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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 140266273661088>"
      ]
     },
     "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 ... 0.1963 0.1961 0.196\n",
       "    x1       (dim_0) float64 800B -50.0 -50.0 -52.5 ... 0.2183 0.2183 0.2183\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 800B 5e+03 5.257e+03 5.256e+03 ... 0.2327 0.1757\n",
       "Attributes:\n",
       "    tuid:                             20240829-164549-514-8b911f\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-af60aaba-e935-4b25-aa30-424f0c54440b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-af60aaba-e935-4b25-aa30-424f0c54440b' 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-7a4f1b23-3969-401a-b4eb-08210f6ed191' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7a4f1b23-3969-401a-b4eb-08210f6ed191' 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 ... 0.1961 0.196</div><input id='attrs-bbebd273-405d-4f7a-b46a-e364c95514fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbebd273-405d-4f7a-b46a-e364c95514fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-464c7ce0-379d-4a7f-ab4e-5dbe3cb91f45' class='xr-var-data-in' type='checkbox'><label for='data-464c7ce0-379d-4a7f-ab4e-5dbe3cb91f45' 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, -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, -8.91332626e-01,\n",
       "        2.67889500e-01, -5.04925251e-01, -3.11721563e-01,  3.23511362e-01,\n",
       "       -2.46222913e-01,  2.64597237e-01,  7.09918737e-01, -7.18750060e-03,\n",
       "       -7.26861507e-02,  1.80276390e-01,  9.59555432e-02,  2.31847912e-01,\n",
       "        3.34990956e-01,  1.55714396e-01,  2.15473250e-01,  1.47527065e-01,\n",
       "        2.67044771e-01,  1.77406492e-01,  2.07285918e-01,  1.73312826e-01,\n",
       "        1.65125495e-01,  1.90911256e-01,  2.24884348e-01,  2.09944635e-01,\n",
       "        1.95004921e-01,  1.82112041e-01,  2.11991468e-01,  1.89581898e-01,\n",
       "        1.93675563e-01,  1.94672582e-01,  2.04189271e-01,  1.93233741e-01,\n",
       "        1.96885584e-01,  1.94340242e-01,  1.96553245e-01,  1.96802500e-01,\n",
       "        2.01560844e-01,  1.96145393e-01,  1.98441480e-01,  1.97212245e-01,\n",
       "        1.97621990e-01,  1.96473946e-01,  1.97293437e-01,  1.97088564e-01,\n",
       "        1.96678819e-01,  1.96986128e-01,  1.96883691e-01,  1.96616978e-01,\n",
       "        1.95878680e-01,  1.95407094e-01,  1.95709565e-01,  1.96012036e-01,\n",
       "        1.96381186e-01,  1.95776244e-01,  1.96229950e-01,  1.96078715e-01,\n",
       "        1.96263289e-01,  1.96329967e-01,  1.96141528e-01,  1.96023631e-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 -52.5 ... 0.2183 0.2183</div><input id='attrs-f15392f3-9513-48e4-a221-1185337ffed0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f15392f3-9513-48e4-a221-1185337ffed0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ca7e733-8760-4835-af30-3dc51d29b2ea' class='xr-var-data-in' type='checkbox'><label for='data-3ca7e733-8760-4835-af30-3dc51d29b2ea' 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, -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,  3.26299667e-01,\n",
       "       -5.75852394e-02,  1.98338032e-01,  1.34357214e-01,  4.29452658e-01,\n",
       "        4.94846702e-02,  1.33051574e-01,  3.01491022e-01,  1.12486258e-01,\n",
       "        1.97358802e-01,  1.49128381e-01,  1.65205188e-01,  1.75487846e-01,\n",
       "        2.28206776e-01,  1.80955585e-01,  1.96705982e-01,  1.91564653e-01,\n",
       "        2.23065447e-01,  1.99439852e-01,  2.07315050e-01,  2.04744386e-01,\n",
       "        2.15353454e-01,  2.28533186e-01,  2.31103851e-01,  2.27166251e-01,\n",
       "        2.23228652e-01,  2.16638786e-01,  2.24513984e-01,  2.18607585e-01,\n",
       "        2.13303051e-01,  2.20747252e-01,  2.20063785e-01,  2.18971635e-01,\n",
       "        2.19335685e-01,  2.18265852e-01,  2.16854285e-01,  2.18715335e-01,\n",
       "        2.18373601e-01,  2.18292789e-01,  2.17501572e-01,  2.18411894e-01,\n",
       "        2.18108454e-01,  2.18504062e-01,  2.17897181e-01,  2.18048901e-01,\n",
       "        2.18352342e-01,  2.18124761e-01,  2.18200621e-01,  2.18170845e-01,\n",
       "        2.18263013e-01,  2.18384957e-01,  2.18331429e-01,  2.18277901e-01,\n",
       "        2.18231817e-01,  2.18338873e-01,  2.18258581e-01,  2.18285345e-01,\n",
       "        2.18262303e-01,  2.18269747e-01,  2.18281446e-01,  2.18311932e-01])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7a944f39-8c39-49e0-8aa6-825c3ec354ca' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7a944f39-8c39-49e0-8aa6-825c3ec354ca' 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 5.257e+03 ... 0.2327 0.1757</div><input id='attrs-a869fff4-950a-4e53-a931-b313154f8fd4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a869fff4-950a-4e53-a931-b313154f8fd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-332b7db0-1054-4447-986e-7152fcade0a1' class='xr-var-data-in' type='checkbox'><label for='data-332b7db0-1054-4447-986e-7152fcade0a1' 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([5.00013536e+03, 5.25659993e+03, 5.25646787e+03, 5.01286810e+03,\n",
       "       4.75654852e+03, 4.51597687e+03, 4.51603605e+03, 4.05030732e+03,\n",
       "       3.61253935e+03, 3.62513279e+03, 2.81577297e+03, 2.11585321e+03,\n",
       "       2.11580758e+03, 1.53209204e+03, 6.19987364e+02, 6.59095364e+01,\n",
       "       1.03765650e+02, 1.10712430e+03, 2.70627743e+02, 4.83500272e+02,\n",
       "       8.81771499e+01, 5.85859362e+00, 1.49974113e+01, 2.05251828e+02,\n",
       "       2.79173328e+01, 4.13752435e+01, 1.63235968e+01, 5.91056388e-01,\n",
       "       1.24467534e+01, 2.08674933e+01, 4.07835715e+00, 9.54254633e+00,\n",
       "       1.48983146e+00, 3.27638166e+00, 8.06905764e-01, 5.40436246e+00,\n",
