{ "cells": [ { "cell_type": "markdown", "id": "da5198c6", "metadata": {}, "source": [ "(sec-dataset-advanced-examples)=\n", "# Quantify dataset - advanced examples\n", "\n", "```{seealso}\n", "The complete source code of this tutorial can be found in\n", "\n", "{nb-download}`Quantify dataset - advanced examples.ipynb`\n", "```\n", "\n", "Here we will explore a few advanced usages of the Quantify dataset and how it can\n", "accommodate them." ] }, { "cell_type": "code", "execution_count": 1, "id": "64643c33", "metadata": { "mystnb": { "code_prompt_show": "Imports and auxiliary utilities" }, "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "from pathlib import Path\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import xarray as xr\n", "from rich import pretty\n", "\n", "from quantify_core.analysis.calibration import rotate_to_calibrated_axis\n", "from quantify_core.analysis.fitting_models import exp_decay_func\n", "from quantify_core.data import handling as dh\n", "from quantify_core.utilities import dataset_examples\n", "from quantify_core.utilities.dataset_examples import (\n", " mk_nested_mc_dataset,\n", " mk_shots_from_probabilities,\n", " mk_surface7_cyles_dataset,\n", ")\n", "from quantify_core.utilities.examples_support import (\n", " mk_iq_shots,\n", " round_trip_dataset,\n", ")\n", "from quantify_core.utilities.inspect_utils import display_source_code\n", "\n", "pretty.install()\n", "\n", "dh.set_datadir(Path.home() / \"quantify-data\") # change me!" ] }, { "cell_type": "markdown", "id": "b075ee74", "metadata": {}, "source": [ "## Dataset for an \"unstructured\" experiment\n", "\n", "Let's take consider a Surface Code experiment, in particular the one portrayed in\n", "Fig. 4b from one of the papers from DiCarlo Lab {cite}`marques_logical_qubit_2021`.\n", "\n", "For simplicity, we will not use exactly the same schedule, because what matters here\n", "are the measurements. It is difficult to deal with the results of these measurements\n", "because we have a few repeating cycles followed by a final measurement that leaves the\n", "overall dataset \"unstructured\".\n", "\n", "```{figure} /images/surface-7-sched.png\n", ":width: 100%\n", "```\n", "\n", "``````{admonition} Source code for generating this schedule and visualizing it\n", ":class: dropdown, info\n", "\n", "If you want to create and visualize the schedule above using `quantify-scheduler`,\n", "you can use this code:\n", "\n", "```{code-block} python\n", "from quantify_scheduler import Schedule\n", "from quantify_scheduler.operations.gate_library import CZ, Y90, Measure, Reset, X\n", "from quantify_scheduler.visualization.circuit_diagram import circuit_diagram_matplotlib\n", "\n", "def mk_surface7_sched(num_cycles: int = 3):\n", " \"\"\"Generates a schedule with some of the feature of a Surface 7 experiment as\n", " portrayed in Fig. 4b of :cite:`marques_logical_qubit_2021`.\n", "\n", " Parameters\n", " ----------\n", " num_cycles\n", " The number of times to repeat the main cycle.\n", "\n", " Returns\n", " -------\n", " :\n", " A schedule similar to a Surface 7 dance.\n", " \"\"\"\n", "\n", " sched = Schedule(\"S7 dance\")\n", "\n", " q_d1, q_d2, q_d3, q_d4 = [f\"D{i}\" for i in range(1, 5)]\n", " q_a1, q_a2, q_a3 = [f\"A{i}\" for i in range(1, 4)]\n", " all_qubits = q_d1, q_d2, q_d3, q_d4, q_a1, q_a2, q_a3\n", "\n", " sched.add(Reset(*all_qubits))\n", "\n", " for cycle in range(num_cycles):\n", " sched.add(Y90(q_d1))\n", " for qubit in [q_d2, q_d3, q_d4]:\n", " sched.add(Y90(qubit), ref_pt=\"start\", rel_time=0)\n", " sched.add(Y90(q_a2), ref_pt=\"start\", rel_time=0)\n", "\n", " for qubit in [q_d2, q_d1, q_d4, q_d3]:\n", " sched.add(CZ(qC=qubit, qT=q_a2))\n", "\n", " sched.add(Y90(q_d1))\n", " for qubit in [q_d2, q_d3, q_d4]:\n", " sched.add(Y90(qubit), ref_pt=\"start\", rel_time=0)\n", " sched.add(Y90(q_a2), ref_pt=\"start\", rel_time=0)\n", "\n", " sched.add(Y90(q_a1), ref_pt=\"end\", rel_time=0)\n", " sched.add(Y90(q_a3), ref_pt=\"start\", rel_time=0)\n", "\n", " sched.add(CZ(qC=q_d1, qT=q_a1))\n", " sched.add(CZ(qC=q_d2, qT=q_a3))\n", " sched.add(CZ(qC=q_d3, qT=q_a1))\n", " sched.add(CZ(qC=q_d4, qT=q_a3))\n", "\n", " sched.add(Y90(q_a1), ref_pt=\"end\", rel_time=0)\n", " sched.add(Y90(q_a3), ref_pt=\"start\", rel_time=0)\n", "\n", " sched.add(Measure(q_a2, acq_index=cycle))\n", " for qubit in (q_a1, q_a3):\n", " sched.add(Measure(qubit, acq_index=cycle), ref_pt=\"start\", rel_time=0)\n", "\n", " for qubit in [q_d1, q_d2, q_d3, q_d4]:\n", " sched.add(X(qubit), ref_pt=\"start\", rel_time=0)\n", "\n", " # final measurements\n", "\n", " sched.add(Measure(*all_qubits[:4], acq_index=0), ref_pt=\"end\", rel_time=0)\n", "\n", " return sched\n", "\n", "sched = mk_surface7_sched(num_cycles=3)\n", "f, ax = circuit_diagram_matplotlib(sched)\n", "f.set_figwidth(30)\n", "```\n", "``````\n", "\n", "How do we store all the shots for this measurement?\n", "We might want this because, e.g., we know we have an issue with leakage to the second\n", "excited state of a transmon and we would like to be able to store and inspect raw data.\n", "\n", "To support such use-cases we will have a dimension in the dataset for the repeating cycles\n", "and one extra dimension for the final measurement." ] }, { "cell_type": "code", "execution_count": 2, "id": "d2f2ddcf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
def mk_iq_shots(\n",
       "    num_shots: int = 128,\n",
       "    sigmas: Union[Tuple[float], NDArray[np.float64]] = (0.1, 0.1),\n",
       "    centers: Union[Tuple[complex], NDArray[np.complex128]] = (-0.2 + 0.65j, 0.7 + 4j),\n",
       "    probabilities: Union[Tuple[float], NDArray[np.float64]] = (0.4, 0.6),\n",
       "    seed: Union[int, None] = 112233,\n",
       ") -> NDArray:\n",
       "    """\n",
       "    Generate clusters of (I + 1j*Q) points with a Gaussian distribution.\n",
       "\n",
       "    Utility to mock the data coming from qubit readout experiments.\n",
       "    Clusters are centered around ``centers`` and data points are distributed between\n",
       "    them according to ``probabilities``.\n",
       "\n",
       "    .. seealso:: :ref:`howto-utilities-examples-ssro`\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_shots\n",
       "        The number of shot to generate.\n",
       "    sigma\n",
       "        The sigma of the Gaussian distribution used for both real and imaginary parts.\n",
       "    centers\n",
       "        The center of each cluster on the imaginary plane.\n",
       "    probabilities\n",
       "        The probabilities of each cluster being randomly selected for each shot.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    """\n",
       "    if not len(sigmas) == len(centers) == len(probabilities):\n",
       "        raise ValueError(\n",
       "            f"Incorrect input. sigmas={sigmas}, centers={centers} and "\n",
       "            f"probabilities={probabilities} must have the same length."\n",
       "        )\n",
       "\n",
       "    rng = np.random.default_rng(seed=seed)\n",
       "\n",
       "    cluster_indices = tuple(range(len(centers)))\n",
       "    choices = rng.choice(a=cluster_indices, size=num_shots, p=probabilities)\n",
       "\n",
       "    shots = []\n",
       "    for idx in cluster_indices:\n",
       "        num_shots_this_cluster = np.sum(choices == idx)\n",
       "        i_data = rng.normal(\n",
       "            loc=centers[idx].real,\n",
       "            scale=sigmas[idx],\n",
       "            size=num_shots_this_cluster,\n",
       "        )\n",
       "        q_data = rng.normal(\n",
       "            loc=centers[idx].imag,\n",
