{ "cells": [ { "cell_type": "markdown", "id": "da5198c6", "metadata": {}, "source": [ "(sec-dataset-advanced-examples)=\n", "# Quantify dataset - advanced examples\n", "\n", "```{seealso}\n", "The complete source code of this tutorial can be found in\n", "\n", "{nb-download}`Quantify dataset - advanced examples.ipynb`\n", "```\n", "\n", "Here we will explore a few advanced usages of the Quantify dataset and how it can\n", "accommodate them." ] }, { "cell_type": "code", "execution_count": 1, "id": "64643c33", "metadata": { "mystnb": { "code_prompt_show": "Imports and auxiliary utilities" }, "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "from pathlib import Path\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", "from rich import pretty\n", "\n", "from quantify_core.analysis.calibration import rotate_to_calibrated_axis\n", "from quantify_core.analysis.fitting_models import exp_decay_func\n", "from quantify_core.data import handling as dh\n", "from quantify_core.utilities import dataset_examples\n", "from quantify_core.utilities.dataset_examples import (\n", " mk_nested_mc_dataset,\n", " mk_shots_from_probabilities,\n", " mk_surface7_cyles_dataset,\n", ")\n", "from quantify_core.utilities.examples_support import (\n", " mk_iq_shots,\n", " round_trip_dataset,\n", ")\n", "from quantify_core.utilities.inspect_utils import display_source_code\n", "\n", "pretty.install()\n", "\n", "dh.set_datadir(Path.home() / \"quantify-data\") # change me!" ] }, { "cell_type": "markdown", "id": "b075ee74", "metadata": {}, "source": [ "## Dataset for an \"unstructured\" experiment\n", "\n", "Let's take consider a Surface Code experiment, in particular the one portrayed in\n", "Fig. 4b from one of the papers from DiCarlo Lab {cite}`marques_logical_qubit_2021`.\n", "\n", "For simplicity, we will not use exactly the same schedule, because what matters here\n", "are the measurements. It is difficult to deal with the results of these measurements\n", "because we have a few repeating cycles followed by a final measurement that leaves the\n", "overall dataset \"unstructured\".\n", "\n", "```{figure} /images/surface-7-sched.png\n", ":width: 100%\n", "```\n", "\n", "``````{admonition} Source code for generating this schedule and visualizing it\n", ":class: dropdown, info\n", "\n", "If you want to create and visualize the schedule above using `quantify-scheduler`,\n", "you can use this code:\n", "\n", "```{code-block} python\n", "from quantify_scheduler import Schedule\n", "from quantify_scheduler.operations.gate_library import CZ, Y90, Measure, Reset, X\n", "from quantify_scheduler.visualization.circuit_diagram import circuit_diagram_matplotlib\n", "\n", "def mk_surface7_sched(num_cycles: int = 3):\n", " \"\"\"Generates a schedule with some of the feature of a Surface 7 experiment as\n", " portrayed in Fig. 4b of :cite:`marques_logical_qubit_2021`.\n", "\n", " Parameters\n", " ----------\n", " num_cycles\n", " The number of times to repeat the main cycle.\n", "\n", " Returns\n", " -------\n", " :\n", " A schedule similar to a Surface 7 dance.\n", " \"\"\"\n", "\n", " sched = Schedule(\"S7 dance\")\n", "\n", " q_d1, q_d2, q_d3, q_d4 = [f\"D{i}\" for i in range(1, 5)]\n", " q_a1, q_a2, q_a3 = [f\"A{i}\" for i in range(1, 4)]\n", " all_qubits = q_d1, q_d2, q_d3, q_d4, q_a1, q_a2, q_a3\n", "\n", " sched.add(Reset(*all_qubits))\n", "\n", " for cycle in range(num_cycles):\n", " sched.add(Y90(q_d1))\n", " for qubit in [q_d2, q_d3, q_d4]:\n", " sched.add(Y90(qubit), ref_pt=\"start\", rel_time=0)\n", " sched.add(Y90(q_a2), ref_pt=\"start\", rel_time=0)\n", "\n", " for qubit in [q_d2, q_d1, q_d4, q_d3]:\n", " sched.add(CZ(qC=qubit, qT=q_a2))\n", "\n", " sched.add(Y90(q_d1))\n", " for qubit in [q_d2, q_d3, q_d4]:\n", " sched.add(Y90(qubit), ref_pt=\"start\", rel_time=0)\n", " sched.add(Y90(q_a2), ref_pt=\"start\", rel_time=0)\n", "\n", " sched.add(Y90(q_a1), ref_pt=\"end\", rel_time=0)\n", " sched.add(Y90(q_a3), ref_pt=\"start\", rel_time=0)\n", "\n", " sched.add(CZ(qC=q_d1, qT=q_a1))\n", " sched.add(CZ(qC=q_d2, qT=q_a3))\n", " sched.add(CZ(qC=q_d3, qT=q_a1))\n", " sched.add(CZ(qC=q_d4, qT=q_a3))\n", "\n", " sched.add(Y90(q_a1), ref_pt=\"end\", rel_time=0)\n", " sched.add(Y90(q_a3), ref_pt=\"start\", rel_time=0)\n", "\n", " sched.add(Measure(q_a2, acq_index=cycle))\n", " for qubit in (q_a1, q_a3):\n", " sched.add(Measure(qubit, acq_index=cycle), ref_pt=\"start\", rel_time=0)\n", "\n", " for qubit in [q_d1, q_d2, q_d3, q_d4]:\n", " sched.add(X(qubit), ref_pt=\"start\", rel_time=0)\n", "\n", " # final measurements\n", "\n", " sched.add(Measure(*all_qubits[:4], acq_index=0), ref_pt=\"end\", rel_time=0)\n", "\n", " return sched\n", "\n", "sched = mk_surface7_sched(num_cycles=3)\n", "f, ax = circuit_diagram_matplotlib(sched)\n", "f.set_figwidth(30)\n", "```\n", "``````\n", "\n", "How do we store all the shots for this measurement?\n", "We might want this because, e.g., we know we have an issue with leakage to the second\n", "excited state of a transmon and we would like to be able to store and inspect raw data.\n", "\n", "To support such use-cases we will have a dimension in the dataset for the repeating cycles\n", "and one extra dimension for the final measurement." ] }, { "cell_type": "code", "execution_count": 2, "id": "d2f2ddcf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
def mk_iq_shots(\n",
       "    num_shots: int = 128,\n",
       "    sigmas: Union[Tuple[float], NDArray[np.float64]] = (0.1, 0.1),\n",
       "    centers: Union[Tuple[complex], NDArray[np.complex128]] = (-0.2 + 0.65j, 0.7 + 4j),\n",
       "    probabilities: Union[Tuple[float], NDArray[np.float64]] = (0.4, 0.6),\n",
       "    seed: Union[int, None] = 112233,\n",
       ") -> NDArray:\n",
       "    """\n",
       "    Generate clusters of (I + 1j*Q) points with a Gaussian distribution.\n",
       "\n",
       "    Utility to mock the data coming from qubit readout experiments.\n",
       "    Clusters are centered around ``centers`` and data points are distributed between\n",
       "    them according to ``probabilities``.\n",
       "\n",
       "    .. seealso:: :ref:`howto-utilities-examples-ssro`\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_shots\n",
       "        The number of shot to generate.\n",
       "    sigma\n",
       "        The sigma of the Gaussian distribution used for both real and imaginary parts.\n",
       "    centers\n",
       "        The center of each cluster on the imaginary plane.\n",
       "    probabilities\n",
       "        The probabilities of each cluster being randomly selected for each shot.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    """\n",
       "    if not len(sigmas) == len(centers) == len(probabilities):\n",
       "        raise ValueError(\n",
       "            f"Incorrect input. sigmas={sigmas}, centers={centers} and "\n",
       "            f"probabilities={probabilities} must have the same length."\n",
       "        )\n",
       "\n",
