{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performing measurements using QCoDeS parameters and DataSet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook shows some ways of performing different measurements using \n",
    "QCoDeS parameters and the [DataSet](DataSet-class-walkthrough.ipynb) via a powerful ``Measurement`` context manager. Here, it is assumed that the reader has some degree of familiarity with fundamental objects and methods of QCoDeS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing a measurement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let us start with necessary imports:"
   ]
  },
  {
   "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-work\\.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-work\\.qcodes\\logs\\210129-14216-qcodes.log\n",
      "Activating auto-logging. Current session state plus future input saved.\n",
      "Filename       : C:\\Users\\Jens-work\\.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-work\\.qcodes\\logs\\210129-14216-qcodes.log\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from pathlib import Path\n",
    "from time import monotonic, sleep\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import qcodes as qc\n",
    "from qcodes.dataset import (\n",
    "    Measurement,\n",
    "    initialise_or_create_database_at,\n",
    "    load_by_guid,\n",
    "    load_by_run_spec,\n",
    "    load_or_create_experiment,\n",
    "    plot_dataset,\n",
    ")\n",
    "from qcodes.dataset.descriptions.detect_shapes import detect_shape_of_measurement\n",
    "from qcodes.instrument_drivers.mock_instruments import (\n",
    "    DummyInstrument,\n",
    "    DummyInstrumentWithMeasurement,\n",
    ")\n",
    "from qcodes.logger import start_all_logging\n",
    "\n",
    "start_all_logging()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In what follows, we shall define some utility functions as well as declare our dummy instruments. We, then, add these instruments to a ``Station`` object. \n",
    "\n",
    "The dummy dmm is setup to generate an output depending on the values set on the dummy dac simulating a real experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# preparatory mocking of physical setup\n",
    "\n",
    "dac = DummyInstrument('dac', gates=['ch1', 'ch2'])\n",
    "dmm = DummyInstrumentWithMeasurement(name='dmm', setter_instr=dac)\n",
    "\n",
    "station = qc.Station(dmm, dac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now make some silly set-up and tear-down actions\n",
    "\n",
    "\n",
    "def veryfirst():\n",
    "    print('Starting the measurement')\n",
    "\n",
    "\n",
    "def numbertwo(inst1, inst2):\n",
    "    print(f'Doing stuff with the following two instruments: {inst1}, {inst2}')\n",
    "\n",
    "\n",
    "def thelast():\n",
    "    print('End of experiment')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note** that database and experiments may be missing.\n",
    "\n",
    "If this is the first time you create a dataset, the underlying database file has\n",
    "most likely not been created. The following cell creates the database file. Please\n",
    "refer to documentation on [The Experiment Container](The-Experiment-Container.ipynb) for details.\n",
    "\n",
    "Furthermore, datasets are associated to an experiment. By default, a dataset (or \"run\")\n",
    "is appended to the latest existing experiments. If no experiment has been created,\n",
    "we must create one. We do that by calling the `load_or_create_experiment` function.\n",
    "\n",
    "Here we explicitly pass the loaded or created experiment to the `Measurement` object to ensure that we are always\n",
    "using the `performing_meas_using_parameters_and_dataset` `Experiment` created within this tutorial. Note that a keyword argument `name` can also be set as any string value for `Measurement` which later becomes the `name` of the dataset that running that `Measurement` produces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "initialise_or_create_database_at(Path.cwd() / \"performing_meas.db\")\n",
    "exp = load_or_create_experiment(\n",
    "    experiment_name='performing_meas_using_parameters_and_dataset',\n",
    "    sample_name=\"no sample\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then run an experiment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting the measurement\n",
      "Doing stuff with the following two instruments: <DummyInstrumentWithMeasurement: dmm>, <DummyInstrument: dac>\n",
      "Starting experimental run with id: 380. \n",
      "End of experiment\n"
     ]
    }
   ],
   "source": [
    "meas = Measurement(exp=exp, name='exponential_decay')\n",
    "meas.register_parameter(dac.ch1)  # register the first independent parameter\n",
    "meas.register_parameter(dmm.v1, setpoints=(dac.ch1,))  # now register the dependent oone\n",
    "\n",
    "meas.add_before_run(veryfirst, ())  # add a set-up action\n",
    "meas.add_before_run(numbertwo, (dmm, dac))  # add another set-up action\n",
    "meas.add_after_run(thelast, ())  # add a tear-down action\n",
    "\n",
    "meas.write_period = 0.5\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "    for set_v in np.linspace(0, 25, 10):\n",
    "        dac.ch1.set(set_v)\n",
    "        get_v = dmm.v1.get()\n",
    "        datasaver.add_result((dac.ch1, set_v),\n",
    "                             (dmm.v1, get_v))\n",
    "\n",
    "    dataset1D = datasaver.dataset  # convenient to have for data access and plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax, cbax = plot_dataset(dataset1D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And let's add an example of a 2D measurement. For the 2D, we'll need a new batch of parameters, notably one with two \n",
    "other parameters as setpoints. We therefore define a new Measurement with new parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qcodes.dataset.measurements.Measurement at 0x1d5524c4b08>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas = Measurement(exp=exp, name='2D_measurement_example')\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(dmm.v2, setpoints=(dac.ch1, dac.ch2))  # now register the dependent oone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 381. \n"
     ]
    }
   ],
   "source": [
    "# run a 2D sweep\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "\n",
    "    for v1 in np.linspace(-1, 1, 200):\n",
    "        for v2 in np.linspace(-1, 1, 200):\n",
    "            dac.ch1(v1)\n",
    "            dac.ch2(v2)\n",
    "            val = dmm.v2.get()\n",
    "            datasaver.add_result((dac.ch1, v1),\n",
    "                                 (dac.ch2, v2),\n",
    "                                 (dmm.v2, val))\n",
    "\n",
    "    dataset2D = datasaver.dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax, cbax = plot_dataset(dataset2D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing and exporting the measured data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "QCoDeS ``DataSet`` implements a number of methods for accessing the data of a given dataset. Here we will concentrate on the two most user friendly methods. For a more detailed walkthrough of the `DataSet` class, refer to [DataSet class walkthrough](DataSet-class-walkthrough.ipynb) notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method `get_parameter_data` returns the data as a dictionary of ``numpy`` arrays. The dictionary is indexed by the measured (dependent) parameter in the outermost level and the names of the dependent and independent parameters in the innermost level. The first parameter in the innermost level is always the dependent parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dmm_v1': {'dmm_v1': array([ 4.94727878,  2.86877729,  1.62188288,  1.08101681,  0.41030687,\n",
       "          0.3159459 ,  0.17529232,  0.23451543,  0.08759799, -0.12921666]),\n",
       "  'dac_ch1': array([ 0.        ,  2.77777778,  5.55555556,  8.33333333, 11.11111111,\n",
       "         13.88888889, 16.66666667, 19.44444444, 22.22222222, 25.        ])}}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1D.get_parameter_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default `get_parameter_data` returns all data stored in the dataset. The data that is specific to one or more measured parameters can be returned by passing the parameter name(s) or by using `ParamSpec` object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dmm_v1': {'dmm_v1': array([ 4.94727878,  2.86877729,  1.62188288,  1.08101681,  0.41030687,\n",
       "          0.3159459 ,  0.17529232,  0.23451543,  0.08759799, -0.12921666]),\n",
       "  'dac_ch1': array([ 0.        ,  2.77777778,  5.55555556,  8.33333333, 11.11111111,\n",
       "         13.88888889, 16.66666667, 19.44444444, 22.22222222, 25.        ])}}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1D.get_parameter_data('dmm_v1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also simply fetch the data for one or more dependent parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'dac_ch1': {'dac_ch1': array([ 0.        ,  2.77777778,  5.55555556,  8.33333333, 11.11111111,\n",
       "         13.88888889, 16.66666667, 19.44444444, 22.22222222, 25.        ])}}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1D.get_parameter_data('dac_ch1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more details about accessing data of a given `DataSet`, see [Accessing data in DataSet notebook](Accessing-data-in-DataSet.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data can also be exported as one or more [Pandas](https://pandas.pydata.org/) DataFrames. \n",
    "The DataFrames cane be returned either as a single dataframe or as a dictionary from measured parameters to DataFrames.\n",
    "If you measure all parameters as a function of the same set of parameters you probably want to export to a single dataframe."
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       "             dmm_v1\n",
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    "However, there may be cases where the data within a dataset cannot be put into a single dataframe. \n",
    "In those cases you can use the other method to export the dataset to a dictionary from name of the measured parameter to Pandas dataframes."
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   "execution_count": 15,
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       " dac_ch1            \n",
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    "When exporting a two or higher dimensional datasets as a Pandas DataFrame a [MultiIndex](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html) is used to index the measured parameter based on all the dependencies"
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       "                     dmm_v2\n",
