{ "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",
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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:                      20250528-190355-303-8e0bfc\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;36m20250528\u001b[0m-\u001b[1;36m190355\u001b[0m-\u001b[1;36m303\u001b[0m-8e0bfc\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:                      20250528-190355-303-8e0bfc\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;36m20250528\u001b[0m-\u001b[1;36m190355\u001b[0m-\u001b[1;36m303\u001b[0m-8e0bfc\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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      ],
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     },
     "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 '20250528-190356-435-397f0c' ....\n",
       "    qubit_freq_tuids      (main_dim) <U26 728B '20250528-190356-436-a0ecd7' ....\n",
       "    t1_tuids              (main_dim) <U26 728B '20250528-190356-436-eff74d' ....\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:                      20250528-190356-437-c2693a\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": [
       "
\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 '20250528-190356-435-397f0c' ....\n",
       "  * qubit_freq_tuids      (main_dim) <U26 728B '20250528-190356-436-a0ecd7' ....\n",
       "  * t1_tuids              (main_dim) <U26 728B '20250528-190356-436-eff74d' ....\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:                      20250528-190356-437-c2693a\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
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       "Coordinates:\n",
       "  * main_dim              \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m object 56B MultiIndex\n",
       "  * flux_bias             \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 56B \u001b[1;36m-0.04\u001b[0m \u001b[1;36m-0.02667\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.02667\u001b[0m \u001b[1;36m0.04\u001b[0m\n",
       "  * resonator_freq_tuids  \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m \n"
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       "  * t1_tuids              (main_dim) <U26 104B '20250528-190356-436-8cbdc6'\n",
       "    qubit_freq_tuids      <U26 104B '20250528-190356-436-ceb51d'\n",
       "Attributes:\n",
       "    unit:                    Hz\n",
       "    long_name:               Qubit frequency\n",
       "    is_main_var:             True\n",
       "    uniformly_spaced:        True\n",
       "    grid:                    True\n",
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       "Coordinates:\n",
       "  * main_dim              \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m object 8B MultiIndex\n",
       "  * flux_bias             \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 8B \u001b[1;36m-0.01333\u001b[0m\n",
       "  * resonator_freq_tuids  \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m \n"
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<xarray.DataArray 'qubit_freq' (main_dim: 1)> Size: 8B\n",
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       "  * resonator_freq_tuids  (main_dim) <U26 104B '20250528-190356-436-ff1a37'\n",
       "  * qubit_freq_tuids      (main_dim) <U26 104B '20250528-190356-436-ceb51d'\n",
       "    t1_tuids              <U26 104B '20250528-190356-436-8cbdc6'\n",
       "Attributes:\n",
       "    unit:                    Hz\n",
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       "Coordinates:\n",
       "  * main_dim              \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m object 8B MultiIndex\n",
       "  * flux_bias             \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 8B \u001b[1;36m-0.01333\u001b[0m\n",
       "  * resonator_freq_tuids  \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m \n"
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<xarray.Dataset> Size: 2kB\n",
       "Dimensions:               (main_dim: 7)\n",
       "Coordinates:\n",
       "    flux_bias             (main_dim) float64 56B -0.04 -0.02667 ... 0.02667 0.04\n",
       "    resonator_freq_tuids  (main_dim) <U26 728B '20250528-190356-435-397f0c' ....\n",
       "    qubit_freq_tuids      (main_dim) <U26 728B '20250528-190356-436-a0ecd7' ....\n",
       "    t1_tuids              (main_dim) <U26 728B '20250528-190356-436-eff74d' ....\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:                      20250528-190356-437-c2693a\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
       "    software_versions:         {}\n",
       "    relationships:             []\n",
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       "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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       "\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": [
    {
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       "\u001b[1m(\u001b[0m\u001b[1;35mdtype\u001b[0m\u001b[1m(\u001b[0m\u001b[32m'\n"
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    {
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       "\u001b[3;92mTrue\u001b[0m"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.t1_tuids.dtype == dataset_multi_indexed.reset_index(\"main_dim\").t1_tuids.dtype"
   ]
  }
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