       "    rng = np.random.default_rng(seed=seed)\n",
       "\n",
       "    cluster_indices = tuple(range(len(centers)))\n",
       "    choices = rng.choice(a=cluster_indices, size=num_shots, p=probabilities)\n",
       "\n",
       "    shots = []\n",
       "    for idx in cluster_indices:\n",
       "        num_shots_this_cluster = np.sum(choices == idx)\n",
       "        i_data = rng.normal(\n",
       "            loc=centers[idx].real,\n",
       "            scale=sigmas[idx],\n",
       "            size=num_shots_this_cluster,\n",
       "        )\n",
       "        q_data = rng.normal(\n",
       "            loc=centers[idx].imag,\n",
       "            scale=sigmas[idx],\n",
       "            size=num_shots_this_cluster,\n",
       "        )\n",
       "        shots.append(i_data + 1j * q_data)\n",
       "    return np.concatenate(shots)\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}iq\\PYZus{}shots}\\PY{p}{(}\n",
       "    \\PY{n}{num\\PYZus{}shots}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{128}\\PY{p}{,}\n",
       "    \\PY{n}{sigmas}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{float}\\PY{p}{]}\\PY{p}{,} \\PY{n}{NDArray}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{float64}\\PY{p}{]}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{0.1}\\PY{p}{,} \\PY{l+m+mf}{0.1}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{centers}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{complex}\\PY{p}{]}\\PY{p}{,} \\PY{n}{NDArray}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{complex128}\\PY{p}{]}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{l+m+mf}{0.2} \\PY{o}{+} \\PY{l+m+mf}{0.65}\\PY{n}{j}\\PY{p}{,} \\PY{l+m+mf}{0.7} \\PY{o}{+} \\PY{l+m+mi}{4}\\PY{n}{j}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{float}\\PY{p}{]}\\PY{p}{,} \\PY{n}{NDArray}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{float64}\\PY{p}{]}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{0.4}\\PY{p}{,} \\PY{l+m+mf}{0.6}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{seed}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n+nb}{int}\\PY{p}{,} \\PY{k+kc}{None}\\PY{p}{]} \\PY{o}{=} \\PY{l+m+mi}{112233}\\PY{p}{,}\n",
       "\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{NDArray}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Generate clusters of (I + 1j*Q) points with a Gaussian distribution.}\n",
       "\n",
       "\\PY{l+s+sd}{    Utility to mock the data coming from qubit readout experiments.}\n",
       "\\PY{l+s+sd}{    Clusters are centered around ``centers`` and data points are distributed between}\n",
       "\\PY{l+s+sd}{    them according to ``probabilities``.}\n",
       "\n",
       "\\PY{l+s+sd}{    .. seealso:: :ref:`howto\\PYZhy{}utilities\\PYZhy{}examples\\PYZhy{}ssro`}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    num\\PYZus{}shots}\n",
       "\\PY{l+s+sd}{        The number of shot to generate.}\n",
       "\\PY{l+s+sd}{    sigma}\n",
       "\\PY{l+s+sd}{        The sigma of the Gaussian distribution used for both real and imaginary parts.}\n",
       "\\PY{l+s+sd}{    centers}\n",
       "\\PY{l+s+sd}{        The center of each cluster on the imaginary plane.}\n",
       "\\PY{l+s+sd}{    probabilities}\n",
       "\\PY{l+s+sd}{        The probabilities of each cluster being randomly selected for each shot.}\n",
       "\\PY{l+s+sd}{    seed}\n",
       "\\PY{l+s+sd}{        Random number generator seed passed to ``numpy.random.default\\PYZus{}rng``.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{k}{if} \\PY{o+ow}{not} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{sigmas}\\PY{p}{)} \\PY{o}{==} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{centers}\\PY{p}{)} \\PY{o}{==} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{probabilities}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{k}{raise} \\PY{n+ne}{ValueError}\\PY{p}{(}\n",
       "            \\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Incorrect input. sigmas=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{sigmas}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{, centers=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{centers}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{ and }\\PY{l+s+s2}{\\PYZdq{}}\n",
       "            \\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{probabilities=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{probabilities}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{ must have the same length.}\\PY{l+s+s2}{\\PYZdq{}}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{rng} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{default\\PYZus{}rng}\\PY{p}{(}\\PY{n}{seed}\\PY{o}{=}\\PY{n}{seed}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{cluster\\PYZus{}indices} \\PY{o}{=} \\PY{n+nb}{tuple}\\PY{p}{(}\\PY{n+nb}{range}\\PY{p}{(}\\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{centers}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\n",
       "    \\PY{n}{choices} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{choice}\\PY{p}{(}\\PY{n}{a}\\PY{o}{=}\\PY{n}{cluster\\PYZus{}indices}\\PY{p}{,} \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots}\\PY{p}{,} \\PY{n}{p}\\PY{o}{=}\\PY{n}{probabilities}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{shots} \\PY{o}{=} \\PY{p}{[}\\PY{p}{]}\n",
       "    \\PY{k}{for} \\PY{n}{idx} \\PY{o+ow}{in} \\PY{n}{cluster\\PYZus{}indices}\\PY{p}{:}\n",
       "        \\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{sum}\\PY{p}{(}\\PY{n}{choices} \\PY{o}{==} \\PY{n}{idx}\\PY{p}{)}\n",
       "        \\PY{n}{i\\PYZus{}data} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{normal}\\PY{p}{(}\n",
       "            \\PY{n}{loc}\\PY{o}{=}\\PY{n}{centers}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{o}{.}\\PY{n}{real}\\PY{p}{,}\n",
       "            \\PY{n}{scale}\\PY{o}{=}\\PY{n}{sigmas}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{p}{,}\n",
       "            \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "        \\PY{n}{q\\PYZus{}data} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{normal}\\PY{p}{(}\n",
       "            \\PY{n}{loc}\\PY{o}{=}\\PY{n}{centers}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{o}{.}\\PY{n}{imag}\\PY{p}{,}\n",
       "            \\PY{n}{scale}\\PY{o}{=}\\PY{n}{sigmas}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{p}{,}\n",
       "            \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "        \\PY{n}{shots}\\PY{o}{.}\\PY{n}{append}\\PY{p}{(}\\PY{n}{i\\PYZus{}data} \\PY{o}{+} \\PY{l+m+mi}{1}\\PY{n}{j} \\PY{o}{*} \\PY{n}{q\\PYZus{}data}\\PY{p}{)}\n",
       "    \\PY{k}{return} \\PY{n}{np}\\PY{o}{.}\\PY{n}{concatenate}\\PY{p}{(}\\PY{n}{shots}\\PY{p}{)}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_iq_shots\u001b[0m\u001b[1m(\u001b[0m\n",
       "    num_shots: int = \u001b[1;36m128\u001b[0m,\n",
