{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ee6bbf81",
   "metadata": {},
   "source": [
    "# Overview\n",
    "\n",
    "A `quantify-core` experiment typically consists of a data-acquisition loop in\n",
    "which one or more parameters are set and one or more parameters are measured.\n",
    "\n",
    "The core of Quantify can be understood by understanding the following concepts:\n",
    "\n",
    "- {ref}`Instruments and Parameters`\n",
    "- {ref}`Measurement Control`\n",
    "- {ref}`Settables and Gettables`\n",
    "- {ref}`Data storage`\n",
    "- {ref}`Analysis`\n",
    "\n",
    "## Code snippets\n",
    "\n",
    "```{seealso}\n",
    "The complete source code of the examples on this page can be found in\n",
    "\n",
    "{nb-download}`concepts.ipynb`\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ba3aa975",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Import common utilities used in the examples"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data will be saved in:\n",
      "/root/quantify-data\n"
     ]
    }
   ],
   "source": [
    "import tempfile\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "from qcodes import Instrument, ManualParameter, Parameter, validators\n",
    "from scipy.optimize import minimize_scalar\n",
    "\n",
    "import quantify_core.data.handling as dh\n",
    "from quantify_core.analysis import base_analysis as ba\n",
    "from quantify_core.analysis import cosine_analysis as ca\n",
    "from quantify_core.measurement import Gettable, MeasurementControl\n",
    "from quantify_core.utilities.dataset_examples import mk_2d_dataset_v1\n",
    "from quantify_core.utilities.examples_support import mk_cosine_instrument\n",
    "from quantify_core.utilities.inspect_utils import display_source_code\n",
    "\n",
    "dh.set_datadir(dh.default_datadir())\n",
    "meas_ctrl = MeasurementControl(\"meas_ctrl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fd1ff2c",
   "metadata": {},
   "source": [
    "# Instruments and Parameters\n",
    "\n",
    "## Parameter\n",
    "\n",
    "A parameter represents a state variable of the system. Parameters:\n",
    "\n",
    "- can be gettable and/or settable;\n",
    "- contain metadata such as units and labels;\n",
    "- are commonly implemented using the QCoDeS {class}`~qcodes.parameters.Parameter` class.\n",
    "\n",
    "A parameter implemented using the QCoDeS {class}`~qcodes.parameters.Parameter` class\n",
    "is a valid {class}`.Settable` and {class}`.Gettable` and as such can be used directly in\n",
    "an experiment loop in the {class}`.MeasurementControl` (see subsequent sections).\n",
    "\n",
    "## Instrument\n",
    "\n",
    "An Instrument is a container for parameters that typically (but not necessarily)\n",
    "corresponds to a physical piece of hardware.\n",
    "\n",
    "Instruments provide the following functionality:\n",
    "\n",
    "- Container for parameters.\n",
    "- A standardized interface.\n",
    "- Logging of parameters through the {meth}`~qcodes.instrument.Instrument.snapshot` method.\n",
    "\n",
    "All instruments inherit from the QCoDeS {class}`~qcodes.instrument.Instrument` class.\n",
    "They are displayed by default in the {class}`.InstrumentMonitor`\n",
    "\n",
    "# Measurement Control\n",
    "\n",
    "The {class}`.MeasurementControl` (meas_ctrl) is in charge of the data-acquisition loop\n",
    "and is based on the notion that, in general, an experiment consists of the following\n",
    "three steps:\n",
    "\n",
    "1. Initialize (set) some parameter(s),\n",
    "2. Measure (get) some parameter(s),\n",
    "3. Store the data.\n",
    "\n",
    "`quantify-core` provides two helper classes, {class}`.Settable` and {class}`.Gettable` to aid\n",
    "in these steps, which are explored further in later sections of this article.\n",
    "\n",
    "{class}`.MeasurementControl` provides the following functionality:\n",
    "\n",
    "- standardization of experiments;\n",
    "- standardization data storage;\n",
    "- {ref}`live plotting of the experiment <plotmon-tutorial>`;\n",
    "- {math}`n`-dimensional sweeps;\n",
    "- data acquisition controlled iteratively or in batches;\n",
    "- adaptive sweeps (measurement points are not predetermined at the beginning of an experiment).\n",
    "\n",
    "## Basic example, a 1D iterative measurement loop\n",
    "\n",
    "Running an experiment is simple!\n",
    "Simply define what parameters to set, and get, and what points to loop over.\n",
    "\n",
    "In the example below we want to set frequencies on a microwave source and acquire the\n",
    "signal from the Qblox Pulsar readout module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "32426643",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Initialize (mock) instruments"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "mw_source1 = Instrument(\"mw_source1\")\n",
    "\n",
    "# NB: for brevity only, this not the proper way of adding parameters to QCoDeS instruments\n",
    "mw_source1.freq = ManualParameter(\n",
    "    name=\"freq\",\n",
    "    label=\"Frequency\",\n",
    "    unit=\"Hz\",\n",
    "    vals=validators.Numbers(),\n",
    "    initial_value=1.0,\n",
    ")\n",
    "\n",
    "pulsar_QRM = Instrument(\"pulsar_QRM\")\n",
    "# NB: for brevity only, this not the proper way of adding parameters to QCoDeS instruments\n",
    "pulsar_QRM.signal = Parameter(\n",
    "    name=\"sig_a\", label=\"Signal\", unit=\"V\", get_cmd=lambda: mw_source1.freq() * 1e-8\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b54a5de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting iterative measurement...\n"
     ]
    },
    {
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       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
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   ],
   "source": [
    "meas_ctrl.settables(\n",
    "    mw_source1.freq\n",
    ")  # We want to set the frequency of a microwave source\n",
    "meas_ctrl.setpoints(np.arange(5e9, 5.2e9, 100e3))  # Scan around 5.1 GHz\n",
    "meas_ctrl.gettables(pulsar_QRM.signal)  # acquire the signal from the pulsar QRM\n",
    "dset = meas_ctrl.run(name=\"Frequency sweep\")  # run the experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8299856b",
   "metadata": {},
   "source": [
    "The {class}`.MeasurementControl` can also be used to perform more advanced experiments\n",
    "such as 2D scans, pulse-sequences where the hardware is in control of the acquisition\n",
    "loop, or adaptive experiments in which it is not known what data points to acquire in\n",
    "advance, they are determined dynamically during the experiment.\n",
    "Take a look at some of the tutorial notebooks for more in-depth examples on\n",
    "usage and application.\n",
    "\n",
    "## Control Mode\n",
    "\n",
    "Batched mode can be used to deal with constraints imposed by (hardware) resources or to reduce overhead.\n",
    "\n",
    "In **iterative mode** , the measurement control steps through each setpoint one at a time,\n",
    "processing them one by one.\n",
    "\n",
    "In **batched mode** , the measurement control vectorizes the setpoints such that they are processed in batches.\n",
    "The size of these batches is automatically calculated but usually dependent on resource\n",
    "constraints; you may have a device that can hold 100 samples but you wish to sweep over 2000 points.\n",
    "\n",
    "```{note}\n",
    "The maximum batch size of the settable(s)/gettable(s) should be specified using the\n",
    "`.batch_size` attribute. If not specified infinite size is assumed and all setpoint\n",
    "are passed to the settable(s).\n",
    "```\n",
    "\n",
    "```{tip}\n",
    "In *Batched* mode it is still possible to perform outer iterative sweeps with an inner\n",
    "batched sweep.\n",
    "This is performed automatically when batched settables (`.batched=True`) are mixed\n",
    "with iterative settables (`.batched=False`). To correctly grid the points in this mode\n",
    "use {meth}`.MeasurementControl.setpoints_grid`.\n",
    "```\n",
    "\n",
    "Control mode is detected automatically based on the `.batched` attribute of the\n",
    "settable(s) and gettable(s); this is expanded upon in subsequent sections.\n",
    "\n",
    "```{note}\n",
    "All gettables must have the same value for the `.batched` attribute.\n",
    "Only when all gettables have `.batched=True`, settables are allowed to have mixed\n",
    "`.batched` attribute (e.g. `settable_A.batched=True`, `settable_B.batched=False`).\n",
    "```\n",
    "\n",
    "Depending on which control mode the {class}`.MeasurementControl` is running in,\n",
    "the interfaces for Settables (their input interface) and Gettables\n",
    "(their output interface) are slightly different.\n",
    "\n",
    "It is also possible for batched gettables to return an array with a length less\n",
    "than the length of the setpoints, and similarly for the input of the Settables.\n",
    "This is often the case when working with resource-constrained devices, for\n",
    "example, if you have *n* setpoints but your device can load only less than *n*\n",
    "datapoints into memory. In this scenario, measurement control tracks how many\n",
    "datapoints were actually processed, automatically adjusting the size of the next\n",
    "batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1dbb3573",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Example"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t --- (None) --- \n",
      "Batched settable(s):\n",
      "\t time \n",
      "Batch size limit: 5\n",
      "\n"
     ]
    },
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       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
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     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff942252520>]"
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     "execution_count": 4,
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = ManualParameter(\n",
    "    name=\"time\",\n",
    "    label=\"Time\",\n",
    "    unit=\"s\",\n",
    "    vals=validators.Arrays(),  # accepts an array of values\n",
    ")\n",
    "signal = Parameter(\n",
    "    name=\"sig_a\", label=\"Signal\", unit=\"V\", get_cmd=lambda: np.cos(time())\n",
    ")\n",
    "\n",
    "time.batched = True\n",
    "time.batch_size = 5\n",
    "signal.batched = True\n",
    "signal.batch_size = 10\n",
    "\n",
    "meas_ctrl.settables(time)\n",
    "meas_ctrl.gettables(signal)\n",
    "meas_ctrl.setpoints(np.linspace(0, 7, 23))\n",
    "dset = meas_ctrl.run(\"my experiment\")\n",
    "dset_grid = dh.to_gridded_dataset(dset)\n",
    "\n",
    "dset_grid.y0.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "602f70da",
   "metadata": {},
   "source": [
    "# Settables and Gettables\n",
    "\n",
    "Experiments typically involve varying some parameters and reading others.\n",
    "In `quantify-core` we encapsulate these concepts as the {class}`.Settable`\n",
    "and {class}`.Gettable` respectively.\n",
    "As their name implies, a Settable is a parameter you set values to,\n",
    "and a Gettable is a parameter you get values from.\n",
    "\n",
    "The interfaces for Settable and Gettable parameters are encapsulated in the\n",
    "{class}`.Settable` and {class}`.Gettable` helper classes respectively.\n",
    "We set values to Settables; these values populate an `X`-axis.\n",
    "Similarly, we get values from Gettables which populate a `Y`-axis.\n",
    "These classes define a set of mandatory and optional attributes the\n",
    "{class}`.MeasurementControl` recognizes and will use as part of the experiment,\n",
    "which are expanded up in the API reference.\n",
    "For ease of use, we do not require users to inherit from a Gettable/Settable class,\n",
    "and instead provide contracts in the form of JSON schemas to which these classes\n",
    "must fit (see {class}`.Settable` and {class}`.Gettable` docs for these schemas).\n",
    "In addition to using a library that fits these contracts\n",
    "(such as the {class}`~qcodes.parameters.Parameter` family of classes).\n",
    "we can define our own Settables and Gettables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e41b8ccb",
   "metadata": {
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.WaveGettable at 0x7ff942183f40>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = ManualParameter(\"time\", label=\"Time\", unit=\"s\")\n",
    "\n",
    "\n",
    "class WaveGettable:\n",
    "    \"\"\"An examples of a gettable.\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        self.unit = \"V\"\n",
    "        self.label = \"Amplitude\"\n",
    "        self.name = \"sine\"\n",
    "\n",
    "    def get(self):\n",
    "        \"\"\"Return the gettable value.\"\"\"\n",
    "        return np.sin(t() / np.pi)\n",
    "\n",
    "    def prepare(self) -> None:\n",
    "        \"\"\"Optional methods to prepare can be left undefined.\"\"\"\n",
    "        print(\"Preparing the WaveGettable for acquisition.\")\n",
    "\n",
    "    def finish(self) -> None:\n",
    "        \"\"\"Optional methods to finish can be left undefined.\"\"\"\n",
    "        print(\"Finishing WaveGettable to wrap up the experiment.\")\n",
    "\n",
    "\n",
    "# verify compliance with the Gettable format\n",
    "wave_gettable = WaveGettable()\n",
    "Gettable(wave_gettable)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a55e8f9e",
   "metadata": {},
   "source": [
    "\"Grouped\" gettable(s) are also allowed.\n",
    "Below we create a Gettable which returns two distinct quantities at once:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2fba6d14",
   "metadata": {
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.DualWave1D at 0x7ff9421c4520>"
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    "t = ManualParameter(\n",
    "    \"time\",\n",
    "    label=\"Time\",\n",
    "    unit=\"s\",\n",
    "    vals=validators.Numbers(),  # accepts a single number, e.g. a float or integer\n",
    ")\n",
    "\n",
    "\n",
    "class DualWave1D:\n",
    "    \"\"\"Example of a \"dual\" gettable.\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        self.unit = [\"V\", \"V\"]\n",
    "        self.label = [\"Sine Amplitude\", \"Cosine Amplitude\"]\n",
    "        self.name = [\"sin\", \"cos\"]\n",
    "\n",
    "    def get(self):\n",
    "        \"\"\"Return the value of the gettable.\"\"\"\n",
    "        return np.array([np.sin(t() / np.pi), np.cos(t() / np.pi)])\n",
    "\n",
    "    # N.B. the optional prepare and finish methods are omitted in this Gettable.\n",
    "\n",
    "\n",
    "# verify compliance with the Gettable format\n",
    "wave_gettable = DualWave1D()\n",
    "Gettable(wave_gettable)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2c664b8",
   "metadata": {},
   "source": [
    "(sec-batched-and-batch-size)=\n",
    "## .batched and .batch_size\n",
    "\n",
    "The {py:class}`.Gettable` and {py:class}`.Settable` objects can have a `bool` property\n",
    "`.batched` (defaults to `False` if not present); and an `int` property `.batch_size`.\n",
    "\n",
    "Setting the `.batched` property to `True` enables the *batched control code**\n",
    "in the {class}`.MeasurementControl`. In this mode, if present,\n",
    "the `.batch_size` attribute is used to determine the maximum size of a batch of\n",
    "setpoints, that can be set.\n",
    "\n",
    "```{admonition} Heterogeneous batch size and effective batch size\n",
    ":class: dropdown, note\n",
    "\n",
    "The minimum `.batch_size` among all settables and gettables will determine the\n",
    "(maximum) size of a batch.\n",
    "During execution of a measurement the size of a batch will be reduced if necessary\n",
    "to comply to the setpoints grid and/or total number of setpoints.\n",
    "```\n",
    "\n",
    "## .prepare() and .finish()\n",
    "\n",
    "Optionally the {meth}`!.prepare` and {meth}`!.finish` can be added.\n",
    "These methods can be used to set up and teardown work.\n",
    "For example, arming a piece of hardware with data and then closing a connection upon\n",
    "completion.\n",
    "\n",
    "The {meth}`!.finish` runs once at the end of an experiment.\n",
    "\n",
    "For `settables`, {meth}`!.prepare` runs once **before the start of a measurement**.\n",
    "\n",
    "For batched `gettables`, {meth}`!.prepare` runs **before the measurement of each batch**.\n",
    "For iterative `gettables`, the {meth}`!.prepare` runs before each loop counting towards\n",
    "soft-averages \\[controlled by {meth}`!meas_ctrl.soft_avg()` which resets to `1`\n",
    "at the end of each experiment\\].\n",
    "\n",
    "(data-storage)=\n",
    "# Data storage\n",
    "\n",
    "Along with the produced dataset, every {class}`~qcodes.parameters.Parameter`\n",
    "attached to QCoDeS {class}`~qcodes.instrument.Instrument` in an experiment run through\n",
    "the {class}`.MeasurementControl` of Quantify is stored in the [snapshot].\n",
    "\n",
    "This is intended to aid with reproducibility, as settings from a past experiment can\n",
    "easily be reloaded \\[see {func}`~quantify_core.utilities.experiment_helpers.load_settings_onto_instrument`\\].\n",
    "\n",
    "## Data Directory\n",
    "The top-level directory in the file system where output is saved to.\n",
    "This directory can be controlled using the {meth}`~quantify_core.data.handling.get_datadir`\n",
    "and {meth}`~quantify_core.data.handling.set_datadir` functions.\n",
    "We recommend changing the default directory when starting the python kernel\n",
    "(after importing {mod}`quantify_core`) and settling for a single common data directory\n",
    "for all notebooks/experiments within your measurement setup/PC\n",
    "(e.g., {code}`D:\\\\quantify-data`).\n",
    "\n",
    "`quantify-core` provides utilities to find/search and extract data,\n",
    "which expects all your experiment containers to be located within the same directory\n",
    "(under the corresponding date subdirectory).\n",
    "\n",
    "Within the data directory experiments are first grouped by date -\n",
    "all experiments which take place on a certain date will be saved together in a\n",
    "subdirectory in the form `YYYYmmDD`.\n",
    "\n",
    "## Experiment Container\n",
    "\n",
    "Individual experiments are saved to their own subdirectories (of the Data Directory)\n",
    "named based on the {class}`~quantify_core.data.types.TUID` and the\n",
    "{code}`<experiment name (if any)>`.\n",
    "\n",
    "```{note}\n",
    "TUID: A Time-based Unique ID is of the form\n",
    "{code}`YYYYmmDD-HHMMSS-sss-<random 6 character string>` and these subdirectories'\n",
    "names take the form\n",
    "{code}`YYYYmmDD-HHMMSS-sss-<random 6 character string><-experiment name (if any)>`.\n",
    "```\n",
    "\n",
    "These subdirectories are termed 'Experiment Containers', with a typical output being the\n",
    "Dataset in hdf5 format and a JSON format file describing Parameters, Instruments and such.\n",
    "\n",
    "Furthermore, additional analyses such as fits can also be written to this directory,\n",
    "storing all data in one location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7b2ae1a2",
   "metadata": {
    "tags": [
     "hide-input"
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   "source": [
    "with tempfile.TemporaryDirectory() as tmpdir:\n",
    "    old_dir = dh.get_datadir()\n",
    "    dh.set_datadir(Path(tmpdir) / \"quantify-data\")\n",
    "    # we generate a dummy dataset and a few empty dirs for pretty printing\n",
    "    (Path(dh.get_datadir()) / \"20210301\").mkdir()\n",
    "    (Path(dh.get_datadir()) / \"20210428\").mkdir()\n",
    "\n",
    "    quantify_dataset = mk_2d_dataset_v1()\n",
    "    ba.BasicAnalysis(dataset=quantify_dataset).run()\n",
    "    dh.set_datadir(old_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f47598b4",
   "metadata": {},
   "source": [
    "An experiment container within a data directory with the name `\"quantify-data\"`\n",
    "thus will look similar to:\n",
    "\n",
    "```{code-block}\n",
    "quantify-data/\n",
    "├── 20210301/\n",
    "├── 20210428/\n",
    "└── 20230125/\n",
    "    └── 20230125-172802-085-874812-my experiment/\n",
    "        ├── analysis_BasicAnalysis/\n",
    "        │   ├── dataset_processed.hdf5\n",
    "        │   ├── figs_mpl/\n",
    "        │   │   ├── Line plot x0-y0.png\n",
    "        │   │   ├── Line plot x0-y0.svg\n",
    "        │   │   ├── Line plot x1-y0.png\n",
    "        │   │   └── Line plot x1-y0.svg\n",
    "        │   └── quantities_of_interest.json\n",
    "        └── dataset.hdf5\n",
    "```\n",
    "\n",
    "## Dataset\n",
    "\n",
    "The Dataset is implemented with a **specific** convention using the\n",
    "{class}`xarray.Dataset` class.\n",
    "\n",
    "`quantify-core` arranges data along two types of axes: `X` and `Y`.\n",
    "In each dataset there will be *n* `X`-type axes and *m* `Y`-type axes.\n",
    "For example, the dataset produced in an experiment where we sweep 2 parameters (settables)\n",
    "and measure 3 other parameters (all 3 returned by a Gettable),\n",
    "we will have *n* = 2 and *m* = 3.\n",
    "Each `X` axis represents a dimension of the setpoints provided.\n",
    "The `Y` axes represent the output of the Gettable.\n",
    "Each axis type are numbered ascending from 0\n",
    "(e.g. {code}`x0`, {code}`x1`, {code}`y0`, {code}`y1`, {code}`y2`),\n",
    "and each stores information described by the {class}`.Settable` and {class}`.Gettable`\n",
    "classes, such as titles and units.\n",
    "The Dataset object also stores some further metadata,\n",
    "such as the {class}`~quantify_core.data.types.TUID` of the experiment which it was\n",
    "generated from.\n",
    "\n",
    "For example, consider an experiment varying time and amplitude against a Cosine function.\n",
    "The resulting dataset will look similar to the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28b3dda9",
   "metadata": {},
   "outputs": [
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       "  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: 24kB\n",
       "Dimensions:  (dim_0: 1000)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 8kB -1.0 -0.7778 -0.5556 ... 0.5556 0.7778 1.0\n",
       "    x1       (dim_0) float64 8kB 0.0 0.0 0.0 0.0 0.0 ... 10.0 10.0 10.0 10.0\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 8kB -1.0 -0.7778 -0.5556 ... -0.6526 -0.8391\n",
       "Attributes:\n",
       "    tuid:                      20241106-153122-024-f3f966\n",
       "    name:                      my experiment\n",
       "    grid_2d:                   True\n",
       "    grid_2d_uniformly_spaced:  True\n",
       "    xlen:                      10\n",
       "    ylen:                      100</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-feee1bfa-ed6b-43fe-b409-8655222e0da5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-feee1bfa-ed6b-43fe-b409-8655222e0da5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>dim_0</span>: 1000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2f37af3f-528f-49ff-ac29-89c9b84e9ab5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2f37af3f-528f-49ff-ac29-89c9b84e9ab5' 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>x0</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.7778 -0.5556 ... 0.7778 1.0</div><input id='attrs-94cea12b-382d-4da4-bcc8-58a1f6cc7a8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94cea12b-382d-4da4-bcc8-58a1f6cc7a8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-865804ec-51ec-47dd-b8b8-776b0a776cc1' class='xr-var-data-in' type='checkbox'><label for='data-865804ec-51ec-47dd-b8b8-776b0a776cc1' 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>name :</span></dt><dd>amp</dd><dt><span>long_name :</span></dt><dd>Amplitude</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><pre>array([-1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "...\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x1</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 10.0 10.0 10.0</div><input id='attrs-7c08d92c-1121-473c-87dc-349d831c9954' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c08d92c-1121-473c-87dc-349d831c9954' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4efdf0dc-3399-4819-a6aa-6fe73f6663db' class='xr-var-data-in' type='checkbox'><label for='data-4efdf0dc-3399-4819-a6aa-6fe73f6663db' 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>name :</span></dt><dd>t</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><pre>array([ 0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.        ,  0.        ,  0.        ,  0.        ,  0.        ,\n",
