{ "cells": [ { "cell_type": "markdown", "id": "da5198c6", "metadata": {}, "source": [ "(sec-dataset-advanced-examples)=\n", "# Quantify dataset - advanced examples\n", "\n", "```{seealso}\n", "The complete source code of this tutorial can be found in\n", "\n", "{nb-download}`Quantify dataset - advanced examples.ipynb`\n", "```\n", "\n", "Here we will explore a few advanced usages of the Quantify dataset and how it can\n", "accommodate them." ] }, { "cell_type": "code", "execution_count": 1, "id": "64643c33", "metadata": { "mystnb": { "code_prompt_show": "Imports and auxiliary utilities" }, "tags": [ "hide-cell" ] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/tmp/ipykernel_349/6278523.py:8: DeprecationWarning: This package has reached its end of life. It is no longer maintained and will not receive any further updates or support. For further developments, please refer to the new Quantify repository: https://gitlab.com/quantify-os/quantify.All existing functionalities can be accessed via the new Quantify repository.\n", " from quantify_core.analysis.calibration import rotate_to_calibrated_axis\n" ] } ], "source": [ "from pathlib import Path\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", "from rich import pretty\n", "\n", "from quantify_core.analysis.calibration import rotate_to_calibrated_axis\n", "from quantify_core.analysis.fitting_models import exp_decay_func\n", "from quantify_core.data import handling as dh\n", "from quantify_core.utilities import dataset_examples\n", "from quantify_core.utilities.dataset_examples import (\n", " mk_nested_mc_dataset,\n", " mk_shots_from_probabilities,\n", " mk_surface7_cyles_dataset,\n", ")\n", "from quantify_core.utilities.examples_support import (\n", " mk_iq_shots,\n", " round_trip_dataset,\n", ")\n", "from quantify_core.utilities.inspect_utils import display_source_code\n", "\n", "pretty.install()\n", "\n", "dh.set_datadir(Path.home() / \"quantify-data\") # change me!" ] }, { "cell_type": "markdown", "id": "b075ee74", "metadata": {}, "source": [ "## Dataset for an \"unstructured\" experiment\n", "\n", "Let's take consider a Surface Code experiment, in particular the one portrayed in\n", "Fig. 4b from one of the papers from DiCarlo Lab {cite}`marques_logical_qubit_2021`.\n", "\n", "For simplicity, we will not use exactly the same schedule, because what matters here\n", "are the measurements. It is difficult to deal with the results of these measurements\n", "because we have a few repeating cycles followed by a final measurement that leaves the\n", "overall dataset \"unstructured\".\n", "\n", "```{figure} /images/surface-7-sched.png\n", ":width: 100%\n", "```\n", "\n", "``````{admonition} Source code for generating this schedule and visualizing it\n", ":class: dropdown, info\n", "\n", "If you want to create and visualize the schedule above using `quantify-scheduler`,\n", "you can use this code:\n", "\n", "```{code-block} python\n", "from quantify_scheduler import Schedule\n", "from quantify_scheduler.operations.gate_library import CZ, Y90, Measure, Reset, X\n", "from quantify_scheduler.visualization.circuit_diagram import circuit_diagram_matplotlib\n", "\n", "def mk_surface7_sched(num_cycles: int = 3):\n", " \"\"\"Generates a schedule with some of the feature of a Surface 7 experiment as\n", " portrayed in Fig. 4b of :cite:`marques_logical_qubit_2021`.\n", "\n", " Parameters\n", " ----------\n", " num_cycles\n", " The number of times to repeat the main cycle.\n", "\n", " Returns\n", " -------\n", " :\n", " A schedule similar to a Surface 7 dance.\n", " \"\"\"\n", "\n", " sched = Schedule(\"S7 dance\")\n", "\n", " q_d1, q_d2, q_d3, q_d4 = [f\"D{i}\" for i in range(1, 5)]\n", " q_a1, q_a2, q_a3 = [f\"A{i}\" for i in range(1, 4)]\n", " all_qubits = q_d1, q_d2, q_d3, q_d4, q_a1, q_a2, q_a3\n", "\n", " sched.add(Reset(*all_qubits))\n", "\n", " for cycle in range(num_cycles):\n", " sched.add(Y90(q_d1))\n", " for qubit in [q_d2, q_d3, q_d4]:\n", " sched.add(Y90(qubit), ref_pt=\"start\", rel_time=0)\n", " sched.add(Y90(q_a2), ref_pt=\"start\", rel_time=0)\n", "\n", " for qubit in [q_d2, q_d1, q_d4, q_d3]:\n", " sched.add(CZ(qC=qubit, qT=q_a2))\n", "\n", " sched.add(Y90(q_d1))\n", " for qubit in [q_d2, q_d3, q_d4]:\n", " sched.add(Y90(qubit), ref_pt=\"start\", rel_time=0)\n", " sched.add(Y90(q_a2), ref_pt=\"start\", rel_time=0)\n", "\n", " sched.add(Y90(q_a1), ref_pt=\"end\", rel_time=0)\n", " sched.add(Y90(q_a3), ref_pt=\"start\", rel_time=0)\n", "\n", " sched.add(CZ(qC=q_d1, qT=q_a1))\n", " sched.add(CZ(qC=q_d2, qT=q_a3))\n", " sched.add(CZ(qC=q_d3, qT=q_a1))\n", " sched.add(CZ(qC=q_d4, qT=q_a3))\n", "\n", " sched.add(Y90(q_a1), ref_pt=\"end\", rel_time=0)\n", " sched.add(Y90(q_a3), ref_pt=\"start\", rel_time=0)\n", "\n", " sched.add(Measure(q_a2, acq_index=cycle))\n", " for qubit in (q_a1, q_a3):\n", " sched.add(Measure(qubit, acq_index=cycle), ref_pt=\"start\", rel_time=0)\n", "\n", " for qubit in [q_d1, q_d2, q_d3, q_d4]:\n", " sched.add(X(qubit), ref_pt=\"start\", rel_time=0)\n", "\n", " # final measurements\n", "\n", " sched.add(Measure(*all_qubits[:4], acq_index=0), ref_pt=\"end\", rel_time=0)\n", "\n", " return sched\n", "\n", "sched = mk_surface7_sched(num_cycles=3)\n", "f, ax = circuit_diagram_matplotlib(sched)\n", "f.set_figwidth(30)\n", "```\n", "``````\n", "\n", "How do we store all the shots for this measurement?\n", "We might want this because, e.g., we know we have an issue with leakage to the second\n", "excited state of a transmon and we would like to be able to store and inspect raw data.\n", "\n", "To support such use-cases we will have a dimension in the dataset for the repeating cycles\n", "and one extra dimension for the final measurement." ] }, { "cell_type": "code", "execution_count": 2, "id": "d2f2ddcf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
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     "data": {
      "text/html": [
       "
def mk_iq_shots(\n",
       "    num_shots: int = 128,\n",
       "    sigmas: Union[Tuple[float], NDArray[np.float64]] = (0.1, 0.1),\n",
       "    centers: Union[Tuple[complex], NDArray[np.complex128]] = (-0.2 + 0.65j, 0.7 + 4j),\n",
       "    probabilities: Union[Tuple[float], NDArray[np.float64]] = (0.4, 0.6),\n",
       "    seed: Union[int, None] = 112233,\n",
       ") -> NDArray:\n",
       "    """\n",
       "    Generate clusters of (I + 1j*Q) points with a Gaussian distribution.\n",
       "\n",
       "    Utility to mock the data coming from qubit readout experiments.\n",
       "    Clusters are centered around ``centers`` and data points are distributed between\n",
       "    them according to ``probabilities``.\n",
       "\n",
       "    .. seealso:: :ref:`howto-utilities-examples-ssro`\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_shots\n",
       "        The number of shot to generate.\n",
       "    sigma\n",
       "        The sigma of the Gaussian distribution used for both real and imaginary parts.\n",
       "    centers\n",
       "        The center of each cluster on the imaginary plane.\n",
       "    probabilities\n",
       "        The probabilities of each cluster being randomly selected for each shot.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    """\n",
       "    if not len(sigmas) == len(centers) == len(probabilities):\n",
       "        raise ValueError(\n",
       "            f"Incorrect input. sigmas={sigmas}, centers={centers} and "\n",
       "            f"probabilities={probabilities} must have the same length."\n",
       "        )\n",
       "\n",
       "    rng = np.random.default_rng(seed=seed)\n",
       "\n",
       "    cluster_indices = tuple(range(len(centers)))\n",
       "    choices = rng.choice(a=cluster_indices, size=num_shots, p=probabilities)\n",
       "\n",
       "    shots = []\n",
       "    for idx in cluster_indices:\n",
       "        num_shots_this_cluster = np.sum(choices == idx)\n",
       "        i_data = rng.normal(\n",
       "            loc=centers[idx].real,\n",
       "            scale=sigmas[idx],\n",
       "            size=num_shots_this_cluster,\n",
       "        )\n",
       "        q_data = rng.normal(\n",
       "            loc=centers[idx].imag,\n",
       "            scale=sigmas[idx],\n",
       "            size=num_shots_this_cluster,\n",
       "        )\n",
       "        shots.append(i_data + 1j * q_data)\n",
       "    return np.concatenate(shots)\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def}\\PY{+w}{ }\\PY{n+nf}{mk\\PYZus{}iq\\PYZus{}shots}\\PY{p}{(}\n",
       "    \\PY{n}{num\\PYZus{}shots}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{128}\\PY{p}{,}\n",
       "    \\PY{n}{sigmas}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{float}\\PY{p}{]}\\PY{p}{,} \\PY{n}{NDArray}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{float64}\\PY{p}{]}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{0.1}\\PY{p}{,} \\PY{l+m+mf}{0.1}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{centers}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{complex}\\PY{p}{]}\\PY{p}{,} \\PY{n}{NDArray}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{complex128}\\PY{p}{]}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{l+m+mf}{0.2} \\PY{o}{+} \\PY{l+m+mf}{0.65}\\PY{n}{j}\\PY{p}{,} \\PY{l+m+mf}{0.7} \\PY{o}{+} \\PY{l+m+mi}{4}\\PY{n}{j}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{float}\\PY{p}{]}\\PY{p}{,} \\PY{n}{NDArray}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{float64}\\PY{p}{]}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{0.4}\\PY{p}{,} \\PY{l+m+mf}{0.6}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{seed}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n+nb}{int}\\PY{p}{,} \\PY{k+kc}{None}\\PY{p}{]} \\PY{o}{=} \\PY{l+m+mi}{112233}\\PY{p}{,}\n",
       "\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{NDArray}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Generate clusters of (I + 1j*Q) points with a Gaussian distribution.}\n",
       "\n",
       "\\PY{l+s+sd}{    Utility to mock the data coming from qubit readout experiments.}\n",
       "\\PY{l+s+sd}{    Clusters are centered around ``centers`` and data points are distributed between}\n",
       "\\PY{l+s+sd}{    them according to ``probabilities``.}\n",
       "\n",
       "\\PY{l+s+sd}{    .. seealso:: :ref:`howto\\PYZhy{}utilities\\PYZhy{}examples\\PYZhy{}ssro`}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    num\\PYZus{}shots}\n",
       "\\PY{l+s+sd}{        The number of shot to generate.}\n",
       "\\PY{l+s+sd}{    sigma}\n",
       "\\PY{l+s+sd}{        The sigma of the Gaussian distribution used for both real and imaginary parts.}\n",
       "\\PY{l+s+sd}{    centers}\n",
       "\\PY{l+s+sd}{        The center of each cluster on the imaginary plane.}\n",
       "\\PY{l+s+sd}{    probabilities}\n",
       "\\PY{l+s+sd}{        The probabilities of each cluster being randomly selected for each shot.}\n",
       "\\PY{l+s+sd}{    seed}\n",
       "\\PY{l+s+sd}{        Random number generator seed passed to ``numpy.random.default\\PYZus{}rng``.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{k}{if} \\PY{o+ow}{not} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{sigmas}\\PY{p}{)} \\PY{o}{==} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{centers}\\PY{p}{)} \\PY{o}{==} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{probabilities}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{k}{raise} \\PY{n+ne}{ValueError}\\PY{p}{(}\n",
       "            \\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Incorrect input. sigmas=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{sigmas}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{, centers=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{centers}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{ and }\\PY{l+s+s2}{\\PYZdq{}}\n",
       "            \\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{probabilities=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{probabilities}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{ must have the same length.}\\PY{l+s+s2}{\\PYZdq{}}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{rng} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{default\\PYZus{}rng}\\PY{p}{(}\\PY{n}{seed}\\PY{o}{=}\\PY{n}{seed}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{cluster\\PYZus{}indices} \\PY{o}{=} \\PY{n+nb}{tuple}\\PY{p}{(}\\PY{n+nb}{range}\\PY{p}{(}\\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{centers}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\n",
