargs\\PYZus{}line}\n",
       "\\PY{l+s+sd}{        Keyword arguments passed to :meth:`matplotlib.axes.Axes.plot`.}\n",
       "\\PY{l+s+sd}{    kwargs\\PYZus{}scatter}\n",
       "\\PY{l+s+sd}{        Keyword arguments passed to :meth:`matplotlib.axes.Axes.scatter`.}\n",
       "\\PY{l+s+sd}{    title}\n",
       "\\PY{l+s+sd}{        Axes title. By default gets formatted with ``var.long\\PYZus{}name``, ``var.name`` and}\n",
       "\\PY{l+s+sd}{        var.shape``.}\n",
       "\\PY{l+s+sd}{    legend}\n",
       "\\PY{l+s+sd}{        Calls :meth:`\\PYZti{}matplotlib.axes.Axes.legend` if ``True``.}\n",
       "\\PY{l+s+sd}{    ax}\n",
       "\\PY{l+s+sd}{        The matplotlib axes. If ``None`` a new axes (and figure) is created.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "    \\PY{k}{if} \\PY{n}{ax} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
       "        \\PY{n}{\\PYZus{}}\\PY{p}{,} \\PY{n}{ax} \\PY{o}{=} \\PY{n}{plt}\\PY{o}{.}\\PY{n}{subplots}\\PY{p}{(}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{if} \\PY{n}{c} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
       "        \\PY{n}{c} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{arange}\\PY{p}{(}\\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{var}\\PY{p}{)}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{if} \\PY{n}{kwargs\\PYZus{}line} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
       "        \\PY{n}{kwargs\\PYZus{}line} \\PY{o}{=} \\PY{p}{\\PYZob{}}\\PY{p}{\\PYZcb{}}\n",
       "\n",
       "    \\PY{k}{if} \\PY{n}{kwargs\\PYZus{}scatter} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
       "        \\PY{n}{kwargs\\PYZus{}scatter} \\PY{o}{=} \\PY{p}{\\PYZob{}}\\PY{p}{\\PYZcb{}}\n",
       "\n",
       "    \\PY{k}{if} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{marker}\\PY{l+s+s2}{\\PYZdq{}} \\PY{o+ow}{not} \\PY{o+ow}{in} \\PY{n}{kwargs\\PYZus{}line}\\PY{p}{:}\n",
       "        \\PY{n}{kwargs\\PYZus{}line}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{marker}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{\\PYZdq{}}\n",
       "\n",
       "    \\PY{n}{var}\\PY{o}{.}\\PY{n}{real}\\PY{o}{.}\\PY{n}{plot}\\PY{p}{(}\\PY{n}{ax}\\PY{o}{=}\\PY{n}{ax}\\PY{p}{,} \\PY{n}{label}\\PY{o}{=}\\PY{n}{label\\PYZus{}real}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs\\PYZus{}line}\\PY{p}{)}\n",
       "    \\PY{n}{var}\\PY{o}{.}\\PY{n}{imag}\\PY{o}{.}\\PY{n}{plot}\\PY{p}{(}\\PY{n}{ax}\\PY{o}{=}\\PY{n}{ax}\\PY{p}{,} \\PY{n}{label}\\PY{o}{=}\\PY{n}{label\\PYZus{}imag}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs\\PYZus{}line}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{for} \\PY{n}{vals} \\PY{o+ow}{in} \\PY{p}{(}\\PY{n}{var}\\PY{o}{.}\\PY{n}{real}\\PY{p}{,} \\PY{n}{var}\\PY{o}{.}\\PY{n}{imag}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{n}{ax}\\PY{o}{.}\\PY{n}{scatter}\\PY{p}{(}\n",
       "            \\PY{n+nb}{next}\\PY{p}{(}\\PY{n+nb}{iter}\\PY{p}{(}\\PY{n}{var}\\PY{o}{.}\\PY{n}{coords}\\PY{o}{.}\\PY{n}{values}\\PY{p}{(}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\\PY{o}{.}\\PY{n}{values}\\PY{p}{,}\n",
       "            \\PY{n}{vals}\\PY{p}{,}\n",
       "            \\PY{n}{marker}\\PY{o}{=}\\PY{n}{marker\\PYZus{}scatter}\\PY{p}{,}\n",
       "            \\PY{n}{c}\\PY{o}{=}\\PY{n}{c}\\PY{p}{,}\n",
       "            \\PY{n}{cmap}\\PY{o}{=}\\PY{n}{cmap}\\PY{p}{,}\n",
       "            \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs\\PYZus{}scatter}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{ax}\\PY{o}{.}\\PY{n}{set\\PYZus{}title}\\PY{p}{(}\\PY{n}{title}\\PY{o}{.}\\PY{n}{format}\\PY{p}{(}\\PY{n}{var}\\PY{o}{.}\\PY{n}{long\\PYZus{}name}\\PY{p}{,} \\PY{n}{var}\\PY{o}{.}\\PY{n}{name}\\PY{p}{,} \\PY{n}{var}\\PY{o}{.}\\PY{n}{shape}\\PY{p}{)}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{if} \\PY{n}{legend}\\PY{p}{:}\n",
       "        \\PY{n}{ax}\\PY{o}{.}\\PY{n}{legend}\\PY{p}{(}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{ax}\\PY{o}{.}\\PY{n}{get\\PYZus{}figure}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,} \\PY{n}{ax}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mplot_xr_complex\u001b[0m\u001b[1m(\u001b[0m\n",
       "    var: xr.DataArray,\n",
       "    marker_scatter: str = \u001b[32m\"o\"\u001b[0m,\n",
       "    label_real: str = \u001b[32m\"Real\"\u001b[0m,\n",
       "    label_imag: str = \u001b[32m\"Imag\"\u001b[0m,\n",
       "    cmap: str = \u001b[32m\"viridis\"\u001b[0m,\n",
       "    c: np.ndarray | \u001b[3;35mNone\u001b[0m = \u001b[3;35mNone\u001b[0m,\n",
       "    kwargs_line: dict | \u001b[3;35mNone\u001b[0m = \u001b[3;35mNone\u001b[0m,\n",
       "    kwargs_scatter: dict | \u001b[3;35mNone\u001b[0m = \u001b[3;35mNone\u001b[0m,\n",
       "    title: str = \u001b[32m\"\u001b[0m\u001b[32m{\u001b[0m\u001b[32m}\u001b[0m\u001b[32m \u001b[0m\u001b[32m[\u001b[0m\u001b[32m{\u001b[0m\u001b[32m}\u001b[0m\u001b[32m]\u001b[0m\u001b[32m; shape = \u001b[0m\u001b[32m{\u001b[0m\u001b[32m}\u001b[0m\u001b[32m\"\u001b[0m,\n",
       "    legend: bool = \u001b[3;92mTrue\u001b[0m,\n",
       "    ax: object = \u001b[3;35mNone\u001b[0m,\n",
       "\u001b[1m)\u001b[0m -> tuple\u001b[1m[\u001b[0mFigure, Axes\u001b[1m]\u001b[0m:\n",
       "    \u001b[32m\"\"\u001b[0m\"Plots the real and imaginary parts of complex data. Points are colored by default\n",
       "    according to their order in the array.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    var\n",
       "        1D array of complex data.\n",
       "    marker_scatter\n",
       "        Marker used for the scatter plot.\n",
       "    label_real\n",
       "        Label for legend.\n",
       "    label_imag\n",
       "        Label for legend.\n",
       "    cmap\n",
       "        The colormap to use for coloring the points.\n",
       "    c\n",
       "        Color of the points. Defaults to an array of integers.\n",
       "    kwargs_line\n",
       "        Keyword arguments passed to :meth:`matplotlib.axes.Axes.plot`.\n",
       "    kwargs_scatter\n",
       "        Keyword arguments passed to :meth:`matplotlib.axes.Axes.scatter`.\n",
       "    title\n",
       "        Axes title. By default gets formatted with ``var.long_name``, ``var.name`` and\n",
       "        var.shape``.\n",
       "    legend\n",
       "        Calls :meth:`~matplotlib.axes.Axes.legend` if ``\u001b[3;92mTrue\u001b[0m``.\n",
       "    ax\n",
       "        The matplotlib axes. If ``\u001b[3;35mNone\u001b[0m`` a new axes \u001b[1m(\u001b[0mand figure\u001b[1m)\u001b[0m is created.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "\n",
       "    if ax is \u001b[3;35mNone\u001b[0m:\n",
       "        _, ax = \u001b[1;35mplt.subplots\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    if c is \u001b[3;35mNone\u001b[0m:\n",
       "        c = \u001b[1;35mnp.arange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mvar\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    if kwargs_line is \u001b[3;35mNone\u001b[0m:\n",
       "        kwargs_line = \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
       "\n",
       "    if kwargs_scatter is \u001b[3;35mNone\u001b[0m:\n",
       "        kwargs_scatter = \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
       "\n",
       "    if \u001b[32m\"marker\"\u001b[0m not in kwargs_line:\n",
       "        kwargs_line\u001b[1m[\u001b[0m\u001b[32m\"marker\"\u001b[0m\u001b[1m]\u001b[0m = \u001b[32m\"\"\u001b[0m\n",
       "\n",
       "    \u001b[1;35mvar.real.plot\u001b[0m\u001b[1m(\u001b[0m\u001b[33max\u001b[0m=\u001b[35max\u001b[0m, \u001b[33mlabel\u001b[0m=\u001b[35mlabel_real\u001b[0m, **kwargs_line\u001b[1m)\u001b[0m\n",
       "    \u001b[1;35mvar.imag.plot\u001b[0m\u001b[1m(\u001b[0m\u001b[33max\u001b[0m=\u001b[35max\u001b[0m, \u001b[33mlabel\u001b[0m=\u001b[35mlabel_imag\u001b[0m, **kwargs_line\u001b[1m)\u001b[0m\n",
       "\n",
       "    for vals in \u001b[1m(\u001b[0mvar.real, var.imag\u001b[1m)\u001b[0m:\n",
       "        \u001b[1;35max.scatter\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[1;35mnext\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35miter\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mvar.coords.values\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m.values,\n",
       "            vals,\n",
       "            \u001b[33mmarker\u001b[0m=\u001b[35mmarker_scatter\u001b[0m,\n",
       "            \u001b[33mc\u001b[0m=\u001b[35mc\u001b[0m,\n",
       "            \u001b[33mcmap\u001b[0m=\u001b[35mcmap\u001b[0m,\n",
       "            **kwargs_scatter,\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    \u001b[1;35max.set_title\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mtitle.format\u001b[0m\u001b[1m(\u001b[0mvar.long_name, var.name, var.shape\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    if legend:\n",
       "        \u001b[1;35max.legend\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    return \u001b[1;35max.get_figure\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m, ax"
      ]
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">plot_xr_complex_on_plane</span><span class=\"p\">(</span>\n",
       "    <span class=\"n\">var</span><span class=\"p\">:</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">DataArray</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">marker</span><span class=\"p\">:</span> <span class=\"nb\">str</span> <span class=\"o\">=</span> <span class=\"s2\">&quot;o&quot;</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">label</span><span class=\"p\">:</span> <span class=\"nb\">str</span> <span class=\"o\">=</span> <span class=\"s2\">&quot;Data on imaginary plane&quot;</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">cmap</span><span class=\"p\">:</span> <span class=\"nb\">str</span> <span class=\"o\">=</span> <span class=\"s2\">&quot;viridis&quot;</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">c</span><span class=\"p\">:</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">ndarray</span> <span class=\"o\">|</span> <span class=\"kc\">None</span> <span class=\"o\">=</span> <span class=\"kc\">None</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">xlabel</span><span class=\"p\">:</span> <span class=\"nb\">str</span> <span class=\"o\">=</span> <span class=\"s2\">&quot;Real</span><span class=\"si\">{}{}{}</span><span class=\"s2\">&quot;</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">ylabel</span><span class=\"p\">:</span> <span class=\"nb\">str</span> <span class=\"o\">=</span> <span class=\"s2\">&quot;Imag</span><span class=\"si\">{}{}{}</span><span class=\"s2\">&quot;</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">legend</span><span class=\"p\">:</span> <span class=\"nb\">bool</span> <span class=\"o\">=</span> <span class=\"kc\">True</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">ax</span><span class=\"p\">:</span> <span class=\"nb\">object</span> <span class=\"o\">=</span> <span class=\"kc\">None</span><span class=\"p\">,</span>\n",
       "    <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">,</span>\n",
       "<span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"nb\">tuple</span><span class=\"p\">[</span><span class=\"n\">Figure</span><span class=\"p\">,</span> <span class=\"n\">Axes</span><span class=\"p\">]:</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;Plots complex data on the imaginary plane. Points are colored by default</span>\n",
       "<span class=\"sd\">    according to their order in the array.</span>\n",
       "\n",
       "\n",
       "<span class=\"sd\">    Parameters</span>\n",
       "<span class=\"sd\">    ----------</span>\n",
       "<span class=\"sd\">    var</span>\n",
       "<span class=\"sd\">        1D array of complex data.</span>\n",
       "<span class=\"sd\">    marker</span>\n",
       "<span class=\"sd\">        Marker used for the scatter plot.</span>\n",
       "<span class=\"sd\">    label</span>\n",
       "<span class=\"sd\">        Data label for the legend.</span>\n",
       "<span class=\"sd\">    cmap</span>\n",
       "<span class=\"sd\">        The colormap to use for coloring the points.</span>\n",
       "<span class=\"sd\">    c</span>\n",
       "<span class=\"sd\">        Color of the points. Defaults to an array of integers.</span>\n",
       "<span class=\"sd\">    xlabel</span>\n",
       "<span class=\"sd\">        Label o x axes.</span>\n",
       "<span class=\"sd\">    ylabel</span>\n",
       "<span class=\"sd\">        Label o y axes.</span>\n",
       "<span class=\"sd\">    legend</span>\n",
       "<span class=\"sd\">        Calls :meth:`~matplotlib.axes.Axes.legend` if ``True``.</span>\n",
       "<span class=\"sd\">    ax</span>\n",
       "<span class=\"sd\">        The matplotlib axes. If ``None`` a new axes (and figure) is created.</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "\n",
       "    <span class=\"k\">if</span> <span class=\"n\">ax</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">_</span><span class=\"p\">,</span> <span class=\"n\">ax</span> <span class=\"o\">=</span> <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">subplots</span><span class=\"p\">()</span>\n",
       "\n",
       "    <span class=\"k\">if</span> <span class=\"n\">c</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">c</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">arange</span><span class=\"p\">(</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">))</span>\n",
       "\n",
       "    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">scatter</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"o\">.</span><span class=\"n\">real</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"o\">.</span><span class=\"n\">imag</span><span class=\"p\">,</span> <span class=\"n\">marker</span><span class=\"o\">=</span><span class=\"n\">marker</span><span class=\"p\">,</span> <span class=\"n\">label</span><span class=\"o\">=</span><span class=\"n\">label</span><span class=\"p\">,</span> <span class=\"n\">c</span><span class=\"o\">=</span><span class=\"n\">c</span><span class=\"p\">,</span> <span class=\"n\">cmap</span><span class=\"o\">=</span><span class=\"n\">cmap</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">unit_str</span> <span class=\"o\">=</span> <span class=\"n\">get_unit_from_attrs</span><span class=\"p\">(</span><span class=\"n\">var</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_xlabel</span><span class=\"p\">(</span><span class=\"n\">xlabel</span><span class=\"o\">.</span><span class=\"n\">format</span><span class=\"p\">(</span><span class=\"s2\">&quot; &quot;</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">,</span> <span class=\"n\">unit_str</span><span class=\"p\">))</span>\n",
       "    <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">set_ylabel</span><span class=\"p\">(</span><span class=\"n\">ylabel</span><span class=\"o\">.</span><span class=\"n\">format</span><span class=\"p\">(</span><span class=\"s2\">&quot; &quot;</span><span class=\"p\">,</span> <span class=\"n\">var</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">,</span> <span class=\"n\">unit_str</span><span class=\"p\">))</span>\n",
       "\n",
       "    <span class=\"k\">if</span> <span class=\"n\">legend</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">legend</span><span class=\"p\">()</span>\n",
       "\n",
       "    <span class=\"k\">return</span> <span class=\"n\">ax</span><span class=\"o\">.</span><span class=\"n\">get_figure</span><span class=\"p\">(),</span> <span class=\"n\">ax</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{plot\\PYZus{}xr\\PYZus{}complex\\PYZus{}on\\PYZus{}plane}\\PY{p}{(}\n",
       "    \\PY{n}{var}\\PY{p}{:} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{DataArray}\\PY{p}{,}\n",
       "    \\PY{n}{marker}\\PY{p}{:} \\PY{n+nb}{str} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{o}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "    \\PY{n}{label}\\PY{p}{:} \\PY{n+nb}{str} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Data on imaginary plane}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "    \\PY{n}{cmap}\\PY{p}{:} \\PY{n+nb}{str} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{viridis}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "    \\PY{n}{c}\\PY{p}{:} \\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray} \\PY{o}{|} \\PY{k+kc}{None} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
       "    \\PY{n}{xlabel}\\PY{p}{:} \\PY{n+nb}{str} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Real}\\PY{l+s+si}{\\PYZob{}\\PYZcb{}}\\PY{l+s+si}{\\PYZob{}\\PYZcb{}}\\PY{l+s+si}{\\PYZob{}\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "    \\PY{n}{ylabel}\\PY{p}{:} \\PY{n+nb}{str} \\PY{o}{=} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Imag}\\PY{l+s+si}{\\PYZob{}\\PYZcb{}}\\PY{l+s+si}{\\PYZob{}\\PYZcb{}}\\PY{l+s+si}{\\PYZob{}\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "    \\PY{n}{legend}\\PY{p}{:} \\PY{n+nb}{bool} \\PY{o}{=} \\PY{k+kc}{True}\\PY{p}{,}\n",
       "    \\PY{n}{ax}\\PY{p}{:} \\PY{n+nb}{object} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
       "    \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{,}\n",
       "\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n+nb}{tuple}\\PY{p}{[}\\PY{n}{Figure}\\PY{p}{,} \\PY{n}{Axes}\\PY{p}{]}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Plots complex data on the imaginary plane. Points are colored by default}\n",
       "\\PY{l+s+sd}{    according to their order in the array.}\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}{    var}\n",
       "\\PY{l+s+sd}{        1D array of complex data.}\n",
       "\\PY{l+s+sd}{    marker}\n",
       "\\PY{l+s+sd}{        Marker used for the scatter plot.}\n",
       "\\PY{l+s+sd}{    label}\n",
       "\\PY{l+s+sd}{        Data label for the legend.}\n",
       "\\PY{l+s+sd}{    cmap}\n",
       "\\PY{l+s+sd}{        The colormap to use for coloring the points.}\n",
       "\\PY{l+s+sd}{    c}\n",
       "\\PY{l+s+sd}{        Color of the points. Defaults to an array of integers.}\n",
       "\\PY{l+s+sd}{    xlabel}\n",
       "\\PY{l+s+sd}{        Label o x axes.}\n",
       "\\PY{l+s+sd}{    ylabel}\n",
       "\\PY{l+s+sd}{        Label o y axes.}\n",
       "\\PY{l+s+sd}{    legend}\n",
       "\\PY{l+s+sd}{        Calls :meth:`\\PYZti{}matplotlib.axes.Axes.legend` if ``True``.}\n",
       "\\PY{l+s+sd}{    ax}\n",
       "\\PY{l+s+sd}{        The matplotlib axes. If ``None`` a new axes (and figure) is created.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "    \\PY{k}{if} \\PY{n}{ax} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
       "        \\PY{n}{\\PYZus{}}\\PY{p}{,} \\PY{n}{ax} \\PY{o}{=} \\PY{n}{plt}\\PY{o}{.}\\PY{n}{subplots}\\PY{p}{(}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{if} \\PY{n}{c} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
       "        \\PY{n}{c} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{arange}\\PY{p}{(}\\PY{l+m+mi}{0}\\PY{p}{,} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{var}\\PY{p}{)}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{ax}\\PY{o}{.}\\PY{n}{scatter}\\PY{p}{(}\\PY{n}{var}\\PY{o}{.}\\PY{n}{real}\\PY{p}{,} \\PY{n}{var}\\PY{o}{.}\\PY{n}{imag}\\PY{p}{,} \\PY{n}{marker}\\PY{o}{=}\\PY{n}{marker}\\PY{p}{,} \\PY{n}{label}\\PY{o}{=}\\PY{n}{label}\\PY{p}{,} \\PY{n}{c}\\PY{o}{=}\\PY{n}{c}\\PY{p}{,} \\PY{n}{cmap}\\PY{o}{=}\\PY{n}{cmap}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{unit\\PYZus{}str} \\PY{o}{=} \\PY{n}{get\\PYZus{}unit\\PYZus{}from\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{var}\\PY{p}{)}\n",
       "    \\PY{n}{ax}\\PY{o}{.}\\PY{n}{set\\PYZus{}xlabel}\\PY{p}{(}\\PY{n}{xlabel}\\PY{o}{.}\\PY{n}{format}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{ }\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{var}\\PY{o}{.}\\PY{n}{name}\\PY{p}{,} \\PY{n}{unit\\PYZus{}str}\\PY{p}{)}\\PY{p}{)}\n",
       "    \\PY{n}{ax}\\PY{o}{.}\\PY{n}{set\\PYZus{}ylabel}\\PY{p}{(}\\PY{n}{ylabel}\\PY{o}{.}\\PY{n}{format}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{ }\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{var}\\PY{o}{.}\\PY{n}{name}\\PY{p}{,} \\PY{n}{unit\\PYZus{}str}\\PY{p}{)}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{if} \\PY{n}{legend}\\PY{p}{:}\n",
       "        \\PY{n}{ax}\\PY{o}{.}\\PY{n}{legend}\\PY{p}{(}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{ax}\\PY{o}{.}\\PY{n}{get\\PYZus{}figure}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,} \\PY{n}{ax}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mplot_xr_complex_on_plane\u001b[0m\u001b[1m(\u001b[0m\n",
       "    var: xr.DataArray,\n",
       "    marker: str = \u001b[32m\"o\"\u001b[0m,\n",
       "    label: str = \u001b[32m\"Data on imaginary plane\"\u001b[0m,\n",
       "    cmap: str = \u001b[32m\"viridis\"\u001b[0m,\n",
       "    c: np.ndarray | \u001b[3;35mNone\u001b[0m = \u001b[3;35mNone\u001b[0m,\n",
       "    xlabel: str = \u001b[32m\"Real\u001b[0m\u001b[32m{\u001b[0m\u001b[32m}\u001b[0m\u001b[32m{\u001b[0m\u001b[32m}\u001b[0m\u001b[32m{\u001b[0m\u001b[32m}\u001b[0m\u001b[32m\"\u001b[0m,\n",
       "    ylabel: str = \u001b[32m\"Imag\u001b[0m\u001b[32m{\u001b[0m\u001b[32m}\u001b[0m\u001b[32m{\u001b[0m\u001b[32m}\u001b[0m\u001b[32m{\u001b[0m\u001b[32m}\u001b[0m\u001b[32m\"\u001b[0m,\n",
       "    legend: bool = \u001b[3;92mTrue\u001b[0m,\n",
       "    ax: object = \u001b[3;35mNone\u001b[0m,\n",
       "    **kwargs,\n",
       "\u001b[1m)\u001b[0m -> tuple\u001b[1m[\u001b[0mFigure, Axes\u001b[1m]\u001b[0m:\n",
       "    \u001b[32m\"\"\u001b[0m\"Plots complex data on the imaginary plane. Points are colored by default\n",
       "    according to their order in the array.\n",
       "\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    var\n",
       "        1D array of complex data.\n",
       "    marker\n",
       "        Marker used for the scatter plot.\n",
       "    label\n",
       "        Data label for the legend.\n",
       "    cmap\n",
       "        The colormap to use for coloring the points.\n",
       "    c\n",
       "        Color of the points. Defaults to an array of integers.\n",
       "    xlabel\n",
       "        Label o x axes.\n",
       "    ylabel\n",
       "        Label o y axes.\n",
       "    legend\n",
       "        Calls :meth:`~matplotlib.axes.Axes.legend` if ``\u001b[3;92mTrue\u001b[0m``.\n",
       "    ax\n",
       "        The matplotlib axes. If ``\u001b[3;35mNone\u001b[0m`` a new axes \u001b[1m(\u001b[0mand figure\u001b[1m)\u001b[0m is created.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "\n",
       "    if ax is \u001b[3;35mNone\u001b[0m:\n",
       "        _, ax = \u001b[1;35mplt.subplots\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    if c is \u001b[3;35mNone\u001b[0m:\n",
       "        c = \u001b[1;35mnp.arange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m0\u001b[0m, \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mvar\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    \u001b[1;35max.scatter\u001b[0m\u001b[1m(\u001b[0mvar.real, var.imag, \u001b[33mmarker\u001b[0m=\u001b[35mmarker\u001b[0m, \u001b[33mlabel\u001b[0m=\u001b[35mlabel\u001b[0m, \u001b[33mc\u001b[0m=\u001b[35mc\u001b[0m, \u001b[33mcmap\u001b[0m=\u001b[35mcmap\u001b[0m, **kwargs\u001b[1m)\u001b[0m\n",
       "\n",
       "    unit_str = \u001b[1;35mget_unit_from_attrs\u001b[0m\u001b[1m(\u001b[0mvar\u001b[1m)\u001b[0m\n",
       "    \u001b[1;35max.set_xlabel\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mxlabel.format\u001b[0m\u001b[1m(\u001b[0m\u001b[32m\" \"\u001b[0m, var.name, unit_str\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\n",
       "    \u001b[1;35max.set_ylabel\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mylabel.format\u001b[0m\u001b[1m(\u001b[0m\u001b[32m\" \"\u001b[0m, var.name, unit_str\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    if legend:\n",
       "        \u001b[1;35max.legend\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    return \u001b[1;35max.get_figure\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m, ax"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_source_code(plot_xr_complex)\n",
    "display_source_code(plot_xr_complex_on_plane)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5acb8de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 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"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "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 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": [
    "plot_xr_complex(dataset_gridded.q0_iq_av)\n",
    "fig, ax = plot_xr_complex_on_plane(dataset_gridded.q0_iq_av)\n",
    "_ = plot_complex_points(centers, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a9a2d7f",
   "metadata": {},
   "source": [
    "### T1 experiment averaged with calibration points\n",
    "\n",
    "It is common for many experiments to require calibration data in order to interpret the\n",
    "results. Often, these calibration data points have different array shapes. E.g. it can be\n",
    "just two simple data points corresponding to the ground and excited states of our\n",
    "transmon.\n",
    "\n",
    "To accommodate this data in the dataset we make use of a secondary dimension along which\n",
    "the variables and its coordinate will lie along.\n",
    "\n",
    "Additionally, since the secondary variable and coordinate used for calibration can have\n",
    "arbitrary names and relate to other variables in more complex ways, we specify this\n",
    "relationship in the dataset attributes\n",
    "(see {class}`~quantify_core.data.dataset_attrs.QDatasetIntraRelationship`).\n",
    "This information can be used later, for example, to run an appropriate analysis on this\n",
    "dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bc2ce765",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Source code for generating the dataset below"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">mk_t1_av_with_cal_dataset</span><span class=\"p\">(</span>\n",
       "    <span class=\"n\">t1_times</span><span class=\"p\">:</span> <span class=\"n\">Optional</span><span class=\"p\">[</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">ndarray</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"kc\">None</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">probabilities</span><span class=\"p\">:</span> <span class=\"n\">Optional</span><span class=\"p\">[</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">ndarray</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"kc\">None</span><span class=\"p\">,</span>\n",
       "    <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">,</span>\n",
       "<span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    Generates a dataset with mock data of a T1 experiment for a single qubit including</span>\n",
       "<span class=\"sd\">    calibration points for the ground and excited states.</span>\n",
       "\n",
       "<span class=\"sd\">    Parameters</span>\n",
       "<span class=\"sd\">    ----------</span>\n",
       "<span class=\"sd\">    t1_times</span>\n",
       "<span class=\"sd\">        Array with the T1 times corresponding to each probability in ``probabilities``.</span>\n",
       "<span class=\"sd\">    probabilities</span>\n",
       "<span class=\"sd\">        The probabilities of finding the qubit in the excited state.</span>\n",
       "<span class=\"sd\">    **kwargs</span>\n",
       "<span class=\"sd\">        Keyword arguments passed to</span>\n",
       "<span class=\"sd\">        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    <span class=\"c1\"># reuse previous dataset</span>\n",
       "    <span class=\"n\">dataset_av</span> <span class=\"o\">=</span> <span class=\"n\">mk_t1_av_dataset</span><span class=\"p\">(</span><span class=\"n\">t1_times</span><span class=\"p\">,</span> <span class=\"n\">probabilities</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"c1\"># generate mock calibration data for the ground and excited states</span>\n",
       "    <span class=\"n\">q0_iq_av_cal</span> <span class=\"o\">=</span> <span class=\"n\">mk_shots_from_probabilities</span><span class=\"p\">([</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">],</span> <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">mean</span><span class=\"p\">(</span><span class=\"n\">axis</span><span class=\"o\">=</span><span class=\"mi\">0</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">secondary_dims</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"s2\">&quot;cal_dim&quot;</span><span class=\"p\">,)</span>\n",
       "    <span class=\"n\">q0_cal_attrs</span> <span class=\"o\">=</span> <span class=\"n\">mk_secondary_var_attrs</span><span class=\"p\">(</span><span class=\"n\">unit</span><span class=\"o\">=</span><span class=\"s2\">&quot;V&quot;</span><span class=\"p\">,</span> <span class=\"n\">long_name</span><span class=\"o\">=</span><span class=\"s2\">&quot;Q0 IQ Calibration&quot;</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">cal_attrs</span> <span class=\"o\">=</span> <span class=\"n\">mk_secondary_coord_attrs</span><span class=\"p\">(</span><span class=\"n\">unit</span><span class=\"o\">=</span><span class=\"s2\">&quot;&quot;</span><span class=\"p\">,</span> <span class=\"n\">long_name</span><span class=\"o\">=</span><span class=\"s2\">&quot;Q0 state&quot;</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">relationships</span> <span class=\"o\">=</span> <span class=\"p\">[</span>\n",
       "        <span class=\"n\">dattrs</span><span class=\"o\">.</span><span class=\"n\">QDatasetIntraRelationship</span><span class=\"p\">(</span>\n",
       "            <span class=\"n\">item_name</span><span class=\"o\">=</span><span class=\"n\">dataset_av</span><span class=\"o\">.</span><span class=\"n\">q0_iq_av</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">,</span>  <span class=\"c1\"># name of a variable in the dataset</span>\n",
       "            <span class=\"n\">relation_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;calibration&quot;</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">related_names</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s2\">&quot;q0_iq_av_cal&quot;</span><span class=\"p\">],</span>  <span class=\"c1\"># the secondary variable in the dataset</span>\n",
       "        <span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">to_dict</span><span class=\"p\">()</span>\n",
       "    <span class=\"p\">]</span>\n",
       "\n",
       "    <span class=\"n\">data_vars</span> <span class=\"o\">=</span> <span class=\"nb\">dict</span><span class=\"p\">(</span>\n",
       "        <span class=\"n\">q0_iq_av</span><span class=\"o\">=</span><span class=\"n\">dataset_av</span><span class=\"o\">.</span><span class=\"n\">q0_iq_av</span><span class=\"p\">,</span>  <span class=\"c1\"># reuse from the other dataset</span>\n",
       "        <span class=\"n\">q0_iq_av_cal</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">secondary_dims</span><span class=\"p\">,</span> <span class=\"n\">q0_iq_av_cal</span><span class=\"p\">,</span> <span class=\"n\">q0_cal_attrs</span><span class=\"p\">),</span>\n",
       "    <span class=\"p\">)</span>\n",
       "    <span class=\"n\">coords</span> <span class=\"o\">=</span> <span class=\"nb\">dict</span><span class=\"p\">(</span>\n",
       "        <span class=\"n\">t1_time</span><span class=\"o\">=</span><span class=\"n\">dataset_av</span><span class=\"o\">.</span><span class=\"n\">t1_time</span><span class=\"p\">,</span>  <span class=\"c1\"># reuse from the other dataset</span>\n",
       "        <span class=\"n\">cal</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">secondary_dims</span><span class=\"p\">,</span> <span class=\"p\">[</span><span class=\"s2\">&quot;|0&gt;&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;|1&gt;&quot;</span><span class=\"p\">],</span> <span class=\"n\">cal_attrs</span><span class=\"p\">),</span>  <span class=\"c1\"># coords can be strings</span>\n",
       "    <span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">dataset</span> <span class=\"o\">=</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">(</span>\n",
       "        <span class=\"n\">data_vars</span><span class=\"o\">=</span><span class=\"n\">data_vars</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">coords</span><span class=\"o\">=</span><span class=\"n\">coords</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">attrs</span><span class=\"o\">=</span><span class=\"n\">mk_dataset_attrs</span><span class=\"p\">(</span><span class=\"n\">relationships</span><span class=\"o\">=</span><span class=\"n\">relationships</span><span class=\"p\">),</span>  <span class=\"c1\"># relationships added here</span>\n",
       "    <span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"k\">return</span> <span class=\"n\">dataset</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}t1\\PYZus{}av\\PYZus{}with\\PYZus{}cal\\PYZus{}dataset}\\PY{p}{(}\n",
       "    \\PY{n}{t1\\PYZus{}times}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
       "    \\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
       "    \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\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 mock data of a T1 experiment for a single qubit including}\n",
       "\\PY{l+s+sd}{    calibration points for the ground and excited 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}{    t1\\PYZus{}times}\n",
       "\\PY{l+s+sd}{        Array with the T1 times corresponding to each probability in ``probabilities``.}\n",
       "\\PY{l+s+sd}{    probabilities}\n",
       "\\PY{l+s+sd}{        The probabilities of finding the qubit in the excited state.}\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",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{c+c1}{\\PYZsh{} reuse previous dataset}\n",
       "    \\PY{n}{dataset\\PYZus{}av} \\PY{o}{=} \\PY{n}{mk\\PYZus{}t1\\PYZus{}av\\PYZus{}dataset}\\PY{p}{(}\\PY{n}{t1\\PYZus{}times}\\PY{p}{,} \\PY{n}{probabilities}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} generate mock calibration data for the ground and excited states}\n",
       "    \\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{,} \\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\\PY{o}{.}\\PY{n}{mean}\\PY{p}{(}\\PY{n}{axis}\\PY{o}{=}\\PY{l+m+mi}{0}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{secondary\\PYZus{}dims} \\PY{o}{=} \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{cal\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\\PY{p}{)}\n",
       "    \\PY{n}{q0\\PYZus{}cal\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}secondary\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\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+s2}{\\PYZdq{}}\\PY{l+s+s2}{Q0 IQ Calibration}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{cal\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}secondary\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Q0 state}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{relationships} \\PY{o}{=} \\PY{p}{[}\n",
       "        \\PY{n}{dattrs}\\PY{o}{.}\\PY{n}{QDatasetIntraRelationship}\\PY{p}{(}\n",
       "            \\PY{n}{item\\PYZus{}name}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}av}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{o}{.}\\PY{n}{name}\\PY{p}{,}  \\PY{c+c1}{\\PYZsh{} name of a variable in the dataset}\n",
       "            \\PY{n}{relation\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{calibration}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{related\\PYZus{}names}\\PY{o}{=}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]}\\PY{p}{,}  \\PY{c+c1}{\\PYZsh{} the secondary variable in the dataset}\n",
       "        \\PY{p}{)}\\PY{o}{.}\\PY{n}{to\\PYZus{}dict}\\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}{q0\\PYZus{}iq\\PYZus{}av}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}av}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{p}{,}  \\PY{c+c1}{\\PYZsh{} reuse from the other dataset}\n",
       "        \\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{o}{=}\\PY{p}{(}\\PY{n}{secondary\\PYZus{}dims}\\PY{p}{,} \\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{p}{,} \\PY{n}{q0\\PYZus{}cal\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "    \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{t1\\PYZus{}time}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}av}\\PY{o}{.}\\PY{n}{t1\\PYZus{}time}\\PY{p}{,}  \\PY{c+c1}{\\PYZsh{} reuse from the other dataset}\n",
       "        \\PY{n}{cal}\\PY{o}{=}\\PY{p}{(}\\PY{n}{secondary\\PYZus{}dims}\\PY{p}{,} \\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{|0\\PYZgt{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{|1\\PYZgt{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]}\\PY{p}{,} \\PY{n}{cal\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}  \\PY{c+c1}{\\PYZsh{} coords can be strings}\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{n}{relationships}\\PY{o}{=}\\PY{n}{relationships}\\PY{p}{)}\\PY{p}{,}  \\PY{c+c1}{\\PYZsh{} relationships added here}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{dataset}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_t1_av_with_cal_dataset\u001b[0m\u001b[1m(\u001b[0m\n",
       "    t1_times: Optional\u001b[1m[\u001b[0mnp.ndarray\u001b[1m]\u001b[0m = \u001b[3;35mNone\u001b[0m,\n",
       "    probabilities: Optional\u001b[1m[\u001b[0mnp.ndarray\u001b[1m]\u001b[0m = \u001b[3;35mNone\u001b[0m,\n",
       "    **kwargs,\n",
       "\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generates a dataset with mock data of a T1 experiment for a single qubit including\n",
       "    calibration points for the ground and excited states.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    t1_times\n",
       "        Array with the T1 times corresponding to each probability in ``probabilities``.\n",
       "    probabilities\n",
       "        The probabilities of finding the qubit in the excited state.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to\n",
       "        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    # reuse previous dataset\n",
       "    dataset_av = \u001b[1;35mmk_t1_av_dataset\u001b[0m\u001b[1m(\u001b[0mt1_times, probabilities, **kwargs\u001b[1m)\u001b[0m\n",
       "\n",
       "    # generate mock calibration data for the ground and excited states\n",
       "    q0_iq_av_cal = \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m, \u001b[1;36m1\u001b[0m\u001b[1m]\u001b[0m, **kwargs\u001b[1m)\u001b[0m\u001b[1;35m.mean\u001b[0m\u001b[1m(\u001b[0m\u001b[33maxis\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    secondary_dims = \u001b[1m(\u001b[0m\u001b[32m\"cal_dim\"\u001b[0m,\u001b[1m)\u001b[0m\n",
       "    q0_cal_attrs = \u001b[1;35mmk_secondary_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33munit\u001b[0m=\u001b[32m\"V\"\u001b[0m, \u001b[33mlong_name\u001b[0m=\u001b[32m\"Q0\u001b[0m\u001b[32m IQ Calibration\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    cal_attrs = \u001b[1;35mmk_secondary_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33munit\u001b[0m=\u001b[32m\"\"\u001b[0m, \u001b[33mlong_name\u001b[0m=\u001b[32m\"Q0\u001b[0m\u001b[32m state\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    relationships = \u001b[1m[\u001b[0m\n",
       "        \u001b[1;35mdattrs.QDatasetIntraRelationship\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[33mitem_name\u001b[0m=\u001b[35mdataset_av\u001b[0m.q0_iq_av.name,  # name of a variable in the dataset\n",
       "            \u001b[33mrelation_type\u001b[0m=\u001b[32m\"calibration\"\u001b[0m,\n",
       "            \u001b[33mrelated_names\u001b[0m=\u001b[1m[\u001b[0m\u001b[32m\"q0_iq_av_cal\"\u001b[0m\u001b[1m]\u001b[0m,  # the secondary variable in the dataset\n",
       "        \u001b[1m)\u001b[0m\u001b[1;35m.to_dict\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
       "    \u001b[1m]\u001b[0m\n",
       "\n",
       "    data_vars = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mq0_iq_av\u001b[0m=\u001b[35mdataset_av\u001b[0m.q0_iq_av,  # reuse from the other dataset\n",
       "        \u001b[33mq0_iq_av_cal\u001b[0m=\u001b[1m(\u001b[0msecondary_dims, q0_iq_av_cal, q0_cal_attrs\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "    coords = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mt1_time\u001b[0m=\u001b[35mdataset_av\u001b[0m.t1_time,  # reuse from the other dataset\n",
       "        \u001b[33mcal\u001b[0m=\u001b[1m(\u001b[0msecondary_dims, \u001b[1m[\u001b[0m\u001b[32m\"|0>\"\u001b[0m, \u001b[32m\"|1>\"\u001b[0m\u001b[1m]\u001b[0m, cal_attrs\u001b[1m)\u001b[0m,  # coords can be strings\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[33mrelationships\u001b[0m=\u001b[35mrelationships\u001b[0m\u001b[1m)\u001b[0m,  # relationships added here\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    return dataset"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_source_code(dataset_examples.mk_t1_av_with_cal_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cbd22722",
   "metadata": {},
   "outputs": [
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       "Coordinates:\n",
       "    t1_time       (main_dim) float64 0.0 4.138e-06 ... 0.0001159 0.00012\n",
       "    cal           (cal_dim) &lt;U3 &#x27;|0&gt;&#x27; &#x27;|1&gt;&#x27;\n",
       "Dimensions without coordinates: main_dim, cal_dim\n",
       "Data variables:\n",
       "    q0_iq_av      (main_dim) complex128 (-0.19894114958423859+0.6515500138845...\n",
       "    q0_iq_av_cal  (cal_dim) complex128 (0.7010588504157614-0.3984499861154196...\n",
       "Attributes:\n",
       "    tuid:                      20231215-162529-205-0b1f3d\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:             [{&#x27;item_name&#x27;: &#x27;q0_iq_av&#x27;, &#x27;relation_type&#x27;: &#x27;c...\n",
       "    json_serialize_exclude:    []</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9ff19c99-8f7b-40ce-a159-52415f70913c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9ff19c99-8f7b-40ce-a159-52415f70913c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>main_dim</span>: 30</li><li><span>cal_dim</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-085242db-f768-447f-8768-5e8a6a9092ca' class='xr-section-summary-in' type='checkbox'  checked><label for='section-085242db-f768-447f-8768-5e8a6a9092ca' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t1_time</span></div><div class='xr-var-dims'>(main_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 4.138e-06 ... 0.0001159 0.00012</div><input id='attrs-cb8e6f58-243c-4394-ad02-4fc6e19a2b70' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb8e6f58-243c-4394-ad02-4fc6e19a2b70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef0d601f-1ffe-4668-9222-4a920f558f20' class='xr-var-data-in' type='checkbox'><label for='data-ef0d601f-1ffe-4668-9222-4a920f558f20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>T1 Time</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.00000000e+00, 4.13793103e-06, 8.27586207e-06, 1.24137931e-05,\n",
       "       1.65517241e-05, 2.06896552e-05, 2.48275862e-05, 2.89655172e-05,\n",
       "       3.31034483e-05, 3.72413793e-05, 4.13793103e-05, 4.55172414e-05,\n",
       "       4.96551724e-05, 5.37931034e-05, 5.79310345e-05, 6.20689655e-05,\n",
       "       6.62068966e-05, 7.03448276e-05, 7.44827586e-05, 7.86206897e-05,\n",
       "       8.27586207e-05, 8.68965517e-05, 9.10344828e-05, 9.51724138e-05,\n",
       "       9.93103448e-05, 1.03448276e-04, 1.07586207e-04, 1.11724138e-04,\n",
       "       1.15862069e-04, 1.20000000e-04])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cal</span></div><div class='xr-var-dims'>(cal_dim)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;|0&gt;&#x27; &#x27;|1&gt;&#x27;</div><input id='attrs-8a3e864c-8027-4cd5-9d82-e23a9c43ab77' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a3e864c-8027-4cd5-9d82-e23a9c43ab77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e383ca04-3aa9-4525-8030-03ef0a1a81cc' class='xr-var-data-in' type='checkbox'><label for='data-e383ca04-3aa9-4525-8030-03ef0a1a81cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Q0 state</dd><dt><span>is_main_coord :</span></dt><dd>False</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;|0&gt;&#x27;, &#x27;|1&gt;&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-644c7eae-0fdd-44d1-b804-4bf71648fa9f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-644c7eae-0fdd-44d1-b804-4bf71648fa9f' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_av</span></div><div class='xr-var-dims'>(main_dim)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(-0.19894114958423859+0.65155001...</div><input id='attrs-119899e3-5696-406f-b5cf-a50e9d71a29c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-119899e3-5696-406f-b5cf-a50e9d71a29c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0234eb17-8b73-479f-8492-4ff5516dfe61' class='xr-var-data-in' type='checkbox'><label for='data-0234eb17-8b73-479f-8492-4ff5516dfe61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ amplitude</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([-0.19894115+0.65155001j, -0.11155212+0.54951255j,\n",
       "       -0.01216713+0.43313537j,  0.05193768+0.35848368j,\n",
       "        0.15504951+0.2389656j ,  0.20661408+0.17861197j,\n",
       "        0.2833043 +0.08961707j,  0.33361813+0.03110011j,\n",
       "        0.37345395-0.01576696j,  0.40534772-0.05352011j,\n",
       "        0.42151034-0.07261241j,  0.47040441-0.12912367j,\n",
       "        0.49717119-0.16057795j,  0.53565518-0.20550725j,\n",
       "        0.56438276-0.23892233j,  0.58075078-0.25822004j,\n",
       "        0.59794213-0.27834108j,  0.60292187-0.28390675j,\n",
       "        0.60292187-0.28390675j,  0.61706292-0.30039156j,\n",
       "        0.6204547 -0.30436927j,  0.62775114-0.31283759j,\n",
       "        0.64350021-0.33151635j,  0.64683289-0.33543496j,\n",
       "        0.65442395-0.34419789j,  0.65442395-0.34419789j,\n",
       "        0.65650831-0.34686819j,  0.66449532-0.35602708j,\n",
       "        0.66901604-0.36113374j,  0.66901604-0.36113374j])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_av_cal</span></div><div class='xr-var-dims'>(cal_dim)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(0.7010588504157614-0.3984499861...</div><input id='attrs-0c7613d2-9540-4b93-bf70-4a567d6919f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c7613d2-9540-4b93-bf70-4a567d6919f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d9cb045-8839-44f7-8fb6-e6d30b43b9ea' class='xr-var-data-in' type='checkbox'><label for='data-0d9cb045-8839-44f7-8fb6-e6d30b43b9ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ Calibration</dd><dt><span>is_main_var :</span></dt><dd>False</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([ 0.70105885-0.39844999j, -0.19894115+0.65155001j])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d74f1cdc-ad8d-43de-adbf-381835a1118a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d74f1cdc-ad8d-43de-adbf-381835a1118a' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-e6560e3e-1943-42af-aa69-c212bc60036c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e6560e3e-1943-42af-aa69-c212bc60036c' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20231215-162529-205-0b1f3d</dd><dt><span>dataset_name :</span></dt><dd></dd><dt><span>dataset_state :</span></dt><dd>None</dd><dt><span>timestamp_start :</span></dt><dd>None</dd><dt><span>timestamp_end :</span></dt><dd>None</dd><dt><span>quantify_dataset_version :</span></dt><dd>2.0.0</dd><dt><span>software_versions :</span></dt><dd>{}</dd><dt><span>relationships :</span></dt><dd>[{&#x27;item_name&#x27;: &#x27;q0_iq_av&#x27;, &#x27;relation_type&#x27;: &#x27;calibration&#x27;, &#x27;related_names&#x27;: [&#x27;q0_iq_av_cal&#x27;], &#x27;relation_metadata&#x27;: {}}]</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39mDimensions:       \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mmain_dim: \u001b[0m\u001b[1;36m30\u001b[0m\u001b[39m, cal_dim: \u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;39m)\u001b[0m\n",
       "\u001b[39mCoordinates:\u001b[0m\n",
       "\u001b[39m    t1_time       \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mmain_dim\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m float64 \u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[39m \u001b[0m\u001b[1;36m4.138e-06\u001b[0m\u001b[39m \u001b[0m\u001b[33m...\u001b[0m\u001b[39m \u001b[0m\u001b[1;36m0.0001159\u001b[0m\u001b[39m \u001b[0m\u001b[1;36m0.00012\u001b[0m\n",
       "\u001b[39m    cal           \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mcal_dim\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m <U3 \u001b[0m\u001b[32m'|0>'\u001b[0m\u001b[39m \u001b[0m\u001b[32m'|1\u001b[0m\u001b[32m>\u001b[0m\u001b[32m'\u001b[0m\n",
       "Dimensions without coordinates: main_dim, cal_dim\n",
       "Data variables:\n",
       "    q0_iq_av      \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.19894114958423859\u001b[0m+\u001b[1;36m0.6515500138845\u001b[0m\u001b[33m...\u001b[0m\n",