       "        0.1010101 ,  0.1010101 ,  0.1010101 ,  0.1010101 ,  0.1010101 ,\n",
       "        0.1010101 ,  0.1010101 ,  0.1010101 ,  0.1010101 ,  0.1010101 ,\n",
       "        0.2020202 ,  0.2020202 ,  0.2020202 ,  0.2020202 ,  0.2020202 ,\n",
       "        0.2020202 ,  0.2020202 ,  0.2020202 ,  0.2020202 ,  0.2020202 ,\n",
       "        0.3030303 ,  0.3030303 ,  0.3030303 ,  0.3030303 ,  0.3030303 ,\n",
       "        0.3030303 ,  0.3030303 ,  0.3030303 ,  0.3030303 ,  0.3030303 ,\n",
       "        0.4040404 ,  0.4040404 ,  0.4040404 ,  0.4040404 ,  0.4040404 ,\n",
       "        0.4040404 ,  0.4040404 ,  0.4040404 ,  0.4040404 ,  0.4040404 ,\n",
       "        0.50505051,  0.50505051,  0.50505051,  0.50505051,  0.50505051,\n",
       "        0.50505051,  0.50505051,  0.50505051,  0.50505051,  0.50505051,\n",
       "        0.60606061,  0.60606061,  0.60606061,  0.60606061,  0.60606061,\n",
       "        0.60606061,  0.60606061,  0.60606061,  0.60606061,  0.60606061,\n",
       "        0.70707071,  0.70707071,  0.70707071,  0.70707071,  0.70707071,\n",
       "        0.70707071,  0.70707071,  0.70707071,  0.70707071,  0.70707071,\n",
       "        0.80808081,  0.80808081,  0.80808081,  0.80808081,  0.80808081,\n",
       "        0.80808081,  0.80808081,  0.80808081,  0.80808081,  0.80808081,\n",
       "        0.90909091,  0.90909091,  0.90909091,  0.90909091,  0.90909091,\n",
       "        0.90909091,  0.90909091,  0.90909091,  0.90909091,  0.90909091,\n",
       "...\n",
       "        9.09090909,  9.09090909,  9.09090909,  9.09090909,  9.09090909,\n",
       "        9.09090909,  9.09090909,  9.09090909,  9.09090909,  9.09090909,\n",
       "        9.19191919,  9.19191919,  9.19191919,  9.19191919,  9.19191919,\n",
       "        9.19191919,  9.19191919,  9.19191919,  9.19191919,  9.19191919,\n",
       "        9.29292929,  9.29292929,  9.29292929,  9.29292929,  9.29292929,\n",
       "        9.29292929,  9.29292929,  9.29292929,  9.29292929,  9.29292929,\n",
       "        9.39393939,  9.39393939,  9.39393939,  9.39393939,  9.39393939,\n",
       "        9.39393939,  9.39393939,  9.39393939,  9.39393939,  9.39393939,\n",
       "        9.49494949,  9.49494949,  9.49494949,  9.49494949,  9.49494949,\n",
       "        9.49494949,  9.49494949,  9.49494949,  9.49494949,  9.49494949,\n",
       "        9.5959596 ,  9.5959596 ,  9.5959596 ,  9.5959596 ,  9.5959596 ,\n",
       "        9.5959596 ,  9.5959596 ,  9.5959596 ,  9.5959596 ,  9.5959596 ,\n",
       "        9.6969697 ,  9.6969697 ,  9.6969697 ,  9.6969697 ,  9.6969697 ,\n",
       "        9.6969697 ,  9.6969697 ,  9.6969697 ,  9.6969697 ,  9.6969697 ,\n",
       "        9.7979798 ,  9.7979798 ,  9.7979798 ,  9.7979798 ,  9.7979798 ,\n",
       "        9.7979798 ,  9.7979798 ,  9.7979798 ,  9.7979798 ,  9.7979798 ,\n",
       "        9.8989899 ,  9.8989899 ,  9.8989899 ,  9.8989899 ,  9.8989899 ,\n",
       "        9.8989899 ,  9.8989899 ,  9.8989899 ,  9.8989899 ,  9.8989899 ,\n",
       "       10.        , 10.        , 10.        , 10.        , 10.        ,\n",
       "       10.        , 10.        , 10.        , 10.        , 10.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3a961e7d-70a6-4131-90c4-93c11d6e9b46' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3a961e7d-70a6-4131-90c4-93c11d6e9b46' class='xr-section-summary' >Data variables: <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>y0</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.7778 ... -0.6526 -0.8391</div><input id='attrs-c04c7095-1166-4e78-91c1-1ef688625e6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c04c7095-1166-4e78-91c1-1ef688625e6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ecf2d5f-356d-4388-965f-5e5b7e837c94' class='xr-var-data-in' type='checkbox'><label for='data-4ecf2d5f-356d-4388-965f-5e5b7e837c94' 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>name :</span></dt><dd>sig</dd><dt><span>long_name :</span></dt><dd>Signal</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><pre>array([-1.        , -0.77777778, -0.55555556, -0.33333333, -0.11111111,\n",
       "        0.11111111,  0.33333333,  0.55555556,  0.77777778,  1.        ,\n",
       "       -0.99490282, -0.7738133 , -0.55272379, -0.33163427, -0.11054476,\n",
       "        0.11054476,  0.33163427,  0.55272379,  0.7738133 ,  0.99490282,\n",
       "       -0.97966323, -0.76196029, -0.54425735, -0.32655441, -0.10885147,\n",
       "        0.10885147,  0.32655441,  0.54425735,  0.76196029,  0.97966323,\n",
       "       -0.95443659, -0.74233957, -0.53024255, -0.31814553, -0.10604851,\n",
       "        0.10604851,  0.31814553,  0.53024255,  0.74233957,  0.95443659,\n",
       "       -0.91948007, -0.71515117, -0.51082226, -0.30649336, -0.10216445,\n",
       "        0.10216445,  0.30649336,  0.51082226,  0.71515117,  0.91948007,\n",
       "       -0.87515004, -0.68067225, -0.48619447, -0.29171668, -0.09723889,\n",
       "        0.09723889,  0.29171668,  0.48619447,  0.68067225,  0.87515004,\n",
       "       -0.8218984 , -0.63925431, -0.45661022, -0.27396613, -0.09132204,\n",
       "        0.09132204,  0.27396613,  0.45661022,  0.63925431,  0.8218984 ,\n",
       "       -0.76026803, -0.59131958, -0.42237113, -0.25342268, -0.08447423,\n",
       "        0.08447423,  0.25342268,  0.42237113,  0.59131958,  0.76026803,\n",
       "       -0.69088721, -0.53735672, -0.38382623, -0.23029574, -0.07676525,\n",
       "        0.07676525,  0.23029574,  0.38382623,  0.53735672,  0.69088721,\n",
       "       -0.61446323, -0.47791584, -0.34136846, -0.20482108, -0.06827369,\n",
       "        0.06827369,  0.20482108,  0.34136846,  0.47791584,  0.61446323,\n",
       "...\n",
       "        0.94478159,  0.73483012,  0.52487866,  0.3149272 ,  0.10497573,\n",
       "       -0.10497573, -0.3149272 , -0.52487866, -0.73483012, -0.94478159,\n",
       "        0.97301068,  0.75678609,  0.54056149,  0.32433689,  0.1081123 ,\n",
       "       -0.1081123 , -0.32433689, -0.54056149, -0.75678609, -0.97301068,\n",
       "        0.99132055,  0.77102709,  0.55073364,  0.33044018,  0.11014673,\n",
       "       -0.11014673, -0.33044018, -0.55073364, -0.77102709, -0.99132055,\n",
       "        0.99952453,  0.77740797,  0.55529141,  0.33317484,  0.11105828,\n",
       "       -0.11105828, -0.33317484, -0.55529141, -0.77740797, -0.99952453,\n",
       "        0.99753899,  0.77586366,  0.55418833,  0.332513  ,  0.11083767,\n",
       "       -0.11083767, -0.332513  , -0.55418833, -0.77586366, -0.99753899,\n",
       "        0.98538417,  0.76640991,  0.54743565,  0.32846139,  0.10948713,\n",
       "       -0.10948713, -0.32846139, -0.54743565, -0.76640991, -0.98538417,\n",
       "        0.96318398,  0.74914309,  0.53510221,  0.32106133,  0.10702044,\n",
       "       -0.10702044, -0.32106133, -0.53510221, -0.74914309, -0.96318398,\n",
       "        0.93116473,  0.72423924,  0.51731374,  0.31038824,  0.10346275,\n",
       "       -0.10346275, -0.31038824, -0.51731374, -0.72423924, -0.93116473,\n",
       "        0.88965286,  0.69195222,  0.49425159,  0.29655095,  0.09885032,\n",
       "       -0.09885032, -0.29655095, -0.49425159, -0.69195222, -0.88965286,\n",
       "        0.83907153,  0.65261119,  0.46615085,  0.27969051,  0.09323017,\n",
       "       -0.09323017, -0.27969051, -0.46615085, -0.65261119, -0.83907153])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-df2b7f9b-1f33-47c6-9e45-82d56409692a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-df2b7f9b-1f33-47c6-9e45-82d56409692a' 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-5b19b761-cc2e-457c-b74d-3eb0b1726e33' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5b19b761-cc2e-457c-b74d-3eb0b1726e33' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20241106-153122-024-f3f966</dd><dt><span>name :</span></dt><dd>my experiment</dd><dt><span>grid_2d :</span></dt><dd>True</dd><dt><span>grid_2d_uniformly_spaced :</span></dt><dd>True</dd><dt><span>xlen :</span></dt><dd>10</dd><dt><span>ylen :</span></dt><dd>100</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 24kB\n",
       "Dimensions:  (dim_0: 1000)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 8kB -1.0 -0.7778 -0.5556 ... 0.5556 0.7778 1.0\n",
       "    x1       (dim_0) float64 8kB 0.0 0.0 0.0 0.0 0.0 ... 10.0 10.0 10.0 10.0\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 8kB -1.0 -0.7778 -0.5556 ... -0.6526 -0.8391\n",
       "Attributes:\n",
       "    tuid:                      20241106-153122-024-f3f966\n",
       "    name:                      my experiment\n",
       "    grid_2d:                   True\n",
       "    grid_2d_uniformly_spaced:  True\n",
       "    xlen:                      10\n",
       "    ylen:                      100"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the columns of the dataset\n",
    "_, axs = plt.subplots(3, 1, sharex=True)\n",
    "xr.plot.line(quantify_dataset.x0[:54], label=\"x0\", ax=axs[0], marker=\".\")\n",
    "xr.plot.line(quantify_dataset.x1[:54], label=\"x1\", ax=axs[1], color=\"C1\", marker=\".\")\n",
    "xr.plot.line(quantify_dataset.y0[:54], label=\"y0\", ax=axs[2], color=\"C2\", marker=\".\")\n",
    "tuple(ax.legend() for ax in axs)\n",
    "# return the dataset\n",
    "quantify_dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9458bbc1",
   "metadata": {},
   "source": [
    "### Associating dimensions to coordinates\n",
    "\n",
    "To support both gridded and non-gridded data, we use {doc}`Xarray <xarray:index>`\n",
    "using only `Data Variables` and `Coordinates` **with a single** `Dimension`\n",
    "(corresponding to the order of the setpoints).\n",
    "\n",
    "This is necessary as in the non-gridded case the dataset will be a perfect sparse array, the usability of which is cumbersome.\n",
    "A prominent example of non-gridded use-cases can be found {ref}`adaptive-tutorial`.\n",
    "\n",
    "To allow for some of Xarray's more advanced functionality,\n",
    "such as the in-built graphing or query system we provide a dataset conversion utility\n",
    "{func}`~quantify_core.data.handling.to_gridded_dataset`.\n",
    "This function reshapes the data and associates dimensions to the dataset\n",
    "\\[which can also be used for 1D datasets\\]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a811cde0",
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       "\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 9kB\n",
       "Dimensions:  (x0: 10, x1: 100)\n",
       "Coordinates:\n",
       "  * x0       (x0) float64 80B -1.0 -0.7778 -0.5556 -0.3333 ... 0.5556 0.7778 1.0\n",
       "  * x1       (x1) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0\n",
       "Data variables:\n",
       "    y0       (x0, x1) float64 8kB -1.0 -0.9949 -0.9797 ... -0.8897 -0.8391\n",
       "Attributes:\n",
       "    tuid:                      20241106-153122-024-f3f966\n",
       "    name:                      my experiment\n",
       "    grid_2d:                   False\n",
       "    grid_2d_uniformly_spaced:  True\n",
       "    xlen:                      10\n",
       "    ylen:                      100</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-45211775-1aae-4845-93d8-37379dd15e6c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-45211775-1aae-4845-93d8-37379dd15e6c' 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'>x0</span>: 10</li><li><span class='xr-has-index'>x1</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9034a0b1-e95c-45ee-9e64-427aa0e70409' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9034a0b1-e95c-45ee-9e64-427aa0e70409' 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 class='xr-has-index'>x0</span></div><div class='xr-var-dims'>(x0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.7778 -0.5556 ... 0.7778 1.0</div><input id='attrs-ec43da85-31b1-4dc1-865d-7915550ba3fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec43da85-31b1-4dc1-865d-7915550ba3fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c769bd3-5639-43a8-9129-90872bb43213' class='xr-var-data-in' type='checkbox'><label for='data-2c769bd3-5639-43a8-9129-90872bb43213' 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>name :</span></dt><dd>amp</dd><dt><span>long_name :</span></dt><dd>Amplitude</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><pre>array([-1.      , -0.777778, -0.555556, -0.333333, -0.111111,  0.111111,\n",
       "        0.333333,  0.555556,  0.777778,  1.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.101 0.202 ... 9.899 10.0</div><input id='attrs-5d6e7e54-f41b-428a-be99-866b20851547' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5d6e7e54-f41b-428a-be99-866b20851547' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7fb4150-e852-47ab-9b47-9518212da0a1' class='xr-var-data-in' type='checkbox'><label for='data-b7fb4150-e852-47ab-9b47-9518212da0a1' 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>name :</span></dt><dd>t</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><pre>array([ 0.      ,  0.10101 ,  0.20202 ,  0.30303 ,  0.40404 ,  0.505051,\n",
       "        0.606061,  0.707071,  0.808081,  0.909091,  1.010101,  1.111111,\n",
       "        1.212121,  1.313131,  1.414141,  1.515152,  1.616162,  1.717172,\n",
       "        1.818182,  1.919192,  2.020202,  2.121212,  2.222222,  2.323232,\n",
       "        2.424242,  2.525253,  2.626263,  2.727273,  2.828283,  2.929293,\n",
       "        3.030303,  3.131313,  3.232323,  3.333333,  3.434343,  3.535354,\n",
       "        3.636364,  3.737374,  3.838384,  3.939394,  4.040404,  4.141414,\n",
       "        4.242424,  4.343434,  4.444444,  4.545455,  4.646465,  4.747475,\n",
       "        4.848485,  4.949495,  5.050505,  5.151515,  5.252525,  5.353535,\n",
       "        5.454545,  5.555556,  5.656566,  5.757576,  5.858586,  5.959596,\n",
       "        6.060606,  6.161616,  6.262626,  6.363636,  6.464646,  6.565657,\n",
       "        6.666667,  6.767677,  6.868687,  6.969697,  7.070707,  7.171717,\n",
       "        7.272727,  7.373737,  7.474747,  7.575758,  7.676768,  7.777778,\n",
       "        7.878788,  7.979798,  8.080808,  8.181818,  8.282828,  8.383838,\n",
       "        8.484848,  8.585859,  8.686869,  8.787879,  8.888889,  8.989899,\n",
       "        9.090909,  9.191919,  9.292929,  9.393939,  9.494949,  9.59596 ,\n",
       "        9.69697 ,  9.79798 ,  9.89899 , 10.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a6687f4d-f1ad-4ff1-909b-fec9f56f113d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a6687f4d-f1ad-4ff1-909b-fec9f56f113d' class='xr-section-summary' >Data variables: <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>y0</span></div><div class='xr-var-dims'>(x0, x1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.9949 ... -0.8897 -0.8391</div><input id='attrs-6ac9f2dd-ae49-491d-8d6d-a987ddb86a20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ac9f2dd-ae49-491d-8d6d-a987ddb86a20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76f4f4ba-b436-451d-abaa-43dc9c9a1388' class='xr-var-data-in' type='checkbox'><label for='data-76f4f4ba-b436-451d-abaa-43dc9c9a1388' 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>name :</span></dt><dd>sig</dd><dt><span>long_name :</span></dt><dd>Signal</dd><dt><span>units :</span></dt><dd>V</dd></dl></div><div class='xr-var-data'><pre>array([[-1.        , -0.99490282, -0.97966323, -0.95443659, -0.91948007,\n",
       "        -0.87515004, -0.8218984 , -0.76026803, -0.69088721, -0.61446323,\n",
       "        -0.53177518, -0.44366602, -0.35103397, -0.25482335, -0.15601496,\n",
       "        -0.0556161 ,  0.04534973,  0.14585325,  0.24486989,  0.34139023,\n",
       "         0.43443032,  0.52304166,  0.60632092,  0.68341913,  0.75355031,\n",
       "         0.81599952,  0.87013012,  0.91539031,  0.95131866,  0.97754893,\n",
       "         0.9938137 ,  0.99994717,  0.9958868 ,  0.981674  ,  0.95745366,\n",
       "         0.92347268,  0.88007748,  0.82771044,  0.76690542,  0.69828229,\n",
       "         0.6225406 ,  0.54045251,  0.45285485,  0.36064061,  0.26474988,\n",
       "         0.16616018,  0.06587659, -0.03507857, -0.13567613, -0.23489055,\n",
       "        -0.33171042, -0.4251487 , -0.51425287, -0.59811455, -0.67587883,\n",
       "        -0.74675295, -0.8100144 , -0.86501827, -0.91120382, -0.94810022,\n",
       "        -0.97533134, -0.99261957, -0.99978867, -0.99676556, -0.98358105,\n",
       "        -0.96036956, -0.9273677 , -0.88491192, -0.83343502, -0.77346177,\n",
       "        -0.70560358, -0.63055219, -0.54907273, -0.46199582, -0.37020915,\n",
       "        -0.27464844, -0.17628785, -0.07613012,  0.0248037 ,  0.12548467,\n",
       "         0.2248864 ,  0.32199555,  0.41582217,  0.50540974,  0.58984498,\n",
       "         0.66826712,  0.7398767 ,  0.8039437 ,  0.859815  ,  0.90692104,\n",
       "         0.94478159,  0.97301068,  0.99132055,  0.99952453,  0.99753899,\n",
       "         0.98538417,  0.96318398,  0.93116473,  0.88965286,  0.83907153],\n",
       "...\n",
       "       [ 1.        ,  0.99490282,  0.97966323,  0.95443659,  0.91948007,\n",
       "         0.87515004,  0.8218984 ,  0.76026803,  0.69088721,  0.61446323,\n",
       "         0.53177518,  0.44366602,  0.35103397,  0.25482335,  0.15601496,\n",
       "         0.0556161 , -0.04534973, -0.14585325, -0.24486989, -0.34139023,\n",
       "        -0.43443032, -0.52304166, -0.60632092, -0.68341913, -0.75355031,\n",
       "        -0.81599952, -0.87013012, -0.91539031, -0.95131866, -0.97754893,\n",
       "        -0.9938137 , -0.99994717, -0.9958868 , -0.981674  , -0.95745366,\n",
       "        -0.92347268, -0.88007748, -0.82771044, -0.76690542, -0.69828229,\n",
       "        -0.6225406 , -0.54045251, -0.45285485, -0.36064061, -0.26474988,\n",
       "        -0.16616018, -0.06587659,  0.03507857,  0.13567613,  0.23489055,\n",
       "         0.33171042,  0.4251487 ,  0.51425287,  0.59811455,  0.67587883,\n",
       "         0.74675295,  0.8100144 ,  0.86501827,  0.91120382,  0.94810022,\n",
       "         0.97533134,  0.99261957,  0.99978867,  0.99676556,  0.98358105,\n",
       "         0.96036956,  0.9273677 ,  0.88491192,  0.83343502,  0.77346177,\n",
       "         0.70560358,  0.63055219,  0.54907273,  0.46199582,  0.37020915,\n",
       "         0.27464844,  0.17628785,  0.07613012, -0.0248037 , -0.12548467,\n",
       "        -0.2248864 , -0.32199555, -0.41582217, -0.50540974, -0.58984498,\n",
       "        -0.66826712, -0.7398767 , -0.8039437 , -0.859815  , -0.90692104,\n",
       "        -0.94478159, -0.97301068, -0.99132055, -0.99952453, -0.99753899,\n",
       "        -0.98538417, -0.96318398, -0.93116473, -0.88965286, -0.83907153]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e93ff55-4427-4805-9266-dc7a894b2c55' class='xr-section-summary-in' type='checkbox'  ><label for='section-9e93ff55-4427-4805-9266-dc7a894b2c55' class='xr-section-summary' >Indexes: <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-index-name'><div>x0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-58b52d71-2c76-4d37-97a1-35c667bc0e64' class='xr-index-data-in' type='checkbox'/><label for='index-58b52d71-2c76-4d37-97a1-35c667bc0e64' 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([                -1.0,  -0.7777777777777778,  -0.5555555555555556,\n",
       "       -0.33333333333333337, -0.11111111111111116,  0.11111111111111116,\n",
       "        0.33333333333333326,   0.5555555555555554,   0.7777777777777777,\n",
       "                        1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-76eac189-3c25-4d96-a551-07ed835b1e61' class='xr-index-data-in' type='checkbox'/><label for='index-76eac189-3c25-4d96-a551-07ed835b1e61' 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, 0.10101010101010101, 0.20202020202020202,\n",
       "       0.30303030303030304, 0.40404040404040403,  0.5050505050505051,\n",
       "        0.6060606060606061,  0.7070707070707071,  0.8080808080808081,\n",
       "        0.9090909090909091,  1.0101010101010102,  1.1111111111111112,\n",
       "        1.2121212121212122,  1.3131313131313131,  1.4141414141414141,\n",
       "        1.5151515151515151,  1.6161616161616161,  1.7171717171717171,\n",
       "        1.8181818181818181,  1.9191919191919191,  2.0202020202020203,\n",
       "         2.121212121212121,  2.2222222222222223,   2.323232323232323,\n",
       "        2.4242424242424243,   2.525252525252525,  2.6262626262626263,\n",
       "         2.727272727272727,  2.8282828282828283,   2.929292929292929,\n",
       "        3.0303030303030303,   3.131313131313131,  3.2323232323232323,\n",
       "        3.3333333333333335,  3.4343434343434343,  3.5353535353535355,\n",
       "        3.6363636363636362,  3.7373737373737375,  3.8383838383838382,\n",
       "        3.9393939393939394,   4.040404040404041,   4.141414141414141,\n",
       "         4.242424242424242,   4.343434343434343,   4.444444444444445,\n",
       "         4.545454545454545,   4.646464646464646,   4.747474747474747,\n",
       "         4.848484848484849,    4.94949494949495,    5.05050505050505,\n",
       "         5.151515151515151,   5.252525252525253,   5.353535353535354,\n",
       "         5.454545454545454,   5.555555555555555,   5.656565656565657,\n",
       "         5.757575757575758,   5.858585858585858,   5.959595959595959,\n",
       "        6.0606060606060606,   6.161616161616162,   6.262626262626262,\n",
       "         6.363636363636363,  6.4646464646464645,   6.565656565656566,\n",
       "         6.666666666666667,   6.767676767676767,  6.8686868686868685,\n",
       "          6.96969696969697,   7.070707070707071,   7.171717171717171,\n",
       "        7.2727272727272725,   7.373737373737374,   7.474747474747475,\n",
       "         7.575757575757575,  7.6767676767676765,   7.777777777777778,\n",
       "         7.878787878787879,   7.979797979797979,   8.080808080808081,\n",
       "         8.181818181818182,   8.282828282828282,   8.383838383838384,\n",
       "         8.484848484848484,   8.585858585858587,   8.686868686868687,\n",
       "         8.787878787878787,    8.88888888888889,    8.98989898989899,\n",
       "          9.09090909090909,   9.191919191919192,   9.292929292929292,\n",
       "         9.393939393939394,   9.494949494949495,   9.595959595959595,\n",
       "         9.696969696969697,   9.797979797979798,     9.8989898989899,\n",
       "                      10.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x1&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cff18480-c929-4308-8ab8-444f040a2979' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cff18480-c929-4308-8ab8-444f040a2979' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20241106-153122-024-f3f966</dd><dt><span>name :</span></dt><dd>my experiment</dd><dt><span>grid_2d :</span></dt><dd>False</dd><dt><span>grid_2d_uniformly_spaced :</span></dt><dd>True</dd><dt><span>xlen :</span></dt><dd>10</dd><dt><span>ylen :</span></dt><dd>100</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 9kB\n",
       "Dimensions:  (x0: 10, x1: 100)\n",
       "Coordinates:\n",
       "  * x0       (x0) float64 80B -1.0 -0.7778 -0.5556 -0.3333 ... 0.5556 0.7778 1.0\n",