       "    sigmas: Union\u001b[1m[\u001b[0mTuple\u001b[1m[\u001b[0mfloat\u001b[1m]\u001b[0m, NDArray\u001b[1m[\u001b[0mnp.float64\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m0.1\u001b[0m, \u001b[1;36m0.1\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    centers: Union\u001b[1m[\u001b[0mTuple\u001b[1m[\u001b[0mcomplex\u001b[1m]\u001b[0m, NDArray\u001b[1m[\u001b[0mnp.complex128\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m-0.2\u001b[0m + \u001b[1;36m0.65j\u001b[0m, \u001b[1;36m0.7\u001b[0m + \u001b[1;36m4j\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    probabilities: Union\u001b[1m[\u001b[0mTuple\u001b[1m[\u001b[0mfloat\u001b[1m]\u001b[0m, NDArray\u001b[1m[\u001b[0mnp.float64\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m0.4\u001b[0m, \u001b[1;36m0.6\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    seed: Union\u001b[1m[\u001b[0mint, \u001b[3;35mNone\u001b[0m\u001b[1m]\u001b[0m = \u001b[1;36m112233\u001b[0m,\n",
       "\u001b[1m)\u001b[0m -> NDArray:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generate clusters of \u001b[1m(\u001b[0mI + \u001b[1;36m1j\u001b[0m*Q\u001b[1m)\u001b[0m points with a Gaussian distribution.\n",
       "\n",
       "    Utility to mock the data coming from qubit readout experiments.\n",
       "    Clusters are centered around ``centers`` and data points are distributed between\n",
       "    them according to ``probabilities``.\n",
       "\n",
       "    .. seealso:: :ref:`howto-utilities-examples-ssro`\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_shots\n",
       "        The number of shot to generate.\n",
       "    sigma\n",
       "        The sigma of the Gaussian distribution used for both real and imaginary parts.\n",
       "    centers\n",
       "        The center of each cluster on the imaginary plane.\n",
       "    probabilities\n",
       "        The probabilities of each cluster being randomly selected for each shot.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    if not \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0msigmas\u001b[1m)\u001b[0m == \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mcenters\u001b[1m)\u001b[0m == \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mprobabilities\u001b[1m)\u001b[0m:\n",
       "        raise \u001b[1;35mValueError\u001b[0m\u001b[1m(\u001b[0m\n",
       "            f\"Incorrect input. \u001b[33msigmas\u001b[0m=\u001b[1m{\u001b[0msigmas\u001b[1m}\u001b[0m, \u001b[33mcenters\u001b[0m=\u001b[1m{\u001b[0mcenters\u001b[1m}\u001b[0m and \"\n",
       "            f\"\u001b[33mprobabilities\u001b[0m=\u001b[1m{\u001b[0mprobabilities\u001b[1m}\u001b[0m must have the same length.\"\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    rng = \u001b[1;35mnp.random.default_rng\u001b[0m\u001b[1m(\u001b[0m\u001b[33mseed\u001b[0m=\u001b[35mseed\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    cluster_indices = \u001b[1;35mtuple\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mcenters\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\n",
       "    choices = \u001b[1;35mrng.choice\u001b[0m\u001b[1m(\u001b[0m\u001b[33ma\u001b[0m=\u001b[35mcluster_indices\u001b[0m, \u001b[33msize\u001b[0m=\u001b[35mnum_shots\u001b[0m, \u001b[33mp\u001b[0m=\u001b[35mprobabilities\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    shots = \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
       "    for idx in cluster_indices:\n",
       "        num_shots_this_cluster = \u001b[1;35mnp.sum\u001b[0m\u001b[1m(\u001b[0mchoices == idx\u001b[1m)\u001b[0m\n",
       "        i_data = \u001b[1;35mrng.normal\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[33mloc\u001b[0m=\u001b[35mcenters\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m.real,\n",
       "            \u001b[33mscale\u001b[0m=\u001b[35msigmas\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m,\n",
       "            \u001b[33msize\u001b[0m=\u001b[35mnum_shots_this_cluster\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "        q_data = \u001b[1;35mrng.normal\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[33mloc\u001b[0m=\u001b[35mcenters\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m.imag,\n",
       "            \u001b[33mscale\u001b[0m=\u001b[35msigmas\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m,\n",
       "            \u001b[33msize\u001b[0m=\u001b[35mnum_shots_this_cluster\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "        \u001b[1;35mshots.append\u001b[0m\u001b[1m(\u001b[0mi_data + \u001b[1;36m1j\u001b[0m * q_data\u001b[1m)\u001b[0m\n",
       "    return \u001b[1;35mnp.concatenate\u001b[0m\u001b[1m(\u001b[0mshots\u001b[1m)\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
def mk_shots_from_probabilities(probabilities: Union[np.ndarray, list], **kwargs):\n",
       "    """Generates multiple shots for a list of probabilities assuming two states.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    probabilities\n",
       "        The list/array of the probabilities of one of the states.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to\n",
       "        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    :\n",
       "        Array containing the shots. Shape: (num_shots, len(probabilities)).\n",
       "    """\n",
       "\n",
       "    shots = np.array(\n",
       "        tuple(\n",
       "            mk_iq_shots(probabilities=[prob, 1 - prob], **kwargs)\n",
       "            for prob in probabilities\n",
       "        )\n",
       "    ).T\n",
       "\n",
       "    return shots\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{,} \\PY{n+nb}{list}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Generates multiple shots for a list of probabilities assuming two states.}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    probabilities}\n",
       "\\PY{l+s+sd}{        The list/array of the probabilities of one of the states.}\n",
       "\\PY{l+s+sd}{    **kwargs}\n",
       "\\PY{l+s+sd}{        Keyword arguments passed to}\n",
       "\\PY{l+s+sd}{        :func:`\\PYZti{}quantify\\PYZus{}core.utilities.examples\\PYZus{}support.mk\\PYZus{}iq\\PYZus{}shots`.}\n",
       "\n",
       "\\PY{l+s+sd}{    Returns}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    :}\n",
       "\\PY{l+s+sd}{        Array containing the shots. Shape: (num\\PYZus{}shots, len(probabilities)).}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "    \\PY{n}{shots} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{array}\\PY{p}{(}\n",
       "        \\PY{n+nb}{tuple}\\PY{p}{(}\n",
       "            \\PY{n}{mk\\PYZus{}iq\\PYZus{}shots}\\PY{p}{(}\\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{prob}\\PY{p}{,} \\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{prob}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "            \\PY{k}{for} \\PY{n}{prob} \\PY{o+ow}{in} \\PY{n}{probabilities}\n",
       "        \\PY{p}{)}\n",