       "            scale=sigmas[idx],\n",
       "            size=num_shots_this_cluster,\n",
       "        )\n",
       "        shots.append(i_data + 1j * q_data)\n",
       "    return np.concatenate(shots)\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}iq\\PYZus{}shots}\\PY{p}{(}\n",
       "    \\PY{n}{num\\PYZus{}shots}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{128}\\PY{p}{,}\n",
       "    \\PY{n}{sigmas}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{float}\\PY{p}{]}\\PY{p}{,} \\PY{n}{NDArray}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{float64}\\PY{p}{]}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{0.1}\\PY{p}{,} \\PY{l+m+mf}{0.1}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{centers}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{complex}\\PY{p}{]}\\PY{p}{,} \\PY{n}{NDArray}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{complex128}\\PY{p}{]}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{l+m+mf}{0.2} \\PY{o}{+} \\PY{l+m+mf}{0.65}\\PY{n}{j}\\PY{p}{,} \\PY{l+m+mf}{0.7} \\PY{o}{+} \\PY{l+m+mi}{4}\\PY{n}{j}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{Tuple}\\PY{p}{[}\\PY{n+nb}{float}\\PY{p}{]}\\PY{p}{,} \\PY{n}{NDArray}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{float64}\\PY{p}{]}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{0.4}\\PY{p}{,} \\PY{l+m+mf}{0.6}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{seed}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n+nb}{int}\\PY{p}{,} \\PY{k+kc}{None}\\PY{p}{]} \\PY{o}{=} \\PY{l+m+mi}{112233}\\PY{p}{,}\n",
       "\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{NDArray}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Generate clusters of (I + 1j*Q) points with a Gaussian distribution.}\n",
       "\n",
       "\\PY{l+s+sd}{    Utility to mock the data coming from qubit readout experiments.}\n",
       "\\PY{l+s+sd}{    Clusters are centered around ``centers`` and data points are distributed between}\n",
       "\\PY{l+s+sd}{    them according to ``probabilities``.}\n",
       "\n",
       "\\PY{l+s+sd}{    .. seealso:: :ref:`howto\\PYZhy{}utilities\\PYZhy{}examples\\PYZhy{}ssro`}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    num\\PYZus{}shots}\n",
       "\\PY{l+s+sd}{        The number of shot to generate.}\n",
       "\\PY{l+s+sd}{    sigma}\n",
       "\\PY{l+s+sd}{        The sigma of the Gaussian distribution used for both real and imaginary parts.}\n",
       "\\PY{l+s+sd}{    centers}\n",
       "\\PY{l+s+sd}{        The center of each cluster on the imaginary plane.}\n",
       "\\PY{l+s+sd}{    probabilities}\n",
       "\\PY{l+s+sd}{        The probabilities of each cluster being randomly selected for each shot.}\n",
       "\\PY{l+s+sd}{    seed}\n",
       "\\PY{l+s+sd}{        Random number generator seed passed to ``numpy.random.default\\PYZus{}rng``.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{k}{if} \\PY{o+ow}{not} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{sigmas}\\PY{p}{)} \\PY{o}{==} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{centers}\\PY{p}{)} \\PY{o}{==} \\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{probabilities}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{k}{raise} \\PY{n+ne}{ValueError}\\PY{p}{(}\n",
       "            \\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Incorrect input. sigmas=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{sigmas}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{, centers=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{centers}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{ and }\\PY{l+s+s2}{\\PYZdq{}}\n",
       "            \\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{probabilities=}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{probabilities}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{ must have the same length.}\\PY{l+s+s2}{\\PYZdq{}}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{rng} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{default\\PYZus{}rng}\\PY{p}{(}\\PY{n}{seed}\\PY{o}{=}\\PY{n}{seed}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{cluster\\PYZus{}indices} \\PY{o}{=} \\PY{n+nb}{tuple}\\PY{p}{(}\\PY{n+nb}{range}\\PY{p}{(}\\PY{n+nb}{len}\\PY{p}{(}\\PY{n}{centers}\\PY{p}{)}\\PY{p}{)}\\PY{p}{)}\n",
       "    \\PY{n}{choices} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{choice}\\PY{p}{(}\\PY{n}{a}\\PY{o}{=}\\PY{n}{cluster\\PYZus{}indices}\\PY{p}{,} \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots}\\PY{p}{,} \\PY{n}{p}\\PY{o}{=}\\PY{n}{probabilities}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{shots} \\PY{o}{=} \\PY{p}{[}\\PY{p}{]}\n",
       "    \\PY{k}{for} \\PY{n}{idx} \\PY{o+ow}{in} \\PY{n}{cluster\\PYZus{}indices}\\PY{p}{:}\n",
       "        \\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{sum}\\PY{p}{(}\\PY{n}{choices} \\PY{o}{==} \\PY{n}{idx}\\PY{p}{)}\n",
       "        \\PY{n}{i\\PYZus{}data} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{normal}\\PY{p}{(}\n",
       "            \\PY{n}{loc}\\PY{o}{=}\\PY{n}{centers}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{o}{.}\\PY{n}{real}\\PY{p}{,}\n",
       "            \\PY{n}{scale}\\PY{o}{=}\\PY{n}{sigmas}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{p}{,}\n",
       "            \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "        \\PY{n}{q\\PYZus{}data} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{normal}\\PY{p}{(}\n",
       "            \\PY{n}{loc}\\PY{o}{=}\\PY{n}{centers}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{o}{.}\\PY{n}{imag}\\PY{p}{,}\n",
       "            \\PY{n}{scale}\\PY{o}{=}\\PY{n}{sigmas}\\PY{p}{[}\\PY{n}{idx}\\PY{p}{]}\\PY{p}{,}\n",
       "            \\PY{n}{size}\\PY{o}{=}\\PY{n}{num\\PYZus{}shots\\PYZus{}this\\PYZus{}cluster}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "        \\PY{n}{shots}\\PY{o}{.}\\PY{n}{append}\\PY{p}{(}\\PY{n}{i\\PYZus{}data} \\PY{o}{+} \\PY{l+m+mi}{1}\\PY{n}{j} \\PY{o}{*} \\PY{n}{q\\PYZus{}data}\\PY{p}{)}\n",
       "    \\PY{k}{return} \\PY{n}{np}\\PY{o}{.}\\PY{n}{concatenate}\\PY{p}{(}\\PY{n}{shots}\\PY{p}{)}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_iq_shots\u001b[0m\u001b[1m(\u001b[0m\n",
       "    num_shots: int = \u001b[1;36m128\u001b[0m,\n",
       "    sigmas: Union\u001b[1m[\u001b[0mTuple\u001b[1m[\u001b[0mfloat\u001b[1m]\u001b[0m, NDArray\u001b[1m[\u001b[0mnp.float64\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m0.1\u001b[0m, \u001b[1;36m0.1\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    centers: Union\u001b[1m[\u001b[0mTuple\u001b[1m[\u001b[0mcomplex\u001b[1m]\u001b[0m, NDArray\u001b[1m[\u001b[0mnp.complex128\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m-0.2\u001b[0m + \u001b[1;36m0.65j\u001b[0m, \u001b[1;36m0.7\u001b[0m + \u001b[1;36m4j\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    probabilities: Union\u001b[1m[\u001b[0mTuple\u001b[1m[\u001b[0mfloat\u001b[1m]\u001b[0m, NDArray\u001b[1m[\u001b[0mnp.float64\u001b[1m]\u001b[0m\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\u001b[1;36m0.4\u001b[0m, \u001b[1;36m0.6\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    seed: Union\u001b[1m[\u001b[0mint, \u001b[3;35mNone\u001b[0m\u001b[1m]\u001b[0m = \u001b[1;36m112233\u001b[0m,\n",
       "\u001b[1m)\u001b[0m -> NDArray:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generate clusters of \u001b[1m(\u001b[0mI + \u001b[1;36m1j\u001b[0m*Q\u001b[1m)\u001b[0m points with a Gaussian distribution.\n",
       "\n",
       "    Utility to mock the data coming from qubit readout experiments.\n",
