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    "(dataset-spec)=\n",
    "# Quantify dataset specification\n",
    "\n",
    "```{seealso}\n",
    "The complete source code of this tutorial can be found in\n",
    "\n",
    "{nb-download}`Quantify dataset - specification.ipynb`\n",
    "```"
   ]
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   "source": [
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "from rich import pretty\n",
    "\n",
    "import quantify_core.data.dataset_adapters as dadapters\n",
    "import quantify_core.data.dataset_attrs as dattrs\n",
    "from quantify_core.data import handling as dh\n",
    "from quantify_core.utilities import dataset_examples\n",
    "from quantify_core.utilities.examples_support import round_trip_dataset\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": "fba7c614",
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    "This document describes the Quantify dataset specification.\n",
    "Here we focus on the concepts and terminology specific to the Quantify dataset.\n",
    "It is based on the Xarray dataset, hence, we assume basic familiarity with the {class}`xarray.Dataset`.\n",
    "If you are not familiar with it, we highly recommend to first have a look at our {ref}`xarray-intro` for a brief overview.\n",
    "\n",
    "(sec-coordinates-and-variables)=\n",
    "\n",
    "## Coordinates and Variables\n",
    "\n",
    "The Quantify dataset is an xarray dataset that follows certain conventions. We define \"subtypes\" of xarray coordinates and variables:\n",
    "\n",
    "(sec-main-coordinates)=\n",
    "\n",
    "### Main coordinate(s)\n",
    "\n",
    "- Xarray **Coordinates** that have an attribute {attr}`~quantify_core.data.dataset_attrs.QCoordAttrs.is_main_coord` set to `True`.\n",
    "\n",
    "- Often correspond to physical coordinates, e.g., a signal frequency or amplitude.\n",
    "\n",
    "- Often correspond to quantities set through {class}`.Settable`s.\n",
    "\n",
    "- The dataset must have at least one main coordinate.\n",
    "\n",
    "  > - Example: In some cases, the idea of a coordinate does not apply, however a main coordinate in the dataset is required. A simple \"index\" coordinate should be used, e.g., an array of integers.\n",
    "\n",
    "- See also the method {func}`~quantify_core.data.dataset_attrs.get_main_coords`.\n",
    "\n",
    "(sec-secondary-coordinates)=\n",
    "\n",
    "### Secondary coordinate(s)\n",
    "\n",
    "- A ubiquitous example is the coordinates that are used by \"calibration\" points.\n",
    "- Similar to {ref}`main coordinates <sec-main-coordinates>`, but intended to serve as the coordinates of {ref}`secondary variables <sec-secondary-variables>`.\n",
    "- Xarray **Coordinates** that have an attribute {attr}`~quantify_core.data.dataset_attrs.QCoordAttrs.is_main_coord` set to `False`.\n",
    "- See also {func}`~quantify_core.data.dataset_attrs.get_secondary_coords`.\n",
    "\n",
    "(sec-main-variables)=\n",
    "\n",
    "### Main variable(s)\n",
    "\n",
    "- Xarray **Variables** that have an attribute {attr}`~quantify_core.data.dataset_attrs.QVarAttrs.is_main_var` set to `True`.\n",
    "- Often correspond to a physical quantity being measured, e.g., the signal magnitude at a specific frequency measured on a metal contact of a quantum chip.\n",
    "- Often correspond to quantities returned by {class}`.Gettable`s.\n",
    "- See also {func}`~quantify_core.data.dataset_attrs.get_main_vars`.\n",
    "\n",
    "(sec-secondary-variables)=\n",
    "\n",
    "### Secondary variables(s)\n",
    "\n",
    "- Again, the ubiquitous example is \"calibration\" datapoints.\n",
    "- Similar to {ref}`main variables <sec-main-variables>`, but intended to serve as reference data for other main variables (e.g., calibration data).\n",
    "- Xarray **Variables** that have an attribute {attr}`~quantify_core.data.dataset_attrs.QVarAttrs.is_main_var` set to `False`.\n",
    "- The \"assignment\" of secondary variables to main variables should be done using {attr}`~quantify_core.data.dataset_attrs.QDatasetAttrs.relationships`.\n",
    "- See also {func}`~quantify_core.data.dataset_attrs.get_secondary_vars`.\n",
    "\n",
    "```{note}\n",
    "In this document we show exemplary datasets to highlight the details of the Quantify dataset specification.\n",
    "However, for completeness, we always show a valid Quantify dataset with all the required properties.\n",
    "```\n",
    "\n",
    "In order to follow the rest of this specification more easily have a look at the example below.\n",
    "It should give you a more concrete feeling of the details that are exposed afterward.\n",
    "See {ref}`sec-dataset-examples` for an exemplary dataset.\n",
    "\n",
    "We use the\n",
    "{func}`~quantify_core.utilities.dataset_examples.mk_two_qubit_chevron_dataset` to\n",
    "generate our dataset."
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">mk_two_qubit_chevron_dataset</span><span class=\"p\">(</span><span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    Generates a dataset that look similar to a two-qubit Chevron experiment.</span>\n",
       "\n",
       "<span class=\"sd\">    Parameters</span>\n",
       "<span class=\"sd\">    ----------</span>\n",
       "<span class=\"sd\">    **kwargs</span>\n",
       "<span class=\"sd\">        Keyword arguments passed to :func:`~.mk_two_qubit_chevron_data`.</span>\n",
       "\n",
       "<span class=\"sd\">    Returns</span>\n",
       "<span class=\"sd\">    -------</span>\n",
       "<span class=\"sd\">    :</span>\n",
       "<span class=\"sd\">        A mock Quantify dataset.</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    <span class=\"n\">amp_values</span><span class=\"p\">,</span> <span class=\"n\">time_values</span><span class=\"p\">,</span> <span class=\"n\">pop_q0</span><span class=\"p\">,</span> <span class=\"n\">pop_q1</span> <span class=\"o\">=</span> <span class=\"n\">mk_two_qubit_chevron_data</span><span class=\"p\">(</span><span class=\"o\">**</span><span class=\"n\">kwargs</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">dims_q0</span> <span class=\"o\">=</span> <span class=\"n\">dims_q1</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"s2\">&quot;repetitions&quot;</span><span class=\"p\">,</span> <span class=\"s2\">&quot;main_dim&quot;</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">pop_q0_attrs</span> <span class=\"o\">=</span> <span class=\"n\">mk_main_var_attrs</span><span class=\"p\">(</span>\n",
       "        <span class=\"n\">long_name</span><span class=\"o\">=</span><span class=\"s2\">&quot;Population Q0&quot;</span><span class=\"p\">,</span> <span class=\"n\">unit</span><span class=\"o\">=</span><span class=\"s2\">&quot;&quot;</span><span class=\"p\">,</span> <span class=\"n\">has_repetitions</span><span class=\"o\">=</span><span class=\"kc\">True</span>\n",
       "    <span class=\"p\">)</span>\n",
       "    <span class=\"n\">pop_q1_attrs</span> <span class=\"o\">=</span> <span class=\"n\">mk_main_var_attrs</span><span class=\"p\">(</span>\n",
       "        <span class=\"n\">long_name</span><span class=\"o\">=</span><span class=\"s2\">&quot;Population Q1&quot;</span><span class=\"p\">,</span> <span class=\"n\">unit</span><span class=\"o\">=</span><span class=\"s2\">&quot;&quot;</span><span class=\"p\">,</span> <span class=\"n\">has_repetitions</span><span class=\"o\">=</span><span class=\"kc\">True</span>\n",
       "    <span class=\"p\">)</span>\n",
       "    <span class=\"n\">data_vars</span> <span class=\"o\">=</span> <span class=\"nb\">dict</span><span class=\"p\">(</span>\n",
       "        <span class=\"n\">pop_q0</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">dims_q0</span><span class=\"p\">,</span> <span class=\"n\">pop_q0</span><span class=\"p\">,</span> <span class=\"n\">pop_q0_attrs</span><span class=\"p\">),</span>\n",
       "        <span class=\"n\">pop_q1</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">dims_q1</span><span class=\"p\">,</span> <span class=\"n\">pop_q1</span><span class=\"p\">,</span> <span class=\"n\">pop_q1_attrs</span><span class=\"p\">),</span>\n",
       "    <span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">dims_amp</span> <span class=\"o\">=</span> <span class=\"n\">dims_time</span> <span class=\"o\">=</span> <span class=\"p\">(</span><span class=\"s2\">&quot;main_dim&quot;</span><span class=\"p\">,)</span>\n",
       "    <span class=\"n\">amp_attrs</span> <span class=\"o\">=</span> <span class=\"n\">mk_main_coord_attrs</span><span class=\"p\">(</span><span class=\"n\">long_name</span><span class=\"o\">=</span><span class=\"s2\">&quot;Amplitude&quot;</span><span class=\"p\">,</span> <span class=\"n\">unit</span><span class=\"o\">=</span><span class=\"s2\">&quot;V&quot;</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">time_attrs</span> <span class=\"o\">=</span> <span class=\"n\">mk_main_coord_attrs</span><span class=\"p\">(</span><span class=\"n\">long_name</span><span class=\"o\">=</span><span class=\"s2\">&quot;Time&quot;</span><span class=\"p\">,</span> <span class=\"n\">unit</span><span class=\"o\">=</span><span class=\"s2\">&quot;s&quot;</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">coords</span> <span class=\"o\">=</span> <span class=\"nb\">dict</span><span class=\"p\">(</span>\n",
       "        <span class=\"n\">amp</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">dims_amp</span><span class=\"p\">,</span> <span class=\"n\">amp_values</span><span class=\"p\">,</span> <span class=\"n\">amp_attrs</span><span class=\"p\">),</span>\n",
       "        <span class=\"n\">time</span><span class=\"o\">=</span><span class=\"p\">(</span><span class=\"n\">dims_time</span><span class=\"p\">,</span> <span class=\"n\">time_values</span><span class=\"p\">,</span> <span class=\"n\">time_attrs</span><span class=\"p\">),</span>\n",
       "    <span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"n\">dataset_attrs</span> <span class=\"o\">=</span> <span class=\"n\">mk_dataset_attrs</span><span class=\"p\">()</span>\n",
       "    <span class=\"n\">dataset</span> <span class=\"o\">=</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">(</span><span class=\"n\">data_vars</span><span class=\"o\">=</span><span class=\"n\">data_vars</span><span class=\"p\">,</span> <span class=\"n\">coords</span><span class=\"o\">=</span><span class=\"n\">coords</span><span class=\"p\">,</span> <span class=\"n\">attrs</span><span class=\"o\">=</span><span class=\"n\">dataset_attrs</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"k\">return</span> <span class=\"n\">dataset</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}two\\PYZus{}qubit\\PYZus{}chevron\\PYZus{}dataset}\\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}{    Generates a dataset that look similar to a two\\PYZhy{}qubit Chevron experiment.}\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}{    **kwargs}\n",
       "\\PY{l+s+sd}{        Keyword arguments passed to :func:`\\PYZti{}.mk\\PYZus{}two\\PYZus{}qubit\\PYZus{}chevron\\PYZus{}data`.}\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}{        A mock Quantify dataset.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{n}{amp\\PYZus{}values}\\PY{p}{,} \\PY{n}{time\\PYZus{}values}\\PY{p}{,} \\PY{n}{pop\\PYZus{}q0}\\PY{p}{,} \\PY{n}{pop\\PYZus{}q1} \\PY{o}{=} \\PY{n}{mk\\PYZus{}two\\PYZus{}qubit\\PYZus{}chevron\\PYZus{}data}\\PY{p}{(}\\PY{o}{*}\\PY{o}{*}\\PY{n}{kwargs}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{dims\\PYZus{}q0} \\PY{o}{=} \\PY{n}{dims\\PYZus{}q1} \\PY{o}{=} \\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}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{pop\\PYZus{}q0\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\n",
       "        \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Population Q0}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{has\\PYZus{}repetitions}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "    \\PY{p}{)}\n",
       "    \\PY{n}{pop\\PYZus{}q1\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}main\\PYZus{}var\\PYZus{}attrs}\\PY{p}{(}\n",
       "        \\PY{n}{long\\PYZus{}name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Population Q1}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{has\\PYZus{}repetitions}\\PY{o}{=}\\PY{k+kc}{True}\n",
       "    \\PY{p}{)}\n",
       "    \\PY{n}{data\\PYZus{}vars} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{pop\\PYZus{}q0}\\PY{o}{=}\\PY{p}{(}\\PY{n}{dims\\PYZus{}q0}\\PY{p}{,} \\PY{n}{pop\\PYZus{}q0}\\PY{p}{,} \\PY{n}{pop\\PYZus{}q0\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{pop\\PYZus{}q1}\\PY{o}{=}\\PY{p}{(}\\PY{n}{dims\\PYZus{}q1}\\PY{p}{,} \\PY{n}{pop\\PYZus{}q1}\\PY{p}{,} \\PY{n}{pop\\PYZus{}q1\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{dims\\PYZus{}amp} \\PY{o}{=} \\PY{n}{dims\\PYZus{}time} \\PY{o}{=} \\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{main\\PYZus{}dim}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\\PY{p}{)}\n",
