{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with Pandas and XArray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrates how Pandas and XArray can be used to work with the [QCoDeS DataSet](DataSet-class-walkthrough.ipynb). It is not meant as a general introduction to Pandas and XArray. We refer to the official documentation for [Pandas](https://pandas.pydata.org/) and [XArray](http://xarray.pydata.org/en/stable/) for this. This notebook requires that both Pandas and XArray are installed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we borrow an example from the measurement notebook to have some data to work with. We split the measurement in two so we can try merging it with Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logging hadn't been started.\n",
      "Activating auto-logging. Current session state plus future input saved.\n",
      "Filename       : C:\\Users\\Jens\\.qcodes\\logs\\command_history.log\n",
      "Mode           : append\n",
      "Output logging : True\n",
      "Raw input log  : False\n",
      "Timestamping   : True\n",
      "State          : active\n",
      "Qcodes Logfile : C:\\Users\\Jens\\.qcodes\\logs\\250323-296-qcodes.log\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from pathlib import Path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import qcodes as qc\n",
    "from qcodes.dataset import (\n",
    "    Measurement,\n",
    "    initialise_or_create_database_at,\n",
    "    load_or_create_experiment,\n",
    ")\n",
    "from qcodes.instrument_drivers.mock_instruments import (\n",
    "    DummyInstrument,\n",
    "    DummyInstrumentWithMeasurement,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preparatory mocking of physical setup\n",
    "dac = DummyInstrument(\"dac\", gates=[\"ch1\", \"ch2\"])\n",
    "dmm = DummyInstrumentWithMeasurement(\"dmm\", setter_instr=dac)\n",
    "station = qc.Station(dmm, dac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "initialise_or_create_database_at(\n",
    "    Path.cwd().parent / \"example_output\" / \"working_with_pandas.db\"\n",
    ")\n",
    "exp = load_or_create_experiment(\n",
    "    experiment_name=\"working_with_pandas\", sample_name=\"no sample\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qcodes.dataset.measurements.Measurement at 0x2112608b620>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas = Measurement(exp)\n",
    "meas.register_parameter(dac.ch1)  # register the first independent parameter\n",
    "meas.register_parameter(dac.ch2)  # register the second independent parameter\n",
    "meas.register_parameter(\n",
    "    dmm.v2, setpoints=(dac.ch1, dac.ch2)\n",
    ")  # register the dependent one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then perform a very basic experiment. To be able to demonstrate merging of datasets in Pandas we will perform the measurement in two parts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 1. \n"
     ]
    }
   ],
   "source": [
    "# run a 2D sweep\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "    for v1 in np.linspace(-1, 0, 200, endpoint=False):\n",
    "        for v2 in np.linspace(-1, 1, 201):\n",
    "            dac.ch1(v1)\n",
    "            dac.ch2(v2)\n",
    "            val = dmm.v2.get()\n",
    "            datasaver.add_result((dac.ch1, v1), (dac.ch2, v2), (dmm.v2, val))\n",
    "\n",
    "dataset1 = datasaver.dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 2. \n"
     ]
    }
   ],
   "source": [
    "# run a 2D sweep\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "    for v1 in np.linspace(0, 1, 201):\n",
    "        for v2 in np.linspace(-1, 1, 201):\n",
    "            dac.ch1(v1)\n",
    "            dac.ch2(v2)\n",
    "            val = dmm.v2.get()\n",
    "            datasaver.add_result((dac.ch1, v1), (dac.ch2, v2), (dmm.v2, val))\n",
    "\n",
    "dataset2 = datasaver.dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two methods exists for extracting data to pandas dataframes. `to_pandas_dataframe` exports all the data from the dataset into a single dataframe. \n",
    "`to_pandas_dataframe_dict` returns the data as a dict from measured (dependent) parameters to DataFrames. \n",
    "\n",
    "Please note that the `to_pandas_dataframe` is only intended to be used when all dependent parameters have the same setpoint. If this is not the case for the DataSet then `to_pandas_dataframe_dict` should be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = dataset1.to_pandas_dataframe()\n",
    "df2 = dataset2.to_pandas_dataframe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets first inspect the Pandas DataFrame. Note how both dependent variables are used for the index. Pandas refers to this as a [MultiIndex](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html). For visual clarity, we just look at the first N points of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>dmm_v2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dac_ch1</th>\n",
       "      <th>dac_ch2</th>\n",
       "      <th></th>\n",
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       "      <th rowspan=\"10\" valign=\"top\">-1.0</th>\n",
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      ],
      "text/plain": [
       "                   dmm_v2\n",
       "dac_ch1 dac_ch2          \n",
       "-1.0    -1.00    0.000127\n",
       "        -0.99    0.000261\n",
       "        -0.98    0.000538\n",
       "        -0.97    0.000014\n",
       "        -0.96    0.000038\n",
       "        -0.95    0.000734\n",
       "        -0.94    0.000411\n",
       "        -0.93    0.000328\n",
       "        -0.92   -0.000081\n",
       "        -0.91   -0.000003"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[:N]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also reset the index to return a simpler view where all data points are simply indexed by a running counter. As we shall see below this can be needed in some situations. Note that calling `reset_index` leaves the original dataframe untouched."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "   dac_ch1  dac_ch2    dmm_v2\n",
       "0     -1.0    -1.00  0.000127\n",
       "1     -1.0    -0.99  0.000261\n",
       "2     -1.0    -0.98  0.000538\n",
       "3     -1.0    -0.97  0.000014\n",
       "4     -1.0    -0.96  0.000038\n",
       "5     -1.0    -0.95  0.000734\n",
       "6     -1.0    -0.94  0.000411\n",
       "7     -1.0    -0.93  0.000328\n",
       "8     -1.0    -0.92 -0.000081\n",
       "9     -1.0    -0.91 -0.000003"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.reset_index()[0:N]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas has built-in support for various forms of plotting. This does not, however, support MultiIndex at the moment so we use `reset_index` to make the data available for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='dac_ch1', ylabel='dac_ch2'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.reset_index().plot.scatter(\"dac_ch1\", \"dac_ch2\", c=\"dmm_v2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, for the other dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='dac_ch1', ylabel='dac_ch2'>"
      ]
     },
     "execution_count": 12,
     "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": [
    "df2.reset_index().plot.scatter(\"dac_ch1\", \"dac_ch2\", c=\"dmm_v2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merging two dataframes with the same labels is fairly simple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.concat([df1, df2], sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='dac_ch1', ylabel='dac_ch2'>"
      ]
     },
     "execution_count": 14,
     "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": [
    "df.reset_index().plot.scatter(\"dac_ch1\", \"dac_ch2\", c=\"dmm_v2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to select a subset of data from the datframe based on the x and y values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
       "columns": [
        {
         "name": "('dac_ch1', 'dac_ch2')",
         "rawType": "object",
         "type": "unknown"
        },
        {
         "name": "dmm_v2",
         "rawType": "float64",
         "type": "float"
        }
       ],
       "conversionMethod": "pd.DataFrame",
       "ref": "0ca87582-597f-48de-85a7-e636bab463ad",
       "rows": [
        [
         "(np.float64(-1.0), np.float64(-1.0))",
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         "(np.float64(-1.0), np.float64(-0.99))",
         "0.00026078329555167"
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         "(np.float64(-1.0), np.float64(-0.98))",
         "0.000537617859419701"
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        [
         "(np.float64(-1.0), np.float64(-0.97))",
         "1.35380429267218e-05"
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         "8.52563274934714e-06"
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         "-0.000591020188501868"
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         "(np.float64(-0.975), np.float64(-0.98))",
         "-0.000746353483560324"
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         "(np.float64(-0.975), np.float64(-0.97))",
         "-6.6800858764138e-05"
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         "(np.float64(-0.97), np.float64(-1.0))",
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         "0.000238771355789873"
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         "-7.13390044775915e-05"
        ],
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         "(np.float64(-0.955), np.float64(-0.98))",
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         "(np.float64(-0.955), np.float64(-0.97))",
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         "0.000934390533874005"
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        [
         "(np.float64(-0.95), np.float64(-0.99))",
         "7.23139870355247e-05"
        ],
        [
         "(np.float64(-0.95), np.float64(-0.98))",
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         "(np.float64(-0.95), np.float64(-0.97))",
         "0.000241475575909176"
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       ],
       "shape": {
        "columns": 1,
        "rows": 44
       }
      },
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>dmm_v2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dac_ch1</th>\n",
       "      <th>dac_ch2</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">-1.000</th>\n",
       "      <th>-1.00</th>\n",
       "      <td>0.000127</td>\n",
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       "    <tr>\n",
       "      <th>-0.99</th>\n",
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       "      <td>0.000241</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   dmm_v2\n",
       "dac_ch1 dac_ch2          \n",
       "-1.000  -1.00    0.000127\n",