       "  * x1       (x1) float64 800B 0.0 0.101 0.202 0.303 ... 9.697 9.798 9.899 10.0\n",
       "Data variables:\n",
       "    y0       (x0, x1) float64 8kB -1.0 -0.9949 -0.9797 ... -0.8897 -0.8391\n",
       "Attributes:\n",
       "    tuid:                      20241106-153122-024-f3f966\n",
       "    name:                      my experiment\n",
       "    grid_2d:                   False\n",
       "    grid_2d_uniformly_spaced:  True\n",
       "    xlen:                      10\n",
       "    ylen:                      100"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gridded_dset = dh.to_gridded_dataset(quantify_dataset)\n",
    "gridded_dset.y0.plot()\n",
    "gridded_dset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc580ef9",
   "metadata": {},
   "source": [
    "## Snapshot\n",
    "\n",
    "The configuration for each QCoDeS {class}`~qcodes.instrument.Instrument`\n",
    "used in this experiment.\n",
    "This information is automatically collected for all Instruments in use.\n",
    "It is useful for quickly reconstructing a complex set-up or verifying that\n",
    "{class}`~qcodes.parameters.Parameter` objects are as expected.\n",
    "\n",
    "(analysis-usage)=\n",
    "\n",
    "# Analysis\n",
    "\n",
    "To aid with data analysis, quantify comes with an {mod}`~quantify_core.analysis` module\n",
    "containing a base data-analysis class\n",
    "({class}`~quantify_core.analysis.base_analysis.BaseAnalysis`)\n",
    "that is intended to serve as a template for analysis scripts\n",
    "and several standard analyses such as\n",
    "the {class}`~quantify_core.analysis.base_analysis.BasicAnalysis`,\n",
    "the {class}`~quantify_core.analysis.base_analysis.Basic2DAnalysis`\n",
    "and the {class}`~quantify_core.analysis.spectroscopy_analysis.ResonatorSpectroscopyAnalysis`.\n",
    "\n",
    "The idea behind the analysis class is that most analyses follow a common structure\n",
    "consisting of steps such as data extraction, data processing, fitting to some model,\n",
    "creating figures, and saving the analysis results.\n",
    "\n",
    "To showcase the analysis usage we generate a dataset that we would like to analyze."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b2dd231c",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Example cosine instrument source code"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
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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\">mk_cosine_instrument</span><span class=\"p\">()</span> <span class=\"o\">-&gt;</span> <span class=\"n\">Instrument</span><span class=\"p\">:</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;A container of parameters (mock instrument) providing a cosine model.&quot;&quot;&quot;</span>\n",
       "\n",
       "    <span class=\"n\">instr</span> <span class=\"o\">=</span> <span class=\"n\">Instrument</span><span class=\"p\">(</span><span class=\"s2\">&quot;ParameterHolder&quot;</span><span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"c1\"># ManualParameter&#39;s is a handy class that preserves the QCoDeS&#39; Parameter</span>\n",
       "    <span class=\"c1\"># structure without necessarily having a connection to the physical world</span>\n",
       "    <span class=\"n\">instr</span><span class=\"o\">.</span><span class=\"n\">add_parameter</span><span class=\"p\">(</span>\n",
       "        <span class=\"s2\">&quot;amp&quot;</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">initial_value</span><span class=\"o\">=</span><span class=\"mf\">0.5</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">unit</span><span class=\"o\">=</span><span class=\"s2\">&quot;V&quot;</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">label</span><span class=\"o\">=</span><span class=\"s2\">&quot;Amplitude&quot;</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">parameter_class</span><span class=\"o\">=</span><span class=\"n\">ManualParameter</span><span class=\"p\">,</span>\n",
       "    <span class=\"p\">)</span>\n",
       "    <span class=\"n\">instr</span><span class=\"o\">.</span><span class=\"n\">add_parameter</span><span class=\"p\">(</span>\n",
       "        <span class=\"s2\">&quot;freq&quot;</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">initial_value</span><span class=\"o\">=</span><span class=\"mi\">1</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">unit</span><span class=\"o\">=</span><span class=\"s2\">&quot;Hz&quot;</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">label</span><span class=\"o\">=</span><span class=\"s2\">&quot;Frequency&quot;</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">parameter_class</span><span class=\"o\">=</span><span class=\"n\">ManualParameter</span><span class=\"p\">,</span>\n",
       "    <span class=\"p\">)</span>\n",
       "    <span class=\"n\">instr</span><span class=\"o\">.</span><span class=\"n\">add_parameter</span><span class=\"p\">(</span>\n",
       "        <span class=\"s2\">&quot;t&quot;</span><span class=\"p\">,</span> <span class=\"n\">initial_value</span><span class=\"o\">=</span><span class=\"mi\">1</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> <span class=\"n\">label</span><span class=\"o\">=</span><span class=\"s2\">&quot;Time&quot;</span><span class=\"p\">,</span> <span class=\"n\">parameter_class</span><span class=\"o\">=</span><span class=\"n\">ManualParameter</span>\n",
       "    <span class=\"p\">)</span>\n",
       "    <span class=\"n\">instr</span><span class=\"o\">.</span><span class=\"n\">add_parameter</span><span class=\"p\">(</span>\n",
       "        <span class=\"s2\">&quot;phi&quot;</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">initial_value</span><span class=\"o\">=</span><span class=\"mi\">0</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">unit</span><span class=\"o\">=</span><span class=\"s2\">&quot;Rad&quot;</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">label</span><span class=\"o\">=</span><span class=\"s2\">&quot;Phase&quot;</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">parameter_class</span><span class=\"o\">=</span><span class=\"n\">ManualParameter</span><span class=\"p\">,</span>\n",
       "    <span class=\"p\">)</span>\n",
       "    <span class=\"n\">instr</span><span class=\"o\">.</span><span class=\"n\">add_parameter</span><span class=\"p\">(</span>\n",
       "        <span class=\"s2\">&quot;noise_level&quot;</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">initial_value</span><span class=\"o\">=</span><span class=\"mf\">0.05</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">unit</span><span class=\"o\">=</span><span class=\"s2\">&quot;V&quot;</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">label</span><span class=\"o\">=</span><span class=\"s2\">&quot;Noise level&quot;</span><span class=\"p\">,</span>\n",
       "        <span class=\"n\">parameter_class</span><span class=\"o\">=</span><span class=\"n\">ManualParameter</span><span class=\"p\">,</span>\n",
       "    <span class=\"p\">)</span>\n",
       "    <span class=\"n\">instr</span><span class=\"o\">.</span><span class=\"n\">add_parameter</span><span class=\"p\">(</span>\n",
       "        <span class=\"s2\">&quot;acq_delay&quot;</span><span class=\"p\">,</span> <span class=\"n\">initial_value</span><span class=\"o\">=</span><span class=\"mf\">0.02</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> <span class=\"n\">parameter_class</span><span class=\"o\">=</span><span class=\"n\">ManualParameter</span>\n",
       "    <span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">cosine_model</span><span class=\"p\">():</span>\n",
       "        <span class=\"n\">sleep</span><span class=\"p\">(</span><span class=\"n\">instr</span><span class=\"o\">.</span><span class=\"n\">acq_delay</span><span class=\"p\">())</span>  <span class=\"c1\"># simulates the acquisition delay of an instrument</span>\n",
       "        <span class=\"k\">return</span> <span class=\"p\">(</span>\n",
       "            <span class=\"n\">cos_func</span><span class=\"p\">(</span><span class=\"n\">instr</span><span class=\"o\">.</span><span class=\"n\">t</span><span class=\"p\">(),</span> <span class=\"n\">instr</span><span class=\"o\">.</span><span class=\"n\">freq</span><span class=\"p\">(),</span> <span class=\"n\">instr</span><span class=\"o\">.</span><span class=\"n\">amp</span><span class=\"p\">(),</span> <span class=\"n\">phase</span><span class=\"o\">=</span><span class=\"n\">instr</span><span class=\"o\">.</span><span class=\"n\">phi</span><span class=\"p\">(),</span> <span class=\"n\">offset</span><span class=\"o\">=</span><span class=\"mi\">0</span><span class=\"p\">)</span>\n",
       "            <span class=\"o\">+</span> <span class=\"n\">np</span><span class=\"o\">.</span><span class=\"n\">random</span><span class=\"o\">.</span><span class=\"n\">randn</span><span class=\"p\">()</span> <span class=\"o\">*</span> <span class=\"n\">instr</span><span class=\"o\">.</span><span class=\"n\">noise_level</span><span class=\"p\">()</span>\n",
       "        <span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"c1\"># Wrap our function in a Parameter to be able to associate metadata to it, e.g. unit</span>\n",
       "    <span class=\"n\">instr</span><span class=\"o\">.</span><span class=\"n\">add_parameter</span><span class=\"p\">(</span>\n",
       "        <span class=\"n\">name</span><span class=\"o\">=</span><span class=\"s2\">&quot;sig&quot;</span><span class=\"p\">,</span> <span class=\"n\">label</span><span class=\"o\">=</span><span class=\"s2\">&quot;Signal level&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> <span class=\"n\">get_cmd</span><span class=\"o\">=</span><span class=\"n\">cosine_model</span>\n",
       "    <span class=\"p\">)</span>\n",
       "\n",
       "    <span class=\"k\">return</span> <span class=\"n\">instr</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{def} \\PY{n+nf}{mk\\PYZus{}cosine\\PYZus{}instrument}\\PY{p}{(}\\PY{p}{)} \\PY{o}{\\PYZhy{}}\\PY{o}{\\PYZgt{}} \\PY{n}{Instrument}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}A container of parameters (mock instrument) providing a cosine model.\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "    \\PY{n}{instr} \\PY{o}{=} \\PY{n}{Instrument}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{ParameterHolder}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} ManualParameter\\PYZsq{}s is a handy class that preserves the QCoDeS\\PYZsq{} Parameter}\n",
       "    \\PY{c+c1}{\\PYZsh{} structure without necessarily having a connection to the physical world}\n",
       "    \\PY{n}{instr}\\PY{o}{.}\\PY{n}{add\\PYZus{}parameter}\\PY{p}{(}\n",
       "        \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{amp}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "        \\PY{n}{initial\\PYZus{}value}\\PY{o}{=}\\PY{l+m+mf}{0.5}\\PY{p}{,}\n",
       "        \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "        \\PY{n}{label}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Amplitude}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "        \\PY{n}{parameter\\PYZus{}class}\\PY{o}{=}\\PY{n}{ManualParameter}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "    \\PY{n}{instr}\\PY{o}{.}\\PY{n}{add\\PYZus{}parameter}\\PY{p}{(}\n",
       "        \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{freq}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "        \\PY{n}{initial\\PYZus{}value}\\PY{o}{=}\\PY{l+m+mi}{1}\\PY{p}{,}\n",
       "        \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Hz}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "        \\PY{n}{label}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Frequency}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "        \\PY{n}{parameter\\PYZus{}class}\\PY{o}{=}\\PY{n}{ManualParameter}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "    \\PY{n}{instr}\\PY{o}{.}\\PY{n}{add\\PYZus{}parameter}\\PY{p}{(}\n",
       "        \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{t}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{initial\\PYZus{}value}\\PY{o}{=}\\PY{l+m+mi}{1}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{s}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{label}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Time}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{parameter\\PYZus{}class}\\PY{o}{=}\\PY{n}{ManualParameter}\n",
       "    \\PY{p}{)}\n",
       "    \\PY{n}{instr}\\PY{o}{.}\\PY{n}{add\\PYZus{}parameter}\\PY{p}{(}\n",
       "        \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{phi}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "        \\PY{n}{initial\\PYZus{}value}\\PY{o}{=}\\PY{l+m+mi}{0}\\PY{p}{,}\n",
       "        \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Rad}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "        \\PY{n}{label}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Phase}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "        \\PY{n}{parameter\\PYZus{}class}\\PY{o}{=}\\PY{n}{ManualParameter}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "    \\PY{n}{instr}\\PY{o}{.}\\PY{n}{add\\PYZus{}parameter}\\PY{p}{(}\n",
       "        \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{noise\\PYZus{}level}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "        \\PY{n}{initial\\PYZus{}value}\\PY{o}{=}\\PY{l+m+mf}{0.05}\\PY{p}{,}\n",
       "        \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{V}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "        \\PY{n}{label}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Noise level}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,}\n",
       "        \\PY{n}{parameter\\PYZus{}class}\\PY{o}{=}\\PY{n}{ManualParameter}\\PY{p}{,}\n",
       "    \\PY{p}{)}\n",
       "    \\PY{n}{instr}\\PY{o}{.}\\PY{n}{add\\PYZus{}parameter}\\PY{p}{(}\n",
       "        \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{acq\\PYZus{}delay}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{initial\\PYZus{}value}\\PY{o}{=}\\PY{l+m+mf}{0.02}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{s}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{parameter\\PYZus{}class}\\PY{o}{=}\\PY{n}{ManualParameter}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{def} \\PY{n+nf}{cosine\\PYZus{}model}\\PY{p}{(}\\PY{p}{)}\\PY{p}{:}\n",
       "        \\PY{n}{sleep}\\PY{p}{(}\\PY{n}{instr}\\PY{o}{.}\\PY{n}{acq\\PYZus{}delay}\\PY{p}{(}\\PY{p}{)}\\PY{p}{)}  \\PY{c+c1}{\\PYZsh{} simulates the acquisition delay of an instrument}\n",
       "        \\PY{k}{return} \\PY{p}{(}\n",
       "            \\PY{n}{cos\\PYZus{}func}\\PY{p}{(}\\PY{n}{instr}\\PY{o}{.}\\PY{n}{t}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,} \\PY{n}{instr}\\PY{o}{.}\\PY{n}{freq}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,} \\PY{n}{instr}\\PY{o}{.}\\PY{n}{amp}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,} \\PY{n}{phase}\\PY{o}{=}\\PY{n}{instr}\\PY{o}{.}\\PY{n}{phi}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,} \\PY{n}{offset}\\PY{o}{=}\\PY{l+m+mi}{0}\\PY{p}{)}\n",
       "            \\PY{o}{+} \\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{randn}\\PY{p}{(}\\PY{p}{)} \\PY{o}{*} \\PY{n}{instr}\\PY{o}{.}\\PY{n}{noise\\PYZus{}level}\\PY{p}{(}\\PY{p}{)}\n",
       "        \\PY{p}{)}\n",
       "\n",
       "    \\PY{c+c1}{\\PYZsh{} Wrap our function in a Parameter to be able to associate metadata to it, e.g. unit}\n",
       "    \\PY{n}{instr}\\PY{o}{.}\\PY{n}{add\\PYZus{}parameter}\\PY{p}{(}\n",
       "        \\PY{n}{name}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{sig}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{label}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Signal level}\\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}{,} \\PY{n}{get\\PYZus{}cmd}\\PY{o}{=}\\PY{n}{cosine\\PYZus{}model}\n",
       "    \\PY{p}{)}\n",
       "\n",
       "    \\PY{k}{return} \\PY{n}{instr}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "def mk_cosine_instrument() -> Instrument:\n",
       "    \"\"\"A container of parameters (mock instrument) providing a cosine model.\"\"\"\n",
       "\n",
       "    instr = Instrument(\"ParameterHolder\")\n",
       "\n",
       "    # ManualParameter's is a handy class that preserves the QCoDeS' Parameter\n",
       "    # structure without necessarily having a connection to the physical world\n",
       "    instr.add_parameter(\n",
       "        \"amp\",\n",
       "        initial_value=0.5,\n",
       "        unit=\"V\",\n",
       "        label=\"Amplitude\",\n",
       "        parameter_class=ManualParameter,\n",
       "    )\n",
       "    instr.add_parameter(\n",
       "        \"freq\",\n",
       "        initial_value=1,\n",
       "        unit=\"Hz\",\n",
       "        label=\"Frequency\",\n",
       "        parameter_class=ManualParameter,\n",
       "    )\n",
       "    instr.add_parameter(\n",
       "        \"t\", initial_value=1, unit=\"s\", label=\"Time\", parameter_class=ManualParameter\n",
       "    )\n",
       "    instr.add_parameter(\n",
       "        \"phi\",\n",
       "        initial_value=0,\n",
       "        unit=\"Rad\",\n",
       "        label=\"Phase\",\n",
       "        parameter_class=ManualParameter,\n",
       "    )\n",
       "    instr.add_parameter(\n",
       "        \"noise_level\",\n",
       "        initial_value=0.05,\n",
       "        unit=\"V\",\n",
       "        label=\"Noise level\",\n",
       "        parameter_class=ManualParameter,\n",
       "    )\n",
       "    instr.add_parameter(\n",
       "        \"acq_delay\", initial_value=0.02, unit=\"s\", parameter_class=ManualParameter\n",
       "    )\n",
       "\n",
       "    def cosine_model():\n",
       "        sleep(instr.acq_delay())  # simulates the acquisition delay of an instrument\n",
       "        return (\n",
       "            cos_func(instr.t(), instr.freq(), instr.amp(), phase=instr.phi(), offset=0)\n",
       "            + np.random.randn() * instr.noise_level()\n",
       "        )\n",
       "\n",
       "    # Wrap our function in a Parameter to be able to associate metadata to it, e.g. unit\n",
       "    instr.add_parameter(\n",
       "        name=\"sig\", label=\"Signal level\", unit=\"V\", get_cmd=cosine_model\n",
       "    )\n",
       "\n",
       "    return instr"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_source_code(mk_cosine_instrument)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50c33548",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Generating a dataset labeled \"Cosine experiment\""
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting iterative measurement...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "79b5dbac8477465ca4c7e0302ffdc765",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
       ".xr-section-details {\n",
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       "\n",
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       "\n",
       ".xr-array-preview,\n",
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       "\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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       ".xr-attrs {\n",
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       "\n",
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       ".xr-var-data,\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "\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",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 800B\n",
       "Dimensions:  (dim_0: 50)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 400B 0.0 0.04082 0.08163 0.1224 ... 1.918 1.959 2.0\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 400B 0.5317 0.4516 0.4674 ... 0.4391 0.5245 0.533\n",
       "Attributes:\n",
       "    tuid:                             20241106-153123-584-19f966\n",
       "    name:                             Cosine experiment\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  False</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-a6027e88-cd6c-44be-9225-5c03659b95bf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a6027e88-cd6c-44be-9225-5c03659b95bf' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>dim_0</span>: 50</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3f2af1cb-6cc5-49c2-8ef3-9ea938d6a6dc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3f2af1cb-6cc5-49c2-8ef3-9ea938d6a6dc' 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>x0</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.04082 0.08163 ... 1.959 2.0</div><input id='attrs-733c70a2-eb04-49f7-8aac-6d125acea97f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-733c70a2-eb04-49f7-8aac-6d125acea97f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5616301-0a22-44c8-957d-e7a2062d1073' class='xr-var-data-in' type='checkbox'><label for='data-b5616301-0a22-44c8-957d-e7a2062d1073' 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>name :</span></dt><dd>ParameterHolder.t</dd><dt><span>long_name :</span></dt><dd>ParameterHolder Time</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([0.        , 0.04081633, 0.08163265, 0.12244898, 0.16326531,\n",
       "       0.20408163, 0.24489796, 0.28571429, 0.32653061, 0.36734694,\n",
       "       0.40816327, 0.44897959, 0.48979592, 0.53061224, 0.57142857,\n",
       "       0.6122449 , 0.65306122, 0.69387755, 0.73469388, 0.7755102 ,\n",
       "       0.81632653, 0.85714286, 0.89795918, 0.93877551, 0.97959184,\n",
       "       1.02040816, 1.06122449, 1.10204082, 1.14285714, 1.18367347,\n",
       "       1.2244898 , 1.26530612, 1.30612245, 1.34693878, 1.3877551 ,\n",
       "       1.42857143, 1.46938776, 1.51020408, 1.55102041, 1.59183673,\n",
       "       1.63265306, 1.67346939, 1.71428571, 1.75510204, 1.79591837,\n",
       "       1.83673469, 1.87755102, 1.91836735, 1.95918367, 2.        ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-85ddf372-b2a7-4210-bc31-b32e1d7482da' class='xr-section-summary-in' type='checkbox'  checked><label for='section-85ddf372-b2a7-4210-bc31-b32e1d7482da' class='xr-section-summary' >Data variables: <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>y0</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5317 0.4516 ... 0.5245 0.533</div><input id='attrs-2326e1bd-90d9-4081-a0f3-7cf4ebccbba0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2326e1bd-90d9-4081-a0f3-7cf4ebccbba0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3193a0ac-8a43-4b00-bb7e-bcf61b724520' class='xr-var-data-in' type='checkbox'><label for='data-3193a0ac-8a43-4b00-bb7e-bcf61b724520' 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>name :</span></dt><dd>sig</dd><dt><span>long_name :</span></dt><dd>Signal level</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([ 0.53169656,  0.45160646,  0.46736019,  0.357237  ,  0.24140173,\n",
       "        0.13233074,  0.01125236, -0.15589795, -0.26996788, -0.37790057,\n",
       "       -0.46984258, -0.4693802 , -0.49798761, -0.48778481, -0.49504448,\n",
       "       -0.35403362, -0.31103443, -0.16700804, -0.03657535,  0.16524666,\n",
       "        0.23802749,  0.35476407,  0.44945495,  0.48671616,  0.47223822,\n",
       "        0.46301048,  0.48838884,  0.35460392,  0.30354708,  0.24419815,\n",
       "        0.08212587, -0.13207605, -0.17663714, -0.32429667, -0.35694686,\n",
       "       -0.49810486, -0.54923018, -0.45579265, -0.44714919, -0.32989809,\n",
       "       -0.29884301, -0.15876823, -0.16916871, -0.00495034,  0.14580075,\n",