       "    \\PY{n}{amp\\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}{Amplitude}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "    \\PY{n}{time\\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}{Time}\\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}{)}\n",
       "    \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{dict}\\PY{p}{(}\n",
       "        \\PY{n}{amp}\\PY{o}{=}\\PY{p}{(}\\PY{n}{dims\\PYZus{}amp}\\PY{p}{,} \\PY{n}{amp\\PYZus{}values}\\PY{p}{,} \\PY{n}{amp\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{n}{time}\\PY{o}{=}\\PY{p}{(}\\PY{n}{dims\\PYZus{}time}\\PY{p}{,} \\PY{n}{time\\PYZus{}values}\\PY{p}{,} \\PY{n}{time\\PYZus{}attrs}\\PY{p}{)}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{n}{dataset\\PYZus{}attrs} \\PY{o}{=} \\PY{n}{mk\\PYZus{}dataset\\PYZus{}attrs}\\PY{p}{(}\\PY{p}{)}\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_two_qubit_chevron_dataset\u001b[0m\u001b[1m(\u001b[0m**kwargs\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Generates a dataset that look similar to a two-qubit Chevron experiment.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    **kwargs\n",
       "        Keyword arguments passed to :func:`~.mk_two_qubit_chevron_data`.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    :\n",
       "        A mock Quantify dataset.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    amp_values, time_values, pop_q0, pop_q1 = \u001b[1;35mmk_two_qubit_chevron_data\u001b[0m\u001b[1m(\u001b[0m**kwargs\u001b[1m)\u001b[0m\n",
       "\n",
       "    dims_q0 = dims_q1 = \u001b[1m(\u001b[0m\u001b[32m\"repetitions\"\u001b[0m, \u001b[32m\"main_dim\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    pop_q0_attrs = \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mlong_name\u001b[0m=\u001b[32m\"Population\u001b[0m\u001b[32m Q0\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"\"\u001b[0m, \u001b[33mhas_repetitions\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "    \u001b[1m)\u001b[0m\n",
       "    pop_q1_attrs = \u001b[1;35mmk_main_var_attrs\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mlong_name\u001b[0m=\u001b[32m\"Population\u001b[0m\u001b[32m Q1\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"\"\u001b[0m, \u001b[33mhas_repetitions\u001b[0m=\u001b[3;92mTrue\u001b[0m\n",
       "    \u001b[1m)\u001b[0m\n",
       "    data_vars = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mpop_q0\u001b[0m=\u001b[1m(\u001b[0mdims_q0, pop_q0, pop_q0_attrs\u001b[1m)\u001b[0m,\n",
       "        \u001b[33mpop_q1\u001b[0m=\u001b[1m(\u001b[0mdims_q1, pop_q1, pop_q1_attrs\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    dims_amp = dims_time = \u001b[1m(\u001b[0m\u001b[32m\"main_dim\"\u001b[0m,\u001b[1m)\u001b[0m\n",
       "    amp_attrs = \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Amplitude\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"V\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    time_attrs = \u001b[1;35mmk_main_coord_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[33mlong_name\u001b[0m=\u001b[32m\"Time\"\u001b[0m, \u001b[33munit\u001b[0m=\u001b[32m\"s\"\u001b[0m\u001b[1m)\u001b[0m\n",
       "    coords = \u001b[1;35mdict\u001b[0m\u001b[1m(\u001b[0m\n",
       "        \u001b[33mamp\u001b[0m=\u001b[1m(\u001b[0mdims_amp, amp_values, amp_attrs\u001b[1m)\u001b[0m,\n",
       "        \u001b[33mtime\u001b[0m=\u001b[1m(\u001b[0mdims_time, time_values, time_attrs\u001b[1m)\u001b[0m,\n",
       "    \u001b[1m)\u001b[0m\n",
       "\n",
       "    dataset_attrs = \u001b[1;35mmk_dataset_attrs\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m\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(dataset_examples.mk_two_qubit_chevron_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "055f7110",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = dataset_examples.mk_two_qubit_chevron_dataset()\n",
    "assert dataset == round_trip_dataset(dataset)  # confirm read/write"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47ee7eb0",
   "metadata": {},
   "source": [
    "### 2D example\n",
    "\n",
    "In the dataset below we have two main coordinates `amp` and `time`; and two main\n",
    "variables `pop_q0` and `pop_q1`.\n",
    "Both main coordinates \"lie\" along a single xarray dimension, `main_dim`.\n",
    "Both main variables lie along two xarray dimensions `main_dim` and `repetitions`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "40661f89",
   "metadata": {},
   "outputs": [
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       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
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       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "\n",
       ".xr-attrs dt {\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 115kB\n",
       "Dimensions:  (repetitions: 5, main_dim: 1200)\n",
       "Coordinates:\n",
       "    amp      (main_dim) float64 10kB 0.45 0.4534 0.4569 ... 0.5431 0.5466 0.55\n",
       "    time     (main_dim) float64 10kB 0.0 0.0 0.0 0.0 ... 1e-07 1e-07 1e-07 1e-07\n",
       "Dimensions without coordinates: repetitions, main_dim\n",
       "Data variables:\n",
       "    pop_q0   (repetitions, main_dim) float64 48kB 0.5 0.5 0.5 ... 0.4818 0.5\n",
       "    pop_q1   (repetitions, main_dim) float64 48kB 0.5 0.5 0.5 ... 0.5371 0.5\n",
       "Attributes:\n",
       "    tuid:                      20240829-164417-502-7f7737\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:    []</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0c900c60-3560-41ba-aaea-409080ea9f77' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0c900c60-3560-41ba-aaea-409080ea9f77' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>repetitions</span>: 5</li><li><span>main_dim</span>: 1200</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e08296e6-dd9b-444e-bb5d-8458b7f034e7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e08296e6-dd9b-444e-bb5d-8458b7f034e7' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(main_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.45 0.4534 0.4569 ... 0.5466 0.55</div><input id='attrs-ad2eee63-5e59-4064-962b-c52a9e36e006' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad2eee63-5e59-4064-962b-c52a9e36e006' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5ba2e1f-f05a-4674-b0dd-ea7917646c99' class='xr-var-data-in' type='checkbox'><label for='data-e5ba2e1f-f05a-4674-b0dd-ea7917646c99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Amplitude</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.45      , 0.45344828, 0.45689655, ..., 0.54310345, 0.54655172,\n",
       "       0.55      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(main_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 1e-07 1e-07 1e-07</div><input id='attrs-aa31fb53-87c2-4b09-96e0-9763c61b5b5c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa31fb53-87c2-4b09-96e0-9763c61b5b5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41a892d5-5b24-4a90-ba10-b373aa05036f' class='xr-var-data-in' type='checkbox'><label for='data-41a892d5-5b24-4a90-ba10-b373aa05036f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.e+00, 0.e+00, 0.e+00, ..., 1.e-07, 1.e-07, 1.e-07])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c61e3814-d630-452d-b706-047566a17dfa' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c61e3814-d630-452d-b706-047566a17dfa' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>pop_q0</span></div><div class='xr-var-dims'>(repetitions, main_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 0.5 0.5 ... 0.4886 0.4818 0.5</div><input id='attrs-e7d1b2a1-b20f-429d-a987-ec1dd6ce4c2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7d1b2a1-b20f-429d-a987-ec1dd6ce4c2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cff3bdf-b958-43d1-b383-528821b6561a' class='xr-var-data-in' type='checkbox'><label for='data-0cff3bdf-b958-43d1-b383-528821b6561a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Population Q0</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[0.5       , 0.5       , 0.5       , ..., 0.47934742, 0.45911157,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.48484689, 0.46978367,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.47697503, 0.48068839,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.4903811 , 0.45931355,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.48855559, 0.48175285,\n",
       "        0.5       ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pop_q1</span></div><div class='xr-var-dims'>(repetitions, main_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 0.5 0.5 ... 0.5243 0.5371 0.5</div><input id='attrs-c8611d5b-6288-4335-bcab-a86ea186f34e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8611d5b-6288-4335-bcab-a86ea186f34e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fb3ecb7-5232-414e-8299-c35045d4a30f' class='xr-var-data-in' type='checkbox'><label for='data-4fb3ecb7-5232-414e-8299-c35045d4a30f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Population Q1</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[0.5       , 0.5       , 0.5       , ..., 0.51325446, 0.53973285,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.50964979, 0.52643865,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.51995816, 0.54094941,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.51040954, 0.53633277,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.52434067, 0.53709612,\n",
       "        0.5       ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-485fd243-148e-42a9-a732-18c2f075311e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-485fd243-148e-42a9-a732-18c2f075311e' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-cc9afe22-fb50-40db-b6fe-876685b4d7d3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cc9afe22-fb50-40db-b6fe-876685b4d7d3' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20240829-164417-502-7f7737</dd><dt><span>dataset_name :</span></dt><dd></dd><dt><span>dataset_state :</span></dt><dd>None</dd><dt><span>timestamp_start :</span></dt><dd>None</dd><dt><span>timestamp_end :</span></dt><dd>None</dd><dt><span>quantify_dataset_version :</span></dt><dd>2.0.0</dd><dt><span>software_versions :</span></dt><dd>{}</dd><dt><span>relationships :</span></dt><dd>[]</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 115kB\n",
       "Dimensions:  \u001b[1m(\u001b[0mrepetitions: \u001b[1;36m5\u001b[0m, main_dim: \u001b[1;36m1200\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "    amp      \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 10kB \u001b[1;36m0.45\u001b[0m \u001b[1;36m0.4534\u001b[0m \u001b[1;36m0.4569\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.5431\u001b[0m \u001b[1;36m0.5466\u001b[0m \u001b[1;36m0.55\u001b[0m\n",
       "    time     \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 10kB \u001b[1;36m0.0\u001b[0m \u001b[1;36m0.0\u001b[0m \u001b[1;36m0.0\u001b[0m \u001b[1;36m0.0\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m1e-07\u001b[0m \u001b[1;36m1e-07\u001b[0m \u001b[1;36m1e-07\u001b[0m \u001b[1;36m1e-07\u001b[0m\n",
       "Dimensions without coordinates: repetitions, main_dim\n",
       "Data variables:\n",
       "    pop_q0   \u001b[1m(\u001b[0mrepetitions, main_dim\u001b[1m)\u001b[0m float64 48kB \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.4818\u001b[0m \u001b[1;36m0.5\u001b[0m\n",
       "    pop_q1   \u001b[1m(\u001b[0mrepetitions, main_dim\u001b[1m)\u001b[0m float64 48kB \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.5371\u001b[0m \u001b[1;36m0.5\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20240829\u001b[0m-\u001b[1;36m164417\u001b[0m-\u001b[1;36m502\u001b[0m-7f7737\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1bbbe76",
   "metadata": {},
   "source": [
    "**Please note** how the underlying arrays for the coordinates are structured!\n",
    "Even for \"gridded\" data, the coordinates are arranged in arrays\n",
    "that match the dimensions of the variables in the xarray. This is\n",
    "done so that the data can support more complex scenarios, such as\n",
    "irregularly spaced samples and measurements taken at unknown locations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ae230c4f",
   "metadata": {},
   "outputs": [
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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": [
    "n_points = 110  # only plot a few points for clarity\n",
    "_, axs = plt.subplots(4, 1, sharex=True, figsize=(10, 10))\n",
    "dataset.amp[:n_points].plot(\n",
    "    ax=axs[0], marker=\".\", color=\"C0\", label=dataset.amp.long_name\n",
    ")\n",
    "dataset.time[:n_points].plot(\n",
    "    ax=axs[1], marker=\".\", color=\"C1\", label=dataset.time.long_name\n",
    ")\n",
    "_ = dataset.pop_q0.sel(repetitions=0)[:n_points].plot(\n",
    "    ax=axs[2], marker=\".\", color=\"C2\", label=dataset.pop_q0.long_name\n",
    ")\n",
    "_ = dataset.pop_q1.sel(repetitions=0)[:n_points].plot(\n",
    "    ax=axs[3], marker=\".\", color=\"C3\", label=dataset.pop_q1.long_name\n",
    ")\n",
    "for ax in axs:\n",
    "    ax.legend()\n",
    "    ax.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "447e03e1",