       "        -0.99    0.000261\n",
       "        -0.98    0.000538\n",
       "        -0.97    0.000014\n",
       "-0.995  -1.00    0.000009\n",
       "        -0.99   -0.000466\n",
       "        -0.98    0.000257\n",
       "        -0.97    0.000357\n",
       "-0.990  -1.00   -0.000072\n",
       "        -0.99   -0.000348\n",
       "        -0.98   -0.000591\n",
       "        -0.97   -0.000341\n",
       "-0.985  -1.00    0.000611\n",
       "        -0.99   -0.000402\n",
       "        -0.98    0.000944\n",
       "        -0.97   -0.000308\n",
       "-0.980  -1.00   -0.000344\n",
       "        -0.99   -0.000513\n",
       "        -0.98   -0.000754\n",
       "        -0.97   -0.000232\n",
       "-0.975  -1.00   -0.000809\n",
       "        -0.99   -0.000467\n",
       "        -0.98   -0.000746\n",
       "        -0.97   -0.000067\n",
       "-0.970  -1.00   -0.000408\n",
       "        -0.99   -0.000546\n",
       "        -0.98   -0.000193\n",
       "        -0.97    0.000239\n",
       "-0.965  -1.00   -0.000365\n",
       "        -0.99    0.000315\n",
       "        -0.98    0.000666\n",
       "        -0.97   -0.000025\n",
       "-0.960  -1.00   -0.000444\n",
       "        -0.99   -0.000147\n",
       "        -0.98   -0.000375\n",
       "        -0.97   -0.000289\n",
       "-0.955  -1.00   -0.000071\n",
       "        -0.99   -0.000584\n",
       "        -0.98   -0.000510\n",
       "        -0.97   -0.000425\n",
       "-0.950  -1.00    0.000934\n",
       "        -0.99    0.000072\n",
       "        -0.98    0.001135\n",
       "        -0.97    0.000241"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(slice(-1, -0.95), slice(-1, -0.97)), :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with XArray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In many cases when working with data on rectangular grids it may be more convenient to export the data to a [XArray](http://xarray.pydata.org) Dataset or DataArray. This is especially true when working in multi-dimentional parameter space. \n",
    "\n",
    "Let's setup and rerun the above measurment with the added dependent parameter `dmm.v1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qcodes.dataset.measurements.Measurement at 0x2112608b620>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas.register_parameter(\n",
    "    dmm.v1, setpoints=(dac.ch1, dac.ch2)\n",
    ")  # register the 2nd dependent parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 3. \n"
     ]
    }
   ],
   "source": [
    "# run a 2D sweep\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "    for v1 in np.linspace(-1, 1, 200):\n",
    "        for v2 in np.linspace(-1, 1, 201):\n",
    "            dac.ch1(v1)\n",
    "            dac.ch2(v2)\n",
    "            val1 = dmm.v1.get()\n",
    "            val2 = dmm.v2.get()\n",
    "            datasaver.add_result(\n",
    "                (dac.ch1, v1), (dac.ch2, v2), (dmm.v1, val1), (dmm.v2, val2)\n",
    "            )\n",
    "\n",
    "dataset3 = datasaver.dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The QCoDeS DataSet can be directly converted to a XArray [Dataset](http://xarray.pydata.org/en/stable/data-structures.html?#dataset) from the `to_xarray_dataset` method. This method returns the data from measured (dependent) parameters to an XArray Dataset. It's also possible to return a dictionary of XArray DataArray's if you were only interested in a single parameter using the `to_xarray_dataarray` method. For convenience we will access the DataArray's from XArray's Dataset directly.\n",
    "\n",
    "Please note that the `to_xarray_dataset` is only intended to be used when all dependent parameters have the same setpoint. If this is not the case for the DataSet then `to_xarray_dataarray` should be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "xaDataSet = dataset3.to_xarray_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "  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: 646kB\n",
       "Dimensions:  (dac_ch1: 200, dac_ch2: 201)\n",
       "Coordinates:\n",
       "  * dac_ch1  (dac_ch1) float64 2kB -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
       "  * dac_ch2  (dac_ch2) float64 2kB -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0\n",
       "Data variables:\n",
       "    dmm_v1   (dac_ch1, dac_ch2) float64 322kB 6.184 5.902 6.03 ... 4.03 4.109\n",
       "    dmm_v2   (dac_ch1, dac_ch2) float64 322kB -0.0008481 -0.000525 ... 0.0005806\n",
       "Attributes: (12/14)\n",
       "    ds_name:                  results\n",
       "    sample_name:              no sample\n",
       "    exp_name:                 working_with_pandas\n",
       "    snapshot:                 {&quot;station&quot;: {&quot;instruments&quot;: {&quot;dmm&quot;: {&quot;functions...\n",
       "    guid:                     91039442-0000-0000-0000-0195c46902ec\n",
       "    run_timestamp:            2025-03-23 20:09:31\n",
       "    ...                       ...\n",
       "    captured_counter:         3\n",
       "    run_id:                   3\n",
       "    run_description:          {&quot;version&quot;: 3, &quot;interdependencies&quot;: {&quot;paramspec...\n",
       "    parent_dataset_links:     []\n",
       "    run_timestamp_raw:        1742756971.260438\n",
       "    completed_timestamp_raw:  1742756982.7415159</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-836c6e38-0776-46d9-9568-1c596fe020d6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-836c6e38-0776-46d9-9568-1c596fe020d6' 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'>dac_ch1</span>: 200</li><li><span class='xr-has-index'>dac_ch2</span>: 201</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d7395900-173f-4e97-8710-d27d00f7449d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d7395900-173f-4e97-8710-d27d00f7449d' 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'>dac_ch1</span></div><div class='xr-var-dims'>(dac_ch1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.9899 -0.9799 ... 0.9899 1.0</div><input id='attrs-489dde72-32c3-49cc-b148-0859339a0037' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-489dde72-32c3-49cc-b148-0859339a0037' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-648a5c2d-c62a-4e27-a17b-d6fa164bf962' class='xr-var-data-in' type='checkbox'><label for='data-648a5c2d-c62a-4e27-a17b-d6fa164bf962' 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>dac_ch1</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate ch1</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate ch1</dd></dl></div><div class='xr-var-data'><pre>array([-1.      , -0.98995 , -0.979899, -0.969849, -0.959799, -0.949749,\n",
       "       -0.939698, -0.929648, -0.919598, -0.909548, -0.899497, -0.889447,\n",
       "       -0.879397, -0.869347, -0.859296, -0.849246, -0.839196, -0.829146,\n",
       "       -0.819095, -0.809045, -0.798995, -0.788945, -0.778894, -0.768844,\n",
       "       -0.758794, -0.748744, -0.738693, -0.728643, -0.718593, -0.708543,\n",
       "       -0.698492, -0.688442, -0.678392, -0.668342, -0.658291, -0.648241,\n",
       "       -0.638191, -0.628141, -0.61809 , -0.60804 , -0.59799 , -0.58794 ,\n",
       "       -0.577889, -0.567839, -0.557789, -0.547739, -0.537688, -0.527638,\n",
       "       -0.517588, -0.507538, -0.497487, -0.487437, -0.477387, -0.467337,\n",
       "       -0.457286, -0.447236, -0.437186, -0.427136, -0.417085, -0.407035,\n",
       "       -0.396985, -0.386935, -0.376884, -0.366834, -0.356784, -0.346734,\n",
       "       -0.336683, -0.326633, -0.316583, -0.306533, -0.296482, -0.286432,\n",
       "       -0.276382, -0.266332, -0.256281, -0.246231, -0.236181, -0.226131,\n",
       "       -0.21608 , -0.20603 , -0.19598 , -0.18593 , -0.175879, -0.165829,\n",
       "       -0.155779, -0.145729, -0.135678, -0.125628, -0.115578, -0.105528,\n",
       "       -0.095477, -0.085427, -0.075377, -0.065327, -0.055276, -0.045226,\n",
       "       -0.035176, -0.025126, -0.015075, -0.005025,  0.005025,  0.015075,\n",
       "        0.025126,  0.035176,  0.045226,  0.055276,  0.065327,  0.075377,\n",
       "        0.085427,  0.095477,  0.105528,  0.115578,  0.125628,  0.135678,\n",
       "        0.145729,  0.155779,  0.165829,  0.175879,  0.18593 ,  0.19598 ,\n",
       "        0.20603 ,  0.21608 ,  0.226131,  0.236181,  0.246231,  0.256281,\n",
       "        0.266332,  0.276382,  0.286432,  0.296482,  0.306533,  0.316583,\n",
       "        0.326633,  0.336683,  0.346734,  0.356784,  0.366834,  0.376884,\n",
       "        0.386935,  0.396985,  0.407035,  0.417085,  0.427136,  0.437186,\n",
       "        0.447236,  0.457286,  0.467337,  0.477387,  0.487437,  0.497487,\n",
       "        0.507538,  0.517588,  0.527638,  0.537688,  0.547739,  0.557789,\n",
       "        0.567839,  0.577889,  0.58794 ,  0.59799 ,  0.60804 ,  0.61809 ,\n",
       "        0.628141,  0.638191,  0.648241,  0.658291,  0.668342,  0.678392,\n",
       "        0.688442,  0.698492,  0.708543,  0.718593,  0.728643,  0.738693,\n",
       "        0.748744,  0.758794,  0.768844,  0.778894,  0.788945,  0.798995,\n",
       "        0.809045,  0.819095,  0.829146,  0.839196,  0.849246,  0.859296,\n",
       "        0.869347,  0.879397,  0.889447,  0.899497,  0.909548,  0.919598,\n",
       "        0.929648,  0.939698,  0.949749,  0.959799,  0.969849,  0.979899,\n",
       "        0.98995 ,  1.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch2</span></div><div class='xr-var-dims'>(dac_ch2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.99 -0.98 ... 0.98 0.99 1.0</div><input id='attrs-778908ab-9547-4be0-9f62-1a52f83d396b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-778908ab-9547-4be0-9f62-1a52f83d396b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9971bb03-b8e1-4307-a76c-ea693760e43a' class='xr-var-data-in' type='checkbox'><label for='data-9971bb03-b8e1-4307-a76c-ea693760e43a' 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>dac_ch2</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate ch2</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate ch2</dd></dl></div><div class='xr-var-data'><pre>array([-1.  , -0.99, -0.98, ...,  0.98,  0.99,  1.  ], shape=(201,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b2c6834b-0267-41d3-91ff-afa47a30e73d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b2c6834b-0267-41d3-91ff-afa47a30e73d' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>dmm_v1</span></div><div class='xr-var-dims'>(dac_ch1, dac_ch2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.184 5.902 6.03 ... 4.03 4.109</div><input id='attrs-e4bd9ac4-2ebe-45ba-8ee0-8a752d4369eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4bd9ac4-2ebe-45ba-8ee0-8a752d4369eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec21b484-2417-4d7c-800f-6be1398ad6f1' class='xr-var-data-in' type='checkbox'><label for='data-ec21b484-2417-4d7c-800f-6be1398ad6f1' 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>dmm_v1</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate v1</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[&#x27;dac_ch1&#x27;, &#x27;dac_ch2&#x27;]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate v1</dd></dl></div><div class='xr-var-data'><pre>array([[6.1839796 , 5.90236482, 6.03001065, ..., 6.07278939, 6.24582387,\n",