       "    Clusters are centered around ``centers`` and data points are distributed between\n",
       "    them according to ``probabilities``.\n",
       "\n",
       "    .. seealso:: :ref:`howto-utilities-examples-ssro`\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_shots\n",
       "        The number of shot to generate.\n",
       "    sigma\n",
       "        The sigma of the Gaussian distribution used for both real and imaginary parts.\n",
       "    centers\n",
       "        The center of each cluster on the imaginary plane.\n",
       "    probabilities\n",
       "        The probabilities of each cluster being randomly selected for each shot.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    if not \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0msigmas\u001b[1m)\u001b[0m == \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mcenters\u001b[1m)\u001b[0m == \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mprobabilities\u001b[1m)\u001b[0m:\n",
       "        raise \u001b[1;35mValueError\u001b[0m\u001b[1m(\u001b[0m\n",
       "            f\"Incorrect input. \u001b[33msigmas\u001b[0m=\u001b[1m{\u001b[0msigmas\u001b[1m}\u001b[0m, \u001b[33mcenters\u001b[0m=\u001b[1m{\u001b[0mcenters\u001b[1m}\u001b[0m and \"\n",
       "            f\"\u001b[33mprobabilities\u001b[0m=\u001b[1m{\u001b[0mprobabilities\u001b[1m}\u001b[0m must have the same length.\"\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    rng = \u001b[1;35mnp.random.default_rng\u001b[0m\u001b[1m(\u001b[0m\u001b[33mseed\u001b[0m=\u001b[35mseed\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    cluster_indices = \u001b[1;35mtuple\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mcenters\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m\n",
       "    choices = \u001b[1;35mrng.choice\u001b[0m\u001b[1m(\u001b[0m\u001b[33ma\u001b[0m=\u001b[35mcluster_indices\u001b[0m, \u001b[33msize\u001b[0m=\u001b[35mnum_shots\u001b[0m, \u001b[33mp\u001b[0m=\u001b[35mprobabilities\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    shots = \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
       "    for idx in cluster_indices:\n",
       "        num_shots_this_cluster = \u001b[1;35mnp.sum\u001b[0m\u001b[1m(\u001b[0mchoices == idx\u001b[1m)\u001b[0m\n",
       "        i_data = \u001b[1;35mrng.normal\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[33mloc\u001b[0m=\u001b[35mcenters\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m.real,\n",
       "            \u001b[33mscale\u001b[0m=\u001b[35msigmas\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m,\n",
       "            \u001b[33msize\u001b[0m=\u001b[35mnum_shots_this_cluster\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "        q_data = \u001b[1;35mrng.normal\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[33mloc\u001b[0m=\u001b[35mcenters\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m.imag,\n",
       "            \u001b[33mscale\u001b[0m=\u001b[35msigmas\u001b[0m\u001b[1m[\u001b[0midx\u001b[1m]\u001b[0m,\n",
       "            \u001b[33msize\u001b[0m=\u001b[35mnum_shots_this_cluster\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "        \u001b[1;35mshots.append\u001b[0m\u001b[1m(\u001b[0mi_data + \u001b[1;36m1j\u001b[0m * q_data\u001b[1m)\u001b[0m\n",
       "    return \u001b[1;35mnp.concatenate\u001b[0m\u001b[1m(\u001b[0mshots\u001b[1m)\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
def mk_shots_from_probabilities(probabilities: Union[np.ndarray, list], **kwargs):\n",
       "    """Generates multiple shots for a list of probabilities assuming two states.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    probabilities\n",
       "        The list/array of the probabilities of one of the states.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to\n",
       "        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    :\n",
       "        Array containing the shots. Shape: (num_shots, len(probabilities)).\n",
       "    """\n",
       "\n",
       "    shots = np.array(\n",
       "        tuple(\n",
       "            mk_iq_shots(probabilities=[prob, 1 - prob], **kwargs)\n",
       "            for prob in probabilities\n",
       "        )\n",
       "    ).T\n",
       "\n",
       "    return shots\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\\PY{n}{probabilities}\\PY{p}{:} \\PY{n}{Union}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{ndarray}\\PY{p}{,} \\PY{n+nb}{list}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Generates multiple shots for a list of probabilities assuming two states.}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    probabilities}\n",
       "\\PY{l+s+sd}{        The list/array of the probabilities of one of the states.}\n",
       "\\PY{l+s+sd}{    **kwargs}\n",
       "\\PY{l+s+sd}{        Keyword arguments passed to}\n",
       "\\PY{l+s+sd}{        :func:`\\PYZti{}quantify\\PYZus{}core.utilities.examples\\PYZus{}support.mk\\PYZus{}iq\\PYZus{}shots`.}\n",
       "\n",
       "\\PY{l+s+sd}{    Returns}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    :}\n",
       "\\PY{l+s+sd}{        Array containing the shots. Shape: (num\\PYZus{}shots, len(probabilities)).}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "    \\PY{n}{shots} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{array}\\PY{p}{(}\n",
       "        \\PY{n+nb}{tuple}\\PY{p}{(}\n",
       "            \\PY{n}{mk\\PYZus{}iq\\PYZus{}shots}\\PY{p}{(}\\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{prob}\\PY{p}{,} \\PY{l+m+mi}{1} \\PY{o}{\\PYZhy{}} \\PY{n}{prob}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "            \\PY{k}{for} \\PY{n}{prob} \\PY{o+ow}{in} \\PY{n}{probabilities}\n",
       "        \\PY{p}{)}\n",
       "    \\PY{p}{)}\\PY{o}{.}\\PY{n}{T}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{shots}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0mprobabilities: Union\u001b[1m[\u001b[0mnp.ndarray, list\u001b[1m]\u001b[0m, **kwargs\u001b[1m)\u001b[0m:\n",
       "    \u001b[32m\"\"\u001b[0m\"Generates multiple shots for a list of probabilities assuming two states.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    probabilities\n",
       "        The list/array of the probabilities of one of the states.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to\n",
       "        :func:`~quantify_core.utilities.examples_support.mk_iq_shots`.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    :\n",
       "        Array containing the shots. Shape: \u001b[1m(\u001b[0mnum_shots, \u001b[1;35mlen\u001b[0m\u001b[1m(\u001b[0mprobabilities\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "\n",
       "    shots = \u001b[1;35mnp.array\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[1;35mtuple\u001b[0m\u001b[1m(\u001b[0m\n",
       "            \u001b[1;35mmk_iq_shots\u001b[0m\u001b[1m(\u001b[0m\u001b[33mprobabilities\u001b[0m=\u001b[1m[\u001b[0mprob, \u001b[1;36m1\u001b[0m - prob\u001b[1m]\u001b[0m, **kwargs\u001b[1m)\u001b[0m\n",
       "            for prob in probabilities\n",
       "        \u001b[1m)\u001b[0m\n",
       "    \u001b[1m)\u001b[0m.T\n",
       "\n",
       "    return shots"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
def mk_surface7_cyles_dataset(num_cycles: int = 3, **kwargs) -> xr.Dataset:\n",
       "    """\n",
       "    See also :func:`mk_surface7_sched` inlined in the documentation as an example in:\n",
       "        :ref:`sec-dataset-advanced-examples`\n",
       "\n",
       "\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_cycles\n",