       "    \\PY{n}{choices} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{choice}\\PY{p}{(}\\PY{n}{a}\\PY{o}{=}\\PY{n}{cluster\\PYZus{}indices}\\PY{p}{,} \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots}\\PY{p}{,} \\PY{n}{p}\\PY{o}{=}\\PY{n}{probabilities}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{shots} \\PY{o}{=} \\PY{p}{[}\\PY{p}{]}\n",
       "    \\PY{k}{for} \\PY{n}{idx} \\PY{o+ow}{in} \\PY{n}{cluster\\PYZus{}indices}\\PY{p}{:}\n",
       "        \\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{sum}\\PY{p}{(}\\PY{n}{choices} \\PY{o}{==} \\PY{n}{idx}\\PY{p}{)}\n",
       "        \\PY{n}{i\\PYZus{}data} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{normal}\\PY{p}{(}\n",
       "            \\PY{n}{loc}\\PY{o}{=}\\PY{n}{centers}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{o}{.}\\PY{n}{real}\\PY{p}{,}\n",
       "            \\PY{n}{scale}\\PY{o}{=}\\PY{n}{sigmas}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{p}{,}\n",
       "            \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "        \\PY{n}{q\\PYZus{}data} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{normal}\\PY{p}{(}\n",
       "            \\PY{n}{loc}\\PY{o}{=}\\PY{n}{centers}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{o}{.}\\PY{n}{imag}\\PY{p}{,}\n",
       "            \\PY{n}{scale}\\PY{o}{=}\\PY{n}{sigmas}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{p}{,}\n",
       "            \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "        \\PY{n}{shots}\\PY{o}{.}\\PY{n}{append}\\PY{p}{(}\\PY{n}{i\\PYZus{}data} \\PY{o}{+} \\PY{l+m+mi}{1}\\PY{n}{j} \\PY{o}{*} \\PY{n}{q\\PYZus{}data}\\PY{p}{)}\n",
       "    \\PY{k}{return} \\PY{n}{np}\\PY{o}{.}\\PY{n}{concatenate}\\PY{p}{(}\\PY{n}{shots}\\PY{p}{)}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_iq_shots\u001b[0m\u001b[1m(\u001b[0m\n",
       "    num_shots: int = \u001b[1;36m128\u001b[0m,\n",
       "    sigmas: Union\u001b[1m[\u001b[0mTuple\u001b[1m[\u001b[0mfloat\u001b[1m]\u001b[0m, NDArray\u001b[1m[\u001b[0mnp.float64\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m0.1\u001b[0m, \u001b[1;36m0.1\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    centers: Union\u001b[1m[\u001b[0mTuple\u001b[1m[\u001b[0mcomplex\u001b[1m]\u001b[0m, NDArray\u001b[1m[\u001b[0mnp.complex128\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m-0.2\u001b[0m + \u001b[1;36m0.65j\u001b[0m, \u001b[1;36m0.7\u001b[0m + \u001b[1;36m4j\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    probabilities: Union\u001b[1m[\u001b[0mTuple\u001b[1m[\u001b[0mfloat\u001b[1m]\u001b[0m, NDArray\u001b[1m[\u001b[0mnp.float64\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m0.4\u001b[0m, \u001b[1;36m0.6\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    seed: Union\u001b[1m[\u001b[0mint, \u001b[3;35mNone\u001b[0m\u001b[1m]\u001b[0m = \u001b[1;36m112233\u001b[0m,\n",
       "\u001b[1m)\u001b[0m -> NDArray:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generate clusters of \u001b[1m(\u001b[0mI + \u001b[1;36m1j\u001b[0m*Q\u001b[1m)\u001b[0m points with a Gaussian distribution.\n",
       "\n",
       "    Utility to mock the data coming from qubit readout experiments.\n",
       "    Clusters are centered around ``centers`` and data points are distributed between\n",
       "    them according to ``probabilities``.\n",
       "\n",
       "    .. seealso:: :ref:`howto-utilities-examples-ssro`\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_shots\n",
       "        The number of shot to generate.\n",
       "    sigma\n",
       "        The sigma of the Gaussian distribution used for both real and imaginary parts.\n",
       "    centers\n",
       "        The center of each cluster on the imaginary plane.\n",
       "    probabilities\n",
       "        The probabilities of each cluster being randomly selected for each shot.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    if not \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0msigmas\u001b[1m)\u001b[0m == \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mcenters\u001b[1m)\u001b[0m == \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mprobabilities\u001b[1m)\u001b[0m:\n",
       "        raise \u001b[1;35mValueError\u001b[0m\u001b[1m(\u001b[0m\n",
       "            f\"Incorrect input. \u001b[33msigmas\u001b[0m=\u001b[1m{\u001b[0msigmas\u001b[1m}\u001b[0m, \u001b[33mcenters\u001b[0m=\u001b[1m{\u001b[0mcenters\u001b[1m}\u001b[0m and \"\n",
       "            f\"\u001b[33mprobabilities\u001b[0m=\u001b[1m{\u001b[0mprobabilities\u001b[1m}\u001b[0m must have the same length.\"\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    rng = \u001b[1;35mnp.random.default_rng\u001b[0m\u001b[1m(\u001b[0m\u001b[33mseed\u001b[0m=\u001b[35mseed\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    cluster_indices = \u001b[1;35mtuple\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mcenters\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\n",
       "    choices = \u001b[1;35mrng.choice\u001b[0m\u001b[1m(\u001b[0m\u001b[33ma\u001b[0m=\u001b[35mcluster_indices\u001b[0m, \u001b[33msize\u001b[0m=\u001b[35mnum_shots\u001b[0m, \u001b[33mp\u001b[0m=\u001b[35mprobabilities\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    shots = \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
       "    for idx in cluster_indices:\n",
       "        num_shots_this_cluster = \u001b[1;35mnp.sum\u001b[0m\u001b[1m(\u001b[0mchoices == idx\u001b[1m)\u001b[0m\n",
       "        i_data = \u001b[1;35mrng.normal\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[33mloc\u001b[0m=\u001b[35mcenters\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m.real,\n",
       "            \u001b[33mscale\u001b[0m=\u001b[35msigmas\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m,\n",
       "            \u001b[33msize\u001b[0m=\u001b[35mnum_shots_this_cluster\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "        q_data = \u001b[1;35mrng.normal\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[33mloc\u001b[0m=\u001b[35mcenters\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m.imag,\n",
       "            \u001b[33mscale\u001b[0m=\u001b[35msigmas\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m,\n",
       "            \u001b[33msize\u001b[0m=\u001b[35mnum_shots_this_cluster\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "        \u001b[1;35mshots.append\u001b[0m\u001b[1m(\u001b[0mi_data + \u001b[1;36m1j\u001b[0m * q_data\u001b[1m)\u001b[0m\n",
       "    return \u001b[1;35mnp.concatenate\u001b[0m\u001b[1m(\u001b[0mshots\u001b[1m)\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "
def mk_shots_from_probabilities(probabilities: Union[np.ndarray, list], **kwargs):\n",
       "    """Generates multiple shots for a list of probabilities assuming two states.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    probabilities\n",
       "        The list/array of the probabilities of one of the states.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to\n",
       "        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    :\n",
       "        Array containing the shots. Shape: (num_shots, len(probabilities)).\n",
       "    """\n",
       "\n",
       "    shots = np.array(\n",
       "        tuple(\n",
       "            mk_iq_shots(probabilities=[prob, 1 - prob], **kwargs)\n",
       "            for prob in probabilities\n",
       "        )\n",
       "    ).T\n",
       "\n",
       "    return shots\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def}\\PY{+w}{ }\\PY{n+nf}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{,} \\PY{n+nb}{list}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Generates multiple shots for a list of probabilities assuming two states.}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    probabilities}\n",
       "\\PY{l+s+sd}{        The list/array of the probabilities of one of the states.}\n",
       "\\PY{l+s+sd}{    **kwargs}\n",
       "\\PY{l+s+sd}{        Keyword arguments passed to}\n",
       "\\PY{l+s+sd}{        :func:`\\PYZti{}quantify\\PYZus{}core.utilities.examples\\PYZus{}support.mk\\PYZus{}iq\\PYZus{}shots`.}\n",
       "\n",
       "\\PY{l+s+sd}{    Returns}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    :}\n",
       "\\PY{l+s+sd}{        Array containing the shots. Shape: (num\\PYZus{}shots, len(probabilities)).}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "    \\PY{n}{shots} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{array}\\PY{p}{(}\n",
       "        \\PY{n+nb}{tuple}\\PY{p}{(}\n",
       "            \\PY{n}{mk\\PYZus{}iq\\PYZus{}shots}\\PY{p}{(}\\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{prob}\\PY{p}{,} \\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{prob}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "            \\PY{k}{for} \\PY{n}{prob} \\PY{o+ow}{in} \\PY{n}{probabilities}\n",
       "        \\PY{p}{)}\n",
       "    \\PY{p}{)}\\PY{o}{.}\\PY{n}{T}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{shots}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0mprobabilities: Union\u001b[1m[\u001b[0mnp.ndarray, list\u001b[1m]\u001b[0m, **kwargs\u001b[1m)\u001b[0m:\n",
       "    \u001b[32m\"\"\u001b[0m\"Generates multiple shots for a list of probabilities assuming two states.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    probabilities\n",
       "        The list/array of the probabilities of one of the states.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to\n",
       "        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    :\n",
       "        Array containing the shots. Shape: \u001b[1m(\u001b[0mnum_shots, \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mprobabilities\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "\n",
       "    shots = \u001b[1;35mnp.array\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[1;35mtuple\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[1;35mmk_iq_shots\u001b[0m\u001b[1m(\u001b[0m\u001b[33mprobabilities\u001b[0m=\u001b[1m[\u001b[0mprob, \u001b[1;36m1\u001b[0m - prob\u001b[1m]\u001b[0m, **kwargs\u001b[1m)\u001b[0m\n",
       "            for prob in probabilities\n",
       "        \u001b[1m)\u001b[0m\n",
       "    \u001b[1m)\u001b[0m.T\n",
       "\n",
       "    return shots"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
def mk_surface7_cyles_dataset(num_cycles: int = 3, **kwargs) -> xr.Dataset:\n",
       "    """\n",
       "    See also :func:`mk_surface7_sched` inlined in the documentation as an example in:\n",
       "        :ref:`sec-dataset-advanced-examples`\n",
       "\n",
       "\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_cycles\n",
       "        The number of repeating cycles before the final measurement.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to :func:`~.mk_shots_from_probabilities`.\n",
       "    """\n",
       "\n",
       "    cycles = range(num_cycles)\n",
       "\n",
       "    mock_data = mk_shots_from_probabilities(\n",
       "        probabilities=[np.random.random() for _ in cycles], **kwargs\n",
       "    )\n",
       "\n",
       "    mock_data_final = mk_shots_from_probabilities(\n",
       "        probabilities=[np.random.random()], **kwargs\n",
       "    )\n",
       "\n",
       "    # %%\n",
       "    data_vars = {}\n",
       "\n",
       "    # NB same random data is used for all qubits only for the simplicity of the mock!\n",
       "    for qubit in (f"A{i}" for i in range(3)):\n",
       "        data_vars[f"{qubit}_shots"] = (\n",
       "            ("repetitions", "dim_cycle"),\n",
       "            mock_data,\n",
       "            mk_main_var_attrs(\n",
       "                unit="V", long_name=f"IQ amplitude {qubit}", has_repetitions=True\n",
       "            ),\n",
       "        )\n",
       "\n",
       "    for qubit in (f"D{i}" for i in range(4)):\n",
       "        data_vars[f"{qubit}_shots"] = (\n",