       "        6.02825979],\n",
       "       [6.04404109, 6.38493506, 6.1109613 , ..., 6.10644736, 6.15946513,\n",
       "        6.05922469],\n",
       "       [6.02753911, 5.94003373, 6.15799005, ..., 6.02933573, 6.00521507,\n",
       "        6.06324342],\n",
       "       ...,\n",
       "       [3.97630273, 4.0573327 , 4.15465123, ..., 4.06528051, 4.03277551,\n",
       "        4.16160207],\n",
       "       [4.03906   , 4.13270187, 4.18937091, ..., 4.09148287, 4.07252051,\n",
       "        4.09736755],\n",
       "       [4.14172693, 3.95176643, 3.92627181, ..., 4.33771175, 4.03037615,\n",
       "        4.1086798 ]], shape=(200, 201))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dmm_v2</span></div><div class='xr-var-dims'>(dac_ch1, dac_ch2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.0008481 -0.000525 ... 0.0005806</div><input id='attrs-9ecfcd50-f7e9-4540-be3c-20dc6fa93bfe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ecfcd50-f7e9-4540-be3c-20dc6fa93bfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e2cc1ff-ab1f-41c7-903e-21b7351b39fa' class='xr-var-data-in' type='checkbox'><label for='data-1e2cc1ff-ab1f-41c7-903e-21b7351b39fa' 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>dmm_v2</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate v2</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[&#x27;dac_ch1&#x27;, &#x27;dac_ch2&#x27;]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate v2</dd></dl></div><div class='xr-var-data'><pre>array([[-8.48128734e-04, -5.25005785e-04,  2.63902660e-04, ...,\n",
       "         2.74689567e-04, -1.42882583e-04,  3.07502119e-04],\n",
       "       [ 4.95598087e-04,  1.64306262e-03,  1.16054805e-04, ...,\n",
       "         4.27937447e-04,  3.06161231e-04,  2.41866121e-04],\n",
       "       [ 1.79865699e-04,  3.20971602e-04, -4.76030839e-04, ...,\n",
       "         3.28896994e-04,  3.35609229e-04,  5.62318401e-05],\n",
       "       ...,\n",
       "       [ 2.37215216e-04,  1.45792375e-04,  3.49907483e-04, ...,\n",
       "        -7.15124372e-04,  6.63749348e-04,  4.38214641e-04],\n",
       "       [ 5.25910520e-04,  2.68390072e-04,  2.56783696e-04, ...,\n",
       "         6.46707854e-04,  1.23851539e-04, -6.93366132e-04],\n",
       "       [ 3.31674190e-04, -1.35628711e-03, -1.13627824e-03, ...,\n",
       "        -4.42975147e-04, -3.46296218e-04,  5.80552877e-04]],\n",
       "      shape=(200, 201))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3cb40ca6-3eb1-4280-99ef-012a08ee8073' class='xr-section-summary-in' type='checkbox'  ><label for='section-3cb40ca6-3eb1-4280-99ef-012a08ee8073' 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>dac_ch1</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-7e678ef9-df76-404f-b2da-9bb2a0061bc5' class='xr-index-data-in' type='checkbox'/><label for='index-7e678ef9-df76-404f-b2da-9bb2a0061bc5' 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, -0.989949748743719, -0.979899497487437,\n",
       "       -0.969849246231156, -0.959798994974874, -0.949748743718593,\n",
       "       -0.939698492462312,  -0.92964824120603, -0.919597989949749,\n",
       "       -0.909547738693467,\n",
       "       ...\n",
       "        0.909547738693467,  0.919597989949749,   0.92964824120603,\n",
       "        0.939698492462312,  0.949748743718593,  0.959798994974874,\n",
       "        0.969849246231156,  0.979899497487437,  0.989949748743719,\n",
       "                      1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;dac_ch1&#x27;, length=200))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dac_ch2</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-aaeef36b-d3b8-4149-89f0-2df1b95c4aa6' class='xr-index-data-in' type='checkbox'/><label for='index-aaeef36b-d3b8-4149-89f0-2df1b95c4aa6' 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, -0.99, -0.98, -0.97, -0.96, -0.95, -0.94, -0.93, -0.92, -0.91,\n",
       "       ...\n",
       "        0.91,  0.92,  0.93,  0.94,  0.95,  0.96,  0.97,  0.98,  0.99,   1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;dac_ch2&#x27;, length=201))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-eca33471-c6f5-4acf-9789-61b0d66f3984' class='xr-section-summary-in' type='checkbox'  ><label for='section-eca33471-c6f5-4acf-9789-61b0d66f3984' class='xr-section-summary' >Attributes: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>ds_name :</span></dt><dd>results</dd><dt><span>sample_name :</span></dt><dd>no sample</dd><dt><span>exp_name :</span></dt><dd>working_with_pandas</dd><dt><span>snapshot :</span></dt><dd>{&quot;station&quot;: {&quot;instruments&quot;: {&quot;dmm&quot;: {&quot;functions&quot;: {}, &quot;submodules&quot;: {}, &quot;parameters&quot;: {&quot;IDN&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dmm_IDN&quot;, &quot;value&quot;: {&quot;vendor&quot;: &quot;QCoDeS&quot;, &quot;model&quot;: &quot;&lt;class &#x27;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&#x27;&gt;&quot;, &quot;serial&quot;: &quot;NA&quot;, &quot;firmware&quot;: &quot;NA&quot;}, &quot;raw_value&quot;: {&quot;vendor&quot;: &quot;QCoDeS&quot;, &quot;model&quot;: &quot;&lt;class &#x27;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&#x27;&gt;&quot;, &quot;serial&quot;: &quot;NA&quot;, &quot;firmware&quot;: &quot;NA&quot;}, &quot;ts&quot;: &quot;2025-03-23 20:08:38&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;instrument_name&quot;: &quot;dmm&quot;, &quot;label&quot;: &quot;IDN&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Anything&gt;&quot;], &quot;vals&quot;: &quot;&lt;Anything&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;IDN&quot;, &quot;unit&quot;: &quot;&quot;}, &quot;v1&quot;: {&quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DmmExponentialParameter&quot;, &quot;full_name&quot;: &quot;dmm_v1&quot;, &quot;value&quot;: 5.056040689387415, &quot;raw_value&quot;: 5.056040689387415, &quot;ts&quot;: &quot;2025-03-23 20:08:38&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;instrument_name&quot;: &quot;dmm&quot;, &quot;label&quot;: &quot;Gate v1&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;v1&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;v2&quot;: {&quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DmmGaussParameter&quot;, &quot;full_name&quot;: &quot;dmm_v2&quot;, &quot;value&quot;: -4.350288627709427e-05, &quot;raw_value&quot;: -4.350288627709427e-05, &quot;ts&quot;: &quot;2025-03-23 20:08:56&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;instrument_name&quot;: &quot;dmm&quot;, &quot;label&quot;: &quot;Gate v2&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;v2&quot;, &quot;unit&quot;: &quot;V&quot;}}, &quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;name&quot;: &quot;dmm&quot;, &quot;label&quot;: &quot;dmm&quot;}, &quot;dac&quot;: {&quot;functions&quot;: {}, &quot;submodules&quot;: {}, &quot;parameters&quot;: {&quot;IDN&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dac_IDN&quot;, &quot;value&quot;: {&quot;vendor&quot;: &quot;QCoDeS&quot;, &quot;model&quot;: &quot;&lt;class &#x27;qcodes.instrument_drivers.mock_instruments.DummyInstrument&#x27;&gt;&quot;, &quot;serial&quot;: &quot;NA&quot;, &quot;firmware&quot;: &quot;NA&quot;}, &quot;raw_value&quot;: {&quot;vendor&quot;: &quot;QCoDeS&quot;, &quot;model&quot;: &quot;&lt;class &#x27;qcodes.instrument_drivers.mock_instruments.DummyInstrument&#x27;&gt;&quot;, &quot;serial&quot;: &quot;NA&quot;, &quot;firmware&quot;: &quot;NA&quot;}, &quot;ts&quot;: &quot;2025-03-23 20:08:38&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;instrument_name&quot;: &quot;dac&quot;, &quot;label&quot;: &quot;IDN&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Anything&gt;&quot;], &quot;vals&quot;: &quot;&lt;Anything&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;IDN&quot;, &quot;unit&quot;: &quot;&quot;}, &quot;ch1&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dac_ch1&quot;, &quot;value&quot;: 1.0, &quot;raw_value&quot;: 1.0, &quot;ts&quot;: &quot;2025-03-23 20:08:56&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;instrument_name&quot;: &quot;dac&quot;, &quot;label&quot;: &quot;Gate ch1&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;ch1&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;ch2&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dac_ch2&quot;, &quot;value&quot;: 1.0, &quot;raw_value&quot;: 1.0, &quot;ts&quot;: &quot;2025-03-23 20:08:56&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;instrument_name&quot;: &quot;dac&quot;, &quot;label&quot;: &quot;Gate ch2&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;ch2&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;fixed_parameter&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dac_fixed_parameter&quot;, &quot;value&quot;: 5, &quot;raw_value&quot;: 5, &quot;ts&quot;: &quot;2025-03-23 20:08:38&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;instrument_name&quot;: &quot;dac&quot;, &quot;label&quot;: &quot;fixed_parameter&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [], &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;fixed_parameter&quot;, &quot;unit&quot;: &quot;&quot;}}, &quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;name&quot;: &quot;dac&quot;, &quot;label&quot;: &quot;dac&quot;}}, &quot;parameters&quot;: {}, &quot;components&quot;: {}, &quot;config&quot;: null}, &quot;parameters&quot;: {&quot;ch1&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dac_ch1&quot;, &quot;value&quot;: 1.0, &quot;raw_value&quot;: 1.0, &quot;ts&quot;: &quot;2025-03-23 20:08:56&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;instrument_name&quot;: &quot;dac&quot;, &quot;label&quot;: &quot;Gate ch1&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;ch1&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;ch2&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dac_ch2&quot;, &quot;value&quot;: 1.0, &quot;raw_value&quot;: 1.0, &quot;ts&quot;: &quot;2025-03-23 20:08:56&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;instrument_name&quot;: &quot;dac&quot;, &quot;label&quot;: &quot;Gate ch2&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;ch2&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;v2&quot;: {&quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DmmGaussParameter&quot;, &quot;full_name&quot;: &quot;dmm_v2&quot;, &quot;value&quot;: -4.350288627709427e-05, &quot;raw_value&quot;: -4.350288627709427e-05, &quot;ts&quot;: &quot;2025-03-23 20:08:56&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;instrument_name&quot;: &quot;dmm&quot;, &quot;label&quot;: &quot;Gate