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    "If your data is on a regular grid it may make sense to view the data as an [XArray](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html) Dataset. The dataset can be directly exported to a XArray Dataset."
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       "}\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",
       "  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;\n",
       "Dimensions:  (dac_ch1: 200, dac_ch2: 200)\n",
       "Coordinates:\n",
       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
       "  * dac_ch2  (dac_ch2) float64 -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
       "Data variables:\n",
       "    dmm_v2   (dac_ch1, dac_ch2) float64 -0.0002192 -4.001e-05 ... -0.0001639\n",
       "Attributes:\n",
       "    sample_name:  no sample\n",
       "    exp_name:     performing_meas_using_parameters_and_dataset</pre><div class='xr-wrap' hidden><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-2d1e5fc6-4824-40d4-9474-24a29feeef87' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2d1e5fc6-4824-40d4-9474-24a29feeef87' 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>: 200</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c83173d9-93ee-4fd1-b6d4-5e46110f4866' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c83173d9-93ee-4fd1-b6d4-5e46110f4866' 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-a34cd0fb-98b6-4eb4-89e0-bc924b1ea4ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a34cd0fb-98b6-4eb4-89e0-bc924b1ea4ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63b431bc-1cae-4a2d-b4e2-f44c9560d3c2' class='xr-var-data-in' type='checkbox'><label for='data-63b431bc-1cae-4a2d-b4e2-f44c9560d3c2' 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></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 -0.9899 -0.9799 ... 0.9899 1.0</div><input id='attrs-74745129-f4fc-4d83-a525-70820d17f098' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74745129-f4fc-4d83-a525-70820d17f098' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a9e36a1-bda2-4397-a9c4-68ed0eb65795' class='xr-var-data-in' type='checkbox'><label for='data-5a9e36a1-bda2-4397-a9c4-68ed0eb65795' 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></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></ul></div></li><li class='xr-section-item'><input id='section-ee6d3cb7-f6a1-4412-865d-2338262a07a5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ee6d3cb7-f6a1-4412-865d-2338262a07a5' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>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'>-0.0002192 ... -0.0001639</div><input id='attrs-6df1aea2-2aaa-4e1e-a99a-34c8c04ef92d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6df1aea2-2aaa-4e1e-a99a-34c8c04ef92d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-192a1bf7-a6b3-4f67-9bb8-295c7543adc2' class='xr-var-data-in' type='checkbox'><label for='data-192a1bf7-a6b3-4f67-9bb8-295c7543adc2' 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></dl></div><div class='xr-var-data'><pre>array([[-2.19176629e-04, -4.00064010e-05, -5.63100829e-04, ...,\n",
       "         4.92344711e-04,  1.13743336e-04,  4.61475600e-04],\n",
       "       [-1.64206923e-04,  4.90867182e-05,  2.52550757e-04, ...,\n",
       "         3.53774946e-04,  1.05182868e-04,  7.11079453e-04],\n",
       "       [ 1.66987131e-05, -4.17155633e-04,  2.26207012e-04, ...,\n",
       "        -3.33816354e-04,  3.87546347e-05, -3.99048509e-04],\n",
       "       ...,\n",
       "       [ 4.07467243e-04,  5.44043252e-04, -2.97859939e-05, ...,\n",
       "         2.86801600e-04,  1.79833873e-04, -2.34794356e-04],\n",
       "       [-3.11745256e-04,  9.00080791e-04,  4.22415634e-04, ...,\n",
       "        -6.71180432e-05,  1.65824734e-04,  1.68343733e-04],\n",
       "       [ 1.24250201e-04,  4.36653302e-04, -5.03244000e-04, ...,\n",
       "        -2.14129378e-04, -2.53412572e-04, -1.63879046e-04]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-45aea91a-8f09-4dfa-986b-c57c2c429592' class='xr-section-summary-in' type='checkbox'  checked><label for='section-45aea91a-8f09-4dfa-986b-c57c2c429592' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>sample_name :</span></dt><dd>no sample</dd><dt><span>exp_name :</span></dt><dd>performing_meas_using_parameters_and_dataset</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (dac_ch1: 200, dac_ch2: 200)\n",
       "Coordinates:\n",
       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
       "  * dac_ch2  (dac_ch2) float64 -1.0 -0.9899 -0.9799 ... 0.9799 0.9899 1.0\n",
       "Data variables:\n",
       "    dmm_v2   (dac_ch1, dac_ch2) float64 -0.0002192 -4.001e-05 ... -0.0001639\n",
       "Attributes:\n",
       "    sample_name:  no sample\n",
       "    exp_name:     performing_meas_using_parameters_and_dataset"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset2D.to_xarray_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note, however, that XArray is only suited for data that is on a rectangular grid with few or no missing values. If the data does not lie on a grid, all the measured data points will have an unique combination of the two dependent parameters. When exporting to XArray, NaN's will therefore replace all the missing combinations of `dac_ch1` and `dac_ch2` and the data is unlikely to be useful in this format."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more details about using Pandas and XArray see [Working With Pandas and XArray](./Working-With-Pandas-and-XArray.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to export the datasets directly to various file formats see [Exporting QCoDes Datasets](./Exporting-data-to-other-file-formats.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reloading datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To load existing datasets QCoDeS provides several functions. The most useful and generic function is called `load_by_run_spec`. \n",
    "This function takes one or more pieces of information about a dataset and will either, if the dataset is uniquely identifiable by the information, load the dataset or print information about all the datasets that match the supplied information allowing you to provide more information to uniquely identify the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we will load a dataset based on the `captured_run_id` printed on the plot above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "380"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1D.captured_run_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_ds = load_by_run_spec(captured_run_id=dataset1D.captured_run_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_ds.the_same_dataset_as(dataset1D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As long as you are working within one database file the dataset should be uniquely identified by `captured_run_id`. However, once you mix several datasets from different database files this is likely not unique. See the following section and [Extracting runs from one DB file to another](Extracting-runs-from-one-DB-file-to-another.ipynb) for more information on how to handle this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataSet GUID"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Internally each dataset is refereed too by a Globally Unique Identifier (GUID) that ensures that the dataset uniquely identified even if datasets from several databases with potentially identical captured_run_id, experiment and sample names.\n",
    "A dataset can always be reloaded from the GUID if known. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset GUID is: aaaaaaaa-0000-0000-0000-01774e8f852e\n"
     ]
    }
   ],
   "source": [
    "print(f\"Dataset GUID is: {dataset1D.guid}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "loaded_ds = load_by_guid(dataset1D.guid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loaded_ds.the_same_dataset_as(dataset1D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Specifying shape of measurement\n",
    "As the context manager allows you to store data of any shape (with the only restriction being that you supply values for both dependent and independent parameters together), it cannot know if the data is being measured on a grid. As a consequence, the Numpy array of data loaded from the dataset may not be of the shape that you expect. `plot_dataset`, `DataSet.to_pandas...` and `DataSet.to_xarray...` contain logic that can detect the shape of the data measured at load time. However, if you know the shape of the measurement that you are going to perform up front, you can choose to specify it before initializing the measurement using ``Measurement.set_shapes`` method.\n",
    "\n",
    "`dataset.get_parameter_data` and `dataset.cache.data` automatically makes use of this information to return shaped data when loaded from the database. Note that these two methods behave slightly different when loading data on a partially completed dataset. `dataset.get_parameter_data` will only reshape the data if the number of points measured matches the number of points expected according to the metadata. `dataset.cache.data` will however return a dataset with empty placeholders (either NaN, zeros or empty strings depending on the datatypes) for missing values in a partially filled dataset. \n",
    "\n",
    "Note that if you use the doNd functions demonstrated in [Using doNd functions in comparison to Measurement context manager for performing measurements](Using_doNd_functions_in_comparison_to_Measurement_context_manager_for_performing_measurements.ipynb) the shape information will be detected and stored automatically.\n",
    "\n",
    "In the example below we show how the shape can be specified manually.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 382. \n"
     ]
    }
   ],
   "source": [
    "n_points_1 = 100\n",
    "n_points_2 = 200\n",
    "\n",
    "meas_with_shape = Measurement(exp=exp, name='shape_specification_example_measurement')\n",
    "meas_with_shape.register_parameter(dac.ch1)  # register the first independent parameter\n",
    "meas_with_shape.register_parameter(dac.ch2)  # register the second independent parameter\n",
    "meas_with_shape.register_parameter(dmm.v2, setpoints=(dac.ch1, dac.ch2))  # now register the dependent oone\n",
    "\n",
    "meas_with_shape.set_shapes(detect_shape_of_measurement((dmm.v2,), (n_points_1, n_points_2)))\n",
    "\n",
    "with meas_with_shape.run() as datasaver:\n",
    "\n",
    "    for v1 in np.linspace(-1, 1, n_points_1):\n",
    "        for v2 in np.linspace(-1, 1, n_points_2):\n",
    "            dac.ch1(v1)\n",
    "            dac.ch2(v2)\n",
    "            val = dmm.v2.get()\n",
    "            datasaver.add_result((dac.ch1, v1),\n",
    "                                 (dac.ch2, v2),\n",
    "                                 (dmm.v2, val))\n",
    "\n",
    "    dataset = datasaver.dataset  # convenient to have for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dmm_v2: data.shape=(100, 200), expected_shape=(100,200)\n",