       "    q0_iq_av_cal  \u001b[1m(\u001b[0mcal_dim\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m0.7010588504157614\u001b[0m-\u001b[1;36m0.3984499861154196\u001b[0m\u001b[33m...\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20231215\u001b[0m-\u001b[1;36m162529\u001b[0m-\u001b[1;36m205\u001b[0m-0b1f3d\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\u001b[32m'item_name'\u001b[0m: \u001b[32m'q0_iq_av'\u001b[0m, \u001b[32m'relation_type'\u001b[0m: 'c\u001b[33m...\u001b[0m\n",
       "    json_serialize_exclude:    \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset_examples.mk_t1_av_with_cal_dataset(**mock_conf)\n",
    "assert dataset == round_trip_dataset(dataset)  # confirm read/write\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8132a26b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/plain": [
       "\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[32m'main_dim'\u001b[0m\u001b[1m]\u001b[0m, \u001b[1m[\u001b[0m\u001b[32m'cal_dim'\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dattrs.get_main_dims(dataset), dattrs.get_secondary_dims(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "44be374f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\u001b[1m[\u001b[0m\n",
       "    \u001b[1m{\u001b[0m\n",
       "        \u001b[32m'item_name'\u001b[0m: \u001b[32m'q0_iq_av'\u001b[0m,\n",
       "        \u001b[32m'relation_type'\u001b[0m: \u001b[32m'calibration'\u001b[0m,\n",
       "        \u001b[32m'related_names'\u001b[0m: \u001b[1m[\u001b[0m\u001b[32m'q0_iq_av_cal'\u001b[0m\u001b[1m]\u001b[0m,\n",
       "        \u001b[32m'relation_metadata'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
       "    \u001b[1m}\u001b[0m\n",
       "\u001b[1m]\u001b[0m"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.relationships"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69094ad3",
   "metadata": {},
   "source": [
    "As before the coordinates can be set to index the variables that lie along the same\n",
    "dimensions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "20be5290",
   "metadata": {},
   "outputs": [
    {
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     "metadata": {},
     "output_type": "display_data"
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
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       "\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:       (t1_time: 30, cal: 2)\n",
       "Coordinates:\n",
       "  * t1_time       (t1_time) float64 0.0 4.138e-06 ... 0.0001159 0.00012\n",
       "  * cal           (cal) &lt;U3 &#x27;|0&gt;&#x27; &#x27;|1&gt;&#x27;\n",
       "Data variables:\n",
       "    q0_iq_av      (t1_time) complex128 (-0.19894114958423859+0.65155001388458...\n",
       "    q0_iq_av_cal  (cal) complex128 (0.7010588504157614-0.3984499861154196j) (...\n",
       "Attributes:\n",
       "    tuid:                      20231215-162529-205-0b1f3d\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:             [{&#x27;item_name&#x27;: &#x27;q0_iq_av&#x27;, &#x27;relation_type&#x27;: &#x27;c...\n",
       "    json_serialize_exclude:    []</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1f11c83d-8fbb-4264-a21d-f69d03e6ab23' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1f11c83d-8fbb-4264-a21d-f69d03e6ab23' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>t1_time</span>: 30</li><li><span class='xr-has-index'>cal</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-af0a9421-bea2-4638-9b03-c06e4aa111fd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-af0a9421-bea2-4638-9b03-c06e4aa111fd' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t1_time</span></div><div class='xr-var-dims'>(t1_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 4.138e-06 ... 0.0001159 0.00012</div><input id='attrs-01d6f05e-76d2-40b1-9545-e685ed55e762' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01d6f05e-76d2-40b1-9545-e685ed55e762' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3414b131-56c6-4539-9c36-d6f29ed9b86b' class='xr-var-data-in' type='checkbox'><label for='data-3414b131-56c6-4539-9c36-d6f29ed9b86b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>T1 Time</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.000000e+00, 4.137931e-06, 8.275862e-06, 1.241379e-05, 1.655172e-05,\n",
       "       2.068966e-05, 2.482759e-05, 2.896552e-05, 3.310345e-05, 3.724138e-05,\n",
       "       4.137931e-05, 4.551724e-05, 4.965517e-05, 5.379310e-05, 5.793103e-05,\n",
       "       6.206897e-05, 6.620690e-05, 7.034483e-05, 7.448276e-05, 7.862069e-05,\n",
       "       8.275862e-05, 8.689655e-05, 9.103448e-05, 9.517241e-05, 9.931034e-05,\n",
       "       1.034483e-04, 1.075862e-04, 1.117241e-04, 1.158621e-04, 1.200000e-04])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cal</span></div><div class='xr-var-dims'>(cal)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;|0&gt;&#x27; &#x27;|1&gt;&#x27;</div><input id='attrs-d38b25d3-3a6f-4295-a58b-1453ce1d4c99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d38b25d3-3a6f-4295-a58b-1453ce1d4c99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2010dbec-f16e-4b65-9f4c-2267f9dc4a65' class='xr-var-data-in' type='checkbox'><label for='data-2010dbec-f16e-4b65-9f4c-2267f9dc4a65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Q0 state</dd><dt><span>is_main_coord :</span></dt><dd>False</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;|0&gt;&#x27;, &#x27;|1&gt;&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f90a2038-247b-4d32-b917-b94b7dff6081' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f90a2038-247b-4d32-b917-b94b7dff6081' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_av</span></div><div class='xr-var-dims'>(t1_time)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(-0.19894114958423859+0.65155001...</div><input id='attrs-a3caf77e-f2ae-48ea-874c-45a232e8d14c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3caf77e-f2ae-48ea-874c-45a232e8d14c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2726b15-cf6d-434a-bbd7-034a33bb8200' class='xr-var-data-in' type='checkbox'><label for='data-a2726b15-cf6d-434a-bbd7-034a33bb8200' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ amplitude</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([-0.19894115+0.65155001j, -0.11155212+0.54951255j,\n",
       "       -0.01216713+0.43313537j,  0.05193768+0.35848368j,\n",
       "        0.15504951+0.2389656j ,  0.20661408+0.17861197j,\n",
       "        0.2833043 +0.08961707j,  0.33361813+0.03110011j,\n",
       "        0.37345395-0.01576696j,  0.40534772-0.05352011j,\n",
       "        0.42151034-0.07261241j,  0.47040441-0.12912367j,\n",
       "        0.49717119-0.16057795j,  0.53565518-0.20550725j,\n",
       "        0.56438276-0.23892233j,  0.58075078-0.25822004j,\n",
       "        0.59794213-0.27834108j,  0.60292187-0.28390675j,\n",
       "        0.60292187-0.28390675j,  0.61706292-0.30039156j,\n",
       "        0.6204547 -0.30436927j,  0.62775114-0.31283759j,\n",
       "        0.64350021-0.33151635j,  0.64683289-0.33543496j,\n",
       "        0.65442395-0.34419789j,  0.65442395-0.34419789j,\n",
       "        0.65650831-0.34686819j,  0.66449532-0.35602708j,\n",
       "        0.66901604-0.36113374j,  0.66901604-0.36113374j])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_av_cal</span></div><div class='xr-var-dims'>(cal)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(0.7010588504157614-0.3984499861...</div><input id='attrs-ac7e3f45-e980-4428-9d57-dcf71a02ab94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac7e3f45-e980-4428-9d57-dcf71a02ab94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebbca3b6-b00c-469b-8df2-b477f0fcbe62' class='xr-var-data-in' type='checkbox'><label for='data-ebbca3b6-b00c-469b-8df2-b477f0fcbe62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ Calibration</dd><dt><span>is_main_var :</span></dt><dd>False</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([ 0.70105885-0.39844999j, -0.19894115+0.65155001j])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fcefc269-6ede-4c77-9140-c606d262178b' class='xr-section-summary-in' type='checkbox'  ><label for='section-fcefc269-6ede-4c77-9140-c606d262178b' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>t1_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f33dc006-3249-4153-91bd-842c1f9b5b1b' class='xr-index-data-in' type='checkbox'/><label for='index-f33dc006-3249-4153-91bd-842c1f9b5b1b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                   0.0,  4.137931034482758e-06,  8.275862068965517e-06,\n",
       "       1.2413793103448275e-05, 1.6551724137931033e-05,  2.068965517241379e-05,\n",
       "        2.482758620689655e-05,  2.896551724137931e-05, 3.3103448275862067e-05,\n",
       "        3.724137931034482e-05,  4.137931034482758e-05,  4.551724137931034e-05,\n",
       "         4.96551724137931e-05, 5.3793103448275856e-05,  5.793103448275862e-05,\n",
       "        6.206896551724137e-05,  6.620689655172413e-05,   7.03448275862069e-05,\n",
       "        7.448275862068965e-05,  7.862068965517241e-05,  8.275862068965516e-05,\n",
       "        8.689655172413792e-05,  9.103448275862069e-05,  9.517241379310344e-05,\n",
       "         9.93103448275862e-05, 0.00010344827586206896, 0.00010758620689655171,\n",
       "       0.00011172413793103448, 0.00011586206896551724,                0.00012],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;t1_time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>cal</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5c395a08-31a3-4408-8f8d-0f5e88a3265d' class='xr-index-data-in' type='checkbox'/><label for='index-5c395a08-31a3-4408-8f8d-0f5e88a3265d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;|0&gt;&#x27;, &#x27;|1&gt;&#x27;], dtype=&#x27;object&#x27;, name=&#x27;cal&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4231eaa2-357c-4b86-9c8d-d2bbd33cfa59' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4231eaa2-357c-4b86-9c8d-d2bbd33cfa59' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20231215-162529-205-0b1f3d</dd><dt><span>dataset_name :</span></dt><dd></dd><dt><span>dataset_state :</span></dt><dd>None</dd><dt><span>timestamp_start :</span></dt><dd>None</dd><dt><span>timestamp_end :</span></dt><dd>None</dd><dt><span>quantify_dataset_version :</span></dt><dd>2.0.0</dd><dt><span>software_versions :</span></dt><dd>{}</dd><dt><span>relationships :</span></dt><dd>[{&#x27;item_name&#x27;: &#x27;q0_iq_av&#x27;, &#x27;relation_type&#x27;: &#x27;calibration&#x27;, &#x27;related_names&#x27;: [&#x27;q0_iq_av_cal&#x27;], &#x27;relation_metadata&#x27;: {}}]</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39mDimensions:       \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mt1_time: \u001b[0m\u001b[1;36m30\u001b[0m\u001b[39m, cal: \u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;39m)\u001b[0m\n",
       "\u001b[39mCoordinates:\u001b[0m\n",
       "\u001b[39m  * t1_time       \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mt1_time\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m float64 \u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[39m \u001b[0m\u001b[1;36m4.138e-06\u001b[0m\u001b[39m \u001b[0m\u001b[33m...\u001b[0m\u001b[39m \u001b[0m\u001b[1;36m0.0001159\u001b[0m\u001b[39m \u001b[0m\u001b[1;36m0.00012\u001b[0m\n",
       "\u001b[39m  * cal           \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mcal\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m <U3 \u001b[0m\u001b[32m'|0>'\u001b[0m\u001b[39m \u001b[0m\u001b[32m'|1\u001b[0m\u001b[32m>\u001b[0m\u001b[32m'\u001b[0m\n",
       "Data variables:\n",
       "    q0_iq_av      \u001b[1m(\u001b[0mt1_time\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.19894114958423859\u001b[0m+\u001b[1;36m0.65155001388458\u001b[0m\u001b[33m...\u001b[0m\n",
       "    q0_iq_av_cal  \u001b[1m(\u001b[0mcal\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m0.7010588504157614-0.3984499861154196j\u001b[0m\u001b[1m)\u001b[0m \u001b[1m(\u001b[0m\u001b[33m...\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20231215\u001b[0m-\u001b[1;36m162529\u001b[0m-\u001b[1;36m205\u001b[0m-0b1f3d\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\u001b[32m'item_name'\u001b[0m: \u001b[32m'q0_iq_av'\u001b[0m, \u001b[32m'relation_type'\u001b[0m: 'c\u001b[33m...\u001b[0m\n",
       "    json_serialize_exclude:    \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset,\n",
    "    dimension=\"main_dim\",\n",
    "    coords_names=dattrs.get_main_coords(dataset),\n",
    ")\n",
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset_gridded,\n",
    "    dimension=\"cal_dim\",\n",
    "    coords_names=dattrs.get_secondary_coords(dataset_gridded),\n",
    ")\n",
    "dataset_gridded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "94386fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 80\u001b[0m\u001b[1;36m0x500\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m2\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\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 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 = plt.figure(figsize=(8, 5))\n",
    "\n",
    "ax = plt.subplot2grid((1, 10), (0, 0), colspan=9, fig=fig)\n",
    "plot_xr_complex(dataset_gridded.q0_iq_av, ax=ax)\n",
    "\n",
    "ax_calib = plt.subplot2grid((1, 10), (0, 9), colspan=1, fig=fig, sharey=ax)\n",
    "for i, color in zip(\n",
    "    range(2), [\"C0\", \"C1\"]\n",
    "):  # plot each calibration point with same color\n",
    "    dataset_gridded.q0_iq_av_cal.real[i : i + 1].plot.line(\n",
    "        marker=\"o\", ax=ax_calib, linestyle=\"\", color=color\n",
    "    )\n",
    "    dataset_gridded.q0_iq_av_cal.imag[i : i + 1].plot.line(\n",
    "        marker=\"o\", ax=ax_calib, linestyle=\"\", color=color\n",
    "    )\n",
    "ax_calib.yaxis.set_label_position(\"right\")\n",
    "ax_calib.yaxis.tick_right()\n",
    "\n",
    "fig, ax = plot_xr_complex_on_plane(dataset_gridded.q0_iq_av)\n",
    "_ = plot_complex_points(dataset_gridded.q0_iq_av_cal.values, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afed9f4e",
   "metadata": {},
   "source": [
    "We can use the calibration points to normalize the data and obtain the typical T1 decay.\n",
    "\n",
    "### Data rotation and normalization utilities\n",
    "\n",
    "The normalization of the calibration points can be achieved as follows.\n",
    "Several of the\n",
    "{mod}`single-qubit time-domain analyses <quantify_core.analysis.single_qubit_timedomain>`\n",
    "provided use this under the hood.\n",
    "The result is that most of the information will now be contained within the same\n",
    "quadrature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "39458494",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 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": [
    "rotated_and_normalized = rotate_to_calibrated_axis(\n",
    "    dataset_gridded.q0_iq_av.values, *dataset_gridded.q0_iq_av_cal.values\n",
    ")\n",
    "rotated_and_normalized_da = xr.DataArray(dataset_gridded.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",
    "_ = plot_xr_complex(rotated_and_normalized_da)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ea18eeb",
   "metadata": {},
   "source": [
    "### T1 experiment storing all shots\n",
    "\n",
    "Now we will include in the dataset all the single qubit states (shot) for each\n",
    "individual measurement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e46844e8",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Source code for generating the dataset below"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
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     "metadata": {},
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">mk_t1_shots_dataset</span><span class=\"p\">(</span>\n",
       "    <span class=\"n\">t1_times</span><span class=\"p\">:</span> <span class=\"n\">Optional</span><span class=\"p\">[</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">ndarray</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"kc\">None</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">probabilities</span><span class=\"p\">:</span> <span class=\"n\">Optional</span><span class=\"p\">[</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">ndarray</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"kc\">None</span><span class=\"p\">,</span>\n",
       "    <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">,</span>\n",
       "<span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    Generates a dataset with mock data of a T1 experiment for a single qubit including</span>\n",
       "<span class=\"sd\">    calibration points for the ground and excited states, including all the individual</span>\n",
       "<span class=\"sd\">    shots (repeated qubit state measurement for the same exact experiment).</span>\n",
       "\n",
       "<span class=\"sd\">    Parameters</span>\n",
       "<span class=\"sd\">    ----------</span>\n",
       "<span class=\"sd\">    t1_times</span>\n",
       "<span class=\"sd\">        Array with the T1 times corresponding to each probability in ``probabilities``.</span>\n",
       "<span class=\"sd\">    probabilities</span>\n",
       "<span class=\"sd\">        The probabilities of finding the qubit in the excited state.</span>\n",
       "<span class=\"sd\">    **kwargs</span>\n",
       "<span class=\"sd\">        Keyword arguments passed to</span>\n",
       "<span class=\"sd\">        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    <span class=\"c1\"># reuse previous dataset</span>\n",
       "    <span class=\"n\">dataset_av_with_cal</span> <span class=\"o\">=</span> <span class=\"n\">mk_t1_av_with_cal_dataset</span><span class=\"p\">(</span><span class=\"n\">t1_times</span><span class=\"p\">,</span> <span class=\"n\">probabilities</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span>\n",
       "    <span class=\"k\">if</span> <span class=\"n\">probabilities</span> <span class=\"ow\">is</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">probabilities</span> <span class=\"o\">=</span> <span class=\"n\">dataset_av_with_cal</span><span class=\"o\">.</span><span class=\"n\">q0_iq_av</span><span class=\"o\">.</span><span class=\"n\">values</span>\n",
       "        <span class=\"n\">probabilities</span> <span class=\"o\">=</span> <span class=\"n\">rotate_to_calibrated_axis</span><span class=\"p\">(</span>\n",
       "            <span class=\"n\">probabilities</span><span class=\"p\">,</span> <span class=\"o\">*</span><span class=\"n\">dataset_av_with_cal</span><span class=\"o\">.</span><span class=\"n\">q0_iq_av_cal</span><span class=\"o\">.</span><span class=\"n\">values</span>\n",
       "        <span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">real</span>\n",
       "    <span class=\"c1\"># generate mock data containing all the shots,</span>\n",
       "    <span class=\"c1\"># NB not the same data that was used for the average above, but this is just a mock</span>\n",
       "    <span class=\"n\">q0_iq_shots</span> <span class=\"o\">=</span> <span class=\"n\">mk_shots_from_probabilities</span><span class=\"p\">(</span><span class=\"n\">probabilities</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">q0_iq_shots_cal</span> <span class=\"o\">=</span> <span class=\"n\">mk_shots_from_probabilities</span><span class=\"p\">([</span><span class=\"mi\">0</span><span class=\"p\">,</span> <span class=\"mi\">1</span><span class=\"p\">],</span> <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"c1\"># the xarray dimensions will now require an outer repetitions dimension</span>\n",
       "    <span class=\"n\">secondary_dims_rep</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"s2\">&quot;repetitions&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;cal_dim&quot;</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">main_dims_rep</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"s2\">&quot;repetitions&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;main_dim&quot;</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">relationships</span> <span class=\"o\">=</span> <span class=\"p\">[</span>\n",
       "        <span class=\"n\">dattrs</span><span class=\"o\">.</span><span class=\"n\">QDatasetIntraRelationship</span><span class=\"p\">(</span>\n",
       "            <span class=\"n\">item_name</span><span class=\"o\">=</span><span class=\"n\">dataset_av_with_cal</span><span class=\"o\">.</span><span class=\"n\">q0_iq_av</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">relation_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;calibration&quot;</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">related_names</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"n\">dataset_av_with_cal</span><span class=\"o\">.</span><span class=\"n\">q0_iq_av_cal</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">],</span>\n",
       "        <span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">to_dict</span><span class=\"p\">(),</span>\n",
       "        <span class=\"n\">dattrs</span><span class=\"o\">.</span><span class=\"n\">QDatasetIntraRelationship</span><span class=\"p\">(</span>\n",
       "            <span class=\"n\">item_name</span><span class=\"o\">=</span><span class=\"s2\">&quot;q0_iq_shots&quot;</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">relation_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;calibration&quot;</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">related_names</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s2\">&quot;q0_iq_cal_shots&quot;</span><span class=\"p\">],</span>\n",
       "        <span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">to_dict</span><span class=\"p\">(),</span>\n",
       "        <span class=\"c1\"># suggestion of a custom relationship</span>\n",
       "        <span class=\"n\">dattrs</span><span class=\"o\">.</span><span class=\"n\">QDatasetIntraRelationship</span><span class=\"p\">(</span>\n",
       "            <span class=\"n\">item_name</span><span class=\"o\">=</span><span class=\"n\">dataset_av_with_cal</span><span class=\"o\">.</span><span class=\"n\">q0_iq_av</span><span class=\"o\">.</span><span class=\"n\">name</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">relation_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;individual_shots&quot;</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">related_names</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s2\">&quot;q0_iq_shots&quot;</span><span class=\"p\">],</span>\n",
       "        <span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">to_dict</span><span class=\"p\">(),</span>\n",
       "    <span class=\"p\">]</span>\n",
       "\n",
       "    <span class=\"c1\"># Flag that these variables use a repetitions dimension</span>\n",
       "    <span class=\"n\">q0_attrs_rep</span> <span class=\"o\">=</span> <span class=\"nb\">dict</span><span class=\"p\">(</span><span class=\"n\">dataset_av_with_cal</span><span class=\"o\">.</span><span class=\"n\">q0_iq_av</span><span class=\"o\">.</span><span class=\"n\">attrs</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">q0_attrs_rep</span><span class=\"p\">[</span><span class=\"s2\">&quot;has_repetitions&quot;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"kc\">True</span>\n",
       "    <span class=\"n\">q0_cal_attrs_rep</span> <span class=\"o\">=</span> <span class=\"nb\">dict</span><span class=\"p\">(</span><span class=\"n\">dataset_av_with_cal</span><span class=\"o\">.</span><span class=\"n\">q0_iq_av_cal</span><span class=\"o\">.</span><span class=\"n\">attrs</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">q0_cal_attrs_rep</span><span class=\"p\">[</span><span class=\"s2\">&quot;has_repetitions&quot;</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"kc\">True</span>\n",
       "\n",
       "    <span class=\"n\">data_vars</span> <span class=\"o\">=</span> <span class=\"nb\">dict</span><span class=\"p\">(</span>\n",
       "        <span class=\"c1\"># variables that are the same as in the previous dataset, and are now redundant,</span>\n",
       "        <span class=\"c1\"># however, we include them to showcase the dataset flexibility</span>\n",
       "        <span class=\"n\">q0_iq_av</span><span class=\"o\">=</span><span class=\"n\">dataset_av_with_cal</span><span class=\"o\">.</span><span class=\"n\">q0_iq_av</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">q0_iq_av_cal</span><span class=\"o\">=</span><span class=\"n\">dataset_av_with_cal</span><span class=\"o\">.</span><span class=\"n\">q0_iq_av_cal</span><span class=\"p\">,</span>\n",
       "        <span class=\"c1\"># variables that contain all the individual shots</span>\n",
       "        <span class=\"n\">q0_iq_shots</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">main_dims_rep</span><span class=\"p\">,</span> <span class=\"n\">q0_iq_shots</span><span class=\"p\">,</span> <span class=\"n\">q0_attrs_rep</span><span class=\"p\">),</span>\n",
       "        <span class=\"n\">q0_iq_shots_cal</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">secondary_dims_rep</span><span class=\"p\">,</span> <span class=\"n\">q0_iq_shots_cal</span><span class=\"p\">,</span> <span class=\"n\">q0_cal_attrs_rep</span><span class=\"p\">),</span>\n",
       "    <span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">dataset</span> <span class=\"o\">=</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">(</span>\n",
       "        <span class=\"n\">data_vars</span><span class=\"o\">=</span><span class=\"n\">data_vars</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">coords</span><span class=\"o\">=</span><span class=\"n\">dataset_av_with_cal</span><span class=\"o\">.</span><span class=\"n\">coords</span><span class=\"p\">,</span>  <span class=\"c1\"># same coordinates as in previous dataset</span>\n",
       "        <span class=\"n\">attrs</span><span class=\"o\">=</span><span class=\"n\">mk_dataset_attrs</span><span class=\"p\">(</span><span class=\"n\">relationships</span><span class=\"o\">=</span><span class=\"n\">relationships</span><span class=\"p\">),</span>  <span class=\"c1\"># relationships added here</span>\n",
       "    <span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"k\">return</span> <span class=\"n\">dataset</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}t1\\PYZus{}shots\\PYZus{}dataset}\\PY{p}{(}\n",
       "    \\PY{n}{t1\\PYZus{}times}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
       "    \\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
       "    \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\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 mock data of a T1 experiment for a single qubit including}\n",
       "\\PY{l+s+sd}{    calibration points for the ground and excited states, including all the individual}\n",
       "\\PY{l+s+sd}{    shots (repeated qubit state measurement for the same exact experiment).}\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}{    t1\\PYZus{}times}\n",
       "\\PY{l+s+sd}{        Array with the T1 times corresponding to each probability in ``probabilities``.}\n",
       "\\PY{l+s+sd}{    probabilities}\n",
       "\\PY{l+s+sd}{        The probabilities of finding the qubit in the excited state.}\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",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{c+c1}{\\PYZsh{} reuse previous dataset}\n",
       "    \\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal} \\PY{o}{=} \\PY{n}{mk\\PYZus{}t1\\PYZus{}av\\PYZus{}with\\PYZus{}cal\\PYZus{}dataset}\\PY{p}{(}\\PY{n}{t1\\PYZus{}times}\\PY{p}{,} \\PY{n}{probabilities}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "    \\PY{k}{if} \\PY{n}{probabilities} \\PY{o+ow}{is} \\PY{k+kc}{None}\\PY{p}{:}\n",
       "        \\PY{n}{probabilities} \\PY{o}{=} \\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{o}{.}\\PY{n}{values}\n",
       "        \\PY{n}{probabilities} \\PY{o}{=} \\PY{n}{rotate\\PYZus{}to\\PYZus{}calibrated\\PYZus{}axis}\\PY{p}{(}\n",
       "            \\PY{n}{probabilities}\\PY{p}{,} \\PY{o}{*}\\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{o}{.}\\PY{n}{values}\n",
       "        \\PY{p}{)}\\PY{o}{.}\\PY{n}{real}\n",
       "    \\PY{c+c1}{\\PYZsh{} generate mock data containing all the shots,}\n",
       "    \\PY{c+c1}{\\PYZsh{} NB not the same data that was used for the average above, but this is just a mock}\n",
       "    \\PY{n}{q0\\PYZus{}iq\\PYZus{}shots} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\\PY{n}{probabilities}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "    \\PY{n}{q0\\PYZus{}iq\\PYZus{}shots\\PYZus{}cal} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{,} \\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} the xarray dimensions will now require an outer repetitions dimension}\n",
       "    \\PY{n}{secondary\\PYZus{}dims\\PYZus{}rep} \\PY{o}{=} \\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}{cal\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{main\\PYZus{}dims\\PYZus{}rep} \\PY{o}{=} \\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}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{relationships} \\PY{o}{=} \\PY{p}{[}\n",
       "        \\PY{n}{dattrs}\\PY{o}{.}\\PY{n}{QDatasetIntraRelationship}\\PY{p}{(}\n",
       "            \\PY{n}{item\\PYZus{}name}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{o}{.}\\PY{n}{name}\\PY{p}{,}\n",
       "            \\PY{n}{relation\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{calibration}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{related\\PYZus{}names}\\PY{o}{=}\\PY{p}{[}\\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{o}{.}\\PY{n}{name}\\PY{p}{]}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{o}{.}\\PY{n}{to\\PYZus{}dict}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{dattrs}\\PY{o}{.}\\PY{n}{QDatasetIntraRelationship}\\PY{p}{(}\n",
       "            \\PY{n}{item\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{q0\\PYZus{}iq\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{relation\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{calibration}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{related\\PYZus{}names}\\PY{o}{=}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{q0\\PYZus{}iq\\PYZus{}cal\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{o}{.}\\PY{n}{to\\PYZus{}dict}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{c+c1}{\\PYZsh{} suggestion of a custom relationship}\n",
       "        \\PY{n}{dattrs}\\PY{o}{.}\\PY{n}{QDatasetIntraRelationship}\\PY{p}{(}\n",
       "            \\PY{n}{item\\PYZus{}name}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{o}{.}\\PY{n}{name}\\PY{p}{,}\n",
       "            \\PY{n}{relation\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{individual\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{related\\PYZus{}names}\\PY{o}{=}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{q0\\PYZus{}iq\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{o}{.}\\PY{n}{to\\PYZus{}dict}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{]}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} Flag that these variables use a repetitions dimension}\n",
       "    \\PY{n}{q0\\PYZus{}attrs\\PYZus{}rep} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{o}{.}\\PY{n}{attrs}\\PY{p}{)}\n",
       "    \\PY{n}{q0\\PYZus{}attrs\\PYZus{}rep}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{has\\PYZus{}repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{True}\n",
       "    \\PY{n}{q0\\PYZus{}cal\\PYZus{}attrs\\PYZus{}rep} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{o}{.}\\PY{n}{attrs}\\PY{p}{)}\n",
       "    \\PY{n}{q0\\PYZus{}cal\\PYZus{}attrs\\PYZus{}rep}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{has\\PYZus{}repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{True}\n",
       "\n",
       "    \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{c+c1}{\\PYZsh{} variables that are the same as in the previous dataset, and are now redundant,}\n",
       "        \\PY{c+c1}{\\PYZsh{} however, we include them to showcase the dataset flexibility}\n",
       "        \\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{p}{,}\n",
       "        \\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{p}{,}\n",
       "        \\PY{c+c1}{\\PYZsh{} variables that contain all the individual shots}\n",
       "        \\PY{n}{q0\\PYZus{}iq\\PYZus{}shots}\\PY{o}{=}\\PY{p}{(}\\PY{n}{main\\PYZus{}dims\\PYZus{}rep}\\PY{p}{,} \\PY{n}{q0\\PYZus{}iq\\PYZus{}shots}\\PY{p}{,} \\PY{n}{q0\\PYZus{}attrs\\PYZus{}rep}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{q0\\PYZus{}iq\\PYZus{}shots\\PYZus{}cal}\\PY{o}{=}\\PY{p}{(}\\PY{n}{secondary\\PYZus{}dims\\PYZus{}rep}\\PY{p}{,} \\PY{n}{q0\\PYZus{}iq\\PYZus{}shots\\PYZus{}cal}\\PY{p}{,} \\PY{n}{q0\\PYZus{}cal\\PYZus{}attrs\\PYZus{}rep}\\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}{dataset\\PYZus{}av\\PYZus{}with\\PYZus{}cal}\\PY{o}{.}\\PY{n}{coords}\\PY{p}{,}  \\PY{c+c1}{\\PYZsh{} same coordinates as in previous dataset}\n",
       "        \\PY{n}{attrs}\\PY{o}{=}\\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{relationships}\\PY{o}{=}\\PY{n}{relationships}\\PY{p}{)}\\PY{p}{,}  \\PY{c+c1}{\\PYZsh{} relationships added here}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{dataset}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_t1_shots_dataset\u001b[0m\u001b[1m(\u001b[0m\n",
       "    t1_times: Optional\u001b[1m[\u001b[0mnp.ndarray\u001b[1m]\u001b[0m = \u001b[3;35mNone\u001b[0m,\n",
       "    probabilities: Optional\u001b[1m[\u001b[0mnp.ndarray\u001b[1m]\u001b[0m = \u001b[3;35mNone\u001b[0m,\n",
       "    **kwargs,\n",
       "\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generates a dataset with mock data of a T1 experiment for a single qubit including\n",
       "    calibration points for the ground and excited states, including all the individual\n",
       "    shots \u001b[1m(\u001b[0mrepeated qubit state measurement for the same exact experiment\u001b[1m)\u001b[0m.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    t1_times\n",
       "        Array with the T1 times corresponding to each probability in ``probabilities``.\n",
       "    probabilities\n",
       "        The probabilities of finding the qubit in the excited state.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to\n",
       "        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    # reuse previous dataset\n",
       "    dataset_av_with_cal = \u001b[1;35mmk_t1_av_with_cal_dataset\u001b[0m\u001b[1m(\u001b[0mt1_times, probabilities, **kwargs\u001b[1m)\u001b[0m\n",
       "    if probabilities is \u001b[3;35mNone\u001b[0m:\n",
       "        probabilities = dataset_av_with_cal.q0_iq_av.values\n",
       "        probabilities = \u001b[1;35mrotate_to_calibrated_axis\u001b[0m\u001b[1m(\u001b[0m\n",
       "            probabilities, *dataset_av_with_cal.q0_iq_av_cal.values\n",
       "        \u001b[1m)\u001b[0m.real\n",
       "    # generate mock data containing all the shots,\n",
       "    # NB not the same data that was used for the average above, but this is just a mock\n",
       "    q0_iq_shots = \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0mprobabilities, **kwargs\u001b[1m)\u001b[0m\n",
       "    q0_iq_shots_cal = \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m, \u001b[1;36m1\u001b[0m\u001b[1m]\u001b[0m, **kwargs\u001b[1m)\u001b[0m\n",
       "\n",
       "    # the xarray dimensions will now require an outer repetitions dimension\n",
       "    secondary_dims_rep = \u001b[1m(\u001b[0m\u001b[32m\"repetitions\"\u001b[0m, \u001b[32m\"cal_dim\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    main_dims_rep = \u001b[1m(\u001b[0m\u001b[32m\"repetitions\"\u001b[0m, \u001b[32m\"main_dim\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    relationships = \u001b[1m[\u001b[0m\n",
       "        \u001b[1;35mdattrs.QDatasetIntraRelationship\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[33mitem_name\u001b[0m=\u001b[35mdataset_av_with_cal\u001b[0m.q0_iq_av.name,\n",
       "            \u001b[33mrelation_type\u001b[0m=\u001b[32m\"calibration\"\u001b[0m,\n",
       "            \u001b[33mrelated_names\u001b[0m=\u001b[1m[\u001b[0mdataset_av_with_cal.q0_iq_av_cal.name\u001b[1m]\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\u001b[1;35m.to_dict\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1;35mdattrs.QDatasetIntraRelationship\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[33mitem_name\u001b[0m=\u001b[32m\"q0_iq_shots\"\u001b[0m,\n",
       "            \u001b[33mrelation_type\u001b[0m=\u001b[32m\"calibration\"\u001b[0m,\n",
       "            \u001b[33mrelated_names\u001b[0m=\u001b[1m[\u001b[0m\u001b[32m\"q0_iq_cal_shots\"\u001b[0m\u001b[1m]\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\u001b[1;35m.to_dict\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        # suggestion of a custom relationship\n",
       "        \u001b[1;35mdattrs.QDatasetIntraRelationship\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[33mitem_name\u001b[0m=\u001b[35mdataset_av_with_cal\u001b[0m.q0_iq_av.name,\n",
       "            \u001b[33mrelation_type\u001b[0m=\u001b[32m\"individual_shots\"\u001b[0m,\n",
       "            \u001b[33mrelated_names\u001b[0m=\u001b[1m[\u001b[0m\u001b[32m\"q0_iq_shots\"\u001b[0m\u001b[1m]\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\u001b[1;35m.to_dict\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m]\u001b[0m\n",
       "\n",
       "    # Flag that these variables use a repetitions dimension\n",
       "    q0_attrs_rep = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0mdataset_av_with_cal.q0_iq_av.attrs\u001b[1m)\u001b[0m\n",
       "    q0_attrs_rep\u001b[1m[\u001b[0m\u001b[32m\"has_repetitions\"\u001b[0m\u001b[1m]\u001b[0m = \u001b[3;92mTrue\u001b[0m\n",
       "    q0_cal_attrs_rep = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0mdataset_av_with_cal.q0_iq_av_cal.attrs\u001b[1m)\u001b[0m\n",
       "    q0_cal_attrs_rep\u001b[1m[\u001b[0m\u001b[32m\"has_repetitions\"\u001b[0m\u001b[1m]\u001b[0m = \u001b[3;92mTrue\u001b[0m\n",
       "\n",
       "    data_vars = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        # variables that are the same as in the previous dataset, and are now redundant,\n",
       "        # however, we include them to showcase the dataset flexibility\n",
       "        \u001b[33mq0_iq_av\u001b[0m=\u001b[35mdataset_av_with_cal\u001b[0m.q0_iq_av,\n",
       "        \u001b[33mq0_iq_av_cal\u001b[0m=\u001b[35mdataset_av_with_cal\u001b[0m.q0_iq_av_cal,\n",
       "        # variables that contain all the individual shots\n",
       "        \u001b[33mq0_iq_shots\u001b[0m=\u001b[1m(\u001b[0mmain_dims_rep, q0_iq_shots, q0_attrs_rep\u001b[1m)\u001b[0m,\n",
       "        \u001b[33mq0_iq_shots_cal\u001b[0m=\u001b[1m(\u001b[0msecondary_dims_rep, q0_iq_shots_cal, q0_cal_attrs_rep\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[35mdataset_av_with_cal\u001b[0m.coords,  # same coordinates as in previous dataset\n",
       "        \u001b[33mattrs\u001b[0m=\u001b[1;35mmk_dataset_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mrelationships\u001b[0m=\u001b[35mrelationships\u001b[0m\u001b[1m)\u001b[0m,  # relationships added here\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    return dataset"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_source_code(dataset_examples.mk_t1_shots_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9e57023f",
   "metadata": {},
   "outputs": [
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       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          (main_dim: 30, cal_dim: 2, repetitions: 256)\n",
       "Coordinates:\n",
       "    t1_time          (main_dim) float64 0.0 4.138e-06 ... 0.0001159 0.00012\n",
       "    cal              (cal_dim) &lt;U3 &#x27;|0&gt;&#x27; &#x27;|1&gt;&#x27;\n",
       "Dimensions without coordinates: main_dim, cal_dim, repetitions\n",
       "Data variables:\n",
       "    q0_iq_av         (main_dim) complex128 (-0.19894114958423859+0.6515500138...\n",
       "    q0_iq_av_cal     (cal_dim) complex128 (0.7010588504157614-0.3984499861154...\n",
       "    q0_iq_shots      (repetitions, main_dim) complex128 (-0.289836545355741+0...\n",
       "    q0_iq_shots_cal  (repetitions, cal_dim) complex128 (0.610163454644259-0.4...\n",
       "Attributes:\n",
       "    tuid:                      20231215-162530-172-6fd8dd\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:             [{&#x27;item_name&#x27;: &#x27;q0_iq_av&#x27;, &#x27;relation_type&#x27;: &#x27;c...\n",
       "    json_serialize_exclude:    []</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-620e0e50-0df5-47d6-988a-0f302baaaf27' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-620e0e50-0df5-47d6-988a-0f302baaaf27' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>main_dim</span>: 30</li><li><span>cal_dim</span>: 2</li><li><span>repetitions</span>: 256</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-96d69337-3a53-4180-b8ec-667349164a20' class='xr-section-summary-in' type='checkbox'  checked><label for='section-96d69337-3a53-4180-b8ec-667349164a20' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t1_time</span></div><div class='xr-var-dims'>(main_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 4.138e-06 ... 0.0001159 0.00012</div><input id='attrs-e634a7a1-fd37-488e-9354-5173c42e9daa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e634a7a1-fd37-488e-9354-5173c42e9daa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75894824-e90a-4d54-b507-c13161792623' class='xr-var-data-in' type='checkbox'><label for='data-75894824-e90a-4d54-b507-c13161792623' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>T1 Time</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.00000000e+00, 4.13793103e-06, 8.27586207e-06, 1.24137931e-05,\n",
       "       1.65517241e-05, 2.06896552e-05, 2.48275862e-05, 2.89655172e-05,\n",
       "       3.31034483e-05, 3.72413793e-05, 4.13793103e-05, 4.55172414e-05,\n",
       "       4.96551724e-05, 5.37931034e-05, 5.79310345e-05, 6.20689655e-05,\n",
       "       6.62068966e-05, 7.03448276e-05, 7.44827586e-05, 7.86206897e-05,\n",
       "       8.27586207e-05, 8.68965517e-05, 9.10344828e-05, 9.51724138e-05,\n",
       "       9.93103448e-05, 1.03448276e-04, 1.07586207e-04, 1.11724138e-04,\n",
       "       1.15862069e-04, 1.20000000e-04])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cal</span></div><div class='xr-var-dims'>(cal_dim)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;|0&gt;&#x27; &#x27;|1&gt;&#x27;</div><input id='attrs-7786b1d1-bf97-4647-81be-a7bc71d6f420' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7786b1d1-bf97-4647-81be-a7bc71d6f420' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3679dcea-f014-4985-9b45-363f1e607692' class='xr-var-data-in' type='checkbox'><label for='data-3679dcea-f014-4985-9b45-363f1e607692' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Q0 state</dd><dt><span>is_main_coord :</span></dt><dd>False</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;|0&gt;&#x27;, &#x27;|1&gt;&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-34d12f4a-2555-4f8a-80d1-e02d6e1d4a6a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-34d12f4a-2555-4f8a-80d1-e02d6e1d4a6a' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_av</span></div><div class='xr-var-dims'>(main_dim)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(-0.19894114958423859+0.65155001...</div><input id='attrs-27889be4-732d-4c81-b392-cf4b593edded' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27889be4-732d-4c81-b392-cf4b593edded' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8689a5b-88bd-4f58-96f6-c98d654e09cb' class='xr-var-data-in' type='checkbox'><label for='data-b8689a5b-88bd-4f58-96f6-c98d654e09cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ amplitude</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([-0.19894115+0.65155001j, -0.11155212+0.54951255j,\n",
       "       -0.01216713+0.43313537j,  0.05193768+0.35848368j,\n",
       "        0.15504951+0.2389656j ,  0.20661408+0.17861197j,\n",
       "        0.2833043 +0.08961707j,  0.33361813+0.03110011j,\n",
       "        0.37345395-0.01576696j,  0.40534772-0.05352011j,\n",
       "        0.42151034-0.07261241j,  0.47040441-0.12912367j,\n",
       "        0.49717119-0.16057795j,  0.53565518-0.20550725j,\n",
       "        0.56438276-0.23892233j,  0.58075078-0.25822004j,\n",
       "        0.59794213-0.27834108j,  0.60292187-0.28390675j,\n",
       "        0.60292187-0.28390675j,  0.61706292-0.30039156j,\n",
       "        0.6204547 -0.30436927j,  0.62775114-0.31283759j,\n",
       "        0.64350021-0.33151635j,  0.64683289-0.33543496j,\n",
       "        0.65442395-0.34419789j,  0.65442395-0.34419789j,\n",
       "        0.65650831-0.34686819j,  0.66449532-0.35602708j,\n",
       "        0.66901604-0.36113374j,  0.66901604-0.36113374j])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_av_cal</span></div><div class='xr-var-dims'>(cal_dim)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(0.7010588504157614-0.3984499861...</div><input id='attrs-18519098-9b77-46df-854e-720b2e74ac76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18519098-9b77-46df-854e-720b2e74ac76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1329b9ca-c3f2-4d72-b205-ff676de85476' class='xr-var-data-in' type='checkbox'><label for='data-1329b9ca-c3f2-4d72-b205-ff676de85476' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ Calibration</dd><dt><span>is_main_var :</span></dt><dd>False</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([ 0.70105885-0.39844999j, -0.19894115+0.65155001j])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_shots</span></div><div class='xr-var-dims'>(repetitions, main_dim)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(-0.289836545355741+0.6397419222...</div><input id='attrs-4cceeabc-d2be-41f0-af03-4c118c2407d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4cceeabc-d2be-41f0-af03-4c118c2407d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24ddef89-3695-4bcb-a66a-23a01c565d29' class='xr-var-data-in' type='checkbox'><label for='data-24ddef89-3695-4bcb-a66a-23a01c565d29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ amplitude</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[-0.28983655+0.63974192j, -0.28983655+0.56980106j,\n",
       "        -0.28983655+0.53688137j, ..., -0.28983655+0.7151281j ,\n",
       "        -0.28983655+0.78818874j, -0.28983655+0.78818874j],\n",
       "       [-0.24253459+0.62214896j, -0.24253459+0.5353232j ,\n",
       "        -0.24253459+0.59304477j, ..., -0.24253459+0.66518369j,\n",
       "        -0.24253459+0.7151281j , -0.24253459+0.7151281j ],\n",
       "       [-0.28481752+0.67074703j, -0.28481752+0.62934539j,\n",
       "        -0.28481752+0.62319817j, ..., -0.28481752+0.77120226j,\n",
       "        -0.28481752+0.66518369j, -0.28481752+0.66518369j],\n",
       "       ...,\n",
       "       [-0.1585822 +0.54251043j,  0.69005581-0.50748957j,\n",
       "         0.6904154 -0.50748957j, ...,  0.72225129-0.50748957j,\n",
       "         0.63121489-0.50748957j,  0.63121489-0.50748957j],\n",
       "       [-0.13095023+0.64399053j,  0.79234273-0.40600947j,\n",
       "         0.78602065-0.40600947j, ...,  0.70347729-0.40600947j,\n",
       "         0.72225129-0.40600947j,  0.72225129-0.40600947j],\n",
       "       [-0.13370834+0.73212141j,  0.71905686-0.31787859j,\n",
       "         0.82586734-0.31787859j, ...,  0.56864489-0.31787859j,\n",
       "         0.70347729-0.31787859j,  0.70347729-0.31787859j]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_shots_cal</span></div><div class='xr-var-dims'>(repetitions, cal_dim)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(0.610163454644259-0.41025807770...</div><input id='attrs-04118286-0b24-41aa-8012-08aba38fefa9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04118286-0b24-41aa-8012-08aba38fefa9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc48b6d1-d2f3-4a3f-833c-5a7e6804d172' class='xr-var-data-in' type='checkbox'><label for='data-dc48b6d1-d2f3-4a3f-833c-5a7e6804d172' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ Calibration</dd><dt><span>is_main_var :</span></dt><dd>False</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.61016345-0.41025808j, -0.28983655+0.63974192j],\n",
       "       [ 0.65746541-0.42785104j, -0.24253459+0.62214896j],\n",
       "       [ 0.61518248-0.37925297j, -0.28481752+0.67074703j],\n",
       "       [ 0.74772749-0.43361903j, -0.15227251+0.61638097j],\n",
       "       [ 0.64265022-0.27384234j, -0.25734978+0.77615766j],\n",
       "       [ 0.60678906-0.3561582j , -0.29321094+0.6938418j ],\n",
       "       [ 0.84395867-0.46878511j, -0.05604133+0.58121489j],\n",
       "       [ 0.57061537-0.37774871j, -0.32938463+0.67225129j],\n",
       "       [ 0.78351922-0.39652271j, -0.11648078+0.65347729j],\n",
       "       [ 0.83818874-0.53135511j, -0.06181126+0.51864489j],\n",
       "       [ 0.7651281 -0.42764291j, -0.1348719 +0.62235709j],\n",
       "       [ 0.71518369-0.62580345j, -0.18481631+0.42419655j],\n",
       "       [ 0.82120226-0.28181242j, -0.07879774+0.76818758j],\n",
       "       [ 0.51610032-0.37325263j, -0.38389968+0.67674737j],\n",
       "       [ 0.77067194-0.39646665j, -0.12932806+0.65353335j],\n",
       "       [ 0.64660115-0.19540354j, -0.25339885+0.85459646j],\n",
       "       [ 0.67313985-0.25608898j, -0.22686015+0.79391102j],\n",
       "       [ 0.73270479-0.51790005j, -0.16729521+0.53209995j],\n",