   "metadata": {},
   "source": [
    "As seen above, in the Quantify dataset the main coordinates do not explicitly index\n",
    "the main variables because not all use-cases fit within this paradigm.\n",
    "However, when possible, the Quantify dataset can be reshaped to take advantage of the\n",
    "xarray built-in utilities.\n",
    "\n",
    "<!-- FIXME when a Quantify dataset has a class re-evaluate the need for following sentence or some equivalent: -->\n",
    "<!-- Note, however, that this reshaping will produce an xarray dataset that does not comply with the Quantify dataset specification. -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ddeb8f38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\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 160\u001b[0m\u001b[1;36m0x300\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m6\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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      "text/plain": [
       "\u001b[1m<\u001b[0m\u001b[1;95mFigure\u001b[0m\u001b[39m size 160\u001b[0m\u001b[1;36m0x300\u001b[0m\u001b[39m with \u001b[0m\u001b[1;36m6\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset,\n",
    "    dimension=\"main_dim\",\n",
    "    coords_names=dattrs.get_main_coords(dataset),\n",
    ")\n",
    "dataset_gridded.pop_q0.plot.pcolormesh(x=\"amp\", col=\"repetitions\")\n",
    "_ = dataset_gridded.pop_q1.plot.pcolormesh(x=\"amp\", col=\"repetitions\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4540870",
   "metadata": {},
   "source": [
    "## Dimensions\n",
    "\n",
    "The main variables and coordinates present in a Quantify dataset have the following required and optional xarray dimensions:\n",
    "\n",
    "### Main dimension(s) \\[Required\\]\n",
    "\n",
    "The main dimensions comply with the following:\n",
    "\n",
    "- The outermost dimension of any main coordinate/variable, OR the second outermost dimension if the outermost one is a {ref}`repetitions dimension <sec-repetitions-dimensions>`.\n",
    "- Do not require to be explicitly specified in any metadata attributes, instead utilities for extracting them are provided. See {func}`~quantify_core.data.dataset_attrs.get_main_dims` which simply applies the rule above while inspecting all the main coordinates and variables present in the dataset.\n",
    "- The dataset must have at least one main dimension.\n",
    "\n",
    "```{admonition} Note on nesting main dimensions\n",
    "Nesting main dimensions is allowed in principle and such examples are\n",
    "provided but it should be considered an experimental feature.\n",
    "\n",
    "- Intuition: intended primarily for time series, also known as \"time trace\" or simply trace. See {ref}`sec-dataset-t1-traces` for an example.\n",
    "```\n",
    "\n",
    "### Secondary dimension(s) \\[Optional\\]\n",
    "\n",
    "Equivalent to the main dimensions but used by the secondary coordinates and variables.\n",
    "The secondary dimensions comply with the following:\n",
    "\n",
    "- The outermost dimension of any secondary coordinate/variable, OR the second outermost dimension if the outermost one is a {ref}`repetitions dimension <sec-repetitions-dimensions>`.\n",
    "- Do not require to be explicitly specified in any metadata attributes, instead utilities for extracting them are provided. See {func}`~quantify_core.data.dataset_attrs.get_secondary_dims` which simply applies the rule above while inspecting all the secondary coordinates and variables present in the dataset.\n",
    "\n",
    "(sec-repetitions-dimensions)=\n",
    "### Repetitions dimension(s) \\[Optional\\]\n",
    "\n",
    "Repetition dimensions comply with the following:\n",
    "\n",
    "- Any dimension that is the outermost dimension of a main or secondary variable when its attribute {attr}`QVarAttrs.has_repetitions <quantify_core.data.dataset_attrs.QVarAttrs.has_repetitions>` is set to `True`.\n",
    "- Intuition for this xarray dimension(s): the equivalent would be to have `dataset_reptition_0.hdf5`, `dataset_reptition_1.hdf5`, etc. where each dataset was obtained from repeating exactly the same experiment. Instead we define an outer dimension for this.\n",
    "- Default behavior of (live) plotting and analysis tools can be to average the main variables along the repetitions dimension(s).\n",
    "- Can be the outermost dimension of the main (and secondary) variables.\n",
    "- Variables can lie along one (and only one) repetitions outermost dimension.\n",
    "\n",
    "#### Example datasets with repetition\n",
    "\n",
    "As shown in the {ref}`xarray-intro` an xarray dimension can be indexed by a `coordinate` variable. In this example the `repetitions` dimension is indexed by the `repetitions` xarray coordinate. Note that in an xarray dataset, a dimension and a data variable or a coordinate can share the same name. This might be confusing at first. It takes just a bit of dataset manipulation practice to gain an intuition for how it works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "db7e9161",
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 20B\n",
       "Dimensions:      (repetitions: 5)\n",
       "Coordinates:\n",
       "  * repetitions  (repetitions) &lt;U1 20B &#x27;A&#x27; &#x27;B&#x27; &#x27;C&#x27; &#x27;D&#x27; &#x27;E&#x27;\n",
       "Data variables:\n",
       "    *empty*</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-46bcea29-7dcf-4ae9-a660-192b8d4fb447' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-46bcea29-7dcf-4ae9-a660-192b8d4fb447' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>repetitions</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c5176270-bcb0-4f4e-8661-d8bd70155322' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c5176270-bcb0-4f4e-8661-d8bd70155322' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>repetitions</span></div><div class='xr-var-dims'>(repetitions)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;A&#x27; &#x27;B&#x27; &#x27;C&#x27; &#x27;D&#x27; &#x27;E&#x27;</div><input id='attrs-632588d1-17f8-496b-8ef7-c0fccb22bae1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-632588d1-17f8-496b-8ef7-c0fccb22bae1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-feab9854-2af0-4a49-88df-8d9bfa1c5bae' class='xr-var-data-in' type='checkbox'><label for='data-feab9854-2af0-4a49-88df-8d9bfa1c5bae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;A&#x27;, &#x27;B&#x27;, &#x27;C&#x27;, &#x27;D&#x27;, &#x27;E&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6591137d-0b50-446d-89bf-b00300e2b1de' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6591137d-0b50-446d-89bf-b00300e2b1de' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-d898d89a-88a9-44f4-a6e4-24df029de544' class='xr-section-summary-in' type='checkbox'  ><label for='section-d898d89a-88a9-44f4-a6e4-24df029de544' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>repetitions</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-47a75b4e-53d0-48b3-996c-e81010e37524' class='xr-index-data-in' type='checkbox'/><label for='index-47a75b4e-53d0-48b3-996c-e81010e37524' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;A&#x27;, &#x27;B&#x27;, &#x27;C&#x27;, &#x27;D&#x27;, &#x27;E&#x27;], dtype=&#x27;object&#x27;, name=&#x27;repetitions&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a3c625ff-fb46-412d-9536-f54341db87dd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a3c625ff-fb46-412d-9536-f54341db87dd' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 20B\n",
       "Dimensions:      \u001b[1m(\u001b[0mrepetitions: \u001b[1;36m5\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "  * repetitions  \u001b[1m(\u001b[0mrepetitions\u001b[1m)\u001b[0m <U1 20B \u001b[32m'A'\u001b[0m \u001b[32m'B'\u001b[0m \u001b[32m'C'\u001b[0m \u001b[32m'D'\u001b[0m \u001b[32m'E'\u001b[0m\n",
       "Data variables:\n",
       "    *empty*"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coord_dims = (\"repetitions\",)\n",
    "coord_values = [\"A\", \"B\", \"C\", \"D\", \"E\"]\n",
    "dataset_indexed_rep = xr.Dataset(coords=dict(repetitions=(coord_dims, coord_values)))\n",
    "\n",
    "dataset_indexed_rep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "db12dae4",
   "metadata": {},
   "outputs": [
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       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 115kB\n",
       "Dimensions:      (repetitions: 5, main_dim: 1200)\n",
       "Coordinates:\n",
       "    amp          (main_dim) float64 10kB 0.45 0.4534 0.4569 ... 0.5466 0.55\n",
       "    time         (main_dim) float64 10kB 0.0 0.0 0.0 0.0 ... 1e-07 1e-07 1e-07\n",
       "  * repetitions  (repetitions) &lt;U1 20B &#x27;A&#x27; &#x27;B&#x27; &#x27;C&#x27; &#x27;D&#x27; &#x27;E&#x27;\n",
       "Dimensions without coordinates: main_dim\n",
       "Data variables:\n",
       "    pop_q0       (repetitions, main_dim) float64 48kB 0.5 0.5 0.5 ... 0.4818 0.5\n",
       "    pop_q1       (repetitions, main_dim) float64 48kB 0.5 0.5 0.5 ... 0.5371 0.5\n",
       "Attributes:\n",
       "    tuid:                      20240829-164417-502-7f7737\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:    []</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e8cfdf89-70cc-447c-b407-a3c391896475' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e8cfdf89-70cc-447c-b407-a3c391896475' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>repetitions</span>: 5</li><li><span>main_dim</span>: 1200</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d480d128-55bf-47bf-8a48-387b707192fe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d480d128-55bf-47bf-8a48-387b707192fe' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(main_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.45 0.4534 0.4569 ... 0.5466 0.55</div><input id='attrs-b348ab2b-aeff-444c-9ccf-7ca15bdcdfe2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b348ab2b-aeff-444c-9ccf-7ca15bdcdfe2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-836f9905-25e4-420d-b2d1-cb226433e3dd' class='xr-var-data-in' type='checkbox'><label for='data-836f9905-25e4-420d-b2d1-cb226433e3dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Amplitude</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.45      , 0.45344828, 0.45689655, ..., 0.54310345, 0.54655172,\n",
       "       0.55      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(main_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 1e-07 1e-07 1e-07</div><input id='attrs-637feba6-f698-463e-ba49-59e2282d4d0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-637feba6-f698-463e-ba49-59e2282d4d0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-803a2cc0-d26d-4918-ad59-73c4b45f633c' class='xr-var-data-in' type='checkbox'><label for='data-803a2cc0-d26d-4918-ad59-73c4b45f633c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.e+00, 0.e+00, 0.e+00, ..., 1.e-07, 1.e-07, 1.e-07])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>repetitions</span></div><div class='xr-var-dims'>(repetitions)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;A&#x27; &#x27;B&#x27; &#x27;C&#x27; &#x27;D&#x27; &#x27;E&#x27;</div><input id='attrs-5c8c8982-3db9-4855-b522-18d2e8555084' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c8c8982-3db9-4855-b522-18d2e8555084' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09fc043e-f654-456d-a951-6396c7dd80c3' class='xr-var-data-in' type='checkbox'><label for='data-09fc043e-f654-456d-a951-6396c7dd80c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;A&#x27;, &#x27;B&#x27;, &#x27;C&#x27;, &#x27;D&#x27;, &#x27;E&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-651e8052-9243-4838-8a22-e08369fc5769' class='xr-section-summary-in' type='checkbox'  checked><label for='section-651e8052-9243-4838-8a22-e08369fc5769' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>pop_q0</span></div><div class='xr-var-dims'>(repetitions, main_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 0.5 0.5 ... 0.4886 0.4818 0.5</div><input id='attrs-1386d272-e8f7-4e12-9952-e14f49acaf14' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1386d272-e8f7-4e12-9952-e14f49acaf14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-096d24c2-3cc8-4699-8fa2-d8140713d739' class='xr-var-data-in' type='checkbox'><label for='data-096d24c2-3cc8-4699-8fa2-d8140713d739' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Population Q0</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[0.5       , 0.5       , 0.5       , ..., 0.47934742, 0.45911157,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.48484689, 0.46978367,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.47697503, 0.48068839,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.4903811 , 0.45931355,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.48855559, 0.48175285,\n",