       "    \\PY{p}{)}\\PY{o}{.}\\PY{n}{T}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{shots}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0mprobabilities: Union\u001b[1m[\u001b[0mnp.ndarray, list\u001b[1m]\u001b[0m, **kwargs\u001b[1m)\u001b[0m:\n",
       "    \u001b[32m\"\"\u001b[0m\"Generates multiple shots for a list of probabilities assuming two states.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    probabilities\n",
       "        The list/array of the probabilities of one of the states.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to\n",
       "        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    :\n",
       "        Array containing the shots. Shape: \u001b[1m(\u001b[0mnum_shots, \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mprobabilities\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "\n",
       "    shots = \u001b[1;35mnp.array\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[1;35mtuple\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[1;35mmk_iq_shots\u001b[0m\u001b[1m(\u001b[0m\u001b[33mprobabilities\u001b[0m=\u001b[1m[\u001b[0mprob, \u001b[1;36m1\u001b[0m - prob\u001b[1m]\u001b[0m, **kwargs\u001b[1m)\u001b[0m\n",
       "            for prob in probabilities\n",
       "        \u001b[1m)\u001b[0m\n",
       "    \u001b[1m)\u001b[0m.T\n",
       "\n",
       "    return shots"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
def mk_surface7_cyles_dataset(num_cycles: int = 3, **kwargs) -> xr.Dataset:\n",
       "    """\n",
       "    See also :func:`mk_surface7_sched` inlined in the documentation as an example in:\n",
       "        :ref:`sec-dataset-advanced-examples`\n",
       "\n",
       "\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_cycles\n",
       "        The number of repeating cycles before the final measurement.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to :func:`~.mk_shots_from_probabilities`.\n",
       "    """\n",
       "\n",
       "    cycles = range(num_cycles)\n",
       "\n",
       "    mock_data = mk_shots_from_probabilities(\n",
       "        probabilities=[np.random.random() for _ in cycles], **kwargs\n",
       "    )\n",
       "\n",
       "    mock_data_final = mk_shots_from_probabilities(\n",
       "        probabilities=[np.random.random()], **kwargs\n",
       "    )\n",
       "\n",
       "    # %%\n",
       "    data_vars = {}\n",
       "\n",
       "    # NB same random data is used for all qubits only for the simplicity of the mock!\n",
       "    for qubit in (f"A{i}" for i in range(3)):\n",
       "        data_vars[f"{qubit}_shots"] = (\n",
       "            ("repetitions", "dim_cycle"),\n",
       "            mock_data,\n",
       "            mk_main_var_attrs(\n",
       "                unit="V", long_name=f"IQ amplitude {qubit}", has_repetitions=True\n",
       "            ),\n",
       "        )\n",
       "\n",
       "    for qubit in (f"D{i}" for i in range(4)):\n",
       "        data_vars[f"{qubit}_shots"] = (\n",
       "            ("repetitions", "dim_final"),\n",
       "            mock_data_final,\n",
       "            mk_main_var_attrs(\n",
       "                unit="V", long_name=f"IQ amplitude {qubit}", has_repetitions=True\n",
       "            ),\n",
       "        )\n",
       "\n",
       "    cycle_attrs = mk_main_coord_attrs(long_name="Surface code cycle number")\n",
       "    final_msmt_attrs = mk_main_coord_attrs(long_name="Final measurement")\n",
       "    coords = dict(\n",
       "        cycle=("dim_cycle", cycles, cycle_attrs),\n",
       "        final_msmt=("dim_final", [0], final_msmt_attrs),\n",
       "    )\n",
       "\n",
       "    dataset = xr.Dataset(\n",
       "        data_vars=data_vars,\n",
       "        coords=coords,\n",
       "        attrs=mk_dataset_attrs(),\n",
       "    )\n",
       "\n",
       "    return dataset\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}surface7\\PYZus{}cyles\\PYZus{}dataset}\\PY{p}{(}\\PY{n}{num\\PYZus{}cycles}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{3}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    See also :func:`mk\\PYZus{}surface7\\PYZus{}sched` inlined in the documentation as an example in:}\n",
       "\\PY{l+s+sd}{        :ref:`sec\\PYZhy{}dataset\\PYZhy{}advanced\\PYZhy{}examples`}\n",
       "\n",
       "\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    num\\PYZus{}cycles}\n",
       "\\PY{l+s+sd}{        The number of repeating cycles before the final measurement.}\n",
       "\\PY{l+s+sd}{    **kwargs}\n",
       "\\PY{l+s+sd}{        Keyword arguments passed to :func:`\\PYZti{}.mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities`.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "    \\PY{n}{cycles} \\PY{o}{=} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}cycles}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{mock\\PYZus{}data} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\n",
       "        \\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{random}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n}{cycles}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{mock\\PYZus{}data\\PYZus{}final} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\n",
       "        \\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{random}\\PY{p}{(}\\PY{p}{)}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} \\PYZpc{}\\PYZpc{}}\n",
       "    \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{p}{\\PYZob{}}\\PY{p}{\\PYZcb{}}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} NB same random data is used for all qubits only for the simplicity of the mock!}\n",
       "    \\PY{k}{for} \\PY{n}{qubit} \\PY{o+ow}{in} \\PY{p}{(}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{A}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{i}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}} \\PY{k}{for} \\PY{n}{i} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{l+m+mi}{3}\\PY{p}{)}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{n}{data\\PYZus{}vars}\\PY{p}{[}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\n",
       "            \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}cycle}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "            \\PY{n}{mock\\PYZus{}data}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{IQ amplitude }\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{has\\PYZus{}repetitions}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{for} \\PY{n}{qubit} \\PY{o+ow}{in} \\PY{p}{(}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{D}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{i}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}} \\PY{k}{for} \\PY{n}{i} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{l+m+mi}{4}\\PY{p}{)}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{n}{data\\PYZus{}vars}\\PY{p}{[}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\n",
