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    "(adaptive-tutorial)=\n",
    "# Tutorial 4. Adaptive Measurements\n",
    "\n",
    "```{seealso}\n",
    "The complete source code of this tutorial can be found in\n",
    "\n",
    "{nb-download}`Tutorial 4. Adaptive Measurements.ipynb`\n",
    "```\n",
    "\n",
    "This tutorial requires familiarity with the **core concepts** of Quantify.\n",
    "We **highly recommended** to read the {ref}`user guide <user-guide>` and follow {ref}`Tutorial 1. Controlling a basic experiment using MeasurementControl` and {ref}`Tutorial 2. Advanced capabilities of the MeasurementControl` first.\n",
    "\n",
    "In this tutorial, we explore the adaptive functionality of the {class}`.MeasurementControl`.\n",
    "With this mode, instead of predefining a grid of values to sweep through, we provide an optimization function and an initial state to the `meas_ctrl`.\n",
    "The `meas_ctrl` will then use this function to build the sweep. We import our usual modules and set up a `meas_ctrl` with visualization:"
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   "execution_count": 1,
   "id": "4874e435",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Imports and auxiliary utilities"
    },
    "tags": [
     "hide-cell"
    ]
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   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "import adaptive\n",
    "import numpy as np\n",
    "from qcodes import Instrument, ManualParameter\n",
    "from scipy import optimize\n",
    "\n",
    "import quantify_core.analysis.optimization_analysis as oa\n",
    "import quantify_core.visualization.pyqt_plotmon as pqm\n",
    "from quantify_core.analysis.interpolation_analysis import InterpolationAnalysis2D\n",
    "from quantify_core.data.handling import set_datadir, default_datadir\n",
    "from quantify_core.measurement.control import MeasurementControl\n",
    "from quantify_core.visualization.instrument_monitor import InstrumentMonitor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07057c0a",
   "metadata": {},
   "source": [
    "Before instantiating any instruments or starting a measurement we change the\n",
    "directory in which the experiments are saved using the\n",
    "{meth}`~quantify_core.data.handling.set_datadir`\n",
    "\\[{meth}`~quantify_core.data.handling.get_datadir`\\] functions.\n",
    "\n",
    "----------------------------------------------------------------------------------------\n",
    "\n",
    "⚠️ **Warning!**\n",
    "\n",
    "We recommend always setting the directory at the start of the python kernel and stick\n",
    "to a single common data directory for all notebooks/experiments within your\n",
    "measurement setup/PC.\n",
    "\n",
    "The cell below sets a default data directory (`~/quantify-data` on Linux/macOS or\n",
    "`$env:USERPROFILE\\\\quantify-data` on Windows) for tutorial purposes. Change it to your\n",
    "desired data directory. The utilities to find/search/extract data only work if\n",
    "all the experiment containers are located within the same directory.\n",
    "\n",
    "----------------------------------------------------------------------------------------"
   ]
  },
  {
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   "execution_count": 2,
   "id": "d6778cc3",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data will be saved in:\n",
      "/root/quantify-data\n"
     ]
    }
   ],
   "source": [
    "set_datadir(default_datadir())"
   ]
  },
  {
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   "execution_count": 3,
   "id": "c93ce3d0",
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   "source": [
    "meas_ctrl = MeasurementControl(\"meas_ctrl\")\n",
    "insmon = InstrumentMonitor(\"InstrumentMonitor\")\n",
    "plotmon = pqm.PlotMonitor_pyqt(\"plotmon_meas_ctrl\")\n",
    "meas_ctrl.instr_plotmon(plotmon.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57a00112",
   "metadata": {},
   "source": [
    "## Finding a minimum\n",
    "\n",
    "We will create a mock Instrument our `meas_ctrl` will interact with. In this case, it is a simple parabola centered at the origin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "882fd32e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qcodes.parameters.parameter.Parameter: amp at 139299658556272>"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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    "para = Instrument(\"parabola\")\n",
    "\n",
    "para.add_parameter(\"x\", unit=\"m\", label=\"X\", parameter_class=ManualParameter)\n",
    "para.add_parameter(\"y\", unit=\"m\", label=\"Y\", parameter_class=ManualParameter)\n",
    "\n",
    "para.add_parameter(\n",
    "    \"noise\", unit=\"V\", label=\"white noise amplitude\", parameter_class=ManualParameter\n",
    ")\n",
    "para.add_parameter(\n",
    "    \"acq_delay\", initial_value=0.1, unit=\"s\", parameter_class=ManualParameter\n",
    ")\n",
    "\n",
    "\n",
    "def _amp_model():\n",
    "    time.sleep(\n",
    "        para.acq_delay()\n",
    "    )  # for display purposes, just so we can watch the live plot update\n",
    "    return para.x() ** 2 + para.y() ** 2 + para.noise() * np.random.rand(1)\n",
    "\n",
    "\n",
    "para.add_parameter(\"amp\", unit=\"V\", label=\"Amplitude\", get_cmd=_amp_model)"
   ]
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   "id": "1999bc2a",
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    "Next, we will use the `optimize` package from `scipy` to provide our adaptive function.\n",
    "You can of course implement your own functions for this purpose, but for brevity we will use something standard and easily available.\n",
    "\n",
    "Then, we set our {ref}`Settables and Gettables<Settables and Gettables>` as usual, and define a new dictionary `af_pars`.\n",
    "The only required key in this object is `adaptive_function`, the value to be used by the adaptive function.\n",
    "The remaining fields in this dictionary are the arguments to the adaptive function itself. We also add some noise into the parabola to stress our adaptive function.\n",
    "\n",
    "**As such, it is highly recommended to thoroughly read the documentation around the adaptive function you are using.**\n",
    "\n",
    "We will use the `optimize.minimize` function (note this is passed by reference as opposed to calling the `minimize` function), which requires an initial state named `\"x0\"` and an algorithm to use named `\"method\"`.\n",
    "In this case, we are starting at `[-50, -50]` and hope to minimize these values relative to our parabola function.\n",
    "Of course, this parabola has its global minimum at the origin, thus these values will tend towards 0 as our algorithm progresses."
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      "Running adaptively...\n",
      "."
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      "/usr/local/lib/python3.9/site-packages/qcodes_loop/plots/pyqtgraph.py:420: UserWarning: zero setpoint range passed to pyqtgraph. ignoring, using default scaling.\n",
      "  warnings.warn('zero setpoint range passed to pyqtgraph. '\n"
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    "meas_ctrl.settables([para.x, para.y])\n",
    "af_pars = {\n",
    "    \"adaptive_function\": optimize.minimize,  # used by meas_ctrl\n",