       "        0.5       ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pop_q1</span></div><div class='xr-var-dims'>(repetitions, main_dim)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 0.5 0.5 ... 0.5243 0.5371 0.5</div><input id='attrs-066a94ed-abdd-4ae2-854f-e4bbbe85cec0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-066a94ed-abdd-4ae2-854f-e4bbbe85cec0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afd58689-7714-4699-a38a-3e6ba639bce1' class='xr-var-data-in' type='checkbox'><label for='data-afd58689-7714-4699-a38a-3e6ba639bce1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Population Q1</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[0.5       , 0.5       , 0.5       , ..., 0.51325446, 0.53973285,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.50964979, 0.52643865,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.51995816, 0.54094941,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.51040954, 0.53633277,\n",
       "        0.5       ],\n",
       "       [0.5       , 0.5       , 0.5       , ..., 0.52434067, 0.53709612,\n",
       "        0.5       ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-23954478-c861-4c0c-bab8-5dd2513f569c' class='xr-section-summary-in' type='checkbox'  ><label for='section-23954478-c861-4c0c-bab8-5dd2513f569c' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>repetitions</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9f88e5d2-65e3-497e-a8eb-e4bfef67f63b' class='xr-index-data-in' type='checkbox'/><label for='index-9f88e5d2-65e3-497e-a8eb-e4bfef67f63b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;A&#x27;, &#x27;B&#x27;, &#x27;C&#x27;, &#x27;D&#x27;, &#x27;E&#x27;], dtype=&#x27;object&#x27;, name=&#x27;repetitions&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-54673e68-b180-4efa-be8e-ff3773c72254' class='xr-section-summary-in' type='checkbox'  checked><label for='section-54673e68-b180-4efa-be8e-ff3773c72254' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20240829-164417-502-7f7737</dd><dt><span>dataset_name :</span></dt><dd></dd><dt><span>dataset_state :</span></dt><dd>None</dd><dt><span>timestamp_start :</span></dt><dd>None</dd><dt><span>timestamp_end :</span></dt><dd>None</dd><dt><span>quantify_dataset_version :</span></dt><dd>2.0.0</dd><dt><span>software_versions :</span></dt><dd>{}</dd><dt><span>relationships :</span></dt><dd>[]</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div></li></ul></div></div>"
      ],
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       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 115kB\n",
       "Dimensions:      \u001b[1m(\u001b[0mrepetitions: \u001b[1;36m5\u001b[0m, main_dim: \u001b[1;36m1200\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "    amp          \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 10kB \u001b[1;36m0.45\u001b[0m \u001b[1;36m0.4534\u001b[0m \u001b[1;36m0.4569\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.5466\u001b[0m \u001b[1;36m0.55\u001b[0m\n",
       "    time         \u001b[1m(\u001b[0mmain_dim\u001b[1m)\u001b[0m float64 10kB \u001b[1;36m0.0\u001b[0m \u001b[1;36m0.0\u001b[0m \u001b[1;36m0.0\u001b[0m \u001b[1;36m0.0\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m1e-07\u001b[0m \u001b[1;36m1e-07\u001b[0m \u001b[1;36m1e-07\u001b[0m\n",
       "  * repetitions  \u001b[1m(\u001b[0mrepetitions\u001b[1m)\u001b[0m <U1 20B \u001b[32m'A'\u001b[0m \u001b[32m'B'\u001b[0m \u001b[32m'C'\u001b[0m \u001b[32m'D'\u001b[0m \u001b[32m'E'\u001b[0m\n",
       "Dimensions without coordinates: main_dim\n",
       "Data variables:\n",
       "    pop_q0       \u001b[1m(\u001b[0mrepetitions, main_dim\u001b[1m)\u001b[0m float64 48kB \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.4818\u001b[0m \u001b[1;36m0.5\u001b[0m\n",
       "    pop_q1       \u001b[1m(\u001b[0mrepetitions, main_dim\u001b[1m)\u001b[0m float64 48kB \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.5371\u001b[0m \u001b[1;36m0.5\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20240829\u001b[0m-\u001b[1;36m164417\u001b[0m-\u001b[1;36m502\u001b[0m-7f7737\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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge with the previous dataset\n",
    "dataset_rep = dataset.merge(dataset_indexed_rep, combine_attrs=\"drop_conflicts\")\n",
    "\n",
    "assert dataset_rep == round_trip_dataset(dataset_rep)  # confirm read/write\n",
    "\n",
    "dataset_rep"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e009fb1",
   "metadata": {},
   "source": [
    "And as before, we can reshape the dataset to take advantage of the xarray built-in utilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cb737344",
   "metadata": {},
   "outputs": [
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       "Data variables:\n",
       "    pop_q0       (repetitions, amp, time) float64 48kB 0.5 0.5 0.5 ... 0.5 0.5\n",
       "    pop_q1       (repetitions, amp, time) float64 48kB 0.5 0.5 0.5 ... 0.5 0.5\n",
       "Attributes:\n",
       "    tuid:                      20240829-164417-502-7f7737\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:    []</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-655782b0-e7ad-4456-86fa-6bc8459b1c0d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-655782b0-e7ad-4456-86fa-6bc8459b1c0d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>amp</span>: 30</li><li><span class='xr-has-index'>time</span>: 40</li><li><span class='xr-has-index'>repetitions</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ef7b66d8-204f-44d4-996c-d7bbad54086b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ef7b66d8-204f-44d4-996c-d7bbad54086b' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>amp</span></div><div class='xr-var-dims'>(amp)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.45 0.4534 0.4569 ... 0.5466 0.55</div><input id='attrs-c92bc5b0-33b4-456a-b9e5-ed9afcdf8027' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c92bc5b0-33b4-456a-b9e5-ed9afcdf8027' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6091bd0-e1f4-45fe-bda0-6238dad7c8ba' class='xr-var-data-in' type='checkbox'><label for='data-e6091bd0-e1f4-45fe-bda0-6238dad7c8ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Amplitude</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.45    , 0.453448, 0.456897, 0.460345, 0.463793, 0.467241, 0.47069 ,\n",
       "       0.474138, 0.477586, 0.481034, 0.484483, 0.487931, 0.491379, 0.494828,\n",
       "       0.498276, 0.501724, 0.505172, 0.508621, 0.512069, 0.515517, 0.518966,\n",
       "       0.522414, 0.525862, 0.52931 , 0.532759, 0.536207, 0.539655, 0.543103,\n",
       "       0.546552, 0.55    ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 2.564e-09 ... 9.744e-08 1e-07</div><input id='attrs-2201b840-7bd8-463c-bbba-26f4be0f3da1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2201b840-7bd8-463c-bbba-26f4be0f3da1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13de0b4e-698c-4cd2-ba9d-9d2385685fa1' class='xr-var-data-in' type='checkbox'><label for='data-13de0b4e-698c-4cd2-ba9d-9d2385685fa1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>is_main_coord :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([0.000000e+00, 2.564103e-09, 5.128205e-09, 7.692308e-09, 1.025641e-08,\n",
       "       1.282051e-08, 1.538462e-08, 1.794872e-08, 2.051282e-08, 2.307692e-08,\n",
       "       2.564103e-08, 2.820513e-08, 3.076923e-08, 3.333333e-08, 3.589744e-08,\n",
       "       3.846154e-08, 4.102564e-08, 4.358974e-08, 4.615385e-08, 4.871795e-08,\n",
       "       5.128205e-08, 5.384615e-08, 5.641026e-08, 5.897436e-08, 6.153846e-08,\n",
       "       6.410256e-08, 6.666667e-08, 6.923077e-08, 7.179487e-08, 7.435897e-08,\n",
       "       7.692308e-08, 7.948718e-08, 8.205128e-08, 8.461538e-08, 8.717949e-08,\n",
       "       8.974359e-08, 9.230769e-08, 9.487179e-08, 9.743590e-08, 1.000000e-07])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>repetitions</span></div><div class='xr-var-dims'>(repetitions)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;A&#x27; &#x27;B&#x27; &#x27;C&#x27; &#x27;D&#x27; &#x27;E&#x27;</div><input id='attrs-06535878-a528-448e-ae04-06017581648f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-06535878-a528-448e-ae04-06017581648f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f0a1ec5-ae2c-4e39-9b32-bc9687bbe8cc' class='xr-var-data-in' type='checkbox'><label for='data-0f0a1ec5-ae2c-4e39-9b32-bc9687bbe8cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;A&#x27;, &#x27;B&#x27;, &#x27;C&#x27;, &#x27;D&#x27;, &#x27;E&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-73016562-91e1-4eb4-9bd1-f26827279db6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-73016562-91e1-4eb4-9bd1-f26827279db6' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>pop_q0</span></div><div class='xr-var-dims'>(repetitions, amp, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5</div><input id='attrs-f2e41a9f-628d-4d64-928a-8ac65b7cd402' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2e41a9f-628d-4d64-928a-8ac65b7cd402' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8327fbaa-017e-4623-bf1d-4d8d0f22996c' class='xr-var-data-in' type='checkbox'><label for='data-8327fbaa-017e-4623-bf1d-4d8d0f22996c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Population Q0</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[[0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ],\n",
       "        [0.5       , 0.52462974, 0.54556513, ..., 0.49369154,\n",
       "         0.46593909, 0.47829501],\n",
       "        [0.5       , 0.53096479, 0.54671207, ..., 0.58439532,\n",
       "         0.52840192, 0.48526448],\n",
       "        ...,\n",
       "        [0.5       , 0.53554992, 0.54556213, ..., 0.53421524,\n",
       "         0.52683234, 0.47934742],\n",
       "        [0.5       , 0.52263221, 0.53324799, ..., 0.49520272,\n",
       "         0.48292625, 0.45911157],\n",
       "        [0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ]],\n",
       "\n",
       "       [[0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ],\n",
       "        [0.5       , 0.511993  , 0.54040107, ..., 0.49323746,\n",
       "         0.46472548, 0.4717735 ],\n",
       "        [0.5       , 0.53839618, 0.59404155, ..., 0.57712699,\n",
       "         0.52809145, 0.47479363],\n",
       "...\n",
       "        [0.5       , 0.5543903 , 0.56225336, ..., 0.55717664,\n",
       "         0.54072803, 0.4903811 ],\n",
       "        [0.5       , 0.51269098, 0.51747944, ..., 0.49306189,\n",
       "         0.46207336, 0.45931355],\n",
       "        [0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ]],\n",
       "\n",
       "       [[0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ],\n",
       "        [0.5       , 0.53282701, 0.53331208, ..., 0.49573131,\n",
       "         0.46313136, 0.45196851],\n",
       "        [0.5       , 0.52376577, 0.55784942, ..., 0.58932903,\n",
       "         0.52031397, 0.47580372],\n",
       "        ...,\n",
       "        [0.5       , 0.53875118, 0.57789726, ..., 0.53942745,\n",
       "         0.52059331, 0.48855559],\n",
       "        [0.5       , 0.52515217, 0.54606107, ..., 0.49641976,\n",
       "         0.46010524, 0.48175285],\n",
       "        [0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pop_q1</span></div><div class='xr-var-dims'>(repetitions, amp, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 0.5 0.5 0.5 ... 0.5 0.5 0.5 0.5</div><input id='attrs-29e96cb8-e1a8-48f3-bb94-9fc76380c91f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29e96cb8-e1a8-48f3-bb94-9fc76380c91f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd1b5703-9808-4946-82c3-a5525649b61e' class='xr-var-data-in' type='checkbox'><label for='data-bd1b5703-9808-4946-82c3-a5525649b61e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>unit :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>Population Q1</dd><dt><span>is_main_var :</span></dt><dd>True</dd><dt><span>uniformly_spaced :</span></dt><dd>True</dd><dt><span>grid :</span></dt><dd>True</dd><dt><span>is_dataset_ref :</span></dt><dd>False</dd><dt><span>has_repetitions :</span></dt><dd>True</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([[[0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ],\n",
       "        [0.5       , 0.47119973, 0.47901959, ..., 0.50297437,\n",
       "         0.51760087, 0.54529387],\n",
       "        [0.5       , 0.44878307, 0.40519978, ..., 0.42239416,\n",
       "         0.46766323, 0.52530065],\n",
       "        ...,\n",
       "        [0.5       , 0.44208814, 0.40902762, ..., 0.44348506,\n",
       "         0.45882752, 0.51325446],\n",
       "        [0.5       , 0.48384946, 0.47746526, ..., 0.50707099,\n",
       "         0.52288233, 0.53973285],\n",
       "        [0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ]],\n",
       "\n",
       "       [[0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ],\n",
       "        [0.5       , 0.47468685, 0.48200051, ..., 0.50796779,\n",
       "         0.53158208, 0.53673514],\n",
       "        [0.5       , 0.43322213, 0.41173447, ..., 0.42695649,\n",
       "         0.45665907, 0.5238828 ],\n",
       "...\n",
       "        [0.5       , 0.44793137, 0.40302014, ..., 0.46628379,\n",
       "         0.45595214, 0.51040954],\n",
       "        [0.5       , 0.48137118, 0.46009508, ..., 0.50666135,\n",
       "         0.51858203, 0.53633277],\n",