v2&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;v2&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;v1&quot;: {&quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DmmExponentialParameter&quot;, &quot;full_name&quot;: &quot;dmm_v1&quot;, &quot;value&quot;: 5.056040689387415, &quot;raw_value&quot;: 5.056040689387415, &quot;ts&quot;: &quot;2025-03-23 20:08:38&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;instrument_name&quot;: &quot;dmm&quot;, &quot;label&quot;: &quot;Gate v1&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;v1&quot;, &quot;unit&quot;: &quot;V&quot;}}}</dd><dt><span>guid :</span></dt><dd>91039442-0000-0000-0000-0195c46902ec</dd><dt><span>run_timestamp :</span></dt><dd>2025-03-23 20:09:31</dd><dt><span>completed_timestamp :</span></dt><dd>2025-03-23 20:09:42</dd><dt><span>captured_run_id :</span></dt><dd>3</dd><dt><span>captured_counter :</span></dt><dd>3</dd><dt><span>run_id :</span></dt><dd>3</dd><dt><span>run_description :</span></dt><dd>{&quot;version&quot;: 3, &quot;interdependencies&quot;: {&quot;paramspecs&quot;: [{&quot;name&quot;: &quot;dac_ch1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate ch1&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: []}, {&quot;name&quot;: &quot;dac_ch2&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate ch2&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: []}, {&quot;name&quot;: &quot;dmm_v2&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v2&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: [&quot;dac_ch1&quot;, &quot;dac_ch2&quot;]}, {&quot;name&quot;: &quot;dmm_v1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v1&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: [&quot;dac_ch1&quot;, &quot;dac_ch2&quot;]}]}, &quot;interdependencies_&quot;: {&quot;parameters&quot;: {&quot;dmm_v2&quot;: {&quot;name&quot;: &quot;dmm_v2&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v2&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;dac_ch1&quot;: {&quot;name&quot;: &quot;dac_ch1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate ch1&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;dac_ch2&quot;: {&quot;name&quot;: &quot;dac_ch2&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate ch2&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;dmm_v1&quot;: {&quot;name&quot;: &quot;dmm_v1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v1&quot;, &quot;unit&quot;: &quot;V&quot;}}, &quot;dependencies&quot;: {&quot;dmm_v2&quot;: [&quot;dac_ch1&quot;, &quot;dac_ch2&quot;], &quot;dmm_v1&quot;: [&quot;dac_ch1&quot;, &quot;dac_ch2&quot;]}, &quot;inferences&quot;: {}, &quot;standalones&quot;: []}, &quot;shapes&quot;: null}</dd><dt><span>parent_dataset_links :</span></dt><dd>[]</dd><dt><span>run_timestamp_raw :</span></dt><dd>1742756971.260438</dd><dt><span>completed_timestamp_raw :</span></dt><dd>1742756982.7415159</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 646kB\n",
       "Dimensions:  (dac_ch1: 200, dac_ch2: 201)\n",
       "Coordinates:\n",
       "  * dac_ch1  (dac_ch1) float64 2kB -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
       "  * dac_ch2  (dac_ch2) float64 2kB -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0\n",
       "Data variables:\n",
       "    dmm_v1   (dac_ch1, dac_ch2) float64 322kB 6.184 5.902 6.03 ... 4.03 4.109\n",
       "    dmm_v2   (dac_ch1, dac_ch2) float64 322kB -0.0008481 -0.000525 ... 0.0005806\n",
       "Attributes: (12/14)\n",
       "    ds_name:                  results\n",
       "    sample_name:              no sample\n",
       "    exp_name:                 working_with_pandas\n",
       "    snapshot:                 {\"station\": {\"instruments\": {\"dmm\": {\"functions...\n",
       "    guid:                     91039442-0000-0000-0000-0195c46902ec\n",
       "    run_timestamp:            2025-03-23 20:09:31\n",
       "    ...                       ...\n",
       "    captured_counter:         3\n",
       "    run_id:                   3\n",
       "    run_description:          {\"version\": 3, \"interdependencies\": {\"paramspec...\n",
       "    parent_dataset_links:     []\n",
       "    run_timestamp_raw:        1742756971.260438\n",
       "    completed_timestamp_raw:  1742756982.7415159"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xaDataSet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned above it's also possible to work with a XArray [DataArray](http://xarray.pydata.org/en/stable/data-structures.html?#dataarray) directly from the DataSet. The DataArray can only contain a single dependent variable and can be obtained from the Dataset by indexing using the parameter name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "xaDataArray = xaDataSet[\"dmm_v2\"]  # or xaDataSet.dmm_v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "  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 0 20px 0 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\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:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0);\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.DataArray &#x27;dmm_v2&#x27; (dac_ch1: 200, dac_ch2: 201)&gt; Size: 322kB\n",
       "array([[-8.48128734e-04, -5.25005785e-04,  2.63902660e-04, ...,\n",
       "         2.74689567e-04, -1.42882583e-04,  3.07502119e-04],\n",
       "       [ 4.95598087e-04,  1.64306262e-03,  1.16054805e-04, ...,\n",
       "         4.27937447e-04,  3.06161231e-04,  2.41866121e-04],\n",
       "       [ 1.79865699e-04,  3.20971602e-04, -4.76030839e-04, ...,\n",
       "         3.28896994e-04,  3.35609229e-04,  5.62318401e-05],\n",
       "       ...,\n",
       "       [ 2.37215216e-04,  1.45792375e-04,  3.49907483e-04, ...,\n",
       "        -7.15124372e-04,  6.63749348e-04,  4.38214641e-04],\n",
       "       [ 5.25910520e-04,  2.68390072e-04,  2.56783696e-04, ...,\n",
       "         6.46707854e-04,  1.23851539e-04, -6.93366132e-04],\n",
       "       [ 3.31674190e-04, -1.35628711e-03, -1.13627824e-03, ...,\n",
       "        -4.42975147e-04, -3.46296218e-04,  5.80552877e-04]],\n",
       "      shape=(200, 201))\n",
       "Coordinates:\n",
       "  * dac_ch1  (dac_ch1) float64 2kB -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
       "  * dac_ch2  (dac_ch2) float64 2kB -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0\n",
       "Attributes:\n",
       "    name:           dmm_v2\n",
       "    paramtype:      numeric\n",
       "    label:          Gate v2\n",
       "    unit:           V\n",
       "    inferred_from:  []\n",
       "    depends_on:     [&#x27;dac_ch1&#x27;, &#x27;dac_ch2&#x27;]\n",
       "    units:          V\n",
       "    long_name:      Gate v2</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'dmm_v2'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>dac_ch1</span>: 200</li><li><span class='xr-has-index'>dac_ch2</span>: 201</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b6caf6db-ec55-41db-a3e0-aa81d1110645' class='xr-array-in' type='checkbox' checked><label for='section-b6caf6db-ec55-41db-a3e0-aa81d1110645' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.0008481 -0.000525 0.0002639 ... -0.000443 -0.0003463 0.0005806</span></div><div class='xr-array-data'><pre>array([[-8.48128734e-04, -5.25005785e-04,  2.63902660e-04, ...,\n",
       "         2.74689567e-04, -1.42882583e-04,  3.07502119e-04],\n",
       "       [ 4.95598087e-04,  1.64306262e-03,  1.16054805e-04, ...,\n",
       "         4.27937447e-04,  3.06161231e-04,  2.41866121e-04],\n",
       "       [ 1.79865699e-04,  3.20971602e-04, -4.76030839e-04, ...,\n",
       "         3.28896994e-04,  3.35609229e-04,  5.62318401e-05],\n",
       "       ...,\n",
       "       [ 2.37215216e-04,  1.45792375e-04,  3.49907483e-04, ...,\n",
       "        -7.15124372e-04,  6.63749348e-04,  4.38214641e-04],\n",
       "       [ 5.25910520e-04,  2.68390072e-04,  2.56783696e-04, ...,\n",
       "         6.46707854e-04,  1.23851539e-04, -6.93366132e-04],\n",
       "       [ 3.31674190e-04, -1.35628711e-03, -1.13627824e-03, ...,\n",
       "        -4.42975147e-04, -3.46296218e-04,  5.80552877e-04]],\n",
       "      shape=(200, 201))</pre></div></div></li><li class='xr-section-item'><input id='section-a7cfa1ce-6889-4115-88fa-ee6b35a064d0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a7cfa1ce-6889-4115-88fa-ee6b35a064d0' 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'>dac_ch1</span></div><div class='xr-var-dims'>(dac_ch1)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.9899 -0.9799 ... 0.9899 1.0</div><input id='attrs-aedc2a88-3055-45f2-83c9-58bd40efadfb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aedc2a88-3055-45f2-83c9-58bd40efadfb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b51e71a-690c-4a13-9f6e-fcd0aa70c5be' class='xr-var-data-in' type='checkbox'><label for='data-9b51e71a-690c-4a13-9f6e-fcd0aa70c5be' 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>dac_ch1</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate ch1</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate ch1</dd></dl></div><div class='xr-var-data'><pre>array([-1.      , -0.98995 , -0.979899, -0.969849, -0.959799, -0.949749,\n",
       "       -0.939698, -0.929648, -0.919598, -0.909548, -0.899497, -0.889447,\n",
       "       -0.879397, -0.869347, -0.859296, -0.849246, -0.839196, -0.829146,\n",
       "       -0.819095, -0.809045, -0.798995, -0.788945, -0.778894, -0.768844,\n",
       "       -0.758794, -0.748744, -0.738693, -0.728643, -0.718593, -0.708543,\n",
       "       -0.698492, -0.688442, -0.678392, -0.668342, -0.658291, -0.648241,\n",
       "       -0.638191, -0.628141, -0.61809 , -0.60804 , -0.59799 , -0.58794 ,\n",
       "       -0.577889, -0.567839, -0.557789, -0.547739, -0.537688, -0.527638,\n",
       "       -0.517588, -0.507538, -0.497487, -0.487437, -0.477387, -0.467337,\n",
       "       -0.457286, -0.447236, -0.437186, -0.427136, -0.417085, -0.407035,\n",
       "       -0.396985, -0.386935, -0.376884, -0.366834, -0.356784, -0.346734,\n",
       "       -0.336683, -0.326633, -0.316583, -0.306533, -0.296482, -0.286432,\n",
       "       -0.276382, -0.266332, -0.256281, -0.246231, -0.236181, -0.226131,\n",
       "       -0.21608 , -0.20603 , -0.19598 , -0.18593 , -0.175879, -0.165829,\n",
       "       -0.155779, -0.145729, -0.135678, -0.125628, -0.115578, -0.105528,\n",
       "       -0.095477, -0.085427, -0.075377, -0.065327, -0.055276, -0.045226,\n",
       "       -0.035176, -0.025126, -0.015075, -0.005025,  0.005025,  0.015075,\n",
       "        0.025126,  0.035176,  0.045226,  0.055276,  0.065327,  0.075377,\n",
       "        0.085427,  0.095477,  0.105528,  0.115578,  0.125628,  0.135678,\n",
       "        0.145729,  0.155779,  0.165829,  0.175879,  0.18593 ,  0.19598 ,\n",
       "        0.20603 ,  0.21608 ,  0.226131,  0.236181,  0.246231,  0.256281,\n",
       "        0.266332,  0.276382,  0.286432,  0.296482,  0.306533,  0.316583,\n",