    "    \"x0\": [-50, -50],  # used by `optimize.minimize` (in this case)\n",
    "    \"method\": \"Nelder-Mead\",  # used by `optimize.minimize` (in this case)\n",
    "    \"options\": {\"maxfev\": 100},  # limit the maximum evaluations of the gettable(s)\n",
    "}\n",
    "para.noise(0.5)\n",
    "meas_ctrl.gettables(para.amp)\n",
    "dset = meas_ctrl.run_adaptive(\"nelder_mead_optimization\", af_pars)"
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   "cell_type": "code",
   "execution_count": 6,
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       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2kB\n",
       "Dimensions:  (dim_0: 100)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 800B -50.0 -52.5 -50.0 ... 0.1582 0.158 0.1579\n",
       "    x1       (dim_0) float64 800B -50.0 -50.0 -52.5 ... -0.007538 -0.007442\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 800B 5e+03 5.256e+03 5.257e+03 ... 0.04095 0.2492\n",
       "Attributes:\n",
       "    tuid:                             20250528-190547-074-457ef0\n",
       "    name:                             nelder_mead_optimization\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-711d047b-b95c-4b70-8943-5414b19d5d6c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-711d047b-b95c-4b70-8943-5414b19d5d6c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>dim_0</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e2e0c24a-1c5e-4502-ba87-adede2fb3482' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e2e0c24a-1c5e-4502-ba87-adede2fb3482' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>x0</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-50.0 -52.5 -50.0 ... 0.158 0.1579</div><input id='attrs-86e3796e-3da3-45a4-b025-82807ab00f18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86e3796e-3da3-45a4-b025-82807ab00f18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80f04a07-611b-49e2-aaa8-b711fbc417fb' class='xr-var-data-in' type='checkbox'><label for='data-80f04a07-611b-49e2-aaa8-b711fbc417fb' 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>parabola.x</dd><dt><span>long_name :</span></dt><dd>parabola X</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([-5.00000000e+01, -5.25000000e+01, -5.00000000e+01, -5.25000000e+01,\n",
       "       -5.00000000e+01, -4.87500000e+01, -4.62500000e+01, -4.50000000e+01,\n",
       "       -4.25000000e+01, -4.50000000e+01, -3.87500000e+01, -3.37500000e+01,\n",
       "       -3.12500000e+01, -2.43750000e+01, -1.56250000e+01, -2.18750000e+00,\n",
       "        7.18750000e+00,  2.93750000e+01,  1.59375000e+01, -1.09375000e+01,\n",
       "        9.21875000e+00, -1.56250000e-01, -3.82812500e+00, -1.15625000e+01,\n",
       "        4.02343750e+00,  6.05468750e+00,  3.99414062e+00, -1.85546875e-01,\n",
       "       -2.29003906e+00, -4.33593750e+00,  1.91162109e+00,  1.88232422e+00,\n",
       "        3.53393555e-01, -1.74377441e+00, -8.29925537e-01, -1.36886597e+00,\n",
       "       -7.71713257e-02,  5.67207336e-01,  2.17924118e-01,  1.09548569e-01,\n",
       "       -3.04913521e-02,  3.72979641e-01,  1.24564171e-01,  1.94584131e-01,\n",
       "       -2.08886862e-01,  2.27513015e-01,  4.52588499e-01,  9.02786106e-02,\n",
       "        2.11048573e-01,  8.20463896e-02,  6.55819476e-02,  1.78119689e-01,\n",
       "        1.54101364e-01,  2.83103548e-01,  1.19962348e-01,  1.74342748e-01,\n",
       "        1.30083039e-01,  1.50324423e-01,  1.38315260e-01,  2.14825515e-01,\n",
       "        1.93639896e-01,  1.72454277e-01,  1.40203731e-01,  1.82574969e-01,\n",
       "        1.60445115e-01,  1.28194569e-01,  1.68979869e-01,  1.55384769e-01,\n",
       "        1.66449696e-01,  1.61389350e-01,  1.59888205e-01,  1.43762932e-01,\n",
       "        1.34199150e-01,  1.53465941e-01,  1.47043677e-01,  1.55106314e-01,\n",
       "        1.58387059e-01,  1.64058750e-01,  1.53605168e-01,  1.54731027e-01,\n",
       "        1.53980455e-01,  1.54168098e-01,  1.54355741e-01,  1.56559043e-01,\n",
       "        1.56183757e-01,  1.56465222e-01,  1.60496540e-01,  1.55890941e-01,\n",
       "        1.57426141e-01,  1.56371400e-01,  1.59441800e-01,  1.57139000e-01,\n",
       "        1.58674200e-01,  1.58290400e-01,  1.57906600e-01,  1.57763030e-01,\n",
       "        1.58243489e-01,  1.58159267e-01,  1.57990822e-01,  1.57906600e-01])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x1</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-50.0 -50.0 ... -0.007538 -0.007442</div><input id='attrs-84c68e2a-0e90-47d1-beb6-3598333f073b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84c68e2a-0e90-47d1-beb6-3598333f073b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9f3451f-4a0b-4d4c-acd4-9245ece8f36e' class='xr-var-data-in' type='checkbox'><label for='data-e9f3451f-4a0b-4d4c-acd4-9245ece8f36e' 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>parabola.y</dd><dt><span>long_name :</span></dt><dd>parabola Y</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([-5.00000000e+01, -5.00000000e+01, -5.25000000e+01, -4.75000000e+01,\n",
       "       -4.75000000e+01, -4.62500000e+01, -4.87500000e+01, -4.50000000e+01,\n",
       "       -4.25000000e+01, -4.00000000e+01, -3.62500000e+01, -3.12500000e+01,\n",
       "       -3.37500000e+01, -3.06250000e+01, -1.93750000e+01, -7.81250000e+00,\n",
       "       -7.18750000e+00,  1.56250000e+01,  4.06250000e+00, -1.90625000e+01,\n",
       "       -1.71875000e+00, -2.34375000e+00,  7.81250000e-02, -8.43750000e+00,\n",
       "       -3.39843750e+00,  2.07031250e+00, -4.00390625e-01,  6.54296875e-01,\n",
       "        2.68066406e+00, -1.28906250e+00, -6.22558594e-01,  2.37548828e+00,\n",
       "       -1.16394043e+00,  1.12915039e-01, -7.09533691e-02,  1.74728394e+00,\n",
       "       -4.36134338e-01,  2.89115906e-01,  1.99098587e-01,  1.28952980e+00,\n",
       "       -4.71830368e-03, -4.59916592e-01, -6.63733482e-01, -1.66094303e-02,\n",
       "        4.38588858e-01, -2.35290229e-01, -2.47181356e-01, -6.53340667e-02,\n",
       "       -1.25949830e-01, -1.06638670e-02,  9.86765325e-02,  9.27309692e-02,\n",
       "        6.68822601e-02, -4.84037027e-02,  6.19064737e-02,  2.51364149e-02,\n",
       "        4.10335511e-02, -7.12294132e-04, -1.36366487e-02, -5.83552755e-02,\n",
       "       -3.35080689e-02, -8.66086222e-03,  2.01606285e-02, -2.95337848e-02,\n",
       "       -2.15852167e-02,  7.23627396e-03, -2.03412701e-02, -1.11487554e-02,\n",
       "       -1.51230395e-02, -4.68657818e-03, -6.30212249e-03,  8.10862286e-03,\n",
       "        1.36984512e-02, -1.30197906e-03,  3.69816436e-03, -3.50720831e-03,\n",
       "       -7.91766681e-03, -1.37255824e-02, -5.12275263e-03, -3.91109439e-03,\n",
       "       -4.71886655e-03, -4.51692351e-03, -4.31498047e-03, -5.91438060e-03,\n",
       "       -6.31826668e-03, -6.01535212e-03, -9.61803846e-03, -5.64074497e-03,\n",
       "       -6.96650946e-03, -6.11632364e-03, -8.76785263e-03, -6.77920589e-03,\n",
       "       -8.10497038e-03, -7.77352926e-03, -7.44208814e-03, -7.34843635e-03,\n",
