{ "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_780/6278523.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 It is no longer maintained and will not receive any further updates or support.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",
       "\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:                      20250818-113008-263-9a09b5\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;36m20250818\u001b[0m-\u001b[1;36m113008\u001b[0m-\u001b[1;36m263\u001b[0m-9a09b5\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:                      20250818-113008-263-9a09b5\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;36m20250818\u001b[0m-\u001b[1;36m113008\u001b[0m-\u001b[1;36m263\u001b[0m-9a09b5\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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",
      "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": [
       "
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       "\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 '20250818-113009-411-a57e91' ....\n",
       "    qubit_freq_tuids      (main_dim) <U26 728B '20250818-113009-411-cb0366' ....\n",
       "    t1_tuids              (main_dim) <U26 728B '20250818-113009-411-f3ab5e' ....\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:                      20250818-113009-412-e5ab0b\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
       "    json_serialize_exclude:    []
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       "\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"
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    {
     "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": [
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     "metadata": {},
     "output_type": "display_data"
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       "\n",
       "\n",
       "
<xarray.Dataset> Size: 2kB\n",
       "Dimensions:               (main_dim: 7)\n",
       "Coordinates:\n",
       "  * main_dim              (main_dim) object 56B MultiIndex\n",
       "  * flux_bias             (main_dim) float64 56B -0.04 -0.02667 ... 0.02667 0.04\n",
       "  * resonator_freq_tuids  (main_dim) <U26 728B '20250818-113009-411-a57e91' ....\n",
       "  * qubit_freq_tuids      (main_dim) <U26 728B '20250818-113009-411-cb0366' ....\n",
       "  * t1_tuids              (main_dim) <U26 728B '20250818-113009-411-f3ab5e' ....\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:                      20250818-113009-412-e5ab0b\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",
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       "\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",
       "  * main_dim              \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m object 56B MultiIndex\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"
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     "metadata": {},
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<xarray.DataArray 'qubit_freq' (main_dim: 1)> Size: 8B\n",
       "array([4.66666667e+09])\n",
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       "  * main_dim              (main_dim) object 8B MultiIndex\n",
       "  * flux_bias             (main_dim) float64 8B -0.01333\n",
       "  * resonator_freq_tuids  (main_dim) <U26 104B '20250818-113009-411-3d2891'\n",
       "  * t1_tuids              (main_dim) <U26 104B '20250818-113009-412-1a05da'\n",
       "    qubit_freq_tuids      <U26 104B '20250818-113009-411-1874e6'\n",
       "Attributes:\n",
       "    unit:                    Hz\n",
       "    long_name:               Qubit frequency\n",
       "    is_main_var:             True\n",
       "    uniformly_spaced:        True\n",
       "    grid:                    True\n",
       "    is_dataset_ref:          False\n",
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       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.DataArray\u001b[0m\u001b[39m \u001b[0m\u001b[32m'qubit_freq'\u001b[0m\u001b[39m \u001b[0m\u001b[1;39m(\u001b[0m\u001b[39mmain_dim: \u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;39m)\u001b[0m\u001b[1m>\u001b[0m Size: 8B\n",
       "\u001b[1;35marray\u001b[0m\u001b[1m(\u001b[0m\u001b[1m[\u001b[0m\u001b[1;36m4.66666667e+09\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "  * main_dim              \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m object 8B MultiIndex\n",
       "  * flux_bias             \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 8B \u001b[1;36m-0.01333\u001b[0m\n",
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<xarray.DataArray 'qubit_freq' (main_dim: 1)> Size: 8B\n",
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       "  * main_dim              (main_dim) object 8B MultiIndex\n",
       "  * flux_bias             (main_dim) float64 8B -0.01333\n",
       "  * resonator_freq_tuids  (main_dim) <U26 104B '20250818-113009-411-3d2891'\n",
       "  * qubit_freq_tuids      (main_dim) <U26 104B '20250818-113009-411-1874e6'\n",
       "    t1_tuids              <U26 104B '20250818-113009-412-1a05da'\n",
       "Attributes:\n",
       "    unit:                    Hz\n",
       "    long_name:               Qubit frequency\n",
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       "Coordinates:\n",
       "  * main_dim              \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m object 8B MultiIndex\n",
       "  * flux_bias             \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 8B \u001b[1;36m-0.01333\u001b[0m\n",
       "  * resonator_freq_tuids  \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m \n"
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<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 '20250818-113009-411-a57e91' ....\n",
       "    qubit_freq_tuids      (main_dim) <U26 728B '20250818-113009-411-cb0366' ....\n",
       "    t1_tuids              (main_dim) <U26 728B '20250818-113009-411-f3ab5e' ....\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:                      20250818-113009-412-e5ab0b\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
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       "\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"
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     "metadata": {},
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    {
     "data": {
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       "\u001b[3;92mTrue\u001b[0m"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all(dataset_multi_indexed.reset_index(\"main_dim\").t1_tuids == dataset.t1_tuids)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27741c5c",
   "metadata": {},
   "source": [
    "But, for example, the `dtype` has been changed to `object`\n",
    "(from fixed-length string):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "62cb3085",
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
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    {
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      "text/plain": [
       "\u001b[1m(\u001b[0m\u001b[1;35mdtype\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'\n"
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     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "\u001b[3;92mTrue\u001b[0m"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.t1_tuids.dtype == dataset_multi_indexed.reset_index(\"main_dim\").t1_tuids.dtype"
   ]
  }
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