       "        0.326633,  0.336683,  0.346734,  0.356784,  0.366834,  0.376884,\n",
       "        0.386935,  0.396985,  0.407035,  0.417085,  0.427136,  0.437186,\n",
       "        0.447236,  0.457286,  0.467337,  0.477387,  0.487437,  0.497487,\n",
       "        0.507538,  0.517588,  0.527638,  0.537688,  0.547739,  0.557789,\n",
       "        0.567839,  0.577889,  0.58794 ,  0.59799 ,  0.60804 ,  0.61809 ,\n",
       "        0.628141,  0.638191,  0.648241,  0.658291,  0.668342,  0.678392,\n",
       "        0.688442,  0.698492,  0.708543,  0.718593,  0.728643,  0.738693,\n",
       "        0.748744,  0.758794,  0.768844,  0.778894,  0.788945,  0.798995,\n",
       "        0.809045,  0.819095,  0.829146,  0.839196,  0.849246,  0.859296,\n",
       "        0.869347,  0.879397,  0.889447,  0.899497,  0.909548,  0.919598,\n",
       "        0.929648,  0.939698,  0.949749,  0.959799,  0.969849,  0.979899,\n",
       "        0.98995 ,  1.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch2</span></div><div class='xr-var-dims'>(dac_ch2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.99 -0.98 ... 0.98 0.99 1.0</div><input id='attrs-77e78817-8dbd-42af-94a0-73198ea4e6d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-77e78817-8dbd-42af-94a0-73198ea4e6d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf7c770f-bf07-4826-8172-1b5a24015eea' class='xr-var-data-in' type='checkbox'><label for='data-bf7c770f-bf07-4826-8172-1b5a24015eea' 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>dac_ch2</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate ch2</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate ch2</dd></dl></div><div class='xr-var-data'><pre>array([-1.  , -0.99, -0.98, ...,  0.98,  0.99,  1.  ], shape=(201,))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8877fb6f-51ae-471a-93c1-7324b405624d' class='xr-section-summary-in' type='checkbox'  ><label for='section-8877fb6f-51ae-471a-93c1-7324b405624d' 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>dac_ch1</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-d1892bc0-435a-46ee-84e0-0876eaf5595f' class='xr-index-data-in' type='checkbox'/><label for='index-d1892bc0-435a-46ee-84e0-0876eaf5595f' 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, -0.989949748743719, -0.979899497487437,\n",
       "       -0.969849246231156, -0.959798994974874, -0.949748743718593,\n",
       "       -0.939698492462312,  -0.92964824120603, -0.919597989949749,\n",
       "       -0.909547738693467,\n",
       "       ...\n",
       "        0.909547738693467,  0.919597989949749,   0.92964824120603,\n",
       "        0.939698492462312,  0.949748743718593,  0.959798994974874,\n",
       "        0.969849246231156,  0.979899497487437,  0.989949748743719,\n",
       "                      1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;dac_ch1&#x27;, length=200))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dac_ch2</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-329ed840-2e87-4937-a94d-2a06ee52bd38' class='xr-index-data-in' type='checkbox'/><label for='index-329ed840-2e87-4937-a94d-2a06ee52bd38' 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, -0.99, -0.98, -0.97, -0.96, -0.95, -0.94, -0.93, -0.92, -0.91,\n",
       "       ...\n",
       "        0.91,  0.92,  0.93,  0.94,  0.95,  0.96,  0.97,  0.98,  0.99,   1.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;dac_ch2&#x27;, length=201))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-befea85c-27b2-434a-aa30-e0e7c25f1043' class='xr-section-summary-in' type='checkbox'  checked><label for='section-befea85c-27b2-434a-aa30-e0e7c25f1043' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>dmm_v2</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate v2</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[&#x27;dac_ch1&#x27;, &#x27;dac_ch2&#x27;]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate v2</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'dmm_v2' (dac_ch1: 200, dac_ch2: 201)> Size: 322kB\n",
       "array([[-8.48128734e-04, -5.25005785e-04,  2.63902660e-04, ...,\n",
       "         2.74689567e-04, -1.42882583e-04,  3.07502119e-04],\n",
       "       [ 4.95598087e-04,  1.64306262e-03,  1.16054805e-04, ...,\n",
       "         4.27937447e-04,  3.06161231e-04,  2.41866121e-04],\n",
       "       [ 1.79865699e-04,  3.20971602e-04, -4.76030839e-04, ...,\n",
       "         3.28896994e-04,  3.35609229e-04,  5.62318401e-05],\n",
       "       ...,\n",
       "       [ 2.37215216e-04,  1.45792375e-04,  3.49907483e-04, ...,\n",
       "        -7.15124372e-04,  6.63749348e-04,  4.38214641e-04],\n",
       "       [ 5.25910520e-04,  2.68390072e-04,  2.56783696e-04, ...,\n",
       "         6.46707854e-04,  1.23851539e-04, -6.93366132e-04],\n",
       "       [ 3.31674190e-04, -1.35628711e-03, -1.13627824e-03, ...,\n",
       "        -4.42975147e-04, -3.46296218e-04,  5.80552877e-04]],\n",
       "      shape=(200, 201))\n",
       "Coordinates:\n",
       "  * dac_ch1  (dac_ch1) float64 2kB -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
       "  * dac_ch2  (dac_ch2) float64 2kB -1.0 -0.99 -0.98 -0.97 ... 0.97 0.98 0.99 1.0\n",
       "Attributes:\n",
       "    name:           dmm_v2\n",
       "    paramtype:      numeric\n",
       "    label:          Gate v2\n",
       "    unit:           V\n",
       "    inferred_from:  []\n",
       "    depends_on:     ['dac_ch1', 'dac_ch2']\n",
       "    units:          V\n",
       "    long_name:      Gate v2"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xaDataArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, 2)\n",
    "xaDataSet.dmm_v2.plot(ax=ax[0, 0])\n",
    "xaDataSet.dmm_v1.plot(ax=ax[1, 1])\n",
    "xaDataSet.dmm_v2.mean(dim=\"dac_ch1\").plot(ax=ax[1, 0])\n",
    "xaDataSet.dmm_v1.mean(dim=\"dac_ch2\").plot(ax=ax[0, 1])\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we demonstrated a few ways to index the data from a DataArray. For instance the DataArray can be directly plotted, the extracted mean or a specific row/column can also be plotted."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with XArray on non gridded data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes your data does not fit well on a regular grid. Perhaps you are sweeping 2 parameters at the same time or you are messuring at random points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 4. \n"
     ]
    }
   ],
   "source": [
    "# run a 2D sweep\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "    for v1, v2 in zip(np.linspace(-1, 1, 200), np.linspace(-1, 1, 201)):\n",
    "        dac.ch1(v1)\n",
    "        dac.ch2(v2)\n",
    "        val1 = dmm.v1.get()\n",
    "        val2 = dmm.v2.get()\n",
    "        datasaver.add_result(\n",
    "            (dac.ch1, v1), (dac.ch2, v2), (dmm.v1, val1), (dmm.v2, val2)\n",
    "        )\n",
    "\n",
    "dataset4 = datasaver.dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "xaDataSet = dataset4.to_xarray_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If this is the case QCoDeS will export the data using a XArray [MultiIndex](https://docs.xarray.dev/en/stable/user-guide/indexing.html#multi-level-indexing). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\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:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0);\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: 8kB\n",
       "Dimensions:      (multi_index: 200)\n",
       "Coordinates:\n",
       "  * multi_index  (multi_index) object 2kB MultiIndex\n",
       "  * dac_ch1      (multi_index) float64 2kB -1.0 -0.9899 -0.9799 ... 0.9899 1.0\n",
       "  * dac_ch2      (multi_index) float64 2kB -1.0 -0.99 -0.98 ... 0.97 0.98 0.99\n",
       "Data variables:\n",
       "    dmm_v1       (multi_index) float64 2kB 6.187 6.159 6.294 ... 4.022 4.066\n",
       "    dmm_v2       (multi_index) float64 2kB -0.0006791 -0.0003731 ... -7.468e-05\n",
       "Attributes: (12/14)\n",
       "    ds_name:                  results\n",
       "    sample_name:              no sample\n",
       "    exp_name:                 working_with_pandas\n",
       "    snapshot:                 {&quot;station&quot;: {&quot;instruments&quot;: {&quot;dmm&quot;: {&quot;functions...\n",
       "    guid:                     abd7a822-0000-0000-0000-0195c4695546\n",
       "    run_timestamp:            2025-03-23 20:09:52\n",
       "    ...                       ...\n",
       "    captured_counter:         4\n",
       "    run_id:                   4\n",
       "    run_description:          {&quot;version&quot;: 3, &quot;interdependencies&quot;: {&quot;paramspec...\n",
       "    parent_dataset_links:     []\n",
       "    run_timestamp_raw:        1742756992.340981\n",
       "    completed_timestamp_raw:  1742756992.4179645</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-9892e239-5d05-4991-86e8-d951d524499d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9892e239-5d05-4991-86e8-d951d524499d' 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'>multi_index</span>: 200</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9401bd26-90ff-44c9-b0e0-74b7857dd231' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9401bd26-90ff-44c9-b0e0-74b7857dd231' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>multi_index</span></div><div class='xr-var-dims'>(multi_index)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>MultiIndex</div><input id='attrs-c973b3a5-8a1f-417d-a972-b616f0ac1c8b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c973b3a5-8a1f-417d-a972-b616f0ac1c8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e33be4a-449b-4fbf-b32b-781e7a0d6dfe' class='xr-var-data-in' type='checkbox'><label for='data-6e33be4a-449b-4fbf-b32b-781e7a0d6dfe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([(-1.0, -1.0), (-0.989949748743719, -0.99), (-0.979899497487437, -0.98),\n",
       "       (-0.969849246231156, -0.97), (-0.959798994974874, -0.96),\n",
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       "       (-0.889447236180904, -0.89), (-0.879396984924623, -0.88),\n",
       "       (-0.869346733668342, -0.87), (-0.85929648241206, -0.86),\n",
       "       (-0.849246231155779, -0.85), (-0.839195979899497, -0.84),\n",
       "       (-0.829145728643216, -0.83), (-0.819095477386935, -0.82),\n",
       "       (-0.809045226130653, -0.81), (-0.798994974874372, -0.8),\n",
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       "       (-0.768844221105528, -0.77), (-0.758793969849246, -0.76),\n",
       "       (-0.748743718592965, -0.75), (-0.738693467336683, -0.74),\n",
       "       (-0.728643216080402, -0.73), (-0.718592964824121, -0.72),\n",
       "       (-0.708542713567839, -0.71), (-0.698492462311558, -0.7),\n",
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       "       (-0.648241206030151, -0.65), (-0.638190954773869, -0.64),\n",
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       "       (-0.608040201005025, -0.61), (-0.597989949748744, -0.6),\n",