       "        The number of repeating cycles before the final measurement.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to :func:`~.mk_shots_from_probabilities`.\n",
       "    """\n",
       "\n",
       "    cycles = range(num_cycles)\n",
       "\n",
       "    mock_data = mk_shots_from_probabilities(\n",
       "        probabilities=[np.random.random() for _ in cycles], **kwargs\n",
       "    )\n",
       "\n",
       "    mock_data_final = mk_shots_from_probabilities(\n",
       "        probabilities=[np.random.random()], **kwargs\n",
       "    )\n",
       "\n",
       "    # %%\n",
       "    data_vars = {}\n",
       "\n",
       "    # NB same random data is used for all qubits only for the simplicity of the mock!\n",
       "    for qubit in (f"A{i}" for i in range(3)):\n",
       "        data_vars[f"{qubit}_shots"] = (\n",
       "            ("repetitions", "dim_cycle"),\n",
       "            mock_data,\n",
       "            mk_main_var_attrs(\n",
       "                unit="V", long_name=f"IQ amplitude {qubit}", has_repetitions=True\n",
       "            ),\n",
       "        )\n",
       "\n",
       "    for qubit in (f"D{i}" for i in range(4)):\n",
       "        data_vars[f"{qubit}_shots"] = (\n",
       "            ("repetitions", "dim_final"),\n",
       "            mock_data_final,\n",
       "            mk_main_var_attrs(\n",
       "                unit="V", long_name=f"IQ amplitude {qubit}", has_repetitions=True\n",
       "            ),\n",
       "        )\n",
       "\n",
       "    cycle_attrs = mk_main_coord_attrs(long_name="Surface code cycle number")\n",
       "    final_msmt_attrs = mk_main_coord_attrs(long_name="Final measurement")\n",
       "    coords = dict(\n",
       "        cycle=("dim_cycle", cycles, cycle_attrs),\n",
       "        final_msmt=("dim_final", [0], final_msmt_attrs),\n",
       "    )\n",
       "\n",
       "    dataset = xr.Dataset(\n",
       "        data_vars=data_vars,\n",
       "        coords=coords,\n",
       "        attrs=mk_dataset_attrs(),\n",
       "    )\n",
       "\n",
       "    return dataset\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}surface7\\PYZus{}cyles\\PYZus{}dataset}\\PY{p}{(}\\PY{n}{num\\PYZus{}cycles}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{3}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    See also :func:`mk\\PYZus{}surface7\\PYZus{}sched` inlined in the documentation as an example in:}\n",
       "\\PY{l+s+sd}{        :ref:`sec\\PYZhy{}dataset\\PYZhy{}advanced\\PYZhy{}examples`}\n",
       "\n",
       "\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    num\\PYZus{}cycles}\n",
       "\\PY{l+s+sd}{        The number of repeating cycles before the final measurement.}\n",
       "\\PY{l+s+sd}{    **kwargs}\n",
       "\\PY{l+s+sd}{        Keyword arguments passed to :func:`\\PYZti{}.mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities`.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "    \\PY{n}{cycles} \\PY{o}{=} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}cycles}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{mock\\PYZus{}data} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\n",
       "        \\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{random}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n}{cycles}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{mock\\PYZus{}data\\PYZus{}final} \\PY{o}{=} \\PY{n}{mk\\PYZus{}shots\\PYZus{}from\\PYZus{}probabilities}\\PY{p}{(}\n",
       "        \\PY{n}{probabilities}\\PY{o}{=}\\PY{p}{[}\\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{random}\\PY{p}{(}\\PY{p}{)}\\PY{p}{]}\\PY{p}{,} \\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} \\PYZpc{}\\PYZpc{}}\n",
       "    \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{p}{\\PYZob{}}\\PY{p}{\\PYZcb{}}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} NB same random data is used for all qubits only for the simplicity of the mock!}\n",
       "    \\PY{k}{for} \\PY{n}{qubit} \\PY{o+ow}{in} \\PY{p}{(}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{A}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{i}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}} \\PY{k}{for} \\PY{n}{i} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{l+m+mi}{3}\\PY{p}{)}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{n}{data\\PYZus{}vars}\\PY{p}{[}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\n",
       "            \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}cycle}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "            \\PY{n}{mock\\PYZus{}data}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{IQ amplitude }\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{has\\PYZus{}repetitions}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{for} \\PY{n}{qubit} \\PY{o+ow}{in} \\PY{p}{(}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{D}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{i}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}} \\PY{k}{for} \\PY{n}{i} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{l+m+mi}{4}\\PY{p}{)}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{n}{data\\PYZus{}vars}\\PY{p}{[}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZus{}shots}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{]} \\PY{o}{=} \\PY{p}{(}\n",
       "            \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{repetitions}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}final}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "            \\PY{n}{mock\\PYZus{}data\\PYZus{}final}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{IQ amplitude }\\PY{l+s+si}{\\PYZob{}}\\PY{n}{qubit}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{has\\PYZus{}repetitions}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{cycle\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Surface code cycle number}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{final\\PYZus{}msmt\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Final measurement}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{cycle}\\PY{o}{=}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}cycle}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{cycles}\\PY{p}{,} \\PY{n}{cycle\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{final\\PYZus{}msmt}\\PY{o}{=}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{dim\\PYZus{}final}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{p}{[}\\PY{l+m+mi}{0}\\PY{p}{]}\\PY{p}{,} \\PY{n}{final\\PYZus{}msmt\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{(}\n",
       "        \\PY{n}{data\\PYZus{}vars}\\PY{o}{=}\\PY{n}{data\\PYZus{}vars}\\PY{p}{,}\n",
       "        \\PY{n}{coords}\\PY{o}{=}\\PY{n}{coords}\\PY{p}{,}\n",
       "        \\PY{n}{attrs}\\PY{o}{=}\\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{dataset}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_surface7_cyles_dataset\u001b[0m\u001b[1m(\u001b[0mnum_cycles: int = \u001b[1;36m3\u001b[0m, **kwargs\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    See also :func:`mk_surface7_sched` inlined in the documentation as an example in:\n",
       "        :ref:`sec-dataset-advanced-examples`\n",
       "\n",
       "\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_cycles\n",
       "        The number of repeating cycles before the final measurement.\n",
       "    **kwargs\n",
       "        Keyword arguments passed to :func:`~.mk_shots_from_probabilities`.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "\n",
       "    cycles = \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_cycles\u001b[1m)\u001b[0m\n",