       "        [0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ]],\n",
       "\n",
       "       [[0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ],\n",
       "        [0.5       , 0.46862904, 0.47483531, ..., 0.50652217,\n",
       "         0.51719523, 0.51827645],\n",
       "        [0.5       , 0.45521752, 0.46430465, ..., 0.43180196,\n",
       "         0.46456878, 0.51473245],\n",
       "        ...,\n",
       "        [0.5       , 0.45105997, 0.39821704, ..., 0.44310153,\n",
       "         0.46092092, 0.52434067],\n",
       "        [0.5       , 0.4840124 , 0.47170094, ..., 0.50421072,\n",
       "         0.54069819, 0.53709612],\n",
       "        [0.5       , 0.5       , 0.5       , ..., 0.5       ,\n",
       "         0.5       , 0.5       ]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7128bad4-7830-45d7-a4ef-2b876dd85fc6' class='xr-section-summary-in' type='checkbox'  ><label for='section-7128bad4-7830-45d7-a4ef-2b876dd85fc6' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>repetitions</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-757e636e-8e0b-437d-9ef5-e5f1e402b923' class='xr-index-data-in' type='checkbox'/><label for='index-757e636e-8e0b-437d-9ef5-e5f1e402b923' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;A&#x27;, &#x27;B&#x27;, &#x27;C&#x27;, &#x27;D&#x27;, &#x27;E&#x27;], dtype=&#x27;object&#x27;, name=&#x27;repetitions&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>amp</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-84ceba3a-96d7-464f-a132-2c130f7ff088' class='xr-index-data-in' type='checkbox'/><label for='index-84ceba3a-96d7-464f-a132-2c130f7ff088' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.45,   0.453448275862069, 0.45689655172413796,\n",
       "       0.46034482758620693,  0.4637931034482759,  0.4672413793103448,\n",
       "        0.4706896551724138, 0.47413793103448276, 0.47758620689655173,\n",
       "        0.4810344827586207,  0.4844827586206897, 0.48793103448275865,\n",
       "        0.4913793103448276,  0.4948275862068966, 0.49827586206896557,\n",
       "        0.5017241379310345,  0.5051724137931035,  0.5086206896551725,\n",
       "        0.5120689655172415,  0.5155172413793103,  0.5189655172413794,\n",
       "        0.5224137931034483,  0.5258620689655172,  0.5293103448275862,\n",
       "        0.5327586206896552,  0.5362068965517242,  0.5396551724137931,\n",
       "        0.5431034482758621,  0.5465517241379311,                0.55],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;amp&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ba037ffd-b8cb-413e-b8c6-dcf5a861968b' class='xr-index-data-in' type='checkbox'/><label for='index-ba037ffd-b8cb-413e-b8c6-dcf5a861968b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                   0.0,  2.564102564102564e-09,  5.128205128205128e-09,\n",
       "        7.692307692307693e-09, 1.0256410256410256e-08,  1.282051282051282e-08,\n",
       "       1.5384615384615385e-08,  1.794871794871795e-08, 2.0512820512820512e-08,\n",
       "       2.3076923076923076e-08,  2.564102564102564e-08, 2.8205128205128203e-08,\n",
       "        3.076923076923077e-08, 3.3333333333333334e-08,   3.58974358974359e-08,\n",
       "        3.846153846153846e-08, 4.1025641025641025e-08,  4.358974358974359e-08,\n",
       "        4.615384615384615e-08, 4.8717948717948716e-08,  5.128205128205128e-08,\n",
       "        5.384615384615384e-08,  5.641025641025641e-08,  5.897435897435897e-08,\n",
       "        6.153846153846154e-08,   6.41025641025641e-08,  6.666666666666667e-08,\n",
       "        6.923076923076923e-08,   7.17948717948718e-08,  7.435897435897436e-08,\n",
       "        7.692307692307692e-08,  7.948717948717949e-08,  8.205128205128205e-08,\n",
       "        8.461538461538461e-08,  8.717948717948718e-08,  8.974358974358974e-08,\n",
       "         9.23076923076923e-08,  9.487179487179487e-08,  9.743589743589743e-08,\n",
       "                        1e-07],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-db1331fb-db58-4b3c-901c-1c1cf88576d6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db1331fb-db58-4b3c-901c-1c1cf88576d6' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20240829-164417-502-7f7737</dd><dt><span>dataset_name :</span></dt><dd></dd><dt><span>dataset_state :</span></dt><dd>None</dd><dt><span>timestamp_start :</span></dt><dd>None</dd><dt><span>timestamp_end :</span></dt><dd>None</dd><dt><span>quantify_dataset_version :</span></dt><dd>2.0.0</dd><dt><span>software_versions :</span></dt><dd>{}</dd><dt><span>relationships :</span></dt><dd>[]</dd><dt><span>json_serialize_exclude :</span></dt><dd>[]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "\n",
       "\u001b[1m<\u001b[0m\u001b[1;95mxarray.Dataset\u001b[0m\u001b[1m>\u001b[0m Size: 97kB\n",
       "Dimensions:      \u001b[1m(\u001b[0mamp: \u001b[1;36m30\u001b[0m, time: \u001b[1;36m40\u001b[0m, repetitions: \u001b[1;36m5\u001b[0m\u001b[1m)\u001b[0m\n",
       "Coordinates:\n",
       "  * amp          \u001b[1m(\u001b[0mamp\u001b[1m)\u001b[0m float64 240B \u001b[1;36m0.45\u001b[0m \u001b[1;36m0.4534\u001b[0m \u001b[1;36m0.4569\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.5431\u001b[0m \u001b[1;36m0.5466\u001b[0m \u001b[1;36m0.55\u001b[0m\n",
       "  * time         \u001b[1m(\u001b[0mtime\u001b[1m)\u001b[0m float64 320B \u001b[1;36m0.0\u001b[0m \u001b[1;36m2.564e-09\u001b[0m \u001b[1;36m5.128e-09\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m9.744e-08\u001b[0m \u001b[1;36m1e-07\u001b[0m\n",
       "  * repetitions  \u001b[1m(\u001b[0mrepetitions\u001b[1m)\u001b[0m <U1 20B \u001b[32m'A'\u001b[0m \u001b[32m'B'\u001b[0m \u001b[32m'C'\u001b[0m \u001b[32m'D'\u001b[0m \u001b[32m'E'\u001b[0m\n",
       "Data variables:\n",
       "    pop_q0       \u001b[1m(\u001b[0mrepetitions, amp, time\u001b[1m)\u001b[0m float64 48kB \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m\n",
       "    pop_q1       \u001b[1m(\u001b[0mrepetitions, amp, time\u001b[1m)\u001b[0m float64 48kB \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[33m...\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[1;36m0.5\u001b[0m\n",
       "Attributes:\n",
       "    tuid:                      \u001b[1;36m20240829\u001b[0m-\u001b[1;36m164417\u001b[0m-\u001b[1;36m502\u001b[0m-7f7737\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_gridded = dh.to_gridded_dataset(\n",
    "    dataset_rep,\n",
    "    dimension=\"main_dim\",\n",
    "    coords_names=dattrs.get_main_coords(dataset),\n",
    ")\n",
    "dataset_gridded"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "defd4a70",
   "metadata": {},
   "source": [
    "It is now possible to retrieve (select) a specific entry along the `repetitions` dimension:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b5100ad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\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;36m2\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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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;36m2\u001b[0m\u001b[39m Axes\u001b[0m\u001b[1m>\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = dataset_gridded.pop_q0.sel(repetitions=\"A\").plot(x=\"amp\")\n",
    "plt.show()\n",
    "_ = dataset_gridded.pop_q0.sel(repetitions=\"D\").plot(x=\"amp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c674ba9d",
   "metadata": {},
   "source": [
    "## Dataset attributes\n",
    "\n",
    "The required attributes of the Quantify dataset are defined by the following dataclass.\n",
    "It can be used to generate a default dictionary that is attached to a dataset under the {attr}`xarray.Dataset.attrs` attribute.\n",
    "\n",
    "```{eval-rst}\n",
    ".. autoclass:: quantify_core.data.dataset_attrs.QDatasetAttrs\n",
    "    :members:\n",
    "    :noindex:\n",
    "    :show-inheritance:\n",
    "```\n",
    "\n",
    "Additionally in order to express relationships between coordinates and/or variables\n",
    "the following template is provided:\n",
    "\n",
    "```{eval-rst}\n",
    ".. autoclass:: quantify_core.data.dataset_attrs.QDatasetIntraRelationship\n",
    "    :members:\n",
    "    :noindex:\n",
    "    :show-inheritance:\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cdc5044e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\u001b[1m{\u001b[0m\n",
       "    \u001b[32m'tuid'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
       "    \u001b[32m'dataset_name'\u001b[0m: \u001b[32m''\u001b[0m,\n",
       "    \u001b[32m'dataset_state'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
       "    \u001b[32m'timestamp_start'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
       "    \u001b[32m'timestamp_end'\u001b[0m: \u001b[3;35mNone\u001b[0m,\n",
       "    \u001b[32m'quantify_dataset_version'\u001b[0m: \u001b[32m'2.0.0'\u001b[0m,\n",
       "    \u001b[32m'software_versions'\u001b[0m: \u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n",
       "    \u001b[32m'relationships'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m,\n",
       "    \u001b[32m'json_serialize_exclude'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
       "\u001b[1m}\u001b[0m"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from quantify_core.data.dataset_attrs import QDatasetAttrs\n",
    "\n",
    "# tip: to_json and from_dict, from_json  are also available\n",
    "dataset.attrs = QDatasetAttrs().to_dict()\n",
    "dataset.attrs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bff4a6c6",
   "metadata": {},
   "source": [
    "Note that xarray automatically provides the entries of the dataset attributes as python attributes. And similarly for the xarray coordinates and data variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dbaf0f11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001b[1m(\u001b[0m\u001b[32m'2.0.0'\u001b[0m, \u001b[3;35mNone\u001b[0m\u001b[1m)\u001b[0m"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.quantify_dataset_version, dataset.tuid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea725680",
   "metadata": {},
   "source": [
    "## Main coordinates and variables attributes\n",
    "\n",
    "Similar to the dataset attributes ({attr}`xarray.Dataset.attrs`), the main coordinates and variables have each their own required attributes attached to them as a dictionary under the {attr}`xarray.DataArray.attrs` attribute.\n",
    "\n",
    "```{eval-rst}\n",
    ".. autoclass:: quantify_core.data.dataset_attrs.QCoordAttrs\n",
    "    :members:\n",
    "    :noindex:\n",
    "    :show-inheritance:\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "38e3e688",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\u001b[1m{\u001b[0m\n",
       "    \u001b[32m'unit'\u001b[0m: \u001b[32m'V'\u001b[0m,\n",
       "    \u001b[32m'long_name'\u001b[0m: \u001b[32m'Amplitude'\u001b[0m,\n",
       "    \u001b[32m'is_main_coord'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
       "    \u001b[32m'uniformly_spaced'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
       "    \u001b[32m'is_dataset_ref'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
       "    \u001b[32m'json_serialize_exclude'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
       "\u001b[1m}\u001b[0m"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.amp.attrs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d78274f",
   "metadata": {},
   "source": [
    "```{eval-rst}\n",
    ".. autoclass:: quantify_core.data.dataset_attrs.QVarAttrs\n",
    "    :members:\n",
    "    :noindex:\n",
    "    :show-inheritance:\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b389218d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "\u001b[1m{\u001b[0m\n",
       "    \u001b[32m'unit'\u001b[0m: \u001b[32m''\u001b[0m,\n",
       "    \u001b[32m'long_name'\u001b[0m: \u001b[32m'Population Q0'\u001b[0m,\n",
       "    \u001b[32m'is_main_var'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
       "    \u001b[32m'uniformly_spaced'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
       "    \u001b[32m'grid'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
       "    \u001b[32m'is_dataset_ref'\u001b[0m: \u001b[3;91mFalse\u001b[0m,\n",
       "    \u001b[32m'has_repetitions'\u001b[0m: \u001b[3;92mTrue\u001b[0m,\n",
       "    \u001b[32m'json_serialize_exclude'\u001b[0m: \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\n",
       "\u001b[1m}\u001b[0m"
      ]
     },
     "execution_count": 14,
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   ],
   "source": [
    "dataset.pop_q0.attrs"
   ]
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   "source": [
    "## Storage format\n",
    "\n",
    "The Quantify dataset is written to disk and loaded back making use of xarray-supported facilities.\n",
    "Internally we write and load to/from disk using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8eef4edf",