      "dac_ch1: data.shape=(100, 200), expected_shape=(100,200)\n",
      "dac_ch2: data.shape=(100, 200), expected_shape=(100,200)\n"
     ]
    }
   ],
   "source": [
    "for name, data in dataset.get_parameter_data()['dmm_v2'].items():\n",
    "    print(f\"{name}: data.shape={data.shape}, expected_shape=({n_points_1},{n_points_2})\")\n",
    "    assert data.shape == (n_points_1, n_points_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performing several measuments concurrently"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to perform two or more measurements at the same time. This may be convenient if you need to measure several parameters as a function of the same independent parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 383. \n",
      "Starting experimental run with id: 384. \n"
     ]
    }
   ],
   "source": [
    "# setup two measurements\n",
    "meas1 = Measurement(exp=exp, name='multi_measurement_1')\n",
    "meas1.register_parameter(dac.ch1)\n",
    "meas1.register_parameter(dac.ch2)\n",
    "meas1.register_parameter(dmm.v1, setpoints=(dac.ch1, dac.ch2))\n",
    "\n",
    "meas2 = Measurement(exp=exp, name='multi_measurement_2')\n",
    "meas2.register_parameter(dac.ch1)\n",
    "meas2.register_parameter(dac.ch2)\n",
    "meas2.register_parameter(dmm.v2, setpoints=(dac.ch1, dac.ch2))\n",
    "\n",
    "with meas1.run() as datasaver1, meas2.run() as datasaver2:\n",
    "\n",
    "    v1points = np.concatenate((np.linspace(-2, -0.5, 10),\n",
    "                               np.linspace(-0.51, 0.5, 200),\n",
    "                               np.linspace(0.51, 2, 10)))\n",
    "    v2points = np.concatenate((np.linspace(-2, -0.25, 10),\n",
    "                               np.linspace(-0.26, 0.5, 200),\n",
    "                               np.linspace(0.51, 2, 10)))\n",
    "\n",
    "    for v1 in v1points:\n",
    "        for v2 in v2points:\n",
    "            dac.ch1(v1)\n",
    "            dac.ch2(v2)\n",
    "            val1 = dmm.v1.get()\n",
    "            datasaver1.add_result((dac.ch1, v1),\n",
    "                                 (dac.ch2, v2),\n",
    "                                 (dmm.v1, val1))\n",
    "            val2 = dmm.v2.get()\n",
    "            datasaver2.add_result((dac.ch1, v1),\n",
    "                                 (dac.ch2, v2),\n",
    "                                 (dmm.v2, val2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax, cbax = plot_dataset(datasaver1.dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax, cbax = plot_dataset(datasaver2.dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interrupting measurements early\n",
    "\n",
    "There may be cases where you do not want to complete a measurement. Currently QCoDeS is designed to allow the user\n",
    "to interrupt the measurements with a standard KeyBoardInterrupt. KeyBoardInterrupts can be raised with either a Ctrl-C keyboard shortcut or using the interrupt button in Juypter / Spyder which is typically in the form of a Square stop button. QCoDeS is designed such that KeyboardInterrupts are delayed around critical parts of the code and the measurement is stopped when its safe to do so. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QCoDeS Array and MultiParameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``Measurement`` object supports automatic handling of ``Array`` and ``MultiParameters``. When registering these parameters \n",
    "the individual components are unpacked and added to the dataset as if they were separate parameters. Lets consider a ``MultiParamter`` with array components as the most general case.\n",
    "\n",
    "First lets use a dummy instrument that produces data as ``Array`` and ``MultiParameters``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qcodes.instrument_drivers.mock_instruments import DummyChannelInstrument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydummy = DummyChannelInstrument('MyDummy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This instrument produces two ``Array``s with the names, shapes and setpoints given below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('this', 'that')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydummy.A.dummy_2d_multi_parameter.names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5, 3), (5, 3))"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydummy.A.dummy_2d_multi_parameter.shapes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(('multi_2d_setpoint_param_this_setpoint',\n",
       "  'multi_2d_setpoint_param_that_setpoint'),\n",
       " ('multi_2d_setpoint_param_this_setpoint',\n",
       "  'multi_2d_setpoint_param_that_setpoint'))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydummy.A.dummy_2d_multi_parameter.setpoint_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MyDummy_ChanA_this': ParamSpecBase('MyDummy_ChanA_this', 'numeric', 'this label', 'this unit'),\n",
       " 'MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint': ParamSpecBase('MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint', 'numeric', 'this setpoint', 'this setpointunit'),\n",
       " 'MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint': ParamSpecBase('MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint', 'numeric', 'that setpoint', 'that setpointunit'),\n",
       " 'MyDummy_ChanA_that': ParamSpecBase('MyDummy_ChanA_that', 'numeric', 'that label', 'that unit')}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas = Measurement(exp=exp)\n",
    "\n",
    "meas.register_parameter(mydummy.A.dummy_2d_multi_parameter)\n",
    "meas.parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When adding the MultiParameter to the measurement we can see that we add each of the individual components as a \n",
    "separate parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 385. \n"
     ]
    }
   ],
   "source": [
    "with meas.run() as datasaver:\n",
    "    datasaver.add_result((mydummy.A.dummy_2d_multi_parameter, mydummy.A.dummy_2d_multi_parameter()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And when adding the result of a ``MultiParameter`` it is automatically unpacked into its components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<AxesSubplot:title={'center':'Run #385, Experiment performing_meas_using_parameters_and_dataset (no sample)'}, xlabel='this setpoint (this setpointunit)', ylabel='that setpoint (that setpointunit)'>,\n",
       "  <AxesSubplot:title={'center':'Run #385, Experiment performing_meas_using_parameters_and_dataset (no sample)'}, xlabel='this setpoint (this setpointunit)', ylabel='that setpoint (that setpointunit)'>],\n",
       " [<matplotlib.colorbar.Colorbar at 0x1d55465b888>,\n",
       "  <matplotlib.colorbar.Colorbar at 0x1d556bfe988>])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dataset(datasaver.dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'MyDummy_ChanA_that': {'MyDummy_ChanA_that': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),\n",
       "  'MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint': array([5., 5., 5., 6., 6., 6., 7., 7., 7., 8., 8., 8., 9., 9., 9.]),\n",
       "  'MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint': array([ 9., 10., 11.,  9., 10., 11.,  9., 10., 11.,  9., 10., 11.,  9.,\n",
       "         10., 11.])}}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasaver.dataset.get_parameter_data('MyDummy_ChanA_that')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>MyDummy_ChanA_that</th>\n",
       "      <th>MyDummy_ChanA_this</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint</th>\n",
       "      <th>MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint</th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>11.0</th>\n",
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       "      <th rowspan=\"3\" valign=\"top\">6.0</th>\n",
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       "    <tr>\n",
       "      <th>10.0</th>\n",
       "      <td>1.0</td>\n",
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       "      <th rowspan=\"3\" valign=\"top\">7.0</th>\n",
       "      <th>9.0</th>\n",
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       "      <td>0.0</td>\n",
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       "      <th>10.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>11.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8.0</th>\n",
       "      <th>9.0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <th>11.0</th>\n",
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       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                       MyDummy_ChanA_that  \\\n",
       "MyDummy_ChanA_multi_2d_setpoint_param_this_setp... MyDummy_ChanA_multi_2d_setpoint_param_that_setp...                       \n",
       "5.0                                                9.0                                                                1.0   \n",
       "                                                   10.0                                                               1.0   \n",
       "                                                   11.0                                                               1.0   \n",
       "6.0                                                9.0                                                                1.0   \n",
       "                                                   10.0                                                               1.0   \n",
       "                                                   11.0                                                               1.0   \n",
       "7.0                                                9.0                                                                1.0   \n",
       "                                                   10.0                                                               1.0   \n",
       "                                                   11.0                                                               1.0   \n",
       "8.0                                                9.0                                                                1.0   \n",
       "                                                   10.0                                                               1.0   \n",
       "                                                   11.0                                                               1.0   \n",
       "9.0                                                9.0                                                                1.0   \n",
       "                                                   10.0                                                               1.0   \n",
       "                                                   11.0                                                               1.0   \n",
       "\n",
       "                                                                                                       MyDummy_ChanA_this  \n",