       "       (-0.587939698492462, -0.59), (-0.577889447236181, -0.58),\n",
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       "       (-0.226130653266332, -0.23), (-0.21608040201005, -0.22),\n",
       "       (-0.206030150753769, -0.21), (-0.195979899497487, -0.2),\n",
       "       (-0.185929648241206, -0.19), (-0.175879396984925, -0.18),\n",
       "       (-0.165829145728643, -0.17), (-0.155778894472362, -0.16),\n",
       "       (-0.14572864321608, -0.15), (-0.135678391959799, -0.14),\n",
       "       (-0.125628140703518, -0.13), (-0.115577889447236, -0.12),\n",
       "       (-0.105527638190955, -0.11), (-0.0954773869346733, -0.1),\n",
       "       (-0.085427135678392, -0.09), (-0.0753768844221105, -0.08),\n",
       "       (-0.0653266331658291, -0.07),\n",
       "       (-0.0552763819095478, -0.0599999999999999),\n",
       "       (-0.0452261306532663, -0.0499999999999999), (-0.035175879396985, -0.04),\n",
       "       (-0.0251256281407035, -0.03), (-0.0150753768844221, -0.02),\n",
       "       (-0.00502512562814073, -0.01), (0.00502512562814061, 0.0),\n",
       "       (0.0150753768844221, 0.01), (0.0251256281407035, 0.02),\n",
       "       (0.035175879396985, 0.03), (0.0452261306532664, 0.04),\n",
       "       (0.0552763819095476, 0.05), (0.0653266331658291, 0.0600000000000001),\n",
       "       (0.0753768844221105, 0.0700000000000001),\n",
       "       (0.085427135678392, 0.0800000000000001),\n",
       "       (0.0954773869346734, 0.0900000000000001), (0.105527638190955, 0.1),\n",
       "       (0.115577889447236, 0.11), (0.125628140703518, 0.12),\n",
       "       (0.135678391959799, 0.13), (0.14572864321608, 0.14),\n",
       "       (0.155778894472362, 0.15), (0.165829145728643, 0.16),\n",
       "       (0.175879396984925, 0.17), (0.185929648241206, 0.18),\n",
       "       (0.195979899497488, 0.19), (0.206030150753769, 0.2),\n",
       "       (0.21608040201005, 0.21), (0.226130653266332, 0.22),\n",
       "       (0.236180904522613, 0.23), (0.246231155778895, 0.24),\n",
       "       (0.256281407035176, 0.25), (0.266331658291457, 0.26),\n",
       "       (0.276381909547739, 0.27), (0.28643216080402, 0.28),\n",
       "       (0.296482412060302, 0.29), (0.306532663316583, 0.3),\n",
       "       (0.316582914572864, 0.31), (0.326633165829146, 0.32),\n",
       "       (0.336683417085427, 0.33), (0.346733668341709, 0.34),\n",
       "       (0.35678391959799, 0.35), (0.366834170854271, 0.36),\n",
       "       (0.376884422110553, 0.37), (0.386934673366834, 0.38),\n",
       "       (0.396984924623116, 0.39), (0.407035175879397, 0.4),\n",
       "       (0.417085427135678, 0.41), (0.42713567839196, 0.42),\n",
       "       (0.437185929648241, 0.43), (0.447236180904523, 0.44),\n",
       "       (0.457286432160804, 0.45), (0.467336683417085, 0.46),\n",
       "       (0.477386934673367, 0.47), (0.487437185929648, 0.48),\n",
       "       (0.49748743718593, 0.49), (0.507537688442211, 0.5),\n",
       "       (0.517587939698493, 0.51), (0.527638190954774, 0.52),\n",
       "       (0.537688442211055, 0.53), (0.547738693467337, 0.54),\n",
       "       (0.557788944723618, 0.55), (0.5678391959799, 0.56),\n",
       "       (0.577889447236181, 0.57), (0.587939698492462, 0.58),\n",
       "       (0.597989949748744, 0.59), (0.608040201005025, 0.6),\n",
       "       (0.618090452261307, 0.61), (0.628140703517588, 0.62),\n",
       "       (0.638190954773869, 0.63), (0.648241206030151, 0.64),\n",
       "       (0.658291457286432, 0.65), (0.668341708542714, 0.66),\n",
       "       (0.678391959798995, 0.67), (0.688442211055276, 0.68),\n",
       "       (0.698492462311558, 0.69), (0.708542713567839, 0.7),\n",
       "       (0.718592964824121, 0.71), (0.728643216080402, 0.72),\n",
       "       (0.738693467336683, 0.73), (0.748743718592965, 0.74),\n",
       "       (0.758793969849246, 0.75), (0.768844221105528, 0.76),\n",
       "       (0.778894472361809, 0.77), (0.78894472361809, 0.78),\n",
       "       (0.798994974874372, 0.79), (0.809045226130653, 0.8),\n",
       "       (0.819095477386935, 0.81), (0.829145728643216, 0.82),\n",
       "       (0.839195979899497, 0.83), (0.849246231155779, 0.84),\n",
       "       (0.85929648241206, 0.85), (0.869346733668342, 0.86),\n",
       "       (0.879396984924623, 0.87), (0.889447236180904, 0.88),\n",
       "       (0.899497487437186, 0.89), (0.909547738693467, 0.9),\n",
       "       (0.919597989949749, 0.91), (0.92964824120603, 0.92),\n",
       "       (0.939698492462312, 0.93), (0.949748743718593, 0.94),\n",
       "       (0.959798994974874, 0.95), (0.969849246231156, 0.96),\n",
       "       (0.979899497487437, 0.97), (0.989949748743719, 0.98), (1.0, 0.99)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch1</span></div><div class='xr-var-dims'>(multi_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.9899 -0.9799 ... 0.9899 1.0</div><input id='attrs-dbc6cbb5-9d57-4afd-a205-4078ec4efb69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbc6cbb5-9d57-4afd-a205-4078ec4efb69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e023ea90-6c1e-4653-a4e0-f07338308da8' class='xr-var-data-in' type='checkbox'><label for='data-e023ea90-6c1e-4653-a4e0-f07338308da8' 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>dac_ch1</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate ch1</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate ch1</dd></dl></div><div class='xr-var-data'><pre>array([-1.      , -0.98995 , -0.979899, -0.969849, -0.959799, -0.949749,\n",
       "       -0.939698, -0.929648, -0.919598, -0.909548, -0.899497, -0.889447,\n",
       "       -0.879397, -0.869347, -0.859296, -0.849246, -0.839196, -0.829146,\n",
       "       -0.819095, -0.809045, -0.798995, -0.788945, -0.778894, -0.768844,\n",
       "       -0.758794, -0.748744, -0.738693, -0.728643, -0.718593, -0.708543,\n",
       "       -0.698492, -0.688442, -0.678392, -0.668342, -0.658291, -0.648241,\n",
       "       -0.638191, -0.628141, -0.61809 , -0.60804 , -0.59799 , -0.58794 ,\n",
       "       -0.577889, -0.567839, -0.557789, -0.547739, -0.537688, -0.527638,\n",
       "       -0.517588, -0.507538, -0.497487, -0.487437, -0.477387, -0.467337,\n",
       "       -0.457286, -0.447236, -0.437186, -0.427136, -0.417085, -0.407035,\n",
       "       -0.396985, -0.386935, -0.376884, -0.366834, -0.356784, -0.346734,\n",
       "       -0.336683, -0.326633, -0.316583, -0.306533, -0.296482, -0.286432,\n",
       "       -0.276382, -0.266332, -0.256281, -0.246231, -0.236181, -0.226131,\n",
       "       -0.21608 , -0.20603 , -0.19598 , -0.18593 , -0.175879, -0.165829,\n",
       "       -0.155779, -0.145729, -0.135678, -0.125628, -0.115578, -0.105528,\n",
       "       -0.095477, -0.085427, -0.075377, -0.065327, -0.055276, -0.045226,\n",
       "       -0.035176, -0.025126, -0.015075, -0.005025,  0.005025,  0.015075,\n",
       "        0.025126,  0.035176,  0.045226,  0.055276,  0.065327,  0.075377,\n",
       "        0.085427,  0.095477,  0.105528,  0.115578,  0.125628,  0.135678,\n",
       "        0.145729,  0.155779,  0.165829,  0.175879,  0.18593 ,  0.19598 ,\n",
       "        0.20603 ,  0.21608 ,  0.226131,  0.236181,  0.246231,  0.256281,\n",
       "        0.266332,  0.276382,  0.286432,  0.296482,  0.306533,  0.316583,\n",
       "        0.326633,  0.336683,  0.346734,  0.356784,  0.366834,  0.376884,\n",
       "        0.386935,  0.396985,  0.407035,  0.417085,  0.427136,  0.437186,\n",
       "        0.447236,  0.457286,  0.467337,  0.477387,  0.487437,  0.497487,\n",
       "        0.507538,  0.517588,  0.527638,  0.537688,  0.547739,  0.557789,\n",
       "        0.567839,  0.577889,  0.58794 ,  0.59799 ,  0.60804 ,  0.61809 ,\n",
       "        0.628141,  0.638191,  0.648241,  0.658291,  0.668342,  0.678392,\n",
       "        0.688442,  0.698492,  0.708543,  0.718593,  0.728643,  0.738693,\n",
       "        0.748744,  0.758794,  0.768844,  0.778894,  0.788945,  0.798995,\n",
       "        0.809045,  0.819095,  0.829146,  0.839196,  0.849246,  0.859296,\n",
       "        0.869347,  0.879397,  0.889447,  0.899497,  0.909548,  0.919598,\n",
       "        0.929648,  0.939698,  0.949749,  0.959799,  0.969849,  0.979899,\n",
       "        0.98995 ,  1.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dac_ch2</span></div><div class='xr-var-dims'>(multi_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.99 -0.98 ... 0.97 0.98 0.99</div><input id='attrs-ca4eba59-2c03-44d3-8911-344e3934ba02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca4eba59-2c03-44d3-8911-344e3934ba02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef94921f-4df2-4d80-a63f-8446b9f7beaa' class='xr-var-data-in' type='checkbox'><label for='data-ef94921f-4df2-4d80-a63f-8446b9f7beaa' 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>dac_ch2</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate ch2</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate ch2</dd></dl></div><div class='xr-var-data'><pre>array([-1.  , -0.99, -0.98, -0.97, -0.96, -0.95, -0.94, -0.93, -0.92, -0.91,\n",
       "       -0.9 , -0.89, -0.88, -0.87, -0.86, -0.85, -0.84, -0.83, -0.82, -0.81,\n",
       "       -0.8 , -0.79, -0.78, -0.77, -0.76, -0.75, -0.74, -0.73, -0.72, -0.71,\n",
       "       -0.7 , -0.69, -0.68, -0.67, -0.66, -0.65, -0.64, -0.63, -0.62, -0.61,\n",
       "       -0.6 , -0.59, -0.58, -0.57, -0.56, -0.55, -0.54, -0.53, -0.52, -0.51,\n",
       "       -0.5 , -0.49, -0.48, -0.47, -0.46, -0.45, -0.44, -0.43, -0.42, -0.41,\n",
       "       -0.4 , -0.39, -0.38, -0.37, -0.36, -0.35, -0.34, -0.33, -0.32, -0.31,\n",
       "       -0.3 , -0.29, -0.28, -0.27, -0.26, -0.25, -0.24, -0.23, -0.22, -0.21,\n",
       "       -0.2 , -0.19, -0.18, -0.17, -0.16, -0.15, -0.14, -0.13, -0.12, -0.11,\n",
       "       -0.1 , -0.09, -0.08, -0.07, -0.06, -0.05, -0.04, -0.03, -0.02, -0.01,\n",
       "        0.  ,  0.01,  0.02,  0.03,  0.04,  0.05,  0.06,  0.07,  0.08,  0.09,\n",
       "        0.1 ,  0.11,  0.12,  0.13,  0.14,  0.15,  0.16,  0.17,  0.18,  0.19,\n",
       "        0.2 ,  0.21,  0.22,  0.23,  0.24,  0.25,  0.26,  0.27,  0.28,  0.29,\n",
       "        0.3 ,  0.31,  0.32,  0.33,  0.34,  0.35,  0.36,  0.37,  0.38,  0.39,\n",
       "        0.4 ,  0.41,  0.42,  0.43,  0.44,  0.45,  0.46,  0.47,  0.48,  0.49,\n",
       "        0.5 ,  0.51,  0.52,  0.53,  0.54,  0.55,  0.56,  0.57,  0.58,  0.59,\n",
       "        0.6 ,  0.61,  0.62,  0.63,  0.64,  0.65,  0.66,  0.67,  0.68,  0.69,\n",
       "        0.7 ,  0.71,  0.72,  0.73,  0.74,  0.75,  0.76,  0.77,  0.78,  0.79,\n",
       "        0.8 ,  0.81,  0.82,  0.83,  0.84,  0.85,  0.86,  0.87,  0.88,  0.89,\n",
