{ "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{+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": [
       "
\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{+w}{ }\\PY{n+nf}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{,} \\PY{n+nb}{list}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Generates multiple shots for a list of probabilities assuming two states.}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    probabilities}\n",
       "\\PY{l+s+sd}{        The list/array of the probabilities of one of the states.}\n",
       "\\PY{l+s+sd}{    **kwargs}\n",
       "\\PY{l+s+sd}{        Keyword arguments passed to}\n",
       "\\PY{l+s+sd}{        :func:`\\PYZti{}quantify\\PYZus{}core.utilities.examples\\PYZus{}support.mk\\PYZus{}iq\\PYZus{}shots`.}\n",
       "\n",
       "\\PY{l+s+sd}{    Returns}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    :}\n",
       "\\PY{l+s+sd}{        Array containing the shots. Shape: (num\\PYZus{}shots, len(probabilities)).}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "    \\PY{n}{shots} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{array}\\PY{p}{(}\n",
       "        \\PY{n+nb}{tuple}\\PY{p}{(}\n",
       "            \\PY{n}{mk\\PYZus{}iq\\PYZus{}shots}\\PY{p}{(}\\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{prob}\\PY{p}{,} \\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{prob}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "            \\PY{k}{for} \\PY{n}{prob} \\PY{o+ow}{in} \\PY{n}{probabilities}\n",
       "        \\PY{p}{)}\n",
       "    \\PY{p}{)}\\PY{o}{.}\\PY{n}{T}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{shots}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0mprobabilities: Union\u001b[1m[\u001b[0mnp.ndarray, list\u001b[1m]\u001b[0m, **kwargs\u001b[1m)\u001b[0m:\n",
       "    \u001b[32m\"\"\u001b[0m\"Generates multiple shots for a list of probabilities assuming two states.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    probabilities\n",
       "        The list/array of the probabilities of one of the states.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to\n",
       "        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    :\n",
       "        Array containing the shots. Shape: \u001b[1m(\u001b[0mnum_shots, \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mprobabilities\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "\n",
       "    shots = \u001b[1;35mnp.array\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[1;35mtuple\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[1;35mmk_iq_shots\u001b[0m\u001b[1m(\u001b[0m\u001b[33mprobabilities\u001b[0m=\u001b[1m[\u001b[0mprob, \u001b[1;36m1\u001b[0m - prob\u001b[1m]\u001b[0m, **kwargs\u001b[1m)\u001b[0m\n",
       "            for prob in probabilities\n",
       "        \u001b[1m)\u001b[0m\n",
       "    \u001b[1m)\u001b[0m.T\n",
       "\n",
       "    return shots"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
def mk_surface7_cyles_dataset(num_cycles: int = 3, **kwargs) -> xr.Dataset:\n",
       "    """\n",
       "    See also :func:`mk_surface7_sched` inlined in the documentation as an example in:\n",
       "        :ref:`sec-dataset-advanced-examples`\n",
       "\n",
       "\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_cycles\n",
       "        The number of repeating cycles before the final measurement.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to :func:`~.mk_shots_from_probabilities`.\n",
       "    """\n",
       "\n",
       "    cycles = range(num_cycles)\n",
       "\n",
       "    mock_data = mk_shots_from_probabilities(\n",
       "        probabilities=[np.random.random() for _ in cycles], **kwargs\n",
       "    )\n",
       "\n",
       "    mock_data_final = mk_shots_from_probabilities(\n",
       "        probabilities=[np.random.random()], **kwargs\n",
       "    )\n",
       "\n",
       "    # %%\n",
       "    data_vars = {}\n",
       "\n",
       "    # NB same random data is used for all qubits only for the simplicity of the mock!\n",
       "    for qubit in (f"A{i}" for i in range(3)):\n",
       "        data_vars[f"{qubit}_shots"] = (\n",
       "            ("repetitions", "dim_cycle"),\n",
       "            mock_data,\n",
       "            mk_main_var_attrs(\n",
       "                unit="V", long_name=f"IQ amplitude {qubit}", has_repetitions=True\n",
       "            ),\n",
       "        )\n",
       "\n",
       "    for qubit in (f"D{i}" for i in range(4)):\n",
       "        data_vars[f"{qubit}_shots"] = (\n",
       "            ("repetitions", "dim_final"),\n",
       "            mock_data_final,\n",
       "            mk_main_var_attrs(\n",
       "                unit="V", long_name=f"IQ amplitude {qubit}", has_repetitions=True\n",
       "            ),\n",
       "        )\n",
       "\n",
       "    cycle_attrs = mk_main_coord_attrs(long_name="Surface code cycle number")\n",
       "    final_msmt_attrs = mk_main_coord_attrs(long_name="Final measurement")\n",
       "    coords = dict(\n",
       "        cycle=("dim_cycle", cycles, cycle_attrs),\n",
       "        final_msmt=("dim_final", [0], final_msmt_attrs),\n",
       "    )\n",
       "\n",