       "        0.30209503,  0.29824451,  0.43914636,  0.52449169,  0.53300654])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f63fc6b7-e5fd-4b78-aee0-8a9c73391c12' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f63fc6b7-e5fd-4b78-aee0-8a9c73391c12' 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-1618617c-1a00-4df6-8190-d8c8e549156c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1618617c-1a00-4df6-8190-d8c8e549156c' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20241106-153123-584-19f966</dd><dt><span>name :</span></dt><dd>Cosine experiment</dd><dt><span>grid_2d :</span></dt><dd>False</dd><dt><span>grid_2d_uniformly_spaced :</span></dt><dd>False</dd><dt><span>1d_2_settables_uniformly_spaced :</span></dt><dd>False</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 800B\n",
       "Dimensions:  (dim_0: 50)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 400B 0.0 0.04082 0.08163 0.1224 ... 1.918 1.959 2.0\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 400B 0.5317 0.4516 0.4674 ... 0.4391 0.5245 0.533\n",
       "Attributes:\n",
       "    tuid:                             20241106-153123-584-19f966\n",
       "    name:                             Cosine experiment\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  False"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pars = mk_cosine_instrument()\n",
    "meas_ctrl.settables(pars.t)\n",
    "meas_ctrl.setpoints(np.linspace(0, 2, 50))\n",
    "meas_ctrl.gettables(pars.sig)\n",
    "dataset = meas_ctrl.run(\"Cosine experiment\")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af91bdd1",
   "metadata": {},
   "source": [
    "## Using an analysis class\n",
    "\n",
    "Running an analysis is very simple:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "48293fd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a_obj = ca.CosineAnalysis(label=\"Cosine experiment\")\n",
    "a_obj.run()  # execute the analysis.\n",
    "a_obj.display_figs_mpl()  # displays the figures created in previous step."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e44aff92",
   "metadata": {},
   "source": [
    "The analysis was executed against the last dataset that has the label\n",
    "`\"Cosine experiment\"` in the filename.\n",
    "\n",
    "After the analysis the experiment container will look similar to the following:\n",
    "\n",
    "```{code-block}\n",
    "20230125-172804-537-f4f73e-Cosine experiment/\n",
    "├── analysis_CosineAnalysis/\n",
    "│   ├── dataset_processed.hdf5\n",
    "│   ├── figs_mpl/\n",
    "│   │   ├── cos_fit.png\n",
    "│   │   └── cos_fit.svg\n",
    "│   ├── fit_results/\n",
    "│   │   └── cosine.txt\n",
    "│   └── quantities_of_interest.json\n",
    "├── dataset.hdf5\n",
    "└── snapshot.json\n",
    "```\n",
    "\n",
    "The analysis object contains several useful methods and attributes such as the\n",
    "{code}`quantities_of_interest`, intended to store relevant quantities extracted\n",
    "during analysis, and the processed dataset.\n",
    "For example, the fitted frequency and amplitude are saved as:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c5912376",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frequency 0.997+/-0.005\n",
      "amplitude 0.508+/-0.008\n"
     ]
    }
   ],
   "source": [
    "freq = a_obj.quantities_of_interest[\"frequency\"]\n",
    "amp = a_obj.quantities_of_interest[\"amplitude\"]\n",
    "print(f\"frequency {freq}\")\n",
    "print(f\"amplitude {amp}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4c1d08f",
   "metadata": {},
   "source": [
    "The use of these methods and attributes is described in more detail in\n",
    "{ref}`analysis-framework-tutorial`.\n",
    "\n",
    "## Creating a custom analysis class\n",
    "\n",
    "The analysis steps and their order of execution are determined by the\n",
    "{attr}`~quantify_core.analysis.base_analysis.BaseAnalysis.analysis_steps` attribute\n",
    "as an {class}`~enum.Enum` ({class}`~quantify_core.analysis.base_analysis.AnalysisSteps`).\n",
    "The corresponding steps are implemented as methods of the analysis class.\n",
    "An analysis class inheriting from the abstract-base-class\n",
    "({class}`~quantify_core.analysis.base_analysis.BaseAnalysis`)\n",
    "will only have to implement those methods that are unique to the custom analysis.\n",
    "Additionally, if required, a customized analysis flow can be specified by assigning it\n",
    "to the {attr}`~quantify_core.analysis.base_analysis.BaseAnalysis.analysis_steps` attribute.\n",
    "\n",
    "The simplest example of an analysis class is the\n",
    "{class}`~quantify_core.analysis.base_analysis.BasicAnalysis`\n",
    "that only implements the\n",
    "{meth}`~quantify_core.analysis.base_analysis.BasicAnalysis.create_figures` method\n",
    "and relies on the base class for data extraction and saving of the figures.\n",
    "\n",
    "Take a look at the source code (also available in the API reference):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b50ee2fe",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "BasicAnalysis source code"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "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\">BasicAnalysis</span><span class=\"p\">(</span><span class=\"n\">BaseAnalysis</span><span class=\"p\">):</span>\n",
       "<span class=\"w\">    </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">    A basic analysis that extracts the data from the latest file matching the label</span>\n",
       "<span class=\"sd\">    and plots and stores the data in the experiment container.</span>\n",
       "<span class=\"sd\">    &quot;&quot;&quot;</span>\n",
       "\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">create_figures</span><span class=\"p\">(</span><span class=\"bp\">self</span><span class=\"p\">):</span>\n",
       "<span class=\"w\">        </span><span class=\"sd\">&quot;&quot;&quot;</span>\n",
       "<span class=\"sd\">        Creates a line plot x vs y for every data variable yi and coordinate xi in the</span>\n",
       "<span class=\"sd\">        dataset.</span>\n",
       "<span class=\"sd\">        &quot;&quot;&quot;</span>\n",
       "        <span class=\"c1\"># NB we do not use `to_gridded_dataset` because that can potentially drop</span>\n",
       "        <span class=\"c1\"># repeated measurement of the same x0_i setpoint (e.g., AllXY experiment)</span>\n",
       "        <span class=\"n\">dataset</span> <span class=\"o\">=</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">dataset</span>\n",
       "        <span class=\"c1\"># for compatibility with older datasets</span>\n",
       "        <span class=\"c1\"># in case &quot;x0&quot; is not a coordinate we use &quot;dim_0&quot;</span>\n",
       "        <span class=\"n\">coords</span> <span class=\"o\">=</span> <span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">coords</span><span class=\"p\">)</span>\n",
       "        <span class=\"n\">dims</span> <span class=\"o\">=</span> <span class=\"nb\">list</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">dims</span><span class=\"p\">)</span>\n",
       "        <span class=\"n\">plot_against</span> <span class=\"o\">=</span> <span class=\"n\">coords</span> <span class=\"k\">if</span> <span class=\"n\">coords</span> <span class=\"k\">else</span> <span class=\"p\">(</span><span class=\"n\">dims</span> <span class=\"k\">if</span> <span class=\"n\">dims</span> <span class=\"k\">else</span> <span class=\"p\">[</span><span class=\"kc\">None</span><span class=\"p\">])</span>\n",
       "        <span class=\"k\">for</span> <span class=\"n\">idx</span><span class=\"p\">,</span> <span class=\"n\">xi</span> <span class=\"ow\">in</span> <span class=\"nb\">enumerate</span><span class=\"p\">(</span><span class=\"n\">plot_against</span><span class=\"p\">):</span>\n",
       "            <span class=\"k\">for</span> <span class=\"n\">yi</span><span class=\"p\">,</span> <span class=\"n\">yvals</span> <span class=\"ow\">in</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">data_vars</span><span class=\"o\">.</span><span class=\"n\">items</span><span class=\"p\">():</span>\n",
       "                <span class=\"c1\"># for compatibility with older datasets, do not plot &quot;x0&quot; vs &quot;x0&quot;</span>\n",
       "                <span class=\"k\">if</span> <span class=\"n\">yi</span><span class=\"o\">.</span><span class=\"n\">startswith</span><span class=\"p\">(</span><span class=\"s2\">&quot;y&quot;</span><span class=\"p\">):</span>\n",
       "                    <span class=\"n\">fig</span><span class=\"p\">,</span> <span class=\"n\">ax</span> <span class=\"o\">=</span> <span class=\"n\">plt</span><span class=\"o\">.</span><span class=\"n\">subplots</span><span class=\"p\">()</span>\n",
       "\n",
       "                    <span class=\"n\">fig_id</span> <span class=\"o\">=</span> <span class=\"sa\">f</span><span class=\"s2\">&quot;Line plot x</span><span class=\"si\">{</span><span class=\"n\">idx</span><span class=\"si\">}</span><span class=\"s2\">-</span><span class=\"si\">{</span><span class=\"n\">yi</span><span class=\"si\">}</span><span class=\"s2\">&quot;</span>\n",
       "\n",
       "                    <span class=\"n\">yvals</span><span class=\"o\">.</span><span class=\"n\">plot</span><span class=\"o\">.</span><span class=\"n\">line</span><span class=\"p\">(</span><span class=\"n\">ax</span><span class=\"o\">=</span><span class=\"n\">ax</span><span class=\"p\">,</span> <span class=\"n\">x</span><span class=\"o\">=</span><span class=\"n\">xi</span><span class=\"p\">,</span> <span class=\"n\">marker</span><span class=\"o\">=</span><span class=\"s2\">&quot;.&quot;</span><span class=\"p\">)</span>\n",
       "\n",
       "                    <span class=\"n\">adjust_axeslabels_SI</span><span class=\"p\">(</span><span class=\"n\">ax</span><span class=\"p\">)</span>\n",
       "\n",
       "                    <span class=\"n\">qpl</span><span class=\"o\">.</span><span class=\"n\">set_suptitle_from_dataset</span><span class=\"p\">(</span><span class=\"n\">fig</span><span class=\"p\">,</span> <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">dataset</span><span class=\"p\">,</span> <span class=\"sa\">f</span><span class=\"s2\">&quot;x</span><span class=\"si\">{</span><span class=\"n\">idx</span><span class=\"si\">}</span><span class=\"s2\">-</span><span class=\"si\">{</span><span class=\"n\">yi</span><span class=\"si\">}</span><span class=\"s2\">&quot;</span><span class=\"p\">)</span>\n",
       "\n",
       "                    <span class=\"c1\"># add the figure and axis to the dicts for saving</span>\n",
       "                    <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">figs_mpl</span><span class=\"p\">[</span><span class=\"n\">fig_id</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">fig</span>\n",
       "                    <span class=\"bp\">self</span><span class=\"o\">.</span><span class=\"n\">axs_mpl</span><span class=\"p\">[</span><span class=\"n\">fig_id</span><span class=\"p\">]</span> <span class=\"o\">=</span> <span class=\"n\">ax</span>\n",
       "</pre></div>\n"
      ],
      "text/latex": [
       "\\begin{Verbatim}[commandchars=\\\\\\{\\}]\n",
       "\\PY{k}{class} \\PY{n+nc}{BasicAnalysis}\\PY{p}{(}\\PY{n}{BaseAnalysis}\\PY{p}{)}\\PY{p}{:}\n",
       "\\PY{+w}{    }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{    A basic analysis that extracts the data from the latest file matching the label}\n",
       "\\PY{l+s+sd}{    and plots and stores the data in the experiment container.}\n",
       "\\PY{l+s+sd}{    \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\n",
       "    \\PY{k}{def} \\PY{n+nf}{create\\PYZus{}figures}\\PY{p}{(}\\PY{n+nb+bp}{self}\\PY{p}{)}\\PY{p}{:}\n",
       "\\PY{+w}{        }\\PY{l+s+sd}{\\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "\\PY{l+s+sd}{        Creates a line plot x vs y for every data variable yi and coordinate xi in the}\n",
       "\\PY{l+s+sd}{        dataset.}\n",
       "\\PY{l+s+sd}{        \\PYZdq{}\\PYZdq{}\\PYZdq{}}\n",
       "        \\PY{c+c1}{\\PYZsh{} NB we do not use `to\\PYZus{}gridded\\PYZus{}dataset` because that can potentially drop}\n",
       "        \\PY{c+c1}{\\PYZsh{} repeated measurement of the same x0\\PYZus{}i setpoint (e.g., AllXY experiment)}\n",
       "        \\PY{n}{dataset} \\PY{o}{=} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{dataset}\n",
       "        \\PY{c+c1}{\\PYZsh{} for compatibility with older datasets}\n",
       "        \\PY{c+c1}{\\PYZsh{} in case \\PYZdq{}x0\\PYZdq{} is not a coordinate we use \\PYZdq{}dim\\PYZus{}0\\PYZdq{}}\n",
       "        \\PY{n}{coords} \\PY{o}{=} \\PY{n+nb}{list}\\PY{p}{(}\\PY{n}{dataset}\\PY{o}{.}\\PY{n}{coords}\\PY{p}{)}\n",
       "        \\PY{n}{dims} \\PY{o}{=} \\PY{n+nb}{list}\\PY{p}{(}\\PY{n}{dataset}\\PY{o}{.}\\PY{n}{dims}\\PY{p}{)}\n",
       "        \\PY{n}{plot\\PYZus{}against} \\PY{o}{=} \\PY{n}{coords} \\PY{k}{if} \\PY{n}{coords} \\PY{k}{else} \\PY{p}{(}\\PY{n}{dims} \\PY{k}{if} \\PY{n}{dims} \\PY{k}{else} \\PY{p}{[}\\PY{k+kc}{None}\\PY{p}{]}\\PY{p}{)}\n",
       "        \\PY{k}{for} \\PY{n}{idx}\\PY{p}{,} \\PY{n}{xi} \\PY{o+ow}{in} \\PY{n+nb}{enumerate}\\PY{p}{(}\\PY{n}{plot\\PYZus{}against}\\PY{p}{)}\\PY{p}{:}\n",
       "            \\PY{k}{for} \\PY{n}{yi}\\PY{p}{,} \\PY{n}{yvals} \\PY{o+ow}{in} \\PY{n}{dataset}\\PY{o}{.}\\PY{n}{data\\PYZus{}vars}\\PY{o}{.}\\PY{n}{items}\\PY{p}{(}\\PY{p}{)}\\PY{p}{:}\n",
       "                \\PY{c+c1}{\\PYZsh{} for compatibility with older datasets, do not plot \\PYZdq{}x0\\PYZdq{} vs \\PYZdq{}x0\\PYZdq{}}\n",
       "                \\PY{k}{if} \\PY{n}{yi}\\PY{o}{.}\\PY{n}{startswith}\\PY{p}{(}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{y}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\\PY{p}{:}\n",
       "                    \\PY{n}{fig}\\PY{p}{,} \\PY{n}{ax} \\PY{o}{=} \\PY{n}{plt}\\PY{o}{.}\\PY{n}{subplots}\\PY{p}{(}\\PY{p}{)}\n",
       "\n",
       "                    \\PY{n}{fig\\PYZus{}id} \\PY{o}{=} \\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{Line plot x}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{idx}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZhy{}}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{yi}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\n",
       "\n",
       "                    \\PY{n}{yvals}\\PY{o}{.}\\PY{n}{plot}\\PY{o}{.}\\PY{n}{line}\\PY{p}{(}\\PY{n}{ax}\\PY{o}{=}\\PY{n}{ax}\\PY{p}{,} \\PY{n}{x}\\PY{o}{=}\\PY{n}{xi}\\PY{p}{,} \\PY{n}{marker}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{.}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\n",
       "                    \\PY{n}{adjust\\PYZus{}axeslabels\\PYZus{}SI}\\PY{p}{(}\\PY{n}{ax}\\PY{p}{)}\n",
       "\n",
       "                    \\PY{n}{qpl}\\PY{o}{.}\\PY{n}{set\\PYZus{}suptitle\\PYZus{}from\\PYZus{}dataset}\\PY{p}{(}\\PY{n}{fig}\\PY{p}{,} \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{dataset}\\PY{p}{,} \\PY{l+s+sa}{f}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{x}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{idx}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZhy{}}\\PY{l+s+si}{\\PYZob{}}\\PY{n}{yi}\\PY{l+s+si}{\\PYZcb{}}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{)}\n",
       "\n",
       "                    \\PY{c+c1}{\\PYZsh{} add the figure and axis to the dicts for saving}\n",
       "                    \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{figs\\PYZus{}mpl}\\PY{p}{[}\\PY{n}{fig\\PYZus{}id}\\PY{p}{]} \\PY{o}{=} \\PY{n}{fig}\n",
       "                    \\PY{n+nb+bp}{self}\\PY{o}{.}\\PY{n}{axs\\PYZus{}mpl}\\PY{p}{[}\\PY{n}{fig\\PYZus{}id}\\PY{p}{]} \\PY{o}{=} \\PY{n}{ax}\n",
       "\\end{Verbatim}\n"
      ],
      "text/plain": [
       "class BasicAnalysis(BaseAnalysis):\n",
       "    \"\"\"\n",
       "    A basic analysis that extracts the data from the latest file matching the label\n",
       "    and plots and stores the data in the experiment container.\n",
       "    \"\"\"\n",
       "\n",
       "    def create_figures(self):\n",
       "        \"\"\"\n",
       "        Creates a line plot x vs y for every data variable yi and coordinate xi in the\n",
       "        dataset.\n",
       "        \"\"\"\n",
       "        # NB we do not use `to_gridded_dataset` because that can potentially drop\n",
       "        # repeated measurement of the same x0_i setpoint (e.g., AllXY experiment)\n",
       "        dataset = self.dataset\n",
       "        # for compatibility with older datasets\n",
       "        # in case \"x0\" is not a coordinate we use \"dim_0\"\n",
       "        coords = list(dataset.coords)\n",
       "        dims = list(dataset.dims)\n",
       "        plot_against = coords if coords else (dims if dims else [None])\n",
       "        for idx, xi in enumerate(plot_against):\n",
       "            for yi, yvals in dataset.data_vars.items():\n",
       "                # for compatibility with older datasets, do not plot \"x0\" vs \"x0\"\n",
       "                if yi.startswith(\"y\"):\n",
       "                    fig, ax = plt.subplots()\n",
       "\n",
       "                    fig_id = f\"Line plot x{idx}-{yi}\"\n",
       "\n",
       "                    yvals.plot.line(ax=ax, x=xi, marker=\".\")\n",
       "\n",
       "                    adjust_axeslabels_SI(ax)\n",
       "\n",
       "                    qpl.set_suptitle_from_dataset(fig, self.dataset, f\"x{idx}-{yi}\")\n",
       "\n",
       "                    # add the figure and axis to the dicts for saving\n",
       "                    self.figs_mpl[fig_id] = fig\n",
       "                    self.axs_mpl[fig_id] = ax"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_source_code(ba.BasicAnalysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2050289e",
   "metadata": {},
   "source": [
    "A slightly more complex use case is the\n",
    "{class}`~quantify_core.analysis.spectroscopy_analysis.ResonatorSpectroscopyAnalysis`\n",
    "that implements\n",
    "{meth}`~quantify_core.analysis.spectroscopy_analysis.ResonatorSpectroscopyAnalysis.process_data`\n",
    "to cast the data to a complex-valued array,\n",
    "{meth}`~quantify_core.analysis.spectroscopy_analysis.ResonatorSpectroscopyAnalysis.run_fitting`\n",
    "where a fit is performed using a model\n",
    "(from the {mod}`quantify_core.analysis.fitting_models` library), and\n",
    "{meth}`~quantify_core.analysis.spectroscopy_analysis.ResonatorSpectroscopyAnalysis.create_figures`\n",
    "where the data and the fitted curve are plotted together.\n",
    "\n",
    "Creating a custom analysis for a particular type of dataset is showcased in the\n",
    "{ref}`analysis-framework-tutorial`.\n",
    "There you will also learn some other capabilities of the analysis and practical\n",
    "productivity tips.\n",
    "\n",
    "```{seealso}\n",
    "{ref}`Analysis API documentation <analysis-api>` and\n",
    "{ref}`tutorial on building custom analyses <analysis-framework-tutorial>`.\n",
    "```\n",
    "\n",
    "# Examples: Settables and Gettables\n",
    "\n",
    "Below we give several examples of experiments that use Settables and Gettables in different control modes.\n",
    "\n",
    "## Iterative control mode\n",
    "\n",
    "### Single-float-valued settable(s) and gettable(s)\n",
    "\n",
    "- Each settable accepts a single float value.\n",
    "- Gettables return a single float value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3560a62c",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "1D example"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting iterative measurement...\n"
     ]
    },
    {
     "data": {
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       "model_id": "f7d6cbc81e0a443fa221285943701db0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
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     },
     "metadata": {},
     "output_type": "display_data"
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       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "html[data-theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\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: 320B\n",
       "Dimensions:  (x0: 20)\n",
       "Coordinates:\n",
       "  * x0       (x0) float64 160B 0.0 0.3684 0.7368 1.105 ... 5.895 6.263 6.632 7.0\n",
       "Data variables:\n",
       "    y0       (x0) float64 160B 1.0 0.9329 0.7406 0.4489 ... 0.9998 0.9399 0.7539\n",
       "Attributes:\n",
       "    tuid:                             20241106-153125-680-813ad8\n",
       "    name:                             my experiment\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  False</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-11adb2e2-3fee-4704-8909-0104d97680bf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-11adb2e2-3fee-4704-8909-0104d97680bf' 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'>x0</span>: 20</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bd140f7f-2646-499b-9a97-5f229aa06588' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bd140f7f-2646-499b-9a97-5f229aa06588' 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'>x0</span></div><div class='xr-var-dims'>(x0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.3684 0.7368 ... 6.632 7.0</div><input id='attrs-0108dfaa-fb74-4aa6-9a7a-1a89f5d2fc44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0108dfaa-fb74-4aa6-9a7a-1a89f5d2fc44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad3a6937-2fd1-48f5-bfdc-00b2849b563c' class='xr-var-data-in' type='checkbox'><label for='data-ad3a6937-2fd1-48f5-bfdc-00b2849b563c' 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>name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([0.      , 0.368421, 0.736842, 1.105263, 1.473684, 1.842105, 2.210526,\n",
       "       2.578947, 2.947368, 3.315789, 3.684211, 4.052632, 4.421053, 4.789474,\n",
       "       5.157895, 5.526316, 5.894737, 6.263158, 6.631579, 7.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c2d54321-3099-42c1-81f3-9cdd00a9e5a7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c2d54321-3099-42c1-81f3-9cdd00a9e5a7' class='xr-section-summary' >Data variables: <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>y0</span></div><div class='xr-var-dims'>(x0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 0.9329 0.7406 ... 0.9399 0.7539</div><input id='attrs-248f5741-f86f-49cc-8ef4-bd11008f6e8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-248f5741-f86f-49cc-8ef4-bd11008f6e8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccaffa10-8db0-4b85-8800-0fd81c5e0caf' class='xr-var-data-in' type='checkbox'><label for='data-ccaffa10-8db0-4b85-8800-0fd81c5e0caf' 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>name :</span></dt><dd>sig_a</dd><dt><span>long_name :</span></dt><dd>Signal</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([ 1.        ,  0.93289715,  0.7405942 ,  0.4488993 ,  0.09695955,\n",
       "       -0.26799272, -0.59697884, -0.84584701, -0.98119769, -0.98486606,\n",
       "       -0.8563598 , -0.61292518, -0.28723252,  0.07700839,  0.43091433,\n",
       "        0.72698911,  0.92549782,  0.99979946,  0.93992232,  0.75390225])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-889b5355-1932-4092-a358-dce1563179fd' class='xr-section-summary-in' type='checkbox'  ><label for='section-889b5355-1932-4092-a358-dce1563179fd' 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>x0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b268eaea-6c4e-4366-9ece-393eb5e39e4b' class='xr-index-data-in' type='checkbox'/><label for='index-b268eaea-6c4e-4366-9ece-393eb5e39e4b' 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, 0.3684210526315789, 0.7368421052631579,\n",
       "       1.1052631578947367, 1.4736842105263157, 1.8421052631578947,\n",
       "       2.2105263157894735, 2.5789473684210527, 2.9473684210526314,\n",
       "         3.31578947368421, 3.6842105263157894,  4.052631578947368,\n",
       "        4.421052631578947,  4.789473684210526,  5.157894736842105,\n",
       "        5.526315789473684,  5.894736842105263,  6.263157894736842,\n",