       "        0.9 ,  0.91,  0.92,  0.93,  0.94,  0.95,  0.96,  0.97,  0.98,  0.99])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-438befa8-9fd9-4926-98f4-4db324f91fa5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-438befa8-9fd9-4926-98f4-4db324f91fa5' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>dmm_v1</span></div><div class='xr-var-dims'>(multi_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.187 6.159 6.294 ... 4.022 4.066</div><input id='attrs-ee6fed07-efef-47e1-bf78-f6d66598c1c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee6fed07-efef-47e1-bf78-f6d66598c1c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-562c47a5-212c-43ed-af22-f3354d68e611' class='xr-var-data-in' type='checkbox'><label for='data-562c47a5-212c-43ed-af22-f3354d68e611' 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>dmm_v1</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate v1</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[&#x27;dac_ch1&#x27;, &#x27;dac_ch2&#x27;]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate v1</dd></dl></div><div class='xr-var-data'><pre>array([6.18683611, 6.15892863, 6.29423606, 6.16766607, 6.18767334,\n",
       "       5.94224744, 5.9827973 , 5.92062603, 6.03982915, 6.04007182,\n",
       "       5.89274329, 5.98202333, 6.03130666, 6.19879249, 5.79466363,\n",
       "       5.8925535 , 5.89124902, 5.74523683, 5.83918478, 5.91154368,\n",
       "       5.97316134, 5.66864818, 6.1011074 , 5.86746175, 5.81002696,\n",
       "       5.79256973, 5.96980468, 5.86164381, 5.75075809, 5.66973134,\n",
       "       5.77471717, 5.98849512, 5.79334456, 5.66024102, 5.71908285,\n",
       "       5.74392072, 5.55650181, 5.85273612, 5.53594221, 5.51251767,\n",
       "       5.7154721 , 5.70862444, 5.65288432, 5.54754781, 5.44417147,\n",
       "       5.49851216, 5.51126301, 5.60144557, 5.65962362, 5.40505865,\n",
       "       5.50738626, 5.61244927, 5.58791327, 5.42912954, 5.4404377 ,\n",
       "       5.52652012, 5.23567302, 5.38202583, 5.3795373 , 5.42018169,\n",
       "       5.42747174, 5.31626175, 5.45068808, 5.39589323, 5.19129987,\n",
       "       5.46508567, 5.35818374, 5.27052787, 5.2899984 , 5.43229735,\n",
       "       5.41525537, 5.48560254, 5.15491286, 5.36160733, 5.25765271,\n",
       "       5.16040699, 5.29296859, 5.27856979, 5.24158077, 5.22456077,\n",
       "       5.4319278 , 5.25354233, 5.25095376, 5.15936651, 5.23009603,\n",
       "       5.08609342, 5.3761493 , 5.28293662, 4.94888076, 4.95543268,\n",
       "       5.00494578, 5.22233037, 5.06967216, 5.07341102, 5.03539266,\n",
       "       5.02214727, 5.0306171 , 5.05737098, 4.83158379, 4.91104954,\n",
       "       4.92369704, 5.02939314, 4.90773453, 4.86050396, 4.91206789,\n",
       "       5.04798876, 5.02336822, 5.04008661, 4.8542089 , 4.9769989 ,\n",
       "       4.87467437, 4.97662696, 4.79621775, 4.95786607, 5.02277377,\n",
       "       4.85725281, 4.89950574, 4.78313275, 4.73427517, 4.83935896,\n",
       "       4.69939299, 5.02920232, 4.83500584, 4.92912948, 4.81238008,\n",
       "       4.72661949, 4.72350332, 4.77213382, 4.77236316, 4.59308591,\n",
       "       4.8222602 , 4.79773074, 4.88636405, 4.74491499, 4.76054453,\n",
       "       4.71498088, 4.5698162 , 4.57577857, 4.60240337, 4.83383064,\n",
       "       4.70220199, 4.5664275 , 4.52101639, 4.61411035, 4.42324734,\n",
       "       4.44667955, 4.5756434 , 4.5715283 , 4.46033931, 4.42184872,\n",
       "       4.62574033, 4.64571418, 4.53669561, 4.43454155, 4.41166202,\n",
       "       4.4617831 , 4.50453601, 4.66395053, 4.32284316, 4.35119409,\n",
       "       4.35156321, 4.29387193, 4.40843251, 4.25132039, 4.57433294,\n",
       "       4.47177884, 4.37495871, 4.38276611, 4.25896928, 4.17923766,\n",
       "       4.13166233, 4.40842981, 4.43199457, 4.35970988, 4.29687332,\n",
       "       4.34470562, 4.26564894, 4.33135907, 4.33307218, 4.27702655,\n",
       "       4.22650884, 4.3271839 , 4.23148632, 4.2603682 , 4.21749224,\n",
       "       4.31800445, 4.25207744, 4.24600239, 4.21136464, 4.32504377,\n",
       "       4.04137144, 4.01535999, 4.12518611, 4.14355952, 4.29043195,\n",
       "       4.10122751, 4.07500067, 3.99338075, 4.0217994 , 4.06614222])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dmm_v2</span></div><div class='xr-var-dims'>(multi_index)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.0006791 ... -7.468e-05</div><input id='attrs-4c2e1fb6-d5e5-4996-ab8d-12cfad133f7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c2e1fb6-d5e5-4996-ab8d-12cfad133f7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed67b4c0-1170-4efe-946b-df96f3d21721' class='xr-var-data-in' type='checkbox'><label for='data-ed67b4c0-1170-4efe-946b-df96f3d21721' 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>dmm_v2</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>Gate v2</dd><dt><span>unit :</span></dt><dd>V</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[&#x27;dac_ch1&#x27;, &#x27;dac_ch2&#x27;]</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Gate v2</dd></dl></div><div class='xr-var-data'><pre>array([-6.79117957e-04, -3.73127227e-04, -3.73049656e-04,  3.46865420e-04,\n",
       "       -5.37498012e-05,  2.81844734e-04, -1.75451440e-04,  9.00586324e-05,\n",
       "        1.34995445e-04,  5.08897799e-04, -1.89960222e-04,  9.87547844e-04,\n",
       "       -3.07437205e-04, -3.23216739e-04, -6.05558173e-04, -5.00747542e-04,\n",
       "        2.64792556e-04,  3.49151276e-05,  1.24351243e-04,  1.29433818e-03,\n",
       "        4.28201297e-04,  4.14251984e-04,  2.67297400e-04,  7.49800400e-04,\n",
       "        5.91617384e-04,  9.22777399e-05, -6.07857939e-05, -3.81921908e-04,\n",
       "       -1.12801156e-03, -1.44229528e-04, -3.65289472e-04, -2.10800318e-04,\n",
       "        1.48286889e-05, -2.87113923e-04, -1.12407571e-03, -3.94431978e-05,\n",
       "       -9.66135627e-05, -6.84392594e-04,  4.10383137e-04,  6.05940706e-05,\n",
       "        1.84321265e-04, -2.61667472e-04,  5.76855280e-05, -1.86086793e-04,\n",
       "        1.30728040e-03, -2.18755231e-04,  6.45727965e-04, -1.81492295e-05,\n",
       "        8.79518462e-04,  1.76278210e-03,  1.07856039e-03,  2.04136072e-03,\n",
       "        8.87403036e-04,  2.23384314e-03,  2.84714379e-03,  3.70820351e-03,\n",
       "        4.06601241e-03,  5.30655102e-03,  5.97002964e-03,  8.50575732e-03,\n",
       "        9.10027658e-03,  1.09825759e-02,  1.24440590e-02,  1.42965190e-02,\n",
       "        1.76916596e-02,  1.96006412e-02,  2.42725102e-02,  2.79631912e-02,\n",
       "        3.25409377e-02,  3.70374366e-02,  4.34034757e-02,  4.97898142e-02,\n",
       "        5.81675848e-02,  6.58105789e-02,  7.54398413e-02,  8.61150204e-02,\n",
       "        9.78217825e-02,  1.10355630e-01,  1.24090893e-01,  1.40008239e-01,\n",
       "...\n",
       "        1.03521428e+00,  1.01665532e+00,  9.94624739e-01,  9.70245862e-01,\n",
       "        9.43009039e-01,  9.12644989e-01,  8.82944254e-01,  8.49123091e-01,\n",
       "        8.14611159e-01,  7.79797039e-01,  7.42937525e-01,  7.06524627e-01,\n",
       "        6.69827767e-01,  6.32985612e-01,  5.94598806e-01,  5.58408085e-01,\n",
       "        5.23359815e-01,  4.87394845e-01,  4.52433229e-01,  4.19111339e-01,\n",
       "        3.87738054e-01,  3.56822907e-01,  3.27495706e-01,  2.99651443e-01,\n",
       "        2.72113438e-01,  2.47422745e-01,  2.24343463e-01,  2.02632315e-01,\n",
       "        1.81753128e-01,  1.62882049e-01,  1.45804636e-01,  1.30399015e-01,\n",
       "        1.15316817e-01,  1.03424412e-01,  9.06857809e-02,  8.05163803e-02,\n",
       "        6.91206168e-02,  6.15116500e-02,  5.33634127e-02,  4.67220264e-02,\n",
       "        3.98990813e-02,  3.40920737e-02,  3.00089078e-02,  2.56141085e-02,\n",
       "        2.19470225e-02,  1.84263307e-02,  1.55048443e-02,  1.34665256e-02,\n",
       "        1.12603848e-02,  9.97859470e-03,  7.91603031e-03,  7.05045081e-03,\n",
       "        5.86026170e-03,  4.27072653e-03,  4.39229345e-03,  3.88691389e-03,\n",
       "        1.93019010e-03,  2.14397728e-03,  1.04207249e-03,  1.71624029e-03,\n",
       "        1.27040601e-03,  1.08898138e-03,  3.83811446e-04,  9.66395178e-04,\n",
       "        7.36042573e-04,  9.13077804e-05,  5.95209688e-04, -1.69341671e-05,\n",
       "       -4.04678751e-04, -2.85742057e-04, -2.83059294e-04, -1.77861005e-04,\n",
       "        6.31276898e-04,  1.93123452e-04,  6.71574820e-04, -1.11635124e-03,\n",
       "        2.04585672e-04, -2.98855481e-04, -1.88725884e-04, -7.46791514e-05])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-87c99c84-34e5-432c-b05d-b65010396bf5' class='xr-section-summary-in' type='checkbox'  ><label for='section-87c99c84-34e5-432c-b05d-b65010396bf5' 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>multi_index<br>dac_ch1<br>dac_ch2</div></div><div class='xr-index-preview'>PandasMultiIndex</div><input type='checkbox' disabled/><label></label><input id='index-bcc2a70b-8343-40fa-acf6-a65f5c45a07e' class='xr-index-data-in' type='checkbox'/><label for='index-bcc2a70b-8343-40fa-acf6-a65f5c45a07e' 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(MultiIndex([(              -1.0,  -1.0),\n",
       "            (-0.989949748743719, -0.99),\n",
       "            (-0.979899497487437, -0.98),\n",
       "            (-0.969849246231156, -0.97),\n",
       "            (-0.959798994974874, -0.96),\n",
       "            (-0.949748743718593, -0.95),\n",
       "            (-0.939698492462312, -0.94),\n",
       "            ( -0.92964824120603, -0.93),\n",
       "            (-0.919597989949749, -0.92),\n",
       "            (-0.909547738693467, -0.91),\n",
       "            ...\n",
       "            ( 0.909547738693467,   0.9),\n",
       "            ( 0.919597989949749,  0.91),\n",
       "            (  0.92964824120603,  0.92),\n",
       "            ( 0.939698492462312,  0.93),\n",
       "            ( 0.949748743718593,  0.94),\n",
       "            ( 0.959798994974874,  0.95),\n",
       "            ( 0.969849246231156,  0.96),\n",
       "            ( 0.979899497487437,  0.97),\n",
       "            ( 0.989949748743719,  0.98),\n",
       "            (               1.0,  0.99)],\n",