       "MyDummy_ChanA_multi_2d_setpoint_param_this_setp... MyDummy_ChanA_multi_2d_setpoint_param_that_setp...                      \n",
       "5.0                                                9.0                                                                0.0  \n",
       "                                                   10.0                                                               0.0  \n",
       "                                                   11.0                                                               0.0  \n",
       "6.0                                                9.0                                                                0.0  \n",
       "                                                   10.0                                                               0.0  \n",
       "                                                   11.0                                                               0.0  \n",
       "7.0                                                9.0                                                                0.0  \n",
       "                                                   10.0                                                               0.0  \n",
       "                                                   11.0                                                               0.0  \n",
       "8.0                                                9.0                                                                0.0  \n",
       "                                                   10.0                                                               0.0  \n",
       "                                                   11.0                                                               0.0  \n",
       "9.0                                                9.0                                                                0.0  \n",
       "                                                   10.0                                                               0.0  \n",
       "                                                   11.0                                                               0.0  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasaver.dataset.to_pandas_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:                                              (MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint: 3, MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint: 5)\n",
       "Coordinates:\n",
       "  * MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint  (MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint) float64 ...\n",
       "  * MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint  (MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint) float64 ...\n",
       "Data variables:\n",
       "    MyDummy_ChanA_that                                   (MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint, MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint) float64 ...\n",
       "    MyDummy_ChanA_this                                   (MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint, MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint) float64 ...\n",
       "Attributes:\n",
       "    sample_name:  no sample\n",
       "    exp_name:     performing_meas_using_parameters_and_dataset</pre><div class='xr-wrap' hidden><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-ee7a1cd9-ac75-4c6f-a31f-57a52efdc757' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ee7a1cd9-ac75-4c6f-a31f-57a52efdc757' 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'>MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint</span>: 3</li><li><span class='xr-has-index'>MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f4d1276b-fbe9-4d16-bba8-2f7dafae9eb6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f4d1276b-fbe9-4d16-bba8-2f7dafae9eb6' 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'>MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint</span></div><div class='xr-var-dims'>(MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 6.0 7.0 8.0 9.0</div><input id='attrs-aa5de5af-039d-484b-8786-94e89f17cfe7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa5de5af-039d-484b-8786-94e89f17cfe7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb14a911-bc87-4c4e-a187-e2c99682046c' class='xr-var-data-in' type='checkbox'><label for='data-eb14a911-bc87-4c4e-a187-e2c99682046c' 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>MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>this setpoint</dd><dt><span>unit :</span></dt><dd>this setpointunit</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([5., 6., 7., 8., 9.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint</span></div><div class='xr-var-dims'>(MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.0 10.0 11.0</div><input id='attrs-fc66d713-f191-4424-9f36-df7e994fd696' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc66d713-f191-4424-9f36-df7e994fd696' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b654199-a79e-42b6-abac-2fd4d6373646' class='xr-var-data-in' type='checkbox'><label for='data-6b654199-a79e-42b6-abac-2fd4d6373646' 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>MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>that setpoint</dd><dt><span>unit :</span></dt><dd>that setpointunit</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[]</dd></dl></div><div class='xr-var-data'><pre>array([ 9., 10., 11.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2215d675-2e3a-4fa2-bd09-c716ffeb35b7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2215d675-2e3a-4fa2-bd09-c716ffeb35b7' 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>MyDummy_ChanA_that</span></div><div class='xr-var-dims'>(MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint, MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0</div><input id='attrs-3f096a1b-86b0-42a9-aef4-54465d221382' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f096a1b-86b0-42a9-aef4-54465d221382' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09cae199-52be-4baf-ae58-083be541bb21' class='xr-var-data-in' type='checkbox'><label for='data-09cae199-52be-4baf-ae58-083be541bb21' 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>MyDummy_ChanA_that</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>that label</dd><dt><span>unit :</span></dt><dd>that unit</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[&#x27;MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint&#x27;, &#x27;MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint&#x27;]</dd></dl></div><div class='xr-var-data'><pre>array([[1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MyDummy_ChanA_this</span></div><div class='xr-var-dims'>(MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint, MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-5b96e73c-3dc3-44b9-bd97-e53869848792' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b96e73c-3dc3-44b9-bd97-e53869848792' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb5a8415-9573-478f-aa29-71541c5a528d' class='xr-var-data-in' type='checkbox'><label for='data-eb5a8415-9573-478f-aa29-71541c5a528d' 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>MyDummy_ChanA_this</dd><dt><span>paramtype :</span></dt><dd>numeric</dd><dt><span>label :</span></dt><dd>this label</dd><dt><span>unit :</span></dt><dd>this unit</dd><dt><span>inferred_from :</span></dt><dd>[]</dd><dt><span>depends_on :</span></dt><dd>[&#x27;MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint&#x27;, &#x27;MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint&#x27;]</dd></dl></div><div class='xr-var-data'><pre>array([[0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dd224945-5c25-40c0-901e-e9acf624aae5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dd224945-5c25-40c0-901e-e9acf624aae5' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>sample_name :</span></dt><dd>no sample</dd><dt><span>exp_name :</span></dt><dd>performing_meas_using_parameters_and_dataset</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:                                              (MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint: 3, MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint: 5)\n",
       "Coordinates:\n",
       "  * MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint  (MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint) float64 ...\n",
       "  * MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint  (MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint) float64 ...\n",
       "Data variables:\n",
       "    MyDummy_ChanA_that                                   (MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint, MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint) float64 ...\n",
       "    MyDummy_ChanA_this                                   (MyDummy_ChanA_multi_2d_setpoint_param_this_setpoint, MyDummy_ChanA_multi_2d_setpoint_param_that_setpoint) float64 ...\n",
       "Attributes:\n",
       "    sample_name:  no sample\n",
       "    exp_name:     performing_meas_using_parameters_and_dataset"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasaver.dataset.to_xarray_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Avoiding verbosity of the Measurement context manager for simple measurements\n",
    "\n",
    "For simple 1D/2D grid-type of measurements, it may feel like an overkill to use the verbose and flexible Measurement context manager construct. For this case, so-called ``doNd`` functions come ti rescue - convenient one- or two-line calls, read more about them in [Using doNd functions](./Using_doNd_functions_in_comparison_to_Measurement_context_manager_for_performing_measurements.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optimizing measurement time\n",
    "\n",
    "There are measurements that are data-heavy or time consuming, or both. QCoDeS provides some features and tools that should help in optimizing the measurement time. Some of those are:\n",
    "\n",
    "* [Saving data in the background](./Saving_data_in_the_background.ipynb)\n",
    "* Setting more appropriate ``paramtype`` when registering parameters, see [Paramtypes explained](./Paramtypes%20explained.ipynb)\n",
    "* Adding result to datasaver by creating threads per instrument, see [Threaded data acquisition](./Threaded%20data%20acquisition.ipynb)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The power of the Measurement context manager construct\n",
    "\n",
    "This new form is so free that we may easily do thing impossible with the old Loop construct."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Say, that from the plot of the above 1D measurement, \n",
    "we decide that a voltage below 1 V is uninteresting,\n",
    "so we stop the sweep at that point, thus,\n",
    "we do not know in advance how many points we'll measure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 386. \n"