       "       -7.82401502e-03, -7.72853330e-03, -7.53756986e-03, -7.44208814e-03])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aca5bb29-0a78-439b-b1ec-de5c53434de9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aca5bb29-0a78-439b-b1ec-de5c53434de9' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y0</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5e+03 5.256e+03 ... 0.04095 0.2492</div><input id='attrs-f72aa268-f9c8-4fa4-97bf-3bc782e6f5ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f72aa268-f9c8-4fa4-97bf-3bc782e6f5ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d24372b-c7c0-45c4-9861-0c5ff6d63492' class='xr-var-data-in' type='checkbox'><label for='data-1d24372b-c7c0-45c4-9861-0c5ff6d63492' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>amp</dd><dt><span>long_name :</span></dt><dd>Amplitude</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([5.00043653e+03, 5.25642507e+03, 5.25663062e+03, 5.01259218e+03,\n",
       "       4.75626123e+03, 4.51568125e+03, 4.51589084e+03, 4.05036193e+03,\n",
       "       3.61270750e+03, 3.62519663e+03, 2.81592980e+03, 2.11592511e+03,\n",
       "       2.11564286e+03, 1.53211723e+03, 6.19954769e+02, 6.61148079e+01,\n",
       "       1.03819485e+02, 1.10746255e+03, 2.70780706e+02, 4.83255306e+02,\n",
       "       8.82376462e+01, 5.70818928e+00, 1.48375604e+01, 2.05019042e+02,\n",
       "       2.82334114e+01, 4.12854606e+01, 1.65854633e+01, 5.42385726e-01,\n",
       "       1.27342719e+01, 2.08125055e+01, 4.43912431e+00, 9.58065199e+00,\n",
       "       1.84987071e+00, 3.14413115e+00, 9.48291447e-01, 5.02118154e+00,\n",
       "       6.56519115e-01, 7.14573496e-01, 4.85201223e-01, 1.67510766e+00,\n",
       "       2.99339692e-01, 4.52930205e-01, 5.54374612e-01, 7.36529697e-02,\n",
       "       6.01758502e-01, 1.61290423e-01, 7.57513706e-01, 3.39879064e-01,\n",
       "       5.25615173e-01, 3.73665251e-01, 2.46975411e-01, 3.36262661e-01,\n",
       "       2.17553751e-01, 2.50648232e-01, 3.21077083e-01, 3.56377768e-01,\n",
       "       2.09011119e-01, 6.08178447e-02, 2.30742645e-01, 1.21224875e-01,\n",
       "       2.62838266e-01, 3.17020520e-01, 3.90140919e-01, 3.11040398e-01,\n",
       "       7.68495335e-02, 4.38619773e-01, 3.92161892e-01, 4.12010147e-01,\n",
       "       2.92287750e-01, 4.01376190e-01, 2.80774192e-01, 2.24728698e-01,\n",
       "       5.16659956e-01, 4.40018892e-01, 3.52798425e-01, 3.39335672e-01,\n",
       "       4.30899049e-02, 2.23855667e-01, 5.01276956e-01, 1.29301896e-01,\n",
       "       7.78139292e-02, 2.98139747e-01, 2.57013572e-01, 2.71473242e-01,\n",
       "       4.93207341e-01, 7.45537240e-02, 4.09302768e-01, 5.21867924e-01,\n",
       "       6.74124671e-02, 2.42978631e-01, 3.14882102e-01, 2.37215885e-01,\n",
       "       1.34744826e-01, 2.72605720e-01, 4.59574619e-01, 3.22121153e-01,\n",
       "       3.71141939e-01, 3.68369466e-01, 4.09533518e-02, 2.49194611e-01])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3c13f5c7-06ca-4fab-a302-618a8e04d392' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3c13f5c7-06ca-4fab-a302-618a8e04d392' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-6a328fe5-1134-4846-9040-558148c6fd09' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6a328fe5-1134-4846-9040-558148c6fd09' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20250528-190547-074-457ef0</dd><dt><span>name :</span></dt><dd>nelder_mead_optimization</dd><dt><span>grid_2d :</span></dt><dd>False</dd><dt><span>grid_2d_uniformly_spaced :</span></dt><dd>False</dd><dt><span>1d_2_settables_uniformly_spaced :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 2kB\n",
       "Dimensions:  (dim_0: 100)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 800B -50.0 -52.5 -50.0 ... 0.1582 0.158 0.1579\n",
       "    x1       (dim_0) float64 800B -50.0 -50.0 -52.5 ... -0.007538 -0.007442\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 800B 5e+03 5.256e+03 5.257e+03 ... 0.04095 0.2492\n",
       "Attributes:\n",
       "    tuid:                             20250528-190547-074-457ef0\n",
       "    name:                             nelder_mead_optimization\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8733dbaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7eb13aa91f40>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotmon.main_QtPlot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f082765c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hwwUFBQkJCWaz+dixYzKZbODAgfZvdTpdSEiIQPDrUK6wsDBCiF6v905zAQDAV7G/96tUKs20sagxf1vF23R782TY8JVMHThwoM1mO3ToUF5eHiEkODh4+vTpoaGh9m/NZrNMJnMuL5FICCEmUzvb7gAAQKDhvuYDRXhaztBXMvXYsWOnTp1KTk6Oi4szm83nz5/fvn37PffcExUVdcvyDMMQQhyjgh3S09PbFrbnNAAABAL2/VS9Xi8RyPqFju6vHHOg6osj/nHvt6Gh4dSpUwMGDHCM442Li/vmm28OHz587733EkJkMpnRaHQ+xd5DlUpbb2WH+AQACHDsF0iyE1BCQsiYqIeGR963vfzd4qbjblftE2sT3rx5k2GYmJgYxxGRSBQZGVlbW2v/qFKpGhsbrVaro0B9fb39eCc3FQAAfBzNUCxfzmeJBbIgoXJm7NLH1G+GSWJud3HXfCJT7c9K7TFpxzCMTqdzPEONi4ujabq0tNRRoKSkRCKROMcwAAAAIYQmApavtufKhIqeQXc80ef9aT0Wu1G1T9z7jY6OViqVZ86cUSgUsbGxFovlzJkzer0+NTXVXkCtVqtUqsOHD0ulUpVKVVhYWFZWNnz48LbrKAEAQIBjuN78bY2SCOQDwyYOUk3O0X50oi6L/Zk+kakCgWDatGl5eXkHDx60HxGJRKmpqUOHDnUUyMzMzMnJycrKsn9MTk5OSUnxWosBAMBX8bLer31Pt/HRj46MnLWt4q0yw3k2Z/lEphJClErl9OnTTSaTfQ390NDQVn3Q0NDQWbNm6XQ6k8mkVCpbTa1xwBr6AAABjuFvT3KJQC4RyGf1ekVrLNpW/la75X0lU+2kUuntJs/Y2Zd6cAE5CgAQ4Hjfk1wmVCQEDXom6ROKaueBo29lKgAAgIc8fZx6KxRFianWszfbQqYCAAA3vr6GPn/3frlCpgIAADc+laBt8TJGyT3IVAAA8Cs8jlHiCpkKAAB+Bf1U3mAuDQBAgOu4fqqFtogFYhcF/C1TkaMAAAGuI/qpRpuxydqkFCtdF/OJ9X4BAAD4wjAUyxebq1kZK83Qm37+/vmzLzLtzdPxt34qAAB4Tq/XMwwTEhLSFZdV56ufyhDGTJuP1B79rPRLlqcgUwEA4H9cv359x44dhJBZs2a5XtvON/EyP7XZ1lLeXP5Ryb9rTTfZn4VMBQCAX5nN5oMHD6pUKuf9N7sWxrN+qok2G20tn5T855y+gOu5/papGPcLAOCJI0eOBAcH9+7d+8iRI95ui5vc7qfSDE1R1JaKrdnane5dwd8yFTkKAOC28vLyK1euPPDAAz///LO32+I+956nGm3GU7ozH1/9pN2BSC74W6YCAIB7LBbLgQMHhgwZolKpunSmcr3322JrqTbVfHj14xstWg+rRqYCAAAhhPz0008ikWjIkCHeboin2N/7jY6OttCWT6+tP153kpeqkakAAEC0Wu2FCxfuu+8+gaD9dQuWLEl3XWD16jye2uUO9uso6XQ6mtCPJcy30tbTurOeV41MBQAAkpeX17NnT5qmtVotIaShoYEQUltbS9N0dHR0q8Lejcx20axLmkwmqUAqFUif7LNQ21L5Ucm/q4zVnlTtb5mKcb8AAG4wGAw3b94sLy93PnjgwAGJRPK73/3OW61yjxvr/QYJg3oHq5ff9bf8uuP/ufaZ21X7W6YiRwEA3HD//fczzK/jXQsLC0+ePDl16tSIiAgvtso97s1PpQglE8pGR478TdSYr8s27q7a48ZF/C1TAQDADaGhoc4fZTIZISQ4OLjV8S7Bk3WURJSIEDIr9r7M7lM/LvnkUsNlTqdjDX0AAPArDOvX7ciEsnCJ6tmk379459Kw9vaicYZ+KgAAtJacnJycnOztVriJr/1Tg4RB/ULvfGPQP/dXH/zm+kY2pyBTAQDAr3i43q8zilASgWRCt3ETuo37nMXuNMhUAADwK4z7awvemlggJoQ8HD/Xxthcl/S3TF2/fr3zxwULFnirJQAA4BV87Z/ailwoa7eMv2UqQhQAIMDx9TzVDW5mKsMwzc3NcrmczSpWAAAAnYaXPcndwyFTaZo+cuRIbm7u2bNnKysraZoWCARRUVGDBg0aNWrUb37zG7FY3HENBQAAYIPvx6kcsMpUmqa3bdv2xRdf1NTURERE9OvXb9iwYQqFwmAw1NXVnT59OicnR6VSPfjgg7Nnz0ayAgCAF/n6vd9nn322rKxs+vTpU6ZMSUhIaFugvLw8Jyfnhx9+yMrK2rBhA89tBAAAX3LmzP5WRwYP9qF1YTtojJKVtlkYq1wodVGGVaZOnTp14sSJcrn8dgViY2Mff/zxRx55ZMeOHZybySusoQ8A0NF8KkFvge+bvwxhTDbLTu2Ju3ukuS7JKlOnT5/OpphYLL733nvZlOw4yFEAgADHbz/VYDVebbqx4sI31UYdP5kKAADQVfD1PLXFZm6xmV6/sPF4XSHLUzhnak1NzXfffXflypX6+nrnjYH69u37yiuvcL0aAAAAvzzvp1oZWkQJPivZten6QU4ncsvU2trahQsX6vX6fv369ejRw/mrbt26cboUAABAR/BwbcJmm+lQdcHrFzcy3B/McsvUPXv2tLS0fP755/Hx8VxrAgAA6ARur6FvsBorWmpXXPjmuqHavStwy1SdTpeUlOTLgYpxvwAAAc6NdZRMtMVCW1dd+ja3+rwnVXPL1OTkZI1GY7FYfHZhB+QoAECAY3/HViQS2RiaIczGsgPrS3Z5XjW31XpHjhw5bNiw1157raamxvO6AQAAeMcwFMtXeHh4i8301PHVvAQq4dpPpShq9uzZS5cu/e1vfxsUFOTcW+3fv/+qVat4aRMAAIDb2M+luXnzZrBI/l7q7w9Wn/vXxU2eV80tU69fv/7MM88IhcLx48eHhYVR1K/t7tmzp+etAQAA8BD7e782m40QEiSUTooeMrX70LVXtn3/c54nVXMe9ysSib766quIiAhPagUAAOggbqz5IBYICSGP95n6UPy4lRc2nKovdq9qbplqsVjUajUCFQAgkPn4GvpuT08NEkqDhNLlyY8WNVa8dmFDrUnP9QrcMjU1NXXbtm3Nzc1BQUFca+ocmEsDANDRfCpB2/JwbcJgkXxwWJ+vRr6YfePYe1e2cDqXW6YOGTIkMzNz2bJlixYtSkhIcB6jRFGUL0ywQY4CAAQ4D9dRIoRQFCUTSqb3HHFPzxHvFm7ecSOf5YncMnX//v0bN24khDz11FOtvho4