       "         6.63157894736842,                7.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bbe7c175-cdd5-459c-9e92-188e14cd3ec5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bbe7c175-cdd5-459c-9e92-188e14cd3ec5' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20241106-153125-680-813ad8</dd><dt><span>name :</span></dt><dd>my experiment</dd><dt><span>grid_2d :</span></dt><dd>False</dd><dt><span>grid_2d_uniformly_spaced :</span></dt><dd>False</dd><dt><span>1d_2_settables_uniformly_spaced :</span></dt><dd>False</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 320B\n",
       "Dimensions:  (x0: 20)\n",
       "Coordinates:\n",
       "  * x0       (x0) float64 160B 0.0 0.3684 0.7368 1.105 ... 5.895 6.263 6.632 7.0\n",
       "Data variables:\n",
       "    y0       (x0) float64 160B 1.0 0.9329 0.7406 0.4489 ... 0.9998 0.9399 0.7539\n",
       "Attributes:\n",
       "    tuid:                             20241106-153125-680-813ad8\n",
       "    name:                             my experiment\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  False"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ELWCxRERERNQCFktERERELfj/yjoEburDMaoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = ManualParameter(\n",
    "    name=\"time\", label=\"Time\", unit=\"s\", vals=validators.Numbers(), initial_value=1\n",
    ")\n",
    "signal = Parameter(\n",
    "    name=\"sig_a\", label=\"Signal\", unit=\"V\", get_cmd=lambda: np.cos(time())\n",
    ")\n",
    "\n",
    "meas_ctrl.settables(time)\n",
    "meas_ctrl.gettables(signal)\n",
    "meas_ctrl.setpoints(np.linspace(0, 7, 20))\n",
    "dset = meas_ctrl.run(\"my experiment\")\n",
    "dset_grid = dh.to_gridded_dataset(dset)\n",
    "\n",
    "dset_grid.y0.plot(marker=\"o\")\n",
    "dset_grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "db03cbc3",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "2D example"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting iterative measurement...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4097a269dca14092be8a1f99bb03322d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<defs>\n",
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "  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: 1kB\n",
       "Dimensions:  (x0: 10, x1: 12)\n",
       "Coordinates:\n",
       "  * x0       (x0) float64 80B 0.0 0.5556 1.111 1.667 ... 3.333 3.889 4.444 5.0\n",
       "  * x1       (x1) float64 96B 0.0 0.4545 0.9091 1.364 ... 3.636 4.091 4.545 5.0\n",
       "Data variables:\n",
       "    y0       (x0, x1) float64 960B 1.5 1.788 2.241 2.955 ... 178.3 195.5 222.6\n",
       "Attributes:\n",
       "    tuid:                             20241106-153125-874-412c85\n",
       "    name:                             my experiment\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         True\n",
       "    1d_2_settables_uniformly_spaced:  False\n",
       "    xlen:                             10\n",
       "    ylen:                             12</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-abc768db-44f9-47f8-9e95-340eca11b4c4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-abc768db-44f9-47f8-9e95-340eca11b4c4' 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'>x0</span>: 10</li><li><span class='xr-has-index'>x1</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bde19880-560d-4c71-94c4-8792552eda99' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bde19880-560d-4c71-94c4-8792552eda99' 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 class='xr-has-index'>x0</span></div><div class='xr-var-dims'>(x0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.5556 1.111 ... 4.444 5.0</div><input id='attrs-653ec19e-5aaa-41eb-a98e-cbba7f242886' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-653ec19e-5aaa-41eb-a98e-cbba7f242886' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f2d4ba3-7138-4bba-b49f-c6158ffc4327' class='xr-var-data-in' type='checkbox'><label for='data-5f2d4ba3-7138-4bba-b49f-c6158ffc4327' 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>name :</span></dt><dd>time_a</dd><dt><span>long_name :</span></dt><dd>Time A</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([0.      , 0.555556, 1.111111, 1.666667, 2.222222, 2.777778, 3.333333,\n",
       "       3.888889, 4.444444, 5.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.4545 0.9091 ... 4.545 5.0</div><input id='attrs-1ee4f2fe-1c04-4a1e-8b81-ad227eba7c18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ee4f2fe-1c04-4a1e-8b81-ad227eba7c18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d0d15e7-a4c8-4b29-8269-6d1486cfefb6' class='xr-var-data-in' type='checkbox'><label for='data-1d0d15e7-a4c8-4b29-8269-6d1486cfefb6' 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>name :</span></dt><dd>time_b</dd><dt><span>long_name :</span></dt><dd>Time B</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([0.      , 0.454545, 0.909091, 1.363636, 1.818182, 2.272727, 2.727273,\n",
       "       3.181818, 3.636364, 4.090909, 4.545455, 5.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a603cb23-94a3-430a-9cb1-1b3daa62be01' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a603cb23-94a3-430a-9cb1-1b3daa62be01' class='xr-section-summary' >Data variables: <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>y0</span></div><div class='xr-var-dims'>(x0, x1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5 1.788 2.241 ... 195.5 222.6</div><input id='attrs-2e78ec16-0464-449b-bb13-d33ca2711660' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e78ec16-0464-449b-bb13-d33ca2711660' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e113703b-7bc3-48de-b304-526f3e9e29bd' class='xr-var-data-in' type='checkbox'><label for='data-e113703b-7bc3-48de-b304-526f3e9e29bd' 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>name :</span></dt><dd>sig_a</dd><dt><span>long_name :</span></dt><dd>Signal A</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([[  1.5       ,   1.78772855,   2.24103254,   2.95519353,\n",
       "          4.08032354,   5.85291761,   8.64556351,  13.04525735,\n",
       "         19.97678625,  30.89711269,  48.10161857,  75.20657955],\n",
       "       [  2.242909  ,   2.53063755,   2.98394154,   3.69810253,\n",
       "          4.82323254,   6.5958266 ,   9.38847251,  13.78816635,\n",
       "         20.71969525,  31.64002169,  48.84452756,  75.94948855],\n",
       "       [  3.53773178,   3.82546033,   4.27876432,   4.99292531,\n",
       "          6.11805532,   7.89064938,  10.68329529,  15.08298912,\n",
       "         22.01451803,  32.93484447,  50.13935034,  77.24431133],\n",
       "       [  5.79449005,   6.0822186 ,   6.53552259,   7.24968358,\n",
       "          8.37481359,  10.14740766,  12.94005356,  17.3397474 ,\n",
       "         24.2712763 ,  35.19160275,  52.39610862,  79.5010696 ],\n",
       "       [  9.72781435,  10.0155429 ,  10.46884689,  11.18300789,\n",
       "         12.30813789,  14.08073196,  16.87337787,  21.2730717 ,\n",
       "         28.2046006 ,  39.12492705,  56.32943292,  83.4343939 ],\n",
       "       [ 16.58324067,  16.87096922,  17.32427321,  18.03843421,\n",
       "         19.16356421,  20.93615828,  23.72880419,  28.12849802,\n",
       "         35.06002692,  45.98035337,  63.18485924,  90.28982022],\n",
       "       [ 28.53162489,  28.81935345,  29.27265744,  29.98681843,\n",
       "         31.11194844,  32.8845425 ,  35.67718841,  40.07688224,\n",
       "         47.00841114,  57.92873759,  75.13324346, 102.23820445],\n",
       "       [ 49.35657127,  49.64429983,  50.09760382,  50.81176481,\n",
       "         51.93689482,  53.70948888,  56.50213479,  60.90182862,\n",
       "         67.83335752,  78.75368397,  95.95818984, 123.06315083],\n",
       "       [ 85.65255772,  85.94028627,  86.39359026,  87.10775125,\n",
       "         88.23288126,  90.00547532,  92.79812123,  97.19781506,\n",
       "        104.12934397, 115.04967041, 132.25417628, 159.35913727],\n",
       "       [148.9131591 , 149.20088765, 149.65419164, 150.36835264,\n",
       "        151.49348264, 153.26607671, 156.05872262, 160.45841645,\n",
       "        167.38994535, 178.3102718 , 195.51477767, 222.61973865]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-78c9516f-c76a-454d-81c2-c75fecde4fb5' class='xr-section-summary-in' type='checkbox'  ><label for='section-78c9516f-c76a-454d-81c2-c75fecde4fb5' class='xr-section-summary' >Indexes: <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-index-name'><div>x0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9359b509-f98d-4ba7-af20-797ab3ab40b5' class='xr-index-data-in' type='checkbox'/><label for='index-9359b509-f98d-4ba7-af20-797ab3ab40b5' 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, 0.5555555555555556, 1.1111111111111112,\n",
       "       1.6666666666666667, 2.2222222222222223, 2.7777777777777777,\n",
       "       3.3333333333333335, 3.8888888888888893,  4.444444444444445,\n",
       "                      5.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fb54331e-8d6f-440e-a566-48ebbf394cee' class='xr-index-data-in' type='checkbox'/><label for='index-fb54331e-8d6f-440e-a566-48ebbf394cee' 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, 0.45454545454545503,  0.9090909090909092,\n",
       "        1.3636363636363638,  1.8181818181818183,   2.272727272727273,\n",
       "        2.7272727272727275,  3.1818181818181817,  3.6363636363636367,\n",
       "         4.090909090909091,   4.545454545454546,                 5.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x1&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b4102888-729c-4b19-94fe-51d677df7861' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b4102888-729c-4b19-94fe-51d677df7861' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20241106-153125-874-412c85</dd><dt><span>name :</span></dt><dd>my experiment</dd><dt><span>grid_2d :</span></dt><dd>False</dd><dt><span>grid_2d_uniformly_spaced :</span></dt><dd>True</dd><dt><span>1d_2_settables_uniformly_spaced :</span></dt><dd>False</dd><dt><span>xlen :</span></dt><dd>10</dd><dt><span>ylen :</span></dt><dd>12</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 1kB\n",
       "Dimensions:  (x0: 10, x1: 12)\n",
       "Coordinates:\n",
       "  * x0       (x0) float64 80B 0.0 0.5556 1.111 1.667 ... 3.333 3.889 4.444 5.0\n",
       "  * x1       (x1) float64 96B 0.0 0.4545 0.9091 1.364 ... 3.636 4.091 4.545 5.0\n",
       "Data variables:\n",
       "    y0       (x0, x1) float64 960B 1.5 1.788 2.241 2.955 ... 178.3 195.5 222.6\n",
       "Attributes:\n",
       "    tuid:                             20241106-153125-874-412c85\n",
       "    name:                             my experiment\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         True\n",
       "    1d_2_settables_uniformly_spaced:  False\n",
       "    xlen:                             10\n",
       "    ylen:                             12"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mzcyx9QlJCgAAFmNOijUo9wAAUA9s2bJFw4cPV0JCggzD0Nq1a33eHz9+vAzD8GlDhw712ef48eMaO3asIiMj1bx5c02aNEmlpaW1+Cl8kaQAAGCxsxNnzTR/lZWVqXv37lq8ePFP7jN06FAVFBR428svv+zz/tixY3XgwAFt2LBB69at05YtWzRlyhS/Y7EK5R4AACxmR7knPT1d6enpF9wnNDRUcXFx533v448/Vk5Ojnbu3KnLLrtMkvSnP/1Jw4YN0+OPP66EhAS/YzKLkRQAACxm1UhKSUmJTysvN7e6+qZNmxQTE6OOHTtq6tSp+uabb7zvbdu2Tc2bN/cmKJKUlpamoKAg7dixw9R5A0WSAgCAQyUmJioqKsrbsrKyAu5r6NChWrFihTZu3KhHHnlEmzdvVnp6ulyuM48LKCwsVExMjM8xjRo1UnR0tAoLC019jkBR7gEAwGIek+WesyMp+fn5ioyM9G4PDQ0NuM9bbrnF+3W3bt2UnJysiy66SJs2bdKgQYMC7rcmMZICAIDFPJI8HhPt+34iIyN9mpkk5cfat2+vli1b6tChQ5KkuLg4FRUV+exz+vRpHT9+/CfnsdQ0khQAABqgL7/8Ut98843i4+MlSampqTpx4oR2797t3eedd96R2+1WSkqKLTFS7gEAwGJuGTJqecXZ0tJS76iIJOXl5Sk3N1fR0dGKjo7W/PnzNWrUKMXFxenw4cO69957dfHFF2vIkCGSpEsvvVRDhw7V5MmT9eyzz6qyslLTpk3TLbfcYsudPRIjKQAAWM6OdVJ27dqlnj17qmfPnpKkWbNmqWfPnpo7d66Cg4O1d+9eXX/99erQoYMmTZqk3r1767333vMpIb300kvq1KmTBg0apGHDhunqq6/Wc889Z9l18RcjKQAA1AP9+/eXx+P5yffXr19fZR/R0dFauXKllWGZQpICAIDF3B5DBs/uMY0kBQAAi529S8fM8WBOCgAAcChGUgAAsFigk19/eDxIUgAAsBxJijVIUgAAsBgTZ63BnBQAAOBIjKQAAGAx7u6xBkkKAAAWO5OkmJmTYmEwdRjlHgAA4EiMpAAAYDHu7rEGSQoAABbzfN/MHA/KPQAAwKEYSQEAwGKUe6xBkgIAgNWo91iCJAUAAKuZHEkRIymSmJMCAAAcipEUAAAsxoqz1iBJAQDAYg1l4uwbb7zh9zHXXnutmjRpUq19bU1SlixZoiVLlujzzz+XJHXp0kVz585Venq6X/0Y/z4pI6i8BiKsQwwHfEMTw5kQgh1SRW1k/98gnpDGdoegoPIQu0OQ4Qm1OwS5Qu3/v9H4pL3/Nxr6r4maMGLECL/2NwxDn332mdq3b1+t/W39jmndurUWLlyo3bt3a9euXRo4cKBuuOEGHThwwM6wAAAwx2OYb3VEYWGh3G53tVrTpk396tvWP7WGDx/u8/rhhx/WkiVLtH37dnXp0sWmqAAAMKehzEkZN25ctUs3knTrrbcqMjKy2vvbPx78PZfLpVdeeUVlZWVKTU097z7l5eUqL//PeF1JSUlthQcAAH5k+fLlfu2/ZMkSv/avVpKyd+9evzqVpM6dO6tRNWri+/btU2pqqk6dOqXw8HCtWbNGnTt3Pu++WVlZmj9/vt+xAABQqxrQYm433XSTfvnLX2rIkCEyLJ4XWK0kpUePHjIMQ55qjj8FBQXp008/rdbEmI4dOyo3N1fFxcV69dVXNW7cOG3evPm8icqcOXM0a9Ys7+uSkhIlJiZWKyYAAGpLQ7m7R5L+/e9/67rrrlNCQoImTJig8ePHV3tibFWqXe7ZsWOHWrVqVeV+Ho9HXbt2rXYAISEhuvjiiyVJvXv31s6dO/XUU09p6dKl5+wbGhqq0FD7Z8kDAIAzNm7cqC+++ELLly/XihUr9PDDD6tfv3765S9/qVGjRpn6vV2tu3v69euniy++WElJSVW2tm3bqm/fvn5NpPkht9vtM+8EAIA6yWOi1TFJSUnKzMzUP//5T23YsEEJCQmaPHmy4uPjlZGRod27dwfUb7VGUt59912/Ov373/9erf3mzJmj9PR0tWnTRidPntTKlSu1adMmrV+/3q/zAQDgJA2p3PNjAwcO1MCBA72/13/7299q6dKlOn36tN99mb67x+Vyad++fUpKStLPfvYzv44tKirS7bffroKCAkVFRSk5OVnr16/XtddeazYsAADs04Amzp5PXl6esrOzlZ2dreLiYqWlpQXUj99JyowZM9StWzdNmjRJLpdL/fr109atW9W0aVOtW7dO/fv3r3ZfL7zwgr+nBwAADnTq1Cm9+uqrWrZsmbZs2aLExERNmjRJEyZMCPgmF7+TlFdffVW33nqrJOnNN99UXl6ePvnkE/35z3/W/fffrw8++CCgQAAAqD+M75uZ4+uGDz/8UMuWLdPq1at16tQp3XjjjcrJydGgQYNM35Ls97L4X3/9teLi4iSdmXsyevRodejQQRMnTtS+fftMBQMAQL1gZtJsgKWiLVu2aPjw4UpISJBhGFq7dq33vcrKSs2ePVvdunVTs2bNlJCQoNtvv11Hjx716aNt27YyDMOnLVy48ILn7dOnj3bs2KGHHnpIR48e1cqVK5WWlmbJmil+j6TExsbqo48+Unx8vHJycryrx3377bcKDg42HRAAAPBfWVmZunfvrokTJ2rkyJE+73377bfas2ePHnzwQXXv3l3//ve/9etf/1rXX3+9du3a5bPvggULNHnyZO/riIiIC553165d6tWrl3Uf5Af8TlImTJign//854qPj5dhGN7JMDt27FCnTp0sDxAAgDrHoomzP378y4XWC0tPT1d6evp534uKitKGDRt8ti1atEhXXHGFjhw5ojZt2ni3R0REeCsmVdm7d6969OhRrX0l6cCBA+rYsWO1VqSXAij3ZGZm6n/+5380ZcoUffDBB96LFRwcrPvuu8/f7gAAqH8segpyYmKioqKivC0rK8uyEIuLi2UYhpo3b+6zfeHChWrRooV69uypxx577IK3Dvfs2VPffPNNtc+ZmpqqI0eOVHv/gG5Bvummm87ZNm7cuEC6AgAAPyE/P9/nqcFWrbp+6tQpzZ49W2PGjPHp/6677lKvXr0UHR2trVu3as6cOSooKNATTzxx3n48Ho8efPBBNW3atFrnraio8CvOaiUpTz/9tKZMmaKwsLBqdfrss89q7NixVdaxAACojzyeM83M8ZIUGRnpk0RYobKyUj//+c/l8XjOeSrxD5+Pl5ycrJCQEN1xxx3Kyso6b4LUt29fHTx4sNrnTk1N9WtF+molKTNnztSYMWOqnaTce++9Gjx4MEkKAKBhcuhibmcTlC+++ELvvPNOlQlQSkqKTp8+rc8//1wdO3Y85/1NmzbVTKDfq1aS4vF4NGjQoGpPdPnuu+9MBQUAAKx1NkH57LPP9O6776pFixZVHpObm6ugoCDFxMTUQoTnqlbWMW/ePL86veGGGxQdHR1QQAAA1Hk/mPwa8PF+Ki0t1aFDh7yv8/LylJubq+joaMXHx+umm27Snj17tG7dOrlcLhUWFkqSoqOjFRISom3btmnHjh0aMGCAIiIitG3bNs2cOVO33nqr34+9sUqNJCkAADRkhudMM3O8v3bt2qUBAwZ4X5+dXzJu3DhlZmbqjTfekKRzbhl+99131b9/f4WGhmrVqlXKzMxUeXm52rVrp5kzZ/rMU6ltph8wCAAAfsSGOSn9+/eX5wKzdS/0niT16tVL27dv9//ENcjvdVIAAACqy+12a926dQEdy0gKAABWs2FOitMcOnRIy5YtU3Z2tr766itVVlb63QcjKQAAWM2GBww6wXfffacVK1aob9++6tixo7Zu3aq5c+fqyy+/DKg/y5KUjz/+WPfcc49V3QEAgDpi586duuOOOxQXF6cnn3xSN9xwgwzD0DPPPKM777xTsbGxAfVrKkkpKyvTCy+8oCuvvFJdunRRTk6Ome4AAKgfGtBISnJyskaPHq0WLVpo69at2rNnj+6++24ZhvmSVUBJygcffKCJEycqNjZWU6ZM0ZVXXqmPPvpI+/fvNx0QAAB1XgNKUg4ePKi+fftqwIAB6ty5s6V9VztJKSoq0qOPPqpOnTrppptuUvPmzbVp0yYFBQVp4sSJ6tSpk6WBAQAA5/vnP/+pjh07aurUqWrdurXuuece/d///V/tjqQkJSVp3759euqpp/Svf/1LTzzxhC677DLTAQAAUO+cvbvHTKsj/uu//kv333+/Dh06pD//+c8qLCzUVVddpdOnTys7O1uffvppwH37laS8//772rJli6kTAgBQ351dcdZMq4sGDhyov/zlLyooKNCiRYv0zjvvqFOnTkpOTg6ov2onKZ988on3xJdffrl69+6tP/7xj5JkyZAOAACoH6KiovSrX/1Ku3bt0p49e9S/f/+A+vFr4uxVV12lZcuWqaCgQHfeeadeeeUVuVwu/epXv9Lzzz+vr776KqAgAACoVxrQxNmq9OjRQ08//XRAxwZ0d094eLgmT56srVu36sCBA+rdu7ceeOABJSQkBBQEAADAj5lezO3SSy/V448/rn/9619avXq1FTEBAFCnGTI5J8XuD+AQlq0426hRI40cOdKq7gAAQANXLx4w6Ckrk8fw/8FF9UqQAx7D5IQJ1EH2x2AEB9sdwhmN7P/vbZw+bXcIjhAcYv/3RKPv7P9+CC63OYDaPD8PGLSE/d+1AADUN2Ynv9aRibP+TIi96667/O6fJAUAAATk7FIkVTEMo3aTlEOHDunw4cPq27evmjRpIo/Hw3opAABIDWYkJS8vr0b793siwzfffKO0tDR16NBBw4YNU0FBgSRp0qRJuvvuuy0PEACAuqahrjhrNb9HUmbOnKlGjRrpyJEjuvTSS73bb775Zs2aNUt/+MMfLA0QAADUDV9++aXeeOMNHTlyRBUVFT7vPfHEE37353eS8tZbb2n9+vVq3bq1z/ZLLrlEX3zxhd8BAABQ7zSQcs8Pbdy4Uddff73at2+vTz75RF27dtXnn38uj8ejXr16BdSn3+WesrIyNW3a9Jztx48fV2hoaEBBAABQrzTAZfHnzJmje+65R/v27VNYWJj+9re/KT8/X/369dPo0aMD6tPvJOWaa67RihUrvK8Nw5Db7dajjz6qAQMGBBQEAACo2z7++GPdfvvtks4s8Prdd98pPDxcCxYs0COPPBJQn36Xex599FENGjRIu3btUkVFhe69914dOHBAx48f1wcffBBQEAAA1CdmJ7/WxYmzzZo1885DiY+P1+HDh9WlSxdJ0tdffx1Qn34nKV27dtWnn36qRYsWKSIiQqWlpRo5cqQyMjIUHx8fUBAAANQrDXDF2T59+uj999/XpZdeqmHDhunuu+/Wvn379Nprr6lPnz4B9RnQOilRUVG6//77AzohAAD1XgOcOPvEE0+otLRUkjR//nyVlpZq9erVuuSSSwK6s0cKMEk5deqU9u7dq6KiIrndbp/3rr/++oACAQAAdVf79u29Xzdr1kzPPvus6T79TlJycnJ0++23n7e+ZBiGXC6X6aAAAKjLGuKclLMqKirOO4jRpk0bv/vy++6e6dOna/To0SooKJDb7fZpJCgAAKhB3oL86aef6pprrlGTJk2UlJSkdu3aqV27dmrbtq3atWsXUJ9+j6QcO3ZMs2bNUmxsbEAnBAAA9c+ECRPUqFEjrVu3TvHx8ZY8z8/vkZSbbrpJmzZtMn1iAADqLbPP7QlgJGXLli0aPny4EhISZBiG1q5d6xuSx6O5c+cqPj5eTZo0UVpamj777DOffY4fP66xY8cqMjJSzZs316RJk7yTYauSm5urpUuXKj09XT169FD37t19WiD8HklZtGiRRo8erffee0/dunVT48aNfd4P5FHMAADUKzbc3VNWVqbu3btr4sSJGjly5DnvP/roo3r66af14osvql27dnrwwQc1ZMgQffTRRwoLC5MkjR07VgUFBdqwYYMqKys1YcIETZkyRStXrqzy/J07dw54PZSf4neS8vLLL+utt95SWFiYNm3a5DOcYxgGSQoAADZIT09Xenr6ed/zeDx68skn9cADD+iGG26QJK1YsUKxsbFau3atbrnlFn388cfKycnRzp07ddlll0mS/vSnP2nYsGF6/PHHlZCQcMHzP/LII7r33nv1+9///ryDGJGRkX5/Jr+TlPvvv1/z58/Xfffdp6Agv6tFAADUfxaNpJSUlPhsDg0NDeg5eXl5eSosLFRaWpp3W1RUlFJSUrRt2zbdcsst2rZtm5o3b+5NUCQpLS1NQUFB2rFjh2688cYLnuNs34MGDfL9KB5PwHf/+p2kVFRU6OabbyZBAQDgJ1h1C3JiYqLP9nnz5ikzM9Pv/goLCyXpnJteYmNjve8VFhYqJibG5/1GjRopOjrau8+FvPvuu37HVRW/k5Rx48Zp9erV+u1vf2t5MAAA4D/y8/N9yiSBjKLUln79+lnep99Jisvl0qOPPqr169crOTn5nJpToEvfAgAAX5GRkQHN5fixuLg4SWeWEfnhc/aOHTumHj16ePcpKiryOe706dM6fvy49/gL2bt373m3G4ahsLAwtWnTxu8ky+8kZd++ferZs6ckaf/+/ecEAgBAg+ewZ/e0a9dOcXFx2rhxozcpKSkp0Y4dOzR16lRJUmpqqk6cOKHdu3erd+/ekqR33nlHbrdbKSkpVZ6jR48eF8wDGjdurJtvvllLly713k1UFb+TlJqoOQEAUJ/YsSx+aWmpDh065H2dl5en3NxcRUdHq02bNpoxY4Z+97vf6ZJLLvHegpyQkKARI0ZIki699FINHTpUkydP1rPPPqvKykpNmzZNt9xyS5V39kjSmjVrNHv2bP3mN7/RFVdcIUn68MMP9Yc//EHz5s3T6dOndd999+mBBx7Q448/Xq3PFNADBgEAgLPs2rVLAwYM8L6eNWuWpDNzSbOzs3XvvfeqrKxMU6ZM0YkTJ3T11VcrJyfHZ1TjpZde0rRp0zRo0CAFBQVp1KhRevrpp6t1/ocfflhPPfWUhgwZ4t3WrVs3tW7dWg8++KA+/PBDNWvWTHfffbe1ScrIkSOVnZ2tyMjI8y4Q80OvvfZatU4MAEC9VsvP3+nfv788np8+qWEYWrBggRYsWPCT+0RHR1dr4bbz2bdvn5KSks7ZnpSUpH379kk6UxIqKCiodp/VSlKioqK8daaoqKhqdw4AQIPksDkptaFTp05auHChnnvuOYWEhEiSKisrtXDhQnXq1EmS9K9//cuvZ/9VK0lZvny5FixYoHvuuUfLly8PIHQAAFCfLV68WNdff71at26t5ORkSWdGV1wul9atWydJ+uc//6lf/epX1e6z2nNS5s+frzvvvFNNmzb1M2wAABoWOybO2u3KK69UXl6eXnrpJX366aeSpNGjR+sXv/iFIiIiJEm33XabX31WO0m5UJ0rUFlZWXrttdf0ySefqEmTJrryyiv1yCOPqGPHjpafCwCAWtMAyz2SFBERoTvvvNOy/vy6u8fqdVA2b96sjIwMXX755Tp9+rR++9vfavDgwfroo4/UrFkzS88FAACs9cYbbyg9PV2NGzfWG2+8ccF9r7/+er/79ytJ6dChQ5WJyvHjx6vdX05Ojs/r7OxsxcTEaPfu3erbt+85+5eXl6u8vNz7+scPXgIAwAkaSrlnxIgR3mf+nF1v5Xxq5QGD8+fPr9G7e4qLiyWduQXqfLKysjR//vwaOz8AAJZoIOUet9t93q+t4leScsstt5zzhESruN1uzZgxQ1dddZW6du163n3mzJnjXZxGOjOS8uMnRAIAgPohqLo71vRzeTIyMrR//36tWrXqJ/cJDQ31PmzJqocuAQBgOY8FrY7Ytm2b9xbjs1asWKF27dopJiZGU6ZM8Zmq4Y9qJyk1cXfPWdOmTdO6dev07rvvqnXr1jV2HgAAasPZOSlmWl2xYMECHThwwPt63759mjRpktLS0nTffffpzTffVFZWVkB9V7vcUxO1Jo/Ho+nTp2vNmjXatGmT2rVrF1g/5RXy2PkvalQ716vfghzwFGwnPIm7cWO7I5AkGW4H/JRzwL+HUXna7hBkVFr/89NfwZX2fz8YNv9TGP7P2wxcA5mTIkm5ubl66KGHvK9XrVqllJQUPf/885KkxMREzZs3T5mZmX73besDBjMyMrRy5Uq9/vrrioiIUGFhoaQzS+83adLEztAAAEA1/Pvf//ZZ6n7z5s1KT0/3vr788suVn58fUN+2DgEsWbJExcXF6t+/v+Lj471t9erVdoYFAIA5DWhOSmxsrPLy8iRJFRUV2rNnj/r06eN9/+TJk2oc4AizrSMpNTnPBQAAuzSUdVIkadiwYbrvvvv0yCOPaO3atWratKmuueYa7/t79+7VRRddFFDftiYpAACgbnvooYc0cuRI9evXT+Hh4XrxxRe9T0GWpGXLlmnw4MEB9U2SAgCA1RrQxNmWLVtqy5YtKi4uVnh4uIKDg33ef+WVVxQeHh5Q3yQpAABYrCGVe876qRXpf2oV+erg3lkAAOBIjKQAAGC1BlTuqUkkKQAAWI0kxRKUewAAgCMxkgIAgMWM75uZ40GSAgCA9Sj3WIIkBQAAizXEW5BrAnNSAACAIzGSAgCA1Sj3WIIkBQCAmkCiYRrlHgAA4EiMpAAAYDEmzlqDJAUAAKsxJ8USlHsAAIAjkaQAAGCxs+UeM80fbdu2lWEY57SMjAxJUv/+/c95784776yBT24tyj0AAFitlss9O3fulMvl8r7ev3+/rr32Wo0ePdq7bfLkyVqwYIH3ddOmTU0EWDtIUgAAqONatWrl83rhwoW66KKL1K9fP++2pk2bKi4urrZDM4VyDwAAFrOq3FNSUuLTysvLqzx3RUWF/vKXv2jixIkyjP88qvCll15Sy5Yt1bVrV82ZM0fffvttTX18yzCSAgCA1Swq9yQmJvpsnjdvnjIzMy946Nq1a3XixAmNHz/eu+0Xv/iFkpKSlJCQoL1792r27Nk6ePCgXnvtNRNB1jySFAAArGZRkpKfn6/IyEjv5tDQ0CoPfeGFF5Senq6EhATvtilTpni/7tatm+Lj4zVo0CAdPnxYF110kYlAaxZJCgAADhUZGemTpFTliy++0Ntvv13lCElKSook6dChQyQpAAA0JHatOLt8+XLFxMTouuuuu+B+ubm5kqT4+PjATlRLSFIAALCaDSvOut1uLV++XOPGjVOjRv/59X748GGtXLlSw4YNU4sWLbR3717NnDlTffv2VXJysokgax5JCgAA9cDbb7+tI0eOaOLEiT7bQ0JC9Pbbb+vJJ59UWVmZEhMTNWrUKD3wwAM2RVp9JCkAAFjM8HhkeAIfSgnk2MGDB8tznuMSExO1efPmgGOxE0kKAABW4wGDlmAxNwAA4EiMpAAAYDG77u6pb0hSAACwGuUeS1DuAQAAjlQvRlI8Lpc8hp35lqvqXWqarZ//+xA8RtU71XgQ9l8HjyrtDsExjGD7/z102v7/n4bL/hjksv9P8yCbL4OnFs9Pucca9SJJAQDAUSj3WIIkBQAAizGSYg0HjMUCAACci5EUAACsRrnHEiQpAADUAEo25lHuAQAAjsRICgAAVvN4zjQzx4MkBQAAq3F3jzUo9wAAAEdiJAUAAKtxd48lSFIAALCY4T7TzBwPyj0AAMChGEkBAMBqlHssQZICAIDFuLvHGiQpAABYjXVSLMGcFAAA4EiMpAAAYDHKPdYgSQEAwGpMnLUE5R4AAOBIjKQAAGAxyj3WIEkBAMBq3N1jCVvLPVu2bNHw4cOVkJAgwzC0du1aO8MBAAAOYmuSUlZWpu7du2vx4sV2hgEAgKXOlnvMNNhc7klPT1d6erqdIQAAYD3u7rFEnZqTUl5ervLycu/rkpISG6MBAAA1qU7dgpyVlaWoqChvS0xMtDskAADOUdvlnszMTBmG4dM6derkff/UqVPKyMhQixYtFB4erlGjRunYsWMWf2rr1akkZc6cOSouLva2/Px8u0MCAOBcbo/55qcuXbqooKDA295//33vezNnztSbb76pV155RZs3b9bRo0c1cuRIKz9xjahT5Z7Q0FCFhobaHQYAABdmw5yURo0aKS4u7pztxcXFeuGFF7Ry5UoNHDhQkrR8+XJdeuml2r59u/r06WMi0JpVp0ZSAABoSEpKSnzaD+dl/thnn32mhIQEtW/fXmPHjtWRI0ckSbt371ZlZaXS0tK8+3bq1Elt2rTRtm3bavwzmGFrklJaWqrc3Fzl5uZKkvLy8pSbm+u9sAAA1EWGTM5J+b6fxMREn7mYWVlZ5z1fSkqKsrOzlZOToyVLligvL0/XXHONTp48qcLCQoWEhKh58+Y+x8TGxqqwsLBGr4NZtpZ7du3apQEDBnhfz5o1S5I0btw4ZWdn2xQVAAAmWbTibH5+viIjI72bf2rKww+X80hOTlZKSoqSkpL017/+VU2aNAk8DpvZmqT0799fHpb+BQDgvCIjI32SlOpq3ry5OnTooEOHDunaa69VRUWFTpw44TOacuzYsfPOYXES5qQAAGAxu1ecLS0t1eHDhxUfH6/evXurcePG2rhxo/f9gwcP6siRI0pNTTX5SWtWnbq7BwCAOqGW7+655557NHz4cCUlJeno0aOaN2+egoODNWbMGEVFRWnSpEmaNWuWoqOjFRkZqenTpys1NdXRd/ZIJCkAANR5X375pcaMGaNvvvlGrVq10tVXX63t27erVatWkqQ//vGPCgoK0qhRo1ReXq4hQ4bomWeesTnqqpGkAABgMcPjkWFizqW/x65ateqC74eFhWnx4sV17oG+9SJJ8bhc8hg2Tq+x89xeLrsDkDxG1fvUNAc8OtQBV+GMYLfdEZi7u8GyGOy/DobL/utgBLCCqeXsDqE2z+/+vpk5HkycBQAAzlQvRlIAAHCS2i731FckKQAAWM2GZ/fURyQpAABYzaIVZxs65qQAAABHYiQFAACLmV011gE3KjoCSQoAAFaj3GMJyj0AAMCRGEkBAMBihvtMM3M8SFIAALAe5R5LUO4BAACOxEgKAABWYzE3S5CkAABgMZbFtwblHgAA4EiMpAAAYDUmzlqCJAUAAKt5JJm5jZgcRRJJCgAAlmNOijWYkwIAAByJkRQAAKzmkck5KZZFUqeRpAAAYDUmzlqCcg8AAHAkRlIAALCaW5Jh8niQpAAAYDXu7rEG5R4AAOBIjKQAAGA1Js5agiQFAACrkaRYgnIPAABwJEZSAACwGiMpliBJAQDAatyCbAmSFAAALMYtyNZgTgoAAHVcVlaWLr/8ckVERCgmJkYjRozQwYMHffbp37+/DMPwaXfeeadNEVcPSQoAAFY7OyfFTPPD5s2blZGRoe3bt2vDhg2qrKzU4MGDVVZW5rPf5MmTVVBQ4G2PPvqolZ/acpR7AACwmtsjGSZKNu4zx5aUlPhsDg0NVWho6Dm75+Tk+LzOzs5WTEyMdu/erb59+3q3N23aVHFxcYHHVcsYSQEAwKESExMVFRXlbVlZWdU6rri4WJIUHR3ts/2ll15Sy5Yt1bVrV82ZM0fffvut5TFbiZEUAACsZtEtyPn5+YqMjPRuPt8oyo+53W7NmDFDV111lbp27erd/otf/EJJSUlKSEjQ3r17NXv2bB08eFCvvfZa4HHWMJIUAAAsZzJJ0ZljIyMjfZKU6sjIyND+/fv1/vvv+2yfMmWK9+tu3bopPj5egwYN0uHDh3XRRReZiLXmkKRYwcMN7ZLkcdtfPTSCnPBvEWx3AGc44RZGtwNicMJ1cAAz0yPqSwx2n782TJs2TevWrdOWLVvUunXrC+6bkpIiSTp06BBJCgAADUYtrzjr8Xg0ffp0rVmzRps2bVK7du2qPCY3N1eSFB8fH0iEtYIkBQAAq7k9OluyCfz46svIyNDKlSv1+uuvKyIiQoWFhZKkqKgoNWnSRIcPH9bKlSs1bNgwtWjRQnv37tXMmTPVt29fJScnBx5nDSNJAQCgjluyZImkMwu2/dDy5cs1fvx4hYSE6O2339aTTz6psrIyJSYmatSoUXrggQdsiLb6SFIAALCax21uvqKfx3qqKA8lJiZq8+bNgcdjE5IUAACsxlOQLUGSAgCA1Wp5Tkp9Zf89owAAAOfBSAoAAFaj3GMJkhQAAKzmkckkxbJI6jTKPQAAwJEYSQEAwGqUeyxBkgIAgNXcbkkm1klxO+E5ZPaj3AMAAByJkRQAAKxGuccSjhhJWbx4sdq2bauwsDClpKToww8/tDskAAACdzZJMdNgf5KyevVqzZo1S/PmzdOePXvUvXt3DRkyREVFRXaHBgAAbGR7kvLEE09o8uTJmjBhgjp37qxnn31WTZs21bJly87Zt7y8XCUlJT4NAADHcXvMN9ibpFRUVGj37t1KS0vzbgsKClJaWpq2bdt2zv5ZWVmKiorytsTExNoMFwCAavF43KYbbE5Svv76a7lcLsXGxvpsj42NVWFh4Tn7z5kzR8XFxd6Wn59fW6ECAFB9HpOjKMxJkVTH7u4JDQ1VaGio3WEAAIBaYGuS0rJlSwUHB+vYsWM+248dO6a4uDibogIAwCSPR6YewMNIiiSbyz0hISHq3bu3Nm7c6N3mdru1ceNGpaam2hgZAAAmuN3mG+wv98yaNUvjxo3TZZddpiuuuEJPPvmkysrKNGHCBLtDAwAANrI9Sbn55pv11Vdfae7cuSosLFSPHj2Uk5NzzmRaAADqDMo9lrA9SZGkadOmadq0aXaHAQCAJTxutzxG4CUbbkE+w/bF3AAAAM7HESMpAADUK5R7LEGSAgCA1dweySBJMYtyDwAAcCRGUgAAsJrHI8nE5FdGUiSRpAAAYDmP2yOPiXKPhyRFEkkKAADW87hlbiSFW5Al5qQAAACHYiQFAACLUe6xBkkKAABWo9xjiTqdpJzNNE+r0tSaObCK/dVDw2PYHYIMh/wFZDjgh5zhtv/fQ277vy89rhC7Q9Dp08F2hyBXhb3fk66KU5JqZ5TC7O+l06q0Lpg6rE4nKSdPnpQkva+/2xwJJDkjUXTZHYCcEYMkldsdgEMctzsASfl2B4AfOnnypKKiomqk75CQEMXFxen9QvO/l+Li4hQSYn+CayfDU4cLX263W0ePHlVERIQMI7C/2EpKSpSYmKj8/HxFRkZaHGHdx/WpGteoalyjqnGNLsyK6+PxeHTy5EklJCQoKKjmRthOnTqliooK0/2EhIQoLCzMgojqrjo9khIUFKTWrVtb0ldkZCQ/GC6A61M1rlHVuEZV4xpdmNnrU1MjKD8UFhbW4JMLq9hfrAUAADgPkhQAAOBIDT5JCQ0N1bx58xQaGmp3KI7E9aka16hqXKOqcY0ujOvTMNXpibMAAKD+avAjKQAAwJlIUgAAgCORpAAAAEciSQEAAI7UoJOUxYsXq23btgoLC1NKSoo+/PBDu0NylC1btmj48OFKSEiQYRhau3at3SE5SlZWli6//HJFREQoJiZGI0aM0MGDB+0Oy1GWLFmi5ORk7wJcqamp+sc//mF3WI61cOFCGYahGTNm2B2KY2RmZsowDJ/WqVMnu8NCLWmwScrq1as1a9YszZs3T3v27FH37t01ZMgQFRUV2R2aY5SVlal79+5avHix3aE40ubNm5WRkaHt27drw4YNqqys1ODBg1VWVmZ3aI7RunVrLVy4ULt379auXbs0cOBA3XDDDTpw4IDdoTnOzp07tXTpUiUnJ9sdiuN06dJFBQUF3vb+++/bHRJqSYO9BTklJUWXX365Fi1aJOnMc4ASExM1ffp03XfffTZH5zyGYWjNmjUaMWKE3aE41ldffaWYmBht3rxZffv2tTscx4qOjtZjjz2mSZMm2R2KY5SWlqpXr1565pln9Lvf/U49evTQk08+aXdYjpCZmam1a9cqNzfX7lBggwY5klJRUaHdu3crLS3Nuy0oKEhpaWnatm2bjZGhLisuLpZ05pcwzuVyubRq1SqVlZUpNTXV7nAcJSMjQ9ddd53PzyT8x2effaaEhAS1b99eY8eO1ZEjR+wOCbWkTj9gMFBff/21XC6XYmNjfbbHxsbqk08+sSkq1GVut1szZszQVVddpa5du9odjqPs27dPqampOnXqlMLDw7VmzRp17tzZ7rAcY9WqVdqzZ4927txpdyiOlJKSouzsbHXs2FEFBQWaP3++rrnmGu3fv18RERF2h4ca1iCTFMBqGRkZ2r9/P7Xy8+jYsaNyc3NVXFysV199VePGjdPmzZtJVCTl5+fr17/+tTZs2MBTc39Cenq69+vk5GSlpKQoKSlJf/3rXykZNgANMklp2bKlgoODdezYMZ/tx44dU1xcnE1Roa6aNm2a1q1bpy1btqh169Z2h+M4ISEhuvjiiyVJvXv31s6dO/XUU09p6dKlNkdmv927d6uoqEi9evXybnO5XNqyZYsWLVqk8vJyBQcH2xih8zRv3lwdOnTQoUOH7A4FtaBBzkkJCQlR7969tXHjRu82t9utjRs3UitHtXk8Hk2bNk1r1qzRO++8o3bt2tkdUp3gdrtVXl5udxiOMGjQIO3bt0+5ubnedtlll2ns2LHKzc0lQTmP0tJSHT58WPHx8XaHglrQIEdSJGnWrFkaN26cLrvsMl1xxRV68sknVVZWpgkTJtgdmmOUlpb6/LWSl5en3NxcRUdHq02bNjZG5gwZGRlauXKlXn/9dUVERKiwsFCSFBUVpSZNmtgcnTPMmTNH6enpatOmjU6ePKmVK1dq06ZNWr9+vd2hOUJERMQ5c5iaNWumFi1aMLfpe/fcc4+GDx+upKQkHT16VPPmzVNwcLDGjBljd2ioBQ02Sbn55pv11Vdfae7cuSosLFSPHj2Uk5NzzmTahmzXrl0aMGCA9/WsWbMkSePGjVN2drZNUTnHkiVLJEn9+/f32b58+XKNHz++9gNyoKKiIt1+++0qKChQVFSUkpOTtX79el177bV2h4Y64ssvv9SYMWP0zTffqFWrVrr66qu1fft2tWrVyu7QUAsa7DopAADA2RrknBQAAOB8JCkAAMCRSFIAAIAjkaQAAABHIkkBAACORJICAAAciSQFAAA4EkkKAABwJJIUoBaNHz9eI0aMsDuMasvOzpZhGDIMQzNmzKjWMePHj/ces3bt2hqND0D9RpICWOTsL+afapmZmXrqqadseaTApk2bfGJp0qSJunTpoueee67KYyMjI1VQUKCHHnqoWud66qmnVFBQYDZkAGi4z+4BrPbDX8yrV6/W3LlzdfDgQe+28PBwhYeH2xGa18GDBxUZGanvvvtOb775pqZOnaqLLrpIgwYN+sljDMNQXFxctc8RFRWlqKgoK8IF0MAxkgJYJC4uztuioqK8v9zPtvDw8HPKPf3799f06dM1Y8YM/exnP1NsbKyef/557xO5IyIidPHFF+sf//iHz7n279+v9PR0hYeHKzY2Vrfddpu+/vrrKmOMiYlRXFyc2rVrp7vuukvt2rXTnj17/P6szzzzjC655BKFhYUpNjZWN910k999AEBVSFIAm7344otq2bKlPvzwQ02fPl1Tp07V6NGjdeWVV2rPnj0aPHiwbrvtNn377beSpBMnTmjgwIHq2bOndu3apZycHB07dkw///nPq31Oj8ejnJwcHTlyRCkpKX7Fu2vXLt11111asGCBDh48qJycHPXt29evPgCgOij3ADbr3r27HnjgAUnSnDlztHDhQrVs2VKTJ0+WJM2dO1dLlizR3r171adPHy1atEg9e/bU73//e28fy5YtU2Jioj799FN16NDhJ8/VunVrSVJ5ebncbrcWLFjgd4Jx5MgRNWvWTP/93/+tiIgIJSUlqWfPnv5+bACoEkkKYLPk5GTv18HBwWrRooW6devm3RYbGytJKioqkiT9v//3//Tuu++ed37L4cOHL5ikvPfee4qIiFB5ebk+/PBDTZs2TdHR0Zo6dWq147322muVlJSk9u3ba+jQoRo6dKhuvPFGNW3atNp9AEB1kKQANmvcuLHPa8MwfLYZhiFJcrvdkqTS0lINHz5cjzzyyDl9xcfHX/Bc7dq1U/PmzSVJXbp00Y4dO/Twww/7laRERERoz5492rRpk9566y3NnTtXmZmZ2rlzp7dvALACSQpQx/Tq1Ut/+9vf1LZtWzVqZO6/cHBwsL777ju/j2vUqJHS0tKUlpamefPmqXnz5nrnnXc0cuRIU/EAwA8xcRaoYzIyMnT8+HGNGTNGO3fu1OHDh7V+/XpNmDBBLpfrgscWFRWpsLBQX3zxhV555RX9+c9/1g033ODX+detW6enn35aubm5+uKLL7RixQq53W517NjRzMcCgHMwkgLUMQkJCfrggw80e/ZsDR48WOXl5UpKStLQoUMVFHThvzvOJhKNGjVSYmKi7rjjDmVmZvp1/ubNm+u1115TZmamTp06pUsuuUQvv/yyunTpEuhHAoDzMjwej8fuIAA4U3Z2tmbMmKETJ074faxhGFqzZk2degwAAGeh3APggoqLixUeHq7Zs2dXa/8777zT9pV1AdQPjKQA+EknT57UsWPHJJ0p87Rs2bLKY4qKilRSUiLpzN1GzZo1q9EYAdRfJCkAAMCRKPcAAABHIkkBAACORJICAAAciSQFAAA4EkkKAABwJJIUAADgSCQpAADAkUhSAACAI/1/mIaNT7oxB0AAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_a = ManualParameter(\n",
