{ "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:                      20241106-152908-767-d0fe11\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;36m20241106\u001b[0m-\u001b[1;36m152908\u001b[0m-\u001b[1;36m767\u001b[0m-d0fe11\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:                      20241106-152908-767-d0fe11\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;36m20241106\u001b[0m-\u001b[1;36m152908\u001b[0m-\u001b[1;36m767\u001b[0m-d0fe11\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{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 '20241106-152909-730-61c7ff' ....\n",
       "    qubit_freq_tuids      (main_dim) <U26 728B '20241106-152909-731-74847a' ....\n",
       "    t1_tuids              (main_dim) <U26 728B '20241106-152909-731-4d1be5' ....\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:                      20241106-152909-732-ccc0ae\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 '20241106-152909-730-61c7ff' ....\n",
       "  * qubit_freq_tuids      (main_dim) <U26 728B '20241106-152909-731-74847a' ....\n",
       "  * t1_tuids              (main_dim) <U26 728B '20241106-152909-731-4d1be5' ....\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",
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       "    dataset_name:              \n",
       "    dataset_state:             None\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 '20241106-152909-730-61c7ff' ....\n",
       "    qubit_freq_tuids      (main_dim) <U26 728B '20241106-152909-731-74847a' ....\n",
       "    t1_tuids              (main_dim) <U26 728B '20241106-152909-731-4d1be5' ....\n",
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       "Data variables:\n",
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       "    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:                      20241106-152909-732-ccc0ae\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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       "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",
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     },
     "execution_count": 16,
     "metadata": {},
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    }
   ],
   "source": [
    "all(dataset_multi_indexed.reset_index(\"main_dim\").t1_tuids == dataset.t1_tuids)"
   ]
  },
  {
   "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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    {
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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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