       "    dataset = xr.Dataset(\n",
       "        data_vars=data_vars,\n",
       "        coords=coords,\n",
       "        attrs=mk_dataset_attrs(),\n",
       "    )\n",
       "\n",
       "    return dataset\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def}\\PY{+w}{ }\\PY{n+nf}{mk\\PYZus{}surface7\\PYZus{}cyles\\PYZus{}dataset}\\PY{p}{(}\\PY{n}{num\\PYZus{}cycles}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{3}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    See also :func:`mk\\PYZus{}surface7\\PYZus{}sched` inlined in the documentation as an example in:}\n",
       "\\PY{l+s+sd}{        :ref:`sec\\PYZhy{}dataset\\PYZhy{}advanced\\PYZhy{}examples`}\n",
       "\n",
       "\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    num\\PYZus{}cycles}\n",
       "\\PY{l+s+sd}{        The number of repeating cycles before the final measurement.}\n",
       "\\PY{l+s+sd}{    **kwargs}\n",
       "\\PY{l+s+sd}{        Keyword arguments passed to :func:`\\PYZti{}.mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities`.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "    \\PY{n}{cycles} \\PY{o}{=} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}cycles}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{mock\\PYZus{}data} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\n",
       "        \\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{random}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n}{cycles}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{mock\\PYZus{}data\\PYZus{}final} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\n",
       "        \\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{random}\\PY{p}{(}\\PY{p}{)}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} \\PYZpc{}\\PYZpc{}}\n",
       "    \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{p}{\\PYZob{}}\\PY{p}{\\PYZcb{}}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} NB same random data is used for all qubits only for the simplicity of the mock!}\n",
       "    \\PY{k}{for} \\PY{n}{qubit} \\PY{o+ow}{in} \\PY{p}{(}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{A}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{i}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}} \\PY{k}{for} \\PY{n}{i} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{l+m+mi}{3}\\PY{p}{)}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{n}{data\\PYZus{}vars}\\PY{p}{[}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\n",
       "            \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}cycle}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "            \\PY{n}{mock\\PYZus{}data}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{IQ amplitude }\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{has\\PYZus{}repetitions}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{for} \\PY{n}{qubit} \\PY{o+ow}{in} \\PY{p}{(}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{D}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{i}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}} \\PY{k}{for} \\PY{n}{i} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{l+m+mi}{4}\\PY{p}{)}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{n}{data\\PYZus{}vars}\\PY{p}{[}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\n",
       "            \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}final}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "            \\PY{n}{mock\\PYZus{}data\\PYZus{}final}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{IQ amplitude }\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{has\\PYZus{}repetitions}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{cycle\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Surface code cycle number}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{final\\PYZus{}msmt\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Final measurement}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{cycle}\\PY{o}{=}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}cycle}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{cycles}\\PY{p}{,} \\PY{n}{cycle\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{final\\PYZus{}msmt}\\PY{o}{=}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}final}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\\PY{p}{,} \\PY{n}{final\\PYZus{}msmt\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{(}\n",
       "        \\PY{n}{data\\PYZus{}vars}\\PY{o}{=}\\PY{n}{data\\PYZus{}vars}\\PY{p}{,}\n",
       "        \\PY{n}{coords}\\PY{o}{=}\\PY{n}{coords}\\PY{p}{,}\n",
       "        \\PY{n}{attrs}\\PY{o}{=}\\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{dataset}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_surface7_cyles_dataset\u001b[0m\u001b[1m(\u001b[0mnum_cycles: int = \u001b[1;36m3\u001b[0m, **kwargs\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    See also :func:`mk_surface7_sched` inlined in the documentation as an example in:\n",
       "        :ref:`sec-dataset-advanced-examples`\n",
       "\n",
       "\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_cycles\n",
       "        The number of repeating cycles before the final measurement.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to :func:`~.mk_shots_from_probabilities`.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "\n",