     ]
    }
   ],
   "source": [
    "meas = Measurement(exp=exp)\n",
    "meas.register_parameter(dac.ch1)  # register the first independent parameter\n",
    "meas.register_parameter(dmm.v1, setpoints=(dac.ch1,))  # now register the dependent oone\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "\n",
    "    for set_v in np.linspace(0, 25, 100):\n",
    "        dac.ch1.set(set_v)\n",
    "        get_v = dmm.v1.get()\n",
    "        datasaver.add_result((dac.ch1, set_v),\n",
    "                             (dmm.v1, get_v))\n",
    "\n",
    "        if get_v < 1:\n",
    "            break\n",
    "\n",
    "    dataset = datasaver.dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax, cbax = plot_dataset(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or we might want to simply get as many points as possible in 10 s\n",
    "randomly sampling the region between 0 V and 10 V (for the setpoint axis)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 387. \n"
     ]
    }
   ],
   "source": [
    "with meas.run() as datasaver:\n",
    "\n",
    "    t_start = monotonic()\n",
    "\n",
    "    while monotonic() - t_start < 3:\n",
    "        set_v = 10/2*(np.random.rand() + 1)\n",
    "        dac.ch1.set(set_v)\n",
    "\n",
    "        # some sleep to not get too many points (or to let the system settle)\n",
    "        sleep(0.04)\n",
    "\n",
    "        get_v = dmm.v1.get()\n",
    "        datasaver.add_result((dac.ch1, set_v),\n",
    "                             (dmm.v1, get_v))\n",
    "\n",
    "    dataset = datasaver.dataset  # convenient to have for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OO38AuRPyBpxfEGdxIsY/pnoTj30Ip//Ig27V2vdxIuNyfAq42K0au3OW9b6O0zn2MZwb76cAVPVrOL+sPu8e+wmcfjBzsQvn180J4Gs4bX3fn+M+FkxV+3E6jf0dzkXyYpwLan5T10LN9nl9FKfTXj/wIE5ziCeAcx6ewKmCfxklbiqq+j2ci85PcT6/byxkf0V8Aviue4xHcS7eaZx26FIKfodwvveHca5D+3Hei1J+D/gLERnD6aD9xRLrF1XGeX0z8O/u9+ZNqroPp4Pfx3Buhs/itGsXE8EZDt+P89rXUHr4/h/hBEljOO95oZtJMZ/A6cf1E5zr8Ww1xH5vweloOohzQ/ysb1mpz2jatUVV9+P0E3gAJwi4jOnX5h3AQyISx6lpep+qHnSbHl4FvBnnXD2F89lECh2nyOu4FXirW+tYjrlck34DeM49T96N07wAs3+X56vgddiv1PslzpQMF+PUpszK66FqADfa/aKq5jePVGLfd+N0Tvpkpfe91IiI4lSzPlty5RVEnPlBjuEMpSwUrJoCROQ1wC2qurnEendTJ98hU3sicjvO/eDOWpdlPip1HRaRf8AZfPDxUuvahCk+qnqMmX0tjCnKbXp4CKeq8o9xatDK+XVct9wmx5/DqaVYi/NL9ms1LZQxeVR1IU1gK4aqfrDcdetixkVjquhqnA5c/Tjtxa9X1QkRuaVA01hcRPL71Sy6JVA2Af4cp6r/UZxh4h92y1aoXHERuWaRyrasyMxUAt6/P6viMe0zMgVZk4cxxhhjFsxqKIwxxhizYNaHYgnp6urSLVu21LoYxhizrDz88MP9qjqvOZBM5VhAsYRs2bKFffv21boYxhizrIhIOTN5miqzJo8qEZFN4kzru1+caWHfV+syGWOMMdViNRTVkwY+qKqPuLPfPSwi33MnazHGGGNWFKuhqBJVPenN3unORHaA4slijDHGmGXNAopFICJbcHLMP1Rg2Y0isk9E9p09e3bRy2aMMcZUggUUVSZOBtKvAO9X1dH85ap6q6puV9Xtq1dbJ2VjjDHLk/WhqCJxMrB+BfjPEunX521PX4Lde8foj2fpaglw/Y4YO3ubSm9ojDHGVJAFFFXiZqn7FHBAVf+xGsfY05fgjvtG6E47KVLj8Sx33DcCYEGFMcaYRWVNHtXzEpw0tT8vIo+5/15byQPs3jtGdxpiOMkRYkB32nneGGOMWUxWQ1ElqvojnPt81fTHs2zKe64F6Itnq3lYY4wxZgaroVjGuloCxPOei7vPG2OMMYvJ7jzL2PU7YhwLwRigOP8fCznPG2OMMYvJmjyWMa/j5VcfHOaZCehqCbLLRnkYY4ypAQsolrmdvU2c1z7OkSNHuOyyy2hoaKh1kYwxxtQha/JYAcLhMABTU1M1Lokxxph6ZQHFCuAFFOl0usYlMcYYU68soFgBrIbCGGNMrVlAsQKEQk5XGAsojDHG1IoFFCtAIBAgGAxaQGGMMaZmbJTHChEOhxctoLCEZMYYY/JZQLFChEKhRemUaQnJjDHGFGJNHivEYtVQWEIyY4wxhVhAsUIsVkDRH8/Skvdci/u8McaY+mUBxQoRDofJZrNkMpmqHscSkhljjCnE7gIrxGJNbmUJyYwxxhRinTJXCP9cFJFIpGrH8TpefunHA4ymgnQ1B9m100Z5GGNMvbOAYoVYzNkyd/Y2EY4/TTab5fnPf37u2MYYY+qXNXmsEIs9/baIAJDNWmdMY4wxFlCsGF6Tx2IlCPMCClVdlOMZY4xZ2iygWCFEhFAotOg1FBZQGGOMAQso5kVEPi0iZ0TkiSLL20Tkv0TkJyLypIi8YzHKtZjTb3ssoDDGGAMWUMzXbcCrZ1n+HmC/qr4AuA74BxFpqHahFjOgsD4Uxhhj/CygmAdVvRcYnG0VICbOXbfFXbfqnRtqEVBYDYUxxhiwYaPV8jFgN3ACJ93Fr6lqwZ/yInIjcCNAT0/Pgg4aDoc5cDrEF24/zcACMoGWk03UAgpjjDF+VkNRHb8APAZsAC4HPiYirYVWVNVbVXW7qm5fvXr1gg66/2SA+59rY1U8ywuBDjcT6J6+RNn72NOX4I57R+gosQ8LKIwxxvhZQFEd7wC+qo5ngUPARdU+6HeegB4NzDkTqKoyPj7OqVOn+Mr9Q3RnSmcTtT4Uxhhj/KzJozqOAC8H7hORtcCFwMFqHnBPX4KxFPT6nhsETgLJeJabbj+da7pQVSYnJxkbG2N0dJSxsbFcUrHhyfWcn7fvFqAvL5uo1VAYY4zxs4BiHkTkDpzRG10icgz4CBAGUNVbgL8EbhORx3F+6H9IVfurWabde8eI4GT+jOEEE8eBLTgBQTye5Y57Rzh7tp+e2AipVApw+l20t7fT2tpKLBZj9dODxONZ/Km+vGyi/r4VbZEYL1yfZcsWCyiMMcZYQDEvqrqrxPITwKsWqTgA9MezbAaewwkiTrr/e4FBDOjOwD3PBPida5pYu3Ytra2tRCKRXG0DOFlD77hvhO60G4jgZBPdsSmSe34TEJ8M8MDhNrq60ryic/FepzHGmKXJ+lCsEF0tARqAjTjtLUmcgMCvBRhNBdm6dStr1qyhsbFxWjABTuKvXde0cbZReQRlsCXArmva2H90ku709L4VPRrgB09aHwpjjDFWQ7Fi+GsWLgae5Fzzh8druihlZ28TW9riHD16NJdN9NN3jbApb70WoG+iUq/AGGPMcmY1FCuEV7Mw1BLgUaAhAkcCMIYzy9YYTtPF9Ttis+/I1djYCMDEhBMxdLUEiOetEwc6ohV6AcYYY5Y1q6FYQXb2Nk2bgMrrRNnnTlC1aw6TXHkBRTKZpLW1let3xLj93hE2Zc71rTgiWV57kcWkxhhjLKBY0fIDjLkIh8MEg0GSyWRuXwMDg/zwKWU0FaQ9oly9foRLN7RVssjGGGOWKQsoTEEiQmNjY67JA6C3K8may0YACIVCpNNpVAtOAGqMMabOWH21KaqxsTFXQwEwOTlJc3MzAOm0k+vMJrYyxhgDFlCYWTQ2NpJOp92aCM0FFKHQuYotCyiMMcaABRRmFtGoM4QjmUwyNTWFqhKJRHLPg+XyMMYY47A+FKYo/9BRrybCCygeeS7NwydijKWCdLWcnleadGOMMSuHBRSmqIaGBgKBAMlkkkDAqcyKRCI8fbaBBw630aMBtuHmCbnP6axpQYUxxtQna/IwRXkjPZLJJJOTk4ATZHz78ey80qQbY4xZuSygMLPyho5OTk7S0NDAvp8lGZssnCekP279KYwxpl5ZQGFm1djYyNTUFIlEgkgkMi1Nul8caItkc2nRjTHG1BcLKMys/CM9IpEI/fEs63HSpPvzhBwErtwwypNPPsnp06dtOKkxxtQZCyjMrLyRHuB0yMxPk/4IcBiIRuCGa88nFotx7NgxDhw4wPj4eG0KbYwxZtHZKA8zq58czfDlx9cwmgrSeUC5dHMje/smcmnS4zhZTN/04jYikQhbt25leHiYo0eP8tRTT7F69Wo2btxIMBis9UsxxhhTRRZQmKL29CX4/H2jdKdDzvDQBOztm2BHb5T9RycLZjEVETo6OmhtbeX48eOcPXuW4eFhNm3aRHt7OyJS2xdljDGmKiygMEXt3jtGd9oZFgrnhofuPzrJX71l7azbBoNBenp66Ozs5MiRIxw8eJDW1lZ6enqIRCJVL7sxxpjFZX0oTFH98eyCh4c2Nzdz0UUX0d3dTTwe58knn+TUqVPWadMYY1YYCyjmQUQ+LSJnROSJWda5TkQeE5EnReSexSxfpXS1BAoOD+1qmdtpIyKsXbuWSy65hLa2No4fP86BAweIx/P3bowxZrmygGJ+bgNeXWyhiLQDHweuV9VLgDcuTrEq6/odMY6Fpg8PPRZynp+PhoYGtm7dytatW0mn0zz99NMcPnw4lwrdGGPM8mV9KOZBVe8VkS2zrPIW4KuqesRd/8yiFKzCvI6Wu/eOFeyAOV/t7e3EYjFOnDjBmTNncp02Ozo6rNOmMcYsUxZQVMcFQFhE7sbpy/jPqvrZQiuKyI3AjQA9PT2LVsBy7extqkrCr2AwyKZNm+js7OTw4cMcOnSI/v5+Nm/ebJ02jTFmGbImj+oIAVcCrwN+Afj/ROSCQiuq6q2qul1Vt69evXoxy7gkNDU1cdFFF7Fp0ybGx8d58sknOXnyJNms5QUxxpjlxGooquMYMKCq48C4iNwLvAB4prbFWppEhDVr1tDe3s7Ro0c5ceIEg4OD9PT0EIvNr7+GMcaYxWU1FNXxdeClIhISkSbgKuBAjcu05HmdNrdt20Y2m+WZZ57hueees06bxhizDFgNxTyIyB3AdUCXiBwDPgKEAVT1FlU9ICLfBn4KZIFPqmrRIaZmura2Ni6++GJOnjzJ6dOnGRkZobu7m1WrVlmnTWOMWaLEJhhaOrZv36779u2rdTGWlEQiwZEjRxgfHycWi9HT0zMtYZkxxojIw6q6vdblqHfW5GGWtKamJi688EJ6enpIJBLs37+fEydOWKdNY4xZYqzJwyx5IsLq1atznTZPnjzJ4OAgmzdvtk6bxhizRFgNhVk2wuEw559/Ptu2bUNVrdOmMcYsIVZDYZadtrY2LrnkEk6ePMmpU6cYHh6mu7ubzs5O9j47we69Y/S7M3teX4GZPY0xxpRmAYVZlgKBABs3bmTVqlUcPnyYw4cP86P9I9zT10R3BjYB8XiWO+4bAbCgwhhjqsyaPMyyFo1GufDCC9m8eTP3H4zQnXHmOhec/7vTTi4SY4wx1WU1FGbZExG6uroYTZ1ka96yFDAQz/LuW09aE4gxxlSRBRRmxehqCRCPZ/HGfQwCJ4BtQAvnmkCePZVi/9FJ62dhjDEVZE0eZsW4fkeMYyEYAxQnmNjC9CaQ1jTcf2CCjniWFwIdbpCxpy9Rs3IbY8xKYAGFWTF29jax65o2hloCPApM4tRM+A0D52H9LIwxptKsycOsKDt7m3LNFzfdfnpaEwhAkplBRgvQF7eZN40xZiGshsKsWPlNIGNAEIjnrRcHWhsyjI6OLnYRjTFmxbCAwqxYXhPI6UiGR1CGWgK89HlRjgWnBxnHgnDV5gR9fX0cOnSIqampGpfcGGOWn7pv8hCRDmADMAE8p6pW972C7OxtYpWcIJVKcfHFFwOwKjLOD/ZnGUuF6GoJsGtHjO1b13Lq1ClOnTqVS5fe2dm5oHTpe/oSNmunMaZu1GVAISJtwHuAXUADcBZoBNaKyIPAx1X1rhoW0VRQQ0MD8fi5ho4Luibp3p7g0ksvnbbehg0bps28OTAwQE9PD9FodM7H3NOX4I77RuhO26ydxpj6UK9NHl8GjgLXqOqFqvpSVd2uqpuAvwVuEJF31raIplIaGhrIZDJkMhkAkskkjY2NBddtbGzkggsuYPPmzUxMTHDgwAGOHz8+53Tpu/eO0Z220STGmPpRlzUUqvrKWZY9DDy8iMUxVdbQ0ABAKpWisbGRZDI5a9pzb+bNtrY2jh07xqlTpxgaGqKnp4fW1tayjtkfz7Ip7zkbTWKMWcnqMqAQkf3A7cAdqvqzWpfHVJc/oAgGg6hq0RoKv3A4zHnnnUdnZydHjhyhr6+PVatWcXKyi/9+JDFr34j8WTvBGU3S1VKvlYLGmJWuXq9uu4Bm4LsiskdEPiAiG2pdKFMd/oAimUwClBVQeFpbW7n44otZt24dD/VN8IUfjZacabPQkNVjIed5Y4xZieoyoFDVn6jqn6rqVuAPgB7gQRG5S0TeVWp7Efm0iJwRkSdKrLdDRNIi8qsVKrqZh3A4DEwPKCKRyJz24aVLf/zsKnqygZJ9I3b2NvGml8Q41ZDmEZQzjVl2XdNmHTKNMStWXTZ5+KnqgzjBxNeBfwI+BnyixGa3uet9ttgKIhIE/jfw3cqU1MyXiNDQ0EAqlSKbzRIIBHJBxlwNjCs9ec8V6xtxeU+QhvgZAoEAIsILtlklmDFm5arLGgqPW4PwjyJyGLgZ+DecOSlmpar34iSznM3vA18Bziy0nGbhvIDCG+Ex3/kluloCBWfaLNQ3Ip1OA9DR0UEmkyGRsARkxpiVqy4DChH5XyLyM+DjwHHgJap6nareoqoDFdj/RuCXgX8tY90bRWSfiOw7e/bsQg9tivAHFHNt7vCbS98IL6BYtWoVgE3tbYxZ0eq1ySMJvFpV+6q0/48CH1LVbKlfwqp6K3ArwPbt27VK5al7XkAB0NnZOe/9eH0gdu8doy+eobUhy5tf0lGwb4Q3hXc0GqWpqYnR0VHWr19f9rFspk1jzHJSrwHFD2cLJkSkFehR1Vk7Xc5iO/B5N5joAl4rImlVvXOe+zML5I30gLmN8CjEy2ja39/P4cOHubi7cIDi1VCEQiFaW1s5deoUmUyGYDBY8hg206YxZrmpyyYP4A0icr+IfFhEXiciO0XkWhH5LRH5HPANYO7zLbtU9TxV3aKqW3Bm5fw9CyZqyx9QLKTJw8+bHGtsrPDsl+l0mmAwiIjkJsQqtm4+m2nTGLPc1GUNhap+QERWAW8A3gisx0kOdgD4N1X90Wzbi8gdwHVAl4gcAz4ChN1931LFopt5evyY8vXH1zCaCtL1VJwbdsqCf+lHIhEaGhoYHR1lzZo1M5ZPTU3lRpM0NzcTCAQYGRmhvb295L5tpk1jzHJTlwEFgKoO4gwPLTVEtNC2u+aw7tvnun9TWXv6EnzlwQm6MyG2AfHxyjUftLa2Mjg4iKrOGDmSTqcJhZyvWCAQIBaLMTo6WnDdfDbTpjFmubGrk1nxdu8doztTneaDWCxGNpstOCTUH1CAE3ykUikmJydL7vf6HTGOBaePJjkaUH5pR8u09fb0Jbjp9tO8+9aT3HT76RkzdhpjzGKp2xoKUz+q2Xzg9aMYHR2lubl52rKpqSlaWs4FAF4/itHR0ZIdQ3f2NtE/MMBdT8FYKkRHFLY0j3PnQ8Jn7hqlqyXAxZsi7O2bsI6bxpglwWoozIo3l8mo5iocDhONRmd0tlRVMpnMtBk5Gxsbc30uSslms2xoHOB3r53ilhvX8/oXtfLcYDNdCXI5RH50YMI6bhpjlgwLKPKISNHU5mZ5qnairlgsRjweJ5s9V+PhHzLq19raytjY2LR1CxkdHSWTyeTmzPivvXE2ZWVa8JDBqWnxa8GpkTHGmMVmAcVMn6p1AUxl7extYtc1bQy1BHgUGGoJVDRRVywWQ1UZHx/PPVcsoGhrayObzU5bt5CBgYHc/BXgBAn5wUMjVK3mxRhj5qou+1CIyO5ii4D5T6NolixvMqpq8Pej8B57s2TmJyErtG6+dDrNyMgIq1evzo0GKTTqox04BJyHUzMRx6l52WUp0o0xNVCXAQVwDfDrzPyBJ8DOxS+OWc6CwSDNzc3T+lEUq6EIBoO0tLQwMjLCxo0bC+7PG4bqnyL8+h2x3MyZXvAwGoKrt0V47OC4M79Gc5BdO216bmNMbdRrQPEgkFDVe/IXiMjTNSiPWeZisdi0qbWLBRTg9KM4ceLEtImv/AYGBnL5PzxekPC1h0Z4JqG54OGybuGi5kMAXHjhhbS0WDBhjKmNugwoVPU1syy7djHLYlYGL6AYGxvjmbMNfPXBNEMT6+l6epAb8pJ6eQHF6OjojERlExMTJBIJuru7ZxxjZ28TvV2THDx4kOc973m5hGMer5mlGEs2ZoypprruvSUif+imGjdmQVpaWhARHngqzh33jbB6QrgCYZU7N4R/wqmmpiaCwWDB4aMDAwPAuZTn+QIB5yvrjRLxBxGzBRResrGOeDY37DS/XMYYsxB1HVDgjL77rojcJyLvFZG1tS6QWZ4CgQAtLS3c9TQl54bwkoV503B7VJXBwUHa2toKNoV4x/HWhfIDCks2ZoyptroOKFT1z1X1EuA9OAnC7hGR79e4WGaZOjQSY2QyUNbcEK2traTTaSYmJnLPjY2NMTU1NaMZxM8b9eGvoQgEAoTD4VkDikLDTm3OCmNMJdVlH4oCzgCngAFgZtpIY0rY05fg2z+FCEIcSib1euZsA197fA1jD4/Q1TLG9TtirA4NEAwGaWtrK3qc/CaPVCrFoeEWHjoSZXgywOqW0wX7RliyMWNMtdX11UREfk9E7gZ+gDP/xLtU9fm1LZVZjrwEZBuA55h9Vs49fQm+9MA461Khc/0Z7h1hz7MTdHR05IKGQvKbPH56DO79WTNrJoNcgRTtG3H9jhhHg1q12UKNMabeayg2Ae9X1cdqXRCzvHkJyLyk5EeAJE7E/o68WTn9/RnA7c+QgX3HY7zu6o5Zj5Pf5PHAoUhuSu7cvty+Ef5j7uxt4tix4zx4JMozkwFWRYVdL6rcbKHGGFPXAYWq/mmty2BWBn+Twir33xjONN/5N21/9tNB4CRu8JEK8uRJ4are4sfxN3moKiOpAFvz1imUSTWZTLK5dZSXvNoZstrZ2UlPjwUTxpjKqesmD2MqpVACsqMBLdik4GU/HQSOAz3AFcA2hM/fNzrrUE5/k0cmk6G1IVNWPo/h4WEAOjo6iEaj0zqDGmNMJVhAYUwFeAnIBprgEZRTDWledWmmYJOCF3ycALYwt6Gc/iaPqakprtwwxrFg6Uyqw8PDNDU10dDQkAso/ENWjTFmoeq6yQNARDYDvar6fRGJAiFVtcH5Zs529jZxWbewf/9+ANauLTytiRdkfOqukYJDOfObK/zyA4rezgk2rF/Htx6b5JkJLZjPY2pqivHxcTZs2ABANBqlv7+fqakpGhoa5vlqjTFmuroOKETkXcCNOE3eW4Fu4Bbg5bUsl1m+IpFI7vFsozV29jaxe+/YnIdyigiBQABVJZVKAXDVBc1cvD7L4cOHufTSS6eVAc41d7S3twNOQAHONN8WUBhjKqXemzzeA7wEGAVQ1T7KmIdCRD4tImdE5Ikiy98qIj8VkcdF5H4ReUFFS22WrEAgkLtJzxZQQOF+F+UM5RSRXA0FOCnSg8EgcG70h9/w8DCRSITGxkZgekBRrj19CW66/TTvvvUkN91+2qbsNsbMUO8BxaSqprw/RCSEc20v5Tbg1bMsPwS8TFUvA/4SuHUhhTTLi3fjLhVQeP0uhloCPIozImTXNaWHcgYCgVxAEQwGCQQCuWNlMplp62YyGcbGxmhvb881l4RCIcLhMMlksqzXY3lAjDHlqOsmD5yptv8MiIrIK4HfA/6r1Eaqeq+IbJll+f2+Px/EaUoxdaJvoJG7nmpk7OEMXUVmrvTs7G2a81wQXpOHP/15/gyanpGREVQ119zhaWxsLLuGouC8GQXmujDG1Ld6Dyj+BHgn8DjwO8B/q+onKnyMdwLfKrZQRG7E6cdBT09PhQ9tFtuevgTffTLEpgxsA+Lur