       "\n",
       "    mock_data = \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mprobabilities\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mnp.random.random\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in cycles\u001b[1m]\u001b[0m, **kwargs\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    mock_data_final = \u001b[1;35mmk_shots_from_probabilities\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mprobabilities\u001b[0m=\u001b[1m[\u001b[0m\u001b[1;35mnp.random.random\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m, **kwargs\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    # %%\n",
       "    data_vars = \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
       "\n",
       "    # NB same random data is used for all qubits only for the simplicity of the mock!\n",
       "    for qubit in \u001b[1m(\u001b[0mf\"A\u001b[1m{\u001b[0mi\u001b[1m}\u001b[0m\" for i in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m:\n",
       "        data_vars\u001b[1m[\u001b[0mf\"\u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m_shots\"\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\n",
       "            \u001b[1m(\u001b[0m\u001b[32m\"repetitions\"\u001b[0m, \u001b[32m\"dim_cycle\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "            mock_data,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33munit\u001b[0m=\u001b[32m\"V\"\u001b[0m, \u001b[33mlong_name\u001b[0m=\u001b[35mf\"\u001b[0mIQ amplitude \u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m\", \u001b[33mhas_repetitions\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    for qubit in \u001b[1m(\u001b[0mf\"D\u001b[1m{\u001b[0mi\u001b[1m}\u001b[0m\" for i in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m:\n",
       "        data_vars\u001b[1m[\u001b[0mf\"\u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m_shots\"\u001b[1m]\u001b[0m = \u001b[1m(\u001b[0m\n",
       "            \u001b[1m(\u001b[0m\u001b[32m\"repetitions\"\u001b[0m, \u001b[32m\"dim_final\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "            mock_data_final,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33munit\u001b[0m=\u001b[32m\"V\"\u001b[0m, \u001b[33mlong_name\u001b[0m=\u001b[35mf\"\u001b[0mIQ amplitude \u001b[1m{\u001b[0mqubit\u001b[1m}\u001b[0m\", \u001b[33mhas_repetitions\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "    cycle_attrs = \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Surface\u001b[0m\u001b[32m code cycle number\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    final_msmt_attrs = \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Final\u001b[0m\u001b[32m measurement\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    coords = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mcycle\u001b[0m=\u001b[1m(\u001b[0m\u001b[32m\"dim_cycle\"\u001b[0m, cycles, cycle_attrs\u001b[1m)\u001b[0m,\n",
       "        \u001b[33mfinal_msmt\u001b[0m=\u001b[1m(\u001b[0m\u001b[32m\"dim_final\"\u001b[0m, \u001b[1m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1m]\u001b[0m, final_msmt_attrs\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    dataset = \u001b[1;35mxr.Dataset\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mdata_vars\u001b[0m=\u001b[35mdata_vars\u001b[0m,\n",
       "        \u001b[33mcoords\u001b[0m=\u001b[35mcoords\u001b[0m,\n",
       "        \u001b[33mattrs\u001b[0m=\u001b[1;35mmk_dataset_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    return dataset"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mock data parameters\n",
    "num_shots = 128  # NB usually >~1000 in real experiments\n",
    "ground = -0.2 + 0.65j\n",
    "excited = 0.7 + 4j\n",
    "centroids = ground, excited\n",
    "sigmas = [0.1] * 2\n",
    "\n",
    "display_source_code(mk_iq_shots)\n",
    "display_source_code(mk_shots_from_probabilities)\n",
    "display_source_code(mk_surface7_cyles_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12829358",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
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       "\n",
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       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "
<xarray.Dataset> Size: 27kB\n",
       "Dimensions:     (repetitions: 128, dim_cycle: 3, dim_final: 1)\n",
       "Coordinates:\n",
       "    cycle       (dim_cycle) int64 24B 0 1 2\n",
       "    final_msmt  (dim_final) int64 8B 0\n",
       "Dimensions without coordinates: repetitions, dim_cycle, dim_final\n",
       "Data variables:\n",
       "    A0_shots    (repetitions, dim_cycle) complex128 6kB (-0.23630343679164473...\n",
       "    A1_shots    (repetitions, dim_cycle) complex128 6kB (-0.23630343679164473...\n",
       "    A2_shots    (repetitions, dim_cycle) complex128 6kB (-0.23630343679164473...\n",
       "    D0_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "    D1_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "    D2_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "    D3_shots    (repetitions, dim_final) complex128 2kB (-0.23630343679164473...\n",
       "Attributes:\n",
       "    tuid:                      20241014-175517-668-824d0c\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;36m20241014\u001b[0m-\u001b[1;36m175517\u001b[0m-\u001b[1;36m668\u001b[0m-824d0c\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+0.7...\n",
       "    A1_shots    (repetitions, cycle) complex128 6kB (-0.23630343679164473+0.7...\n",
       "    A2_shots    (repetitions, cycle) complex128 6kB (-0.23630343679164473+0.7...\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:                      20241014-175517-668-824d0c\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;36m0.7\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;36m0.7\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;36m0.7\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;36m20241014\u001b[0m-\u001b[1;36m175517\u001b[0m-\u001b[1;36m668\u001b[0m-824d0c\n",
       "    dataset_name:              \n",
       "    dataset_state:             \u001b[3;35mNone\u001b[0m\n",
       "    timestamp_start:           \u001b[3;35mNone\u001b[0m\n",
       "    timestamp_end:             \u001b[3;35mNone\u001b[0m\n",
       "    quantify_dataset_version:  \u001b[1;36m2.0\u001b[0m.\u001b[1;36m0\u001b[0m\n",
       "    software_versions:         \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m\n",
       "    relationships:             \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
       "    json_serialize_exclude:    \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset, dimension=\"dim_cycle\", coords_names=[\"cycle\"]\n",
    ")\n",
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset_gridded, dimension=\"dim_final\", coords_names=[\"final_msmt\"]\n",
    ")\n",
    "dataset_gridded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a216dc93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
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      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 64\u001b[0m\u001b[1;36m0x480\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_gridded.A0_shots.real.mean(\"repetitions\").plot(marker=\"o\", label=\"I-quadrature\")\n",
    "dataset_gridded.A0_shots.imag.mean(\"repetitions\").plot(marker=\"^\", label=\"Q-quadrature\")\n",
    "_ = plt.gca().legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41b4f6af",
   "metadata": {},
   "source": [
    "(sec-nested-mc-example)=\n",
    "## Dataset for a \"nested MeasurementControl\" experiment\n",
    "\n",
    "Now consider a dataset that has been constructed by an experiment involving the\n",