       "       [ 0.8270149 -0.40109669j, -0.0729851 +0.64890331j],\n",
       "       [ 0.83712465-0.3367254j , -0.06287535+0.7132746j ],\n",
       "...\n",
       "       [ 0.64294178-0.26650726j, -0.25705822+0.78349274j],\n",
       "       [ 0.77779463-0.50086946j, -0.12220537+0.54913054j],\n",
       "       [ 0.87940344-0.38703376j, -0.02059656+0.66296624j],\n",
       "       [ 0.79740773-0.36164035j, -0.10259227+0.68835965j],\n",
       "       [ 0.63267527-0.4135685j , -0.26732473+0.6364315j ],\n",
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       "       [ 0.76629166-0.31787859j, -0.13370834+0.73212141j]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1cc21e50-863b-4115-b18f-836c2cbbe376' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1cc21e50-863b-4115-b18f-836c2cbbe376' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-a55b8122-f98a-4c3c-97f8-54ebd72d5504' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a55b8122-f98a-4c3c-97f8-54ebd72d5504' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20231215-162530-172-6fd8dd</dd><dt><span>dataset_name :</span></dt><dd></dd><dt><span>dataset_state :</span></dt><dd>None</dd><dt><span>timestamp_start :</span></dt><dd>None</dd><dt><span>timestamp_end :</span></dt><dd>None</dd><dt><span>quantify_dataset_version :</span></dt><dd>2.0.0</dd><dt><span>software_versions :</span></dt><dd>{}</dd><dt><span>relationships :</span></dt><dd>[{&#x27;item_name&#x27;: &#x27;q0_iq_av&#x27;, &#x27;relation_type&#x27;: &#x27;calibration&#x27;, &#x27;related_names&#x27;: [&#x27;q0_iq_av_cal&#x27;], &#x27;relation_metadata&#x27;: {}}, {&#x27;item_name&#x27;: &#x27;q0_iq_shots&#x27;, &#x27;relation_type&#x27;: &#x27;calibration&#x27;, &#x27;related_names&#x27;: [&#x27;q0_iq_cal_shots&#x27;], &#x27;relation_metadata&#x27;: {}}, {&#x27;item_name&#x27;: &#x27;q0_iq_av&#x27;, &#x27;relation_type&#x27;: &#x27;individual_shots&#x27;, &#x27;related_names&#x27;: [&#x27;q0_iq_shots&#x27;], &#x27;relation_metadata&#x27;: {}}]</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div></li></ul></div></div>"
      ],
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       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39mDimensions:          \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mmain_dim: \u001b[0m\u001b[1;36m30\u001b[0m\u001b[39m, cal_dim: \u001b[0m\u001b[1;36m2\u001b[0m\u001b[39m, repetitions: \u001b[0m\u001b[1;36m256\u001b[0m\u001b[1;39m)\u001b[0m\n",
       "\u001b[39mCoordinates:\u001b[0m\n",
       "\u001b[39m    t1_time          \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mmain_dim\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m float64 \u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[39m \u001b[0m\u001b[1;36m4.138e-06\u001b[0m\u001b[39m \u001b[0m\u001b[33m...\u001b[0m\u001b[39m \u001b[0m\u001b[1;36m0.0001159\u001b[0m\u001b[39m \u001b[0m\u001b[1;36m0.00012\u001b[0m\n",
       "\u001b[39m    cal              \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mcal_dim\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m <U3 \u001b[0m\u001b[32m'|0>'\u001b[0m\u001b[39m \u001b[0m\u001b[32m'|1\u001b[0m\u001b[32m>\u001b[0m\u001b[32m'\u001b[0m\n",
       "Dimensions without coordinates: main_dim, cal_dim, repetitions\n",
       "Data variables:\n",
       "    q0_iq_av         \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.19894114958423859\u001b[0m+\u001b[1;36m0.6515500138\u001b[0m\u001b[33m...\u001b[0m\n",
       "    q0_iq_av_cal     \u001b[1m(\u001b[0mcal_dim\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m0.7010588504157614\u001b[0m-\u001b[1;36m0.3984499861154\u001b[0m\u001b[33m...\u001b[0m\n",
       "    q0_iq_shots      \u001b[1m(\u001b[0mrepetitions, main_dim\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.289836545355741\u001b[0m+\u001b[1;36m0\u001b[0m\u001b[33m...\u001b[0m\n",
       "    q0_iq_shots_cal  \u001b[1m(\u001b[0mrepetitions, cal_dim\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m0.610163454644259\u001b[0m-\u001b[1;36m0.4\u001b[0m\u001b[33m...\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20231215\u001b[0m-\u001b[1;36m162530\u001b[0m-\u001b[1;36m172\u001b[0m-6fd8dd\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\u001b[32m'item_name'\u001b[0m: \u001b[32m'q0_iq_av'\u001b[0m, \u001b[32m'relation_type'\u001b[0m: 'c\u001b[33m...\u001b[0m\n",
       "    json_serialize_exclude:    \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset_examples.mk_t1_shots_dataset(**mock_conf)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0c2357c2",
   "metadata": {},
   "outputs": [
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       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          (t1_time: 30, cal: 2, repetitions: 256)\n",
       "Coordinates:\n",
       "  * t1_time          (t1_time) float64 0.0 4.138e-06 ... 0.0001159 0.00012\n",
       "  * cal              (cal) &lt;U3 &#x27;|0&gt;&#x27; &#x27;|1&gt;&#x27;\n",
       "Dimensions without coordinates: repetitions\n",
       "Data variables:\n",
       "    q0_iq_av         (t1_time) complex128 (-0.19894114958423859+0.65155001388...\n",
       "    q0_iq_av_cal     (cal) complex128 (0.7010588504157614-0.3984499861154196j...\n",
       "    q0_iq_shots      (repetitions, t1_time) complex128 (-0.289836545355741+0....\n",
       "    q0_iq_shots_cal  (repetitions, cal) complex128 (0.610163454644259-0.41025...\n",
       "Attributes:\n",
       "    tuid:                      20231215-162530-172-6fd8dd\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:             [{&#x27;item_name&#x27;: &#x27;q0_iq_av&#x27;, &#x27;relation_type&#x27;: &#x27;c...\n",
       "    json_serialize_exclude:    []</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-37319392-bafc-44f0-bd06-779c36b6adcd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-37319392-bafc-44f0-bd06-779c36b6adcd' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>t1_time</span>: 30</li><li><span class='xr-has-index'>cal</span>: 2</li><li><span>repetitions</span>: 256</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7f9f36e8-b7e1-426a-b339-2960198a6447' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7f9f36e8-b7e1-426a-b339-2960198a6447' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t1_time</span></div><div class='xr-var-dims'>(t1_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 4.138e-06 ... 0.0001159 0.00012</div><input id='attrs-c91527a4-a7a9-43e8-97c6-5144f489ef7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c91527a4-a7a9-43e8-97c6-5144f489ef7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53e99092-aa32-4713-8866-472659ed5f9f' class='xr-var-data-in' type='checkbox'><label for='data-53e99092-aa32-4713-8866-472659ed5f9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>T1 Time</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.000000e+00, 4.137931e-06, 8.275862e-06, 1.241379e-05, 1.655172e-05,\n",
       "       2.068966e-05, 2.482759e-05, 2.896552e-05, 3.310345e-05, 3.724138e-05,\n",
       "       4.137931e-05, 4.551724e-05, 4.965517e-05, 5.379310e-05, 5.793103e-05,\n",
       "       6.206897e-05, 6.620690e-05, 7.034483e-05, 7.448276e-05, 7.862069e-05,\n",
       "       8.275862e-05, 8.689655e-05, 9.103448e-05, 9.517241e-05, 9.931034e-05,\n",
       "       1.034483e-04, 1.075862e-04, 1.117241e-04, 1.158621e-04, 1.200000e-04])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cal</span></div><div class='xr-var-dims'>(cal)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;|0&gt;&#x27; &#x27;|1&gt;&#x27;</div><input id='attrs-8a430b9b-36c8-45d8-9c9d-f1e817d0f86a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a430b9b-36c8-45d8-9c9d-f1e817d0f86a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bb670f3-fe3b-4673-bce3-05f76d468885' class='xr-var-data-in' type='checkbox'><label for='data-9bb670f3-fe3b-4673-bce3-05f76d468885' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Q0 state</dd><dt><span>is_main_coord :</span></dt><dd>False</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;|0&gt;&#x27;, &#x27;|1&gt;&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46f996c1-c9b4-4be9-8b82-7bdd27ab96e0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-46f996c1-c9b4-4be9-8b82-7bdd27ab96e0' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_av</span></div><div class='xr-var-dims'>(t1_time)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(-0.19894114958423859+0.65155001...</div><input id='attrs-c6a80ebd-9fd5-4a3b-9acf-feafdbadaafa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6a80ebd-9fd5-4a3b-9acf-feafdbadaafa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a273a8c1-dbd2-45cd-8391-d071fbee0dfb' class='xr-var-data-in' type='checkbox'><label for='data-a273a8c1-dbd2-45cd-8391-d071fbee0dfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ amplitude</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([-0.19894115+0.65155001j, -0.11155212+0.54951255j,\n",
       "       -0.01216713+0.43313537j,  0.05193768+0.35848368j,\n",
       "        0.15504951+0.2389656j ,  0.20661408+0.17861197j,\n",
       "        0.2833043 +0.08961707j,  0.33361813+0.03110011j,\n",
       "        0.37345395-0.01576696j,  0.40534772-0.05352011j,\n",
       "        0.42151034-0.07261241j,  0.47040441-0.12912367j,\n",
       "        0.49717119-0.16057795j,  0.53565518-0.20550725j,\n",
       "        0.56438276-0.23892233j,  0.58075078-0.25822004j,\n",
       "        0.59794213-0.27834108j,  0.60292187-0.28390675j,\n",
       "        0.60292187-0.28390675j,  0.61706292-0.30039156j,\n",
       "        0.6204547 -0.30436927j,  0.62775114-0.31283759j,\n",
       "        0.64350021-0.33151635j,  0.64683289-0.33543496j,\n",
       "        0.65442395-0.34419789j,  0.65442395-0.34419789j,\n",
       "        0.65650831-0.34686819j,  0.66449532-0.35602708j,\n",
       "        0.66901604-0.36113374j,  0.66901604-0.36113374j])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_av_cal</span></div><div class='xr-var-dims'>(cal)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(0.7010588504157614-0.3984499861...</div><input id='attrs-e40cfd70-22c1-4e54-9eb8-b19db46b3d68' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e40cfd70-22c1-4e54-9eb8-b19db46b3d68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f928034-ec3e-49ea-82ac-a9e9afef0a2c' class='xr-var-data-in' type='checkbox'><label for='data-7f928034-ec3e-49ea-82ac-a9e9afef0a2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ Calibration</dd><dt><span>is_main_var :</span></dt><dd>False</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([ 0.70105885-0.39844999j, -0.19894115+0.65155001j])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_shots</span></div><div class='xr-var-dims'>(repetitions, t1_time)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(-0.289836545355741+0.6397419222...</div><input id='attrs-d5d77801-0056-4616-a802-c45216e8c451' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5d77801-0056-4616-a802-c45216e8c451' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cae43ce-4191-4dda-8898-60b73abb1db8' class='xr-var-data-in' type='checkbox'><label for='data-9cae43ce-4191-4dda-8898-60b73abb1db8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ amplitude</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[-0.28983655+0.63974192j, -0.28983655+0.56980106j,\n",
       "        -0.28983655+0.53688137j, ..., -0.28983655+0.7151281j ,\n",
       "        -0.28983655+0.78818874j, -0.28983655+0.78818874j],\n",
       "       [-0.24253459+0.62214896j, -0.24253459+0.5353232j ,\n",
       "        -0.24253459+0.59304477j, ..., -0.24253459+0.66518369j,\n",
       "        -0.24253459+0.7151281j , -0.24253459+0.7151281j ],\n",
       "       [-0.28481752+0.67074703j, -0.28481752+0.62934539j,\n",
       "        -0.28481752+0.62319817j, ..., -0.28481752+0.77120226j,\n",
       "        -0.28481752+0.66518369j, -0.28481752+0.66518369j],\n",
       "       ...,\n",
       "       [-0.1585822 +0.54251043j,  0.69005581-0.50748957j,\n",
       "         0.6904154 -0.50748957j, ...,  0.72225129-0.50748957j,\n",
       "         0.63121489-0.50748957j,  0.63121489-0.50748957j],\n",
       "       [-0.13095023+0.64399053j,  0.79234273-0.40600947j,\n",
       "         0.78602065-0.40600947j, ...,  0.70347729-0.40600947j,\n",
       "         0.72225129-0.40600947j,  0.72225129-0.40600947j],\n",
       "       [-0.13370834+0.73212141j,  0.71905686-0.31787859j,\n",
       "         0.82586734-0.31787859j, ...,  0.56864489-0.31787859j,\n",
       "         0.70347729-0.31787859j,  0.70347729-0.31787859j]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_shots_cal</span></div><div class='xr-var-dims'>(repetitions, cal)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(0.610163454644259-0.41025807770...</div><input id='attrs-6478e4bc-f43b-4fa5-bbd1-9c50d9300800' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6478e4bc-f43b-4fa5-bbd1-9c50d9300800' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd3f7dd7-ab0c-4ba5-9c84-41dca5315f15' class='xr-var-data-in' type='checkbox'><label for='data-bd3f7dd7-ab0c-4ba5-9c84-41dca5315f15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ Calibration</dd><dt><span>is_main_var :</span></dt><dd>False</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.61016345-0.41025808j, -0.28983655+0.63974192j],\n",
       "       [ 0.65746541-0.42785104j, -0.24253459+0.62214896j],\n",
       "       [ 0.61518248-0.37925297j, -0.28481752+0.67074703j],\n",
       "       [ 0.74772749-0.43361903j, -0.15227251+0.61638097j],\n",
       "       [ 0.64265022-0.27384234j, -0.25734978+0.77615766j],\n",
       "       [ 0.60678906-0.3561582j , -0.29321094+0.6938418j ],\n",
       "       [ 0.84395867-0.46878511j, -0.05604133+0.58121489j],\n",
       "       [ 0.57061537-0.37774871j, -0.32938463+0.67225129j],\n",
       "       [ 0.78351922-0.39652271j, -0.11648078+0.65347729j],\n",
       "       [ 0.83818874-0.53135511j, -0.06181126+0.51864489j],\n",
       "       [ 0.7651281 -0.42764291j, -0.1348719 +0.62235709j],\n",
       "       [ 0.71518369-0.62580345j, -0.18481631+0.42419655j],\n",
       "       [ 0.82120226-0.28181242j, -0.07879774+0.76818758j],\n",
       "       [ 0.51610032-0.37325263j, -0.38389968+0.67674737j],\n",
       "       [ 0.77067194-0.39646665j, -0.12932806+0.65353335j],\n",
       "       [ 0.64660115-0.19540354j, -0.25339885+0.85459646j],\n",
       "       [ 0.67313985-0.25608898j, -0.22686015+0.79391102j],\n",
       "       [ 0.73270479-0.51790005j, -0.16729521+0.53209995j],\n",
       "       [ 0.8270149 -0.40109669j, -0.0729851 +0.64890331j],\n",
       "       [ 0.83712465-0.3367254j , -0.06287535+0.7132746j ],\n",
       "...\n",
       "       [ 0.64294178-0.26650726j, -0.25705822+0.78349274j],\n",
       "       [ 0.77779463-0.50086946j, -0.12220537+0.54913054j],\n",
       "       [ 0.87940344-0.38703376j, -0.02059656+0.66296624j],\n",
       "       [ 0.79740773-0.36164035j, -0.10259227+0.68835965j],\n",
       "       [ 0.63267527-0.4135685j , -0.26732473+0.6364315j ],\n",
       "       [ 0.79144079-0.44017147j, -0.10855921+0.60982853j],\n",
       "       [ 0.68482914-0.37939136j, -0.21517086+0.67060864j],\n",
       "       [ 0.85136549-0.38836868j, -0.04863451+0.66163132j],\n",
       "       [ 0.6485531 -0.22668462j, -0.2514469 +0.82331538j],\n",
       "       [ 0.69385278-0.44030654j, -0.20614722+0.60969346j],\n",
       "       [ 0.64327725-0.46894991j, -0.25672275+0.58105009j],\n",
       "       [ 0.73337178-0.39917798j, -0.16662822+0.65082202j],\n",
       "       [ 0.68419873-0.53952882j, -0.21580127+0.51047118j],\n",
       "       [ 0.72360952-0.19834449j, -0.17639048+0.85165551j],\n",
       "       [ 0.77753549-0.33238509j, -0.12246451+0.71761491j],\n",
       "       [ 0.76165034-0.55445959j, -0.13834966+0.49554041j],\n",
       "       [ 0.64390147-0.33097297j, -0.25609853+0.71902703j],\n",
       "       [ 0.7414178 -0.50748957j, -0.1585822 +0.54251043j],\n",
       "       [ 0.76904977-0.40600947j, -0.13095023+0.64399053j],\n",
       "       [ 0.76629166-0.31787859j, -0.13370834+0.73212141j]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0f2883bf-2950-4971-a505-605506432c6c' class='xr-section-summary-in' type='checkbox'  ><label for='section-0f2883bf-2950-4971-a505-605506432c6c' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>t1_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d7c83d70-af31-405c-8fc5-1034e2828ce7' class='xr-index-data-in' type='checkbox'/><label for='index-d7c83d70-af31-405c-8fc5-1034e2828ce7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                   0.0,  4.137931034482758e-06,  8.275862068965517e-06,\n",
       "       1.2413793103448275e-05, 1.6551724137931033e-05,  2.068965517241379e-05,\n",
       "        2.482758620689655e-05,  2.896551724137931e-05, 3.3103448275862067e-05,\n",
       "        3.724137931034482e-05,  4.137931034482758e-05,  4.551724137931034e-05,\n",
       "         4.96551724137931e-05, 5.3793103448275856e-05,  5.793103448275862e-05,\n",
       "        6.206896551724137e-05,  6.620689655172413e-05,   7.03448275862069e-05,\n",
       "        7.448275862068965e-05,  7.862068965517241e-05,  8.275862068965516e-05,\n",
       "        8.689655172413792e-05,  9.103448275862069e-05,  9.517241379310344e-05,\n",
       "         9.93103448275862e-05, 0.00010344827586206896, 0.00010758620689655171,\n",
       "       0.00011172413793103448, 0.00011586206896551724,                0.00012],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;t1_time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>cal</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d794d2c0-73c2-4ac4-9594-10fac9b695e4' class='xr-index-data-in' type='checkbox'/><label for='index-d794d2c0-73c2-4ac4-9594-10fac9b695e4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;|0&gt;&#x27;, &#x27;|1&gt;&#x27;], dtype=&#x27;object&#x27;, name=&#x27;cal&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c90299e9-9959-4b59-af5c-be96b38fa8bc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c90299e9-9959-4b59-af5c-be96b38fa8bc' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20231215-162530-172-6fd8dd</dd><dt><span>dataset_name :</span></dt><dd></dd><dt><span>dataset_state :</span></dt><dd>None</dd><dt><span>timestamp_start :</span></dt><dd>None</dd><dt><span>timestamp_end :</span></dt><dd>None</dd><dt><span>quantify_dataset_version :</span></dt><dd>2.0.0</dd><dt><span>software_versions :</span></dt><dd>{}</dd><dt><span>relationships :</span></dt><dd>[{&#x27;item_name&#x27;: &#x27;q0_iq_av&#x27;, &#x27;relation_type&#x27;: &#x27;calibration&#x27;, &#x27;related_names&#x27;: [&#x27;q0_iq_av_cal&#x27;], &#x27;relation_metadata&#x27;: {}}, {&#x27;item_name&#x27;: &#x27;q0_iq_shots&#x27;, &#x27;relation_type&#x27;: &#x27;calibration&#x27;, &#x27;related_names&#x27;: [&#x27;q0_iq_cal_shots&#x27;], &#x27;relation_metadata&#x27;: {}}, {&#x27;item_name&#x27;: &#x27;q0_iq_av&#x27;, &#x27;relation_type&#x27;: &#x27;individual_shots&#x27;, &#x27;related_names&#x27;: [&#x27;q0_iq_shots&#x27;], &#x27;relation_metadata&#x27;: {}}]</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39mDimensions:          \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mt1_time: \u001b[0m\u001b[1;36m30\u001b[0m\u001b[39m, cal: \u001b[0m\u001b[1;36m2\u001b[0m\u001b[39m, repetitions: \u001b[0m\u001b[1;36m256\u001b[0m\u001b[1;39m)\u001b[0m\n",
       "\u001b[39mCoordinates:\u001b[0m\n",
       "\u001b[39m  * t1_time          \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mt1_time\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m float64 \u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[39m \u001b[0m\u001b[1;36m4.138e-06\u001b[0m\u001b[39m \u001b[0m\u001b[33m...\u001b[0m\u001b[39m \u001b[0m\u001b[1;36m0.0001159\u001b[0m\u001b[39m \u001b[0m\u001b[1;36m0.00012\u001b[0m\n",
       "\u001b[39m  * cal              \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mcal\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m <U3 \u001b[0m\u001b[32m'|0>'\u001b[0m\u001b[39m \u001b[0m\u001b[32m'|1\u001b[0m\u001b[32m>\u001b[0m\u001b[32m'\u001b[0m\n",
       "Dimensions without coordinates: repetitions\n",
       "Data variables:\n",
       "    q0_iq_av         \u001b[1m(\u001b[0mt1_time\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.19894114958423859\u001b[0m+\u001b[1;36m0.65155001388\u001b[0m\u001b[33m...\u001b[0m\n",
       "    q0_iq_av_cal     \u001b[1m(\u001b[0mcal\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m0.7010588504157614-0.3984499861154196j\u001b[0m\u001b[33m...\u001b[0m\n",
       "    q0_iq_shots      \u001b[1m(\u001b[0mrepetitions, t1_time\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.289836545355741\u001b[0m+\u001b[1;36m0\u001b[0m\u001b[33m...\u001b[0m.\n",
       "    q0_iq_shots_cal  \u001b[1m(\u001b[0mrepetitions, cal\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m0.610163454644259\u001b[0m-\u001b[1;36m0.41025\u001b[0m\u001b[33m...\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20231215\u001b[0m-\u001b[1;36m162530\u001b[0m-\u001b[1;36m172\u001b[0m-6fd8dd\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\u001b[32m'item_name'\u001b[0m: \u001b[32m'q0_iq_av'\u001b[0m, \u001b[32m'relation_type'\u001b[0m: 'c\u001b[33m...\u001b[0m\n",
       "    json_serialize_exclude:    \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset,\n",
    "    dimension=\"main_dim\",\n",
    "    coords_names=dattrs.get_main_coords(dataset),\n",
    ")\n",
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset_gridded,\n",
    "    dimension=\"cal_dim\",\n",
    "    coords_names=dattrs.get_secondary_coords(dataset_gridded),\n",
    ")\n",
    "dataset_gridded"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f14d060f",
   "metadata": {},
   "source": [
    "In this dataset we have both the averaged values and all the shots. The averaged values\n",
    "can be plotted in the same way as before."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "eff69184",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 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"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "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 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": [
    "_ = plot_xr_complex(dataset_gridded.q0_iq_av)\n",
    "_, ax = plot_xr_complex_on_plane(dataset_gridded.q0_iq_av)\n",
    "_ = plot_complex_points(dataset_gridded.q0_iq_av_cal.values, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1d27387",
   "metadata": {},
   "source": [
    "Here we focus on inspecting how the individual shots are distributed on the IQ plane\n",
    "for some particular `Time` values.\n",
    "\n",
    "Note that we are plotting the calibration points as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fbcc3811",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ly5eje/fuWLp0KQCgX79++Omnn/D8889j2rRpTZ5jt9tht9s9X1ut1nMLmhBCCCGdVqcaUWqvLVu2YPLkyV7Hpk2bhi1btjR7zpIlSxAREeF50ERuQggh5PzVqUaU2quwsBDx8fFex+Lj42G1WlFXV4eQkBCfcxYuXIj58+d7vq6fDEYIIYQEmqIocDqdWocRlHQ6HURR7PDrdOlE6WwYDAYYDAatwyCEEHIe45yjsLAQFRUVWocS1CwWCxISEjp0ncMunSglJCSgqKjI61hRURHMZnOTo0mEEEJIMKhPkuLi4mAymWjB4zNwzlFbW4vi4mIAQGJiYoddq0snSmPHjsXatWu9jm3YsAFjx47VKCJCCCGkZYqieJKk6OhorcMJWvUDHsXFxYiLi+uw23CdajJ3dXU1du/ejd27dwNwlf/v3r0b2dnZAFzzi2644QZP+zvuuAMnT57EQw89hMOHD+PVV1/Ff//7X9x///1ahE8IIYS0qn5Okslk0jiS4Ff/HnXkPK5OlSht374dw4YNw7BhwwAA8+fPx7Bhw/DYY48BAAoKCjxJEwB0794dX331FTZs2IAhQ4Zg6dKleOONN5pdGoAQQggJFnS7rXWBeI861a23iRMngnPe7PPvvPNOk+fs2rWrA6MihBBCSFfVqUaUCCGEEEICqVONKHUV9jo7Nr7/E3Zu2IMLfz8G468ag5wj+fjitW+gN0iYcdc0xHaLxncf/ITt63dj3O9GYfzsC5B/vBBfvPYNBFHAzLsvQ1xKNDZ99DN++3oXLpgxEhP+byzyTxThy9e+ARgw865piE+Pw/erfsHWr3ZgzBUjMGHOOBRlFuPzV9cDHLjyzqlI6hGP7/+7Bb9+sR2jLh+GSddciOKc0/ji1fWQnTJm3jUNST0T8OPHv+KXz37DyGlDccl1F6Ek19XGYZcx486pSOmThB//txU/r9mK4VOG4NLrL0JZQQU+f3U9bDU2zLhzGtL6d8NPn2zFj59sxbBLBuLSP16MiuJKfPHqetRYa3HlHVPRfWAqfv50G374+FcMmTgAk/90MaylVfj81fWorqjBlX+egowhafjls+34/r+/YPDF/THlhotRVV6DL15dj8rTVbjyz1PQY2g6fv1iBzav+hkDLuyLaTdNRHVFLb54bT3Kiypxxe2T0XtkD2z9aie+++BH9B/bB9NunoTaqjp8+do3KM0vwxW3T0GfUT2wbe0ufPfBj+g7uhem3TIJtho7vnhtPUpzT2P63MnoM7oXtu3OxDc/HESfHgm48tJBcFTV4svlG1CUVYLLb70E/cf1wY4Ne/Hte9+j59DuuOzWSyA7FXy5/BsUnCzC5bdeigEX9sHOb/dhw7ub0X1QGqbPvRSqouKr179F7rF8XHbLJRg0vh92bzqAb97ZhPQBKZg+dzI45/hqxbfIOZyHqTdNxJCJA7D3+4NY//YmdOuThCv/PAWMMaxd+S0yD+Rg6k2TMHTSAOz78RDWvfUdknsm4oo/T4EoCfj6je9wcm8mptwwEcMnD8KBn4/g6zc3IjEjHlfeMRWSTsS6N7/DsV2nMOVPF2P4lME49OsxfP3GRsSlxmDGnVNhCNHj6ze/w9EdJ3Hp9eMxctoQHNl2HGtXfouYbtGYcec0GEMNWP/WJhzedgyXXDceo6cPw5HfTuCrFRsQnRiJGXdOhclswvq3N+HgliOYdO1FGHPFcBzfeQpfrdiAiNgIzLxrGsIsJqx/ZzMO/HwYE+dciAtmjMCJ3Zn48vUNiIgOx4w7pyI8Kgwb3v0Be384iAn/Nw7jfjcSJ/dk4cvXNyDMEoqZd02DOSYc3773A/ZsPoCLZ1+AC2eNxqn92fhy+TcwhYdgxl3TEBlvwcb//IBdG/dh/FUX4KI/jEHWwVx8sfwbGE0GzLxrGqISLfTz3eV+vnti+/o92Pj+j+g9IgOX33oJ7HWODvv5PrDlKJxSHSpCKxGbpAPnQGWpFQ6bAxHR4QgJD0FdlQ2Vp63QG/WIiDGDMaCyxAp7nQPmmHCYwkNQV21DZakVeoMOEbFmMMZQWVoFe60d5uhwmMyuNtbSKugMUrNtbDU2VJZUQdJLsMR5twmPDkOo2QRbjR2VpVZs/e1X3PeXeThx4gSspVWw1dgRHhWG0IiGNpJORERsBARRcLexedoE4+1Gxlu6l0VgtVoRERGByspKv2yKu3PjPjxx9bOoqaiFIDCoKofOIMFplyFKAjgAVVah00twOuT2tan/WnINFKqy6jmvvo2klyC32kaE7FAgiAIYA5SW+mmqjShAVVRIOhGys5l+6tvoRShOBUxopo2qQpJEyLICoYU2oiRClVUwgbWzjQ5OuxOCKICr3NO2/odVVZpq43oPGAAwBkUngk8ZArskQhAYOOfQHyuAeDAXjHOAMd9+OAcTGKACQAtt3HHU/7vZfngrbZjrNatqS21c9/ubawPOwQEIQsN75NPGHasgCp73utl+Wmrj7kcUBShebRr+mwLw+f5o3EaUBHDubqOTIDvl5tsoqut7ui1tHA3fvy22oZ/vLvHzfVZtzvHn25IQjttfux7xMQkQmbuSiwNg9d25fs/Uf4363OKMNrlWO9ZlVaCoxon4UB0uS7cgOUzffD+Ad59ntjnjGk212br9Vyxc/Fd898X3AAcqrBV48l9PYNNP30FgDFMvmYZHHvwbQkNCffqRDBK69U6C3qBr82eqzWbDqVOn0L17dxiNRq/n/PX5TSNKAbb/x0Ooq7IBcH1oAYDTLgNw/WDXczrk9rep/7pxG7t3G7lNbRTX10ob+mmqjfuY7Gyhn/o27mvxc+xHcdb3w9vZxundRna3QUttuLuN63/VqDA4JNHr/UFeGbiionHLM/vhasM1mm3TKA7ehn6abdOWfrz+t+k2AKCoStvbKOfWRvZp4/t9d+b3R1M/K2d+vwakDf18d4mf77Ntc64/32ec6vVvz/gGb77NulPleG57IRgD3H+v4b+Hy/DAyARMTbc03U8TfZ5rmwcfnY+S0hK8/co7cMoyHn7ir3jsqUex9O/PA9y1v2pNbTWiIqMh22U46hztSpQCgeYoaYAG8boWVmuH/ufDEDOLAVmBcLoKrLpO67AIIeepvCoHntte6Bqd5PD6/6XbC5FX7QhIHCdOHcePW37A3x99GkMGDsXIoSPx6IOP4atvvkRRiWsx6NKyUlw8/SLc9eAd2LDpm6DcroUSpQAbN2sUMgalah0G8SOhxg6xuBK6Xadg/HI7DD8cBHP/dUsIIYG2LrMCzU31YQxYd6oiIHHs2rcL5nAzBvUf5Dk2bvSFEAQBe/fvAQAkJybjo7dWIykhGYv+8Tf06tcT8+bNw44dOwISY1tQohRgPYd2x2s7n8HdL92idSjEzxgA1tRQOCGEBFBRrRPN3rjgrucDofR0CaIivVcWlyQJEeYIlJwu8Rwb2G8gVry5HIVFhXj77bdRUFCACy+8EIMGDcKzzz7rsxVZoFGipIGK4koc23FS6zAIIYR0QfEmXbMjSmCu54NNXbUNAhMwY8YMrF69GqdOnUJCQgL+8pe/YMmSJZrGRpO5A+ynNVvx92uehyrTrRlCCCH+d1m6Bf89Utbkc5wDl3W3BCSOmOhYlJWf9jomyzIqrZWIjY71Ol6ScxqleWXIOn0Kq/77EVavXo3IyEg89thjuPXWWwMSb3MoUQqwk3uywDlvfliUEEIIOQfJ4Xo8MDIBS91Vb/Ul+JwDD4xMcC0READDBg2DtcqK/Yf2Y2C/gQCAX7dvgaqqGDxwiKfdqaxT+Gztp/h83WeotFZg9uzZ+PTTTzFhwoSgWFeJEqUAY4ydUTZKCCGE+NfUdAsGxJiw7lQFimqdiDfpcFl3S8CSJADo0b0nxo+9GH976mEsXvgknLKMJ59ZjCumXon42HgAQH5hPqb/3zSMHj4G826/DzfedgPik+MCFmNbUKIUYJf+cTyO7jiBX78Mnhn9hBBCup7kMD1uHaRt0vHsk8/hyWcW48a7bnAvOHkZHn3wb57nIy2R2PjZZiQlJsEcHY6YhOgWetMGJUoBltQjAU9+vgDfffAjlvzxJa3DIYQQQjqMJcLiWlyyGSHGEIQkhKD7wFTogmyhyXpU9aaBzAM52PTRT1qHQQghhASFihKrZ/X5YEMjSgG24d3v8a+bXoEgaj9BjRBCCAkG5UUVKC+qQGrfbjCGGrQOxwuNKAVYwcki1+aNCk3oJoQQ0rUkJ3bDDdfc1P4TueshO4NvVIlGlAKsfvfqrowbJMjd4yGnx4HV2aE7WgChoBw0hkYIIV1bt6RuuOm6m7UOw68oUQqw6XMvRVlBOb5+6zvPDt1dCddLsE0bBggMYAzcqIMaHgKxoFzr0AghhAQpQRJgiYuAyRyidSg+6NZbgEUlRGLeq3Ox6OMHtA6lQ3CdCIgCPOvnMwZW5wANJxFCCGlO+oBUxCRFQRCCLy0Jvoi6OM45dmzYg/ef/kTrUDpG/WKajZceZ6CVyAkhJNA4h+Aoh1SbD8FRHtS/iEtySmGrtWsdRpPo1luAff7qerxy75tdtuqN1Tmg234ccu8kcLMJUDmUCBNEnQg4aX87QgjpaIKzCuacL2DJ/Aj62lzPcYepGyrSr4E1ZQZUXbiGEfqqKq9GVVk1UvomIyTMqHU4XihRCrDKEitEnQiliyYNDICUcxpizmlwiwnM5gSzOf16De6+EGvhj6O2tCGEkK7GVPwLknb8BUyx+Tynq81D7MGliDmyDPkjnkFt3DgNImyG+3e1EoQbxtOttwCT9BJUuWtXvQGuhEmoqPVrksQZg5wSA/slg2D73WjYx/SCYjH5tukWDfukgY3ahPotBkIICVam4l+QvG0emGIDAweD91+K9ceYYkPyb/NgKv7F7zFs3fErLpk54azPD4ZNcM9EiVKAzbr3ctz4xByERYZpHUqnI/frBufIHuDmEIAxqLFmCJV13m36JsM5qid4hMnVJi4CgrVWo4gJISQwBGcVknb8BWgiQToTAwc4R9KOv0BwVnVoXK+99SquueVqDLloIEZOGtZsO51Rh/i0WKp6I4ApPATXP3IVHl11v9ahdDpcJ7omI9b/xaFysDMmJ3Kd6JpQ3riNSvffCCFdmznnC89IUlvUjyyZc7/s0LicTgcum3w5rr3qumbb5BfmI7VfMiJizTSiRFyrjq5/ZxNe+PPrWofS+Zy5UCeDz68EpqreSxE00YYQQroUzmHJ/OisTrWc+rBDq+Hm/fn/4abrbkHvnn2abXPp7yZiwviJWPn6G6ipqemwWM4WJUoBtubFtXj2lldRmFmsdSidju5QHqQDOWA2BwBAqLFDifGu3JAON9Em1hzwWAkhJFAEZwX0tbltHk2qx8Chr82F4KzsoMja5qtV6zCw70A8/vgiJCQk4JZbbsH3338PHiTLGVDVW4DVVdsg6UTIjareVIMOzOHs1BVanAFcr4Ng92+FG+Ba7RuyAiYr0B0rgHS8EDxED6GJNTeYrLrbFICHGJpsQwghXYkg17XeqMXza6HqLf4J5ixkpGdg/l0P4v47H8CJwqNY/clqzJgxAzExMbjhhhtw4403onv37prFR4lSgIWEGV1JksCgxFsg90qEGh0O2J2QjuZDOlnUqebUcJ0IOSMBco94wKCDcLoK0v5siGXV59y3Eh3uen8SLICiQjxRCOloAQRZAWslAWIcrbYhhJCuQJXObQK0Kplab9TRGMDAMHHCREyfMR0vvvgi7rzzTixevBi7d+/Gp59+qllolCgF2Kx5l8McE473lq1DZmpsw71hgw5CVV3DytadhHNgKpS0WM/kaTVE75ckieslOC7u3zB5WxIh2J1gQbjGBiGEaEnVWeAwdYOuNq9dt984GJymZKi6iA6Mrm1CwkIQmRCBQ8cO4b333sOHH34Ixhjmz5+P2267TdPYaI5SgOn0Oky7aRLuW/ln14HGM/wVtfNtiSZ6fwv5bTRMaNgrzkPlQVkRQQghmmIMFenXnNWpFd2v9f49q4HCogK89793MGbcaFx44YXIy8vDm2++idzcXCxduhT9+vXTND4aUQqw2qo6rHlpLf775kZgWEbDiAnngE4CRyfbP7Z+rpX7dXBR8M9rUNSG0TV30tSwPAC6dimb+/uh030vEEI0Y02ZgZgjy4A2LhHAIYCLBli7XdmhceUX5qOysgL5hflQVBWHjhwEAKSmpCHU5FoMeNLMCRg0YDBuueEW3Dz3ZkRFRXVoTO1FiVKAffry1/j3Y6vAOYehohbOnglQk6PBamzgktjpkgDdviwIFTWQeyaChxkhlFrBzSFg1nObXMicCgwb90LukQClexzAGOR+3cDDTdAdygGrdfjpFQQPLglgsgolNhxytxgIVXWQjhd06kn+hJDAUHXhyB/xDJJ/m+f+e7L5XxzcNSEI+SOf7fA9315a/gLWfNWwCfysP84EALy7/D8YM+ICAMCXq75Gj/QeAACDGFz7vAGd8NbbsmXLkJ6eDqPRiDFjxmDbtm0ttn/hhRfQp08fhISEICUlBffffz9sNt89cAJFdsgQJNfbLlTUwLD9BIxf/AbDt3sh5ZR2ug9FpnJIWSUwbNzreh3bT0A4xySpnlBtg35PJlhRBVBnBwQBSmoMnL2T/NL/2VLDjFBD9H7oxwDV1NCPGh4C+7g+cFzQB2paLOSBqVAtQTDJkhDSKdTGjUPeqJfARWP9ZiVez9cf46IReaNfRm3s2A6P6R+P/wtHfjvu86hPkgB4kiQAQbMkQGOdakRp1apVmD9/PpYvX44xY8bghRdewLRp03DkyBHExcX5tP/ggw+wYMECvPXWWxg3bhyOHj2Km266CYwxPPfccxq8AiAi1gzFqYAJDNx9a6kzVbk1hwEdNhHduOUoOAA13gKuEyDml3fIdVqjJFgg90mCGhUOcA4h5zT0+7PB2rkkghIX4eonxrW+k5B3Grq9WRDLayD+cgRcL0GJj4BQ64BQTtuvEELarjZuHE5OXgdz7pewnPoQ+tpcz3NOUzIqul8La7crO3wkqd3cd1NESdQ6Eh+dKlF67rnnMHfuXNx8880AgOXLl+Orr77CW2+9hQULFvi0/+WXX3DhhRfiuutcS6enp6fj2muvxdatWwMad2Mz75qGbr0T8c6iVTj86zHN4uhsGACxqEKz63PG4Ligd8MBxiBU1wFnsW6UY2xvr8mTQrXNa/Ng5pAh5Zw+p3gJIecvVReOiu7XoiL9GgjOStc6SZLJVd0WpAUx4ZFhiEywwGgyaB2Kj05z683hcGDHjh2YPHmy55ggCJg8eTK2bNnS5Dnjxo3Djh07PLfnTp48ibVr12L69OnNXsdut8NqtXo9/IkxhhFThuD6h//g135JB2Nw/YJp/EuGn+VO1032c84REkKIN8ag6i2QTUmuBSUD8IsmObEbbrjmpnafF5sSE5RJEtCJRpRKS0uhKAri4+O9jsfHx+Pw4cNNnnPdddehtLQUF110ETjnkGUZd9xxBx5++OFmr7NkyRIsXrzYr7E3VlZYjv888TG+fuu7DrtGV1F/I6+lH+22tGm3xhPq6ysSVe4aPdI3/MjwEP3Z7ZFkcwJGXUM/Rl2rE/g9FXD18RBCSBDqltQNN113c7vPyzyQDUtcBKISLBCE4BrDCa5o/Gzz5s14+umn8eqrr2Lnzp345JNP8NVXX+HJJ59s9pyFCxeisrLS88jJyfFrTGtXbsSXKzZAdsh+7ber4Eada7XvHgmwTxwAuX83cNZcm3jYJwyA3D/Fp81ZX9+ggxoeAsfw7rBf2BdKlKt8lQEwbtgDaX82WK0DzFoHoazqrK5r/HYvdHuzwGrtYFU2CGXVzfajGnVQw4xwDu0O2/h+tG8dIecBznlQTmruSKqsoiy/HLXtLAYKxPvUaUaUYmJiIIoiioqKvI4XFRUhISGhyXP+9re/4U9/+pNnVc9BgwahpqYGt99+Ox555JEms1aDwQCDoeOG/1RFhSAIUFRaYbopiiUUSmoM1HgLIImQI8PAauwQKmsgVLgmNsuJkZCHNuz7I1tCIZ4s9Jrn0xZqiB5qVBjEokowWQEHYJ/QHzy0oTxVtjkgnnatNM6cCqQThXBKdRBsCsScatfgk1EHJTocYnElmLP1/65MViCdLIJ0sqjVtnJGPHh0uGubG8bgEEXodCLEEisYJduEdElVp2sgOxSoUCAi+CY3B5PaWtfngk6na6Xl2es0iZJer8eIESOwceNGzJo1CwCgqio2btyIe+65p8lzamtrfZIhUXR902mVrSdmxEORFQgig6qcX38xtIVYWAGpsAKqKMA5qifUBAucwzMAAKyiBlAU8Ch3tQbnEE8UQXcsv11JkhpqgHNACtSkKIAxOGUFrLwaCAtx3U4DwGpskA7kQMwr85xnvSAO5VOTIUe5EmljZh0i9zvAQsMBgcGpqNDtPAkp77Tf1sLSH3RVrCjRYXCM6AFYTHCO7gWnqkK3OxNSdkmnWneLENI6e40Dv32xB+OvMSDKEgXhfEiW3KPqiqq0aQkfzjlqa2tRXFwMi8Xi+WzvCJ0mUQKA+fPn48Ybb8TIkSMxevRovPDCC6ipqfFUwd1www1ITk7GkiVLAAAzZszAc889h2HDhmHMmDE4fvw4/va3v2HGjBkd+qa2ZMoNE9BrRAbeXPg+fv1yhyYxBLP6O1BMEqEmRno9xyPcawrVT0h0yNDvy2r3NZTu8Z4kCQAgieCx3nsdiaeKIeaVeeLhIkPJNRle84N03AiEGxqiFhik3I6pVlOjwgGToSFmQYCY67+EjBASXDa//QsAYNSMIZD0YpffvslkDoHJbEJ+UV67zrNYLM3eVfKXTpUozZkzByUlJXjsscdQWFiIoUOHYt26dZ4J3tnZ2V4jSI8++igYY3j00UeRl5eH2NhYzJgxA0899ZRWLwEAkD4gBZOuuZASpZY09TvBT78oeP1k7Va6Y01NnD4zBsa0S1bOszkMhJxPOAc2vfULfv5oO8wxoV0+Ufrnt39DbHJMu87R6XQBGfRg/HybMdZOVqsVERERqKyshNl87hNp808U4rX736EkqRVcFGCbOgQwNqosq/9Fobr3e1NUGDfsad/cJMYgp8bAOay7bwVZo6/F7FLodp5sGFFiQPYjQyHHGD0La4aUAJbD3LWaOmPgnMP47V4I1ba2b0XTeF+3Fs5REiPhGNPL6xzDpv0QKmlBSkJI5yfqREy9YQL+vPRGhJr9syOBvz6/O9WIUlew8T8/YuvanVqHEfSYosK4fjeUbtGQu8eD2RyQjhcCdieUHglQY8IhZpcCctsnxavmEHCTAUpKtOtAjQ1MUSHmnIaYVQw1NgJyRjyYQ4aYU+odDwdSn96DmiFRqBgfD8GmwLK5EIZcG+wjUiCnRqMulqHokcEIOVKO2I9OQrS1HJtqDgE36iH3SAAPN0I6mNvsrTuxoByG9buhZMRDSYiEmF8GZut6+90RQs5PilPB129+h7EzR2HsjJFah+OFEqUA45x7bV/SVXGdCKVbNFitA4J7RW01OgzcEgYx7zSYzelqkxwNZnNAKHS3iQoDj2xoI+aXA4LganO6yrVC957Ms4pJSY6CajZBjTYDjEHML4d+f7bneSH3dItzjJjKEbbrNMJ2ebdhWQVwRDlg6x4FNcSImqHR0BXbYMivQejecjCVQzWHQImLgFhQDqHG7oon3gJuCYUaZwYEAXKvRHCjDmJhBYRqG7jAoCRHuyat55dBqHNAOJAD3QH/LllBCCHBIhhvclGiFGAZQ9LAGOuy6wZyBjgHpUFJjwME9wrUdQ4wzsFNBoBzOAemAtU2INTQ0MbmAFNUV2l+fZsam2sCs7uNkF8G/c6TbSrBb4p0KM91R0wvgetET8JyrkKya2DMrkH0Z9ko/mMPVI+IQcWUJIAx6IudiNrjhKAPATiHPDDVVRmXXQrpWIHrDmJkKBxjeoFbQiFHmCAPSgOrqnMtRCmJrtuFZVXQbz0GoZ1LIBBCSGdhMocguWfHTsw+G116wclgdNHvx+DD7OWYcuNErUPpENxkgNIjARCFhjlFIXpP2T0YcyU+5hDvNka9K5Fq3Cbcu01b1ylqjqeiziH7LUny6psB1SNjvbYoMdRIYHr3ukzu42J2qVc8alyEay5WfRsAPMwI6KSG115a1e51ogghpLO447kb8VHeCqT1T9E6FB+UKGnAEheBXu61gc4bbanYaFObcw8loJjnf9p5Xmd7oYQQcvYGXdwPIY0W+w0mlCgF2Ik9mbhz+F+wbN5bWodyzri7Mr7+AQDM5gSrci9BX79HGj/jAQCq2tDGq9Nm2qgcamSYb3PPMBHz5CON4zkrrfXDAC4wz/U9bThgPOHeRFnhgMLhCHc/0ej1q9FhXt0J5TWAonq/R/WvXXW/dkuoa5SNEEK6oLtHLsDCy59CRUml1qH4oDlKAfbzmm04uS+79YZBTomPgBoeAiUjAVxk0O86BbGwAkxRYfh2L9S4CMjpcWB1dkgnisAUBXL3eKiRoRCzSyHkl4PHml0l71LDOhjMWgtW54SYUwohrww8Jhxy9zgwmxPSiULXnB5zCBCih5ISAyUpEkJZNcSTRYBR79ryQy9BtzfrrBZ/VOLM4KFGVz9GPXT7syFllXi1qe0dAWeUAdaLEyBH6BH1RTYithSDAUh6+SDs6WGoHB8PZlcR8WMh9CUOKGmxUOIjXJOyK7xL+sXiShi/3gUlLQZKvAVifjnEnFJwcwicGfFgKod0sgisixcAEELOb9vX78bBLUcxbuYorUPxQomSBhhj4J14SWUxRUDIPAuc1UZUH9QBDtdmsvUYXB/+YrH3Xwa6w2esuFpcCd3BHFfClRLjWm06swS6RnugqTU2CFV1rpEqmxOcAUpqLJReiZ42PNQIbjaBGyTXXChRAK+f89MOcoQOZX/oCSbpYCoEBAWQu8eBh4dAyC+DUFYNGHWoG5cBZ7QezliASwzWC+PhjA9B6J7TMJ6qhjHT9WhMOlEI6URhs9dmsgLpRBGkEw2vXXHaUWuxASqH2UlLARBCiBYoUQqwgeP7ISTMiJrOuFCgAYh8MgTG8RLAywEGhOpzUfm0Dag5u0nW4okiiAB0e7MBgYG510XiDHCM7Ak1OcqzirZzYKrrtpRO8tye020/4drKw71Yo25fNiAK7Zr0zQEUX5uB6lGxrgMMsEUBUQcBITIMsiUU6JUI2J2AXkJIORBSARhLgbKBgKNbKBxJJlROTET820cRuqfsnKdSlc5KQ+VF8Z7bbTVDopC48gjEGtoIlxDSNcWnxaL7oFStw/BBc5QCbPilg7AqfwWufmCm1qG0m5QgIORinWt5A3cxmuN7O1B7bpVoDABTVU+SBAAw6KB2i26ogGPMdYtO587tGQMcMqTc067zeX0/vN2VcVwvoHqMezkD97WM5QCr76Z+YrVBBzDmev0A7JEAr79rKDBA5QjzQ5IEAJXj4wFJ8MQUctwKoZaSJEJI1/Twh/fh38dfRmL3eK1D8UGJkgYknQRjqEHrMAghhJCgYAo3ee3VGkyCM6oubP9Ph3Bt6h1474nVWofSIg6AMwYuMM9sKqVIhX2na1SDyxxc5TBcIAGiu/KrvlpMavrbyquN6GrjqZxjABeEhplbdtmzorenck5RAUVpOKaToESHu76uH5oSAbSQg3qq9NzXBwOYU4XpQIVXA5sF4PUvo77Uzql4qtI4AH0FGiZYu/efq+lvadfsM8/rF73fs/DfSj0Vb1A56nqYoRo6fvNHQgjRwqNXLsHcwQ+g+Izto4IBzVEKsJ3f7kNliVXrMFokp8VCNZugpMUAjEG38yTEvDLABpy+qxZSbwGmGXooeSpqv3AAsnvrEbMJSmoM1KgwMGstDN8fBFNUT7+eNikxUKPDwKx1kLJKwI06yOlxgChAtzcTUmYJGOcw/HIEangI5PRYV9VbZjGgqFC6RUONCoeYWwrhdBUAIGS6DlKaANNMHQQTQ+VLNtR+7L1Ao5waAx5mdF1LJ0LMLYNQaoXSLRqWEjPCtnHUJAKCDJgKAEEFWGUNhPIaiHllEIorwUMNqLi6P5yRetQmAlxk0OXXwphVhbA9ZQg5UtnmW29yt+iGeAw6SEfzoTuUCwCI++gkIr/Jg3VsHJjKYf6lqNW94wghpDPLOpCD47tOIS4lRutQvFCipIVg37tEVlzzhdyjJDijLF0+qsK61Ob5mutEyD0SXHOK6o9FhMIxPANCVR2kU8VgdqerH1nxbGTLI0xwDk5r6Ngpu0aNGhGq6qA/YzkFKbsUyPb+q4PXcfAa7qrA0wP8jIW3VYsJzkFpgL7hW15JjYGS2vADKdUBESe934czq/AcFhGVPQSo4a50iDkUmLcWw/J98xVtzWGKCjgV19YtnDeMlrnpyuyI/or2dSOEEC1RohRgo6cPw8b3f0B+ozLwYCPllYHnlUE6lOupJmtulEROjYFzaPcmF0NUk6OgwrWtiW7XSYgVNUBlDcT8MtgnDAC3hHrairmnodt50idRaivbJtm1hci7DtftN/e8Zw7AcVFfqLER7UpQhcIK6H873pDUASj4c1/U9bN4EseQQxWI//cxCGc50iMWlIMzQDxR6JkMTggh56t+Y3ujz6ieWofhg+YoBVjf0b3w9pGXcNs/rtc6lBZ5qtFaSJIA9z5l9VVpPp24N7MtsYLV5wDcteZS4yQJAITiSlfl29kGzAFXVgZPkgQA0ImuJKk+njYSSq1gitIQj8hcSRLgSQpDTlohOJRzqnLzqtY7h34IIaQzW7p5MV76+SlEJ0ZqHYoPSpQ0UFdVh+Ls4JuwprkgG1Bp0wBUkMVMCCGdUe7RAjgdwbnxNyVKAbZ17U7MSbodn7+6XutQ/ELMLgWrda8aXb9PWa29oVINgJpo8aqEYzYHxMxi7zYJDW3q929zRujAhbblIp4BK0n0rBLOAcCpQCgoa/ttN/drUOIiAF2jrVUUjohN+YBT9cRd2ycCqqnpu9eNq+u40b1quft1CbEMaOPC4Z5+BNbQDyGEdDHP374c16XcgdxjBVqH4oPmKAXYkW3H4XR0nYUDxeJKCN/shppggRphgph7GkKNHdygg5waA1Zrh5hfDtYoUWEc0O86BX4w19WmzgExrwyMc9T2DIetZwSqh0fDGRcCscqBlKf3QKxreR6Q3CsR3BwCJTkaEFy3+4TTVVCSo8HNIQA4IsRahAgO1Kl6VCohaOqmoni8AIK1DmLuaZ+91WI+y0bkhjxUjYyFYFcQtrMUgrPpBEzpEe+qHOwWDYgCjFIJwiPyEDJBB11vEWoVR8nN1VByW07g5O5x4BGuSkFIIoTMYhh2nWrxHEII6YwqSqzIPpSLbo22qAoGlChpgAdJ1ZsaaoDcIwHcbIKYXQKhoAw1gyJhHRsHfbENET8UQF9ka7UfBkAsrIBYWNFwzO6ErtFfBs4oAyrHx8OeGobw30oRtr0YwhltAECqciLkSCWUcB1ksw7Gk9VgzoYJ3mI8g2m2HvrBIurWO1H7lROwA8LpKnCbA6p7zzehpBJCaRVCQxVMTcvHVbE70M1Q7uknz2nBWnN//JyRguKjMajaZwbLroLucB6Y3PyEcrFWgeWHhgo3btBB7h4HJS4CUl4ZxMxiMEUFK6+BYHOCh4dANRmA8hrYDysQTAxCAoPzkArV2vB9IIdJsI6LR20/C0L3lsG8pQiiTXVtoGt39xMdDjUuAraL+0MsKId0qqjFWAH31K2YcMgZCQADpBNFrvlXLZ5FCCGkHuPB8qkdpKxWKyIiIlBZWQmz2XzO/WUdysWLd67Avh8O+SG6s+fsnQR5QIpnCQBVx1A8AlCN7uorDli+L0DU59nn/KFaOT4epb9Pd33BAGZXkPKPvdBVNL/Ra/3ilMy9zxs4YJqpQ8Rfja5uGIPqVFFyXQ2UPO7pG7zh3FEZpXjs6j0w6FyjUY0L8+rTC4co4qUhF+C7+8KhZKmePtpCSYqCY1TPhoEpzmHYtB+Ctc4dD/MsTlkfXkNbuG58q0DNAAsKb+7tnhTvei75xQMwZlV7TlTiIuC4wN3G3aH+x0MQ3etINcd+YV/XhHv3f2eh1Ar9z0e8RvgIIURzDJgweyzmvTYX5qhwv3Tpr89vGlEKsLR+3fDc5ifw9Zsb8dzc5ZrFoUaGuubjuLMHRe9OkgDPMUN2tV9GHmwpYV79inVKi0kS4M4nGlXKAYCurwiAgbmnDvFaNCRJjdoxACMzSvH3a3eCocmVCzyT8/SKggd2/oJyaRi2I6Zdk7NVi8l9wYYLeJIkwDMvyuvyjft3Z2uOpNCGfebcJxhyarzOUc0m93VYw1yn8urWY4zyfu+ZtS741/EihJx33tj/PNL6ddM6jCbRZG4NFGYWY8eGvVqH0Sb++Eg9M0856z75GWc3k8WFGpx47Oo9riSple9wAQADx2NX70GooZ0VF/WjXeesiXfEX/fGOCgxIoQEvR8//hXWspZHyLVCiVKAbV71M27oeQ9++HiLpnGIhRVAo/ktUpWC8L3VYPaGSdO1/S3gxvbtL8YZoFpCvarcTIcqvBZlVE0SavpFtN5PhAlcarh+zRoHHLsa+hEiGMJvN4CFe2cVU4fkw6BTWk2SPP0AMOgUXDqqYRFQp0UPe5Kp5fNKrYCtUXLFGByDUsENvgO1nsExBigWk1ebsB2nYTpQ3pDQcI7KyWleVW5iqRWodXi1kVNjWk2odPuywGwNo3dqVBi4iTZkJoQEl3cX/xfXdvszTu7N0joUH3TrLcCyD+VBEBiUVibhdhTH0HSoEaHg7lsyQm4phKJKSPnlMMkKYvQCagZHQVdqgyGz7bfe5EQL1IRIKElRrm1CZAVVoRVwxuphywgHGINpXxlC95cjdF8ZxNqmq9jk+AioSVFQEiMBgw5QVBjX7QJzyJCPqzh9Ty1Cr9Uh4r4QMMYQfosBYdfrUfSHaqinXcX0vxud3WTfLWLAzEsLsKLHRKghEup6uxbSDNtShPhV3lVmzv7doFpCXXN/zljEUumZCCUjHoaN+yBUN0yEVy2hUNLjoCRYgBA9oKrQf7cPYpUNujI7Et88CltGJKov6w3jaUA0xcM2LQ5CYTnAGNQEi+taFTXQnSqCUFQJoa7l25cAIGWVQMwq8ZwvFJY3LP5JCCFBgqscDpsThZnFyGi8tVUQoEQpwJjAoGq4VYV0NB/cbILcI9614e2RfDBrnSchEhwqwre3fzFMobIWQmUtmM0BuVsMxMJyWAqKIUcbUDExEXJSBEIrTNBZTFBT9RBOFoI5vJMlNTIUcp9k8Gj3RD6nAjG7BHA2Go2yhKLSmoiKV80IG1iJ8IEVqMsMQ92o7kC1A1E5WUiOqkN7CQDSeDnSNp9ChWiC4FBhT4+EQbCgbtpQSLmnIZ4ohGBzQswqgVBYAVlWIQ0OQcSocpi616D6sBmVWyPAD1rBzkhiWJ0D0uE8CFV1kNNjIZTXQKj1bmPIrYL+rV1Qk6M8baQTheCMQZEVqOYQSEfyIOWXoz3qqxIJISTYCW29FRBAlCgF2LSbJiLvWAG+++gncCXwCZNQ6wDqnA0fnH6aC1P/oc8O5UE6lOfpWl9mhw5hkPtHAMwAmAC5Rzyk496byHqq8OpvLckKjOt3uTaNrW/TMwHyoDRXmyqGii3RqNji3og3BIDRCCkp9Zxeh6WiGvYqBbrvQuAcFgnO9K6Yeya4khYAQo0dqLUjPD4TlttCwFWACYB5xGnY/p4Ftdj3v6tgd92iE04WQjxR2OTbzhyya15V4zbuhuL2E+5G5/TyCCEkKBlMelz556kYMmmA1qH4oEQpwOJSY7HgvXm48Pej8cTspdoE0Xhyr59zNZ/PcZWDhxnc13RXXskqmOw9msRD3W3qb2U5FTDnmW2M3m3OvBoD6tQ2LnndjFqHCKjueFQOVl+JxgHWeD4SB8RkAVwGmNQQTVNJkveLaEOu07jNmd3RbTNCSBf0ztGXEZMUpXUYTQq+Ma7zwMFfj3aZLUza5IyqK97U5rRnVma1pU0TxytlI3LtkWjv3U1VBfLKQlBV12ibkMYhNJXdqACEM+Nu33UJIYQA7//9Y+QdD77tSwBKlAJu7cpvcd+4R7D3+wNahxIQHIBTroY9TEH90ovMvYBiY0JZNeCQGyU9HHK8xauNdLwQQnapK6sBAIcMIb8MrMbuacNOV+N/pwa2P1AG/O9gD9RnOkJ5tXdFmyDAMbIH1PAQz6GqzXpUbQ0Fd+9Iw22AcYrk9VOlRoZCSbCAN7WY0zngogDFvW0MIYR0dmvf2Iib+8zD4W3HtA7FB916C7DSvDKIkghFbnnvsq6g8OZesCeFQo51raZt/rUUUd+VQCixelaGto/uCW42gbsTECHvNKTMEtc2G2cMCwm1dhh2ngQ/kA3VEurqR3WlX2p0OJjDCaHKhk0GCbffL8IgtW2JAAUMNibhzSsuR6jkhKiI4GbfBERJiYHSLRpCWbVr890IE+p+AMq/KkdYzUk4dsrg7pzNMTQdalQ4uDuREQrKYfj16Fm+kw1UiwnOft2gRps9m/bqvz8Asaz1xScJISRYqe5K8LIgLDyhRCnAREmEqmqzNEBjXGCoHhqFykmJcMSHIHxrCSI25UNf1lCJxRmDkhTp3nDWBDGrBGJ+GZQEC5T0OLBaO6Sj+a4NZJu41RX3/gnIkQZUTEgAN4iwfJsLsdR77zj9jpPgIXrIPRPAdRJ0h3K9yuqbwuwyxKLKhq8Bz1YeHIA1MhaPHpyBfw35DArnEFsYzKn/TzGvbiZQaQQLNbrXteSuZROOF0AorYKSHAU5NQZiWTWkk0XgogBnr0SoabGQDeGoCB0OYdBpCAUlqBoYjspL4gDOEJbDEbbXCuloXouvqa1YtR36X49CTYiEnBYDwVoHoaKm9RNbwHUi5LRYyD0TAcYgHS+AdKrYZx4ZIYR0NEkXfGlJ8EXUxc24ayrqquvw2bJ1sDW6ZRRoJbPTUTUu3rUHmMBQ08+CmDWZXm2cg9OgZMR7JlAr6XFQuse5nmTMNQqk1zW7Lo/gUKEvrkPsf09573PWCFNUsGobdLszm23THnKfZMj9u2Er5/hLpgl/T10No+AEuPcq3arqupZdFrHo42E40q8PwiQO5g5AOlYA3YEcz35tUu5pSLmnPYtxM7iq39RGC2Iq3aKRNycGXETDnmyFRQj5MbPpOVdnoT55EQsrIBaU+2VxcPuEAeBhRvcFGFRLKCVJhJCAik6Owuz7r8SwS89i6kQHo0QpwMxR4bjtH3/EkEkD8PDlT2sWhxqm89rrTXSqYGcOdOkl7yqzM+fZMAbYnS1vJNtSBVfjrtrQpi24QfIkf9uqe+AP++7BjMK1mDU622t9pYKKEHy6LRXf7ElCjWwABoneCYdDdr3eM27/ebVpdC3Xkwxcx7wai9VOcJGB+XspiKb2kTvbrow6r0SOOWT/7c5CCCFtsHLfUoRbwrQOo0mdbjL3smXLkJ6eDqPRiDFjxmDbtm0ttq+oqMDdd9+NxMREGAwG9O7dG2vXrg1QtL4URcGP//sVKx76j2YxuALhXkkJb7Kq64wP90bbZ3gILKi2EmPcu/6+WjXi021puOmVi/CHZybijy+Oxx+emYgbX7kIn25LQ63dnTDWPzwdMfDWSufUJmr9Ofd637gouN7rYKao3v+tGaMkiRASUP+6aRn2/3RI6zCa1KkSpVWrVmH+/PlYtGgRdu7ciSFDhmDatGkoLi5usr3D4cCUKVOQmZmJjz/+GEeOHMHKlSuRnJwc4MgbfPbyOjxx9VJkHcjRLAYAiPk0CxE/NKyOLThV1HX3zuZ1B7JdC0O6b8Mway3EYwUQyt1zYhQVapgRkNq3H9y5qk/q1DAjnBnxUGPC3ccZUG1zjQa5P/iZXYYSbwHAUFWnR6HVhPKoBCjd4z37yDGVQ//zYQgl7nlPKocaogPXSw3XMhngzIj3qtaTjhdCOpTrGlWDa7J5/P8KYTri7odzOPrGwz6kG7gusO9Rexi2HIGQX+ZJFrlehGo6t/WoCCGkPX5buxP3X/wY9v5wUOtQfDDOg2k8oGVjxozBqFGj8MorrwAAVFVFSkoK7r33XixYsMCn/fLly/HMM8/g8OHD0Ol0Ps+3hdVqRUREBCorK2E2m88pfgB49/H/4oMln0BxBsccECVEhCPeCGNmTbOjCFwSwEMMYFUNW52oYUYwu9NnUcgzz4Ostjg6oegFCHLDbT8OAJLoNUfmzDbOpEgofbuBm0M8t4xCnFWQBR2comuuDcs9Df2RPK/tWWzj+oBbQl17yMG1z53htxNe8agmA5iigNldNf9KTDicfZPBY8yeaxm+3QOhqmHCOXfP12LWWs+1aqb0A48Ig6i4/hYRiipg+OWI9/vjvmV3ZnVfS5p6f/yFG3XgjHntIcclEZAbVkc/m5gJIaStFn/6EMbNHOWXvvz1+d1p5ig5HA7s2LEDCxcu9BwTBAGTJ0/Gli1bmjzn888/x9ixY3H33Xfjs88+Q2xsLK677jr89a9/hSg2/Re+3W6H3d4wydpqtfr1deiNOtfSAC3N6wkAxSiiamwcKiYmQjHrYDpQgai1OTDk1/q0ZbIKVuW9f1pLlWmqJRTOXglQk6MBhwzpWD6kk8VgSsMkKHu3UFRMSkT1sGgItTIifixCWJYCNTUOPNQIobgSirUM1iHhqBkWA8GmIOLHIoRmyuDdE8CNrhGPHsmnMefSvZg88jjq7Dr8d0N/rPl3Ihz7SjwraavmEMg9E8HrN7FVVIjZpdAd8a1EE2q9J9gL5TUw/nQYanQ45KRICNZasCrv1844B7N6v2+mbw8DAoOcHgeE6CCeahj1VMOMkHskQEmLdU0UP1kE8Wg+hJaSTuaaLC73SgSPCIVQXg3xUC7Eokq/3SZjNqfn21JJjoLSKxFqZBhYZS3ErBJwkx5KehzAGMTMYkhH8yE0XmuKEELOkuDeB1VvDL7R7E6TKJWWlkJRFMTHx3sdj4+Px+HDh5s85+TJk/juu+9w/fXXY+3atTh+/DjuuusuOJ1OLFq0qMlzlixZgsWLF/s9/nq/v286jKFGfLjkE03Xiyi9Kh3VI2JciQMDnPHGJpOk9uIGHewT3Xv1MAYYdGCy6poH46aESsh9YKB7ZWsGNUwHpWcyZFND1ZmcaEbh7AjXp7bAoIZKUHomQQlpaGMyOvDGgk/AOSCJHOEmB/j+Ijh/VT1tuCjAPmlQQzwApCP5kI7ktSnBqE/uhNNV0J+uavMMZ8Y5oHDoTrj3tGt0nn3SQFcJnnt0hjPWYpIEAEp6PJxD0z3zqFRLKPTlzY8CngslJQbOkT081+LmEMiD07wn9uskSpIIIX6TMTQdc/7yO4yYMljrUHx0qjlK7aWqKuLi4rBixQqMGDECc+bMwSOPPILly5c3e87ChQtRWVnpeeTk+HcukSHEgFn3Xo6//udev/bbXtwgNtS5A2Cyf4a3uCS4k69GH+Gy6vWlqnO3abTAEVMB1qgRF919CM230UkKRIFDEhtit9cKEBun/wJreNT3IysNe7i1+wWe3Wle54mCdzyK2uryAVwS3JPHGyrs0FEl/JLgnRSd+f9AwwJUhBDiB0s3LcbEORd6/Y4PFp0mUYqJiYEoiigqKvI6XlRUhISEhCbPSUxMRO/evb1us/Xr1w+FhYVwOBxNnmMwGGA2m70e/mSvs2PNS2vxzz+97Nd+24vZFdeHd30hm9SOb84WmjJZ9a0gkwTvQjmnu02jajAunlF0psLdD5pt43QKUFQGWW4IyGBSoMiNAlJ5w6O+H0louqKtTUNMbWjT2nlnVJlxUfCpJAS8KxGZrLrXZmpUedhRk+hl1bN+lOdajf8fQJuWPCeEkDZ6YNIibF71M4Jx2nSn+W2n1+sxYsQIbNy40XNMVVVs3LgRY8eObfKcCy+8EMePH/daCfvo0aNITEyEXq/NfdA1L32NV+9/W/Nl2mP+l4noL7IhWh0A59AX1sGe1PS+YVwUXPNWEixwDE6DkhTV7MAKszth+P4AhLzTrmTA7nSNhogN32pijYzk5/YjbLerjeDk4ALAG303CjIQsxMIKXYlOczJoTJ3G/dIii3HhruuHYbvPrFAdgJVFSJ0OhWmsIb/3kxRYdi0D2KOe484h+xKAiTRlaDANWHbMTgNckqM7+tyt1Gjw+EYlAo5NdanTX0/alQ4HIPSIKfHggsMXBLg7JEA58AUqCaDp71h036ImcWuhElWwDiHqhNdI20AbCmhKPl9Gqzj4j3JkphZBN3242CVrtujQkUNlMjQDpnmJuaUQrftmKe6kVnrIO3NglhfAamogFOGajy7AglCCDnTyd2ZeOraF7Dz271ah+Kj08xRAoD58+fjxhtvxMiRIzF69Gi88MILqKmpwc033wwAuOGGG5CcnIwlS5YAAO6880688soruO+++3Dvvffi2LFjePrppzFv3jzNXoOjzuHa602jqjc1zABW54RoU2DZVICI7wugGkWItQ3xOGIMEKudEG2uhENJjoScFgse7ar8UnokwPjFb66RhyYI5TUw/HYCXJfpuu3WxF8IxpwaGN87jrCKcDCdvmF1b1kBau1AeAj01Qz6w4Bltw0QRDC9u1otvwz6fdlgDhmZAJb+mIplSzIg16iQq31jEqx10O88Cb43C1BVT8WWEmeGs3eSp6JNASCUVYFVN0zoViJDvareFADM5oBQXgPmcA1dqRYTnH27gcc2tJF7JLgSqFBXFZ5qNnmq3oRqG/R7MsH3ZwNomAdlSzXh9JVpsPU0e0aV9Hk1CMmsBuOAlHMaQm4ZeHQYhNNVza6Ifq4YACmvDFJeGbheAhxyQ9XboVyvmAkhxB9U9+9le13Td3u01KkSpTlz5qCkpASPPfYYCgsLMXToUKxbt84zwTs7OxtCo1sCKSkpWL9+Pe6//34MHjwYycnJuO+++/DXv/5Vq5eAMEsoFKdrjkyrCxr6SX0Vk9wrETwyDJAViMcLoDtaAKaoEGsVcAA1Q6JQcUkS7GlhYE4VEZvyYdmYDzHnNKTs01DNIVBizRDLql23glrR0tIBSoIFcq9ECJLeFaDd6dpj7KRrjzE13AglNgKitRZCaZVr37nkKEASIeaUej6olbgIyL0TURcbAagqxFNF0B3Ka/LaZ5bUiyVWiMVW8FAD5HgLhKo6CNXeVW/i6SqIPx0GDzXAMTAVamIkHOP6ukbCsoqhO5QLsawa4s+HwU0GyAkWCNU2iMWV4AxQEyOhGnWQck77xnNGsmHIrUXyq4cgW/SoHhwFXakNxkzXZrdcFCBnxEPp6ar4Y3UOSPuzIOWWtfrf4VzUJ4PNxUwIIf4gSgIUWUWYJVTrUHx0qnWUtODvdZQURcEvn23Hu4v/i8x92X6IsHVqeAjskwd7TdDVbTsGMa/MM1LgiDUi55GhXltyxL17DGG7mtjw9hyXNuAGHWzTh3vHs/sUxFPFvlOAzrhW4601uE6E7cqRXv1Ie7MgnSj0ezUYZwy237nX9qi/1qFcSIdbr5472+1AOIPnvZd7JMA5KNVrQrXxi9/alLASQkiwu2DmSMx5cCYGXtTPb32ed+sodRWiKGL8H8bAGGrAw5c/FZiLNtqLrB5TudeHN6+vQBOab9PQ+NzC4U3EA5W7qh3OzNvP+NIrniaqsRjnroo2f4/WMe/rAGg+5iZOPatLNk4Q6zfZbdwZLfpICOkiHnrnbtrrjbhYy6qw8q//wROznw3cRWXFuxKNc9fmsY2INsVVhaY0tFHCdGeVFHGglco494TgxvHUb8DbHorq3Q8abYp7RlLDm/p3C2WoPm1UDjjlFmOun3jd5L5554jVb9LbuAJNL2m5ZikhhPjN3EEP4OPnvoDslFtvHGCUKAXYF69+g9VLP4etxt5643PE9RK4KECNtwA1ds/aO6yyFpAVrw9ZqcKB1Kd3I+KnQjC7An1+LYQ6uc0f+lwvgbtXonaM6wNnj4TmK+OcCgwb9kA6VgA4ZbBqG5jd2e4EgykqjN/shnQ035NAyH2SYb+gN5ToMHDGIKfEwDauD+Q+SeA6EWqoAc7BabCP7gk1PMT3dUiCa1+3Qamwj+4F1exqwwAYN+yBdDgPsDvBau0QamzgggAlRAQXGKqHRiN/bh+UT+vWMGrmJ2JWCfQ/HoJQWOGaH1VQDjXE0Op5hBDSGZzOK8PrD76Lnd/u0zoUH3TrLcAUWYEgCFDUjq96kxMjoSZHQY2NcI1GqBzGr3Y0u0+X7rQdMWuyELMmq93XUmLNUNJiocaaAUGAGhcBKasEaGZCt1DngHAgB7pWNgfmcJXmM7vTs20KN+igWkwQSqxgNiekg7mQU2IAkwFgzDXhOjHS+72Ii4AaZwHXi+Bm1zIIDkmE7kShqx/3e+IcnA41MtTTxslVz35wzC5DdzgPusPeW5/Yk8JQeUky6nqawQ0i6vpbELqnDIaChpXOuU6EGhUOodQKpqiuJawiQ11JT2XTK6Kf2UYstUIs9e+WOoQQEkyCcUSJEqUAi0mOciVLogC1gyuIpKwSsKwScIMENTIMQkVNh21mKnrKyd0JgbW2xaq31nAASlqsayTIXWLPSq2uiri4CNeChw4ZQqnVldSYmh9dEbNLIB3J9yRaSlIUHEPSwOMi4Ii3AE4Zup0nIRWUQ7/zJAC4kiW9BKG49cQk5EQ1TCeOQDWIqO1lhr7EBn2Ra288btDB2TfZta+bKABOBUJJJXiY0ZOMCYXl0O3O9NqMVkmMdC1L4K4AEYoqoNt1yqsNIYR0FYIkgCsqohIsWofigxKlAJs+dzLSB6Xi7Uc/xO7v9nfotepv/jC7DLGDF7j0XMuh+OdaBgnO4Rnec4Ciw93zn9xX00tQk6JantvkVKDfcdLrkBoVChh0XpPBpfxyrzb1iy22Rf1rF+wKwvZ79yOnx0LpHtdojzTRJ2ahshbsjATIMaqn18T6ptoQQkhXMf22SzF7/gwk90zUOhQfNEdJA/0v6I2Zd03TOozg1tT+YmfuIXdm26Y0kURxxs65cq/NmrtW45h95543XWUXfFsgEUKIX1z/6OygTJIASpQCrjinFEv++CKenPOc1qG0G2/0aLPGH+7t2ezQqbi2G6mv1muc8KiNKr/OfHiCdf9bYK6J5o3iEWrsXqM1YK5quY7InVj9tZq75ck5eKjBdxmEWrvneQDgJoNruQBKlgghXdBNve/F8gf+jboam9ah+KBbbwG2/u1N2PTRzwFbldsfVJMeAIOSHgs5OQpSQQWk/dmtfmarJgN4mBFyzwRwSYRuXxbENt7SYooK4/pdUFJjIXePB7M5IB0vBOxOyD0SoMaEQ8ouhZhdAjXOAjkjDszudLWxOdxtzJBySgHmyi/UUAN4iB5KrNmVgLhvZUm5p8EZ65C/GqScUggVNXAOSIF6xgRzVm0DHE4IBeU+SyQZNu6D0i0azsFpgE6C0i0atggTpIM5PrcJCSGks7PXOvC/57/E0EkDccGVI7QOxwslSgHGVQ5BYFA6U6JkCYUab3FtIaKTIPcKAauxgVXWQiirbjZhknsmgEeYoEaHu6rR4i1tTpQAgMkqpJNFkE4WeR0Xd5zw+lo4VQTp1JltvOclAYDcPd4Vj3tPNrGwAvo9mZ7nucCgJEaCORUIxZVtHrxRQw1QY80QCyrA7E6f54WqOog5pV6JEmeAs7YEYnktDPkVPtdiKoeUXQoeoocaGQY1wQIeHgIlJYYSJUJIl9V4E/tgQYlSgKX0TYaq8oDu9XaupPxyIL8c6t5MOC/oDTU2As6h3QEAYk4JdHuymqxw0+91LTOghhoAUYBgrQto3D7xuDehVU16QCd5leU7eyVC7pXomuQNV5WZfscJMHvzpar1azKp8RaAMTiHcOh2nICUe9q7zaA0qI0qOWqSgKo0QNWnAgCMJ6yIe+8YdBW+SZbuSD4AV/UcD9GBVTS9lAAhhHRmTGDQ6SUkpMdpHYoPmqMUYJOuuRDvHn8FF88eq3Uo7RdigBpn8ZprJBRWtroMgFBj1zxJakyodXglSVwUIA9M9SRJACCUWIEWkiQAUFJjPUkSAIDBK0kCACUlxpUkNXrPKnsCqq7Rnm0nqiBV+iZJjTG7E0JFLU1RIoR0STcs+j98mPs6MganaR2KD0qUNJCQHoeRU4doHQZpRatzz9u6OXCTVW+tNSCEkPPH+NkXwBwVrnUYTaJEKcCyDuVi/oTHsPS217QOpd2Y3emZAA3OAZW7FmbUNqxzp6hgVvcIk/t18QhTq1vPCRWNznE/6rc88bSprAXgXZmnq3I/qbr21nMkhbZczebO2NpdcUgIIZ3EbQPvx9/nPAdrWVXrjQOM5igF2PerfsH+nw9rHcZZYU4FxvW7oSRFQukeB1Ztg5RZ0ulvBzG4qszUBAvk7vGAQ4Z0srDV1yXml8G4bhfk7nFQ4iIg5ZW5lgNo3Kag3NUmPR5KfATE/DJ0+6oEtowwVI6PB3NyRPxQCDQzf1GJDHVV6vVIgBpmhHQoD7rMYr+8bkIICRoc+H71Flxy/XiMmzlK62i8UKKkAcYYeCcdG2CcQ3JvV9IeHIAaHwHVEgox97RrLaOm2sRFQI1svk1HYQBgrUad3ghBUaCramb/NZ0IJTUGkFWIOaVgdmeT+7959W2XoTuSB92RhjamI5UwHalsNS7VEuqq1LOEApIINTIUyGzniyOEEHLWKFEKsD6je0Knl2A/j7ajUGLNcA7r7tqzjXPIfZKh33IEYknDPmpKTDicwzLAw9xt+iZDv+UoxOLWk4lzxQGc/n0aKsfFA6Jr0aWqkTFIeOcYxNqGCd3OgamQM+I9i1XKqTEwbD0G5ui4TRylU67RI93eLEAvgdlanvRNCCGdlSXWjNS+yVqH4YPmKAXYmOnDsSp/xXm1hYmSGgNev2ktY2B2p1eSBLiqw3hoQxvY5YAkSQDA9QIqJyQCOsGVBDEG0+EKCHUNCRAXmGv5AFHwbKUiFlW6Vg/vQKz+oXJKkgghXdb9K+7ABznL0a13ktah+KBESQMh4SGIS43ROgziB+3ZlYUQQkjTuvVOhE6va72hBihRCrDD247h5j7z8MaC97UOJWCE4kpXhZe76osbdFDNJu82JVZAURuqwwwS1AhTMz36F3OqCKmfL6S4qt5sGWZwsVEWpHIIRRWef0PlUKPDXBvsEkIIOScPTFyEeRc+gtMFwbfzAM1RCrBta3eh8NT5VbUk5ZyGWFgBOS0WqiUUUk5pQzl+fZtcVxslLRZKZCik3NNglYFZhZpxIOm1Q7B3M6FyXDzEOgXmnwohyA0T7hkAwy9HoEaYIHePA5MViCeLwTrJ6uqEEBLsDm05iiO/HaeqNwL3/Zrz6wOWORXojhe23EZWIJ0o9PqmVMNDIKfHgtmckDKLW10FvDlqmBFKepyr9D+zuMkJ2IbcWsT991SL/QiVtdDvzjyrGAghhHQ+lCgF2LBLB+HLFRtQXlihdShBjTMGx9jeri1CVA4wQI0Mg3778XaN4nAAjgt6QU2MaugnOgz6bcfBlODbfJEQQs5Xaf27oeew7lqH4YPmKAXYoPH98EHWa/jTY1drHUpwM0iuJAnwVKKJJZXtv9WlE11JUuN+iq1gQbhDNSGEnK/+/uVCrNz3HOJSgq/QiRIlDSiyAlsAF1MkTTi/7nwSQkhQq62qhRqkf8BSohRgOzfuw5zk27F66edahxLc7E4Iuae99khT4iLAxaa/Zev3QeMCA9eJDU84FQjZJb79SPStTwghweLpa1/Ejb3uRcGpIq1D8UFzlAJs/4+HUFdl0zqMoMc4YPjtONQDeihprsncYnZps/OKlIx4qOFGKCmxgCRA2p8D3fECV7XajpNQD+a6+nHIrn7k4PzLhRBCzldFmSU4tS8bid3jtQ7FCyVKGuCtbUtPPIRaB4RDze+jVo9V2yAAUG0OcL0EZvPeIkaoc0BoYT82QgghpCmUKAXYuFmj8Mtn23BiT5bWoXQpYnElUFIJ6WQRTT8ihJBOaOTUIeg/trfWYfhgnIY3WmS1WhEREYHKykqYzWa/9Mk5x2fL1mHZvLf80h8hhBDSmS3b/g/0Ht7Dr3366/ObZrRqoKK4Esd2nNQ6DEIIISQo7Pv+IOpqgnP+LiVKAfbTmq24NvUObHh3s9ahBIX64UxukKCaDJrGQgghRBvLH3gX1yTfjqyDOVqH4oPmKAXYyT1Z4JyDbni6yP1ToJpDoCZYAADS4TzoaNI1IYScd2qtdcg7Xoi0/ilah+KFEqUAY4yB00aqHmJuKVhYCKCTwHUihNNVWodECCFEI4wxrUPw0eluvS1btgzp6ekwGo0YM2YMtm3b1qbzPvroIzDGMGvWrI4NsBWX/nE8xkwfrmkMwUSw1kEqKIfhx4MwfrcPYqlV65AIIYQEmKgTcfmtl2DwhP5ah+KjUyVKq1atwvz587Fo0SLs3LkTQ4YMwbRp01BcXNzieZmZmXjwwQcxfvz4AEXavKQeCXjy8wVY+J95WocSPBrfh6TBNkIIOe+8c+RFzF95J0LNJq1D8dGpEqXnnnsOc+fOxc0334z+/ftj+fLlMJlMeOut5svsFUXB9ddfj8WLFyMjIyOA0TYv80AONn30k9ZhEEIIIUHhi1e/QWl+mdZhNKnTJEoOhwM7duzA5MmTPccEQcDkyZOxZcuWZs974oknEBcXh1tvvbVN17Hb7bBarV4Pf9rw7veYO2g+tn29y6/9EkIIIZ3Vx89/iT+m34ljO4Nv6ZxOkyiVlpZCURTEx3vvARMfH4/CwsImz/npp5/w5ptvYuXKlW2+zpIlSxAREeF5pKT4d/Z9wckiiJIIVaF7TIQQQggAqIoKRVZRknta61B8dJpEqb2qqqrwpz/9CStXrkRMTEybz1u4cCEqKys9j5wc/67pIIgCVJU2ZCWEEELOJIrBl5Z0muUBYmJiIIoiioqKvI4XFRUhISHBp/2JEyeQmZmJGTNmeI7VJyiSJOHIkSPo0cN3uXSDwQCDoeMWPpw+91KUFZTj67e+g+yQO+w6hBBCSGcRFhmK3987HcMuHaR1KD6CL3Vrhl6vx4gRI7Bx40bPMVVVsXHjRowdO9anfd++fbFv3z7s3r3b85g5cyYmTZqE3bt3+/2WWltFJURi3qtzsejjBzS5PiGEEBJs3j7yEm54/P+gN+q1DsVHpxlRAoD58+fjxhtvxMiRIzF69Gi88MILqKmpwc033wwAuOGGG5CcnIwlS5bAaDRi4MCBXudbLBYA8DkeSJxz7Px2L95/+hPNYiCEEEKCyfL738HsB2ag59DuWofio1MlSnPmzEFJSQkee+wxFBYWYujQoVi3bp1ngnd2djYEIbgHyT5/dT1eufdNCGLwrT5KCCGEaGHzqp+x8f0f8cJPf8eAcX20DsdLp0qUAOCee+7BPffc0+RzmzdvbvHcd955x/8BtVNliRWiToTiVLQOhRBCCAkKiuyaQ1wZhLszBPfwSxck6SWoMlW9EUIIIfXq93jT6YNv/Cb4IuriZt17OTjn+Pi5L1FdXq11OIQQQojmknom4P8enInhUwZrHYoPxjmnlQ9bYLVaERERgcrKSpjNZr/1u+PbvVgw9Um/9UcIIYR0Vp9W/Nvv+7z56/Obbr0FmOyUsf6dTXjhz69rHQohhBASFBb97p/YunYngnHshhKlAFvz4lo8e8urKMws1joUQgghJCjs++kwHr1yCXZv2q91KD4oUQqwumobJJ0IBF/STAghhGhCVVxFTnXVNo0j8UWJUoCFhBkhOxWAllEihBBCALj2QQVcn5HBhqreAmzWvMthjgnH+3//GAUn6fYbIYQQMmh8P1z94EwMnaTdzhnNoUQpwHR6HabdNAkx3aKwYOrftQ6HEEII0dziTx/ye9Wbv9CttwCrrarD+0/9D3+f87zWoRBCCCFB4e5RC7B25bdQlODbtYISpQD79OWv8e/HVqG6vEbrUAghhJCgkH+8EM//+XXs3LBX61B8UKIUYLJDhiDR204IIYTUq18/yemQNY7EF31iB1hErBmKUwETqOyNEEIIAQDRPYAQEeO/HTD8hRKlAJt51zT8Y/2j6DO6p9ahEEIIIUFh4pwL8drOf2HAuD5ah+KDqt4CjDGGEVOGwGl34m8z/6l1OIQQQojm7nj+JliCcDQJoBGlgCsrLMdLd63E4tlLtQ6FEEIICQo395mHdx//Lxw2h9ah+KBEKcDWrtyIL1dsgByEE9YIIYQQLVSX1+C9J1Zj18Z9WofigxKlAFMVFYJAbzshhBByJsW951swoTlKAZaYEQ9FViCIDKpCO+MSQgghgiiAMSC2W7TWofigoY0Am3LDBKzc9xxGXz5c61AIIYSQoDD7/ivxn8zX0Gt4htah+KBESQPpA1Iw6ZoLtQ6DEEIICQoz7pqKmKQorcNoEiVKAZZ/ohB/m/kPLPnjS1qHQgghhASFm/rch+fmvoYaa63WofigRCnANv7nR2xdu1PrMAghhJCgoTgVfP3md9j7/UGtQ/FBiVKAcc5p+xJCCCGkCfV7vgUTSpQCLGNIGhhjYJQrEUIIIR4mcwiSeyZoHYYPSpQC7KLfj8GH2csx5caJWodCCCGEBIU7nrsRH+WtQFr/FK1D8UGJkgYscRFBWQJJCCGEaGHQxf0QEmrUOowmUaIUYCf2ZOLO4X/BsnlvaR0KIYQQEhTuHrkACy9/ChUllVqH4oMSpQD7ec02nNyXrXUYhBBCSFDZvn43Dm45qnUYPihR0gCjmdyEEEJIp0CJUoANHN8PIWHBeR+WEEII0Up8Wiy6D0rVOgwflCgF2PBLB2FV/gpc/cBMrUMhhBBCgsLDH96Hfx9/GYnd47UOxQclShqQdBKMoQatwyCEEEKCgincBEEIzpQkOKPqwvb/dAjXpt6B955YrXUohBBCSFB49MolmDv4ARTnlGodio9zSpRKS0thtVr9FUubLFu2DOnp6TAajRgzZgy2bdvWbNuVK1di/PjxiIyMRGRkJCZPntxi+0DY+e0+VJYE9j0jhBBCgl3WgRwc33VK6zB8tDtRqqiowN13342YmBjEx8cjMjISCQkJWLhwIWprO3bX31WrVmH+/PlYtGgRdu7ciSFDhmDatGkoLi5usv3mzZtx7bXXYtOmTdiyZQtSUlIwdepU5OXldWicrQrCvWwIIYQQ4ovxduxAV1ZWhrFjxyIvLw/XX389+vXrBwA4ePAgPvjgA/Tt2xc//fQT9u7di19//RXz5s3za7BjxozBqFGj8MorrwAAVFVFSkoK7r33XixYsKDV8xVFQWRkJF555RXccMMNTbax2+2w2+2er61WK1JSUlBZWQmz2XzOr+HwtmNYcv2LyD9RdM59EUIIIV1Fv7G9sejjBxGdGOmX/qxWKyIiIs7581tqT+MnnngCer0eJ06cQHx8vM9zU6dOxZ/+9Cd88803eOmll846qKY4HA7s2LEDCxcu9BwTBAGTJ0/Gli1b2tRHbW0tnE4noqKimm2zZMkSLF68+JzjbU7f0b3w9pGXsPrZz/HGgvc77DqEEEJIZ7F082IMvri/1mE0qV233j799FM8++yzPkkSACQkJOBf//oX/ve//2H+/Pm48cYb/RYk4JoPpSiKz7Xj4+NRWFjYpj7++te/IikpCZMnT262zcKFC1FZWel55OTknFPcTamrqkNxdvBNWCOEEEK0kHu0AE6HU+swmtSuRKmgoAADBgxo9vmBAwdCEAQsWrTonAPzt3/84x/46KOPsGbNGhiNzS/4aDAYYDabvR7+tHXtTsxJuh2fv7rer/0SQgghndXzty/HdSl3IPdYgdah+GhXohQTE4PMzMxmnz916hTi4uLONaZmry2KIoqKvOf2FBUVISEhocVzn332WfzjH//AN998g8GDB3dIfG11ZNtxOB2ypjEQQgghwaaixIrsQ7lah+GjXYnStGnT8Mgjj8DhcPg8Z7fb8be//Q2XXXaZ34JrTK/XY8SIEdi4caPnmKqq2LhxI8aOHdvsef/617/w5JNPYt26dRg5cmSHxNZe7Zg/TwghhBANtXsy98iRI9GrVy/cfffd6Nu3LzjnOHToEF599VXY7Xa8++67HRWrZ+7TyJEjMXr0aLzwwguoqanBzTffDAC44YYbkJycjCVLlgAA/vnPf+Kxxx7DBx98gPT0dM9cprCwMISFhXVYnC2ZMGccdm/ej30/HNLk+oQQQkjQYcCE2WMx8KK+Wkfio13LAwCu22t33XUXvvnmG8/ICGMMU6ZMwSuvvIKePXt2SKD1XnnlFTzzzDMoLCzE0KFD8dJLL2HMmDEAgIkTJyI9PR3vvPMOACA9PR1ZWVk+fSxatAiPP/54m67nr/LCM3395kY8N3e53/ojhBBCOqs3DjyPtH7d/Nqnvz6/250o1SsvL8exY8cAAD179myx5L4z64hEqTCzGG8seB/f//cXv/RHCCGEdGY3Lp6DmXdPgzkq3G99ap4onS/8nShtXvUznr7+RQAAV+mtJ4QQQpjAoNNLePnXJcgYnOaXPv31+U2b4gZY9qE8CAKjJIkQQghx4yqHw+ZEYWbTW5JpiRKlAGMCg0pJEiGEEOJDEIIvLQm+iLq4aTdNxCXXXgQmMq1DIYQQQoKCwaTHVfdfiSGTml/UWis0R6kVHVX19uMnv+KJ2Uv91h8hhBDSWX2Y+zpikvxbFEZzlDqxg78epS1MCCGEELf3//4x8o4H3/YlACVKAbd25be4b9wj2Pv9Aa1DIYQQQoLC2jc24uY+83B42zGtQ/FBiVKAleaVQZREqArd8SSEEEIAQJVVcA6UFVZoHYoPSpQCTJREqKqqdRiEEEJI0JF07dpZLSCCL6IubsZdU1FXXYfPlq2DrcaudTiEEEKI5qKTozD7/isx7NKBWofig6reWtFRVW+/rd+Fhy9/2m/9EUIIIZ3VJ2VvI9zi383qqeqtk1IUBT/+71eseOg/WodCCCGEBIV/3bQM+386pHUYTaJEKcA+e3kdnrh6KbIO5GgdCiGEEBIUflu7E/df/Bj2/nBQ61B8UKIUYNUVNRB1Iu31RgghhLgpsqvIqbqiRuNIfFGiFGB6ow6KrAC0gwkhhBACABAE14ei3qjXOBJfVPUWYL+/bzqMoUZ8uOSToFwvghBCCAm0jKHpmPOX32HElMFah+KDEqUAM4QYMOvey5E6IBl/nfyk1uEQQgghmlu6aTFM4SFah9EkuvUWYPY6O9a8tBb//NPLWodCCCGEBIUHJi3C5lU/IxhXLKIRpQBb89LXePPh97UOgxBCCAkaJ3dn4qlrX0B4VBhGTBmidTheaEQpwBx1DoiSCARf0kwIIYRoQnVXgtvrHBpH4osSpQALs4RCcSpgApW9EUIIIQAgSq50JMwSqnEkvihRCrDf3XsZHvv4QaQNSNE6FEIIISQojJo+HM//8AQGX9xf61B80BylABNFEeP/MAbGUAMevvwprcMhhBBCNPfQO3f7fa83f6ERpQCzllVh5V//gydmP6t1KIQQQkhQmDvoAXz83BeQnbLWofigRCnAvnj1G6xe+jlsNXatQyGEEEKCwum8Mrz+4LvY+e0+rUPxQYlSgCmyAkGgt50QQgg5E40oEcQkR7mSJZHeekIIIQQABEkAY0BUgkXrUHzQp3WATZ87GS/+8hQGTwi+mf2EEEKIFqbfdinePvIS+o7upXUoPihR0kD/C3pj5l3TtA6DEEIICQrXPzobyT0TtQ6jSZQoBVhxTimW/PFFPDnnOa1DIYQQQoLCTb3vxfIH/o26GpvWofigRCnA1r+9CZs++hlcoT1MCCGEEACw1zrwv+e/xJ5NB7QOxQclSgHGVQ6Bti8hhBBCfKiqqnUIPihRCrCUvslQVU57vRFCCCFuTGDQG3VISI/TOhQfnS5RWrZsGdLT02E0GjFmzBhs27atxfarV69G3759YTQaMWjQIKxduzZAkTZt0jUX4t3jr+Di2WM1jYMQQggJFjcs+j98mPs6MganaR2Kj06VKK1atQrz58/HokWLsHPnTgwZMgTTpk1DcXFxk+1/+eUXXHvttbj11luxa9cuzJo1C7NmzcL+/fsDHLm3hPQ4jJw6RNMYCCGEkGAxfvYFMEeFax1GkxjnvNPMKh4zZgxGjRqFV155BYDrXmZKSgruvfdeLFiwwKf9nDlzUFNTgy+//NJz7IILLsDQoUOxfPnyNl3TarUiIiIClZWVMJvN5/wasg7l4sU7VmDfj4fOuS9CCCGkS2DAhNljMe+1uX5LmPz1+d1pRpQcDgd27NiByZMne44JgoDJkydjy5YtTZ6zZcsWr/YAMG3atGbbA4DdbofVavV6+NP3q37B/p8P+7VPQgghpFPjwPert2D/T8H3+dhpEqXS0lIoioL4+Hiv4/Hx8SgsLGzynMLCwna1B4AlS5YgIiLC80hJSTn34M/AGE3kJoQQQjqDTpMoBcrChQtRWVnpeeTk5Pi1/z6je0Knl/zaJyGEENLZWWLNSO2brHUYPjpNohQTEwNRFFFUVOR1vKioCAkJCU2ek5CQ0K72AGAwGGA2m70e/jRm+nCsyl9BW5gQQgghbvevuAMf5CxHt95JWofio9MkSnq9HiNGjMDGjRs9x1RVxcaNGzF2bNOl9mPHjvVqDwAbNmxotn2ghISHIC41RtMYCCGEkGDRrXcidHqd1mE0qVPdA5o/fz5uvPFGjBw5EqNHj8YLL7yAmpoa3HzzzQCAG264AcnJyViyZAkA4L777sOECROwdOlSXHHFFfjoo4+wfft2rFixQrPXcHjbMSy5/kXknyhqvTEhhBByHnhg4iL0G9sbiz5+ENGJkVqH46VTJUpz5sxBSUkJHnvsMRQWFmLo0KFYt26dZ8J2dnY2BKFhkGzcuHH44IMP8Oijj+Lhhx9Gr1698Omnn2LgwIFavQRsW7sLhaeaXveJEEIIOV8d2nIUR347jnEzR2kdipdOlSgBwD333IN77rmnyec2b97sc+zqq6/G1Vdf3cFRtRNjADrN8lWEEELIeavTzFHqKoZdOggRsf6dIE4IIYR0dmn9u6HnsO5ah+GDEqUAGzS+Hz7Ieg1/eizIRrkIIYQQjfz9y4VYue85xKUEX6ETJUoaUGQFthq71mEQQgghQaG2qhaqqmodRpMoUQqwnRv3YU7y7Vi99HOtQyGEEEKCwtPXvogbe92LglPBVxFOiVKA7f/xEOqqbFqHQQghhASVoswSnNqXrXUYPihR0gDnVPFGCCGEdAaUKAXYuFmjkDEoVeswCCGEkKAycuoQ9B/bW+swfDBOwxstslqtiIiIQGVlpd/2feOc47Nl67Bs3lt+6Y8QQgjpzJZt/wd6D+/h1z799flNI0oaqCiuxLEdJ7UOgxBCCAkK+74/iLqa4Jy/S4lSgP20ZiuuTb0DG97drHUohBBCSFBY/sC7uCb5dmQdzNE6FB+UKAXYyT1Z4JyDbngSQgghDWqtdcg7Xqh1GD4oUQowxhi4SlkSIYQQcibGmNYh+KBEKcAu/eN4jJk+XOswCCGEkKAh6kRcfuslGDyhv9ah+KCqt1Z0RNUbAHz3wY9Y8seX/NYfIYQQ0lm9d/IVJKTH+7VPqnrrxDIP5GDTRz9pHQYhhBASFL549RuU5pdpHUaTKFEKsA3vfo+5g+Zj29e7tA6FEEIICQofP/8l/ph+J47tDL6lcyhRCrCCk0UQJRGqQnc8CSGEEABQFRWKrKIk97TWofigRCnABFGAqqpah0EIIYQEHVEMvrQk+CLq4qbPvRRX3j4Fkl7SOhRCCCEkKIRFhuJPj12NYZcO0joUH1T11oqOqnr79cvt+NvMf/qtP0IIIaSzWl38Jiwx/vuMBajqrdPinGPHhj14/+lPtA6FEEIICQrL738Hx3ef0jqMJlGiFGCfv7oeC6b9HUd/O651KIQQQkhQ2LzqZ9w5/CEc+OWI1qH4oEQpwCpLrBB1VPVGCCGE1FNkV5FTZalV40h8UaIUYJJegipT1RshhBBSr36PN10QFjoFX0Rd3Kx7LwfnHB8/9yWqy6u1DocQQgjRXFLPBPzfgzMxfMpgrUPxQVVvreioqrcd3+7FgqlP+q0/QgghpLP6tOLfCDWb/NonVb11UrJTxvp3NuGFP7+udSiEEEJIUFj0u39i69qdCMaxG0qUAmzNi2vx7C2vojCzWOtQCCGEkKCw76fDePTKJdi9ab/WofigRCnA6qptkHQiEHxJMyGEEKIJVXEVOdVV2zSOxBclSgEWEmaE7FQApnUkhBBCSHAQ3Hu8hYQZNY7EF1W9BdiseZfDHBOO9//+MQpO0u03QgghZND4frj6wZkYOmmg1qH4oEQpwHR6HabdNAkx3aKwYOrftQ6HEEII0dziTx/ye9Wbv9CttwCrrarD+0/9D3+f87zWoRBCCCFB4e5RC7B25bdQFEXrUHxQohRgn778Nf792CpUl9doHQohhBASFPKPF+L5P7+OnRv2ah2Kj06TKJWVleH666+H2WyGxWLBrbfeiurq5le2Lisrw7333os+ffogJCQEqampmDdvHiorKwMYtS/ZIUOQOs3bTgghpAviAoMSFQYuBsfnUf36SU6HrHEkvjrNHKXrr78eBQUF2LBhA5xOJ26++Wbcfvvt+OCDD5psn5+fj/z8fDz77LPo378/srKycMcddyA/Px8ff/xxgKNvEBFrhuJUwAQGrtIaAYQQQgKHiwLkXomQM+IBgw5wytDtz4GUVazpsjWiJECRVUTE+G8HDH/pFFuYHDp0CP3798dvv/2GkSNHAgDWrVuH6dOnIzc3F0lJSW3qZ/Xq1fjjH/+ImpoaSFLbckR/b2HCOcfOb/finUWrcPjXY+fcHyGEENJWcnosnMMyvI4Z1+4Eszs1isjl0uvHY/YDM9BzaHe/9XlebWGyZcsWWCwWT5IEAJMnT4YgCNi6dWub+6l/s1pKkux2O6xWq9fDnxhjGDFlCK5/+A9+7ZcQQghpFWPAmeMjQTBecsfzN/k1SfKnTpEoFRYWIi4uzuuYJEmIiopCYWFhm/ooLS3Fk08+idtvv73FdkuWLEFERITnkZKSctZxN6WssBwv3bUSi2cv9Wu/hBBCSGuYzelKluqnfnAObtRrvlnEzX3m4d3H/wuHzaFxJL40TZQWLFgAxliLj8OHD5/zdaxWK6644gr0798fjz/+eIttFy5ciMrKSs8jJyfnnK/f2NqVG/Hlig2Qg3DCGiGEkK5NLCiHYeNeiNklgFOGkHsaELTfLKK6vAbvPbEauzbu0zgSX5pO5n7ggQdw0003tdgmIyMDCQkJKC72XsValmWUlZUhISGhxfOrqqpw2WWXITw8HGvWrIFOp2uxvcFggMFgaFP8Z0NVVAiCAEUNvrUiCCGkq+IA1LgIMKcCVl6teWKgJcFaB/2uU8CuU1qH4kNx7/kWTDRNlGJjYxEbG9tqu7Fjx6KiogI7duzAiBEjAADfffcdVFXFmDFjmj3ParVi2rRpMBgM+Pzzz2E0ar+HTGJGPBRZgSAyqIrWg52EENL1yd3jIPdOAje5/ggWCsqh23USgp1G9oOFIApgDIjtFq11KD46xRylfv364bLLLsPcuXOxbds2/Pzzz7jnnntwzTXXeCre8vLy0LdvX2zbtg2AK0maOnUqampq8Oabb8JqtaKwsBCFhYWarvw55YYJWLnvOYy+fLhmMRBCyPmCiwKcQ7uDh+g9x4TTVWCUJAWV2fdfif9kvoZewzNabxxgnSJRAoD3338fffv2xaWXXorp06fjoosuwooVKzzPO51OHDlyBLW1tQCAnTt3YuvWrdi3bx969uyJxMREz8Pf847aK31ACiZdc6GmMRBCyHmFsTO+PJ9vvgWfGXdNRUxSlNZhNKlTrKOkJX+vo5R/ohCv3f8Ofv1yhx+iI4QQ0hLOAPvUoa7bbu6POzG7FLqdJ8/reUrBRtSJmHrDBPx56Y1+2xz3vFpHqSvZ+J8fsXXtTq3DIISQ8wLjgOGbPdD9dhysrBpCYQXEnFKtwyJnUJwKvn7zO+z9/qDWofjoNFuYdBWcc9q+hBBCAohxDin3NKTc01qHQloRjDe5aEQpwDKGpLnXiNI6EkIIISR4mMwhSO7Z8pI/WqBEKcAu+v0YfJi9HFNunKh1KIQQQkhQuOO5G/FR3gqk9ffvbhj+QImSBixxEUFZAkkIIYRoYdDF/RASqv1ah02hRCnATuzJxJ3D/4Jl897SOhRCCCEdyT3HgjNovpdasLt75AIsvPwpVJRUah2KD0qUAuznNdtwcl+21mEQQgjpQEqsGXJqDGyXDITtihFQesRrHVLQ275+Nw5uOap1GD4oUdIALXRGCCFdGzcZwMNDwEMMgCRCDem4PURJx6LlAQJs4Ph+CAkzoqayVutQCCGEdBAxqwQAIB3IBiQRzEkbobcmPi0W3Qelah2GDxpRCrDhlw7CqvwVuPqBmVqHQgghpIOw+gcHJUlt8PCH9+Hfx19GYvfgu0VJiZIGJJ0EYygNwxJCCCEAYAo3QRCCMyUJzqi6sP0/HcK1qXfgvSdWax0KIYQQEhQevXIJ5g5+AMVBuL0MJUoBtvPbfagssWodBiGEEBJUsg7k4PiuU1qH4YMSJS0E4V42hBBCCPFFiVKAjZ4+DAnd47QOgxBCCAkq/cb2Rp9RPbUOwwclSgHWd3QvvH3kJdz2j+u1DoUQQggJCks3L8ZLPz+F6MRIrUPxQYmSBuqq6lCcHXwT1gghhBAt5B4tgNPh1DqMJlGiFGBb1+7EnKTb8fmr67UOhRBCSADVz07lRh24wGj/t0aev305rku5A7nHCrQOxQetzB1gR7Ydh9Mhax0GIYSQAFLCQ6AmR0FJjAS3hAJOGYbNB8CqbVqHFjQqSqzIPpSLbr0StQ7FCyVKGuBU9UYIIR2GG3WQM+KhxJgh5Z6GmFUMpmj3e5frJSg9EqDUF/JwDqG8Boz+aO4UKFEKsAlzxmH35v3Y98MhrUMhhJAuR06JhnN4D9f+IQCckaEQiis1G7lREixwjOkFNNoMXf/DQYhl1Z4YCQAGTJg9FgMv6qt1JD4oUQqwtH7d8NzmJ/D1mxvx3NzlWodDCCFdimoJdf2jPjFRVAga3t5SI0yuWBolSkJ5jesfdHPB4439zyOtXzetw2gSTebWQGFmMXZs2Kt1GIQQ0uUwjuAaqaFkqE1+/PhXWMuqtA6jSZQoBdjmVT/jhp734IePt2gdCiGEdDliVgmE4sqGAwKDnBSlXTy5pyEUlDfsyMA55NSY4ErmgsC7i/+La7v9GSf3Zmkdig+69RZg2YfyIAgMiqxqHQohhHQ5QlUdDL8cgRpmhBoTDrGgHMyu3aRpodYOw9ZjUE0GqHFmiIWVYDaHZvEEK65yOGxOFGYWI2NwmtbheKFEKcCYwKCqNBZLCCEdSai2aTI3iQsMSrdoyD0SAFWFdLwQYl4ZhFo7hMySgMfT2QhC8N3ookQpwKbdNBF5xwrw3Uc/gWtYrkoIIcT/7JMHg4caPbfa1MRISHllGkcV/AwmPa7881QMmTRA61B8UKIUYHGpsVjw3jxc+PvReGL2Uq3DIYQQ4kfcZHD9w13lxmrsrvlI9Hdxi945+jJiNJxL1pLgG+M6Dxz89ShtYUIIIV0RR8PEbQBg3l+Spr3/94+Rdzz4ti8BKFEKuLUrv8V94x7B3u8PaB0KIYQQP9P/dgzMWuf5Wo0wgYfoNYyoc1j7xkbc3GceDm87pnUoPujWW4CV5pVBlEQosqJ1KIQQQvxMzC+HkF8OHhkKqBxCZa3WIXUKqrsSvKywQttAmkCJUoCJkghVpaUBCCEkULhBBzkjDnJGAphThnSsAGJWCVg7K5C5XoLcPR5yj3hAcVW0sRo7lO5xUOMiIBSUQTpaALGiBqx+9W3SLpIu+NKS4Iuoi5tx11TUVdfhs2XrYKuxax0OIYR0aZwx2KYOAUQBYAxcJ4KbDO1PkgDYpgwBdKJrojbnkAelev4NxqAmRUHYndkhr6Ori06Owuz7r8SwSwdqHYoPSpQCzBwVjtv+8UcMmTQAD1/+tNbhEEJI1yYwQBIbvmYMcMgNCU576Bt9ZDbau83zb8bgCA1B1QW94IwIg66yGuZ9x6AvD86tOYLJyn1LEW4J0zqMJnWaydxlZWW4/vrrYTabYbFYcOutt6K6urpN53LOcfnll4Mxhk8//bRjA22Foij48X+/YsVD/9E0DkIIOS9w3vCoJzDwsylFU8/o54x+a8wRyL719ygfPRDVfdJRPnogsm79PawDe57DCzg//OumZdj/0yGtw2hSp0mUrr/+ehw4cAAbNmzAl19+iR9++AG33357m8594YUXwFhwbKzz2cvr8MTVS5F1IEfrUAghpMtjKof+58MQShr2f5N7JkLumwzuHiFSTQY4M+KhxEU03w8A/U+HIBS5+1E5WF4ZxJNFgN0Jp06H8rhEQBC8H4yh6LJxcFjCO/Jldnq/rd2J+y9+DHt/OKh1KD46xa23Q4cOYd26dfjtt98wcuRIAMDLL7+M6dOn49lnn0VSUlKz5+7evRtLly7F9u3bkZiY2Oq17HY77PaGuUNWq/XcX0Aj1RU1EHUiFCdVvRFCSEfhACCJYLICscQKscQK28QB4GYToJcg9+sGuVciWHk1eIzZc/vM8O0eCFVNb30inq6CuOUI1BA9mMrB7E7XtfZlo3ryaCCVe9+SAzy3+KyDeyHmh50d81oF9+KWnXh7rPr9T6srgm8SfKdIlLZs2QKLxeJJkgBg8uTJEAQBW7duxe9///smz6utrcV1112HZcuWISEhoU3XWrJkCRYvXuyXuJuiN+pcSwPQSq2EEOJ3XGBQUmMg90wEDw+BUGqFdDAXwukqGL4/AIgi5PRYcIMOUlYJhGob1OhwyEmREKy1YM0kSY0Jdd6b2jLOIRsNLZ7jjPD//BvVZIDcIwFKeizAGMRTRZCO5kPQcBPgsyW490HVG4NvzalOkSgVFhYiLi7O65gkSYiKikJhYWGz591///0YN24cfve737X5WgsXLsT8+fM9X1utVqSkpLQ/6Gb8/r7pMIYa8eGST4JyvQhCCOnM5D5JkPske75WLaEQyqrBANcfp7IC6bjrc6N+7Ec4XQX96aqGA2dBV9nynNnWnj8b9gn9Ab3ONWEdAESxUyZJAJAxNB1z/vI7jJgyWOtQfGg6R2nBggVgjLX4OHz48Fn1/fnnn+O7777DCy+80K7zDAYDzGaz18OfDCEGzLr3cvz1P/f6tV9CCCEAl0RPuX49dsbEbYZmcqJzGOU373OvKH3mJHH31+a9HbDitCQ2JEkAoKqd9kbF0k2LMXHOhUEzn7gxTUeUHnjgAdx0000ttsnIyEBCQgKKi4u9jsuyjLKysmZvqX333Xc4ceIELBaL1/GrrroK48ePx+bNm88h8rNnr7Nj7cqN+OifazS5PiGEdGVMVrzWNgJcaymdmSz5m768CvHrfkHRZeN8kqX4db9AX9EBSwTIinvSeP2IknAug2KaemDSIsz5y+8w4f/GBV2yxPhZ1UgG1qFDh9C/f39s374dI0aMAAB88803uOyyy5Cbm9vkZO7CwkKUlpZ6HRs0aBBefPFFzJgxA927d2/Tta1WKyIiIlBZWemX0aWP/vkp3nz4fdcXQf/OE0JI59LSHKWz+fjlogCmqFCjwiEnR0GoqoWYWdJsXw5LOKyDG62jtPdYxyRJqJ+jFA8lPa7LzFH6x/pHMWLKEL/06a/P704xR6lfv3647LLLMHfuXCxfvhxOpxP33HMPrrnmGk+SlJeXh0svvRTvvvsuRo8ejYSEhCZHm1JTU9ucJHUER53DtdcbVb0RQojfMZVDyiyBmFkC6CUwx7klDarFBGffbuCxrso4BXDNeWq08S0HwMNDwKrqoK+o6rDqtjMJtXbo92WDH8wFADCl+e2xuEECZwyCzRmQ2NpLdVfs2c+YKB8MOkWiBADvv/8+7rnnHlx66aUQBAFXXXUVXnrpJc/zTqcTR44cQW1tcG9AGGYJheJUwAQG3olLOQkhJJgxwLUC9zkSy6oh/nwY3GSAnGCBUG2D4E6SuCRAzkiA3DMBMOjAau2Q9mVByi8/5+u2R0sJkmoxwdkrCWpyFABAyC+Dbn82hNrgSkhESYAiqwizhGodio9OcetNS/6+9aYoCn75bDveXfxfZO7L9kOEhBBCtODsnQS5fzeviePGz7YF1XpGdZcP86qME08VQ7/7lMZR+bpg5kjMeXAmBl7Uz299nle33roSURQx/g9jYAw14OHLn9I6HEIIIWdLYK77bo0nLAVRkgTAtRlw48o4zn1CDgYPvXM37fVGXKxlVVj51//gidnPah0KIYSQc8DssjtZcidHnHtvnBsEmM3ptScd10tBlyQBwNxBD+Dj576A7Ay+ieiUKAXYF69+g9VLP4etxt56Y0IIIUFLPFXUsP+byiHkl0E1tbxCd6AZvj8AaX82YHMCNieEihrXWlNB5nReGV5/8F3s/Haf1qH4CK7U9zygyAoEQYCiUtUbIYQEO66XoEaGQSi1+kyaZoBnH7lgxZwKdMcLoTve/C4WwSQYR5QoUQqwmOQoV7IkClBbqFQghBCiHW7Uwdk3GUpqrGuej1OBbvcpSHmnaQ28DiBIAriiIirBonUoPujWW4BNnzsZL/7yFAZP6K91KIQQQpohd49zLeQouj8mJQFiLiVJHWX6bZfi7SMvoe/oXlqH4oMSJQ30v6A3Zt41TeswCCGENIcxn6QoGCdBdxXXPzobyT0TtQ6jSZQoBVhxTimW/PFFPDnnOa1DIYQQ0gxWY3dVtKkNFW1qiJ4GlDrITb3vxfIH/o26GpvWofigRCnA1r+9CZs++hlcoR83QggJVlJWCQwb90LMLgFq7ZBOFALgNKrUQey1Dvzv+S+xZ9MBrUPxQZO5A4yrHILAoATbomSEEEK8CNY66HcF3yrWXZmqBl+RE40oBVhK32SoKgcT6O8SQgghBACYwKA36pCQHqd1KD4oUQqwSddciHePv4KLZ4/VOhRCCCEkKNyw6P/wYe7ryBicpnUoPihR0kBCehxGTh2idRiEEEJIUBg/+wKYo8K1DqNJlCgFWNahXMyf8BiW3vaa1qEQQgghreJotFJCB80auW3g/fj7nOdgLavqmAucA0qUAuz7Vb9g/8+HtQ6DEEIIaZGqF6FEhUHumwzbZcNgv7g/VKO+Yy7Gge9Xb8H+n4Lv85ESJQ0wRhO5CSGko3AASowZzr7JUMNDOv5aUWGua0WYOvRaAScIUOMioMZGAEYd1AgTeEclSkGMlgcIsD6je0Knl2Cvc2gdCiGEdDlqZBgcw7uDm00A55D7JkO39RjEgnK/3zVSzSY4RmSAW0Jd1+rXDbrtJyDllPr5StoQbE6ww3lgh/OgGnVgTgXowD1KLbFmpPZN7rD+zxaNKAXYmOnDsSp/BW1hQgghHUDuFgVeP4rEGKCokDogSQIAJSkSvH4UyX2nQOwiSVK9+vdNsDnBFLXDFty8f8Ud+CBnObr1TuqgK5w9SpQ0EBIegrjUGK3DIIQQQoJCt96J0Ol1WofRJEqUAuzwtmO4uc88vLHgfa1DIYSQLkcssQKyCnDueogClKiwDrmWcLoKcCoN1/r/7d17UJN3ugfwby4k3BJCgHATL6CCWlTUioBgVVTUsbr1rFYp1Y6r3aluZ22nu3TdHpy1F6fT03bsuN1Zu213W1vXemqPa6mtghRRBIpQrSBKod7KRUUgCgokv/MHEo0QawJJiHw/M/kjP5/3fZ+H+CZPfnkvAIw6H7ts60H3/CMZeDZhA67UXHV2Kt2wUXKwwswS1FbXOzsNIqIHkqy2Ee5fHYPb9z9BerEBisIzkF69Zp9tXWru3FZJNaQXG+BWeAbSS8122dZAUJ5/GhVFlc5OoxsezO0MEgnAe1ATEdmFxGCEvLoeckd8KRUC6DBAcuvRNbNk9WoAGEN8YQjUQFZzFdLaRt6At5/gjJKDxcyMhk+A2tlpEBFRLxn8vHEjJQbtk0fAMDgAbfFRMAb7Wv012OjrhRspMWiLHdm5nrhIGEL9BtzX6SGjB2F4zDBnp9ENGyUHi04chU/Ovou0//61s1MhIqJeMAb4AMpbByDfutG51IYz7Az+asDdfD2ymoYBNaP08t4Xse3Em9CF9b8TndgoOYGhw4Ab1286Ow0iIqJ+4ZqnEkZj/5xDY6PkYMeyTmBp6Bp89j97nJ0KERHdh66Pb+Emg5Dcfi6ra4Sk60vvrWOTDKFaq38yk9U3QXLthvl6Bj3YP70JoPNvKQEMg/2RsSUTS9e+h5/rGp2dWjc8mNvBfjhUjlb9DWenQURE98Gg9oAI8IEhVAuj1hu42Q5lzklIWtsgvXodyv3fwxjoA4POB7Kfr0J6RW/1T2bSphYoDxyHMUANQ5AGsppGSC83P9A/vRlCtBBqD3QMDQA8lACA2kvNqDp3GSGBGucmdxc2Sk4gbDwrgoiIHEzphvaxQ24/d1cAHgrg1m2oJABkdU2Q1TX1ajMSdF5uQDZALi9gDFDDMExnuqJ5f8af3hwsftHDCI8eDOD2zXH9B2nhpnTrfC4BlJ4K+IX4mseEaqFwvyPGQwG/UG239dwZo/Bwg//dMaFaKD0UgKRzzM29e4xfiBZKz9sxCqUb/AfdHeMLpafy9noU8u4xwb5w93Y3jcmV3WO0wRp4dMVIJZArZPAf5Gd6bopR3Y6Ruclur+dWjG+QBp63blsgkUggk0u7xwT6wFPtcXs9cikC7orRBPrA69YtCSRSCaQyabd8NDr1XTGSzhiJeYy3r5cpRiKVICDMPMYnQNUtxn+QFhKJxBSj9ldBdetieRJp5+saMMjPLEblp4LKr4cY6R0xWm+o/VRmdQSEmcd4+3rDx19tHjPID1KZ9I4YL9NZm11j/nfFeGm8oNHdez1ePp7Q3Low3+31aCGT3xnjAd/Au2JCtZC5yUzPPVQe8L317dNsPW4y0/8xT5U7tEGabuuR37keb3dog833uYBBfpAr5KYY9x5iOvdduem5u5ey+77L/dv0t3fF/dvtWisC5dJb2wYAQBUzDN537HPcv63fv330rdCa/v90/l0njx+KMf3wFiYSwemNe2puboaPjw+ampqgVvfNaf1CCJw8UoHib75Hwq8mY/j4YbjWeB0HPsqFm1KOGamJcPdUoiz/NL77uhTxCx/GiAnhuN50HQc+PgSZXIaZqVPh4e2BsqOnUfRVCaYsmITISRG43tyCrI8PQSIBZj6RBE+VByqKKnF0bzFi509A1OQRaNG3Imv7IQijwMwnEuGl9kTFdz/i6H++w8NzYzB6yki0XmtF1vY8dLR3YFZaErx8vHDmWBWO/F8RJs0Zj9FxI3Gj5SYOfpKHthvtSE5LgrfGC5Wl1TjyRREmJEdjTEIUbra2IfuTPNxsuYnktCSofL1Rdfws8j4vwPgZDyE6cRTabrTh4KeH0dLciuQnk6DWqlD9wzkc2nUU4x4Zg7HTRnfG7DiC643XMevJaVD7qXC27DxyPzuKhxKjMH76Q2i/2Y6cfx9B8xU9ktOSoAnwwdnyC8jdmY8xCZGImRmN9rYOfLvzCBrrmpD85DT46nxwvuIicv59BKOmjMTEWWPR0d6B3M+OoqHmKpLTkuAbqMGFMzU4+GkeoiYPx8TZ42DoMODQrqO4dKEBs55MgjbIFxcra3Dw08MYMTEcD6eMh9FgxKH/LUDd2UtITkuCf4gWNVV1yP4kDxHjh2LyvBgYDUYc3l2Imqq6zphQP9T+VI+s7YcwLHowYudPAARw+ItCXDxTi5lPJEIX5o+6s5dw4ONcDB0ThikLJgICOLLnO5w/dRHJTyRCNzgA9ecu4cDHhxAWFYr4RycBEuDof4rx08nzSH4iCYFDAnDpwhVkfZyLkOFBSFg0GZAABV8eQ/Xxc5iROhXBwwJx+eIVHPgoF8HhgUj41WRIZVIUZpagsqQaM5ZPRUhEEK7UXMWBj3KhG+yPxMWxkMqkKNpXijPFVZi+LAGhw4PRUNsZ4x+qReJ/TYFMLkPxN9/jVGElpj+egEEjQ3C1rhEHPsqFb5AG05bEQe4mR/H+4yjPP41HHo9HWGQoGi81Yf+/cqHRqTFtSTzcFHKUZJ3AycMVSFoShyGjBqHpcjP2/+tbqP1UeGRpPNyUbvg+5ySOf1uGpF/HYeiYMDRf0WP/v76Fl8YL0x+Ph8JdgePfluH7nJNIXByLYdFD0Nygx4GPcuGp8sD0ZQlQuCtw4lA5SrN/wNTHYhE+dgj0V6/hwEe5UHoqMWP5VCg9FNy/H8D9+0LNVRzIO4XI8EDExgyD0WDg/t0H+3fR92fxw+mfkZwQhcG3mvq+0lef32yUfoE9GiUiIiKyr776/OZPb0REREQWsFEiIiIissBlGqWGhgakpqZCrVZDo9Fg1apVuHbtl290mJ+fjxkzZsDLywtqtRpJSUlobW11QMZERETk6lymUUpNTcXJkyexf/9+7N27F7m5uVizZs09l8nPz0dKSgpmz56NwsJCFBUVYd26dZBKXaZsIiIiciKXOJi7vLwco0ePRlFRESZNmgQA2LdvH+bNm4cLFy4gJKTn0wmnTJmCWbNmYdOmTTZvmwdzExERuZ4BdTB3fn4+NBqNqUkCgOTkZEilUhQUFPS4TH19PQoKCqDT6RAfH4/AwEBMmzYNeXl599zWzZs30dzcbPYgIiKigcklGqXa2lrodDqzMblcDq1Wi9ra2h6XqaqqAgBs3LgRq1evxr59+zBhwgTMnDkTZ86csbit1157DT4+PqZHWFhY3xVCRERELsWpjVJ6enrnFUjv8Th16pRN6zYajQCAp59+Gk899RRiYmLw1ltvITIyEu+//77F5V588UU0NTWZHufPn7dp+0REROT6nHqvt+effx4rV668Z0x4eDiCgoJQX19vNt7R0YGGhgYEBQX1uFxwcDAAYPTo0Wbjo0aNwrlz5yxuT6lUQqlU3kf2RERE9KBzaqMUEBCAgICAX4yLi4tDY2MjiouLMXHiRABAdnY2jEYjYmNje1xm6NChCAkJQUVFhdn46dOnMXfu3N4nT0RERA88lzhGadSoUUhJScHq1atRWFiIw4cPY926dXj88cdNZ7xdvHgRUVFRKCwsBNB548QXXngBW7Zswa5du1BZWYmXXnoJp06dwqpVq5xZDhEREbkIp84oWWP79u1Yt24dZs6cCalUisWLF2PLli2mf29vb0dFRQVaWlpMY7///e9x48YNrF+/Hg0NDRg3bhz279+PiIgIZ5RARERELsYlrqPkTE1NTdBoNDh//jyvo0REROQimpubERYWhsbGRvj4+Ni8HpeZUXIWvV4PALxMABERkQvS6/W9apQ4o/QLjEYjfv75Z6hUKkgkEmen02tdHfZAnCFj7aydtQ8cA7X2gVo30L12IQT0ej1CQkJ6desyzij9AqlUikGDBjk7jT6nVqsH3E7UhbWz9oGGtQ+82gdq3YB57b2ZSeriEme9ERERETkDGyUiIiIiC9goDTBKpRIZGRkD8urjrJ21DzSsfeDVPlDrBuxXOw/mJiIiIrKAM0pEREREFrBRIiIiIrKAjRIRERGRBWyUiIiIiCxgozQANDQ0IDU1FWq1GhqNBqtWrcK1a9fua1khBObOnQuJRIIvvvjCvonagbW1NzQ04He/+x0iIyPh4eGBwYMH49lnn0VTU5MDs7bN1q1bMXToULi7uyM2NhaFhYX3jP/ss88QFRUFd3d3REdHIzMz00GZ9j1rat+2bRsSExPh6+sLX19fJCcn/+Lfqj+z9nXvsmPHDkgkEixatMi+CdqJtXU3NjZi7dq1CA4OhlKpxMiRI132/7y1tb/99tum97SwsDCsX78eN27ccFC2fSc3NxcLFixASEjIfX8m5eTkYMKECVAqlRg+fDg+/PBD6zcs6IGXkpIixo0bJ44ePSoOHTokhg8fLpYtW3Zfy7755pti7ty5AoDYvXu3fRO1A2trP3HihHjsscfEnj17RGVlpcjKyhIjRowQixcvdmDW1tuxY4dQKBTi/fffFydPnhSrV68WGo1G1NXV9Rh/+PBhIZPJxOuvvy7KysrEn//8Z+Hm5iZOnDjh4Mx7z9raly9fLrZu3SpKSkpEeXm5WLlypfDx8REXLlxwcOa9Z23tXaqrq0VoaKhITEwUCxcudEyyfcjaum/evCkmTZok5s2bJ/Ly8kR1dbXIyckRpaWlDs6896ytffv27UKpVIrt27eL6upq8fXXX4vg4GCxfv16B2fee5mZmWLDhg3i888/v6/PpKqqKuHp6Smee+45UVZWJt555x0hk8nEvn37rNouG6UHXFlZmQAgioqKTGNfffWVkEgk4uLFi/dctqSkRISGhoqamhqXbJR6U/uddu7cKRQKhWhvb7dHmn1i8uTJYu3atabnBoNBhISEiNdee63H+CVLloj58+ebjcXGxoqnn37arnnag7W1362jo0OoVCrxz3/+014p2o0ttXd0dIj4+Hjx3nvviRUrVrhko2Rt3e+++64IDw8XbW1tjkrRbqytfe3atWLGjBlmY88995xISEiwa572dj+fSX/4wx/EmDFjzMaWLl0q5syZY9W2+NPbAy4/Px8ajQaTJk0yjSUnJ0MqlaKgoMDici0tLVi+fDm2bt2KoKAgR6Ta52yt/W5NTU1Qq9WQy/vnrRHb2tpQXFyM5ORk05hUKkVycjLy8/N7XCY/P98sHgDmzJljMb6/sqX2u7W0tKC9vR1ardZeadqFrbX/5S9/gU6nw6pVqxyRZp+zpe49e/YgLi4Oa9euRWBgIB566CG8+uqrMBgMjkq7T9hSe3x8PIqLi00/z1VVVSEzMxPz5s1zSM7O1Ffvc/3znZ/6TG1tLXQ6ndmYXC6HVqtFbW2txeXWr1+P+Ph4LFy40N4p2o2ttd/p8uXL2LRpE9asWWOPFPvE5cuXYTAYEBgYaDYeGBiIU6dO9bhMbW1tj/H3+3fpL2yp/W5//OMfERIS0u0Ntb+zpfa8vDz84x//QGlpqQMytA9b6q6qqkJ2djZSU1ORmZmJyspKPPPMM2hvb0dGRoYj0u4TttS+fPlyXL58GVOnToUQAh0dHfjtb3+LP/3pT45I2aksvc81NzejtbUVHh4e97Uezii5qPT0dEgkkns+7veD4m579uxBdnY23n777b5Nuo/Ys/Y7NTc3Y/78+Rg9ejQ2btzY+8Sp39m8eTN27NiB3bt3w93d3dnp2JVer0daWhq2bdsGf39/Z6fjUEajETqdDn//+98xceJELF26FBs2bMDf/vY3Z6dmdzk5OXj11Vfx17/+FceOHcPnn3+OL7/8Eps2bXJ2ai6DM0ou6vnnn8fKlSvvGRMeHo6goCDU19ebjXd0dKChocHiT2rZ2dn48ccfodFozMYXL16MxMRE5OTk9CLz3rNn7V30ej1SUlKgUqmwe/duuLm59TZtu/H394dMJkNdXZ3ZeF1dncU6g4KCrIrvr2ypvcsbb7yBzZs348CBAxg7dqw907QLa2v/8ccf8dNPP2HBggWmMaPRCKBzprWiogIRERH2TboP2PKaBwcHw83NDTKZzDQ2atQo1NbWoq2tDQqFwq459xVban/ppZeQlpaG3/zmNwCA6OhoXL9+HWvWrMGGDRsglT648yWW3ufUavV9zyYBnFFyWQEBAYiKirrnQ6FQIC4uDo2NjSguLjYtm52dDaPRiNjY2B7XnZ6ejuPHj6O0tNT0AIC33noLH3zwgSPKuyd71g50ziTNnj0bCoUCe/bs6fczDQqFAhMnTkRWVpZpzGg0IisrC3FxcT0uExcXZxYPAPv377cY31/ZUjsAvP7669i0aRP27dtndgybK7G29qioKJw4ccJsv3700Ucxffp0lJaWIiwszJHp28yW1zwhIQGVlZWmxhAATp8+jeDgYJdpkgDbam9paenWDHU1jOIBv9Vrn73PWXecObmilJQUERMTIwoKCkReXp4YMWKE2SnyFy5cEJGRkaKgoMDiOuCCZ70JYX3tTU1NIjY2VkRHR4vKykpRU1NjenR0dDirjF+0Y8cOoVQqxYcffijKysrEmjVrhEajEbW1tUIIIdLS0kR6erop/vDhw0Iul4s33nhDlJeXi4yMDJe+PIA1tW/evFkoFAqxa9cus9dXr9c7qwSbWVv73Vz1rDdr6z537pxQqVRi3bp1oqKiQuzdu1fodDrx8ssvO6sEm1lbe0ZGhlCpVOLTTz8VVVVV4ptvvhERERFiyZIlzirBZnq9XpSUlIiSkhIBQLz55puipKREnD17VgghRHp6ukhLSzPFd10e4IUXXhDl5eVi69atvDwA9ezKlSti2bJlwtvbW6jVavHUU0+ZfShUV1cLAOLgwYMW1+GqjZK1tR88eFAA6PFRXV3tnCLu0zvvvCMGDx4sFAqFmDx5sjh69Kjp36ZNmyZWrFhhFr9z504xcuRIoVAoxJgxY8SXX37p4Iz7jjW1DxkypMfXNyMjw/GJ9wFrX/c7uWqjJIT1dR85ckTExsYKpVIpwsPDxSuvvNKvv/zcizW1t7e3i40bN4qIiAjh7u4uwsLCxDPPPCOuXr3q+MR7ydL7c1e9K1asENOmTeu2zPjx44VCoRDh4eHigw8+sHq7EiEe8Lk3IiIiIhvxGCUiIiIiC9goEREREVnARomIiIjIAjZKRERERBawUSIiIiKygI0SERERkQVslIiIiIgsYKNEREREZAEbJSIiIiIL2CgR0YC1cuVKLFq0yNlpEFE/xkaJiIiIyAI2SkREREQWsFEiIiIisoCNEhEREZEFbJSIiIiILGCjRERERGQBGyUiIiIiC9goEREREVnARomIiIjIAokQQjg7CSIiIqL+iDNKRERERBawUSIiIiKygI0SERERkQVslIiIiIgsYKNEREREZAEbJSIiIiIL2CgRERERWcBGiYiIiMgCNkpEREREFrBRIiIiIrKAjRIRERGRBf8PITLvbVJbXjAAAAAASUVORK5CYII=",
      "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"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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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": [
    "chosen_time_values = [\n",
    "    t1_times[1],  # second value selected otherwise we won't see both centers\n",
    "    t1_times[len(t1_times) // 5],  # a value close to the end of the experiment\n",
    "]\n",
    "for t_example in chosen_time_values:\n",
    "    shots_example = (\n",
    "        dataset_gridded.q0_iq_shots.real.sel(t1_time=t_example),\n",
    "        dataset_gridded.q0_iq_shots.imag.sel(t1_time=t_example),\n",
    "    )\n",
    "    plt.hexbin(*shots_example)\n",
    "    plt.xlabel(\"I\")\n",
    "    plt.ylabel(\"Q\")\n",
    "    calib_0 = dataset_gridded.q0_iq_av_cal.sel(cal=\"|0>\")\n",
    "    calib_1 = dataset_gridded.q0_iq_av_cal.sel(cal=\"|1>\")\n",
    "    plot_complex_points([calib_0, calib_1], ax=plt.gca())\n",
    "    plt.suptitle(f\"Shots for t = {t_example:.5f} [s]\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea8da075",
   "metadata": {},
   "source": [
    "We can collapse (average along) the `repetitions` dimension:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c321a4a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 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"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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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": [
    "q0_iq_shots_mean = dataset_gridded.q0_iq_shots.mean(dim=\"repetitions\", keep_attrs=True)\n",
    "plot_xr_complex(q0_iq_shots_mean)\n",
    "_, ax = plot_xr_complex_on_plane(q0_iq_shots_mean)\n",
    "_ = plot_complex_points(centers, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ca2bdcc",
   "metadata": {},
   "source": [
    "(sec-dataset-t1-traces)=\n",
    "\n",
    "### T1 experiment storing digitized signals for all shots\n",
    "\n",
    "Finally, in addition to the individual shots we will store all the digitized readout\n",
    "signals that are required to obtain the previous measurement results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "078c7002",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Source code for generating the dataset below"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">mk_t1_traces_dataset</span><span class=\"p\">(</span>\n",
       "    <span class=\"n\">t1_times</span><span class=\"p\">:</span> <span class=\"n\">Optional</span><span class=\"p\">[</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">ndarray</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"kc\">None</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">probabilities</span><span class=\"p\">:</span> <span class=\"n\">Optional</span><span class=\"p\">[</span><span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">ndarray</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"kc\">None</span><span class=\"p\">,</span>\n",
       "    <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">,</span>\n",
       "<span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    Generates a dataset with mock data of a T1 experiment for a single qubit including</span>\n",
       "<span class=\"sd\">    calibration points for the ground and excited states, including all the individual</span>\n",
       "<span class=\"sd\">    shots (repeated qubit state measurement for the same exact experiment); and</span>\n",
       "<span class=\"sd\">    including all the signals that had to be digitized to obtain the rest of the data.</span>\n",
       "\n",
       "<span class=\"sd\">    Parameters</span>\n",
       "<span class=\"sd\">    ----------</span>\n",
       "<span class=\"sd\">    t1_times</span>\n",
       "<span class=\"sd\">        Array with the T1 times corresponding to each probability in ``probabilities``.</span>\n",
       "<span class=\"sd\">    probabilities</span>\n",
       "<span class=\"sd\">        The probabilities of finding the qubit in the excited state.</span>\n",
       "<span class=\"sd\">    **kwargs</span>\n",
       "<span class=\"sd\">        Keyword arguments passed to</span>\n",
       "<span class=\"sd\">        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    <span class=\"n\">dataset_shots</span> <span class=\"o\">=</span> <span class=\"n\">mk_t1_shots_dataset</span><span class=\"p\">(</span><span class=\"n\">t1_times</span><span class=\"p\">,</span> <span class=\"n\">probabilities</span><span class=\"p\">,</span> <span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">shots</span> <span class=\"o\">=</span> <span class=\"n\">dataset_shots</span><span class=\"o\">.</span><span class=\"n\">q0_iq_shots</span><span class=\"o\">.</span><span class=\"n\">values</span>\n",
       "    <span class=\"n\">shots_cal</span> <span class=\"o\">=</span> <span class=\"n\">dataset_shots</span><span class=\"o\">.</span><span class=\"n\">q0_iq_shots_cal</span><span class=\"o\">.</span><span class=\"n\">values</span>\n",
       "\n",
       "    <span class=\"c1\"># generate mock traces for all shots</span>\n",
       "    <span class=\"n\">q0_traces</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">(</span><span class=\"nb\">tuple</span><span class=\"p\">(</span><span class=\"nb\">map</span><span class=\"p\">(</span><span class=\"n\">mk_trace_for_iq_shot</span><span class=\"p\">,</span> <span class=\"n\">shots</span><span class=\"o\">.</span><span class=\"n\">flatten</span><span class=\"p\">())))</span>\n",
       "    <span class=\"n\">q0_traces</span> <span class=\"o\">=</span> <span class=\"n\">q0_traces</span><span class=\"o\">.</span><span class=\"n\">reshape</span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"n\">shots</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">,</span> <span class=\"n\">q0_traces</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">])</span>\n",
       "    <span class=\"c1\"># generate mock traces for calibration points shots</span>\n",
       "    <span class=\"n\">q0_traces_cal</span> <span class=\"o\">=</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">array</span><span class=\"p\">(</span><span class=\"nb\">tuple</span><span class=\"p\">(</span><span class=\"nb\">map</span><span class=\"p\">(</span><span class=\"n\">mk_trace_for_iq_shot</span><span class=\"p\">,</span> <span class=\"n\">shots_cal</span><span class=\"o\">.</span><span class=\"n\">flatten</span><span class=\"p\">())))</span>\n",
       "    <span class=\"n\">q0_traces_cal</span> <span class=\"o\">=</span> <span class=\"n\">q0_traces_cal</span><span class=\"o\">.</span><span class=\"n\">reshape</span><span class=\"p\">(</span><span class=\"o\">*</span><span class=\"n\">shots_cal</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">,</span> <span class=\"n\">q0_traces_cal</span><span class=\"o\">.</span><span class=\"n\">shape</span><span class=\"p\">[</span><span class=\"o\">-</span><span class=\"mi\">1</span><span class=\"p\">])</span>\n",
       "\n",
       "    <span class=\"n\">traces_dims</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"s2\">&quot;repetitions&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;main_dim&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;trace_dim&quot;</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">traces_cal_dims</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"s2\">&quot;repetitions&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;cal_dim&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;trace_dim&quot;</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">trace_times</span> <span class=\"o\">=</span> <span class=\"n\">mk_trace_time</span><span class=\"p\">()</span>\n",
       "    <span class=\"n\">trace_attrs</span> <span class=\"o\">=</span> <span class=\"n\">mk_main_coord_attrs</span><span class=\"p\">(</span><span class=\"n\">long_name</span><span class=\"o\">=</span><span class=\"s2\">&quot;Trace time&quot;</span><span class=\"p\">,</span> <span class=\"n\">unit</span><span class=\"o\">=</span><span class=\"s2\">&quot;s&quot;</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">relationships_with_traces</span> <span class=\"o\">=</span> <span class=\"n\">dataset_shots</span><span class=\"o\">.</span><span class=\"n\">relationships</span> <span class=\"o\">+</span> <span class=\"p\">[</span>\n",
       "        <span class=\"n\">dattrs</span><span class=\"o\">.</span><span class=\"n\">QDatasetIntraRelationship</span><span class=\"p\">(</span>\n",
       "            <span class=\"n\">item_name</span><span class=\"o\">=</span><span class=\"s2\">&quot;q0_traces&quot;</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">related_names</span><span class=\"o\">=</span><span class=\"p\">[</span><span class=\"s2\">&quot;q0_traces_cal&quot;</span><span class=\"p\">],</span>\n",
       "            <span class=\"n\">relation_type</span><span class=\"o\">=</span><span class=\"s2\">&quot;calibration&quot;</span><span class=\"p\">,</span>\n",
       "        <span class=\"p\">)</span><span class=\"o\">.</span><span class=\"n\">to_dict</span><span class=\"p\">(),</span>\n",
       "    <span class=\"p\">]</span>\n",
       "\n",
       "    <span class=\"n\">data_vars</span> <span class=\"o\">=</span> <span class=\"nb\">dict</span><span class=\"p\">(</span>\n",
       "        <span class=\"n\">q0_iq_av</span><span class=\"o\">=</span><span class=\"n\">dataset_shots</span><span class=\"o\">.</span><span class=\"n\">q0_iq_av</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">q0_iq_av_cal</span><span class=\"o\">=</span><span class=\"n\">dataset_shots</span><span class=\"o\">.</span><span class=\"n\">q0_iq_av_cal</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">q0_iq_shots</span><span class=\"o\">=</span><span class=\"n\">dataset_shots</span><span class=\"o\">.</span><span class=\"n\">q0_iq_shots</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">q0_iq_shots_cal</span><span class=\"o\">=</span><span class=\"n\">dataset_shots</span><span class=\"o\">.</span><span class=\"n\">q0_iq_shots_cal</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">q0_traces</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">traces_dims</span><span class=\"p\">,</span> <span class=\"n\">q0_traces</span><span class=\"p\">,</span> <span class=\"n\">dataset_shots</span><span class=\"o\">.</span><span class=\"n\">q0_iq_shots</span><span class=\"o\">.</span><span class=\"n\">attrs</span><span class=\"p\">),</span>\n",
       "        <span class=\"n\">q0_traces_cal</span><span class=\"o\">=</span><span class=\"p\">(</span>\n",
       "            <span class=\"n\">traces_cal_dims</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">q0_traces_cal</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">dataset_shots</span><span class=\"o\">.</span><span class=\"n\">q0_iq_shots_cal</span><span class=\"o\">.</span><span class=\"n\">attrs</span><span class=\"p\">,</span>\n",
       "        <span class=\"p\">),</span>\n",
       "    <span class=\"p\">)</span>\n",
       "    <span class=\"n\">coords</span> <span class=\"o\">=</span> <span class=\"nb\">dict</span><span class=\"p\">(</span>\n",
       "        <span class=\"n\">t1_time</span><span class=\"o\">=</span><span class=\"n\">dataset_shots</span><span class=\"o\">.</span><span class=\"n\">t1_time</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">cal</span><span class=\"o\">=</span><span class=\"n\">dataset_shots</span><span class=\"o\">.</span><span class=\"n\">cal</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">trace_time</span><span class=\"o\">=</span><span class=\"p\">((</span><span class=\"s2\">&quot;trace_dim&quot;</span><span class=\"p\">,),</span> <span class=\"n\">trace_times</span><span class=\"p\">,</span> <span class=\"n\">trace_attrs</span><span class=\"p\">),</span>\n",
       "    <span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">dataset</span> <span class=\"o\">=</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">(</span>\n",
       "        <span class=\"n\">data_vars</span><span class=\"o\">=</span><span class=\"n\">data_vars</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">coords</span><span class=\"o\">=</span><span class=\"n\">coords</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">attrs</span><span class=\"o\">=</span><span class=\"n\">mk_dataset_attrs</span><span class=\"p\">(</span><span class=\"n\">relationships</span><span class=\"o\">=</span><span class=\"n\">relationships_with_traces</span><span class=\"p\">),</span>\n",
       "    <span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"k\">return</span> <span class=\"n\">dataset</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}t1\\PYZus{}traces\\PYZus{}dataset}\\PY{p}{(}\n",
       "    \\PY{n}{t1\\PYZus{}times}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
       "    \\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{]} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
       "    \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\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 mock data of a T1 experiment for a single qubit including}\n",
       "\\PY{l+s+sd}{    calibration points for the ground and excited states, including all the individual}\n",
       "\\PY{l+s+sd}{    shots (repeated qubit state measurement for the same exact experiment); and}\n",
       "\\PY{l+s+sd}{    including all the signals that had to be digitized to obtain the rest of the data.}\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}{    t1\\PYZus{}times}\n",
       "\\PY{l+s+sd}{        Array with the T1 times corresponding to each probability in ``probabilities``.}\n",
       "\\PY{l+s+sd}{    probabilities}\n",
       "\\PY{l+s+sd}{        The probabilities of finding the qubit in the excited state.}\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",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{n}{dataset\\PYZus{}shots} \\PY{o}{=} \\PY{n}{mk\\PYZus{}t1\\PYZus{}shots\\PYZus{}dataset}\\PY{p}{(}\\PY{n}{t1\\PYZus{}times}\\PY{p}{,} \\PY{n}{probabilities}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "    \\PY{n}{shots} \\PY{o}{=} \\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}shots}\\PY{o}{.}\\PY{n}{values}\n",
       "    \\PY{n}{shots\\PYZus{}cal} \\PY{o}{=} \\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}shots\\PYZus{}cal}\\PY{o}{.}\\PY{n}{values}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} generate mock traces for all shots}\n",
       "    \\PY{n}{q0\\PYZus{}traces} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{array}\\PY{p}{(}\\PY{n+nb}{tuple}\\PY{p}{(}\\PY{n+nb}{map}\\PY{p}{(}\\PY{n}{mk\\PYZus{}trace\\PYZus{}for\\PYZus{}iq\\PYZus{}shot}\\PY{p}{,} \\PY{n}{shots}\\PY{o}{.}\\PY{n}{flatten}\\PY{p}{(}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\n",
       "    \\PY{n}{q0\\PYZus{}traces} \\PY{o}{=} \\PY{n}{q0\\PYZus{}traces}\\PY{o}{.}\\PY{n}{reshape}\\PY{p}{(}\\PY{o}{*}\\PY{n}{shots}\\PY{o}{.}\\PY{n}{shape}\\PY{p}{,} \\PY{n}{q0\\PYZus{}traces}\\PY{o}{.}\\PY{n}{shape}\\PY{p}{[}\\PY{o}{\\PYZhy{}}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{)}\n",
       "    \\PY{c+c1}{\\PYZsh{} generate mock traces for calibration points shots}\n",
       "    \\PY{n}{q0\\PYZus{}traces\\PYZus{}cal} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{array}\\PY{p}{(}\\PY{n+nb}{tuple}\\PY{p}{(}\\PY{n+nb}{map}\\PY{p}{(}\\PY{n}{mk\\PYZus{}trace\\PYZus{}for\\PYZus{}iq\\PYZus{}shot}\\PY{p}{,} \\PY{n}{shots\\PYZus{}cal}\\PY{o}{.}\\PY{n}{flatten}\\PY{p}{(}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\n",
       "    \\PY{n}{q0\\PYZus{}traces\\PYZus{}cal} \\PY{o}{=} \\PY{n}{q0\\PYZus{}traces\\PYZus{}cal}\\PY{o}{.}\\PY{n}{reshape}\\PY{p}{(}\\PY{o}{*}\\PY{n}{shots\\PYZus{}cal}\\PY{o}{.}\\PY{n}{shape}\\PY{p}{,} \\PY{n}{q0\\PYZus{}traces\\PYZus{}cal}\\PY{o}{.}\\PY{n}{shape}\\PY{p}{[}\\PY{o}{\\PYZhy{}}\\PY{l+m+mi}{1}\\PY{p}{]}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{traces\\PYZus{}dims} \\PY{o}{=} \\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}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{trace\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{traces\\PYZus{}cal\\PYZus{}dims} \\PY{o}{=} \\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}{cal\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{trace\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{trace\\PYZus{}times} \\PY{o}{=} \\PY{n}{mk\\PYZus{}trace\\PYZus{}time}\\PY{p}{(}\\PY{p}{)}\n",
       "    \\PY{n}{trace\\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}{Trace time}\\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}{)}\n",
       "\n",
       "    \\PY{n}{relationships\\PYZus{}with\\PYZus{}traces} \\PY{o}{=} \\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{relationships} \\PY{o}{+} \\PY{p}{[}\n",
       "        \\PY{n}{dattrs}\\PY{o}{.}\\PY{n}{QDatasetIntraRelationship}\\PY{p}{(}\n",
       "            \\PY{n}{item\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{q0\\PYZus{}traces}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{related\\PYZus{}names}\\PY{o}{=}\\PY{p}{[}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{q0\\PYZus{}traces\\PYZus{}cal}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]}\\PY{p}{,}\n",
       "            \\PY{n}{relation\\PYZus{}type}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{calibration}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{o}{.}\\PY{n}{to\\PYZus{}dict}\\PY{p}{(}\\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}{q0\\PYZus{}iq\\PYZus{}av}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av}\\PY{p}{,}\n",
       "        \\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}av\\PYZus{}cal}\\PY{p}{,}\n",
       "        \\PY{n}{q0\\PYZus{}iq\\PYZus{}shots}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}shots}\\PY{p}{,}\n",
       "        \\PY{n}{q0\\PYZus{}iq\\PYZus{}shots\\PYZus{}cal}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}shots\\PYZus{}cal}\\PY{p}{,}\n",
       "        \\PY{n}{q0\\PYZus{}traces}\\PY{o}{=}\\PY{p}{(}\\PY{n}{traces\\PYZus{}dims}\\PY{p}{,} \\PY{n}{q0\\PYZus{}traces}\\PY{p}{,} \\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}shots}\\PY{o}{.}\\PY{n}{attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{q0\\PYZus{}traces\\PYZus{}cal}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{n}{traces\\PYZus{}cal\\PYZus{}dims}\\PY{p}{,}\n",
       "            \\PY{n}{q0\\PYZus{}traces\\PYZus{}cal}\\PY{p}{,}\n",
       "            \\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{q0\\PYZus{}iq\\PYZus{}shots\\PYZus{}cal}\\PY{o}{.}\\PY{n}{attrs}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "    \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{t1\\PYZus{}time}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{t1\\PYZus{}time}\\PY{p}{,}\n",
       "        \\PY{n}{cal}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}shots}\\PY{o}{.}\\PY{n}{cal}\\PY{p}{,}\n",
       "        \\PY{n}{trace\\PYZus{}time}\\PY{o}{=}\\PY{p}{(}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{trace\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\\PY{p}{)}\\PY{p}{,} \\PY{n}{trace\\PYZus{}times}\\PY{p}{,} \\PY{n}{trace\\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{n}{relationships}\\PY{o}{=}\\PY{n}{relationships\\PYZus{}with\\PYZus{}traces}\\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_t1_traces_dataset\u001b[0m\u001b[1m(\u001b[0m\n",
       "    t1_times: Optional\u001b[1m[\u001b[0mnp.ndarray\u001b[1m]\u001b[0m = \u001b[3;35mNone\u001b[0m,\n",
       "    probabilities: Optional\u001b[1m[\u001b[0mnp.ndarray\u001b[1m]\u001b[0m = \u001b[3;35mNone\u001b[0m,\n",
       "    **kwargs,\n",
       "\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generates a dataset with mock data of a T1 experiment for a single qubit including\n",
       "    calibration points for the ground and excited states, including all the individual\n",
       "    shots \u001b[1m(\u001b[0mrepeated qubit state measurement for the same exact experiment\u001b[1m)\u001b[0m; and\n",
       "    including all the signals that had to be digitized to obtain the rest of the data.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    t1_times\n",
       "        Array with the T1 times corresponding to each probability in ``probabilities``.\n",
       "    probabilities\n",
       "        The probabilities of finding the qubit in the excited state.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to\n",
       "        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    dataset_shots = \u001b[1;35mmk_t1_shots_dataset\u001b[0m\u001b[1m(\u001b[0mt1_times, probabilities, **kwargs\u001b[1m)\u001b[0m\n",
       "    shots = dataset_shots.q0_iq_shots.values\n",
       "    shots_cal = dataset_shots.q0_iq_shots_cal.values\n",
       "\n",
       "    # generate mock traces for all shots\n",
       "    q0_traces = \u001b[1;35mnp.array\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mtuple\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mmap\u001b[0m\u001b[1m(\u001b[0mmk_trace_for_iq_shot, \u001b[1;35mshots.flatten\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\n",
       "    q0_traces = \u001b[1;35mq0_traces.reshape\u001b[0m\u001b[1m(\u001b[0m*shots.shape, q0_traces.shape\u001b[1m[\u001b[0m\u001b[1;36m-1\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n",
       "    # generate mock traces for calibration points shots\n",
       "    q0_traces_cal = \u001b[1;35mnp.array\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mtuple\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mmap\u001b[0m\u001b[1m(\u001b[0mmk_trace_for_iq_shot, \u001b[1;35mshots_cal.flatten\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\n",
       "    q0_traces_cal = \u001b[1;35mq0_traces_cal.reshape\u001b[0m\u001b[1m(\u001b[0m*shots_cal.shape, q0_traces_cal.shape\u001b[1m[\u001b[0m\u001b[1;36m-1\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    traces_dims = \u001b[1m(\u001b[0m\u001b[32m\"repetitions\"\u001b[0m, \u001b[32m\"main_dim\"\u001b[0m, \u001b[32m\"trace_dim\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    traces_cal_dims = \u001b[1m(\u001b[0m\u001b[32m\"repetitions\"\u001b[0m, \u001b[32m\"cal_dim\"\u001b[0m, \u001b[32m\"trace_dim\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    trace_times = \u001b[1;35mmk_trace_time\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
       "    trace_attrs = \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Trace\u001b[0m\u001b[32m time\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"s\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    relationships_with_traces = dataset_shots.relationships + \u001b[1m[\u001b[0m\n",
       "        \u001b[1;35mdattrs.QDatasetIntraRelationship\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[33mitem_name\u001b[0m=\u001b[32m\"q0_traces\"\u001b[0m,\n",
       "            \u001b[33mrelated_names\u001b[0m=\u001b[1m[\u001b[0m\u001b[32m\"q0_traces_cal\"\u001b[0m\u001b[1m]\u001b[0m,\n",
       "            \u001b[33mrelation_type\u001b[0m=\u001b[32m\"calibration\"\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\u001b[1;35m.to_dict\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m]\u001b[0m\n",
       "\n",
       "    data_vars = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mq0_iq_av\u001b[0m=\u001b[35mdataset_shots\u001b[0m.q0_iq_av,\n",
       "        \u001b[33mq0_iq_av_cal\u001b[0m=\u001b[35mdataset_shots\u001b[0m.q0_iq_av_cal,\n",
       "        \u001b[33mq0_iq_shots\u001b[0m=\u001b[35mdataset_shots\u001b[0m.q0_iq_shots,\n",
       "        \u001b[33mq0_iq_shots_cal\u001b[0m=\u001b[35mdataset_shots\u001b[0m.q0_iq_shots_cal,\n",
       "        \u001b[33mq0_traces\u001b[0m=\u001b[1m(\u001b[0mtraces_dims, q0_traces, dataset_shots.q0_iq_shots.attrs\u001b[1m)\u001b[0m,\n",
       "        \u001b[33mq0_traces_cal\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            traces_cal_dims,\n",
       "            q0_traces_cal,\n",
       "            dataset_shots.q0_iq_shots_cal.attrs,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "    coords = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mt1_time\u001b[0m=\u001b[35mdataset_shots\u001b[0m.t1_time,\n",
       "        \u001b[33mcal\u001b[0m=\u001b[35mdataset_shots\u001b[0m.cal,\n",
       "        \u001b[33mtrace_time\u001b[0m=\u001b[1m(\u001b[0m\u001b[1m(\u001b[0m\u001b[32m\"trace_dim\"\u001b[0m,\u001b[1m)\u001b[0m, trace_times, trace_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[33mrelationships\u001b[0m=\u001b[35mrelationships_with_traces\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    return dataset"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_source_code(dataset_examples.mk_t1_traces_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ceb30e07",
   "metadata": {},
   "outputs": [
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       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          (main_dim: 30, cal_dim: 2, repetitions: 256, trace_dim: 300)\n",
       "Coordinates:\n",
       "    t1_time          (main_dim) float64 0.0 4.138e-06 ... 0.0001159 0.00012\n",
       "    cal              (cal_dim) &lt;U3 &#x27;|0&gt;&#x27; &#x27;|1&gt;&#x27;\n",
       "    trace_time       (trace_dim) float64 0.0 1e-09 2e-09 ... 2.98e-07 2.99e-07\n",
       "Dimensions without coordinates: main_dim, cal_dim, repetitions, trace_dim\n",
       "Data variables:\n",
       "    q0_iq_av         (main_dim) complex128 (-0.19894114958423859+0.6515500138...\n",
       "    q0_iq_av_cal     (cal_dim) complex128 (0.7010588504157614-0.3984499861154...\n",
       "    q0_iq_shots      (repetitions, main_dim) complex128 (-0.289836545355741+0...\n",
       "    q0_iq_shots_cal  (repetitions, cal_dim) complex128 (0.610163454644259-0.4...\n",
       "    q0_traces        (repetitions, main_dim, trace_dim) complex128 (-0.289836...\n",
       "    q0_traces_cal    (repetitions, cal_dim, trace_dim) complex128 (0.61016345...\n",
       "Attributes:\n",
       "    tuid:                      20231215-162531-854-50fae7\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:             [{&#x27;item_name&#x27;: &#x27;q0_iq_av&#x27;, &#x27;relation_type&#x27;: &#x27;c...\n",
       "    json_serialize_exclude:    []</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fb419456-abd3-4bb4-b47d-799d423b29c0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fb419456-abd3-4bb4-b47d-799d423b29c0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>main_dim</span>: 30</li><li><span>cal_dim</span>: 2</li><li><span>repetitions</span>: 256</li><li><span>trace_dim</span>: 300</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2efb5571-b9a8-4b11-b7af-bfad1f903b92' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2efb5571-b9a8-4b11-b7af-bfad1f903b92' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>t1_time</span></div><div class='xr-var-dims'>(main_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 4.138e-06 ... 0.0001159 0.00012</div><input id='attrs-9d442937-4847-488a-906a-525b47d5ee02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d442937-4847-488a-906a-525b47d5ee02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84e7c0c0-6047-40ed-aa97-fb4b9b3454a8' class='xr-var-data-in' type='checkbox'><label for='data-84e7c0c0-6047-40ed-aa97-fb4b9b3454a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>T1 Time</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.00000000e+00, 4.13793103e-06, 8.27586207e-06, 1.24137931e-05,\n",
       "       1.65517241e-05, 2.06896552e-05, 2.48275862e-05, 2.89655172e-05,\n",