3mgYjdff5OH17xSrMljeHiYUChEc3PztOej0Shnz55FVXM1F8X4583wtADPxLPcdPvpiqRDt9Tqxix/9d7k8fuq+glVfaOq/qqqfkJE3lepnYvIz+EEFB8qto6q3qqq21V1++rVqyt1aFMju/eOsSlT3ayeXpNHKpWatYYim80yMjIyrbnDE41GUVUmJydLHs+bN8PvBBCGijSDWJOKMStDvQcUbyvw3NsrsWMReT7wSeAGVR2oxD7N0rcYWT39fSjyAwp/H4qxsTGy2eyM5g6YW8fM63fEOBqYngfkLHAeMAXsB54BMmn44v0jc349llrdmJWhLps8RGQX8BbgPBHZ7VsUw5nAcKH77wG+CvyGqj6z0P2Z5WMxsnqKSG7IaH5A8djhNPd8z2mGaG9UrljfxAtjM0eN+AOKjo7Z84fs2BblucNH2HusmdFUkNUtQTLxLCnOTc7VgvM6D046NQ5zaa4o1qQy23wcxpilpy4DCuB+nBQKXcA/+J4fA35aamMRuQO4DugSkWPAR3BqgFHVW4AP42Qv/bhb1ZxW1e0VLL9Zoq7fEeOO+0boTp+7yR4Lwa4KZvUMBAK5pgovoAgGg/QNRHnwKGzKODfoeFK4/3AbW36WnHGDDwQCRCKRsmooJicn2doxTm/nBCLC5Zdfzk23n+ZkPJub6RP3//OZe2dNS61uzMpQlwGFqh4GDgNXz3P7XSWW/zbw2/PZt1nevBvp7r1j9LkdDHdVuIOhfziqF1CICA8dbc313wDn/01ZKXqDLzenx/j4OABtbW0MDQ2RzWa5fkdsXjN9FrIYQZgxpvrqMqDwiMiLgP8LPA9oAILAuKq21rRgZlmbz1DQufB3sPRGeezpS5DIBOZ0g49GowwPD5PJZHKjRApJJBIEAgFaW1sZGhoilUqxs7eJL90/yolJZRgnW2oj0M7caxZ29jahKF/+8SCjqSCdTQF2XdVqozyMWWbqOqAAPga8GfgSsB34TeCCmpbImBL8NRShkPMV3r13jAjCCSj7Bu/1o0gmkzOGlfqNj4/nEouBkxuksbGRF57fyP0HEpyH5GoWDgEv3hQpuq9iXrg5TGj0DADnnXceq1ZZMGHMclP3jZSq+iwQVNWMqn6G2WfANKbmvIAiFArlHve7fRD6OZcOvcf9++IiN/hyRnqoKolEYlpA4XUI3X90kvOQaaMzznOfnytvn/mPjTHLR73XUCREpAF4TET+DqejZt0HWWZp85o8vP4T4NRCjMaznM/MTpLFbvCRSAQRmTWg8NKcNzc3547n5RCp5OgMfxDh7d8Ys7zU+83zN3Deg/cC48Am4A01LZExJXi1Ev6A4vodMSbROc2BISIlO2Z6HTKbm5tzeUO8G36hCa/mOzrDCyhCoZAFFMYsU3UdUKjqYVVNquqoqv65qv6h2wRizJJVKKDY2dtEc0jnfIMvJ6AIBoO55o6Ghobczf/6HTGOBLJzzpZaSCqVIhAIEI1GrcnDmGWqLgMKEblBRN7j+/shETno/vvVWpbNmFK8Jg/vJu+5dluSIzK3G3w0GiWdThetFUgkEjQ3N09rZvHW3dnbxNU9I5yNKo+gnIlky8qWWkgqlaKhoWHa/o0xy0u99qH4HzijOzwRYAfQDHwG+HItCmVMKXv6EnztwSyDE+tZFRV++UXnZqW8aM0UyeQkPznTQV+CsubA8HfM9Nd4gDON98TExLSpuxsaGhgdHQWcXCG9nRO87LIOkskkY2NjPL9347xel5forKGhgampqbKSlhljlpZ6DSgaVPWo7+8fufk2BkSk+Pg5Y2rIS6LVnYYtCPEJpmUyVVV6Oyd4zVUbaWtrK2uf/oCitXX69CuJhJOcq6npXEDi1SCoai4RmddUMTg4SDqdzg1lnYtUKkVTUxPhcBhVJZ1OzwhwjDFLW70GFNOSF6jqe31/WspPsyT5k2jBzCRaX94TY2Syjc4DE7z+qnBZTQ+hUIhQKFSwH4UXUPjnqPDPReEJBoO5m38ymaSlJb9r6Oyy2SzpdDrX5OHtPz+gsBTnxixtddmHAnhIRN6V/6SI/A6wpwblMaakYplMz7rpvtdOBrkCoTOhZaf/9kZ6JJPJGcvGx8en3eSBaTd8L7OpV0MB5WUvzed1wvSaPPzPeSzFuTFLX73WUHwAuFNE3gI84j53JU5fitfXqlDGzKZYEq2QULTmopxf8I2NjQwMDMzot+DNkOnnr6HwZzoNh8MEAoEFBxT5c114ZqudsVoKY5aGuqyhUNUzqvpi4C+B59x/f6GqV6vq6VqWzZhirt8R41iIGaM40sqc5p/IF41GyWaz02oF0uk0qVRqxpTc3g0/lUrl+lAEg8Gy5rQoplBAkV9DUax2ptzXaIypvnqtoQBAVX8I/LDW5TCmHMUyme7eO7ag9N/+5opIxJmm2z+hlV8oFEJEcqMy4Ny8GNFolKGhoTmP0PCCh3A4jIgUHDpqKc6NWfrqOqAwZrkplsl0Iem//QFFe3s7e/oSfPXBcYYm1tP1VIIbdgZzx/Ru+I8eTvPjgxMMJtbT9dQYN+wUtrRF6e/vn/MIjVQqlWsyASewyK+huH5HjNvvGWZTVizFuTFLlIX3xixzO3ub2HVNG0MtAR4FhloCc5pgypsJc2JiItf5cfWEcAXCqvGZnR+fHWzi+/vDdCVw13E6ge4/5aRAn2uzhzeplcebi8LvyvMjvKhnmLON7iRajfOfRMsYUx1WQ2HMClCs5qJcXv+H3T8p3fnxwcONbMrKjHW+93iaX7mo8JwWs/HmoPCEw2HGxsamrZNIJHJzbAwPDzMwMMALzl8379drjKk8CyiMMUSjUUZGRsrKIDqSFM4vtM54tuicFsWoKqlUatpEXA0NDWQyGTKZDMGgU+sRjztZSpqbmwmFQvT39zM4OMjq1Ys3bcxC58GweTTMSmdNHsaYXD+KVc1SMsFYR7T4OsXmtCgmk8mgqtOaPAoNHY3H4zQ2NhIKhWhqasoNdV0sC50Hw+bRMPXAAooqEZFPi8gZEXmi1mUxphQvoHj5xQGOBmcOTfUnGHvlZcGiSci8phNVLeu4/iGjnvyAQlUZHx/PzcApInR1dTE+Pj6vYarz4Z8HQ5g5S2m1tzdmObCAonpuA15d60IYU47GxkZEhAtXp3j5RSlON2SKdvDcua2JqzePcKohzSPotHUaGxtnzGkxm0IBRf5smclkkkwmM20I66pVqwAWrZZiofNg2Dwaph5YH4oqUdV7RWRLrcthTDlEhMbGRiYmJjivbZKLr4mydevWguuGw2F6Oyfo7Zxg9erV9PRsyC0rNKfFbMqpofDmxPDnCAmHw7S3tzMwMMDGjRurnpm0s1mIj+u858GweTRMPbCz2RgDOMFAIpFgcnIyFxgUUii3h38fQNn9KFKpFCIyLUNpMBgkGAzmgo14PE4wGJwRoHR2dpJOpxkZGSnrWPOVTCZ54boRjgR0WjPP0eD0pqDZFJvltNztjVkOLKCoMRG5UUT2ici+s2fP1ro4po5Fo1HS6TTAjBwefoFAIBcA5AcU/jktyuHNQZFfw+CfLdPrP5G/TltbW27ER7VMTk7S19fHts4J3nh1U26ujzORLC/ePMKV55euhYFzc4WcjTrzaJxtVJtHw6w41uRRY6p6K3ArwPbt28vryWZMFTx1JsQ3H1/DaCpI11OT3LAzUfCGt6cvwZcf62RkMkDngTSvv2r6el7TSTnyJ7XyeLNlptNpkskknZ2dM9YRETo7Ozl9+nTBdOcLNTU1RV9fH5lMhgsuuICmpiZeeomzLJFIcODAKU6dOsXGjRvL2t/O3iY2RAc5ffo0HR0dnH++BRNmZbGAwhjDnr4EX987RXcmxDYg7s6QCUwLFrzhj93pIFuBuJsq3b9eNBplbGysrJweqVSq4CRYDQ0NjI6OFs0p4unq6uL06dMMDAywbt3CJ7ryzxXRGsmwfUOQ1169ZUaNTVNTEx0dHZw5c4Y1a9aUHcx4CdXyZwI1ZiWwJo8qEZE7gAeAC0XkmIi8s9ZlMqaY3XvH6M6UHtZYzvDHaDSKqpbsR6Gq05KM+XlNHt6MmcUCisbGRpqbm3Pp1xcif66ItZNBHjjSzv6ThS+TGzZsIJvNcvLkybKP4QUU5Y6CMWY5sYCiSlR1l6quV9Wwqnar6qdqXSZjiil3WGM565XbMdP7lV4ooPCeGx4epqmpKZc4rJCuri6SyWSuNmO+CgZLmeJzRTQ2NtLV1UV/fz+Tk5NlHcNfQ7HQAMiYpcYCCmOMM6wx77lCwxrLWa+xsREonSSs0JBRj9eEMDk5OW24aCEdHR0EAoEFz0kxn7ki1q9fD8CJEyfKOoYXUKhqrgOsMSuFBRTGmLKHNZazXiAQIBKJlB1QFOp/4A8yijV3eILBIB0dHQwODpLJZGZddzblBlX55VyzZg2Dg4NldUT1AgpYHv0o9vQluOn207z71pPcdPtpmyrczMo6ZRpjch0qd+8do89NXrWrQPKqctfzpuCezWw1FI8dyfC13IiTqaIjTjydnZ0MDAwwPDxccERIOa7fEeP2e0fYlHFqJuI4wdKuEnNFrFu3jv7+fo4fP862bdtmXTebzRIMBslkMlUNKCqRiOxcB1zYBMTjhTvqGuOxgMIYA5SfAr2c9aLRKMPDw2Sz2aL9H1KpVG4SK789fQm+dH+c7vTsI078WlpaiEQi9Pf3zzug2NnbRP/AAHc/BX2pUNFgKV8oFGLdunUcP36ceDw+axNNJpPh0EgL9x+KMPZwgq6WZMWzjlYqEPD3KYHCqeyN8bOAwhhTcf6OmcUmySo2B8V8bmTenBQnTpxgcnKyrGm/C+ntTHLeVWme97znzWm7NWvWcObMGY4fP84FF1xQdLjsgdMhfnSwiU1ZcYKlKvzqr1QgUE4qe2P8rA+FMabi/Dk9iikWUMw3kZZXM7GQmTPnG4wEAgHWr19PPB5ndHS06Hp7jjSzKStVzTpaqURk8+lTYuqbnRnGmIqLRCKIyLwCivneyBoaGmhtbZ33nBSquqDaja6uLiKRCMePHy96/JFUoOpZRysVCFy/I8axEqnsjfGzgMIYU3H+7KWFZDIZMplMwYBiIYm0urq6mJqamrWWoBhvLglv2OtciQgbNmxgYmKCoaGhGctVldaGTNV/9S/k/fOP6ti9d4zLzw9xqiHNo3lp6o0pxPpQGGOqwpuCu5DZRniUO5KkkLa2NoLBIAMDA7S1tc2pvF5AMd8aCnDmxLj7pwN8YXeC0dTktBEWqsqVG8Z48GjHnEeSzMXO3iYy2SxffWCI0VSQVU3CrqtKBwKFOnM+djDLVZvGeNEFTWzZsqViZTQrkwUUxpiqiEajubkh8kdyzBZQQPkjTvIFAgE6Ozs5e/Ys6XR6Wlr0UryZPRcSUOx9doL7ftZCdwYn14mv0+UV5zXQ2zlB56oO7n4mwDPxDO2Nyq6rOyr+q//yniAN8TOAM0X4+vWl91+wM2cGHj4RY+e2pT9nhqk9CyiMMVXhnzEzfyhlqYBiITo7Ozlz5gyDg4OsWbOm7O0mJycJBoNzCkLy+XOiwPROl5dv7gDg8s0hXrl9NQcPHiQej3PZtg3zPl4x/jkuyp0WvNiojtFUkGy2vH2Y+mZ9KIwxVTHbSA/vhlfplOPgZAJtamqa82iPZDKZ60w6X7ONsPBmyfRqa2KxGFNTU2Xf8OfCe3+DwWDZ+y/WmbO1IWN5R0xZrIbCGFMVDQ0NBAKBGQHFnr4EX30AhpLrWf30mYpP7AROLcXRo0dJJBJF58HINzk5WXKa71K6WgLE41n8PSJOAEGB9392iFjDGl4byvJzq5yAAmBsbGzeHUGL8fKEtLS0lDUlODidNu+4d4RuX/+Oo0F40YYxstlgqc3npRIzepqlw2oojDFVISJEo9FpWUe9jn+rk8IVCB1uH4NK54hYtWoVIlJ2LUU2myWVSi34xp4/wuI4MIByvsILgXWpELv3TbGnL0EkEiEcDhftuLoQU1NTiAhNTU2kUqlpOUSK2dnbxA07wpxqSPMIykCT8NoXQG/nRFnbz1V+uvhqnQtm8VhAYYypmvycHgVThFd4YidwpsNub29ncHCwrJthJUZ4gHNT3nVNG0MtAR4F+gXOQwqmRBcRYrEYY2NjFW9SmJqaIhwO516P12ellIvWpHnzZWe48cqT/NFrG7hkvfPeVSOgWKxzwSweCyiMMVXT2NhIOp3OtelXahbHcnR1dZHJZBgeHi657kLnoPDb2dvEX71lLbfcuJ6MMuvrjcVipNPpabU4lZAfUJTbjyKZTOb6ePhrNqoRUCzmuWAWhwUUxpiqye+YuZjTOcdiMcLhMAMDAyXXrVQNRb5Sr9ffj6KS5htQTExM0NTURCAQYGpqqqoBhU3tvfLYJ2eMqRp/kjBw+hgcXaTpnEWErq4uRkdHS1b5e7/MFzJktJCCs1YGz73eSCRCQ0ND1QKKUChEIBAoK6BQVZLJJNFolHA4XPWAYjHPBbM4LKAwxlRNKBQiFArlaih29jbx8gsnOR3J8ChUfTpnL2FYqVqKycnJio+0gHN9Kvqj8AjKqYY0b3rp9JEMle5Hkc1myWQyhMNhRISGhoayAgqviaOxsZFwOEwqlSKTyeT2Wel+Hju2Rbnm/DHORLKLci6Y6rNho8aYqimU02Nz6yiXXhvjvPO6q378SCRCLBajv7+fdevWFZ1jIplM5pofKm1nbxO9XZMcPHgQgBf2bpy2PBaLMTAwkGtuWChvyKhX2xKJRMoKKLxapGg0SkNDA/F4nEDg3G9OVV3QHB35EokEW1rHeNnrVtHV1VWx/ZrasRqKeRKRV4vI0yLyrIj8SYHlPSJyl4g8KiI/FZHX1qKcxtSaN9JDVZmammJqaqoiN85ydXZ2kkqliMfzW+wd2WyWqampivef8PNPPZ5/U650P4r8ScO8gKJUDYMX9Hk1FP4mD5ieOOym208veHjnwMAAIkJHR8eC9mOWDgso5kFEgsC/AK8BLgZ2icjFeavdBHxRVV8IvBn4+OKW0pilIRqN5uZ5GB8fB1jwBFJz0dHRQSAQKDonRSVHeBTj/6WfH1A0NDQQiUSqGlB4wdxskslkrt9FOBxGVXN9T/oGonzhR2MVmzMim80yODhIe3v7jDwvZvmygGJ+dgLPqupBVU0BnwduyFtHgVb3cRvOhHnG1B1/x8xEIjHtucUQCARYtWoVQ0NDuT4BfpVIClZKqZtmJftRFAoooPRcFBMTE7mgytvW29fDJ2K5HCWVmDNiZGSETCaT6+NiVgYLKOZnI3DU9/cx9zm/m4FfF5FjwH8Dv19oRyJyo4jsE5F9Z8+erUZZjakp/9DR8fFxotHoov8q7erqQlUZHBycsWyxaygKicViZLPZXMC1EMUCitn6UfhHeMD0pG3BYJDRVLCic0YMDAwQDodpbW0tvbJZNiygqJ5dwG2q2g28FviciMx4v1X1VlXdrqrbV69eveiFNKbagsEg4XA4F1AsZv8JT1NTE42NjQVHe0xOThIKhaoa5JRTQwGV6UcxNTVFKBTKNa14wcFsAYV/hAdMT9oWCoVobchUbM6IqakpRkZGctOjm5XDAor5OQ7TMv12u8/5vRP4IoCqPgA0AtaV2dSlaDTK6OgomUxmUftPeLw5KcbHx2cky/KyjFZTqYAiHA7T2NhYsYDCHxAEAoGSQ0f9Izy88vjLduWGMY4GtCJzRgwNDQFYc8cKZMNG52cv0Csi5+EEEm8G3pK3zhHg5cBtIvI8nIDC2jRMXfICCqAmNRTgJAw7duwYAwMDdHefG7I6OTlZ9ar3vc9O8KXH1zCaCrL66dMFs2oeHmvl7qdg9K6T0zJvzjUjp1dD4Vdq6Kh/hAc4QUgwGCSTyRAKhejtjBMOh3jocJRnUkHaI8quF3fMa86IgYEBmpqaFrUfjVkcFlDMg6qmReS9wHeAIPBpVX1SRP4C2Kequ4EPAp8QkQ/gBPVv10rPDGPMMvH02Qa+7d5Qu56Oc8MOWfQJjMLhMO3t7QwMDLBx40ZEhEwmU/Uho15Wze50iG1A3B0hAeTegz19Cb73ZIjuDGz1rfPsqRR7+yboTjtVooW2zZdOp2f0B4lEIrPmNPGP8PA0NDQwMTGRe25L6xiXvdgJDs+ePcvlW9fP+b2YmJggkUhMC+jMymFNHvOkqv+tqheo6lZV/Wv3uQ+7wQSqul9VX6KqL1DVy1X1u7UtsTG1sacvwTceybAuFeIKhFU1TFPd2dlJOp1mZMS5KVcrh4dfOVk1d+8dKziK4sdPTcwpI6c3PNTfZAHO60un0wVHucD0ER4ebx/+IKO5uZm2tjZUdV7NM14fllWrVs15W7P0WUBhjKmqYjfLWqSpbmtrIxQK5eakWIwRHuVk1Sy2Tlp1TqMrMpkMqlowoIDCHTPzR3iAEwR+6oFmbn14PR/7YYC+AWdZS0sLLS0tiEiuCatc3iibtra2GeUzK4MFFMaYqlpKaapFhM7OTkZGRpiamlqUOSjKyapZbJ2QyJxGV+QPGfXMFlDkj/DwmmhWJ4UrEBom4N7n2rn14fX83TeS7PuZM025V8tTrtHRUaampqwz5gpmAYUxpqqWWppq74Y2ODi4KENGC2YczRshUWydl1wULbmtX7GAYraho/kjPPxNNEPAILAVJ7hYNe40Vz03GmNycrLstOjgvN/BYJC2traytzHLiwUUxpiqKueGupii0SjNzc309/eTTCar2twB5zKODrUEimbVLLbOW65p9z3vZCt949XNRTtkFgsovKCpUACQP8LDX6N0EtjCzOaqu59y5o8ot5Yik8kwNDTEqlWrSk7yZZYvG+VhjKkq7+a3e+8Yfe7Qx10lhj5W27FEB9/5aYrRVJCORviVQKKq5dnZ21Ry/8XW8Z6fnJzkiSeeYG1MODer/3TFAgooPnQ0f4RHV0uAeDxLDEhCweaqvvEskUiE0dFR1qxZM+vrAmfuCVW1zpgrnAUUxpiqK+eGulj29CX45qNZujPuMM4kJYdiLgWRSIT29nb6+/tZv359wWaadDqNiBSsBYhEIrnkbH75Izyu3xFzh7k6k+fEcWomPF5zVWtrKwMDA2Sz2ZK1DgMDA0QikZpMamYWj9U9GWPqylIadTJXa9euJZPJFMxJAudmySw0pXUkEiGVSk1LQFZohIe/+SUJHKJwc1VrayvZbLZoWnjP5OQk8Xiczs5Om2p7hbMaCmNMXemPZ6fNmw9uNX4NRp3MVXNzM01NTZw+fZqurq4ZN+hCc1B4/FlH/Y/9Izw8/holb6bO/OaqTCbCs4NRvnRnnOHkeNFZPL25J2x0x8pnAYUxpq74+wh4ajnqZC5EhLVr13Lo0CFGR0dnjJiYmpoq2snUP3TUe5w/wqOQYs1VDx+c5IHD7WzKCudReBZPb+6JWCw2LYOpWZmW/jfIGGMqaKmNOpmr9vZ2wuEwp0+fnrGsnBoKf8fM/BEec7F77xibsjJr09H4+DiTk5NWO1EnLKAwxtSVcoZxLmWBQIDVq1czNjY2LXNqNpslk8kUDSi8vhX+gKJQDo9ylTNh2cDAAIFAgPb29jnv3yw/1uRhjKk7S2nUyXysXr2akydPcubMGTZv3gw4IzyAosGBiMxIY14oh0e5SjUdZbNZhoaGaG9vr+rEYWbpsBoKY4xZZkKhEJ2dnQwMDOTmnphtDgqPfy6KQiM85qJU09Hw8DCZTMaaO+qIBRTGGLMMrVmzBlXNJTqbS0ChqkVHeJTLazoaaIJHUAaaZFrT0cDAAOFwmFhsefRNMQtnTR7GGLMMRaNRWltbOXPmDGvXri07oMhms6TT6bJGeJSys7eJK8+P8Nhjj7Fu3To2bnSCiampKUZHR1m3bp3NPVFHrIbCGGOWqbVr15JOpxkaGio7oABn/omFjPDwCwaDtLS0TMvr4U28Zc0d9cUCCmOMWaZisRiNjY2cPn2aqakpQqHQrDUC/qGjCxnhka+trY2JiYlcUDMwMEBTU1PVE6+ZpcUCCmOMWaZEhDVr1jAxMcHIyMistRMwPaBYyAiPfK2tTrKy0dFREokEExMTVjtRh6wPhTHGLGOdnZ3c9ZMB9h5rZjQVZPVjpwtOgQ3OHBbhcJhkMkkymaSrq6siZYhGo4RCIUZGRkgkEoiIZRatQxZQGGPMMrbvZ0nuP9zKpow42VMLTIHtF4lEiMfjCxrhkU9EODbewT2PBhhJBWhvXEs6lmJnr91i6ok1ecyDiLxaRJ4WkWdF5E+KrPMmEdkvIk+KyO2LXUZjTH3YvXeMTZnZp8D287KOwsJGePjt6Uvw/QNh1qaCXIGwJhngjvtG2NOXqMj+zfJgAcUciUgQ+BfgNcDFwC4RuThvnV7gT4GXqOolwPsXu5zGmPpQzhTYfl4/Clj4CA/Pck4JbyrH6qPmbifwrKoeBBCRzwM3APt967wL+BdVHQJQ1TOLXkpjTF2Ya/bUA6eDfOvxNU5/i6cHiva3mIvlnBLeVI7VUMzdRuCo7+9j7nN+FwAXiMiPReRBEXl1sZ2JyI0isk9E9p09e7YKxTXGrGRzyZ66py/Bfz2cYV0qxBUIHW5/i4U2TXS1BIjnPbdcUsKbyrFPuzpCQC9wHbAL+ISItBdaUVVvVdXtqrp99erVi1dCY8yKMJfsqdVqmljuKeFNZViTx9wdh2m1e93uc37HgIdUdQo4JCLP4AQYexeniMaYelJu9tRqNU14x969d4y+eJaulgC7KtCUYpYXCyjmbi/QKyLn4QQSbwbekrfOnTg1E58RkS6cJpCDi1lIY4zJN9f+FnOx3FPCm4WzJo85UtU08F7gO8AB4Iuq+qSI/IWIXO+u9h1gQET2A3cBf6yqA7UpsTHGOKxpwlSTqGqty2Bc27dv13379tW6GMaYFWxPX4Lde8fod5smKjHKo9ZE5GFV3V7rctQ7a/Iwxpg6Yk0TplqsycMYY4wxC2YBhTHGGGMWzAIKY4wxxiyYBRTGGGOMWTALKIwxxhizYDZsdAkRkbPA4Xls2gX0V7g4y4G97vpir7u+zOV1b1ZVy11QYxZQrAAisq8ex2