    "operation of two\n",
    "{class}`.MeasurementControl` objects. The second of\n",
    "them performs a \"meta\" outer loop in which we sweep a flux bias and then perform\n",
    "several experiments to characterize a transmon qubit, e.g. determining the frequency of\n",
    "a read-out resonator, the frequency of the transmon, and its T1 lifetime.\n",
    "\n",
    "Below we showcase what the data from the dataset containing the T1 experiment results\n",
    "could look like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d839806c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 64\u001b[0m\u001b[1;36m0x480\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m1\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "rng = np.random.default_rng(seed=112244)  # random number generator\n",
    "\n",
    "num_t1_datasets = 7\n",
    "t1_times = np.linspace(0, 120e-6, 30)\n",
    "\n",
    "for tau in rng.uniform(10e-6, 50e-6, num_t1_datasets):\n",
    "    probabilities = exp_decay_func(\n",
    "        t=t1_times, tau=tau, offset=0, n_factor=1, amplitude=1\n",
    "    )\n",
    "    dataset = dataset_examples.mk_t1_av_with_cal_dataset(t1_times, probabilities)\n",
    "\n",
    "    round_trip_dataset(dataset)  # confirm read/write\n",
    "    dataset_g = dh.to_gridded_dataset(\n",
    "        dataset, dimension=\"main_dim\", coords_names=[\"t1_time\"]\n",
    "    )\n",
    "    # rotate the iq data\n",
    "    rotated_and_normalized = rotate_to_calibrated_axis(\n",
    "        dataset_g.q0_iq_av.values, *dataset_g.q0_iq_av_cal.values\n",
    "    )\n",
    "    rotated_and_normalized_da = xr.DataArray(dataset_g.q0_iq_av)\n",
    "    rotated_and_normalized_da.values = rotated_and_normalized\n",
    "    rotated_and_normalized_da.attrs[\"long_name\"] = \"|1> Population\"\n",
    "    rotated_and_normalized_da.attrs[\"units\"] = \"\"\n",
    "    rotated_and_normalized_da.real.plot(ax=ax, label=dataset.tuid, marker=\".\")\n",
    "ax.set_title(\"Results from repeated T1 experiments\\n(different datasets)\")\n",
    "_ = ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae491ce5",
   "metadata": {},
   "source": [
    "Since the raw data is now split among several datasets, we would like to keep a\n",
    "reference to all these datasets in our \"combined\" datasets. Below we showcase how this\n",
    "can be achieved, along with some useful xarray features and known limitations.\n",
    "\n",
    "We start by generating a mock dataset that combines all the information that would have\n",
    "been obtained from analyzing a series of other datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f7149f12",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Source code for mk_nested_mc_dataset function"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "
def mk_nested_mc_dataset(\n",
       "    num_points: int = 12,\n",
       "    flux_bias_min_max: tuple = (-0.04, 0.04),\n",
       "    resonator_freqs_min_max: tuple = (7e9, 7.3e9),\n",
       "    qubit_freqs_min_max: tuple = (4.5e9, 5.0e9),\n",
       "    t1_values_min_max: tuple = (20e-6, 50e-6),\n",
       "    seed: Optional[int] = 112233,\n",
       ") -> xr.Dataset:\n",
       "    """\n",
       "    Generates a dataset with dataset references and several coordinates that serve to\n",
       "    index the same variables.\n",
       "\n",
       "    Note that the each value for ``resonator_freqs``, ``qubit_freqs`` and ``t1_values``\n",
       "    would have been extracted from other dataset corresponding to individual experiments\n",
       "    with their own dataset.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_points\n",
       "        Number of datapoints to generate (used for all variables/coordinates).\n",
       "    flux_bias_min_max\n",
       "        Range for mock values.\n",
       "    resonator_freqs_min_max\n",
       "        Range for mock values.\n",
       "    qubit_freqs_min_max\n",
       "        Range for mock values.\n",
       "    t1_values_min_max\n",
       "        Range for mock random values.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    """\n",
       "    rng = np.random.default_rng(seed=seed)  # random number generator\n",
       "\n",
       "    flux_bias_vals = np.linspace(*flux_bias_min_max, num_points)\n",
       "    resonator_freqs = np.linspace(*resonator_freqs_min_max, num_points)\n",
       "    qubit_freqs = np.linspace(*qubit_freqs_min_max, num_points)\n",
       "    t1_values = rng.uniform(*t1_values_min_max, num_points)\n",
       "\n",
       "    resonator_freq_tuids = [dh.gen_tuid() for _ in range(num_points)]\n",
       "    qubit_freq_tuids = [dh.gen_tuid() for _ in range(num_points)]\n",
       "    t1_tuids = [dh.gen_tuid() for _ in range(num_points)]\n",
       "\n",
       "    coords = dict(\n",
       "        flux_bias=(\n",
       "            "main_dim",\n",
       "            flux_bias_vals,\n",
       "            mk_main_coord_attrs(long_name="Flux bias", unit="A"),\n",
       "        ),\n",
       "        resonator_freq_tuids=(\n",
       "            "main_dim",\n",
       "            resonator_freq_tuids,\n",
       "            mk_main_coord_attrs(\n",
       "                long_name="Dataset TUID resonator frequency", is_dataset_ref=True\n",
       "            ),\n",
       "        ),\n",
       "        qubit_freq_tuids=(\n",
       "            "main_dim",\n",
       "            qubit_freq_tuids,\n",
       "            mk_main_coord_attrs(\n",
       "                long_name="Dataset TUID qubit frequency", is_dataset_ref=True\n",
       "            ),\n",
       "        ),\n",
       "        t1_tuids=(\n",
       "            "main_dim",\n",
       "            t1_tuids,\n",
       "            mk_main_coord_attrs(long_name="Dataset TUID T1", is_dataset_ref=True),\n",
       "        ),\n",
       "    )\n",
       "\n",
       "    data_vars = dict(\n",
       "        resonator_freq=(\n",
       "            "main_dim",\n",
       "            resonator_freqs,\n",
       "            mk_main_var_attrs(long_name="Resonator frequency", unit="Hz"),\n",
       "        ),\n",
       "        qubit_freq=(\n",
       "            "main_dim",\n",
       "            qubit_freqs,\n",
       "            mk_main_var_attrs(long_name="Qubit frequency", unit="Hz"),\n",
       "        ),\n",
       "        t1=(\n",
       "            "main_dim",\n",
       "            t1_values,\n",
       "            mk_main_var_attrs(long_name="T1", unit="s"),\n",
       "        ),\n",
       "    )\n",
       "    dataset_attrs = mk_dataset_attrs()\n",
       "\n",
       "    dataset = xr.Dataset(data_vars=data_vars, coords=coords, attrs=dataset_attrs)\n",
       "\n",
       "    return dataset\n",
       "
\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}nested\\PYZus{}mc\\PYZus{}dataset}\\PY{p}{(}\n",
       "    \\PY{n}{num\\PYZus{}points}\\PY{p}{:} \\PY{n+nb}{int} \\PY{o}{=} \\PY{l+m+mi}{12}\\PY{p}{,}\n",
       "    \\PY{n}{flux\\PYZus{}bias\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{o}{\\PYZhy{}}\\PY{l+m+mf}{0.04}\\PY{p}{,} \\PY{l+m+mf}{0.04}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{resonator\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{7e9}\\PY{p}{,} \\PY{l+m+mf}{7.3e9}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{qubit\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{4.5e9}\\PY{p}{,} \\PY{l+m+mf}{5.0e9}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{t1\\PYZus{}values\\PYZus{}min\\PYZus{}max}\\PY{p}{:} \\PY{n+nb}{tuple} \\PY{o}{=} \\PY{p}{(}\\PY{l+m+mf}{20e\\PYZhy{}6}\\PY{p}{,} \\PY{l+m+mf}{50e\\PYZhy{}6}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{n}{seed}\\PY{p}{:} \\PY{n}{Optional}\\PY{p}{[}\\PY{n+nb}{int}\\PY{p}{]} \\PY{o}{=} \\PY{l+m+mi}{112233}\\PY{p}{,}\n",