       "            \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}final}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "            \\PY{n}{mock\\PYZus{}data\\PYZus{}final}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{IQ amplitude }\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{has\\PYZus{}repetitions}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{cycle\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Surface code cycle number}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{final\\PYZus{}msmt\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Final measurement}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{cycle}\\PY{o}{=}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}cycle}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{cycles}\\PY{p}{,} \\PY{n}{cycle\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{final\\PYZus{}msmt}\\PY{o}{=}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}final}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\\PY{p}{,} \\PY{n}{final\\PYZus{}msmt\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{(}\n",
       "        \\PY{n}{data\\PYZus{}vars}\\PY{o}{=}\\PY{n}{data\\PYZus{}vars}\\PY{p}{,}\n",
       "        \\PY{n}{coords}\\PY{o}{=}\\PY{n}{coords}\\PY{p}{,}\n",
       "        \\PY{n}{attrs}\\PY{o}{=}\\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{dataset}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_surface7_cyles_dataset\u001b[0m\u001b[1m(\u001b[0mnum_cycles: int = \u001b[1;36m3\u001b[0m, **kwargs\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    See also :func:`mk_surface7_sched` inlined in the documentation as an example in:\n",
       "        :ref:`sec-dataset-advanced-examples`\n",
       "\n",
       "\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_cycles\n",
       "        The number of repeating cycles before the final measurement.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to :func:`~.mk_shots_from_probabilities`.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "\n",
       "    cycles = \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_cycles\u001b[1m)\u001b[0m\n",
       "\n",
       "    mock_data = \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mprobabilities\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mnp.random.random\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in cycles\u001b[1m]\u001b[0m, **kwargs\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    mock_data_final = \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mprobabilities\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mnp.random.random\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m, **kwargs\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    # %%\n",
       "    data_vars = \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
       "\n",
       "    # NB same random data is used for all qubits only for the simplicity of the mock!\n",
       "    for qubit in \u001b[1m(\u001b[0mf\"A\u001b[1m{\u001b[0mi\u001b[1m}\u001b[0m\" for i in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m:\n",
       "        data_vars\u001b[1m[\u001b[0mf\"\u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m_shots\"\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\n",
       "            \u001b[1m(\u001b[0m\u001b[32m\"repetitions\"\u001b[0m, \u001b[32m\"dim_cycle\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "            mock_data,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33munit\u001b[0m=\u001b[32m\"V\"\u001b[0m, \u001b[33mlong_name\u001b[0m=\u001b[35mf\"\u001b[0mIQ amplitude \u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m\", \u001b[33mhas_repetitions\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    for qubit in \u001b[1m(\u001b[0mf\"D\u001b[1m{\u001b[0mi\u001b[1m}\u001b[0m\" for i in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m:\n",
       "        data_vars\u001b[1m[\u001b[0mf\"\u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m_shots\"\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\n",
       "            \u001b[1m(\u001b[0m\u001b[32m\"repetitions\"\u001b[0m, \u001b[32m\"dim_final\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "            mock_data_final,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33munit\u001b[0m=\u001b[32m\"V\"\u001b[0m, \u001b[33mlong_name\u001b[0m=\u001b[35mf\"\u001b[0mIQ amplitude \u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m\", \u001b[33mhas_repetitions\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    cycle_attrs = \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Surface\u001b[0m\u001b[32m code cycle number\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    final_msmt_attrs = \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Final\u001b[0m\u001b[32m measurement\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    coords = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mcycle\u001b[0m=\u001b[1m(\u001b[0m\u001b[32m\"dim_cycle\"\u001b[0m, cycles, cycle_attrs\u001b[1m)\u001b[0m,\n",
       "        \u001b[33mfinal_msmt\u001b[0m=\u001b[1m(\u001b[0m\u001b[32m\"dim_final\"\u001b[0m, \u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m, final_msmt_attrs\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    dataset = \u001b[1;35mxr.Dataset\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mdata_vars\u001b[0m=\u001b[35mdata_vars\u001b[0m,\n",
       "        \u001b[33mcoords\u001b[0m=\u001b[35mcoords\u001b[0m,\n",
       "        \u001b[33mattrs\u001b[0m=\u001b[1;35mmk_dataset_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    return dataset"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mock data parameters\n",
    "num_shots = 128  # NB usually >~1000 in real experiments\n",
    "ground = -0.2 + 0.65j\n",
    "excited = 0.7 + 4j\n",
    "centroids = ground, excited\n",
    "sigmas = [0.1] * 2\n",
    "\n",
    "display_source_code(mk_iq_shots)\n",
    "display_source_code(mk_shots_from_probabilities)\n",
    "display_source_code(mk_surface7_cyles_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12829358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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       "\n",
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       "\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:                      20241018-040738-010-da2604\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;36m20241018\u001b[0m-\u001b[1;36m040738\u001b[0m-\u001b[1;36m010\u001b[0m-da2604\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:                      20241018-040738-010-da2604\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;36m20241018\u001b[0m-\u001b[1;36m040738\u001b[0m-\u001b[1;36m010\u001b[0m-da2604\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: Optional[int] = 112233,\n",
       ") -> xr.Dataset:\n",
       "    """\n",
       "    Generates a dataset with dataset references and several coordinates that serve to\n",
       "    index the same variables.\n",
       "\n",
       "    Note that the each value for ``resonator_freqs``, ``qubit_freqs`` and ``t1_values``\n",