Db664v9rrrS72+7uXMmjyMMcYYs2AWUBhjjDFmwSygWBlurXUBasRed32x111f6vV1L1vWh8IYY4wxC2Y1FMYYY4xZMAsojDHGGLNgFlAscyLynIg8LiKPiUjd5D4XkXYR+bKIPCUiB0Tk6lqXqdpE5EL3c/b+jYrI+2tdrmoTkQ+IyJMi8oSI3CEijbUu02IQkfe5r/nJlf45i8inReSMiDzhe26ViHxPRPrc/ztqWUZTmgUUK8PPqerldTZm+5+Bb6vqRcALgAM1Lk/VqerT7ud8OXAlkAC+VttSVZeIbAT+ANiuqpcCQeDNtS1V9YnIpcC7gJ045/cvisi22paqqm4DXp333J8AP1DVXuAH7t9mCbOAwiw7ItIGXAt8CkBVU6o6XNNCLb6XAz9T1fnMrLrchICoiISAJuBEjcuzGJ4HPKSqCVVNA/cAv1LjMlWNqt4LDOY9fQPw7+7jfwdev5hlMnNnAcXyp8B3ReRhEbmx1oVZJOcBZ4HPiMijIvJJEWmudaEW2ZuBO2pdiGpT1ePA3wNHgJPAiKp+t7alWhRPANeISKeINAGvBTbVuEyLba2qnnQfnwLW1rIwpjQLKJa/l6rqFcBrgPeIyLW1LtAiCAFXAP+qqi8Exqmj6lARaQCuB75U67JUm9tufgNOELkBaBaRX69tqapPVQ8A/xv4LvBt4DEgU8sy1ZI68xvYHAdLnAUUy5z7Cw5VPYPTnr6ztiVaFMeAY6r6kPv3l3ECjHrxGuARVT1d64IsglcAh1T1rKpOAV8FXlzjMi0KVf2Uql6pqtcCQ8AztS7TIjstIusB3P/P1Lg8pgQLKJYxEWkWkZj3GHgVTlXpiqaqp4CjInKh+9TLgf01LNJi20UdNHe4jgAvEpEmERGcz3rFd8AFEJE17v89OP0nbq9tiRbdbuBt7uO3AV+vYVlMGWymzGVMRM7nXC//EHC7qv51DYu0aETkcuCTQANwEHiHqg7VtFCLwA0cjwDnq+pIrcuzGETkz4FfA9LAo8Bvq+pkbUtVfSJyH9AJTAF/qKo/qHGRqkZE7gCuw0lZfhr4CHAn8EWgBzgMvElV8ztumiXEAgpjjDHGLJg1eRhjjDFmwSygMMYYY8yCWUBhjDHGmAWzgMIYY4wxC2YBhTHGGGMWzAIKY5YREVkrIreLyEF3uvUHROSXS2yzRUTeUqHj3yYiv1pk2bdFZFhEvlFiHx8VkWtF5CMi8jd5yy4XkQPu4+9bhkljlg8LKIxZJtyJne4E7lXV81X1SpycHt0lNt0CVCSgKOH/AL8x2woi0gm8yE0GdQfO/BJ+/hwlnwN+r9KFNMZUhwUUxiwfPw+kVPUW7wlVPayq/xdyNRH3icgj7j9viuq/xUk09ZiIfEBEgiLyf0Rkr4j8VER+p9DBROQ33eU/EZHP+RZdKyL3u7UkudoKd+KlsRKv4Q04uSlQ1WeAIRG5yrf8TZwLKHbjzApqjFkGQrUugDGmbJcAj8yy/AzwSlVNikgvzo15O07itD9S1V8EcLPSjqjqDhGJAD8Wke+q6iFvRyJyCXAT8GJV7ReRVb7jrAdeClyEc9P/8hxew0vy1r8Dp1biIRF5ETCoqn0AqjokIhER6VTVgTkcwxhTA1ZDYcwyJSL/4tYe7HWfCgOfEJHHcTKRXlxk01cBvykijwEP4Uzv3Ju3zs8DX1LVfoC8KY/vVNWsqu5n7iml1+Oknvd8AfhVEQlQOCX7GZwso8aYJc5qKIxZPp7EaTIAQFXfIyJdwD73qQ/g5EF4Ac6PhWSR/Qjw+6r6nXmWw59HQ+a47QTQ6P2hqkdF5BDwMpzXdnXe+o3uNsaYJc5qKIxZPn4INIrI7/qea/I9bgNOqmoWp3Nk0H1+DIj51vsO8LsiEgYQkQvcpGP5x3qj24mSvCaPhTgAbMt77g7gn4CDqnrMe9LthLoOeK5CxzbGVJEFFMYsE+pk8ns98DIROSQie4B/Bz7krvJx4G0i8hOc/g3j7vM/BTJu88gHcLK07gceEZEngH8jr7ZSVZ8E/hq4x93fP5Yqn5sd80vAy0XkmIj8QoHVvomTVdLvSzj9Q/KbO64EHlTVdKljG2Nqz7KNGmMWlYj8CPhFVR0usd4/A7tXctpuY1YSq6Ewxiy2DwI9Zaz3hAUTxiwfVkNhjDHGmAWzGgpjjDHGLJgFFMYYY4xZMAsojDHGGLNgFlAYY4wxZsEsoDDGGGPMgv0/nsc7aOKCGsYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes, cbax = plot_dataset(dataset)\n",
    "# we slightly tweak the plot to better visualise the highly non-standard axis spacing\n",
    "axes[0].lines[0].set_marker('o')\n",
    "axes[0].lines[0].set_markerfacecolor((0.6, 0.6, 0.9))\n",
    "axes[0].lines[0].set_markeredgecolor((0.4, 0.6, 0.9))\n",
    "axes[0].lines[0].set_color((0.8, 0.8, 0.8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finer sampling in 2D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the plot of the 2D measurement above, we may decide to sample more finely in the central region:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qcodes.dataset.measurements.Measurement at 0x1d55690bf08>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas = Measurement(exp=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(dmm.v2, setpoints=(dac.ch1, dac.ch2))  # now register the dependent oone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 388. \n"
     ]
    }
   ],
   "source": [
    "with meas.run() as datasaver:\n",
    "\n",
    "    v1points = np.concatenate((np.linspace(-1, -0.5, 5),\n",
    "                               np.linspace(-0.51, 0.5, 200),\n",
    "                               np.linspace(0.51, 1, 5)))\n",
    "    v2points = np.concatenate((np.linspace(-1, -0.25, 5),\n",
    "                               np.linspace(-0.26, 0.5, 200),\n",
    "                               np.linspace(0.51, 1, 5)))\n",
    "\n",
    "    for v1 in v1points:\n",
    "        for v2 in v2points:\n",
    "            dac.ch1(v1)\n",
    "            dac.ch2(v2)\n",
    "            val = dmm.v2.get()\n",
    "            datasaver.add_result((dac.ch1, v1),\n",
    "                                 (dac.ch2, v2),\n",
    "                                 (dmm.v2, val))\n",
    "\n",
    "    dataset = datasaver.dataset  # convenient to have for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax, cbax = plot_dataset(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple adaptive 2D sweep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ".. or even perform an adaptive sweep... ooohh...\n",
    "(the example below is a not-very-clever toy model example,\n",
    "but it nicely shows a semi-realistic measurement that the old Loop\n",
    "could not handle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 389. \n",
      "130 46 0.2505535776516436\n",
      "----------\n"
     ]
    }
   ],
   "source": [
    "v1_points = np.linspace(-1, 1, 250)\n",
    "v2_points = np.linspace(1, -1, 250)\n",
    "\n",
    "threshold = 0.25\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "    # Do normal sweeping until the peak is detected\n",
    "\n",
    "    for v2ind, v2 in enumerate(v2_points):\n",
    "        for v1ind, v1 in enumerate(v1_points):\n",
    "            dac.ch1(v1)\n",
    "            dac.ch2(v2)\n",
    "            val = dmm.v2.get()\n",
    "            datasaver.add_result((dac.ch1, v1),\n",
    "                                 (dac.ch2, v2),\n",
    "                                 (dmm.v2, val))\n",
    "            if val > threshold:\n",
    "                break\n",
    "        else:\n",
    "            continue\n",
    "        break\n",
    "\n",
    "    print(v1ind, v2ind, val)\n",
    "    print('-'*10)\n",
    "\n",
    "    # now be more clever, meandering back and forth over the peak\n",
    "    doneyet = False\n",
    "    rowdone = False\n",
    "    v1_step = 1\n",
    "    while not doneyet:\n",
    "            v2 = v2_points[v2ind]\n",
    "            v1 = v1_points[v1ind+v1_step-1]\n",
    "            dac.ch1(v1)\n",
    "            dac.ch2(v2)\n",
    "            val = dmm.v2.get()\n",
    "            datasaver.add_result((dac.ch1, v1),\n",
    "                                 (dac.ch2, v2),\n",
    "                                 (dmm.v2, val))\n",
    "            if val < threshold:\n",
    "                if rowdone:\n",
    "                    doneyet = True\n",
    "                v2ind += 1\n",
    "                v1_step *= -1\n",
    "                rowdone = True\n",
    "            else:\n",
    "                v1ind += v1_step\n",
    "                rowdone = False\n",
    "\n",
    "dataset = datasaver.dataset  # convenient to have for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-01-29 15:33:09,196 ¦ py.warnings ¦ WARNING ¦ warnings ¦ _showwarnmsg ¦ 110 ¦ c:\\users\\jens-work\\source\\repos\\qcodes\\qcodes\\dataset\\data_export.py:172: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  return np.array(rows)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax, cbax = plot_dataset(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random sampling "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We may also chose to sample completely randomly across the phase space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 390. \n"
     ]
    }
   ],
   "source": [
    "meas2 = Measurement(exp=exp, name='random_sampling_measurement')\n",
    "meas2.register_parameter(dac.ch1)\n",
    "meas2.register_parameter(dac.ch2)\n",
    "meas2.register_parameter(dmm.v2, setpoints=(dac.ch1, dac.ch2))\n",
    "\n",
    "threshold = 0.25\n",
    "\n",
    "npoints = 5000\n",
    "\n",
    "with meas2.run() as datasaver:\n",
    "    for i in range(npoints):\n",
    "        x = 2*(np.random.rand()-.5)\n",
    "        y = 2*(np.random.rand()-.5)\n",
    "        dac.ch1(x)\n",
    "        dac.ch2(y)\n",
    "        z = dmm.v2()\n",
    "        datasaver.add_result((dac.ch1, x),\n",
    "                             (dac.ch2, y),\n",
    "                             (dmm.v2, z))\n",
    "dataset = datasaver.dataset  # convenient to have for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax, cbax = plot_dataset(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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>0.616079</th>\n",
       "      <th>0.577508</th>\n",
       "      <td>0.043870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.458416</th>\n",
       "      <th>0.271791</th>\n",
       "      <td>0.089667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.482792</th>\n",
       "      <th>0.269050</th>\n",
       "      <td>0.071653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.301025</th>\n",
       "      <th>0.294020</th>\n",
       "      <td>0.763959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.689742</th>\n",
       "      <th>0.800379</th>\n",
       "      <td>-0.000130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.399536</th>\n",
       "      <th>-0.912901</th>\n",
       "      <td>0.000191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.440590</th>\n",
       "      <th>0.601735</th>\n",
       "      <td>0.029985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.321103</th>\n",
       "      <th>-0.840414</th>\n",
       "      <td>0.000613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.808781</th>\n",
       "      <th>0.598168</th>\n",
       "      <td>0.005690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.148221</th>\n",
       "      <th>-0.508436</th>\n",
       "      <td>0.019341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       dmm_v2\n",
       "dac_ch1   dac_ch2            \n",
       " 0.616079  0.577508  0.043870\n",
       "-0.458416  0.271791  0.089667\n",
       "-0.482792  0.269050  0.071653\n",
       " 0.301025  0.294020  0.763959\n",
       "-0.689742  0.800379 -0.000130\n",
       "-0.399536 -0.912901  0.000191\n",
       "-0.440590  0.601735  0.029985\n",
       "-0.321103 -0.840414  0.000613\n",
       " 0.808781  0.598168  0.005690\n",
       " 0.148221 -0.508436  0.019341"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasaver.dataset.to_pandas_dataframe()[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unlike the data measured above, which lies on a grid, here, all the measured data points have an unique combination of the two dependent parameters. When exporting to XArray NaN's will therefore replace all the missing combinations of `dac_ch1` and `dac_ch2` and the data is unlikely to be useful in this format. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (dac_ch1: 5000, dac_ch2: 5000)\n",
       "Coordinates:\n",
       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.9997 -0.9984 ... 0.9992 0.9994 0.9997\n",
       "  * dac_ch2  (dac_ch2) float64 -0.9997 -0.9988 -0.9982 ... 0.999 0.9991 0.9992\n",
       "Data variables:\n",
       "    dmm_v2   (dac_ch1, dac_ch2) float64 nan nan nan nan nan ... nan nan nan nan\n",
       "Attributes:\n",
       "    sample_name:  no sample\n",