       "\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Generates a dataset with dataset references and several coordinates that serve to}\n",
       "\\PY{l+s+sd}{    index the same variables.}\n",
       "\n",
       "\\PY{l+s+sd}{    Note that the each value for ``resonator\\PYZus{}freqs``, ``qubit\\PYZus{}freqs`` and ``t1\\PYZus{}values``}\n",
       "\\PY{l+s+sd}{    would have been extracted from other dataset corresponding to individual experiments}\n",
       "\\PY{l+s+sd}{    with their own dataset.}\n",
       "\n",
       "\\PY{l+s+sd}{    Parameters}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    num\\PYZus{}points}\n",
       "\\PY{l+s+sd}{        Number of datapoints to generate (used for all variables/coordinates).}\n",
       "\\PY{l+s+sd}{    flux\\PYZus{}bias\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock values.}\n",
       "\\PY{l+s+sd}{    resonator\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock values.}\n",
       "\\PY{l+s+sd}{    qubit\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock values.}\n",
       "\\PY{l+s+sd}{    t1\\PYZus{}values\\PYZus{}min\\PYZus{}max}\n",
       "\\PY{l+s+sd}{        Range for mock random values.}\n",
       "\\PY{l+s+sd}{    seed}\n",
       "\\PY{l+s+sd}{        Random number generator seed passed to ``numpy.random.default\\PYZus{}rng``.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{n}{rng} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{default\\PYZus{}rng}\\PY{p}{(}\\PY{n}{seed}\\PY{o}{=}\\PY{n}{seed}\\PY{p}{)}  \\PY{c+c1}{\\PYZsh{} random number generator}\n",
       "\n",
       "    \\PY{n}{flux\\PYZus{}bias\\PYZus{}vals} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{o}{*}\\PY{n}{flux\\PYZus{}bias\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "    \\PY{n}{resonator\\PYZus{}freqs} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{o}{*}\\PY{n}{resonator\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "    \\PY{n}{qubit\\PYZus{}freqs} \\PY{o}{=} \\PY{n}{np}\\PY{o}{.}\\PY{n}{linspace}\\PY{p}{(}\\PY{o}{*}\\PY{n}{qubit\\PYZus{}freqs\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "    \\PY{n}{t1\\PYZus{}values} \\PY{o}{=} \\PY{n}{rng}\\PY{o}{.}\\PY{n}{uniform}\\PY{p}{(}\\PY{o}{*}\\PY{n}{t1\\PYZus{}values\\PYZus{}min\\PYZus{}max}\\PY{p}{,} \\PY{n}{num\\PYZus{}points}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{resonator\\PYZus{}freq\\PYZus{}tuids} \\PY{o}{=} \\PY{p}{[}\\PY{n}{dh}\\PY{o}{.}\\PY{n}{gen\\PYZus{}tuid}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}points}\\PY{p}{)}\\PY{p}{]}\n",
       "    \\PY{n}{qubit\\PYZus{}freq\\PYZus{}tuids} \\PY{o}{=} \\PY{p}{[}\\PY{n}{dh}\\PY{o}{.}\\PY{n}{gen\\PYZus{}tuid}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}points}\\PY{p}{)}\\PY{p}{]}\n",
       "    \\PY{n}{t1\\PYZus{}tuids} \\PY{o}{=} \\PY{p}{[}\\PY{n}{dh}\\PY{o}{.}\\PY{n}{gen\\PYZus{}tuid}\\PY{p}{(}\\PY{p}{)} \\PY{k}{for} \\PY{n}{\\PYZus{}} \\PY{o+ow}{in} \\PY{n+nb}{range}\\PY{p}{(}\\PY{n}{num\\PYZus{}points}\\PY{p}{)}\\PY{p}{]}\n",
       "\n",
       "    \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{flux\\PYZus{}bias}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{flux\\PYZus{}bias\\PYZus{}vals}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Flux bias}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{A}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{resonator\\PYZus{}freq\\PYZus{}tuids}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{resonator\\PYZus{}freq\\PYZus{}tuids}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Dataset TUID resonator frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{is\\PYZus{}dataset\\PYZus{}ref}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{qubit\\PYZus{}freq\\PYZus{}tuids}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{qubit\\PYZus{}freq\\PYZus{}tuids}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\n",
       "                \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Dataset TUID qubit frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{is\\PYZus{}dataset\\PYZus{}ref}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{t1\\PYZus{}tuids}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{t1\\PYZus{}tuids}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}coord\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Dataset TUID T1}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{is\\PYZus{}dataset\\PYZus{}ref}\\PY{o}{=}\\PY{k+kc}{True}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{resonator\\PYZus{}freq}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{resonator\\PYZus{}freqs}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Resonator frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Hz}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{qubit\\PYZus{}freq}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{qubit\\PYZus{}freqs}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Qubit frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Hz}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{t1}\\PY{o}{=}\\PY{p}{(}\n",
       "            \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "            \\PY{n}{t1\\PYZus{}values}\\PY{p}{,}\n",
       "            \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{T1}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{s}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "    \\PY{n}{dataset\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{(}\\PY{n}{data\\PYZus{}vars}\\PY{o}{=}\\PY{n}{data\\PYZus{}vars}\\PY{p}{,} \\PY{n}{coords}\\PY{o}{=}\\PY{n}{coords}\\PY{p}{,} \\PY{n}{attrs}\\PY{o}{=}\\PY{n}{dataset\\PYZus{}attrs}\\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{dataset}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mmk_nested_mc_dataset\u001b[0m\u001b[1m(\u001b[0m\n",
       "    num_points: int = \u001b[1;36m12\u001b[0m,\n",
       "    flux_bias_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m-0.04\u001b[0m, \u001b[1;36m0.04\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    resonator_freqs_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m7e9\u001b[0m, \u001b[1;36m7.3e9\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    qubit_freqs_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m4.5e9\u001b[0m, \u001b[1;36m5.0e9\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    t1_values_min_max: tuple = \u001b[1m(\u001b[0m\u001b[1;36m20e-6\u001b[0m, \u001b[1;36m50e-6\u001b[0m\u001b[1m)\u001b[0m,\n",
       "    seed: Optional\u001b[1m[\u001b[0mint\u001b[1m]\u001b[0m = \u001b[1;36m112233\u001b[0m,\n",
       "\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generates a dataset with dataset references and several coordinates that serve to\n",
       "    index the same variables.\n",
       "\n",
       "    Note that the each value for ``resonator_freqs``, ``qubit_freqs`` and ``t1_values``\n",
       "    would have been extracted from other dataset corresponding to individual experiments\n",
       "    with their own dataset.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    num_points\n",
       "        Number of datapoints to generate \u001b[1m(\u001b[0mused for all variables/coordinates\u001b[1m)\u001b[0m.\n",
       "    flux_bias_min_max\n",
       "        Range for mock values.\n",
       "    resonator_freqs_min_max\n",
       "        Range for mock values.\n",
       "    qubit_freqs_min_max\n",
       "        Range for mock values.\n",