    "    name=\"time_a\", label=\"Time A\", unit=\"s\", vals=validators.Numbers(), initial_value=1\n",
    ")\n",
    "time_b = ManualParameter(\n",
    "    name=\"time_b\", label=\"Time B\", unit=\"s\", vals=validators.Numbers(), initial_value=1\n",
    ")\n",
    "signal = Parameter(\n",
    "    name=\"sig_a\",\n",
    "    label=\"Signal A\",\n",
    "    unit=\"V\",\n",
    "    get_cmd=lambda: np.exp(time_a()) + 0.5 * np.exp(time_b()),\n",
    ")\n",
    "\n",
    "meas_ctrl.settables([time_a, time_b])\n",
    "meas_ctrl.gettables(signal)\n",
    "meas_ctrl.setpoints_grid([np.linspace(0, 5, 10), np.linspace(5, 0, 12)])\n",
    "dset = meas_ctrl.run(\"my experiment\")\n",
    "dset_grid = dh.to_gridded_dataset(dset)\n",
    "\n",
    "dset_grid.y0.plot(cmap=\"viridis\")\n",
    "dset_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "875b7694",
   "metadata": {},
   "source": [
    "For more dimensions, you only need to pass more settables and the corresponding setpoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a1b8cbe1",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "1D adaptive example"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running adaptively...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = ManualParameter(\n",
    "    name=\"time\", label=\"Time\", unit=\"s\", vals=validators.Numbers(), initial_value=1\n",
    ")\n",
    "signal = Parameter(\n",
    "    name=\"sig_a\", label=\"Signal\", unit=\"V\", get_cmd=lambda: np.cos(time())\n",
    ")\n",
    "meas_ctrl.settables(time)\n",
    "meas_ctrl.gettables(signal)\n",
    "dset = meas_ctrl.run_adaptive(\"1D minimizer\", {\"adaptive_function\": minimize_scalar})\n",
    "\n",
    "dset_ad = dh.to_gridded_dataset(dset)\n",
    "# add a grey cosine for reference\n",
    "x = np.linspace(np.min(dset_ad[\"x0\"].values), np.max(dset_ad[\"x0\"].values), 101)\n",
    "y = np.cos(x)\n",
    "plt.plot(x, y, c=\"grey\", ls=\"--\")\n",
    "_ = dset_ad.y0.plot(marker=\"o\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbd2af86",
   "metadata": {},
   "source": [
    "### Single-float-valued settable(s) with multiple float-valued gettable(s)\n",
    "\n",
    "- Each settable accepts a single float value.\n",
    "- Gettables return a 1D array of floats, with each element corresponding to a *different Y dimension*.\n",
    "\n",
    "We exemplify a 2D case, however, there is no limitation on the number of settables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cdd4275d",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "2D example"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting iterative measurement...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e9714b0758684dd2840f623c0e822b76",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cERERyYFo5LIaN2RLhskRERGRDIji7WJMf5IGl9WIiIiIauDMERERkRzwnCPZYHJEREQkB7yVXzaYHBEREcmAKAoQjZj9MaYvaeOeIyIiIqIaOHNEREQkB1xWkw0mR0RERHLADdmywWU1IiIioho4c0RERCQD3JAtH5w5IiIikgO1BMUASUlJcHd3h5WVFfz9/ZGdnV1v29TUVAiCoFWsrKy02oiiiLi4OLi4uKBVq1YIDAzEzz//bFhwJsLkiIiIqJnasGEDYmJiEB8fj9zcXPTq1QvBwcEoLi6ut49CoUBhYaGmXLhwQev99957D0uXLkVycjIOHTqEBx54AMHBwbh582ZjX45kmBwRERHJwZ0N2cYUPS1cuBAREREIDw+Ht7c3kpOTYW1tjZSUlHr7CIIApVKpKc7Ozn9fgihi8eLFmDlzJoYNG4aePXtizZo1uHz5MrZs2WLIt2ISTI6IiIhkQFQLRhd9VFVVIScnB4GBgZo6MzMzBAYGIisrq95+5eXlaN++Pdzc3DBs2DCcPHlS815+fj5UKpXWmHZ2dvD3929wTLlhckRERPQPUlZWplUqKyvrbHflyhVUV1drzfwAgLOzM1QqVZ19vLy8kJKSgq1bt+LTTz+FWq1Gv3798NtvvwGApp8+Y8oRkyMiIiI5kGhZzc3NDXZ2dpqSmJgoWYgBAQEICwuDj48PBgwYgE2bNqFNmzZYvny5ZJ8hB7yVn4iISAakupW/oKAACoVCU29paVln+9atW8Pc3BxFRUVa9UVFRVAqlTp9ZsuWLeHr64tz584BgKZfUVERXFxctMb08fHR+VpMjTNHREREciAKfz9CxJDy/8mRQqHQKvUlRxYWFvDz80NGRoamTq1WIyMjAwEBATqFXF1djePHj2sSIQ8PDyiVSq0xy8rKcOjQIZ3HlAPOHBERETVTMTExGDt2LPr06YO+ffti8eLFqKioQHh4OAAgLCwMbdu21SzNzZkzB4888gg8PT1RUlKC999/HxcuXMCECRMA3L6TLTo6Gm+//TY6deoEDw8PvPXWW3B1dUVoaKipLlNvTI6IiIjkwATPVhsxYgR+//13xMXFQaVSwcfHB2lpaZoN1RcvXoSZ2d+LTH/++SciIiKgUqng4OAAPz8/HDhwAN7e3po206dPR0VFBSZOnIiSkhI8+uijSEtLq3VYpJwxOSIiIpIBUbxdjOlviKioKERFRdX5XmZmptbrRYsWYdGiRQ2OJwgC5syZgzlz5hgWkAxwzxERERFRDZw5IiIikoM7G6uN6U+SYHJEREQkA1Ldyk/G47IaERERUQ2cOSIiIpIDE9ytRnVjckRERCQDhjw89u7+JA0uqxERERHVwJkjIiIiORBh5LKaZJE0e0yOiIiIZIB3q8kHkyMiIiI54DlHssE9R0REREQ1cOaIiIhIBkz1bDWqjckRERGRDHDPkXxwWY2IiIioBs4cERERyQE3ZMsGkyMiIiIZ4LKabnr37q1Xe0EQ8PXXX6Nt27Y692FyRERERPeNvLw8TJ06FTY2NvdsK4oi3n33XVRWVur1GUyOiIiIZMHIB8+iecwcAcC0adPg5OSkU9sFCxboPT6TIyIiIhngsppu8vPz0aZNG53bnzp1Cq6urnp9Bu9WIyIiovtG+/btcfLkSZ3bu7m5wdzcXK/PYHJEREQkB3fuVjOmNBM9e/aEv78/Pv74Y1y7dk3y8ZkcERERycCdE7KNKc3Fnj170K1bN0ydOhUuLi4YO3YsfvjhB8nGZ3JEREQkA3f2HBlTmot//etfSElJQWFhIT744AP8+uuvGDBgADp37ox58+ZBpVIZNb5Jk6PExEQ8/PDDsLW1hZOTE0JDQ3H27Nl79tu4cSO6dOkCKysr9OjRA9u2bWuCaImIiEhOHnjgAYSHh2PPnj346aefMHz4cCQlJeGhhx7CM888Y/C4Jk2O9uzZg8jISBw8eBDp6em4desWgoKCUFFRUW+fAwcOYNSoURg/fjyOHj2K0NBQhIaG4sSJE00YORERkcREwfjSjHl6euKNN97AzJkzYWtri++++87gsUx6K39aWprW69TUVDg5OSEnJwePPfZYnX2WLFmCkJAQTJs2DQCQkJCA9PR0LFu2DMnJyY0eMxERUaNQCxD5+BCD7N27FykpKfjqq69gZmaGF154AePHjzd4PFmdc1RaWgoAcHR0rLdNVlYWYmJitOqCg4OxZcuWOttXVlZqnYxZVlZmfKBERERkUpcvX0ZqaipSU1Nx7tw59OvXD0uXLsULL7yABx54wKixZZMcqdVqREdHo3///ujevXu97VQqFZydnbXqnJ2d6918lZiYiNmzZ0saKxERkdR4CKTuBg8ejJ07d6J169YICwvDSy+9BC8vL8nGl01yFBkZiRMnTmDfvn2SjhsbG6s101RWVgY3NzdJP4OIiMhoxu4bakbJUcuWLfHll1/iqaee0vuAR13IIjmKiorCt99+i71796Jdu3YNtlUqlSgqKtKqKyoqglKprLO9paUlLC0tJYuViIiITOvrr79u1PFNereaKIqIiorC5s2bsWvXLnh4eNyzT0BAADIyMrTq0tPTERAQ0FhhEhERNTqec6Sb5557Tq/9w6NHj0ZxcbFen2HSmaPIyEisW7cOW7duha2trWbfkJ2dHVq1agUACAsLQ9u2bZGYmAgAmDJlCgYMGIAFCxZg6NChWL9+PY4cOYIVK1aY7DqIiIiMJapvF2P6Nwdbt27F77//rlNbURTxzTffICEhAU5OTjp/hkmTo48++ggAMHDgQK36VatWYdy4cQCAixcvwszs7wmufv36Yd26dZg5cybeeOMNdOrUCVu2bGlwEzcRERH9M4iiiM6dOzfqZ5g0ORJ1eBBMZmZmrbrhw4dj+PDhjRARERGRiXBDtk52796td5+2bdvq1V4WG7KJiIiaO97Kr5sBAwY0+mcwOSIiIpIBJkfyYdK71YiIiMi0kpKS4O7uDisrK/j7+yM7O7veth9//DH+9a9/wcHBAQ4ODggMDKzVfty4cRAEQauEhIQ09mVIiskRERGRHJjgwbMbNmxATEwM4uPjkZubi169eiE4OLjeW98zMzMxatQo7N69G1lZWXBzc0NQUBAuXbqk1S4kJASFhYWa8vnnnxv0lZgKkyMiIiIZEEVA/P+HzxpU7n2PUy0LFy5EREQEwsPD4e3tjeTkZFhbWyMlJaXO9p999hn+97//wcfHB126dMEnn3wCtVpd6/xBS0tLKJVKTXFwcDDkKzEZJkdERET/IGVlZVql5sPXa6qqqkJOTg4CAwM1dWZmZggMDERWVpZOn3X9+nXcunWr1gPjMzMz4eTkBC8vL0yaNAl//PGH4Rd0D3/99Rd27tyJ5cuX49q1awBuP5S2vLzc4DGZHBEREcmAVCdku7m5wc7OTlPuHKJ8tytXrqC6ulqvh7nf7fXXX4erq6tWghUSEoI1a9YgIyMD8+bNw549ezB48GBUV1cb+M3U78KFC+jRoweGDRuGyMhIzeGQ8+bNw2uvvWbwuLxbjYiISA7E/y/G9AdQUFAAhUKhqW6s54u+++67WL9+PTIzM2FlZaWpHzlypObPPXr0QM+ePdGxY0dkZmZi0KBBksYwZcoU9OnTBz/++CMefPBBTf2zzz6LiIgIg8dlckRERPQPolAotJKj+rRu3Rrm5uZ6Pcz9jvnz5+Pdd9/Fzp070bNnzwbbdujQAa1bt8a5c+ckT45++OEHHDhwABYWFlr17u7utTaJ64PLakRERDLQ1A+etbCwgJ+fn9Zm6jubqxt6mPt7772HhIQEpKWloU+fPvf8nN9++w1//PEHXFxc9IpPF2q1us7lut9++w22trYGj8vkiIiISAaaOjkCgJiYGHz88cdYvXo1Tp8+jUmTJqGiogLh4eEAbj/8PTY2VtN+3rx5eOutt5CSkgJ3d3eoVCqoVCrN5ufy8nJMmzYNBw8exK+//oqMjAwMGzYMnp6eCA4OluaLqiEoKAiLFy/WvBYEAeXl5YiPj8eQIUMMHpfLakRERDJw55Z8Y/rra8SIEfj9998RFxcHlUoFHx8fpKWlaTZp3/3w948++ghVVVV4/vnntcaJj4/HrFmzYG5ujmPHjmH16tUoKSmBq6srgoKCkJCQ0Ch7nxYsWIDg4GB4e3vj5s2b+M9//oOff/4ZrVu3NupsJSZHREREzVhUVBSioqLqfO/uh7//+uuvDY7VqlUrbN++XaLI7q1du3b48ccfsX79ehw7dgzl5eUYP348Ro8ejVatWhk8LpMjIiIiOTDwlGut/s1QixYtMGbMGGnHlHQ0IiIiMggfPKubr7/+Wue2zzzzjEGfweSIiIiI7huhoaFarwVBgHjXs1ME4XaiaOjBk7xbjYiISAZMcbfa/UitVmvKjh074OPjg++//x4lJSUoKSnB999/j969eyMtLc3gz+DMERERkQyIIgx6eGzN/s1NdHQ0kpOT8eijj2rqgoODYW1tjYkTJ+L06dMGjatTcnTs2DG9B/b29kaLFsy9iIiIqHGcP38e9vb2tert7OzueWddQ3TKXnx8fOpc06uPmZkZfvrpJ3To0MHgwIiIiJoTbsjW38MPP4yYmBisXbtWczZTUVERpk2bhr59+xo8rs5TO4cOHUKbNm3u2U4URXTv3t3ggIiIiJoltXC7GNO/mUlJScGzzz6Lhx56CG5ubgBuP3i3U6dO2LJli8Hj6pQcDRgwAJ6ennVOXdXlscceM+rwJSIiIqJ78fT0xLFjx5Ceno4zZ84AALp27YrAwEDNHWuG0Ck52r17t16Dbtu2zaBgiIiImisuqxlGEAQEBQUhKChIsjGN3jFdXV2N48ePo3379nBwcJAiJiIiomaHyZH+5syZ0+D7cXFxBo2rd3IUHR2NHj16YPz48aiursaAAQNw4MABWFtb49tvv8XAgQMNCoSIiKg5Y3Kkv82bN2u9vnXrFvLz89GiRQt07Nix6ZKjL7/8UvMMk2+++Qb5+fk4c+YM1q5dizfffBP79+83KBAiIiIifRw9erRWXVlZGcaNG4dnn33W4HH1PiH7ypUrUCqVAG7vLRo+fDg6d+6Ml156CcePHzc4ECIioubN2NOxm9/MUV0UCgVmz56Nt956y+Ax9E6OnJ2dcerUKVRXVyMtLQ1PPvkkAOD69eswNzc3OBAiIqJmTRSMLwQAKC0tRWlpqcH99V5WCw8PxwsvvAAXFxcIgoDAwEAAt89B6tKli8GBEBEREelj6dKlWq9FUURhYSHWrl2LwYMHGzyu3snRrFmz0L17dxQUFGD48OGwtLQEAJibm2PGjBkGB0JERNScierbxZj+zc2iRYu0XpuZmaFNmzYYO3YsYmNjDR7XoFv5n3/++Vp1Y8eONTgIIiKi5o53q+kvPz+/UcbVac/R0qVLcfPmTZ0HTU5OxrVr1wwOioiIiOheXnrppTrzjYqKCrz00ksGj6tTcvTqq6/qlexMnz4dv//+u8FBERERNTfG3Klm7KzT/Wr16tW4ceNGrfobN25gzZo1Bo+r07KaKIoYNGgQWrTQbRWurkCJiIioflxW011ZWRlEUYQoirh27RqsrKw071VXV2Pbtm1wcnIyeHydsp34+Hi9Bh02bBgcHR0NCoiIiIioIfb29hAEAYIgoHPnzrXeFwQBs2fPNnj8RkmOiIiISD+iaNzsjyhKGIzM7d69G6Io4oknnsBXX32lNSFjYWGB9u3bw9XV1eDxjX7wLBEREUnA2IMcm9Gy2oABAwDcvlvtoYcegiBIe+1MjoiIiGSAe450c+zYMXTv3h1mZmYoLS1t8NFlPXv2NOgzmBwRERHRfcPHxwcqlQpOTk7w8fGBIAgQ61hTFAQB1dXVBn0GkyMiIiIZ4MyRbvLz89GmTRvNnxuDZMnR6dOnsXLlSsyfP1+qIYmIiJoNPj5EN+3bt6/zz1IyKjmqqKjA+vXrsXLlShw8eBDe3t5MjoiIiKjRfP311zq3feaZZwz6DIOSo/3792PlypX44osvcOPGDbz66qtISUlBly5dDAqCiIioueOymm5CQ0N1amfMniOdHh8CAMXFxXjvvffQpUsXPP/887C3t0dmZibMzMzw0ksvMTEiIiIyAh8fohu1Wq1TMTQxAvSYOWrfvj2ef/55LFmyBE8++STMzHTOq4iIiIjuGzpnOO3bt8e+ffuwd+9e/PTTT40ZExERUbPDmSPDZGRk4KmnnkLHjh3RsWNHPPXUU9i5c6dRY+qcHJ05cwaffvopCgsL8fDDD8PPzw+LFi0CAMlPpiQiImpumBzp78MPP0RISAhsbW0xZcoUTJkyBQqFAkOGDEFSUpLB4+q1Ibt///7o378/li5dis8//xyrVq1CdXU1/ve//+E///kPQkNDNWcPEBERETWmuXPnYtGiRYiKitLUvfLKK+jfvz/mzp2LyMhIg8Y1aOOQjY0NIiIicODAAZw8eRJ+fn6YOXOm3g9527t3L55++mm4urpCEARs2bKlwfaZmZmap/DWLCqVypDLICIikg1TzRwlJSXB3d0dVlZW8Pf3R3Z2doPtN27ciC5dusDKygo9evTAtm3b7roOEXFxcXBxcUGrVq0QGBiIn3/+2aDY7qWkpAQhISG16oOCglBaWmrwuEbvqu7atSvmz5+PS5cuYcOGDXr1raioQK9evfSe+jp79iwKCws1xcnJSa/+REREcmOK5GjDhg2IiYlBfHw8cnNz0atXLwQHB6O4uLjO9gcOHMCoUaMwfvx4HD16FKGhoQgNDcWJEyc0bd577z0sXboUycnJOHToEB544AEEBwfj5s2bBn839XnmmWewefPmWvVbt27FU089ZfC4kp2Q3aJFCzz33HN69Rk8eDAGDx6s92c5OTnB3t5e735ERESyJQqA2oh9QwYkRwsXLkRERATCw8MBAMnJyfjuu++QkpKCGTNm1Gq/ZMkShISEYNq0aQCAhIQEpKenY9myZUhOToYoili8eDFmzpyJYcOGAQDWrFkDZ2dnbNmyBSNHjjT8+urg7e2Nd955B5mZmQgICAAAHDx4EPv378fUqVOxdOlSTdtXXnlF53Hvy2er+fj4oLKyEt27d8esWbPQv3//ettWVlaisrJS87qsrKwpQiQiIjKJu/+es7S0hKWlZa12VVVVyMnJQWxsrKbOzMwMgYGByMrKqnPsrKwsxMTEaNUFBwdrtsXk5+dDpVIhMDBQ876dnR38/f2RlZUleXK0cuVKODg44NSpUzh16pSm3t7eHitXrtS8FgThn5scubi4IDk5GX369EFlZSU++eQTDBw4EIcOHULv3r3r7JOYmIjZs2c3caRERET6keqEbDc3N636+Ph4zJo1q1b7K1euoLq6Gs7Ozlr1zs7OOHPmTJ2foVKp6mx/Z+/vnf/fUBspyf7Bs03By8sLXl5emtf9+vXD+fPnsWjRIqxdu7bOPrGxsVpZbllZWa0fDhERkalJlRwVFBRAoVBo6uuaNaKGGZwcnTt3DufPn8djjz2GVq1aQRRFk5x31LdvX+zbt6/e9+ubTiQiIvonUigUWslRfVq3bg1zc3MUFRVp1RcVFUGpVNbZR6lUNtj+zv8vKiqCi4uLVhsfHx99LkMnoijiyy+/xO7du1FcXAy1Wq31/qZNmwwaV++71f744w8EBgaic+fOGDJkCAoLCwEA48ePx9SpUw0Kwhh5eXla/wUQERHdj0TR+KIPCwsL+Pn5ISMjQ1OnVquRkZGh2dx8t4CAAK32AJCenq5p7+HhAaVSqdWmrKwMhw4dqndMY0RHR+PFF19Efn4+bGxsYGdnp1UMpffM0auvvooWLVrg4sWL6Nq1q6Z+xIgRiImJwYIFC3Qeq7y8HOfOndO8zs/PR15eHhwdHfHQQw8hNjYWly5dwpo1awAAixcvhoeHB7p164abN2/ik08+wa5du7Bjxw59L4OIiEhejD3l2oC+MTExGDt2LPr06YO+ffti8eLFqKio0Ny9FhYWhrZt2yIxMREAMGXKFAwYMAALFizA0KFDsX79ehw5cgQrVqwAcHvjc3R0NN5++2106tQJHh4eeOutt+Dq6orQ0FDDr60ea9euxaZNmzBkyBBJx9U7OdqxYwe2b9+Odu3aadV36tQJFy5c0GusI0eO4PHHH9e8vrM3aOzYsUhNTUVhYSEuXryoeb+qqgpTp07FpUuXYG1tjZ49e2Lnzp1aYxAREZFuRowYgd9//x1xcXFQqVTw8fFBWlqaZkP1xYsXtR40369fP6xbtw4zZ87EG2+8gU6dOmHLli3o3r27ps306dNRUVGBiRMnoqSkBI8++ijS0tJgZWUlefx2dnbo0KGD5OPqnRxVVFTA2tq6Vv3Vq1f13tszcOBAiA3MA6ampmq9nj59OqZPn67XZxAREd0PpNqQra+oqCitx2/UlJmZWatu+PDhGD58eL3jCYKAOXPmYM6cOQbFo49Zs2Zh9uzZSElJQatWrSQbV+89R//61780y1zA7S9BrVbjvffe4wwOERGRgfjgWf298MIL+PPPP+Hk5IQePXqgd+/eWsVQes8cvffeexg0aBCOHDmCqqoqTJ8+HSdPnsTVq1exf/9+gwMhIiIi0sfYsWORk5ODMWPGwNnZWbK75vVOjrp3746ffvoJy5Ytg62tLcrLy/Hcc88hMjKSd40REREZyFTLavez7777Dtu3b8ejjz4q6bgGnXNkZ2eHN998U9JAiIiImjNRLUA04tlqxvS9X7m5uel0ppO+DEqObt68iWPHjtV54NIzzzwjSWBERETNye2zioyZOZIwmPvEggULMH36dCQnJ8Pd3V2ycfVOjtLS0hAWFoYrV67Uek8QBFRXV0sSGBEREVFDxowZg+vXr6Njx46wtrZGy5Yttd6/evWqQePqnRxNnjwZw4cPR1xcXK0HyxEREZFhuOdIf4sXL26UcfVOjoqKihATE8PEiIiISEJMjvQ3duzYet8zdNYIMOCco+eff77OQ6GIiIiITG3Hjh144YUX0LZtW4PH0HvmaNmyZRg+fDh++OEH9OjRo9b63iuvvGJwMERERM0VZ44Md+HCBaSkpGD16tX4888/MXjwYK0Dq/Wld3L0+eefY8eOHbCyskJmZqbWgUuCIDA5IiIiMgCTI/1UVVVh06ZN+OSTT7B//34EBgbit99+w9GjR9GjRw+jxtY7OXrzzTcxe/ZszJgxQ+thdERERERNYfLkyfj888/RqVMnjBkzBhs2bMCDDz6Ili1bwtzc3Ojx9U6OqqqqMGLECCZGREREEuLMke4++ugjvP7665gxYwZsbW0lH1/vDGfs2LHYsGGD5IEQERE1Z3zwrO7Wrl2L7OxsuLi4YMSIEfj2228lPWdR75mj6upqvPfee9i+fTt69uxZa0P2woULJQuOiIiI6G6jRo3CqFGjkJ+fj9TUVERGRuL69etQq9U4deoUvL29jRpf75mj48ePw9fXF2ZmZjhx4gSOHj2qKXl5eUYFQ0RE1FyJoqB5vppBpRnNHN3h4eGB2bNn49dff8Wnn36Kf//73xgzZgzatWtn1A1ies8c7d692+APIyIiorpxz5HhBEFAcHAwgoODcfXqVaxZswarVq0yeDzuqiYiIpKB2w+eNa4Q4OjoiOjoaPz4448Gj6HTzNFzzz2H1NRUKBQKPPfccw223bRpk8HBEBEREZmaTsmRnZ2d5rBHOzu7Rg2IiIioOVKLAtRGLI0Z05e06ZQcrVq1CnPmzMFrr71m1BoeERER1Y17juRD5z1Hs2fPRnl5eWPGQkRERGRyOidHInd6ERERNR5jD4BspjNHP/zwA8aMGYOAgABcunQJwO1DIvft22fwmHrdrVbzIbNEREQkHZ6Qrb+vvvoKwcHBaNWqFY4ePYrKykoAQGlpKebOnWvwuHolR507d4ajo2ODhYiIiKgpvP3220hOTsbHH3+s9cSO/v37Izc31+Bx9ToEcvbs2bxbjYiIqBFwQ7b+zp49i8cee6xWvZ2dHUpKSgweV6/kaOTIkXBycjL4w4iIiKhuolqAaMT2FVHd/JIjpVKJc+fOwd3dXat+37596NChg8Hj6rysxv1GREREJCcRERGYMmUKDh06BEEQcPnyZXz22Wd47bXXMGnSJIPH1XnmiHerERERNR4uq+lvxowZUKvVGDRoEK5fv47HHnsMlpaWeO211zB58mSDx9U5OVKr1QZ/CBERETWMyZH+BEHAm2++iWnTpuHcuXMoLy+Ht7c3bGxsjBpXrz1HRERE1DiYHBnOwsIC3t7eko3H5IiIiIjuSxUVFXj33XeRkZGB4uLiWqtcv/zyi0HjMjkiIiKSAbVo3MNj1c1wa/CECROwZ88evPjii3BxcZHs5jEmR0RERDLAZTX9ff/99/juu+/Qv39/ScfV64RsIiIiIrlwcHBolKdzMDkiIiKSAbk/W+3q1asYPXo0FAoF7O3tMX78eJSXlzfYfvLkyfDy8kKrVq3w0EMP4ZVXXkFpaalWO0EQapX169frFFNCQgLi4uJw/fp1o67tblxWIyIikgFRBEQjTs1p7OMIR48ejcLCQqSnp+PWrVsIDw/HxIkTsW7dujrbX758GZcvX8b8+fPh7e2NCxcu4OWXX8bly5fx5ZdfarVdtWoVQkJCNK/t7e11imnBggU4f/48nJ2d4e7urvV8NQAGP1+NyRERERE16PTp00hLS8Phw4fRp08fAMAHH3yAIUOGYP78+XB1da3Vp3v37vjqq680rzt27Ih33nkHY8aMwV9//YUWLf5OQezt7aFUKvWOKzQ0VP+L0QGTIyIiIhmQakN2WVmZVr2lpSUsLS2Nii0rKwv29vaaxAgAAgMDYWZmhkOHDuHZZ5/VaZzS0lIoFAqtxAgAIiMjMWHCBHTo0AEvv/wywsPDdbrzLD4+Xr8L0RGTIyIiIhlQi4KRt/Lf7uvm5qZVHx8fj1mzZhkTGlQqVa0Hz7do0QKOjo5QqVQ6jXHlyhUkJCRg4sSJWvVz5szBE088AWtra+zYsQP/+9//UF5ejldeecWomI3B5IiIiOgfpKCgAAqFQvO6oVmjGTNmYN68eQ2Od/r0aaNjKisrw9ChQ+Ht7V0rUXvrrbc0f/b19UVFRQXef//9epMjR0dH/PTTT2jdujUcHBwanGG6evWqQfEyOSIiIpIBqZbVFAqFVnLUkKlTp2LcuHENtunQoQOUSiWKi4u16v/66y9cvXr1nnuFrl27hpCQENja2mLz5s21Nk3fzd/fHwkJCaisrKwzsVu0aBFsbW01f5bq4MeamBwRERHJgCkOgWzTpg3atGlzz3YBAQEoKSlBTk4O/Pz8AAC7du2CWq2Gv79/vf3KysoQHBwMS0tLfP3117CysrrnZ+Xl5cHBwaHeGa+xY8dq/nyvxM5QPOeIiIhIBuR8zlHXrl0REhKCiIgIZGdnY//+/YiKisLIkSM1d6pdunQJXbp0QXZ2NoDbiVFQUBAqKiqwcuVKlJWVQaVSQaVSobq6GgDwzTff4JNPPsGJEydw7tw5fPTRR5g7dy4mT56sU1y5ubk4fvy45vXWrVsRGhqKN954A1VVVQZfL5MjIiIiuqfPPvsMXbp0waBBgzBkyBA8+uijWLFiheb9W7du4ezZs5oDGXNzc3Ho0CEcP34cnp6ecHFx0ZSCggIAQMuWLZGUlISAgAD4+Phg+fLlWLhwoc53of33v//FTz/9BOD2Q2ZHjBgBa2trbNy4EdOnTzf4WrmsRkREJAOikXerNfYJ2Y6OjvUe+AgA7u7uEGucRDlw4ECt13UJCQnROvxRXz/99BN8fHwAABs3bsSAAQOwbt067N+/HyNHjsTixYsNGpfJERERkQyIonGnXDf2CdlyJIoi1Orbx4rv3LkTTz31FIDbxxlcuXLF4HFNuqy2d+9ePP3003B1dYUgCNiyZcs9+2RmZqJ3796wtLSEp6cnUlNTGz1OIiIikp8+ffrg7bffxtq1a7Fnzx4MHToUAJCfnw9nZ2eDxzVpclRRUYFevXohKSlJp/b5+fkYOnQoHn/8ceTl5SE6OhoTJkzA9u3bGzlSIiKixiWqBaNLc7N48WLk5uYiKioKb775Jjw9PQEAX375Jfr162fwuCZdVhs8eDAGDx6sc/vk5GR4eHhgwYIFAG7vnt+3bx8WLVqE4ODgxgqTiIio0ZniVv77Xc+ePbXuVrvj/fffh7m5ucHj3ld7jrKyshAYGKhVFxwcjOjoaNMERERERCaXk5OjOcnb29sbvXv3Nmq8+yo5UqlUtdYQnZ2dUVZWhhs3bqBVq1a1+lRWVqKyslLz+u4H8hEREcmBVM9Wa06Ki4sxYsQI7NmzB/b29gCAkpISPP7441i/fr1OB1zW5R9/zlFiYiLs7Ow05e4H8hEREcnBnbvVjCnNzeTJk1FeXo6TJ0/i6tWruHr1Kk6cOIGysjKjHlx7XyVHSqUSRUVFWnVFRUVQKBR1zhoBQGxsLEpLSzXlzsFTREREdH9LS0vDhx9+iK5du2rqvL29kZSUhO+//97gce+rZbWAgABs27ZNqy49PR0BAQH19rG0tGzwicRERERywA3Z+lOr1XU+yLZly5aa848MYdKZo/LycuTl5SEvLw/A7Vv18/LycPHiRQC3Z33CwsI07V9++WX88ssvmD59Os6cOYMPP/wQX3zxBV599VVThE9ERCSZO3uOjCnNzRNPPIEpU6bg8uXLmrpLly7h1VdfxaBBgwwe16TJ0ZEjR+Dr6wtfX18AQExMDHx9fREXFwcAKCws1CRKAODh4YHvvvsO6enp6NWrFxYsWIBPPvmEt/ETEdF9j3uO9Lds2TKUlZXB3d0dHTt2RMeOHeHh4YGysjJ88MEHBo9r0mW1ez13pa7TrwcOHIijR482YlRERER0P3Bzc0Nubi527tyJM2fOALh9BuLdx/7o677ac0RERPRPxT1HhhEEAU8++SSefPJJyca8r+5WIyIi+qfiniPd7dq1C97e3nWeXVhaWopu3brhhx9+MHh8JkdERER0X1m8eDEiIiKgUChqvWdnZ4f//ve/WLhwocHjMzkiIiKSAVEERLURpRltyP7xxx8REhJS7/tBQUHIyckxeHzuOSIiIpIBURQggnuOdFFUVFTn+UZ3tGjRAr///rvB43PmiIiIiO4rbdu2xYkTJ+p9/9ixY3BxcTF4fCZHREREMsAN2bobMmQI3nrrLdy8ebPWezdu3EB8fDyeeuopg8fnshoREZEciIBR24aa0Z6jmTNnYtOmTejcuTOioqLg5eUFADhz5gySkpJQXV2NN9980+DxmRwRERHRfcXZ2RkHDhzApEmTEBsbqzlQWhAEBAcHIykpCc7OzgaPz+SIiIhIBtQioDZiQ7a6Gc0cAUD79u2xbds2/Pnnnzh37hxEUUSnTp3g4OBg9NhMjoiIiGRANHJZrTndyl+Tg4MDHn74YUnHZHJEREQkA7yVXz54txoRERFRDZw5IiIikoHbe46M60/SYHJEREQkA9xzJB9cViMiIiKqgTNHREREMqAWBSNv5eeGbKkwOSIiIpIBLqvJB5fViIiIiGrgzBEREZEMcOZIPpgcERERyQD3HMkHl9WIiIiIauDMERERkQyIMHJZTapAiDNHREREcqAWjS+N6erVqxg9ejQUCgXs7e0xfvx4lJeXN9hn4MCBEARBq7z88stabS5evIihQ4fC2toaTk5OmDZtGv7666/GvJR74swRERGRDIgw8sGzRvTVxejRo1FYWIj09HTcunUL4eHhmDhxItatW9dgv4iICMyZM0fz2traWvPn6upqDB06FEqlEgcOHEBhYSHCwsLQsmVLzJ07t9Gu5V6YHBEREVGDTp8+jbS0NBw+fBh9+vQBAHzwwQcYMmQI5s+fD1dX13r7WltbQ6lU1vnejh07cOrUKezcuRPOzs7w8fFBQkICXn/9dcyaNQsWFhaNcj33wmU1IiIiGRCNXFK7cyt/WVmZVqmsrDQ6tqysLNjb22sSIwAIDAyEmZkZDh061GDfzz77DK1bt0b37t0RGxuL69eva43bo0cPODs7a+qCg4NRVlaGkydPGh23oThzREREJANSbch2c3PTqo+Pj8esWbOMGBlQqVRwcnLSqmvRogUcHR2hUqnq7fef//wH7du3h6urK44dO4bXX38dZ8+exaZNmzTj1kyMAGheNzRuY2NyRERE9A9SUFAAhUKheW1paVlv2xkzZmDevHkNjnf69GmDY5k4caLmzz169ICLiwsGDRqE8+fPo2PHjgaP29iYHBEREcmAWgTURvYHAIVCoZUcNWTq1KkYN25cg206dOgApVKJ4uJirfq//voLV69erXc/UV38/f0BAOfOnUPHjh2hVCqRnZ2t1aaoqAgA9BpXakyOiIiIZMAU5xy1adMGbdq0uWe7gIAAlJSUICcnB35+fgCAXbt2Qa1WaxIeXeTl5QEAXFxcNOO+8847KC4u1izbpaenQ6FQwNvbW8+rkQ43ZBMREVGDunbtipCQEERERCA7Oxv79+9HVFQURo4cqblT7dKlS+jSpYtmJuj8+fNISEhATk4Ofv31V3z99dcICwvDY489hp49ewIAgoKC4O3tjRdffBE//vgjtm/fjpkzZyIyMrLB5cDGxuSIiIhIBuR+CORnn32GLl26YNCgQRgyZAgeffRRrFixQvP+rVu3cPbsWc3daBYWFti5cyeCgoLQpUsXTJ06Ff/+97/xzTffaPqYm5vj22+/hbm5OQICAjBmzBiEhYVpnYtkClxWIyIikgG5Pz7E0dGxwQMf3d3dIYp/R+Hm5oY9e/bcc9z27dtj27ZtksQoFc4cEREREdXAmSMiIiIZUMPIu9WkCoSYHBEREcmB3JfVmhMmR0RERDLAmSP54J4jIiIioho4c0RERCQDIv5+eKyh/UkaTI6IiIhkgMtq8sFlNSIiIqIaOHNEREQkA7xbTT6YHBEREcmACOOWxpgcSYfLakREREQ1cOaIiIhIBrghWz6YHBEREckA9xzJB5fViIiIiGqQRXKUlJQEd3d3WFlZwd/fH9nZ2fW2TU1NhSAIWsXKyqoJoyUiIpKeWoJC0jB5crRhwwbExMQgPj4eubm56NWrF4KDg1FcXFxvH4VCgcLCQk25cOFCE0ZMREQkPVGC/5A0TJ4cLVy4EBEREQgPD4e3tzeSk5NhbW2NlJSUevsIggClUqkpzs7OTRgxERGR9DhzJB8mTY6qqqqQk5ODwMBATZ2ZmRkCAwORlZVVb7/y8nK0b98ebm5uGDZsGE6ePFlv28rKSpSVlWkVIiIiovqYNDm6cuUKqqura838ODs7Q6VS1dnHy8sLKSkp2Lp1Kz799FOo1Wr069cPv/32W53tExMTYWdnpylubm6SXwcREZGxRAkKScPky2r6CggIQFhYGHx8fDBgwABs2rQJbdq0wfLly+tsHxsbi9LSUk0pKCho4oiJiIjujctq8mHSc45at24Nc3NzFBUVadUXFRVBqVTqNEbLli3h6+uLc+fO1fm+paUlLC0tjY6ViIiImgeTzhxZWFjAz88PGRkZmjq1Wo2MjAwEBAToNEZ1dTWOHz8OFxeXxgqTiIio0YkQIQpGFC6sScbkJ2THxMRg7Nix6NOnD/r27YvFixejoqIC4eHhAICwsDC0bdsWiYmJAIA5c+bgkUcegaenJ0pKSvD+++/jwoULmDBhgikvg4iIyCh8fIh8mDw5GjFiBH7//XfExcVBpVLBx8cHaWlpmk3aFy9ehJnZ3xNcf/75JyIiIqBSqeDg4AA/Pz8cOHAA3t7eproEIiIi+gcxeXIEAFFRUYiKiqrzvczMTK3XixYtwqJFi5ogKiIioqbDmSP5kEVyRERERMbuG+KeI6ncd7fyExERETUmzhwRERHJAJfV5IPJERERkQwY+/BY3sovHSZHREREMsCZI/ngniMiIiKiGjhzREREJAOicLsY3F/zf8hYTI6IiIhk4PaymuHZDZfVpMNlNSIiIqIaOHNEREQkA9yQLR+cOSIiIpIBUYL/NKarV69i9OjRUCgUsLe3x/jx41FeXl5v+19//RWCINRZNm7cqGlX1/vr169v1Gu5F84cERER0T2NHj0ahYWFSE9Px61btxAeHo6JEydi3bp1dbZ3c3NDYWGhVt2KFSvw/vvvY/DgwVr1q1atQkhIiOa1vb295PHrg8kRERGRDMh5We306dNIS0vD4cOH0adPHwDABx98gCFDhmD+/PlwdXWt1cfc3BxKpVKrbvPmzXjhhRdgY2OjVW9vb1+rrSlxWY2IiEgG1BCNLo0lKysL9vb2msQIAAIDA2FmZoZDhw7pNEZOTg7y8vIwfvz4Wu9FRkaidevW6Nu3L1JSUiCKpj2TgDNHRERE/yBlZWVary0tLWFpaWnUmCqVCk5OTlp1LVq0gKOjI1QqlU5jrFy5El27dkW/fv206ufMmYMnnngC1tbW2LFjB/73v/+hvLwcr7zyilExG4MzR0RERDJw5xBIYwpwe6+PnZ2dpiQmJtb7mTNmzKh30/SdcubMGaOv7caNG1i3bl2ds0ZvvfUW+vfvD19fX7z++uuYPn063n//faM/0xicOSIiIpIBY5fG7vQtKCiAQqHQ1Dc0azR16lSMGzeuwXE7dOgApVKJ4uJirfq//voLV69e1Wmv0Jdffonr168jLCzsnm39/f2RkJCAyspKo2e8DMXkiIiISBaMvR3/dl+FQqGVHDWkTZs2aNOmzT3bBQQEoKSkBDk5OfDz8wMA7Nq1C2q1Gv7+/vfsv3LlSjzzzDM6fVZeXh4cHBxMlhgBTI6IiIjoHrp27YqQkBBEREQgOTkZt27dQlRUFEaOHKm5U+3SpUsYNGgQ1qxZg759+2r6njt3Dnv37sW2bdtqjfvNN9+gqKgIjzzyCKysrJCeno65c+fitddea7JrqwuTIyIiIhmQ8638APDZZ58hKioKgwYNgpmZGf79739j6dKlmvdv3bqFs2fP4vr161r9UlJS0K5dOwQFBdUas2XLlkhKSsKrr74KURTh6emJhQsXIiIiopGvpmFMjoiIiGRAqj1HjcXR0bHeAx8BwN3dvc5b8OfOnYu5c+fW2SckJETr8Ee54N1qRERERDVw5oiIiEgGRECC7dgkBSZHREREMqAWRKgF+S6rNSdcViMiIiKqgTNHREREMiD3DdnNCZMjIiIiGeCeI/ngshoRERFRDZw5IiIikgEuq8kHkyMiIiIZYHIkH0yOiIiIZEDujw9pTrjniIiIiKgGzhwRERHJgPj//zGmP0mDyREREZEMiEbuOWJyJB0uqxERERHVwJkjIiIiGVALIgQ+W00WmBwRERHJgBqAYGR/kgaX1YiIiIhq4MwRERGRDKghQuAhkLLA5IiIiEgGeCu/fHBZjYiIiKgGzhwRERHJAJfV5IPJERERkQwwOZIPJkdEREQywORIPrjniIiIiKgGzhwRERHJwO1DII2ZOSKpMDkiIiKSAVEA1EYckc1FNelwWY2IiIioBlkkR0lJSXB3d4eVlRX8/f2RnZ3dYPuNGzeiS5cusLKyQo8ePbBt27YmipSIiKhxqCEaXUgaJk+ONmzYgJiYGMTHxyM3Nxe9evVCcHAwiouL62x/4MABjBo1CuPHj8fRo0cRGhqK0NBQnDhxookjJyIikg6TI/kweXK0cOFCREREIDw8HN7e3khOToa1tTVSUlLqbL9kyRKEhIRg2rRp6Nq1KxISEtC7d28sW7asiSMnIiKifyKTJkdVVVXIyclBYGCgps7MzAyBgYHIysqqs09WVpZWewAIDg6ut31lZSXKysq0ChERkdxUQzS6kDRMmhxduXIF1dXVcHZ21qp3dnaGSqWqs49KpdKrfWJiIuzs7DTFzc1NmuCJiIgkxGU1+TD5slpji42NRWlpqaYUFBSYOiQiIiKSMZOec9S6dWuYm5ujqKhIq76oqAhKpbLOPkqlUq/2lpaWsLS0lCZgIiKiRnJ75oePD5EDk84cWVhYwM/PDxkZGZo6tVqNjIwMBAQE1NknICBAqz0ApKen19ueiIjoflAtqI0uJA2Tn5AdExODsWPHok+fPujbty8WL16MiooKhIeHAwDCwsLQtm1bJCYmAgCmTJmCAQMGYMGCBRg6dCjWr1+PI0eOYMWKFaa8DCIiIqNUQ4TImSNZMPmeoxEjRmD+/PmIi4uDj48P8vLykJaWptl0ffHiRRQWFmra9+vXD+vWrcOKFSvQq1cvfPnll9iyZQu6d+9uqksgIiL6x3vnnXfQr18/WFtbw97eXqc+oigiLi4OLi4uaNWqFQIDA/Hzzz9rtbl69SpGjx4NhUIBe3t7jB8/HuXl5Y1wBboz+cwRAERFRSEqKqrO9zIzM2vVDR8+HMOHD2/kqIiIiJqO2siZI2P66qKqqgrDhw9HQEAAVq5cqVOf9957D0uXLsXq1avh4eGBt956C8HBwTh16hSsrKwAAKNHj0ZhYSHS09Nx69YthIeHY+LEiVi3bl1jXk6DZJEcERERNXfVgghBkG9yNHv2bABAamqqTu1FUcTixYsxc+ZMDBs2DACwZs0aODs7Y8uWLRg5ciROnz6NtLQ0HD58GH369AEAfPDBBxgyZAjmz58PV1fXRrmWe2l2yZEo3v7xXLsm3cY1dXm1ZGMBwLWqKknHuylel3S8avGGZGOJ4k3JxgKAWxJfa8Vf0sZXduMvScerLJP2tyc1SwmvV+r/LqT+rUj9W64WW0o63k1R2n/cS/nPqVYS/zO0vEy6f77f+bvizt8djUlEpTE3q93uD9Q67NhUd23n5+dDpVJpHdxsZ2cHf39/ZGVlYeTIkcjKyoK9vb0mMQKAwMBAmJmZ4dChQ3j22WebPG6gGSZH165dAwB06yDn845+kXi8DRKPJ19fSD3eIYkHlHq8SInHk9w5CceS+suTmLT/ToNSaYdDrMRbOGI/lnAwKcdqJNeuXYOdnV2jjG1hYQGlUgmV6l2jx7Kxsal12HF8fDxmzZpl9Nj6unM4c0MHN6tUKjg5OWm936JFCzg6OtZ7uHNTaHbJkaurKwoKCmBrawtBEOptV1ZWBjc3NxQUFEChUDRhhMZj7KZzP8fP2E2DsZuGrrGLoohr16416vKOlZUV8vPzUSXBbJwoirX+bmto1mjGjBmYN29eg2OePn0aXbp0MTq2+0mzS47MzMzQrl07ndsrFIr77n/0dzB207mf42fspsHYTUOX2BtrxqgmKysrzQblpjR16lSMGzeuwTYdOnQwaOw7hzMXFRXBxcVFU19UVAQfHx9Nm+LiYq1+f/31F65evVrv4c5NodklR0RERHRbmzZt0KZNm0YZ28PDA0qlEhkZGZpkqKysDIcOHcKkSZMA3D7YuaSkBDk5OfDz8wMA7Nq1C2q1Gv7+/o0Sly5Mfs4RERERyd/FixeRl5eHixcvorq6Gnl5ecjLy9M6k6hLly7YvHkzAEAQBERHR+Ptt9/G119/jePHjyMsLAyurq4IDQ0FAHTt2hUhISGIiIhAdnY29u/fj6ioKIwcOdJkd6oBnDmql6WlJeLj4+/L57IxdtO5n+Nn7KbB2E3jfo7dVOLi4rB69WrNa19fXwDA7t27MXDgQADA2bNnUVr69+0E06dPR0VFBSZOnIiSkhI8+uijSEtL01pC/OyzzxAVFYVBgwbBzMwM//73v7F06dKmuah6CGJT3J9IREREdJ/gshoRERFRDUyOiIiIiGpgckRERERUA5MjIiIiohqadXKUlJQEd3d3WFlZwd/fH9nZ2Q2237hxI7p06QIrKyv06NED27Zta6JIa9Mn9tTUVAiCoFVMcdgYAOzduxdPP/00XF1dIQgCtmzZcs8+mZmZ6N27NywtLeHp6anzQw+lpm/smZmZtb53QRBMciR+YmIiHn74Ydja2sLJyQmhoaE4e/bsPfvJ4TdvSOxy+c1/9NFH6Nmzp+agwYCAAHz//fcN9pHDdw7oH7tcvvO6vPvuu5rbyhsil++eTK/ZJkcbNmxATEwM4uPjkZubi169eiE4OLjWSZ13HDhwAKNGjcL48eNx9OhRhIaGIjQ0FCdOnGjiyPWPHbh9CmxhYaGmXLhwoQkj/ltFRQV69eqFpKQkndrn5+dj6NChePzxx5GXl4fo6GhMmDAB27dvb+RIa9M39jvOnj2r9d3f/RyhprBnzx5ERkbi4MGDSE9Px61btxAUFISKiop6+8jlN29I7IA8fvPt2rXDu+++i5ycHBw5cgRPPPEEhg0bhpMnT9bZXi7fOaB/7IA8vvO7HT58GMuXL0fPnj0bbCen755kQGym+vbtK0ZGRmpeV1dXi66urmJiYmKd7V944QVx6NChWnX+/v7if//730aNsy76xr5q1SrRzs6uiaLTHQBx8+bNDbaZPn262K1bN626ESNGiMHBwY0Y2b3pEvvu3btFAOKff/7ZJDHpo7i4WAQg7tmzp942cvrN16RL7HL9zYuiKDo4OIiffPJJne/J9Tu/o6HY5fidX7t2TezUqZOYnp4uDhgwQJwyZUq9beX+3VPTapYzR1VVVcjJyUFgYKCmzszMDIGBgcjKyqqzT1ZWllZ7AAgODq63fWMxJHYAKC8vR/v27eHm5nbPf/uTE7l878bw8fGBi4sLnnzySezfv9/U4QCA5pA2R0fHetvI9bvXJXZAfr/56upqrF+/HhUVFQgICKizjVy/c11iB+T3nUdGRmLo0KG1vtO6yPW7J9NolsnRlStXUF1dDWdnZ616Z2fneveDqFQqvdo3FkNi9/LyQkpKCrZu3YpPP/0UarUa/fr1w2+//dYUIRulvu+9rKwMN27cMFFUunFxcUFycjK++uorfPXVV3Bzc8PAgQORm5tr0rjUajWio6PRv39/dO/evd52cvnN16Rr7HL6zR8/fhw2NjawtLTEyy+/jM2bN8Pb27vOtnL7zvWJXU7fOQCsX78eubm5SExM1Km93L57Mi0+PqQZCAgI0Pq3vX79+qFr165Yvnw5EhISTBjZP5uXlxe8vLw0r/v164fz589j0aJFWLt2rcniioyMxIkTJ7Bv3z6TxWAoXWOX02/ey8sLeXl5KC0txZdffomxY8diz5499SYZcqJP7HL6zgsKCjBlyhSkp6fLZlM43V+aZXLUunVrmJubo6ioSKu+qKgISqWyzj5KpVKv9o3FkNjv1rJlS/j6+uLcuXONEaKk6vveFQoFWrVqZaKoDNe3b1+TJiVRUVH49ttvsXfvXrRr167BtnL5zd+hT+x3M+Vv3sLCAp6engAAPz8/HD58GEuWLMHy5ctrtZXbd65P7Hcz5Xeek5OD4uJi9O7dW1NXXV2NvXv3YtmyZaisrIS5ublWH7l992RazXJZzcLCAn5+fsjIyNDUqdVqZGRk1LueHhAQoNUeANLT0xtcf28MhsR+t+rqahw/fhwuLi6NFaZk5PK9SyUvL88k37soioiKisLmzZuxa9cueHh43LOPXL57Q2K/m5x+82q1GpWVlXW+J5fvvD4NxX43U37ngwYNwvHjxzVPjc/Ly0OfPn0wevRo5OXl1UqMAPl/99TETL0j3FTWr18vWlpaiqmpqeKpU6fEiRMnivb29qJKpRJFURRffPFFccaMGZr2+/fvF1u0aCHOnz9fPH36tBgfHy+2bNlSPH78uOxjnz17trh9+3bx/PnzYk5Ojjhy5EjRyspKPHnyZJPHfu3aNfHo0aPi0aNHRQDiwoULxaNHj4oXLlwQRVEUZ8yYIb744oua9r/88otobW0tTps2TTx9+rSYlJQkmpubi2lpabKPfdGiReKWLVvEn3/+WTx+/Lg4ZcoU0czMTNy5c2eTxz5p0iTRzs5OzMzMFAsLCzXl+vXrmjZy/c0bErtcfvMzZswQ9+zZI+bn54vHjh0TZ8yYIQqCIO7YsaPOuOXynRsSu1y+8/rcfbeanL97Mr1mmxyJoih+8MEH4kMPPSRaWFiIffv2FQ8ePKh5b8CAAeLYsWO12n/xxRdi586dRQsLC7Fbt27id99918QR/02f2KOjozVtnZ2dxSFDhoi5ubkmiPrv29vvLnfiHTt2rDhgwIBafXx8fEQLCwuxQ4cO4qpVq5o87jtx6BP7vHnzxI4dO4pWVlaio6OjOHDgQHHXrl0mib2uuAFofZdy/c0bErtcfvMvvfSS2L59e9HCwkJs06aNOGjQIE1yUVfcoiiP71wU9Y9dLt95fe5OjuT83ZPpCaIoik03T0VEREQkb81yzxERERFRfZgcEREREdXA5IiIiIioBiZHRERERDUwOSIiIiKqgckRERERUQ1MjoiIiIhqYHJEREREVAOTIyKZGzduHEJDQ00dhs5SU1MhCAIEQUB0dLROfcaNG6fps2XLlkaNj4joXpgcEZnQnYSgvjJr1iwsWbIEqampTR5bZmamViytWrVCt27dsGLFinv2VSgUKCwsREJCgk6ftWTJEhQWFhobMhGRJFqYOgCi5qxmQrBhwwbExcXh7NmzmjobGxvY2NiYIjSNs2fPQqFQ4MaNG/jmm28wadIkdOzYEYMGDaq3jyAIUCqVOn+GnZ0d7OzspAiXiMhonDkiMiGlUqkpdnZ2mqTiTrGxsam1rDZw4EBMnjwZ0dHRcHBwgLOzMz7++GNUVFQgPDwctra28PT0xPfff6/1WSdOnMDgwYNhY2MDZ2dnvPjii7hy5co9Y3RycoJSqYSHhwdeeeUVeHh4IDc3V+9r/fDDD9GpUydYWVnB2dkZzz//vN5jEBE1BSZHRPeh1atXo3Xr1sjOzsbkyZMxadIkDB8+HP369UNubi6CgoLw4osv4vr16wCAkpISPPHEE/D19cWRI0eQlpaGoqIivPDCCzp/piiKSEtLw8WLF+Hv769XvEeOHMErr7yCOXPm4OzZs0hLS8Njjz2m1xhERE2Fy2pE96FevXph5syZAIDY2Fi8++67aN26NSIiIgAAcXFx+Oijj3Ds2DE88sgjWLZsGXx9fTF37lzNGCkpKXBzc8NPP/2Ezp071/tZ7dq1AwBUVlZCrVZjzpw5eic2Fy9exAMPPICnnnoKtra2aN++PXx9ffW9bCKiJsHkiOg+1LNnT82fzc3N8eCDD6JHjx6aOmdnZwBAcXExAODHH3/E7t2769y/dP78+QaTox9++AG2traorKxEdnY2oqKi4OjoiEmTJukc75NPPon27dujQ4cOCAkJQUhICJ599llYW1vrPAYRUVNhckR0H2rZsqXWa0EQtOoEQQAAqNVqAEB5eTmefvppzJs3r9ZYLi4uDX6Wh4cH7O3tAQDdunXDoUOH8M477+iVHNna2iI3NxeZmZnYsWMH4uLiMGvWLBw+fFgzNhGRXDA5ImoGevfuja+++gru7u5o0cK4/9mbm5vjxo0bevdr0aIFAgMDERgYiPj4eNjb22PXrl147rnnjIqHiEhq3JBN1AxERkbi6tWrGDVqFA4fPozz589j+/btCA8PR3V1dYN9i4uLoVKpcOHCBWzcuBFr167FsGHD9Pr8b7/9FkuXLkVeXh4uXLiANWvWQK1Ww8vLy5jLIiJqFJw5ImoGXF1dsX//frz++usICgpCZWUl2rdvj5CQEJiZNfzvSHcSmBYtWsDNzQ3//e9/MWvWLL0+397eHps2bcKsWbNw8+ZNdOrUCZ9//jm6detm6CURETUaQRRF0dRBENE/R2pqKqKjo1FSUqJ3X0EQsHnz5vvqcSlE9M/DZTUiklxpaSlsbGzw+uuv69T+5ZdfNvlJ4EREd3DmiIgkde3aNRQVFQG4vZzWunXre/YpLi5GWVkZgNt3zz3wwAONGiMRUUOYHBERERHVwGU1IiIiohqYHBERERHVwOSIiIiIqAYmR0REREQ1MDkiIiIiqoHJEREREVENTI6IiIiIamByRERERFTD/wGq14rK0c9VnAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\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: 10kB\n",
       "Dimensions:  (x0: 21, x1: 20)\n",
       "Coordinates:\n",
       "  * x0       (x0) float64 168B 0.0 0.15 0.3 0.45 0.6 ... 2.4 2.55 2.7 2.85 3.0\n",
       "  * x1       (x1) float64 160B 0.0 0.2105 0.4211 0.6316 ... 3.579 3.789 4.0\n",
       "Data variables:\n",
       "    y0       (x0, x1) float64 3kB 1.5 1.617 1.762 1.94 ... 34.6 38.0 42.2 47.38\n",
       "    y1       (x0, x1) float64 3kB 0.0 0.0 0.0 ... 3.674e-16 3.674e-16 3.674e-16\n",
       "    y2       (x0, x1) float64 3kB 1.0 0.7891 0.2455 ... 0.2455 0.7891 1.0\n",
       "Attributes:\n",
       "    tuid:                             20241106-153126-258-4e5651\n",
       "    name:                             my experiment\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         True\n",
       "    1d_2_settables_uniformly_spaced:  True\n",
       "    xlen:                             21\n",
       "    ylen:                             20</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-cb987eca-7458-4039-9771-21e9834681c2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cb987eca-7458-4039-9771-21e9834681c2' 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'>x0</span>: 21</li><li><span class='xr-has-index'>x1</span>: 20</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0dfd88a2-3225-48d4-92ce-b837eded6193' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0dfd88a2-3225-48d4-92ce-b837eded6193' 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 class='xr-has-index'>x0</span></div><div class='xr-var-dims'>(x0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.15 0.3 0.45 ... 2.7 2.85 3.0</div><input id='attrs-b0b87d7b-ccf5-4414-a921-1045001d4576' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0b87d7b-ccf5-4414-a921-1045001d4576' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d2337d0-34f0-442e-8ebf-78f3d22a4c88' class='xr-var-data-in' type='checkbox'><label for='data-2d2337d0-34f0-442e-8ebf-78f3d22a4c88' 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>name :</span></dt><dd>time_a</dd><dt><span>long_name :</span></dt><dd>Time A</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([0.  , 0.15, 0.3 , 0.45, 0.6 , 0.75, 0.9 , 1.05, 1.2 , 1.35, 1.5 , 1.65,\n",
       "       1.8 , 1.95, 2.1 , 2.25, 2.4 , 2.55, 2.7 , 2.85, 3.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.2105 0.4211 ... 3.789 4.0</div><input id='attrs-5f8e0796-52e8-463d-a385-1a95ea2781a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f8e0796-52e8-463d-a385-1a95ea2781a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c61de8c-8020-432f-8bbd-b8704adeb649' class='xr-var-data-in' type='checkbox'><label for='data-8c61de8c-8020-432f-8bbd-b8704adeb649' 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>name :</span></dt><dd>time_b</dd><dt><span>long_name :</span></dt><dd>Time B</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([0.      , 0.210526, 0.421053, 0.631579, 0.842105, 1.052632, 1.263158,\n",
       "       1.473684, 1.684211, 1.894737, 2.105263, 2.315789, 2.526316, 2.736842,\n",
       "       2.947368, 3.157895, 3.368421, 3.578947, 3.789474, 4.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4ba886fa-99f2-405c-b0d1-95377a2473ef' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4ba886fa-99f2-405c-b0d1-95377a2473ef' class='xr-section-summary' >Data variables: <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>y0</span></div><div class='xr-var-dims'>(x0, x1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5 1.617 1.762 ... 38.0 42.2 47.38</div><input id='attrs-2746ba15-3eb4-4e91-a486-8302238f0a3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2746ba15-3eb4-4e91-a486-8302238f0a3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51931555-db45-457d-baba-bce0b2ebaf3a' class='xr-var-data-in' type='checkbox'><label for='data-51931555-db45-457d-baba-bce0b2ebaf3a' 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>name :</span></dt><dd>sig_a</dd><dt><span>long_name :</span></dt><dd>Signal A</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([[ 1.5       ,  1.61716377,  1.76178223,  1.94028878,  2.16062434,\n",
       "         2.43259058,  2.768286  ,  3.1826441 ,  3.69409771,  4.32539898,\n",
       "         5.10463153,  6.06645972,  7.25367073,  8.71907798, 10.5278705 ,\n",
       "        12.76051291, 15.51632491, 18.91789955, 23.11655678, 28.29907502],\n",
       "       [ 1.66183424,  1.77899801,  1.92361647,  2.10212303,  2.32245858,\n",
       "         2.59442482,  2.93012024,  3.34447834,  3.85593195,  4.48723322,\n",
       "         5.26646577,  6.22829396,  7.41550498,  8.88091223, 10.68970474,\n",
       "        12.92234715, 15.67815915, 19.07973379, 23.27839102, 28.46090926],\n",
       "       [ 1.84985881,  1.96702258,  2.11164104,  2.29014759,  2.51048315,\n",
       "         2.78244939,  3.1181448 ,  3.5325029 ,  4.04395651,  4.67525779,\n",
       "         5.45449034,  6.41631853,  7.60352954,  9.06893679, 10.87772931,\n",
       "        13.11037172, 15.86618372, 19.26775835, 23.46641559, 28.64893382],\n",
       "       [ 2.06831219,  2.18547595,  2.33009442,  2.50860097,  2.72893652,\n",
       "         3.00090276,  3.33659818,  3.75095628,  4.26240989,  4.89371117,\n",
       "         5.67294371,  6.6347719 ,  7.82198292,  9.28739017, 11.09618269,\n",
       "        13.3288251 , 16.0846371 , 19.48621173, 23.68486897, 28.8673872 ],\n",
       "       [ 2.3221188 ,  2.43928257,  2.58390103,  2.76240759,  2.98274314,\n",
       "         3.25470938,  3.5904048 ,  4.0047629 ,  4.51621651,  5.14751778,\n",
       "         5.92675033,  6.88857852,  8.07578954,  9.54119678, 11.3499893 ,\n",
       "        13.58263171, 16.33844371, 19.74001835, 23.93867558, 29.12119382],\n",
       "...\n",
       "       [11.52317638, 11.64034015, 11.78495861, 11.96346517, 12.18380072,\n",
       "        12.45576696, 12.79146238, 13.20582048, 13.71727409, 14.34857536,\n",
       "        15.12780791, 16.0896361 , 17.27684712, 18.74225436, 20.55104688,\n",
       "        22.78368929, 25.53950129, 28.94107593, 33.13973316, 38.3222514 ],\n",
       "       [13.30710378, 13.42426755, 13.56888601, 13.74739257, 13.96772812,\n",
       "        14.23969436, 14.57538978, 14.98974788, 15.50120149, 16.13250276,\n",
       "        16.91173531, 17.8735635 , 19.06077452, 20.52618177, 22.33497428,\n",
       "        24.56761669, 27.32342869, 30.72500333, 34.92366056, 40.1061788 ],\n",
       "       [15.37973172, 15.49689549, 15.64151396, 15.82002051, 16.04035606,\n",
       "        16.3123223 , 16.64801772, 17.06237582, 17.57382943, 18.20513071,\n",
       "        18.98436325, 19.94619144, 21.13340246, 22.59880971, 24.40760223,\n",
       "        26.64024463, 29.39605664, 32.79763127, 36.99628851, 42.17880674],\n",
       "       [17.78778184, 17.90494561, 18.04956407, 18.22807063, 18.44840618,\n",
       "        18.72037242, 19.05606784, 19.47042594, 19.98187955, 20.61318082,\n",
       "        21.39241337, 22.35424156, 23.54145258, 25.00685982, 26.81565234,\n",
       "        29.04829475, 31.80410675, 35.20568139, 39.40433862, 44.58685686],\n",
       "       [20.58553692, 20.70270069, 20.84731916, 21.02582571, 21.24616126,\n",
       "        21.5181275 , 21.85382292, 22.26818102, 22.77963463, 23.41093591,\n",
       "        24.19016845, 25.15199664, 26.33920766, 27.80461491, 29.61340742,\n",
       "        31.84604983, 34.60186183, 38.00343647, 42.2020937 , 47.38461194]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y1</span></div><div class='xr-var-dims'>(x0, x1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 3.674e-16 3.674e-16</div><input id='attrs-3aa73d26-5f64-4350-bef4-3dd8c7d07169' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3aa73d26-5f64-4350-bef4-3dd8c7d07169' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a701684f-6920-4e6e-a0da-20b97ec492bf' class='xr-var-data-in' type='checkbox'><label for='data-a701684f-6920-4e6e-a0da-20b97ec492bf' 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>name :</span></dt><dd>sin</dd><dt><span>long_name :</span></dt><dd>Sine Amplitude</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
       "         0.00000000e+00,  0.00000000e+00],\n",
       "       [ 4.53990500e-01,  4.53990500e-01,  4.53990500e-01,\n",
       "         4.53990500e-01,  4.53990500e-01,  4.53990500e-01,\n",
       "         4.53990500e-01,  4.53990500e-01,  4.53990500e-01,\n",
       "         4.53990500e-01,  4.53990500e-01,  4.53990500e-01,\n",
       "         4.53990500e-01,  4.53990500e-01,  4.53990500e-01,\n",
       "         4.53990500e-01,  4.53990500e-01,  4.53990500e-01,\n",
       "         4.53990500e-01,  4.53990500e-01],\n",
       "       [ 8.09016994e-01,  8.09016994e-01,  8.09016994e-01,\n",
       "         8.09016994e-01,  8.09016994e-01,  8.09016994e-01,\n",
       "         8.09016994e-01,  8.09016994e-01,  8.09016994e-01,\n",
       "         8.09016994e-01,  8.09016994e-01,  8.09016994e-01,\n",
       "         8.09016994e-01,  8.09016994e-01,  8.09016994e-01,\n",
       "         8.09016994e-01,  8.09016994e-01,  8.09016994e-01,\n",
       "...\n",
       "         8.09016994e-01,  8.09016994e-01,  8.09016994e-01,\n",
       "         8.09016994e-01,  8.09016994e-01,  8.09016994e-01,\n",
       "         8.09016994e-01,  8.09016994e-01,  8.09016994e-01,\n",
       "         8.09016994e-01,  8.09016994e-01,  8.09016994e-01,\n",
       "         8.09016994e-01,  8.09016994e-01,  8.09016994e-01,\n",
       "         8.09016994e-01,  8.09016994e-01],\n",
       "       [ 4.53990500e-01,  4.53990500e-01,  4.53990500e-01,\n",
       "         4.53990500e-01,  4.53990500e-01,  4.53990500e-01,\n",
       "         4.53990500e-01,  4.53990500e-01,  4.53990500e-01,\n",
       "         4.53990500e-01,  4.53990500e-01,  4.53990500e-01,\n",
       "         4.53990500e-01,  4.53990500e-01,  4.53990500e-01,\n",
       "         4.53990500e-01,  4.53990500e-01,  4.53990500e-01,\n",
       "         4.53990500e-01,  4.53990500e-01],\n",
       "       [ 3.67394040e-16,  3.67394040e-16,  3.67394040e-16,\n",
       "         3.67394040e-16,  3.67394040e-16,  3.67394040e-16,\n",
       "         3.67394040e-16,  3.67394040e-16,  3.67394040e-16,\n",
       "         3.67394040e-16,  3.67394040e-16,  3.67394040e-16,\n",
       "         3.67394040e-16,  3.67394040e-16,  3.67394040e-16,\n",
       "         3.67394040e-16,  3.67394040e-16,  3.67394040e-16,\n",
       "         3.67394040e-16,  3.67394040e-16]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y2</span></div><div class='xr-var-dims'>(x0, x1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 0.7891 0.2455 ... 0.7891 1.0</div><input id='attrs-eb8d5c88-11db-4403-8809-86c113791312' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb8d5c88-11db-4403-8809-86c113791312' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35ccf351-4281-4214-b060-0c59a0319162' class='xr-var-data-in' type='checkbox'><label for='data-35ccf351-4281-4214-b060-0c59a0319162' 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>name :</span></dt><dd>cos</dd><dt><span>long_name :</span></dt><dd>Cosine Amplitude</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([[ 1.        ,  0.78914051,  0.24548549, -0.40169542, -0.87947375,\n",
       "        -0.9863613 , -0.67728157, -0.08257935,  0.54694816,  0.94581724,\n",
       "         0.94581724,  0.54694816, -0.08257935, -0.67728157, -0.9863613 ,\n",
       "        -0.87947375, -0.40169542,  0.24548549,  0.78914051,  1.        ],\n",
       "       [ 1.        ,  0.78914051,  0.24548549, -0.40169542, -0.87947375,\n",
       "        -0.9863613 , -0.67728157, -0.08257935,  0.54694816,  0.94581724,\n",
       "         0.94581724,  0.54694816, -0.08257935, -0.67728157, -0.9863613 ,\n",
       "        -0.87947375, -0.40169542,  0.24548549,  0.78914051,  1.        ],\n",
       "       [ 1.        ,  0.78914051,  0.24548549, -0.40169542, -0.87947375,\n",
       "        -0.9863613 , -0.67728157, -0.08257935,  0.54694816,  0.94581724,\n",
       "         0.94581724,  0.54694816, -0.08257935, -0.67728157, -0.9863613 ,\n",
       "        -0.87947375, -0.40169542,  0.24548549,  0.78914051,  1.        ],\n",
       "       [ 1.        ,  0.78914051,  0.24548549, -0.40169542, -0.87947375,\n",
       "        -0.9863613 , -0.67728157, -0.08257935,  0.54694816,  0.94581724,\n",
       "         0.94581724,  0.54694816, -0.08257935, -0.67728157, -0.9863613 ,\n",
       "        -0.87947375, -0.40169542,  0.24548549,  0.78914051,  1.        ],\n",
       "       [ 1.        ,  0.78914051,  0.24548549, -0.40169542, -0.87947375,\n",
       "        -0.9863613 , -0.67728157, -0.08257935,  0.54694816,  0.94581724,\n",
       "         0.94581724,  0.54694816, -0.08257935, -0.67728157, -0.9863613 ,\n",
       "        -0.87947375, -0.40169542,  0.24548549,  0.78914051,  1.        ],\n",
       "...\n",
       "       [ 1.        ,  0.78914051,  0.24548549, -0.40169542, -0.87947375,\n",
       "        -0.9863613 , -0.67728157, -0.08257935,  0.54694816,  0.94581724,\n",
       "         0.94581724,  0.54694816, -0.08257935, -0.67728157, -0.9863613 ,\n",
       "        -0.87947375, -0.40169542,  0.24548549,  0.78914051,  1.        ],\n",
       "       [ 1.        ,  0.78914051,  0.24548549, -0.40169542, -0.87947375,\n",
       "        -0.9863613 , -0.67728157, -0.08257935,  0.54694816,  0.94581724,\n",
       "         0.94581724,  0.54694816, -0.08257935, -0.67728157, -0.9863613 ,\n",
       "        -0.87947375, -0.40169542,  0.24548549,  0.78914051,  1.        ],\n",
       "       [ 1.        ,  0.78914051,  0.24548549, -0.40169542, -0.87947375,\n",
       "        -0.9863613 , -0.67728157, -0.08257935,  0.54694816,  0.94581724,\n",
       "         0.94581724,  0.54694816, -0.08257935, -0.67728157, -0.9863613 ,\n",
       "        -0.87947375, -0.40169542,  0.24548549,  0.78914051,  1.        ],\n",
       "       [ 1.        ,  0.78914051,  0.24548549, -0.40169542, -0.87947375,\n",
       "        -0.9863613 , -0.67728157, -0.08257935,  0.54694816,  0.94581724,\n",
       "         0.94581724,  0.54694816, -0.08257935, -0.67728157, -0.9863613 ,\n",
       "        -0.87947375, -0.40169542,  0.24548549,  0.78914051,  1.        ],\n",
       "       [ 1.        ,  0.78914051,  0.24548549, -0.40169542, -0.87947375,\n",
       "        -0.9863613 , -0.67728157, -0.08257935,  0.54694816,  0.94581724,\n",
       "         0.94581724,  0.54694816, -0.08257935, -0.67728157, -0.9863613 ,\n",
       "        -0.87947375, -0.40169542,  0.24548549,  0.78914051,  1.        ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9903405a-6cc7-4d36-95de-5b70b296b4a0' class='xr-section-summary-in' type='checkbox'  ><label for='section-9903405a-6cc7-4d36-95de-5b70b296b4a0' class='xr-section-summary' >Indexes: <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-index-name'><div>x0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2f65314f-f9cd-4668-9822-736152fc72e7' class='xr-index-data-in' type='checkbox'/><label for='index-2f65314f-f9cd-4668-9822-736152fc72e7' 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,                0.15,                 0.3,\n",
       "       0.44999999999999996,                 0.6,                0.75,\n",
       "        0.8999999999999999,                1.05,                 1.2,\n",
       "        1.3499999999999999,                 1.5,                1.65,\n",
       "        1.7999999999999998,                1.95,                 2.1,\n",
       "                      2.25,                 2.4,                2.55,\n",
       "        2.6999999999999997,                2.85,                 3.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a67d89ec-93e1-4dd9-ab98-493cf26d17a9' class='xr-index-data-in' type='checkbox'/><label for='index-a67d89ec-93e1-4dd9-ab98-493cf26d17a9' 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, 0.2105263157894739, 0.4210526315789478,\n",
       "       0.6315789473684212, 0.8421052631578947, 1.0526315789473686,\n",
       "       1.2631578947368425,  1.473684210526316, 1.6842105263157894,\n",
       "       1.8947368421052633,  2.105263157894737, 2.3157894736842106,\n",
       "        2.526315789473684,  2.736842105263158,  2.947368421052632,\n",
       "       3.1578947368421053, 3.3684210526315788, 3.5789473684210527,\n",
       "       3.7894736842105265,                4.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x1&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d3b438f2-60f4-4a1a-b726-330b69f43673' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d3b438f2-60f4-4a1a-b726-330b69f43673' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20241106-153126-258-4e5651</dd><dt><span>name :</span></dt><dd>my experiment</dd><dt><span>grid_2d :</span></dt><dd>False</dd><dt><span>grid_2d_uniformly_spaced :</span></dt><dd>True</dd><dt><span>1d_2_settables_uniformly_spaced :</span></dt><dd>True</dd><dt><span>xlen :</span></dt><dd>21</dd><dt><span>ylen :</span></dt><dd>20</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 10kB\n",
       "Dimensions:  (x0: 21, x1: 20)\n",
       "Coordinates:\n",
       "  * x0       (x0) float64 168B 0.0 0.15 0.3 0.45 0.6 ... 2.4 2.55 2.7 2.85 3.0\n",
       "  * x1       (x1) float64 160B 0.0 0.2105 0.4211 0.6316 ... 3.579 3.789 4.0\n",
       "Data variables:\n",
       "    y0       (x0, x1) float64 3kB 1.5 1.617 1.762 1.94 ... 34.6 38.0 42.2 47.38\n",
       "    y1       (x0, x1) float64 3kB 0.0 0.0 0.0 ... 3.674e-16 3.674e-16 3.674e-16\n",
       "    y2       (x0, x1) float64 3kB 1.0 0.7891 0.2455 ... 0.2455 0.7891 1.0\n",
       "Attributes:\n",
       "    tuid:                             20241106-153126-258-4e5651\n",
       "    name:                             my experiment\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         True\n",
       "    1d_2_settables_uniformly_spaced:  True\n",
       "    xlen:                             21\n",
       "    ylen:                             20"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_a = ManualParameter(\n",