       "       3.57236927e-01, 6.49435212e-01, 9.30989274e-02, 1.19026276e+00,\n",
       "       5.59451923e-01, 3.93816747e-01, 2.96535843e-01, 6.08630596e-01,\n",
       "       1.89207251e-01, 1.87136086e-01, 6.36979260e-01, 1.41865361e-01,\n",
       "       4.48096754e-01, 5.53064706e-01, 3.53851597e-01, 2.81440107e-01,\n",
       "       6.41106804e-01, 4.43031185e-01, 1.70428067e-01, 2.57671240e-01,\n",
       "       5.66612166e-01, 3.70636063e-01, 4.53201897e-01, 3.89250675e-01,\n",
       "       2.50039441e-01, 1.19540514e-01, 1.38395838e-01, 3.10768094e-01,\n",
       "       3.01974448e-01, 3.89163454e-01, 4.86271319e-01, 1.99835277e-01,\n",
       "       4.64656199e-01, 1.73205785e-01, 3.44666858e-01, 5.11664160e-01,\n",
       "       4.75082529e-01, 3.25275824e-01, 5.32618252e-01, 2.79824018e-01,\n",
       "       4.68813815e-01, 8.94812461e-02, 5.85842012e-01, 3.04815213e-01,\n",
       "       1.16264399e-01, 5.75068306e-01, 4.91557763e-01, 1.30985436e-01,\n",
       "       5.41209100e-01, 3.38220593e-01, 2.86562293e-01, 1.98621369e-01,\n",
       "       1.26621649e-01, 1.39104055e-01, 2.19301967e-01, 3.34223739e-01,\n",
       "       4.02545766e-01, 5.29745116e-01, 4.19636094e-01, 5.55664204e-01,\n",
       "       3.37893570e-01, 5.61096263e-01, 2.32731812e-01, 1.75735884e-01])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ddc43cbb-e47f-4022-9368-22d2abcfe5a8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ddc43cbb-e47f-4022-9368-22d2abcfe5a8' 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-06f7e780-053e-4ee0-8d94-9d4fafffb5ce' class='xr-section-summary-in' type='checkbox'  checked><label for='section-06f7e780-053e-4ee0-8d94-9d4fafffb5ce' 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>20240829-164549-514-8b911f</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 ... 0.1963 0.1961 0.196\n",
       "    x1       (dim_0) float64 800B -50.0 -50.0 -52.5 ... 0.2183 0.2183 0.2183\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 800B 5e+03 5.257e+03 5.256e+03 ... 0.2327 0.1757\n",
       "Attributes:\n",
       "    tuid:                             20240829-164549-514-8b911f\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 0x7f924969ebb0>"
      ]
     },
     "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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",
      "text/plain": [
       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7f924969ec10>"
      ]
     },
     "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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DqASb0Wf08zEW2jcr251sW2E6CaCT3bJnkH1PfUE9ZJsTLyKtALSG/rdZWK+f5GFIyzz4Dto3WCMd7M9Tch4jfj2ANvrxWrcpj6w93Rug9Wq/qge09vtx9v1b1L6D9h55w36FiLjLraEjHX1OmgA8Z7fZfmi9+8/q661C4Nzflq3N0L41HaenAVo9BS0N5Se78u7Qhym2ad8ovT0H9MV5+TsvdCLSHVr+/HtKqdXZFCstn1lEpVpp7KmvJSJroKWetIU2asW3Silrb89GaB/6a0VkLrQc0JHQvk52tocyr3UcB7BAtDF7I6ENX1YRmS8CPoAWEP8sIp9Au2l1BLTepYHZfc2ulIoVkR0AJuuBwAVoqS32vWEOicgAaOkwLyqlMlIIlFKHROQzAM+LyCKl1O/ZVqKVvyIiW6GlJ5SB1nNvqyuAT0VkJbTz4Q4tzSAd2gWB05RSiSLyN4AhInIc2rk6opQ6Au2i7lcAf4nIfGi9dRWhvReqQuttzc06AI/recB/69t2x63h46wO6+1/Wb+oSwawRQ9O7c2DFiwsEpF7oV2oPQKgPYAXlFI3nTz8wvQlgC9E5Adoo+U0hTaCybUi2t8JaEMzzoF2IfcCtHP6oU2Zwnj98jqkpTO+gZZe9oX+jdxv0AKm+vryXtCCU0c+hPZeDxORWbg1pOUZAE2shfS/5dH6vg6KyHJowWV1aDd4/gZt2EiXUkpt1z/3XhGRZtA+D1Oh9bQPgpbK9z20UVJiACzWP9MUtPMgdvWlijZu/1xoQ5yugPb59QTymFOvlLoqIu9DG9IyTP9fUA/ahcTvsLkhWncR2t9vTWifS0OgjdT1jLo1DONJaPcZPSvafADxAPbq9wndDsugvQ/CReQxu3WblDYcbGn5zCIq3YpiSJ3i+MCtIS3vhnbjZiy0YG82ALNd2X7QvtJNhJaqMxnaPxAFfXg5vVwEtB5nR/vLUx3QAu0/oOWUHoODoR2hjRKwEto/wkRoIyM8YFemJrIOaRkE7QaqGGj/fL6DdnGhkMOwftAuRs5BG8LNzcH6MtAuEg44Wu+g/NP6PmMdnPNa0EbVOKEfWxS0r9q7OVHvItgMG6cvawstiEq2P079PC6GNixhCrSbCddCuziylgnRt2vhYH/+0EaYuApt6LYwaIFBBGyGKrQ55pPQ8p0zhoqDg2HpoN1AaK03GdrQjSF2Zayv7yQH7crx9czpuHBrmMfONssM0C4mr0ILVMIA1LY/zhzqfEtfXs7B6xXn6JigXfSdhfZ3sAM2Q80W4utnPdYcz5XN63TEyfecEdrf+RG9/dHQ3oNvAvC1+5u3f5801veVqB/P69Au7jN9Xti0Pwza33IitL+ZrwDcm905zsujoK+nzfKR+vEnQPub/xNarnxlmzLtoOV4J0D7LJmGW0PndrarbzS0ID4JWgDeEbkM75jDMY6B9jmbAu3+hM8B+Dt67aGNcrNLP9cRAMY4qK8/tHlJUmHz+Wv/PkE2f78278lH7JZneS3sjxm3hhl29LB+3vijhH5m8cEHH84/RCmXpwPeFnJrwpHySqmi6mkkIqI7gGizp5ZTSmWZ1ZiIqDgqjTn1RERERER3lNKYU09EVOqIiCdyH5M8WmWe4KnE0G8Uzukm7hSlVPTtag8R0e3GoJ6IqHQYAi33PiddoOVOl0S/I+cZkbdDy10nIrojlZqceiKi0kwfBrRhLsUOqFszfZYoItIegGcORWKUUgdyWE9EVKIxqCciIiIiKuF4oywRERERUQnHoJ5KDBEJERGlTwSTW9kIEVlU9K0iIisRqan/jU5ydVucISIvicgpEUkXkcOubg8RUUEwqKfbQkTaichbNlPEF1siUl9EPhSRwyJyU0QuichPItIim/JBIvKdiFwXkVgR+VFE7rIrU01EporIPhGJEZFrIrJNn+I9t/bM1wOlddms7y8iB0UkSUTOisjb+nTt9uXuFZF1InJZROJE5E8RGScibjZlAvVAZ4eIXNWPaY+IDLGvz6bOMP24b4rIRn0WUaeIyBARWSIi4foxbsul/D0iskZEokUkQUSOiMi4HMr7i8gVve5H7NZ11pc7erTJT536+oKek4hs2vSFXbnKIvKBiGzV96NEpLMT9efY/hy2y/X9k1ci0kNEftVfyxgR+d7RRbuz7xMR8dHf/2H6e0SJSEg2ZXtCm833N2gTA76a3+MgIioOOPoN3S7toE3+tQjaTJj58Q2A5dBmLixKTwN4CsAP0Gaa9IM2HfoeEemtlNpsLSgiPgC26mX+C21GyQkAtotIM6WUdRr2BwG8DGA1tNlQ3QH8H4BNIvKkUsrhqCT6hUQItFk0Ha2/X69zG4Cx0GYmfR3aTI+jbcpZZ8UMhzZrZwKA+wHMgjZL7Hi9aFsA7wFYD+BdaLNKDgSwXEQaKKWm2tR5D4Bfoc06/Da0ToLn9GNvpZT611Gb7YyGNmPn7wACcyqoB2Froc1w/B8AcXrbq+aw2TsAvHJpwyf6/m2dyE+dhXROAOAwgI/slh23e14P2nsqHMBf0F47ZzhzTjLJw/snL3X2BfAjgIMApgDw1ev5VUSaK6Wu2hR39n1SDtoMvmehzdDdOYeyXQFYADxVUofxJCLKxNVT2vJROh4AJsHBlPdFuL8I2E1/nodt7wXgY7csEMAVAL/aLZ+sH1dLm2X1oQXD/7VZ1hDa7JS223pAm6r+XDbtEGiB1AL9eNY5KHMUWgDobrPsXWjBSn2bZfOgXQwF2G2/HcANm+e1ANRw0I5foF1YeNss/wlANIBAm2WVoU1D/4OT57oaAIP++xHYTUNvU84XwGUAq6zlnai7EbSLrDf01+gRu/WdHS0vYJ2FcU4cvtYOypWxvp4AHtHb07kg7c9hO2ffPzX1eic5UedRaBcJJptlTQGkA/gon+8TDwCV9N9b6G0JyabsQgBxzr72fPDBBx/F/cH0GypyIvIWgP/pT0/bpBPUlFs5uCEOtlP6ttbnWXLqRfO6iJzXv8LfKiIOh+0TkdoiUju39iqlDiil4uyWRQHYCeBuu+KPAPhdKfW7Tdl/oAXBg22WHVVKXbOrMxlaj3hVESnjoCmPQwvCXsvmeBoAaABgnlIqzWbV59ACcdvUCl9oQfl1u2ouAUi0adNppdQZu3YqaN8GeACwTSvqCGCzuvVtBJRSl6AFen31bzFypJQ6p5Sy5FYOwHAAFQG8ppSyiIi3iOT2+TULQCi01y1HIlJGHKQs5aPOAp8TmzaZRMQ7u/VKqZsq75MpOX1O7Dj1/rElIhNE5IyIJIrIdhFpZLMuANp7N1TZ9JIrpf6AdqE71LYuZ98nSqlkpdTl3MqJiIKWcuNt