       "    t1_values_min_max\n",
       "        Range for mock random values.\n",
       "    seed\n",
       "        Random number generator seed passed to ``numpy.random.default_rng``.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    rng = \u001b[1;35mnp.random.default_rng\u001b[0m\u001b[1m(\u001b[0m\u001b[33mseed\u001b[0m=\u001b[35mseed\u001b[0m\u001b[1m)\u001b[0m  # random number generator\n",
       "\n",
       "    flux_bias_vals = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m*flux_bias_min_max, num_points\u001b[1m)\u001b[0m\n",
       "    resonator_freqs = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m*resonator_freqs_min_max, num_points\u001b[1m)\u001b[0m\n",
       "    qubit_freqs = \u001b[1;35mnp.linspace\u001b[0m\u001b[1m(\u001b[0m*qubit_freqs_min_max, num_points\u001b[1m)\u001b[0m\n",
       "    t1_values = \u001b[1;35mrng.uniform\u001b[0m\u001b[1m(\u001b[0m*t1_values_min_max, num_points\u001b[1m)\u001b[0m\n",
       "\n",
       "    resonator_freq_tuids = \u001b[1m[\u001b[0m\u001b[1;35mdh.gen_tuid\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_points\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n",
       "    qubit_freq_tuids = \u001b[1m[\u001b[0m\u001b[1;35mdh.gen_tuid\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_points\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n",
       "    t1_tuids = \u001b[1m[\u001b[0m\u001b[1;35mdh.gen_tuid\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m for _ in \u001b[1;35mrange\u001b[0m\u001b[1m(\u001b[0mnum_points\u001b[1m)\u001b[0m\u001b[1m]\u001b[0m\n",
       "\n",
       "    coords = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mflux_bias\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            flux_bias_vals,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Flux\u001b[0m\u001b[32m bias\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"A\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mresonator_freq_tuids\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            resonator_freq_tuids,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33mlong_name\u001b[0m=\u001b[32m\"Dataset\u001b[0m\u001b[32m TUID resonator frequency\"\u001b[0m, \u001b[33mis_dataset_ref\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mqubit_freq_tuids\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            qubit_freq_tuids,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "                \u001b[33mlong_name\u001b[0m=\u001b[32m\"Dataset\u001b[0m\u001b[32m TUID qubit frequency\"\u001b[0m, \u001b[33mis_dataset_ref\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mt1_tuids\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            t1_tuids,\n",
       "            \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Dataset\u001b[0m\u001b[32m TUID T1\"\u001b[0m, \u001b[33mis_dataset_ref\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    data_vars = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mresonator_freq\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            resonator_freqs,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Resonator\u001b[0m\u001b[32m frequency\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"Hz\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mqubit_freq\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            qubit_freqs,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Qubit\u001b[0m\u001b[32m frequency\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"Hz\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "        \u001b[33mt1\u001b[0m=\u001b[1m(\u001b[0m\n",
       "            \u001b[32m\"main_dim\"\u001b[0m,\n",
       "            t1_values,\n",
       "            \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"T1\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"s\"\u001b[0m\u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "    dataset_attrs = \u001b[1;35mmk_dataset_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    dataset = \u001b[1;35mxr.Dataset\u001b[0m\u001b[1m(\u001b[0m\u001b[33mdata_vars\u001b[0m=\u001b[35mdata_vars\u001b[0m, \u001b[33mcoords\u001b[0m=\u001b[35mcoords\u001b[0m, \u001b[33mattrs\u001b[0m=\u001b[35mdataset_attrs\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "    return dataset"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_source_code(mk_nested_mc_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "09512820",
   "metadata": {},
   "outputs": [
    {
     "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 '20241014-175518-726-e65fee' ....\n",
       "    qubit_freq_tuids      (main_dim) <U26 728B '20241014-175518-727-6a85ba' ....\n",
       "    t1_tuids              (main_dim) <U26 728B '20241014-175518-727-cec4c2' ....\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:                      20241014-175518-728-7f46e3\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 '20241014-175518-726-e65fee' ....\n",
       "  * qubit_freq_tuids      (main_dim) <U26 728B '20241014-175518-727-6a85ba' ....\n",
       "  * t1_tuids              (main_dim) <U26 728B '20241014-175518-727-cec4c2' ....\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:                      20241014-175518-728-7f46e3\n",
       "    dataset_name:              \n",
       "    dataset_state:             None\n",
       "    timestamp_start:           None\n",
       "    timestamp_end:             None\n",
       "    quantify_dataset_version:  2.0.0\n",
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       "    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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       "    unit:                    Hz\n",
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       "Coordinates:\n",
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       "  * 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 '20241014-175518-726-c80233'\n",
       "  * qubit_freq_tuids      (main_dim) <U26 104B '20241014-175518-727-2edb19'\n",
       "    t1_tuids              <U26 104B '20241014-175518-727-296f55'\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 '20241014-175518-726-e65fee' ....\n",
       "    qubit_freq_tuids      (main_dim) <U26 728B '20241014-175518-727-6a85ba' ....\n",
       "    t1_tuids              (main_dim) <U26 728B '20241014-175518-727-cec4c2' ....\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:                      20241014-175518-728-7f46e3\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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       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 2kB\n",
       "Dimensions:               \u001b[1m(\u001b[0mmain_dim: \u001b[1;36m7\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "    flux_bias             \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 56B \u001b[1;36m-0.04\u001b[0m \u001b[1;36m-0.02667\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.02667\u001b[0m \u001b[1;36m0.04\u001b[0m\n",
       "    resonator_freq_tuids  \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m \n"
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     "metadata": {},
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       "\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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    {
     "data": {
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       "\u001b[3;92mTrue\u001b[0m"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.t1_tuids.dtype == dataset_multi_indexed.reset_index(\"main_dim\").t1_tuids.dtype"
   ]
  }
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