       "       3.31034483e-05, 3.72413793e-05, 4.13793103e-05, 4.55172414e-05,\n",
       "       4.96551724e-05, 5.37931034e-05, 5.79310345e-05, 6.20689655e-05,\n",
       "       6.62068966e-05, 7.03448276e-05, 7.44827586e-05, 7.86206897e-05,\n",
       "       8.27586207e-05, 8.68965517e-05, 9.10344828e-05, 9.51724138e-05,\n",
       "       9.93103448e-05, 1.03448276e-04, 1.07586207e-04, 1.11724138e-04,\n",
       "       1.15862069e-04, 1.20000000e-04])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cal</span></div><div class='xr-var-dims'>(cal_dim)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;|0&gt;&#x27; &#x27;|1&gt;&#x27;</div><input id='attrs-daf088b7-5b04-44c3-9ad2-8a06d63641fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-daf088b7-5b04-44c3-9ad2-8a06d63641fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2276ccb4-e465-45fa-8121-2780e6c12e2b' class='xr-var-data-in' type='checkbox'><label for='data-2276ccb4-e465-45fa-8121-2780e6c12e2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Q0 state</dd><dt><span>is_main_coord :</span></dt><dd>False</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;|0&gt;&#x27;, &#x27;|1&gt;&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>trace_time</span></div><div class='xr-var-dims'>(trace_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1e-09 ... 2.98e-07 2.99e-07</div><input id='attrs-e602030d-d347-4777-b4ac-e64f3b9c3fcd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e602030d-d347-4777-b4ac-e64f3b9c3fcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2a37ba1-d821-42df-8b4d-9ca79accf3b4' class='xr-var-data-in' type='checkbox'><label for='data-c2a37ba1-d821-42df-8b4d-9ca79accf3b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Trace time</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.00e+00, 1.00e-09, 2.00e-09, 3.00e-09, 4.00e-09, 5.00e-09,\n",
       "       6.00e-09, 7.00e-09, 8.00e-09, 9.00e-09, 1.00e-08, 1.10e-08,\n",
       "       1.20e-08, 1.30e-08, 1.40e-08, 1.50e-08, 1.60e-08, 1.70e-08,\n",
       "       1.80e-08, 1.90e-08, 2.00e-08, 2.10e-08, 2.20e-08, 2.30e-08,\n",
       "       2.40e-08, 2.50e-08, 2.60e-08, 2.70e-08, 2.80e-08, 2.90e-08,\n",
       "       3.00e-08, 3.10e-08, 3.20e-08, 3.30e-08, 3.40e-08, 3.50e-08,\n",
       "       3.60e-08, 3.70e-08, 3.80e-08, 3.90e-08, 4.00e-08, 4.10e-08,\n",
       "       4.20e-08, 4.30e-08, 4.40e-08, 4.50e-08, 4.60e-08, 4.70e-08,\n",
       "       4.80e-08, 4.90e-08, 5.00e-08, 5.10e-08, 5.20e-08, 5.30e-08,\n",
       "       5.40e-08, 5.50e-08, 5.60e-08, 5.70e-08, 5.80e-08, 5.90e-08,\n",
       "       6.00e-08, 6.10e-08, 6.20e-08, 6.30e-08, 6.40e-08, 6.50e-08,\n",
       "       6.60e-08, 6.70e-08, 6.80e-08, 6.90e-08, 7.00e-08, 7.10e-08,\n",
       "       7.20e-08, 7.30e-08, 7.40e-08, 7.50e-08, 7.60e-08, 7.70e-08,\n",
       "       7.80e-08, 7.90e-08, 8.00e-08, 8.10e-08, 8.20e-08, 8.30e-08,\n",
       "       8.40e-08, 8.50e-08, 8.60e-08, 8.70e-08, 8.80e-08, 8.90e-08,\n",
       "       9.00e-08, 9.10e-08, 9.20e-08, 9.30e-08, 9.40e-08, 9.50e-08,\n",
       "       9.60e-08, 9.70e-08, 9.80e-08, 9.90e-08, 1.00e-07, 1.01e-07,\n",
       "       1.02e-07, 1.03e-07, 1.04e-07, 1.05e-07, 1.06e-07, 1.07e-07,\n",
       "       1.08e-07, 1.09e-07, 1.10e-07, 1.11e-07, 1.12e-07, 1.13e-07,\n",
       "       1.14e-07, 1.15e-07, 1.16e-07, 1.17e-07, 1.18e-07, 1.19e-07,\n",
       "...\n",
       "       1.80e-07, 1.81e-07, 1.82e-07, 1.83e-07, 1.84e-07, 1.85e-07,\n",
       "       1.86e-07, 1.87e-07, 1.88e-07, 1.89e-07, 1.90e-07, 1.91e-07,\n",
       "       1.92e-07, 1.93e-07, 1.94e-07, 1.95e-07, 1.96e-07, 1.97e-07,\n",
       "       1.98e-07, 1.99e-07, 2.00e-07, 2.01e-07, 2.02e-07, 2.03e-07,\n",
       "       2.04e-07, 2.05e-07, 2.06e-07, 2.07e-07, 2.08e-07, 2.09e-07,\n",
       "       2.10e-07, 2.11e-07, 2.12e-07, 2.13e-07, 2.14e-07, 2.15e-07,\n",
       "       2.16e-07, 2.17e-07, 2.18e-07, 2.19e-07, 2.20e-07, 2.21e-07,\n",
       "       2.22e-07, 2.23e-07, 2.24e-07, 2.25e-07, 2.26e-07, 2.27e-07,\n",
       "       2.28e-07, 2.29e-07, 2.30e-07, 2.31e-07, 2.32e-07, 2.33e-07,\n",
       "       2.34e-07, 2.35e-07, 2.36e-07, 2.37e-07, 2.38e-07, 2.39e-07,\n",
       "       2.40e-07, 2.41e-07, 2.42e-07, 2.43e-07, 2.44e-07, 2.45e-07,\n",
       "       2.46e-07, 2.47e-07, 2.48e-07, 2.49e-07, 2.50e-07, 2.51e-07,\n",
       "       2.52e-07, 2.53e-07, 2.54e-07, 2.55e-07, 2.56e-07, 2.57e-07,\n",
       "       2.58e-07, 2.59e-07, 2.60e-07, 2.61e-07, 2.62e-07, 2.63e-07,\n",
       "       2.64e-07, 2.65e-07, 2.66e-07, 2.67e-07, 2.68e-07, 2.69e-07,\n",
       "       2.70e-07, 2.71e-07, 2.72e-07, 2.73e-07, 2.74e-07, 2.75e-07,\n",
       "       2.76e-07, 2.77e-07, 2.78e-07, 2.79e-07, 2.80e-07, 2.81e-07,\n",
       "       2.82e-07, 2.83e-07, 2.84e-07, 2.85e-07, 2.86e-07, 2.87e-07,\n",
       "       2.88e-07, 2.89e-07, 2.90e-07, 2.91e-07, 2.92e-07, 2.93e-07,\n",
       "       2.94e-07, 2.95e-07, 2.96e-07, 2.97e-07, 2.98e-07, 2.99e-07])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e07854fd-215a-4068-8935-ea0bec8a3125' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e07854fd-215a-4068-8935-ea0bec8a3125' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_av</span></div><div class='xr-var-dims'>(main_dim)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(-0.19894114958423859+0.65155001...</div><input id='attrs-55a802de-224a-4c26-be07-32c7e6e37654' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-55a802de-224a-4c26-be07-32c7e6e37654' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-318750b9-af2a-4923-8e7a-f1fac793dc15' class='xr-var-data-in' type='checkbox'><label for='data-318750b9-af2a-4923-8e7a-f1fac793dc15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ amplitude</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([-0.19894115+0.65155001j, -0.11155212+0.54951255j,\n",
       "       -0.01216713+0.43313537j,  0.05193768+0.35848368j,\n",
       "        0.15504951+0.2389656j ,  0.20661408+0.17861197j,\n",
       "        0.2833043 +0.08961707j,  0.33361813+0.03110011j,\n",
       "        0.37345395-0.01576696j,  0.40534772-0.05352011j,\n",
       "        0.42151034-0.07261241j,  0.47040441-0.12912367j,\n",
       "        0.49717119-0.16057795j,  0.53565518-0.20550725j,\n",
       "        0.56438276-0.23892233j,  0.58075078-0.25822004j,\n",
       "        0.59794213-0.27834108j,  0.60292187-0.28390675j,\n",
       "        0.60292187-0.28390675j,  0.61706292-0.30039156j,\n",
       "        0.6204547 -0.30436927j,  0.62775114-0.31283759j,\n",
       "        0.64350021-0.33151635j,  0.64683289-0.33543496j,\n",
       "        0.65442395-0.34419789j,  0.65442395-0.34419789j,\n",
       "        0.65650831-0.34686819j,  0.66449532-0.35602708j,\n",
       "        0.66901604-0.36113374j,  0.66901604-0.36113374j])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_av_cal</span></div><div class='xr-var-dims'>(cal_dim)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(0.7010588504157614-0.3984499861...</div><input id='attrs-f663e0b6-edc4-48de-a962-369d93893c1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f663e0b6-edc4-48de-a962-369d93893c1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-729ed195-5b06-43c2-8608-d698c99bfd93' class='xr-var-data-in' type='checkbox'><label for='data-729ed195-5b06-43c2-8608-d698c99bfd93' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ Calibration</dd><dt><span>is_main_var :</span></dt><dd>False</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([ 0.70105885-0.39844999j, -0.19894115+0.65155001j])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_shots</span></div><div class='xr-var-dims'>(repetitions, main_dim)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(-0.289836545355741+0.6397419222...</div><input id='attrs-e1e0c2e8-4e83-4c35-acee-d8dee2e43212' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1e0c2e8-4e83-4c35-acee-d8dee2e43212' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-924ee7a9-5afa-499a-83a4-20535ef8fa1c' class='xr-var-data-in' type='checkbox'><label for='data-924ee7a9-5afa-499a-83a4-20535ef8fa1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ amplitude</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[-0.28983655+0.63974192j, -0.28983655+0.56980106j,\n",
       "        -0.28983655+0.53688137j, ..., -0.28983655+0.7151281j ,\n",
       "        -0.28983655+0.78818874j, -0.28983655+0.78818874j],\n",
       "       [-0.24253459+0.62214896j, -0.24253459+0.5353232j ,\n",
       "        -0.24253459+0.59304477j, ..., -0.24253459+0.66518369j,\n",
       "        -0.24253459+0.7151281j , -0.24253459+0.7151281j ],\n",
       "       [-0.28481752+0.67074703j, -0.28481752+0.62934539j,\n",
       "        -0.28481752+0.62319817j, ..., -0.28481752+0.77120226j,\n",
       "        -0.28481752+0.66518369j, -0.28481752+0.66518369j],\n",
       "       ...,\n",
       "       [-0.1585822 +0.54251043j,  0.69005581-0.50748957j,\n",
       "         0.6904154 -0.50748957j, ...,  0.72225129-0.50748957j,\n",
       "         0.63121489-0.50748957j,  0.63121489-0.50748957j],\n",
       "       [-0.13095023+0.64399053j,  0.79234273-0.40600947j,\n",
       "         0.78602065-0.40600947j, ...,  0.70347729-0.40600947j,\n",
       "         0.72225129-0.40600947j,  0.72225129-0.40600947j],\n",
       "       [-0.13370834+0.73212141j,  0.71905686-0.31787859j,\n",
       "         0.82586734-0.31787859j, ...,  0.56864489-0.31787859j,\n",
       "         0.70347729-0.31787859j,  0.70347729-0.31787859j]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_shots_cal</span></div><div class='xr-var-dims'>(repetitions, cal_dim)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(0.610163454644259-0.41025807770...</div><input id='attrs-c00e7a44-b8b3-4c89-aeb0-4fa1a936f89e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c00e7a44-b8b3-4c89-aeb0-4fa1a936f89e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cd4ccd5-40c9-4b23-87d7-14f05653f3e9' class='xr-var-data-in' type='checkbox'><label for='data-2cd4ccd5-40c9-4b23-87d7-14f05653f3e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ Calibration</dd><dt><span>is_main_var :</span></dt><dd>False</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.61016345-0.41025808j, -0.28983655+0.63974192j],\n",
       "       [ 0.65746541-0.42785104j, -0.24253459+0.62214896j],\n",
       "       [ 0.61518248-0.37925297j, -0.28481752+0.67074703j],\n",
       "       [ 0.74772749-0.43361903j, -0.15227251+0.61638097j],\n",
       "       [ 0.64265022-0.27384234j, -0.25734978+0.77615766j],\n",
       "       [ 0.60678906-0.3561582j , -0.29321094+0.6938418j ],\n",
       "       [ 0.84395867-0.46878511j, -0.05604133+0.58121489j],\n",
       "       [ 0.57061537-0.37774871j, -0.32938463+0.67225129j],\n",
       "       [ 0.78351922-0.39652271j, -0.11648078+0.65347729j],\n",
       "       [ 0.83818874-0.53135511j, -0.06181126+0.51864489j],\n",
       "       [ 0.7651281 -0.42764291j, -0.1348719 +0.62235709j],\n",
       "       [ 0.71518369-0.62580345j, -0.18481631+0.42419655j],\n",
       "       [ 0.82120226-0.28181242j, -0.07879774+0.76818758j],\n",
       "       [ 0.51610032-0.37325263j, -0.38389968+0.67674737j],\n",
       "       [ 0.77067194-0.39646665j, -0.12932806+0.65353335j],\n",
       "       [ 0.64660115-0.19540354j, -0.25339885+0.85459646j],\n",
       "       [ 0.67313985-0.25608898j, -0.22686015+0.79391102j],\n",
       "       [ 0.73270479-0.51790005j, -0.16729521+0.53209995j],\n",
       "       [ 0.8270149 -0.40109669j, -0.0729851 +0.64890331j],\n",
       "       [ 0.83712465-0.3367254j , -0.06287535+0.7132746j ],\n",
       "...\n",
       "       [ 0.64294178-0.26650726j, -0.25705822+0.78349274j],\n",
       "       [ 0.77779463-0.50086946j, -0.12220537+0.54913054j],\n",
       "       [ 0.87940344-0.38703376j, -0.02059656+0.66296624j],\n",
       "       [ 0.79740773-0.36164035j, -0.10259227+0.68835965j],\n",
       "       [ 0.63267527-0.4135685j , -0.26732473+0.6364315j ],\n",
       "       [ 0.79144079-0.44017147j, -0.10855921+0.60982853j],\n",
       "       [ 0.68482914-0.37939136j, -0.21517086+0.67060864j],\n",
       "       [ 0.85136549-0.38836868j, -0.04863451+0.66163132j],\n",
       "       [ 0.6485531 -0.22668462j, -0.2514469 +0.82331538j],\n",
       "       [ 0.69385278-0.44030654j, -0.20614722+0.60969346j],\n",
       "       [ 0.64327725-0.46894991j, -0.25672275+0.58105009j],\n",
       "       [ 0.73337178-0.39917798j, -0.16662822+0.65082202j],\n",
       "       [ 0.68419873-0.53952882j, -0.21580127+0.51047118j],\n",
       "       [ 0.72360952-0.19834449j, -0.17639048+0.85165551j],\n",
       "       [ 0.77753549-0.33238509j, -0.12246451+0.71761491j],\n",
       "       [ 0.76165034-0.55445959j, -0.13834966+0.49554041j],\n",
       "       [ 0.64390147-0.33097297j, -0.25609853+0.71902703j],\n",
       "       [ 0.7414178 -0.50748957j, -0.1585822 +0.54251043j],\n",
       "       [ 0.76904977-0.40600947j, -0.13095023+0.64399053j],\n",
       "       [ 0.76629166-0.31787859j, -0.13370834+0.73212141j]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q0_traces</span></div><div class='xr-var-dims'>(repetitions, main_dim, trace_dim)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(-0.289836545355741+0.6397419222...</div><input id='attrs-2f2a6bec-b2b3-4a0f-afb9-69eceeba21c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f2a6bec-b2b3-4a0f-afb9-69eceeba21c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbd140d0-6c5a-429b-bc07-b5f671d130ab' class='xr-var-data-in' type='checkbox'><label for='data-dbd140d0-6c5a-429b-bc07-b5f671d130ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ amplitude</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.28983655+0.63974192j, -0.47334206+0.51886631j,\n",
       "         -0.61051356+0.34720044j, ...,  0.34720044+0.61051356j,\n",
       "          0.14154818+0.68792373j, -0.07795981+0.69799514j],\n",
       "        [-0.28983655+0.56980106j, -0.45172915+0.45234859j,\n",
       "         -0.56940335+0.29061709j, ...,  0.29061709+0.56940335j,\n",
       "          0.10043797+0.63134039j, -0.09957272+0.63147743j],\n",
       "        [-0.28983655+0.53688137j, -0.4415564 +0.42104011j,\n",
       "         -0.55005364+0.2639845j , ...,  0.2639845 +0.55005364j,\n",
       "          0.08108826+0.6047078j , -0.10974547+0.60016894j],\n",
       "        ...,\n",
       "        [-0.28983655+0.7151281j , -0.49663767+0.59056283j,\n",
       "         -0.65482444+0.40818914j, ...,  0.40818914+0.65482444j,\n",
       "          0.18585906+0.74891244j, -0.0546642 +0.76969166j],\n",
       "        [-0.28983655+0.78818874j, -0.51921465+0.66004762j,\n",
       "         -0.69776841+0.46729644j, ...,  0.46729644+0.69776841j,\n",
       "          0.22880303+0.80801974j, -0.03208722+0.83917646j],\n",
       "        [-0.28983655+0.78818874j, -0.51921465+0.66004762j,\n",
       "         -0.69776841+0.46729644j, ...,  0.46729644+0.69776841j,\n",
       "          0.22880303+0.80801974j, -0.03208722+0.83917646j]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[-0.13370834+0.73212141j, -0.35340215+0.65497068j,\n",
       "         -0.53850249+0.51370687j, ...,  0.51370687+0.53850249j,\n",
       "          0.32215784+0.67089045j,  0.09907377+0.73760699j],\n",
       "        [ 0.71905686-0.31787859j,  0.7820936 -0.08011972j,\n",
       "          0.76857357+0.16548183j, ...,  0.16548183-0.76857357j,\n",
       "          0.39488487-0.6798202j ,  0.58563382-0.5245213j ],\n",
       "        [ 0.82586734-0.31787859j,  0.88367641-0.04711346j,\n",
       "          0.85498507+0.22826346j, ...,  0.22826346-0.85498507j,\n",
       "          0.48129637-0.74260183j,  0.68721663-0.55752755j],\n",
       "        ...,\n",
       "        [ 0.56864489-0.31787859j,  0.63904332-0.12659957j,\n",
       "          0.64688773+0.0770719j , ...,  0.0770719 -0.64688773j,\n",
       "          0.27319903-0.59141027j,  0.44258354-0.47804144j],\n",
       "        [ 0.70347729-0.31787859j,  0.76727655-0.08493407j,\n",
       "          0.75596943+0.15632439j, ...,  0.15632439-0.75596943j,\n",
       "          0.38228073-0.67066276j,  0.57081677-0.51970694j],\n",
       "        [ 0.70347729-0.31787859j,  0.76727655-0.08493407j,\n",
       "          0.75596943+0.15632439j, ...,  0.15632439-0.75596943j,\n",
       "          0.38228073-0.67066276j,  0.57081677-0.51970694j]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q0_traces_cal</span></div><div class='xr-var-dims'>(repetitions, cal_dim, trace_dim)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(0.610163454644259-0.41025807770...</div><input id='attrs-6789230c-ddd8-46dd-a588-deb73486283c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6789230c-ddd8-46dd-a588-deb73486283c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20ed5768-b165-4a18-9281-f8c9e2245ee7' class='xr-var-data-in' type='checkbox'><label for='data-20ed5768-b165-4a18-9281-f8c9e2245ee7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ Calibration</dd><dt><span>is_main_var :</span></dt><dd>False</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.61016345-0.41025808j,  0.70707665-0.20162774j,\n",
       "          0.73477625+0.02673932j, ...,  0.02673932-0.73477625j,\n",
       "          0.25248896-0.69055084j,  0.45352321-0.57872949j],\n",
       "        [-0.28983655+0.63974192j, -0.47334206+0.51886631j,\n",
       "         -0.61051356+0.34720044j, ...,  0.34720044+0.61051356j,\n",
       "          0.14154818+0.68792373j, -0.07795981+0.69799514j]],\n",
       "\n",
       "       [[ 0.65746541-0.42785104j,  0.7575    -0.20374254j,\n",
       "          0.78338522+0.04030971j, ...,  0.04030971-0.78338522j,\n",
       "          0.28041615-0.73258723j,  0.49307351-0.6100785j ],\n",
       "        [-0.24253459+0.62214896j, -0.42291871+0.51675151j,\n",
       "         -0.56190459+0.36077082j, ...,  0.36077082+0.56190459j,\n",
       "          0.16947537+0.64588734j, -0.03840951+0.66664613j]],\n",
       "\n",
       "       [[ 0.61518248-0.37925297j,  0.70226892-0.17058917j,\n",
       "          0.72061239+0.05477309j, ...,  0.05477309-0.72061239j,\n",
       "          0.27477378-0.66841729j,  0.46787769-0.55079285j],\n",
       "        [-0.28481752+0.67074703j, -0.47814979+0.54990488j,\n",
       "         -0.62467742+0.37523421j, ...,  0.37523421+0.62467742j,\n",
       "          0.163833  +0.71005728j, -0.06360533+0.72593178j]],\n",
       "...\n",
       "       [[ 0.7414178 -0.50748957j,  0.86195313-0.25354056j,\n",
       "          0.89811448+0.02522676j, ...,  0.02522676-0.89811448j,\n",
       "          0.30152471-0.84636213j,  0.54830733-0.71176196j],\n",
       "        [-0.1585822 +0.54251043j, -0.31846558+0.46695349j,\n",
       "         -0.44717533+0.34568788j, ...,  0.34568788+0.44717533j,\n",
       "          0.19058393+0.53211244j,  0.01682431+0.56496268j]],\n",
       "\n",
       "       [[ 0.76904977-0.40600947j,  0.85687362-0.1484885j ,\n",
       "          0.86082071+0.12356755j, ...,  0.12356755-0.86082071j,\n",
       "          0.38352796-0.78050467j,  0.60594597-0.6237874j ],\n",
       "        [-0.13095023+0.64399053j, -0.32354509+0.57200555j,\n",
       "         -0.4844691 +0.44402867j, ...,  0.44402867+0.4844691j ,\n",
       "          0.27258718+0.5979699j ,  0.07446295+0.65293724j]],\n",
       "\n",
       "       [[ 0.76629166-0.31787859j,  0.82701656-0.06552336j,\n",
       "          0.80678732+0.19324575j, ...,  0.19324575-0.80678732j,\n",
       "          0.43309862-0.70758412j,  0.63055679-0.53911765j],\n",
       "        [-0.13370834+0.73212141j, -0.35340215+0.65497068j,\n",
       "         -0.53850249+0.51370687j, ...,  0.51370687+0.53850249j,\n",
       "          0.32215784+0.67089045j,  0.09907377+0.73760699j]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-103e09a6-feae-417e-be63-59a7c37b15a0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-103e09a6-feae-417e-be63-59a7c37b15a0' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-e8e2aacb-ba5a-4913-955c-c3421df74640' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e8e2aacb-ba5a-4913-955c-c3421df74640' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20231215-162531-854-50fae7</dd><dt><span>dataset_name :</span></dt><dd></dd><dt><span>dataset_state :</span></dt><dd>None</dd><dt><span>timestamp_start :</span></dt><dd>None</dd><dt><span>timestamp_end :</span></dt><dd>None</dd><dt><span>quantify_dataset_version :</span></dt><dd>2.0.0</dd><dt><span>software_versions :</span></dt><dd>{}</dd><dt><span>relationships :</span></dt><dd>[{&#x27;item_name&#x27;: &#x27;q0_iq_av&#x27;, &#x27;relation_type&#x27;: &#x27;calibration&#x27;, &#x27;related_names&#x27;: [&#x27;q0_iq_av_cal&#x27;], &#x27;relation_metadata&#x27;: {}}, {&#x27;item_name&#x27;: &#x27;q0_iq_shots&#x27;, &#x27;relation_type&#x27;: &#x27;calibration&#x27;, &#x27;related_names&#x27;: [&#x27;q0_iq_cal_shots&#x27;], &#x27;relation_metadata&#x27;: {}}, {&#x27;item_name&#x27;: &#x27;q0_iq_av&#x27;, &#x27;relation_type&#x27;: &#x27;individual_shots&#x27;, &#x27;related_names&#x27;: [&#x27;q0_iq_shots&#x27;], &#x27;relation_metadata&#x27;: {}}, {&#x27;item_name&#x27;: &#x27;q0_traces&#x27;, &#x27;relation_type&#x27;: &#x27;calibration&#x27;, &#x27;related_names&#x27;: [&#x27;q0_traces_cal&#x27;], &#x27;relation_metadata&#x27;: {}}]</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39mDimensions:          \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mmain_dim: \u001b[0m\u001b[1;36m30\u001b[0m\u001b[39m, cal_dim: \u001b[0m\u001b[1;36m2\u001b[0m\u001b[39m, repetitions: \u001b[0m\u001b[1;36m256\u001b[0m\u001b[39m, trace_dim: \u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;39m)\u001b[0m\n",
       "\u001b[39mCoordinates:\u001b[0m\n",
       "\u001b[39m    t1_time          \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mmain_dim\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m float64 \u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[39m \u001b[0m\u001b[1;36m4.138e-06\u001b[0m\u001b[39m \u001b[0m\u001b[33m...\u001b[0m\u001b[39m \u001b[0m\u001b[1;36m0.0001159\u001b[0m\u001b[39m \u001b[0m\u001b[1;36m0.00012\u001b[0m\n",
       "\u001b[39m    cal              \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mcal_dim\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m <U3 \u001b[0m\u001b[32m'|0>'\u001b[0m\u001b[39m \u001b[0m\u001b[32m'|1\u001b[0m\u001b[32m>\u001b[0m\u001b[32m'\u001b[0m\n",
       "    trace_time       \u001b[1m(\u001b[0mtrace_dim\u001b[1m)\u001b[0m float64 \u001b[1;36m0.0\u001b[0m \u001b[1;36m1e-09\u001b[0m \u001b[1;36m2e-09\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m2.98e-07\u001b[0m \u001b[1;36m2.99e-07\u001b[0m\n",
       "Dimensions without coordinates: main_dim, cal_dim, repetitions, trace_dim\n",
       "Data variables:\n",
       "    q0_iq_av         \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.19894114958423859\u001b[0m+\u001b[1;36m0.6515500138\u001b[0m\u001b[33m...\u001b[0m\n",
       "    q0_iq_av_cal     \u001b[1m(\u001b[0mcal_dim\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m0.7010588504157614\u001b[0m-\u001b[1;36m0.3984499861154\u001b[0m\u001b[33m...\u001b[0m\n",
       "    q0_iq_shots      \u001b[1m(\u001b[0mrepetitions, main_dim\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.289836545355741\u001b[0m+\u001b[1;36m0\u001b[0m\u001b[33m...\u001b[0m\n",
       "    q0_iq_shots_cal  \u001b[1m(\u001b[0mrepetitions, cal_dim\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m0.610163454644259\u001b[0m-\u001b[1;36m0.4\u001b[0m\u001b[33m...\u001b[0m\n",
       "    q0_traces        \u001b[1m(\u001b[0mrepetitions, main_dim, trace_dim\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.289836\u001b[0m\u001b[33m...\u001b[0m\n",
       "    q0_traces_cal    \u001b[1m(\u001b[0mrepetitions, cal_dim, trace_dim\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m0.61016345\u001b[0m\u001b[33m...\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20231215\u001b[0m-\u001b[1;36m162531\u001b[0m-\u001b[1;36m854\u001b[0m-50fae7\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\u001b[32m'item_name'\u001b[0m: \u001b[32m'q0_iq_av'\u001b[0m, \u001b[32m'relation_type'\u001b[0m: 'c\u001b[33m...\u001b[0m\n",
       "    json_serialize_exclude:    \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = dataset_examples.mk_t1_traces_dataset(**mock_conf)\n",
    "assert dataset == round_trip_dataset(dataset)  # confirm read/write\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c5d7d06c",
   "metadata": {},
   "outputs": [
    {
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     "data": {
      "text/plain": [
       "\u001b[1m(\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m256\u001b[0m, \u001b[1;36m30\u001b[0m, \u001b[1;36m300\u001b[0m\u001b[1m)\u001b[0m, \u001b[1m(\u001b[0m\u001b[1;36m256\u001b[0m, \u001b[1;36m2\u001b[0m, \u001b[1;36m300\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "dataset.q0_traces.shape, dataset.q0_traces_cal.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "afb96381",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:          (t1_time: 30, cal: 2, repetitions: 256, trace_time: 300)\n",
       "Coordinates:\n",
       "  * t1_time          (t1_time) float64 0.0 4.138e-06 ... 0.0001159 0.00012\n",
       "  * cal              (cal) &lt;U3 &#x27;|0&gt;&#x27; &#x27;|1&gt;&#x27;\n",
       "  * trace_time       (trace_time) float64 0.0 1e-09 2e-09 ... 2.98e-07 2.99e-07\n",
       "Dimensions without coordinates: repetitions\n",
       "Data variables:\n",
       "    q0_iq_av         (t1_time) complex128 (-0.19894114958423859+0.65155001388...\n",
       "    q0_iq_av_cal     (cal) complex128 (0.7010588504157614-0.3984499861154196j...\n",
       "    q0_iq_shots      (repetitions, t1_time) complex128 (-0.289836545355741+0....\n",
       "    q0_iq_shots_cal  (repetitions, cal) complex128 (0.610163454644259-0.41025...\n",
       "    q0_traces        (repetitions, t1_time, trace_time) complex128 (-0.289836...\n",
       "    q0_traces_cal    (repetitions, cal, trace_time) complex128 (0.61016345464...\n",
       "Attributes:\n",
       "    tuid:                      20231215-162531-854-50fae7\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:             [{&#x27;item_name&#x27;: &#x27;q0_iq_av&#x27;, &#x27;relation_type&#x27;: &#x27;c...\n",
       "    json_serialize_exclude:    []</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-91dba48e-8220-4c5f-a7c3-9a95f6e69be5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-91dba48e-8220-4c5f-a7c3-9a95f6e69be5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>t1_time</span>: 30</li><li><span class='xr-has-index'>cal</span>: 2</li><li><span>repetitions</span>: 256</li><li><span class='xr-has-index'>trace_time</span>: 300</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-04dc3e9a-798a-4ea8-b42f-1580796e7700' class='xr-section-summary-in' type='checkbox'  checked><label for='section-04dc3e9a-798a-4ea8-b42f-1580796e7700' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t1_time</span></div><div class='xr-var-dims'>(t1_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 4.138e-06 ... 0.0001159 0.00012</div><input id='attrs-8d58a4d3-1ef6-4338-b3bf-76249a6a7336' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d58a4d3-1ef6-4338-b3bf-76249a6a7336' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a9553c0-d872-4e95-a77e-6fe6b272273e' class='xr-var-data-in' type='checkbox'><label for='data-6a9553c0-d872-4e95-a77e-6fe6b272273e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>T1 Time</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.000000e+00, 4.137931e-06, 8.275862e-06, 1.241379e-05, 1.655172e-05,\n",
       "       2.068966e-05, 2.482759e-05, 2.896552e-05, 3.310345e-05, 3.724138e-05,\n",
       "       4.137931e-05, 4.551724e-05, 4.965517e-05, 5.379310e-05, 5.793103e-05,\n",
       "       6.206897e-05, 6.620690e-05, 7.034483e-05, 7.448276e-05, 7.862069e-05,\n",
       "       8.275862e-05, 8.689655e-05, 9.103448e-05, 9.517241e-05, 9.931034e-05,\n",
       "       1.034483e-04, 1.075862e-04, 1.117241e-04, 1.158621e-04, 1.200000e-04])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>cal</span></div><div class='xr-var-dims'>(cal)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;|0&gt;&#x27; &#x27;|1&gt;&#x27;</div><input id='attrs-f19dd618-cc8e-4d93-95ee-23441b30d417' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f19dd618-cc8e-4d93-95ee-23441b30d417' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42317966-2c9d-4f7d-b10f-6b92d61f2977' class='xr-var-data-in' type='checkbox'><label for='data-42317966-2c9d-4f7d-b10f-6b92d61f2977' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Q0 state</dd><dt><span>is_main_coord :</span></dt><dd>False</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;|0&gt;&#x27;, &#x27;|1&gt;&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>trace_time</span></div><div class='xr-var-dims'>(trace_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1e-09 ... 2.98e-07 2.99e-07</div><input id='attrs-a4e1e9b7-ba45-45f8-91c1-0e2e647b292a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4e1e9b7-ba45-45f8-91c1-0e2e647b292a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e329697-6bb8-4059-a829-4dc0e60e690f' class='xr-var-data-in' type='checkbox'><label for='data-5e329697-6bb8-4059-a829-4dc0e60e690f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Trace time</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.00e+00, 1.00e-09, 2.00e-09, ..., 2.97e-07, 2.98e-07, 2.99e-07])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f785dba7-f2d2-4eec-a17c-cd45943bb879' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f785dba7-f2d2-4eec-a17c-cd45943bb879' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_av</span></div><div class='xr-var-dims'>(t1_time)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(-0.19894114958423859+0.65155001...</div><input id='attrs-15227cf8-1815-42f2-8b12-7724862118a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15227cf8-1815-42f2-8b12-7724862118a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8aa6a61a-bb13-4a78-a5b2-cc717ba2b2c6' class='xr-var-data-in' type='checkbox'><label for='data-8aa6a61a-bb13-4a78-a5b2-cc717ba2b2c6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ amplitude</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([-0.19894115+0.65155001j, -0.11155212+0.54951255j,\n",
       "       -0.01216713+0.43313537j,  0.05193768+0.35848368j,\n",
       "        0.15504951+0.2389656j ,  0.20661408+0.17861197j,\n",
       "        0.2833043 +0.08961707j,  0.33361813+0.03110011j,\n",
       "        0.37345395-0.01576696j,  0.40534772-0.05352011j,\n",
       "        0.42151034-0.07261241j,  0.47040441-0.12912367j,\n",
       "        0.49717119-0.16057795j,  0.53565518-0.20550725j,\n",
       "        0.56438276-0.23892233j,  0.58075078-0.25822004j,\n",
       "        0.59794213-0.27834108j,  0.60292187-0.28390675j,\n",
       "        0.60292187-0.28390675j,  0.61706292-0.30039156j,\n",
       "        0.6204547 -0.30436927j,  0.62775114-0.31283759j,\n",
       "        0.64350021-0.33151635j,  0.64683289-0.33543496j,\n",
       "        0.65442395-0.34419789j,  0.65442395-0.34419789j,\n",
       "        0.65650831-0.34686819j,  0.66449532-0.35602708j,\n",
       "        0.66901604-0.36113374j,  0.66901604-0.36113374j])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_av_cal</span></div><div class='xr-var-dims'>(cal)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(0.7010588504157614-0.3984499861...</div><input id='attrs-5282752b-e5cf-4d88-b1aa-3c610521d983' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5282752b-e5cf-4d88-b1aa-3c610521d983' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a8409ad-2f92-4824-b882-864bc1e0bccd' class='xr-var-data-in' type='checkbox'><label for='data-0a8409ad-2f92-4824-b882-864bc1e0bccd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ Calibration</dd><dt><span>is_main_var :</span></dt><dd>False</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([ 0.70105885-0.39844999j, -0.19894115+0.65155001j])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_shots</span></div><div class='xr-var-dims'>(repetitions, t1_time)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(-0.289836545355741+0.6397419222...</div><input id='attrs-95d914e1-94f3-4eda-9315-74b320f19cc9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95d914e1-94f3-4eda-9315-74b320f19cc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23455e23-e7a1-4994-86b9-a9fcd7308171' class='xr-var-data-in' type='checkbox'><label for='data-23455e23-e7a1-4994-86b9-a9fcd7308171' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ amplitude</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[-0.28983655+0.63974192j, -0.28983655+0.56980106j,\n",
       "        -0.28983655+0.53688137j, ..., -0.28983655+0.7151281j ,\n",
       "        -0.28983655+0.78818874j, -0.28983655+0.78818874j],\n",
       "       [-0.24253459+0.62214896j, -0.24253459+0.5353232j ,\n",
       "        -0.24253459+0.59304477j, ..., -0.24253459+0.66518369j,\n",
       "        -0.24253459+0.7151281j , -0.24253459+0.7151281j ],\n",
       "       [-0.28481752+0.67074703j, -0.28481752+0.62934539j,\n",
       "        -0.28481752+0.62319817j, ..., -0.28481752+0.77120226j,\n",
       "        -0.28481752+0.66518369j, -0.28481752+0.66518369j],\n",
       "       ...,\n",
       "       [-0.1585822 +0.54251043j,  0.69005581-0.50748957j,\n",
       "         0.6904154 -0.50748957j, ...,  0.72225129-0.50748957j,\n",
       "         0.63121489-0.50748957j,  0.63121489-0.50748957j],\n",
       "       [-0.13095023+0.64399053j,  0.79234273-0.40600947j,\n",
       "         0.78602065-0.40600947j, ...,  0.70347729-0.40600947j,\n",
       "         0.72225129-0.40600947j,  0.72225129-0.40600947j],\n",
       "       [-0.13370834+0.73212141j,  0.71905686-0.31787859j,\n",
       "         0.82586734-0.31787859j, ...,  0.56864489-0.31787859j,\n",
       "         0.70347729-0.31787859j,  0.70347729-0.31787859j]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q0_iq_shots_cal</span></div><div class='xr-var-dims'>(repetitions, cal)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(0.610163454644259-0.41025807770...</div><input id='attrs-cc9d3887-06bc-49d8-a2cf-2a74b5dfd64c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cc9d3887-06bc-49d8-a2cf-2a74b5dfd64c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc6e8bf2-6fdf-40c4-beb6-495336d8523f' class='xr-var-data-in' type='checkbox'><label for='data-bc6e8bf2-6fdf-40c4-beb6-495336d8523f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ Calibration</dd><dt><span>is_main_var :</span></dt><dd>False</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.61016345-0.41025808j, -0.28983655+0.63974192j],\n",
       "       [ 0.65746541-0.42785104j, -0.24253459+0.62214896j],\n",
       "       [ 0.61518248-0.37925297j, -0.28481752+0.67074703j],\n",
       "       [ 0.74772749-0.43361903j, -0.15227251+0.61638097j],\n",
       "       [ 0.64265022-0.27384234j, -0.25734978+0.77615766j],\n",
       "       [ 0.60678906-0.3561582j , -0.29321094+0.6938418j ],\n",
       "       [ 0.84395867-0.46878511j, -0.05604133+0.58121489j],\n",
       "       [ 0.57061537-0.37774871j, -0.32938463+0.67225129j],\n",
       "       [ 0.78351922-0.39652271j, -0.11648078+0.65347729j],\n",
       "       [ 0.83818874-0.53135511j, -0.06181126+0.51864489j],\n",
       "       [ 0.7651281 -0.42764291j, -0.1348719 +0.62235709j],\n",
       "       [ 0.71518369-0.62580345j, -0.18481631+0.42419655j],\n",
       "       [ 0.82120226-0.28181242j, -0.07879774+0.76818758j],\n",
       "       [ 0.51610032-0.37325263j, -0.38389968+0.67674737j],\n",
       "       [ 0.77067194-0.39646665j, -0.12932806+0.65353335j],\n",
       "       [ 0.64660115-0.19540354j, -0.25339885+0.85459646j],\n",
       "       [ 0.67313985-0.25608898j, -0.22686015+0.79391102j],\n",
       "       [ 0.73270479-0.51790005j, -0.16729521+0.53209995j],\n",
       "       [ 0.8270149 -0.40109669j, -0.0729851 +0.64890331j],\n",
       "       [ 0.83712465-0.3367254j , -0.06287535+0.7132746j ],\n",
       "...\n",
       "       [ 0.64294178-0.26650726j, -0.25705822+0.78349274j],\n",
       "       [ 0.77779463-0.50086946j, -0.12220537+0.54913054j],\n",
       "       [ 0.87940344-0.38703376j, -0.02059656+0.66296624j],\n",
       "       [ 0.79740773-0.36164035j, -0.10259227+0.68835965j],\n",
       "       [ 0.63267527-0.4135685j , -0.26732473+0.6364315j ],\n",
       "       [ 0.79144079-0.44017147j, -0.10855921+0.60982853j],\n",
       "       [ 0.68482914-0.37939136j, -0.21517086+0.67060864j],\n",
       "       [ 0.85136549-0.38836868j, -0.04863451+0.66163132j],\n",
       "       [ 0.6485531 -0.22668462j, -0.2514469 +0.82331538j],\n",
       "       [ 0.69385278-0.44030654j, -0.20614722+0.60969346j],\n",
       "       [ 0.64327725-0.46894991j, -0.25672275+0.58105009j],\n",
       "       [ 0.73337178-0.39917798j, -0.16662822+0.65082202j],\n",
       "       [ 0.68419873-0.53952882j, -0.21580127+0.51047118j],\n",
       "       [ 0.72360952-0.19834449j, -0.17639048+0.85165551j],\n",
       "       [ 0.77753549-0.33238509j, -0.12246451+0.71761491j],\n",
       "       [ 0.76165034-0.55445959j, -0.13834966+0.49554041j],\n",
       "       [ 0.64390147-0.33097297j, -0.25609853+0.71902703j],\n",
       "       [ 0.7414178 -0.50748957j, -0.1585822 +0.54251043j],\n",
       "       [ 0.76904977-0.40600947j, -0.13095023+0.64399053j],\n",
       "       [ 0.76629166-0.31787859j, -0.13370834+0.73212141j]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q0_traces</span></div><div class='xr-var-dims'>(repetitions, t1_time, trace_time)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(-0.289836545355741+0.6397419222...</div><input id='attrs-58ef696c-fb5f-482c-825a-895537e3a50a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58ef696c-fb5f-482c-825a-895537e3a50a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-352b623b-2a27-464c-bb03-513565e99b1b' class='xr-var-data-in' type='checkbox'><label for='data-352b623b-2a27-464c-bb03-513565e99b1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ amplitude</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.28983655+0.63974192j, -0.47334206+0.51886631j,\n",
       "         -0.61051356+0.34720044j, ...,  0.34720044+0.61051356j,\n",
       "          0.14154818+0.68792373j, -0.07795981+0.69799514j],\n",
       "        [-0.28983655+0.56980106j, -0.45172915+0.45234859j,\n",
       "         -0.56940335+0.29061709j, ...,  0.29061709+0.56940335j,\n",
       "          0.10043797+0.63134039j, -0.09957272+0.63147743j],\n",
       "        [-0.28983655+0.53688137j, -0.4415564 +0.42104011j,\n",
       "         -0.55005364+0.2639845j , ...,  0.2639845 +0.55005364j,\n",
       "          0.08108826+0.6047078j , -0.10974547+0.60016894j],\n",
       "        ...,\n",
       "        [-0.28983655+0.7151281j , -0.49663767+0.59056283j,\n",
       "         -0.65482444+0.40818914j, ...,  0.40818914+0.65482444j,\n",
       "          0.18585906+0.74891244j, -0.0546642 +0.76969166j],\n",
       "        [-0.28983655+0.78818874j, -0.51921465+0.66004762j,\n",
       "         -0.69776841+0.46729644j, ...,  0.46729644+0.69776841j,\n",
       "          0.22880303+0.80801974j, -0.03208722+0.83917646j],\n",
       "        [-0.28983655+0.78818874j, -0.51921465+0.66004762j,\n",
       "         -0.69776841+0.46729644j, ...,  0.46729644+0.69776841j,\n",
       "          0.22880303+0.80801974j, -0.03208722+0.83917646j]],\n",
       "\n",
       "...\n",
       "\n",
       "       [[-0.13370834+0.73212141j, -0.35340215+0.65497068j,\n",
       "         -0.53850249+0.51370687j, ...,  0.51370687+0.53850249j,\n",
       "          0.32215784+0.67089045j,  0.09907377+0.73760699j],\n",
       "        [ 0.71905686-0.31787859j,  0.7820936 -0.08011972j,\n",
       "          0.76857357+0.16548183j, ...,  0.16548183-0.76857357j,\n",
       "          0.39488487-0.6798202j ,  0.58563382-0.5245213j ],\n",
       "        [ 0.82586734-0.31787859j,  0.88367641-0.04711346j,\n",
       "          0.85498507+0.22826346j, ...,  0.22826346-0.85498507j,\n",
       "          0.48129637-0.74260183j,  0.68721663-0.55752755j],\n",
       "        ...,\n",
       "        [ 0.56864489-0.31787859j,  0.63904332-0.12659957j,\n",
       "          0.64688773+0.0770719j , ...,  0.0770719 -0.64688773j,\n",
       "          0.27319903-0.59141027j,  0.44258354-0.47804144j],\n",
       "        [ 0.70347729-0.31787859j,  0.76727655-0.08493407j,\n",
       "          0.75596943+0.15632439j, ...,  0.15632439-0.75596943j,\n",
       "          0.38228073-0.67066276j,  0.57081677-0.51970694j],\n",
       "        [ 0.70347729-0.31787859j,  0.76727655-0.08493407j,\n",
       "          0.75596943+0.15632439j, ...,  0.15632439-0.75596943j,\n",
       "          0.38228073-0.67066276j,  0.57081677-0.51970694j]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q0_traces_cal</span></div><div class='xr-var-dims'>(repetitions, cal, trace_time)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(0.610163454644259-0.41025807770...</div><input id='attrs-39d46f6b-c2e7-447e-be8c-8bfd4862bea7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39d46f6b-c2e7-447e-be8c-8bfd4862bea7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19af421a-1501-4dfe-b8c7-471a0713f53b' class='xr-var-data-in' type='checkbox'><label for='data-19af421a-1501-4dfe-b8c7-471a0713f53b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Q0 IQ Calibration</dd><dt><span>is_main_var :</span></dt><dd>False</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.61016345-0.41025808j,  0.70707665-0.20162774j,\n",
       "          0.73477625+0.02673932j, ...,  0.02673932-0.73477625j,\n",
       "          0.25248896-0.69055084j,  0.45352321-0.57872949j],\n",
       "        [-0.28983655+0.63974192j, -0.47334206+0.51886631j,\n",
       "         -0.61051356+0.34720044j, ...,  0.34720044+0.61051356j,\n",
       "          0.14154818+0.68792373j, -0.07795981+0.69799514j]],\n",
       "\n",
       "       [[ 0.65746541-0.42785104j,  0.7575    -0.20374254j,\n",
       "          0.78338522+0.04030971j, ...,  0.04030971-0.78338522j,\n",
       "          0.28041615-0.73258723j,  0.49307351-0.6100785j ],\n",
       "        [-0.24253459+0.62214896j, -0.42291871+0.51675151j,\n",
       "         -0.56190459+0.36077082j, ...,  0.36077082+0.56190459j,\n",
       "          0.16947537+0.64588734j, -0.03840951+0.66664613j]],\n",
       "\n",
       "       [[ 0.61518248-0.37925297j,  0.70226892-0.17058917j,\n",
       "          0.72061239+0.05477309j, ...,  0.05477309-0.72061239j,\n",
       "          0.27477378-0.66841729j,  0.46787769-0.55079285j],\n",
       "        [-0.28481752+0.67074703j, -0.47814979+0.54990488j,\n",
       "         -0.62467742+0.37523421j, ...,  0.37523421+0.62467742j,\n",
       "          0.163833  +0.71005728j, -0.06360533+0.72593178j]],\n",
       "...\n",
       "       [[ 0.7414178 -0.50748957j,  0.86195313-0.25354056j,\n",
       "          0.89811448+0.02522676j, ...,  0.02522676-0.89811448j,\n",
       "          0.30152471-0.84636213j,  0.54830733-0.71176196j],\n",
       "        [-0.1585822 +0.54251043j, -0.31846558+0.46695349j,\n",
       "         -0.44717533+0.34568788j, ...,  0.34568788+0.44717533j,\n",
       "          0.19058393+0.53211244j,  0.01682431+0.56496268j]],\n",
       "\n",
       "       [[ 0.76904977-0.40600947j,  0.85687362-0.1484885j ,\n",
       "          0.86082071+0.12356755j, ...,  0.12356755-0.86082071j,\n",
       "          0.38352796-0.78050467j,  0.60594597-0.6237874j ],\n",
       "        [-0.13095023+0.64399053j, -0.32354509+0.57200555j,\n",
       "         -0.4844691 +0.44402867j, ...,  0.44402867+0.4844691j ,\n",
       "          0.27258718+0.5979699j ,  0.07446295+0.65293724j]],\n",
       "\n",
       "       [[ 0.76629166-0.31787859j,  0.82701656-0.06552336j,\n",
       "          0.80678732+0.19324575j, ...,  0.19324575-0.80678732j,\n",
       "          0.43309862-0.70758412j,  0.63055679-0.53911765j],\n",
       "        [-0.13370834+0.73212141j, -0.35340215+0.65497068j,\n",
       "         -0.53850249+0.51370687j, ...,  0.51370687+0.53850249j,\n",
       "          0.32215784+0.67089045j,  0.09907377+0.73760699j]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-473a2db9-c006-41d4-8db7-9d0f99e7b065' class='xr-section-summary-in' type='checkbox'  ><label for='section-473a2db9-c006-41d4-8db7-9d0f99e7b065' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>t1_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4847ffdf-e161-4f4c-b680-1d51cc801bb5' class='xr-index-data-in' type='checkbox'/><label for='index-4847ffdf-e161-4f4c-b680-1d51cc801bb5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                   0.0,  4.137931034482758e-06,  8.275862068965517e-06,\n",
       "       1.2413793103448275e-05, 1.6551724137931033e-05,  2.068965517241379e-05,\n",
       "        2.482758620689655e-05,  2.896551724137931e-05, 3.3103448275862067e-05,\n",
       "        3.724137931034482e-05,  4.137931034482758e-05,  4.551724137931034e-05,\n",
       "         4.96551724137931e-05, 5.3793103448275856e-05,  5.793103448275862e-05,\n",
       "        6.206896551724137e-05,  6.620689655172413e-05,   7.03448275862069e-05,\n",
       "        7.448275862068965e-05,  7.862068965517241e-05,  8.275862068965516e-05,\n",
       "        8.689655172413792e-05,  9.103448275862069e-05,  9.517241379310344e-05,\n",
       "         9.93103448275862e-05, 0.00010344827586206896, 0.00010758620689655171,\n",
       "       0.00011172413793103448, 0.00011586206896551724,                0.00012],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;t1_time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>cal</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e58076a6-6caa-4c39-8b33-5dca13a65700' class='xr-index-data-in' type='checkbox'/><label for='index-e58076a6-6caa-4c39-8b33-5dca13a65700' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;|0&gt;&#x27;, &#x27;|1&gt;&#x27;], dtype=&#x27;object&#x27;, name=&#x27;cal&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>trace_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8e988a9b-4a32-452b-be81-992aac23b97d' class='xr-index-data-in' type='checkbox'/><label for='index-8e988a9b-4a32-452b-be81-992aac23b97d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([     0.0,    1e-09,    2e-09,    3e-09,    4e-09,    5e-09,    6e-09,\n",
       "          7e-09,    8e-09,    9e-09,\n",
       "       ...\n",
       "        2.9e-07, 2.91e-07, 2.92e-07, 2.93e-07, 2.94e-07, 2.95e-07, 2.96e-07,\n",
       "       2.97e-07, 2.98e-07, 2.99e-07],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;trace_time&#x27;, length=300))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f607a309-d458-4e08-9c5d-a39bdd1eb009' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f607a309-d458-4e08-9c5d-a39bdd1eb009' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20231215-162531-854-50fae7</dd><dt><span>dataset_name :</span></dt><dd></dd><dt><span>dataset_state :</span></dt><dd>None</dd><dt><span>timestamp_start :</span></dt><dd>None</dd><dt><span>timestamp_end :</span></dt><dd>None</dd><dt><span>quantify_dataset_version :</span></dt><dd>2.0.0</dd><dt><span>software_versions :</span></dt><dd>{}</dd><dt><span>relationships :</span></dt><dd>[{&#x27;item_name&#x27;: &#x27;q0_iq_av&#x27;, &#x27;relation_type&#x27;: &#x27;calibration&#x27;, &#x27;related_names&#x27;: [&#x27;q0_iq_av_cal&#x27;], &#x27;relation_metadata&#x27;: {}}, {&#x27;item_name&#x27;: &#x27;q0_iq_shots&#x27;, &#x27;relation_type&#x27;: &#x27;calibration&#x27;, &#x27;related_names&#x27;: [&#x27;q0_iq_cal_shots&#x27;], &#x27;relation_metadata&#x27;: {}}, {&#x27;item_name&#x27;: &#x27;q0_iq_av&#x27;, &#x27;relation_type&#x27;: &#x27;individual_shots&#x27;, &#x27;related_names&#x27;: [&#x27;q0_iq_shots&#x27;], &#x27;relation_metadata&#x27;: {}}, {&#x27;item_name&#x27;: &#x27;q0_traces&#x27;, &#x27;relation_type&#x27;: &#x27;calibration&#x27;, &#x27;related_names&#x27;: [&#x27;q0_traces_cal&#x27;], &#x27;relation_metadata&#x27;: {}}]</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[39m>\u001b[0m\n",
       "\u001b[39mDimensions:          \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mt1_time: \u001b[0m\u001b[1;36m30\u001b[0m\u001b[39m, cal: \u001b[0m\u001b[1;36m2\u001b[0m\u001b[39m, repetitions: \u001b[0m\u001b[1;36m256\u001b[0m\u001b[39m, trace_time: \u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;39m)\u001b[0m\n",
       "\u001b[39mCoordinates:\u001b[0m\n",
       "\u001b[39m  * t1_time          \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mt1_time\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m float64 \u001b[0m\u001b[1;36m0.0\u001b[0m\u001b[39m \u001b[0m\u001b[1;36m4.138e-06\u001b[0m\u001b[39m \u001b[0m\u001b[33m...\u001b[0m\u001b[39m \u001b[0m\u001b[1;36m0.0001159\u001b[0m\u001b[39m \u001b[0m\u001b[1;36m0.00012\u001b[0m\n",
       "\u001b[39m  * cal              \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mcal\u001b[0m\u001b[1;39m)\u001b[0m\u001b[39m <U3 \u001b[0m\u001b[32m'|0>'\u001b[0m\u001b[39m \u001b[0m\u001b[32m'|1\u001b[0m\u001b[32m>\u001b[0m\u001b[32m'\u001b[0m\n",
       "  * trace_time       \u001b[1m(\u001b[0mtrace_time\u001b[1m)\u001b[0m float64 \u001b[1;36m0.0\u001b[0m \u001b[1;36m1e-09\u001b[0m \u001b[1;36m2e-09\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m2.98e-07\u001b[0m \u001b[1;36m2.99e-07\u001b[0m\n",
       "Dimensions without coordinates: repetitions\n",
       "Data variables:\n",