83kUktt2RETFGYN6uh1WAVim/z4BWrD6OICr2W7hvHegpWL8AeAlAKcAbATgKBj6RX/kVyUAGYG5HlQ2AbDfQdl9AGpnE6zb15mgPzLo202D1tufXZDSXP+Zaf9KqYsAztusB7T0HF8Ac0XkbhGpISLPAngYwPu5tNHaTsDm+KEF+Y4CugQAJmgXJIWlO4BYAEEi8i+01JtYEZkjImb7wiIyCFrK12Qn6v5KrztJvyjM7t4JZ+osrHPSVd8mTs+xz3N6i708nhN725C398//ARgH4DN9fSMAW0Skor7eQ/+Z3bmqIiKVHKwrLI9Du7BJxq3Pox1FuD8ioiLHnHoqckqpP0XkIIBhAFYrpSKs60Sb2j1f9G0nQ0t56Kf3KENE3kMh3/QmIh2h5Sy/a7M4AFpwcsnBJtZlVQA4zKMWkTrQgqKVSql0u9VvQgt4Ps6hWZXt9mW//yo2z+dDSwEaBe2eAUBLc3heKfUFcqD3qj4NYKfe62z1L4A2IuJmbb+ImAC01tcH5VRvHgVD+7z6EVo60ivQ0mfGAvCH9t6yttcTwHQAHyulIhzdeKlLgXbfxHpoFysNoKWJ7RSRdkqpQ/moszDOyZ/Q8vL/hZb2FQJgpohUUUq97MT2WeSh/dnJ6/unDoBgpdQFff9hAPZCuwdgIoBIaL3+7e3aGQjtdQC0c5Vrr3t+KKWWiHaT+j1KqSVFsQ8iotuNPfVUknWH1vs52xrQ62Y6KqyUqqmUqpnXnYhIBQDfAjgNbbQMK+vslY5u3E2yK2NfpxeAldAC9yl26+pCu2HwJT1FJzu57T9j33qAeRLABgAjAAyBdtPpbBF5KLsd6N9GLIUWOI+1W/05gLoAFohIAz294mvcutjIaXbPvPKBdnPn10qpcUqpVUqpcQDmAhgqIsE2ZacAMEK7cTlbSqldSqlHlFILlVJrlFIfAGgDLQ/bvvfZqTpRCOdEKdVfKfWhUupHpdRCAPdBe90mikhONwXnxNn2Z9emvL5/VlsDen37fdCC+j76cwu0166biLwvIsH6zbjfQfubBgr3/UNEdMdjUE8lWQ39Z7jtQqWNmlEoU93rOc3roN2U+KBdrr01dcAjy4aA2a6MbZ1u0EbxaQDtRsWLdkVmAdillPohl+bltv+MfYvIFGi9pMOUUl8rpb5TSg2A1iP8WQ755LMB9AbwtJ7vnEHvof0vtHz3o9BGYKmNWxc+cfq+A0Skks3DL5fjyulYl9kt/1b/2VbfV01oaViv2d8X4Qyl1Alo3wZ00V+nPNVZFOdEv2D9GNo3FZ3zekzOtl/P4a9k97Ceg7y+f8KR1XFoN9JavQntW5fJ+rr90G4wX6Cvz/PrR0RUmjGoJ1dTjhZKAca+Lix62sQqaHnzDyqljtgViYbWS17ZflubZfYBO6ClMvSFNirHFrt9doUWRM+SWzcS14QW0Hnqz3314tZUmOz2b7vv5wBscRDUrYGWplPTvgIRmapvN0Up9Y2DfUAp9Rq0G1g7AmiilGqJW58r1iEYV+lttT5mOaorF9ZjibRbfkX/WVb/+Q6ACwC22Zw7a252eX1Zbp9756D1Flvvy8hTnUV0Ts7pPwNyKeeIs+1vZ9emS9BGnQHy8f7JjVIqRSn1tL59JwD1lFK9oA0Pa0HOw4oSEZEd5tTT7eIweMetHnV/u+U1kDvrKC3B0G6QBZCRa1/W4RZO0oOcrwF0AzBYKbXdvow+Cstf0IbOs9cawCml1E27ev8HbdSNF5RS9r3OAFBd/7nKwbogaClAE6ClGB3Wl7eAdmOudR9VoI3dPs9m24oAHF0oGfWfmT4LRGQMgLcAzFRKTXOwXQalVAy0Hlur7tBu1P1Hf/4iMr8eji50cnMAQA9o58D2HgXrfQPWm66rQ8vnPoWsPtd/lkXOcyXcBS19yRrA5rnOIjgn1lGH8nNzubPt/wPaObZlzWnP0/sH2t+kvbrQhuvMRCkVCf1iTb+Y7wxgb36+aSEiKs0Y1NPtEq//9LddqJSKFZFr0HrqZtqses6JOjdDG3N7rIhstMmrf8FRYdGHs1RKnXSi7tnQ8oZHKaUcBdhW3wP4QERaKKX26/upB230kul2+38J2o2Y/1VKZdczuwXAAAfL50G7iHkPWkoHlFJHReQfAM+IyFybm21HQ7uI+t5m++MAeohIoHW4RT2AGgxtDPWMcyLa7LGfQMuln5jDsWehb9sS2jjlFr2dB/JSRza+g5YX/hS0c2T1NLSUjW3689ehTUBkqxG0EZI+BLAb+ntRRMqrzBMcQUSaAugP4GebIRSdrtORvJwT/abkG7Y3TouIUT/2FGgTneWVU+1XSqVC+5tyxOn3j+4hEQmyuVG2FbQL3Zm5tHUStG+Z7O/fICKiXDCop9vFGsS8JyLLoQXja5VS8QC+BDBFRL6EllfbCVqvXo6UUldFZDq0kVDWich6aMM43o/MQy9aWYezrJlTvSLyArSLit0AEkTkMbsioXq7Aa2ncySAn/S2pOLW6B4ZM4KKyABoAVQ4gGMO6tyklIpUSp2FNhumfZtmAohUSq22W/UStBSIjfp5bQTgeQBfKqWO2ZT7AMASAHtFZB60HPVh0Cbael0P6KzB19cAoqCdr0dFxHZ/u5RSp/SynaDlRW/Uy7eB9i1EGJxMsdHr6KQ/LQ9t3PDX9ec7lFI7AEApdUhEFgJ4Us/f3g6tR3cQgPet9yUopWx7x637uK7/+rvd+VshIonQJvi6Au0eh2egDamYcfNyXuoshHPSH8DrIvI9tG9lAqDl5zcC8Kr98KY258o6N8PjItJBb/e7eW1/Dpx6/9g4AW1m2DnQ7vl4Adr5yLjRXP8bGAhtKMk4aN9mDIb23s10P4mz7xO97PPQOg+s3+L0s7nBeLZS6oYTx0tEVPKoYjADFh+l4wGtx/A8tKHwMmaXhTbKxZfQ0hdiAayA9o9bAXjLZvsQ2+30ZQZoQdRFaMHYVmgBTgTsZpTVl0U40c5F+n6ye9S0K18V2kg2N6D1Wq4FUMeuzFu51Nk5lzZFIJtZRgE8BOAQtJSRc9B6YI0OyvWC1qN9Fdq9AH9C+ybCtkxILu0MsSlbG9poKFf1fR+DFgybcjqWPJyXt+zKGgFM1c9FCrQLpBec2EdnOJ79dRy0EVmioF2MXQTwjf1rl8c6C3ROoAXJa6D9nSTr76edAAZlUz7b1yo/7c9lG2fePzX1eidBu7g9q5+HHdDuL7At2wraxVk0tIuEw9CGzJQCvk8icihr+9mxCJxRlg8++LiDHqJUdqnORERERERUEnD0GyIiIiKiEo5BPRERERFRCcegnoiIiIiohGNQT0RERERUwjGoJyIiIiIq4RjUExERERGVcAzqiYiIiIhKOAb1REREREQlHIN6IiIiIqISjkE9EREREVEJ5+7qBriCiAiAKgBuurotRERElCdlAFxUSilXN4SoOCmVQT20gP68qxtBRERE+VIVwAVXN4KoOCmtQf1NADh37hx8fX1d3RYiIiJyQmxsLKpVqwbwm3aiLEprUA8A8PX1ZVBPRERERCUeb5QlIiIiIirhGNQTEREREZVwDOqJiIiIiEo4BvVERERERCUcg3oiIiIiohKOQT0RERERUQnHoJ6IiIiIqIRjUE9EREREVMIxqCciIiIiKuFK9YyyRERERCJiAlAXQHUAZgDi2hYRZWIBkADgBIDTSimLo0IM6omIiKjUEpF2NWrUGFi1atUqderUMfr7+8NgMChXt4vIKi0tDVevXpVTp04lnDlz5rSIfKOUOmFfjkE9ERERlUoi0r558+ZPP/bYY8aHH374TM2aNZNc3SYiRywWC44cOeKzdOnSBt9///0YEflUKXXStgyDenKp1HQLZm85gf0R0WhRMwBju9aB0Y23ehARUdESEXONGjUG/t///Z/xhRdeyNLrSVScGAwGNGnSJK5u3brHUlJSGn311Vd9AMy2LcOgnlxq9pYTmP1LOBSA3SejAAATe9R1baOIiKg0qFutWrUqAwcOPO3qhhA5y2w2W3r16nVp06ZNjUUkQCkVbV3HLlFyqf0R0bAmLir9ORER0W1Qo169eu7VqlVLdnVDiPKiQ4cO0eXLly8LoIbtcgb15FItagZk/C52z4mIiIqQ2d/fnzfEUonj4+OT7unpCWgjNWVg+g251NiudbDm8AWYjW7o2bASxnat4+omERFR6SBubm4cupJKJDc3N8Bu6FX21JNLGd0MqOzniSfb18LEHnV5kywRERUL//77r1fXrl0f8Pf3n+Du7v56mTJlJjVo0OCxRYsWVXN124gcYU89uVxiajqi4lNc3QwiIqIMDzzwwJC0tDS3Dz74ILRZs2Yxp0+f9vnpp59qXb582cvVbSsMcXFxbj4+PumubgcVHnaLksslpaYjJoFBPRERFQ9nz541nzx5svprr7226dlnn41o06bNjWHDhl1YsmTJr1OmTPl39+7d/iIyNTQ0tJLtNiIydd68eTUBYN68eTVFZOqsWbNqBwUFjTIaja/VrVt3xLFjx7xnzJhRp2LFimPMZvMrrVu3Hnjt2jWjtZ7g4OCQHj163N+7d+/enp6eL/v4+EwaP378PVeuXDF26NDhQQ8Pj1fKlSs3bsaMGRn5qikpKdKpU6f+AQEB441G42sVKlR4fuTIka1tj6l9+/YPNWvWbOjw4cM7+vr6vli9evXnhwwZcl+lSpWesz/+oKCgZwcOHNilSE4uFRkG9eRyPh7uiIpjUE9ERMVDhQoVUkwmU8qqVavqx8bGuhWkro8//rjze++9t37FihULoqOjffv37z/oyy+/bDNnzpwfPvvss6VHjhypPXHixFa22+zcubNZ2bJlE9avXz+/X79++z799NO+Xbt2HXzvvfeeCwsLm9u0adOTb7zxxsPWi4G0tDSpWLFi7Jw5c1Zu27bts6effnr74sWLu02dOrWhbb3Hjh2rFRERUW758uVfL1my5NvJkycfunLlSrnly5dXsZZZtWpVpUuXLlWcMGHC4YIcN91+TL8hl/P1NOJGYqqrm0FERARAGwv89ddfX/3+++/3DwwMbFGtWrVLTZo0OfPkk08e6d+/f2Re6po8efKWkJCQcwAQGhp6aMmSJd127tz5SYcOHWIAYOnSpX/v37+/FoDfrNsEBQVdXrZs2Q4AaN++/c5Vq1Z18PX1TZg1a9ZBAChfvvz2xo0btwgLC6v42GOPnffy8rKsXLlym3X79u3bX//999+rrVu3ruHbb7991LrcZDKlbty4cY1t2k2DBg1Ozp8/v/nQoUMvAsAXX3zRvE6dOhHW9lHJwZ56cjmDAABHFSMiouLjjTfeOHbx4sWPZsyYsaxVq1Yn/vjjj5oDBgwYNWnSpGZ5qadbt24ZFwEVKlSIMxqNqbYBc2BgYFxsbKy37Ta1atXK2MZkMikvL6+EunXrZixr0KBBHACcP38+Y7vRo0e3rFat2jPe3t4veXh4vLp169Z7r1275mdbb1BQUKR9Hv2wYcMO7Nq1q9H169fd4+Li3H777bfGDz/88KG8HCMVDwzqyaWUYjBPRETFk7+/f9rYsWNPLV++fMfp06cXtG/f/vCCBQs6u7m5KSDz/7DExESHMZWnp6fF+ruIwM3NzWK7XkSglMo0NKG7u3uWMrbLDAZtVxaLRQDgjTfeaPTll1/27Nev36Fvvvnmm40bN37RsWPHQ2lpaZlSh8xmc5avxV988cXj7u7u6dOnT6//8ccf101PTzdMnjz579zODRU/TL8hl0pNVzC6GZCabsm9MBERkQvVqVPn6oEDB+rXqVMnHgBOnz5dBsBlANi4cWOlHDcuQnv27Kl21113nfv8889/ty67dOmSU7M5ms1mS5cuXQ7/8MMPzd3d3dPbtm17JCAgIK3oWktFhUE9uVRiSjo8jVpHQnJaOjzcC3Q/EhERUYGdOHHCs0+fPoMHDhx4qG3btpGBgYHJmzZtqrJy5cr299577z8BAQFpNWrUOD9nzpwOjRo1ijl79qz39OnTu7qqvbVq1YreuXNn01mzZtVu3Ljx9c8++6xJRERElcDAwOvObD9hwoSD3bt3fx4Avv766wVF2lgqMgzqyaUSU9PhaXKD2eSG6wmpqOjLoJ6IiFyrQoUKKXfffff5JUuWtJk5c2ZAenq6wd/fP7Znz54H5s+fvxMA5s6d++Ozzz77YL9+/UaVL1/+2pQpUzaNGzfucVe0d/r06fv//vvvSlOmTBkEQLVr1+5Ir169ft+/f3+wM9t36dIlulatWufi4uI8H3300QtF3FwqIlIac5pFxBfAjRs3bsDX19fVzSnVTl2Nw7d7z8LT5Ib7G1VGgyp8PYiIyLHY2Fj4+fkBgJ9SKrYgdYnI4MmTJw+eNm3akcJpXcllsVhQoUKFcf379/994cKFu13dHspdv379Gq5bt+5jpdQu6zLeKEsulZiaDi+TG8p6mTgBFRER0W3277//eo0ePbrVzZs3fd58802OelOCMf2GXCopNR1mkxsCfUyIimdQT0REdDvVr1//JS8vr4RJkyatrVmzZpKr20P5x6CeXCpBv1G2rJcJp6/Fu7o5REREpYpS6m1Xt4EKB9NvyKUSU7T0mwBv9tQTERER5ReDenKpxNR0mI1aUB/DoJ6IiIgoXxjUk0tZx6kP8DYhmkE9ERERUb4wqCeX0ka/cYfZ6IbktHRXN4eIiIioRGJQTy6lTT7FtyERERFRQTCaIpfS0m84CBMRERFRQTCoJ5dKTEmHp8lNfyYojTMcExERERVUsQvqRaSTiKwVkYsiokTkIbv1IiLviMglEUkUkc0iEuyi5lIBJaRqN8oCgK/ZHbFJaS5uEREREVHJU+yCegDeAP4AMCab9ZMBjAPwLIDWAOIBbBAR8+1pHhWmJJue+rIc1pKIiIgoX4pdMrNS6mcAPwOAiGRaJ9qCFwC8q5T6UV/2fwAiATwEYPltbCoVgkSbnnrrBFQ1y3m7uFVEREREJUtx7KnPSS0AlQBsti5QSt0AsBdA2+w2EhEPEfG1PgCUKfKWklNS0y0wumkXb5yAioiIiCh/SlpQX0n/GWm3PNJmnSOvALhh8zhf+E2j/LJ+I1PWixNQEREREeVHSQvq8+t9AH42j6qubQ45EuhjQnQCg3oiIiKivCp2OfW5uKz/rAjgks3yigAOZ7eRUioZQLL1uX2uPhUPZb2YfkNERESUHyWtp/40tMC+m3WBniPfGsBuVzWKCkegfqMsEREREeVNseupFxEfAHVsFtUSkWYAopVSZ0VkJoDXRSQcWpD/HwAXAay+zU2lQubnacSNxFRXN4OIiIioxCl2QT2AFgC22jyfof9cDCAEwIfQxrKfB8AfwK8Aeiulkm5fE6koGAycUZaIiIgoP4pdUK+U2gYg26R3pUV9b+oPKsEYwBMREREVjpKWU093kOQ0Czz0iaeIiIjoloCAgBeefvrpNtbnIjL1v//9b/3bsa/bpX379g81a9ZsaF62KcrzUNIxqCeXSUhJh5ddUO9uMCAlzeKiFhEREWX2zTffVDUYDG82atRouCvb8ddff330/PPPhwPA7t27/UVkamhoaE5z9BR7y5cv/3nNmjWr87KN7XmgzBjUk8skpqbD05Q5qC/rbUIMx6onIqJiYv78+ff07NlzX3h4eI3Dhw+7bEb6Ro0axfn6+qa7av9FoVq1asnVq1fP0z2Rd+J5KCwM6sllElPS4GnXUx/ozVlliYioeIiMjDTt3bu34aRJk35v0qRJ+P/+979mtuvnzZtXU0Smzpo1q3ZQUNAoo9H4Wt26dUccO3bMe8aMGXUqVqw4xmw2v9K6deuB165dM1q3Cw4ODunRo0efHj169DGbzVO8vb0nDxw4sIvFkv031bZpJ+3atRsPAA8//PAoEZkaHBwcYq23d+/evW23a9as2dD27ds/ZH1+7Ngx7yZNmgwzGo2vBQQEjH/11Vcb2+/r7Nmz5k6dOvX39vZ+yWw2v1K3bt0Rq1evrphd26zfHEydOrVh7dq1nzAaja9Vr1595JYtWwKXL19epVq1as94eHi82rBhw0f//fdfL+t29uk3wcHBIT179ry/X79+Pby8vF4uU6bMpMGDB3fO7jzkd7/OnKeAgIAXhg0b1qldu3YDPDw8Xi1btuwLH3zwQb1///3Xq2nTpkM9PDxerVKlyugVK1ZUye683G4M6sllElMsjnvqGdQTEVExMG3atIYVK1a81r1796jBgwf/uWHDhuaOAu+PP/6483vvvbd+xYoVC6Kjo3379+8/6Msvv2wzZ86cHz777LOlR44cqT1x4sRWttvs2LGjqZubm2XdunXzx48f//PatWvbTpgw4R5n2rVs2bL5APDZZ599/ddff320YcOGFc4e06BBgx6Kiory++qrrxbPnTv3u+XLl7eMi4vzti3Ts2fPQdevX/deuHDh0p9//nlucHDwpUcffXTEqVOnPHOq+4svvug8bty4HevWrZtrMBgsI0aMGPjmm2/2+M9//vPzsmXLFkZGRgaMHj26S0517Nixo6mnp2fKTz/9NP+5557b9P333983e/bsu4p6v478+OOPbe69996zYWFhX9xzzz3hb7311oCHH354wIABA/78+eef51aoUCF63LhxA2zfEyIyddKkSc3yuq/CUOxGv6HSIzE13WFPPSegIiKi4mD16tXNe/Xq9ScAjB079sRbb73l8eWXX9Z85plnImzLTZ48eUtISMg5AAgNDT20ZMmSbjt37vykQ4cOMQCwdOnSv/fv318LwG/Wbfz9/WPXr18fZjAY0L1796ijR49WXL58edtZs2YdzK1dNWrUSACAypUrJzZq1CjO2ePZsmVL4NGjR+ssW7Zs/tChQy/q7VjTs2fPMdYyX331VfWIiIigK1eu/M+a5tKlS5eN5cqVqz9z5swGn3zyyYHs6h8+fPiu8ePHnwSAxx9/fO+777478PPPP//a9txs2LChWU5tDAoKivzuu++26/uNXr58eavNmzffNXbs2FNFuV9HmjZtGj579uwDAFC+fPntjRs3bnH33XdffOutt/4GgEmTJv32+OOPP/X333/7WF+H8uXLR5UtW9Ylw6wzqCeXSUhJg5eDnvozUfEuahEREZFm8+bNgWfOnAmaOHHiCgAwm82Wdu3aHf3qq6+a2wf13bp1i7T+XqFChTij0ZhqDegBIDAwMO748eNBttvUrVv3vMFwK2GiTZs259atW9c2JSVFTCZTkYz5vHfv3nIGg8HyyCOPXLIu69GjxzWz2ZwRhO7bt69iSkqKqXz58i/bbpuamuoeERFRNqf6W7dunXEegoKC4gCga9eumc6N/bcC9mrVqhVp+9zf3/9mdHR0jtsUxn4dqVevXkYdDRo0iAOARo0aZSyrVatWHACcOnXK2xrUX7ly5dO87qewMKgnl0lKTYfZQU/9wTMx2WxBRER0e8yaNesei8ViaNSo0YvWZUopuLu7p587d259tWrVkq3LPT09M/IvRARubm6ZcnREBEqpbOfgKSwiouzngElLS8tTqnV8fLypTJkycWvWrFlkv65KlSo59kB7eHhkHLf1gsX+3OR2Htzd3bOcO4vFkuM2ed2vs+fJti3Wek0mU5Zl6enpRf7aOoNBPbkMR78hIqLiKCkpybBt27amTzzxxMaBAweetF03YsSIodOmTWv86aef7i/IPsLDwzP13O/Zs6dquXLlop3ppff09EwHgLS0tEzBpJ+fX3xUVFTGCD0pKSly4cKFCn5+fhEA0LJly2sWi8Xw/fffV7am32zevDkwKSnJbN2mZcuWl5YuXepjMpksbdu2vV6QYyyucjtPJRVvlCWXSUhJz5J+E+DFnHoiInKtGTNm1E1MTDS//fbbBx944IErto927dr9vWbNmuYF3UdMTIxfnz59em3evDnw9ddfbxQWFtZ68ODBe5zZtn79+vFGozEtNDS0ztGjR73PnTvnAQBt2rQ5/eeffwZPnz49eNOmTeV69uzZNzExMSNg7969e1SDBg1OTJo0qd/SpUuDVq5cWXnUqFH9jUZjmrXMmDFjTtWsWfPcI488MvSTTz6pvXv3bv/FixdXGzBgQNfiNNJLQeR2ngqiQoUKz7/33nsumRyLPfXkMokpWdNvPE1uSE7l8LNEROQ6y5cvb16/fv1Ttik2VsOGDTu2du3a9mvWrMl2iEdndOzY8Y+kpCT3vn37jjQYDKpPnz57Zs2ale1NqLbMZrPlueee+3nRokX3LV++vEvt2rXPhoeHL5o2bdqhv//+u9Kbb745wGAwWAYMGLDn+vXrEbbbrlix4sdhw4b1DwkJecLHxydu1KhRW+bNm+dnXW8wGPDrr78uHTFiRLc33njjwfj4eG8fH5+4+vXrn6ldu7bTN+UWZ86cp/y6evVqYExMTKFcIOSV2OcUlQYi4gvgxo0bN+Dr6+vq5pRan24JR/s65dC8eub7bp5e/Du+HNHSRa0iIqLiKjY2Fn5+fgDgp5SKLUhdIjJ48uTJg6dNm3akcFrnvODg4JDatWtfDgsLC7vd+6Y7Q79+/RquW7fuY6XULusypt+Qy2jpN/yyiIiIiKigGNSTyzgap96qNH6DRERERJRf7CYll0lMyTr6DQCUMRtxMzkNvmajg62IiIhKtvDw8EWubgPdedhTTy7jaEhLACjrZUIMR8AhIiIichqDenKZxBTH6TcB3kYOa0lERKVaQEDAC08//XSbgtQxePDgzkFBQc8WVpuoeGNQTy5jUQpuhqyTsAV4e7CnnoiIqBho2bLlIzVq1BiZkpKS8Q87ISHBUK1atWfatGnzcF7qGj58eMdatWo9ZTKZXvP09JziqMznn39eq1atWk95eHi8UqZMmUn9+/fvnpSUlCletVgsCAkJaVe+fPmx7u7ur/v5+U0cPnx4x4LuW0Sm2j/eeOONRnk5RldiTj0VO+ypJyKiO5HFYkFKSorBbDZbXN0WZy1btuyn5s2bjwkJCenw7bff7gSA//u//7vvxo0bZXbu3Pl1XupKTU1169q169FLly6d27p16z3261evXl1x3Lhxjw4cOHDnc889F3r8+PEyr7/+et+BAwcafvrpp43Wcr17977/8OHDtV988cWNHTt2jLx48aLn5cuXPQuyb6sXX3zxx5CQkBPW51WrVk3KyzG6EnvqqdhhTz0REblacHBwSI8ePfr06NGjj9lsnuLt7T154MCBXSyWW/H4lClTmlSrVu0Za69y69atBx47dszbun7evHk1RWTqjBkz6lSrVu0Zk8n0xtdff119x44dZZs2bTrUx8dnkoeHx6vVq1cf+emnn95l34a4uDhTq1atBppMplf9/Pwmjh49OtMkLvv27fNr2rTpUA8Pj1fNZvMrLVq0GHT06FFv+3qsli9fXqV+/fqPe3t7TzabzVPq1KkT8v3331fO6TzUqVMn8bXXXlv73Xff3bdmzZqKK1asqBIaGtph6tSpa2rWrJmngHflypXbFixYsKdBgwZXHK1ftGhRoypVqkSuWLFi+3333Rc9cuTIMy+++OKmjRs3trx06ZIJADZs2FDul19+afHtt98umzJlyr/t27e/PmjQoEtjx449VZB9WwUEBCQ1atQozvrw9/dPy67s7t27/UVk6tSpUxvWrl37CaPR+Fr16tVHbtmyJXD58uVV9PfGqw0bNnz033//9bJu1759+4eaNWs29NFHH+3o4+MzydPTc8qQIUPuS0pKMvTt27eHl5fXy35+fhNffPHFZjmeUDsM6qlYSU23YO0fF7B4dwRmbDqO1PQS05lBRER3mB07djR1c3OzrFu3bv748eN/Xrt2bdsJEyZk9PKmpKS4TZgwYcsvv/zyxZw5c5ZfuXLFf9CgQQ/Z1zN9+vTukydP3rxly5