       "    exp_name:     performing_meas_using_parameters_and_dataset</pre><div class='xr-wrap' hidden><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-a95cf8da-23d8-464f-8f21-211beb2b75ae' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a95cf8da-23d8-464f-8f21-211beb2b75ae' 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>: 5000</li><li><span class='xr-has-index'>dac_ch2</span>: 5000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b5a2c484-3253-4140-9ac3-9b57547fd61d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b5a2c484-3253-4140-9ac3-9b57547fd61d' 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 -0.9997 ... 0.9994 0.9997</div><input id='attrs-64ef29cf-81e0-4ea4-8a38-222e2fadb9ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64ef29cf-81e0-4ea4-8a38-222e2fadb9ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9140e605-4789-478f-8553-d7ea7d74ac7f' class='xr-var-data-in' type='checkbox'><label for='data-9140e605-4789-478f-8553-d7ea7d74ac7f' 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></dl></div><div class='xr-var-data'><pre>array([-0.999984, -0.999736, -0.998374, ...,  0.999179,  0.99938 ,  0.999746])</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'>-0.9997 -0.9988 ... 0.9991 0.9992</div><input id='attrs-37f99dad-d665-4250-816b-98e64006698c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37f99dad-d665-4250-816b-98e64006698c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-714ac512-3711-49d5-9481-69049832901e' class='xr-var-data-in' type='checkbox'><label for='data-714ac512-3711-49d5-9481-69049832901e' 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></dl></div><div class='xr-var-data'><pre>array([-0.99968 , -0.998807, -0.998224, ...,  0.998968,  0.999112,  0.9992  ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6f8f354c-e408-41bf-883b-f4ee67d810ea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6f8f354c-e408-41bf-883b-f4ee67d810ea' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-04446a8f-826f-459b-a8e2-b83e3a9ce687' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04446a8f-826f-459b-a8e2-b83e3a9ce687' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7652e922-3748-478b-a65a-f5ccbd4216b0' class='xr-var-data-in' type='checkbox'><label for='data-7652e922-3748-478b-a65a-f5ccbd4216b0' 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></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       ...,\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan],\n",
       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-959ee2b2-56d7-424d-9da7-1a54b2869ae4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-959ee2b2-56d7-424d-9da7-1a54b2869ae4' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>sample_name :</span></dt><dd>no sample</dd><dt><span>exp_name :</span></dt><dd>performing_meas_using_parameters_and_dataset</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (dac_ch1: 5000, dac_ch2: 5000)\n",
       "Coordinates:\n",
       "  * dac_ch1  (dac_ch1) float64 -1.0 -0.9997 -0.9984 ... 0.9992 0.9994 0.9997\n",
       "  * dac_ch2  (dac_ch2) float64 -0.9997 -0.9988 -0.9982 ... 0.999 0.9991 0.9992\n",
       "Data variables:\n",
       "    dmm_v2   (dac_ch1, dac_ch2) float64 nan nan nan nan nan ... nan nan nan nan\n",
       "Attributes:\n",
       "    sample_name:  no sample\n",
       "    exp_name:     performing_meas_using_parameters_and_dataset"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasaver.dataset.to_xarray_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimiser"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An example to show that the algorithm is flexible enough to be used with completely unstructured data such as the output of an downhill simplex optimization. The downhill simplex is somewhat more sensitive to noise and it is important that 'fatol' is set to match the expected noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.optimize import minimize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 391. \n"
     ]
    }
   ],
   "source": [
    "def set_and_measure(*xk):\n",
    "    dac.ch1(xk[0])\n",
    "    dac.ch2(xk[1])\n",
    "    return dmm.v2.get()\n",
    "\n",
    "\n",
    "noise = 0.0005\n",
    "x0 = [np.random.rand(), np.random.rand()]\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "    def mycallback(xk):\n",
    "        dac.ch1(xk[0])\n",
    "        dac.ch2(xk[1])\n",
    "        datasaver.add_result((dac.ch1, xk[0]),\n",
    "                             (dac.ch2, xk[1]),\n",
    "                             (dmm.v2, dmm.v2.cache.get()))\n",
    "\n",
    "    res = minimize(lambda x: -set_and_measure(*x),\n",
    "                   x0,\n",
    "                   method='Nelder-Mead',\n",
    "                   tol=1e-10,\n",
    "                   callback=mycallback,\n",
    "                   options={'fatol': noise})\n",
    "\n",
    "    dataset = datasaver.dataset  # convenient to have for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " final_simplex: (array([[0.09648282, 0.20168964],\n",
       "       [0.09648282, 0.20168964],\n",
       "       [0.09648282, 0.20168964]]), array([-1.13469849, -1.13333984, -1.1328577 ]))\n",
       "           fun: -1.134698492197985\n",
       "       message: 'Maximum number of function evaluations has been exceeded.'\n",
       "          nfev: 403\n",
       "           nit: 160\n",
       "        status: 1\n",
       "       success: False\n",
       "             x: array([0.09648282, 0.20168964])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-01-29 15:33:10,709 ¦ py.warnings ¦ WARNING ¦ warnings ¦ _showwarnmsg ¦ 110 ¦ c:\\users\\jens-work\\source\\repos\\qcodes\\qcodes\\dataset\\data_export.py:172: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  return np.array(rows)\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax, cbax = plot_dataset(dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subscriptions\n",
    "\n",
    "The ``Measurement`` object can also handle subscriptions to the dataset. Subscriptions are, under the hood, triggers in the underlying SQLite database. Therefore, the subscribers are only called when data is written to the database (which happens every `write_period`).\n",
    "\n",
    "When making a subscription, two things must be supplied: a function and a mutable state object. The function **MUST** have a call signature of `f(result_list, length, state, **kwargs)`, where ``result_list`` is a list of tuples of parameter values inserted in the dataset, ``length`` is an integer (the step number of the run), and ``state`` is the mutable state object. The function does not need to actually use these arguments, but the call signature must match this.\n",
    "\n",
    "Let us consider two generic examples:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subscription example 1: simple printing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 392. \n",
      "Added points to measurement, step 0.\n",
      "The run now holds 2 rows\n",
      "Added points to measurement, step 1.\n",
      "The run now holds 3 rows\n",
      "Added points to measurement, step 2.\n",
      "The run now holds 4 rows\n",
      "Added points to measurement, step 3.\n",
      "The run now holds 5 rows\n",
      "Added points to measurement, step 4.\n",
      "The run now holds 6 rows\n",
      "Added points to measurement, step 5.\n",
      "The run now holds 7 rows\n",
      "Added points to measurement, step 6.\n",
      "The run now holds 7 rows\n",
      "The run now holds 7 rows\n"
     ]
    }
   ],
   "source": [
    "def print_which_step(results_list, length, state):\n",
    "    \"\"\"\n",
    "    This subscriber does not use results_list nor state; it simply\n",
    "    prints how many results we have added to the database\n",
    "    \"\"\"\n",
    "    print(f'The run now holds {length} rows')\n",
    "\n",
    "\n",
    "meas = Measurement(exp=exp)\n",
    "meas.register_parameter(dac.ch1)\n",
    "meas.register_parameter(dmm.v1, setpoints=(dac.ch1,))\n",
    "\n",
    "meas.write_period = 0.2  # We write to the database every 0.2s\n",
    "\n",
    "meas.add_subscriber(print_which_step, state=[])\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "    for n in range(7):\n",
    "        datasaver.add_result((dac.ch1, n), (dmm.v1, n**2))\n",
    "        print(f'Added points to measurement, step {n}.')\n",
    "        sleep(0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subscription example 2: using the state\n",
    "\n",
    "We add two subscribers now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting experimental run with id: 393. \n",
      "Added points to measurement, step 0.\n",
      "First parameter value list: []\n",
      "Added points to measurement, step 1.\n",
      "First parameter value list: []\n",
      "The run now holds 2 rows\n",
      "Added points to measurement, step 2.\n",
      "First parameter value list: [0, 1, 2]\n",
      "Added points to measurement, step 3.\n",
      "First parameter value list: [0, 1, 2]\n",
      "The run now holds 5 rows\n",
      "Added points to measurement, step 4.\n",
      "First parameter value list: [0, 1, 2, 3, 4]\n",
      "Added points to measurement, step 5.\n",
      "First parameter value list: [0, 1, 2, 3, 4]\n",
      "The run now holds 7 rows\n",
      "Added points to measurement, step 6.\n",
      "First parameter value list: [0, 1, 2, 3, 4, 5, 6]\n",
      "Added points to measurement, step 7.\n",
      "First parameter value list: [0, 1, 2, 3, 4, 5, 6]\n",
      "The run now holds 9 rows\n",
      "Added points to measurement, step 8.\n",
      "First parameter value list: [0, 1, 2, 3, 4, 5, 6, 7, 8]\n",
      "Added points to measurement, step 9.\n",
      "First parameter value list: [0, 1, 2, 3, 4, 5, 6, 7, 8]\n",
      "The run now holds 10 rows\n",
      "The run now holds 10 rows\n",
      "The run now holds 10 rows\n"
     ]
    }
   ],
   "source": [
    "def get_list_of_first_param(results_list, length, state):\n",
    "    \"\"\"\n",
    "    Modify the state (a list) to hold all the values for\n",
    "    the first parameter\n",
    "    \"\"\"\n",
    "    param_vals = [parvals[0] for parvals in results_list]\n",
    "    state += param_vals\n",
    "\n",
    "\n",
    "meas = Measurement(exp=exp)\n",
    "meas.register_parameter(dac.ch1)\n",
    "meas.register_parameter(dmm.v1, setpoints=(dac.ch1,))\n",
    "\n",
    "meas.write_period = 0.2  # We write to the database every 0.2s\n",
    "\n",
    "first_param_list = []\n",
    "\n",
    "meas.add_subscriber(print_which_step, state=[])\n",
    "meas.add_subscriber(get_list_of_first_param, state=first_param_list)\n",
    "\n",
    "with meas.run() as datasaver:\n",
    "    for n in range(10):\n",
    "        datasaver.add_result((dac.ch1, n), (dmm.v1, n**2))\n",
    "        print(f'Added points to measurement, step {n}.')\n",
    "        print(f'First parameter value list: {first_param_list}')\n",
    "        sleep(0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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