       "    q0_iq_av         \u001b[1m(\u001b[0mt1_time\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.19894114958423859\u001b[0m+\u001b[1;36m0.65155001388\u001b[0m\u001b[33m...\u001b[0m\n",
       "    q0_iq_av_cal     \u001b[1m(\u001b[0mcal\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m0.7010588504157614-0.3984499861154196j\u001b[0m\u001b[33m...\u001b[0m\n",
       "    q0_iq_shots      \u001b[1m(\u001b[0mrepetitions, t1_time\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.289836545355741\u001b[0m+\u001b[1;36m0\u001b[0m\u001b[33m...\u001b[0m.\n",
       "    q0_iq_shots_cal  \u001b[1m(\u001b[0mrepetitions, cal\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m0.610163454644259\u001b[0m-\u001b[1;36m0.41025\u001b[0m\u001b[33m...\u001b[0m\n",
       "    q0_traces        \u001b[1m(\u001b[0mrepetitions, t1_time, trace_time\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m-0.289836\u001b[0m\u001b[33m...\u001b[0m\n",
       "    q0_traces_cal    \u001b[1m(\u001b[0mrepetitions, cal, trace_time\u001b[1m)\u001b[0m complex128 \u001b[1m(\u001b[0m\u001b[1;36m0.61016345464\u001b[0m\u001b[33m...\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20231215\u001b[0m-\u001b[1;36m162531\u001b[0m-\u001b[1;36m854\u001b[0m-50fae7\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\u001b[32m'item_name'\u001b[0m: \u001b[32m'q0_iq_av'\u001b[0m, \u001b[32m'relation_type'\u001b[0m: 'c\u001b[33m...\u001b[0m\n",
       "    json_serialize_exclude:    \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset,\n",
    "    dimension=\"main_dim\",\n",
    "    coords_names=[\"t1_time\"],\n",
    ")\n",
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset_gridded,\n",
    "    dimension=\"cal_dim\",\n",
    "    coords_names=[\"cal\"],\n",
    ")\n",
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset_gridded, dimension=\"trace_dim\", coords_names=[\"trace_time\"]\n",
    ")\n",
    "dataset_gridded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e86d695e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001b[1m(\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m256\u001b[0m, \u001b[1;36m30\u001b[0m, \u001b[1;36m300\u001b[0m\u001b[1m)\u001b[0m, \u001b[1m(\u001b[0m\u001b[32m'repetitions'\u001b[0m, \u001b[32m't1_time'\u001b[0m, \u001b[32m'trace_time'\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_gridded.q0_traces.shape, dataset_gridded.q0_traces.dims"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8b5e3e7",
   "metadata": {},
   "source": [
    "All the previous data is also present, but in this dataset we can inspect the IQ signal\n",
    "for each individual shot. Let's inspect the signal of shot number 123 of the last\n",
    "\"point\" of the T1 experiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "fdf8914e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001b[1m(\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m300\u001b[0m,\u001b[1m)\u001b[0m, \u001b[1;35mdtype\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'complex128'\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace_example = dataset_gridded.q0_traces.sel(\n",
    "    repetitions=123, t1_time=dataset_gridded.t1_time[-1]\n",
    ")\n",
    "trace_example.shape, trace_example.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d5a1035",
   "metadata": {},
   "source": [
    "Now we can plot these digitized signals for each quadrature. For clarity, we plot only\n",
    "part of the signal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "20686f92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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s9hfQaDQajUaj0Wg0Go1Go9FoCg3flZd+61vf4qtf/SodHR0cfPDBfOMb3+Coo46a8/U33XQTt9xyC7t27aKxsZHzzjuP66+/nkgkktH9kskke/fupaqqKqux7xqNRqPRaDQajUaj0Wg0msLCNE2Gh4dZunQpgcD8uWy+crrdeeedXHHFFdx6661s2rSJm266iTPOOIPNmzfT3Nw84/U/+clPuPLKK7n99ts59thjefnll3n3u9+NYRjceOONGd1z7969eoKURqPRaDQajUaj0Wg0Go3GZvfu3Sxbtmze1/iqp9umTZs48sgj+eY3vwmILLTly5fzkY98hCuvvHLG6y+77DJefPFF7r33Xvu5T3ziEzz66KM8+OCDGd1zcHCQ2tpadu/eTXV1tTv/EI1Go9FoNBqNRqPRaDQaje8YGhpi+fLlDAwMUFNTM+9rfZPpFovFeOKJJ7jqqqvs5wKBAKeddhqPPPLIrO859thjueOOO3jsscc46qij2LZtG3/6059417veNed9otEo0WjUvh4eHgagurpaO900Go1Go9FoNBqNRqPRaDQZtSDzjdOtp6eHRCJBS0vLlOdbWlp46aWXZn3PO97xDnp6ejj++OMxTZN4PM4HP/hBPv3pT895n+uvv54vfOELrn53jUaj0Wg0Go1Go9FoNBpNcVHQ00vvv/9+rrvuOr797W/z5JNP8utf/5o//vGPXHvttXO+56qrrmJwcND+s3v3boXfWKPRaDQajUaj0Wg0Go1GUwj4JtOtsbGRYDBIZ2fnlOc7OztZsmTJrO+5+uqrede73sX73vc+ADZu3Mjo6Cjvf//7+cxnPjPrlIlwOEw4HHb/H6DRaDQajUaj0Wg0Go1GoykafJPpVlpayuGHHz5lKEIymeTee+/lmGOOmfU9Y2NjMxxrwWAQECNeNRqNRqPRaDQajUaj0Wg0Ghn4JtMN4IorruCiiy7iiCOO4KijjuKmm25idHSUiy++GIALL7yQtrY2rr/+egDOPvtsbrzxRg499FA2bdrEK6+8wtVXX83ZZ59tO980Go1Go9FoNBqNRqPRaDQat/GV0+3888+nu7ubz33uc3R0dHDIIYdw991328MVdu3aNSWz7bOf/SyGYfDZz36WPXv20NTUxNlnn82XvvSlxfonaDQajUaj0Wg0Go1Go9FoigDD1HWW8zI0NERNTQ2Dg4NUV1cv9tfRaDQajUaj0Wg0Go1Go9EsEtn4iXzT002j0Wg0Go1Go9FoNBqNRqPxC9rpptFoNBqNRqPRaDQajUaj0biMdrppNBqNRqPRaDQajUaj0Wg0LqOdbhqNRqPRaDQajUaj0Wg0Go3LaKebZk7aB8d5eGsP7YPj8m82uAe2PyB+arJGmay0nPJCy8kf6L3PH2g5+QMtJ/+gdZQ/0HLyB3rv8wdaThoVhBb7C2i8yZ2P7+KqXz9L0oSAAdefu5Hzj1wh52ZP/hB+/zEwk2AE4Oyb4bAL5dyrAFEmKy2nvNBy8gd67/MHWk7+QMvJP2gd5Q+0nPyB3vv8gZaTRhWGaZrmYn8JL5PNKFg/0z44zvaeUVY3VgBw3JfvI5n2mxE0DB688hRaa8ryv9ngHujbCvVrwUzATQcBaTczgnD5s1DTlv+9Cox0ObXWlLG5Y4jX3fT39P8992SVLidMuGmjUBQWWk5zMl1OO3tHOfmr9yuQE3DTBi2nLEiXVTJpcvx//RVTxt6XLqeaNuh8Hm45Dr33ZcZ0HXXsl+9TI6febfCNw9ByyozFsyWSQkdpOWXEdB21pXOY07/+gNZRHiRdVpOJJCf9l7YlvEi6nEzT5LivKLIlul6Cbx+N3vsyY9F0VGIS/t8haDkVFtn4iXSmm2aKl98wYHld2ZQNCCBhmuzoGct/E0r38mNAaSVTNiAQjri+bXoTmsZ0OR25qo6ndg1M/99zR1bT5VTRNNX4Ai2nOZgeNTt532b+uaNPjZxqV2o5ZcGUNQVURUJMD0O5LicjAMuPglefQO99mTFdTi3VETVyWvsa2P0oWk6ZMV1HrWooV2dLRKrRcsqM6Tpq0+p6dbZE5RKto7Jg+t5XVhKQLyfDgLo1Wk5ZMF1OteUlanTUimPh1X+i977MmK6jWmsi6nRUSRlaTsWNdroVOe2D4/YGBGCasKtvZk17wIBVjeX53WxwT9oGBGBCbHjm64wg1K/J714Fxmxyemx7/6yvDRpGfrKaTU6jXTNfp+U0g+lySppw30uz/N8hSU4DO2a+TstpVmasKWBoIj7jda7LyUzCrn/M/lotqxnMJqeOoYkZr5Mip1f+MvtrtZxmMJuO2t4zNuN10myJicGZr9NymsFsOuqRbX2zvlaKjhppn/k6LadZmW3vG5tMznid+3ufKbJzpqPlNCuzyal/bHLG66ToqJ0Pzv5aLasZzKaj9g7MZktI0lGTM/WhllNxoQcpFDnbe0ZnePkB3nbEMgKGc33Rsavy9/r3bZ0ZOQM4+kMiYmNx1te1138ac8npmrMP4MvnbrSvDeC6czfkJ6u55HTkJVPldMaXtJymMZecLj9tHde9eQNpS0qenA69UChyi6M/pOU0C3PJ6t3Hrpqy9117zoFy5PT6/4Kz/1/aEwacfZOW1TTmktM7N62YIqcrTl8vR04nfxrOuhns1avlNBtzyekth7URTJPT245YLs+W2PTBabbEjVpO05hLTp9+w/58+dyN6b/l8nTUEe+dKqfXfE7LaRbmktV7j1+FkbampMnp4HdOtSUOv0jLaRbmktNFx6ycoqM+/0ZJtsTpX0rZElpHzcdccvq3I5dPkdOHT9lH4nn3Ukg/CWg5FRXa6VbkrG6smOIIABGN+fhr1/PQladyxgEtAPSPxvK/mdUnIh0jCMd8BD70CARKxHOtB+d/rwJjdWPFFKUAQk6v27CEtx+1gmvO3h+AttoIbztieX43q18L038rjCAc/3G4/DlRdgAQLM3vPgXIXHI6/8jlvGPTSn76/qMBke3xmv1b8rvZXHI6+UrRI2L968Rz47NnRBY7Vj+PdIKGwQdOWsP9/34yVRGRCL60Nk/jq37t1AMmCDntd5Y4xJz+RfFc3Wo49F353asAmUtHXXbqPjx05alsbBM9NOKJPNvTziWnQy+AI94NF/7OeW6/s/K7VwGyurFiiiMAhJw+eca+PHjlqZx76FIAOmfJUsyauWyJYz8KH3kSSlJru251/vcqMObSUWcf3Mrbj1rBf513EAC15aW85bBl+d1sLh11wieELdGSChiaifzuU6DMpaPed8Ia7rr0OPt/9ug1DfndaC45nfoZYUtsOE88NzJ71n6xM5ecPnjyWh781Ck0Vgpbuaa8JL8bzaWjDnyzsCXOvkk8V9EMh7wzv3sVIHPZEh89bR0PXXkqR6+pB2A06sJ+NOd591J43/85z605Jf97aXyDdroVOa01ZRy1ut6+DhqGHTVrrSnjAyeLjePPz3cyGp1ZepUV4aqpCsMIOl7+5v1gvzPF88/+Ir/7FCCtNWWcm2YAp8sJ4G1HrKCsJMirAxP869VZymyyoaYNqpY41+lyqmmDI98jnn/m5/ndpwBprSnj8tPW2dcBY2oU+ug1DRy0rIakCX/41978blbTBis2OdfT5XTcx8TzL9wFkwrGoPuMpsow4ZCzH6WvqRX1Fbz5UBF9/N3TLshp49uc63Q5gZhcFQxD/zboeCa/exUgrTVlrGlyDjXTddS7jxWOld8+vYe85kLVtMGJ/+FcT5fTmpOEk8CMwwu/zf0+BUprTRknrW+yr6fL6dJTxb74wJYeekei+d2sokmsGYt0WdWvhoPeKp7XOmoGrTVlvHPTSvt6ui1xzqFt1JWX0D8W46GtvfndrKZtaunUdB216f3i+Wd+zowGWBqMaS6CdFltXFbL8esaAZd0VPrhf7qcTviEeP7lP8PY7KXIxcyS6gg1ZU63pnQ5La0t5+2pSZi/fWpPfjeqaRPONYvpOurgf4NIDYx2ws6H8rtXAdJaU8aBS51G99N11PuOF3vV75/ZSzwxS5ZaNkwPJKTLatkRsOoE8bw+7xYV2ulW5Jimyd5BcSD/5OnrefDKU6aMSj50eS0rG8oZn0zwlxc687vZ9gdEum3tSrjoDyKClj4q+aDzxc9nfwlJHfmczsSk+D9586FLZ8ipIhzi9ANF5lTein1oLwyn+q68/acz5bThLYABu/8B/Tvyu1cBUl4qjK+NbdU8dOWpM0aPn3OIMJB+m6+hbJowsFs8Pu0LM+W0/GioWSH6Jm7+3/zuVYA8vXuAaDxJdSTEj9+3acaaOifldPvz8x2MxfIMOEyOip8Hv3OmnCI1sG8qK1E7CWYwFouzO9Vn9GtvO3iGnM7YsIRISYDtPaM8k2/AoSSV1dh25Ew5ARyUcp5qOc1K97Bwpn345LUz5LS2qZKDltWQSJr84ZlZ+nplw+5HIRGFSB1c+PuZsrKc3C/qgMNsJFMOrtMPaJkhp5JggLMOElmJv8vXlpgYgoGd4vFbvjtTTvu/UThPu1+Cjmfzu1cB8sCWbgD2X1LFTy85eqaOsmyJp/IMOACMdIifJ/77TDm1HAAtGyA5CS/8Lr/7FCCvdI0wOB6nJGjwg4uPnMWWEOvpgZe78w84JFK2yP7nzJRTKAwHnCMeP3NnfvcpQOKJJDv7RF+1a9904Aw5nbi+ibryErqHozycb8DhlXvFz9ZDZj/vbkwLDOmAQ9GgnW5Fzo7eMXb3jVMSNLj4uNUz6tgNw+BNKcX+m3wNsK2pTWj9GbD6hJl17PucBmV1QvlvfyC/exUYiaTJg6/0APCOTStn7TdgGWB/yDdKs/U+8XPpYbDfG2bKqXoprD5RPNZRmhk8sEXI6Y0Ht80qp7MObiVgCKfP9p7R3G/UvRmG9kAoAps+MFNOgQBsTJWFaDnN4IGXxYHmhPVNHLdP4wxZWQGHsVieAYdEHLal9rMj3zN7/w4dcJiTf2zrJZZI0lZbxrmHzlxTleEQrz1AZOa6pqMOeuvsctp4HmDArkdgYFd+9yowuoejPL93CGBWWwKwbYnfPu2SnNa9FtacOFNWK46BmuUQHYKX787vXgWGaZq2M2eu/nqWk+DufAMO2x+AZFyUWm2cZU2V1Qp7EOBZ7ciejqWjTjughWPWNsyQlRVw2NYzyrN78gg4DO2FrhcAA47+8Bw6Sgcc5uJvKTkdvaaBk/ZtniGnfZqr2NBWTTxp8sdn8wg4mKZjnx/+rvnl9MJdMOlCKX8B8fTuAYYn4tSWl8x6jioNBTjzoFbARR217xtmP+8e8CbRoqf7Reh8Lr97aXyDdroVOZZSP3xlHRXh2YfZnnOIMMAefKWHnlyjNKbpeP7Xvmb214RKRW8C0E6CaTy3Z5CBsUmqwiEOWV4762uOX9dIQ0UpPSMx20GXE5ZS32cOOUGaAfYLHaVJY2IywaPbRITsxLRSq3SaqyIcv0783e/yUeyWUl95rJOhMx3LmbPlL7osZBqWc/SkdbPLKT3gkFf26J4nIDooAgpLD539Nfu8FiK1IuCw4++536sAeeBlIacT1zdhTG8aluLNKSdBXgGH2KgzVXYuHVW9VBjQoHXUNB5K6ZwDWqtpqgrP+pqzUwGHp3YNsLM3j4DDQjoqPeDwjJZTOumB1mPWzt4L7LAVdSyvL8s/4GDpqHltCSvg8CsdcEgjPdA6ly2RHnD47VN5ZM5v/av4ufRQKK+f/TUbrIDDwzrgMA3LljhxDlsCpmYl5kx6oHXlcbO/ZsWxUL1MBBy2/Dn3exUglpyO26eR4PTGlimstiJ/fq6D8ViO+1F6oHWuvS894KAd2UWDdroVOZbTbS6lDrCmqZKDrbKQXPtQ9W0TZQaBElh1/Nyv25gepdFlIRaWnI7dp4GS4OzLVpSFpKI0uSr2ZMIxwOY6eALsf7YoC+nZrPtQpfHY9j6i8SRLqiOsb6mc83WWI/t3T+/NvSxkISc2iF6JSzaKspDnf5PbfQqQgbEYz7w6AMAJ6xvnfJ0lpwe25BFwsA6ea06GQHD214RK4cBzxGPtJJiCtfedNI+cTljXRH0q4JBzH6odD0IiBrUroGGWJsgWG9MyPnTAwSYTW6K5KsJx+wg55uwkGOmG9n+Jx2tPnft1lpy23KMDDmlkEmg1DMN2EuTcLyyTQCuIbMVIDQzvFWtQA2QWaAVHR931rzwCDpk4R2vaHNv92V/mdp8CJJNAK8AbD15KwIAndw2wq3cst5tlEmidEnDQzpx0bFtiHueoFXAYjSX4y4s5BhwyCbSCo6Oe/SUk8+whp/EF2ulWxMTiSR6xlMU8mxA4ZSG/ydUAs4yvFUdDeG5nBMs36T5Us2CVg8yn1AHeZPehynHwRfvTMN4H4WrR7HMuIjWw7+vFY63YbeySxXWNc2blAJx+oNOHKqfBF5PjTqPc+QxlSFPs2plj8eArPSRNWN9SOe9o+DVpfaj+mGsfqkwOnuBkfLzwOx1wSLG7b4xtPaMEAwbH7jO3060kGODMjXkGHNLlNM/a5QDdh2o6yaTpZHvM4xwF0pw5Ofah2pYKCi3ZCJXNc7+u5QAx+CI5qQdfpJGJcxQcm+9vufahyjTQmt6HSpeY2mQSaAWnD1XPSI59qDINtMLUElMdcAAyD7Q2V0c4dm0q4JBrhUPGtoQOOEwn00CrYRi86eCUjsrVlsgk0Aqw7nQn4LBTBxyKAe10K2Ke2NnPWCxBY2UpB7RWz/vasw9eSjBg8K9c+1BlEkkDEaXRk8emMDQxyZO7BoCFnaN5D754JVW2s/pECC4w3vyg9CiNLguBzJ2jleEQp9tlITko9p0PQ3wCqpZC037zvza9D1X/zuzvVYDYB88F1hM4ToKc+oWN9cHeJ8Xj+bJyIDX4YrkIOOg+VICzng5dXkt1ZP79KO/BF5nqqCmDL3SzaoAXO4boGYlSXhrk8JV18742vQ9VToMvMj14QpotoQMOkF2gdZ/mSja2pQIOufShyjTQCmkBB92HyiJTWyJ98EVOtkSmgVZIDb7QfajSyTTQCo6OymnSdjaB1pYDxeCLREwPvkiRaaAVnJ6Wf3u5m77RWPY3y1RHlUREbzfQ590iQTvdihhLqZ+wronAHPXtFk1V4bSykCwVezwG21N9ihY6eIKTmfPKX2A0zwkyBcDDr/SSSJqsbqxgeX35vK+d0ocql2iadfDMRE77vFYPvkijY3CClztHMAw4fp6sHItz8ulDZfU0Wnvq/Fk5oPtQTcM0TbtP2AkLHGhg6uCLHdkGHLbdLyY2N+03e9PjdAKBqROtNPzdklMGztHDVtSyoj7HwRf9O6H3FTCCzpCY+bB01HO6DxU4ffeOXtNAODRPZJ9pfaiy1VHJ5NS9byF0H6opWIHWhoqFA60Ab0qVLuYUcMhGTiuOcfpQ6YBDVoFWcGyJnAIO2QRay2phvQ44pPP3LfP33UvnjANbCIcCbOvOYfDFrkcyD7SCY0tomw9Id44uLKcpgy+eybK6K5tAK0yrcNABh0JHO92KGKfMYGEHATjNqn/1xG4efqWH9sEMS6B2PwqTo1DRJMo9FqJ5P1hykJh69cBXYTDPKTI+x454rstMTnYfqpe7ufu59szlNDEIux8TjxeKpIHoQ2WVhTz8DS2nlJwOaquhrqJ0wden96G67e/bMpcTpDUSz0Cpg6PYn/oxbPtbUctqS9cIHUMThEMBNq2eo2l0GumDL75x35Ys5ZRFVg442aMv3wMv/rGo5RRPJHloa2Yli2D1oRJ7388e28XDW7PQUdZ6WnakyGRbiHWpwRfD7fCPbxW1nCA9czQ7W+K3T+3h71u6M5dT53Mw2gUl5SKDaiHS+1D97b+0nLY4WTkLBVrB6UP11K4BfvPUq5nLKR5zBsJkYkukVzg88QMRxCtiWWUTaIWpfai+/ddXctRRmdoSKR31r5/D1vuLWk4dgxNs7hzOONBaFSnhtQe0AHDLX7dmJ6dX0uS0UKAVnAqHnQ+JapQillN6oDUT5yg4FQ53Pr47O1sim0ArTB188dDXi1pOxYB2uhUp3cNRnt87BGTm+Qc4/YAllAQNXh2Y4B3ffZTjvnwfdz6eQfQ4XakHMvyVq18jfj56C9y0AZ78YWbvKzCEssiszMBiTVMly+rKSJrwwTuezFxO2x8AMwH1a6FuVWZf0Dqgbr23qOUEmffKsSgJBtg31QPkK3dvzlxOQ3uh6wXAgDWnZPbl9j8bjBD0b4MfvrGoZWXJ6ajV9URK5s/KsVhSLaYx/urJPZnLyTSdLIJMnaPN+0N1G5hxuPMdRS2np3cPMDwRp7a8hIOW1Wb0Hqun5SPb+njHbTnoqEwcBCD6ULUcIB7fc3VRy2k0GuefO0XfoEz3vhPWNVFRGqR/bJJ3/c9j2ctp1QlCBplQs1z8fOpHRS0nyF5HNVdHWNskdNTH7/xX5nLa/SjERjIPtIKTPbrtPvjB2UUtq2wDrYZhsK65CoBv/nVr5nLKNtAKog9VqAxGO+FHb9JyAg5aVptRoBWgIfW6/32+I3M5QfaB1ppl0LCPePyr9xa1nLINtIIIOBjAc3uHcrMlMg20BgLQvK94fP+Xi1pOxYB2uhUpD74ilMWBS6tprMzMeB2amGQy4fQhSJrw6V8/t3AEIJseLCA8/S/e5VybSfj95UUZAdjeM8qr/eOUBA2OXtOQ0XvaB8fZ0+/IJGs5ZWp8De6Bh/+fc13EckokTR58JbtIWvvgOP/Y7jS5zVhOlvHVdhiUZ2ZAEB0RjhyLIpbV3+xpmJnL6ZdPvGpfZyyn7pdEg9xQBFYel9mXG9wjnKoWRSwny0Fw3D6NBDPIygEoL53qRM1IVok4bEuVx2ejo3Y94lwXsZz+sa2XyYTJsroyVjdWZPSenpEoYzGnLFeqjnrmZ851Ecspl0Br++A4r3SN2NeZ66gcAq3TM0yLVFa5BFrbB8e5f3OXfZ2xnHIJtI72iDJH+wsXp5wgfRpmZs7R9sFxfvQPp69uxnLKJdA6uEe0TLDQcsoq0JowTdK77mUkq1wCrYN7nEEmUNRyKga0061IyTbVFph1gELCNNnRM8/465Eu6HhGPM40fb1vq9h40jETYhpWkWH1izhiZT0V4VBG79neM8r0Fq0Lysk0s4/QaDnZPLtnkIGxSarCIQ5ZXpvRe7b3jM4YALagnCB7JzYIWU2nCGU1MZngse3ZZeVs7xklmYucLOfoymOhZP7GvTZ9W2H66i1COQH8LbX3nZShgwBy1FF7/gnRQdGfcukhmd2ob+vM6X1FKqf0nkYLNRK3yElHxUZh1z/EY62jssYKtB7QWk1TVWaB1pzkBFpH5cGO3rGsA61566hMndigdVSKXAKtecspm0CrlpONNVk700Ar5GhLdG/OPtCqdVRRoZ1uRUgyafJ3O309801odWMF0xMOgobBqsZ5ek5YHvwlB0FlhveqXwvGtF9NI+iUnBYR2UY8IUc59W0TjaYDJU4PnIXQcrKx5HTsPg2UBDPbVnOSUzIB21JrKhtDWcsKgMe29xGNJ1lSHWFd8wIT9VLkJCfI7eCp5QRA/2iMZ14dAOCEDHuOgpDVdL/PgrKy5LTmFAhkFgXXcnLIZhKwRU5raseDkJyE2hXQsDazG2k52eQSaM1JTrkEWkHIimk3K0JZWespm0BrTnIyTa2j8sAOtEYyD7RqW0I9E5MJHrUmNme592VtS1iJC9kEWrWcigrtdCtCXmgfomckRnlpkMNX1mX8vtaaMr70Zqc/R8CA687dMP/45Wx75YBoPnn2zVObhZ59U2ZNKQuIWDzJI7ayyPzg2VpTxvXnOnIyMpGTpdRXHA3hzJwRjpzStpGzvl50coLcnKOWnKzfcoMM5LT3aRjvh3ANtB2R+ResaYMzv+5cG4GiXFPpw2Myzcqx5JRuLC8op8lx0cAYctv7LIpUTg++0oNpwvqWyvn/n6fRWlPGla9zJrtJ11Hpq7cI5bS7b4xtPaMEAwbH7pNZVg7M1FEZySn94Jnh2p1VRxWhnKYEWmXbErkEWkHI5DWfc66NYFHKSpkt0bcNBnZmF2gFIY+zbnKui1RH2e0P1jYSyjDQmpMtkWugdYYtUZw6KpdAKwhZff7sA+zrrHVUpmhboqjQTrcixGr+ecyaBkpD2f0K/NtRK9hviWjY+sVzNnD+kSvmfnEymTY2PotNCOCwC+GtPxCPq5aK6yLjnzv7GIslaKwMs/+S6qzee/6RK3jLYWLTfsemFfPLCXI7eIKQy6WPO9f7vj679xcAQxOTPLV7AMgu2wOEnD575v4AHLy8NnM5rTkRgplFwW2OeDc0phwSb/xGUa4pu0F1FgcaEHL6ySViWmIkFOCthy+f/w07HxZ9b6qWiilW2XDYhbDhPPH4yEuKU045ZE9ZvP/ENVSERcbaD95z1PxraqwP9jwpHmeTlQNCLq/9gni84ujilFNqPR22opbqSElW7z3/yBUctUoE/T71uv3k6qgLfi0el1bCoe/K7v0FQHqg9YiVGZanpTj/yBW8+5hVALzx4FZ5cgI47nJRmgVw0V1Ft6ZyDbSCkNNX3nIQAGubKxeWUy6BVovDL4K2I8Xj079UdHKC3JyjIOR012WOk/ONBy/gXMk10ApCLke8Rzw+6O1FLqfMA60WFx27miXVYj/61jsOm39N5RpoBSGXs1JB8ZYDilJOxYJ2uhUhf3mhExCH/FzY2CYa3nYORed/4dZ7YbQbSsph+absb7T6RPFzeC9MDGX/fp/zp2c7ADhiZR2BDBuJp3PoCnGgSR+qMCvxGGz7m3ic6aSxdBr3SZWGAJ3PZ/9+n/PHf+0lkTRZUVfG8voFygRmYVOqb8uO3lHM6X2ipvPSn8TPpYdnfR8AWoVRznBHbu/3Mf/a3c/LnSMYwPH7ZHegAbEOS0MBJuJJdvcv0IPlhdQgmBWbMs/KSWf5UeLn4O7s3+tzTNPkr6mm4BvaahZ49UwMw7CDFH2jsflf/NyvABMa1kH10qzvZfdtKdL+K/c8L/aRQ3O1JVJTaTuGJuZ/4a5HU03BA45dkA0rj4NASEzUHHx14dcXGH98VgxnOXR5bdaBVoDDU87RXX0L2BLJJGz5i3i85OCs7yMm+aWyS0Z7sn+/z/nz8+2MxRLUlZdmHWgFOGatsCV29Y4xmUjO/+KX/ih+LsvSkWPRdqj4Obx3/tcVIC93DvPkrn4ge+coCL1mTTHd0jU8/4uf/434ufzI7AOt4Jy9BjKcklpg3PuSOO9uXJa9LQFwwFKxDntGFjjvvnCXCLRWtGQfaAUxkRugd5vIbtQUJNrpVmT88OEdPLVrAICb/u/lzMdVp7FvKtPt5c55lMWTP4Qfv1U8nhybOkEsU8rqRKYIiEmARcSdj+/ijtSUoz8/35GTnKyMxJc7FlDq9/0nxFPG9E/emtu46maRrUXXi9m/18fc+fgurvrNcwDs7h/PSU77NFcSMGBgbJLu4XkU+6P/De1Picf3/WducmpJHWiKUE7nfOthQLQW/vPz2TsdQ8GAXZ6web419eQP4cnvi8fP/zbP9fRC9u/1Of/v3i30jAhn2RU/fzqnNbU+tfctKKc/fVI87n0lNzlZxvVIJ4z2Zv9+H/OTR3fyt1SfsNse3C7Xlrj9jNRFEl74Xdb3IVQqHKtQlHvfLfcLp/DDW3vzk1PH8PyBob9dD+Opidy/eo/WUVlw5+O7+OhPnwagfyzGL57IPuDSVltGRWmQWCLJzt6ZjeBtHr8dtt8vHj90s7b5suDOx3dxxtcfsAciPPRKbs7h9S0Z6qhHviEev3JvjnKy1tMLM4f/FDj//cBWtqcGH1zzu+fz2vs2L6SjfvMB8Xi0E576Udb3oX61yPKNj0P/juzfr/EF2ulWRLQPjnPN751MpIzHVU9j34UONIN74PcfY8rknFxHIFuKvYgyqNoHx7nq18/a1ya5yWldSqnvHZxgcHxy9hcN7oGHv+lc5zquuuVA8bNLyylbOUVKgqxqrADgpfnW1P9+yrnOVU7pBliRYMkp3dzMRU4A+y5kKNt7n4WZn5z6d0B0JNuv6VvaB8e56f+22Ne56qj9MtZRFjnKKVwJtSvF4yJbU5/57XP2tZmrLZHxenLBlrCdOVpHZSun1Y0VlAQNRmMJXp0rc35wD/ztq8513jqq+OSUr44KBAzb7pvXlvjTFc51znJK2XydxbXvuWZLqNJRjetFf8Tx/qKqcGgfHOfLf3KSNXI+76rSUYEgNO0rHheRLVFsaKdbEbG9Z3RGoCOjcdXTsDahHb2jTEzOkgbr5gjkIox65jxWfBo1ZSUsrRH9CLbMFaVxa6x4EUY93ZITOGtqzowP1+SUWk89L4uy4iLAVTktFPV0a++raISKZvG4e3OW39K/bO8Znf5bnpOs7CwC2XKCtIBDce19btgS61oqMQzoGYnNXr7jppy0jgJyk1NJMMDaJpHlq0xHaTnlpKMWrHDo2zoz4yknOaWyfIf3CodOEeBLW6Ik4kx7LiJnjlu2RLpzdNYsX1d1VPHtfcWGdroVETmPq55GU1WYuvISkia80jVLFoabI5CLMDNndWMF07tA5SIncMqs5ox6Wr3Y0slFVlbUs+sl0delCHBrPYGj2KXLqWYZhKshGU/1SCp83JTTgmWL9Wth+urNde8rwsyc1amMz3RykZXlxH61f5yRaHzmC1zVUZYzp7jk5IaOKi8NsSLVB3NWJ4Grciq+zBw3bQllOsqy+fq2iebkRYCrOmqhTDe3dFSkBmpSA4WKxEkgw+ZTYksUYbsKt2yJNU0VBAMGQxPx2XuPyjjvFlFlV7GhnW5FhDWuOphq7B00jIVHIM+CYRjz9yOoaYPj09LX8xn9nr4JFUk/gtaaMk5Y5zRnzVVOkIFir2wR8rHIVVb1ayAYhslRMYa+CGitKePfjnKmGeUlp4Uy3WraoG6Vc52rnAyj6Ayw1poy/uN1TmPbjEa/z4GVRbCtZ5RofJYs35o2WH2Sc+3G3lckBxoQsqoMO82ic11TdRWlNFeFgTnWVE3b1CmWruio4lhPIOS0X6vT6N2NvW9WJ0FNG5z6Wec6Lzml9r2ezZCYxRFbgLTWlHH6gS32dT5yWr+QjqpeCqG0z81VVpXNUN4gskeKpJdva00Z7zlutX2dj5z2WyiDqqbNWQvgzpoqIlvimrMPsK/zsSWs9dQ1HKV/toE/NW2w/nXOdV5yKr6AQ2tNGU2VpfZ1rmsqHAqyZr72LzVtcNQHnGtt82nmIYdRKBo/c/6RKzhxfRM7esZY1Viek7IAodgf3d43t2JvXC9+tmyAd/w8tw0IRI27ERDNeUe6oKpl4fcUACVB4Q9/z3GruOTENTnLad+FyqwGdopU6GAE3vkLkYaei6yCIWhaDx3PCgOsfvXC7ykArHHiJ65r5CvnHZS7nNIaiieSJsHp4VTTdEo43vhNWHtq7muqeX/Y/WjRGMoAB6QcBEtrI/zqQ8fmLKcl1RGqIiGGJ+Js6x5l/zTHg00g5cQ+5iNw9IfykFPxRT0HxyftzLTbLzqC/ZdW57WmuoajbO4Y5rDUJOcpVKZ0yboz4Kyv5y+nrhfFOs1lWq0PiaZaS3zmDftz1sGtecnpnhc653bmtGwQP+tWwbv/lLucaldCSYUIDPVtdfrnFDgVpcLMP//I5Vx+2rq8bD6YJ4A31usMZHrnr4SeyUVWhiHW1I6/izW19NCcvq/fsCafH76yjm++49Cc5WRlY+/qG2MsFqe8dJZjXiw1ZOGML8MBb8xv79tyT1E5cw5ZLnRJbVkJ/3v5CTnLqTIcYlldGa/2j7O5c5ijU1Psp1CS+uzD3wMnfjI/mw+KyuabTCTpHxO9rL/5b4dy+Kq6vNbUlq4RXu4Y5pR9m2e+oDaV8bnyODj3ttzlZFU39L4C8SiEwrl9jsaz6Ey3IqS1poxj1jbkvAFBBmVWPS+Ln8uOyH0DAqF0rDTdIirf2dotynZP278lLzkt2I/AklPjOlhzYn6yKsJSYEtOm9bkt55WNlQQDgWYmEyyu2+WnhOj3TAxCBiw8bw85VR8UU9LThuW1uQlJ8MwFj589qQGAez3BpfWU/FEPbel5NRcFebUfPe+hRogW3vf6hPyk1PDPhAogdgwDGY/cdCPxOJJdqb2qXwcbpBB2aIlp9ZD8pNTIOD0oSpCHXXy+qb8bL7UetraPcJkYpYWEpacalbAutPc2fuKKOBgyemIPJwDAI2VYRorSzFN2NI5S/uXyXEYSE1x3PgWraOyxJLTfq1VeckJMqhwsGyJ9afnJyer72j3S5CcJUO/ANnVN0Y8aVJWEuQNG/PTUftlakusOCY/OVW1irJtM+F8pqag0E43TU4sePDsTSkLK+MtH4qsAXI0nmB3akLY2ubKvD5rbVMlwYDB4PgkXcOzNKq2lHrjurzuAxRlmdW2HhExtppM50owYLCuRXzGrIdPS061K5zoZ64UYdRzW3dKTnmuJ1igSX9szHG85Lv3WQ6C0S4Y7cnvs3yCLac81xNkUFpv9TTMV06hUmf/LBIdtatvjETSpLw0aGf75op18NzSOUxyepdySNNRbtgSxaWjTNN0be9bVldGRWmQyYTJ9pTem4KrtkRx2Xzg7t43r47q2waY4nBf0ZTfjdL7jhZJ+xcZOmpWmy+ZdE9H1a0Spd/xCTERvQiw5LSmqYLA9MqRLFm/UMl2j0tyMoyiDIoXE9rppskJayx5x9AEg6kU3im4aigX1ya0q1ccaCrDIbsvUa5ESoKsahBlC7M7c6xMNxcPNEViKJumydbUIJF9mmc2bc2WeXvmyJDTwE6IzmFEFBhWdNoNQ3negIM1wa+sTvQlyofSCqePX5E4SG05ubCe0ku2Z5BMpB1o3Aw4FEdmTvp6MvIsp13VWEFpMMBoLMGegVka58twuhXJeuoejjIcjRMwYGVD9s3e0zEMY/4KBzd1lD0RuDjkBO7qqHkDDulyyrcUvnG96GE1MQjD7fl9lk+QIadZh8gMvSrKtQMlojQ+HwJBp5y+CHVUvlg235auEeLzZfm6GnAonr2vmPCd0+1b3/oWq1atIhKJsGnTJh577LF5Xz8wMMCll15Ka2sr4XCY9evX86c//UnRty1cqiMltNWKjJsZ3n/XDzTFtQk5yqIi7wMNLKDY3YxO2/0ItkB8lsawBUbnUJTRWIJgwGBFff5OgnmdOW4ePCsanH5W3Zvz/zwfkL6m8mXeITJuHmig6AIObhrK65qrMAzoHY3RPT3Ld3C3iPoHS/M/0EDRZea4uZ5KggHWpD5n/jXloo4qElvilZScVtSXEw4FF3j1wmSmo1yQU1Mqy3e4Hcb68v88jzMSjdM+KCYjurGm5i1bdNOWCIVFeT0Un45yIWt+37QMqhntX6x9r2Gt6JmcL0UWFLcC4m7YEsvryikrCU5pq2Az3i+qEUDrKM2C+Mrpduedd3LFFVdwzTXX8OSTT3LwwQdzxhln0NXVNevrY7EYr33ta9mxYwe//OUv2bx5M7fddhttbXnUXGts1qfK4WY43QZ2QiImpllaI8XzYUo/glmiDAXGVjstOn9lAbBvi2j2Lj3TrboNwjWQjDvlxQWMZXytrC+nNJT/VjpvSYgtp33yvg9QVJk5wxOTdA4Jp4sba8oylPcMjDM8MS3L182DJxRhwMG90p2y0iArU83JZxw+LTnVr3UGX+RDkWXmbO1yV0fNOXFxrA/GUqXVDS7sfda+17ddlIIXOG6uJ8hUR7mw90WqRW84KAonwfaUnBoqSqktL13g1Qszb9mim3KCotJRiaTJjh6xb1gTLfNhTWMloYDB8ITjdLWxdJQb+x5MLQUuAiz7fI0LTuxAwHDOu9PXlFVaWrUUwlV536vYnKPFhq+cbjfeeCOXXHIJF198MQcccAC33nor5eXl3H777bO+/vbbb6evr4/f/va3HHfccaxatYqTTjqJgw8+WPE3L0z2XSKcOZs7hqb+hbUJNezjzoGmbrVw4E2OwcCO/D/P47iZRQCw7xKhLGYcPEd7xVRYENG0fDEMxwArgqjnNheVOsB+qfW0vWeUaHxas1s3eyRCUSl2q/9QY2WYmrKSvD+vtryUlmpR9v3y9EbVtqHs0oGmiKKe8USSnb1OHxY3mPPwKcs52vMyJGZpt1BgbOtxL4sA5hnMZGXMV7dB2IV7VTZDeSNgiiBegeO2jpqzbDEeFcFWcFFHFY8zx+31ZLV/6R6O0jc6rerAdR1VPAGHV/vHiCWShEMBu9InH0pD82T5upmRCEWVjW2apryAwwwdZcnJrYB4Sk6Du1PD0zSFhG+cbrFYjCeeeILTTjvNfi4QCHDaaafxyCOPzPqeu+66i2OOOYZLL72UlpYWNmzYwHXXXUciMff0lmg0ytDQ0JQ/mtmxnTkd0w+eLkfSgqG0fgSFr9jdVhaWc/TlzmES6Y2qLWVRs1z0j3KDIjKU3ZZTS3WY6kiIRNK0M0kAmJyAfpcPNEUU9XTbiQ3pAYfphrKLmaMw1Tla4I2qd/ePM5kwiZQEWJrnVDgLu8xKtpxqVkBppcjw7t3qzmd6lPRelm703oN5yhbdtiWgqA6frtsSqfW0q2+MsVjc+Yu+bWAmIVzttC7IlyIKOLi9nirDIZbXp9q/pK8p05TozCkCOdlO7Mq8m/NbzJk96rotkXKO9m4VNmUB0zcaY3B8EsOA1S5kJMI8AQe35VRWJ7LmALoKPzBUbPjG6dbT00MikaClZapCb2lpoaOjY9b3bNu2jV/+8pckEgn+9Kc/cfXVV/O1r32NL37xi3Pe5/rrr6empsb+s3y5C+WRBYpTtjg0tR+B25sQFE1mjmmabOtyr2cEiH4ukZIA0XiSXen9CGQcaIoo6ulm/ykQjaqtbLfNnWnOfqs5vxvTxiyK6eDZ5d7kUot9W2bJHnVz2phFwz6ikXJsBAZ2ufOZHsU6eK5pdO9AYzlHX5qrvNQtOQUCTh+qAt/7ekZiDE3EMQxY1eDOgcY6eG7tHiEWT2shIcOWKCYd5bIt0VAZprFylizfdFvCjV6WUDQ2H7jvHIU5+roN7YXJUQiEoH61Ozey5NS9WfRzLmBsW8LFAN7cAQeXdVTVEojUgplw1muBYq2nttoyykpdqLZinsFMbssJiiooXmz4xumWC8lkkubmZr7zne9w+OGHc/755/OZz3yGW2+9dc73XHXVVQwODtp/du/erfAb+4u1zRUEAwZDE3G7ZxLgfukOFM0m5Oa0MYtgwGBds6XY05w5Up2jxXSgcTODypLTLAeaBhcPNE37AQaMdsNItzuf6VHs6LRLEU9Ic+akr6ehPaIEPlACdS405wcIljhZvgV++HSzQbWFtZ62dA6TTM4WGHKpJASKJjPHktOyujIiJe4caNpqy6gMh4gnTbscHJBzoCkSHTWeNg3WTWfOfrMNZkrXUW5h9x19oeCzfN3sP2Uxa2m9Jae61UK3uEHdagiVicE0fdvd+UyPkp7p5hazZs1PDMFIKpnELR1lGEUTcJAjJ7GedvSOMjGZ5lyWko1dPAGHYsM3TrfGxkaCwSCdnZ1Tnu/s7GTJkiWzvqe1tZX169cTDDqG4f77709HRwex2OzTFcPhMNXV1VP+aGYnHAraqbtTDp+9EpxuRbIJWRGa5S5NG7Nw+hGkO3PSeu+5hZVBNbALorM08S0QxmJx9qYa365pdE+xO72N0p2jLmdPgSgnrlslHhe4AbatW0ammxOdtrN8rX2v3sUDDaRlJRZ2wMGSk5vO0VUNYsjJWCzBq/3CAcH4gDNtTIaToMB11DYJWTmGYcw+mMntZuIw1ZlTwFjOy9ryEuor8m/Ob2HZElOdOS5Oq7doXAdGEKKDIkOrQEmkOZrdXFPrZ8t065Ugp0AAmq0s3+LQUa62qkjJ6ZXuEeKJVJavZUtUtogKB7coklLgbRJaijRVhqkrLyFpwiupgDuJScfRLCvgoCkofON0Ky0t5fDDD+fee++1n0smk9x7770cc8wxs77nuOOO45VXXiGZNvHy5ZdfprW1ldJS94yQYmZGyu1Yn8ieATmbUM8W0bS3QHG7ZNHCmQ4nOdOtvB6qWsXjAu5HYBlf9RWl1Ll4oLGzCOYq3XGTIoh6ph9o9nFxTa1rqcQwoH9sku6R1H4kIysHisaZIyPTLRQM2HK3nTnWwbOqVUxJdIsimQgsS0fNGMyUmIT+1IHG1Uy3lINgpEPYKwWKbFtiijNHhi0RCjs6r4B11N6BcaLxJKXBAMvq3KluAGcw08vpgSFZtoTVL6xYdJSLa2pZXRnlpUFi8SQ7elPtX2TbEgXuzJFRrm0Yxszs0f6dkJyEknIx7Mct0rPmCzzLt9jwjdMN4IorruC2227jBz/4AS+++CIf+tCHGB0d5eKLLwbgwgsv5KqrrrJf/6EPfYi+vj4+9rGP8fLLL/PHP/6R6667jksvvXSx/gkFx77To572gWapO9PGLKqXQrgm1Y9gi3uf6zFklMLBLNPh4lHo3yEeu67YCz8zR0ZzfoD1qTLgPQPjDE2kpiBKM5QLP+ppTRsrDQVY6sK0MYtISdDuZ2UPkpEmp2IxlOWsqX2nZ4/apXAuZk+BI6f+HRAbnfelfkZGKRw4fRLtbOy+7ZCMQ0mF0P9uEa6C2hXicQHvfdJ01PSD55Tm/FpHZcsrKTmtaiwn6FIvSxAN5EMBg+Gok5UvxTkKaVPrC9fm6x+N0ZuaBOvm3hcIGPa0Wds+l62jisQ56r6OmhZwSJdTwEV3SuN6MAIw3gcjnQu/XuMbfOV0O//887nhhhv43Oc+xyGHHMLTTz/N3XffbQ9X2LVrF+3t7fbrly9fzp///Gcef/xxDjroID760Y/ysY99jCuvvHKx/gkFx4xMN1kHT8Moip45WyWUwoETnd7ROyb6EfRtFw7M0irRYNVNikCxy4ikAdSUl9BaEwFEHypM0/3m/BZF4MxJd2K7eaCB9IDDNGeO23Ky9r2el0X2TwHSNxqjf0z829ws14Y0p1vndOeoy3KqbEoNOjGhu3CzfKVnunVOX08u9rK0KILMHFk6yioD7hmJ0jsSheEOiA2LQ2L9GlfvZcupkHVUl5z1VBoK2J/pBBwkZVC1FL7Nt61HyGlpTYTy0pCrn73f9Ammsp2jQ6+KNgsFSDSeYHdqYJyb1Q2Q3stXspxKyqB+rXhcwOfdYsRXTjeAyy67jJ07dxKNRnn00UfZtGmT/Xf3338/3//+96e8/phjjuEf//gHExMTbN26lU9/+tNTerxp8sM6eG7pHCGRlDCOPJ0iiHpuk3Sgaa4KU1NWQiJpikOT3XdvHwkHmsIvs9omKZIG03rmDLeLyZVGUDQsdhN76thLYvJmASKj/5TF+hkBB6tHossBh5rlwjmenHQcsAWGtZ7cnDZm4fTfk3zwhIKfCjwx6fTGc9/pJuS0u2+c0WhcTm9YiyLIzNkmoZk4QHlpiBX1ogxyc+ewI6e6VaIk1E2KweaT0M/NYn36YKboiBj2A/IyqPq2wuS4u5/tEWQFxGGWXr4y+vgClNU6ZZAFGhja2TtG0oSqcIimKnf3o32XpKbW25VdKnRU4e59xYjvnG4ab7GivpxISYBoPMnO3lHJB5rCzsyZOm3MXWdOej+CzR3D8iI0UBRRT1lZBDBtOpwlp/rVEHK5D2XDWgiWCqfe4C53P9sjyCqxgrQ+iR3DYmjIcKrZt5sTMUE4xQv88CmrHAQcZ8627lFi8aS8Ujgo+MycHb2jmCZUR0I0Vrq7H9VXlNqHpJc7h+XaEi2FnemWTJpSmr5b7DubjpJpS3RvhkTc/c/3ADKmoFvsl+7MsQI25Y2i966bVLZAWT2YSef3ocCQleEL03r5JuLCeQmSdFRhB8Wt9bSmuRLD5YQCKyDeMTTB4NikXFuiwHVUsaKdbpq8CASMqVOSbAPM5YMnFHzZ4vYecaCpKXN32pjFvukp7DKVReO+gAFjPTDS7f7nLzLJpMn2HnkG2PrZ5OR29hSICZvWQalA19TWrtRETIlyerlzhGR3Sk4VzVBW5/q9Cj2DSqYTu7UmQlUkRDxpsr1rEPq2ib+QGZ0uVOdo2npy+0AD03rmyGpVAVPXUwE2qm4fmmB8MkFJ0GB5vXvN+S0cW2JEzoRZi9pVokl5IuoM1SgwttpTm2XaEiNyndiGUfD2ubP3yatu2NE7SrRnOyRiEIqILHe3KXhbImWbu9wXG6AqUkJbqjfw5k7JAYcCtyWKFe100+SNbSjv7ZczbczC2oQGd8HE0Pyv9SHbepysHCkHmimZbhINsNJyp7dLAQ5T2Ds4zsSkNW3Mveb8FulyMmUePKHgo57bJDpHVzWUUxoKMD6ZoG9X6v9PxnoCJ+pZoBlU2yRMLrUwDMPWUbu3vSDKdENlUL3M9XsV+kRgWe0PLOzpcO1DcnVUwzoIhCA66JTcFRCWnFY2VFASdN/MnzKcRKacAgFoSk2bLUAdNTg+SU9q+rUMZ46VQbW1a4REt2RbokXbErnSVBWmoaIU04T2rc+KJ91uzm9R8DpKXhkwOHvfjl07YbwfMJz+a25iZc0XcPuXYkQ73TR5Y5fvbHnOmTZW5eK0MYvyeqhqFY//9TMYLCxj2YqkyT7QPLu7n6TMCA04DtIX/1B4ckop9VWN5YQkHGj2aa7EAPrHJhnZk4pGypbTtr8VnJwGxmL0jLg/bcwiFAzYjXq3vfiUeFKaczQlpz1PFJycID3TzX05gdMzZ9uLT4snGiUdaJr2FT9HOqHjOfc/f5GxswgklMJBWgbVtu0wMYC0A02o1MkefuYXBbemnOb8kuSUWk8vtA8R79osnpSmo1LOnM13F5ycLOdoS3WYqkiJ65/fVltGpCRALJGkf1dqP5JtS+x4qODkNJlIsqtXNOeXZZ9b2W7bXlJkS7T/CwZflXOPRURmSxGYRU61y0WigdvUr4ZgGCbHYPej7n++ZlHQTjdN3rSnxpGPphwEfWUr5BxoACK14uf//jvctAGe/KGc+ywCWyVmewA8t2cQgMBYD4HoEEkkTBuzSKb6rzx+W+HJyeoZIaEcBOB3T+/BKngaejUVjZRlKFvjyHc8UHhySjlyWmsiVITdnTZmESkR+1z3DhGdfmqsUcp9bAfOSEfBySkaT7CrT+6BZjQq9qOelJx2BiRkuQE8/xvn8X8fX1ByArllwID9exDvFI6ckbKlUBKRci9KUlnK936+4NaUXbIoSU7/3NEnHkyOExpOHdxl6ahJ8TvBMz8tWDnJWk+/eGI3E5MiS6Z7u+V0k+TMGUj1hd37RMHJaWfvGPGkSUVpkJZql4eFpLCKWzq3CR31XKxFyn149Z/iZ2wEbtpYUHIyTVP6mhoYE4Hc/l3CNm8vkVACDPD0j0VZPcD3Xl9QcipmtNNNkxftg+N87yFRUrrWEI3EH+yvo31QwgSjwT1TJ+6YSfj95QUTVbObiUvoRdA+OM61fxBKYm1AyGm32UT7qIS05cE98PKfnesClZOMbI/2wXGu+rUwusqYoM3oBaCjVIKTYHAPPPYd57pA5SQjyw2ErJ7aNQDAWqMdgG88E3B/7xvcA/d8xrkuMDnt6h0jkTSpDIdodnnaGAg53fUvsedZcvrtrnI5cvr9x5xr0ywoOZmmmVZeKmfv+/b9otm7paP+OdIkz5bY+5RzXWBrSmbT9/bBcT77W+HAWZNaT/1mJe1xCdkeg3umOrILVE4ydFS6LWGQZHVKVl3hFa7fi8E98NDNznXByklOL8v2wXEe2SpsPWvv++4LITk66k+fdK4LTE5dw1FGonECBqxocH8/ah8c585/7gac8+7d7VXybQkKy5YoZrTTTZMX23tGSabSciwD7JXkUnb0jLl/s76twLSmx2bCaYztY6ZMG5OQ6abl5B7bJEbSpsqpA4Bes4rtoxKyPfq2CqMrHS2njNneM4oJBEiyOiWrl5Ot7q+pApdTemmpjAONNaAGYE3qQPOKllPWdA5FGY0lCAYMVtS77ySYVUeZEuQERaSj1Mhpq6ltiVyQ2SMxXU5tRi8RY5KoGWJrrMH1exX63idzPYFjS4CzprZoHZU1lnN0RX054VDQ9c+fYkuknG6vyNj7ClxOxYx2umnyYnVjBYHUOcmK0GxnKasaZdS4rwWmHcqMoLwSSYV0pKaNhQIGKyRMG5sip5Sy2G5KlJMxbWspEDmB3CyC2eS0TcspJ2TKCYSsDAPajG7CxiQTZgmdNLkvKy2nvJh176NNyylLLDmtrBcDRNxmdh0lQU5Q0LIaicbpGBItP2SUl2od5R4yS+Fmk9MOs5VVzVWu36vw5aRGR9UyTKMhhsTtknGOKng5yQ20KtNRBS6nYkY73TR50VpTxvXnbgRMexM689QTaa1xf6ojNW1wyqedayMIZ98knvc59oGmoVzKtDFLTgaOsjj40CPkyemsm5xrI1AwchqamKRrWN60MUtOAcNxYtev3CBPTmenlYQUkJxAvqHcWlPG1WcekHagWcIXzz3IfVnZcrICDkZByklWGbC1puoYot4Q93r3G18jT07pxnJBykneepquo15zwrHy9r7X/qdzXUC2hJU91VgZpqbM/eb8tpzSdNT6Aw/TOipLJhNJdvbKq26YYkuk1lP50v0ky6mwdZSsfsutNWVc+6YNdpbbXrOBz54rwT4vdDl1yZfT9edupJRJVhhdAJz/+lO0LaHJGO100+TN+Ueu4F0byqk1RjExeN2Jx8u72TGXOo8/+BAcdqG8eynEbs4v6UADQk5XvWE/2wA78oijpd2Lwy9ypsOdc2vByMkqM2iukjNtDISc7njvJltOa/c7RMp9ACGXA98sHh99acHIKX3amCxnDsDFx61iv5AoLW1avZHzj5TQLweEXF5zjXi8+qSCkRPIj06DWFNvaB0GYCSyhPOO3lfOjQ67EN7xC/E4UldYcpI8EROEnN591BKWG90AnHr8CdLuxdGXQqBUPL74fwtGVrKn94GQ0/Vv3mjrqEMOOVLavTjsQlh6mHj8+v8qGDnt7htjMmESKQnQWi1nWMj5R67gt5ceZ8upbZ+DpNwHEHI57KLU44sKRk6maabZ5/LW1DuPXsmh5cKRE16yr1xb4g03iMethxSMnEBuX2yL849cwXmrYgQNk2iwgjcdf5icGx12Ibwn1Rs7UAKHXCDnPhqlaKebxhUOqxRNQPtKWpypYDIorYDyVE+K5KS8+yhmW4/8gyfAQc1h2owecSFr2piFlQo9KaHXyyIhswdLOoesqLWjnmM1klPKm1Ij5Cf65d5HIbv6xLSx8tIgSyQdaAAMw2BDWBjKI1WS5dS6Ufwc7ZJ7H4VMac4vKTptcXCZcOT0RlZJvQ/LDhc/J/ohNir3XgpRpaMOq+onYJiMBiqhoknejQIBqE1NnkvE5N1HMTJ7w6azYWmV3ctSui3RsI/4GRuRex+FWHJa01hJIOB+L0uLDUtrWBsQtsRAxSpp9wGg5QDxc7xX7n0U0jsaY2gijmHAqgZ5zhyAjSlbYqhitdT7sPQQ8dOaXl8gqNr7Dq0QZ6ieyEpn7KwMlh4msrCTkwUnq2JFO900rrAaMVVlV0DClMXp1KYiQIO75d9LESqi0wArjQ4Chkm/WYlZVi/1XgUtJwmTS9MpDwVYkzKU94YkjSS3sOQ0UEBySotMyzzQAOyTKrHaE5S899WuFD8HdmN38/U53SNRhifEtLGVEqaNpWNNLn1VtpwitRCuFo8HX5V7L4U4pTty9741li1htMk90EBh6yjJztEVoQHKjSgxM8hEpey9rwB1lKJgQyBgsC4o9r49QW1LZIu17y2vKydS4n5z/nQs56h8WyIlp+F2iBdGwGE8lmDPgJgiKnvvW2MIHbVb9nk3GILqVElpAemoYkY73TSu0DopNoSX463yb1aTMhwKSrGridA0RXeK+5lL6RmVnClYW8BykqzUGdxNhBhRM8TOeKPce1lyKiClrqJk0WJZQjhWXkkulXujmpSBFxuGiQG591KEtZ5kTRtLpzW+CxBT4aRiGAWno0ajcfYOpprzN8pdU0tStsTmeCumbOdyQesouc7RqhExSW+nuYQ9w3Gp9ypMHaUm0Mr4AA2myGLfZsrWUYUoJzXrCaAtLv7fXpEtp4omCEUAE4b2yL2XIrb1iPVUV15CfUWp1HvZ592EgvOuraN2yb+XRjra6aZxhdqhlwDYGS0nnkgu8Op8b2ZF0wpjE0qfNlZZGpJ6r9DeJwFoT9bzar/kss8CkxPASx1islRtuVylzq5/ALDHbGT3oORIpJ3t8SokJa9dRTy7ZwCApsqw3BuN91MZFwea3bIrn0rKoKJZPC6QNfXkLvF/t7RWYkuCFHUjrwCwc0zy7wSk7X075d9LAY/v6AOgtqyEOskHmpqBFwDYG69maFy2M6ewdFQiadqHz6qIXFvCePVxAPaYDbzaPy71XoUmJ4AX20WPSdnriV2PANBl1rBjWPKRz3IQjPUWTGn9M68OANAisU0FAPEYtRMigLdrRG4AampgqDDW1D93CltiWZ3cjHmAuuGXAdg5Lvl3Agpy72sfHOfhrT20D0rWGx5EO900+fPkDynd/SAAnwz+nKGHb5d7P9tJUBib0H//bav9+HU3P8Cdj0v6dz35Q/jHNwE4M/gPAk//SM59LGoKq3TnJ4/uZEeqOf8nfv60XDn95gMArDY6aNxyp5z7WFQtFX0jErGC6Btx5+O7+NOzotfQ/zy4XZ6cAB76hv3wqu0XCdnJpIAyc+58fBc3/HkzAI9s7ZUrp8dvJzIqIvqfnfgaySd+IO9eUFCZOXc+vouLvyccLAPjk3Ll9OQPCb38RwA+GPo9I/+QbEvUFJ4tMZkQ2YFvvfURuTrq/i8DcGLgGcLP/FjOfSxq0soWC6C0/s7Hd/HsnkEAPn/X83Ll9NN/A6CJQZZu+7mc+1hEasQfKBgd9bPHd6ce75ZsS9xMABH0/EzHR9TZEgWioz7/u+cBeHbPoFw5PfEDIn0vAvDp5HeIPvZ9efeCgsse/c4D2zj2y/fxjtse5bgv3ydXVh5EO900+TG4B37/MXsAdcAwqbvvP8Tzsiggz3/74DjfvO8V+zppwqd//Zz7EYCUnCwCBmx88ho1chra6/u+Ee2D43z2t8/Z1/LlJA4WhgGv3/EVuXJK7xvh8zXVPjjOVb9+1r42kSQnEDJ58Eb7MoAJv79c730ZYMnJOj5Ll9OfPmHrqKBhYvzh41pOGTBdTiBZTlN0lMnSv1+p5ZQh7YPjfPWezfa1Kh0VMOCo574gV05Waf3kKIz1ybuPAmboKIW2xLl7b5ArJyiYNaXclvjrl+zLICamtiUyQrmO+sPlU2yJ0v+9QsspQ9oHx7n+Ty/acRNpOsrDaKebJj/6toI5tSTNMBPQt03ePQuoX872nlGmx20TpsmOHpdLP2eRU4CkXDlVNEKojELoG7G9Z5TkNEGpklNQtpygYKKeyuQEQlbTV6+qvU/LKXO0jsqZxZeT5L3P3vf2QDIh7z4K2N4zOiMJrGBsiZIIVLaIxz7PSix4W6JAskcX25bQOiozFl9HSZZTAVU3KDvvehjtdNPkR/3aGRPGkgSgfo28e1qb0HgfRP09Qn5148zmrEHDYFWjy30J6teCMXW5J2TLyTCcCLXPozSrGyuYPkevYOQEBdODanVjBdOHlUqREwhZTcM0gpL3vsKIeqqX0zQdZcjWUVpOWTPL3ifdlqhqhUAIkpMw3CHvPgooHh3l/zW1aHIyAyTrVrt7n+kUSOP3xdZR8m0Jaxq6llPGzGZLyN770nvv+by0Xtl518Nop5smP2ra4OB32pdxM8Cvln5SPC+L9L4RPs/4aK0pY98WZxpc0DC47twNtNa43Fi8pg1O/rR9GTcD3Fz2YblygrT+e/6X02v2b7avpcrprJvsy4RpcNXkexkra3H3PjPuWxjRtNaaMi46dpV9LU1OIGTVuK99GTcDbN30RblrqkAOnq01ZVx91gH2dcBArpzWvda+jJsB/rb+s2rkNNIB8ai8+0imtaaM68/daF9Ll9MR77Mv42aAO5o+LldOgaBTWl8AOurg5TX2tVQd9dpr7cuEGeDLwQ/KtyUKSEeddZAz9VCqnM6+2b5MmAZXxd9LlyF7Gnpa/z0f01pTxgdPcgJr0m2J1oPty7gZ4LnDviBZR1lZvv63Jb54zgb7WrqOOuBN9mXcDHD36qvkyqlmGWBAfFwMKPExrTVlLKtz5CJ1TXkU7XTT5E+diJh0NJ/A8dGb+Y3xGvn3LJDDJzhJ5Ve9fj8evPIUzj9yhZwbtR0GwGTVco6P3sx3R0/AlB05KZCoJ2CPIX/b4cvkyilNqb/RuImfJ05hj54OlzH7NAsn9mHLa+XKCUQ5KfCjmg9yfPRmnmw8W969oKAmjh21uh6A6kiIh648Va6cyuoA+FfzORwfvZn/i5wu714A5Q1QkoreDr4q916SeevhyylJpRL84oPHyJVT4zoA+huP4Pjozfw8eYq8e1kU0N5XEhQm/Udfs4/cvW/FMQAkIvUcF72Z744dTyyup9ZnijUJ8w0blsiV08HvwDrmXRL+L2FLDEgu5SogHXXA0moA9m2plG9LBMTE0t/XX8zx0Zt5tPZMefeCtID4HkhInhItmRPWNQEQChj8/T8kyylV5v5Ky+s4Pnoz/1v62gXekCehMFQtEY99XokCMDEpbObrz90gf015EO100+RP6lARbz2cDhrYM6CgKWJN4Rhg7QMTALxm/xa5Hv+UnALN+9FBA2OxBANjk/LuBwUT9QTYm5LTpjUNcuVk9b8rqydZJyKtr8peUwXS0w2c9XRgW41cOZmmvabal5wi9j7pztGUnCYGYGJI7r0kY62nVY0V8iOdqUbHY0uPUaOjDKNgDp+9ozEmkyaGAQctq5V7s9T+Y7YeTAcNvCp7PUFBOXOsNXXyvs2SdVTKlmjch4GSJkwT+c2wC0lHDQo5HbGqXq6cRjqAJARKGKs/EED+miqQ6gaAvSk9sV9rtTId1bP0JDU6qnIJBEpE4HC4Xe69JGOtp7a6MtrqJJcqpmy+aOumlI5S0I+sQM5RE5MJekbEUL3Xb2gtqgw3C+100+RPahOqaBYZb3sHxklO72zpNgViKA9NTDIcFVGmpbURyTcTSj1Yu4ymqjCgwAArIOfo3tShYmmtbOMrlRlTs4y21L2UGcoDu33fN8IylKXLaawP4sLYq2oW/3/S5RSusrO2/H6osQ7pS1UYXqn/q/KmVYACOUHB6ChrPbVURexMKmmkdFR5k7AlBsYmGYlKzsIoEOdoImnSMZQ6fCrSUYZSHVUYPagA26Ei35ZIBfCqW1laL/opKbMlRjphckLuvSRjObGlyykeTTlIoSK190mXUyBQMD2XbZtPhS2RCjiUp8670gOtUDA6qiPlHC0vDVJTVrLI32Zx0E43Tf6kDLDq5lWEAgaTCZOuYcl9bAok6mkpi9ryEspLQ3JvNoszR3qpgR319LeyME0zzZkj2Tlq/U7XLLP7H0hX7NVpfSNGe+TeSzJ7VMupsoXW+prUvXXUM1MsObXKllMyCUN7AahfKpqI7+kfV1daXyA6SrqcwNZRkYaVtlGurLTe53LqGp4gkTQJBQwaK8Nyb2bbEm0sS2WWSJdTgfR0AxbBllhuy0m6M6esDkpTfYp9XlrvOHNkB8SFfiIUoalZ9AdT4swpEB2lzJYA+3e6vlUMT+gajtolk9IoEB1l2xI1EQxj+jiZ4kA73TT5YZpOBlXdCpaklJO6vhH+3oSsUjg12R6WobyctjpV0emUnIb2+rpvxMDYJBOTomfNEtkGmBWdTne6yS41CJU6fSN87iC1Sg2kR6eHHDm1qZITOHufzw0wa++TnpUz2iWmUxoBmttWATA+maBfdml9geiovarWE6TtfcsVBoYKRE6p9bSkJkJw+jg/t5nNllDVAiE6COMDcu8lkVg8SfeICEqr1FHLahXpqPTSem1LZIYlp+o22upTTmylbXr8vfdZWfPSbYlJZ5hB9ZJVlJUEU/eXnNFZIDpKWYavh9FON01+TAxAbEQ8rl66COVw/lbqyrJyYFp0WpGcrL4Rybiv+0ZYcmqsDBMOBeXezJJTdVvaelKZQeXfNZVMmrYB1irdOerIyVpP7QMi20Qqtpz83VTXiXoqKteuaiVcGqbZLq1XlOXr4/UECrM9EnEYTmV81LSpCwylO7F9XFqvtMRqMXRUaYUYUAK+Djh0Dk1gmlAaCtCQGs4kjXQ51am0JQqjHE65jqpx1tPg+CTDE6p6LvvdlhBOL1V99yitxIjUqltTBdKmx+69p51uGk2OWMqivAFKy9WlsFvKYrRLRB98irL+U2kZiUqjnul9I3xsKFtyalPqHF2mrnQHCiIzp2ckymTCJGA4E+KkkVa601wVIRQwiCdNOodkRz0LIzq9GOXagLqS7QIrCZGuo4bbwRRN36loVlha3wZGQPRnHO2Wey+JKFtPME1HKZITFISO2pPmxJZeYjWLnPYOqCit97+OmphM0Dsqmr7L75Ho2BIV4RB15anSej1AKyMWxZYwjMWxJQogMFSMAxQstNNNkx9ppXCAuuh0gfSNUJa+PtabavpuQNVSdc5RKIiopzI5gd2oNb10R2nfCB/LySqFa6lW0PTd3vvaCAYM+3dDWWaOj+UUTyTpTPX9lH+gma6jFAeGhvZAQnLGgkSUlZfaJVZLIRBwMqiUlNa3isc+XlPKdFQ8KoKdkOoVpmjfg4LQUe2qBjLBFKdba00ZhgETk0nbmSSNAtBR1nqqKA1SXSa73/Ic56g+XTGUCU5QXF0ZcPr9pDtHrcSF6JCoLvMpji2hIDDkUbTTTZMflue/eloWgcq+ET5WGHvSGktKJa3pO6FSpweVHnedEcoiNMmE01S3po268hLKSxX3jfBx1HOvqvUEUw40wCIMJ/GvnLqGoySSJiVBhU3fU4ayMh1V0QzBUpG9Za1pH6KsbDGtTxigODDk/8On00xc0cEzFIHyeltOHUMTxBNJufcugL1PWSkcTNFRpaEALVVCLyoLOPhaTs56UpaRaOmoWkV93ezS+lfFwCEfMhKNMzQh+kVL3/um2XzKdFRpOVQ0iccFcI7S5aUaTa4MTY3QLNM9qLJCWQPQORwEQxNxhmT3jajxf98IZb33RrpE/zsjCJVLMAxDXc+cAlhPykrhYBYDTFV0OmUoj3ZDTME+KwFLTktqIgRkN30fmurMUbaeAgHfB4ai8QTdw1bT9wItAwbfywkUtkBIz8oxDJoqw5QGAySSJh3KSuv9a0sok1NsDMb7xONpGVTKyuF8vJ6UNn0fmqtiSLKOqm4TtmYiBiOdcu8lifaUnKojISrDsjMSnTJgULie0u7p1zVlmuYUR3axop1umvyYw/O/p19F3wh/Z+YkkiYdqkpC0krhgKl9I3TUc0GUNQC1I55LISgMCGWHz/RJVj7tG7FX1UTMxCSMdIjH1dMONLKj05FaCFeLxz4trbfLDBRnewCKy+H8raM6B4XDLRwKUC+96ftUHWXJqWdEYWm9T+UECstLp62nQMCwHbLydZT/e7opCwxZjpzSKojUAOl7n6IA3nA7xCWXskrCma6tMmveyvJVZEsEQ8LWBN/ufUqdo3PoKKVJJj6V09B4nLGY0ONKKlE8ina6afIjbeoOiMwFw4BoPEnPiGRl6/NoWnrTd2uinjSmRWhAYWp0AfR0Uxahscu12+ynlPVJtOQUG4bxfrn3koSy8lKr6Xuw1E77V7aeCqC0fnEyEqeVl+qyxQVJP9CobPoOUFNWQkWqtF5ZQ3Gfymk8lqAv1adL2aTFVLABVOoof68nSCsvVdacP01OqnpQVTSJ8mMz6Tj/fIayliITg6JXF9g6qk1Vf1jw/Zqy1tOiVDek7tkxNMGk9NJ6f+soa89pqCglUhJc5G+zeGinmyY/bM+/2BBKQwGWpKYGKutH4NOop11iVR0hJL3p+1RlAWkGmKqop0/7RsQTSXsipfQSq2llBpCWPSp7PZWUOX0jfBpNU9akOr0HS0CsXWUHGkhbU/40wJRNG5tMm0hpl5eK9TQcjTM4rqq03u/rSX22h2EY6qeh+1ROe1NyqgyHqI5ILrEammlLKOtBZR08x/sgOiL3XpLYa7cUkV0GPIucFiMw5FNbYq8yWyJl85XVQWkFwCJNrfenLaFMR5nmjDXVWBmmNBQgaWJXLUmjdqX46VM5KQ20ehjtdNPkTjIxq5NAXQ8qv29CKidiTp26AwrLrKqW+rpvROdwlKQJpcEAjRWKmr4vxnoC30c996haU4OzOUedDKpkUlFpvU/lpGzvs/a9knJxqAHKSoM0pEol1fVJ9GcPKmVDFMBx5syS5auutH6XL0vr2wecoJDqjERQ2IMqUmOXSvrRmTM8Mcmw1fRdekbi1FI4UNyDyvc6SpEzZ5711DsaYywWl3t/39t8ipw54/0QT62blI4KBAx1WYnaOVoQaKebJndGOsFMQCAkpmKmUNesNbUJ+bRvhLUJKWkqOY9i130j5kdp0/f55KQ06uk/OUXjCXpGrKbv6kt3ltRECBgQSyTt7yENH8sJFDpz0oMNac4I5TrKh/seOL33pOuo6IhT0j5rOZxkZ451z8lRX5bWKyuFg9mdOSqzfH2cPWr13aspK6FCetP3eaobBhT0XPaxjhJN3xX1HR2aWa5dU1ZCVer3Y6+q7FGf6qh2VXKy/n8qmiHkBN+V7X0+l9MelVObPYx2umlyx1LqVUsh4NRoK8ugsvpGYDqKy0com4gZj8Fwqun7YpQagK+jacoinjBHSYjKvhH+lZOV3h8pCdhDQqQxODMrpyQYsA2K3bq30byoK92ZuZ5AoY6aUloveRiABJRNWrSco+FqiFTbTyuTU0nECRz6MCtRWbaHac7RH3YxelBpOc2L3R92pi0xoqK03sc6amBskvHU8JYlsvvDzqGjrMCQtiXmR70t0TblaWXDFKz9drwfosNy7yUBx5bQTjeNJjds42vqJtSmqr/HlIbi/vP+K8v2GG4HTAiGobzRflptdNq/qdHKIp4wqzOnsUJl3wj/Tkjak7aepJdYzVJWDzrqmQljsTgDY+LA16qsdGe6jlIkp6pWkQmejDuBDx+hLINqgYOnzvKdH7t0R7aDYGIQYqlearOUAbcPTKgrrffh3ufYfAoCeLPoqEhJkMZKq7ReT62fC8uR01ipoOn7As4cpROBfVZan0yadqab9OFZs7QUgfTe2LKn1leLyfXgbx2lnW4aTY7MsQmpnQ7nXwPMKjVQGqEJOEveMpT7VPaN8KGclDUAnRyHsR7xOG1NBQKGPSVJ942Ym/ZFmWK1fMrT6soWU/0shzsgLrmU1WUsJ3ZVOER1RFFG4jQ5KWtUHQj6urRe2ZqaMyNxEbKxfSgn5T0Sy+qhtNx+ekl1hGDAIJZI0q1L6+dE2Xqapem7RZvy4ST+syXUTsScOozOQtkALev3Iz4OY71y7+UyvaMxYokkhqEiI3Fmhi/AsvpFyPL1sY6SHmj1OL5zun3rW99i1apVRCIRNm3axGOPPZbR+372s59hGAbnnHOO3C9YTCwQnX61f0x+3whfK3ZFZYuzDFGAVN+I1KQzZb2NfCgnZRGaob3iZ0mF3fTdQlmjar2eMmOxSw3KGyBUBqQdrnyC0ojnXDrKcmLL7hUGvh34MzQxyXBUBGMWS0dZcuocniAWl11a718dtdjl2qGgM7Ve66i5URbAG+uDeCor3nL6p1imKsvXck4M7fFdab2TOaqwDHguHSXbNg+FRUY2+K5k21pPLVURSoKS3Rlz6ihFzlHw7d6XSJp0DIn9SJeX+og777yTK664gmuuuYYnn3ySgw8+mDPOOIOurq5537djxw4++clPcsIJJyj6pkXCLKVw4Cyq0VhCft8In0Y9JyYT9IyI4Q/KGoBOi9BA+uFTVdTTX3KCtAag0kvh0sq1p5VHLlM19MI6eE4MwMSQ3Hu5jHXwlF4KFx0W/z8w596npLTep1FPu2RRpXN0mpys6LTaskV/GcqWnGrLSygvVd/0HUR5VzgUwDSdg7A0fKqjRNN3VZMWZ3cQgMo+if6tblDWx9f6v6lsmdL0HRQGhqqWQKAkVVrfLvdeLrNHlY5KJp1g63QdZWfNKwgM+fQctSi2xByVXXsHxklIL633p9Ota3iCRNIkFDBorAwv/IYCxldOtxtvvJFLLrmEiy++mAMOOIBbb72V8vJybr/99jnfk0gkeOc738kXvvAF1qxZo/DbFgFDs5fuiL4RYmHJN8D8mUVg9eYqKwlSq6rp+7SsHFBYvpN+8PRZ3whlDUDnKNdOv7d0OYWrnCw7nx1qrPR1ZXIK10xp+g6qy+H86czZo7LEamj20h3rd6R/bJLRqKLSep/JSdlUOJjTmWMYhrqS7Rp/yql/bJKJSZEFKL/Eah4dpczplrL5RjphUnKPU5dR3lKkeqbNp2w9BYKOzemzNaXMlhjtguQkGAEn2yyFsvUEvtVRe1WtJ5jT6dZSHSEUMIgnTbqGJe9HPg/gLakRbQiKGd843WKxGE888QSnnXaa/VwgEOC0007jkUcemfN9//mf/0lzczPvfe97M7pPNBplaGhoyh/NHMyxCYFKA8yKevpzE1paG5Hf9H0eQ1lds1Z/9o0YTZvyJb9R68LrSTcUnxtlpTtDmclJemm9T+XUrsqJPTHgNH2fFnCoipRQbZXW66EXs6J20mIGAQdlcvKnLdFUFSYcUtX0fRZbQlWWb1mdaMGQ/n18QDJpKmxVsfB6UjtAy197X7uqvc/6/a1aCsGp2cSWnLqGo0TjkstzfaqjlAXEE2nZmtPWVDBg2MEOZW16fCcnhc5Rj+Mbp1tPTw+JRIKWlpYpz7e0tNDRMftUsAcffJD/+Z//4bbbbsv4Ptdffz01NTX2n+XLZ5bkaUg1fU85T2bNoFLcg2pwj9gYfYIXIjSgUE4+7RthGclVkRBVspu+W86c6tnklMqgUtKDyn9Rz/QSK3UTMWfKySobGp9M0Dcak/s9fCgnSC8DVpSVU94AJTP3WScrUfegmg3HQSBZTvM0fYdFyMaeGBR/fMKiTMScJYNKmZzSS+t9ZEv0jEaZTJgEDGipklxiNU9LEbXZ2P6sRHGmNi/OdG2A+opSylKTUy2nhTR8rqOky2m4HcykKJeuaJ7x1+pK6/0pJ6U6yuP4xumWLcPDw7zrXe/itttuo7GxMeP3XXXVVQwODtp/du/2l0dZGdaBprTSGWOchrKoZ2Wqb4SZ8FXfCGcTUuh0m8WZo6Oe87NHVZkBzJ/plrp/+8CEur4RPsr4GJqIMxoT0WD5PRLnNpTDoSDNqQOV/Awqv/Z0W9yJmBbqyhat6PSrviqtVyan0R5IRAFDZHxMQ1k2drhSTOUEX+koZRm+MH9/WJU9qHyY8WGtp5bqCCHZTd/n0VGWnAbHJxmekNxz2YfZo/FEUl3T93l01OKU1vtnPYHCVhX2GWopBGauXWXDFKx9d7RbJL74BKU6yuP4xunW2NhIMBiks7NzyvOdnZ0sWbJkxuu3bt3Kjh07OPvsswmFQoRCIX74wx9y1113EQqF2Lp166z3CYfDVFdXT/mjmQXL2Kme2fQdFHr+AwFHYfnI+68sK2diCKKpqP1i9nQDX0Zp2lVFPGFeQzm9b0TnkI56TsdaT3XlJZSVyi6xmrt0B3TUcz6mNH1XNUBmlmADKJRTdZvo2ROfgJH5hz55iT2q9j4rw7eyBUKlM/5aWTY2+HJNWX3CpA+QSSacpu/zVDfsGVBQWu9DOSnLnoJ5dVRlOGT3EVYWGPKRnLqGoyRNKAkqaPo+T+89WISKIZ/1XFZmS8zRG9ZCmZzK6qC0Sjz2kYNUaWWXx/GN0620tJTDDz+ce++9134umUxy7733cswxx8x4/X777cezzz7L008/bf954xvfyCmnnMLTTz+ty0bzZZ6eEaC4B5Ufo56qNiFLTpEa0SR/GpacuoejTEzqvhHTURahMc00Q3nm3hQMGLaDVlk0zUdKXVmvHFjQmdOWcmQry6Aa2uub0vq+0RjReBLDgJYayQeahXSUql5hoVKntN5He5+1ptRle8x+8FSaje1DHaVsIuZIl5hEaQRFhcE0WmvKMAyYmEzSK7u03oc6Sm1G4tzVDZC2ppQN0PKfnJbURAjIbvo+xzA6C2V7n6UjY2mT2T1ONJ6gezgKqJzaPIeOUhXAMwxfZo8qm67tA3zjdAO44ooruO222/jBD37Aiy++yIc+9CFGR0e5+OKLAbjwwgu56qqrAIhEImzYsGHKn9raWqqqqtiwYQOlpTMjqposWKB0R1m/HPBlNE3dRMz5lXpdeQnlpVbfCB31nI6y9PXxfpgcFY+rZ5ZYASyr1T2o5kKZnCCDvU9R1LOyBYKlorTecjB5HKvEqqlycZu+w2Jl+fqjB1UiadoTthe7DNiSU/vgBPFEUu538WEPKuW2RPXMpu8ApaEALVXiUKWzfGeibCJmYnLOpu8WyrOxB3dDUvLadYk9i9H6ZbF1VGk5VDSJxz5ZU52DwuEWDgWor5B8ns/Q5lOTZOLHvU+Xl1r4yul2/vnnc8MNN/C5z32OQw45hKeffpq7777bHq6wa9cu2tv909fL19ie//kjaUMTcbZ0Dsv9LlY/gt2POdlCHmZK0/dFnIgJqb4RKVn95YVOO7tBCpacul7yhZxAYTNxS07ljbM2fQcnmvbI1l65crIiaWM90POKvPu4iLJGrclkWonV/Hvfs3sG5copEICqVMbJ3qfk3cdF7CEKSp2j85fubO8elSsncIIe2x7wxd7XM+I0fW+W3vR9/sBQc1WYUEA4Ap/dI3nAgfUd9jzpCzlBWnmpdKdbWkuRObB01F9f6pKso1K2RO8rvpGTMptvuB0wRUDGcqRMw+pB9fiOPrlyql4KGJCIQccz8u7jIu2LMuRs/gyqF/cOqdNRm+/2xZpKn65tzNLiyFUWaimSWk+7+8bYK3vYmSWnHQ/5Qk7jsQT9Y6J3pPQWCD7AV043gMsuu4ydO3cSjUZ59NFH2bRpk/13999/P9///vfnfO/3v/99fvvb38r/ksXAApvQH57Zaz8+/aYHuPNxiV55K3X9lb/ATRvgyR/Ku5cLDI3HGbOaviuLTs9tKFsZ9Nf/70sc9+X75MnKcgwM7PCFnGAxekbMvp4ABsZEyc7P//mqXDm9cJfz+FtH+kJO7aoiaWNpTd/nyEjc2j0CwJO7BuTK6ckfOtHOX7zbF3JysnJU9jWa3Znz+I4+AIYmJuXKCZxpmE9+3xd7n11ipbLp+xw66hdP7CaeSpI595aH5cqpN9Xrd9fDvpDTZCJp9/iUHhjKQEclUtlMN9+7Re6a2v2o+DnW4ws5gcIWCAs0fQfoHha/M394pl2unP71UyDVI+y2U3whJ2WlcJMToiE+zKmjXmofEj87h+XrKDO1yd5/nS/WlLKAOCxYrv33LUKOk0mT47/yV7lysn5nnvulL+RkBVorwyGqIzMzpIsN3zndNB5hngyq9sFxrvr1s/a1acKnf/2cnEjN4B741x3OtZmE31/u6QiAFaFpqCglUrK4JVbtg+Ns7hyxr5OyZDW4RyhzCx/IyTRNdb33MpDTvS86TdilyukPlzvXPpATqJyImXLwVy2BYMmMv24fHOcHD++wr6XK6fcfS3vC9ImcFDmxk4l5nQTtg+Nc+4cXnJfL1lFb7nGufbCmlK0n8JYt8c/vOtc+kFPn0ARJE0qDARorVGUkzq2j/rXbyUSUuvfd81nn2gdygkWYtDiHI6d9cJw/PONUBCnTUT6Rk7JSOEs/lZSLBvnTaB8c59a/OQP/pOuo9n851z6QlTJbAuat7GofHOfq3z1nX0uX0wu/c679JKfaiPyMRB+gnW6a7DFNR2HMEp3e3jNKctoAnIRpsqNHQtpt39aZ03bMBPRtc/9eLtFul1gpiNAsMHVne8/ojOekyKpvqxNJs/C4nHpHY8RSTd+XLHIZ8PaeUabPlNJyctirrAx4/mwPZXufb+WkqBRupFP8fwRCovfdNJTrqOmr1+OyaldZBjyPc1S9LeGvNWWVwilp+q51VM5E4wl6Rqym74sbwNNymh874KCquqG6TTTIn4bWUfOjzJaIjjjDJbygo3wmp/YBRdO1fYJ2ummyZ7wfJlMbyixOt9WNFUy3/4KGwarGcve/S/1aMKb9GhtBqF/j/r1cYnEiNLOX7qxurGC6upciKx/LqbkqTImqEqs5DGVla8qHclqUpu9zlMJpOc2PsvJSS05VSyEwM5tY66j5UTYRMx6D4Q7xeJa9T8tpfpROhctAR2lbYnYs/RQpCVBXPjND2lW0jsqLvarLgBfb5gMhq+mr1+OyUmZLWM7RcDVEqmf8tdZR87NHD1GYgna6abLHcuRUNEPJzA2vtaaM68/daG/hBnDduRvkeLpr2uDsm51rIwBn3zSnk8kLKCszyKDpe2tNGe89frV9HTQMObLyoZy8UmIFQk7XnH2AfR0wJK0pW05pq9fjcuoejhJPmgQDBs1Vi9vXyNr7LKTLKd0A87icwIl6KisDXkBOynTUGWml9UbQ87JSNhHTbvoeFkNkpmHLKe1Qo20JB6/pqEtP3ce+lm9L+EdHKW36noGO+tKbFeooH8lpLBZnINX0XdnwrMW2JUDI5PiPO9c+0lGL7RxVbkuceaNz7QMdZWXNK+nj6wO0002TPXaJ1dwL/fwjV3DVG/YDYNPqes4/coW873PYhdCwTjx+83+Law+jrAHoaLeYGoUBVa1zvuz8I0XpaUVpkAevPEWerA67EA58s3h8zGWel5PSjES71GDuJtUXHrOKSInYsn/2/qPlyuk114jHq0/yvpwGnabvQeklVvM7c0DsfYevrAXgc2cdIFdO7/iFeFzW4Hk5TSaSdA4rKjXIUEddcPRKAM47fJlcHbXpgxBIZbi8527Py8qeiCldTgs3fT//yBVcd444fB64tFq+LbH0MPH4DTd4Xk7KdNTkuBhaAPMOZXpXaj0FDPjbf5wsd+877KLU44s8L6d2VSWLsKCTAODfjlpBU6XoAfjdi46QK6c33CAeLz3M83KynNhV4RBVEUUZiQvYEievFxNoP/7a9XL3viMuFj8DIbj8Gc/LSlnZYgbD6M4/cgUfPHktAGdsWCJfTmX14vE7f+l5Oe3V5aVT0E43TfZkoCwA9l0iUnEHxidlfyOoTfUsSyi4V54oj9BUtc7a9N2iuVo4/0ZjCerKS+V+p4ZUJDw2s5ec11BWupOIL5iRCGAYhq24pveQcJ3m/cXPiX7JN8ofL5VYWayorwBgwhq7KIvWg8XP8T7P730dgxOYJpSGAjRUSN5nMpTT2iYhp5FoXO73MQyoTgU+pvcg9SDK1lSGctqvtQqA/tGY3O8DUCccR8Sj8u+VJ+qavqf0U0nFrE3fLRoqSgkY2MMdpNKYCrTGhuXexwXU6qiFA0Pp3yWekLwftRwofo73yr2PCyhbT5Dx3reyQZQpjscScr+P1fs0GYeQtx0kQxOTDKd0tld01LrmSgCGJxTYYVYwMSn5d8IFlK4pH6CdbprssZT6PFk5AC3VIpJmjbSXipXJNdw+/+s8gDLP/1BmyqI6ErIzqKTLqmqJ+Gn18fEwyrI9RjrSmr43z/vS5ipFa8pHcrKUupJI2gKDFCyaVe195Q2pDCpTDA/wMM56Utj0fZ7oNEBLKuCgdZTDxGSCnhHh3JLfTHz+SYsW1iCbruEoSdkRB5/ICdKbics+eKb1hp2nPDIUDNBYae19kp2WftJRg4p0VHQYJlITZDPd+4YVysnjAQelQ84y1FHNto6SLKdQWNgT4Pm9z7L5astLKC8Nyb3ZAuXaFktUyQl8o6NM01Q35MwnaKebJnuy3IT6xyaJxiV75O1NyNsGWCJp0pE64Envl2NHaOZX6oZhqFMYVUvFT48rC1DYANRy5FTP3vQ9HfvwKVtO1Sk5jXSJTDwPo6yvUTwqHKSwYMDBWk/S5RQI+ObwqbZcOzNnTkvNYhjK3paT1fS9rCRIraqm7wvoqMbKMIYB8aRJ35jkbDefyAkU9t7LMNgAjo6S7siu9o8tYekoZXIK18za9D2dFltHyQ7gpdZTfMKZAulRlPVbNs20c9QCAQdLTsMqAkPWmvL23qe2XDuzzNHmRQngeVtO/WOTTEyKag9LLxQ72ummyZ4M021rykooDYlfMemHT/vg6W0DrGt4gkTSJBQwaEplLUkjQzmBQoXhEzmBygONpdTnN77AMZQ7pGdQNYpmuj7IoFI3xSpVYhUMQ8XMpu/pqM2gSq0p6/t5FKVTrDLc+1rSDjTqMqi8Laf0UjjpTd8zlFNJMEBDhdCXllNQGj7JIhiNxhlMte5olX2gycaWqFKko+x9r93zGVReafqejlWJIn09lUSckuQhb68pZTbfxADERsTjBQIOts0nW07gG/vcm7aEWE/DE3HGYpID1T6zJZqqwoRD8ycUFAva6abJngyjnoZhqCsx9YmhbEU8W5Q0fbfS1zMxwFQ53VJyGun0dD+CWDxJ94hwFEsvNbCHKCw8gUhZeamPMqiUlQEPpTXnX8AZYR9oVDrdPC8nRWUGk+MwluoftMCBxmomPpkw6ZeeQeUPOVkli2oONAsPkLGw1pT0jA+fHDyt9VQVUdD0fSgbWyIlJ2UZVONOSaUHMU3TaYEg3ZbILHMU0gKtsstLwTf2uV1eKt2Jndr3yhugZP59Vm2bHn/tfdJtCdNM01Hzr6nKcIjyUuFY0qX1Aqe6QWe5WWinmyY7EnHHu55JqYGqssVqf6TbKoukQVZRzyWqFHtlsxhzbSbFdFWP0jnkvabvoLC8FHxjKHszi8CRkyk7C8MnJdvKyoAtI7m0EiK18760NBSgsVKsb/k6yi9yUlgGnJWOUmVL+KPEao+qkkXwppxKypz17WFZDU3EGU01wZe+pnKQk3TnKPimHE6djsrClkjZfEMTcfnDFHyjoxTJabQHElHAcP5v5mBqmx5dWg96iMJsaKebJjuG24XDxAhmNC1PXdlimlJPSp4YmAcvtQ8BUCe7Vw5A/07xM7iw06hFlaEcCDpTkjysMKz09fryUvnZSj1bxM/SygVf6jQ/1lFPEE3fe1MTDUOyM0c7nxc/yxoWfKlVOh5LJBkYkzzNyidRz529YmJxpERymYFVrl1Wl1HJrVUOJ31N+UROr3SJaZBVEckNqieGIJrKUMqgjFWZLWHpp9iIaEzvUV7cK2yJetlBIYC+7eJnaOGMBbU6yvuBIevgWRUJMTAuOZu2+2XxMzx/PzfQQ2SmY5ome/rHACgJSrYlOp4VP8vnb1MBUBUOUZbSmeqyfL2to7b3CFuirFSRLRGpFQ64BVA2QMsncnq5S5RQ15QpOO/6BO1002THP/9H/DQTcPNB8OQP5315S5UixV5hZVAlPJtBdefju/j2/VsBuOeFTu58fJe8mz1+uzOi/afnLyinZlW9wmBqLxaP8punRCSyY2iC4758nzxZPflD2PZX8fi+azNeTx2DEwoyqLxvKH/3we3249fd/IBcOT38DfH4hd8uKKdwKGgfhuX3NvJ+f487H9/F1m5hKH/i50/L3fue/YX4Obgbbtqw8JqyDGVVvcI8vO/d+fgu7vqX+H7/8+B2uXL6xy3O428fnbmcZK+ncKXjtPCorO58fBdfufslAB7e2itXTk/8APpT++yv35eBLaGoVxj4IjBkyWZ4Ii7flnjxd+LxQzdnvJ5Es3PZg868L6fbH9xOLCFsqrfe+ohcOf31S+Lx1vsWlFN6mx5l/Sw93B/2zsd38fTuAQA+f9fzcve+p38sfk70Z2hLqG7T05VR8sticOfju/jJo7tSj3fLlZOP0E43TeYM7oEHb3KuzST8/nKnnGcWltQoMpSDIeF4A08q9vbBca769bNYbhIT+PSvn7N7E7jK4B740yec60zkpLTUwNup0e2D4/z88Vft66QpSVaDe+D3H0t7wlxQTtaBJhpPMjQuuVmrx0u22wfH+do9m+1r+XJKW70LyAkUGmA+kNNVv37WvpYmJxAyefonznVGOkpRlq918IwNezKDarqcpOuo+693rrPQUWonzXpPR023JUCynP5wuXOdxXrqUtErzONlVu2D4/zg4Z32tZdsiZqyEsKpQWfdsmXlAx31xT+9aF971paQLSePlwHP0FGybYnH/8e59pKOKm+EQAgx6KxL7r1yQKkt4TO0002TOX1bgWnZNWYC+rbN+RZlZYvg6ZTb7T2jTB+OlzBNdvSMuX+zvq1CQaSzoJws56iKHlTelRMIWU3/H5AiqxzkFCkJUpsqTZZfDufdgyek5KRiTeUgJ0hvKK7KUPaunJTufVnqKGXlpeEqKK0Sj4e9NxHY63JalInAHtRRnrclUuupbzRGNK4qg8p7cgJv2xIig0p1+xfv6ihv2xKKguKWnEa7RP9uj+F1HaWsBUIgAJXe3fuUyslnaKebJnPq1wLTeh0YQahfM+dbmlWVl4Kny6xWN1YwveVU0DBY1Vju/s3ykNP4ZILhaHGPu17dWDHjOSmyql8rSqLTWUBOMLXEVCoeLwNe3Vgx/bfck3JS1t9jYhBi3jNqVjdWzGjZJXfvm8YCsrIPnkrL4by393leRymd4uddHeV1OdWWl1AaFPulsoCDR8vhPG9LqJqyrW0JQb5ykq2jKhrF9/HooDPv2xKLMWnWe3ufUh3lM7TTTZM5NW2w9DDn2gjC2TfNO558cTYh73n+W2vK+NKbN9rXAQOuO3cDrTKmWdW0wYFvdq4zkFNZaZDqVONs+b2NvCsnELJaUe/IJWgYcmRV0wbHXe5cZyAnUNms1dvR6daaMo7dxxlqIFVOZ97oXBuBjOSk7EATroaSlDHjQVm11pRx/hHL7WtpcgIhkxrnXlnpKKXDSby397XWlPHJ0/e1r4OyddTK45zrDORkle70jMSYTEgeluRxOV1/rkJb4pB3OtcZyMkwDFtHFXvj99aaMvZtcQYkSdVRJ1/lXGdoSyirRLF7UHVCUnL2Yw601pRx6v7N9rVUOZ19s3OdsS2hqLx0yqAz7zlzWmvKePexq+xr6bZE43rnOgsdVeyVXa01ZXz2zAPsa6k6ymdIHk+lKThKUovmxH+Hwy/OWFmMxhKMRONUhiX+ynm8v8dp+7dwFaLO/W//fjLL62dGQV3DGkW+/5vgddcvKCcQvViGJkboHIqyrqVK3nfzeH8PAOtMd+2bDuS0A1rkKYulh4ifTfvDBb/KTE5WqYGq/h4TAzA57qx9D1FbJoYVXHTMSj548lp5clr3WvHTCMDHnoHa5fO/HmhR1SvMMISs+raKNdUwS4R2kVnTJPa649Y2cMPbDpZrfFmNhc+6Cdad7p2DJ3heRx2xqh4QjsjfXnqcXDlFasTPoz8Ex3xkQTnVlZdSEjSYTJh0D0dZWivxu3lcTm86pI1P/UrYEnd/7ETWL5GorxtSmR1rT4M3/r+MddSr/eMKnDmWnLxrSwQCIrfhU6/bl3MObZO3ppYfJX5WL4f3/jkjOSkrW5wy6KwHqlrk3i8HmlMTx887bBmfOGO9PDkdeC7c9RHx+MP/gKZ95389ikvrq1uFw82ja2q/1F53yPIabrngcMmOnFS61ulfFHLL2JYQg86MDKZy54zHdZQVEK+KhLjn4ydqh1sKnemmyY6R1Ea85uSMlHpFOERVytFW7OVwVtS3sTIs1+EGIqII0HZYRnKCRZi849GSENM0bVmdur9Ehxs4vZ0a12UtJ+nrKVIDodS/3aOK3ZLTUasb1MipqjUjhxs45aXSsz3A81mJVpnZgW01cuWUTIh+NAD7vj6rg2fPSFRdBpXHddTK+gr5RrJlS6w8PiM5BQKG3Qah2MvhrMb3paEA69IyqaRg7X2tB3lPR1lyGumApOS1myPdqTV10vpmNTqqYXUWclKUjT1l0Jk37T5LRx2+qk6unCzbvLQyI4cbqO5n6Q9bYt+WanU6ap/XZrSmmqqcQWeD45KninpcR1lyaqst0w63NLTTTZMdVvTD2pgzwC41UFYO580IjbUJWYaOVCyFmY2clB1oUt9pvA/iCrJLsqR/bJLJ1Oj4pkrJsspBTspKtg3D0yns4GQnSV9TtpyWZPwWZQdPSJOTNw0wqyzGyiaQxmi36EdjBKCiKaO3NFSUEgoYmKZwvEnF4wcaaz01K9FR2dsSLdqWABznaEt1WG42BeRmS6gq2a5sBgxIxmGsR+69cmAykaRnJAao1FHZrCc9nMSiM21NSSUnW8Kx+ZQNOvOoM0eZnGJjok8uZCyrSEmQOmvQWZEP0LL2FGu4hEaQUa3fXXfdlfUHv/a1r6WsTHs3C4roMMRGxGOr7j8DltRE2No9WvTTFq1NqEXFJmQfaDJX7EtqFB1oyuogGIZEVHzPupVy75cllpwaKkopDUmOS+QgJ2X9PUCksPdv9+SaMk1T3ZrK5UCTWk89I1HiiSShoMTfJY+XbCuXU2WL6E+TASKDKszewQk6h6Jyo7Jed+aoklMy6WR85LL3qTzQmCYzuncvMnawocqjtoRdtihZTsES4Xgb6RSyqmxe+D0KsTISS4IGdeWlcm+Why0hXU4gbIn2pz1pS0BawEH2msop2CC+08RkkqGJODVlJTK+mcDjOsoJDEmWk5XlFipzWiFkQEt1hP6xSTqHJthXZtm/x+Vktb9pkR1o9RkZOd3OOeecrD7UMAy2bNnCmjXzT2TR+Awrfb20CsKZlzQ40xYVGcpjPSKDKuStxd6lKtsD0srhPHigsTKoBnYKA8xjTjdLTk0q5DSSh9NNZQaVB6OeQxNxonFRUiRdVpaDIItgQ0NFmGDAIJE06R2NyXVkeLxku1vV3jecvZxAGPB7BydEVmJm1cO54eGpmKBQR431iuwkjKwcJXb2qOzAkPX7k5wU37WiUe79sqTLziLwto5SluU70il0VOvB8u+XBbYtURkmMH2cn9tYcqrMQU4qelB52JaIJ5L0jijK8rWcJFnoqEhJkJqyEgbHhTNHjdPNe3IChTrKPkO1ZBV0aa6O8FLHsLqKIa/aEip1lI/IOPTe0dFBMpnM6E95uR4LW5DkkBYNTkRCegp7eT0EU9FE65DsIZSl28bGIJpdWjQ4ET41U/y8q9i9npFofa/ukSiJpOxSA+/KyVLqNWUlREoyy2rKmRwy3YIBwy5Plt8n0bulO17PSIS0skWV0xZllwnlgHI5VTSKbKUMUVYOFyqF8pSjzYN7n1OuLVlOppmTjlJWXgqe1lFKS6xysiWEnMZSg86k4mE59Y7GSJpCZzdUeK+8FBS2FfGwLQEKs7FztCWWKGuBkJLTxKA483kMp/WLLi9NJyOn20UXXZRVqegFF1xAdXV1zl9K41FyUOqQtgnJNsA83oPKSV+XnZWT+reXlEM483W4xJq2qCI67eFyODtCoyQj0SqHy3xNNVaWEjBIZVCpKrPynpyUrSfIKXMUnAmm8huKe3eS1Ug0zlgsASjMIshyOt4SVc4c6/cnEYPxfrn3yoFOVXtfDqWlkN7TTUU5nJf3PkVZBOP94ncVsmsporJs0cM6Sq0tkX3ZYnlpiKqIKHhSV7LtPTlZ66mxspSg7IzEHM9RyipR7KmY3sugSiZNJ2te9t6Xs45SJKdIjTjjgXPm8xBWwEXJ3ucjMnK6fe9736OqKvPa5FtuuYXGRm+l42tcIIcyA1C4CYGno2nddgNQRT0jKrNLi3ayPaIkizmDalhRhCYedQ7eWaypUDBAY6Wiw6eHndhdqtYT5G4oV1kZHwrl5LEMKms9VYVDlJdm1NEid0ayP3hCeja2ZDmFwlDeIB57eO+TnpmTQ7AB9BQ/i267X44iW6KsPqt2Hdbvz0g0XtQZVMpsiSkZidkFHJy+bqrK4TxoS6jMyrGdOdlmYysODI33w6SCfTYL+sZixJMmhoFtA0sjRx2lrLLL40kmXap67/mMjMtLzzvvPO6++275k1M03iXHg2ez6v4e4Mm+EeonLWan1BsrwxgGxJMmfWMxCV8sDU/LyXLmKMrKCYbFcIksUNYzx8N9I9ROWsy1JETVgSb1veLjMDEg915ZoiwrB1zIIlDozPHY3jceSzA8IRwkyva+HOUkvV8O+ERHebPEqjIcojJsZVCpKofzspwkr6eJQbH3Q9ZOgiWq1pQtJw/aEnZWjooAnsfLSyO1EEr9P3gsg8oZchamROZgKsijsmsxbAlvrSnTdDISdXnpVDL+re3v7+fMM89kxYoVfO5zn2Pbtm0yv5fGi9gZVDmWhAyrGHftzahnMmnSbTVqlR6dTmsAmgUlwYDdz0KdM8dbcoL0QQqS5TSSJqcsGxi3qOqZ4+kMKkWGcmJSDGeBnPc+6euppEwYy+C5qGe3qv5TkLeOUmMoe9NJYK2nspKg7TCRRs5ONyGn4Yk4YzGdQaWuxCo7WwLS+ropy6DysJxU2RLhGijNrqe2IydVg856RZa/h+hSGsCzhv14NDCUnkHlsYCD2mF0+ekoNZVd3sx0GxibJJZIDTmTnZHoMzJ2ut17771s27aN9773vdxxxx2sW7eOU089lZ/85CdEo97aQDWSyDXTLWVwTCZM+scm3f5WU/FoCnvvaIyEnRYte3R8btFpgCU1qhqKe1NOkF5q4M2MRFBYsm19t8kxiA7JvVeWKJOTdaAJhJzSwAyx5SS7vBTSerF4y1BWlu0BLkSni7cHVXomttQphpCznCrDIcpLxdAU+aX13pTTxGSCgZQdJb+8NA9bQlVfN0/33lM1ETO37ClQ6Mzx8KAzu1WF7PUUHYbYsHicYxmwGh3lTVuiywe2hB505iQD1FeUUhqSnJHoM7L631i5ciWf//zn2bZtG3/5y19YunQpl1xyCa2trVx66aU88cQTsr6nxgvkqNhLQwEaKoSyLdZyuPS06JDstOgcG4CCY3Soa6rrLWVhmqaTQaWy916W2AaY7PVUWi4atoLnop5O03dFmaOVSyCQ3dpVVl4Kno1Od6rq7ZFMwGiXeJxjC4TB8UkmJhNuf7OpeFxHKclIzLH3nmEYCsvhvCknK3O0NBSgukxRRmIeOkqZnEa7IS65LUaWdKnWUTnYfMrK4TycQaXOOZqSU2klhDPvkw6qWyB4M4PKGZ7lXR3VWBl2Bp2NFOc5SumQM5+R8+n/1FNP5Y477qCjo4Prr7+en/3sZ2zatMnN76bxGjk2AIW05pIqy+E8hFPf7s2JmBbKmoBacoqNiOifR+gfm2QyIaJT0tOic5g2ZqGsvBQ8q9i7VK0pO9iQh3O0iBu/KysJGe0GMwlGACqasnprdSREpESYQ+p6UHlLRykrWYScy4BBZdmiV+XkZHuoy0jMxeZTJKeyegiUiMceyqCaTCTpHRVOQHU6KpdMN5Wl9V7VUYoyqHIcRgd60BkolFNsTPRJhKwDDsGAQZM1QKtIB53ZwQbdz20GeaXcbN++nRtuuIHrrruOwcFBTjvtNLe+l8ZrRIeFgwRyinousRSGbMVul1h5axNS1vgYck6LBoVRz3AlhKvFYw/JyslIVJAWnYecFmcisHfkZJqm55uJg7Oe+sdUZlB5R06wCE3fK1sgEMzqrekZVNLXlEfLgLtUySmZzC8bW1nZYkpOI12QkNw/LgvsMmCVPRLzsCWkyykQ8OTeZwVaS4IGdeWyW4rkLidlk5vBk3IChRlUeTixm1KDzhJJ03bmSqPam043ZVnzlnM0VOZUemSBsmCrV20Je7q2znSbTtanyomJCe644w5OPfVU1q1bxw9/+EPe+973sn37du6++24Z31HjBey06CrhMMkSZ9qiIs9/dAiiI3LvlQVqG4DmnpG4KE1APTR5xxmioEBOeUU9FyODyjtyGpqIE42nGrXKllUeDoLqshDhlPO2W3ZfN4826O9WtffZZcDZB4Ugbcq2qgwqj5VYKdNRY72QjAMGVDZn/XZl5aXljWAEAdNTGVROFoE/dJTSSbMe0lG2LVEZJhCQnJGYYykcpDlHhycUZlB5R07xRNIuA5RfXpp7uXYoGKCxUvVwEm85R5XpqPRy7RyyidWV1qfZEh4adKZUR/mMjJ1ujz32GB/84AdpbW3lkksuYcmSJdx9991s27aNz33ucyxfvlzm99QsNnmkr4PC8tJwleiXAJ5SGJ2q0m1jYxBNpUV7vhzOe6nRfslItL5f32iMaFx2BpX35GQp9ZqyEiIl2WU1ZU0e5dqGYahbUx4sCfFLRiIo7L9n96DyWgaVYjlVNEKwJOu3K2uBEAh4cu/rVDUR0zTz1FGLMRHYQ3JSWWKVhzPHClqJQWeSM6g8KKfe0RhJU5QFNlR4twwYFK4pjwbwlGVjuyQnZbZEfNwph/UAzlAmXV46nYydbkcffTSPPvoo1157LXv37uUnP/kJp512mvyeEhpvkIfxBemlBiozc7yjMJQ1lrQiniXlTvlmFqh1unkvNdppfOzt3nt15SWUBhVlUHkwhV1po9Y8mlSDwswcD07xG4nGGYsJp7CyLIIcgg3gtECQvvdVNIkMKjMp+tB5hE5Ve18emaOQfqBRWQ7npb1PURbBeD8kUk6YPAYpdA1FMWVnYXhQR6m1JXLPdCsJBmisFOWvxVhab62nxspSgrIzEl06RykbdOahDKpk0nSy5lVNrM9VR6kaSFdSBpFa8dhDdp+VXKMHKcwkY6fbP//5T5566ikuu+wy6urqZH4njRfJo8wAHENZbamBdxR7t90AVFHPiMqWHNOihZx6RmJMJpJufrOZeFBOTnN+yXKKR8WhBnJaU4ZhqG8o7qFyuC5V6wnyNpSbVZVsp5eEJCVnP2aItZ6qwiHKSyVPWsyjxArSS0Jk96AKOk4MD5VZOYMUFGUR5BBsAIVObPCkjrKHMqnqP1VWD6HsD09WBlUskaR/bNLNbzYTT+ooRbbElIzE3AIO6rKxPSgnlVk5eQyjg0VogTA56plBZ31jMeJJE8PALrOVRp46qqVGpY7yXsl2l6reez4kI6fbM888wyGHHJLxhz7//PPE494pm9C4QJ4Hz8Vp/O4dxe6k26pKX89NqdeVl1ISFM46+T2ovCgny5mjKCsnGIay3IIYytaUB/t7OA11FWYk5rn3Sc/yrWgWkzvNBIz2yL1XhijLygEXnKPFW1o/FoszPCFsNmV7X962xIT8DCpP6yhvl2uHQ0HqK6wMquIrrVdmS0wMivIyyN1JoLwFgjf2PUjPylERwMvTlqhSZEuUVkA4NUDAI7JyhpyFKQl6d8gZFHebHtM01QbFfUZGv7mHHnoovb29GX/oMcccw65du3L+UvPxrW99i1WrVhGJRNi0aROPPfbYnK+97bbbOOGEE6irq6Ouro7TTjtt3tdr5iFfz39q8fWMRInLzqDyWJlVImnSbTVqlR6dzi8tOhAw7O8of/KOt+QEjjOnSdkUq9wyEkHhpFnLUB7pEJMHPYBTCidZTolJGEs5sHI8fCqTUzAkHG/gmcNnl6qpcOBaBpWSFggeK7Oy5FRWEqQyLDkjMY9SOHAyqKLxJEPjkoO7HtZRXi/XhiJ35qja+6zsqUgNlJbn9BHKBmhZdmls2DMZVGoDeFYlSo46qkZhn0SPTTDtUtpSJN+s+VQLBNmJC+A5W6J/bJLJhAiGNcnOSPQhGVlXpmly9dVXU16e2YYei8lpxnnnnXdyxRVXcOutt7Jp0yZuuukmzjjjDDZv3kxz88wpWPfffz//9m//xrHHHkskEuErX/kKp59+Os8//zxtbW1SvmPBkqczp6FC9EtIJE16RmIsqZFoiHgs6tk3GiNhp0XLHh2fXyQNhMLYMzBelNFpu3RHes+I/JQ6KCxbrGwGDDFxcKwXKpvk3i8D1Mkpte8FQqLMKgeaVZfWj3R45vDpRDy933sv/eBpmqbcfrUei053pa0n6X1683TmREqC1JWX0D82SefwBDXl2Q9jyBiP6aiJyQSD46JUU3p5qQs6qqU6zIvtCvrvedDp1qWq/1SewQZIc44qGXRWJZxuw53iepGxW7/IXk/RYYiNiMc57n3NSiuGlkD3S57Z+9TaEvnpKCuAZw06C4ckDvvynC0h5FRfUUppSHJGog/JyOl24oknsnnz5ow/9JhjjqGsrCznLzUXN954I5dccgkXX3wxALfeeit//OMfuf3227nyyitnvP7HP/7xlOvvfve7/OpXv+Lee+/lwgsvdP37FTR5OnNEBlWY9sEJOoYmJDvdvNU3Ij0tOiQ7LTrPBqCgsmwxTVmYZs4ZX26RnhYtv6+RFfH0QRZBsEQ0fx/tEn0jPOB0U5bpZjlyKpeIaYY5kN5QXDpVrdD+tGf6e3Sq6u2RTIjfT8i9vDT1uzQ+mWBoIk5NmUxnjjd1lJKMRFecORH6xybpGJxgfYvEg7vH5GQFG0pDAarLFGUk5qOjUr9PynpQRQchNirK4xaZLtU6yg2bb1BRYKh3WOioxn3k328B1GWOpuRUWpmzs7FFVRUKeC7goCxzFPLWUTVlJZSGAsTiSbqGoiyvzy0DNSPsoRces/n0EIVZyUhr33///ZK/xsLEYjGeeOIJrrrqKvu5QCDAaaedxiOPPJLRZ4yNjTE5OUl9/dwZC9FolGjUORwNDQ3l/qULiTwbgII4eLUPThRdBpWyrBzIuw8LKHTmWJHZRAzG+qCiQe79FkBpWnSe6eugcHw8CEN5tEt879aD5d9vAbpUrSlXMkeLt7+Hne0h2wAb7RbTQI2AcBDnQFlpkOpIiKGJOF1DE5Kdbt7SUcqyciDvfjkgbImXOoaLzpZIz/ZQl5HoAx0VroKSCtH4fbgDGtbKvd8CTCaS9I6Kih+v9/GFNDnJznSDlNNti4d0lKIMqjyH0YHzHXtHY8TiSbmZRJ6zJRTJKTYm+iRCzrIyDIOW6jC7+8bpGp6Q7HTzmJzsPr4KnKM+xDe5fz09PSQSCVpapkbdWlpa6OjI7JftU5/6FEuXLuW0006b8zXXX389NTU19p/ly5fn9b0LgvS06DyinkusOneV/T08MO5aWeNjcDWDSnp0OlQK5Y3isQcONU5GooK0aBcOnkqdOR7qG2Ga5iI0E8/fUB6NJRiekDzFz0NygkVo+l7ZIqaD5oiVgV1sw0m6VMkpmXQCePmUw1Up6pljyWliACbH5d4rA+yBTEp6JLqgo1StJ8PwVA8qK9BaEjSoK5fdUiS/UjggrY9v8fWgUpZB5YITu74ibdDZiOy9LyUnr2VQydZRlnM0VAbh6pw/pkXVmrLk5BVbwp6urTPdZsM3Trd8+fKXv8zPfvYzfvOb3xCJzL1or7rqKgYHB+0/u3fvVvgtPYq1mEurIFyZ88coc+ZYRmIiCuP9cu+VAUrTbe1Sg/yjnsrK4cATBpjlIGhSIic3M6gU9fcAT5RZDY3HicbFQAfpsnKhXLu8NERVRCSVKyvZ9oCcIL3ESlHmaB7BBlCpo6x9zysHGkVyGusVvSExUr0ic8NyjnbILoeL1IjDF3hKR/klI3FRyuE8sPfZtkRlmEBAckaiC+Xa1nrqGYkyKXvQmYd0VDyRpMcacqas917uOsownEFn0vc+r2ZQSbcl0my+PLKJW1TpKEtOHhl0plRH+RDfON0aGxsJBoN0dnZOeb6zs5MlS+Y3Cm644Qa+/OUvc88993DQQQfN+9pwOEx1dfWUP0WPC8YXKHQShMJO03MPGMrK+oTFxkRPE/DHxDFIU+xekJNVsqiiZ4R7fVhGonFGopKn+HnIOWqtp5qyEiIlEhvUgitNqiG9r5vCLN9FRvRIVLSmXMgiANRNbrbW/Xg/TCrYZxdAnZxS66miUfSKzJFmVTrKMDx1+HTKtSXLyTRdKocrbltCSYmVCwGH+vJSQgED08R2QknDQ7ZE72gM04RgwKChQlVLkXxtiUWoGPIAynVUvnKybAnZJdtTBp31yL1XBljJGkrOUT7EN0630tJSDj/8cO699177uWQyyb333ssxxxwz5/v+67/+i2uvvZa7776bI444QsVXLTxcd7oVVzmcXRKiqmdESXl+adFK5eQdxe6UWPmj915lOERlWGRQFZMBpmw9gStNqsGZZiXdAPPQgWYkGmcslgBUZBG4o6OW1Cg60JTVQShllI54YU0pik67EGwAp3SlU3Z5KXjMllBUBjzeL3qtQn6tKlLrqWckSlx6BpV3dJRaWyL/gIM16AyKq7Q+PSMxKDsj0aXAkNMCQbKOSi/XXuQ2PcmkwgCeWzpKVcVQsMTJGveCjrKSTFS0QPAhvnG6AVxxxRXcdttt/OAHP+DFF1/kQx/6EKOjo/Y00wsvvHDKoIWvfOUrXH311dx+++2sWrWKjo4OOjo6GBkZWax/gj9xy/OvuvE7eCKFvUvVJpR+8MwnLTolp6GJOOOpQ7M0PDR5R1lvj8kJp+w5z3I465BcTOVwSictupVBZclpUNGBZqwH4gocEvNgraeqcIjyUtmTFt3SUYrKS9MzqLygo5T1Nco/2ABpB09V0xbBY3JSVApXVi8qB3KkoUI4M5Im9IzEXPpyc+ApHaUwI9GtoLiycjgPyskn5drg/E51yHbmWLZpclK0BVhEekdjJJImhgGNlbJ7JLqro6SvJ/CmjtLlpbOSk9PtRz/6EccddxxLly5l586dANx000387ne/c/XLTef888/nhhtu4HOf+xyHHHIITz/9NHfffbc9XGHXrl20tzu/dLfccguxWIzzzjuP1tZW+88NN9wg9XsWHK55/hWVl4K3SkJUZea4VApXGQ5RXirK9pSVWXlBTsqmWKXWUzAsMl7ywEphlx5N85ScVBrKlgHmTq8w6eupvB6CKaPU+j1bJJyyegVycklHKW0o7pGsxLFYnOFUebp8HZV/KRw466l7JEoiKTkLwyNygnQd5Y9y7WDAsCeBa1tCAhODEE8N+HCpHK5LejZ2mpwWOYNKWUAcXCnXBoWtKoIlziTwRd77LDk1VIQJBRUNOcs3IK6qvBQ8o6NESxFFOsqnZP3be8stt3DFFVfwhje8gYGBARIJkQlTW1vLTTfd5Pb3m8Fll13Gzp07iUajPProo2zatMn+u/vvv5/vf//79vWOHTswTXPGn89//vPSv2dB4XJfo8HxSSYmZWdQeaMkJJE07SlD8rMI3Dl4GobhlMNJN5S9ISdwDtpNyjISW/LKSASVpQYpOY12Q0LyBM4FUJbplph0emTkG/WsVnSg8VAPKmXZU+CajrLWk/QDDXimzMqSU1lJ0C5Xl4ZLzpyGilIChtCvvaPFVA6nqul7mo7KkxbVOspDtoT0vc8KNkRqoKQsr49SVoliDzqLLfqgs0XJdMtbR1ml9cWno9QMo3NHRy3OQLrFlVP/2CSTCeFIt4Itmqlk7XT7xje+wW233cZnPvMZgkGnifURRxzBs88+6+qX03gEl5w51ZEQkRLxK6cu6rm4BlifD9OiQWXZojfkBNBt94xQ1HvPT3Iqq4dAqvH5Iit2dXJK7XuBEmcwS4602OWlCg3lRS7ZVpbtAa7pKNtQHo6SVJZBtdhyctaTkWcQYEFcKrEKBQP25OJO6SXb3tBRE5MJBsdFwKNFujPHPR1l999TZUsMLX4PKmXZ2C7afE55qYJBZ+UN4vEi66huS0fJXk/RYYil2hrlmzWvanopFKkt4W5GotpBZ96QU31FKaUhX3UvU0bW/yvbt2/n0EMPnfF8OBxmdHTUlS+l8RguKXbDMNSVmHok3dYyNBsrFaRF2yVWLkSn7RR2RXIa6YKEZMU0D6JRq+rSnfyUOigsLw0EPJNBpayZuOXIqWwR//48aC7C0vpOVVOsEnEY7RKP89RRjZVhDAPiSZPeUdk9qLwiJ6sM2D8lVqCwZNsjtoQVbAiHAlSXqcpIdFNOsntQWRlU0UXPoOpaDB2VJ8rKS8EzmTnKhjJZciqtgnBVXh/VrMo2Bw/pKIUTMV3SURXhEFWpzPFiKa3vVJmR6FOyPkmsXr2ap59+esbzd999N/vvv78b30njNSQ4CYplE3J6RvhjIqaFsgNNRSMYQcB0Ds6LQP9YzE6LbpSdFu1SKRwonjTrkYyPTlVryqXm/JDmxB6ewJSdheERJ4E9GU62nEa7wUyCERD7SR6UBAM0VKjKzPHKwVOljnLPllDWM8cjPajSJ8zKz0h0U0cpWk8lEadH6iKuqVg8aTvs1ekoH9l84B1bQtXUZpd6w4KznoajcUaVZVB5RE6y11NsTPRJBHd0lLKSbY/JSfdzm5OsnW5XXHEFl156KXfeeSemafLYY4/xpS99iauuuor/+I//kPEdNYtJdBgmUxmMbkTTUinsj27rpX1wPO/PmxO7v0cHDOySd58F6FIZoXGpASg43/e5vYNy5RQIOs1a25+Rd58FsMpBGlSkRbtUCgdOf4+dfaNy5QSOYt/xIAzukXuvOTBNU92actHpZhmLkwmTF9uH8v68ebHk1P6vRZMTpJctKiqFq2wR+0meNFSKMuqXOiTLqTolp76tiyqnblVySiadbGwXnDnW3vfPHf1y9z5rPU2OQfdmefdZAHs9KemR6J6Osn6vNncOy9dR5SlbomPxWt30pHr4lgQN6spltxSxnNj523zWetrTP67Oltj1D0/oKGW991xwjlZFSihLtel5fu9g3p83L5aO6nzOG3JSZUuEyiBcnffHWT1iH9rSI/m8m5LTwC5v2BI6021Osj5dvu997+MrX/kKn/3sZxkbG+Md73gHt9xyCzfffDNvf/vbZXxHzWJiKfXSKghX5v1xA6kI4B2P7uK4L9/HnY9Lcoi9fHfqgQk3HwxP/lDOfRZAabrtsHuKfWu36D/xj219cuX05A8dRffTty+inBRl5YCrzpzHtvcB0D0ckysncCKAj/033LRhUWQ1NB4nGk8CCmTl0kRMgF8/+ar9+MxvPChXTr1bxc9tf100OYFTYiU/28O9YMOdj+9ic4fY+/79F8/IldOuR8XPob2LKidlWQRjvZCMAwZUNuf9cZbz/TdP7ZG79z33S+fxLccsvpx81NcI4KWOYQCeeXVQvi3R+7J4/JsPLLqcmirDBAKSMxJd7L334CtiaNBoLCHflhjpFj+fvmPR9r54Imk7SJVlurmko8YnhQ10/nf+IVdOXS+Kn68+XiS2RJrN50I28XhMDAz81v1b5a6p7X8XPycGvWFLqNBRPiWnlI53vvOdbNmyhZGRETo6Onj11Vd573vf6/Z303gBF42v9sFxW7EDJE349K+fcz8CMLgH/vBx59pMwu8vX5QIgF1eKjtCExuDqDtp0e2D4/z0MUc5SJXT7z+W9oS5iHJS2TPCHWdO++A4X/2zk3khTU4gZLL9Aed6kdaUtZ5qykqIlOSf1TQvLjlH2wfHuerXTuaFKVtOT//IuV4kOYnR8aoyEt05eM6QE5Ll9NcvOteLqqMUZ45WNEGwJK+Pah8c5y8vdNrXynSUB+QkPSvHNF3ra9Q+OM73HtpuXxeTLaGkxMol+7x9cJz//P0L9rV0W2LLn53rRVpTvaMxTBOCAcNuKSANF+Wk1JZ47DvOtQf2PnXVDfk7sdsHx3lq14B9LXXvu+czzvViykllZZdPyauOqry8nObm/COWGg/jotNte88o0zuiJEyTHT1jeX/2FPq2io0nHTMBfdvcvU8GKGvUahnJJeV5N2rd3jM6o3VNocvJaXzsn95723tGmT5gUYqcQMhq+updBFkpW0+Q1qQ6v71PuZymL95FkNNINM5YKsorP4vAHR2lXk7e2PuURaddHPRTnLaEoub84/2QSA0RyTMzR9ma8pCc1NoSVpavz/Y+T9gSTkZiUHZGokuBoWLUUWLImapWFVpH5YPTb1k73eYioxFIhx56aMaNW5988sm8vpDGY7hYCre6sQLDmHomDBoGqxrL8/7sKdSvFU210zciIwj1a9y9TwZ0qdqE0g+eeaZFr26sIGAwRbkXupycMmDJcpqccCar5XmgUSYnELLCYIqxvAiyckrhVGYR5GcoK5eTB9aUtZ6qwiHKS2VPWnRHRxWjnMCJTsvXUe5lERSlLaGqVYUlp7J6COV3L21LSMQ0XQs4FOPeZ8vJR+XaxSin3tEYiaSJYUBjpeweiS7rKKa6lwt97+tSuaZ8SkaZbueccw5vetObeNOb3sQZZ5zB1q1bCYfDnHzyyZx88slEIhG2bt3KGWecIfv7alTjYl+j1poyrjhtnX0dNAyuO3cDrTVleX/2FGra4OybEU4CxM+zbxLPK6ZL2Uhy95RFa00Z15+70b4OGMiVU7qTcLHkNKwoOm2tp2DYmbSWI9PlZMiSEwiZnPQp59oILoqsnNId//Tes+SUthvJldNZNznXRmCR5KSwt4dLOsqSU3rSg1Q5nX2zc71IchqLxRlOTcCTr6Pcy5pvrSnj02/Y376WrqO8YEvYOsof5dqwGLZE2pFm0eUkeT1NDEI8Varm0t5nId2WeM01zvWi2RIKA3gulWsrtyU8oKMsOTVUhAkFZQ85c1dHvf9Ex/El/7ybwlgcHSVaiijSUT4mo9/ga665xv7T3d3NRz/6UR555BFuvPFGbrzxRh5++GEuv/xyOjs7F/4wjb9wcXQ8wIXHrrYf3/uJkzj/yBWufO4MDrsQNn1QPD7obeJaMYmkSfeIqiwCqxQu/7RogPOPXMGKOqEcvvFvh8qV0xu/JR43778ocgIn6tmkLCOxxZVGrecfuYLXHSjW5odOWitPTgBHXeI8/siTiyIrZZluiUkYS/WfdMEAO//IFXz8tesBOGnfJrlyOvwiZ3rz2360KHJSlj0Fruqo849cwXcuPAIQGUVS5XTYhbD+deLxif++qHIqKwlSGZadkehOKZzFe45bbR8+f3fpcXJ11In/Lh7v+7pF11HqyrXdsyX2bxWTAK9780a5cnpbqoF4ddviy0nVRMxIDZTkf5A//8gVvPXwZQC8a9NKuXvf0R92Hn/ggUWyJRYh080lHfW5s0XA4bCVtfJ1VNN+4vGbvr3ItoS/5ATw9qOEbCKhAA9eeYrcvW/j28TjTR9cFDn1j00ymRB5fU2VOtNtLrJ2G//iF7/gwgtnCvSCCy7gV7/6lStfSuMhXPT8A1RHQoRD4tcu4ILTYV4a9xE/oyNy7zMHvaNRX6ZFW7TWCkMuMb0xgdu0HCB+jvVLvtHcKOvDIkFOKxtEuvrEZHKBV+ZJWT0EUgfzoOQD+hwoz0gMlIh/twusaxbTn4cn4q583rxUi8MTZkL+vWahczH6Grmkow5IOQj6RmMkpzfQcRurBGRSQiPsDEiXU6YtRHLGZTkFA4Y9wVj6d2/aV/ycGJZ7nzmYmEwwOD4JQIuPyoAtlqUCeJOy11PLBvFzrHdmb0tFKOuRKEFOqxorABiblKw3QqWOXpW9dufAtvlkr6foMMRSZxCXHNnrW4SOGhxXYEvUphxFyUn595oFP9sSlqNwIp6kOpLf8KAFaVgrfsYk9PfLAEtO9RWllIYkZyT6mKz/Z8rKynjooYdmPP/QQw8RieiUwoLDxVIDEMaxZYx0j0y48plzYmV9jSxOBqYVoWmsVJAW7WIDUAvrQNOdKumThhVVGu2GpHonQTItI1Fdo1Z3lDqkyWlEspwCAahIDc5Z5DUlv8QqLXM04M7aVbaewNkHRrrk32sWlDU+TsTFvgGu6ajGVJQ2njQZGJd80Ki01tPiyknJpEWXSqzSUaejFteWsP594VCA6jLJAQ8JOqpZtZziExAdknuvOehWNmnR3eoGUK2jUr9fi2VL2HKS7RxN/ftKq/IecmahbD1Bmo5abDn5T0dVhEOUlwYBlTpqkW0JFRmJPiZr7X355ZfzoQ99iCeffJKjjjoKgEcffZTbb7+dq6++2vUvqFlkXPb8g0g93d03bh+gpVG52ErdKoXzz0TMdKzyCOvfIY2KJsAQWTljvY6SV0T/WMxOi26UnRbtcrk2OIayFbmVSlULDO91DEnFdKpaUy4OkLFIX0+macrNzrEMMGv/Vow9GU62nEa7RQNhIwAVja58ZGkoQF15Cf1jk3QNT1BfITFL2dZRiysnpaU7LjtznkeBjlpkp1t69pT8jER5OqpbtpxKyyFcLRxuw52i9FIhsXiS3lEx+VWdjnLT5kvZEqqcOV0vLJ4toTwj0X3n6OD4JBOTCSIlQdc+ewbWPrDYcpK9nmJjok8iuK6jdvSO0TUctTNJpWDrqEW2JXQ/t3nJ2ul25ZVXsmbNGm6++WbuuOMOAPbff3++973v/X/2zjzMsbLK/5+btapS+17ddNPV0EBDI/sqCIoKMqCIMzIjSrO5oqyOio6AijtgA4qOOoooo60/kBFUQEFA9maTfevF3mpfUqlU9tzfH2/uTaqX6lrue3KTyvd5+kludSrvTZ287znveb/ne/jgBz/o+A1WUEQkIpCKqucOnqblN5+aHXtdQaBsmuI0djFWDujZ0FiMRN3JUa9PbZijA+pzCCfdrO9hiwQtOqKDRaC+XzKnnsVLEpimKch0cz7pZgXK8VSWSCKtt9ygyMkcsdNp6/PVdoDHuY1He12VSrqNJdjHua/A9rB8VJE2NGKsnGy2gEGl4WBIt4+y7JQYU5uzgIZugVPAnk8iGon6fJR2O4FaCxJjam1o20v/eAUYzLHN/V6DphrdkiI6kthWLCFwgOcSHyWmvefgutdQ7Sfg85BMZxmIJFjUrHE9qnOJnaRiCX8uce8Q2uuqckk3zXPKLbFEhek2JWbFU//gBz9YSbDNB1hOPVAHwVrH3tZO5uhOElilcJkkxEagxhltpumiT1QA1Co1cJaRCAJli6DuOzpQFGq0GCsH9DCopOYTFLUcbiyWJpFWunXabaWhxKo64KUu6COSSDMQSehNuhW7vFTqdDpSkHRzEO31QV7ri5R92aIYi2BiCLJpwMj7ZQcgVlofrAdfteoWOd4Hzd27/h0HIcbKAW2MRJCKJTpg6I3ixhK1QTwezYe8Gsu1h6JJ0pmsXlmUIvqodCZrJ0jFmG4O+ijDMGirDbJlNMbAuOakW7ElEMRiiYJybQcJGvIyPf3qkMshWZTpQtRHlTAqancV7BwaEgSQT+Zoz/z7q6CqUT0vwqbGLoXTfUKTnICEBlp0vVW2KJnMkT9NK3VGouXUI4k0saRmTTzrvotQtmjNp4Zqv95yCtC39knNqSKWl5qmaR84yDESnWMRQKGPEgqUY8OQTuodawcQt1OozdEmLGI+yjCKqm0k1hHTNLUmc0RiibrirX35jpilGUu0hAJ4PQamiV0mqw1F9FGD40lMUzVjaQlJdQN2OJaQmlO1xYv5oAg+yulYQqpkO9SmHrNpFU8II9/wolJeOhVmHP14PJ4pNSUymeJ0S6tAAzSUGUBBoCwl1hofVQ6jfbn+8QqQT+boZuVYtOiQY0KtIKjpBkVN5oh1xISCDY1zjr0u6KPK7yGeytIfibN7i4RuhPzGU2w+QcHa52wA1l4XZN1AtKw1qMYTaWK57nf6WQR6fJSdHNVtp5pm1SE3m1K2alykd7xtYOuOSnUDdjqWqBOyE6h7H/1nkX2U5g1NbERVBoCzkiK579fgeIJs1tTLAiti2eKAZCzhcJMzAI/HoLU2QN9Ygv6xhN7vWzFjiUiekejVzUjUYCcobKYgVLZYBJkeXU3OstksyeQ2SeXxMNQuguZ9IO7c33Rxo4+FdV7isThxB993h2jdX+13h7eC17nqtOkgk0qysM5LV8ir/3MWAYFAAI8D7MEZJ91+//vfT7pOpVI8++yz/OIXv+ArX/nKnG+oAhdh4FX1WOVcfTtIa1B1qM9RBGr05hHVutmnm+ZrOfWqehjbCg0LHXlby6mPTKRIprN69c6KGICtG1C6hTV+zV3hUnG1qQEl/u4QDMOgva6KjcMTDEQSepNuRew49lqv6kbXUK259TrA6Cb16HF2LLG1z7KT1RHYQb2zXcE6ma72ewjHUtQENM6roTfVo9/Z8hoxOxmGWvvGNisfJZx06w2r4NhA80aq/2X1aDHPHUKblD4sFNVHbRpWsUTAK9REIVAH0UHHYonCjsAjE0ladDYsKmLZ4tp+FUuEdK55oJIfYzlbmc6+dXtdlUq6ReKAxkYUxWTN53xUTcBLTzhGV0O1vsFG/qkevc5q/InJihR2BI6HobpR73gFGIomyWTVFzzlEKEnmUyyfv16stltYvDAXvDWayFQC+vXOzIWwAENaa56eztVfljv4PvuEId/TR2ajGRgXPNY2+BD+1aT2aeKFsKsXz8uOrYEPB4P3d3dBAJzm8cz9gzve9/7tvvZv/7rv7LffvuxevVqzjvvvDndUAUuwTO3wN+vVc9f/ZO6PvgsR95ajG4LRevosnrNRl7tjQDwhdufB0zOOGyxnsGeX60eIz2wagWcer0jtmqs8eP3GqQyJoPjCRY0agxMirShWb1mI7c/uwWAXzy2geUL6vTZ6fEf5p//6BjH7ARqTm0cnhAohyuOWOvqNRu5+o+vALBmwwir12zUZ6dnboHB3IHDHZ+ATMLxtU97oFzTiuoInFWbZwc7p+0Kq9dsBCCWyvLWb93PN0/fX4+tnrkFXvx/6vnjN0H7PiXqo9pzSTdZH/XLxzYQzZWjn/nTx/Xa6S9Xqucb/u5oLNFeMJ/EOgIXwUet2aAOa67+0yvUVvn0rX3PquZoJCOOxhJ+r4fmUIDhaJL+SEJv0q1IZYur12zk5sc2APD7Z7dwxNJmfXZ64r8hk2OT3Pwex2MJEEzmFCE5+qcXtgKwbjCq30dtWaOe3/05JXnjlJ1qhQ4c/NUQbFASNuP9okm3Xz6+wX5+3HcfmLOdTNOkp6cHr9fLokWL8syliREYjwG5PU5tI9Q0zXqcQkQTKXwjMQI+L906u5cCjHhU88O6Tqh25v6ng9GJJMlq6wAPmuqraNTdSEYQ2WyWrVu30tPTw+LFi+cUZzh2HHPkkUfysY99zKm3q6CYCG+BOy8if4Rmwp0Xwx4nOHLyaQXKQ+MJMllTL727CB1desIxLr/9BfvaNOGLt7/I2/Zqc/5ELbwFnv5F/trMOmYrS6x1azhOf0Rz0s0NdkKzne7/av7aQTtBQZnVmGDZolCpgWWnwgN9rXa686L8tS476Q6UvT6l8RHtV8kcoaRbTzjGT/+eP2HN6lr7trWTJh8lwsYuAuOjJxzjyj+8ZF/rt5OeWMJKECTSWcbiab0sWDf4KN2xROHBkIa1z0q6LXe20m4yipAcFY8l7rk8f12qPsqyUzICySgENCckcugJx7jj2a32tZiPMh32UaIyPR25pJtcR+CecIwb73/TvnbCTul0momJCRYsWEBNTY4dn05CvBd8BbFsvBfqW8A398SR6fFjRDKYHg9VVZrlAaqqwJxQn0X3WDkk01kGJuIYBX+rgQmT5vqA3uooYbS1tbF161bS6TR+/+zjDEf+IrFYjBtuuIGFC52holdQZAyv3b78zczA8DpH3r6lNojHUIvoUFRIBFQwAFs/GCW7DeU/Y5psGJxwfrDhtWxXX+CgrdpyOgr6kznyOizidtI4p8S6w1li4tlUvlRWM8rKTlJaYVCUJMH6weh21U5abCU1n0TZ2HKMD7E5pdlOVX4v9VXq7Fi7ttF88FE6YwmpOVUECYSy8lFSOonBurwsgOCBQ8VHzQJFqHBYPxjFdHhOWZrzk0oEMzv5++3s5zOEPycJkM5myW77gZyG1aQom9Y7TgGS6cx288nEJJl2TkLHDbC+M3PtWzBjpltTU9Mkap1pmkQiEWpqavjVr341p5upwCVo3gMMz2SHYXiheakjb+/1GDSHggyOK7FWrR25inDq2d0awjCY5DC8hsGSVg1tvZv32P5nDtrK6uInlswZ7xdjUHW3huzkrwW9djKYtKlx0k5Snax8QUVbj42oQLmmWe94FMFOGtc+qyRELlB+QX7tY/LWXYutdNspN5/GE2kmkmm9unRFkEAoFzuBstVYPE1/JMGe7c41EtoORYolys5HSTWRiY1AOqF8lmaUo51k9CzbYWSDivtadhDLasCOSvxKce0Tm09QvH0UenzUpPJA707Wh539fIbwegwMDExMMhkTj0/j3sbSIM6k9I2xDQK+7fWCDYyyYrkBjklXzPiv8r3vfW/SvxtuuIG77rqLf/7zn7z3ve915KYqKDIaFiqNCAuGB05d5ZioLgie0hShfXxXQzVnH73EvvYaBt84fYUesdaGhdC6d/7a8DpqK5uZozuZY51OpyYgEdE7Vg5dDdV8+ZR97WuPgV47LXt3/tppO4kKissyProaqvnm6fvb19rtdOj5+Wtd86kM7QTKVgcsyotva1v7GhbCu6/OXztsp9qgj2q/Cib1r33yLIKuhmpOeUu+xk+rnbTHElLNSYpjp08cl09GaI8lug7IX+vyUbrnU3VTXrReKEnQ1VDN1aetsK+1+6h9C7S1HbaTbHOS4vio3ZrydtG69h19Yf5a03waHM83G9CGuuLY6Yil+UNdbXbyBaBht8k/a1jkSGkpqGSNL8d2S23bvMFpeHNJt6xk0s1Dc4F+m4HBwqaqsku6OYUZH9++4x3vYNGiRTvM+m3cuJHFizUJh1YgixX/Cn/4jHr+ycehfe+pXz9DtNcHeblHQqzVchayYq3Lu1TH1wN2a+BHHzlEb3ckqzPhu74GKz6gJTmqPQALhFTHtGREBcoOd8zdGd66ZysAoYCXv152nF471bapx4M+DMd/0VE7tUl1sgK1+Rx4RXROnXHYYq78w0vEU1l+87EjOby7Rd9gHblE7G6Hw7/drGU+jU6kSKQzBHdwSugYipAkAKjOdQG+4O178OEjd9c3p5Ycqx6rGuGTjzpqJ8MwaK8P8s+hCQbGEyzRKYBcBAkEgN2aFWPgxP06uOq9++mz00EfgbsuVRuBc++BRYc7+vZiB0OWnaIDkEnnS3k04y27NQKwR1uIX51/hF4f5c+993Gfh4NX6jlo1c2atzoChzflOgLL7ElOWN4Bv38RjwEPfe7t7NakgeVmwUoS7HsanPgNZ+0kNZ+gaD4qnVFJqqtPW8EJy9v1zSkrid2+H5z5O0ft1FobwDAgk+sI3CrSnETWTo3VKplzzluX8LG3LdVnp6C13zCgfV/HEm4WfF6DVCb/vdMGm+kmV1569tln0zc4xLd/9Etqgz52a6qpJNymwIz/Mt3d3QwMDGz386GhIbq7ux25qQpcAOtEw1etRThTTDfCKltMhCEV0ztWAazkx7KOOr1BMuRZfHu+01GnDoKlBpAPwAQ3n1ZguaCxWsBOuc+16AjH7SSWHIWidIcbT6SJp9Qp4X4LGnbx6rkOlrNTx76O26mh2k/Aq9zu4HjS0ffeDkVK5lhr+lv3aNU7p6zP1bDIcTtBYXMSqS5+wnbKfa637Nao106xkfzJeyGTyiGISSCEWhVTDxMmBvWOVQBLq26Ptlq5WGLp8c77KOtgSCKZY8V9gj7Kmk+ttUG9CTfIrxW7Hep8zFcwn0zdGlRF8FHZrMlgbq3QmnCD/OdqXea4nXxeDy0hlRwqWx+VW/sOX9Ks106WBprX73jCDcCf65I6W6bb2WefzWmnnbbrF9qabnJMN8hLKVUHvLNOuN188800NjY6d1MuxYz/OjtbhMfHx/V35qhADhaLpa5Di75Wm1SSoKoBfLnvpWgAppyFtXHThnQSYsPquUUBdxD50h1B3QhBOw2M5+xUr1/3xQ5Yap23kzWfhqIJ0hnNFPYiBGDWfAoFvISCmhkm1vdPg50MwyjQ3xM6cBAOlK0EvfY5ZX0uTZ1ZxTRzCg8bdJefFMD6XNp9lGWn6iYt+lp5Zo5mO3m8qiMwyPooqfkE+biv1vk5ZSVzZDSo5Mvh7PkkYSeNPspa95LpLGMxzYyZIviokYkk6Vw5plZ2GBT4KOftBPn7F/VRguiXWvssDTSPntjSKi8VY7qZWcjOTfB/JrDSQj7P9imlZFLz4fIOkErJJh1ngmkn3S699FIuvfRSDMPgiiuusK8vvfRSLrroIs444wwOPPBAjbdagSg0OnUQ1GGxSg1AtBzOOnXXvqGJ5j6Tx682NQ6jKAyqIjDd2nQHX6A1SdASUh2BTROGo5qdXBG6w+U3ngIHO/bGs13L24sdOFh2EkwQxFMZxuJqo2Y1jdCGcvFRIasjcFqsIzAIzin7sEFPclRWz7IIPipi+SjNdkpEIBVVzzXYyvqeybLmBWM+az7pbAxmQaOPqvJ7aahWm3f9yRx5H2XNp+ZQAL9XcxlcRPPaJzWnauXtZJqm3JyymGGWJto00BOO8ejaQXrCu66g8uW+Z7oOxPv7+zn11FOpDtXSfeQp3Hr7n1iydCmrVq0CYMOGDRiGwXPPPWf/zujoKIZh8MADDwCqK+d5551Hd3c31dXV7L333lx//fWTxslkMlx66aU0NjbS0tLC5z73OUzTtLuy+r0Gxx9/PJ/+9Ke5+OKLaW1t5cQTTwTguuuuY//99ycUCrFo0SI+9alPMT4+DsADDzzAOeecQzgcxjAMDMPgqquuAtRB9R133DHpPhobG7n55psnfbbVq1dz3HHHUVVVxa233grAT3/6U5YvX05VVRX77LMPN910k0N/8dlj2mndZ599FlAT4YUXXpjUcjcQCHDAAQfw2c9+1vk7rKA4sANlPRtP8WTO6D9lTz2tZI5uZ1Ho1DUyEgfHE2SzJh6Pxs47RUjm9EttPLMZpQUEWgIwr8egtTZIfyRBfySh9/MUQd8jv/GUSI7m1glNp9NiJduFhw1CHYGtzxTweaiv1sxI1OyjxJKjvgBUNyvG8ngvhDTqFRZgQGpOad54ykogdELv80XyUUJ2CtRCsNbxt7divmgyQzSR1stYLgJrvtx8VDiWYiCSYFmHzo7A8prL9nzSfSAOeTtpO3AQ8lGWj42PinUEHounSaRVkqpNk61M0ySWykAsAaks+LyQ3DW787anN3PlH14ia6qmKV9573584JDddvr6dDqrDiRjaZpC6v2r/V7HOmKeffbZbN26lb/97W/4xzZx4Ze+Tn//9jJgUyGbzbLbbrvxu9/9jpaWFh599FE+9rGP0dXVxQc/+EEArr32Wm6++WZ+9rOfsXz5cq699lp+//vfc/jRbwPyycVf/OIXfPKTn+SRRx6x39/j8XDDDTfQ3d3NunXr+NSnPsXnPvc5brrpJo4++mhWrVrFFVdcwWuvvQZAbe3MfNAXvvAFrr32Wg466CA78XbFFVfw/e9/n4MOOohnn32Wj370o4RCIVauXDmj93YS0/Z6f/vb3wA455xzuP7666mvlxE6r6BIiMhsPEVKDYog1ioWKNvBlx6nbtHXUxmT0ViK5pDzegc2bB2WIthJOyNxQFG+DU++RMlhtNVZSbc4oFH3zE7mFGFDI1K6o68MGCQD5Zyd0jFIjKlSe80oLFl0KqDcKcR8lFAyJzasPlPHftqHS2WyDOUYsXI+qsTnExTJR0mVAetNEISCPmoCXiaSGfojCbolkm6iyVGh8tJUDOJh9VxjMufN/nG5ZI7ogbiyk65EziRY64RmCQTtBw7VTeANQiah5pRAcxJL0qauykeVX0/DqVgqw75X3FPwk17gxRm9R9aEL//fS3z5/16a0e+9/NUTqQnMfQ18/fXX+fOf/8yTTz7JYYcdBoPN/M+1V7D8uA/M6H38fj9f+cpX7Ovu7m4ee+wxfvvb39pJt1WrVnH55Zdz+umnA/CjH/2Ie+65hyw5pluOlLFs2TK+853vTHr/iy++2H6+ZMkSrr76aj7xiU9w0003EQgEaGhowDAMOjtnFydcfPHF9n0BXHnllVx77bX2z7q7u3n55Zf57//+76Im3WbMrf35z39eSbjNB2jU9oDJpTvlJtY6mRYtpBmhyU4Bn8dOtOlveiGvw2I5du0BmGWnUFu+26zDaJcKwOrkT6fF5pNpCrB8hXQSAzX5rlxCSQKbPSXCItDtowQZVMISCEO5Jh5ej0FzjcaDFNBerm3Np3AsRTylWcemKD5KaE5p1p+CYvioIkggaLdTbj55g9oOUsQanVl2ig6KdVy0pF9kfJTMAZ6oTI9QLCHKSCwR3HrrrdTW1tr//v73v/PKK6/g8/k45JBD1Is8fvbZs5vGxpmvDT/4wQ845JBDaGtro7a2lh//+Mds3LgRgHA4TE9PD0cccYT9ejXuoWBpuuW06+x7KcBf//pXTjjhBBYuXEhdXR0f+chHGBoaYmJiYsb3uSMceuih9vNoNMratWs577zzJv29rr76atauXevIeLPFtNKsp59+OjfffDP19fWTMok7wu233+7IjVVQZGg+9bQcXjyVJZJIU181/Vr6GUOYmTOeSCvKMgKOXXPpDqhyieFokv6xBPvoi8eLykiUs5OejScUaBtp72RldQQeg+SESu5oRr9UcrSw06LmtU+7nUB9hsSYWvs0dKHeFuVVulOEJjJCPsqaT621Ab2SAaBde6++2kfA5yGZzjIQSbCoWeN6JKxBlcmadpdj7bpGQj5qw9CEXNf6cowlCrVhNbGJxXxUTQsYXjAzSp+4foHe8ch/Ju3zKZPKdznWrmcp4aPaIbxRzEdJHDZU+728/NUTYfB1xSBt6oaqqUlFveE477zuQbIFXBGPAX+99Dg6G3b8nUqmM7zeN45hGOzbVYdhGFTPgr333ve+d1LSa+HChdx7772TX2Tp0hXeX67BQSHBZdtmA7/5zW/47Gc/y7XXXstRRx1FXV0d3/3ud3niiSemvCfrPT2GgSe3HoVCoUmv2bBhA6eccgqf/OQn+frXv05zczMPP/ww5513HslkkpqanftswzC2I+bsqFFC4ZiWVtxPfvKTSX8vAK9XD+lhuphW0s2i/VnPK5gHiOg99awOeKkL+ogk0vSPJfQm3YSTOVbwVRv0OUIfnhKaS3dAlUu81hcR1KASPJ2WCsDG9W48QbAcLlgPvmpVtjjeC81L9Y6HoEi1tZmubtbSOh4KTqfHhZJuQ2+IMajENjSmKVa6MxRNks5kbb0SLZD2UVJ2Au0MKsMwaKsNsmU0xsC45qSbXQ4nM5+Go0kyWRPDUAlSrZD0UdoPhiwGVb/qCLyDrnpOQ07HV7+d7AMH3T7K6gg83qvWCYGkmzgj0eNTyUUNEJdAALH4XMJHGYah9mieLPg9UFUFu9izLW2r5Zun788Xb3+RjGniNQy+cfoKlrbtXIOsyu+1S2SDPu+sY4m6ujrq6iZrLO6zzz6k02mefvppVV7q8fHamxsYDYft17S1KUmbnp4eDjroIIBJTRUAHnnkEY4++mg+9alP2T8rZIU1NDTQ1dXFE088wdvepjTc0uk0zzzzDMv23R+f19ippMjTTz9NNpvl2muvtROAv/3tbye9JhAIkMlsz1Rva2ujp6fHvn7jjTd2yY7r6OhgwYIFrFu3jjPPPHPK10pjWhmBn//85zt8XkEZQ3PZIih9pshAmoFIgj3bnRfutSFcXirm1EF7iRUIOnbLTrFhSCe1JT0sxJIZIglV0qBf10hvggDyn0Gk1KCuA0Y2qO+faNKtDEqs6gWZbnYyR/Z0WrudEhGV9AVta19LKIDXY5DJmgxFk3RobU4i7KOkumuDSCzRXq+SbmLJHGG2R0sooDfpC/lYQqOPapM6cKgt7Ag8DKFWrcOZpik3pwRjCTEfNd4rLoEgFvOF2rUlfYsigSBlJ6n5ZJoF3UunR5I447DFvG2vNjYMTrCktYauhuopX+8xDDuWSGdNfA6Srfbee29OOukkPv7xj/PDH/4QX3qci//zq1RX5+OV6upqjjzySL71rW/R3d1Nf38///Vf/zXpfZYtW8Ytt9zCPffcQ3d3N7/85S9Zs2YN3d3d9msuuugivvWtb7Fs2TL22WcfrrvuOsLhUQD8U3zH99xzT1KpFDfeeCOnnnoqjzzyCD/60Y8mvWbJkiWMj49z3333ccABB1BTU0NNTQ3veMc7+P73v89RRx1FJpPh85//PH7/rkk6X/nKV7jwwgtpaGjgpJNOIpFI8NRTTzEyMsKll146nT+tFug//qmg9JBJa+20aEFMN8I+nZZlurWKCLXqLbECwaYXNc3gyS2mArayApWgz0OdTlFnECkDFptPIN4dTrx0R2OJ1bYdgbVCOEkgVgZs2SlQB4HQ1K+dJTwew2YXiZVsC7MIRMXENUsggEApcCEjUbcWLYVlwOURS4glc7z+PLtIwEeNxdIkNXdatCFxIF4rGUsUyUfpnlMCsYQ1nyaSGcYTmjXxpCUQpBpeZAv+bp7pV1x1NVRz1B4tu0y4WbCSUqlMdka3Nx38/Oc/Z8GCBRx33HGc/h/n8LEzT6e9tXnSa372s5+RTqc55JBDuPjii7n66qsn/f/HP/5xTj/9dM444wyOOOIIhoaGJrHeAC677DI+8pGPsHLlSrsE9T2nvBfI67ntCAcccADXXXcd3/72t1mxYgW33nor3/zmNye95uijj+YTn/gEZ5xxBm1tbXYjhmuvvZZFixZx7LHH8qEPfYjPfvazU5ajWjj//PP56U9/ys9//nP2339/jjvuOG6++eZJScRiYFo7zYMOOmjancieeeaZOd1QBS5AdAAwc50W9Z0QFjZT0ApbrHUAshltQvYWLGchyiLQeeopZSdLrHVsszp1b1ykdbjCbmPaOy2KlO5Y+h6Swu/6kwTJdJZhq9Oidu09/XZqrQ1iGJDOmoxMJGnRGfxb64JUeakUi8DuXKpv3QO19vWNCXQEFtYKE+uImYpBIlfmUg4sX2vdyyRUB8nqRq3D5eeTYBmw1oMhSQ2qTpgYyvneFVqHsj5PvcZOizYkykul5hMU0UcJlQFrZM3XBHzUBn2MJ9L0j8WpnaLEcc4o11jCYrl5fNo0EiGXlEqruG+muPnmm6f8/87OTu666y51kYrBwKt8+buTmWTLly/n0UcfnfSzQq20YDDIz3/+8+2qGQuTYz6fj1WrVrFq1Sr7Zz3hGAORBP4cE/uBBx7Y4T1ecsklXHLJJZN+9pGPfGTS9Q9/+EN++MMfTvrZggULuOeeeyb9bHR01H6+ZMmSnTZj/NCHPsSHPvShHf5fsTCtpNtpp52m+TYqcBUm0aL1BRBi7a5DbSqBaGZVlyTNGzUx/alsVnt3JChkUEkkc9pzSTf9m08xO4FI6U5hqYFpmnoTiYL6HoO5MgOfx6BJe6dF/Ulsv9dDc02AoWiS/khCb9JNmJEoNqcE1j0Q9FE220N2Q9MmtfH0Vec76WpAu9SBg78agg0qkTjepz3pJitVob+0Xmw+gVrD+18SmVNFSY5qLQNWn2MsniaeyuhNJAr6qGgizURS6UaJabppTGKDmlPjCSXTM5Wu2JxRK30wJNXwIsd0mwHLbTawklJpDUy3SfBYaR1ThI2dzqgxfLobMpUJppV0u/LKK3Xfx7Txgx/8gO9+97v09vZywAEHcOONN3L44Yfv9PW/+93v+PKXv8yGDRtYtmwZ3/72tzn55JMF77gEIeDUQTCZ4/FCTasS1R3vlUu66T6hiY3kqdGhNm3DyAbKco7d3niKlu7o39Ak0lnG4mkaqnV2BJbrDlfYxUp7p0UBtgeoz2Il3ZZ3aRxIkJGoOi0WoYOfRoj5KGs+JSOQjGormbUgLiZe266VRSArKN6hkm6RXmjbW+tQEh38AKWhOjGkngsc4MlqUEke4JWHRmJ9lWBHYEEfZa0PNQEvId2SIgJNzkCtDesHo3I+qty0sWeo5zZbWOWXqYzmRJjHB+R8bXb7xgROw2LuadccLRPM+q/01FNP8ctf/pJf/vKXPP30007e006xevVqLr30Uq688kqeeeYZDjjgAE488UT6+3d8kvXoo4/yH//xH5x33nk8++yznHbaaZx22mm8+OKLIvdbshDQ9oACfQ+JUgPB7nBiyRzLqde0aG06UJRAWeR0Ol9eqhWmKaLvUeX3Ul+lAgft2kaCOixiem4goj8FgolsQUbiUDRB1lS5lZaQZkailI+S0kkM1oE/t7EVTBLoT47KbDzL3kfptlO0oNNidZO2YazPMRRNatE2mgTRZI6Q/hSI+CjDMOQOHAR9lKj0S0R/zAeCB0O2nXIdgTUinsoQjqlkmPY5lbHKS/Uy3Xwei+mmOelmGODxseGJP3LxZz6pdyzyGnX+KTTdKshjxkm3zZs3c+yxx3L44Ydz0UUXcdFFF3HYYYdxzDHHsHnzZh33aOO6667jox/9KOeccw777rsvP/rRj6ipqeFnP/vZDl9//fXXc9JJJ/Gf//mfLF++nK997WscfPDBfP/739d6nyUPKVp0rZBWGIh2hxNL5giwpyBfMjGeSDORLB+xVrlOi2OQzm3aBU49ocwCZamNJwgmCYS0jaz5FBuBtN7vRL7TYlCu02K5JEctPUvQnswxTVNu7RNKYstqhRXDR0mVa3do67QI0FQTsMuQhsaT2sYBhJM5UqycTD5BKpbIFvJREqz5caH5BOUngRBqAwwwM3lWrCZYnyXg8+it2IB8tZBXd3lpjummOWEJ5Fl7Wc37NQrLSytMt+lgxn+l888/n1QqxSuvvMLw8DDDw8O88sorZLNZzj//fB33CEAymeTpp5/mne98p/0zj8fDO9/5Th577LEd/s5jjz026fUAJ5544k5fD5BIJBgbG5v0b95BauNZL5QggDINlAtKdzQiFPBSndP00N51rBiMRKmNZ7BBaQJphFjTi6KUlwpq7wklc7TPp+om8OZYZ5o3n6KMRLHSnWI0J9Hro8KxFMlM+XRahMKOwEkyujsCF0MCoUySo6ojsBB7VNJHSSVzJoaUNjGGkkzRCLEDvMJ1T7MGlWjXZgGNRBA8cCjsCKzZR1nzqa1WoMlZpqCRgkZYhw3amW6QZ+1Zn00TTNMknUsiTtW9tII8Zpx0e/DBB/nhD3/I3nvntSz23ntvbrzxRh566CFHb64Qg4ODZDIZOjomBwUdHR309u54Aejt7Z3R60F16mhoaLD/LVqkt4OiKyFcujM6kSKR1lx7LpTMSaazjEwI0aKFNp6GYeS7WY1LCYpLnk7rTo5a80lvchQKEtm6kzmWnaIDeSFaTRDbeCYnFCsRxNY+7fNJkEE1IMX2ALnSHan5BGI+yppPDdV+gj7NnRZtH6V3PrXWBjAMpSs4MqGZQSXISBRjUI3LxHxQBB8lIYEglcyxYvNQm3YNKrkDvNx3LpOE+KjWocRiCSFJERAurRdij4omR21NN83lpVYjBRGmW+6zZPUm3awEooFRaaQwTcw46bZo0SJSqe0NmclkWLBggSM3VUxcfvnlhMNh+9+mTZuKfUvyEGJ7NFT7CeQWokHdpQZCyRxrE+33GjTVaKZFC51OQ4FuhPZAWU6HZUBM9F2mHATyOoLakzmhVtURGBMmBrUOJb7x9NcojS2NsJPYEskcm/EhczotKyaumelWMJ921pbeMQj5KLH5BAU+Sq+dfF6PrSMo56P0zqdoMkMspQ4j5XyU/lhCzEcValBphpgEglADGRCM+fxVUNWgnms+cBBtcpbJ7WnKRQIBxEqBByQlRYQ03azy0kzWJKubjW0z3fQeiKcKWG7aGYllghkn3b773e/ymc98hqeeesr+2VNPPcVFF13ENddc4+jNFaK1tRWv10tf3+TJ3tfXR2fnjgO6zs7OGb0eIBgMUl9fP+nfvIMggypfZiVUaqA76VbQREH7IiRUugOFpQaa7VRXkHTTeCKUyZoMSSUJhJijUMgi0GwnjzffMbdckjlCnRYhv/GU0aCSYXxY3zmRTouxYfVcSCMxmc4yFtOtZynko8aFNEdB1EeJlS1KMRJz8ykk0WlRSB8WJJluVkfgcUiMax1KLJlTjjEfyPkoq+GF9iZnOTtVN4FP71jlKNMjNp+gQNNN7xrrMQw8hpCum/VZNJeX5vXcKgm36WLGSbezzz6b5557jiOOOIJgMEgwGOSII47gmWee4dxzz6W5udn+5yQCgQCHHHII9913n/2zbDbLfffdx1FHHbXD3znqqKMmvR7gL3/5y05fXwGKFi1UugNFEH7XnCAQ23iC8KmnkLZRKPedy6bzG2sNGBpXnRY9BrRIdZkVYSQWQ4NKc5JAak6JbjwF7SRctijG9vD4tXZaBNUR2BJy1p/MkfJR+YMh7ZD0UVJzSixBYG08JUXfJWI+IQ2qYB34Q+q5Rh8VT2UYi6uNu9UcTBuKkRwtIx81IDWnJA9ac/NpOJokmdaczBGOJbTPp2wmp5GIdqabYRi27pl2XTePTHmplTz0626cVUaYcWp31apVGm5jerj00ktZuXIlhx56KIcffjirVq0iGo1yzjnnAHDWWWexcOFCvvnNbwJw0UUXcdxxx3HttdfyL//yL/zmN7/hqaee4sc//nHRPoPrEQ9DJudkBRy7GDW6UIfFNLUxWfolRd8FAzAxO/kCUN2sEm7jfaqMUQMsO7XUBvHqPqWRLN2R1vfofV7rhsY0zTzTTXegXITSnYlkhmgirZfJIpUcldrQFMofCJQ0tNUFCcdSDEQSLOvQWHZsJ3P0lsOJJXOyGaX5CDJJArFYIpeYiochFVflcRowIKU/BWKi7yDtozpgeJ36fC17aBmisNNifbVmRmIRDlpFyxalfJQYa16/nRqr/fg8BumsyVA0QVeDxmZdUhIIUkw3iwlmeFT1hmb4PB6SZElndDPdrKSbXna+zXSboonCzTffzMUXX8zo6KjWeykVzNhDrFy5Usd9TAtnnHEGAwMDXHHFFfT29nLggQdy9913280SNm7ciKegbe3RRx/N//7v//Jf//VffPGLX2TZsmXccccdrFixolgfwf2wFtOqBm3BZCHapZhulvNLx5RYuqUh4TBEadGCjl2MkQgq8I8Nq6Rix35ahigsA9YOydNpSTsJdIcbnUiRyjn21tqAtnEA0dKdUNBHTcDLRDJDfyRBdxkk3cREqoXE+S201wV5s39cwEdZ5aVCpTu67RQdVCwCw6Pt8KQQYsmc6ibwBtXh5HgfNO2uZRjRbsBC2nsg7aNySTeN7NFChm85SooMjifIZE29h5MCPiqVyTIUVTpr5dLkDFRH4La6ID3hOP1jupNusjI92n2UlZTSzHKzYOm6pWap6bZp0yauvPJK7r77bgYHB+nq6uK0007jiiuuoKWlJf9CT0F5qUaSiZU89Hn0M902bNhAd3c3zz77LAceeKD28XRh1pF+f38//f39ZLepTX7LW94y55uaCp/+9Kf59Kc/vcP/e+CBB7b72b/927/xb//2b1rvqawgSIuGwtM0zaUGgRoI1quEW6RPW9KtXyqZk4xCMqKei4rqSuh7dED/y1oduy18LKprJNC9NDefwrEU8VSGKr/G0zuBMitrPjXWCHRaFGxMAmpObRiaoH8sTndrSN9AAmWLpmnKiYmL+ygprTCrI/CgEkDWpDFj6xpJbTxDbSIsAjE7WR2Bwxs1J92E5pNgp0UQ7rYokMwZkJpPIOqjWkKqI3DWhKFoQm+XdwEfNZhjzPs8Bs01mg/wBCV6QM2pnnC8fGR6pOaUUOdSC3YH01kw3datW8dRRx3FXnvtxa9//Wu6u7t56aWX+M///E/+/Oc/8/jjj+dlvezPYyrGuaZYwjoQ90/BdNsVkskkgYDm+eiCMS3MOD359NNPs2LFCrq6unjLW97CgQceaP876KCDdNxjBZIQZE9BQRe/MgvAtCdzLKfnD2nvtAj5ZM6g7o5jINKWXOwkDURLd+qrfQR8Vkdgqe5wAslRETvJnU5DwYGDbjsVltZrwngiTTylAkmxTotiPkqozKqmBQwvYObLMjVAjEElnsQWLIerk4glLB+lueIgNpLffIo0+8nbSXtHYBEfJRlLyPko1RFYqmRbLuZrrQ3i0S4pIscchbycjahMjyZksiaD44qRqH3tszuXziIpFd4C6x9Sj9OE3zN7TbcLLriAQCDAvffey3HHHcfixYt5z3vew1//+le2bNnCl770pfyLDU8ulgCyKTKZDJdeeimNjY20tLTwuc99jpUrV3LaaafZv7JkyZLt5MMOPPBArrrqKvv6uuuuY//99ycUCrFo0SK+9J8XMxEdt5OJoMpJFy9eTE1NDe9///sZGhqa9J5XXXUVBx54ID/96U/p7u6mqkrZ+O677+aYY46x7/GUU05h7dq19u91d3cDcNBBB2EYBscffzwAxx9/PBdffPGkMU477TTOPvvsSZ/ta1/7GmeddRb19fV87GMfA+Dhhx/m2GOPpbq6mkWLFnHhhRcSjUZ3ZYo5YcZJt3PPPZe99tqLRx99lHXr1rF+/Xr737p163TcYwWSEN545rv4lUfSrV8qUBY8mYb8Bm0omtSvRyBQtii28Uwn1KYGRDY0hmHIzSkJO40JzScQTxLkOzcLBcrRfm0dga3vWm3QR01At65Rmfqowo7AGtmjA1JzSrCBDAhLIFifSSPjQ0zTzfoM1c1KU1UzLJmAZCZLOKZX6LusfJRwkzMQnFMCZYt2A5kyO2gFwU6z1rqXikIiomWI4WiSTNbEMAQkRTJJSMXUoUMyOv1/T/4EVq2AX5yqHp/8ybR+z5eZwEhNkE5E1XyeJoaHh7nnnnv41Kc+RXX15PLhzs5OzjzzTFavXj35EMNiu2VSXHvttdx888387Gc/4+GHH2Z4eJjf//73M/5zeTwebrjhBl566SV+8Ytf8NjfH+R7X7/S7l76xBNPcN555/HpT3+a5557jre//e1cffXV273Pm2++yW233cbtt9/Oc889B0A0GuXSSy/lqaee4r777sPj8fD+97/frqZ88sknAfjrX/9KT08Pt99++4zu/ZprruGAAw7g2Wef5ctf/jJr167lpJNO4gMf+ADPP/88q1ev5uGHH95pJaVTmHF0vG7dOm677Tb23HNPHfdTQbEhXboj1T4eCjrv6A+UxTr4CTn1llAAr8ewT6E6GzQGmBJli2Ibz5ydvAHtnRYttNcH2TIaE0jm6LeT3URBMlAutyRBbTtgKP2SiSGobXN8iPx8Kq8SKyiCjxrv1ZYkiCUzRBJKx0Y7G1tQ9B0KJRAUg0qrvpbEAZ7UnBJOYgd9Xhpr/IxOpOiPJGjUWeon4aOkYr5ERGkSgxiDqr0uyCs9+US9Nthli2XCSCyaBIJmOwVrIVALyXFlKw1VNtZ8agkFJjGotCARgZ+/Z27vYWbhT59V/3aB5tw/AL64FQLTkxV54403ME2T5cuX7/D/ly9fzsjICAMDA7S35xLYFnsvm2bVqlVcfvnlnH766QD86Ec/4p577pnW2IUoZJTtvvvuXPCf/8XVl1+C36uaU15//fWcdNJJfO5znwOwCVp33333pPdJJpPccssttLXlY9EPfOADk17zs5/9jLa2Nl5++WVWrFhhv7alpYXOzpmvf+94xzu47LLL7Ovzzz+fM8880/5My5Yt44YbbuC4447jhz/8oc3Acxoz/kafcMIJ/OMf/9BxLxW4AdKlOwVli9lZiktOG5oDsGzWFDydlt14ejyGfeqkncJul+7oo7CLJXOEOy1CoWaObg2qgvbxmsqExE6nM+l8SZ9UealUab3Xr0oXQVuSwJpPoiwC8eSohJ6lXh9lfdeCPg91Opt3gKg4P+TtFEtliCYzegeTkECwuzZL+SgZ9hRMTpBqRaGP0gQ5jcTcZwjWK41iAdixhJQEQiKsmEcaUK5NzkD4YEjzgYM1n1olmpxl9Hb3dBq7KsePx+PU1taqf0sO5Bs3/A/hkSF6eno44ogj7Nf5fD4OPfTQGY//17/+lRNOOIGFCxdSX1/Ply76OKMjwyTjymavvPLKpHEAjjrqqO3eZ/fdd5+UcAOVWPyP//gPli5dSn19PUuWLAFUg0wnsO3n/cc//sHNN9+c/3vV1nLiiSeSzWZZv369I2PuCDOOun7605+ycuVKXnzxRVasWIHfP1mA8L3vfa9jN1dBESB86tlSq8Ra01mTkYkkLToXWpvCrieZMzKRJJ21Oi0KnU4LOXVQQWXfWCLnFPU0ogBESnfEAmXhE08Q7OJnfaZMAuJhqG50fAgxO0UHAFNpYdS07PLlTiBftijUnGRiMLduON+922qwIlu6I8sikNEd1eujChvI6O+0KLv2hYI+QgEv0WSG/rE4tW21+gbTXLaYTGcZtjot6o4lBLtrW2irC/J63zgD40LlcBKSIlI6voLJ0bwEgmY7VTVs0xF4ieND2LGE7vmUiqnkIYj5KOszaU+OQq4j8FptB0P5+SQgKeLxwjl/hqalUDVN1t7YVvjB4YrhZsHwwgVPQP2CKX81lcnyam8EA9jPV810PfCee+6JYRi88sorvP/979/u/1955RXa2tpYsGCBXa5JpJfmQDqvW7cLeDye7ZJ6qVT+dzds2MApp5zCJz/5Sb7+9a8Tqmvg9j/fx1X/+RnS6ZnJBIRC2zP8Tj31VHbffXd+8pOfsGDBArLZLCtWrCCZTM7pvnc25vj4OB//+Me58MILt3vt4sWLp/MxZoUZJ90ee+wxHnnkEf785z9v93+GYZDJaD5hrEAvhBlUfq+H5poAQ9Ek/ZGE3qSb5s47lsNrDgVsMXttsDZlQk4dLFbimEA5nF4WgWmaguWlsklsyH8m7XbyV0OwQQWY431akm4DUgGY9V0LtYt0WgRBgX5Q60T/S9qSBGKi79ls0USqx+Jp/R2BdfsoKTtBcXxUfRXrB6P0RxIs1Zp008tILOy02KS706JwEhsKfJSUBMLEoNp8auhSKDanhNc9ECxbNAz1/RvdqHyUlqRbjo2tO5aw1m5ftWIlCsCOJXQnR0E7e1S0yZmZUXFsdYN6nA5al8Gp18OdF6vfN7xw6ir1813AZ5rgz5AF0iZMdzVqaWnhXe96FzfddBOXXHLJJF233t5ebr31Vi644AJ8Pl9e+mu8Hsa2QFUNXV1dPPHEE7ztbW8DIJ1O8/TTT3PwwQfb79PW1kZPT499PTY2Nonx9fTTT5PNZrn22mvxeDyMx1MM9N066T6XL1/OE088Melnjz/++C4/39DQEK+99ho/+clPOPbYYwHV5KAQVrfRbXNM2953JpPhxRdf5O1vf/uUYx588MG8/PLL4lJpM84MfOYzn+HDH/4wPT09ZLPZSf8qCbcygHDpDkhqG2mmRVulcBK06CKcTosxPiynnhyHxLjjbx9JpEmkpTstFqF0R7KLn+YkgfY5VZSNpySDSm8iW6x0JzaitOkg33RAM+qrfARzhyhy3eF0le6Ut48SY/lqlkAolKmQ67Qov/Zp91E1LXltIw22Uhq3QqX1xfBRkgdDUj5KMuaTlhQZF+gILGQn7fPJzOZjCc8Mk/EHnwUXvwAr71KPB581rV8zDANvTqdupg3pvv/975NIJDjxxBN56KGH2LRpE3fffTfvete72Guvvbjiiism/4Kt6Zbioosu4lvf+hZ33HEHr776Kp/61KcYHR2d9PJ3vOMd/PKXv+Tvf/87L7zwAitXrsTrzR8w7rnnnqRSKW688UbWrVvHr371K373q59Peo8LL7yQu+++m2uuuYY33niD73//+9vpue0ITU1NtLS08OMf/5g333yT+++/n0svvXTSa9rb26murubuu++mr6+PcDhs3/cf//hH/vjHP/Lqq6/yyU9+crvPtiN8/vOf59FHH7WbPrzxxhv83//9n/ZGCjNOug0NDXHJJZfQ0SG38FcghFQc4qPquaBjlwuU9ToLsTIDKHKgrPk0LVAL/pxmiQZbWcnRuqCP6oBmVlM5bzxBewt5+dId+fk0FE2SkuoIXOrJHItZVNMi0mkRch2BpZIE2n1UvrxUK0xTvMM2CCZzCte9rPOHzWLdtUG8ugEEfZTHo9jLoGVODUUTZE2VW2kJaV6PiuijZDoC6/VRYsmcIlQ3WHI2qYzJ6ITmjsD2gYNmHyWh4wuAMbvqhoaF0H2sepwB/LlDlFRmZsnRZcuWsWbNGpYuXcoHP/hBdt99d97znvew11578cgjj1Bbuw2zu6B76WWXXcZHPvIRVq5cyVFHHUVdXd12ZaqXX345xx13HKeccgr/8i//wmmnncYee+xh//8BBxzAddddx7e//W1WrFjBb3/zay78wpcnvceRRx7JT37yE66//noOOOAA7r33Xv7rv/5rl5/N4/Hwm9/8hqeffpoVK1ZwySWX8N3vfnfSa3w+HzfccAP//d//zYIFC3jf+94HwLnnnsvKlSs566yzOO6441i6dOkuWW4Ab3nLW3jwwQd5/fXXOfbYYznooIO44oorWLBg6hLhuWLG5aWnn346f/vb3yYZo4Iygd1pMQhVjWLD5svhhPQ9YiOQToDP2UVdzKlDUU498/oeAqUGtR0wsl59zhZn1xpb20MyOVqM0h0prTDQUmY1kUwzbnValGx4IYSmmgA+j0E6x5boaphmecNsoLlsUSyZU4QkNqjv3+aRmP7mJLaeZYmzPRJjkM79rQQ3n2JNL0K5jsBmJtcR2NnEotjGE4qSJJBtTtIOka1akgT5TotB/Z0Wi+CjCu2kvSOwRh9lmqbc2leEJHbA56Gpxs9IriNwk84EsGbNZTmpilxy0uMTYyQCap1IZUhnZ37QumTJEm6++Wb7+sorr+S6667j+eef58gjj5z8You9l03j8/lYtWoVq1atsv/77LPPnvTy+vp6fvOb30z62cqVKyddX3LJJVxyySUAbB2NMTie4NyVK2lszMeu5557Lueee+6k3yvsGnrVVVdx1VVXbffZ3vnOd/Lyyy9P+tm2rM3zzz+f888/f9LP/H4/N910EzfddNN272lhw4YNO/z5YYcdxr333rvT39OBGSfd9tprLy6//HIefvhh9t9//+0aKexIlK6CEkEROi2CYOed6ibwBiCTVAFYo7NiiWKi75k0RAfVc1EGVa7UQEKsta4zn3RzGKKaEcVgJObm0+B4kkzWxKuzPEljoGzZqcrvoVZ3p8UibDxVR+AgvWNxBiKak26aGYlyukby5dogqJNYmMQ2Tcf9sBiDytp4BmegleMALDvp7wjsg1CrasAy3uf49zF/gCeovSfKoBKaT6DW9B60+ChRRmIR9WHjqSzjiTR1Vc5r4tnQWLYYjqVIZqQkReRjPlC2Ukm3OHt3TrMpwGwgVN0gso8CLTqPU8FiuqVnyHTbEb7yla+wZMkSHn/8cQ4//HA8noLEvzcXM5sZxcZ2WKvYahqo/bChzDCr7qW1tbU8+OCDPPjgg5P+zzCMStKtlGE7dVlnIdZ5x2JQhTcph+F40k1o4xntR3Va9Ip1WgTptuT6usOJbmiKcOrZElIdgTO5jsBaO+lqDMAK55P2TotFKN0BNad6x+ICguL6GInJdJaRXElLOZbuQDE6AieV1EN1k6NvL95ARjw5KlxaHx1Qa3zn/o6+tdjGMzmhWIlQnuWloNVHDYwJHuDZsYTcnKoOeKkL+ogk0vRHEpqTbvrKS6351FDtJ+jTLClSrH1UXZDX+iKCMj16GIn9UnOqkOkmCCtJlco6o713zjnn7Pg/DC9gAKbSrnM66ZZLYvu8cgSdcsCMU5Tr16/f6b9169bpuMcKpFDEjSfA2r5xesIxvYNZHRb7XnL8rcUCsMLW8R65UwYrOdo3Fmfr6ITewaxTz81rILzF0bful2K6ZbO5BCmiSQKf10NjtQqOX94ypncwa60YeNV5O0luaIp0Om3NqcfWDeld+6zvX3ir43ayDkv8XoOmGs2nxkVIYkP+O/jC5rBeO/mrIJhjKfQ87/jbizPdipQc3TAY1R9LWN/B9Q+V7tpnbZz9NfnvnQCsmC8ST7NhMKp3MMtOW5/VEEsUoQxYuLTeWtNf2ao5lrDWisE3S3c+QYGPkpdAAFizYVjv2mfNp4khGN7g6FuPJ9LEUkojUz/TLZd0E2a6WUmqeDJDMq1Ry9cw8gnF1Pbfh5tvvpk77rhj1m9vadL5dTf6KTNUeIEV5FGEMgOAF7eoLiSv9kV467fuZ/WajXoGeuYW6H1BPb/zInXtIAbEulgVx04Pvj4AKFrxMd/+mz47gdJgAXjx/8GqFY7aSqy8NDac645kiHVaBFi9ZqPNPFp585N67dT7fP7RcTsJlWtDgfaebKAcSSg7/c/D6/WufWvvV4+ZuLb51FYb1M9ILFJydMOQSgw88PqAfh+ViKjnv3yfo3bKZE2Go1LJnOLY6ZmNIwBsGonptROo7toAj17v/JwaL99OiwB/er7Hfv6Oax/Qa6eRDerx9T/rW/t02ymdUFrEIOqjVq/ZyMYRtWH/zK+f1WunzU+px5F1GuaTkOYoFG3tG8qt7b9+cpPete/VP+af33iQlvkUCngJ6ZYUsZluskm3RC6pGE2mea13zPbJjiM6mP+MI+vzkkQOwdKkq5SXzgyz+mtt3ryZm266iS984Qtceumlk/5VUMIoQulOTzjGjx/KMySzJnzx9hedP6kJb1GJNhsm3Hmxoydq/WNCp57j8ozEnnCMK/7vRftam51A2eSVu/LXZtZRW4lp71mMxJoWsdO0nnCMy29/wb42ddvp8QLxUsftJLTxNM2ilO70hGOsWT9iX2td+/78ufy103YaK0ZyVHbtu/3Z/N9KzEeZzvqooXHVadFjQItUl1lhH3XDfW/Y19p91KYn89cOz6kBqTlVhMYkPeEYX/x93kdpt9MLv8tfl6qPspKjHr/jJec7w3axBJrt9Pdr8teO+yih7tpQNB/10Ov5pIpWH3XXxflrXfOpXq/8gWmaBZpucuWlyXSWoWgyfx/AlpG484y3dFLJKBUivEn93AFksyYZS9NtnjDdtm3qMFvM+Nt233338d73vpelS5fy6quvsmLFCjZs2IBpmhx88MGO3FQFRUIRSnfWD0bZtrQ9Y5psGJxwVlh8eK1yEIUwMzC8bsYtn3eEaCJNNKlOMHQ7jHzpThnaCZSt2GYwB20lrmskmMQWt5PGOSUVgBEfhUzutFFw87l+MLrtt7wk175+UY1E+STB+sEo28ZbpWynltqg3uYqUPFRc4Bpmnmmm+61rwgJgnKxEwj6qEL2lBAjsRJLzALZjNJ5hEosMQvolj/wepWuWTKZpLoITLdkOrPdz0xMkuksAZ+DjLHMTthzmQT45t7Z1mK5GYahP5ZwCZJJlbC0vkOzxYyTbpdffjmf/exn+cpXvkJdXR233XYb7e3tnHnmmZx00klzupkKiowilFh1t4bwGExy7l7DYElrjbMDNe8BhmeywzC80LzUkbe3aNE1Aa9Ap0V5zQgxO4F+W9kbmvIrAy4rO0mV7lh2qmpQmlpC6G4NYRhMSuiU8tonU7ojr5FYbj5KViOxDO0EylaWSLUFh2w1OpGy9XJaa+e+QZoS88FO5TCnipAcrdhpFogOqs9geFR3YyGUXSyhyU4+n4+amhoGBgbwBxJ4MCGVBSOuZbxtkU1nIZ2clCA1MMimk8TjaecGSpvq37ZImWDO/bNOJNOY6SQ+j4dEQqAZTpGRzWYZGBigpqYGn29u+/sZ//Yrr7zCr3/9a/XLPh+xWIza2lq++tWv8r73vY9PfvKTc7qhCooIOwCTK7Hqaqjmm6fvz+dvUzR2jwHfOH2F8ydpDQvh1OtV+Y7lME5d5cjpDEi3ji+enb5w2wuYqO2GFjuBssm7vw73XK6uDa9jtkqkM4xanRZ1lxoUoTGJbafbX7ADMK12OvV6+MNn1LXhKc05VaQGMl0N1Vxw/J58/29vAipI1rr2/eFCsGavDjvpnk/JKCRzemfCa9/X37+/XWql3Udps1MRyoDLMZYAZZNjLoaHv6euHfRR1nxqrBHotFiEsnrxWOLka+GPl6hrB32UaZryUhWVWGJWELOTVd0QanO8W+RU6Gqo5rPv3pvv3vMaAN6Kj9ohDMOgq6uL9evX888tOV3JiF+0g2kykbY1lw3UOr8lpmH8ZAYmRrAPhmqaIepMGXAsmWEomiTg82BEBeIJF8Dj8bB48eI56xbP2NKhUMim2XV1dbF27Vr2228/AAYHnRXqq0AQ2UwBM0dWTPyMwxazes0mntk4ypWn7ssZhy3WM9DBZ0FdF9z6r1Ddoq4dgmgXq4h82SIoO72wZYxfPf5P/vWQ3fTZCeDIT8K9XwYzDefeDYsOd+RtrZO0gNdDo+5Oi0U4nQZlp2wWLv/9C+y3oF6vnQ4+C9b8DHqehZOvcXRODUjNqSIJHwN8+Mjd+f7f3sRjwIOfO57dmjSwCEDZZfPT8MzNcPBKPXbSzXSz1j1/SLTTIsB/HL6Y6+59nYHxBD9deSjv2EfTd+Xgs5Ro+p8+CwsOdtZHiXbwK56P+tMLPTz4+iCXvGsvvWvfIeeopJvHBxf9Axp2c+Rti9IRswh22jA0wQ8fWMuJKzr12umwc+G+r0J8BM78f7DnCY68bSSRJp5SB7j6pSqK46POOGwxNQEvn/n1cyxurtYfS7xwG6x/AN5xhcPxuXDX5iLEEue8dYmddLv74rexrEOTjzz4LBh4HR67EfZ7v7OxhID0SyAQYNnCFpL/d5pKjH7iUUdKLmeCs372BFtGYnzx5OWs0BVLALz6J/jrFdC4O3z4Nsfe9o5nt3Dj3zbz1j1b+er79nHsfd2MQCCAxzP3EuAZJ92OPPJIHn74YZYvX87JJ5/MZZddxgsvvMDtt9/OkUceOecbqqBImBhStfnCnRYtLGqu4ZmNoyR3RIl1El0HqMfYsBLSdEhEM0+LFihPK1JyFGCPthAAE8nttQkchWFA/QIIb0SdBzmDwuBLe6fFIuhPWdi7SwVcFqtPK5p2V0m3jHNjpTN5wVmxDY3wxhNU+ZiRK+HRzmpp3VM9pqKOvm2/1NpXpCS2ha7GKgbGE2Qd1jzeDh3qEJP4yNSvmyHyHTE12ymdUDqJUJTN5+4tIWCQREqzoaz1IpsGv3PJcjHNUShIEsivfXu21QJKD1c7GnZT88khMWzIx3x1QR/VAc1rdxF91L4LGgAYiQrEEs1LYD0716SaJSSSOUDRktgANQEfdUEfkUQaj26drba91KPVwdkhSHVt9kT7qZrYCtXNUFuvdawdIRisYktknHjWS1WVxu9k1z4wvgkyUXBwnM1jabZEMgSDVXrvvwwx47TdddddxxFHHAHAV77yFU444QRWr17NkiVL+J//+R/Hb7ACIVhOPdQq2s3FQkdO4LRvTHNtfU2rKgXBhGi/Y28rdpJmmgWd4eRKQixYQYt2O0F+cx3pcewtrUC5VYRFYCVH5e1kzaeBSMKxrjs7hRVgOminoWgSM9dpsTmk+RSySOWloNqtt4TUd1H7nLI21tbndQhy2nvFYxFAwdoX0W0na93rdTRJYHfwk7KTNyDWabEQ1oZN+3zyBfOfz8E5ZW08y7UM2IJYzAdaYgmx+QRFKQO2YM2nSCLNRFJzgrTW+VgilswQySV25XyUvJ0gzzaXiyWcsxNI+qjixXyQ/3zaYwkrNo8Nq8MwhyAW85UhZpxdWbo0L5gYCoX40Y9+5OgNVVAkFPHEE/KO3UpeaYPHoxbayFYVKNcvcORtxZxFbAQyubbPRXAYHfVCdoK8w7ACGQfQLyomXrxTT0tfK5nJMjqRokln4kqHnXLzqVWi02KRkzkd9UEGxxN2IKMNdc4n3bJZU06kuoilO1Cw9o0J2Sk1AYkIVDlzEi9WtlhoJ6FOi4WwOhTK+Kgu5ZPHe6FjX0feUqwMOJPOd1osgo8qTizh3Nonq5FYPNZ8bdBHTcDLRDJD/1iCJa0aD+VtH+VcLGH5p6DPQ32VZkJBkfdRHfVVrB2ICsYSztkJCnyUdqmK4rLmrQMH7bFEdZM6/MokVZzb6Ex5uKgEQpnBwR61FZQ07ARBkVgERTn1dP50Wr/+lNVpsVG006KFQqabdgaVhtO0gTFJ7b3iJQkCPo/NENPPzNFgp3Gh4AuKWroDgswcDUm3kYkk6azVaVHodLpodrKSOZrtFAhBMJdo0+GjtHdtLjYjUWg+wWRWokMQS+ZEBwBTMf9rWvSOtQNY82l0IkU8pVmuQgPLN9+1WVBSpAjxuWEYRfBRDjISCxI52iVFiu6jhO0UHXBMViSZztoNBuQ0EotNMtFsJ8PQsvb1S3asLzNUkm4VKBSb7SHFdAN1Og0OlxoId0cq1oYmt8gm0lnGYppLDTQkCcRKdxLjee2som8+S49BZTNHdSdyoKilO1BYZiVkp2REfT8dgDWfmmr8BHyaw4kilmtDnpmj3U7g+ObTNM2COSWka1Q0RqIw0w20SCCIlcIJd1q0UF/tI5hbM0qR5Tsg1bU5my16fG4filfsNDWK7qOEYolCmZ5xZ2R6BnOxhM9j0Fgt1OSs3O0EWhLZ+TlV0XObKSpJtwoUily6Y5eEiDDdnC+HE2ukUGRadJXfS0POIWo/pbE3NM4nc8RO0gK1EKzVO9ZOIDandNhJtDFJ8Up3oLAcTrOdgnXq+wiOrX2you/FtpPQ6TQ47qPG4mkS6VynxXlSujMcTZJMCzVT0FAOV+6NSQzDkJtTOn2U7vkUG1bNOgBCxU0SiMUS0X7IOsN+FI0liuyj2iRlehwu2S7UxdbeCKJI3bUtdBQjlnDIR2Wypp0grTDdZo5K0q0CBZfQoqPJDOO6u1k5XA6XKuy0qL10p7hOHSQZVDpKd6T0p4rL9gBB9qhlp/gopGKOvKWYtkcqBvGwel6s0nqp+QQF5XDOrH2iZQZFThLkS+sl7OSsjxrIzae6Kh9Vft2dFovro5pq/Pi9atNmMTG1QUs5nNCcKnKCAKBDak7pYGOLaSTm7rmmA0dcdAAAt6hJREFUBXyamwrtBGKay6E2MDxgZvN6g3OEWCxhmkX3UaLNSRwure+XlH4pNnO0YN0rtUZnQ9EEWVNVrrbobnJWhph10m1wcJCxsTEn76WCYmJ0k3r0yHcuBQgFVbtrkNSNcCbzPzSuEm4eAxK6tUmG1qlHf43ecaZAhxQzxzr1dFD8uDes7lm7vnf/K+qxqlHzQDuHWABW1Qi+3CmyQ8ycTcMTAHIlix4/JKN6x9oJ8p1mJU49nWV8rB1QZaqhgIDfGNuaeyIvzg95Ow2OJ0hnSotBZW2Wa4M+esLOJMZ3ipGN6tFbnIBcaVBJMXOcZSROJNP2oWMmq3kzNvimegyE9I4zBcR8VKGdss7M3a2jah6JNfoJ1EJ4i96xdoJ8ExnNdvJ482w+h3zUP4eUX6/SHUskxiCd+/s4xNKbKcQYieB4LPFmLpao093sAvLzyJAvq4c8IzEpKdPjkI+ymNh1QZ/+Q60yxIxWodHRUS644AJaW1vp6OigqamJzs5OLr/8ciYmJnTdYwW68cwtsPUZ9fxPl6nrIqBNrDucszosv3rinwBkTTj2O39j9ZqNjrzvdnjmFnjm5tzzXxTNTu1S2kaWnSaGHGl3/esnN9pO4qO3PKXXTn/6rHq+9emi20n7fDIMR5kEq9ds5MHXBwG47t7X9dkJ4Omb1WM2Bav2L4qtiqMV5oydfvTAWgDuealXr52eulmVWQHc+q9FsVNLKIDXY2Ca2MxmbXDYR/3hOZWw7AnHeeu37te79q1/QD2/7ytFX/vEfJRDdrr5kQ3285NWPaTXTo/dqJ6/9PvixXxSDCqL0ZJN5deROWD1mo2sH1R7nktWP6d37XvxdvU4+k9YtaJIPqoYGlTO+Ki7nlfv85O/r9drpyd+nH/+/UOLYqdCRqIcg8oZO3337tcAePjNIb12euYWGN2gnt92blHsVOX30lijZHq0Nzpz2Ef9v6c3A0qyQmssUaaYdtJteHiYI444gl/84hd84AMf4Nprr+Xaa6/lve99LzfeeCNve9vbiMfjPPnkk9xwww0677kCJxHeAndelL82Tbjz4qKcqHVIdYdzsGyxJxzjB397077OmvDF2190nk2wrZ0onp0KO5hqhdXuGuZ8StMTjvGl379gX+u3U0HAU2w7STCoHCqH6wnHuPz2vJ1MNNkJlE0e/l7+2swWxVaWnQbGE/rZLQ6VGlh2su5Wu53+eEn+ukh28ngMW4xbPzPHWR+1es0m+3p++CghzZzCEqs5bnJ7wjG+e+9r9rWcjypizCfFdPP6lfg7OO6jtNkJlE2euzV/XaS1z0qOisQSDvsoC9p91ANfz18XK5bIHTZMSMj0aIolQLOdJu13i2MnEOw062AZcE84NulgSOvaV6aYdtLtq1/9KoFAgLVr1/Lf//3fXHzxxVx88cX8+Mc/5s033ySZTPKRj3yEd73rXTQ0NOi85wqcxPBatfAUwszA8DrxWxFj5thirQOQmZtjWj8Y3S7WzpgmGwYdZn66yE4WM0d7x7FJ7a7nlnRbPxhl23xGudtJbD6BY+VwYnYCZSu2GawItmqtDWAYqpxsWDuDyplSA3E7zbc55WBp/frB6Lbf8rJf+/JlVkJaYZkkxEbm9FbzMZawNp7aYwkoYHyU2NrnAh9lSyBIxhIl56OKb6eaQF6mR7+Wb6nayR1rn5yPcq4MWCyWKGNMO+l2xx13cM0119DRsb3wYGdnJ9/5zne47bbbuPTSS1m5cqWjN1mBRjTvoYRLC2F4oXmp+K2InXoWtruOzq3ddXdraDuFIa9hsKTVYc01F9lJjOkGBYyPuZ2mdbeGttNxK3c7FWrvlYpYa3driG0lcrTYCZSttp29RbCVz+uhJSR16ulMSch8tBMIskcdZFB1t26v2VXua58Yi8AXVIxscGROzbdYQlT4vdRiCcitfdugCLay5lMkkWYiWRqNzuatj6qXjiVKzE7bTt4i2UmMPWrF5rHhOcv0iMUSZYxpJ916enrYb7/9dvr/K1aswOPxcOWVVzpyYxUIoWEhHPmp/LXhhVNXqZ8Lww6UJdpdO0S57Wqo5tAlTfa11zD4xukr6GqontP7boeGhfCeb+evi2infLvr0jn17Gqo5vSD8n8rrXY69fr8teEpmp2sUrhUxmRkIqV3MAftdOE7ltnX2uwEyia7HZ6/dsGc0s74cEiHpauhmm+8f3/72mOg1077/1v+2gV2Euu2mJqARGROb9XVUD0pWNa69h17Wf66mLGEfeAgyKCaIyuxq6Gaty1rta+12umUVfnrIvqoouhZOmCnDx2+2L7W7qOauvPXRZpTtUEfNQElOi/GHp0jI7GroZrPn7SPfe3V7aOWHpe/LqqPEmb5OmCnb54uGEscVEAKmg92clCmp6uhmn276u1rrWtfmWLabUJaW1vZsGEDu+222w7/f/369bS3tzt2YxUIojO34HUdAP/+66IsQFAQKIucenZCZKsjlNvaHJ37o8d2c+4x3foWoKVvV4++avjM00WzU+HptGmaGDpbgTooArq4WW083753G984fX99djr4LLj7i5CMwFl/gO5j9YyzCwR8HppDAYajSfojcZp1tvd20E4HLm4EYFFTNb/9xFF6HXogd0L31kvg8I8WdU69tHVMQCvMuVKD96zo4gs5zZz7LzueJTs4BXUMTburx71PhpOvKZ6PsvT3dJ9OB0IQbIBEWNmqqn7XvzMFLKbrFacs5z37d+mbUwsPUY/Ne8DKO13ho7SjrhP6X3ZkTjXl1ugPHb6Iz5ywTJ+d9j4Z7rxQPb/wufz8EoY1n8KxFPFUhiq/xm6CDq59e7bXAnBEdxOr/v0gvT7K6oR58jXKbkWYU4Zh0FFfxfrBKH1jcb1rvYOxxOHdzQC0hPzcdeGxeu1kMV4P+ygcc0kRfZSQnuUkmZ6U0k2cJT5w8G58/jYVS/zfp9/K/gsbHbjBnaB1T/W45G3w/h8Vz0dJ2clqdDa6Ua19jYt3/TtTwO9V+73L3rUX/3robpWE2wwxbabbiSeeyJe+9CWSye01ZxKJBF/+8pc56aSTHL25CoQwpjqb0b5f0RYgKFyEJDWo5u7YrVPao/ds1bsAWffauKiodrJo0QmJdtcOioBaNO637Nao107JqEq4ASw4UN8400C+zEqoO5wDdrJO/vZor9Xv0K37XXpcUeeUmJ2sEqtkBBLjc3oraz411vj1bsIAxnJr38JDiuujRJk5zpTDmaZp3+8JyztkfFTb3q6YTyKxhENlVpD/Xh2xtEWznXIxX21H0RJuAPXVPoI+tQ3RzvJ1MpbI2WnfBQ167WSa+e/VXie5Iu7TXoniYBMZy06LW0L6YwnLRy05psg+SqjTbE0LeHyACeNzk+kZHFd5Ba/HYL8uzZrw1vdqwQHF9VGSHYE1+Kjj9m6rJNxmgWkz3b761a9y6KGHsmzZMi644AL22WcfTNPklVde4aabbiKRSHDLLcVpO17BHGEtQlYiqkholz6dhjnTbSF/v1b3VW2wnHqR7VTl99JQ7SccS9EfidNQM/sTrl3CwdPpvnDOTvWa7WTda6AWgnV6x9oF2uureLU3UlIMql6p+QT5zad1/0VCe4H+nlYE69T3Mjmu1r5g7azfqjcsaSdr7Su2nYT0ckCt84Ovz9lHjcXTxFKKLdM+T3yUtcYPR5Mk01kCvmmfL88cDpVZQf57pd1OLon5DMOgvT7IpuEYfWNxFjVr1AZyMpYYE4olJoYgm5OGsJKGRUKHVCWKzaDqVyw/z+zZj2KxObjGR7VJ6VlaMj1jW1TJ9hwSWL32uhfEs63Am9MYc0fMl5fpkewIPDcflcmaDIyrpJv2ta9MMe2k22677cZjjz3Gpz71KS6//HK7XMEwDN71rnfx/e9/n8WL50ZbrKBIcImzsE6nrXbXVtmmFjiU+U+mswzlOg5ai6g2uMROoGwVjqXoG0uwrENjYsnJU8+IFShL2am4GxrIs0f1a4Xl7BQfhVQM/LM/ActvaDTbKTkB8bB6XuwDBymmG6hAeXhcfU9bdiDUPU3YCQLddgLXJAmsZEgpMaisTXJ9lY/qgMbyPXCNj2qq8eP3GqQyapOwsFHjibyjrHlpH1X8WKKjropNwzG5bouOJt2E7FTTCj6N8hDTgBh7NNSmdAbNrCpdnMOaL2Yn03SNj+qQ1LO0km5znFP5WEIiOeoOO+Ub0iUEZHqc8VFD4wkyWROPAS065WrKGDM6/uvu7ubPf/4zg4ODPP744zz++OMMDAxw9913s+eee+q6xwp0w1qE6osbgIWC+XbX+pk5zmT+ray/32vo1cyCAmfhgkBZipnjkEg15BMa2k9oxly0oZFij1Y1gi/3d50jM8e2U4NmO1nfKX8NVGkuadgFLDtp1woDxxgfVlDfKRIo506n6xfoH2sKWHYaHE+QzmT1DuaQj7LmU6fu+QSu8VGGYeQTpFKxxBzXvfFEmmhSMRIrPkoDCu2UndvcFWO6uSQ2hwJmjnYGlRdCOX3wOSdzhGKJ2AhkckkutyTdRNjYDsUSuXvtFDnAs9a+4sYSFiMxKSHT45CPsuZTW10Qn1cje7yMMau/WlNTE4cffjiHH344zc3NTt9TBdJw0alnm+3YdZ96OiPWapVYtddV6T2pANeUwkFhmZWQnSaG5tTuOpXJMihFi3bRfGqXmk+WWCs4duopWq6te+7uAkXp4jdHO/VKlWun4mpTA0Xf0LSEAng9BqaJzXDWBqd8lFSCAFy59on5KIdiibqgj5BOlj+4yk5tUgwqqzwzm4LY8JzeSu4Azz0xn5hWGJReLGEzElvAJ5A4mgKFjESrIk0bnIolpHxUoUaiC2R6GnPSPH1S5IVSiiXKFNP27Keffvq0Xnf77bfP+mamwvDwMJ/5zGe488478Xg8fOADH+D666+ntnbHmjTDw8NceeWV3HvvvWzcuJG2tjZOO+00vva1r9HQUFxWg6uQzbqGbgvKOa4biAowqJwpW+yXoq+Dq+yUp0ZrtpPV7jqTVKc0s+y8MziewDTB5zH006LdaCcJBlVtJ4xsmLNjl2MRuGfjaXfFzNH3vTp1TRwqNRAr3bEYib4qxagsIjweg7baIL1jcfrG4nq/ow75KDGdMHDNhgYEu/gVCvSb5qwT+P3zsFwbBJluXr8q05wYVN/TUOus3mY8kWY8oZgpYkw3F9gp30hBgkHVCT1UYolZwFo/LJmeuiqdmstOxRJCSez4KKRz318XzKn2uiCjEyn6xuLspVOmx6EmMqKxRJli2ky3hoaGaf3ThTPPPJOXXnqJv/zlL9x111089NBDfOxjH9vp67du3crWrVu55pprePHFF7n55pu5++67Oe+887TdY0kiNpwTajWKLtQKgkLVk9pdz57aa92nTOlOzrEVucQK8htt7VphhlGgbTR7arTl1EWEWl1kJzGmGzhSDpcuZCQ26NbLcUcpHEBrbQDDUEK1w9oZVA6VGkSkmKMFdioyIxGKwKCaY2m9Xbqjez6lE4qRDK5Y+/JlVkJaYZlknpE5C1jJjPkWS9jJUREfZTE+Zr/2WfOpNujTqzsMrimFgwIJBMlYYs7lcEJrn4tiiZpAoUyP0NrnkJ3EYonqpjnpDjsFOR/lcBmw7vlUxpi2x/j5z3+u8z6mxCuvvMLdd9/NmjVrOPTQQwG48cYbOfnkk7nmmmtYsGB7h7RixQpuu+02+3qPPfbg61//Oh/+8IdJp9P4fJqdZanAoq+H2tRJYJEhtgjVtILhBTOjuiTNMvjstZM5ErRo95x6ip1Og/q84Y1zOk2zy4BFS6zcY6f+SFxArHXuFPbB8SRZU7WObwnNn4YXPq+H1togA5EEfWNxm1WgBQ4FYGLdgF1UYgXWWh8WYGMXlO7MgUElVhJifZ+8AbWpKTLEfJQvCNXN6gAz0gs1s5Nc6Q3nktjzjJEopg8L6vP2vTC3WEKUkeg+O0USaSaSaWoCGvdQDsQSsWSGsbg6UNce97mka7OF9vogkYE0/ZE4e7bPvkP5LuFQ2aIYa96VsYREeWnuexkbVodjsyyB7pUq1y5jlIQS3mOPPUZjY6OdcAN45zvficfj4Yknnpj2+4TDYerr66dMuCUSCcbGxib9K2u4KJEDBV38dDOorHbXMKfNZ7/UhmZiWJ2kQ575VUSIdbKCfJnVHE7T+qU6l4KrSg3aatXnTWVMRiZSegdzwE5W8NVWG9RbYgmushPk55R29qgD655o63iX+Sgx/T1rnU9NQCIy67fpkzoYKrSTCxiJYlphUMD4mP2cEuvgl0kphj+4Yu2T1bO0fNRcYr5iJEeLb6faoI+aXPdj7Yfito+aeyxR7ffazC9tcJGdQJC84ICdQLC81GWxhFglSnUTeHN7nznF50J2KmOURNKtt7eX9vb2ST/z+Xw0NzfT2zs95zk4OMjXvva1KUtSAb75zW9OKpddtGjRrO+7JOAyZ9EuzaCCOW0+8yUh86d1PExmEegXa537aZpdZiAi1OqeUoOAz2N31RUr2XbATtq7jYGrOsOBZBe/uTPdhqL51vGttbo1Et1TCgfIdcUM1EAwJ9nhwMGQ9rJFF5XCQRHY2DA3O1mxhHaNxNymy+NXwu9FhjWfwrEU8VRG72AOrH2ykiLz3Uc5EPM1SDQ5c5edbPKCqEzP7A5146kM4Zj6XTntPZf4KCndUcNwRCNWND4vUxQ16faFL3wBwzCm/Pfqq6/OeZyxsTH+5V/+hX333ZerrrpqytdefvnlhMNh+9+mTZvmPL6rYW9o3OEsOqTYHuCICGif1KmnixI5kGcRJCTaXTvAzLHZHrqdemzEVUKtIMhKdMJOlk6YzvJKC247cKgTYnxYwVcyAonxWb2FdTLbWivQOt5FJVaQZ+aIsnxn6aOyWdO+T+0sX7exCCSZbrUOxhJipXDu0Eisr/YR9Kk1pBRYvvlYQvN8yqRhvF89d4mPapOqRHEiQRDJ6/hqR8RdZYv5km3dMj0t4PEBZv67OkNYsUSV30N9lW5Gost8lGRHYAd8lFgsUcYoqrDZZZddxtlnnz3la5YuXUpnZyf9/ZMndDqdZnh4mM7OqSdPJBLhpJNOoq6ujt///vf4/VPrlgWDQYLBefSFctvGcxsGlV4NKqskZA50WymtMNupu8NZVPm9NFT7CcdS9EfiNNTo7JDk3Om0nFBrc9Fbx1tor6/i1d5ISTCoxHTCTNOFOixC2kbBOgjUQnJcrX3BmWu+9ErZCVx34CDW7AfUd3Pw9Vn7qKFoknTWxDBUglQrXLrxHI4mSaazBHwak8MONJER0x11WRLbMAza64NsGo7RNxZnUXONvsGcjCV0H7SO9wGm0h6umV2nVaeRL1uUYlD1QzYDHu+M30IslgDXJXPapJhulkzP2BZVst2wcMZvUag5qp+R6K61L3+AJ8nGnp2PSqQzdpOviqbb7FHUpFtbWxttbW27fN1RRx3F6OgoTz/9NIcccggA999/P9lsliOOOGKnvzc2NsaJJ55IMBjkD3/4A1VVlS/KdnCZs7BOpWTaXc+Nwh5NpInYrePnF4sA1GcOx1L0jSVYprPdtRNlwFJCrS5LYkMBhV17oJyzU3wUUrFZdYcSs1M8DOmYeu4SW8lqG3XC0Jvq+9qyx4x/va8oGonuWPts8WPRbouz81HWfGoJBfFrZyS6y0c11fjxew1SGaU/uLBRY7e6OdrJNE053VGX2QnUJk4l3dzfxU/8AK+uUyU3XIAOKfZoqA0MD5hZVbo4i++qWCyRzeQPRVwTS1jJUaFYYmzLrOeUWBIbXBefF8YSbm90Zn2XAj4PjTpJFmWOGSXdkskkd9xxB4899pitpdbZ2cnRRx/N+973PgIBPdouy5cv56STTuKjH/0oP/rRj0ilUnz605/m3//93+3OpVu2bOGEE07glltu4fDDD2dsbIx3v/vdTExM8Ktf/WpSU4S2tja83pmfnJQl7G4u7qhxDwVVu+tIIk1/JKE36TbHUgMr8AgFvHrvE1ynawTKYbzeNy7AoHKudEe7ppvLyrVBsNSgqgF8Vaq8NtILzd0zfgu7vFRqQ1PV6IrW8VCgFSZx6llrJd1mGygL2WkSI9Edc8r6zEPRBOlMVm957Zx9lKVrJJgcdYmPMgyD9roqtowqBpXepNvc7DQykSKVUdqo+hte5GI+l9gJBDuYFjZSyGZnlcwS1/F1yboHgixfjxdC7cpOkZ7ZJd2kYonxfpUcNLwqWegCiHYEnmPZ4nzW8bUYicl0lnAsRWONRn3cOfqowkMh7YzEMsa0Pc6bb77J8uXLWblyJc8++yzZbJZsNsuzzz7LWWedxX777cebb76p7UZvvfVW9tlnH0444QROPvlkjjnmGH784x/b/59KpXjttdeYmJgA4JlnnuGJJ57ghRdeYM8996Srq8v+V/Y6bTOBC08926Qc+xxPPUVLrFxWCgcFnXe063vk7GS1u54hCoVa51vpDggGyoYx55JtsZIQl5XCQUGpgdTpNMw+6SZlp0QEUlH13CVzqiUUwOsxME0YHE/qHWzOPkqw06KbfZQUg2qWXTGtWKIlFNBbBgvujPmk9CytJHY2reKJGcI0TcFuwO6bT+IMKph1OZxcLJGzU23HrMpgdaBQH1Z/o7M52slmugkwEl0mVVHl99qsMbF91Kx9lGAsUcaYNtPtk5/8JPvvvz/PPvss9fX1k/5vbGyMs846iwsuuIB77rnH8ZsEaG5u5n//9393+v9LliyZtLgcf/zx+hebUofLWsdb6KirYt1AVL+orkOZf+2CuuDOU0+bGq05mVPdBN4AZJIqmdO4eEa/bt2frFCr++wkJig+smH2p54RqUDZfRtPy04D46ozqNcjoGc5ZzsJlcIFGyAQ0jvWNOHxGLTVBukdi9MfievtYjhHH2WtfdoPG8Cla5+QZk4hI9E0Z9ygoC8iaSf3xRJizByvX+mjTQyqv0NoZlppoxMpkuksIBD3udBO+UYKAgyquk7ooRJLzALWdzOWkpDpmSvTTYiRGB0EM6PKlkPteseaAdrrgoxOpOgbi7OXTpmeObLmxcrqyxzTPlJ75JFHuPrqq7dLuAHU19fzta99jb///e+O3lwFmjHeD5iuaR1vQYyZM6nd9cw7cNotyefphsbacGtPjhpGAYV95qdphaWl2mnRLiuFA0G2B8zp1DOeyjA6oRiJcmXA7imxaq0NYBiQyZq2YK02zJWRKBUo26Vw7plPUOij3H06bZeXSjASkxH13EWbTzFmjvWZM0nVwXqG6LdjCYEDPDf6qDpJH2WxR2cRS+TmU3MoQNCnmdXkslI4yM+nAclYYhY+SjEShdY+F5Zr1wSUTA9I+Ki5xhJC5aVWzBdqB29R5ewnQc5HzY01L5bELnNMO+nW2NjIhg0bdvr/GzZsoLGx0YFbqkAMhfR1lwi1Qn5Sa3cWNa1KhwFTdUmaIWy6re5FKJPO35+LAuW8nYROPWFWp2m9omwP921oClkE+ksNZi/WagUdQZ+H+mrNQZELS+F8Xo/dYdLtpfVFERN3EcRYvoVlwLOYu3kJBCFGYqBOdcd1CcR8lC+oOlbDrOaUWCwBLj3AK5FYwuowq7sUDgr0lt1np0giTTQx84PqGWEOscRYLE08JcVIdKmPsg9bpWKJOWq66Z5TLizXhoJYQrueZe5zz1Kmp08qlihzTDvTcv7553PWWWfxve99j+eff56+vj76+vp4/vnn+d73vsfZZ5/Nxz72MZ33WoHTcO0iJKQVZrW7hlk5DLGSkGihUKs7WsdDgW6ESKnB7KnR/ZK0aBcGYG25RE4qYzKSY5JpwxzsVHiSJtc63j0bGihGOdzM171JrePFNBLdZacOKT1Li+GbmoDE2Ix/XY6R6M5YIl8OJ8nydXEskYxCIqyeu8hWYvMJ5hhLFCM56h471QZ91AQUw0//2jf3WKKh2k+VXzcj0Z1rn1gDrTnYqVAjcb7GEmKVKNVN4M0lzGYzpyTXvjLGtOkEX/3qVwmFQnz3u9/lsssuszdFpmnS2dnJ5z//eT73uc9pu9EKNMCFJ56QDzzFTj0jW2dVatAvRl8vZCS6Q6gVJtOixdpdz6LMqk+qdKewdbyLSg0CPg/NoQDD0SR9Y3GaQzo7JDlhp/nJ9gA1p17aOiZYajDzdc8qJw94PTTpbh3vwlI4KNBJ1O2jAjVKzy4RVraqapjRr/dL6xq5qBQOCn2UUCzR//KsyqzkYgmLkVgLVdtLxRQL1nwKx1LEUxm9iZKS8VHW2ueeWALUnFo/GKV/LE53q0adzTmwsUXtNOZOO+WbKQgx3aKDSifcO/2YIJJIE0tlgHnso6QOWg1DHTiMblQ+qmn3Gf16pbzUGcyopvDzn/88W7du5c033+Thhx/m4YcfZu3atWzdurWScCtFuJC+DgWLkEigPHtqdK/NoNKczHFhKRzkWQSJdJaxmO5Sg4IyqxmiV0yodaBAqNUdreMtyAVgc7CTVbozTxuTQH4t0a/DkjudTkYgMT6jX82L8wu0jne9ndxbDpdMZ+3uqvp9lEtjCVEGVQnEEi5l5dRX+wjmuraK6e/NKpYQslMqBvFR9dxltmqXYo+WTCzhPkYiSMr0tIDHB5g5nfDpwypZrK/yUR3QTChwrY8SshPMyUdVykudwayEvJYuXcqyZcvYb7/96O7udvqeKpCCS51FewEtWr8GVW7zOcPT6QotWrW7bqi22l3rLoebfQAm1sHPha3jLbSLlRrM3k7WvWmfT9msa9e+NrvTrOb5FKxTbBeY8donWmbgUju1F6McboZ2GhhX9+b3GjTVaGS3gnvtlJtPw9Gk3XVSG+wyq9k3+5HTSHRXLGEYRsGccnMsoewkFkv4qmfMbtWNdin2qLWWRPtVFcEMIBZLgGvj8zYpBlWhTM8M2aOVWAK5dQ9m7aPGE2miSSFGYpljRkm30dFRLrjgAlpbW+no6KCpqYnOzk4uv/xyJiYmdN1jBbrgUmdhnaRNJFW7a62YZeY/HJNsHe/OQBkkmTlzSOZIlRq4tBQOBNmjlp3io+q0fgYQKwmZyLWOx8gHIS6B2HyCWTOoilJi5aJybShspODe02n7sKGuCo9HipHoLjs11fjxe9Vnt5KQ2jBLO6UzWQbHhTafLmV7AHRIzak5lC2KdQMuLIXTzSaeITqkNJdDbapqwMyqKoIZQMxHpeJKmB7cV7Yo1RUTZh2f23bS3bkUCvZR7vJRhbGEWxudWXaqC/oIBd3T+bUUMe2/3vDwMEcddRRbtmzhzDPPZPny5QC8/PLL3HjjjfzlL3/h4Ycf5vnnn+fxxx/nwgsv1HbTFTgEe0PjLmcRCqp215FEmv5IgroqjbpBsxQBtQLDphq/XOt4l53QgHIYr/eNC5YtzsxZTGYkSpXuuGs+gaCoblUD+KogHVff2+bpM6ELyxa1wmYkuqt1PBRohYmcenbC0JuzXvu028nFjERrPg1FE6QzWXxejd2/Z+mj+qVK4cC1djIMg/a6KraMxugbi7OwsVrfYLMU6B8cT2Ka4PUYtOjU2wTX2gkmd9nWiroCVk42q5g604Rc12b3xhLtUqX1Hi+E2pWdIj0z+s72Sa19FrPLVwVVjXrHmiHE5hMUsEdnmMyxGsjUSTIS3bX2WYzEZDpLOJaiUScrfZY+Siw2nweYUSOFQCDA2rVr6ejo2O7/3v3ud/ORj3yEe++9lxtuuMHxG61AA1zs2Nvqg0QG0vSNxdmjrVbfQLM89eyVCr5ANXoAV9pJrMzK+uxWu2vf9Bb/sXheqFW7Y3fxhkYsUDYM9flHNqhyuBkl3aTYHu4MvqBAg6oETqe12yk2DNlct12XMRJbQgG8HoNM1mRwPKn3pH62Pipc8VGg1r4tozG55iQzLLHqtRmJQUFGovvsZHea1W0nay3JptUaM82O8OlM1m4iM191fKEIDKrx3hmXw/WKlQEXxHwuYyS2F8wn/Y3OrFhihlIVUjph6YSqcADXrX1Vfi+NNX5GJ1L0RxKak26z81FiMd88wLSPeO644w6uueaa7RJuAJ2dnXznO9/htttu49JLL2XlypWO3mQFGpCcgLj7Wsdb6KgTcuxz3Hhqd+rg6mROXgRUczKnugm8OWc0A20ji+0hItTq5o2ny8vhFCOxwiKwPvvAeIJMVnepwdzKS8XE+UNtM+qIJgGPx6Ct1t3NSfqkdI1M090+Soo9WminGZQJycYSpZDM0Wwnrz/f6GgGa99QNEnWYiTWzl/WvB1LSDCoZlkO1y8VS7i4XNs6aI2lMkRcKtMjdtBq7Rm8Aahp1jvWLJAvrXdpLCGpvVfmmHbSraenh/3222+n/79ixQo8Hg9XXnmlIzdWgWZYi6M/BEH3tI63ICYuaYu1DkBm+o4prxMm2GnRZbpGkD9NG9DNdDOMAgr79JNulrMQ1YxwWbk25OeTdjvBrMRaxxNpJmyh1vmrkdgSCmAYkMmaDEeTegez1r4ZN1KQSo66104gyfK17OTS0+nYCGRy31UXJnPapdij1rqXSaq/yTRRiSUU2qW0wmCWsYSyU1ttEK92RqJ71z47lhA5wJt5E5ls1rS/Q2Laey60U01AyfSABHlhds1+rMStXCzhPkYiSPool1c3zANMO+nW2trKhg0bdvr/69evp7293Yl7qkACLl+E5Npdt4LhBUzVJWmaECsvTcXyAbwLNzRiTDeYFTNHtgzYxWyPAn2PrHYG1cxPPW2h1iofNQHNOmsuZiT6vB5axRhUsytbnO9dmy20F+N0ehYMKjFGYk3LtMv+JSHmo3xBqM6xKGYwp8R8lNsZiW6PJaRK4aAkGImRRJqoCxlUg1HFEjcMaK3VrZHoch9lJ3OkYokZMt2k5pSLGYkgyB611hNLpmeaEIsl5gGmnXQ78cQT+dKXvkQyuf3peyKR4Mtf/jInnXSSozdXgUa43VnUCW08PZ5ZBWByreNzQbILhVqhoNuiSKnBzE9pCjv4aYeLHbtVCpfKmIxMCDGoZmSnSut4Cx3SLN8ZrHvjibTdUVou6eZyO+n2UdbpdGoCEmPT/jW55Kh72R5QEEtIMKhmdeAgZKfYiGpwA/nvlIvQIaU7CrPzURGhmA9cHZ/XBn2EclIdYizfGdjJYgu11gb1NriBEvBRwsmcGdipkJEoynRzIcS0fKubwJtLnLk1Pi9zzKiRwqGHHsqyZcu44IIL2GeffTBNk1deeYWbbrqJRCLBLbfcovNeK3ASLu1caqHdZuYIlcONbZlRqUG+JERwQ+NCRmJ7gfaemFjrDMqsbDs1CAi1Wq3jXRgoB3wemkMBhqNJ+iMJvZo0s7BTn9R8AleXWIE1p8YESw1mvu7VBn3U6m4dXxJ2EvBRgRoINkAirGxV1TCtX6toJCq0S2mFgSqz6n9pRmVW4uXa1c3gd9/myZpPY/E08VSGKr9GDda5xBKSjEQXx+frB6P0j8Xpbg3pG2gW5XCysYRlJ7f6KOGyxeggZFLT0mAdnkiSzjESrSYq2mD7KJfbSXdy1DCUjxrdqHxU0+7T+rVKealzmPYxwG677cZjjz3Gvvvuy+WXX85pp53G+9//fr70pS+x77778sgjj7B48WKd91qBk3B75r9OiEUAszqd7pWi27q4FA7yzjKRzjIW011qMPMATKx0x7onb1CdJrkQYuzROdhJpCW5izvDQSHjQ0iHJRmBxPi0fkXUTm73UUVh5kzPR00k00TiFiNRakPjbjuJHODNobRezk7ujCXqq30EfWpL4sYGWmLlpfGwYrWCKxmJIMgenVPMN7/LgEFSpqcFPD7AhPHpyfRY86klFMRfYSQC7mx0ZpqmvR5XykvnjhkdVXd3d/PnP/+ZkZER3njjDQD23HNPmpvd1w2kgl3A5QFYIdNNP4NqZiKgmaxZ0Dp+ftOiq/xeGqr9hGMp+iNxGmo0dhmc1alnriREd3lpoVN3ISMR1Jx6tTeif/M5Czv1S9HX00nXto630CbVbTFYB4FaSI6rtS9Yu8tfse00z8u1QbCRAigfNfjatH2Ute7VBLxyjES32in3XR2OJkmmswR8Gjd4s2giI6+R6M5YwjAM2uuDbBqO0R+Js7ilRt9gs4klpMpLrXuqalAsVxdCjD1qNzrrh2wGPLtmP4pJv5hmwQGeO9e+NikGlceTrxga74WGhbv8lX67iYJkctSddhJrHAgz9lEjEymSmSwgJNNT5phV9NHU1MThhx/O4YcfXkm4lSpKRIdlIpnhzf7psTBmDetvsPVZCG/Z5cuHxhNkTfAY2KLn2uDyEiuA5pBKtL3cM329oVnBCsCG1k7LTlBYXiq0oXGxnSz26FMbhukJx/QNZNkpPqpsNQ2IlYRYSQuPX53OuhBWEPpKT0SvnUA1kgHofWFaL++Tmk9QAj5K/Q02Dcf028n6G/zzkWmtfYXzSeuBFbi+FK6pxo+VZ3tpa1jvYJadel+Ylp3iqQzhWAoQTLq51E6QT+b//Y1BzT4q9zcY2TDzWEIsOer+WOK5TaN67RRqAwwws9P2UWJ2SkQgFVXPXZrIttaUN/vGBWKJXC6g76VpvdxKjsqWAbtz7bNiiZ7ROFtHJ/QOZq19m56YUSzREgroPbCaJ6j8BecrXM4iuOv5rfbzE1c9xOo1G/UNNrxePb5xL6xaAc9MrU1oOYu2OsnW8e506qvXbGT9oHISF//mOb122rxGPY5umJadJgu16k6OuttOAEPjqoHCb5/azFu/db8+W738f/nn3z90l3YCyRKrgkSOSxmJr/dGAHhm44heOz1zi5pLAL87e5p2slgEmu2USUF0QD13qY96fN0QAGPxlF47QZ6d+fTN0/RRkmXA7mYR/PapTaTVQT2n//BRvXYaeFU9bnx0WnaymKNVfg/1VboZie5OYoOSqQC4/r439M6pfz6iHicGp2UnKIL2notjiZ5caeBdz/fotdNztwK5js0/ebs7Y4lgAwQ06trNAdYhw6t9Ef2xhJUU/cOFM7KTTGMSd699D76uYp101uSYb/9Nr4+yJIte/H8zjCUqLDcnUEm6zUe4vHV8TzjG5bfnT7WyJnzx9hf1nNSEt8A/fp2/NrNw58VTngCI6YSBq+nr29rJRLOdHvx2/noadhqK5oVatTMSXZ7E7gnH+Ntrea0NbXMqvAXuujh/PQ07gWQ3YMtO7lv3QNnplsf/aV9rtdOdFxX8wJymnXJrn+4yg/E+dU8uZST2hGN840+v2NfafdSb9+evpzGnRIWPXayRuJ2P0m2np3+ev55hLKGdkehiO4Gy1Ytb8kxErWvfX6/MX0/DTvFUhpEJi5E4v3V8e8Ix/vRCXg9KzEdNM5borcQSgLLTjx9aZ1+7NpbQPZ8S4/mu3y60VU84xhX/96J9rT2WeOWu/PWMYomKnpsTqCTd5iPiYUjnJrQLF6H1g1Gy5uSfZUyTDYMaaLfDa7FP0iyYGRhet8OXg/CGxsUsAnE7mdnJP5umnVprJYRa3X2Stn4wuu23XI+tZmEnxUiUKt1xd5nB+sEopsScmoWdQLC8tPBQyOO+MMX9PkqodCeTVnpL4MpyuFLxUbKxhPvsBO72UZaGb9DnoaFao24tlIaP2uZnbrETSJYBu99OImvfrGMJIR9l2SlQp3RsXYZKLDG/4L5otgL9sIKv6ibwVxf3XnaA7tYQ21Zteg2DJa0aRGWb9wBjm2lgeKF56U5/pV8q8+9yRqLr7VQRarXR3RrarppSi61mYaeRiSSpjAoE5FrHu9dOInNqFnYC6JOaUy4XfXf72idWEhIdUBsuwwuhVr1jzQKlYieZpJt7YwnI+ahtfuaWta9PkpFY8VEKs7BTMp1lKKpkNOQak1TsNKtYQqxcuxJL2HBzLDFPUEm6zUe43Fl0NVTzzdP3twMwA/jG6SvoatCQIGxYCKesyl8bHjh11ZTdd3qlSqwSY64WarXsVOgwtNrp1Ovz19OxU1iw06LLNzRdDdV8+V/2ta89hiZb2XYqmL3TnE+ttQH9jESXl1hZc8qCdjsVBmC7sJNpmoLdgN0/n0R91Lu+lr82vLu0lZyuUa7EqrZjWp0FpWHbKWco7XaaoY/Kl2trtlM2k28i4+K47xPH72Ffew3DdT5K5ACvBHzU1aetsK/dFEtYB61+r0FTjRAj0cV2cmssAYK6oy5PuonHEidfk792UywxT1BJus1HuNxZAJxx2GIuf88+ABzR3cwZhy3WN9ghK9UJAMD7fwwHnzXly62Np5igrouFWs84bDG3nHsEAPVVPr12Ovgs2Pf96vlRn56GnSSFWt2dyAY4561LqMp1H/rNx47UZ6uDz4J3fFk9X3r8Lu3UL5XIgZKw0xmHLeagxY0AXHnqvnrt9KHfqufVLbu00+hEimRO6Fx7oOxyjURQdjrzyN0B+NdDdtO79h35KTByQvvn3uM+H+XyWOIbuSTBvgvq9fuoroPU85OvcY+dogOqjMjw5DpCuhMrj1oCqATBg587Xu/ad1DONoecPW07VUTfFT50xO601gYA+OnKQ/Xa6eTvqucLDp6+neokNBJLw0cdt5diIF/yrr302umcu9VzTwAO+siUL09lsgyOVxiJFs44bDGfOF6xzU5c0anXRx12HlQ1qedn/m76PkoiPp8HqCTd5iNsZ+FObQ8LyxfUA9h0ca1o7laP6fguX2pn/rXrGuWchUs1IywcsKgBgLF4mmgirXewtr3VoyWMOgXEdMISEUiOq+cu3nwahsGCJnV6ls5sq8riMDpzJ6xW18UpIKYTBiWxoQFVcgAQTWb0DrTwEPUYG4LU1GufVVraHAoQ9GlmNZWInfbqqAUgHEvpHcjjgcbd1PPs1GusYiRK6RpZPsrdscR+C5WPGhxP6B+sJXeAl9q1Jo94LFHbAV7NXVLngLa6ID6PQdZEf2f4dnWoS3x0ly8V0wnLZmG8NNa+Rc2q/M06iNGGrgPUo9XNegr0V2KJ7dDdqnzUuO7YfMFBgAHZJESnjvssjUS/16C5JqD3vlyuvWdhn0613x2dENjvNqnDQjK7Hks0Pp8HqCTd5iNK4HQaYEGjShBsHY1hbqsu7jQachua8OZdvlSMbuvyMgMLdVV+6qpUIK+l404hZmCn3rBUiZXFSKyHYK3eseaIhbk5tWXURXaSpK+XwKkn5O20VbedqpvAn9MOGdtFV7jcfGrXXQoHJWOnBbkSkK261z2AhkXqcRdzKhxLkchthLVrJJaIj7Jiif5IQn+SYDaxhHYty9KI+bwew2a+aF/73OijJgZzSXUDatv1jjVHLLBjiV0fVM8Jlp3Gtqgy6SkgG0uURtItH0totpMvkF9fwpumfKllp/a6Kjy6k+slwEiEwv2uZjvBtNe+dCZrH1Rpr26YJ6gk3eYjXF7jbsHa0ESTGcbimk9p6i3HPvUilEgXtI7XrmtUGhtPKNh8ag/ActoDu2hHDoKlO2Pubh1fiK7caVVPWMhOsRFIRqd8qZhOmMtbxxfC0vPo0T2fDAPqc7baRdKtX2o+Qcn4qK7G3HySCJRtO03to6z51Fjjp8ovxUh0t51aQgECPg+mmU90acM0NzSFGolyPqoEYolGK+nmplhCWPQ91AZezZpkc8QCK5bQnRyt7QCPT5VHW7qEO4FYLJHNlqCPEjgYmnYsIaTnBiXjo6zYvDccJ7ttO1OnMU0fNTietFnHLaFK0s0JVJJu8xElUhJSHfDaYqhuOfW0Np4Bn4dGMaHWUgqUddvJYntsUt1dp0C+e6mUrlEp2EmIQVXVoJh/sMtNjVhJiBWwB2qhql7vWHOENZ+0MxJh2mufWMkilIyPslgEQ9Ek8ZTmUmA328nlUhWGYdhJArfEEpFEmljuO1PxUXmI+Sgrloj0QGbq8nCxA4cSKYWDAjvpZvl6vPn1ZZfxuVAsERuGbO474/Jkjth8ghn4KDWfKj4qj476KjwGJDNZBqOaZRBmGEu01wX1l/vPE1SSbvMRJZL5h7zDcEvZYj6RE5z3reMLIebYrZO05DjEwzt92WShVqnuSCVkJ91MNyiYU1OXGvQVzCmtKJGTaRAsL4XpB2BSdkpO5Oe2y23VUO2nJqDYZPrZozMMlEVF391tJxBMEkz7AE/Zqb7KR3VANyOx9HyU9vlU0wreIGDmmYA7gTjTrYTspL28FNwbS5QAI9GKJfoiCdIZd5TWi80n0ywZH+X3egpK6+dhLDFPUEm6zTdksyV16mmVWenXjSgoNZiCQdUbFuzkUkJJArFkTqAGqpvV8yko7P0FQq1N2oVaS8hODYLJnGmWGlhzSntJyFjpbGi6cvNpLJ7WL4A8zQDMtpPUxtNfk2dLuhSGYdhlIXIMqqnnk5hOGECkdMoWu6QkEKx1b2IQUjv/TtixRKVcexIsRqJ2lq/Hk2fSTuGjIvGU3dCmouObxwJbAkHiYGh6pcC2jq9YLOF+O7XWquYkmaxpx8XaMN1YQqq8NDYCmdxnLgFbdUmVbNcX6CROAdFYYp6gknSbb7Bax2PsklLvBiyUKlu0AuV0DCaGd/qyN/oi6uXVAqdbluNy+UkaCJaXwrQcu+UsGqr89umnNgyvU4+WIL2LUWgnNzQnSRUIteomjjLwqnqsatA80NxRG/RRbzUncUk53OYR1Y0x4NUcNliHQlWNu2SguAELxJuTTM32WDegNBRrg5q7VKbialNTIhCLJaqbwK+6D0/1/X2tV+lLNumWqQAYzX1nvJoPoByA28rhrFK4ar9Hf5fioTfVo9/dDZkgH0v0RxIk0u4orbeSbtrF+ftfVo9VzXrHcQBej2GX27qltH7TsIolqny6Y4lccjRYv8uOqm6AeCwxthUyOz/UXduvYgmrUV4Fc0cl6TbfsOanuScm3HAgPHNLMe9ml7BLDXQvQr4ghHLdonayqVm9ZiPX3/cGAH97tZ/Vazbqu5+nf5F3GL/5kOvt1CXJoJrG5vP3zyinPxhN8tZv3a/PVs/cAm/cq54/8E3X28maTxPJDGMx3Qwq63R65wHYzx5Zbz8/9caH9drp79eq56/+0fV2AsEArH7Xdlq9ZiOv9qoDh8/f/rzete/536rHyFZYtcL1tlpo+yghBlV8VDUF2QFWr9nI7c+q0+ubH92g106P35R//qO3ut5OXVLJHMMoWPt2Hktc/cdXAHhyw4heOz1zCwy+pp7f8QnX20msvBSmFUtYtomlsvpjiRf/n3r++A9cb6fmUIBgLmnSF9bMoJqGj7rl0Q02I/FDP3lcr53+epV6vuEh19sJBCtRphlLrNmgDmu+9sdX9K59z/5SPSbGSiKWEOtgWtte0Jykd4cvWb1mIzc/tgGA25/ZotdO8wiVpNt8QngLPPTd/LWZhTsvnlb3pmKhqxhtlHdAue0Jx7j89hewuEEm8MXbX9SjNRfeAnddnL8uATstLHDqcgyqHf89esIxfvV43kFkTU22Cm+BOy8q+IHpejtV+b00hxTbQf9pmtX0YscBWE84xrf+/Kp9rd9OBbPX5XYCwc2nZaexHZfWW2ufBVOXnUDZ5Jlf5K9LYO0TO3CoqodgjqU5hY+yoN1H3f/V/HUJ2Kk4yZxdxxKg2U6FPqoU7JSbT8PRJLGkFINq57HET/+ePxiqxBJ5GIYhqJNo+aidxxJX3fmSfV2JJSZDrolMzk7jfZBObvff4rHE4z/KX5fE2pcrL5VoTmKV1k/hoyxojSXmGSpJt/mE4bXANpsqM5Mvj3Mh7JIQick+BTV6/WCUbbs4Z0yTDYMTzt/H8FrlIArhcjt11FdhGJBMZxmKbu9sHcUuKOzrB6Pbfsv12KoE7QT5spBiNydZPxjdLsdTsVMech2BC5uTjG733+JrX4n5qAVF8VHbM3PkfVRp2WmhSzoCV2KJqVFf7SNkNydxgY/a5mcVH5WHnI/atZ1E5lTJ2kmoYihU0Jwksn1pfSWWmBqypfXWoXiRY4l5hkrSbT6heQ8wtjG54YXmpcW5n2nAWoR6w3Ey264CTmMKx97dGmJbmQivYbCkVYOOV/MewDaDudxOAZ+HtloltilWZrUTEdDu1tB2P9NiqxKcTyDYTKHQTjtgUHW3hrb9llfsVAAx4Xd/NdS0qOc7OPXsbg1tp7end+3bBi63lWyn2Z0Lild81NSw5lMkniYS16zNZQtVV2KJmWISg0p7LDG1oHgllpga1pzSz8a2mpMM7bA5SSWWmBpinWYnldZXYomZQqwMGKbcR4n6qHmGStJtPqFhIZx6vVp4QD2euiq/SLoQ7XVVeD0G6azJQBE773Q1VPO1962wrz0GfOP0FXbQ4ex9LIR935e/LgE7gaQI6M5PaEDZaremvF28hqHHVg0L4ajP5K9Lzk66NzQLAAPScRUsb4OuhmqOWJoXItZqp5OvyV+XiJ1kkzlTr33/dshu9rU2O4GyiVX2ACVhq0IJhGI2J+lqqOaSd+5lX3t1+6jFR+WvS8BOoaCPhlwDJP1Jgqnt9I33729fa48lDvj3/HUJ2AkE9fd2oenW1VDNnu35pgZafdRxn89fl4idxGK+qkYI5Oywg2ROV0M1b9+73b7WaqdTVuWvDU+J2Mkdjc66Gqr5yJG729faY4nmPfPXJTCnrPk0UOTmJF0N1Xzx5OX2tVYfNc9QaUkx33DwWbDHCYpi27zU1QsQ5Drv1FexZTTG1nDM7sKjBbsQAT12WRsAfo/Bg587ngWNGrP+ITUWK/4N3vUV19sJlGN/bpNgOdzYVshmlD7BNpjIacF86/T9OW7vNn3Ooj3nmDoPgP/4dcnYCQRKd3xBJdg63qc2NaHW7V5S7Ve2++ix3Zx7TLc+O3W/LXdPVfCZp/MBh4thtY8XKVus3w16/rHTzefC3Fr39r3a+MYH9tcbfFlNAk77IXQf5/o5ZdkplsoQjqVorNHYIXIXPmr/3ZTm26Kman77iaP02smnmM289WI4/GOutxMoW4VjKbaMxtiro07fQLtoIvOeFV18IaeZc9+lx9Pdtj2jyjFY35m9ToZ/uaYk7CQmK2L9LeJhSEQguP13Ip1R5YRfPmU5J+/fpW9OdR2gHpv3gJV3loSdxLTCDEN9jwdfUz6qdc/tXlJfrbaz/3H4Ii48YZk+O+39Hrgz9/wzz0LzEj3jOIi8nqVQLAE7jSUs9ujhS5q4/j8O0uujMjmpm/d8F/b5F9fPqaYaP0Gfh0Q6S284zu4tGv3CLnzUIbs3AdAc8vPHC4+tJNwcQskw3YaHhznzzDOpr6+nsbGR8847j/HxHXfw2hamafKe97wHwzC444479N5oKaBhIXQf6/oFyIKcbkSBoPgOsHlU1bMvaq7Rm3CDvMNacnTp2KlByLHXdqpTq2waxvu3+++JZJrhnK7ce3QGyZC3U9f+pWOnojCodjynrBPyY5dpTIwCjOYaazR1l0TCDSYLv2elSut3svZtya19B+3epNdOsVHVaQwU27cE5lSV30trrXBzkp0Iilvj79VRpz9Itta+PU8oCTuBYKdZm42949J6K5ZoCQX0Jtwgb6fFh5eMncQkEIJ1UJVrTrIDH5XNmnaJ67v37dTso3J2al9eOnaSmk8wDR+lvitH79EqY6e6BSWRcIO8nUYmUkwkdXet34WdRpSd3rJbo+aEWzp/D8tPKYk5ZRhGQYWDlI+aOpZY2lpbSbg5iJJJup155pm89NJL/OUvf+Guu+7ioYce4mMf+9i0fnfVqlUY2xaSV1AyEOsOZzmLSA9kttd82ZxzFgubBBYgK0nQuFj/WA5BTIfF6yvovLO9w7Ccel1VvpxIG0b/qR4bd5/6dS6CmFYYTElhN03TnlO76Z5TJTifOhvc05xE3E41rRDQnIxwEGJzyi12ymbzm88SmlNiBw6Wf0pFITay3X+L2QlKcu2T7Vq/883nwHiCZCaL12PYjFZtKMFYojgHeMX2UZadSmc+1Vf5qQ0qJuC88VGRrapxgsevDupLBGJzyi12mmcoiaTbK6+8wt13381Pf/pTjjjiCI455hhuvPFGfvOb37B16/YdUgrx3HPPce211/Kzn/1M6G4rcBpiyZxQm1qgzaxKvG2DLVKLkGnmNzQNpePYF0h2h7PLrLansG/OjW+dGGmFbadF+sdyCNbfpXdMoDnJFKUGoxMpuwx4gW5bWeM3lo6d/F4P7XWqhE+sZHsXp57a51QJ2gkkOwIXiFTvgEG1RepgKDoAmYTSNKp3P4PAQpcUa95frRLHMOXBkMwBXunGEjKl9VPEEjk7ddZX4fNq3i6V4Npn2SmSSDOmuznJFPp7yXSWvjG1P9A+p0rQTlAMH7WLWKJJc7WQve7tBp6SSHUABbIiUo3OYsOQ3L4rqaiPmkcoiW/iY489RmNjI4ceeqj9s3e+8514PB6eeOKJnf7exMQEH/rQh/jBD35AZ+f0Mt2JRIKxsbFJ/yooLsTKSz2eKTvv5DP/mp1FbASSEfW8hBy7qG7EFBR2MTtBSbII2uqC+DwGmaxJf0To1HMKO7XVBanyb6/L5yhK0E4gOKcKy+G2gSqxys2pZt2BcmnayWK6aT9wqMs1J8kkIDq43X9vHlHBs/a1z7JT3QLwamYTOwi7dGe++KhMSjE+oKTmVGF5qVhzkh3aSc2nSnXDjlET8NFYk2tOIsag2t5OveE4WROCPg9ttUG991GCdgLJiqGdxxJQ6KMq1Q07glgH06oGCOQ0LKdY+0T2UfMIJZF06+3tpb29fdLPfD4fzc3N9Pb27vT3LrnkEo4++mje97737fQ12+Kb3/wmDQ0N9r9Fi0on6VGuyGuFFbfUwNI1EmN7hNrUiXmJwHIW/ZEEyXRW72BTUKPFGInZbN5ZlVBy1Osx6Ki3EtnFKzUQm09QkoxEEOw0W7jxzE7umtUfSZDKmOp7U6d7Q1OadhLTCvMFoC53gLgDxoccI9Ha0JSWncRY8+COtW9si2Lue4P55kwlAKthVjyVZXRCikG1IzvlYomKj9opxPT3prDT5oL5pF1KqFTtJLX2WQyqRBjik0krE8k0I7n5XGEk7hhiXesNY0r2qFgsMc9Q1KTbF77wBQzDmPLfq6++Oqv3/sMf/sD999/PqlWrZvR7l19+OeFw2P63adOOO7BUIAdR3QjLYexAqDpPi66c0OwILaEAAZ8H08Sm+mvDVAGY1EnaeJ/qjmR4cwyU0oGYY58Gc1SWRVA6ejmQ7w7Xo9tOtR3g8SkNlPG+Sf9lzaeuBokSq9Jc+4rjoybPqUQ6Q38kAVR81M5gle70SjYn2aGPktKfKs0SK9WcRCX49Tcn2fUBnvb5lIzCRI65WmJzSqwUuHDd24b9KBpLlGwyR6hiKFgLVY3q+TY+qlBvub6qore8I3RJlQHDTuNzUb3leQZfMQe/7LLLOPvss6d8zdKlS+ns7KS/f3KXwnQ6zfDw8E7LRu+//37Wrl1LY2PjpJ9/4AMf4Nhjj+WBBx7Y4e8Fg0GCQc2n+RXMCJZTH4omiacyekvRdhKAZbKmzWKQC5RLy6kbhsGChio2DE2wdTTGIp2laNM4ndZ+QmNtPOsXquYOJQQxbSPrO2w1JykoRRNz6ulkXqOxxALlBVLlcJ5c4ji8Uc2p+nwSWfTEs1STOVLzCdTat+Wp7da+ntE4pglVfg8toYDeeyhRH9VRX4XHgGQmy2A0QXudRnH86fioSnJ0p1jQWMXgeIKtozFWLGzQN5AbkqPW2MF6qG7UO5bDkGtOkksQpCaUBEtNs/1fsnrLpXmAZ5eXiiRzFkF8VH2v25fbP5aVfilNH2VXN4yo0nqtzM2drH2iesvzDEXdKba1tdHWtmvK+1FHHcXo6ChPP/00hxxyCKCSatlsliOOOGKHv/OFL3yB888/f9LP9t9/f773ve9x6qmnzv3mKxBDQ7WfmoCXiWSGnnCc7laNXe12IgLaNxYnnTXxeQy9gTqUdKDc1VCtkm5Sp55TBsqaHXuJnnhCoVaYZkZiTSt4A4oROLYVmvKBqljpzthmwARfVUmVWEGhVphEOdzCXNJtEyw63P5xJVDeNayEZF8kQTqT1csI3EmgXJgc1V9iVZo+SjUnqaJ3LM7W0bheX74THzWeSNslk5XGJDvHgoZqnt8c1u+jChlU2ewkRmB+TlW0LHcGsc7N/pz/jg6o73VB0k1Wb3lcPbfW4RLBAikJBFCxRN8L25UtyjY5K805ZZVrR5MZxuJpGqo1MgLrdxxLiOotzzOUBN98+fLlnHTSSXz0ox/lySef5JFHHuHTn/40//7v/86CBepEfsuWLeyzzz48+eSTAHR2drJixYpJ/wAWL15Md3d30T5LBTOHYRiCbZR3LAJqLUILGqvxejRvaOxAubScBQjqRlgBz8QgpPLfiXgqw4BYiVXptY63kNcKE2hOspNyOLFAuTCRozsZ4TDyWmGCwu87Wfu0z6fEuOqkBSWXJGirDeL3Ws1JEnoH20nSTVT4uJSTOY1CJdtWLLGTEquGaj912kusSnPjCZIMKqs5STJf4olVYiUs+l5ihw0g2OgMduqjxDQSLTuF2ktKbxnyf5stks1JtoslhOZTieotA1QHvDRZzUm0N9CytHy3PcAT1FueZyiJpBvArbfeyj777MMJJ5zAySefzDHHHMOPf/xj+/9TqRSvvfYaExPbt76toPQh1kZ5J8KSsqLvpRuAielGVDeBP8d4HNtq/9g6Fa/25x2XNpQoKwcKtMIku/hty8yR6gxXBgmC/kiCRDqzi1fPEbtgUGlnJFp2qmpQ/0oInoLmJGKB8nbzSSg5apoFa1/pJnPEtMLGtkImbf+4OLFEKdrJ0grTfIDn9UNdl3peEPcNR5PEU6ohlFU+rg0l7KOK0hF4ZyzfSiyxU3Q0KNmkRDprNzPQhl34qIre8tSQI5lMzXQT0UicZygZIaLm5mb+93//d6f/v2TJkl1m77Vn9yvQhoVSDCqLlRMfVcyLYC0Am4cFRSVL+HS6S7TzzkIYfF0FQi17AJNP0iolVjtHcbr45Tc0Y/EUY3G1ERU7nS5BOzWHAgR9HhLpLH3hBItbNLKYdlIOJ872KEE7gZpTm0dibBmNc4hOuZ+d2knIR00MQyqqnpdYiRUIrn217ao5STYN473230pUoLqE55Roc5KGhRDZqubUQiVhY9mpoz5I0Ke5xKqE7WTFfFZzEo/OSpD67WMJWb3l0rVT0OelrS7IQETpJDbr1P3cRdmimJ1KUG8ZVMn2S1vHBLrWF8QSpmlXglSaKOhDyTDdKpjfEAvAquohmGNZFJSFiJ2kxcdUwg9K8jRNTCsMdkhhF2N7QEmfelp2Go4miSWlGFTb26mpxk8oqDkoKmFG4qTSeu0MKqscLh8om6YpN6dKmJUDkh2Bc3Ya71NNQnIQ08uxOszWdiidpRKDGMvX4803JCmGj8pmSrbECqSTbjvwUaL6U6XrozrqgngMSGVMBseFSusLYnNZveXStRMUg+W7M93Rit7yVFgoJYFgHeCl4+owLQex6oZ5iErSrYKSgF1eKkphz5+miQVg1pjVTRCs0zuWBljOQrtThx1So8VOaCaVWJWeY6+v8hEKqNP7YpTDiSZHS7TbmAUxzZwd2GlwPEkincUw8oLZ2lDigbLlo7QHyqFW8AYBU7FzchAr3SlhtgdIJ3Msjdh8LCGmZRnpVSw7jy9fPllCsJKjfWNx0pms3sGm9FESDWRKd075vB4664Xivilivore8q6xQMpHFSaxs2ruVvSWpw8xH+ULqsMzKI6PmoeoJN0qKAmIsQhghx1MiyL6XoKwNuaReJpIXLNuxA5KDcRO0qKDkI4BRkmWWE1uTqK7ZHtHgXKuZFG3nSDPzCnZZI4Ugyq37k0MQVLZx5pPHXVVBHyaw4US3nhCIYtA83yySuvBnlPpTJbeMavEquKjpoKYnWCHpcBijMTCEitP6XWga801J8maqiuwVuwglhArq08nVPkxlOza1yUVS+woOSqqkVgmyRzdlSh1XWB4IJuCaL8aM7fu1QQqesu7gth8gh36KDG95XmIStKtgpJA4SIk3XknmzXzdNsKi2BKhII+u8W19hLTHZQaiAXKViKnrlOdFpUgxPT3dlBqIFaunUnnS4ZKdE6JBcpVjRBQGpZWcxKx+QQlHygXs4tf71icTNYk4PXQVqt5PSpxH2XNp8FxweYkhVIVYj6qtFk5Ho9Bp3QDrR1Jimivbsj5RX8N1LToHUsT8rIiQnaK9NjNSWT1lkvbR1lsbO2MRG8BuzbnowrnU0VveWrYDekkK7tya5+o3vI8RCXpVkFJwHIWsVSGcEw3g2py5n9wPEEyncVjYAeB2hAubWcBgo59B4xEcf2pEraTmGO37BQPQyICCJYBR3rAzIDHD7WdesfShAVSG0/DKFj71OaiOGXApTmnxDaesB0zJ19iVaVXyLxgzFJljjbV+AnmWJu92g+GJvuoeCrD4LjS4dN/gFfarByABdIs32JIVRR2q9edjNCEBVIxX6hd+XIzq3w7Fb3lmcBKomgvL4XtYglRcf4S91FWdUNvWB2macU2ckqiesvzEJWkWwUlgSq/l9Za1W1HfzJnsqC4VQ7SWV+F31spsdoV8o5d94bG0svZAqZJalKJle5AubRPPEFwQxOsg6pcc5IdnHpqhb2h2Q08penuFkjNJ9ju1FMsUE7F7DKUUl37LDuNTKTkmpPk7FTRSJw+DMOQ64Zu+6hcLJGzU20BI1wbysBHidtpvA/SiUkNZCrVDbuGmI/yePLNSbbzUULi/CWqtwxF6lov7aMK9ZZLdE611wXxegzSWcHmJGHh+TRPUZq7kArmJazsv/5kzmTdCNFFqAwCZTERUCv4SkUhNqJa1psQ8HloDVVKrHaFLpuZIxGA7XjzKRYol+iJJxSri5+yk1y3sRzDJFCrNjUliPoqP7W5k2H97NGd+CgJjcSKj5o+djqfKiVW00GX1cVP93yqaQFfrophbCtjsTSRhFViVfFRu4JYh23YLpYQP8Arg/nUH4mTEm5OkpeqENRbri89vWUobnMSW8+tUlqqBZWkWwUlgwXS5XA5BlWlxGpm6JLSNvJXQ02reh7ezCZbnL+6UmI1DSyQ7DRbUGowkUwzHFUlVpUy4F3DslMkkWZMd3OShm3LFoX0p8qgxAokO81OLoezxcR12yk2Comwel7Ca1+XVMm2te7FhiE5IauRWBY+Sig5Oqm0fjObc/OpJRSgOqC5CUUZ+Cix+QQFa98mpbcsxkgs/cOG1lCQgNejmpOMSZEXcmWL0snRui7wBfSOpRFyPmpnJJNK0k0HKkm3CkoG+a5jmhehugWAAZkERAftQFm7s0hOwMSgel7CgfJC0VPPPIW9KMnRhtINlAs7Aos1JymwU51IiVXp26km4KMx1+1LjuW7ZVKJlVxytHTXPZBkUBWU1pMPlPWLvuc2njUtEAjpHUsj5JqTNEAgV4om6aOy2bJIEoh2mi3wUZtFY4nSt5O17gyOJ4mnhErrw1uU3nJGSG+5DDQSPR6j4FBcLpYA5OZUiXertyBWsm3ZabwXMik5jcR5ikrSrYKSwQKp8lJfQHWlBAhvkutcam1ogvWqk2CJoii6EeHNcic0ZaAZAfkgNZ7KMjohxaDabGskVpij04ecoHjeTqMTKaI5bTKxZE6J26mrQWjts1g5iTDEw5Xu2jPEQkkGVQHjQ8xHRQfUoaHhyX9XShB2zCdatrhJjj0FJa+RCNBY46farxiB+puT5H3Uppyduhqq9estl42PEirZLrBTKpO1mXUVHzU9iJFMQm3gDdjNSSqabnpRSbpVUDIQ1TYqKDWQY3sUnHiWcIlVoVPPCnbeEaOvx0chGZk8fgki6PPSWqu07yR1I0Q3NGVQYgWCpcCT1j3F8G2tDVLl111iVfpsDyjoCKy9OUmtfTCTHd1sjyfqo0oYYhIIMKkUWE4jMWenEi+xsta90YkUE8m03sF2aCfN8ymTgshW9byEfZRhGHJzqqAcTsxOUDZrn1gyx7JTtJ/eobCg3nK52EloPhU2J5GeU/MQlaRbBSWDBbaorhyDypzEoNIdKJcHLbqjvgqPAamMyWBUrvOOeClcqA0CpX0atFBqTu2Qkaj5b5fN5gX6Sz4AE2J8WEm3dIy+vh6gwkicCRYUoTnJaO8GUhkTr8ewxZe1oUzYHguKUVov6qNKvxQOoK7KT11VrjmJYDmcmOj72FbFMPEGINSudyzNWChVsm2XAQseiEPZ+KiFUmWLNc3gU2P1b10HVPSWZ4I8y1culkgMb5TTW56nqCTdKigZWIFy71ictFDnnfjQRmI5jYou7ZoR5eHU/V4P7XVCuhE7ED/WHiiXyUkaFJbDCSVzxrawdSQKCJykjfdBJgmGt6RLrECwbNFfpZLJwFjfekCakVjaa5/YfAKbmRPJ2amzvgqf7hKrMknmWBuaaDLDWFwzgyrH+MiMbqIvIlViVT4+Sqy0vr4ITLfCBjKe0t6OiQm/W4zE2Aj9g0oDWft8KhO9ZRD0UYaR91G9G4BKcnQmEGVj11t2UrFEXZWA3vI8RWmv8hXMK7TVBvF7DTJZk/6IDIMqMaQ2Ge11lRKrmUCui5/6W5nhzfbJXaV1/PQhVrJdbzUnSRIZUuU0YhqJ9QvA69M7lmaIdprdZu3bTfd8SicV4wNKfk4tLCjdkWJQ2XaqiL5PG9UBL0255iRSOonJoX9imlDl99AS0lzyWVY+SjaWmMTGbpbSsizt+QSCsURVg9I9BuLD6nsupjla4nrLUORYQlJvuYSbZ0H+Oz0UlWtOkhgSmk/zGJWkWwUlA4/HsMXf5URAVeedygnNzCDXxS9np0gPZjaNz2PQIVZiVQ6Bcm5Do5vC7vUrfSEgOyo0p8poPi2UKi8Fe05ZdtIeKI9tAUzw5Vl2pYqOhiCGAYl0lhGh5iTZio+aFcRKtreNJRqrMXRrtpaVj5IqW8wxqJIRMhNhoHKANxOI2Ql24KMEqxtKWG8ZCmMJQTvlZD60z6fYSF5vucTXvoZqPzUBRfSQkn8xc3aqNFHQh0rSrYKSgkWN/ttrA3qD5RzdNji2nk6GZBahMgyUn94wotdOte1geDHMDPuxnq7GKry6NSPKoNuYBctOr/WOCWw+1Zw6IP6kzJwqLN0pcXQVJLGtBgfakCuHWzj2HJ0MySVHy2BDE/R5aapRLKbnN4/qHSxnp/rwqzLzKTEOsWH1vAx8lBVL/P2NQb1rX27dC4xvppNBFkrEEmWYzHluk+ZYIhBSLCrgLcZaGqr91FVpLrEqE1YO5MuA3+yPiGmP7j2+RshHlUdZPeRjiXAsxdr+cb2D5XxUR/gFGR9l7aFCbeAvbbaWYRi01apY4oUto3oHyyXdQmNv5uxU2n87N6OSdKugpJDKabl9//43eeu37mf1mo16Btr4GADV6TEeCV7Ie5J/0TOOhXQCIkq4vBySOb25oOvPL/XqtdNzt4KpqNd3BK/kw/6H9IxTiDJK5ry0dQyA1/vG9doJIKu0ky7138YjwQtpevXX+saCstp4PvhaPwCZLBz7nb/ptVOkF4DjUn/nkeCF7Nv7f/rGgrI6bFi9Ji9EfM7P1+i1U9+LACyIr+WR4IUcO/YnfWNB3k5VDXZyopQxkVBMxJ8/skHv2rf2AQC8ZopHghfxvux9esaxUEYlVgAbh5UG6EOvD+q10zO3QFwx3H4Z+BbnVP9dzziFKKNkznObRgClO6o9lkipg6dzPXfxSPBCdlv/O31jQVn5qD8+v9V+/q7vPajXTrkY7ODEkzwSvJADBv6gb6yC8cphPq1es5F/Dqt91EW/fk6vnbY8A0BTYiuPBC/kHbG79Y01z1FJulVQMugJx3hu46h9nTXhi7e/6PypWngL3Ptf9qXXMDlp/Tft8hAtsLos+qqhpkXfOALoCce48x899rVWO915kX3pMUw+Gr5er52gbAKwnnCMHz7wpn2tzU6gbLL1OfvSa5gYd12ieU6Vj53+644X7Wvtdnr5DvvSa5h0PPgFvXYqE52wnnCMy29/wb420WynR2+wL72GyaEvfkXITqW/oekJx3hs3bB9rdVH/eky+9JrmLx/83f02ik2AimVqLJLW0sUPeEYv31qs30tGUt8ZuL7lVhimugJx7juL6/b19p91D8ftS+9hon/T5dWfNQ0sK2P0m6nf+QPVr2GyZLHvlSx0zQgHks8+C370muYHPvq1frXvnmKStKtgpLB+sEo20pTZ0yTDYMOl1sNr1Vt3AvgIQvD65wdpxCFJzQlXmJV1nZKRNSmBkresa8fjJLdxlBa7ATKVtt+K8yM3JwqYRTbTkbFTtOCuJ22WfsMU7ePKh9WTln7KMtOtR2qG3EJY/1glG37kUjZyavbTtlM/rC1xOdUsX1UJZaYHoptp0osMT2UfSwxj1FJulVQMuhuDW2Xj/IaBktaHdYJaN4DjMlTwzS80LzU2XEKUSYnnqDstK2smpSdsoZHr52sk7SqRqiq1zeOAMTsBDlbbTOYzjk1qcSqtOeUvJ22CQvE1r7SDpSLbaeKj5o+iumjtNupTNY9KHIsgeZYItKrJBc8PrvJUKmi2GufnI8q7Tk1f+xUiSWmjWL4qHmMStKtgpJBV0M1V5yyr33tMeAbp6+wBZEdQ8NCOPV6TNSqlzUh+Z7r8t2tdKBMTmhA2embp+9vX+u2k+UwTBPWH/H1ip2mCctOlm830GQnUDZ599ftyyweOHWVPltFByEdA4ySL7Gy7VQQhGm106nX2+fTWQy9doKyYVBZdvKI2innozAwtNupvNa+q09bYV9LxhJj7/xuxU7TRDFjiZcO+aqMneoXgserbxwBiMcSJ19T4KM0xxJlpLdciSVKAzuKJa5+v1AsYRpwyvf02mkeo5J0q6CkcM5bu2mqUR2l/mflYZxxmKbF9eCz6D3qKgBeMJYRPPxsPeNYKKPTaYAzDlvMW/dQ2nSXvntvrXbKnnUXAHH8BA47S884FsrkJM3CGYct5qPHdgNw0opOfXYCOPJTRFEndY8f/RM4WKOtwrkNTV0n+IL6xhHCGYct5nsfPBCAJS01eu108Fn8s1aNdd/CT+m1UyYNYzlh5zJY+844bDF3X/w2+/rk/TUyWA4+iw17fASAh6uO02snKDsf9aEjdmdxrkvbqn8/UKuPGnnXKgDWsYC6o87VM46FMmHlWDjjsMWctF8nAB9721KtduLjqhGTCXje8m96xrFQhrHEJe/aC4BjlrXq9VGHncewtw2Avx/4Hc2xRPnoLYOy00/POhSA5lBAeyyxvvlYAO5r/XDFR80AZxy2mAf+83g78XbcXm36Bjv4LP654gIAnvQdjHHISn1jzXNUkm4VlByWddQBMBZPaR3nnzX7AbDYGNA6DlBWp9MW9luoOtwNRhJaxxlsOoCk6aXaSNHJoNaxyuUkrRAHLm4CoHcsrncgw2C9oVhnnUHNY5VRh1kLh3U3A7BlNGZ3cdaFdV5VWtDpjWgdh0hPrsTKrxKkZYC9Oupoq1OJ3vWDUa1jrQvuDcACY3gXr3QAto8qnzm1V6eSCBid0BxL1L4FgEXGoNJ004kyjCX2303FEv2aY4l483JGzFo8Biwyt+76F+YCK5YoIx91cC6W2DKqQfB9G6w11Pe7w695rDLSW7ZweC6WGI4mte+j1vn2BKDLO6Z1HOJjEB9Vz8vERy1uDtHdGgJg7cC41rE2VKkqsoWeIa3jzHdUkm4VlBz2aKsFYG2/3kXojaxiKTSZo3nxfF0os1NPgD3aZJzF5rEUG0y1YfePvLmLV88RZXSSZqFwPpnbqlY7iFQmy6tpZaf2hMb255C3UxnNp676Kqr9XlIZk03DGgR1C/BqWq19nSnNdrLWvYbSL7EqhNTa93pG2akrtUnrOKTiEO1Xz0u8xKoQe7Tn7KQ5lliXaiFh+giSzH/ndaGMusxayM8nvUnsLeE4a80FANRF1msdqxx9lDWfNg5NaD8YejWlYonOpJCPKpNEDkBdlZ+OenUwtE7znHotI2yn6iYI1ukdSxBi+10rlkhvUU1eKtCCStKtgpKDVAC2IeKhx1QnQgxqTOaUWYmVBctZ6HbqW0ZirMsFygy+oXWscmR77N5Sg2HAWDzN4HhS2zi94ThvZpWdQmOaOyOVYaDs8RgsFVr7/hFTpQz1Ud0bz/JjJAIstQNlvXZ6PmenUHoEJjSy3awSK39IbWrKBPaGRnsyJ2kfDFV81Mxhzad1mg+GNo/EWJvzUcaQZjuVoY/qrK+iJuAlnTX555C+g6FwLGUf4NWNV3zUbLC0VSaZ8+xEOwANExu0jlOOB+IAe7TL+KhXYg0kTD8+M5Vn4VbgOCpJtwpKDvlAWa+zWNs/zroc2w2dAdjYFtXy3ONTpVZlAitQ3jIaI5bUd3Lycs8Ya82cnXRvaEY2qEdfld5xBFHl97KoSWmt6ZxTm0dirMvZKdX/mrZxABh4XT0GS7vD7LawN58a7TSRTPPMhErmBCKblJC0LvS+pB5rWvWNUQRI+ajXhrNstQ6GhjQeDG15Wj3WdZVNiRXkD/B0zieA1/vGbAaVVh8VD0MirJ4b5cMc3b2lBo8BkUSaAY0lpi9vHbN9lPZYYih38OTX0JGwSDCM/MGQzjm1eWTCTo6auu1kxSpVjXrHEYbN8tVop2zW5MmxRgD8iRGIaixd7HlePYY0ap8VAUtz5aXrBjXvdwdjrLMPhjRXDIW3wPqH1OM8QyXpVkHJwdrQrB+MksnqOfVcvWYjD7w+YAfKL7/wlJZxAFjzU/WYTcP1b4FnbtE3liCaQwG76YUuh7F6zUZ++MBaOwDrW/+ClnEAWPM/EMuxSf73g2VjJyjcfOo7Tbv92c32fEr1vcbqJzWdpj1zC6x/QD2/76tlaSedgfL//H0dAzQyZlZjmFn+/NAjegZ65hZ47Eb1/OU7ytJOOufTb57cyLrBqL32PbnmMT0DPXML/P7j6vnwm2VlJ4vtsTUcJ5rQc+C1es1G7nq+11773nzlGS3jAPDYTfnnNx1RNrYK+rwsbrYOhvTMqdVrNvKdu1+17TSy8SUt4wDw9C9gdIN6ftt5ZWMnkGGPrn5yk20nz+hGfve4psTbM7fAq39Qzx9ZVZZ20umj/ueR9Yxlgmw21aHafQ8/rGegZ26BB76hnq+9v7zs1K6fNb96zUb+sTlsz6lnn31C21g8vAq+tx/84lRYtaKsbDUdVJJuFZQcFjZVE/B5SKSzbNUg2NoTjnH57Sp5Yy1Cm1//Bz1hDYKt4S3w6I35azMLd15cNicAOgOwHdmJwdf12elPn81fl62d9CRzesIx/t9Tm/mn2UHK9BIyEtzw+4ect1V4C9x5UcEPzDK1k54ArCcc47q/vgEYdsn2nfc9qNFO1qFJedpJ18FQTzjGF38/ee179pk1AnairOzUFArQEgoAeppeTPJRueTo4IYX9fmoB7+dv674qGnDspNJfj4Fw+voGdWwzoa3wF0X568rdpoResIxfvn4PxmknrBZg8cw+dkf7qvEEjOEhJ2+8adXAOyKob889HAllpgh9sgdDPWOxRnXcDC0o33Uay8+o89H/fUqbFuV2do3HVSSbhWUHLweg+4WxSR4U4PDWD8YxdonWYvQUmMrGwY1aFQMr2XShgZUqemwZs0rIegUAS20k5Ug6DBG2bS1z/GxGF6rHEQhyshOSzUHYOsHo5hAGh8bTaXxscTY4vycKnM76Q6U1w9GMe21TwXK3WhY+8rcTgsbqwn6PCQzWTaPOO83xHxUmdsJ9M6pyT5Kzac9KrHErLBUI8u30E6bzDaSppcaI8HWjWsdH6vc55SIjwIKD4a6qcQSM4U1nzYMRUlraHoxOZbI2akS880YDTV+WmutpheafVQuOdpd8VHaUEm6VVCS0Nl1rLs1ZMvWWIvQYqOPJU1+x8eieY/tf2Z4oXmp82MVAba+hwYWQXdrCE/OThFq6Dcb1ZierY6Ppey0jZZRGdlJd9mi1fYc8gnSPY1elrQ6rGdT5nay/o6jEymGo843vSi0k8XM2dPTo8dOxjbhRxnZyeMx7L+ljjnV3Rqyv+VWMmepUbHTbKCzOcmkWCJnpzYjTHdtyvGxyj2W0MnyLYwl1MFQBwBLqMQSM8XSgtJ6HU0vJvkos+KjZosFDdVU+T2qG/qI86ymQh9l2WmPio+aFZZqlKvYsZ22Om8nKHsfNR1Ukm4VlCRsPQINyZyuhmpO2V8FyD00M2EGCRgZurL9jo9Fw0KoKugGZ3jh1FXq52UAnUy3roZqPn5cfhG3kjmtcQ2tyRsWQtcB+etys1NON2LzSIx4yvmmF82hgL35tBz7h/eM09VQ7exADQth3/fmr8vMTtUBLwsb1d9MRzKnq6E6//45O72teUSPnY76TP66zOwE+TmlI1DuaqjmiKWqgYKVHO329tNV63N2oIaF8J7v5K8NT/nZSSMzp6uhmn89ZDcAxqmh11S+vjO12fGxaFgI9Qvy12U2p/LzSY+dLnnnXva1FUu0xDTojjYshMVH5q/LzE5WojkcSzGk4WCos76KoE9tXa217/TFE3p81FvOyF+XmZ08HsPWtNQ1p2w9stx8OqJ+SI+d3va5/HWZ2Qn0+6jj91bNJ+w9lDFGl19DeWldF3gKyCtlaKtdoZJ0q6AkoTOZA1ATUJuX9x+0G/6OXDA2+LrzA8VGIT6inv/Hb+DiF+Dgs5wfp0iwA+XBcbIatI2srptv2a2B/Q88VP1Qh50AkrnN8zu/WnZ2agkFaKj2Y5qq3MBprO1XpQa1QS/vPPYYAJZ5exwfBwBfLqg78MNlZycoFNZ1fu3LZE0Gx1V3wH878e1AbuOpgbFA4yL1uNth5WknzWVW2VxFzQeOP4ysvwaPmYbh9c4PtPBg9VjVABe/WH520siaB2isVpuME/froGnxfuqHOnxUJgXjA+r5B/6n7ObUHpq7oVsSC3u0hTj6yKPUD3XFEpkc0/Ftny87O1X5vezWlDu40TCntozGSKSz+Dxw6juOA2CJqUkTqqpBPe57WtnZCQpiCU0+anRCfc8/8G4VS9RNbNbTDb0ld/jevm952klzJYo3R/P94NH7kKnNHdzo6IY+sh6yKfAG4aw/lKWtdoVK0q2CkoTOkhCA1/oiALxjeQf+9r3VD3UEYAOvqsf6hbD3e8ou47+oqRq/1yCeyrJVgzDn6zk7HdHdTKhrufqhDjul4jk9AuAtHyw7OxmGkZ9TGrokWXZa3lXPnvvmNvGDmjqO9b+sHstwPoHeAGzj8ASJdJYqv4e3H3WkYjYlIxDpdXws205LjilvO2mYT6Zp2j7q5LcswNOq8WCoX4lh03VAmdop3/RCx8HQa31qnh63VzvBDo2xxNBataEJ1MJ+p5edrZpDARpr1MGQjqYX1nw6ZPcmahfuq36ow07ZbD7uW1F+dgK9pcBWLLFHWx37vuUQ9cOhN/UcDFk+atm7ytJOS1v1+aih8YR9gPeeIw+EQF1Ov0vDwZBlp8VHlqWddHcwtda+E/frxNu2TP1QZyzRvg8sPa4sbbUrVJJuFZQkrFPJwfEE4Qln9VGyWdN27Pt01oG1oRnSkCToy7Wlb9/X+fd2AXxeD0ta9OkRvNo7BsDenfV5Ow1qOKEZfE2JtVY3QV2n8+/vAuhk5rzaq+bT3p110LKn+uHYFkg4PFYmDQOvqecd5Tmn7NJ6DfPptdx8WtZe9//be/P4KMps///T3Ul39g7ZEwgJCZAgmyAQQRAQZJVBHQUdZBu3O1e+o6OOg/deRa7ecbledZzhp84MgjrKoggoKsqOLLIvAZJAQhYIWcjW2ZNOd/3+eLqqu9N7dTWku8779cqLqu6nluZUPec85znPOVCpQ4Be6ewLn/R9JkM5YbD05+4B+PJ9qmrsgK5ND5VSwa7jUx0V2HLq0ysMahWrhl7ug2roBYKOsrAlfDHhUM3bEoMAZWCa9r58pwpulC2hKwM6mwGV2hyhE2CYdZSPbYle/dgStc5moMkHkfNC3xegtoQPI914R07fmDCEhwQDcSZnjk91VGDKqT8/MVQrfTX05o4uXKljes/3Oiqw5eQOgamZiYAnQhOEpKgQAEBRjbQK42p9G1o7DVCrlEiLDQfiTE4Cn3RCvOd/kPTn7iH4quoYx3Eo4A2wxEizUq8rYs4XKRHkdAuE5GQBxg0Z0CRGAmExQFgc+0LqEPb6YsDQAQSHAdHp0p67h+DLKn4FleycAxMj2Qe+iqDiuIDv+/iE37UtnWholTa3ET/ZkB4bhpBglbnv86mhHJhyUikVQtJoqd+phtZOVDWyaI+BiRE+llNgv0+Ab6N8rWwJfmKo6RrQ0STthXg5xQ0EVD4oztUD8KWOumjpdAtSAzH92BdS66jm60BrDQAFEJ8t7bl7CDfifTLbEjcigiownTkp0aFQBynR2WVEucRFLy6ZnKPxkRrEhKvJ6eZjyOlG+C18LhapIz74GZrMhAgEq5TmTuh6gfQh7HwnlBiYUQSA75w515s7UN+qh0IBDEiMALSpQFAIYOgEGiROgBzgEYmA2QDzRQTVRdMSq6ykKPaBrxQ7L6f47ICN9uBnPdlSUGlzGxVUMWdOdlJ3Q1liOTWWAx06QBlkfhYCjHBNEFK0pokhid8pcyQ2/z75ckAjJx0lsS1hGnj2jg5FZEiw+Vmvu2zO6yUVAR6RCPguyret04DSulYAJmdOaDQQnsC+9JWOCmhbwnfLS/MtnaOA72wJPnI0ph+g9kElxx4AX0ih3gfV0K1WCwG+syXaG1n0KBCwEw4qpcK8FFjicRSvo2zl5ANbIsAjEt3Bb0YldXV1WLBgAaKiohAdHY1HH30Uzc2uH77Dhw/jrrvuQnh4OKKionDnnXeirc0HVTmIGw6vMKTvhPioHHZ+ocR7ewPQWivdhTgu4KMIAMuiFxIPPE1ROemx4SzaQ6k0z1BLboAFfhRBhoVzlJPQudzYrheWbWX5etYzwGc8ATYjGakJgpEDSmtbJT23MDvNG2CxPpZTbH8WrRCgZPhowiHfJorAIiJRyomhllqguYptx2dJd94ehq8mhviBZxb/PkX1ZoVejHqgXuKJIRnYEr56ny5VN4HjWN64uAhTf+QzZ07g2xL8+3SlvlXSauh6g1FwuArvlGDzkS3hKZbV0KVeCpzf3ZbwVdQ8nx8xMpmtoghQfKWj+CATm4jE+mJpJ4a6OswrWwI09Ys7+I3TbcGCBTh//jx27NiBbdu2Yf/+/XjiiSecHnP48GHMmDED06ZNw9GjR3Hs2DEsW7YMygCNgJAb5kTVUndC3aJy1GHmSntSGmBNlUBbPUtWHhfAAxqLCqZSkm+5ZJHHV3kjZBDtkRYbhiClAq2dBlQ2tkt2Xj58PSkqBNow03IaX+Wg4menA1ipKxQKZCRInzOnXW9AicmJl21jKEu8DFgG0R6A75bv2DhzhIkhHdBSI92F+H4vOg3QRDpv68eYo+Z9M/AU5KRUmtNVSNn3dbYA9SVsO4B1lGU0tpRFLyyXlir49BFkS4gmLkKNqJAgcBJPDJXUtKDTYES4hbPI51HzAa6jfLEUmOM4YRmwXVtCyokhmcgp00fFAwu666jIFCA4HDB2mXWKFNRcZIU0QrTMQSpT/ML7lJeXh+3bt+Of//wncnJyMH78ePz1r3/F+vXrce3aNYfH/eEPf8Dvf/97LF++HIMHD0ZWVhbmzZsHjUZzA++e8BW+SgJqTqgbYf7QFxEfvPEVkwkEh0h33h4Gr9SrGjvQ1C7dzAk/8BRm0gDfzKa1NbDlcEDA5vYAgGCVEn1jTbmNJIxKtJnxBHy31EAGUQQAkBknvQFWdL0ZBiMHbWgwEiJNOpJ/n3RlQKeEUXUyiCIAfFN1zGDkcEmYGDK9U8EhQHRfti2pjpKHnMxR875ZBmw9MeQDHXU9HwAHhMcD4XHSnbeHkRoThmCVAm16AyoknBiyGXgCvonG7uo0ny+AdRSrhi69fc5H5QxIjIRSyTtHKSLRG3yxFPhqfRtaOg0IVimE3KaIyWABBh06oLlasmvJRU6+ivK10VGWE0M+sSUGB2xebHfwC6fb4cOHER0djVGjRgmfTZ06FUqlEkeOHLF7THV1NY4cOYKEhASMGzcOiYmJmDhxIg4cOOD0Wh0dHWhsbLT6I3omGRa5jfQGoyTn7OyyDF+PMn/hC0NZmPEM7AFNVEgw4k2DeClzsdjkIgAsnKMSGmC8sojqw3K9BDD84FPKqESbGU/AIoqgEDBKtPxE38ZyJQEBndcIsHTmSDigsRh4CtEe4bGsYi8gbdELGUQkAr55n0prW9DRZURIsBJ9YyxyDflER8lETqaJoetNrCqsFHAcZxvpBvhmAk8muXKCVeZnXsqoxILukaOAb5w5tYUsgkQdyXLQBjDmtCLS6yi7tkTjVemqoRuNZrsvgCMSAcvoUQltPj4vdrwpLzYABGlYxDTgo3FUoMtJ+tUNNc0dqGnuNOfF5vGJjrKori1j/MLpVllZiYSEBKvPgoKCEBMTg8rKSrvHXL7MBl+vvPIKHn/8cWzfvh0jR47ElClTcOmSYyX6+uuvQ6vVCn+pqYGtGP2Z5KgQhAaroDdwuFInTRTG5ZpmdBk5RFokwQbgm8gcmUQRANIvszIaOSE5/0B7y0t9odRloCz4ZVZSGso2+acAZnyp1EBXO6C7Is2FrhcAnBEIjQEiEly392N8sWyxwF5UDiC9M8fQBVwP/GgPwPw+ldVKNzHEDzwHJERCpbSYMfaFk0AmOioyJBiJUfzEkDTvVGVjO5rau1gSbNP7CoBsCS/xpTPHri1RWyTdxJClLRHg0R6CLSGljrInp7AYICyWbUs1MaQrA/QtzEaJyZDmnD0UX0S62bX5AOltCY6TjTOH1yE1zZ3QtUozMcRPiPeNCUOYOsj8hS9tiQCfwHPFTXW6LV++HAqFwulffn6+qHMbjczAffLJJ7F06VKMGDEC7777LrKysvDxxx87PO7FF1+ETqcT/q5ckWhASEiO0sKYlUphWCYSVyjsDGikzO8hk1wEgPRVx67Ut6JNb4A6SIn0WItoDz6pbmst0FonybXkEpEISG+AcRxnW8UKAJQqiwTIEhnKljOegT6gsXifpCp6YXeJFWAdlSgFdZcBQwfLGxKdLs05eyhJUSEIU6vQZeQky21kNyoHkD4HFcfJ0pkjlY7iB54ZceHQBKnMX/hkQCOPiETAMq2INHKqb+lEdVMHgG7vlFANvQNoKJPkWnK0JS7XSLi6wZ4tAVjY5xLpKD5yNC4LUAVLc84eCv8+SVkN3SbnKI/UtkRzNdBWB0AR0KlfAFYNPZmvhi5R5LxNJWAen0wMySMa2xU31en23HPPIS8vz+lfRkYGkpKSUF1tvQa8q6sLdXV1SEpKsnvu5GSWqO+WW6wFPGjQIJSVOVagGo0GUVFRVn9Ez0Xqii4uB571JawKi7cYDSwyB5BFJyR1PgJztEcEglQW3Zgmgi0DBaRTGJa5CAIcqd+n600dqG/VQ6kA+idEWH8pddUxGUUk9o0Ng1IBNHV04XqTBP0RHCwDBqSfnRbklM3yhwQwCoXCZzrKVk4SR/nqrgIdjYAyyPyuBjBSJxS/6MiW4P8v2+pYdVgpIB0lGt6R06dXKCI0FtEeSpWpQAl8YEsEvs1nWehMiomh1s4ulJlWtDi0z6XWUTJwjiZEahChCYLByKFMqomhG6WjhLzYGUBwqDTn7MFkSFw80O6EOCB9NfT2RvOKFhnY5864qRZvfHw8srOznf6p1WqMHTsWDQ0NOHHihHDs7t27YTQakZOTY/fc6enpSElJQUFBgdXnFy9eRFpamk9/F3Hj8FUnZOP5j0gENFFs+RqfN8ob6kuArjY2kxrTz/vz9XCkXg5X4GiGBpA2CaiMwtcBs5wqdO1o6ejy+nz8gCY9NhwhwSrrL6V25sgkrxEAaIJUQm6jQgneKV2bHtd0LDH5gO7vlNT5PWTkHAWkd+bw75TDpTv1pYBegiTzvJziBgJBau/P18Px2QRedzmpw8z5vKR4p1pqgeYqth0fuFXQeSR3jjqy+QDpnQQyWt3QNyYcKqUCLZ0GVDV6PzF0qaoZHMcqo8ZGdCuGRzpKNKzohXTvlN5gFM7j8+WlMnKOAtKvRLFb5AwAYvlq6A3SVEPnJxsiU8w5gmWKX0wzDxo0CDNmzMDjjz+Oo0eP4uDBg1i2bBkeeughpKSkAADKy8uRnZ2No0ePAmAdyR//+Ee8//77+Oqrr1BYWIiXXnoJ+fn5ePTRR2/mzyEkROoQdruJjwG2XE3KkFteWcRnsRnVAIeXU0lNKwxG72dOHC6xAqRdCtxUyRSPQmU+bwATHaZGbDgbYBdL8E45jBwFpF8SIqMoAkDa5XCXTO9TijYE2tBuy2kEORWxBNPeIgxoAj8qB5BWTu16A0pM76XN7HR4PBCiBcBJMzEko4EnIP3yUuc6SsKlwLyceqWzSO8AJ9NUnKSqsQPNEkwMObT5AGltiY5moKGUbctAR6mDlEiTsOiFWzafZKkq5BM5CkjrzCmuaYHewCFcrUKfXt2iz3g5NVxhha+8RWZLFqUspmA0coLdZ2NLBIcC0aaJISl1lEyco87wC6cbAHz++efIzs7GlClTMGvWLIwfPx5///vfhe/1ej0KCgrQ2moOj33mmWfw4osv4g9/+AOGDx+OXbt2YceOHcjMzLwZP4HwAXwnVChBCHtzRxeu1jNFYH/WU8JZmip5DTx7R4dCE6REp8GIq/Xeh7AXOJqhAaTNmcPnyonNBIJDnLcNEKSM+LCb+JhHyiiCtnqg6RrblouTIEE6OTmc8QSAXmmAMhjQtwKN5V5fy9z3yUROEr5PhdXNMHJAdJi5IrSAQuEjHSUPQ5l/n0pqW9DlZdGLLoMRl0zR986dBBLISWYDT21YMOIipCt64XAZMCCtLXHdlJ86IpFVhZYBUqYVccuWqL3k/cRQV6f5vZSNjpIu0s1hXmyAFbwI7QWAY5N43iI3HSXh+1Te0IaWTgPUKiXSYsNtG/hER8njfXKG3zjdYmJi8MUXX6CpqQk6nQ4ff/wxIiLMs3rp6engOA6TJk2yOm758uW4cuUKWlpacOjQIYwfP/4G3znhS/rFhUOhYMuj6lo6vToXv8wgIVKDXuF2ltP4ItJNJp0QK3ohjcLo6DIIUVg2MzSAtM4cmUVPAdJWMHWY+Bgwy6m5Cmhr8O5CvJy0qUCIPPJwZkpYRMZh4mOAJZLmK7h5+07p28xRWInymHCwfJ+8nRiyXLJoM6ABJJ5wkFffZ1UNvd67KIzSulZ0dhkRGqxCaq8w2wY+sSXkISdAOicBx3HuRSTSwFMU5gqm0ukou7aElNXQawsBYxdLJ6Pt4925/AQpI90c5nMDpJ0YMhrNjmyZ9H38+1QqQTV0Xk6ZCREIVtlxBfnElpCHzecMv3G6EYQ9QtUqpGhZCLO3CsPpjCcgbd4IGYbbmvPveSeny9db0GXkEBkShKQoO9FnvJzqitmspTfIbCYNADLipDHAjEZz5VK7EVSaSCCSFbzxeompjHLl8AhObAmcow6rWPFI5SS4ng+AYzPe4fHenctPSI9lE0ON7V2oaZZmYsixjpIon6VBD9SY8uHKREcplQr0i5NmwsEclRMBpdKOc1RKW0JmkaOAZd/nnY6q0LWjqb0LQUqFoPes4N+nlussmtobZLa6ATAvBZY0GtuejlIFWUwMeamjLJ2jAV4FnYd/ny5LMTHkKOcoT6xEtkRDCYu+V2nMsg9wLKuh80VFxCJMNiQ6SEkglS0hs7zYriCnG+H38MtCvF1q4HrgaZGDyhvFpG83h1bLyElgzr/nnZwsZzztRntEpQDB4QBnYAUrvEGGzlHz7LR3ciqra0W73gh1kBLp9sLXAemcOcJMmnyUOv8+XdO1oa3TIPo8HMe5duZIlYPKMnpKJgOakGBzbhvJdJRDOUmUg6ruMmDoZP2otq935/IjBFvCSx3lNJclYF30wptq6BxnfqdkEjkKmCPdpJJTRnw41EF2hkOaCCCqN9v2Nl+YjCPdvM2TWNfSKVTpdujMkUxHyW+iNc2yGnqzd0UvXPd9EtsS8QOZ01UGWBa98PadMsvJwcoQqSLdmqtZpW6FUhaFflxBTjfC7+ENsJ8vXUeFTvyyEKdROYCpyqgS6GgEKs6Ivg4rw2xgSa/5SB8ZwMvpVGmDV3JyOuMJWBe9OL8Z0InMQ2U0yC58HbBYalDd7FX+PX4mbUBCBFT2oj0As2Iv3CleToAsDeWYcDW0IUHgOOBYcZ3o81Q3daChVQ+VUiHI3gZeTleOeicnGUYkAuZ36qcLVZLoKJcTQ9V5gO6q6OuY5ZQNKOVjJvI66lBhrVdycpp/CgAik8wTQ2W/iL4OdFeAziaWc5GPTJABvHM096rOOzm5isoBzLZE3rekozyEjx4sb2jzasKBf59SY0IRrnHgYOH7vsv7vNRR8pNTSLAKqaaiF9+cvib6nWrt7BIisFzqqPKTJCcR8LbE7nzvbAmz082FzVdfAtR6UZiJz4sdk8EKNMgc+VhTRMBSb8rl9l1uJe54Yzc2HCsTdR6nuQgA4OwGAKZ19P+YBJz8VNR1rNa3yyTaAzAv2cmvavJKThddyQkAlCbDbO+fgfeGiJNVfQnLERIUyirDyYRDRbUAAL2Rw51v7fH6fXI44wmYc7md+0q8nDhOlhGJG46VQdfOqvctXnPUazmlx4YhJNhBJWV++W/lWfFyAiyicuQjJwDoMrDI6NUHikX3fbpWPSp07QCcTAyVHjRdsB14b6gEOkpecqpuZFEeey9e905HCdHYDqIITn0G6E2RCp/O9V5OcQNZ7kWZkF/RCAC4Ut/mW5sPADiTzXfoL+L7vubrbIkqwBzZMuGnC5XC9pR39nn9PmUlOsnX2lxtavyDlzpKfrYEAISadP9r3+WJfqcuVjEbPy5Cg9gIjf1G/P9vXZE0cpKZjmo1rWpYd/SKaDl1dhmFlSwOI90ubjdtcMDfbiNbQiLI6Ub4NRW6Nmw9c03YN3LAf3x9zuMZgJrmDtS2dEKhAAYk2DHAdOXAt0+b9zkO+PYZcTM11fJb316ha8Pf9piXZ4iVE+BGpJuuHCg/Yd7njOJkxUd7xGcBSgfOiACjQteG/9ycK+x7I6cCV1E5unLg3Cbzvlg5NV4D2nWAQmWenQtwKnRtePFrs5w4eCEnV85RXTlw4F3zvlg5AbI0lCt0bThYWCPsi32nLlYzOaVoQxAVYsfBoisHvnvWvE9y8ogKXRvWWQxgxMqpXW9ASS1zqA20F0XQ3ZaAF7aEDHPlVOja8L8/Fgj7Xukod2yJ4p/N+2LfKf596pUOqB2kWggwbHSUBDafw6gcXTlw+nPzvlg5dTQBDaVsW2Z9H/8uAF7oKHfktOd/zPukozyiQteGnReqhH2xciquMeXF1gQhRWsnL7auHNj2jHnfGznJNCLREeR0I/ya4poWm/RqBo5DSY1ny+J4hZMWE4ZQtR0HS12RecaThzOYK/F5ggyjPYprWmCUQE5N7XqUNzAF49BJUFcE5oawQIysZJgrRyo5AW44c6SWU2x/IMjB7GqAIamcXEURSNX3tdYBTRVsO14+0R7FNS3dn3JRsnKZz01SHSW/aA+pbInC6mYYObb8O95etAfZEl4hVd/XZTCi0BTt4TAiUWodJaMiClLqKHPOUR/rqOsmZ25EEhAW4+Fd+i+S6yhf2xJdHeboe5n1fVLIqcAilZLdvNhkS/gMcroRfk2/uHB0TxelUiiQHhfm0XlcRk/FZLJEkJYoVOKq5sjQ8y+VnPjw9cQoDaLD1PYbSSUrGUYkSiWnji4DimtYtIdDJwHJSTT25KRUwGM5AW7k9pBMTqaBp7YvEOJkmVCAIdU7VVDJltQ5zjkqkZw6W1jlZ4B0lFe2RIT9AY2UtoQMoz2kklNJbQs6u4wIU5sLndhAOko09uXkuY7iOM4cQeVr+1yGkaOAhDqqiukon9sSNZcAYxeg0ZoLncgAyW0JX79PRqMs82I7g5xuhF+TrA3F6/cPhWU/9Of7hyBZ61nCRpd5wrS9gTl/YR0Pzx2/Z597QrsOaDQluJaRYuflZKkw/uc+EXJyNeMJmGUloADmvOe5rGSYi8CenF6YmeWxnIqqW2AwcogKCUJSlJ3wdcBCTvzFvJSTjCISeTmpLAb1C3LSPJaTwcjhUrWLd0ro+yzMhXveFSEnec54dtdRCojVUXxUjisdZSGnaa96LqfrBQA4ICwOiEjw7Fg/RpCTRd8nSk6u8rnZsyVGP+a5nAx6VpQJkL2OevXewR7LqcD0Pg1IjITSUaEfyXWUvOX0+6kDPJbTNV07mjq6EKxSoF+cg6W5NnIC2RIewMuKR6EQ1/fx75RntsQ7Xtjmg2SVF1sqW6LAbVvCQkdN/g/P5dRQAuhbAZVG3KRSAEJON8LvmT+6L/65eBQAIFytwv0j+3h8jrPlOgBAfJST5WkjFwHP5ALpE9i+UkTi4uL97N/wRCC0l+fH+zHzR/fF7ucmIdhkhY1M8/z3nyytBwD0jnbgyOEZuQiY+Ce2nTaW7XuCvh2oMYWvh8pnmQHA5HRw+V1CJb9eoQ4iCp1wpJjlsOoXF24/2oNn5CJg/mdsOyQKuHWBx9fCtVPsXxk5CAAmpwPLJ2P64EQAbJmBp5worUO73ghNkAJ9Y5zMlo5cBDx11FygJO0Oz2/46jH2r9bz/tnfmT+6L1bMYQPu/gkRmD+6r0fHcxyHCxVMR8U4ivAFTDrqHNDLZOCGxXl+s3wxBhkayfNH98WGJ24HAAQpgXuGpXh8jjNXGgAAiY4mGwCzLTFwpukDz99dlB4CDJ1AcBigTfX8eD9m/ui+2P/CZERo2KBwgLPqow44XsIqPqf2csOWmPYq204a4rktYTSaI6jEvI9+DG9LDOujBQCEqx1UHnXC4SJmS6T2CoM6yMmQdeQiYNFWtq3SAEMf9PhauHaS/RuZ5Pmxfs780X2x9I50AMDkrASPddSFazrUNLNCNAMSHES6AUxOvz/NqjcDQOJQx20dccVU8Tnas3sMBOaP7ov/fXAYACBRG4J5ozzv+8+bbInYcFe2RC6QxK4Fjed9rJAPM6afbPJiu4KcbkRAMCkrAb3CgtHSacBpk9HrLuuOliHPVA1rxdbzzqvBaHsDwx9i20W7PLvJk58CGxay7ZYq8dVg/Jj0uHCM7c8Mz/0Xr3t07IZjZfjyBIsSXH/0iuuqPYPvZ/+WnwT0HibvPfAOhEq1/7xLdrJK1oZitmnAue+S53L672/ZTOSZqzrXcho4ky0TaNcB1057dqMn1prD17f9QZZymj+aGV37L14H54HjbcOxMsz/iBmvHV0cvjpxxfkBcQOAtHFsW0zfd3YD2z62WnZyAoB7hrP36VJ1M643dXh07D9+LkZzB6tatnTtMdc6avBcti1GTj+9xLavHpOlnEanxyA1JhRdRuCXy7UeHbvhWBmOFDNnzls/5ruW04hH2HahCDl9apKxvhU4/S/Pjg8A+vQKw+RsNuEgxpZYc6gEAPDd2UrXOmrIr9m/ledYbkpPOPgXJiMA+Oxe2b1TydpQ/MrU9+0TIac/fnkWAHC5psW1nPrdyZYbGjrMkwfucvJT4MoRtr3jZdnJCQDuGZYMADhRWg9D94R8TthwrAyz/3pA2N929pqT1gB6pQH972LbYnTUsX+y7dwvZSmnWUOToVYpUalrF9K4uMunh0pQ0cCqoP9+/SnXOorv+8ToKL5g0PV8WcrJHuR0IwIClVKB8QPiAXhmgImq1phpUhblJ903wISKZRaKTGw1GD/nzgHM6eaJASaqWmN8FjPAuto9M8B05cC+t8z73lTu8WMmDmRyOnCpxm0DjJeTZWuXclIFARl3sm1PDDBdOXO08chUTrdnxEKtUuJqfZvbBpgoOQFA5hT2rycGmJTVGv2YuAgNhvRmy25+9sCRXaFrw+s/5An77ukok5yKdrNIG3ew0VHylJNCocCdIm0Jj6s19ruTRY/WFQH1Je5diGwJAd6W8EpOcENOUSmmJbwccHmP+zeoKwd2rTTvy1RHTRzI3qejxXVo1xvcOkaUjlIozPZ54W73b7C7jpKpnIb3iUZkSBB0bXqcvdrg1jGCnCwERbaEbwlTB2FUOlsp5Gnft+Lb88K+W7ZEf5OcSn5mBSzcgXSUQ8jpRgQMYgwwURWWxBhgUlaD8XPEGGCi5CTWAJOqYpmfI8YAE12xTIwBRu8UAHEGmGg5iTHASE4CYpw5oqpqpuYA6gig5TpQleu4nSUkJ4E7TTpq/6Uat48R9U6FRAF9xrBtd/s+kpMAL6ez5TrUtXS6dYx4HUW2hFj6J0QgWRuCji6jEAnqCq91lCcTePROAQCCVEqMF1aiuNf3eS2nq8fYKgd3IDkJiNVRHtsSiUOAiEQWrVv2i3sXIjk5hJxuRMAgxgCzl5jVrWownhpgUlYs83PEGGD94sLRPTOYW3ISY4BF2ck5JUNZiTHAvJaTJwZYTKbtZzKUE+C5ASa6CpYYAywmE+j+VMhcTj9fqoHRzejRNDu59lzKKkhtzj3qrjOH5CQwLjMWQUoFimtacKXOxeDRhGhbQlhmRbaEpyRGhSA7KRIcBxwovEE6qmgXbEaujuiVbvuZDGUlJnqU5YO1/swtOWVMYu/H9XxAd9W9GyRbQsBsS7gvJ1G2RHRfIHYAc8Zc3ufezZGOEuDfp8NFtejoci94QZSOsgxecHccRXJyCDndiIBBjAGm6NYxqBQK96rBeGqAaXszY0C4sEpcdaUAQIwBlhgZAm2ouXCF23ISY4BVnu12w/KVlacGWLI2VFhCB3ggJzEGGNfN0JCznDw0wJK1oZgx2Jws2m05iTHAIpMAjUU1MxnLaWTfXghXq1Db0okLpjyirrhab730w3Md5aYzR9ubOVV5ZCynyJBgjOzLokfdTYMQpg6yqibstpz4KN/L+1hFUldoewNZs8z7MpYTYKGj3JRTsjYUiRYFs9yWU99xQFAo0FRhrp7oitoi630Zy0qMnEZZFNtyW06hvYDet7Ftd/u+4FBrRzbJCaevNEDX5ro/StaG4r4R5v8nt+UEeD4pHpViXTBLxnIalByJ+EgN2vQGnCipd+uYhlZreXqso9wNMtH2NkdwA7KWU3c8LyVDED2YOwfGI7+yCfsvXheStzrjmzNsjfnwPlosnzkI6XFh7imL7gaYq1LwRgNQbUr4Pvk/gFsfkXUHdOfAeGw4fsVtA+x4aT0a2vQIU6vwwSMjMTAx0j058QbY1WPMAHOn8ljul+zf0Y8Bt9zLZmdkKqvuBpil49Meje16FFSxcuSv3z8Ek7IS3C9n3n8KUHuJGWC3/Mp1e15OfcYAU16WtZx4A+x6UwdOlNRjXH/XVfKKa1kEz6Pj0/HYhAz35ZQ5BTizjhlgd/+36/YlB4AOHXO8PfgJy7UoUzmpg5QYmxmHnXlV2HfxOob01ro8ZstppqPuGZaEBTnp7uso3jla9gvQ0QxonFSUA1h+Ur4oydxVQMZk2coJAO4cGIejJXXYf/E6Hrk9zWX77ecqYOA49IsLw5/vG4r0uHD35JR8KxAWC7TWMj3FFytxBMcBtabK2nc8DYx5Ut5yGhCPv++/jJ8vsUIyTqtlA8ivbERlYweClQp88MhtGNw7yj05BYcA6XcAhTuZjnJl8wFmHTV0HrM9ZKyjxvePg1LBCslca2hDSrTz//OOLgMKKpsAAP81exBmD0v2TEddPcaifN2x+c5vZsvh4rOBWW/LWk69o0ORGR+OoustOFRYg5lDk10ew08MPTw6Fb+fOsAzOR35kNkSHAeb0MbuVJwBmqtYddp5n7FqwjKVk0KhwIQBcfj6ZDn2Xbruls3H2xITB8bh3yb298CWmAxAwVJVNFUBkYnO2+vbgOoLbHvmW0D2PbKVU3co0o0IKPiID94Ac8WWU6zKzoOjUjE2M9Z9ZcEbYIB7szSlB4Gma0CIFrjjGdl3QN0NMFfwymLW0GRMHOiBIwfwLF9Yax1w6Se2PepRoN8EWcuKN8AMRg6H3Ige3Z5bic4uIwYkROCh0X1Fymm36+hRjgPObmTbIxfKXk68AQa4V222oLIJeRWNCFYp8P/u8sBIBkwGGMwGmCtyTXIacj9bSidjOQHmAiXuTDh0dBnw3dkKAMBvxqR5pqNiM9nyNqOe5eBzxYUtrG3iUFZVU+Zy4iccDhXVQm9wXYyCtyUeuC0VYzPj3JeTUskcnIB7OqoylzlHVRpgwnOyl9Oo9F4ICVaiqrEDBVVNLtvzcpqcnYCptyT6zpbQtwEXvjHd5FLZ6yhtWDCGp0YDcK+QzJ7862hs70JSVAh+e0c/z+TER1Bd3ssmvF3BO0dHPCJ7OQGerXAob2gT0sQsm+KhLZF+B6BSA7oy80SCM3g5Zc0EsqbLXk4ThehR17a50cjhm9Os73todF/PbInwOCB5ONt2J3q04Aegs4mtYBnzhOzlZAk53YiAwhMD7GJVEy6YBp6z3ZjNscETA+zsBvbvLfcCQRqnTeWAJwZYZ5dRGHhahrG7DR/x4Y4BdmErYOhkA093ZrJlwIQB7htgm08x5+i9I3q7jDiwwRMDzHLgOciNqDgZ4IkBxjuxJ2UlIDpM7dmFPDHA9O3mgeew+Z5dJ0Dh36cTpfVo7uhy2tZy4JmTEev5xTzSUSbn6LB5nl8nABmSokVMuBrNHV04VdbgtG2Frg2/FNcCAObe6jrC3gZPlmzztkTWTDaJJ3NCglXI6cfeDVeObDbwZH2fKFuCd+aUHgI6XeT64wee2r5A6u2eXysAMacVcUNHmWyJubemQNk9aZgrUkYCGi3Q3gCUn3Tetr4UKDsMQAEMecCz6wQolnJyFbzAO3Jy+sWgt4voRRvU4UDfsWzblY4yGoDcr9g22RIAWPCCQgHkVTSiuqndadsjxXWo0LUjMiQIk7MTnLa1i0c6ymRLDJ3nOnpRZpDTjQgoPDHAeKU+cWACeoV7OPAE3DfArAaeNKDhcdcA21tQDV2bHgmRGtwuZuDZ+zb3DTB+Jm3Yg55fJ0CxdOY4M8Aqde3eDTw9McD46KmB04HQaM+vFYC4a4BZznjee6vIGUjemePKALu4HehoBLSpNPA0kR4Xjr4xYegycjhcVOu07VaTg+BXt6ZA5enAE3A/Z47lwHMoDTwBQKlUWBSScW5LfHP6GjgOGJMegz69XCQQtwc/oLl2Gmhx8kwYDcC5TWybbAmBO92ccDhaUodr3gw84wayQkuGDmb3OUNYWvoAi2YkBDkdKKyBwUkhGV2bHrvzqwEAc8XoKFUQkDGRbbvq+3g59bsTiBIx+R6A5GTEQK1SoryhDUXXW5y25XXUvWKc2ID7Oqp4P9BcyVLG9J8q7loBRmyEBkNS2MTLzy76Pl5Os4YkIyRY5fnFLHPEGp1EfrfUAoU72DbpKBtIExABhzsGmNHIYSs/8BwhwkEAuG+A8QPPqD4sFxwBwH0DjI/KmSt24OmuAdZQxpYB04ynFe4aYN+cKQfHAaPTe4kbeALuGWA042kXdw2wYyV1KG9oQ6QmCFMGiRh4Au4bYMKMJw08LbnTjSWmujY9dgkDT5E6Kn0CoAwC6i4DdcWO2wkDzwksWTUBwP1lVpYRvqKISgYSBgPggMt7HLcr+ZnlkQ2JBvrfLe5aAQi/ZPtoSR3aOh1Hs/MDz5lDksQNPBUKi2qzTnSUZZoK0lECw/toERUSBF2bHmeuNjhs90NuBToNRmQlRmJQcqS4i/V3I8rXMk0FOQgEwtRBGN2PFbFwpqPyKhqRX9kEtUqJWUNEOiz5CbySA0BXh+N2vJwG38eqcxMALGwJJzqqXW/Ad7lstZBoHdVnDKCOYLlHK884bndhM2DsApKGsfy9hBVkBRMBhzsG2PHSepQ3tCFCE4Spg1wkhXSEuwYYzXjaxR0DrLFdj515Xsx48rhjgPGOnPTxlIPAAncNsM2neCe2F/937hhgJQdMA08tMIAGnpa4Y4BtMU02zBA78ATcM8Bo4OmQO91Ysr39XAU6u4wYmBiBW5KjHLZzSkgUkJrDth3pKMuB51AaeFpypylPYm65DnUtnXbb5FeygWewSoFZQ5PstnEL3pZwpqPOmmwJGnhakRkfgRRtCDq7jEK0dXcs8yNKoqOcyek8P/AcCiRki79WgBGkUmL8ANcTDsJE64gUz9NU8PByKj8OtDmo7lh5FqgpAIJCgEFzxF0nQHFHR/FympwdD22Y8yJbDkkcDEQkAfpWU7S1HTpbgbxv2TbpKCvMecxrYHQQvLC3oBpN7V1I1oYgp1+MuAsFqVk0KOCejiKbzy7kASACDncMMF5ZeDXwBFwbYK11wMUf2TZ1Qla4Y4BZJuYfnCJy4Am4NsBoxtMprgywi1XmxPyi8iPyuGOAWc14Un5ES1wZYGzgyZxuonIa8bhjgFkm5k8YJP5aAcjYzFgEKRUorW1Faa396FE+4fvcW0XkR7SEX7pY6CD/Hj/wVGncqxosIxKiQpCdFAmOc5x7VEjMLyY/oiWZFtGj9pbx69tYzlGAdFQ3FAqFxQoH+3KyzI94ez8RaSp4MiYCCiV7Z3RX7bcRbAmy+bpjTitiX07XGtrwy2WWmN+ridboVLYahTMCl/fZb8PLaeAMyo/YDf59+uVyLdr1tsELRiOHb71NUwGw4IVMFxMOFy0S8/OTSAQAYGRaL0RoglDX0onz1xrttuEjsX8lJj+iJUJeNwe2RH0JcOUXsNVCvxZ/nQCGnG5EwGFpgG08egUVOuvqmJaJ+b1SFoC1AXZuE6Art/7+wlbTwHMIJea3A2+AfZ9bYSMnwOwcFZWY3xJLA+zIh7ZyqjoHXM9jifwpMb8N/Pt0uKgG+y5W28jKMj+iVwNPSwPsxCe2ctK3AXmm/Ig042mDpQG2/liZjZz2FrCBZ2KURlxifkt4OZ3fbCsnwGLGk/IjdicyJBgj01j06NpDJTZy8joxvyWWlfyKdtvKih94ZtHA0x58TstNJ6/ayMkyMb9X0VMAy2cZFMryFp36zFZOF7ebEvNTfkR78Drqx/OVdm0Jy/yIXg08Q3sBvUexbXu2BA08ncLL6VRZA3ZcqLKR1TdnmCNnjJjE/N3hHdmnP7eVk1WaCrIlupOdFImESA3a9UZ8ethWR3mdH9ESXkflfevclhj6IK0W6kawSomxmcyW+9cvpTZy0rXqsSefObi9Hu/ycir7hemj7rKi/IguoaeXCEj43F8/nK/EHW/sxoZjZcJ3lon5+c5KNKG9gOg0tv3Vb4H3hgAnPzV/T9FTTtG16QEAF6uabeRUqWvH4cts4Pmr4RLkGYo0nWPvG3bkZKoIN3AGJea3Q3ZSJCI1Qejo4rD442NWsrLMj+hV9BSPyuS0O/+1rZwsE/PzRRcIgWCVEn1jWD69/9h8zuadMleE6y0uP6IlHaZZ1apztnJqKAPKDoHyIzqml2k5zpqDJTZy8joxvyVJw9lSYH0L8Nl91rKi/IguMZqizvZfrLGRkzDw1AThLm8HnsEhQEw/tv3N/3NsS9DA0y5Vjax4zLWGdhs56dr02GVKU+H1wBNgFZwB4NBfbeVkNfCk/IjdSYkORUKkBhyAxz897lBHSWJL8Fz6yVZOVon5KU1FdxQKhaB7/vx9vkM5iU7Mb0kzezdRX2wrJ8vE/DTRapdwNfv/33D8io2cvj/H8iNmJ0VikNg0FTwxGUBYHMAZgC/mW8vKarUQ2RKOIM1NBBwVujasO2rudIwc8OLXuajQtaFC14bVB1hC6V8NF5mY3xJdOZvZ5OGMwLfPsM+vHDUNPEEDTztU6Nrw5vZ8YZ+X07UGVgn2X7+UgONY7rfUGC8HnrpyoNhiiQFnBL59mn3eUAac+oJ9Ts5Ru1Q2tqOpo0vYt3ynfjxfifKGNoSpVeIT8/PoyoFTFgaXpZx05cAvH7DPKT+iXSp0bcirMC8xsJTTxaom7LhQBUCC6CldObD7NfO+ICfTcqtjq9m/fUZTfkQ7VOja8JNJFoCtjvr8CNNfc8UW+bGkqQLobDbvW+qo85vZwFMTRQNPO1jaC4C1nABg3dFSAMDErDjvB566cqA6z7xvKaeq8xZpKkhHdadC14ZXt10Q9nk5ldczW2L90TJ0GozoFxcmPjE/j66cTf7wWOmoq8DxT9jnJCe7VOjacL3JnK/V8p36+dJ1lh9RqRCfmJ9HVw4c/ci8bykngEXSA6zfo/yINlTo2nCqzJyKxVJOpbUtQoSv1zpKVw789J/mfV5ODVfY/ok1LD9ifDblR7RDha5NiA4FbG2JTw6VAPByqTaPrpzl8eWx1FGXdgA1F1maCsqP6JCgm30DBCE1xTUt6J7OyMgB9606iKrGDvBfham9NJIBoK4IQLeLcQZg/cNAxVnzZ0W7gJGLvL9eAOFITg98cBij+8UI0VNnr+qw4VgZ5o/uK/5iduVkBD6dC9QWmr9rdl6lTq4U19jmnTJywFOfn8DJMh0AoLXTgK2ny72XE9etGiZnBFbfDTRegyCnIC+XnQQoxTUt3Z9yGDlg6ZpjKKhsEr7LvarD4BQvlhI6ktPHM1gEYq5pxvPqMTYTSn2fFcU1LTZpu4wc8NBHv6CsrlWQU2eXk8qw7lJXZPsZZwC+XApcPcL2O5qAs+tJTt1wpKMWrj6C29J6Yetplqbiu9xKTPCJjjIAX8xjTjf+u6vHKEdiNxzafP/fIYxOjxEq95XUtGLj8Su+0VFr72GROrycOh1X+pYzjnTUv312AmeuMltCb+Sw/XyFb+T0r1+zyaALm9ln575kVZup77PCkZwWrT6Kwupm4bvS2laMy/TiQg5tvmlA2liWtgcArheQLWEHR33fgx8eRnl9myAnb+NLADjWURsWAtdOsH1DB8vnS3KyC4UKEAFHv7hwux1MpYXDDQBW7Sm0m/vDI2IyWU637lScgVXnxM8GEAKO5HRN1y443AD2v/gfX5/zTlaO5FR7CVZy+v55kpMdHMmKd7jx+ExOjeWwktO+N0lOdnAkp3wLhxsA/OdmH8lJd8XscAMAcNT32cGRnEotHG4A8Nq2PN/pKN7hBoDkZB9HciqsbsGGY+Yk+hznw76v6hzIlnCOIzlVN3UIDjfAx7ZE/WVYyWn7iyQnOziSFe9w4/GZnK7ndYump77PHo7kdMnC4QYA/+UrW6LpmtnhBoB0lH0cyemqhcMNAN7aXuA7W4J3uPGQnBxCTjci4EjWhuL1+4dCZUq8r1Io8JucVJt2Bo7NfHqFtjcw5y+AwhQ1p1AB2XYS8XMGoO6yd9cKMOzJaeWvbsHisWk2bQ0c552s7MnJXt4BkpNdustKqQCm2Mlh5BM5jVxi247kZBfbdwq4Z5jtMh2fyGnmW8Dox23bkqxssNf3PXib7fIPr+UE2JfVgOm27UhONtiT03/NHoQHbutj09Yn79Tg+2zbkZxssCenV+cOxqPj+9m09Ymchv/Gth3JyS72bIlpgxJt2vlETtNeA4Y9bNuWZGWDPVtirp3cyj6R06y3gZzf2bYlOdlgr++bP8reeNdHtkTWLNt2JCeH0PJSIiCZP7ov7hwYj5KaVqTHsXxg649esQrDVSkUwndeMXIRq5JUd5klmgSAgm3WIdMKlfk7QqC7nJK1oajQteGzX0qll5U9OeV+SXJyE3vv1J43dt8YOZ36lOTkJvbk9H1uhe/lpO3NZjePryZZuYE9OW06WX7jdNR7Q0hObuBIR3198uqN6fsubCU5uYEjOa05WHxj5HR2PcnJTez1fTvzq26cjsrdQLJyA3ty+vbstRsnp6MfkZzcwJ6cvjxxA8e7F7eTnNyEIt2IgCVZG4qxmbFI1obanQ348/1DkKyVKDeUtjfLC6HtbX82YM57lFDcAZZy4vd9JiuSk1fcsHeK5OQVN0VO/D7Jym1IR/kHpKP8A5KT/0A6yj8gOfkHZEv4BwqO657Ol7CksbERWq0WOp0OUVFeltslbjoVujarmVCfoiu3nrkhPOKGyYrk5BUkJ/+A+j7/gOTkP1Df5x+QnPwD6vv8A5KTf0ByujF44icip5sLyOlGEARBEARBEARBEARBAJ75iWh5KUEQBEEQBEEQBEEQBEFIDDndCIIgCIIgCIIgCIIgCEJiyOlGEARBEARBEARBEARBEBJDTjeCIAiCIAiCIAiCIAiCkBhyuhEEQRAEQRAEQRAEQRCExJDTjSAIgiAIgiAIgiAIgiAkxm+cbnV1dViwYAGioqIQHR2NRx99FM3NzU6PqaysxMKFC5GUlITw8HCMHDkSmzZtukF3TBAEQRAEQRAEQRAEQcgVv3G6LViwAOfPn8eOHTuwbds27N+/H0888YTTYxYtWoSCggJ88803yM3Nxf3334958+bh1KlTN+iuCYIgCIIgCIIgCIIgCDmi4DiOu9k34Yq8vDzccsstOHbsGEaNGgUA2L59O2bNmoWrV68iJSXF7nERERH44IMPsHDhQuGz2NhYvPnmm3jsscfsHtPR0YGOjg5hv7GxEampqdDpdIiKipLwVxEEQRAEQRAEQRAEQRD+RGNjI7RarVt+Ir+IdDt8+DCio6MFhxsATJ06FUqlEkeOHHF43Lhx47BhwwbU1dXBaDRi/fr1aG9vx6RJkxwe8/rrr0Or1Qp/qampUv4UgiAIgiAIgiAIgiAIQgb4hdOtsrISCQkJVp8FBQUhJiYGlZWVDo/buHEj9Ho9YmNjodFo8OSTT2Lz5s3o37+/w2NefPFF6HQ64e/KlSuS/Q6CIAiCIAiCIAiCIAhCHtxUp9vy5cuhUCic/uXn54s+/0svvYSGhgbs3LkTx48fx7PPPot58+YhNzfX4TEajQZRUVFWfwRBEARBEARBEARBEAThCUE38+LPPfcclixZ4rRNRkYGkpKSUF1dbfV5V1cX6urqkJSUZPe4oqIi/O1vf8O5c+cwePBgAMDw4cPx888/Y9WqVfjwww/dukc+5V1jY6Nb7QmCIAiCIAiCIAiCIIjAhPcPuVMi4aY63eLj4xEfH++y3dixY9HQ0IATJ07gtttuAwDs3r0bRqMROTk5do9pbW0FACiV1sF8KpUKRqPR7XtsamoCAMrtRhAEQRAEQRAEQRAEQQBg/iKtVuu0jV9ULwWAmTNnoqqqCh9++CH0ej2WLl2KUaNG4YsvvgAAlJeXY8qUKfj0008xZswY6PV63HLLLUhOTsbbb7+N2NhYbNmyBX/84x+xbds2zJo1y63rGo1GXLt2DZGRkVAoFL78iTcMviLrlStXaPksAYCeCcIaeh6I7tAzQXSHngmiO/RMEJbQ80B0h54Jojv+/ExwHIempiakpKTYBHp156ZGunnC559/jmXLlmHKlClQKpX49a9/jffff1/4Xq/Xo6CgQIhwCw4Oxvfff4/ly5djzpw5aG5uRv/+/fHJJ5+47XADWKRcnz59JP89PQHKWUd0h54JwhJ6Hoju0DNBdIeeCaI79EwQltDzQHSHngmiO/76TLiKcOPxG6dbTEyMENVmj/T0dJv1tAMGDMCmTZt8fWsEQRAEQRAEQRAEQRAEYcVNrV5KEARBEARBEARBEARBEIEIOd1kiEajwYoVK6DRaG72rRA9BHomCEvoeSC6Q88E0R16Joju0DNBWELPA9EdeiaI7sjlmfCbQgoEQRAEQRAEQRAEQRAE4S9QpBtBEARBEARBEARBEARBSAw53QiCIAiCIAiCIAiCIAhCYsjpRhAEQRAEQRAEQRAEQRASQ043giAIgiAIgiAIgiAIgpAYcroFAKtWrUJ6ejpCQkKQk5ODo0ePOm3/5ZdfIjs7GyEhIRg6dCi+//57q+85jsPLL7+M5ORkhIaGYurUqbh06ZIvfwIhMZ48E//4xz8wYcIE9OrVC7169cLUqVNt2i9ZsgQKhcLqb8aMGb7+GYSEePJMrF271kbeISEhVm2on/B/PHkmJk2aZPNMKBQKzJ49W2hD/YT/sn//fsyZMwcpKSlQKBTYsmWLy2P27t2LkSNHQqPRoH///li7dq1NG0/tE6Ln4Okz8fXXX+Puu+9GfHw8oqKiMHbsWPz4449WbV555RWbPiI7O9uHv4KQEk+fib1799rVG5WVlVbtqJ/wTzx9HuzZCAqFAoMHDxbaUB/h37z++usYPXo0IiMjkZCQgHvvvRcFBQUuj5ODb4Kcbn7Ohg0b8Oyzz2LFihU4efIkhg8fjunTp6O6utpu+0OHDuHhhx/Go48+ilOnTuHee+/Fvffei3Pnzglt3nrrLbz//vv48MMPceTIEYSHh2P69Olob2+/UT+L8AJPn4m9e/fi4Ycfxp49e3D48GGkpqZi2rRpKC8vt2o3Y8YMVFRUCH/r1q27ET+HkABPnwkAiIqKspJ3aWmp1ffUT/g3nj4TX3/9tdXzcO7cOahUKjz44INW7aif8E9aWlowfPhwrFq1yq32xcXFmD17NiZPnozTp0/jmWeewWOPPWblZBHT7xA9B0+fif379+Puu+/G999/jxMnTmDy5MmYM2cOTp06ZdVu8ODBVn3EgQMHfHH7hA/w9JngKSgosJJ5QkKC8B31E/6Lp8/DX/7yF6vn4MqVK4iJibGxI6iP8F/27duHp556Cr/88gt27NgBvV6PadOmoaWlxeExsvFNcIRfM2bMGO6pp54S9g0GA5eSksK9/vrrdtvPmzePmz17ttVnOTk53JNPPslxHMcZjUYuKSmJ+9///V/h+4aGBk6j0XDr1q3zwS8gpMbTZ6I7XV1dXGRkJPfJJ58Iny1evJibO3eu1LdK3CA8fSbWrFnDabVah+ejfsL/8bafePfdd7nIyEiuublZ+Iz6icAAALd582anbV544QVu8ODBVp/Nnz+fmz59urDv7TNG9BzceSbsccstt3ArV64U9lesWMENHz5cuhsjbhruPBN79uzhAHD19fUO21A/ERiI6SM2b97MKRQKrqSkRPiM+ojAorq6mgPA7du3z2EbufgmKNLNj+ns7MSJEycwdepU4TOlUompU6fi8OHDdo85fPiwVXsAmD59utC+uLgYlZWVVm20Wi1ycnIcnpPoOYh5JrrT2toKvV6PmJgYq8/37t2LhIQEZGVl4Xe/+x1qa2slvXfCN4h9Jpqbm5GWlobU1FTMnTsX58+fF76jfsK/kaKfWL16NR566CGEh4dbfU79hDxwZUtI8YwR/o3RaERTU5ONLXHp0iWkpKQgIyMDCxYsQFlZ2U26Q+JGceuttyI5ORl33303Dh48KHxO/YS8Wb16NaZOnYq0tDSrz6mPCBx0Oh0A2OgBS+TimyCnmx9TU1MDg8GAxMREq88TExNt8iXwVFZWOm3P/+vJOYmeg5hnojt/+tOfkJKSYtW5zZgxA59++il27dqFN998E/v27cPMmTNhMBgkvX9CesQ8E1lZWfj444+xdetW/Otf/4LRaMS4ceNw9epVANRP+Dve9hNHjx7FuXPn8Nhjj1l9Tv2EfHBkSzQ2NqKtrU0SXUT4N2+//Taam5sxb9484bOcnBysXbsW27dvxwcffIDi4mJMmDABTU1NN/FOCV+RnJyMDz/8EJs2bcKmTZuQmpqKSZMm4eTJkwCksVkJ/+TatWv44YcfbOwI6iMCB6PRiGeeeQZ33HEHhgwZ4rCdXHwTQTf7BgiC6Dm88cYbWL9+Pfbu3WuVOP+hhx4StocOHYphw4YhMzMTe/fuxZQpU27GrRI+ZOzYsRg7dqywP27cOAwaNAgfffQRXn311Zt4Z0RPYPXq1Rg6dCjGjBlj9Tn1EwRBAMAXX3yBlStXYuvWrVb5u2bOnClsDxs2DDk5OUhLS8PGjRvx6KOP3oxbJXxIVlYWsrKyhP1x48ahqKgI7777Lj777LObeGfEzeaTTz5BdHQ07r33XqvPqY8IHJ566imcO3eOcvKZoEg3PyYuLg4qlQpVVVVWn1dVVSEpKcnuMUlJSU7b8/96ck6i5yDmmeB5++238cYbb+Cnn37CsGHDnLbNyMhAXFwcCgsLvb5nwrd480zwBAcHY8SIEYK8qZ/wb7x5JlpaWrB+/Xq3jF/qJwIXR7ZEVFQUQkNDJel3CP9k/fr1eOyxx7Bx40abJUPdiY6OxsCBA6mPkBFjxowR5E39hDzhOA4ff/wxFi5cCLVa7bQt9RH+ybJly7Bt2zbs2bMHffr0cdpWLr4Jcrr5MWq1Grfddht27dolfGY0GrFr1y6rKBVLxo4da9UeAHbs2CG079evH5KSkqzaNDY24siRIw7PSfQcxDwTAKsK8+qrr2L79u0YNWqUy+tcvXoVtbW1SE5OluS+Cd8h9pmwxGAwIDc3V5A39RP+jTfPxJdffomOjg488sgjLq9D/UTg4sqWkKLfIfyPdevWYenSpVi3bh1mz57tsn1zczOKioqoj5ARp0+fFuRN/YQ82bdvHwoLC92avKM+wr/gOA7Lli3D5s2bsXv3bvTr18/lMbLxTdzsSg6Ed6xfv57TaDTc2rVruQsXLnBPPPEEFx0dzVVWVnIcx3ELFy7kli9fLrQ/ePAgFxQUxL399ttcXl4et2LFCi44OJjLzc0V2rzxxhtcdHQ0t3XrVu7s2bPc3LlzuX79+nFtbW03/PcRnuPpM/HGG29warWa++qrr7iKigrhr6mpieM4jmtqauKef/557vDhw1xxcTG3c+dObuTIkdyAAQO49vb2m/IbCc/w9JlYuXIl9+OPP3JFRUXciRMnuIceeogLCQnhzp8/L7ShfsK/8fSZ4Bk/fjw3f/58m8+pn/BvmpqauFOnTnGnTp3iAHDvvPMOd+rUKa60tJTjOI5bvnw5t3DhQqH95cuXubCwMO6Pf/wjl5eXx61atYpTqVTc9u3bhTaunjGiZ+PpM/H5559zQUFB3KpVq6xsiYaGBqHNc889x+3du5crLi7mDh48yE2dOpWLi4vjqqurb/jvIzzH02fi3Xff5bZs2cJdunSJy83N5Z5++mlOqVRyO3fuFNpQP+G/ePo88DzyyCNcTk6O3XNSH+Hf/O53v+O0Wi23d+9eKz3Q2toqtJGrb4KcbgHAX//6V65v376cWq3mxowZw/3yyy/CdxMnTuQWL15s1X7jxo3cwIEDObVazQ0ePJj77rvvrL43Go3cSy+9xCUmJnIajYabMmUKV1BQcCN+CiERnjwTaWlpHACbvxUrVnAcx3Gtra3ctGnTuPj4eC44OJhLS0vjHn/8cTKI/AxPnolnnnlGaJuYmMjNmjWLO3nypNX5qJ/wfzzVHfn5+RwA7qeffrI5F/UT/s2ePXvs6gH+GVi8eDE3ceJEm2NuvfVWTq1WcxkZGdyaNWtszuvsGSN6Np4+ExMnTnTanuM4bv78+VxycjKnVqu53r17c/Pnz+cKCwtv7A8jROPpM/Hmm29ymZmZXEhICBcTE8NNmjSJ2717t815qZ/wT8TojYaGBi40NJT7+9//bvec1Ef4N/aeBwBW9oFcfRMKjuM4n4XREQRBEARBEARBEARBEIQMoZxuBEEQBEEQBEEQBEEQBCEx5HQjCIIgCIIgCIIgCIIgCIkhpxtBEARBEARBEARBEARBSAw53QiCIAiCIAiCIAiCIAhCYsjpRhAEQRAEQRAEQRAEQRASQ043giAIgiAIgiAIgiAIgpAYcroRBEEQBEEQBEEQBEEQhMSQ040gCIIgCIIgCIIgCILosezfvx9z5sxBSkoKFAoFtmzZ4tPrpaenQ6FQ2Pw99dRTHp2HnG4EQRAEQRCE26Snp+O999674dddsmSJYPC6a2hbGswNDQ0+vT+CIAiCIHxHS0sLhg8fjlWrVt2Q6x07dgwVFRXC344dOwAADz74oEfnIacbQRAEQRDEDcTerKnl3yuvvHKzbxEAsHbtWkRHR9t8fuzYMTzxxBM3/oYAzJgxAxUVFZg5c6Zb7Y8dO4ZNmzb5+K4IgiAIgvA1M2fOxGuvvYb77rvP7vcdHR14/vnn0bt3b4SHhyMnJwd79+4Vfb34+HgkJSUJf9u2bUNmZiYmTpzo0XmCRN8BQRAEQRAE4TEVFRXC9oYNG/Dyyy+joKBA+CwiIkLY5jgOBoMBQUE9x2SLj4+/adfWaDRISkpyu318fDxiYmJ8eEcEQRAEQfQEli1bhgsXLmD9+vVISUnB5s2bMWPGDOTm5mLAgAFenbuzsxP/+te/8Oyzz0KhUHh0LEW6EQRBEARB3EAsZ021Wi0UCoWwn5+fj8jISPzwww+47bbboNFocODAARQVFWHu3LlITExEREQERo8ejZ07d1qdt6OjA3/605+QmpoKjUaD/v37Y/Xq1cL3586dw8yZMxEREYHExEQsXLgQNTU1du9x7969WLp0KXQ6nU0EXvflpQqFAh999BHuuecehIWFYdCgQTh8+DAKCwsxadIkhIeHY9y4cSgqKrK6xtatWzFy5EiEhIQgIyMDK1euRFdXl0f/l52dnVi2bBmSk5MREhKCtLQ0vP766x6dgyAIgiAI/6asrAxr1qzBl19+iQkTJiAzMxPPP/88xo8fjzVr1nh9/i1btqChoQFLlizx+FhyuhEEQRAEQfQwli9fjjfeeAN5eXkYNmwYmpubMWvWLOzatQunTp3CjBkzMGfOHJSVlQnHLFq0COvWrcP777+PvLw8fPTRR0LUXENDA+666y6MGDECx48fx/bt21FVVYV58+bZvf64cePw3nvvISoqSshl8vzzzzu831dffRWLFi3C6dOnkZ2djd/85jd48skn8eKLL+L48ePgOA7Lli0T2v/8889YtGgRnn76aVy4cAEfffQR1q5di//5n//x6P/p/fffxzfffIONGzeioKAAn3/+OdLT0z06B0EQBEEQ/k1ubi4MBgMGDhyIiIgI4W/fvn3CpF9+fr7LFB/Lly+3e/7Vq1dj5syZSElJ8fjees5aBYIgCIIgCAIA8N///d+4++67hf2YmBgMHz5c2H/11VexefNmfPPNN1i2bBkuXryIjRs3YseOHZg6dSoAICMjQ2j/t7/9DSNGjMCf//xn4bOPP/4YqampuHjxIgYOHGh1fbVabRWF54qlS5cKDrw//elPGDt2LF566SVMnz4dAPD0009j6dKlQvuVK1di+fLlWLx4sXCvr776Kl544QWsWLHC7f+nsrIyDBgwAOPHj4dCoUBaWprbxxIEQRAEERg0NzdDpVLhxIkTUKlUVt/xE5AZGRnIy8tzep7Y2Fibz0pLS7Fz5058/fXXou6NnG4EQRAEQRA9jFGjRlntNzc345VXXsF3332HiooKdHV1oa2tTYh0O336NFQqlcPkvmfOnMGePXus8sXxFBUV2TjdPGXYsGHCdmJiIgBg6NChVp+1t7ejsbERUVFROHPmDA4ePGgV2WYwGNDe3o7W1laEhYW5dd0lS5bg7rvvRlZWFmbMmIF77rkH06ZN8+q3EARBEAThX4wYMQIGgwHV1dWYMGGC3TZqtRrZ2dken3vNmjVISEjA7NmzRd0bOd0IgiAIgiB6GOHh4Vb7zz//PHbs2IG3334b/fv3R2hoKB544AF0dnYCAEJDQ52er7m5GXPmzMGbb75p811ycrLX9xscHCxs8wmG7X1mNBqF+1m5ciXuv/9+m3OFhIS4fd2RI0eiuLgYP/zwA3bu3Il58+Zh6tSp+Oqrr0T9DoIgCIIgeibNzc0oLCwU9ouLi3H69GnExMRg4MCBWLBgARYtWoT/+7//w4gRI3D9+nXs2rULw4YNE+0wMxqNWLNmDRYvXiy6qBU53QiCIAiCIHo4Bw8exJIlS3DfffcBYIZnSUmJ8P3QoUNhNBqxb98+YXmpJSNHjsSmTZuQnp7uttGoVqthMBgkuX9791NQUID+/ft7fa6oqCjMnz8f8+fPxwMPPIAZM2agrq6OqpYSBEEQRABx/PhxTJ48Wdh/9tlnAQCLFy/G2rVrsWbNGrz22mt47rnnUF5ejri4ONx+++245557RF9z586dKCsrw29/+1vR5yCnG0EQBEEQRA9nwIAB+PrrrzFnzhwoFAq89NJLQtQYwCqKLl68GL/97W/x/vvvY/jw4SgtLUV1dTXmzZuHp556Cv/4xz/w8MMP44UXXkBMTAwKCwuxfv16/POf/7TJf8Kfs7m5Gbt27cLw4cMRFhbm9rJPV7z88su455570LdvXzzwwANQKpU4c+YMzp07h9dee83t87zzzjtITk7GiBEjoFQq8eWXXyIpKQnR0dGS3CdBEARBED2DSZMmgeM4h98HBwdj5cqVWLlypWTXnDZtmtNrugNVLyUIgiAIgujhvPPOO+jVqxfGjRuHOXPmYPr06Rg5cqRVmw8++AAPPPAA/v3f/x3Z2dl4/PHH0dLSAgBISUnBwYMHYTAYMG3aNAwdOhTPPPMMoqOjoVTaNwfHjRuHf/u3f8P8+fMRHx+Pt956S7LfM336dGzbtg0//fQTRo8ejdtvvx3vvvuux4UQIiMj8dZbb2HUqFEYPXo0SkpK8P333zv8TQRBEARBEDcSBeet244gCIIgCIIgfMySJUvQ0NCALVu2eHTc3r17MXnyZNTX11MEHEEQBEEQNxSaBiQIgiAIgiD8gm3btiEiIgLbtm1zq/3gwYMxc+ZMH98VQRAEQRCEfSjSjSAIgiAIgujxVFdXo7GxEQCruNq9wqs9SktLodfrAQAZGRm07JQgCIIgiBsKOd0IgiAIgiAIgiAIgiAIQmJouo8gCIIgCIIgCIIgCIIgJIacbgRBEARBEARBEARBEAQhMeR0IwiCIAiCIAiCIAiCIAiJIacbQRAEQRAEQRAEQRAEQUgMOd0IgiAIgiAIgiAIgiAIQmLI6UYQBEEQBEEQBEEQBEEQEkNON4IgCIIgCIIgCIIgCIKQGHK6EQRBEARBEARBEARBEITE/P/aBNIQhuznMAAAAABJRU5ErkJggg==",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 150\u001b[0m\u001b[1;36m0x500\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": [
    "trace_example_plt = trace_example[:200]\n",
    "trace_example_plt.real.plot(figsize=(15, 5), marker=\".\", label=\"I-quadrature\")\n",
    "trace_example_plt.imag.plot(marker=\".\", label=\"Q-quadrature\")\n",
    "plt.gca().legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "file_format": "mystnb",
  "jupytext": {
   "text_representation": {
    "extension": ".md",
    "format_name": "myst"
   }
  },
  "kernelspec": {
   "display_name": "python3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}