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">write_dataset</span><span class=\"p\">(</span><span class=\"n\">path</span><span class=\"p\">:</span> <span class=\"n\">Path</span> <span class=\"o\">|</span> <span class=\"nb\">str</span><span class=\"p\">,</span> <span class=\"n\">dataset</span><span class=\"p\">:</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"kc\">None</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;Writes a :class:`~xarray.Dataset` to a file with the `h5netcdf` engine.</span>\n",
       "\n",
       "<span class=\"sd\">    Before writing the</span>\n",
       "<span class=\"sd\">    :meth:`~quantify_core.data.dataset_adapters.AdapterH5NetCDF.adapt`</span>\n",
       "<span class=\"sd\">    is applied.</span>\n",
       "\n",
       "<span class=\"sd\">    To accommodate for complex-type numbers and arrays ``invalid_netcdf=True`` is used.</span>\n",
       "\n",
       "<span class=\"sd\">    Parameters</span>\n",
       "<span class=\"sd\">    ----------</span>\n",
       "<span class=\"sd\">    path</span>\n",
       "<span class=\"sd\">        Path to the file including filename and extension</span>\n",
       "<span class=\"sd\">    dataset</span>\n",
       "<span class=\"sd\">        The :class:`~xarray.Dataset` to be written to file.</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>  <span class=\"c1\"># pylint: disable=line-too-long</span>\n",
       "    <span class=\"n\">_xarray_numpy_bool_patch</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"p\">)</span>  <span class=\"c1\"># See issue #161 in quantify-core</span>\n",
       "    <span class=\"c1\"># Only quantify_dataset_version=&gt;2.0.0 requires the adapter</span>\n",
       "    <span class=\"k\">if</span> <span class=\"s2\">&quot;quantify_dataset_version&quot;</span> <span class=\"ow\">in</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">attrs</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">dataset</span> <span class=\"o\">=</span> <span class=\"n\">da</span><span class=\"o\">.</span><span class=\"n\">AdapterH5NetCDF</span><span class=\"o\">.</span><span class=\"n\">adapt</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"p\">)</span>\n",
       "    <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">to_netcdf</span><span class=\"p\">(</span><span class=\"n\">path</span><span class=\"p\">,</span> <span class=\"n\">engine</span><span class=\"o\">=</span><span class=\"s2\">&quot;h5netcdf&quot;</span><span class=\"p\">,</span> <span class=\"n\">invalid_netcdf</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">)</span>\n",
       "</pre></div>\n"
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       "\\PY{k}{def} \\PY{n+nf}{write\\PYZus{}dataset}\\PY{p}{(}\\PY{n}{path}\\PY{p}{:} \\PY{n}{Path} \\PY{o}{|} \\PY{n+nb}{str}\\PY{p}{,} \\PY{n}{dataset}\\PY{p}{:} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{k+kc}{None}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}Writes a :class:`\\PYZti{}xarray.Dataset` to a file with the `h5netcdf` engine.}\n",
       "\n",
       "\\PY{l+s+sd}{    Before writing the}\n",
       "\\PY{l+s+sd}{    :meth:`\\PYZti{}quantify\\PYZus{}core.data.dataset\\PYZus{}adapters.AdapterH5NetCDF.adapt`}\n",
       "\\PY{l+s+sd}{    is applied.}\n",
       "\n",
       "\\PY{l+s+sd}{    To accommodate for complex\\PYZhy{}type numbers and arrays ``invalid\\PYZus{}netcdf=True`` is used.}\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}{    path}\n",
       "\\PY{l+s+sd}{        Path to the file including filename and extension}\n",
       "\\PY{l+s+sd}{    dataset}\n",
       "\\PY{l+s+sd}{        The :class:`\\PYZti{}xarray.Dataset` to be written to file.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}  \\PY{c+c1}{\\PYZsh{} pylint: disable=line\\PYZhy{}too\\PYZhy{}long}\n",
       "    \\PY{n}{\\PYZus{}xarray\\PYZus{}numpy\\PYZus{}bool\\PYZus{}patch}\\PY{p}{(}\\PY{n}{dataset}\\PY{p}{)}  \\PY{c+c1}{\\PYZsh{} See issue \\PYZsh{}161 in quantify\\PYZhy{}core}\n",
       "    \\PY{c+c1}{\\PYZsh{} Only quantify\\PYZus{}dataset\\PYZus{}version=\\PYZgt{}2.0.0 requires the adapter}\n",
       "    \\PY{k}{if} \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{quantify\\PYZus{}dataset\\PYZus{}version}\\PY{l+s+s2}{\\PYZdq{}} \\PY{o+ow}{in} \\PY{n}{dataset}\\PY{o}{.}\\PY{n}{attrs}\\PY{p}{:}\n",
       "        \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{da}\\PY{o}{.}\\PY{n}{AdapterH5NetCDF}\\PY{o}{.}\\PY{n}{adapt}\\PY{p}{(}\\PY{n}{dataset}\\PY{p}{)}\n",
       "    \\PY{n}{dataset}\\PY{o}{.}\\PY{n}{to\\PYZus{}netcdf}\\PY{p}{(}\\PY{n}{path}\\PY{p}{,} \\PY{n}{engine}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{h5netcdf}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{invalid\\PYZus{}netcdf}\\PY{o}{=}\\PY{k+kc}{True}\\PY{p}{)}\n",
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       "\n",
       "def \u001b[1;35mwrite_dataset\u001b[0m\u001b[1m(\u001b[0mpath: Path | str, dataset: xr.Dataset\u001b[1m)\u001b[0m -> \u001b[3;35mNone\u001b[0m:\n",
       "    \u001b[32m\"\"\u001b[0m\"Writes a :class:`~xarray.Dataset` to a file with the `h5netcdf` engine.\n",
       "\n",
       "    Before writing the\n",
       "    :meth:`~quantify_core.data.dataset_adapters.AdapterH5NetCDF.adapt`\n",
       "    is applied.\n",
       "\n",
       "    To accommodate for complex-type numbers and arrays ``\u001b[33minvalid_netcdf\u001b[0m=\u001b[3;92mTrue\u001b[0m`` is used.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    path\n",
       "        Path to the file including filename and extension\n",
       "    dataset\n",
       "        The :class:`~xarray.Dataset` to be written to file.\n",
       "    \u001b[32m\"\"\u001b[0m\"  # pylint: \u001b[33mdisable\u001b[0m=\u001b[35mline\u001b[0m-too-long\n",
       "    \u001b[1;35m_xarray_numpy_bool_patch\u001b[0m\u001b[1m(\u001b[0mdataset\u001b[1m)\u001b[0m  # See issue #\u001b[1;36m161\u001b[0m in quantify-core\n",
       "    # Only \u001b[33mquantify_dataset_version\u001b[0m=>\u001b[1;36m2.0\u001b[0m.\u001b[1;36m0\u001b[0m requires the adapter\n",
       "    if \u001b[32m\"quantify_dataset_version\"\u001b[0m in dataset.attrs:\n",
       "        dataset = \u001b[1;35mda.AdapterH5NetCDF.adapt\u001b[0m\u001b[1m(\u001b[0mdataset\u001b[1m)\u001b[0m\n",
       "    \u001b[1;35mdataset.to_netcdf\u001b[0m\u001b[1m(\u001b[0mpath, \u001b[33mengine\u001b[0m=\u001b[32m\"h5netcdf\"\u001b[0m, \u001b[33minvalid_netcdf\u001b[0m=\u001b[3;92mTrue\u001b[0m\u001b[1m)\u001b[0m"
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       ".output_html .vm { color: #19177C } /* Name.Variable.Magic */\n",
       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">load_dataset</span><span class=\"p\">(</span>\n",
       "    <span class=\"n\">tuid</span><span class=\"p\">:</span> <span class=\"n\">TUID</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">datadir</span><span class=\"p\">:</span> <span class=\"n\">Path</span> <span class=\"o\">|</span> <span class=\"nb\">str</span> <span class=\"o\">|</span> <span class=\"kc\">None</span> <span class=\"o\">=</span> <span class=\"kc\">None</span><span class=\"p\">,</span>\n",
       "    <span class=\"n\">name</span><span class=\"p\">:</span> <span class=\"nb\">str</span> <span class=\"o\">=</span> <span class=\"n\">DATASET_NAME</span><span class=\"p\">,</span>\n",
       "<span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;Loads a dataset specified by a tuid.</span>\n",
       "\n",
       "<span class=\"sd\">    .. tip::</span>\n",
       "\n",
       "<span class=\"sd\">        This method also works when specifying only the first part of a</span>\n",
       "<span class=\"sd\">        :class:`~quantify_core.data.types.TUID`.</span>\n",
       "\n",
       "<span class=\"sd\">    .. note::</span>\n",
       "\n",
       "<span class=\"sd\">        This method uses :func:`~.load_dataset` to ensure the file is closed after</span>\n",
       "<span class=\"sd\">        loading as datasets are intended to be immutable after performing the initial</span>\n",
       "<span class=\"sd\">        experiment.</span>\n",
       "\n",
       "<span class=\"sd\">    Parameters</span>\n",
       "<span class=\"sd\">    ----------</span>\n",
       "<span class=\"sd\">    tuid</span>\n",
       "<span class=\"sd\">        A :class:`~quantify_core.data.types.TUID` string. It is also possible to specify</span>\n",
       "<span class=\"sd\">        only the first part of a tuid.</span>\n",
       "<span class=\"sd\">    datadir</span>\n",
       "<span class=\"sd\">        Path of the data directory. If ``None``, uses :meth:`~get_datadir` to determine</span>\n",
       "<span class=\"sd\">        the data directory.</span>\n",
       "<span class=\"sd\">    name</span>\n",
       "<span class=\"sd\">        Name of the dataset.</span>\n",
       "\n",
       "<span class=\"sd\">    Returns</span>\n",
       "<span class=\"sd\">    -------</span>\n",
       "<span class=\"sd\">    :</span>\n",
       "<span class=\"sd\">        The dataset.</span>\n",
       "\n",
       "<span class=\"sd\">    Raises</span>\n",
       "<span class=\"sd\">    ------</span>\n",
       "<span class=\"sd\">    FileNotFoundError</span>\n",
       "<span class=\"sd\">        No data found for specified date.</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">load_dataset_from_path</span><span class=\"p\">(</span><span class=\"n\">_locate_experiment_file</span><span class=\"p\">(</span><span class=\"n\">tuid</span><span class=\"p\">,</span> <span class=\"n\">datadir</span><span class=\"p\">,</span> <span class=\"n\">name</span><span class=\"p\">))</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{load\\PYZus{}dataset}\\PY{p}{(}\n",
       "    \\PY{n}{tuid}\\PY{p}{:} \\PY{n}{TUID}\\PY{p}{,}\n",
       "    \\PY{n}{datadir}\\PY{p}{:} \\PY{n}{Path} \\PY{o}{|} \\PY{n+nb}{str} \\PY{o}{|} \\PY{k+kc}{None} \\PY{o}{=} \\PY{k+kc}{None}\\PY{p}{,}\n",
       "    \\PY{n}{name}\\PY{p}{:} \\PY{n+nb}{str} \\PY{o}{=} \\PY{n}{DATASET\\PYZus{}NAME}\\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{}Loads a dataset specified by a tuid.}\n",
       "\n",
       "\\PY{l+s+sd}{    .. tip::}\n",
       "\n",
       "\\PY{l+s+sd}{        This method also works when specifying only the first part of a}\n",
       "\\PY{l+s+sd}{        :class:`\\PYZti{}quantify\\PYZus{}core.data.types.TUID`.}\n",
       "\n",
       "\\PY{l+s+sd}{    .. note::}\n",
       "\n",
       "\\PY{l+s+sd}{        This method uses :func:`\\PYZti{}.load\\PYZus{}dataset` to ensure the file is closed after}\n",
       "\\PY{l+s+sd}{        loading as datasets are intended to be immutable after performing the initial}\n",
       "\\PY{l+s+sd}{        experiment.}\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}{    tuid}\n",
       "\\PY{l+s+sd}{        A :class:`\\PYZti{}quantify\\PYZus{}core.data.types.TUID` string. It is also possible to specify}\n",
       "\\PY{l+s+sd}{        only the first part of a tuid.}\n",
       "\\PY{l+s+sd}{    datadir}\n",
       "\\PY{l+s+sd}{        Path of the data directory. If ``None``, uses :meth:`\\PYZti{}get\\PYZus{}datadir` to determine}\n",
       "\\PY{l+s+sd}{        the data directory.}\n",
       "\\PY{l+s+sd}{    name}\n",
       "\\PY{l+s+sd}{        Name of the dataset.}\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}{        The dataset.}\n",
       "\n",
       "\\PY{l+s+sd}{    Raises}\n",
       "\\PY{l+s+sd}{    \\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}\\PYZhy{}}\n",
       "\\PY{l+s+sd}{    FileNotFoundError}\n",
       "\\PY{l+s+sd}{        No data found for specified date.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "    \\PY{k}{return} \\PY{n}{load\\PYZus{}dataset\\PYZus{}from\\PYZus{}path}\\PY{p}{(}\\PY{n}{\\PYZus{}locate\\PYZus{}experiment\\PYZus{}file}\\PY{p}{(}\\PY{n}{tuid}\\PY{p}{,} \\PY{n}{datadir}\\PY{p}{,} \\PY{n}{name}\\PY{p}{)}\\PY{p}{)}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "def \u001b[1;35mload_dataset\u001b[0m\u001b[1m(\u001b[0m\n",
       "    tuid: TUID,\n",
       "    datadir: Path | str | \u001b[3;35mNone\u001b[0m = \u001b[3;35mNone\u001b[0m,\n",
       "    name: str = DATASET_NAME,\n",
       "\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "    \u001b[32m\"\"\u001b[0m\"Loads a dataset specified by a tuid.\n",
       "\n",
       "    .. tip::\n",
       "\n",
       "        This method also works when specifying only the first part of a\n",
       "        :class:`~quantify_core.data.types.TUID`.\n",
       "\n",
       "    .. note::\n",
       "\n",
       "        This method uses :func:`~.load_dataset` to ensure the file is closed after\n",
       "        loading as datasets are intended to be immutable after performing the initial\n",
       "        experiment.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    tuid\n",
       "        A :class:`~quantify_core.data.types.TUID` string. It is also possible to specify\n",
       "        only the first part of a tuid.\n",
       "    datadir\n",
       "        Path of the data directory. If ``\u001b[3;35mNone\u001b[0m``, uses :meth:`~get_datadir` to determine\n",
       "        the data directory.\n",
       "    name\n",
       "        Name of the dataset.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    :\n",