       "            ("repetitions", "dim_final"),\n",
       "            mock_data_final,\n",
       "            mk_main_var_attrs(\n",
       "                unit="V", long_name=f"IQ amplitude {qubit}", has_repetitions=True\n",
       "            ),\n",
       "        )\n",
       "\n",
       "    cycle_attrs = mk_main_coord_attrs(long_name="Surface code cycle number")\n",
       "    final_msmt_attrs = mk_main_coord_attrs(long_name="Final measurement")\n",
       "    coords = dict(\n",
       "        cycle=("dim_cycle", cycles, cycle_attrs),\n",
       "        final_msmt=("dim_final", [0], final_msmt_attrs),\n",
       "    )\n",
       "\n",
       "    dataset = xr.Dataset(\n",
       "        data_vars=data_vars,\n",
       "        coords=coords,\n",
       "        attrs=mk_dataset_attrs(),\n",
       "    )\n",
       "\n",
       "    return dataset\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def}\\PY{+w}{ }\\PY{n+nf}{mk\\PYZus{}surface7\\PYZus{}cyles\\PYZus{}dataset}\\PY{p}{(}\\PY{n}{num\\PYZus{}cycles}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{3}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    See also :func:`mk\\PYZus{}surface7\\PYZus{}sched` inlined in the documentation as an example in:}\n",
       "\\PY{l+s+sd}{        :ref:`sec\\PYZhy{}dataset\\PYZhy{}advanced\\PYZhy{}examples`}\n",
       "\n",
       "\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    num\\PYZus{}cycles}\n",
       "\\PY{l+s+sd}{        The number of repeating cycles before the final measurement.}\n",
       "\\PY{l+s+sd}{    **kwargs}\n",
       "\\PY{l+s+sd}{        Keyword arguments passed to :func:`\\PYZti{}.mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities`.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "    \\PY{n}{cycles} \\PY{o}{=} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}cycles}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{mock\\PYZus{}data} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\n",
       "        \\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{random}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n}{cycles}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{mock\\PYZus{}data\\PYZus{}final} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\n",
       "        \\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{random}\\PY{p}{(}\\PY{p}{)}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} \\PYZpc{}\\PYZpc{}}\n",
       "    \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{p}{\\PYZob{}}\\PY{p}{\\PYZcb{}}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} NB same random data is used for all qubits only for the simplicity of the mock!}\n",
       "    \\PY{k}{for} \\PY{n}{qubit} \\PY{o+ow}{in} \\PY{p}{(}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{A}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{i}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}} \\PY{k}{for} \\PY{n}{i} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{l+m+mi}{3}\\PY{p}{)}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{n}{data\\PYZus{}vars}\\PY{p}{[}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\n",
       "            \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}cycle}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "            \\PY{n}{mock\\PYZus{}data}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{IQ amplitude }\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{has\\PYZus{}repetitions}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{for} \\PY{n}{qubit} \\PY{o+ow}{in} \\PY{p}{(}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{D}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{i}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}} \\PY{k}{for} \\PY{n}{i} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{l+m+mi}{4}\\PY{p}{)}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{n}{data\\PYZus{}vars}\\PY{p}{[}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\n",
       "            \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}final}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "            \\PY{n}{mock\\PYZus{}data\\PYZus{}final}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{IQ amplitude }\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{has\\PYZus{}repetitions}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{cycle\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Surface code cycle number}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{final\\PYZus{}msmt\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Final measurement}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{cycle}\\PY{o}{=}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}cycle}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{cycles}\\PY{p}{,} \\PY{n}{cycle\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{final\\PYZus{}msmt}\\PY{o}{=}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}final}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\\PY{p}{,} \\PY{n}{final\\PYZus{}msmt\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{(}\n",
       "        \\PY{n}{data\\PYZus{}vars}\\PY{o}{=}\\PY{n}{data\\PYZus{}vars}\\PY{p}{,}\n",
       "        \\PY{n}{coords}\\PY{o}{=}\\PY{n}{coords}\\PY{p}{,}\n",
       "        \\PY{n}{attrs}\\PY{o}{=}\\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{dataset}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_surface7_cyles_dataset\u001b[0m\u001b[1m(\u001b[0mnum_cycles: int = \u001b[1;36m3\u001b[0m, **kwargs\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    See also :func:`mk_surface7_sched` inlined in the documentation as an example in:\n",
       "        :ref:`sec-dataset-advanced-examples`\n",
       "\n",
       "\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_cycles\n",
       "        The number of repeating cycles before the final measurement.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to :func:`~.mk_shots_from_probabilities`.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "\n",
       "    cycles = \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_cycles\u001b[1m)\u001b[0m\n",
       "\n",
       "    mock_data = \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mprobabilities\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mnp.random.random\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in cycles\u001b[1m]\u001b[0m, **kwargs\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    mock_data_final = \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mprobabilities\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mnp.random.random\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m, **kwargs\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    # %%\n",
       "    data_vars = \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
       "\n",
       "    # NB same random data is used for all qubits only for the simplicity of the mock!\n",
       "    for qubit in \u001b[1m(\u001b[0mf\"A\u001b[1m{\u001b[0mi\u001b[1m}\u001b[0m\" for i in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m:\n",
       "        data_vars\u001b[1m[\u001b[0mf\"\u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m_shots\"\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\n",
       "            \u001b[1m(\u001b[0m\u001b[32m\"repetitions\"\u001b[0m, \u001b[32m\"dim_cycle\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "            mock_data,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33munit\u001b[0m=\u001b[32m\"V\"\u001b[0m, \u001b[33mlong_name\u001b[0m=\u001b[35mf\"\u001b[0mIQ amplitude \u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m\", \u001b[33mhas_repetitions\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    for qubit in \u001b[1m(\u001b[0mf\"D\u001b[1m{\u001b[0mi\u001b[1m}\u001b[0m\" for i in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m:\n",
       "        data_vars\u001b[1m[\u001b[0mf\"\u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m_shots\"\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\n",
       "            \u001b[1m(\u001b[0m\u001b[32m\"repetitions\"\u001b[0m, \u001b[32m\"dim_final\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "            mock_data_final,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33munit\u001b[0m=\u001b[32m\"V\"\u001b[0m, \u001b[33mlong_name\u001b[0m=\u001b[35mf\"\u001b[0mIQ amplitude \u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m\", \u001b[33mhas_repetitions\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    cycle_attrs = \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Surface\u001b[0m\u001b[32m code cycle number\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    final_msmt_attrs = \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Final\u001b[0m\u001b[32m measurement\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    coords = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mcycle\u001b[0m=\u001b[1m(\u001b[0m\u001b[32m\"dim_cycle\"\u001b[0m, cycles, cycle_attrs\u001b[1m)\u001b[0m,\n",
       "        \u001b[33mfinal_msmt\u001b[0m=\u001b[1m(\u001b[0m\u001b[32m\"dim_final\"\u001b[0m, \u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m, final_msmt_attrs\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    dataset = \u001b[1;35mxr.Dataset\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mdata_vars\u001b[0m=\u001b[35mdata_vars\u001b[0m,\n",
       "        \u001b[33mcoords\u001b[0m=\u001b[35mcoords\u001b[0m,\n",
       "        \u001b[33mattrs\u001b[0m=\u001b[1;35mmk_dataset_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    return dataset"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mock data parameters\n",
    "num_shots = 128  # NB usually >~1000 in real experiments\n",
    "ground = -0.2 + 0.65j\n",
    "excited = 0.7 + 4j\n",
    "centroids = ground, excited\n",
    "sigmas = [0.1] * 2\n",
    "\n",
    "display_source_code(mk_iq_shots)\n",
    "display_source_code(mk_shots_from_probabilities)\n",
    "display_source_code(mk_surface7_cyles_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12829358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.Dataset> Size: 27kB\n",
       "Dimensions:     (repetitions: 128, dim_cycle: 3, dim_final: 1)\n",
       "Coordinates:\n",
       "    cycle       (dim_cycle) int64 24B 0 1 2\n",
       "    final_msmt  (dim_final) int64 8B 0\n",
       "Dimensions without coordinates: repetitions, dim_cycle, dim_final\n",
       "Data variables:\n",
       "    A0_shots    (repetitions, dim_cycle) complex128 6kB (-0.23630343679164473...\n",
       "    A1_shots    (repetitions, dim_cycle) complex128 6kB (-0.23630343679164473...\n",
       "    A2_shots    (repetitions, dim_cycle) complex128 6kB (-0.23630343679164473...\n",
       "    D0_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "    D1_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "    D2_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "    D3_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "Attributes:\n",
       "    tuid:                      20250904-040824-815-9cdf74\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
       "    json_serialize_exclude:    []
"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 27kB\n",
       "Dimensions:     \u001b[1m(\u001b[0mrepetitions: \u001b[1;36m128\u001b[0m, dim_cycle: \u001b[1;36m3\u001b[0m, dim_final: \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "    cycle       \u001b[1m(\u001b[0mdim_cycle\u001b[1m)\u001b[0m int64 24B \u001b[1;36m0\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;36m2\u001b[0m\n",
       "    final_msmt  \u001b[1m(\u001b[0mdim_final\u001b[1m)\u001b[0m int64 8B \u001b[1;36m0\u001b[0m\n",
       "Dimensions without coordinates: repetitions, dim_cycle, dim_final\n",
       "Data variables:\n",