       "    cycles = \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_cycles\u001b[1m)\u001b[0m\n",
       "\n",
       "    mock_data = \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mprobabilities\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mnp.random.random\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in cycles\u001b[1m]\u001b[0m, **kwargs\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    mock_data_final = \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mprobabilities\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mnp.random.random\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m, **kwargs\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    # %%\n",
       "    data_vars = \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
       "\n",
       "    # NB same random data is used for all qubits only for the simplicity of the mock!\n",
       "    for qubit in \u001b[1m(\u001b[0mf\"A\u001b[1m{\u001b[0mi\u001b[1m}\u001b[0m\" for i in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m:\n",
       "        data_vars\u001b[1m[\u001b[0mf\"\u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m_shots\"\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\n",
       "            \u001b[1m(\u001b[0m\u001b[32m\"repetitions\"\u001b[0m, \u001b[32m\"dim_cycle\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "            mock_data,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33munit\u001b[0m=\u001b[32m\"V\"\u001b[0m, \u001b[33mlong_name\u001b[0m=\u001b[35mf\"\u001b[0mIQ amplitude \u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m\", \u001b[33mhas_repetitions\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    for qubit in \u001b[1m(\u001b[0mf\"D\u001b[1m{\u001b[0mi\u001b[1m}\u001b[0m\" for i in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m:\n",
       "        data_vars\u001b[1m[\u001b[0mf\"\u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m_shots\"\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\n",
       "            \u001b[1m(\u001b[0m\u001b[32m\"repetitions\"\u001b[0m, \u001b[32m\"dim_final\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "            mock_data_final,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33munit\u001b[0m=\u001b[32m\"V\"\u001b[0m, \u001b[33mlong_name\u001b[0m=\u001b[35mf\"\u001b[0mIQ amplitude \u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m\", \u001b[33mhas_repetitions\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    cycle_attrs = \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Surface\u001b[0m\u001b[32m code cycle number\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    final_msmt_attrs = \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Final\u001b[0m\u001b[32m measurement\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    coords = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mcycle\u001b[0m=\u001b[1m(\u001b[0m\u001b[32m\"dim_cycle\"\u001b[0m, cycles, cycle_attrs\u001b[1m)\u001b[0m,\n",
       "        \u001b[33mfinal_msmt\u001b[0m=\u001b[1m(\u001b[0m\u001b[32m\"dim_final\"\u001b[0m, \u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m, final_msmt_attrs\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    dataset = \u001b[1;35mxr.Dataset\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mdata_vars\u001b[0m=\u001b[35mdata_vars\u001b[0m,\n",
       "        \u001b[33mcoords\u001b[0m=\u001b[35mcoords\u001b[0m,\n",
       "        \u001b[33mattrs\u001b[0m=\u001b[1;35mmk_dataset_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    return dataset"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mock data parameters\n",
    "num_shots = 128  # NB usually >~1000 in real experiments\n",
    "ground = -0.2 + 0.65j\n",
    "excited = 0.7 + 4j\n",
    "centroids = ground, excited\n",
    "sigmas = [0.1] * 2\n",
    "\n",
    "display_source_code(mk_iq_shots)\n",
    "display_source_code(mk_shots_from_probabilities)\n",
    "display_source_code(mk_surface7_cyles_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12829358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.Dataset> Size: 27kB\n",
       "Dimensions:     (repetitions: 128, dim_cycle: 3, dim_final: 1)\n",
       "Coordinates:\n",
       "    cycle       (dim_cycle) int64 24B 0 1 2\n",
       "    final_msmt  (dim_final) int64 8B 0\n",
       "Dimensions without coordinates: repetitions, dim_cycle, dim_final\n",
       "Data variables:\n",
       "    A0_shots    (repetitions, dim_cycle) complex128 6kB (-0.23630343679164473...\n",
       "    A1_shots    (repetitions, dim_cycle) complex128 6kB (-0.23630343679164473...\n",
       "    A2_shots    (repetitions, dim_cycle) complex128 6kB (-0.23630343679164473...\n",
       "    D0_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "    D1_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "    D2_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "    D3_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "Attributes:\n",
       "    tuid:                      20250320-201128-571-abc308\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;36m20250320\u001b[0m-\u001b[1;36m201128\u001b[0m-\u001b[1;36m571\u001b[0m-abc308\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+0.5...\n",
       "    A1_shots    (repetitions, cycle) complex128 6kB (-0.23630343679164473+0.5...\n",
       "    A2_shots    (repetitions, cycle) complex128 6kB (-0.23630343679164473+0.5...\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:                      20250320-201128-571-abc308\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;36m0.5\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;36m0.5\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;36m0.5\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;36m20250320\u001b[0m-\u001b[1;36m201128\u001b[0m-\u001b[1;36m571\u001b[0m-abc308\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{+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}{Optional}\\PY{p}{[}\\PY{n+nb}{int}\\PY{p}{]} \\PY{o}{=} \\PY{l+m+mi}{112233}\\PY{p}{,}\n",
       "\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Generates a dataset with dataset references and several coordinates that serve to}\n",
       "\\PY{l+s+sd}{    index the same variables.}\n",
       "\n",
       "\\PY{l+s+sd}{    Note that the each value for ``resonator\\PYZus{}freqs``, ``qubit\\PYZus{}freqs`` and ``t1\\PYZus{}values``}\n",
       "\\PY{l+s+sd}{    would have been extracted from other dataset corresponding to individual experiments}\n",
       "\\PY{l+s+sd}{    with their own dataset.}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    num\\PYZus{}points}\n",
       "\\PY{l+s+sd}{        Number of datapoints to generate (used for all variables/coordinates).}\n",
       "\\PY{l+s+sd}{    flux\\PYZus{}bias\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock values.}\n",
       "\\PY{l+s+sd}{    resonator\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock values.}\n",
       "\\PY{l+s+sd}{    qubit\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock values.}\n",
       "\\PY{l+s+sd}{    t1\\PYZus{}values\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock random values.}\n",
       "\\PY{l+s+sd}{    seed}\n",
       "\\PY{l+s+sd}{        Random number generator seed passed to ``numpy.random.default\\PYZus{}rng``.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{n}{rng} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{default\\PYZus{}rng}\\PY{p}{(}\\PY{n}{seed}\\PY{o}{=}\\PY{n}{seed}\\PY{p}{)}  \\PY{c+c1}{\\PYZsh{} random number generator}\n",
       "\n",
       "    \\PY{n}{flux\\PYZus{}bias\\PYZus{}vals} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{o}{*}\\PY{n}{flux\\PYZus{}bias\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "    \\PY{n}{resonator\\PYZus{}freqs} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{o}{*}\\PY{n}{resonator\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "    \\PY{n}{qubit\\PYZus{}freqs} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{o}{*}\\PY{n}{qubit\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "    \\PY{n}{t1\\PYZus{}values} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{uniform}\\PY{p}{(}\\PY{o}{*}\\PY{n}{t1\\PYZus{}values\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{resonator\\PYZus{}freq\\PYZus{}tuids} \\PY{o}{=} \\PY{p}{[}\\PY{n}{dh}\\PY{o}{.}\\PY{n}{gen\\PYZus{}tuid}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}points}\\PY{p}{)}\\PY{p}{]}\n",
       "    \\PY{n}{qubit\\PYZus{}freq\\PYZus{}tuids} \\PY{o}{=} \\PY{p}{[}\\PY{n}{dh}\\PY{o}{.}\\PY{n}{gen\\PYZus{}tuid}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}points}\\PY{p}{)}\\PY{p}{]}\n",
       "    \\PY{n}{t1\\PYZus{}tuids} \\PY{o}{=} \\PY{p}{[}\\PY{n}{dh}\\PY{o}{.}\\PY{n}{gen\\PYZus{}tuid}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}points}\\PY{p}{)}\\PY{p}{]}\n",
       "\n",
       "    \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{flux\\PYZus{}bias}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{flux\\PYZus{}bias\\PYZus{}vals}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Flux bias}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{A}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{resonator\\PYZus{}freq\\PYZus{}tuids}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{resonator\\PYZus{}freq\\PYZus{}tuids}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Dataset TUID resonator frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{is\\PYZus{}dataset\\PYZus{}ref}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{qubit\\PYZus{}freq\\PYZus{}tuids}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{qubit\\PYZus{}freq\\PYZus{}tuids}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Dataset TUID qubit frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{is\\PYZus{}dataset\\PYZus{}ref}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{t1\\PYZus{}tuids}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{t1\\PYZus{}tuids}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Dataset TUID T1}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{is\\PYZus{}dataset\\PYZus{}ref}\\PY{o}{=}\\PY{k+kc}{True}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{resonator\\PYZus{}freq}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{resonator\\PYZus{}freqs}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Resonator frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Hz}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{qubit\\PYZus{}freq}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{qubit\\PYZus{}freqs}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Qubit frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Hz}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{t1}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{t1\\PYZus{}values}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{T1}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{s}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "    \\PY{n}{dataset\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{(}\\PY{n}{data\\PYZus{}vars}\\PY{o}{=}\\PY{n}{data\\PYZus{}vars}\\PY{p}{,} \\PY{n}{coords}\\PY{o}{=}\\PY{n}{coords}\\PY{p}{,} \\PY{n}{attrs}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}attrs}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{dataset}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_nested_mc_dataset\u001b[0m\u001b[1m(\u001b[0m\n",
       "    num_points: int = \u001b[1;36m12\u001b[0m,\n",
       "    flux_bias_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m-0.04\u001b[0m, \u001b[1;36m0.04\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    resonator_freqs_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m7e9\u001b[0m, \u001b[1;36m7.3e9\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    qubit_freqs_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m4.5e9\u001b[0m, \u001b[1;36m5.0e9\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    t1_values_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m20e-6\u001b[0m, \u001b[1;36m50e-6\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    seed: Optional\u001b[1m[\u001b[0mint\u001b[1m]\u001b[0m = \u001b[1;36m112233\u001b[0m,\n",
       "\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generates a dataset with dataset references and several coordinates that serve to\n",
       "    index the same variables.\n",
       "\n",
       "    Note that the each value for ``resonator_freqs``, ``qubit_freqs`` and ``t1_values``\n",
       "    would have been extracted from other dataset corresponding to individual experiments\n",
       "    with their own dataset.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_points\n",
       "        Number of datapoints to generate \u001b[1m(\u001b[0mused for all variables/coordinates\u001b[1m)\u001b[0m.\n",
       "    flux_bias_min_max\n",
       "        Range for mock values.\n",
       "    resonator_freqs_min_max\n",
       "        Range for mock values.\n",
       "    qubit_freqs_min_max\n",
       "        Range for mock values.\n",
       "    t1_values_min_max\n",
       "        Range for mock random values.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    rng = \u001b[1;35mnp.random.default_rng\u001b[0m\u001b[1m(\u001b[0m\u001b[33mseed\u001b[0m=\u001b[35mseed\u001b[0m\u001b[1m)\u001b[0m  # random number generator\n",
       "\n",
       "    flux_bias_vals = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m*flux_bias_min_max, num_points\u001b[1m)\u001b[0m\n",
       "    resonator_freqs = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m*resonator_freqs_min_max, num_points\u001b[1m)\u001b[0m\n",
       "    qubit_freqs = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m*qubit_freqs_min_max, num_points\u001b[1m)\u001b[0m\n",
       "    t1_values = \u001b[1;35mrng.uniform\u001b[0m\u001b[1m(\u001b[0m*t1_values_min_max, num_points\u001b[1m)\u001b[0m\n",
       "\n",
       "    resonator_freq_tuids = \u001b[1m[\u001b[0m\u001b[1;35mdh.gen_tuid\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_points\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n",
       "    qubit_freq_tuids = \u001b[1m[\u001b[0m\u001b[1;35mdh.gen_tuid\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_points\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n",
       "    t1_tuids = \u001b[1m[\u001b[0m\u001b[1;35mdh.gen_tuid\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_points\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n",
       "\n",
       "    coords = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mflux_bias\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            flux_bias_vals,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Flux\u001b[0m\u001b[32m bias\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"A\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mresonator_freq_tuids\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            resonator_freq_tuids,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33mlong_name\u001b[0m=\u001b[32m\"Dataset\u001b[0m\u001b[32m TUID resonator frequency\"\u001b[0m, \u001b[33mis_dataset_ref\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mqubit_freq_tuids\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            qubit_freq_tuids,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33mlong_name\u001b[0m=\u001b[32m\"Dataset\u001b[0m\u001b[32m TUID qubit frequency\"\u001b[0m, \u001b[33mis_dataset_ref\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mt1_tuids\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            t1_tuids,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Dataset\u001b[0m\u001b[32m TUID T1\"\u001b[0m, \u001b[33mis_dataset_ref\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    data_vars = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mresonator_freq\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            resonator_freqs,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Resonator\u001b[0m\u001b[32m frequency\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"Hz\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mqubit_freq\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            qubit_freqs,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Qubit\u001b[0m\u001b[32m frequency\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"Hz\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mt1\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            t1_values,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"T1\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"s\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "    dataset_attrs = \u001b[1;35mmk_dataset_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    dataset = \u001b[1;35mxr.Dataset\u001b[0m\u001b[1m(\u001b[0m\u001b[33mdata_vars\u001b[0m=\u001b[35mdata_vars\u001b[0m, \u001b[33mcoords\u001b[0m=\u001b[35mcoords\u001b[0m, \u001b[33mattrs\u001b[0m=\u001b[35mdataset_attrs\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    return dataset"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_source_code(mk_nested_mc_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "09512820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.Dataset> 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 '20250320-201129-784-f1b897' ....\n",
       "    qubit_freq_tuids      (main_dim) <U26 728B '20250320-201129-785-c4eebe' ....\n",
       "    t1_tuids              (main_dim) <U26 728B '20250320-201129-785-60ee55' ....\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:                      20250320-201129-786-fa5111\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
       "    json_serialize_exclude:    []
"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 2kB\n",
       "Dimensions:               \u001b[1m(\u001b[0mmain_dim: \u001b[1;36m7\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "    flux_bias             \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 56B \u001b[1;36m-0.04\u001b[0m \u001b[1;36m-0.02667\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.02667\u001b[0m \u001b[1;36m0.04\u001b[0m\n",
       "    resonator_freq_tuids  \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m \n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 100\u001b[0m\u001b[1;36m0x1000\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m4\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 1, figsize=(10, 10), sharex=True)\n",
    "\n",
    "_ = dataset.t1.plot(x=\"flux_bias\", marker=\"o\", ax=axs[0].twiny(), color=\"C0\")\n",
    "x = \"t1_tuids\"\n",
    "_ = dataset.t1.plot(x=x, marker=\"o\", ax=axs[0], color=\"C0\")\n",
    "_ = dataset.resonator_freq.plot(x=x, marker=\"o\", ax=axs[1], color=\"C1\")\n",
    "_ = dataset.qubit_freq.plot(x=x, marker=\"o\", ax=axs[2], color=\"C2\")\n",
    "for tick in axs[2].get_xticklabels():\n",
    "    tick.set_rotation(15)  # avoid tuid labels overlapping"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92c30a1b",
   "metadata": {},
   "source": [
    "It is possible to work with an explicit MultiIndex within a (python) xarray object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3d1df2bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.Dataset> Size: 2kB\n",
       "Dimensions:               (main_dim: 7)\n",
       "Coordinates:\n",
       "  * 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 '20250320-201129-784-f1b897' ....\n",
       "  * qubit_freq_tuids      (main_dim) <U26 728B '20250320-201129-785-c4eebe' ....\n",
       "  * t1_tuids              (main_dim) <U26 728B '20250320-201129-785-60ee55' ....\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:                      20250320-201129-786-fa5111\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",
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       "Attributes:\n",
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       "    dataset_name:              \n",
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       "    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",
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     "execution_count": 16,
     "metadata": {},
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   "source": [
    "all(dataset_multi_indexed.reset_index(\"main_dim\").t1_tuids == dataset.t1_tuids)"
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   "id": "27741c5c",
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    "But, for example, the `dtype` has been changed to `object`\n",
    "(from fixed-length string):"
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  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "62cb3085",
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "dataset.t1_tuids.dtype == dataset_multi_indexed.reset_index(\"main_dim\").t1_tuids.dtype"
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