       "        The dataset.\n",
       "\n",
       "    Raises\n",
       "    ------\n",
       "    FileNotFoundError\n",
       "        No data found for specified date.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    return \u001b[1;35mload_dataset_from_path\u001b[0m\u001b[1m(\u001b[0m\u001b[1;35m_locate_experiment_file\u001b[0m\u001b[1m(\u001b[0mtuid, datadir, name\u001b[1m)\u001b[0m\u001b[1m)\u001b[0m"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_source_code(dh.write_dataset)\n",
    "display_source_code(dh.load_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9c5a464",
   "metadata": {},
   "source": [
    "Note that we use the `h5netcdf` engine which is more permissive than the default NetCDF engine to accommodate arrays of complex numbers.\n",
    "\n",
    "```{note}\n",
    "Furthermore, in order to support a variety of attribute types (e.g. the `None` type) and shapes (e.g. nested dictionaries) in a seamless dataset round trip, some additional tooling is required. See source codes below that implements the two-way conversion adapter used by the functions shown above.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d4e70eb0",
   "metadata": {},
   "outputs": [
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       ".output_html .il { color: #666666 } /* Literal.Number.Integer.Long */</style><div class=\"highlight\"><pre><span></span><span class=\"k\">class</span> <span class=\"nc\">AdapterH5NetCDF</span><span class=\"p\">(</span><span class=\"n\">DatasetAdapterBase</span><span class=\"p\">):</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    Quantify dataset adapter for the ``h5netcdf`` engine.</span>\n",
       "\n",
       "<span class=\"sd\">    It has the functionality of adapting the Quantify dataset to a format compatible</span>\n",
       "<span class=\"sd\">    with the ``h5netcdf`` xarray backend engine that is used to write and load the</span>\n",
       "<span class=\"sd\">    dataset to/from disk.</span>\n",
       "\n",
       "<span class=\"sd\">    .. warning::</span>\n",
       "\n",
       "<span class=\"sd\">        The ``h5netcdf`` engine has minor issues when performing a two-way trip of the</span>\n",
       "<span class=\"sd\">        dataset. The ``type`` of some attributes are not preserved. E.g., list- and</span>\n",
       "<span class=\"sd\">        tuple-like objects are loaded as numpy arrays of ``dtype=object``.</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "\n",
       "    <span class=\"nd\">@classmethod</span>\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">adapt</span><span class=\"p\">(</span><span class=\"bp\">cls</span><span class=\"p\">,</span> <span class=\"n\">dataset</span><span class=\"p\">:</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">        Serializes to JSON the dataset and variables attributes.</span>\n",
       "\n",
       "<span class=\"sd\">        To prevent the JSON serialization for specific items, their names should be</span>\n",
       "<span class=\"sd\">        listed under the attribute named ``json_serialize_exclude`` (for each ``attrs``</span>\n",
       "<span class=\"sd\">        dictionary).</span>\n",
       "\n",
       "<span class=\"sd\">        Parameters</span>\n",
       "<span class=\"sd\">        ----------</span>\n",
       "<span class=\"sd\">        dataset</span>\n",
       "<span class=\"sd\">            Dataset that needs to be adapted.</span>\n",
       "\n",
       "<span class=\"sd\">        Returns</span>\n",
       "<span class=\"sd\">        -------</span>\n",
       "<span class=\"sd\">        :</span>\n",
       "<span class=\"sd\">            Dataset in which the attributes have been replaced with their JSON strings</span>\n",
       "<span class=\"sd\">            version.</span>\n",
       "<span class=\"sd\">        &quot;&quot;&quot;</span>\n",
       "\n",
       "        <span class=\"k\">return</span> <span class=\"bp\">cls</span><span class=\"o\">.</span><span class=\"n\">_transform</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"p\">,</span> <span class=\"n\">vals_converter</span><span class=\"o\">=</span><span class=\"n\">json</span><span class=\"o\">.</span><span class=\"n\">dumps</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"nd\">@classmethod</span>\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">recover</span><span class=\"p\">(</span><span class=\"bp\">cls</span><span class=\"p\">,</span> <span class=\"n\">dataset</span><span class=\"p\">:</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">        Reverts the action of ``.adapt()``.</span>\n",
       "\n",
       "<span class=\"sd\">        To prevent the JSON de-serialization for specific items, their names should be</span>\n",
       "<span class=\"sd\">        listed under the attribute named ``json_serialize_exclude``</span>\n",
       "<span class=\"sd\">        (for each ``attrs`` dictionary).</span>\n",
       "\n",
       "<span class=\"sd\">        Parameters</span>\n",
       "<span class=\"sd\">        ----------</span>\n",
       "<span class=\"sd\">        dataset</span>\n",
       "<span class=\"sd\">            Dataset from which to recover the original format.</span>\n",
       "\n",
       "<span class=\"sd\">        Returns</span>\n",
       "<span class=\"sd\">        -------</span>\n",
       "<span class=\"sd\">        :</span>\n",
       "<span class=\"sd\">            Dataset in which the attributes have been replaced with their python objects</span>\n",
       "<span class=\"sd\">            version.</span>\n",
       "<span class=\"sd\">        &quot;&quot;&quot;</span>\n",
       "\n",
       "        <span class=\"k\">return</span> <span class=\"bp\">cls</span><span class=\"o\">.</span><span class=\"n\">_transform</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"p\">,</span> <span class=\"n\">vals_converter</span><span class=\"o\">=</span><span class=\"n\">json</span><span class=\"o\">.</span><span class=\"n\">loads</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"nd\">@staticmethod</span>\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">attrs_convert</span><span class=\"p\">(</span>\n",
       "        <span class=\"n\">attrs</span><span class=\"p\">:</span> <span class=\"nb\">dict</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">inplace</span><span class=\"p\">:</span> <span class=\"nb\">bool</span> <span class=\"o\">=</span> <span class=\"kc\">False</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">vals_converter</span><span class=\"p\">:</span> <span class=\"n\">Callable</span><span class=\"p\">[</span><span class=\"n\">Any</span><span class=\"p\">,</span> <span class=\"n\">Any</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">json</span><span class=\"o\">.</span><span class=\"n\">dumps</span><span class=\"p\">,</span>\n",
       "    <span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"nb\">dict</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">        Converts to/from JSON string the values of the keys which are not listed in the</span>\n",
       "<span class=\"sd\">        ``json_serialize_exclude`` list.</span>\n",
       "\n",
       "<span class=\"sd\">        Parameters</span>\n",
       "<span class=\"sd\">        ----------</span>\n",
       "<span class=\"sd\">        attrs</span>\n",
       "<span class=\"sd\">            The input dictionary.</span>\n",
       "<span class=\"sd\">        inplace</span>\n",
       "<span class=\"sd\">            If ``True`` the values are replaced in place, otherwise a deepcopy of</span>\n",
       "<span class=\"sd\">            ``attrs`` is performed first.</span>\n",
       "<span class=\"sd\">        &quot;&quot;&quot;</span>\n",
       "        <span class=\"n\">json_serialize_exclude</span> <span class=\"o\">=</span> <span class=\"n\">attrs</span><span class=\"o\">.</span><span class=\"n\">get</span><span class=\"p\">(</span><span class=\"s2\">&quot;json_serialize_exclude&quot;</span><span class=\"p\">,</span> <span class=\"p\">[])</span>\n",
       "\n",
       "        <span class=\"n\">attrs</span> <span class=\"o\">=</span> <span class=\"n\">attrs</span> <span class=\"k\">if</span> <span class=\"n\">inplace</span> <span class=\"k\">else</span> <span class=\"n\">deepcopy</span><span class=\"p\">(</span><span class=\"n\">attrs</span><span class=\"p\">)</span>\n",
       "        <span class=\"k\">for</span> <span class=\"n\">attr_name</span><span class=\"p\">,</span> <span class=\"n\">attr_val</span> <span class=\"ow\">in</span> <span class=\"n\">attrs</span><span class=\"o\">.</span><span class=\"n\">items</span><span class=\"p\">():</span>\n",
       "            <span class=\"k\">if</span> <span class=\"n\">attr_name</span> <span class=\"ow\">not</span> <span class=\"ow\">in</span> <span class=\"n\">json_serialize_exclude</span><span class=\"p\">:</span>\n",
       "                <span class=\"n\">attrs</span><span class=\"p\">[</span><span class=\"n\">attr_name</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">vals_converter</span><span class=\"p\">(</span><span class=\"n\">attr_val</span><span class=\"p\">)</span>\n",
       "        <span class=\"k\">return</span> <span class=\"n\">attrs</span>\n",
       "\n",
       "    <span class=\"nd\">@classmethod</span>\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">_transform</span><span class=\"p\">(</span>\n",
       "        <span class=\"bp\">cls</span><span class=\"p\">,</span> <span class=\"n\">dataset</span><span class=\"p\">:</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">,</span> <span class=\"n\">vals_converter</span><span class=\"p\">:</span> <span class=\"n\">Callable</span><span class=\"p\">[</span><span class=\"n\">Any</span><span class=\"p\">,</span> <span class=\"n\">Any</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">json</span><span class=\"o\">.</span><span class=\"n\">dumps</span>\n",
       "    <span class=\"p\">)</span> <span class=\"o\">-&gt;</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">:</span>\n",
       "        <span class=\"n\">dataset</span> <span class=\"o\">=</span> <span class=\"n\">xr</span><span class=\"o\">.</span><span class=\"n\">Dataset</span><span class=\"p\">(</span>\n",
       "            <span class=\"n\">dataset</span><span class=\"p\">,</span>\n",
       "            <span class=\"n\">attrs</span><span class=\"o\">=</span><span class=\"bp\">cls</span><span class=\"o\">.</span><span class=\"n\">attrs_convert</span><span class=\"p\">(</span>\n",
       "                <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">attrs</span><span class=\"p\">,</span> <span class=\"n\">inplace</span><span class=\"o\">=</span><span class=\"kc\">False</span><span class=\"p\">,</span> <span class=\"n\">vals_converter</span><span class=\"o\">=</span><span class=\"n\">vals_converter</span>\n",
       "            <span class=\"p\">),</span>\n",
       "        <span class=\"p\">)</span>\n",
       "\n",
       "        <span class=\"k\">for</span> <span class=\"n\">var_name</span> <span class=\"ow\">in</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">variables</span><span class=\"o\">.</span><span class=\"n\">keys</span><span class=\"p\">():</span>\n",
       "            <span class=\"c1\"># The new dataset generated above has already a deepcopy of the attributes.</span>\n",
       "            <span class=\"n\">_</span> <span class=\"o\">=</span> <span class=\"bp\">cls</span><span class=\"o\">.</span><span class=\"n\">attrs_convert</span><span class=\"p\">(</span>\n",
       "                <span class=\"n\">dataset</span><span class=\"p\">[</span><span class=\"n\">var_name</span><span class=\"p\">]</span><span class=\"o\">.</span><span class=\"n\">attrs</span><span class=\"p\">,</span> <span class=\"n\">inplace</span><span class=\"o\">=</span><span class=\"kc\">True</span><span class=\"p\">,</span> <span class=\"n\">vals_converter</span><span class=\"o\">=</span><span class=\"n\">vals_converter</span>\n",
       "            <span class=\"p\">)</span>\n",
       "\n",
       "        <span class=\"k\">return</span> <span class=\"n\">dataset</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{class} \\PY{n+nc}{AdapterH5NetCDF}\\PY{p}{(}\\PY{n}{DatasetAdapterBase}\\PY{p}{)}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    Quantify dataset adapter for the ``h5netcdf`` engine.}\n",
       "\n",
       "\\PY{l+s+sd}{    It has the functionality of adapting the Quantify dataset to a format compatible}\n",
       "\\PY{l+s+sd}{    with the ``h5netcdf`` xarray backend engine that is used to write and load the}\n",
       "\\PY{l+s+sd}{    dataset to/from disk.}\n",
       "\n",
       "\\PY{l+s+sd}{    .. warning::}\n",
       "\n",
       "\\PY{l+s+sd}{        The ``h5netcdf`` engine has minor issues when performing a two\\PYZhy{}way trip of the}\n",
       "\\PY{l+s+sd}{        dataset. The ``type`` of some attributes are not preserved. E.g., list\\PYZhy{} and}\n",
       "\\PY{l+s+sd}{        tuple\\PYZhy{}like objects are loaded as numpy arrays of ``dtype=object``.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "    \\PY{n+nd}{@classmethod}\n",