       "    A0_shots    \u001b[1m(\u001b[0mrepetitions, dim_cycle\u001b[1m)\u001b[0m complex128 6kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "    A1_shots    \u001b[1m(\u001b[0mrepetitions, dim_cycle\u001b[1m)\u001b[0m complex128 6kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "    A2_shots    \u001b[1m(\u001b[0mrepetitions, dim_cycle\u001b[1m)\u001b[0m complex128 6kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D0_shots    \u001b[1m(\u001b[0mrepetitions, dim_final\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D1_shots    \u001b[1m(\u001b[0mrepetitions, dim_final\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D2_shots    \u001b[1m(\u001b[0mrepetitions, dim_final\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D3_shots    \u001b[1m(\u001b[0mrepetitions, dim_final\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m\u001b[33m...\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20250904\u001b[0m-\u001b[1;36m040824\u001b[0m-\u001b[1;36m815\u001b[0m-9cdf74\n",
       "    dataset_name:              \n",
       "    dataset_state:             \u001b[3;35mNone\u001b[0m\n",
       "    timestamp_start:           \u001b[3;35mNone\u001b[0m\n",
       "    timestamp_end:             \u001b[3;35mNone\u001b[0m\n",
       "    quantify_dataset_version:  \u001b[1;36m2.0\u001b[0m.\u001b[1;36m0\u001b[0m\n",
       "    software_versions:         \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
       "    relationships:             \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
       "    json_serialize_exclude:    \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = mk_surface7_cyles_dataset(\n",
    "    num_shots=num_shots, sigmas=sigmas, centers=centroids\n",
    ")\n",
    "\n",
    "assert dataset == round_trip_dataset(dataset)  # confirm read/write\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b71d9599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001b[1m(\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m128\u001b[0m, \u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m, \u001b[1m(\u001b[0m\u001b[1;36m128\u001b[0m, \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.A1_shots.shape, dataset.D1_shots.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4cf70976",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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       "
<xarray.Dataset> Size: 27kB\n",
       "Dimensions:     (repetitions: 128, cycle: 3, final_msmt: 1)\n",
       "Coordinates:\n",
       "  * cycle       (cycle) int64 24B 0 1 2\n",
       "  * final_msmt  (final_msmt) int64 8B 0\n",
       "Dimensions without coordinates: repetitions\n",
       "Data variables:\n",
       "    A0_shots    (repetitions, cycle) complex128 6kB (-0.23630343679164473+0.6...\n",
       "    A1_shots    (repetitions, cycle) complex128 6kB (-0.23630343679164473+0.6...\n",
       "    A2_shots    (repetitions, cycle) complex128 6kB (-0.23630343679164473+0.6...\n",
       "    D0_shots    (repetitions, final_msmt) complex128 2kB (-0.2363034367916447...\n",
       "    D1_shots    (repetitions, final_msmt) complex128 2kB (-0.2363034367916447...\n",
       "    D2_shots    (repetitions, final_msmt) complex128 2kB (-0.2363034367916447...\n",
       "    D3_shots    (repetitions, final_msmt) complex128 2kB (-0.2363034367916447...\n",
       "Attributes:\n",
       "    tuid:                      20250904-040824-815-9cdf74\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
       "    json_serialize_exclude:    []
"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 27kB\n",
       "Dimensions:     \u001b[1m(\u001b[0mrepetitions: \u001b[1;36m128\u001b[0m, cycle: \u001b[1;36m3\u001b[0m, final_msmt: \u001b[1;36m1\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "  * cycle       \u001b[1m(\u001b[0mcycle\u001b[1m)\u001b[0m int64 24B \u001b[1;36m0\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;36m2\u001b[0m\n",
       "  * final_msmt  \u001b[1m(\u001b[0mfinal_msmt\u001b[1m)\u001b[0m int64 8B \u001b[1;36m0\u001b[0m\n",
       "Dimensions without coordinates: repetitions\n",
       "Data variables:\n",
       "    A0_shots    \u001b[1m(\u001b[0mrepetitions, cycle\u001b[1m)\u001b[0m complex128 6kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0.6\u001b[0m\u001b[33m...\u001b[0m\n",
       "    A1_shots    \u001b[1m(\u001b[0mrepetitions, cycle\u001b[1m)\u001b[0m complex128 6kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0.6\u001b[0m\u001b[33m...\u001b[0m\n",
       "    A2_shots    \u001b[1m(\u001b[0mrepetitions, cycle\u001b[1m)\u001b[0m complex128 6kB \u001b[1m(\u001b[0m\u001b[1;36m-0.23630343679164473\u001b[0m+\u001b[1;36m0.6\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D0_shots    \u001b[1m(\u001b[0mrepetitions, final_msmt\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.2363034367916447\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D1_shots    \u001b[1m(\u001b[0mrepetitions, final_msmt\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.2363034367916447\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D2_shots    \u001b[1m(\u001b[0mrepetitions, final_msmt\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.2363034367916447\u001b[0m\u001b[33m...\u001b[0m\n",
       "    D3_shots    \u001b[1m(\u001b[0mrepetitions, final_msmt\u001b[1m)\u001b[0m complex128 2kB \u001b[1m(\u001b[0m\u001b[1;36m-0.2363034367916447\u001b[0m\u001b[33m...\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20250904\u001b[0m-\u001b[1;36m040824\u001b[0m-\u001b[1;36m815\u001b[0m-9cdf74\n",
       "    dataset_name:              \n",
       "    dataset_state:             \u001b[3;35mNone\u001b[0m\n",
       "    timestamp_start:           \u001b[3;35mNone\u001b[0m\n",
       "    timestamp_end:             \u001b[3;35mNone\u001b[0m\n",
       "    quantify_dataset_version:  \u001b[1;36m2.0\u001b[0m.\u001b[1;36m0\u001b[0m\n",
       "    software_versions:         \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
       "    relationships:             \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
       "    json_serialize_exclude:    \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset, dimension=\"dim_cycle\", coords_names=[\"cycle\"]\n",
    ")\n",
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset_gridded, dimension=\"dim_final\", coords_names=[\"final_msmt\"]\n",
    ")\n",
    "dataset_gridded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a216dc93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/DN3GQvUOhf50hw4dIjw8nJtvvpnp06cTFhbGrl27ePHFF5k/fz7r1q3D39+/0OMoaCkpKbi5FeFaUnZ6zkx2b7m5WkZGhs0YmSuFh4ezZMkSm22LFi3KcYyOiBSQi2dgwX/hkyawaaKZ2FTvCE8shd7THCuxyWSxwO2vw+2jzPsr34OFr5pT3KVoGAYseQvO7jP/LYKf/ZAhQ3Bzc+PPP/+kXbt2VK5cmW7durF48WJOnDjBq6++muOx6enpjBgxgrJlyxIQEMBLL71Ev3796Nmzp3WfqlWrMn78eJvjGjduzJtvvmm9/+GHH9KgQQO8vb0JDQ3lmWee4eLFizbHREREULlyZby8vLj33nuztCq9+eabNG7cmG+//dZmcO6CBQu49dZbrTHefffdHDx40HpcZsHLJk2aYLFYaN++PQDt27fPUj+rZ8+e9O/f3+ba3nnnHfr27Yuvry9PPvkkAKtWraJt27Z4enoSGhrKsGHDSEhIyPHnWBDsmtyMHDmSlStXcvjwYXbs2MHIkSNZvnw5jz76KAB9+/Zl5MiR1v2fe+45FixYwLhx49i7dy9vvvkmmzZtYujQofa6BBHHduk8LHnbrNS97nNIT4bKbaD/H+aKvjc1s3eEha/tCOj2vvn/dZ/Dr8+Zqy1L3hmG2fKV39u+P+DkVvMcJ7ea9/N7jnwkROfOnWPhwoU888wzeHp62jwWFBTEo48+ysyZM8lpkvG4ceOIiIhg4sSJrFq1inPnzjFnzpx8/7icnJz45JNP2LVrF5MnT2bp0qW89NJL1sfXr1/PwIEDGTp0KNu2baNDhw5Zqo8DHDhwgNmzZ/Pzzz9bu5kSEhIYMWIEmzZtYsmSJTg5OXHvvfeSkZEBwIYNGwBYvHgxUVFR/Pzzz/mK/YMPPqBRo0Zs3bqV119/nYMHD9K1a1fuv/9+tm/fzsyZM1m1alWhf27btVvq9OnT9O3bl6ioKPz8/GjYsCELFy6kc+fOABw9ehSnK4rntWnThmnTpvHaa6/x3//+l5o1azJ37lzq13fAb40i9pQcD+smwJpPITnW3BbSBDq+brbYlLaumVZPgpsX/PIsbJlszqLq+aU5AFmuLTUR/lcA1ddnXMfMtf+eBDfvPO26f/9+DMOgTp062T5ep04dzp8/z5kzZ6hYsWKWx8ePH8/IkSO57777AJgwYQILFy7Md8hXtpBUrVqVd999l6effpovvvgCgI8//piuXbtaE56bb76ZNWvWsGDBApvzpKSk8P3331OhQgXrtvvvv99mn4kTJ1KhQgV2795N/fr1rfsGBATkOuQjJx07duT555+33h80aBCPPvqo9Zpq1qzJJ598Qrt27fjyyy8LbakAuyY33333Xa6PL1++PMu2Bx98kAcffLCQIhIp5VIvwcZvYdVHkHi5mbtiPej4KtS6s/QlNVdq8pj5ITl7EOyYBSmJ8OAkcNGYPkdzreXfkpKSKFPm34Uo//vf/zJkyBCioqJo1aqVdbuLiwvNmze/5vmutnjxYkaPHs3evXuJi4sjLS2NpKQkEhMT8fLyYs+ePdx77702x4SHh2dJbqpUqWKT2ICZwL3xxhusX7+es2fPWltsjh49WiANBc2bN7e5//fff7N9+3amTp1q3WYYBhkZGURGRuaYSN4ouw8oFpFiIC3FbJFY+QFcvLy0gn91cwBtvfvMAbYC9e4FVy+Y2Qf2/Q7TH4ZeU81WHcmZq5fZgpJXhgERd0L0TjCu6AK0OJvju/r/kfdE2zXvr02NGjWwWCzZJg8Ae/bsoUKFCoSEhNjMJsrPAGMnJ6csyU5qaqr1/4cPH+buu+9m8ODB/N///R/+/v6sWrWKgQMHkpKSgpdX3q/H2ztri1X37t2pUqUK33zzDSEhIWRkZFC/fn1SUlJuKO6cnvPixYs89dRTDBs2LMu+lStXzstlXBclNyKlWXoabJ8By8dC7FFzm19laP8yNHwYnPUnIoubu8Cjs2B6bzi4FKbcD4/MBA+teJ4jiyXPXUMAHFhslu24mpFubj+2Dmp0Krj4LgsICKBz58588cUX/Oc//7EZdxMdHc3UqVMZMmQILi4u1lm+VwoODmb9+vXcdtttAKSlpbF582aaNm1q3adChQpERUVZ78fFxREZGWm9v3nzZjIyMhg3bpx1WMaPP/5o8zx16tRh/fr1NtvWrVt3zeuLiYlh3759fPPNN7Rt2xYwB/teKXN2U3q67biyq+NOT09n586ddOiQ+wy2pk2bsnv37mx/XoVJX8dESqOMDNjxk7lOzbwhZmJTJhDu/ACe3WR2wSixyVm1dtB3Lrj7wdE18P09kHjO3lE5BsOApe+S88eTk/l4Ic2c+uyzz0hOTqZLly6sXLmSY8eOsWDBAjp37szNN9/MG2+8keOxzz33HGPGjGHu3Lns3buXZ555hgsXLtjs07FjR3744Qf++usvduzYQb9+/XB2drY+XqNGDVJTU/n00085dOgQP/zwQ5byQsOGDWPBggV88MEH7N+/n88++yxLl1R2ypUrR0BAAF9//TUHDhxg6dKlWVb9r1ixIp6enixYsIBTp04RGxtrjfv333/n999/Z+/evQwePDjLtWXn5ZdfZs2aNdbBz/v372fevHmFPqBYyY1IaWIYsPd3mHArzB4IMQfA0x86vwPDtpl1oDSGJG9CW0L/X8ErwJzJE3EXxJ+69nGSu/QUcz0lMnLYIQPiTpj7FYKaNWuyceNGqlWrxkMPPUSVKlXo1q0bN998M6tXr7YZa3O1559/nj59+tCvXz/Cw8Px8fHJ0r01cuRI2rVrx913381dd91Fz549qV69uvXxRo0a8eGHHzJ27Fjq16/P1KlTGT16tM05WrduzTfffMPHH39Mo0aN+PPPP3nttdeueW1OTk7MmDGDzZs3U79+ff7zn//w/vvv2+zj4uLCJ598wldffUVISAg9evQA4PHHH6dfv3707duXdu3aUa1atWu22gA0bNiQFStW8M8//9C2bVuaNGnCG2+8QUhIAQwwz0WxK5xZ2FQ4U0olwzC7UJa+Cye3mNvcfaHNs9B6MLj72De+kuz0Xvi+hzlWyb869J0HZUt3mZcbLpwZexwSzub8uHcF8Lvp+gPMp1GjRvHhhx+yaNEiWrduna9j+/fvz4ULF3KtgSj/crjCmSJSSI6sgSXvmN0nYA6wbPW0mdh4lbyVVoudirXh8fkwuQecOwiTupkJTkD1ax8r2fOrZN6KibfeeouqVauybt06WrZsabNEiRRPSm5EHNWJzbD0/+Dg5VW9nd2hxSC49T9QpkLux0r++FczE5zve5hdfZO6QZ+5EJh9EWApeQYMGGDvECQflNyIOJpTu2DZ/2Dvb+Z9JxezyvVtLxZpU36p41cJBsyHH+6FUzvNqcx95piLH0qpFRERYe8QSiW1rYk4irMH4KeB8OUtZmJjcYJGvWHoJug+XolNUShTEfr9apaluHQeJt8DR9baOyqRUkfJjUhJd+GoOZ3785aw8yfAgLo94Zl1cO8E8A+zd4Sli5e/Oeamyi2QHAdT7jMrW5dCpWy+ihSAgnrPKLkRKaniouD3F+CTprB1irnA2c1d4am/4KHJUKGWvSMsvdx94NGfzIXmUhNh2kOw9w97R1VkMtdtudaqtyJXy3zPXLn2z/XQmBuRkiYhBlZ/BBu+gbQkc1tYO7OoZWgL+8Ym/3LzgoenwU+Pm92EMx+D+76GBg/YO7JC5+LigpeXF2fOnMHV1VWziyRPMjIyOHPmDF5eXri43Fh6ouRGpKS4dAHWfg7rvoCUi+a20FbQ8TUIu82uoUkOXNzhwclmt+H2GWbRzdREaNrX3pEVKovFQnBwMJGRkRw5csTe4UgJ4uTkROXKlbHcYJFeJTcixV3yRdjwFaz+BJIumNuCG5ktNTU6le5K3SWBswv0/NJsydk0EX55FlISzMUTHZibmxs1a9ZU15Tki5ubW4G09Cm5ESmuUpPMD8NVH0LCGXNbhdrQ4VWo011JTUni5AR3fWgWj1zzKSx4xUxwbnvB3pEVKicnp+tboVjkBim5ESlu0lJg2xRY8T7EnzS3lQuD9iPN8RpONzbQTuzEYjFreLn5wPL/wdJ3zO7F20cpURUpYEpuRIqLjHTY/iMsHw0XLo9T8L0J2r0EjR8FZ1f7xic3zmKB9i+bXVR/vgarPjJbcLqONVt3RKRAKLkRsbeMDNgzz1xV+Ow/5jbvitD2eWjWH1zVrO9w2jxrdlH9NgI2fG0mOPd8qlY5kQKi5EbEXgwD/lkIy96F6B3mNo+ycOtwaPmk+eEnjqv54+DqDXMHw7ap5iyqe78GFzd7RyZS4im5EbGHQ8th6btwfKN5380HwodA+DPg4WfX0KQINepldlHNGgC75kDqJXPquFrrRG6IOnlFitLR9RBxt1k9+vhGcPGEW56D4duhw0glNqVRne7Qewa4eMA/C2Dag+b0fxG5bkpuRIrCyW0w9UGYeAcc/guc3aDlU/Dc39D5bbMekZReNTvBY7PBrQxErjQri1+6YO+oREosJTcihen0HpjZB75uB/v/BIuzuTrts1vgzvfAJ9DeEUpxUfVW6PuLOe7q+AaYfDcknLV3VCIlkpIbkcIQcxB+fhK+CIc9vwAWaPAQDN1ozoopG2rvCKU4qtQM+v8O3hXMQeaT7oS4k/aOSqTE0YBikYIUexxWvPdvlW4wx1S0/y8E1rVvbFIyBNWHAfPNcVln98GkbmaLTrkq9o5MpMRQciNSEOJPmWUSNk2E9Mu1dGp0ho6vQkgT+8YmJU/5mpcTnHvg/GGY2BX6/WJuF5FrUnIjciMSz8Hqj82F2FITzW1VbjUrdVcJt29sUrKVqwIDFti24PSZa7bsiEiulNyIXI+kOFj3Baz9HJLjzG03NYfbX4ewdqoVJAXDNxgG/GHOnoreDhF3wWM/m2NzRCRHGlAskh8pCbBqPHzc0KwBlRwHgQ3MdUoGLYZq7ZXYSMHyLg/9foXQVpB0weyqOrzK3lGJFGtKbkTyIi0Z1n8FHzeGxaPg0nkIqAkPTIKnVkKtbkpqpPB4ljVbbMJuMyuJT7kf9i+2d1QixZaSG5HcpKfC5snwSVOY/xIknIaylaHnl/DMOqh/n6o5S9FwLwOPzIKbu0JaEkx/GHb/Yu+oRIol/VUWyU5GOmz/ET5vCb8Og7jj4BMMd30IQzdD40fAWUPWpIi5ekCvKVDvXshIhVn94e+Z9o5KpNjRX2eRKxkG7PkVlv0Pzuwxt3mVh7YjLldx9rRvfCLOrnD/d2ZF8W1TYM5TkJpgvj9FBFByI2IyDDiwGJa+A1F/m9s8/KDNMGj1tNklIFJcODmbK127ecOGr+C3/5iD3ds8a+/IRIoFJTcikX/B0nfh2DrzvlsZaD0YwoeaAzlFiiMnJ+g21kxwVn0If75mVhNv/4oGt0upp+RGSq/jm8yWmkPLzfsuHtBiENz6H3P6rUhxZ7FAp1FmgrP0HVgxxpxNdce7SnCkVFNyI6VP1HZzTM0/8837Tq7QrB+0fcFcNE2kpLntBbPFccHLsPYzs4vqrg81k09KLSU3Unqc+QeW/w92zTHvW5yg0SPQ7iUVJZSSr/XTZgvOL8/C5klmOZAeX2hWn5RKeteL4zsXaVbq3j4DjAxzW/37of1IFSIUx9K0jzmjb85TsH2m2YLzwERwcbd3ZCJFSsmNOK7YE/DXB7Dle8hIM7fVugs6/FfFB8VxNXgAXL1gVj/Y+xtM722ujePmZe/IRIqMOmTF8Vw8Awv+C580gU0TzcSmekcYtBR6T1NiI46v9p3wyI9mknNwCUx9wCz2KlJK2DW5GT16NC1atMDHx4eKFSvSs2dP9u3bl+sxERERWCwWm5uHh0cRRSzF2qXzsORt+LgRrPsc0pOhcjj0/wP6zFElZSldqncw3/fuvnBkNXzfAxLP2TsqkSJh1+RmxYoVDBkyhHXr1rFo0SJSU1O54447SEhIyPU4X19foqKirLcjR44UUcRSLCXHw4r3YXwj+GucuVprSBN4bDYMmA9Vb7F3hCL2Ubm1WVHc0x9OboGIu+HiaXtHJVLo7DrmZsGCBTb3IyIiqFixIps3b+a2227L8TiLxUJQUFBhhyfFXeol2PgtrPoIEmPMbRXrQodXofZdWudDBCCkMQz4A77vCad3waRu0Hce+FWyd2QihaZYjbmJjY0FwN/fP9f9Ll68SJUqVQgNDaVHjx7s2rUrx32Tk5OJi4uzuUkJl5YCG76Bjxubq7ImxoB/dbPeztOroc7dSmxErlSxjpng+IVCzAGY2A3OHbJ3VCKFxmIYhmHvIAAyMjK45557uHDhAqtWrcpxv7Vr17J//34aNmxIbGwsH3zwAStXrmTXrl1UqpT1m8ibb77JW2+9lWV7bGwsvr6+BXoNUsjS08zp3MvHQuxRc5tfKLR7GRr11noeItcSexwm3wPnDkKZILMFp2Jte0clkidxcXH4+fnl6fO72CQ3gwcPZv78+axatSrbJCUnqamp1KlTh969e/POO+9keTw5OZnk5GTr/bi4OEJDQ5XclCQZGbDrZ1g+2vzWCVAmEG57EZr21RoeIvkRfwp+6Amnd4NXADz2s9l1JVLM5Se5KRZfdYcOHcpvv/3GypUr85XYALi6utKkSRMOHDiQ7ePu7u64u+vDr0QyDNj3Byz9P3OsAJgDI2/9j1kDSut2iOSfTyD0/x2m3Acnt5otOY/Ogsqt7B2ZSIGx65gbwzAYOnQoc+bMYenSpYSFheX7HOnp6ezYsYPgYNUEchiGAQeWwDcdYcYjZmLj7msOFB6+HW4ZpsRG5EZ4+UPfX6ByG0iOhR/u/beArIgDsGvLzZAhQ5g2bRrz5s3Dx8eH6OhoAPz8/PD09ASgb9++3HTTTYwePRqAt99+m9atW1OjRg0uXLjA+++/z5EjRxg0aJDdrkMK0JE1sPRdc10OMBcha/U0tHnW/IMsIgXDw9dcLmHmo3BwKUx9CB6aDLW62TsykRtm1+Tmyy+/BKB9+/Y22ydNmkT//v0BOHr0KE5XVLY9f/48TzzxBNHR0ZQrV45mzZqxZs0a6tatW1RhS2E4sdnsfjq4xLzv7A4tBppdUGUq2jc2EUfl5gW9Z8BPj5ulGmY+Bvd9bdZeEynBis2A4qKSnwFJUgRO7YJl/zP/sAI4uUCTPuZgYb+b7BubSGmRngpzB8OOWWBxgns+hSaP2TsqERslbkCxlEJnD5izn3bOBgzzD2rDXtDuJfCvZu/oREoXZ1e49yuzG3jLZJg3xKwo3uope0cmcl2U3EjRunAUVoyFbdPBSDe31e1pVuquUMuuoYmUak7O0P1jcCtj1mab/xKkXIS2z9s7MpF8U3IjRSMuyqz7tDkCMlLNbTd3NZOa4EZ2DU1ELrNYoMv/gXsZ80vIkrfNFpyOr2vVbylRlNxI4UqIgdUfmeUS0pLMbWHtoONrENrSvrGJSFYWi/mlw80bFr1hfilJSYAuo8GpWFXsEcmRkhspHJcuwNrPYd0XZtM2QKWWcPvrEJZzUVQRKSZuec5McH5/HtZPMH+Pu39idl+JFHNKbqRgpSSYfwhXfwJJF8xtQQ3NZu2andW0LVKStBgErt4w7xnYOgVSEs2p4s6u9o5MJFdKbqRgpCbBpomw6kNIOGNuq1DbbN6u3V3N2SIlVePe5no4Pw00a7ylXoIHI8DVw96RieRIyY3cmPRU2PoDrHgf4k+a28pVhfb/hQYPqAlbxBHU7QG9vcxF/v6ZD9Megt7TzW4rkWJIX6fl+mSkm9O5P2sOv/3HTGx8bzKnkg7dBI16KbERcSQ1O8OjP5lTxSNXwA/3QVKsvaMSyZaSG8mfjAzYNQe+aA1zn4bzh8G7AnQdC89ugWb91R8v4qjC2kLfeeDhB8fWweTu5oxIkWJG3VKSN4YB/yyEZe9C9A5zm0dZuHU4tHxSzdMipUWl5tD/d/i+J0T9DRF3mgmPT5C9IxOxUnIj13ZouVmp+/hG876bD4QPgfBnzG9wIlK6BDWAAfPh+x5wZi9M7Ar9foGyle0dmQig5EZyc3Q9LH0HDv9l3nfxhFZPQpvnwDvAvrGJiH1VuBkenw+T74HzkWaC0/cXKF/D3pGJKLmRbJzcBsv+D/b/ad53doNmA6DtCDU9i8i/ylWFxxeYLThn/4FJ3aDvXAisZ+/IpJTTgGL51+k9MLMPfN3OTGwsztC0Lzy7Ge58T4mNiGTlGwL9/zC7qhJOw6Q74cRme0clpZySG4GYg/Dzk/BFOOz5BbBAgwdh6Ea451P1o4tI7spUgH6/QqUW5srkk3vA4dX2jkpKMSU3pVnscfhlGHzWArbPBAyo0x0Gr4H7v4WA6vaOUERKCs9y0GcOVG0LKfEw5X44sNjeUUkppeSmNIo/BfNfhk+awJbJYKRDjc7w5HLoNQUC69o7QhEpidx94NFZUPMOSLsE03vDnt/sHZWUQkpuSpPEc7BoFHzS2CxumZ4CVW6FAQvgsZ8gpIm9IxSRks7VE3pNNUs2pKfAj31h+4/2jkpKGc2WKg2S4mDdF7D2c0iOM7fd1Mys1F2tvSp1i0jBcnGD+yeC67Pw9zRzTF9KAjQfYO/IpJRQcuPIUhJhw9ewejxcOm9uC6wPHV+Dm7sqqRGRwuPsAj0+N1cv3/gN/DYcUhPNBUBFCpmSG0eUlgybI2DlB+bUTICAmtDhv1C3JzipN1JEioCTE9z5vpngrB4PC/9rtuDc9qK+XEmhUnLjSNJTYds0WPEexB03t5WtDO1HQoOHzG9SIiJFyWKBTm+CexmzjMuy/4PkeOj8thIcKTT6tHMEGemwczYsHw3nDpnbfILNb0dN+pj93yIi9mKxmH+PXL1h4UhY84nZgnPnB2pJlkKh5KYkMwzY8yss+x+c2WNu8ypvlklo/rg5a0FEpLgIf8bsovr1Odj0nTkG557P1KosBU7vqJLIMMzFsZa+A1F/m9s8/KDNMGj1tNn8KyJSHDXrZyY4Pz8Jf083W3Du/04tzFKglNyUNJF/mf3Wx9aZ9129zW9D4UPBs6xdQxMRyZMGD5gty7P6myVfZjwCvX5Qa7MUGHV2lhTHN5mVdyffbSY2Lh5mQjN8uzm1W4mNiJQkte+CR2aCiyccWARTHjAHGosUACU3xV3Udpj2MHx7OxxaDk6u0HwgDNsKXf4PvMvbO0IRketTvSP0+RncfODIKvi+579rconcACU3xdWZf8wm26/awj/zweIEjR+DZzfB3R+Cb4i9IxQRuXFV2kC/X8zCmyc2QUR3uHjG3lFJCafkprg5FwlzBsMXrWDXHHNbvftgyAbo+TmUq2rX8ERECtxNTaH/H+BdEU7tgEndIPaEvaOSEkwDiouLuJOw8n3Y8j1kpJnbat0JHV6FoPr2jU1EpLAF1oXHF8DkeyBmP0zqCn1/Af8we0cmJZCSG3u7eAZWfQQbv4X0ZHNbtQ5mUctKzewbm4hIUQqoDo/PNydPnDtktuD0nQcVatk7MilhlNzYy6XzsOZTWDcBUhPMbZXDzZlPVW+1b2wiIvZStjIMmG8OLj6zx0xw+syF4Ib2jkxKECU3RS053kxo1nwKybHmtuDGcPvrUP121VoREfEJggF/wA/3QtQ2cwmMR2dDaAt7RyYlhAYUF5XUS2ZC83EjWPaumdhUrAu9psKTy6FGJyU2IiKZvPzNWVSVwyEp1uyqilxp76ikhFByU9jSUmDDN/BxY/jzNUiMAf/q5nLjT6+COncrqRERyY6HHzw22xyHmJoAUx+Ef/60d1RSAii5KUgHl8FnLc1/09Ng6xT4tBn88QJcjAa/ULNI3JAN5vLjTs72jlhEpHhz84beM8zZo2lJZqmGXXPtHZUUcxpzU1AMA5a8BWf3wW/DweIM5w6aj5UJhNtehKZ9wcXdrmGKiJQ4rh7w0Pcw52nY+RP8NMCsKN74EXtHJsWUkpuCcmAJnNxq/v/8YfNfT3+49T/QYhC4edktNBGREs/ZFe772vxbuuV7mDvYrCje8gl7RybFkJKbgmAYsOAV220+wfDMevD0s09MIiKOxskZun8Crt6w/kuzyz/lovklUuQKdh1zM3r0aFq0aIGPjw8VK1akZ8+e7Nu375rHzZo1i9q1a+Ph4UGDBg34448/iiDaXBxcYq6oeaX4KDix0T7xiIg4KosFuo42u/oBFr8JS981v2SKXGbX5GbFihUMGTKEdevWsWjRIlJTU7njjjtISEjI8Zg1a9bQu3dvBg4cyNatW+nZsyc9e/Zk586dRRj5FQzD/MWyXDU42OKsXzgRkcJgsZgLnnZ607y/8n1Y+F/9vRUri2EUn3fDmTNnqFixIitWrOC2227Ldp9evXqRkJDAb7/9Zt3WunVrGjduzIQJE675HHFxcfj5+REbG4uvr++NB31gMUy5P+fHH5ttrmEjIiIFb8M3ZvcUmJM27h6vmagOKj+f38VqKnhsrLlir7+/f477rF27lk6dbJOFLl26sHbt2mz3T05OJi4uzuZWYDJbbXL8MTqp9UZEpDC1fAJ6fAEWJ3Og8c9PQnqqvaMSOys2yU1GRgbDhw/nlltuoX79nKtgR0dHExgYaLMtMDCQ6OjobPcfPXo0fn5+1ltoaGjBBZ2eArEngIwcdsiAuBPmfiIiUjiaPAoPTAQnF3Oq+I/9IDXJ3lGJHRWb2VJDhgxh586drFq1qkDPO3LkSEaMGGG9HxcXV3AJjos7PLkMEs7mvI93Ba1tIyJS2OrdC65eMLMP7Psdpj8MD081FwGUUqdYJDdDhw7lt99+Y+XKlVSqVCnXfYOCgjh16pTNtlOnThEUFJTt/u7u7ri7F2Jy4VfJvImIiH3d3AUenQXTe8OhZeZ4yEdmmmUcpFSxa7eUYRgMHTqUOXPmsHTpUsLCwq55THh4OEuWLLHZtmjRIsLDwwsrTBERKSmqtYO+c8HdD46uhcn3QOI5e0clRcyuyc2QIUOYMmUK06ZNw8fHh+joaKKjo7l06ZJ1n759+zJy5Ejr/eeee44FCxYwbtw49u7dy5tvvsmmTZsYOnSoPS5BRESKm9CW0P9X8AqAqG0QcRfEn7rmYeI47JrcfPnll8TGxtK+fXuCg4Ott5kzZ1r3OXr0KFFRUdb7bdq0Ydq0aXz99dc0atSIn376iblz5+Y6CFlEREqZ4EYwYL65Wvzp3TCpK1w4Zu+opIgUq3VuikKBr3MjIiLF17lI+P4euHAU/EKh7zwIqG7vqOQ6lNh1bkRERAqUfxgMWAABNSH2GEzqBqd22zsqKWRKbkRExLH53WR2UQXWh4unIOJOOLHF3lFJIVJyIyIijq9MBej3K9zUDC6dN2dRHcl+ZXsp+ZTciIhI6eDlb465qXIrpMTDD/fCwaX2jkoKgZIbEREpPdx9zIX+anSCtEswrRfs/d3eUUkBU3IjIiKli5sXPDwN6nQ3a//N7AM7frJ3VFKAlNyIiEjp4+IOD0RAw4fBSIfZg2DzZHtHJQVEyY2IiJROzi7Q80to/jhgwK/DYO0X9o5KCoCSGxERKb2cnOCuD6HNs+b9hSNh5ftQuta3dThKbkREpHSzWKDzO9D+v+b9pe/C4jeV4JRgSm5EREQsFmj/Mtzxf+b91ePhjxchI8OuYcn1UXIjIiKSqc1QuHs8YIGN38AvQyE9zd5RST7lO7k5duwYx48ft97fsGEDw4cP5+uvvy7QwEREROyi+QC472uwOMO2qTB7IKSl2DsqyYd8JzePPPIIy5YtAyA6OprOnTuzYcMGXn31Vd5+++0CD1BERKTINXwIHpoMzm6wey7MfAxSL9k7KsmjfCc3O3fupGXLlgD8+OOP1K9fnzVr1jB16lQiIiIKOj4RERH7qNMdek8HF0/YvxCmPgjJF+0dleRBvpOb1NRU3N3dAVi8eDH33HMPALVr1yYqKqpgoxMREbGnGp3gsdng5gOH/4IfepqFN6VYy3dyU69ePSZMmMBff/3FokWL6Nq1KwAnT54kICCgwAMUERGxq6q3QL954FEWjm+Eyd0h4ay9o5Jc5Du5GTt2LF999RXt27end+/eNGrUCIBffvnF2l0lIiLiUG5qBv1/B+8KEL0DJnWDuJP2jkpyYDGM/K9SlJ6eTlxcHOXKlbNuO3z4MN7e3lSoUKFAAyxocXFx+Pn5ERsbi6+vr73DERGRkuTsAfj+Hog7AWWrQL9foFxVe0dVKuTn8zvfLTcdO3YkPj7eJrEB8Pf3p1evXvk9nYiISMlRvgYMmA/lwuDCEZjYDc78Y++o5Cr5Tm6WL19OSkrW+f5JSUn89ddfBRKUiIhIsVWuipngVKgN8SfNLqroHfaOSq7gktcdt2/fbv3/7t27iY6Ott5PT09nwYIF3HTTTQUbnYiISHHkGwz9/zBnT0Vvh4i74LGfoVJze0cm5GPMjZOTExaLBYDsDvH09OTTTz/l8ccfL9gIC5jG3IiISIG5dAGmPQTH1oNbGeg9A8La2jsqh5Sfz+88t9xERkZiGAbVqlVjw4YNNgOH3dzcqFixIs7OztcftYiISEnjWdZssZnRGyJXwtQHoNcUqNnZ3pGVatc1W6okU8uNiIgUuNQkmNUP/lkATq7wwHdQt4e9o3IohTpbCuDgwYM8++yzdOrUiU6dOjFs2DAOHjx4XcGKiIiUeK4eZotNvfsgIxVm9Ye/Z9g7qlIr38nNwoULqVu3Lhs2bKBhw4Y0bNiQ9evXU69ePRYtWlQYMYqIiBR/zq5w/7fQ5DEwMmDOU7DxO3tHVSrlu1uqSZMmdOnShTFjxthsf+WVV/jzzz/ZsmVLgQZY0NQtJSIihSojAxaOhPUTzPud34Fbhtk3JgdQqN1Se/bsYeDAgVm2P/744+zevTu/pxMREXEsTk7QdQy0fd68v+h1WDYaStcQV7vKd3JToUIFtm3blmX7tm3bqFixYkHEJCIiUrJZLHD7G+YNYMUY+PM1JThFJM9TwTM98cQTPPnkkxw6dIg2bdoAsHr1asaOHcuIESMKPEAREZESq+3z5vo381+CtZ9BykW460Nw0tIphSnfY24Mw2D8+PGMGzeOkyfNiqghISG8+OKLDBs2zLrQX3GlMTciIlLktvwAvw4zBxo3eAh6fgnO+W5fKNXy8/l9Q+vcxMfHA+Dj43O9pyhySm5ERMQuds6Gn5+EjDSofTc8MBFc3O0dVYlR6OvcZPLx8SlRiY2IiIjd1L/fXAvH2R32/gbTH4aURHtH5ZDyndycOnWKPn36EBISgouLC87OzjY3ERERyUGtbvDoj+DqBQeXwpT7ISnO3lE5nHx3+PXv35+jR4/y+uuvExwcXOzH2IiIiBQr1dpDnzkw9UE4uga+7wGPzQYvf3tH5jDyPebGx8eHv/76i8aNGxdSSIVLY25ERKRYOLkNfrgXLp2DivWg71wooyVVclKoY25CQ0MpZbU2RURECl5IYxjwB5QJgtO7YGJXiD1u76gcQr6Tm/Hjx/PKK69w+PDhQghHRESkFKlYx0xw/ELh3EGY2A1iVIj6RuWpW6pcuXI2Y2sSEhJIS0vDy8sLV1dXm33PnTtX8FEWIHVLiYhIsRN73Bx7E3PAbMnpOw8q1rZ3VMVKfj6/8zSgePz48QURl4iIiGTHrxIMmA/f9zS7qCZ1MwcdhzS2d2Ql0g0t4nejVq5cyfvvv8/mzZuJiopizpw59OzZM8f9ly9fTocOHbJsj4qKIigoKE/PqZYbEREpthLPmdPDT24Bd1949Ceo3MreURULhTqgeMuWLezYscN6f968efTs2ZP//ve/pKSk5OtcCQkJNGrUiM8//zxfx+3bt4+oqCjrTQU7RUTEIXj5m11SVW6B5Dj4oSccWm7vqEqcfCc3Tz31FP/88w8Ahw4dolevXnh5eTFr1ixeeumlfJ2rW7duvPvuu9x77735Oq5ixYoEBQVZb05ON7TQsoiISPHhcbnFpvrtkJoIUx+CffPtHVWJku+s4J9//rGucTNr1izatWvHtGnTiIiIYPbs2QUdX7YaN25McHAwnTt3ZvXq1bnum5ycTFxcnM1NRESkWHPzgt7TzRpU6ckw8zGzNpXkSb6TG8MwyMjIAGDx4sXceeedgLn+zdmzZws2uqsEBwczYcIEZs+ezezZswkNDaV9+/Zs2bIlx2NGjx6Nn5+f9RYaGlqoMYqIiBQIF3d4cLJZRTwjDX4aaFYXl2vK94Dijh07EhoaSqdOnRg4cCC7d++mRo0arFixgn79+l33+jcWi+WaA4qz065dOypXrswPP2T/gicnJ5OcnGy9HxcXR2hoqAYUi4hIyZCRAb//BzZHmPe7joXWT9s1JHso1AHF48ePZ8uWLQwdOpRXX32VGjVqAPDTTz/Rpk2b64v4BrRs2ZIDBw7k+Li7uzu+vr42NxERkRLDyQnuHg/hQ837C16GlR/YNaTiLt+FMxs2bGgzWyrT+++/b1MVfPr06dxzzz14e3vfWITXsG3bNoKDgwv1OUREROzKYoE73gW3MrBiDCx9B1IS4PY3zMfERr6Tm5x4eHjY3H/qqado1aoV1apVy/GYixcv2rS6REZGsm3bNvz9/alcuTIjR47kxIkTfP/994DZahQWFka9evVISkri22+/ZenSpfz5558FdRkiIiLFk8UCHUaCmzcseh1WfWgmOF3HmK07YlVgyc3V8jKUZ9OmTTaL8o0YMQKAfv36ERERQVRUFEePHrU+npKSwvPPP8+JEyfw8vKiYcOGLF68ONuF/URERBzSLcPM2VS/Pw8bvjITnHs+ASfnax9bShTaCsU+Pj78/fffubbc2INWKBYREYewbTrMewaMDKh3H9z3NTi7Xvu4EqpQBxSLiIhIMdC4NzwYAU6usOtnmNkHUpPsHVWxoORGRESkpKrbw1zsz8UD/pkP0x6C5Iv2jsrulNyIiIiUZDU7m+Ua3MpA5AqYch9cumDvqOyq0JKbKlWq4OrquH1/IiIixUZYW7PgpocfHFsPk7tDQoy9o7KbfA8oTktLY9euXURHRwMQFBRE3bp1S0wiowHFIiLisKJ3wPc9IfEsVKgNfeaCr2OsBVcoA4ozMjJ47bXXqFChAk2aNKFbt25069aNJk2aULFiRV5//XVrzSkRERGxg6AG8PgC8AmBM3thUje4cPTaxzmYPCc3r7zyCl9//TVjxozh0KFDJCQkkJCQwKFDhxg7dixff/01I0eOLMxYRURE5FrK14TH50O5qnA+EiZ2hbM5lylyRHnulgoKCmLy5Ml06dIl28cXLlxI3759OXXqVIEGWNDULSUiIqVC3En4vgec/Qe8K5hdVEH17R3VdSuUbqn4+HhCQkJyfDw4OJiEhIS8RykiIiKFxzcEBsw3u6oSzkDEXXB8s72jKhJ5Tm7at2/PCy+8wNmzZ7M8dvbsWV5++WXat29fkLGJiIjIjfAuD/1+g0otIOkCfH8PHF5t76gKXZ67pY4dO8add97J3r17adCgAYGBgQCcOnWKHTt2UKdOHX7//XdCQ0MLNeAbpW4pEREpdZIvwvSH4fBf4OIJD0+BGp3sHVW+5OfzO19TwTMyMli4cCHr1q2zmQoeHh7OHXfcwe7du6lfv3j35ym5ERGRUin1EvzYD/YvNEs2PDgJ6nS3d1R5VmjJTXbi4+OZPn063333HZs2bSI9Pf1GTlfolNyIiEiplZYCPz8Bu+eCxRl6fgmNetk7qjwpksKZK1eupF+/fgQHB/PBBx/QoUMH1q1bd72nExERkcLm4gb3fweNHgEjHeY8BZsm2TuqAueSn52jo6OJiIjgu+++Iy4ujoceeojk5GTmzp1L3bp1CytGERERKSjOLtDjc3Dzho3fwG/DISUB2gy1d2QFJs8tN927d6dWrVps376d8ePHc/LkST799NPCjE1EREQKg5MT3Pk+3DLcvP/nq7B8LNzYSJViI88tN/Pnz2fYsGEMHjyYmjVrFmZMIiIiUtgsFuj0JriXgaXvwvL/QUo8dH7HfKwEy3PLzapVq4iPj6dZs2a0atWKzz77LNs1b0RERKSEsFjgtheh6xjz/ppP4fcRUMJrReY5uWndujXffPMNUVFRPPXUU8yYMYOQkBAyMjJYtGgR8fHxhRmniIiIFJbWg+GeTwELbJoIcwdDepq9o7puNzQVfN++fXz33Xf88MMPXLhwgc6dO/PLL78UZHwFTlPBRUREcrDjJ/j5SXMmVZ17zJlVLm72jgoooqngALVq1eK9997j+PHjTJ8+/UZOJSIiIvbW4AHo9QM4u8GeX2BGb0hJtHdU+XbDi/iVNGq5ERERuYaDS2HGo5CaCFVuhUdmgLuPXUMqspYbERERcUDVO8JjP4O7LxxZBd/3gMRz9o4qz5TciIiISFZVwqHfL+BZDk5shsnd4eJpe0eVJ0puREREJHshTaD/H1AmEE7thEndIPaEvaO6JiU3IiIikrPAujBgPvhWgpgDMKkrnDtk76hypeRGREREchdQHR5fAP7V4MJRmHQnnNln76hypORGRERErq1sqNmCU6EOxEeZXVRRf9s7qmwpuREREZG88QmCAX9AcGNIjIGI7nBsg72jykLJjYiIiOSdl785i6pyOCTHwvc94dAKe0dlQ8mNiIiI5I+HHzw2G6p1gNQEmPog/LPQfOzgMvispfmvnSi5ERERkfxz84beM6DWXZCeDDMegZ0/w5K34Ow+8187FUFQciMiIiLXx9UDHpoMDR6EjDT46XE4udV87ORWOLjELmEpuREREZHr5+wK934FTfoAV7TUWJxh6bt2ab1RciMiIiI3xskZ6va03Wak2631RsmNiIiI3BjDgGXvmq01V7JT642SGxEREbkxB5eYrTRGuu12O7XeKLkRERGR62cYZutMjimFU5G33ii5ERERkeuXnnK5UnhGDjtkQNwJc78i4lJkzyQiIiKOx8UdnlwGCWdz3se7grlfUYVUZM8kIiIijsmvknkrJuzaLbVy5Uq6d+9OSEgIFouFuXPnXvOY5cuX07RpU9zd3alRowYRERGFHqeIiIiUHHZNbhISEmjUqBGff/55nvaPjIzkrrvuokOHDmzbto3hw4czaNAgFi5cWMiRioiISElh126pbt260a1btzzvP2HCBMLCwhg3bhwAderUYdWqVXz00Ud06dKlsMIUERGREqREzZZau3YtnTp1stnWpUsX1q5dm+MxycnJxMXF2dxERETEcZWo5CY6OprAwECbbYGBgcTFxXHp0qVsjxk9ejR+fn7WW2hoaFGEKiIiInZSopKb6zFy5EhiY2Ott2PHjtk7JBERESlEJWoqeFBQEKdOnbLZdurUKXx9ffH09Mz2GHd3d9zdi25uvYiIiNhXiWq5CQ8PZ8kS2/oUixYtIjw83E4RiYiISHFj1+Tm4sWLbNu2jW3btgHmVO9t27Zx9OhRwOxS6tu3r3X/p59+mkOHDvHSSy+xd+9evvjiC3788Uf+85//2CN8ERERKYbsmtxs2rSJJk2a0KRJEwBGjBhBkyZNeOONNwCIioqyJjoAYWFh/P777yxatIhGjRoxbtw4vv32W00DFxERESuLYRRhmc5iIC4uDj8/P2JjY/H19bV3OCIiIpIH+fn8LlFjbkRERESuRcmNiIiIOBQlNyIiIuJQlNyIiIiIQ1FyIyIiIg5FyY2IiIg4FCU3IiIi4lCU3IiIiIhDUXIjIiIiDkXJjYiIiDgUJTciIiLiUJTciIiIiENRciMiIiIORcmNiIiIOBQlNyIiIuJQlNyIiIiIQ1FyIyIiIg5FyY2IiIg4FCU3IiIi4lCU3IiIiIhDUXIjIiIiDkXJjYiIiDgUJTciIiLiUJTciIiIiENRciMiIiIORcmNiIiIOBQlNyIiIuJQlNyIiIiIQ1FyIyIiIg5FyY2IiIg4FCU3IiIi4lCU3IiIiIhDUXIjIiIiDkXJjYiIiDgUJTciIiLiUJTciIiIiENRciMiIiIORcmNiIiIOBQlNyIiIuJQlNyIiIiIQ1FyIyIiIg6lWCQ3n3/+OVWrVsXDw4NWrVqxYcOGHPeNiIjAYrHY3Dw8PIowWhEpjtIzDNYejGHethOsPRhDeoZh75BExE5c7B3AzJkzGTFiBBMmTKBVq1aMHz+eLl26sG/fPipWrJjtMb6+vuzbt89632KxFFW4IlIMLdgZxVu/7iYqNsm6LdjPg1Hd69K1frAdIxMRe7B7y82HH37IE088wYABA6hbty4TJkzAy8uLiRMn5niMxWIhKCjIegsMDMxx3+TkZOLi4mxuIuI4FuyMYvCULTaJDUB0bBKDp2xhwc4oO0UmIvZi1+QmJSWFzZs306lTJ+s2JycnOnXqxNq1a3M87uLFi1SpUoXQ0FB69OjBrl27ctx39OjR+Pn5WW+hoaEFeg0iYj/pGQZv/bqb7DqgMre99etudVGJlDJ2TW7Onj1Lenp6lpaXwMBAoqOjsz2mVq1aTJw4kXnz5jFlyhQyMjJo06YNx48fz3b/kSNHEhsba70dO3aswK9DRIqeYRgs3BWdpcXGZh8gKjaJDZHnii4wEbE7u4+5ya/w8HDCw8Ot99u0aUOdOnX46quveOedd7Ls7+7ujru7e1GGKCIFJD3D4OSFSxyJSeRwTAJHYhI4EpNo3s4lkJSakafzTFodiWEYNK1SDg9X50KOWkTsza7JTfny5XF