cOC6des4XQ0AAIB3bq+j1IpEICKEPJN0z9yE8a9d2HBRX9buKdwytU+fPgsXLrzlV9HR0ZwuBQAA0BH42pfGTiGSKUSyVYMXntNda7cwt0xNSEhISEhws10AANCJbDabyWTy2eEvHYfvLckJISRYJB8R0c9Im10Xw/6pAAD+o7m5effu3fn5+QUFBTqdjmEYsVjcq1evwYMHT5gwYdCgQd5uYGfgt5/qIKCoIKHUdRlWmcp+gV/PlwKurq5uaGgQi8VRUVGt/nlls9m0Wq3RaFSpVLebz4NxvwAQmJqamj7//POtW7cajcZevXoNGTIkLCxMKpU2NDRUV1fn5OT8+OOPCQkJCxcuHDt2rLcb27E8H6PkNlaZ+vTTT0+YMGHmzJkKheJ2ZYxGY3Z29qZNmzZtcnN5p5s3b+7Zs6eurs5xZMaMGY7lmWpqajQajcFgEAqFNptNrVZnZGQIhcJWF0GOAkAA0ul08+bNU6lUCxcuzMjIUKlUrQowDHPhwoVdu3atWLEiPz//T3/6k1fa2Tn4GqPkBlaZOm/evLVr13766adjxoxJTU294447IiIiFApFS0vLzZs3CwsLT58+nZubGxQU9OSTT7rXjqampm3btsnl8nvuuSc6OtpkMlVVVTki3Gq1ajQasVg8d+5cpVJZVFS0d+/e/Pz8UaNGuVcdAIA/oShqyZIlEydOFAhuvTMKRVEDBgwYMGDAggULTp061cnN62S+3k8dN27cqFGjdu7cuXnz5n379rUtoFarn3rqqczMTLlc7l478vPzrVbr9OnTg4ODCSFisdj+xq64uNhgMEybNk2pVBJCkpKSysrKCgoK0tLSfGFSLACAdymVytTU1NsFqrPw8PBJkyZ1QpO8yNf7qYQQiUQyY8aMGTNmVFZWnjt3rrKysqmpKTg4ODo6Ojk5uXv37p40wmazXb16tU+fPvYcpWm61X8Z5eXlIpEoNjbWcSQ+Pr6oqEir1cbFxXlSNQCAf1i7dm1RUVFmZuaUKVPa3vsNKB3UT220mAQUUYhcDVPiPO43JiYmJibGg1bdQl1dnc1mCw8PP3DgQFFRkdVqDQ8PT0tL6927t72ATqcLCQlxDtqwsDBCiF7PeTFGAAC/lJaWdu7cubVr13744YcjR46cNm3ayJEj2w46CQw891ObrWaD1fTiyS3rRjzkuqRPzKVpaWkhhJw+fTo4ONg+IO3cuXO7du2aOnWqWq0mhJjNZplM5nyKRCIhhJhMplaXSk9Pb3v9vDyP9u4BAPB9U6dOnTJlyunTp7Ozsw8ePHjo0KHw8PDJkydPmzYt0NYVoPnrp1ppWiQQvH/pwGfFR9mU94lMtWMYZsaMGfbsTEhI+Prrr0+cOGHP1FsWJoQ4b+Bqh/gEgIBFUdSQIUOGDBny3HPP7du3T6PRbNy4cePGjf369cvMzMzIyHAxd4MTX9+Xhqf5qQarefeNS38+yWEZfZ/IVKlUSgjp3r27ozMqkUh69ux59epV+7NVmUxmNBqdT7H3UO0nAgCAs6CgoOnTp0+fPr2iokKj0ezcufOtt95as2bNc889l5mZ6fn1fSpBO0KTxVRmuPnC8R+vNd3kdKJPZKpKpaIoqtW4JPtjAHt/VKVSXblyxWq1ikS/NLi+vt5+vNMbCwDQZfTs2fPxxx+/66673n333Rs3blRUVHi7RZ3Bk36q0WYx2awvn9q2V3vZjdNZZarZbLY/v+wgEomke/fu1dXVNpvNHqU0TVdWViqVSvvHuLi4y5cvl5aWJiYm2k8pKSmRSCS8j5YCAPAbpaWl2dnZOTk5N2/elMlkd99998SJE73dqM7g3lwammFowvz7yuF1lw+6XTWrTM3JySkoKFi8eDFf9+LbGjp06Pbt2/fu3Tt06FBCyKlTpxoaGhwLaKnVapVKdfjwYalUqlKpCgsLy8rKhg8fHqhD2gAAbquxsXHPnj0ajebSpUuEkAEDBixcuHDixIlurx/Q5bgxRqnJajpSdfXFk1uMNosnVbPK1O7du7/99tsnTpxYtmyZPfN417NnzwkTJhw6dOjq1auEEJFINGLEiP79+9u/FQgEmZmZOTk5WVlZ9o/JyckpKSkd0RIAgK6Ipuljx45pNJq8vDyLxRIREfHwww9nZmYG4iR+1vd+g4KCDFZTZUvDCyd+vKSr9LxmVpmampr66aefvvbaa88999zMmTN///vft5rZwou+ffv26dOnrq6OYZjw8HDHo1O70NDQWbNm6XQ6k8mkVCpv1wCsoQ8AgWnVqlU7duwQiUSjR4/OzMwcMWIEm2WV/BL7bmpQUJBCJP2+9DQvgUrYj1FSq9UfffTR119//dlnnx07duyll17qiD2DhEJhVFSUiwL2pR5cQI4CQGDq0aPH4sWLp0yZYl/DNZCxX0eptraWEPKHfuMeTxq57OSWI9UlHlbNYdyvUCicP3/+mDFjli9fvnjx4tDQUOdv+/fvv2rVKg9bAwAA7pk/f779TUNDQ6vfzw4lJSWO9en8GrcxSgqRRCGSvDPsgSv6qj+d3Kxtdn+FPs5zaYqLi6urq4OCghxDcO169erldiMAAIAvn3/++dixY5OTk1sdX7NmjVQqDYhMdWsdpVCxLDUiLmviM1uun/3H2Wz3auaQqXV1dW+++WZeXt6wYcOWLVvm+iYtAAB4RUhIyLJlyz744IP4+HjHwX//+9/ffvvta6+95sWGdRq319CnKCpIJJmVkDI7IXXFuexN105yve0BJ0QAACAASURBVALbJ9j79u2bP3/+iRMnli5d+tZbbyFQAQB80yOPPHLXXXctXbq0rq7OfuTLL7/84osvnn/++VuuiO6PKNavW5AIRCKBYOmAjN2Tnx0Swe0WLKtM3bdv39/+9rfevXt/8cUXM2fO5FRBJ9vvxNttAQDwAqFQuHz5cqVSuXTp0paWlm+//fbjjz9evHixj//25hPD+nV7wSJprEL1wci5H496WCUJYlkzq3u/NE0vWbLk/vvvb7tmva/BuF8AALlcvmrVqkWLFj3xxBNlZWVPPvnk7Nmzvd2ozsPj9qmhYtnobn32T/2/L6/mv3VhT7vlWWWq328KDwDQpTEMY7H8zwJAwcHBK1euXLx48Zw5cx588EGz2UwIEQqFvCw/5+P70rBf84ENAUVJhaJ5fYY93DtN0F7H0ifW0AcAAE9UVFTMmTPnll9t2LBhw4YN9vfz589/4oknPK/OtxK0DR77qQ4yoZgQYqVp18WQqQAAXZ5SqVy4cGG7xTpirR5f1BGhSgghRNTe0lTIVACALi8kJOTRRx/1dit8Bq/3fjnxt0zFer8AEIBsNlvbXahvx2KxiMXijm6SF3VYN7V9/papyFEACEA1NTVLly59/PHHx44d62IUUm1t7bfffltRUeHniz94L1T9LVMBAAJQZGTk5MmT//nPf77zzjvjx49PTk5OTEwMCwuTSqWNjY1VVVWXLl366aefTp48mZiYuGTJkltehKbpurq6xsZGmqZDQ0MjIyN9f/7kbXRUsxmGcf1nwjlTaZret2/fyZMndTqdzWZzHFer1U8//bQ7bQQAAM+IRKL58+dPnz598+bNWVlZmzdvblWAoqjU1NTly5f/5je/uWUqGI3GL7/80mq1Oo6oVKrx48dHR0d3bNM7gNtrE7pgoWmRQGBlaDHlajISt0ylaXrZsmVHjx7t3r27Vqvt1atXXV2dwWCIiIiIiIjwrMEAAOCR8PDw3/3udwsWLCgpKbl48WJtba19w+levXoNGjTodpvV2AkEgrS0tB49eoSGhlIUdePGjQMHDmg0mkceeYSXKa2diu9MNVjM2WWFLxzOvjzvedcPorllan5+/tGjR1999dUJEyakp6evWLFCrVbv3r37vffemzFjhictBgAAXggEgsTExFZbh7VLIpEMHjzY8VGtVut0up9++unmzZvdunXju40di+Hv3m+jxVSir/tj3vZrDfVsynPL1PPnz/fp02fChAmOIxRFTZ48ubS0dO3atWvWrOHWWAAA8FX2+8ByudzbDeGOj35qi9VistleOJy9++ci9mdxy1Sr1eq4xysWixsbG+3v+/fvv2nTJk6X6iCYSwMA4Daz2dzU1GSxWKqqqs6cOTNgwICQkBBvN6qz0QxDM8wHBT+9d/YI13O5ZWpkZGR+fr79fWxs7Llz5+yrchQXF8tkMq51dwTkKACA20pLS/fu3Wt/37dv31GjRnm3Pe5hPFjzodFiyrtR+lxeltFmbb90G9wyNSUlZe3atVVVVdHR0RkZGZ9++umNGzcIITt37szIyHCjegAA8B0JCQn2Bfe1Wu2pU6e2bds2c+bMtktJ7Ny5/panT526oOPbyIJb936bLCatoXFJ3vaLddVu18wtU5OSkpYvX84wDCFk7ty5Op0uJyeHEJKRkfHss8+63QgAAPAFEokkPDycEBITExMcHLxnz57i4uK+ffu2KuYr2XkbXMcomWxWmmFeOZqz5dpFD6vmPD917Nix9jdCoXDx4sWLFy/2sAUAAOCD7MN9dTqdtxvCHet+anh4uIW2fXbp5OsnD/BSM6tMtS+r0c6FRCKFQsFHkwAAwFNGo7GgoKCurm7QoEHR0dEMw9A0zWmmqVarJYQEBwd3WBu9z2w2m6y2lKge3RWhWkOD5xdklamPPvpoTU2N6zIDBw5ct26d5w3y0Pr1/3OXf8ECn75BAQDQETQazerVqw0GAyFk5cqV0dHR165de/TRRz/++ON+/frd8pSLFy/W1dXFxsYGBwdbLJaKioozZ84oFAqu81x9Aut+alNTU7BEkhbda++9C78vPv/X/N0e1swqU5944onm5mbXZSIjIz1sCi8QogAQ4E6cOPH6669PmTJl5syZr776qv1g7969k5KS8vLybpepMpmspKTk/PnzjiO9evVKT0+XSCSd0Wh+cRyjRBESJBI/lDRobt/Bf83f/c2VM27XzCpT7777brcrAACAzrRly5ahQ4e+/PLLhBDnm729e/e+fv367c7q3bt37969m5qaDAYDRVFKpVIqlXZGczuEO3NpJEIhIeSloeOfGjj8//KyTlZXuHERN/el0el0paWlZrM5KioqPj6e5aZ9AADQ0W7cuDFu3Li2xxUKRbtP8YKDg/3hAaoH6ygFiyXBYslnEx84WVPxf3lZ9aYWTqdzztTa2tq33nrr8OHDzH9X/o+Jifn9739/y79CAADoZKGhodXVt5hhWVxcHBMT0/nt6YpCJNLf9FD/9MAz6y+d/CeXIcHc+pcWi+WPf/xjfn7+tGnTli1b9uqrrz755JMikeivf/3rkSOc13ACAADejRw5cteuXUVF/7NKrUajOXfu3OjRo73Vqs5EMWxfLggoSioULeiXeunh5+5V92dZNbd+6v79+ysqKlavXp2cnOw4+NBDDz377LPr16/voqtYAQD4k3vvvXfPnj2LFi0aPny4Xq/Pzs7etGnT2bNnhw8fHig3FPnb600qFBFCVoyc/EzyyD/mbW+3PLdMLS4uvvPOO50DlRAiFotnzJjxxhtvcLpUB8Ea+gAQ4KRS6fvvv//555/v2rWrqanp0KFDUVFRCxYseOSRRwJl7Avf+6cGi6V9w6Sbps5l2tvunFumikSilpZbPLA1Go0+smktchQAQCaTLVq0aNGiRUaj0f7R2y3qZLztn+osRNz+QGhu/2ZJTk6+evXqtm3bnA/W1NRs2LChVecVAAA6DU3TTbditVqtVqvjo9ls9nZLOwXD+sU3bv3U4cOHjxgx4o033ti6detdd90ll8tv3Lhx5MgRoVD42muv8d86AABg4caNG3PmzGm32Pz585944gnPqztzZn+rI4MH4x4hIVwzlaKolStXbtiwITs7e/PmzYQQuVw+cuTIhQsXJiQkdEgDAQCgPSqVynlHk/379xcWFo4bNy4uLs5ms127du3QoUODBw8eOXIkL9X5eoJ2QAeUJc7zU8Vi8fz58+fPn282my0WS1BQEEV1yJ1rAABgSaFQzJ492/4+Ly/vxo0bX331VY8ePRwFioqKnn32WavVnX22u54ulKkOEonEB9eBxLhfAAhwW7duzczMdA5UQkhSUlJ6evrWrVsHDx7srYb5gRaLRS4WuyjgTqbW1NTs3bu3pKTEYrFERUUNGzZs6NCh7raQZ8hRAAhwOp3ullM+GIapr6/v/PZ4AcP/3dNGk+lqfV2/yCjXxThnalZW1jvvvGM2m2UymUwm0+v1GzZsGDZs2D/+8Y+goCB3WwsAAPxITEzcsWPH9OnTY2NjHQcvX7588ODBmTNnerFhXVSLxWK0WV/cvWvPtasXn1niujC3TC0qKnrjjTeGDRv2zDPPqNVqQkhLS8v27dvXrVv3/vvvv/jii+63GgAA+PDYY48dPXp0/vz5o0aNio+Pp2n62rVrR48e7dat28MPP+zt1nUKnp6n2miaUNS6E8fWHv+J5SncMjU3NzciImLlypXi/95Qlsvls2fP1uv1P/zwAzIVAMDrYmJiPvnkk08//fTQoUMHDx4khISHh993332PPfZYWFiYt1vXGVwv5MtSo9mUW1b6fI7GbLOxP4tbplosFrVaLW7zhLZv374Wi4XTpQAAoINERUUtW7aMENLc3CwQCAJvHSWPNJnNFY0Nz+3KvlTbztZ4bXHL1JSUlKysrMbGxpCQEOfjJ0+eTE1N5Vp3R8C4XwAAhwAd5uJuP9VotdoY+uV9OduvFLp3BVaZarFY7KPIUlJSJk2atGTJkgULFtx5550ymayysjIrKys/P//NN990rwX8Qo4CQICzWCy3m4cqEona3mj0Q9zH/dIMY6Xpz86cWnUkz5OaWWXqgw8+2Gp3+JdeeqlVmddff33dunWeNAUAADy3evXqrVu33vIrvtYm9Bv2jXoazaYTNyqe26XRm4weXpBVps6dO9dgMLguEx0d7WFTAADAc+PGjevZs6fjI03T5eXle/bsGTJkyIgRI7zYsM7D+t5vZGRkk9m8YOuPp7Q3eKmZVabOmjWLl8oAAKCjDR06tO06PA899NDTTz/N1+J3vr6GPutM1el0UqFw06yHXj2w76vzZzyv2f21CQEAoKuIj48fOnTo999///LLL3t+Nd9KUA+YzWaxUEgIeXF0+qLUtOd3a45VlHtyQc6ZStP0vn37Tp48qdPpbE6zdtRq9dNPP+1JUxwMBoPFYpHL5VLp/2wAa7PZtFqt0WhUqVQRERG81AUAECDEYnFdXZ23W9EZ3JifqpBIFBLJx9PvPVtV+dyu7Jstze5VzS1TaZpetmzZ0aNHu3fvrtVqe/XqVVdXZzAYIiIi+Ao5g8GwadMmk8k0cuRI57Wea2pqNBqNwWAQCoU2m02tVmdkZAiFwlanYy4NAEArDMMcO3bs4MGD9913n7fb4tNCpdLRveLyFjzx1bkzKw8ddOMK3DI1Pz//6NGjr7766oQJE9LT01esWKFWq3fv3v3ee+/NmDHDjerbys3NDQoKMplMzgetVqtGoxGLxXPnzlUqlUVFRXv37s3Pzx81alSr05GjABDgPvjgg507dzofaWxstFgsPXv2nDdvnrda1ak8WEdJQFEykWhe8uB5yYNf3r9786WLnE7nlqnnz5/v06fPhAkTHEcoipo8eXJpaenatWvXrFnD6WptFRUVabXaSZMm7dixw/l4cXGxwWCYNm2aUqkkhCQlJZWVlRUUFKSlpQXEXCsAANb69u1rNP46J4SiqJCQkPj4+LFjxwbKL0yP1yaUiUSEkL+PnfjM0OHP7co+X13F8kRumWq1Wh33eMVicWNjo/19//79N23axOlSbbW0tBw6dGjkyJFtF/4oLy8XiUTOeyzEx8fbAzguLs7DegEA/ElqaurQoUPtPRBntbW1EokkNDTUK63qXPzs9RYikYRIwr+674Ej5defz9E0s1iCV8CpgsjIyNraWvv72NjYc+fO2d8XFxd7vp6kfYH+fv36tf1Kp9OFhITYJ+fa2VeC1uv1HlYKAOBnPvnkk88//7zt8RUrVnje+ekSKIbti40QqXSSus/pRX9YMnxku4U5r/e7du3aqqqq6OjojIyMTz/99MaNG4SQnTt3ZmRkcLpUKyUlJdevX589e/Ytv7Vv1+p8xD7LqtVjVwAAuB2r1ercM/FnPO315iAUCISEPJmaZqVp1yW5ZWpSUtLy5cvta//OnTtXp9Pl5OQQQjIyMp599lm3m2symXJzc295s+J27G2gqNYd/PT09LaF8/I8Wr8RAMD30TTd3NxMCLFYLBRFNTU1OX9148aNq1evTpo0yXsN7PLkovafRnOenzp27Fj7G6FQuHjx4sWLF3NuVxvHjx8nhERHR2u1WvLfO7qNjY1arTYyMlIsFstkMudH7uS/PdRWE1gJ4hMAAtXVq1cff/xxx8ctW7a0KhASEjJmzJjObZSX8N1PZY9bpn755ZdHjx7lfa18g8HQ0tLSatHngoKCgoKCBx54IDIyUqVSXblyxWq1ikS/NLi+vp4QolKp+G0JAEAXFRUV9cwzzxBCDh48KBKJRo8e7fhKIBAolcrhw4cHyO9MXvYkdw+3TDUaja36i7xIT093Xtm5vr5eo9GkpKT069fPvlFrXFzc5cuXS0tLExMT7WVKSkokEklMTAzvjQEA6IrCwsLmzJlDCOnRo4dQKAyULqmP4ZapQ4cO3bhxY319Pb//2Gk1ecZisRBCZDKZ4/GqWq1WqVSHDx+WSqUqlaqwsLCsrGz48OFt11ECAAhwjid0gaur9FMHDRr029/+dunSpYsWLUpKSlIoFI6vKIrquNnEAoEgMzMzJycnKyvL/jE5OTklJaWDqgMA6Fpomm5sbBSJRAqFwmg03m5OhFQq9XzeI/H9fWk6RovFYqOZYKmrvX24Zer+/fs3btxICHn++edbfTVw4EC+nrNGRka2XY4/NDR01qxZOp3OZDIplcrb/WeB9X4BIADduHFjzpw5qamp77777po1azp6T3JfT1C++6lWmhZQ1Nrcn/7wm3amqHLL1D59+ixcuPCWX3XOnuT2pR5cQI4CQABSqVR//OMf7b+HJ06cqFarb1nslovq+B9+xyg1mcwHikr+tHWXjaZ5ztSEhISEhAT3mwYAAB1AoVDcf//99vcpKSkB/2iMn7UJm0zmcp1+6RZNUfVNlqdgT3IAAPAvHvdTjVar1Ua/sj1Hc6mI04m+uCc5AABwYh+j1G4xvsYo+TGaYWw0/Z+jJ1cfOOLG6T63JzkAAHBlH6PUbjG+xij5Onf7qU0m07Gyihe2aJpMZveu4HN7kgMAAFf2MUrtFsMYpdsxmMw1BsMLm3eeu1HpSdW+tSe55zCXBgACkPMYJWBPIpGYbTbCkBW7Dvx49oLnF/ShPcl5gRwFACCEFBYW5uXlabVaiqJiY2PHjRsXQLM2WPdTVSqV1UYv2rjlWFk5LzVzy9TIyMj8/Hz7e/ue5IMGDSI87UkOAACeo2n67bffti/7EBoaarPZDAbDf/7zn7lz5z711FPebl2nYJ2p1dXVQRLx2tkzCrRVL2zZWdtk8LBmX9mTHAAAePHjjz9u3bp1zpw5c+bMsa/NXlNT85///Ofrr7/u3bv35MmTvd1AH2LfijtUJh2R0GvvHx7feOrc6zkHPbmgT+xJDgAAfDlw4MDYsWPt+77ZRUVFvfjii9evX9+/f38gZKobY5QEFCUTi+akJs9JTf7bjr2bz110r2qf2JMcAAD4YrFY7rjjjrbHk5KSysv5eWro69ydSyMViQghr0wdv2jMsBe2aM7fqOJ6BX9bRwnjfgEgwKWkpJw6dWrevHkU9esSfVar9ezZs3x1Un19XxrP1lEKlkqCpZL18+7PLy3/05adBjOHuaruZGpNTc3evXtLSkosFktUVNSwYcOGDh3qxnU6AnIUAALcvHnzXnjhhb/85S+zZ8+Oj4+32WylpaVffvmlSqWaMWOG2WwmHu/O6VsJ2gYvq/2GSKXj+/bOX/rUx4ePv3fwKMuzOGdqVlbWO++8YzabZTKZTCbT6/UbNmwYNmzYP/7xj1ZbiwMAQOf78MMPCwoKCCEHD7YebjN16lT7G4FA0PZb/8HTvjRCihIKhY+PHDp/WMpL23fnXG5/7V9umVpUVPTGG28MGzbsmWeese8l1NLSsn379nXr1r3//vsvvviimw0HAACejBkzJioqynUZgUDQOY3xDl73epOLRUQsWnlPxtPpw6j2usDcMjU3NzciImLlypWOmwZyuXz27Nl6vf6HH35ApgIAeN2IESNGjBjh7Vb4mxCZtH9MtxaLxXUxbplqsVjUanXbu/B9+/a1tFcTAABAJ+B3T3Jn8vYeQnNe8yErK6uxsTEkJMT5+MmTJ1NTUzm3rgNg3C8AwOXLlzdv3lxeXt7U1GRfUcBu5syZAbEscIdlaru4ZWpaWtqkSZOWLFmyYMGCO++8UyaTVVZWZmVl5efnr1q1yvzfAcdisZhq965zx0COAkCAO3bs2AsvvCCXy/v27dujRw/nr0JDQ12fW19fr9PpKIpSqVRKpbIjm+mfuGXq/v37f/jhB0LISy+91OqrefPmOd6vXbs2OTnZ88YBAABXP/zwQ58+fdasWcNpLsb169dzc3OdNzaPj48fP368XC7vgDZ2sK7ST+3Tp8/ChQvbLRYTE+NuewAAwCM6nW7UqFFcJzc2NDTExMSMGzcuKiqKpunLly//9NNPe/bsueeeezqonR2n456ntotbpiYkJATQbkEAAF1QcnLytWvXuJ511113DRgwwPExJSWlqqrq2rVrJpNJKpXy2sCuymAyiwQCqdhVbvr1FCUAgMDz6KOPVlRUfPXVVy0tLezPajsIJjg4mBBis9n4bFznYFi/2Gkymi9XVM9dvaHdM/xtvV8AgAAXHBw8Z86clStXfvTRRyEhIc7LOzz44IOPPPIIm4vYVzRUqVRdcoE8/u79Gi1Wi832102795xrfxEl4n+Zun79euePCxYs8FZLApfzv3YZ7z3WAAhUOTk5K1as6N69+8CBA+19TYc+ffqwvMihQ4caGxtv9zB1yZJ016evXp3HsqKOwMu0E5phbDTz2YGTa3ceYX+Wv2UqQhQAAtz27dsHDhy4Zs0atxcgPHXq1MWLF0eMGBEbG3vLAt6NzPZ5/I/5JqP5+NWf//z1ToOJw6Y0xP8yFQAgwFksltTUVLcD9ezZs/n5+ampqSkpKfw2rPN4kKkGk7lGb/jzN5qCnzlvnkqQqcA/3O8F8KrU1NRLly65d+65c+eOHDmSkpIybNgwflvl+8xWG8Mwr2/ev/X4RbcvgnG/AAB+Ze7cuVar9a233iorKzMajWYnrgfxFhQUHD58eNCgQV19CX6KYfuyYxjGaLFuOnJ26LL3PQlUgn4qAICf+eCDD06fPn369OktW7a0+mr+/PlPPPHELc+6evVqXl6eUqlUqVTO3Vy1Wi2TyTqwuR2B9c2y4OBgg9Fc8HPlsq931jYaPK/Z3zIVa+gDQIBzsX/qoEGDbnfWzZs3CSF6vf7AgQPOxyMjI7teprImkUjEImGR9iYvgUr8L1ORowAQ4NzbP3XYsGH+8wyVdT+1rq5OIhLOHjVw9qjk5d/v8fDGL8HzVACAAMEwjE6n83YrOgPF+mUnEYkkIuGf7x2fteyxgXEerVePTAUA8HOVlZXr16+fPXv2d9995+22dAq31iZUyCTxUaqPFv32vcdnBMsk7tXsb/d+AQDAzmQyHTx4MDs7+9SpUwzDJCUluXie6lc8mNAXIpOO7d87d/lT6/efeF/DYQUlO2QqAIC/uXTp0o4dO/bs2WMwGAghEydOXLBgQXx8vLfb1Uk83OtNQFECoXD+b1Lnjkn527c5OWdZrfRr52+ZinG/ABCw6urqdu3alZ2dXVpaKpPJxo0bd/fddy9dunTo0KGBE6h8kUlEhJC/z854ctLwZV9riitvsjnL3zIVOdohnBc5o2nvtQMAbuvzzz9fv349wzCDBw9++OGHx40b58dzYNrB32JuITLpHT2ivlz80MGLJS9v2NVueX/LVACAwHT9+nWbzXb33XfPnz//dmvf8+XMmf2tjgwe7EP9GV72pXEWLJNMGdz37pQ7bLZ2OhXIVAAAf3DfffeZTKY9e/ZoNJoBAwZMnTp14sSJrfZ644tPJegtdMCi4yKBgBBCCduZLINMhdtw3gYV93sBfN6AAQNWrFih1+tzcnKys7PffPPN1atXp6en0wH4/2+HbeRBUe30gZGpAAD+Q6lUPvDAAw888EBhYWF2dvbu3bttNtuHH35YWFg4efLkAQMGtJsK4AlkKgCAH7rjjjvuuOOOP/zhD7m5uTt27Ni2bduWLVt+97vfPfbYY95uWoejvLfjpL9lKubSeELg9OiFbmryYksAgBdisXjixIkTJ06srq7Ozs4ODw/3dov8nL9lKnIUAKCtbt26BUIP9Rcd1k212miRy2FK/papAAAQ4DxcR+mWjGarxWqTikVE6KoY1tAHAAC4LRvNWG30l7uOj392LdPek1r0U+FXeIYKAP6Av35qU4v5xOXrf/1E02yysCmPTAUAAP/CR6YajOYaXdNf/q25VFbF/iwfylSDwVBXV2c2m0NDQyMjI9tOorLZbFqt1mg0qlSqiIiIW14E434BAAKchzNwzRYrzZBV3+zbceQi13N9IlObmpp27txZU1PjOBIeHj5u3Ljo6GjHkZqaGo1GYzAYhEKhzWZTq9UZGRlCYeuHxchRAIBA524/lWEYs9X23f6z73570L0r+ESmGo1GsVg8adKkHj16iMXiioqK/fv3Z2dnP/zwwxKJhBBitVo1Go1YLJ47d65SqSwqKtq7d29+fv6oUaO83XYAAPAxbmVqU4vpwrXKv/xbU9fY7HbNPpGpERERM2fOdHxUq9VNTU2HDh0qLy/v3bs3IaS4uNhgMEybNk2pVBJCkpKSysrKCgoK0tLSxGKx19oNABCQ/GxfmmaTRd/U8tdPNKeLKjys2icyte2jU/teCo6ln8vLy0UikfPuRfHx8UVFRVqtNi4urtPa2dVRTv/+YCysxrABALTlUwl6C6z7qd26daMZ5v3v877bf4aXmn10fmpxcbFAIOjevbv9o06nCwkJETjtjB0WFkYI0ev13mkfAAB0fQ0NDS1Gy9yMIQN7d+flgr6YqcXFxcXFxSkpKQqFwn7EbDbbH6w62D+aTCYvtA8AAHwYxbB9GY1GhVzSq1vYe3/87TuL7w0JknpYtU/c+3VWUVGxb9++hISEtLQ0F8Xsi1m0vWmcnp7etnBeXh6PLexahAm9HO9tpT97sSUAAJ2E+xilkCDp6GT1rref+mrXiXWbD7tds29lqlar1Wg03bt3nzx5snNeymQyo9HoXNLeQ5VKW/+bIpDjEwAACHFz3K+AoiQi4dyM1Acnpqz4fPfu44XuXMSdmjtGZWXljh07unXrdvfdd7eaeKpSqRobG61Wq+NIfX29/XhntxIAAHwbxfrVlkwiUsgkL8+ftOHvjyTGRnKt2lcytaqqKisrKzIy8u677xaJWvee4+LiaJouLS11HCkpKZFIJDExMZ3ayq5DGK6yv2ylPzte3m4UAECnYFi/biNYLk2KjfrkxQdfezLT9eZurfjEvd+GhoasrCyGYXr37l1cXOw43q1bN/sahGq1WqVSHT58WCqVqlSqwsLCsrKy4cOHt11HCQAAAl17OjhT2wAAIABJREFUu8ewFCyXThzaNyPtjnU/HvpMc5zNKb6SqWazmRBy+PD/PBkeOXKkPVMFAkFmZmZOTk5WVpb9Y3JyckpKildaCwAAvozH/VNFAgEh5HfTRzw8OfXv/9nVfnneavZAbGzs008/7bpMaGjorFmzdDqdyWRSKpUymeyWxQJ5DX3LpNRfP+w56b2GAAD4FblULJeKX3sy00rTrmfb+ESmsmdf6sGFQMtRAABojb9+qrNgefuzV7tYpgIAALjG471frpCpAADAjY+voe9FyNSujZL+umSjGM9QAaBT+HqCop8KAADAC9z75U0gj/sFAICOQ9OMyWyVy1xt2u1vmRoQOeq0zhRjMnuxIQAAvqgD+qmGZvPxc6Ujh/R2XczfMhUAAAIcv/d+m1vM1Tcbl7+748q16r0b/ui6MDIVAAD8C0+ZarZYaZp56+M9u3IvsjwFmdplCPsl2t/YLhW7LgkAENg8DVWGYcwW24+aM2u/OMDpRGQqAAD4FQ/v/TY1my5e0S5fna1raOZ6LjIVAAD8i7uZ2txibmg0Ln9vx7lLFe5dwd8y1c/m0jQ8PMLxPvTrn7zYEgCALoN7plqtNkpAffBl7uZdZzyp2d8y1Q9yFAAAPEGxLikUChmGGE2WnNyLb3y02/Oq/S1TAQAg0LHup0ZERDQ1mxb/dVNxaTUvNSNTfU7FslGO9z3/ecSLLQEA6JJYZ2pdXZ1CLvn4nw9v2n7io6/zPK8ZmQoAANz4+L407Mf9Wq1WgYCSCISzp6fef3fKqg9z9hy67EnVyFQAAODGpxL0VjgPUpJKRISQFxZlzL9/xKvv7rhaVuNexf6WqV103C/u9wIA8MbduTSKIGnvOOm6FXN+Ol2y4n2NxWLjegV/y9QulKMAANAhPFvzQREkGTey7/iRd3yy8fAXP3CbxCjwqGYAAAAfQ7F+3Y5QIBAIqPn3D9/x2e/HDO3DvmpkKgAA+BeG9cslmVSsDJG/siTz438+HBsTxqZmf7v324UUfZjmeJ/0FJ6hAgDwhOFzs7fgIGn/pO7/eWv+vsOF7RZGpgIAwK/MZnNzc7NEIgkKCvJ2W9zE7/6pdkEyyZSx/a1W2nUxf8vU9evXO39csGCBt1oCANC15ObmlpeX6/V6QkhSUtKkSZO83SLfIhYJxSKh6zL+lqm+H6KW3fH2N0kZx73bEgAAZzqdrlu3bnfdddfRo0e93RbPdEA/lSV/y1QAAHDPjBkz7G+6eqZSvD5P5QSZCgAA/gX9VP9myol3vJdmlHmxJQAA0HGQqQAAwM3OnetveXzqVN8Y0dIx/dRmg4khRKGQuiiDTAUAAG58JTtvg/e5NCaTlWGYd9/d9fzSTNcl/S1TfWcN/aTjEsf7ojTc7wUA6Cz8jVFiGMZisW3ZfOLjj/YTQgIuU7GGPgBAoOMpUg0G08ULFa+v3KbXt7A8xd8yFQAAApyLxfFZamk26xtaXn9tW0FBOacTkal8mnf51z/9r+6M9WJLAAC4Ki8vb2xstL9vaGi4dOkSISQ2NjYkJMSr7eLOg36q1WqjCPXhh/uytp9243RkKgAAEELIhQsXSkpK7O+rqqqqqqoIIZMnTw6QTGUYYjJZcnadX/3uLrdrRqYCAAAhhEyZMsXbTeAJ9zFKBoOp5Gr1yte2VVc3eFKzv2Wq74z7BQAAr2D/PFUmkxmNluZm879e337ixDXPq/a3TPVKjjoeo+IZKgCA97HupoaGhoqEgg8/2MtLoBJCBLxcBQAAwFcwDMtXdXW1SCx8dsmUT9c/kZgU7XnNyFQAAPAvDOsXIYSQ4GBpQkLk2+88/Le/3yeVij2p2d/u/Xaal6+edbx/rc8gL7YEAACcuTc/VaGQjknvu33Mc19+cejLLw67VzX6qQAA4F9Y3/ttdZ5AIBAKBQ/NGbll2x9Hj0lyo2Z/66di3C8AQEc7c2Z/qyODB/vS71vP1iaUycQymfhPL06vmFf/+mvbf/75Jvtz/S1TOzRHP73+k+P97+JGdFxFAAC+zLcStC0+1tAPDpbdcUf3dR8+eijvyr/+mcXyLNz7BQAAv0IxbF/tCgqSTpjYf+/+P896YBibqv2tnwoAAIGOv73eCCEikZAQ8tiC9NkPDqfaG/6ETG3Hrhu/ju+d0gP3ewEAApFcLpHLJWaz1XUxZCoAAPgXXvupziSSdkKzK2WqzWbTarVGo1GlUkVERHi7OQAA4JM6KlLb12UytaamRqPRGAwGoVBos9nUanVGRoZQKGxVjK+5NI5bvlN6YD0HAICuhOqwfmq7ukamWq1WjUYjFovnzp2rVCqLior27t2bn58/atSoViUxJxUAALyla8ylKS4uNhgMo0ePViqVhJCkpKTExMSCggKLxeLtpgEAgI9xdx0lz3WNfmp5eblIJIqN/XUntfj4+KKiIq1WGxcXx1ct/y476ng/pcdIvi4LAACdqgNu/TIMY7XSFCEicetnjs66Rj9Vp9OFhIQIBL+2NiwsjBCi1+u91ygAAPBJHPelaZehyXQ878qcca/bbLTrkl2jn2o2m2UymfMRiURCCDGZTF5qEQAA+Cgexyi1NJt1N5v+9efvLp/7mU35rpGpbTEMQwih2qxpkZ6e3rZwXl5eZ7QJAAB8AR+RarHYCCEfrcre+eMJ9md1jUyVyWRGo9H5iL2HKpVKW5X0JD4TepU73u9up38PABC4fH1fGs9ClWEYs8m6a/PJda+zXTrfoWtkqkqlunLlitVqFYl+aXB9fb39uFfbBQAQiHwsQdvwIFKbm4zFl7T/WvbtzZpGN07vGpkaFxd3+fLl0tLSxMRE+5GSkhKJRBITE+PdhgEAgM9x63mqscVsaDS+8fIPZ/Kvul1z18hUtVqtUqkOHz4slUpVKlVhYWFZWdnw4cPbrqMEAAABjs0mbs5sVlogpD57f8+Wr454WHXXyFSBQJCZmZmTk5OVlWX/mJycnJKS4u12AQCAD2IbqmFhYSaj5eCu82//5UdeKu4amUoICQ0NnTVrlk6nM5lMSqWy1dQaB77W+wUAgK6KdT+VpmnaRgcHy+RBkpZms+c1d5lMtbMv9eACchQAINCxfp7a0NAgV0iHj7vzu7yXN35y8KsP9nlYc9dYRwkAAIAtjuv9CoUCkVg4a0H694deGTWxvyc1I1MBAMC/uLU2oUwmDg6VPb/8t+9vfCaud5R7NSNTAQDAv3iwL40iRJbUv8e7Xz/1wmuz2i7V1y5kKgAA+BeP93oLUkjH3j1Qc/Yf988fzalmZOovbrlQsJ/x+58RP6Af8Puf0e9/QJ/Ax/6pIpGQEDLvmYnf7HtxyMhEljV3sXG/7cJcGgCAQMff/qnyIIk8SPLymw+VXKlc9efv2i3vb/3U8U683ZZ2rF+/3ttN6Fh+/wOSAPgZ/f4HJIHxM/LuzJn9rV7ebtH/4qOf6kwRIhswJP7fW5cIRe2Epr9lqjPnPiub4+wLc7oyp+o6v0ZertzJf6SdX2NX/CPt/BrxR8p7jVx/e3SawYPHt3p5u0Wt8L0pOSEURcnkEvsNYRf87d4vAAAEOv7u/XKFTAUAAP/i1r40vKAY79XNOwyoAwD/lpeX5+0mkPT09NWrvd8Mh7q6uvDwcMfH1FRqau/nWJ67s+RtfkPQr/qpvvBfGwAAeBnu/QIAAPCjw+6/mk1WidRVbvrzuF8AAAhEfM+lIYS0NBkrSqrbLYZMBQAAP8PnXBqL2WoxWT945fuFo19t9+Er7v0SQoher6+trRWJRD169BCLxd5ujqcMBkNdXZ3ZbA4NDY2MjGy7DLTNZtNqtUajUaVSRUREeKWRvDCZTC0tLSKRKDg4uNVXfvB3arPZKisrm5ub5XJ5dHR0q5/CaDRqtVqGYbp169b2x+8SamtrGxoaRCJRRESEQqFoW6CqqqqxsTEoKKh79+5urGbuFQ0NDTRNh4aGCgS36LG4/on85n9M7+Pp1i/DMGajZefXhz/8y/csT0Gmktzc3AsXLggEApqmpVLppEmT4uLivN0oNzU1Ne3cubOmpsZxJDw8fNy4cdHR0Y4jNTU1Go3GYDAIhUKbzaZWqzMyMoTCdiYy+yCGYXbs2FFVVRUfH5+Zmen8lR/8nV69ejU3N9doNNo/CgSChQsXOv6aLl++nJubS9M0RVEMw6SlpaWmpnqvsZzp9fo9e/ZUV1fb/44EAsFdd901evRoR8z8f3vnHtXkkTbwN8mbe0IICQgaEBCoKOBt0QoLarWAUut3qEoRrd2tuq79qtvtRWvraU+Pp9jit5XVdT1eurbV9bLWugql5X5AKiAqCraIAQSDCYSEJJAbb5L3+2PatzGBEC5KEuZ3+CPvZJ6ZeZhMnszMM88YDIb8/HyZTAY+pXw+f8WKFV5eXuPbbAdIpdKamhq5XI5hGIIg69at4/F41hmG1MhjBqZLMBb7qVqNXlz/8LPXTyo71c5LTXSbWl9ff/fu3QULFsyePdtoNP7www8FBQUZGRkD/mp2fQwGA5VKXbZsGZicdXR0lJaWfvfdd5mZmTQaDUEQk8mUn59PpVLBmL9//35xcXF1dXVcXNx4t33Y3LlzR6fToajtZ9gD+rStra2wsHDq1Knz58/38vLS6XTt7e2EvZHL5WVlZcHBwc899xyFQrl27VpNTY1AIAgODh7XVg+D4uJihUKRmpoaGBhoMpmuXbtWX1/v4+MzY8Yv10GXlZV1d3e/8MILgYGB3d3deXl5BQUFq1evHt9mO0Cr1SIIMmPGDI1G09raap/BsUaeNDBdgtHZVL3WqNXo97/x5e3KpuHKTvT91Lq6Oj8/v7lz55LJZCaTuXjxYgzD7t69O97tGiECgWDVqlXh4eFsNptGo4WEhMTGxhoMBolEAjKIxWKtVhsfHw9+RIeHh4eFhTU0NIAf126EWq2uqalJTEy0X2Fz9z7FcbyiokIgEKSkpAgEAiqVyuPxoqOjCU3v3LlDJpOXLFlCo9EoFEp8fDyHw6mrqxvfZjuP2Wzu7OwMCQkJCgoikUhUKhXMUDs6OkAGYJaio6MDAwMRBBEKhbGxsXK5nPgYuyBhYWGrVq2Ki4vz9R3gLushNfKYgekqjNRHyYyZcRw/mXV5w9z3R2BQkQluU1UqVV9fn/Wve29vb29vb1ceuo6x36EBO20WiwU8SiQSFEVFIhGRYerUqWAX56k1ckwoKysLDQ21X9H1gD6VSqW9vb1RUVFgXdc+g0QiCQgIoNPp4JFEIgUFBclkMnf5/qVQKHQ63Ww2EylmsxnHcRaLBR5BZ02dOpXIADrUjTrRhiE18piB6SqMyEVJrzUWXahZMfl/L58oG3HNE3rtV61WIwji7e1tncjj8WQy2Ti1aOwRi8VkMjkgIAA8qlQqLpdrPbcD6oN/hbvQ0NCgVCqTk5Pt3/KAPu3q6kIQhMFgXLlypaOjg0QiTZ48OS4uDjitYBim0+lCQkKsRby9vXEc12g07uLYEhsbW1lZ2dDQEBwc3N/fX1NTw2AwoqOjwbsqlQp5vBNZLBaVSgXp7siQGrndwLS/iMa1wugPc+1Xq9FLmjs/e/3ko1b50LkdMqFtan9/P4IgYKORgEajgXQPQCwWi8XiefPmEVuJ/f39DAbDOg9Q32g0jkP7RkRvb29VVdWiRYtsFAF4QJ8Cv6TS0tIpU6YkJSXpdLra2tr//ve/a9as4XK5gymIuFUnRkdHm83mq1evgthnHA7nhRdeIBx2PKATbRhSI7cbmK5lQe1x2qZOmjSp34Blv/FldUH9mNQ8odd+AZ4U8diajo6OkpKS4ODg2NhYB9mA+u5yUAFBkLKysoCAgPDwcAd53LpPQeN9fHySk5NDQ0OjoqJSUlKMRmN9/aBj3u06saampqqqKiYmZuXKlcnJyV5eXleuXCH81QdUBMdxN1LQhhFo5HZ96lo4vZ+qUqlwHH/n4Mb41NljUvOEtqlgR8rml6DRaBxwAuReSKXS/Pz8gICApKQk62HJYDCI4xkAoD6xOefitLS0SCSSiIgI6a/gOG40GqVSKXC89IA+BU213hL29/dnMplgTXgwBQlB10ej0dy8eTMqKiouLk4kEoWGhqampqIoWllZCTIAHW0+qP39/e7yKbVnSI3cfWC6HE7bVKPRSGfS2F7MN/+2/mDBrqCIgFHWPKHXfvl8PoIgPT091ok9PT0g3X2RyWR5eXl+fn7Lly+3Od/G5/ObmppMJhNxBAWo7y4q63Q6BEGKioqsE2Uy2aVLl+Lj42NiYjygT8ENGzYdR6FQgKMZiqJcLtdeQTKZ7MrHN61RKBQ4jvv7+xMpKIoKhcJHjx6BR6ITiT2L3t5ek8nkRp1ow5AaufvA9ADYXsyw6MDPc9+uKrizf/tXuGWEa10Tep7K5XL5fH5LSwuxVCiXy3t7e90uPoA1nZ2dubm5QqFw+fLl9mc3g4KCLBbLgwcPiJSWlhYajWb9BefKRERErHscKpU6ZcqUdevWTZ8+HfGIPp08eTKKotYOn2q1uq+vj/A/CgoK6uzs7OvrA48mk6m9vV0kErlLfAAwn7b+WYDjuEqlIubZgYGBJBKppaWFyNDc3IwgiBt1og1DauTuA9PVwC24k382giwuI3HVvDzJwZf+vGxkVU/oeSqCIPPmzSsqKiovL58zZ45ery8tLWUymcTBc7dDo9Hk5ubiOB4aGioWi4l0Pz8/8I0cEhLC5/MrKyvpdDqfz793715bW9uCBQvc5euYRqPZOHqQSCQURa1j1rh7n1Kp1Dlz5ly/fv3GjRvh4eE6na6iooJCoRBusbNmzbp3715hYWFiYiKFQqmurjYYDHPnzh3fZjvPpEmTeDxeXV0dm80WiUQYhtXV1anVaiIUFIvFioyM/Omnn0Agi87OztraWpFIZB0OzNUwGo3AZIJd4ebmZiaTSafTQ0NDESc0cveB6XKMwqMCRSkIgqx/e8VLf176tx1f15b+NCxxj7qTfGTcunWrtrbWZDIhCMLj8Z5//vkBT227BRKJ5MqVK/bpCxcunD37lx14jUZTUFAARj6ZTAbbWu7rCnHixImAgACb2ITu3qc4joPQQmC9l81mL1682HqW9vDhw5KSErASTqPREhISIiIixq25w0etVldUVDx8+BA8oig6a9as3/3ud8RhEpPJVFZWdv/+ffAoEomef/55V94wViqV586ds0n08fFJT08Hr4fUyHUG5pBhll3/TvJkwWYnZX9QHHNgBLUa/YOfH2W/8WXnQwVIudR6gM5wFD8c2lQEQRAMw3p6elAUte4Yz0alUhmNRh6P58rfU6PBA/rUaDSqVCqggv1Xm8ViUSqVOI77+Pi46WzGaDSCGPpeXl4DqqDT6cCXO5fLffrNexIMqdH4Dkwnwyy7gU312eSk7A/K446NII7jBl1/yTc1h3aeRZywqRN97RdApVL9/PzGuxVPFZuoCJ6HB/QpnU53sNpJJpOFQuHTbM+YQ6fTHa8fsFgsIriSZzCkRuM7MN0uzPKgjN1ckUQiMdn0pPSFKZnx/9x9fsj8E9pHCQKBQCAAdwyzPCgW3Nk/56DSUQqF/McP/sfUb3KcE9pUCAQCgXhUmGWnI+gPbzrL4jLYXkzHeeDaLwQCgUA8K8zy+PkJQZsKgUAgkCcYZlmpVI6yhAHJy/slSEhq6mTb96BNhUAgEMg4MqygxDt2JDgubf/+6cTrJ+S1/fLLv722vecQ2lQIBAKBjCNEUGIigCIyeJhlcKHQaKiuHvQI6YIFx0ZX9vFxtKnQRwkyQdm7d29BQcHIZLu7uz/66KOGhoYRyCqVyra2NhCPwhqJREIEvB0QsViclZU14oW4r7/+Oj8/f2SykImAfazsJxpmeTDDOWqDiiDIMGLoD6vUfgOm69U7zgNtKmSCUlJSQgS1GS5arba4uLizs3MEsg8fPtywYcO//vUv68TKysqMjIzSUtt7nq3JyclBUdRmu8t5goKCPv/88ye0swXxAJ5+mGVgPjs7tXo9Zp0yenCncb5Ao64/90gBBR0ivgq0qRDIU2XWrFlpaWmnT59ubGwEKRqNJjs7OzIyMiMjYzCpmpqaurq6devWjbjexMREoVB46tSpEZcA8WyIoMR3797VarUtLS1PIczyrFkHa2sfFRa25OeLlcq0mpqa5uZmcKfpqMrFnf5zAq1aV1fS8ErY6//868khM8P9VIhLIJfLWSwWm82+d+9eV1dXUFCQ9Tk5gEQi6ejowDAsODhYJBIR6RiGqVQqHo9HoVAaGhrUavWzzz4L5nODiVjjoEaNRvPzzz9jGDZt2rSAAEcXKxoMBrFYrFQqvby8IiMjHV97uXXr1qqqqk8++eTEiRNUKvXAgQMajSYnJ4eIdmvPxYsXY2JipkyZQqQQ/7H79+93dnaGhISAd3Ecb2xsVKlU4eHh1oGWSCRScnLy2bNnt2zZ4qkBKSGjJD4+HsOw8vLy8vJy5NegxE+0RoVCERHxUnj4HYOBrddf7ehgisUcg4FNodB8fHwEAoFQKBQIBHw+38HoGIAx2k/V9xn6erSfbjx4u+yukyLQpkJcgvT09NTU1Pb29rq6OgqFgmHYihUrdu7cCQaSwWB45ZVXpFIpjUYzmUwWi2XRokUffvghlUpFEKSpqWnr1q1vvPHGxYsXOzo6EAT5z3/+4+3t7UAEYDAYduzYMWCNCIKcOXPm2LFjJpOJQqGYTKaUlJR3333XWpzg/PnzR44cwTAM3CzN4/H27NmzYMGCwZRlMBi7du3avn37iRMnZs6cWVhYuG3bNnuLTqDVaq9du7Z582M+Henp6StXrpRIJNevX0cQhEQi/elPfwIqNDY2WiwWKpW6e/fuZct+u7IqLi7u+PHj1dXVixYtcq5bIBMLFEWXLVsWFxf31MIsk8lkgSCsqcmbRFIymVomU+/trWAwtBhG1+vZXV2s9na2Xs/BcSqfzydMrI+Pz4Aj8TdGbVNN/SYKSvli978vHRqeFwK0qRBXIS8vLykpad++fSiKAisVHBxMLIempaUtXbpUKBRaLJbCwsJ9+/Z99dVXr732GiF+7NixzZs3Jycnm81mDodjsViGFHFQY1lZ2eHDh5OTk//yl7/QaLRLly4dOnSIy+Vu377dvuX+/v779++fOXMmnU6XyWRZWVkffvjhhQsXOBzOYMrOnj07LS3tzJkzHA5nxowZxO0lA3Lnzh2LxWJ/XV1ubm5aWtrHH39MIpH27t179OjR8vLyxYsX5+TkGI3G3bt3Hzhw4Pe//z0xK502bRqDwbh58ya0qRAHPM0wywEBAWAFqK+vT6FQdHd3KxQKmUxhMimYTC2TqeNypUxmn8WC6nQslYotlbL0erbZTPfy8gL2FWC79DI6m6rvM5Serfx8y5ERyEKbCnEVuFzuW2+9BX5+ZmZmVlVVnT9/Hlg4BoPx8q+H0SgUSkpKSlVVVVlZmbWBXLRo0dq1a60LHFLEQY1nz54VCoU7d+4E765du/bWrVuXLl3atGmT/ddNYmIi8drf3/+tt97KzMy8fv36kiVLHOi7cePGb7/9VqPRvP32247Xtdrb20HJNumBgYHbtm0DxwdfffXViooKLpdL/McyMjLee+89sVgcFRUF8pPJ5EmTJrW1tTmoCwIZFzgcDofDIVZr9Hq94lc6OhR6vZzJ1DIYWoFAzmQ+QBDcYGD39bG6u9l6Pau/n8HhcMASMTgIZMYxR5U9jlarpdFoYKRr1br2nyVZ6/8ubRmJByICbSrEdZgzZ471es78+fPr6urkcjm4uuTRo0e5ublisVin01ksFplMptVqrcXtL+UeUmSwGgUCQWNjY1JSkvW7cXFxV69eFYvFMTExNhVZLJaSkpJr1651d3djGAbcK7q6uhzr++WXXyIIQiKRioqKwsPDHeRUq9UIgtgvxEVFRRHn8YEjCWE+iZTu7m5rES6XC0qDQFwZJpMpEokIHwgMw4hZbGenoq9PTqP1slg6Hk/h7/8QRU16PUuvZzc1oTdutPj6suWkn7gkp2748bWwz507FRsbFz4twqg1Zv/xH9V5N0fTcmhTIa6CzS1X4FRcX1+fr69vS0vL1q1bJ02alJCQIBAIKBRKeXl5XV2dA/ERiBA1cjgcs9ls8y54tLHKgKysrKKioqVLlz777LNMJtNkMjU0NGC2kV0e4/bt2xcvXszIyDAajWfPnl28eHFkZORgmYHHk32BTOZv4bzB/aP2KWaz2VrEaDQ69p+CQFwQKpXq7+9PLNWYzWalUklMZFWqLipVw2RqeTxNcjKalra2p8fgdNnBRqPCYrGc3vvN2X3fjr6p0KZCXAWbo5PgkcfjIQhy4cIFGo129OhRwmbU19c7Ls0ZkcFqZDAYVCpVoVBYvwse7e9nlsvl33///ZYtWzZs2ABSJBJJTk6Og7YZDIasrKygoKDXXnvNYrH8+OOPWVlZwAd4wPwCgQBBEJVKNfr71dVq9TPPPDPKQiCQ8YVCofj6+hKX7+I4rlKpFArFgwcPCgoKtNo+H58p9v4Hg2HUGbMyDioax2b9BtpUiKtw48YNg8FA+BpUVFRMnjwZWBGNRiMQCAjrqNfra2trHZfmjIiDGqOjo6urq61ndaWlpUwmMywszKaQ3t5e5PFz8UOGbTty5IhUKj18+DA48LNr164dO3acPHnSxrOXAHw7NDc3h4aGOi7ZMRqNpqurKy0tbTSFQCCuBolE4vP5fD4/LCwMOLpjGDaEY/DjrFmztjr3xmev/sNsMg+d2yEw5gPEVcAw7KOPPmpra5PJZAcOHGhsbCRCHEyfPr21tfXy5csajeb+/fvvvfee0Wh0XJozIg5qzMzMVKvVe/bsaWlpkUqlBw8evHnzZnp6uv3CqUgkYrPZZ86caWtrUyqVV65cOXfunIOGgVXf9PT0mTNngpS5c+e++OKLp0+fbmpqGlAkJCTE19d3ZKEQrQEz9fnz54+yHAik1I7xbtFjDMugIghOX06zAAACqElEQVTC9mIlronLN5xJf3fVKKuG81SIq7BixYqenp7169cjCEImk9evX79q1S+f7zVr1ty+fTs7Ozs7O5tMJqekpISHh3/zzTcOSnNGxEGN8+fP371796FDhzZu3IggCIqiGRkZf/jDH+wrotFo77//flZWFijH19f3gw8+ePPNNwdsFVj1DQwM3LRpk3X6tm3bQBSI48ePo6jtqCSRSKmpqZcvX96xY8fwTr4/TnFxcUREhGN/KAjEGRz7tLsjKJWCIEjmB6tX/3Vl9qv/qPn+1sjKIY02BBQEMhY899xzL7300uuvv97V1dXV1SUSiWxchBAEkclkCoUiICDA+W1FZ0Qc1GgymVpbW/v7+4ODg60v67DHYDC0t7dTKJSQkJDRmL3B6O7uzsjI2LNnj/W5nWGhVqtXr179zjvvJCUljW3bIBONhISE0d9L48r0qbRtP0n2bfi7rNXWez9Xe5rOdBRzG85TIa6Fn5+fn5/fgG9ZO/45iTMiDmpEUdTJWR2DwYiIiBhW24aFUCh8+eWXv/jii4SEhAHvsxySU6dOBQcHP+lQcxCIB8DxZs9YGHH09v8V/7siZ+vRYcnC/VQIxD3YsGHDrl277C+Jc5Lly5d/+umnI7PHEMhEg0QiMTmM5I1LfsDOrfxzsvOC0KZCXILNmzcvXLhwvFvh0tBotOnTpw/X+YIgNDR09EdxIJAJBZWOkinkTfsyT7Uejk4Y9AS5NXA/FQKBQCDDwOP3UwdEq9L+XCOOToh0vJ8K56kQCAQCgQwB25s9b1mMxWxxnA3OUyEQCAQCGRvgPBUCgUAgkLEB2lQIBAKBQMYGaFMhEAgEAhkboE2FQCAQCGRsgDYVAoFAIJCxAdpUCAQCgUDGBmhTIRAIBAIZG6BNhUAgEAhkbPh/19Bi3YAl1S4AAAAASUVORK5CYII=",
      "text/plain": [
       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7eb13aa91fa0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotmon.secondary_QtPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dacd3b6c",
   "metadata": {},
   "source": [
    "We can see from the graphs that the values of the settables in the dataset snake towards 0 as expected. Success!\n",
    "\n",
    "### Analysis\n",
    "\n",
    "There are several analysis classes available in `quantify-core` that can be used to visualize and extract relevant information from the results of these adaptive measurements.\n",
    "\n",
    "The {class}`~quantify_core.analysis.optimization_analysis.OptimizationAnalysis` class searches the dataset for the optimal datapoint and provides a number of useful plots to visualize the convergence of the measurement result around the minimum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "35a9c0d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a_obj = oa.OptimizationAnalysis(dset)\n",
    "a_obj.run()\n",
    "a_obj.display_figs_mpl()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aaf2250",
   "metadata": {},
   "source": [
    "The analysis generates plots of each of the variables versus the number of iteration steps completed. The figures show the data converging on the optimal value.\n",
    "\n",
    "The {class}`~quantify_core.analysis.interpolation_analysis.InterpolationAnalysis2D` class can be used to generate a 2-dimensional heatmap that interpolates between a set of irregularly spaced datapoints."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8f010339",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a_obj = InterpolationAnalysis2D(dset)\n",
    "a_obj.run()\n",
    "a_obj.display_figs_mpl()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6be8ebb3",
   "metadata": {},
   "source": [
    "## Adaptive Sampling\n",
    "\n",
    "Quantify is designed to be modular and the adaptive functions support is no different. To this end, the `meas_ctrl` has first-class support for the `adaptive` package.\n",
    "Let's see what the same experiment looks like with this module. Note the fields of the `af_pars` dictionary have changed to be compatible with the different adaptive functions that we are using.\n",
    "\n",
    "As a practical example, let's revisit a Resonator Spectroscopy experiment. This time we only know our device has a resonance in 6-7 GHz range.\n",
    "We really don't want to sweep through a million points, so instead let's use an adaptive sampler to quickly locate our peak."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "075258bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qcodes.parameters.parameter.Parameter: S21 at 139299635635104>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = Instrument(\"Resonator\")\n",
    "\n",
    "res.add_parameter(\"freq\", unit=\"Hz\", label=\"Frequency\", parameter_class=ManualParameter)\n",
    "res.add_parameter(\"amp\", unit=\"V\", label=\"Amplitude\", parameter_class=ManualParameter)\n",
    "_fwhm = 15e6  # pretend you don't know what this value is\n",
    "_res_freq = 6.78e9  # pretend you don't know what this value is\n",
    "_noise_level = 0.1\n",
    "\n",
    "\n",
    "def lorenz():\n",
    "    \"\"\"A Lorenz model function.\"\"\"\n",
    "    time.sleep(0.02)  # for display purposes, just so we can watch the graph update\n",
    "    return (\n",
    "        1\n",
    "        - (\n",
    "            res.amp()\n",
    "            * ((_fwhm / 2.0) ** 2)\n",
    "            / ((res.freq() - _res_freq) ** 2 + (_fwhm / 2.0) ** 2)\n",
    "        )\n",
    "        + _noise_level * np.random.rand(1)\n",
    "    )\n",
    "\n",
    "\n",
    "res.add_parameter(\"S21\", unit=\"V\", label=\"Transmission amp. S21\", get_cmd=lorenz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "312542e3",
   "metadata": {
    "tags": [
     "hide-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running adaptively...\n",
      "."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "."