       "    would have been extracted from other dataset corresponding to individual experiments\n",
       "    with their own dataset.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_points\n",
       "        Number of datapoints to generate (used for all variables/coordinates).\n",
       "    flux_bias_min_max\n",
       "        Range for mock values.\n",
       "    resonator_freqs_min_max\n",
       "        Range for mock values.\n",
       "    qubit_freqs_min_max\n",
       "        Range for mock values.\n",
       "    t1_values_min_max\n",
       "        Range for mock random values.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    """\n",
       "    rng = np.random.default_rng(seed=seed)  # random number generator\n",
       "\n",
       "    flux_bias_vals = np.linspace(*flux_bias_min_max, num_points)\n",
       "    resonator_freqs = np.linspace(*resonator_freqs_min_max, num_points)\n",
       "    qubit_freqs = np.linspace(*qubit_freqs_min_max, num_points)\n",
       "    t1_values = rng.uniform(*t1_values_min_max, num_points)\n",
       "\n",
       "    resonator_freq_tuids = [dh.gen_tuid() for _ in range(num_points)]\n",
       "    qubit_freq_tuids = [dh.gen_tuid() for _ in range(num_points)]\n",
       "    t1_tuids = [dh.gen_tuid() for _ in range(num_points)]\n",
       "\n",
       "    coords = dict(\n",
       "        flux_bias=(\n",
       "            "main_dim",\n",
       "            flux_bias_vals,\n",
       "            mk_main_coord_attrs(long_name="Flux bias", unit="A"),\n",
       "        ),\n",
       "        resonator_freq_tuids=(\n",
       "            "main_dim",\n",
       "            resonator_freq_tuids,\n",
       "            mk_main_coord_attrs(\n",
       "                long_name="Dataset TUID resonator frequency", is_dataset_ref=True\n",
       "            ),\n",
       "        ),\n",
       "        qubit_freq_tuids=(\n",
       "            "main_dim",\n",
       "            qubit_freq_tuids,\n",
       "            mk_main_coord_attrs(\n",
       "                long_name="Dataset TUID qubit frequency", is_dataset_ref=True\n",
       "            ),\n",
       "        ),\n",
       "        t1_tuids=(\n",
       "            "main_dim",\n",
       "            t1_tuids,\n",
       "            mk_main_coord_attrs(long_name="Dataset TUID T1", is_dataset_ref=True),\n",
       "        ),\n",
       "    )\n",
       "\n",
       "    data_vars = dict(\n",
       "        resonator_freq=(\n",
       "            "main_dim",\n",
       "            resonator_freqs,\n",
       "            mk_main_var_attrs(long_name="Resonator frequency", unit="Hz"),\n",
       "        ),\n",
       "        qubit_freq=(\n",
       "            "main_dim",\n",
       "            qubit_freqs,\n",
       "            mk_main_var_attrs(long_name="Qubit frequency", unit="Hz"),\n",
       "        ),\n",
       "        t1=(\n",
       "            "main_dim",\n",
       "            t1_values,\n",
       "            mk_main_var_attrs(long_name="T1", unit="s"),\n",
       "        ),\n",
       "    )\n",
       "    dataset_attrs = mk_dataset_attrs()\n",
       "\n",
       "    dataset = xr.Dataset(data_vars=data_vars, coords=coords, attrs=dataset_attrs)\n",
       "\n",
       "    return dataset\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}nested\\PYZus{}mc\\PYZus{}dataset}\\PY{p}{(}\n",
       "    \\PY{n}{num\\PYZus{}points}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{12}\\PY{p}{,}\n",
       "    \\PY{n}{flux\\PYZus{}bias\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{l+m+mf}{0.04}\\PY{p}{,} \\PY{l+m+mf}{0.04}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{resonator\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{7e9}\\PY{p}{,} \\PY{l+m+mf}{7.3e9}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{qubit\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{4.5e9}\\PY{p}{,} \\PY{l+m+mf}{5.0e9}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{t1\\PYZus{}values\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{20e\\PYZhy{}6}\\PY{p}{,} \\PY{l+m+mf}{50e\\PYZhy{}6}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{seed}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n+nb}{int}\\PY{p}{]} \\PY{o}{=} \\PY{l+m+mi}{112233}\\PY{p}{,}\n",
       "\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Generates a dataset with dataset references and several coordinates that serve to}\n",
       "\\PY{l+s+sd}{    index the same variables.}\n",
       "\n",
       "\\PY{l+s+sd}{    Note that the each value for ``resonator\\PYZus{}freqs``, ``qubit\\PYZus{}freqs`` and ``t1\\PYZus{}values``}\n",
       "\\PY{l+s+sd}{    would have been extracted from other dataset corresponding to individual experiments}\n",
       "\\PY{l+s+sd}{    with their own dataset.}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    num\\PYZus{}points}\n",
       "\\PY{l+s+sd}{        Number of datapoints to generate (used for all variables/coordinates).}\n",
       "\\PY{l+s+sd}{    flux\\PYZus{}bias\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock values.}\n",
       "\\PY{l+s+sd}{    resonator\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock values.}\n",
       "\\PY{l+s+sd}{    qubit\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock values.}\n",
       "\\PY{l+s+sd}{    t1\\PYZus{}values\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock random values.}\n",
       "\\PY{l+s+sd}{    seed}\n",
       "\\PY{l+s+sd}{        Random number generator seed passed to ``numpy.random.default\\PYZus{}rng``.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{n}{rng} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{default\\PYZus{}rng}\\PY{p}{(}\\PY{n}{seed}\\PY{o}{=}\\PY{n}{seed}\\PY{p}{)}  \\PY{c+c1}{\\PYZsh{} random number generator}\n",
       "\n",
       "    \\PY{n}{flux\\PYZus{}bias\\PYZus{}vals} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{o}{*}\\PY{n}{flux\\PYZus{}bias\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "    \\PY{n}{resonator\\PYZus{}freqs} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{o}{*}\\PY{n}{resonator\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "    \\PY{n}{qubit\\PYZus{}freqs} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{o}{*}\\PY{n}{qubit\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "    \\PY{n}{t1\\PYZus{}values} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{uniform}\\PY{p}{(}\\PY{o}{*}\\PY{n}{t1\\PYZus{}values\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{resonator\\PYZus{}freq\\PYZus{}tuids} \\PY{o}{=} \\PY{p}{[}\\PY{n}{dh}\\PY{o}{.}\\PY{n}{gen\\PYZus{}tuid}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}points}\\PY{p}{)}\\PY{p}{]}\n",
       "    \\PY{n}{qubit\\PYZus{}freq\\PYZus{}tuids} \\PY{o}{=} \\PY{p}{[}\\PY{n}{dh}\\PY{o}{.}\\PY{n}{gen\\PYZus{}tuid}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}points}\\PY{p}{)}\\PY{p}{]}\n",