2dubUqVM220+dOkVQUFCezuHq6kqTJk04cOBAYYQoIoUsJS2DY+cTbRKXwzEJHI1J5Nj5RFLTc+5ScrJAXnqc/tx9ij93n8LNxYmmlcsSXq08bWoE0KhSWdxc7D70UEQKmF2TGzc3N5o1a8aSJUvo2bMnABkZGSxZsoShQ4fm6Rzp6ens2LGDO++8sxAjFZEbkZiSxtFziRw+m8jRcwkcjvk3mTl54VKuCYqbsxOVA7yoGuBFZX9vqpb3okqAN1X8vQjy86DDB8uJjk3KdtwNgJ+nKx1qVWDtoRhOxSWz7tA51h06x0eLwdPVmeZVyxFePYA21ctTP8QXF2clOyIlnd27pUaMGEG/fv1o3rw5LVu2ZPz48SQkJDBgwAAA+vbty0033cTo0aMBePvtt2ndujU1atTgwoULvP/++xw5coRBgwbZ8zJESr3YS6kczab76HBMAqfjk3M91svNmSoB3mYCE+BF1QBvqlz+N8jXAyennJd7GNW9LoOnbMECNglO5hFj729A1/rBGIZB5NkE1hyMYe2hGNYdjCEmIYW/9p/lr/1ngX2UcXehZZg/baoH0LpaAHWDfXN9bhEpnuye3PTq1YszZ87wxhtvEB0dTePGjVmwYIF1kPHRo0dxcvr3m9T58+d54okniI6Oply5cjRr1ow1a9ZQt25de12CSKlgGAYxCSmXkxaz9eVozL+tMOcTU3M9vqyXK1X8va5IYsx/qwR4U76M23WvV9W1fjBfPtY0yzo3QVetc2OxWKhWoQzVKpThsdZVMAyDf05dZM3Bs6w9GMO6QzHEJaWxdO9plu49bY25VZg/baqXJ7x6ADUrltG6WiIlgMUwjFI1RzIuLg4/Pz9iY2Px9fW1dzgixUpGhsGp+KRsu4+OxCRyMTkt1+Mr+Lj/230U4EWV8t6XExovynq5FWrs6RkGGyLPcTo+iYo+HrQM88c5H60u6RkGe6LirMnOhshzJKSk2+xTvow7rav9m+xUDfBSsiNSRPLz+a3kRqSUSUvP4OSFJJvuo8wk5ui5RJLTcp6BZLFAiJ8nVQK8Lt+8rclMlQAvvN3t3hhcYFLTM9hxIpa1B2NYezCGTUfOZZmdFeznQXi1AMKrm7dK5bzsFK2I41NykwslN1IaJKelc+zcpWy7j46fv0RaLiN4XZwsVCrnmW33UaVynqV2KnVyWjrbjl5g7aEY1hyMYdvRC6Sk2yY7lf29bJKdQF/VvRMpKEpucqHkRhxFQnIaR2Ky7z46GXuJ3H6z3V2cqHzF+JfM7qOqAd6ElPXQjKE8uJSSzuYj51l76CxrDsaw/XhslpWQq1Xwpk31AMKrlad1NX8CymjNLZHrpeQmF0pupCSJTUw1u4/OJXLk7OVWmMvJzJlrzEAq4+6SbfdR1fJeBPrkPgNJ8u9ichobI89dbtk5y66TcVkSzNpBPmarTrUAWlULwM/T1T7BipRASm5yoeRGihPDMDh7MSXb7qMj5xK5cI0ZSOW8XLPtPqoS4EWA9/XPQJIbF5uYyrrIGOtMrL3R8TaPO1mgXoifOe28egAtqvpTxoHGLIkUNCU3uVByI0UtI8MgKi7pisG7CZfXgzGTmatn5Fytoo+7dd2Xf1thvKkc4KVv/iXI2YvJrD90zpyNdSiGQ2cSbB53cbLQsJKfdSZWM5WKELGh5CYXSm6kMKSmZ3Di/CWz+ygmwWYq9dFziaTkMgPJyQIhZT2z7T6q7O+Fl5u+zTui6Ngk1l3uwlp7KIZj5y7ZPO7m7ESTymWtqyc3DlWpCCndlNzkQsmNXK+k1HSOnUu0Gbx7+PL06ePnL2UZTHolV2cLoeWyrr5bOcCLSuU8cXfRN/TS7ti5RNYeirFOPY+Os6127uHqRIuq/rSuFkCb6gE0uMlPA7+lVFFykwslN5Kbi8lp2XQfmfej45JynYHk4epEFf+s3UdVArwI9tMMJMm7zFIRmdPOM0tFXCmzVETm1HOVihBHp+QmF0puSjfDMLiQmGrTfXTkXIK1pMDZiym5Hu/j7kKV8ldMob6czFQt701FH3cN4JVCYRgG+09fZM0Bswtr3aFzxF6yHWzu5+lK62pmstOmRnmVihCHo+QmF0puHJ9hGJyJT862++jw2QTiknIvIRDg7WbTfXRlK0w5L1d9YIjdZZaKWHu5COiGyHNZSmOUL+NGq8tdWOHVAggr7633rpRoSm5yoeTGMaRnGETFXrIuWmdOpf53EbtLqbnPQAry9ci2+6hygBe+HpqBJCVL2uVSEWsuTzvfeDhrqYggXw/rysnh1QII9VepCClZlNzkQslNyZGSlsGJC5fMpOXs5YXsLrfCHD93KcvS91dyssBN5TzNQbv+VwziLe9NaDkvPN00gFccV3JaOn8fi7UWAd2aTamIUH9Pswvr8tRzlYqQ4k7JTS6U3BQvSanp1u4im+6jmAROnL9ELhOQcHN2opK/57/dR/5mGYGqAd7cVNZT02ZFLktKvVwq4qA59fzvHEpFZCY7KhUhxZGSm1wouSl68Ump1u6iKytRZ85Ayo2nq7O1+8hMYv4dBxPs54mzZoeI5NvF5DQ2Hj7HuoPmbKydJ2OzLRXR+vJMrNZhAfh5qbtW7EvJTS6U3BQ8wzA4n1kD6YrEJXMq9dVTWK/m4+FCWHnb7qPM2UgVNANJpNDFJqayPjLGus7O1aUiLBaoF+JrdmFVC6BFmEpFSNFTcpMLJTfXxzAMTscnW7uPjlxViTr+GjOQypdx+7fV5fLqu1UCzErUZTUDSaRYibmYzLpD56wVz68uFeFsLRVhVjxvVqWcxrFJoVNykwslNzlLzzA4eeFStt1HR84lZJl9cbVgPw+blXevbIXRtzyRkutUXJJ15eS1h2I4ei7R5nE3ZycaVy5rnXbeuHJZrbotBU7JTS5Ke3KTkpbBsfOJ2XYfHTufSGp6zm8HZycLlcp5Ztt9FOrvpSJ/IqXE8fOJ1mRnTQ6lIppX8bdOPW+oUhFSAJTc5KI0JDeJKWmXZyD9W7wxM5k5eeHaM5DMVpd/izdmdh/dVM4TV/2BEpErGIbB4ZhE60ysdYdisqz07e3mbJaKuFwEtE6wryYDSL4pucmFoyQ3sZdSr6h7ZNsKczo+Oddjvdyc/60+fVUhxyBfD9WnEZHrllkq4t9kJ2upCF8PF+tMrDbVy3NzoEpFyLUpuclFYSU36RkGGyLPcTo+iYo+HrQM87+hbyaGYRCTkHJF+YAru5ISOJ+YmuvxZb1czXVfrEnM5VpIAd6UL+OmPyQiUiQyMgx2R8Wx7nIR0OxKRQR4u9H68nidNtVVKkKyp+QmF4WR3CzYGcVbv+4mKvbffudgPw9Gda9L1/rBOR6XkWFwKj4p2+6jIzGJWf4AXK2Cj7tNAlOlvPfl+16U9XIrkGsTESlImaUiMqedZ1cqItDX3TrtPLy6SkWISclNLgo6uVmwM4rBU7Zw9Q8x8zvHZ480ocFNZW26jzKTmKPnEklOy3kGksUCIX6eV9VAMsfCVAnwwlszkESkhMssFZHZjZVdqYhK5S6XiqhhTj0P8lOpiNJIyU0uCjK5Sc8wuHXsUpsWm/xyuTwDKbvuo0rlPDUDSURKlStLRaw9FMPfxy6QdnWpiPLetK5udmG1rhZAeZWKKBXy8/mtr/43YEPkuTwlNq5OFqqW9/63++iKStQhZT00RVJE5DIPV2duqVGeW2qUByDhcqmIzGRn54lYDp1N4NDZBKatPwpArUAf67RzlYoQUHJzQ07H563F5v0HG9GzyU2FHI2IiOPxdnehfa2KtK9VETBnim6IPGeteL43Op59p8xbxJrD1lIRmUVAVSqidNIrfgMq+uSt3zfQV/3DIiIFwc/Tlc51A+lcNxAwS0WsvyLZOXgmgZ0n4th5Io5v/oq0lorIHJzcvIq/SkWUAhpzcwMyx9xExyZlGVAM5qDiID8PVr3cUQtWiYgUgVNxSea08wPZl4pwdbbQJLSctRuriUpFlBgaUJyLwpotBdgkOJmpzJePNc11OriIiBQea6mIy1PPrx4n6e7iRPOq5WhTvTytqwXQsJKfVmIvppTc5KI4rXMjIiJFxzAMjsQkssaa7JzNtlREizB/a8XzuiEqFVFcKLnJRUlZoVhERAqXYRgcOH3RTHYOxrAuMoYLiVlLRbS6vHJyePUAbq7ooxI1dqLkJheOUltKREQKVkaGwZ7oOGvF8/XZlIrw93YjvFqAdZ2daioVUWSU3ORCyY2IiORFWnoGO0/GWVdP3nT4PJdS0232qejjbm3VaVO9vEpFFCIlN7lQciMiItcjJS2Dv49fsCY7W45eIOWqEjo3lfW0Jjvh1QMI9vO0U7SOR8lNLpTciIhIQUhKTWfLkfOsvVzxPLtSEWHlvc1Ep5pZKqKCj0pFXC8lN7lQciMiIoXBWiri8rTznSdiuSrX4ebAMpcXFCxP62r+lPVys0+wJZCSm1wouRERkaKQWSoic52dPVFxNo9bLFA32Nda8bxFVX98PFQXKydKbnKh5EZEROzhXEIK6y93Ya09FMOB0xdtHnd2stDgJr/Lg5NVKuJqSm5yoeRGRESKg9NxSdYurLWHYjgSk32piMxp56W9VISSm1wouRERkeLoxIVL1plY6w7GcDKHUhGZRUAbVipbqkpFlLjk5vPPP+f9998nOjqaRo0a8emnn9KyZcsc9581axavv/46hw8fpmbNmowdO5Y777wzT8+l5EZERIq7zFIRmTOx1h6M4ezFZJt9vNycaVHV3zr1vF6In0OvjF+ikpuZM2fSt29fJkyYQKtWrRg/fjyzZs1i3759VKxYMcv+a9as4bbbbmP06NHcfffdTJs2jbFjx7Jlyxbq169/zedTciMiIiWNYRgcPPNvqYi1h7KWivDxcKFV2L+lImoFOlapiBKV3LRq1YoWLVrw2WefAZCRkUFoaCjPPvssr7zySpb9e/XqRUJCAr/99pt1W+vWrWncuDETJkzIsn9ycjLJyf9mu3FxcYSGhiq5ERGREisjw2BvdLzZhXUohvWHzhGfTamI1tX8Ca9envBqAVSvULJLReQnuXEpopiylZKSwubNmxk5cqR1m5OTE506dWLt2rXZHrN27VpGjBhhs61Lly7MnTs32/1Hjx7NW2+9VWAxi4iI2JuTk4W6Ib7UDfFlUNtqpKVnsOtknHUm1sbIc5xLSOGPHdH8sSMaMEtFZM7ECq9WnlB/zxKd7OTGrsnN2bNnSU9PJzAw0GZ7YGAge/fuzfaY6OjobPePjo7Odv+RI0faJEOZLTciIiKOwsXZiUahZWkUWpbB7auTkpbB9uMXrN1Ym4+e53R8MvO2nWTetpOAWSoic/XkNjUcq1SEXZObouDu7o67u5a7FhGR0sPNxYnmVf1pXtWfYbfXNEtFHD3PuoPmAOVtxy5w4sIlftp8nJ82HwegaoCX2YV1OeEpyaUi7JrclC9fHmdnZ06dOmWz/dSpUwQFBWV7TFBQUL72FxERKe08XJ1pU708baqXZwRmqYhNR86bg5MPnmXHiVgOxyRyOOYo0zccBaBmxTLWwcmtwgIo533tUhHpGQYbIs9xOj6Jij4etAzzt8sMLrsmN25ubjRr1owlS5bQs2dPwBxQvGTJEoYOHZrtMeHh4SxZsoThw4dbty1atIjw8PAiiFhERKTk83Z3od3NFWh3cwUA4pJS2XDonHXq+Z6oOPafvsj+0xeZvPYIFgvUCfK1Jjstw7KWiliwM4q3ft1N1BXr8wT7eTCqe1261g8u0uuz+2ypmTNn0q9fP7766itatmzJ+PHj+fHHH9m7dy+BgYH07duXm266idGjRwPmVPB27doxZswY7rrrLmbMmMH//vc/TQUXEREpIJmlIjKTnexKRdS/ye/y4OQAziemMHzGNq5OKDLbbL58rOkNJzglaio4wGeffWZdxK9x48Z88skntGrVCoD27dtTtWpVIiIirPvPmjWL1157zbqI33vvvadF/ERERArJ6fgk1h6MYd3lchGHryoVkRsLEOTnwaqXO95QF1WJS26KkpIbERGRG3PSWioihuX7ThGTkHrNY6Y/0Zrw6gHX/Zz5+fwuPUUpREREpECElPXk/maVGPdQI964u16ejjkdn3TtnQqIkhsRERG5bhV9PfK2n0/e9isISm5ERETkurUM8yfYz4OcRtNYMGdNtQzzL7KYlNyIiIjIdXN2sjCqe12ALAlO5v1R3esW6Xo3Sm5ERETkhnStH8yXjzUlyM+26ynIz6NApoHnl8OXXxAREZHC17V+MJ3rBmmFYhEREXEczk6WG5ruXVDULSUiIiIORcmNiIiIOBQlNyIiIuJQlNyIiIiIQ1FyIyIiIg5FyY2IiIg4FCU3IiIi4lCU3IiIiIhDUXIjIiIiDqXUrVBsGAYAcXFxdo5ERERE8irzczvzczw3pS65iY+PByA0NNTOkYiIiEh+xcfH4+fnl+s+FiMvKZADycjI4OTJk/j4+GCxFGwxr7i4OEJDQzl27Bi+vr4Feu7iwNGvDxz/GnV9JZ+jX6OjXx84/jUW1vUZhkF8fDwhISE4OeU+qqbUtdw4OTlRqVKlQn0OX19fh3zDZnL06wPHv0ZdX8nn6Nfo6NcHjn+NhXF912qxyaQBxSIiIuJQlNyIiIiIQ1FyU4Dc3d0ZNWoU7u7u9g6lUDj69YHjX6Our+Rz9Gt09OsDx7/G4nB9pW5AsYiIiDg2tdyIiIiIQ1FyIyIiIg5FyY2IiIg4FCU3IiIi4lCU3FzD559/TtWqVfHw8KBVq1Zs2LAh1/1nzZpF7dq18fDwoEGDBvzxxx82jxuGwRtvvEFwcDCenp506tSJ/fv3F+Yl5Co/1/fNN9/Qtm1bypUrR7ly5ejUqVOW/fv374/FYrG5de3atbAvI0f5ub6IiIgssXt4eNjsU9xeP8jfNbZv3z7LNVosFu666y7rPsXpNVy5ciXdu3cnJCQEi8XC3Llzr3nM8uXLadq0Ke7u7tSoUYOIiIgs++T397qw5Pf6fv75Zzp37kyFChXw9fUlPDychQsX2uzz5ptvZnn9ateuXYhXkbP8Xt/y5cuzfX9GR0fb7FdcXj/I/zVm9/tlsVioV6+edZ/i9BqOHj2aFi1a4OPjQ8WKFenZsyf79u275nH2/ixUcpOLmTNnMmLECEaNGsWWLVto1KgRXbp04fTp09nuv2bNGnr37s3AgQPZunUrPXv2pGfPnuzcudO6z3vvvccnn3zChAkTWL9+Pd7e3nTp0oWkpKSiuiyr/F7f8uXL6d27N8uWLWPt2rWEhoZyxx13cOLECZv9unbtSlRUlPU2ffr0oricLPJ7fWCuqHll7EeOHLF5vDi9fpD/a/z5559trm/nzp04Ozvz4IMP2uxXXF7DhIQEGjVqxOeff56n/SMjI7nrrrvo0KED27ZtY/jw4QwaNMgmAbie90Vhye/1rVy5ks6dO/PHH3+wefNmOnToQPfu3dm6davNfvXq1bN5/VatWlUY4V9Tfq8v0759+2zir1ixovWx4vT6Qf6v8eOPP7a5tmPHjuHv75/ld7C4vIYrVqxgyJAhrFu3jkWLFpGamsodd9xBQkJCjscUi89CQ3LUsmVLY8iQIdb76enpRkhIiDF69Ohs93/ooYeMu+66y2Zbq1atjKeeesowDMPIyMgwgoKCjPfff9/6+IULFwx3d3dj+vTphXAFucvv9V0tLS3N8PHxMSZPnmzd1q9fP6NHjx4FHep1ye/1TZo0yfDz88vxfMXt9TOMG38NP/roI8PHx8e4ePGidVtxeg2vBBhz5szJdZ+XXnrJqFevns22Xr16GV26dLHev9GfWWHJy/Vlp27dusZbb71lvT9q1CijUaNGBRdYAcnL9S1btswAjPPnz+e4T3F9/Qzj+l7DOXPmGBaLxTh8+LB1W3F9DQ3DME6fPm0AxooVK3Lcpzh8FqrlJgcpKSls3ryZTp06Wbc5OTnRqVMn1q5dm+0xa9eutdkfoEuXLtb9IyMjiY6OttnHz8+PVq1a5XjOwnI913e1xMREUlNT8ff3t9m+fPlyKlasSK1atRg8eDAxMTEFGnteXO/1Xbx4kSpVqhAaGkqPHj3YtWuX9bHi9PpBwbyG3333HQ8//DDe3t4224vDa3g9rvU7WBA/s+IkIyOD+Pj4LL+D+/fvJyQkhGrVqvHoo49y9OhRO0V4fRo3bkxwcDCdO3dm9erV1u2O9vqB+TvYqVMnqlSpYrO9uL6GsbGxAFnec1cqDp+FSm5ycPbsWdLT0wkMDLTZHhgYmKX/N1N0dHSu+2f+m59zFpbrub6rvfzyy4SEhNi8Qbt27cr333/PkiVLGDt2LCtWrKBbt26kp6cXaPzXcj3XV6tWLSZOnMi8efOYMmUKGRkZtGnThuPHjwPF6/WDG38NN2zYwM6dOxk0aJDN9uLyGl6PnH4H4+LiuHTpUoG874uTDz74gIsXL/LQQw9Zt7Vq1YqIiAgWLFjAl19+SWRkJG3btiU+Pt6OkeZNcHAwEyZMYPbs2cyePZvQ0FDat2/Pli1bgIL5u1WcnDx5kvnz52f5HSyur2FGRgbDhw/nlltuoX79+jnuVxw+C0tdVXApGGPGjGHGjBksX77cZtDtww8/bP1/gwYNaNiwIdWrV2f58uXcfvvt9gg1z8LDwwkPD7feb9OmDXXq1OGrr77inXfesWNkheO7776jQYMGtGzZ0mZ7SX4NS5Np06bx1ltvMW/ePJsxKd26dbP+v2HDhrRq1YoqVarw448/MnDgQHuEmme1atWiVq1a1vtt2rTh4MGDfPTRR/zwww92jKxwTJ48mbJly9KzZ0+b7cX1NRwyZAg7d+602/if/FDLTQ7Kly+Ps7Mzp06dstl+6tQpgoKCsj0mKCgo1/0z/83POQvL9Vxfpg8++IAxY8bw559/0rBhw1z3rVatGuXLl+fAgQM3HHN+3Mj1ZXJ1daVJkybW2IvT6wc3do0JCQnMmDEjT38o7fUaXo+cfgd9fX3x9PQskPdFcTBjxgwGDRrEjz/+mKX5/2ply5bl5ptvLhGvX3Zatmxpjd1RXj8wZwtNnDiRPn364Obmluu+xeE1HDp0KL/99hvLli2jUqVKue5bHD4LldzkwM3NjWbNmrFkyRLrtoyMDJYsWWLz7f5K4eHhNvsDLFq0yLp/WFgYQUFBNvvExcWxfv36HM9ZWK7n+sAc4f7OO++wYMECmjdvfs3nOX78ODExMQQHBxdI3Hl1vdd3pfT0dHbs2GGNvTi9fnBj1zhr1iySk5N57LHHrvk89noNr8e1fgcL4n1hb9OnT2fAgAFMnz7dZgp/Ti5evMjBgwdLxOuXnW3btlljd4TXL9OKFSs4cOBAnr5g2PM1NAyDoUOHMmfOHJYuXUpYWNg1jykWn4UFMizZQc2YMcNwd3c3IiIijN27dxtPPvmkUbZsWSM6OtowDMPo06eP8corr1j3X716teHi4mJ88MEHxp49e4xRo0YZrq6uxo4dO6z7jBkzxihbtqwxb948Y/v27UaPHj2MsLAw49KlS8X++saMGWO4ubkZP/30kxEVFWW9xcfHG4ZhGPHx8cYLL7xgrF271oiMjDQWL15sNG3a1KhZs6aRlJRU7K/vrbfeMhYuXGgcPHjQ2Lx5s/Hwww8bHh4exq5du6z7FKfXzzDyf42Zbr31VqNXr15Zthe31zA+Pt7YunWrsXXrVgMwPvzwQ2Pr1q3GkSNHDMMwjFdeecXo06ePdf9Dhw4ZXl5exosvvmjs2bPH+Pzzzw1nZ2djwYIF1n2u9TMrztc3depUw8XFxfj8889tfgcvXLhg3ef55583li9fbkRGRhqrV682OnXqZJQvX944ffp0sb++jz76yJg7d66xf/9+Y8eOHcZzzz1nODk5GYsXL7buU5xeP8PI/zVmeuyxx4xWrVple87i9BoOHjzY8PPzM5YvX27znktMTLTuUxw/C5XcXMOnn35qVK5c2XBzczNatmxprFu3zvpYu3btjH79+tns/+OPPxo333yz4ebmZtSrV8/4/fffbR7PyMgwXn/9dSMwMNBwd3c3br/9dmPfvn1FcSnZys/1ValSxQCy3EaNGmUYhmEkJiYad9xxh1GhQgXD1dXVqFKlivHEE0/Y7Y+OYeTv+oYPH27dNzAw0LjzzjuNLVu22JyvuL1+hpH/9+jevXsNwPjzzz+znKu4vYaZU4OvvmVeU79+/Yx27dplOaZx48aGm5ubUa1aNWPSpElZzpvbz6wo5ff62rVrl+v+hmFOfQ8ODjbc3NyMm266yejVq5dx4MCBor2wy/J7fWPHjjWqV69ueHh4GP7+/kb79u2NpUuXZjlvcXn9DOP63qMXLlwwPD09ja+//jrbcxan1zC7awNsfq+K42eh5XLwIiIiIg5BY25ERETEoSi5EREREYei5EZEREQcipIbERERcShKbkRERMShKLkRERERh6LkRkRERByKkhsRERFxKEpuRIqxr7/+mtDQUJycnBg/fry9wykwy5cvx2KxcOHCBXuHkiOLxcLcuXPtHcZ1Keqfb/v27bFYLFgsFrZt2wbA4cOHrdsaN25cJHGIZFJyI3IDzpw5w+DBg6lcuTLu7u4EBQXRpUsXVq9efcPnjouLY+jQobz88sucOHGCJ598sgAiFikcTzzxBFFRUdSvXx+A0NBQoqKieP755+0cmZRGLvYOQKQku//++0lJSWHy5MlUq1aNU6dOsWTJEmJiYq77nIZhkJ6eztGjR0lNTeWuu+4qsRWdxbGkpKTg5uaW7WNeXl4EBQVZ7zs7OxMUFESZMmWKKjwRK7XciFynCxcu8NdffzF27Fg6dOhAlSpVaNmyJSNHjuSee+4B/m2az2yqzzzOYrGwfPly4N8uhPnz59OsWTPc3d2ZMmUKDRo0AKBatWpYLBYOHz7MwYMH6dGjB4GBgZQpU4YWLVqwePFim7iSk5N5+eWXCQ0Nxd3dnRo1avDdd99ZH9+5cyfdunWjTJkyBAYG0qdPH86ePZvrta5evZr27dvj5eVFuXLl6NKlC+fPn7c+37Bhw6hYsSIeHh7ceuutbNy40eb4P/74g5tvvhlPT086dOjA4cOHszzHqlWraNu2LZ6enoSGhjJs2DASEhJyjevXX3+lRYsWeHh4UL58ee69914A3n77bWsLwpUaN27M66+/br0/ceJE6tWrh7u7O8HBwQwdOjTH5zp27BgPPfQQZcuWxd/fnx49emR7HZkyX9clS5bQvHlzvLy8aNOmDfv27bPu079/f3r27Glz3PDhw2nfvr31fvv27Xn22WcZPnw45cqVIzAwkG+++YaEhAQGDBiAj48PNWrUYP78+VliWL16NQ0bNsTDw4PWrVuzc+dOm8ev9TOvWrUq77zzDn379sXX11eth1JiKLkRuU5lypShTJkyzJ07l+Tk5Bs+3yuvvMKYMWPYs2cPnTt3tiYtGzZsICoqitDQUC5evMidd97JkiVL2Lp1K127dqV79+4cPXrUep6+ffsyffp0PvnkE/bs2cNXX31l/fZ84cIFOnbsSJMmTdi0aRMLFizg1KlTPPTQQznGtW3bNm6//Xbq1q3L2rVrWbVqFd27dyc9PR2Al156idmzZzN58mS2bNlCjRo16NKlC+fOnQPMpOC+++6je/fubNu2jUGDBvHKK6/YPMfBgwfp2rUr999/P9u3b2fmzJmsWrUq12Tj999/59577+XOO+9k69atLFmyhJYtWwLw+OOPs2fPHpska+vWrWzfvp0BAwYA8OWXXzJkyBCefPJJduzYwS+//EKNGjWyfa7U1FS6dOmCj48Pf/31F6tXr6ZMmTJ07dqVlJSUHGMEePXVVxk3bhybNm3CxcWFxx9/PNf9szN58mTKly/Phg0bePbZZxk8eDAPPvggbdq0YcuWLdxxxx306dOHxMREm+NefPFFxo0bx8aNG6lQoQLdu3cnNTUVyPvP/IMPPqBRo0Zs3brVJjEUKdYKrL64SCn0008/GeXKlTM8PDyMNm3aGCNHjjT+/vtv6+ORkZEGYGzdutW67fz58wZgLFu2zDAMw1i2bJkBGHPnzrU599atWw3AiIyMzDWGevXqGZ9++qlhGIaxb98+AzAWLVqU7b7vvPOOcccdd9hsO3bsmAEY+/bty/aY3r17G7fccku2j128eNFwdXU1pk6dat2WkpJihISEGO+9955hGIYxcuRIo27dujbHvfzyywZgnD9/3jAMwxg4cKDx5JNP2uzz119/GU5OTsalS5eyfe7w8HDj0UcfzfYxwzCMbt26GYMHD7bef/bZZ4327dtb74eEhBivvvpqjscDxpw5cwzDMIwffvjBqFWrlpGRkWF9PDk52fD09DQWLlyY7fGZr+vixYut237//XcDsF5Tv379jB49etgc99xzzxnt2rWz3m/Xrp1x6623Wu+npaUZ3t7eRp8+fazboqKiDMBYu3atzXPPmDHDuk9MTIzh6elpzJw50zCMvP3Mq1SpYvTs2TPHn9GVMT733HPZPjZq1CijUaNG1zyHSEFSy43IDbj//vs5efIkv/zyC127dmX58uU0bdqUiIiIfJ+refPm19zn4sWLvPDCC9SpU4eyZctSpkwZ9uzZY2252bZtG87OzrRr1y7b4//++2+WLVtmbXUqU6YMtWvXBsxv8tnJbLnJzsGDB0lNTeWWW26xbnN1daVly5bs2bMHgD179tCqVSub48LDw7PEFRERYRNXly5dyMjIIDIyMt9xgTnAdfr06SQlJZGSksK0adOsrSanT5/m5MmTuR5/dXwHDhzAx8fHGp+/vz9JSUk5/twyNWzY0Pr/zLFTp0+fztPzZncOZ2dnAgICrN2WAIGBgdme98qfs7+/P7Vq1bK+Lnn9meflfSlS3GhAscgN8vDwoHPnznTu3JnXX3+dQYMGMWrUKPr374+Tk/n9wTAM6/6Z3QJX8/b2vuZzvfDCCyxatIgPPviAGjVq4OnpyQMPPGDtGvH09Mz1+IsXL9K9e3fGjh2b5bGcBi1f65wF4eLFizz11FMMGzYsy2OVK1fO9phrxdW9e3fc3d2ZM2cObm5upKam8sADD+Tp2Ozia9asGVOnTs3yWIUKFXI91tXV1fp/i8UCQEZGBgBOTk427w3I/v1x5Tkyz5PbefMirz/zvLwvRYobtdyIFLC6detaB2VmfvBFRUVZH79ycHF+rV69mv79+3PvvffSoEEDgoKCbAa1NmjQgIyMDFasWJHt8U2bNmXXrl1UrVqVGjVq2Nxy+hBr2LAhS5Ysyfax6tWr4+bmZjP1PTU1lY0bN1K3bl0A6tSpw4YNG2yOW7duXZa4du/enSWmGjVq5Dg7J7e4AFxcXOjXrx+TJk1i0qRJPPzww9akxsfHh6pVq+Z6/NXx7d+/n4oVK2aJz8/PL0/nyE6FChVs3htwY++Pq135cz5//jz//PMPderUAa7vZy5SUii5EblOMTExdOzYkSlTprB9+3YiIyOZNWsW7733Hj169ADMFoLWrVtbBwqvWLGC11577bqfs2bNmvz8889s27aNv//+m0ceecTm23rVqlXp168fjz/+OHPnziUyMpLly5fz448/AjBkyBDOnTtH79692bhxIwcPHmThwoUMGDDAOkD4aiNHjmTjxo0888wzbN++nb179/Lll19y9uxZvL29GTx4MC+++CILFixg9+7dPPHEEyQmJjJw4EAAnn76afbv38+LL77Ivn37mDZtWpZuu5dffpk1a9YwdOhQtm3bxv79+5k3b16uA4pHjRrF9OnTGTVqFHv27GHHjh1ZWqQGDRrE0qVLWbBgQZaBvG+++Sbjxo3jk08+Yf/+/WzZsoVPP/002+d69NFHKV++PD169OCvv/6y/lyHDRvG8ePHc4zxWjp27MimTZv4/vvv2b9/P6NGjcoyo+lGvP322yxZsoSdO3fSv39/ypcvb52ddT0/c5GSQsmNyHUqU6YMrVq14qOPPuK2226jfv36vP766zzxxBN89tln1v0mTpxIWloazZo1Y/jw4bz77rvX/Zwffvgh5cqVo02bNnTv3p0uXbrQtGlTm32+/PJLHnjgAZ555hlq167NE088YW1JCgkJYfXq1aSnp3PHHXfQoEEDhg8fTtmyZa1daFe7+eab+fPPP/n7779p2bIl4eHhzJs3DxcXs1d7zJgx3H///fTp04emTZty4MABFi5cSLly5QCzi2P27NnMnTuXRo0aMWHCBP73v//ZPEfDhg1ZsWIF//zzD23btqVJkya88cYbhISE5PizaN++PbNmzeKXX36hcePGdOzYMUsLUc2aNWnTpg21a9fOMu6nX79+jB8/ni+++IJ69epx9913s3///myfy8vLi5UrV1K5cmXuu+8+6tSpw8CBA0lKSsLX1zfHGK+lS5cuvP7667z00ku0aNGC+Ph4+vbte93nu9qYMWN47rnnaNasGdHR0fz666/WVpnr+ZmLlBQW4+oOXxERB2EYBjVr1uSZZ55hxIgR9g7HYbVv357GjRtnWyLkzTffZO7cuQXa3SZyLWq5ERGHdObMGT777DOio6Ota9tI4fniiy8oU6YMO3bsAODo0aOUKVMmSyudSFFQy42IOCSLxUL58uX5+OOPeeSRR+wdjkM7ceIEly5dAsxuSDc3N9LS0qyD3d3d3QkNDbVjhFLaKLkRERERh6JuKREREXEoSm5ERETEoSi5EREREYei5EZEREQcipIbERERcShKbkRERMShKLkRERERh6LkRkRERBzK/wMfVLI+CvKbpAAAAABJRU5ErkJggg==",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 64\u001b[0m\u001b[1;36m0x480\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_gridded.A0_shots.real.mean(\"repetitions\").plot(marker=\"o\", label=\"I-quadrature\")\n",
    "dataset_gridded.A0_shots.imag.mean(\"repetitions\").plot(marker=\"^\", label=\"Q-quadrature\")\n",
    "_ = plt.gca().legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41b4f6af",
   "metadata": {},
   "source": [
    "(sec-nested-mc-example)=\n",
    "## Dataset for a \"nested MeasurementControl\" experiment\n",
    "\n",
    "Now consider a dataset that has been constructed by an experiment involving the\n",
    "operation of two\n",
    "{class}`.MeasurementControl` objects. The second of\n",
    "them performs a \"meta\" outer loop in which we sweep a flux bias and then perform\n",
    "several experiments to characterize a transmon qubit, e.g. determining the frequency of\n",
    "a read-out resonator, the frequency of the transmon, and its T1 lifetime.\n",
    "\n",
    "Below we showcase what the data from the dataset containing the T1 experiment results\n",
    "could look like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d839806c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 64\u001b[0m\u001b[1;36m0x480\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "rng = np.random.default_rng(seed=112244)  # random number generator\n",
    "\n",
    "num_t1_datasets = 7\n",
    "t1_times = np.linspace(0, 120e-6, 30)\n",
    "\n",
    "for tau in rng.uniform(10e-6, 50e-6, num_t1_datasets):\n",
    "    probabilities = exp_decay_func(\n",
    "        t=t1_times, tau=tau, offset=0, n_factor=1, amplitude=1\n",
    "    )\n",
    "    dataset = dataset_examples.mk_t1_av_with_cal_dataset(t1_times, probabilities)\n",
    "\n",
    "    round_trip_dataset(dataset)  # confirm read/write\n",
    "    dataset_g = dh.to_gridded_dataset(\n",
    "        dataset, dimension=\"main_dim\", coords_names=[\"t1_time\"]\n",
    "    )\n",
    "    # rotate the iq data\n",
    "    rotated_and_normalized = rotate_to_calibrated_axis(\n",
    "        dataset_g.q0_iq_av.values, *dataset_g.q0_iq_av_cal.values\n",
    "    )\n",
    "    rotated_and_normalized_da = xr.DataArray(dataset_g.q0_iq_av)\n",
    "    rotated_and_normalized_da.values = rotated_and_normalized\n",
    "    rotated_and_normalized_da.attrs[\"long_name\"] = \"|1> Population\"\n",
    "    rotated_and_normalized_da.attrs[\"units\"] = \"\"\n",
    "    rotated_and_normalized_da.real.plot(ax=ax, label=dataset.tuid, marker=\".\")\n",
    "ax.set_title(\"Results from repeated T1 experiments\\n(different datasets)\")\n",
    "_ = ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae491ce5",
   "metadata": {},
   "source": [
    "Since the raw data is now split among several datasets, we would like to keep a\n",
    "reference to all these datasets in our \"combined\" datasets. Below we showcase how this\n",
    "can be achieved, along with some useful xarray features and known limitations.\n",
    "\n",
    "We start by generating a mock dataset that combines all the information that would have\n",
    "been obtained from analyzing a series of other datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f7149f12",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Source code for mk_nested_mc_dataset function"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
def mk_nested_mc_dataset(\n",
       "    num_points: int = 12,\n",
       "    flux_bias_min_max: tuple = (-0.04, 0.04),\n",
       "    resonator_freqs_min_max: tuple = (7e9, 7.3e9),\n",
       "    qubit_freqs_min_max: tuple = (4.5e9, 5.0e9),\n",
       "    t1_values_min_max: tuple = (20e-6, 50e-6),\n",
       "    seed: int | None = 112233,\n",
       ") -> xr.Dataset:\n",
       "    """\n",
       "    Generates a dataset with dataset references and several coordinates that serve to\n",
       "    index the same variables.\n",
       "\n",
       "    Note that the each value for ``resonator_freqs``, ``qubit_freqs`` and ``t1_values``\n",
       "    would have been extracted from other dataset corresponding to individual experiments\n",
       "    with their own dataset.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_points\n",
       "        Number of datapoints to generate (used for all variables/coordinates).\n",
       "    flux_bias_min_max\n",
       "        Range for mock values.\n",
       "    resonator_freqs_min_max\n",
       "        Range for mock values.\n",
       "    qubit_freqs_min_max\n",
       "        Range for mock values.\n",
       "    t1_values_min_max\n",
       "        Range for mock random values.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    """\n",
       "    rng = np.random.default_rng(seed=seed)  # random number generator\n",
       "\n",
       "    flux_bias_vals = np.linspace(*flux_bias_min_max, num_points)\n",
       "    resonator_freqs = np.linspace(*resonator_freqs_min_max, num_points)\n",
       "    qubit_freqs = np.linspace(*qubit_freqs_min_max, num_points)\n",
       "    t1_values = rng.uniform(*t1_values_min_max, num_points)\n",
       "\n",
       "    resonator_freq_tuids = [dh.gen_tuid() for _ in range(num_points)]\n",
       "    qubit_freq_tuids = [dh.gen_tuid() for _ in range(num_points)]\n",
       "    t1_tuids = [dh.gen_tuid() for _ in range(num_points)]\n",
       "\n",
       "    coords = dict(\n",
       "        flux_bias=(\n",
       "            "main_dim",\n",
       "            flux_bias_vals,\n",
       "            mk_main_coord_attrs(long_name="Flux bias", unit="A"),\n",
       "        ),\n",
       "        resonator_freq_tuids=(\n",
       "            "main_dim",\n",
       "            resonator_freq_tuids,\n",
       "            mk_main_coord_attrs(\n",
       "                long_name="Dataset TUID resonator frequency", is_dataset_ref=True\n",
       "            ),\n",
       "        ),\n",
       "        qubit_freq_tuids=(\n",
       "            "main_dim",\n",
       "            qubit_freq_tuids,\n",
       "            mk_main_coord_attrs(\n",
       "                long_name="Dataset TUID qubit frequency", is_dataset_ref=True\n",
       "            ),\n",
       "        ),\n",
       "        t1_tuids=(\n",
       "            "main_dim",\n",
       "            t1_tuids,\n",
       "            mk_main_coord_attrs(long_name="Dataset TUID T1", is_dataset_ref=True),\n",
       "        ),\n",
       "    )\n",
       "\n",
       "    data_vars = dict(\n",
       "        resonator_freq=(\n",
       "            "main_dim",\n",
       "            resonator_freqs,\n",
       "            mk_main_var_attrs(long_name="Resonator frequency", unit="Hz"),\n",
       "        ),\n",
       "        qubit_freq=(\n",
       "            "main_dim",\n",
       "            qubit_freqs,\n",
       "            mk_main_var_attrs(long_name="Qubit frequency", unit="Hz"),\n",
       "        ),\n",
       "        t1=(\n",
       "            "main_dim",\n",
       "            t1_values,\n",
       "            mk_main_var_attrs(long_name="T1", unit="s"),\n",
       "        ),\n",
       "    )\n",
       "    dataset_attrs = mk_dataset_attrs()\n",
       "\n",
       "    dataset = xr.Dataset(data_vars=data_vars, coords=coords, attrs=dataset_attrs)\n",
       "\n",
       "    return dataset\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def}\\PY{+w}{ }\\PY{n+nf}{mk\\PYZus{}nested\\PYZus{}mc\\PYZus{}dataset}\\PY{p}{(}\n",
       "    \\PY{n}{num\\PYZus{}points}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{12}\\PY{p}{,}\n",
       "    \\PY{n}{flux\\PYZus{}bias\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{l+m+mf}{0.04}\\PY{p}{,} \\PY{l+m+mf}{0.04}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{resonator\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{7e9}\\PY{p}{,} \\PY{l+m+mf}{7.3e9}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{qubit\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{4.5e9}\\PY{p}{,} \\PY{l+m+mf}{5.0e9}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{t1\\PYZus{}values\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{20e\\PYZhy{}6}\\PY{p}{,} \\PY{l+m+mf}{50e\\PYZhy{}6}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{seed}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{|} \\PY{k+kc}{None} \\PY{o}{=} \\PY{l+m+mi}{112233}\\PY{p}{,}\n",
       "\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Generates a dataset with dataset references and several coordinates that serve to}\n",
       "\\PY{l+s+sd}{    index the same variables.}\n",
       "\n",
       "\\PY{l+s+sd}{    Note that the each value for ``resonator\\PYZus{}freqs``, ``qubit\\PYZus{}freqs`` and ``t1\\PYZus{}values``}\n",
       "\\PY{l+s+sd}{    would have been extracted from other dataset corresponding to individual experiments}\n",
       "\\PY{l+s+sd}{    with their own dataset.}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    num\\PYZus{}points}\n",
       "\\PY{l+s+sd}{        Number of datapoints to generate (used for all variables/coordinates).}\n",
       "\\PY{l+s+sd}{    flux\\PYZus{}bias\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock values.}\n",
       "\\PY{l+s+sd}{    resonator\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock values.}\n",
       "\\PY{l+s+sd}{    qubit\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock values.}\n",
       "\\PY{l+s+sd}{    t1\\PYZus{}values\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock random values.}\n",
       "\\PY{l+s+sd}{    seed}\n",
       "\\PY{l+s+sd}{        Random number generator seed passed to ``numpy.random.default\\PYZus{}rng``.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{n}{rng} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{default\\PYZus{}rng}\\PY{p}{(}\\PY{n}{seed}\\PY{o}{=}\\PY{n}{seed}\\PY{p}{)}  \\PY{c+c1}{\\PYZsh{} random number generator}\n",