     ]
    }
   ],
   "source": [
    "_noise_level = 0.0\n",
    "res.amp(1)\n",
    "meas_ctrl.settables([res.freq])\n",
    "af_pars = {\n",
    "    \"adaptive_function\": adaptive.learner.Learner1D,\n",
    "    \"goal\": lambda l: l.npoints > 99,\n",
    "    \"bounds\": (6.0e9, 7.0e9),\n",
    "}\n",
    "meas_ctrl.gettables(res.S21)\n",
    "dset = meas_ctrl.run_adaptive(\"adaptive sample\", af_pars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "41581199",
   "metadata": {},
   "outputs": [
    {
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       "html[data-theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2kB\n",
       "Dimensions:  (dim_0: 100)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 800B 6e+09 7e+09 6.5e+09 ... 6.77e+09 6.754e+09\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 800B 0.9999 0.9988 0.9993 ... 0.6186 0.6391 0.9237\n",
       "Attributes:\n",
       "    tuid:                             20250528-190601-395-18ada8\n",
       "    name:                             adaptive sample\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6e5842cc-762e-4ed5-b21f-c19ac45e7b79' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6e5842cc-762e-4ed5-b21f-c19ac45e7b79' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>dim_0</span>: 100</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ff315609-7c7b-44c2-98d8-512b78826d84' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff315609-7c7b-44c2-98d8-512b78826d84' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>x0</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6e+09 7e+09 ... 6.77e+09 6.754e+09</div><input id='attrs-2840a9f5-6b1f-44d4-8034-d0255a0ffbf2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2840a9f5-6b1f-44d4-8034-d0255a0ffbf2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d815d221-c7de-497f-9a25-18b69ff1e236' class='xr-var-data-in' type='checkbox'><label for='data-d815d221-c7de-497f-9a25-18b69ff1e236' 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>Resonator.freq</dd><dt><span>long_name :</span></dt><dd>Resonator Frequency</dd><dt><span>units :</span></dt><dd>Hz</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([6.00000000e+09, 7.00000000e+09, 6.50000000e+09, 6.25000000e+09,\n",
       "       6.75000000e+09, 6.62500000e+09, 6.87500000e+09, 6.68750000e+09,\n",
       "       6.81250000e+09, 6.71875000e+09, 6.84375000e+09, 6.73437500e+09,\n",
       "       6.82812500e+09, 6.74218750e+09, 6.82031250e+09, 6.74609375e+09,\n",
       "       6.81640625e+09, 6.37500000e+09, 6.12500000e+09, 6.74804688e+09,\n",
       "       6.72656250e+09, 6.73828125e+09, 6.83593750e+09, 6.81445312e+09,\n",
       "       6.82421875e+09, 6.78125000e+09, 6.79687500e+09, 6.76562500e+09,\n",
       "       6.78906250e+09, 6.77343750e+09, 6.78515625e+09, 6.77734375e+09,\n",
       "       6.76953125e+09, 6.77539062e+09, 6.78320312e+09, 6.78710938e+09,\n",
       "       6.79296875e+09, 6.77148438e+09, 6.78417969e+09, 6.77636719e+09,\n",
       "       6.77441406e+09, 6.79101562e+09, 6.75781250e+09, 6.78613281e+09,\n",
       "       6.77246094e+09, 6.78222656e+09, 6.76757812e+09, 6.93750000e+09,\n",
       "       6.56250000e+09, 6.43750000e+09, 6.31250000e+09, 6.18750000e+09,\n",
       "       6.06250000e+09, 6.78808594e+09, 6.80468750e+09, 6.76171875e+09,\n",
       "       6.77050781e+09, 6.79003906e+09, 6.79492188e+09, 6.77929688e+09,\n",
       "       6.77832031e+09, 6.77587891e+09, 6.78466797e+09, 6.78369141e+09,\n",
       "       6.77490234e+09, 6.80078125e+09, 6.78564453e+09, 6.77685547e+09,\n",
       "       6.77392578e+09, 6.78271484e+09, 6.78662109e+09, 6.76855469e+09,\n",
       "       6.76367188e+09, 6.77294922e+09, 6.79199219e+09, 6.78759766e+09,\n",
       "       6.77783203e+09, 6.65625000e+09, 6.90625000e+09, 6.59375000e+09,\n",
       "       6.96875000e+09, 6.53125000e+09, 6.46875000e+09, 6.40625000e+09,\n",
       "       6.34375000e+09, 6.28125000e+09, 6.21875000e+09, 6.15625000e+09,\n",
       "       6.09375000e+09, 6.03125000e+09, 6.77197266e+09, 6.78857422e+09,\n",
       "       6.78173828e+09, 6.76660156e+09, 6.77099609e+09, 6.79394531e+09,\n",
       "       6.79882812e+09, 6.78955078e+09, 6.77001953e+09, 6.75390625e+09])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fd27309e-cf96-461f-8166-4d47a8cd600c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fd27309e-cf96-461f-8166-4d47a8cd600c' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>y0</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9999 0.9988 ... 0.6391 0.9237</div><input id='attrs-6a51bbdf-a1cf-4048-adf9-d29422ae6cec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a51bbdf-a1cf-4048-adf9-d29422ae6cec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d5c5939-3145-49ee-b234-8a19355b9191' class='xr-var-data-in' type='checkbox'><label for='data-9d5c5939-3145-49ee-b234-8a19355b9191' 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>S21</dd><dt><span>long_name :</span></dt><dd>Transmission amp. S21</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>batched :</span></dt><dd>False</dd></dl></div><div class='xr-var-data'><pre>array([0.99990755, 0.99883916, 0.99928304, 0.99979979, 0.94117647,\n",
       "       0.99766416, 0.99380592, 0.9934688 , 0.9494382 , 0.98522774,\n",
       "       0.98634812, 0.97368902, 0.97628849, 0.9621476 , 0.96654469,\n",
       "       0.95335371, 0.95928825, 0.99965718, 0.99986891, 0.94778382,\n",
       "       0.98068216, 0.96869267, 0.9823405 , 0.95475633, 0.97203646,\n",
       "       0.02702703, 0.83505155, 0.78603269, 0.59350741, 0.43362832,\n",
       "       0.32095491, 0.11145391, 0.66082732, 0.27415925, 0.15426264,\n",
       "       0.47328113, 0.74937452, 0.56316064, 0.23697556, 0.19003362,\n",
       "       0.35679514, 0.68326632, 0.89745416, 0.40071204, 0.50259739,\n",
       "       0.08099619, 0.73284633, 0.99773756, 0.99881235, 0.99952072,\n",
       "       0.9997427 , 0.99983979, 0.99989075, 0.53754093, 0.91550535,\n",
       "       0.85593697, 0.61565168, 0.64179453, 0.79832374, 0.00871249,\n",
       "       0.04776173, 0.23190799, 0.27921516, 0.19500811, 0.31599382,\n",
       "       0.88475967, 0.36159877, 0.14950657, 0.3961102 , 0.11584931,\n",
       "       0.43799952, 0.69959173, 0.82577462, 0.46915691, 0.71883795,\n",
       "       0.50646803, 0.07711373, 0.99634035, 0.99648334, 0.99838108,\n",
       "       0.99842361, 0.99909176, 0.9994197 , 0.99959748, 0.99970452,\n",
       "       0.99977392, 0.99982146, 0.99985544, 0.99988057, 0.99989968,\n",
       "       0.53392314, 0.56653154, 0.05097923, 0.76141813, 0.59037383,\n",
       "       0.7756477 , 0.86305482, 0.61855982, 0.63909863, 0.92369092])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-24b8d283-f73c-474e-95bb-eb7980739216' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-24b8d283-f73c-474e-95bb-eb7980739216' class='xr-section-summary'  title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-7eceef41-e80a-476f-9ecf-3579b3c13248' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7eceef41-e80a-476f-9ecf-3579b3c13248' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>tuid :</span></dt><dd>20250528-190601-395-18ada8</dd><dt><span>name :</span></dt><dd>adaptive sample</dd><dt><span>grid_2d :</span></dt><dd>False</dd><dt><span>grid_2d_uniformly_spaced :</span></dt><dd>False</dd><dt><span>1d_2_settables_uniformly_spaced :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 2kB\n",
       "Dimensions:  (dim_0: 100)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 800B 6e+09 7e+09 6.5e+09 ... 6.77e+09 6.754e+09\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 800B 0.9999 0.9988 0.9993 ... 0.6186 0.6391 0.9237\n",
       "Attributes:\n",
       "    tuid:                             20250528-190601-395-18ada8\n",
       "    name:                             adaptive sample\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8f2e3381",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7eb13aa91f40>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotmon.main_QtPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0795fee",
   "metadata": {},
   "source": [
    "## FAQ\n",
    "\n",
    "### Can I return multi-dimensional data from a Gettable in Adaptive Mode?\n",
    "\n",
    "Yes, but only the first dimension (`y0`) will be considered by the adaptive function;\n",
    "the remaining dimensions will merely be saved to the dataset."
   ]
  }
 ],
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  "file_format": "mystnb",
  "jupytext": {
   "text_representation": {
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  "kernelspec": {
   "display_name": "python3",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.22"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}