       "    \\PY{n}{t1\\PYZus{}tuids} \\PY{o}{=} \\PY{p}{[}\\PY{n}{dh}\\PY{o}{.}\\PY{n}{gen\\PYZus{}tuid}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}points}\\PY{p}{)}\\PY{p}{]}\n",
       "\n",
       "    \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{flux\\PYZus{}bias}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{flux\\PYZus{}bias\\PYZus{}vals}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Flux bias}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{A}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{resonator\\PYZus{}freq\\PYZus{}tuids}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{resonator\\PYZus{}freq\\PYZus{}tuids}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Dataset TUID resonator frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{is\\PYZus{}dataset\\PYZus{}ref}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{qubit\\PYZus{}freq\\PYZus{}tuids}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{qubit\\PYZus{}freq\\PYZus{}tuids}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Dataset TUID qubit frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{is\\PYZus{}dataset\\PYZus{}ref}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{t1\\PYZus{}tuids}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{t1\\PYZus{}tuids}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Dataset TUID T1}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{is\\PYZus{}dataset\\PYZus{}ref}\\PY{o}{=}\\PY{k+kc}{True}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{resonator\\PYZus{}freq}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{resonator\\PYZus{}freqs}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Resonator frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Hz}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{qubit\\PYZus{}freq}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{qubit\\PYZus{}freqs}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Qubit frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Hz}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{t1}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{t1\\PYZus{}values}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{T1}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{s}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "    \\PY{n}{dataset\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{(}\\PY{n}{data\\PYZus{}vars}\\PY{o}{=}\\PY{n}{data\\PYZus{}vars}\\PY{p}{,} \\PY{n}{coords}\\PY{o}{=}\\PY{n}{coords}\\PY{p}{,} \\PY{n}{attrs}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}attrs}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{dataset}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_nested_mc_dataset\u001b[0m\u001b[1m(\u001b[0m\n",
       "    num_points: int = \u001b[1;36m12\u001b[0m,\n",
       "    flux_bias_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m-0.04\u001b[0m, \u001b[1;36m0.04\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    resonator_freqs_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m7e9\u001b[0m, \u001b[1;36m7.3e9\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    qubit_freqs_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m4.5e9\u001b[0m, \u001b[1;36m5.0e9\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    t1_values_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m20e-6\u001b[0m, \u001b[1;36m50e-6\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    seed: Optional\u001b[1m[\u001b[0mint\u001b[1m]\u001b[0m = \u001b[1;36m112233\u001b[0m,\n",
       "\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generates a dataset with dataset references and several coordinates that serve to\n",
       "    index the same variables.\n",
       "\n",
       "    Note that the each value for ``resonator_freqs``, ``qubit_freqs`` and ``t1_values``\n",
       "    would have been extracted from other dataset corresponding to individual experiments\n",
       "    with their own dataset.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_points\n",
       "        Number of datapoints to generate \u001b[1m(\u001b[0mused for all variables/coordinates\u001b[1m)\u001b[0m.\n",
       "    flux_bias_min_max\n",
       "        Range for mock values.\n",
       "    resonator_freqs_min_max\n",
       "        Range for mock values.\n",
       "    qubit_freqs_min_max\n",
       "        Range for mock values.\n",
       "    t1_values_min_max\n",
       "        Range for mock random values.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    rng = \u001b[1;35mnp.random.default_rng\u001b[0m\u001b[1m(\u001b[0m\u001b[33mseed\u001b[0m=\u001b[35mseed\u001b[0m\u001b[1m)\u001b[0m  # random number generator\n",
       "\n",
       "    flux_bias_vals = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m*flux_bias_min_max, num_points\u001b[1m)\u001b[0m\n",
       "    resonator_freqs = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m*resonator_freqs_min_max, num_points\u001b[1m)\u001b[0m\n",
       "    qubit_freqs = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m*qubit_freqs_min_max, num_points\u001b[1m)\u001b[0m\n",
       "    t1_values = \u001b[1;35mrng.uniform\u001b[0m\u001b[1m(\u001b[0m*t1_values_min_max, num_points\u001b[1m)\u001b[0m\n",
       "\n",
       "    resonator_freq_tuids = \u001b[1m[\u001b[0m\u001b[1;35mdh.gen_tuid\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_points\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n",
       "    qubit_freq_tuids = \u001b[1m[\u001b[0m\u001b[1;35mdh.gen_tuid\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_points\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n",
       "    t1_tuids = \u001b[1m[\u001b[0m\u001b[1;35mdh.gen_tuid\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_points\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n",
       "\n",
       "    coords = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mflux_bias\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            flux_bias_vals,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Flux\u001b[0m\u001b[32m bias\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"A\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mresonator_freq_tuids\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            resonator_freq_tuids,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33mlong_name\u001b[0m=\u001b[32m\"Dataset\u001b[0m\u001b[32m TUID resonator frequency\"\u001b[0m, \u001b[33mis_dataset_ref\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mqubit_freq_tuids\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            qubit_freq_tuids,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33mlong_name\u001b[0m=\u001b[32m\"Dataset\u001b[0m\u001b[32m TUID qubit frequency\"\u001b[0m, \u001b[33mis_dataset_ref\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mt1_tuids\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            t1_tuids,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Dataset\u001b[0m\u001b[32m