       "\n",
       "    \\PY{n}{flux\\PYZus{}bias\\PYZus{}vals} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{o}{*}\\PY{n}{flux\\PYZus{}bias\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "    \\PY{n}{resonator\\PYZus{}freqs} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{o}{*}\\PY{n}{resonator\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "    \\PY{n}{qubit\\PYZus{}freqs} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{o}{*}\\PY{n}{qubit\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "    \\PY{n}{t1\\PYZus{}values} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{uniform}\\PY{p}{(}\\PY{o}{*}\\PY{n}{t1\\PYZus{}values\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{resonator\\PYZus{}freq\\PYZus{}tuids} \\PY{o}{=} \\PY{p}{[}\\PY{n}{dh}\\PY{o}{.}\\PY{n}{gen\\PYZus{}tuid}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}points}\\PY{p}{)}\\PY{p}{]}\n",
       "    \\PY{n}{qubit\\PYZus{}freq\\PYZus{}tuids} \\PY{o}{=} \\PY{p}{[}\\PY{n}{dh}\\PY{o}{.}\\PY{n}{gen\\PYZus{}tuid}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}points}\\PY{p}{)}\\PY{p}{]}\n",
       "    \\PY{n}{t1\\PYZus{}tuids} \\PY{o}{=} \\PY{p}{[}\\PY{n}{dh}\\PY{o}{.}\\PY{n}{gen\\PYZus{}tuid}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}points}\\PY{p}{)}\\PY{p}{]}\n",
       "\n",
       "    \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{flux\\PYZus{}bias}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{flux\\PYZus{}bias\\PYZus{}vals}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Flux bias}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{A}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{resonator\\PYZus{}freq\\PYZus{}tuids}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{resonator\\PYZus{}freq\\PYZus{}tuids}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Dataset TUID resonator frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{is\\PYZus{}dataset\\PYZus{}ref}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{qubit\\PYZus{}freq\\PYZus{}tuids}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{qubit\\PYZus{}freq\\PYZus{}tuids}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Dataset TUID qubit frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{is\\PYZus{}dataset\\PYZus{}ref}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{t1\\PYZus{}tuids}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{t1\\PYZus{}tuids}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Dataset TUID T1}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{is\\PYZus{}dataset\\PYZus{}ref}\\PY{o}{=}\\PY{k+kc}{True}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{resonator\\PYZus{}freq}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{resonator\\PYZus{}freqs}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Resonator frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Hz}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{qubit\\PYZus{}freq}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{qubit\\PYZus{}freqs}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Qubit frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Hz}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{t1}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{t1\\PYZus{}values}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{T1}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{s}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "    \\PY{n}{dataset\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{(}\\PY{n}{data\\PYZus{}vars}\\PY{o}{=}\\PY{n}{data\\PYZus{}vars}\\PY{p}{,} \\PY{n}{coords}\\PY{o}{=}\\PY{n}{coords}\\PY{p}{,} \\PY{n}{attrs}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}attrs}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{dataset}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_nested_mc_dataset\u001b[0m\u001b[1m(\u001b[0m\n",
       "    num_points: int = \u001b[1;36m12\u001b[0m,\n",
       "    flux_bias_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m-0.04\u001b[0m, \u001b[1;36m0.04\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    resonator_freqs_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m7e9\u001b[0m, \u001b[1;36m7.3e9\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    qubit_freqs_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m4.5e9\u001b[0m, \u001b[1;36m5.0e9\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    t1_values_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m20e-6\u001b[0m, \u001b[1;36m50e-6\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    seed: int | \u001b[3;35mNone\u001b[0m = \u001b[1;36m112233\u001b[0m,\n",
       "\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generates a dataset with dataset references and several coordinates that serve to\n",
       "    index the same variables.\n",
       "\n",
       "    Note that the each value for ``resonator_freqs``, ``qubit_freqs`` and ``t1_values``\n",
       "    would have been extracted from other dataset corresponding to individual experiments\n",
       "    with their own dataset.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_points\n",
       "        Number of datapoints to generate \u001b[1m(\u001b[0mused for all variables/coordinates\u001b[1m)\u001b[0m.\n",
       "    flux_bias_min_max\n",
       "        Range for mock values.\n",
       "    resonator_freqs_min_max\n",
       "        Range for mock values.\n",
       "    qubit_freqs_min_max\n",
       "        Range for mock values.\n",
       "    t1_values_min_max\n",
       "        Range for mock random values.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    rng = \u001b[1;35mnp.random.default_rng\u001b[0m\u001b[1m(\u001b[0m\u001b[33mseed\u001b[0m=\u001b[35mseed\u001b[0m\u001b[1m)\u001b[0m  # random number generator\n",
       "\n",
       "    flux_bias_vals = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m*flux_bias_min_max, num_points\u001b[1m)\u001b[0m\n",
       "    resonator_freqs = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m*resonator_freqs_min_max, num_points\u001b[1m)\u001b[0m\n",
       "    qubit_freqs = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m*qubit_freqs_min_max, num_points\u001b[1m)\u001b[0m\n",
       "    t1_values = \u001b[1;35mrng.uniform\u001b[0m\u001b[1m(\u001b[0m*t1_values_min_max, num_points\u001b[1m)\u001b[0m\n",
       "\n",
       "    resonator_freq_tuids = \u001b[1m[\u001b[0m\u001b[1;35mdh.gen_tuid\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_points\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n",
       "    qubit_freq_tuids = \u001b[1m[\u001b[0m\u001b[1;35mdh.gen_tuid\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_points\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n",
       "    t1_tuids = \u001b[1m[\u001b[0m\u001b[1;35mdh.gen_tuid\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_points\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n",
       "\n",
       "    coords = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mflux_bias\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            flux_bias_vals,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Flux\u001b[0m\u001b[32m bias\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"A\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mresonator_freq_tuids\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            resonator_freq_tuids,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33mlong_name\u001b[0m=\u001b[32m\"Dataset\u001b[0m\u001b[32m TUID resonator frequency\"\u001b[0m, \u001b[33mis_dataset_ref\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mqubit_freq_tuids\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            qubit_freq_tuids,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33mlong_name\u001b[0m=\u001b[32m\"Dataset\u001b[0m\u001b[32m TUID qubit frequency\"\u001b[0m, \u001b[33mis_dataset_ref\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mt1_tuids\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            t1_tuids,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Dataset\u001b[0m\u001b[32m TUID T1\"\u001b[0m, \u001b[33mis_dataset_ref\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    data_vars = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mresonator_freq\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            resonator_freqs,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Resonator\u001b[0m\u001b[32m frequency\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"Hz\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mqubit_freq\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            qubit_freqs,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Qubit\u001b[0m\u001b[32m frequency\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"Hz\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mt1\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            t1_values,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"T1\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"s\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "    dataset_attrs = \u001b[1;35mmk_dataset_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    dataset = \u001b[1;35mxr.Dataset\u001b[0m\u001b[1m(\u001b[0m\u001b[33mdata_vars\u001b[0m=\u001b[35mdata_vars\u001b[0m, \u001b[33mcoords\u001b[0m=\u001b[35mcoords\u001b[0m, \u001b[33mattrs\u001b[0m=\u001b[35mdataset_attrs\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    return dataset"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_source_code(mk_nested_mc_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "09512820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.Dataset> Size: 2kB\n",
       "Dimensions:               (main_dim: 7)\n",
       "Coordinates:\n",
       "    flux_bias             (main_dim) float64 56B -0.04 -0.02667 ... 0.02667 0.04\n",
       "    resonator_freq_tuids  (main_dim) <U26 728B '20250904-040825-928-729f2f' ....\n",
       "    qubit_freq_tuids      (main_dim) <U26 728B '20250904-040825-928-46eaea' ....\n",
       "    t1_tuids              (main_dim) <U26 728B '20250904-040825-929-a0404a' ....\n",
       "Dimensions without coordinates: main_dim\n",
       "Data variables:\n",
       "    resonator_freq        (main_dim) float64 56B 7e+09 7.05e+09 ... 7.3e+09\n",
       "    qubit_freq            (main_dim) float64 56B 4.5e+09 4.583e+09 ... 5e+09\n",
       "    t1                    (main_dim) float64 56B 4.238e-05 ... 4.154e-05\n",
       "Attributes:\n",
       "    tuid:                      20250904-040825-929-883636\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
       "    json_serialize_exclude:    []
"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 2kB\n",
       "Dimensions:               \u001b[1m(\u001b[0mmain_dim: \u001b[1;36m7\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "    flux_bias             \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 56B \u001b[1;36m-0.04\u001b[0m \u001b[1;36m-0.02667\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.02667\u001b[0m \u001b[1;36m0.04\u001b[0m\n",
       "    resonator_freq_tuids  \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m \n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 100\u001b[0m\u001b[1;36m0x1000\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m4\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 1, figsize=(10, 10), sharex=True)\n",
    "\n",
    "_ = dataset.t1.plot(x=\"flux_bias\", marker=\"o\", ax=axs[0].twiny(), color=\"C0\")\n",
    "x = \"t1_tuids\"\n",
    "_ = dataset.t1.plot(x=x, marker=\"o\", ax=axs[0], color=\"C0\")\n",
    "_ = dataset.resonator_freq.plot(x=x, marker=\"o\", ax=axs[1], color=\"C1\")\n",
    "_ = dataset.qubit_freq.plot(x=x, marker=\"o\", ax=axs[2], color=\"C2\")\n",
    "for tick in axs[2].get_xticklabels():\n",
    "    tick.set_rotation(15)  # avoid tuid labels overlapping"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c30a1b",
   "metadata": {},
   "source": [
    "It is possible to work with an explicit MultiIndex within a (python) xarray object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3d1df2bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
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