    "    name=\"time_a\", label=\"Time A\", unit=\"s\", vals=validators.Numbers(), initial_value=1\n",
    ")\n",
    "time_b = ManualParameter(\n",
    "    name=\"time_b\", label=\"Time B\", unit=\"s\", vals=validators.Numbers(), initial_value=1\n",
    ")\n",
    "\n",
    "signal = Parameter(\n",
    "    name=\"sig_a\",\n",
    "    label=\"Signal A\",\n",
    "    unit=\"V\",\n",
    "    get_cmd=lambda: np.exp(time_a()) + 0.5 * np.exp(time_b()),\n",
    ")\n",
    "\n",
    "\n",
    "class DualWave2D:\n",
    "    \"\"\"A \"dual\" gettable example that depends on two settables.\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        self.unit = [\"V\", \"V\"]\n",
    "        self.label = [\"Sine Amplitude\", \"Cosine Amplitude\"]\n",
    "        self.name = [\"sin\", \"cos\"]\n",
    "\n",
    "    def get(self):\n",
    "        \"\"\"Returns the value of the gettable.\"\"\"\n",
    "        return np.array([np.sin(time_a() * np.pi), np.cos(time_b() * np.pi)])\n",
    "\n",
    "\n",
    "dual_wave = DualWave2D()\n",
    "meas_ctrl.settables([time_a, time_b])\n",
    "meas_ctrl.gettables([signal, dual_wave])\n",
    "meas_ctrl.setpoints_grid([np.linspace(0, 3, 21), np.linspace(4, 0, 20)])\n",
    "dset = meas_ctrl.run(\"my experiment\")\n",
    "dset_grid = dh.to_gridded_dataset(dset)\n",
    "\n",
    "for yi, cmap in zip((\"y0\", \"y1\", \"y2\"), (\"viridis\", \"inferno\", \"plasma\")):\n",
    "    dset_grid[yi].plot(cmap=cmap)\n",
    "    plt.show()\n",
    "dset_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "350a94b2",
   "metadata": {},
   "source": [
    "## Batched control mode\n",
    "\n",
    "### Float-valued array settable(s) and gettable(s)\n",
    "\n",
    "- Each settable accepts a 1D array of float values corresponding to all setpoints for a single *X dimension*.\n",
    "- Gettables return a 1D array of float values with each element corresponding to a datapoint *in a single Y dimension*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0f387ca7",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "2D example"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t --- (None) --- \n",
      "Batched settable(s):\n",
      "\t time \n",
      "Batch size limit: 20\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1456f472a9024babbb69a195875206c0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "NOTE: The gettable returns an array:\n",
      "\n",
      "[ 1.          0.93289715  0.7405942   0.4488993   0.09695955 -0.26799272\n",
      " -0.59697884 -0.84584701 -0.98119769 -0.98486606 -0.8563598  -0.61292518\n",
      " -0.28723252  0.07700839  0.43091433  0.72698911  0.92549782  0.99979946\n",
      "  0.93992232  0.75390225]\n"
     ]
    },
    {
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-has-index {\n",
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       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
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       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
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       "\n",
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       "  padding-right: 5px;\n",
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       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
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       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
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       "\n",
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       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       "\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",
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       "\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",
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       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
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       "\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",
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       "\n",
       "dl.xr-attrs {\n",
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       "  margin: 0;\n",
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       "  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: 320B\n",
       "Dimensions:  (x0: 20)\n",
       "Coordinates:\n",
       "  * x0       (x0) float64 160B 0.0 0.3684 0.7368 1.105 ... 5.895 6.263 6.632 7.0\n",
       "Data variables:\n",
       "    y0       (x0) float64 160B 1.0 0.9329 0.7406 0.4489 ... 0.9998 0.9399 0.7539\n",
       "Attributes:\n",
       "    tuid:                             20241106-153126-802-fecd88\n",
       "    name:                             my experiment\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  False</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-83336774-9ce2-4774-8dee-4e95aa5a9152' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-83336774-9ce2-4774-8dee-4e95aa5a9152' 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'>x0</span>: 20</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-dc30ae51-a82b-4f6d-8e25-a6f4d01caa1a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dc30ae51-a82b-4f6d-8e25-a6f4d01caa1a' 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'>x0</span></div><div class='xr-var-dims'>(x0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.3684 0.7368 ... 6.632 7.0</div><input id='attrs-1fa3833e-d0d9-4279-be32-ffa2e872b18c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1fa3833e-d0d9-4279-be32-ffa2e872b18c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3976a72-862b-4458-9488-145a7a87fe68' class='xr-var-data-in' type='checkbox'><label for='data-b3976a72-862b-4458-9488-145a7a87fe68' 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>name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>batched :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><pre>array([0.      , 0.368421, 0.736842, 1.105263, 1.473684, 1.842105, 2.210526,\n",
       "       2.578947, 2.947368, 3.315789, 3.684211, 4.052632, 4.421053, 4.789474,\n",
       "       5.157895, 5.526316, 5.894737, 6.263158, 6.631579, 7.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fb20d638-26a4-48b1-b1e6-376d0d5b00b7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fb20d638-26a4-48b1-b1e6-376d0d5b00b7' class='xr-section-summary' >Data variables: <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>y0</span></div><div class='xr-var-dims'>(x0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 0.9329 0.7406 ... 0.9399 0.7539</div><input id='attrs-d84b2ff4-c822-444f-b597-ace35b4914b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d84b2ff4-c822-444f-b597-ace35b4914b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b45991e-79e1-49a5-9b4d-cf08372afe25' class='xr-var-data-in' type='checkbox'><label for='data-8b45991e-79e1-49a5-9b4d-cf08372afe25' 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>name :</span></dt><dd>sig_a</dd><dt><span>long_name :</span></dt><dd>Signal</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>batched :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><pre>array([ 1.        ,  0.93289715,  0.7405942 ,  0.4488993 ,  0.09695955,\n",
       "       -0.26799272, -0.59697884, -0.84584701, -0.98119769, -0.98486606,\n",
       "       -0.8563598 , -0.61292518, -0.28723252,  0.07700839,  0.43091433,\n",
       "        0.72698911,  0.92549782,  0.99979946,  0.93992232,  0.75390225])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e6646e68-b30d-433d-b882-3fcc6f368c94' class='xr-section-summary-in' type='checkbox'  ><label for='section-e6646e68-b30d-433d-b882-3fcc6f368c94' 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>x0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8e165141-bd2e-4a18-8114-4f99e7565a7b' class='xr-index-data-in' type='checkbox'/><label for='index-8e165141-bd2e-4a18-8114-4f99e7565a7b' 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, 0.3684210526315789, 0.7368421052631579,\n",
       "       1.1052631578947367, 1.4736842105263157, 1.8421052631578947,\n",
       "       2.2105263157894735, 2.5789473684210527, 2.9473684210526314,\n",
       "         3.31578947368421, 3.6842105263157894,  4.052631578947368,\n",
       "        4.421052631578947,  4.789473684210526,  5.157894736842105,\n",
       "        5.526315789473684,  5.894736842105263,  6.263157894736842,\n",
       "         6.63157894736842,                7.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x0&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-34f672d7-b39f-4297-b449-2b6c4dbe65ff' class='xr-section-summary-in' type='checkbox'  checked><label for='section-34f672d7-b39f-4297-b449-2b6c4dbe65ff' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20241106-153126-802-fecd88</dd><dt><span>name :</span></dt><dd>my experiment</dd><dt><span>grid_2d :</span></dt><dd>False</dd><dt><span>grid_2d_uniformly_spaced :</span></dt><dd>False</dd><dt><span>1d_2_settables_uniformly_spaced :</span></dt><dd>False</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 320B\n",
       "Dimensions:  (x0: 20)\n",
       "Coordinates:\n",
       "  * x0       (x0) float64 160B 0.0 0.3684 0.7368 1.105 ... 5.895 6.263 6.632 7.0\n",
       "Data variables:\n",
       "    y0       (x0) float64 160B 1.0 0.9329 0.7406 0.4489 ... 0.9998 0.9399 0.7539\n",
       "Attributes:\n",
       "    tuid:                             20241106-153126-802-fecd88\n",
       "    name:                             my experiment\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  False"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = ManualParameter(\n",
    "    name=\"time\",\n",
    "    label=\"Time\",\n",
    "    unit=\"s\",\n",
    "    vals=validators.Arrays(),\n",
    "    initial_value=np.array([1, 2, 3]),\n",
    ")\n",
    "signal = Parameter(\n",
    "    name=\"sig_a\", label=\"Signal\", unit=\"V\", get_cmd=lambda: np.cos(time())\n",
    ")\n",
    "\n",
    "time.batched = True\n",
    "signal.batched = True\n",
    "\n",
    "meas_ctrl.settables(time)\n",
    "meas_ctrl.gettables(signal)\n",
    "meas_ctrl.setpoints(np.linspace(0, 7, 20))\n",
    "dset = meas_ctrl.run(\"my experiment\")\n",
    "dset_grid = dh.to_gridded_dataset(dset)\n",
    "\n",
    "dset_grid.y0.plot(marker=\"o\")\n",
    "print(f\"\\nNOTE: The gettable returns an array:\\n\\n{signal.get()}\")\n",
    "dset_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1cb7ae4",
   "metadata": {},
   "source": [
    "### Mixing iterative and batched settables\n",
    "\n",
    "In this case:\n",
    "\n",
    "- One or more settables accept a 1D array of float values corresponding to all setpoints for the corresponding *X dimension*.\n",
    "- One or more settables accept a float value corresponding to its *X dimension*.\n",
    "\n",
    "Measurement control will set the value of each of these iterative settables before each batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8a94ebbc",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "2D (1D batch with iterative outer loop) example"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t time_a \n",
      "Batched settable(s):\n",
      "\t time_b \n",
      "Batch size limit: 12\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4c928454fafd454083e70811fa16d4dc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
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       "\n",
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       "\n",
       ".xr-attrs dt {\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 1kB\n",
       "Dimensions:  (x0: 10, x1: 12)\n",
       "Coordinates:\n",
       "  * x0       (x0) float64 80B 0.0 0.5556 1.111 1.667 ... 3.333 3.889 4.444 5.0\n",
       "  * x1       (x1) float64 96B 0.0 0.3636 0.7273 1.091 ... 2.909 3.273 3.636 4.0\n",
       "Data variables:\n",
       "    y0       (x0, x1) float64 960B 1.5 1.719 2.035 2.488 ... 161.6 167.4 175.7\n",
       "Attributes:\n",
       "    tuid:                             20241106-153126-984-84a65a\n",
       "    name:                             my experiment\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         True\n",
       "    1d_2_settables_uniformly_spaced:  False\n",
       "    xlen:                             10\n",
       "    ylen:                             12</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-bb1ef46a-421a-4206-b725-70b46c3fba7f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bb1ef46a-421a-4206-b725-70b46c3fba7f' 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'>x0</span>: 10</li><li><span class='xr-has-index'>x1</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8160247b-3475-4aa9-97c0-703bc6f17f89' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8160247b-3475-4aa9-97c0-703bc6f17f89' 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 class='xr-has-index'>x0</span></div><div class='xr-var-dims'>(x0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.5556 1.111 ... 4.444 5.0</div><input id='attrs-76384db4-5db8-4a64-a976-4d2e1cc64da7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76384db4-5db8-4a64-a976-4d2e1cc64da7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34a83890-26f0-49a3-b5dd-35750484d605' class='xr-var-data-in' type='checkbox'><label for='data-34a83890-26f0-49a3-b5dd-35750484d605' 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>name :</span></dt><dd>time_a</dd><dt><span>long_name :</span></dt><dd>Time A</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([0.      , 0.555556, 1.111111, 1.666667, 2.222222, 2.777778, 3.333333,\n",
       "       3.888889, 4.444444, 5.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.3636 0.7273 ... 3.636 4.0</div><input id='attrs-1633c39e-78cf-4d1a-a584-1d0da6e63c03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1633c39e-78cf-4d1a-a584-1d0da6e63c03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3994b27-2e0f-4614-bb11-cabb4cabaf84' class='xr-var-data-in' type='checkbox'><label for='data-e3994b27-2e0f-4614-bb11-cabb4cabaf84' 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>name :</span></dt><dd>time_b</dd><dt><span>long_name :</span></dt><dd>Time B</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>batched :</span></dt><dd>True</dd><dt><span>batch_size :</span></dt><dd>12</dd></dl></div><div class='xr-var-data'><pre>array([0.      , 0.363636, 0.727273, 1.090909, 1.454545, 1.818182, 2.181818,\n",
       "       2.545455, 2.909091, 3.272727, 3.636364, 4.      ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9075029a-7b9d-46a7-8830-73d2e500fcb3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9075029a-7b9d-46a7-8830-73d2e500fcb3' class='xr-section-summary' >Data variables: <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>y0</span></div><div class='xr-var-dims'>(x0, x1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5 1.719 2.035 ... 167.4 175.7</div><input id='attrs-957f3812-7187-46fe-bd64-5ff912759086' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-957f3812-7187-46fe-bd64-5ff912759086' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49e6f1dc-541f-44e9-8107-08bf3975c9a8' class='xr-var-data-in' type='checkbox'><label for='data-49e6f1dc-541f-44e9-8107-08bf3975c9a8' 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>name :</span></dt><dd>sig_a</dd><dt><span>long_name :</span></dt><dd>Signal A</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>batched :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><pre>array([[  1.5       ,   1.7192755 ,   2.0347145 ,   2.48848959,\n",
       "          3.14126821,   4.08032354,   5.43120254,   7.37451089,\n",
       "         10.17005908,  14.19159774,  19.97678625,  28.29907502],\n",
       "       [  2.242909  ,   2.4621845 ,   2.7776235 ,   3.23139859,\n",
       "          3.88417721,   4.82323254,   6.17411154,   8.11741989,\n",
       "         10.91296807,  14.93450674,  20.71969525,  29.04198402],\n",
       "       [  3.53773178,   3.75700728,   4.07244628,   4.52622137,\n",
       "          5.17899999,   6.11805532,   7.46893432,   9.41224267,\n",
       "         12.20779085,  16.22932952,  22.01451803,  30.33680679],\n",
       "       [  5.79449005,   6.01376556,   6.32920455,   6.78297964,\n",
       "          7.43575826,   8.37481359,   9.72569259,  11.66900094,\n",
       "         14.46454913,  18.48608779,  24.2712763 ,  32.59356507],\n",
       "       [  9.72781435,   9.94708986,  10.26252886,  10.71630395,\n",
       "         11.36908256,  12.30813789,  13.65901689,  15.60232524,\n",
       "         18.39787343,  22.41941209,  28.2046006 ,  36.52688937],\n",
       "       [ 16.58324067,  16.80251618,  17.11795518,  17.57173027,\n",
       "         18.22450888,  19.16356421,  20.51444321,  22.45775156,\n",
       "         25.25329975,  29.27483841,  35.06002692,  43.38231569],\n",
       "       [ 28.53162489,  28.7509004 ,  29.0663394 ,  29.52011449,\n",
       "         30.1728931 ,  31.11194844,  32.46282744,  34.40613578,\n",
       "         37.20168397,  41.22322264,  47.00841114,  55.33069991],\n",
       "       [ 49.35657127,  49.57584678,  49.89128578,  50.34506087,\n",
       "         50.99783948,  51.93689482,  53.28777382,  55.23108216,\n",
       "         58.02663035,  62.04816902,  67.83335752,  76.15564629],\n",
       "       [ 85.65255772,  85.87183322,  86.18727222,  86.64104731,\n",
       "         87.29382593,  88.23288126,  89.58376026,  91.52706861,\n",
       "         94.32261679,  98.34415546, 104.12934397, 112.45163273],\n",
       "       [148.9131591 , 149.13243461, 149.44787361, 149.9016487 ,\n",
       "        150.55442731, 151.49348264, 152.84436164, 154.78766999,\n",
       "        157.58321818, 161.60475684, 167.38994535, 175.71223412]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-407cbb15-e567-41fb-8c7f-84605b5fb378' class='xr-section-summary-in' type='checkbox'  ><label for='section-407cbb15-e567-41fb-8c7f-84605b5fb378' class='xr-section-summary' >Indexes: <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-index-name'><div>x0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-716e6d45-e572-437c-bfa2-4c9480d16ff5' class='xr-index-data-in' type='checkbox'/><label for='index-716e6d45-e572-437c-bfa2-4c9480d16ff5' 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, 0.5555555555555556, 1.1111111111111112,\n",
       "       1.6666666666666667, 2.2222222222222223, 2.7777777777777777,\n",
       "       3.3333333333333335, 3.8888888888888893,  4.444444444444445,\n",
       "                      5.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c939a00f-494f-41fa-8aff-fdeae4d4564b' class='xr-index-data-in' type='checkbox'/><label for='index-c939a00f-494f-41fa-8aff-fdeae4d4564b' 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, 0.3636363636363633, 0.7272727272727271,\n",
       "       1.0909090909090908, 1.4545454545454546, 1.8181818181818183,\n",
       "       2.1818181818181817, 2.5454545454545454,  2.909090909090909,\n",
       "       3.2727272727272725, 3.6363636363636362,                4.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x1&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-77bff8bb-560e-47d7-803d-911b62a39268' class='xr-section-summary-in' type='checkbox'  checked><label for='section-77bff8bb-560e-47d7-803d-911b62a39268' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20241106-153126-984-84a65a</dd><dt><span>name :</span></dt><dd>my experiment</dd><dt><span>grid_2d :</span></dt><dd>False</dd><dt><span>grid_2d_uniformly_spaced :</span></dt><dd>True</dd><dt><span>1d_2_settables_uniformly_spaced :</span></dt><dd>False</dd><dt><span>xlen :</span></dt><dd>10</dd><dt><span>ylen :</span></dt><dd>12</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 1kB\n",
       "Dimensions:  (x0: 10, x1: 12)\n",
       "Coordinates:\n",
       "  * x0       (x0) float64 80B 0.0 0.5556 1.111 1.667 ... 3.333 3.889 4.444 5.0\n",
       "  * x1       (x1) float64 96B 0.0 0.3636 0.7273 1.091 ... 2.909 3.273 3.636 4.0\n",
       "Data variables:\n",
       "    y0       (x0, x1) float64 960B 1.5 1.719 2.035 2.488 ... 161.6 167.4 175.7\n",
       "Attributes:\n",
       "    tuid:                             20241106-153126-984-84a65a\n",
       "    name:                             my experiment\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         True\n",
       "    1d_2_settables_uniformly_spaced:  False\n",
       "    xlen:                             10\n",
       "    ylen:                             12"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_a = ManualParameter(\n",
    "    name=\"time_a\", label=\"Time A\", unit=\"s\", vals=validators.Numbers(), initial_value=1\n",
    ")\n",
    "time_b = ManualParameter(\n",
    "    name=\"time_b\",\n",
    "    label=\"Time B\",\n",
    "    unit=\"s\",\n",
    "    vals=validators.Arrays(),\n",
    "    initial_value=np.array([1, 2, 3]),\n",
    ")\n",
    "signal = Parameter(\n",
    "    name=\"sig_a\",\n",
    "    label=\"Signal A\",\n",
    "    unit=\"V\",\n",
    "    get_cmd=lambda: np.exp(time_a()) + 0.5 * np.exp(time_b()),\n",
    ")\n",
    "\n",
    "time_b.batched = True\n",
    "time_b.batch_size = 12\n",
    "signal.batched = True\n",
    "\n",
    "meas_ctrl.settables([time_a, time_b])\n",
    "meas_ctrl.gettables(signal)\n",
    "# `setpoints_grid` will take into account the `.batched` attribute\n",
    "meas_ctrl.setpoints_grid([np.linspace(0, 5, 10), np.linspace(4, 0, time_b.batch_size)])\n",
    "dset = meas_ctrl.run(\"my experiment\")\n",
    "dset_grid = dh.to_gridded_dataset(dset)\n",
    "\n",
    "dset_grid.y0.plot(cmap=\"viridis\")\n",
    "dset_grid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8a85881",
   "metadata": {},
   "source": [
    "### Float-valued array settable(s) with multi-return float-valued array gettable(s)\n",
    "\n",
    "- Each settable accepts a 1D array of float values corresponding to all setpoints\n",
    "  for a single *X dimension*.\n",
    "- Gettables return a 2D array of float values with each row representing a\n",
    "  *different Y dimension*, i.e. each column is a datapoint corresponding\n",
    "  to each setpoint."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e1f7a1c4",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "1D example"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t --- (None) --- \n",
      "Batched settable(s):\n",
      "\t time \n",
      "Batch size limit: 100\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "38125949b72d4b39a0d8a209b2578f8b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = ManualParameter(\n",
    "    name=\"time\",\n",
    "    label=\"Time\",\n",
    "    unit=\"s\",\n",
    "    vals=validators.Arrays(),\n",
    "    initial_value=np.array([1, 2, 3]),\n",
    ")\n",
    "\n",
    "\n",
    "class DualWaveBatched:\n",
    "    \"\"\"A \"dual\" batched gettable example.\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        self.unit = [\"V\", \"V\"]\n",
    "        self.label = [\"Amplitude W1\", \"Amplitude W2\"]\n",
    "        self.name = [\"sine\", \"cosine\"]\n",
    "        self.batched = True\n",
    "        self.batch_size = 100\n",
    "\n",
    "    def get(self):\n",
    "        \"\"\"Returns the value of the gettable.\"\"\"\n",
    "        return np.array([np.sin(time() * np.pi), np.cos(time() * np.pi)])\n",
    "\n",
    "\n",
    "time.batched = True\n",
    "dual_wave = DualWaveBatched()\n",
    "\n",
    "meas_ctrl.settables(time)\n",
    "meas_ctrl.gettables(dual_wave)\n",
    "meas_ctrl.setpoints(np.linspace(0, 7, 100))\n",
    "dset = meas_ctrl.run(\"my experiment\")\n",
    "dset_grid = dh.to_gridded_dataset(dset)\n",
    "\n",
    "_, ax = plt.subplots()\n",
    "dset_grid.y0.plot(marker=\"o\", label=\"y0\", ax=ax)\n",
    "dset_grid.y1.plot(marker=\"s\", label=\"y1\", ax=ax)\n",
    "_ = ax.legend()"
   ]
  }
 ],
 "metadata": {
  "file_format": "mystnb",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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