       "           name=&#x27;multi_index&#x27;, length=200))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-66e80b39-674f-4db7-aeaa-8b8f633259f2' class='xr-section-summary-in' type='checkbox'  ><label for='section-66e80b39-674f-4db7-aeaa-8b8f633259f2' class='xr-section-summary' >Attributes: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>ds_name :</span></dt><dd>results</dd><dt><span>sample_name :</span></dt><dd>no sample</dd><dt><span>exp_name :</span></dt><dd>working_with_pandas</dd><dt><span>snapshot :</span></dt><dd>{&quot;station&quot;: {&quot;instruments&quot;: {&quot;dmm&quot;: {&quot;functions&quot;: {}, &quot;submodules&quot;: {}, &quot;parameters&quot;: {&quot;IDN&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dmm_IDN&quot;, &quot;value&quot;: {&quot;vendor&quot;: &quot;QCoDeS&quot;, &quot;model&quot;: &quot;&lt;class &#x27;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&#x27;&gt;&quot;, &quot;serial&quot;: &quot;NA&quot;, &quot;firmware&quot;: &quot;NA&quot;}, &quot;raw_value&quot;: {&quot;vendor&quot;: &quot;QCoDeS&quot;, &quot;model&quot;: &quot;&lt;class &#x27;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&#x27;&gt;&quot;, &quot;serial&quot;: &quot;NA&quot;, &quot;firmware&quot;: &quot;NA&quot;}, &quot;ts&quot;: &quot;2025-03-23 20:08:38&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;instrument_name&quot;: &quot;dmm&quot;, &quot;label&quot;: &quot;IDN&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Anything&gt;&quot;], &quot;vals&quot;: &quot;&lt;Anything&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;IDN&quot;, &quot;unit&quot;: &quot;&quot;}, &quot;v1&quot;: {&quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DmmExponentialParameter&quot;, &quot;full_name&quot;: &quot;dmm_v1&quot;, &quot;value&quot;: 4.108679799299617, &quot;raw_value&quot;: 4.108679799299617, &quot;ts&quot;: &quot;2025-03-23 20:09:42&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;instrument_name&quot;: &quot;dmm&quot;, &quot;label&quot;: &quot;Gate v1&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;v1&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;v2&quot;: {&quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DmmGaussParameter&quot;, &quot;full_name&quot;: &quot;dmm_v2&quot;, &quot;value&quot;: 0.0005805528766549148, &quot;raw_value&quot;: 0.0005805528766549148, &quot;ts&quot;: &quot;2025-03-23 20:09:42&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;instrument_name&quot;: &quot;dmm&quot;, &quot;label&quot;: &quot;Gate v2&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;v2&quot;, &quot;unit&quot;: &quot;V&quot;}}, &quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;name&quot;: &quot;dmm&quot;, &quot;label&quot;: &quot;dmm&quot;}, &quot;dac&quot;: {&quot;functions&quot;: {}, &quot;submodules&quot;: {}, &quot;parameters&quot;: {&quot;IDN&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dac_IDN&quot;, &quot;value&quot;: {&quot;vendor&quot;: &quot;QCoDeS&quot;, &quot;model&quot;: &quot;&lt;class &#x27;qcodes.instrument_drivers.mock_instruments.DummyInstrument&#x27;&gt;&quot;, &quot;serial&quot;: &quot;NA&quot;, &quot;firmware&quot;: &quot;NA&quot;}, &quot;raw_value&quot;: {&quot;vendor&quot;: &quot;QCoDeS&quot;, &quot;model&quot;: &quot;&lt;class &#x27;qcodes.instrument_drivers.mock_instruments.DummyInstrument&#x27;&gt;&quot;, &quot;serial&quot;: &quot;NA&quot;, &quot;firmware&quot;: &quot;NA&quot;}, &quot;ts&quot;: &quot;2025-03-23 20:08:38&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;instrument_name&quot;: &quot;dac&quot;, &quot;label&quot;: &quot;IDN&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Anything&gt;&quot;], &quot;vals&quot;: &quot;&lt;Anything&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;IDN&quot;, &quot;unit&quot;: &quot;&quot;}, &quot;ch1&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dac_ch1&quot;, &quot;value&quot;: 1.0, &quot;raw_value&quot;: 1.0, &quot;ts&quot;: &quot;2025-03-23 20:09:42&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;instrument_name&quot;: &quot;dac&quot;, &quot;label&quot;: &quot;Gate ch1&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;ch1&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;ch2&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dac_ch2&quot;, &quot;value&quot;: 1.0, &quot;raw_value&quot;: 1.0, &quot;ts&quot;: &quot;2025-03-23 20:09:42&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;instrument_name&quot;: &quot;dac&quot;, &quot;label&quot;: &quot;Gate ch2&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;ch2&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;fixed_parameter&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dac_fixed_parameter&quot;, &quot;value&quot;: 5, &quot;raw_value&quot;: 5, &quot;ts&quot;: &quot;2025-03-23 20:08:38&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;instrument_name&quot;: &quot;dac&quot;, &quot;label&quot;: &quot;fixed_parameter&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [], &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;fixed_parameter&quot;, &quot;unit&quot;: &quot;&quot;}}, &quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;name&quot;: &quot;dac&quot;, &quot;label&quot;: &quot;dac&quot;}}, &quot;parameters&quot;: {}, &quot;components&quot;: {}, &quot;config&quot;: null}, &quot;parameters&quot;: {&quot;ch1&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dac_ch1&quot;, &quot;value&quot;: 1.0, &quot;raw_value&quot;: 1.0, &quot;ts&quot;: &quot;2025-03-23 20:09:42&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;instrument_name&quot;: &quot;dac&quot;, &quot;label&quot;: &quot;Gate ch1&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;ch1&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;ch2&quot;: {&quot;__class__&quot;: &quot;qcodes.parameters.parameter.Parameter&quot;, &quot;full_name&quot;: &quot;dac_ch2&quot;, &quot;value&quot;: 1.0, &quot;raw_value&quot;: 1.0, &quot;ts&quot;: &quot;2025-03-23 20:09:42&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrument&quot;, &quot;instrument_name&quot;: &quot;dac&quot;, &quot;label&quot;: &quot;Gate ch2&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;ch2&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;v2&quot;: {&quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DmmGaussParameter&quot;, &quot;full_name&quot;: &quot;dmm_v2&quot;, &quot;value&quot;: 0.0005805528766549148, &quot;raw_value&quot;: 0.0005805528766549148, &quot;ts&quot;: &quot;2025-03-23 20:09:42&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;instrument_name&quot;: &quot;dmm&quot;, &quot;label&quot;: &quot;Gate v2&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;v2&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;v1&quot;: {&quot;__class__&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DmmExponentialParameter&quot;, &quot;full_name&quot;: &quot;dmm_v1&quot;, &quot;value&quot;: 4.108679799299617, &quot;raw_value&quot;: 4.108679799299617, &quot;ts&quot;: &quot;2025-03-23 20:09:42&quot;, &quot;instrument&quot;: &quot;qcodes.instrument_drivers.mock_instruments.DummyInstrumentWithMeasurement&quot;, &quot;instrument_name&quot;: &quot;dmm&quot;, &quot;label&quot;: &quot;Gate v1&quot;, &quot;inter_delay&quot;: 0, &quot;validators&quot;: [&quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;], &quot;vals&quot;: &quot;&lt;Numbers -800&lt;=v&lt;=400&gt;&quot;, &quot;post_delay&quot;: 0, &quot;name&quot;: &quot;v1&quot;, &quot;unit&quot;: &quot;V&quot;}}}</dd><dt><span>guid :</span></dt><dd>abd7a822-0000-0000-0000-0195c4695546</dd><dt><span>run_timestamp :</span></dt><dd>2025-03-23 20:09:52</dd><dt><span>completed_timestamp :</span></dt><dd>2025-03-23 20:09:52</dd><dt><span>captured_run_id :</span></dt><dd>4</dd><dt><span>captured_counter :</span></dt><dd>4</dd><dt><span>run_id :</span></dt><dd>4</dd><dt><span>run_description :</span></dt><dd>{&quot;version&quot;: 3, &quot;interdependencies&quot;: {&quot;paramspecs&quot;: [{&quot;name&quot;: &quot;dac_ch1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate ch1&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: []}, {&quot;name&quot;: &quot;dac_ch2&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate ch2&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: []}, {&quot;name&quot;: &quot;dmm_v2&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v2&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: [&quot;dac_ch1&quot;, &quot;dac_ch2&quot;]}, {&quot;name&quot;: &quot;dmm_v1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v1&quot;, &quot;unit&quot;: &quot;V&quot;, &quot;inferred_from&quot;: [], &quot;depends_on&quot;: [&quot;dac_ch1&quot;, &quot;dac_ch2&quot;]}]}, &quot;interdependencies_&quot;: {&quot;parameters&quot;: {&quot;dmm_v2&quot;: {&quot;name&quot;: &quot;dmm_v2&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v2&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;dac_ch1&quot;: {&quot;name&quot;: &quot;dac_ch1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate ch1&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;dac_ch2&quot;: {&quot;name&quot;: &quot;dac_ch2&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate ch2&quot;, &quot;unit&quot;: &quot;V&quot;}, &quot;dmm_v1&quot;: {&quot;name&quot;: &quot;dmm_v1&quot;, &quot;paramtype&quot;: &quot;numeric&quot;, &quot;label&quot;: &quot;Gate v1&quot;, &quot;unit&quot;: &quot;V&quot;}}, &quot;dependencies&quot;: {&quot;dmm_v2&quot;: [&quot;dac_ch1&quot;, &quot;dac_ch2&quot;], &quot;dmm_v1&quot;: [&quot;dac_ch1&quot;, &quot;dac_ch2&quot;]}, &quot;inferences&quot;: {}, &quot;standalones&quot;: []}, &quot;shapes&quot;: null}</dd><dt><span>parent_dataset_links :</span></dt><dd>[]</dd><dt><span>run_timestamp_raw :</span></dt><dd>1742756992.340981</dd><dt><span>completed_timestamp_raw :</span></dt><dd>1742756992.4179645</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 8kB\n",
       "Dimensions:      (multi_index: 200)\n",
       "Coordinates:\n",
       "  * multi_index  (multi_index) object 2kB MultiIndex\n",
       "  * dac_ch1      (multi_index) float64 2kB -1.0 -0.9899 -0.9799 ... 0.9899 1.0\n",
       "  * dac_ch2      (multi_index) float64 2kB -1.0 -0.99 -0.98 ... 0.97 0.98 0.99\n",
       "Data variables:\n",
       "    dmm_v1       (multi_index) float64 2kB 6.187 6.159 6.294 ... 4.022 4.066\n",
       "    dmm_v2       (multi_index) float64 2kB -0.0006791 -0.0003731 ... -7.468e-05\n",
       "Attributes: (12/14)\n",
       "    ds_name:                  results\n",
       "    sample_name:              no sample\n",
       "    exp_name:                 working_with_pandas\n",
       "    snapshot:                 {\"station\": {\"instruments\": {\"dmm\": {\"functions...\n",
       "    guid:                     abd7a822-0000-0000-0000-0195c4695546\n",
       "    run_timestamp:            2025-03-23 20:09:52\n",
       "    ...                       ...\n",
       "    captured_counter:         4\n",
       "    run_id:                   4\n",
       "    run_description:          {\"version\": 3, \"interdependencies\": {\"paramspec...\n",
       "    parent_dataset_links:     []\n",
       "    run_timestamp_raw:        1742756992.340981\n",
       "    completed_timestamp_raw:  1742756992.4179645"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xaDataSet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note how the expected coordinates can be seen above along with a coordinate called `multi_index`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QCoDeS has build in support for exporting such datasets to NetCDF files using [cf_xarray](https://cf-xarray.readthedocs.io/en/latest/coding.html) to compress and decompress the data. Note however, that if you manually export or import such XArray datasets to / from NetCDF you will be responsible for compressing / decompressing as needed. "
   ]
  }
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