TUID T1\"\u001b[0m, \u001b[33mis_dataset_ref\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    data_vars = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mresonator_freq\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            resonator_freqs,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Resonator\u001b[0m\u001b[32m frequency\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"Hz\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mqubit_freq\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            qubit_freqs,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Qubit\u001b[0m\u001b[32m frequency\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"Hz\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mt1\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            t1_values,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"T1\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"s\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "    dataset_attrs = \u001b[1;35mmk_dataset_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    dataset = \u001b[1;35mxr.Dataset\u001b[0m\u001b[1m(\u001b[0m\u001b[33mdata_vars\u001b[0m=\u001b[35mdata_vars\u001b[0m, \u001b[33mcoords\u001b[0m=\u001b[35mcoords\u001b[0m, \u001b[33mattrs\u001b[0m=\u001b[35mdataset_attrs\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    return dataset"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_source_code(mk_nested_mc_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "09512820",
   "metadata": {},
   "outputs": [
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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 '20241018-040739-026-18439f' ....\n",
       "    qubit_freq_tuids      (main_dim) <U26 728B '20241018-040739-026-cc31a9' ....\n",
       "    t1_tuids              (main_dim) <U26 728B '20241018-040739-026-d1f9b6' ....\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:                      20241018-040739-027-ef8e05\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
       "    json_serialize_exclude:    []
"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 2kB\n",
       "Dimensions:               \u001b[1m(\u001b[0mmain_dim: \u001b[1;36m7\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "    flux_bias             \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 56B \u001b[1;36m-0.04\u001b[0m \u001b[1;36m-0.02667\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.02667\u001b[0m \u001b[1;36m0.04\u001b[0m\n",
       "    resonator_freq_tuids  \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m \n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 100\u001b[0m\u001b[1;36m0x1000\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m4\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 1, figsize=(10, 10), sharex=True)\n",
    "\n",
    "_ = dataset.t1.plot(x=\"flux_bias\", marker=\"o\", ax=axs[0].twiny(), color=\"C0\")\n",
    "x = \"t1_tuids\"\n",
    "_ = dataset.t1.plot(x=x, marker=\"o\", ax=axs[0], color=\"C0\")\n",
    "_ = dataset.resonator_freq.plot(x=x, marker=\"o\", ax=axs[1], color=\"C1\")\n",
    "_ = dataset.qubit_freq.plot(x=x, marker=\"o\", ax=axs[2], color=\"C2\")\n",
    "for tick in axs[2].get_xticklabels():\n",
    "    tick.set_rotation(15)  # avoid tuid labels overlapping"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c30a1b",
   "metadata": {},
   "source": [
    "It is possible to work with an explicit MultiIndex within a (python) xarray object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3d1df2bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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       "\n",
       "\n",
       "\n",
       "\n",
       "\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 '20241018-040739-026-18439f' ....\n",
       "  * qubit_freq_tuids      (main_dim) <U26 728B '20241018-040739-026-cc31a9' ....\n",
       "  * t1_tuids              (main_dim) <U26 728B '20241018-040739-026-d1f9b6' ....\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:                      20241018-040739-027-ef8e05\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",
       "  * 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"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.DataArray 'qubit_freq' (main_dim: 1)> Size: 8B\n",
       "array([4.66666667e+09])\n",
       "Coordinates:\n",
       "  * main_dim              (main_dim) object 8B MultiIndex\n",
       "  * flux_bias             (main_dim) float64 8B -0.01333\n",
       "  * resonator_freq_tuids  (main_dim) <U26 104B '20241018-040739-026-d7270b'\n",
       "  * t1_tuids              (main_dim) <U26 104B '20241018-040739-027-29eb45'\n",
       "    qubit_freq_tuids      <U26 104B '20241018-040739-026-f38ce6'\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",
       "    has_repetitions:         False\n",
       "    json_serialize_exclude:  []
"
      ],
      "text/plain": [
       "\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",
       "  * 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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       "\n",
       "\n",
       "\n",
       "
<xarray.DataArray 'qubit_freq' (main_dim: 1)> Size: 8B\n",
       "array([4.66666667e+09])\n",
       "Coordinates:\n",
       "  * main_dim              (main_dim) object 8B MultiIndex\n",
       "  * flux_bias             (main_dim) float64 8B -0.01333\n",
       "  * resonator_freq_tuids  (main_dim) <U26 104B '20241018-040739-026-d7270b'\n",
       "  * qubit_freq_tuids      (main_dim) <U26 104B '20241018-040739-026-f38ce6'\n",
       "    t1_tuids              <U26 104B '20241018-040739-027-29eb45'\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",
       "    has_repetitions:         False\n",
       "    json_serialize_exclude:  []
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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",
       "  * resonator_freq_tuids  \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m \n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
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       "\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 '20241018-040739-026-18439f' ....\n",
       "    qubit_freq_tuids      (main_dim) <U26 728B '20241018-040739-026-cc31a9' ....\n",
       "    t1_tuids              (main_dim) <U26 728B '20241018-040739-026-d1f9b6' ....\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:                      20241018-040739-027-ef8e05\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"
      ],
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\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": {
      "text/html": [
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "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"
    },
    {
     "data": {
      "text/plain": [
       "\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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