       "    \\PY{k}{def} \\PY{n+nf}{adapt}\\PY{p}{(}\\PY{n+nb+bp}{cls}\\PY{p}{,} \\PY{n}{dataset}\\PY{p}{:} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\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}{        Serializes to JSON the dataset and variables attributes.}\n",
       "\n",
       "\\PY{l+s+sd}{        To prevent the JSON serialization for specific items, their names should be}\n",
       "\\PY{l+s+sd}{        listed under the attribute named ``json\\PYZus{}serialize\\PYZus{}exclude`` (for each ``attrs``}\n",
       "\\PY{l+s+sd}{        dictionary).}\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}{        dataset}\n",
       "\\PY{l+s+sd}{            Dataset that needs to be adapted.}\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}{            Dataset in which the attributes have been replaced with their JSON strings}\n",
       "\\PY{l+s+sd}{            version.}\n",
       "\\PY{l+s+sd}{        \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "        \\PY{k}{return} \\PY{n+nb+bp}{cls}\\PY{o}{.}\\PY{n}{\\PYZus{}transform}\\PY{p}{(}\\PY{n}{dataset}\\PY{p}{,} \\PY{n}{vals\\PYZus{}converter}\\PY{o}{=}\\PY{n}{json}\\PY{o}{.}\\PY{n}{dumps}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n+nd}{@classmethod}\n",
       "    \\PY{k}{def} \\PY{n+nf}{recover}\\PY{p}{(}\\PY{n+nb+bp}{cls}\\PY{p}{,} \\PY{n}{dataset}\\PY{p}{:} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\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}{        Reverts the action of ``.adapt()``.}\n",
       "\n",
       "\\PY{l+s+sd}{        To prevent the JSON de\\PYZhy{}serialization for specific items, their names should be}\n",
       "\\PY{l+s+sd}{        listed under the attribute named ``json\\PYZus{}serialize\\PYZus{}exclude``}\n",
       "\\PY{l+s+sd}{        (for each ``attrs`` dictionary).}\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}{        dataset}\n",
       "\\PY{l+s+sd}{            Dataset from which to recover the original format.}\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}{            Dataset in which the attributes have been replaced with their python objects}\n",
       "\\PY{l+s+sd}{            version.}\n",
       "\\PY{l+s+sd}{        \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "        \\PY{k}{return} \\PY{n+nb+bp}{cls}\\PY{o}{.}\\PY{n}{\\PYZus{}transform}\\PY{p}{(}\\PY{n}{dataset}\\PY{p}{,} \\PY{n}{vals\\PYZus{}converter}\\PY{o}{=}\\PY{n}{json}\\PY{o}{.}\\PY{n}{loads}\\PY{p}{)}\n",
       "\n",
       "    \\PY{n+nd}{@staticmethod}\n",
       "    \\PY{k}{def} \\PY{n+nf}{attrs\\PYZus{}convert}\\PY{p}{(}\n",
       "        \\PY{n}{attrs}\\PY{p}{:} \\PY{n+nb}{dict}\\PY{p}{,}\n",
       "        \\PY{n}{inplace}\\PY{p}{:} \\PY{n+nb}{bool} \\PY{o}{=} \\PY{k+kc}{False}\\PY{p}{,}\n",
       "        \\PY{n}{vals\\PYZus{}converter}\\PY{p}{:} \\PY{n}{Callable}\\PY{p}{[}\\PY{n}{Any}\\PY{p}{,} \\PY{n}{Any}\\PY{p}{]} \\PY{o}{=} \\PY{n}{json}\\PY{o}{.}\\PY{n}{dumps}\\PY{p}{,}\n",
       "    \\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n+nb}{dict}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{        Converts to/from JSON string the values of the keys which are not listed in the}\n",
       "\\PY{l+s+sd}{        ``json\\PYZus{}serialize\\PYZus{}exclude`` list.}\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}{        attrs}\n",
       "\\PY{l+s+sd}{            The input dictionary.}\n",
       "\\PY{l+s+sd}{        inplace}\n",
       "\\PY{l+s+sd}{            If ``True`` the values are replaced in place, otherwise a deepcopy of}\n",
       "\\PY{l+s+sd}{            ``attrs`` is performed first.}\n",
       "\\PY{l+s+sd}{        \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "        \\PY{n}{json\\PYZus{}serialize\\PYZus{}exclude} \\PY{o}{=} \\PY{n}{attrs}\\PY{o}{.}\\PY{n}{get}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{json\\PYZus{}serialize\\PYZus{}exclude}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{p}{[}\\PY{p}{]}\\PY{p}{)}\n",
       "\n",
       "        \\PY{n}{attrs} \\PY{o}{=} \\PY{n}{attrs} \\PY{k}{if} \\PY{n}{inplace} \\PY{k}{else} \\PY{n}{deepcopy}\\PY{p}{(}\\PY{n}{attrs}\\PY{p}{)}\n",
       "        \\PY{k}{for} \\PY{n}{attr\\PYZus{}name}\\PY{p}{,} \\PY{n}{attr\\PYZus{}val} \\PY{o+ow}{in} \\PY{n}{attrs}\\PY{o}{.}\\PY{n}{items}\\PY{p}{(}\\PY{p}{)}\\PY{p}{:}\n",
       "            \\PY{k}{if} \\PY{n}{attr\\PYZus{}name} \\PY{o+ow}{not} \\PY{o+ow}{in} \\PY{n}{json\\PYZus{}serialize\\PYZus{}exclude}\\PY{p}{:}\n",
       "                \\PY{n}{attrs}\\PY{p}{[}\\PY{n}{attr\\PYZus{}name}\\PY{p}{]} \\PY{o}{=} \\PY{n}{vals\\PYZus{}converter}\\PY{p}{(}\\PY{n}{attr\\PYZus{}val}\\PY{p}{)}\n",
       "        \\PY{k}{return} \\PY{n}{attrs}\n",
       "\n",
       "    \\PY{n+nd}{@classmethod}\n",
       "    \\PY{k}{def} \\PY{n+nf}{\\PYZus{}transform}\\PY{p}{(}\n",
       "        \\PY{n+nb+bp}{cls}\\PY{p}{,} \\PY{n}{dataset}\\PY{p}{:} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{,} \\PY{n}{vals\\PYZus{}converter}\\PY{p}{:} \\PY{n}{Callable}\\PY{p}{[}\\PY{n}{Any}\\PY{p}{,} \\PY{n}{Any}\\PY{p}{]} \\PY{o}{=} \\PY{n}{json}\\PY{o}{.}\\PY{n}{dumps}\n",
       "    \\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{:}\n",
       "        \\PY{n}{dataset} \\PY{o}{=} \\PY{n}{xr}\\PY{o}{.}\\PY{n}{Dataset}\\PY{p}{(}\n",
       "            \\PY{n}{dataset}\\PY{p}{,}\n",
       "            \\PY{n}{attrs}\\PY{o}{=}\\PY{n+nb+bp}{cls}\\PY{o}{.}\\PY{n}{attrs\\PYZus{}convert}\\PY{p}{(}\n",
       "                \\PY{n}{dataset}\\PY{o}{.}\\PY{n}{attrs}\\PY{p}{,} \\PY{n}{inplace}\\PY{o}{=}\\PY{k+kc}{False}\\PY{p}{,} \\PY{n}{vals\\PYZus{}converter}\\PY{o}{=}\\PY{n}{vals\\PYZus{}converter}\n",
       "            \\PY{p}{)}\\PY{p}{,}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "        \\PY{k}{for} \\PY{n}{var\\PYZus{}name} \\PY{o+ow}{in} \\PY{n}{dataset}\\PY{o}{.}\\PY{n}{variables}\\PY{o}{.}\\PY{n}{keys}\\PY{p}{(}\\PY{p}{)}\\PY{p}{:}\n",
       "            \\PY{c+c1}{\\PYZsh{} The new dataset generated above has already a deepcopy of the attributes.}\n",
       "            \\PY{n}{\\PYZus{}} \\PY{o}{=} \\PY{n+nb+bp}{cls}\\PY{o}{.}\\PY{n}{attrs\\PYZus{}convert}\\PY{p}{(}\n",
       "                \\PY{n}{dataset}\\PY{p}{[}\\PY{n}{var\\PYZus{}name}\\PY{p}{]}\\PY{o}{.}\\PY{n}{attrs}\\PY{p}{,} \\PY{n}{inplace}\\PY{o}{=}\\PY{k+kc}{True}\\PY{p}{,} \\PY{n}{vals\\PYZus{}converter}\\PY{o}{=}\\PY{n}{vals\\PYZus{}converter}\n",
       "            \\PY{p}{)}\n",
       "\n",
       "        \\PY{k}{return} \\PY{n}{dataset}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "\n",
       "class \u001b[1;35mAdapterH5NetCDF\u001b[0m\u001b[1m(\u001b[0mDatasetAdapterBase\u001b[1m)\u001b[0m:\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "    Quantify dataset adapter for the ``h5netcdf`` engine.\n",
       "\n",
       "    It has the functionality of adapting the Quantify dataset to a format compatible\n",
       "    with the ``h5netcdf`` xarray backend engine that is used to write and load the\n",
       "    dataset to/from disk.\n",
       "\n",
       "    .. warning::\n",
       "\n",
       "        The ``h5netcdf`` engine has minor issues when performing a two-way trip of the\n",
       "        dataset. The ``type`` of some attributes are not preserved. E.g., list- and\n",
       "        tuple-like objects are loaded as numpy arrays of ``\u001b[33mdtype\u001b[0m=\u001b[35mobject\u001b[0m``.\n",
       "    \u001b[32m\"\"\u001b[0m\"\n",
       "\n",
       "    @classmethod\n",
       "    def \u001b[1;35madapt\u001b[0m\u001b[1m(\u001b[0mcls, dataset: xr.Dataset\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "        \u001b[32m\"\"\u001b[0m\"\n",
       "        Serializes to JSON the dataset and variables attributes.\n",
       "\n",
       "        To prevent the JSON serialization for specific items, their names should be\n",
       "        listed under the attribute named ``json_serialize_exclude`` \u001b[1m(\u001b[0mfor each ``attrs``\n",
       "        dictionary\u001b[1m)\u001b[0m.\n",
       "\n",
       "        Parameters\n",
       "        ----------\n",
       "        dataset\n",
       "            Dataset that needs to be adapted.\n",
       "\n",
       "        Returns\n",
       "        -------\n",
       "        :\n",
       "            Dataset in which the attributes have been replaced with their JSON strings\n",
       "            version.\n",
       "        \u001b[32m\"\"\u001b[0m\"\n",
       "\n",
       "        return \u001b[1;35mcls._transform\u001b[0m\u001b[1m(\u001b[0mdataset, \u001b[33mvals_converter\u001b[0m=\u001b[35mjson\u001b[0m.dumps\u001b[1m)\u001b[0m\n",
       "\n",
       "    @classmethod\n",
       "    def \u001b[1;35mrecover\u001b[0m\u001b[1m(\u001b[0mcls, dataset: xr.Dataset\u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "        \u001b[32m\"\"\u001b[0m\"\n",
       "        Reverts the action of ``\u001b[1;35m.adapt\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m``.\n",
       "\n",
       "        To prevent the JSON de-serialization for specific items, their names should be\n",
       "        listed under the attribute named ``json_serialize_exclude``\n",
       "        \u001b[1m(\u001b[0mfor each ``attrs`` dictionary\u001b[1m)\u001b[0m.\n",
       "\n",
       "        Parameters\n",
       "        ----------\n",
       "        dataset\n",
       "            Dataset from which to recover the original format.\n",
       "\n",
       "        Returns\n",
       "        -------\n",
       "        :\n",
       "            Dataset in which the attributes have been replaced with their python objects\n",
       "            version.\n",
       "        \u001b[32m\"\"\u001b[0m\"\n",
       "\n",
       "        return \u001b[1;35mcls._transform\u001b[0m\u001b[1m(\u001b[0mdataset, \u001b[33mvals_converter\u001b[0m=\u001b[35mjson\u001b[0m.loads\u001b[1m)\u001b[0m\n",
       "\n",
       "    @staticmethod\n",
       "    def \u001b[1;35mattrs_convert\u001b[0m\u001b[1m(\u001b[0m\n",
       "        attrs: dict,\n",
       "        inplace: bool = \u001b[3;91mFalse\u001b[0m,\n",
       "        vals_converter: Callable\u001b[1m[\u001b[0mAny, Any\u001b[1m]\u001b[0m = json.dumps,\n",
       "    \u001b[1m)\u001b[0m -> dict:\n",
       "        \u001b[32m\"\"\u001b[0m\"\n",
       "        Converts to/from JSON string the values of the keys which are not listed in the\n",
       "        ``json_serialize_exclude`` list.\n",
       "\n",
       "        Parameters\n",
       "        ----------\n",
       "        attrs\n",
       "            The input dictionary.\n",
       "        inplace\n",
       "            If ``\u001b[3;92mTrue\u001b[0m`` the values are replaced in place, otherwise a deepcopy of\n",
       "            ``attrs`` is performed first.\n",
       "        \u001b[32m\"\"\u001b[0m\"\n",
       "        json_serialize_exclude = \u001b[1;35mattrs.get\u001b[0m\u001b[1m(\u001b[0m\u001b[32m\"json_serialize_exclude\"\u001b[0m, \u001b[1m[\u001b[0m\u001b[1m]\u001b[0m\u001b[1m)\u001b[0m\n",
       "\n",
       "        attrs = attrs if inplace else \u001b[1;35mdeepcopy\u001b[0m\u001b[1m(\u001b[0mattrs\u001b[1m)\u001b[0m\n",
       "        for attr_name, attr_val in \u001b[1;35mattrs.items\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m:\n",
       "            if attr_name not in json_serialize_exclude:\n",
       "                attrs\u001b[1m[\u001b[0mattr_name\u001b[1m]\u001b[0m = \u001b[1;35mvals_converter\u001b[0m\u001b[1m(\u001b[0mattr_val\u001b[1m)\u001b[0m\n",
       "        return attrs\n",
       "\n",
       "    @classmethod\n",
       "    def \u001b[1;35m_transform\u001b[0m\u001b[1m(\u001b[0m\n",
       "        cls, dataset: xr.Dataset, vals_converter: Callable\u001b[1m[\u001b[0mAny, Any\u001b[1m]\u001b[0m = json.dumps\n",
       "    \u001b[1m)\u001b[0m -> xr.Dataset:\n",
       "        dataset = \u001b[1;35mxr.Dataset\u001b[0m\u001b[1m(\u001b[0m\n",
       "            dataset,\n",
       "            \u001b[33mattrs\u001b[0m=\u001b[1;35mcls\u001b[0m\u001b[1;35m.attrs_convert\u001b[0m\u001b[1m(\u001b[0m\n",
       "                dataset.attrs, \u001b[33minplace\u001b[0m=\u001b[3;91mFalse\u001b[0m, \u001b[33mvals_converter\u001b[0m=\u001b[35mvals_converter\u001b[0m\n",
       "            \u001b[1m)\u001b[0m,\n",
       "        \u001b[1m)\u001b[0m\n",
       "\n",
       "        for var_name in \u001b[1;35mdataset.variables.keys\u001b[0m\u001b[1m(\u001b[0m\u001b[1m)\u001b[0m:\n",
       "            # The new dataset generated above has already a deepcopy of the attributes.\n",
       "            _ = \u001b[1;35mcls.attrs_convert\u001b[0m\u001b[1m(\u001b[0m\n",
       "                dataset\u001b[1m[\u001b[0mvar_name\u001b[1m]\u001b[0m.attrs, \u001b[33minplace\u001b[0m=\u001b[3;92mTrue\u001b[0m, \u001b[33mvals_converter\u001b[0m=\u001b[35mvals_converter\u001b[0m\n",
       "            \u001b[1m)\u001b[0m\n",
       "\n",
       "        return dataset"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_source_code(dadapters.AdapterH5NetCDF)"
   ]
  }
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