ZPu3fvHhkdHW3q1KlT+IoVK74OCwv7omXLlicmTJgwbN++fX62261evbp9vXr1IsPCwuYOHTr013nz5t0/e/bsuwAgLS1NBgwYMDQuLs5zyZIlXy1YsODryMjIsg899NCg7I4nJibGY8CAAX+sXr164Y8//vhllSpVop944olHrQFzdqZMmfJv69atj4wePXrA+PHjH2rfvv0fL774YrhtmeDg4JD27dtnOfa8SElJcTMajZmCaC8vr7S0tDT3tWvXVgGAb7/9tl5gYGDM0qVL6wYEBIwPCAh4oVOnTv1PnTqVY0+9s6ZNm9bH29t7co0aNUZOnDixue1FXHa++OKLzuPGjduxbt26uQaDwTJixIiBb775Zo///Oc/Py9btmxhZGRkwOjRo7vYbvPPP//UunLlSpnVq1d/NXr06A3fffdd53vuuWe4n59f0k8//TS/T58++2fNmtXvwIEDTs+SyvQbKlZmbzmBJXvOQgGY/Yv2eTGxR13XNoqIiEolf3//2PXr14cZDAZ079496ujRoxWXL1/edtasWQcBYMaMGYesZTt06BBjNBp/Hjp06MjIyEhTxYoVM75yHjt27FbbnuS77ror8aGHHoq0Pu/SpcvWSpUq3b1w4cJ6rVq12mddXrt27bPffPPNrwDQtWvXqIMHD1afO3du27Fjx56aM2dOrcuXL1fcu3fvzBYtWsQCQGBgYOj999//3PLly6sMHTr0ov3xjB49+rTt827duq318fGZsmTJkpovvfTS8ZzOxdKlS8Pq1q37ooeHR/KyZcs22K8vX778jXLlyt3M+YzmrHfv3ifDwsLavP76643efPPNo0ePHvWZOXPmfQBw9uxZHwA4d+5c2ZiYGP+tW7c2/N///heanp5uePvtt3v17t178PHjxxcXZP9DhgzZ2rt379O+vr6pq1evrj179uwH4uLiTPPmzdub03bDhw/fNX78+JMA8Pjjj+999913B37++edfh4SEnAOA0NDQQxs2bGhmu42Xl1fizz///LO7u7vq3r171JIlS9qnpKQYly5duhMA2rdvv3PVqlUdVq9eXf3ee+91atZjBvVUrOyPiIZ12imlPyciInKFunXrnjcYbiU1tGnT5ty6devapqSkiMlkUitXrqz8n//8p/PZs2crJSYmmpVSAgCHDh3y692791Xrdr17984UYEdGRpqefPLJzvv37w+OjY0tY7FYDKmpqe4XLlzI1FPfqFGj87bPmzVrdu6HH35oAwB//fVXeT8/vxvWgF7fz1Wz2Zx06NCh8o6C+qNHj3o/99xzXY8ePVozLi7OWyllSE1NNZ4+fdrPvqy9Dz/8sDEAlZCQ4LVjx45yw4YNu2C7fteuXaG51ZGbcePGnTx8+PCm6dOn933//fcfdnNzSxsyZMiOkydPVjcYDAoALBaLpKWluS1cuDC0a9euUQAQEBCwZtCgQc9s3rw5sHv37lH53f/y5ct3WH9/+OGHLyckJJi+++67drkF9a1bt864QAsKCooDgK5du2Ysq1ChQlxcXFymtKgqVapccXd3z5hp09fXN65mzZoZqUEmk0l5eXklREZGZptOZY/pN1SstKgZAOvt9aI/JyIiKm6uXLliDAkJedzLyyv5o48++mHdunXzp0+fvgIAEhMTM43XHBgYmGr7fNiwYT337NlTf9y4cb989913Czdu3PhF5cqVr6SmpjqeZr2QDBo0aMDp06crvfLKK2GrVq1asHHjxi88PT0TUlJSctzvr7/+WnbBggU9JkyY8FPbtm3/eOGFFx6KjY0tkrYuXLhwd0JCwgcHDhz4+PLlyx8+9thj/wBAgwYNYgCgfPnyNw0Gg8Ua0ANA165drwLAsWPHcr04yYs2bdqcv3Hjhm9ux+rh4ZGRo2O9CPT09MxYJiKwXvBZubu7Z8rrERGHyywWS9ZhArPBoJ5cIt2iIJL1fTq2ax2M7RaMsl5GjO0WjLFd67igdUREREB4eHiQ7fM9e/ZULVeuXLTJZFLbt28vl5CQ4Pnpp59ufuqpp8726NHj2qVLl5zqVT127Fi1Hj16HH7ttdf+6dev35X69evHRUdH+9uXO3r0aFXb53/88UfVKlWqXAOAxo0bX71x44bf/v37M3Kuw8LCyiclJZnvueeeq/Z1AcDJkyerDR8+fO+LL74Y3qdPn6ve3t5pCQkJXo7KWqWlpUlISMhD9evXPzVt2rQ/li1bFpacnGx67LHHuuS0XUEYDAY0a9bsZkBAQNr8+fMb+/n5xT788MOXAKBjx47nLBaLYceOHWWt5Xfs2BEIAA0bNrxRmO04ePBgJU9PzyRfX9/0wqy3qDCoJ5dITE2Hl4PZZI1uBkzsURf31iiLiT3qwujGtygREblGTEyMX58+fXpt3rw58PXXX28UFhbWevDgwXsAoEmTJjfc3NzS33777da//vpr2Q8++KDeggULOjlTb6VKlaJ37tx5d2hoaKXVq1dX7NGjx0D7nlwAOHHiRLURI0a037JlS+Do0aNbHjhwoOHIkSP3AMCYMWNOVapUKXLo0KEDv//++8rLli0LGjly5IA6deqcGTJkSJbUGwAoX7589I8//th0w4YN5ZYuXRo0dOjQgfY3ptobNWpU64sXL5ZfunTpOgCoVq1a8ttvv73mp59+arts2bKMi5527doNePDBB7vlVNe+ffv8QkNDK507d87PYrFIaGhopdDQ0EqRkZEZN+qGhIS0W7t2bYX169eXHzp0aKfVq1d3mDx58s8mk0kBwHPPPXeqatWql5588skHV61aVWnlypWVX3jhhX7169c/Ze29X7ZsWVCFChWeP3ToUBln9/3hhx/WfeGFF+756aefKmzfvj1gzJgxLb7//vuODzzwQI6pN8UJc+rJJbKbTfYWp79tIiIiKhIdO3b8Iykpyb1v374jDQaD6tOnz55Zs2YdAIB69eolTJkyZfXnn3/erUuXLq2rVq16adKkSRtffvnlYbnV+/nnn28YMWLEg0OGDHnKy8srYdiwYb8mJiZ62Jd78MEHdx89erRK79697zObzclPPfXUBusNmQaDAaGhoctHjhx5/6OPPvqEiKhGjRqdWLx48frs9vvJJ5/8+MILL/Tr27fvKH9//9ixY8f+MnPmzJ7Zld+yZUvgN9980+2ll15a06hRozjr8vHjx58MDQ09/MILLzz0wAMPfOHr65t+9epVPxFR2dUFABMmTOiya9euptbnDz/88CgAmDt37uJnnnkmAgB27doVvGLFik5paWluQUFBkR9++OGyiRMnZowb7+7urtauXbvsySefvH/YsGFPmEym1KZNm4Z//fXXGePY37x503j16tXApKQkN2f3bTKZLCtXrmw5Z86cXgCkXLly0aNGjdrw8ccfH8zpmIoTUSrH839HEhFfADdu3LgBX1+nRwqiQnQuOgELfj2Nt/o3dLj+6cW/48sRLR2uK45S0y2YveUE9kdEo0XNAIztWoffMhARFbLY2Fj4+fkBgJ9SKja38jkRkcGTJ08ePG3aNIcjiwQHB4fUrl37clhYWFhB9kNUFPr169dw3bp1HyuldlmXsaeeXCIhxXH6TUk1e8sJzP4lHArA7pPavTvODMXJiwEiIiIqDAzqySUSU3NLvylZfj+dv6E483sxQERERGSLQT25RGJKOjxz7KkXKOV4hJyilN+e8wBvY8bveRmKM78XA0REVLTCw8MXuboNRHnBoJ5cIjE1Lceg3sNoQHKaBebb3Jufn55zpRRik9Lw7H21sXzfWfxfu5pOD8VZteytWa0LMi4/03iIiIhKN/7XJ5dITLHkmH7jaXRDYsrtHxY2PzPaHjgTg4ZV/DDl/vroUr8CRrSt4XRA7edpRIc6gagR4FWgcfmtFyO7TkZh9i/hmL3lRO4bERFRsRUQEPDC008/3aYgdQwePLhzUFDQs4XVJireGNSTS2Q3Tr2Vp9ENiam3P6i37Sl3tuf8q10ReKJ9TX37sjhwJsbp/f0beRPz/68l6lYqU6Bx+fNzMUJERJSTTp069a9cufLouLi4TP+wP/roo2B3d/c3vv/++8o5bb97927/Tp069Q8ICBhvNBpfCwwMHDdo0KDOtvVdv37dvX379g9Vrlx5tJub25vNmjUb6qiuV199tXGVKlWeNZlMr/n6+r7YsWPHB0+cOOHpqCwArF69umKrVq0G+vv7TzAaja9VrFhxzMiRI1vblxs9enTLihUrjjEaja9VqFDh+Zdffrmpo/pKAgb15BKJKWk5ptZ4mVwT1I/tWgd3VyqDhlV80biqX6495yevxsHb5IaKvmYAQIsaAdjvZFB/8moc7irnDU+TG5RSSEmz5L5RNqr4F04aDxERFR2LxYKkpKQSE3stW7ZsQ3Jyssejjz7a2brs7Nmz5nfeeaffI488sv2RRx65lNP2e/fuLWexWOStt95at3Xr1s9feumlDT///HOLIUOGZExSlZKSIh4eHqmDBw/eW69evVOO6lm0aFG1Dz74YEDfvn0Pbd269bNZs2atDA8PDxo0aFD/7Pa9Y8eOKmXLlo3/3//+t2rr1q2fP/nkkzsXL17c/dlnn21lLTNmzJgWX331VfdRo0Zt27p16+ejRo3aNnPmzD4ffvhhiRyxosS8sejOktvoN2YXpd8Y3Qyo5GfGT+M6ws/TCEsu8zh8ufM0nu54V8bz4Ao+CI+86dS+Nv0die4NKgIAmlXzx+Fz1/Pd7uj4ZIzsWAtlvYx4ptNd+U7jISIiTXBwcEiPHj369OjRo4/ZbJ7i7e09eeDAgV0sllsdMFOmTGlSrVq1Zzw8PF4pU6bMpNatWw88duyYt3X9vHnzaorI1BkzZtSpVq3aMyaT6Y2vv/66+o4dO8o2bdp0qI+PzyQPD49Xq1evPvLTTz+9y74NcXFxplatWg00mUyv+vn5TRw9enSmCVz27dvn17Rp06EeHh6vms3mV1q0aDHo6NGj3vb1WC1fvrxK/fr1H/f29p5sNpun1KlTJySn3vagoKDk//73v6t/+umndkuXLg0CgGHDhvX29/e/uWjRol9zO4cvvPDCiV9//fXHcePGnezQoUPMlClT/n3kkUd27dmz525rmQoVKqRu2bLlp1mzZh0sW7ZsnKN6tm3bVq1s2bLX582bt7d9+/bXn3jiibMPPvjg/lOnTgU5Kg8AM2bMOLRhw4awkSNHnunQoUPM+++//2enTp0ObdmyJWPf69evb9q9e/f977zzztEOHTrE/Oc//znSrVu3A1988UWH7Oq1vqazZs2qHRQUNMpoNL5Wt27dEceOHfOeMWNGnYoVK44xm82vtG7deuC1a9cyRtHQ30/39+7du7enp+fLPj4+k8aPH3/PlStXjB06dHjQw8PjlXLlyo2bMWNGvv+BM6gnl0hMscDLlP192p4u6qm3WBQM+og7PRpUxJZjV7Ite/VmMmLiU1C3YsYs1DAYBCZ3A5KcaPveU1FoXSsQANC2djn8duJavtq8+2QU6lb0xWsPNMD7DzdGRV9zib1JNjXdghmbjmP4/D2Ysek4UtPz/+0FEVFB7dixo6mbm5tl3bp188ePH//z2rVr206YMOEe6/qUlBS3CRMmbPnll1++mDNnzvIrV674Dxo06CH7eqZPn9598uTJm7ds2fJp9+7dI6Ojo02dOnUKX7FixddhYWFftGzZ8sSECROG7du3z892u9WrV7evV69eZFhY2NyhQ4f+Om/evPtnz559FwCkpaXJgAEDhsbFxXkuWbLkqwULFnwdGRlZ9qGHHhqU3fHExMR4DBgw4I/Vq1cv/PHHH7+sUqVK9BNPPPHopUuXTNlt8+yzz0Z079799wkTJgx46623Gvz+++8Nv/zyy1Cz2WwJDg4Oad++fZbjzUlsbKzZy8srMS/bdO7c+dyNGzf8Pvroo2CLxYKjR496//LLLw2aN28enpd64uLizGXKlMnYd1pampuHh0eabRmz2Zx29uzZoISEhBz/kX788ced33vvvfUrVqxYEB0d7du/f/9BX375ZZs5c+b88Nlnny09cuRI7YkTJ7ay3Wbnzp3NypYtm7B+/fr5/fr12/fpp5/27dq16+B77733XFhY2NymTZuefOONNx62vRjIC45+Qy6RkJoGT1P2fy+uulH2fExixog0/ZpUwSur/sL9jTN3YlhHmll18Dxa1QpAarolUxDdpKo//jh3Ha3vCsx2P1dvJsPP0wiTu7Zd06p++Gxr3m9uVUph/s5TmD5ISwHsfndFjPhqH0La1YTBcHuHAy0MHLefiIoTf3//2PXr14cZDAZ079496ujRoxWXL1/edtasWQcBrTfYWrZDhw4xRqPx56FDh46MjIw0VaxYMcW6buzYsVvHjh2bkVpy1113JT700EOR1uddunTZWqlSpbsXLlxYr1WrVvusy2vXrn32m2+++RUAunbtGnXw4MHqc+fObTt27NhTc+bMqXX58uWKe/fundmiRYtYAAgMDAy9//77n1u+fHmVoUOHXrQ/ntGjR5+2fd6tW7e1Pj4+U5YsWVLzpZdeOp7defj22283169fv84777zzyJNPPrmxR48e1wCgfPnyN8qVK+fc19MAtm/fHrB+/fpWo0aN2ujsNgAQEhJyLiIi4odXX331kcmTJ7tbLBZD48aNj69fv/4nZ+tYvHhxtd9//73hxx9//K112b333nty06ZN96xcufKfgQMHXlq5cmWVTZs23ZOenm44ceKEV5MmTRx+cwAAkydP3hISEnIOAEJDQw8tWbKk286dOz/p0KFDDAAsXbr07/3799cC8Jt1m6CgoMvLli3bAQDt27ffuWrVqg6+vr4J1vdT+fLltzdu3LhFWFhYxccee+x8Xs4RUIJ76kVkjIhEiEiSiOwVkVa5b0XFRVJKeo459a66UfZ45E0E6z3vZb1NcHcTXL2ZnKmMNfA8H5OI0IMXsow006JG2Vzz6rf8E4mud1fMeO7uZoDJzYD45LQctspq18koNKjsiwBvU0Y97WqXw47wq3mqp7Dlp8ddKYVfjkXetht++a0AEeWmbt265w2GW6FSmzZtzl27di0gJSVFAGDlypWVmzRpMszf33+Ch4fHK48//ngIABw6dChTj3vv3r0zBdiRkZGmBx54oGfFihXHeHp6TvHw8Hj1ypUr5S5cuJBpu0aNGmUK7Jo1a3bu4sWL5QDgr7/+Ku/n53fDGtDr+7lqNpuTDh06VN7R8Rw9etT7vvvu61euXLmxekrRKykpKabTp0/7OSpvFRAQkDZ8+PBdRqMxdd68eXusy3ft2hX6448//pLTtlaHDh0qM3DgwMdatWr1tzWIdVZYWFj5GTNm3D9s2LDtoaGh82bOnLnk2rVr/r179+7rzPY//fRTheeee27o4MGDt48bN+6kdfnChQu3N23a9MTQoUOfNhqNbz7zzDNDu3TpchgA3Nzccsy/7datW8ZFWYUKFeKMRmOqNaAHgMDAwLjY2NhMqVC1atXK2MZkMikvL6+EunXrZixr0KBBHACcP38+2xSqnJTInnoRGQJgBoBnAewF8AKADSJSTymVfb4EFRsJKem5pt84k8JS2I5fuYkWNW7dZDqgeRB+PHwhU978vlNROQaeTav5Y+FvmTpDstj271VMe6RJpmUtawVgX0Q0utSrkGl5dmPQW3vpZw5plqn80JbV8FroEXTOpZ5n77sLX2w/levY9vkZA9+ZHnfbegN9TLiekAovkxsEyDi/Tar657ifvLTRvozFovDZ1hOZ2ji2a51cz5H1+Aq7TFHWzTayja5sY0lNB8zNlStXjCEhIY83btz4xEcfffRD9erVE44dO+Y3fvz4xxITEzP1WgUGBqbaPh82bFjPP/74466JEydubNKkSbSvr2/a8OHDB6emphbp5CyDBg0aEBcX5/nKK6+E3X333de9vb3T+/Tp81RKSkqu+3V3d7cYDAZle5HjrMOHD5fp2bNnSN26dc9t2bJlbV63f/vttzsEBwefXbRo0S59UaSvr+9PTz755BN//vnnlpx61MPCwsoPGTLk/3r27HnA2ktuFRAQkLZz584fExIS1h47dsyncePGNydMmHCvh4dHSr169RJyapOnp2dGT5CIwM3NLVPPkIhAKZXpK3N3d/csZWyXWc+txWLJ11ftJTKoBzARwHyl1FcAICLPAngAwJMAPnBlw8g5ud0o62l0w7W45GzXOyM/wejxyzcxvFX1jOed6pbHol0ReKpDLYgI0i0KUfEpGYGno5FmzEY3pKRZtPx8BykwiSnpSE1X8DVnTplrXycQqw5eyBLU2wfI6RYFN4Ng09+XUcZshLdH5j/jQB8P+JjdcSYqHjUCb13sf7zpOD7fpnVQ7DoZhZX7z+HSjSQAOae6zNx8HJ9tPZlrOVu5XfgAyBhP31pu1H13YVLPehmvWRU/Txw8G403Vv+Fk1fjcww+Pt50HHO2nbx1jtItcHMzYH9ENJpU9UOvBpXwxY6T2HA0MuP4zUZDpjZ+t/8c/r54A78cu5JRz89/XUL4lbiMYz8XnQCzuwHLfz+XUWb1ofM4G52YUe/vEdHwNrnZ1XMRJ67EZzw/eTUORoPgx8MXM5ZdjU0CBFi+71bdG49exr+Xb2Y8P3rhBtIsFmw/fi1jfz8eOo8zNvv/51IsRICNRyMztgs7cgnhkXEZz09cuYm0dIWNf986Hz/9eRGnrt5q46mrWvn1f1661e4r2rfs6/+6nLHspz8v4uTV+IxzdDzyJgRA2JFbZdb/dREnbY7/hF7Pzzb1rNfrsT633nBuW499G/+9HAsRwQabMuGR2vmyXeZM3bezjDPbOTxHzpzHQipTWHUDJTuFLjw8PNONmHv27Klarly5aJPJpLZv314uISHB89NPP91s7S3fsmVLFWfqPXbsWLUePXocfu211/4BtJ776Ohof/tyR48erWr7/I8//qhapUqVawDQuHHjqwsWLPDbv3+/r3X/YWFh5ZOSksz33HOPw69rT548WW3ChAk/vfjii+EAsH//ft+EhAQvZ9qcX4cOHSrTs2fPkFq1al3ctm3band395xHoHAgOTnZaB80W4PhnALg9evXlx88ePCILl26/BEaGrolu3JeXl6We++9NxYANmzY0Khp06bH89NOVytxQb2ImADcC+B96zKllEVENgNom802HgA8bBaVcVSObp+kVAvMxhxy6k1uSChgTv3sX8LxiZ4a4+w/l9ikNPh73bpfyOhmQL2KZXD0YiwaBfnh3Z/+xjOd7sK5mMQsvVW2giuWQfiVONSrlPWttjP8KjoGl8uyvG6FMvj3ctbUxH2nMwfIy/aeQUxCasZFxewtJ7Ic1+NtauCb3Wfwet8GAIBz0QlYuvdspjI3k26l+uSU6vL9gfNOlbM6fO46zl9PzNTjHpeUhmtxyfh69xnsj4hGjQAvhB29DNtPzL/O34DRzZDpWN5d9ze+/FX71mP3yShcu5kMT6MbFv52GgpaMLruj4uISUjJdI4W/HoaSfoQobtORuHg2euIiU+BrXI+HrgQk5hxHns1qIjddhcj52NuddQoAIfOxiDNojKViYrLXG9cUhqi45Lt6knM9PyfS7GwKGRatlO/Udp2WcS1+EzPL1xPhP114lW7/UdExUPZ1X02OiHT8/DIOIhdPRevZ27jsUuxWdpjfX/aLrt4/db9bgrAySvaxUCmdtsd//HIuCz1XLDb/4krWcvYt/GUfjGR23bO1H07yzizncNz5Mx5tCsTns96Cmv/JX3OjJiYGL8+ffr0mjhx4v5t27ZVDgsLa/3MM89sAIAmTZrccHNzS3/77bdbv/zyy/t//fXXCgsWLOjkTL2VKlWK3rlz592hoaHHRUS9+eabXe17dQHgxIkT1UaMGNF+xIgR/6xcufKuAwcONJwxY8ZSABgzZsypDz74IHLo0KEDP/jgg7DU1FTD5MmTH6hTp86ZIUOGZMmnB4Dy5ctH//jjj027dOly8dq1ax5Tp07taTQa85b3aaNdu3YDypcvH5tdCs6hQ4fK9OjRI6RcuXI35s6du/H48eMZPU2NGjXK6F0PCwsrn5iY6Hbz5k3PpKQkU2hoaCUAGDBgwGUA6Nq16/GPP/6435gxY1oMGzbs5IkTJ3zefPPN3tWrV7/QrFmzmwDw3nvv1Z81a1b3K1eufApoKTdDhgwZ0bhx4xPvv//+7iNHjvgAgNFotFh74bds2RK4adOmoJ49e56/fPmy54wZM9pevHixwjfffLM6v+fElUpcUA+gHAA3AJF2yyMB1M9mm1cATC3KRlFeKYh9VGHDXAg59duP3+qocOafS7pFZQmYAODBZlXwWuhfuJGYikp+Zrza5+5ce/xb1CiL3yOiHQb1m49FYnz3rBcXBoPAz9OImPgUlPW+dWGRkJKe6ZsBCHL9p9mgii/GLjuEvy7cQGU/M67cTMaA5kFYvCsio54GVXzx++lbk1bVKpc1he/bvWdRq5w3rsTeClLLehsxY9PxLBc1szaHY/XhC/DxcMfKUW2w7PfzGWXa1ArAQ5/9hvMxt3qUm1fzx/WE6xntcTS2/t+XMlJFoQDsOH4VqRZLpouBCmU80LdplYxvMwRAuTIeGfsCAHeD4P7GlXHCpsyDzYLgZpBMxzF7ywmER94q07iqf8Y5EgD9m2mddrb7ahjkl6lMl/raNy3Hc6jngSZVstTz8D1VsyxrUi3zdj0bVgIAHLt0M2NZI7v9926k3dhte6xN7PZvvfk7p2N11MY+DpbZb2etO6cyfZwoY63nRB7L5He721nGme2cOUeFdR4d1VNY+y/pc2Z07Njxj6SkJPe+ffuONBgMqk+fPntmzZp1AADq1auXMGXKlNWff/55ty5durSuWrXqpUmTJm18+eWXh+VW7+eff75hxIgRDw4ZMuQpLy+vhGHDhv2amJjoYV/uwQcf3H306NEqvXv3vs9sNic/9dRTG8aPH38S0FI1QkNDl48cOfL+Rx999AkRUY0aNTqxePHi9dnt95NPPvnxhRde6Ne3b99R/v7+sWPHjv1l5syZPfN7fq5eveonIiq79UuXLq0dFRUVEBUVFXDPPfdMtF2nlHrb+vvw4cMfjYmJ8bM+f/jhh0fZlpk+ffrhmzdvmn744YdW8+fP72U2m5MaNGhwes6cOZus28TExJivXr2aMULF4sWLG8THx3vt2bOnSePGjTPyXcuWLXsjOjp6JgCkpqbK119/3fajjz4qZzAY0uvXrx+xZs2aBW3btr2e33PiSiUxqM+P96Hl4FuVAZDnu4rp9vEq5Jx6Z/65nI1OQLWArN9Cbjp2BX+cvwEAOBOV4LBn3N69Ncri3Z+O4bE2NTKWpaZb8Mkv4dh4NBKV/DwdpgO1rR2I3aeiMv5Zbjx6GfdWL4vO9So4zAXP7rhmbzmBs9EJOBut9TQ/17k2JvSoC19Po8Pc14ZVfPHn+Ru4ejMZ5cto/1cOnInBbyevYfETrfDZtpPYHxGN5tX9seXYFfz052UAt1JCImOTcORibMa5Xvb7+SznqGpZz0yBtofRgLHdgnP8xqNFzQDsPhl1K/C9N2vg2+quwIxtczpH9mUcnX/7MtnlEBdFmaKsm21kG4tDG0sid3d3S1hYWBgAh6OsvPvuu0fefffdI7bLJk+enBGsPvPMMxHPPPPM2/bbtW3b9vrx48cX2y3+3faJNfDMSatWrW788ccfy7Nb/913320DsM36/OGHH7788MMPz7ct8+abb/6d234ALbCePn36Ydtl4eHhi/K6jSPOHOvcuXP3zZ07d1926+33ZX/sjvTq1evahQsX5ua2b1uOXlNHx2m/f0fnytFx217s5JWoXCbXKW709JsEAI8opVbbLF8MwF8p9aATdfgCuHHjxg34+voWWVspe08v3o8vR7TIdn3EtXgs2XMrfSSvklLT8fTX+5Gcmo6UNAvuq1ch15z6DUcvIyY+BUNtcuoBYPj8Pdilp+8AQLvagfh2ZJtc2zBi4T4sfvLWoEwzNh3PFIyO7RacJfA9G5WAuTtO4r0BjXH5RhJe+v4PfDmiBTzcb91/4My9Avlp84krN/H22r/ROMgP+05H43JsEtY83x4B3pk7j+zrrlfRBz5mIw7YjPjjaH/OHL89R8cKZM2pz+2m2Dv5Zj2i0iQ2NhZ+fn4A4KeUis2tfE5EZPDkyZMHT5s27Yij9cHBwSG1a9e+rAf1RMVKv379Gq5bt+5jpZT15uGS11OvlEoRkQMAugFYDQAiYtCff+rCplGe5Hwx6WlyQ0IBeuo3H4tEr4aVYBCgkq8Z3WyGj8zO8cs30a5O1lx3+95iZ79Orujrgcs3klDJzwwA2HDkUq5pM9UCPHE2OgHpFoXXQv/CW/0bZgroAWTJO3ckP22uU6EMAn1MGTfTCoBFu85k2Zd93b30dI+DZ2Jy3J+jnvLcZHesuR2/M+eIiIjoTlLignrdDACLRWQ/gH3QhrT0BvCVKxtFhcdsdENSAW6UXXP4Iv43qCn2R0RnjPCSm/ArcRjRvmaW5fkJRlPTLYiOT8H/LdyL7ndXREx8Crw93HMcNQcA0iwKUXHJaP/BFjQM8kV1B+lAzshPmwHgSuytEYeyu/DIqe6c9sdAm4hKktxSS4iKmxIZ1CulVohIeQDvAKgE4DCA3kop+5tnqdjKeQjWgkw+dfF6Irw93OHnaUQlPzMOn7vu1HZxyWlZhpkE8heMzt5yImNIw+ORcRjQPAgrRrV1mEpiv531JsjI2CSn8vcdyW8A7UwPf357z4mIiKjolNgkU6XUp0qpGkopD6VUa6XUXle3ifIi5/Qbk7sBqemOy+Q2E+iqg+cxUB9JpLKfp1M99anpFhhyGI0nr/ZHRGc6wsjYpIxg+NuRbTCxR12HOd6227liOLixXetgbLdgtKsdiLHdgkv8TW5ERCVVQEDAC08//XTGzUkiMvW///1vdqP8Feq+qGQqsUE9lVyp6Ra452NGOivrZEy7TkZlTGBkZbEo7DoZhXa1tVGtynoZs4xP7siZqHjUKld482+0qBmQ8V1EXvLw87tdYXHmwoOIqDT55ptvqhoMhjcbNWo03JXt+Ouvvz56/vnnwwFg9+7d/iIy1Tqee3H16quvNq5SpcqzJpPpNV9f3xc7duz44IkTJzyt6xMSEgxDhgy5LzAwcJzRaHy9SpUqz86cOdPp3qTt27cHeHh4vOLp6TnFft3TTz/dpkKFCs8bjcbX/P39J9x///29rl+/npGh8uWXX9Zo0qTJMF9f3xeL6oIpLi7Ozdvbe/Ljjz/ewdH6oUOHdvLx8ZmUkJBQKP9s+R+bbruElHR4mZyZCdtxT31Ovdn79NQW60yuOY2Fb+t4ZByCKxbenGT57fFmTzkRUfEyf/78e3r27LkvPDy8xuHDh102eWWjRo3ifH19C2+s5yK2aNGiah988MGAvn37Htq6detns2bNWhkeHh40aNCg/tYyQ4cO7RoWFnbvq6+++vPWrVs/GzBgwP6XXnppyKpVq3K9WElISDA8/vjjA4ODg8/ar3vttdcaL168uPvIkSO379ix47N33nlnzW+//dZo+PDh3axlYmNjjXXq1Il8+eWXHQ5XWhh8fHzSO3bs+OeGDRua2a+zWCzYtGlTs86dO//h5eVlcbB5njGop9suKTUdZqeCesdse7Ot9VnHtP/+wHkMureq4w1zcDzyJuoWYlCf3x5v9pQTERUfkZGRpr179zacNGnS702aNAn/3//+18x2/bx582qKyNRZs2bVDgoKGmU0Gl+rW7fuiGPHjnnPmDGjTsWKFceYzeZXWrduPfDatWsZN20FBweH9OjRo0+PHj36mM3mKd7e3pMHDhzYxWLJPraz7U1u167deECbpElEpgYHB4dY6+3du3dv2+2aNWs2tH379g9Znx87dsy7SZMmw4xG42sBAQHjX3311cb2+zp79qy5U6dO/b29vV8ym82v1K1bd8Tq1atzH0bOxrZt26qVLVv2+rx58/a2b9/++hNPPHH2wQcf3H/q1KkgmzJNhw8fvvPFF18M79ChQ8xnn322v3HjxuEffvhhu9zqHzp0aNegoKBrvXv3Pmq/bt++fdXuuuuus++9995fbdu2vT5u3LiTHTp0+Ouff/7J2PfEiRNPrFq1astrr732j7PHNHjw4M5BQUHPTpw4sbm/v/8EDw+PV7t16/ZASkqKjBgxon2ZMmUm+fj4vDR8+PCO1m3GjBlz8OrVq4FfffVVpvGyv/zyy5rR0dFlx48ff8jZ/eeGEQPddokp6fAyOhPUO+5lH9u1Dp7tXBuB3iaM6xaMUffdhZCv9mHCisP45VgkVh44nynP3sfsjptJqTnuKfxKHIIr+OTlMIiI6A43bdq0hhUrVrzWvXv3qMGDB/+5YcOG5o4C748//rjze++9t37FihULoqOjffv37z/oyy+/bDNnzpwfPvvss6VHjhypPXHixFa22+zYsaOpm5ubZd26dfPHjx//89q1a9tOmDDhHmfatWzZsvkA8Nlnn339119/fbRhw4YVzh7ToEGDHoqKivL76quvFs+dO/e75cuXt4yLi8s0pXjPnj0HXb9+3XvhwoVLf/7557nBwcGXHn300RGnTp3yBG6l/8ybN69mdvvp3LnzuRs3bvh99NFHwRaLBUePHvX+5ZdfGjRv3jzcWiYtLc3NbDan2W5nMpnSjh8/Xj1rjbfMmTOn1m+//dbw+++/dzh7bqtWrc6dPXu2yrJly4IA4Ndffy174MCB4LZt24Y7Kp8X165dK7t9+/Y6y5cvX/Kf//zn++3btzdv1qzZo5cuXfINDQ39atSoUZuWLVvWdenSpUEA0K9fvyvVq1e/uHDhwua29SxevLhZrVq1zvXo0eNaQdtkVSJHv6GSLSElHZ4F6Kk3uhnwRPuauJ6QkjHiyoEz1zFvxykA2myjwK3RWCr5mXH5RhLKOBjZJqNNyWnw9uCfAxER3bJ69ermvXr1+hMAxo4de+Ktt97y+PLLL2s+88wzEbblJk+evCUkJOQcAISGhh5asmRJt507d37SoUOHGABYunTp3/v3768F4DfrNv7+/rHr168PMxgM6N69e9TRo0crLl++vO2sWbMO5tauGjVqJABA5cqVExs1ahTn7PFs2bIl8OjRo3WWLVs2f+jQoRf1dqzp2bPnGGuZr776qnpERETQlStX/mdN9+nSpcvGcuXK1Z85c2aDTz755IDZbE4vX758VJkyZbLtMQsJCTkXERHxw6uvvvrI5MmT3S0Wi6Fx48bH169fn5Hu0rhx45PLli1r279//zMdO3aMmTNnTq2DBw/ebbFYss2dPXHihOcrr7zy0PTp01cFBQUlOyrz3nvv/XX16lWvxx9//MnHHnsMFovF0KVLl/1Lly7d6ey5yo5SStatW/dj5cqVU3r37n110aJFEZcvXw78888/l7q7u6vu3btHffPNNx3CwsJqPfrooxcAoH///gfnzZvXMzIy8ueKFSumXLp0yfT77783GDNmzM8FbY8t9tTTbZeYmg6zUz312Y+QE5eUBh+bIPzIhRuZtrLNs6/sa85xBJyUNAvTXIiIKJPNmzcHnjlzJmjixIlHAMBsNlvatWt39KuvvmpuX7Zbt24ZQ2pXqFAhzmg0ploDegAIDAyMi42NzdQbXrdu3fMGm0Ej2rRpc+7atWsBKSkphTcUm529e/eWMxgMlkceeeSSdVmPHj2umc3mjH+S+/btq5iSkmIqX778yx4eHq9aH9HR0f4RERFlAaB58+Y3r1y58umwYcMuZLevsLCw8jNmzLh/2LBh20NDQ+fNnDlzybVr1/x79+7d11rm66+//rlSpUrR3bp1e97Dw+ON//znP306dep0SESyDQAGDRrU/7777vvr6aefPpNdmXnz5tVctmxZx+eff/6n1atXz3333XdXHDx4sO7QoUM75eV8ORIQEHC9cuXKGSNwlC1bNq5KlSpX3d3dM9rs6+sbFxUVlfF6T5ky5YhSyjBt2rSGADBt2rRGIqKmTJmSJXWoINg1SbddUqqzN8oCSimHN7vG2fWs5zS+eiU/My7HZh/Un74Wj1rlvbNdT0REpc+sWbPusVgshkaNGr1oXaaUgru7e/q5c+fWV6tWLaOX2NPTMyMnR0Tg5uaWKUdHRKCUKrJg3WY/SqnM8XBaWlqeeq3i4+NNZcqUiVuzZs0i+3VVqlRxbjZHAG+//XaH4ODgs4sWLdqlL4r09fX96cknn3zizz//3NKkSZO4evXqJRw+fHj59evX3U+fPu3ZtGnTmw899FD3wMDAmOzq/eeff2r9+eef9dzc3NoB2muilBI3N7c3x48fv3bGjBmHpk2b1uW+++77Y+bMmQcBLQUmLi7O9NFHH/VLS0vbaRuA55X9awsA7u7uWV5v228bgoKCklu0aPH3jz/+2GzGjBmH1qxZ06x169ZHK1asmPvwfHnAoJ5uu4SUdHg60VPv4e6G5DSLw179uOTMPfU5zXJayc8TxyOvZruf45E3UbeCywY0ICKiYiYpKcmwbdu2pk888cTGgQMHnrRdN2LEiKHTpk1r/Omnn+4vyD7Cw8ODbJ/v2bOnarly5aJNJlOuAaenp2c6AKSlpWW6UPDz84uPiorK+IeWkpIiFy5cqODn5xcBAC1btrxmsVgM33//fWVr+s3mzZsDk5KSzNZtWrZseWnp0qU+JpPJ0rZt2+v5Pb7k5GSjfQBsDX7t02v8/f3TmjdvfjMhIcGwa9euBh06dMi2B3v16tVf2l6oLF26tN6qVas6/PDDDwsaNmwYqx+30WAwZDqP1rbkdDNyUXriiScOPvPMMyH/+9//6p4+fbra1KlTNxX2PhjU022XmOpcTr2nyU0bKcdRUJ+UhjLmW2/fnGZQreyXffpNaroFX++OQEqaBWeiEzC2ax2m4hARlXIzZsyom5iYaH777bcP2vbIA0C7du3+XrNmTfOCBvUxMTF+ffr06TVx4sT927ZtqxwWFtb6mWee2eDMtvXr1483Go1poaGhdRo0aBDr6+ubVq1ateQ2bdqcnjt3bq/p06cHN23aNOa9995rm5iYmBGwd+/ePapBgwYnJk2a1C89PX2dyWSyTJkypbfRaMy4WXXMmDGnZs6cee6RRx4Z+vLLL29q2bJl1PHjx8usXr06eOjQof8MGTLk4qFDh8r06tVrxKxZs0KzS8Hp2rXr8Y8//rjfmDFjWgwbNuzkiRMnfN58883e1atXv9CsWbObALB06dKg06dP+3bu3Pnyv//+W+bDDz/srJSSTz75JOPeg2effbbV1q1b6//7779fA0CvXr0y3Vi6devWKiKiHnjggSvWZW3btj2+Zs2atm+88cbl3r17nz948GDAF1980bVp06b/Wi+aIiMjTbt27cr4Wv/kyZP+oaGhlYKCghJbtWp1A4XsqaeeOvPKK69ET506dUCFChWujRgx4lxh74NBPd12SSnp8PfK/qZVK0+jGxJS0uHvYE4o+/SbnJTz8cC1OIf30mD2lhP4PUL7lu/P89rfcHYXB0REVDosX768ef369U/ZB/QAMGzYsGNr165tv2bNmjwN8WivY8eOfyQlJbn37dt3pMFgUH369Nkza9asA85sazabLc8999zPixYtum/58uVdateufTY8PHzRtGnTDv3999+V3nzzzQEGg8EyYMCAPdevX4+w3XbFihU/Dhs2rH9ISMgTPj4+caNGjdoyb948P+t6g8GAX3/9demIESO6vfHGGw/Gx8d7+/j4xNWvX/9M7dq14wAgKSnJ7erVq4E3b97M9p/59OnTD9+8edP0ww8/tJo/f34vs9mc1KBBg9Nz5szJ6KFOSEhwnz17dte33367rMlkSmnatGn4ihUrQqtXr57RExcdHe119erVPM3E+PXXX+/4v//7PzVnzpyuH3zwQRlvb++EFi1a/Ltw4cIt1jI//vhjlVGjRo2wPl+4cGGvhQsXol27dn/89ttvq/OyP2cYDAb07t370NKlS7sNHjy4wDfsOiL2uVelgYj4Arhx48YN+Pr6uro5pc6i306jdgUfdAwun2O5/64/hiEtq6F2+axDTX6zOwLVArzQuV4Fp/b51KLfsSCkZZblw+fvwa6TURnP29UOxLcjOVM2EVFxFBsbCz8/PwDwU0rFFqQuERk8efLkwdOmTTtSOK1zXnBwcEjt2rUvh4WFhd3ufdOdoV+/fg3XrVv3sVLKes8CR7+h2y8x1eLUjbJmoxsSUxxPnnczOXP6TX41qeqf8bv9DbZEREREJQXTb+i2S0xJc2pISy89p96R+DyOK+9hNDjMz+9SrxwOn4uBQSTLDbZEREREJQWDerrtElOdG/3G0+iGxGyCevtx6nNTydcTl28koWa5zENXHr8Sj6c73IXuDQqUGklEROS08PDwRa5uA915mH5Dt11iajq8TLkH5NYbZR25mZzHoN7Pw+FY9ccuxeLuKryvgoiIiEo2BvV02zk7Tr25ENNvKvlpPfX2Ll1PRBU/s4MtiIiIiEoOBvV02yWlpsNsyv2t55nDjbLpFpWn8eQdjVVvsWiz1TqasZaIiIioJGFQT7ddSpqCyYmA3MuUfU59XlXyNePyjcRMy85GJ6B6gINB8ImIiIhKGAb15BLO9I6bc8ipz6uKvmZExmaeQ+TYpVg0qMx8eiIiIir5GNRTseVpzD6nPq9M7gakplsyLTt2KRZ3M6gnIiKiOwCDeiq2PE3Z59QXhuORcQiumHW2WiIiIqKShkE9FVvZ5dRbLAra/K9542aQTL31yWlZJ6MiIiIiKokY1FOxZc5m8qn4lDT4eOQ9GK/g64ErN7W8+huJqShjNha4jURERETFAYN6cgHlVKnshrSMT07P0xj1VpVtxqr/51Is6lcuk+c6iIiIiIojBvV0WynlXEAPAEY3yXJzKwDEJafCx5z3oF4b1lIL6nmTLBEREd1JGNTTbZWcZoGHk3ns2Q17eTMpDT6m/PTUm3FJH6v+2KWbHM6SiIiI7hgM6um2SU23YMbG49h3OhozNh132AufVdbAPj45PX899X63euqv3ExChTIeea6DiIiIqDjKe2RElE+zt5zA/J2noADM/iUcADCxR91ctsqarhOXnAqffOTUV/Iz43JsEtLSLXAziFMTYBERERGVBOypp9tm3+mojBBdAdgfEZ2vem4mpeUrqPcyuSMxJR0RUfGoGeidr30TERERuZpSKkvPJIN6ui3+vhiLCzGJGck0AqBFzYB81RWfnJav9JuMtly6yZtkiYgoNTEx0fnRG4iKCYvFguTkZABItV3O9BsqMqnpFszaHI4f/7gAT6Mblo1sje8OXMD+iGi0qBmAsV3rOFFL1hSZuOT89dQDgIh2gdG/aZV8bU9ERHeM6FOnTklSUpLBbDY7c5MXUbFw5MgRn9jY2AQAUbbLGdRTkZm95QQ+3XoCgBaaf3fgghM59FkppTLlv98sQFAf6O2Bfaej8tUOIiK6o/x99uzZmO3btwf06tXrmqsbQ+Ss7du3V7hw4cJZAGdslzP9hoqMbc58fnPoPdwNSE7L3IFSkPSbSn5mJKZaYHLnW5+IqDRTSl09ffr07/Pmzaty9OhR3mhFJcL69evL//DDD6YLFy7sUEplmqGTPfVUZFrUDMCuk9o3Q/nNoTcb3ZCUmg6zzdj2cfm8UTY13YIjF27g6s0kzNh0HGO71oHRjcE9EVFpFRcX982mTZtMMTExHe+9915D06ZNb/j5+aW4ubkx156KjbS0NMPVq1c99u/f73vo0KHEAwcOrASw2b6cU5GRiPTPRxs2KaUS87Ed3SHGdq2DtYcvoLK/Zx5y6DPzNBmQmJoOf5tl8Snp8M7H5FOzt5zAln+u5HFITSIiulMppRJF5MutW7fu3bNnT+MKFSo0NplMHiLCHh8qNiwWiyU5OfnqxYsXN6Snpx8F8LdSKst9IM5GRqvzuH8FIBjAqTxuR3cQi1KoX9kXcx67N991WIehtKWUgsGQ9zHm90dEF8qQmkREdOdQSiUB+B3A76LdwGWEo1EaiFwnXSmVlluhvHR3VlJKXXGmoIjczEO9dIeKiU9FWW9TgeowG92QYBfU51eLmgHYfVIbK78gQ2oSEdGdSSmlAKS4uh1E+eFsUL8YQF5SaZYAiM17c+hOEhWfjMACBvWeek59YbCm/+RtSE0iIiKi4s+poF4p9UReKlVKjc5fc+hOEhOfirJeBQ3qtZz6wmB0MzCHnoiIiO5IxepGEBF5WEQ2ikiUiCgRaeagjFlEPtPLxInIDyJS0QXNpVxEJ6Qg0KdgQb2jnHoiIiIiyizPQ4iIiBnAWABdAFSA3YWBUuqeArTHG8CvAL4DMD+bMh8DeADAIAA3AHwKYBWA9gXYLxWB6Lhk3FXep0B1mE1umXrq09ItMAjvXyIiIiKylZ9x6hcA6AngewD7ABTaWK5KqW8AQERqOlovIn4AngIwXCm1RV/2BIBjItJGKbUnm+08AHjYLCpTWG2m7EUnpKJFIeTUR8UlZzyPT07P98RTRERERHeq/ERHfQH0UUr9VtiNccK90IaayhhwXyn1j4icBdAWgMOgHsArAKYWffPIVnR8MgIKIai37am/mZyar4mniIiIiO5k+cmpvwDAVUNWVgKQopS6brc8Ul+XnfcB+Nk8qhZJ6yiTmPjUggf1psw3ysYnpzOoJyIiIrKTn6D+RQDTRKRGQXYsIo/qN7paHx0LUl9OlFLJSqlY6wOuuygpVZJS02E2uhWoDk+jO5JsbpSNS05l+g0RERGRnfxER/sBmAGcEpEEAKm2K5VSzs7oswbAXpvnF5zY5jIAk4j42/XWV9TX0R3G05R58qk49tQTERERZZGf6GgZgCAAr0JLe8nXjbJKqZvIe4/5AWgXEd0A/AAAIlIPQHUAu/PTDire7HPq45LSGNQTERER2clPdNQOQFul1B+F3RgRCYAWoFfRF9UTbfjCy0qpy0qpGyKyAMAMEYmGNmvtbAC7sxv5hlxDKYXCGHkyS1CfnAr/Ak5oRURERHSnyU9O/T8APAu7Ibr+AA4B+El/vlx//qxNmQkA1kHrqd8BLe3m4SJqD+VTbFIafM3GAtfjaXJDUmrm9Jsy7KknIiIiyiQ/0dEUAB+JyGsA/kLWnPrY/DZGKbUIwKJcyiQBGKM/qJiKjk9B2QKOfAMARjdBStqtDK+4pDTeKEtERERkJz/RUZj+8xe75QItv75gw53QHSE6PqXAw1kCgJZ+ZRPUJ6fCmz31RERERJnkJzrqUuitoDtOYQX19ph+Q0RERJRVnqMjpdT2omgI3Vli4lNQtghuaI1LZvoNERERkT2nbpQVkSYi4vRNtSLSUEQYeZViUfEpCPQprKD+1jA6iSlp8CzghFZEREREdxpnA/VDAALzUO9uaENTUikVk1CYPfWZp0KQwhgrk4iIiOgO4mxvugD4jz6DrDM4kHgpFxWXgsAiyKknIiIioqycDep3AKiXh3p3A0jMe3PoTnEjMRV+ngUfp95Km8yKPfREREREjjgV1CulOhdxO+iOo2AwFE4Q7uHuhpR0CzzcmUtPRERE5Eh+ZpQluq3MRjckpVhc3QwiIiKiYotBPRV7niYDElLTkJyWDqMb37JERERE9jjsJBW6wg6+PY1uSExJR7x7Onw48RQRERFRFoyQqNBdT0hF2UIc+cbT6IbE1HS4J6XBm0E9ERERURaFGiGJiFEplVqYdVLJExWXgoBCnE3W0+SOpNR0CARlOJssERERURZ5mSX2GxHxzWF9C2iTVFEpF5OQUsg99QYkpKQjLjmN6TdEREREDuQl8bkBgL9FpJftQhExisj7AHYB+LUwG0clU1R84U485WnScurjklOZfkNERETkQF6C+tYAFgJYKyJzRcRH750/DGA4gL5KqWeLoI1UwsTEF25PvVnPqY9LTmf6DREREZEDTgf1Sqk0pdSbANoBaA/gOLTe+T0AGiulNhZNE6mkKeyeei89pz4uKQ3eJgb1RERERPbyM+5gEoBUAH4AUgBsVUrFFmqrqEQr7J76jCEtk9Pgw556IiIioizycqOsiMgrAPZDS7kJAjAZwBwRWSUi5YumiVTSRCcU9ug3BiSkpuMmb5QlIiIicigvPfV7AIwFMEgp9YRS6rpS6nMATQGUg3YT7ZCiaCSVLMmp6fA0uRVafWajG5JStPQbBvVEREREWeUlqD8NoJFSaq3tQqXUKQD3AfgvgAWF2DYiAFpOfWIq02+IiIiIspOXG2WHKqWis1mnlFIfA2heaC0j0nlmjH7DnnoiIiIiR/Jzo2y2lFLhhVkflTxKqUKv09PohoSUdCSnpcPDvVDfskRERER3BEZIVKhik9JQxmws1DrNJgOSUtMBACJSqHUTERER3QkY1FOhiolPQdlCHPkGAExuBqSkWQq1TiIiIqI7CYN6KlRR8SkI9CncoF7rnWcPPREREVF2GNRToSqKnnpN4efqExEREd0p8jWUiIh4QxvGsjqATBGcUuqTQmgXlVDRCSkIKMTZZK2UAphOT0RERORYnoN6EWkOYD0ALwDeAKKhTT6VAOAKAAb1pVh0fApqBnoXer3JaRb4eRXuDbhEREREd4r8pN98DGAtgLIAEgG0AVADwAEAkwqvaVQSxcQXTU99QkoaynCMeiIiIiKH8hPUNwPwkVLKAiAdgIdS6hyAydBmlaVSLKqIgnqTuwHeDOqJiIiIHMpPUJ8KwDq+4BVoefUAcANAtcJoFJVcNxJT4edZ+GkynkY3ziZLRERElI38REmHALQEEA5gO4B3RKQcgMcBHCnEtlEJZLEouBkK/45WT5MbypgZ1BMRERE5kp+e+lcBXNJ/fw1ADIA5AMoDeKaQ2kWUidnoxvQbIiIiomzkOUpSSu23+f0KgN6F2iIiB7xMTL8hIiIiyg6jJCo0KWkWGN0Kfz6z1HQL/r4YiwNnYhB+JQ5ju9Ypkv0QERERlVROBfUicghOTumplLqnQC2iEut6QgrKFsHIN7O3nMDBs9cBAP9cugkAmNijbqHvh4iIiKikcranfnVRNoJKvtR0C2ZuDseO41cxY9PxQu1N3x8RnfG7sntORERERE4G9Uqpt4u6ISJiBPAugD4A7oI2ROZmAFOUUhdtygUAmA2gH7ShNX8AMF4pFVfUbaTszd5yAsv2nYUCMPuXcACF15veomYAdp+MggIg+nMiIiIiuiXfOfUici+Au/WnR5VShwrYFi8A9wD4D4A/oM1YOwvAGgAtbMotBVAZQA8ARgBfAZgHYHgB908FsD8iOiM/q7B708d2rZOxjxY1AzKeExEREZEmz0G9iFQAsBxAZwDX9cX+IrIVwFCl1NX8NEQpdQNaoG67r+cB7BOR6kqpsyJyN7TRdlpaR+ERkbEA1ovIJNsefbq9WtQMwK6TUQAKvzfd6GZgDj0RERFRDvKT9DwbQBkADZVSAUqpAACNAPgC+KQwGwfAD1rH73X9eVsA122H1YSWomMB0Dq7SkTEQ0R8rQ+9/VSIxnatg1Y1y6JpVT+M7RbM3nQiIiKi2yg/6Te9AXRXSh2zLlBK/S0iYwBsLKyGiYgZwDQAy5RSsfriSgCu2JZTSqWJSLS+LjuvAJhaWG2jrIxuBgRXLIPnu9ZBZT9PVzeHiIiIqFTJT0+9AUCqg+WpealPRB4VkTibR0ebdUYA30HL5Bidjzbaex9ar7/1UbUQ6iQ70fEpCCiCIS2JiIiIKGf56anfAmCWiAyz5rCLSBCAjwH8kod61gDYa/P8gl6XNaCvAaCrTS89AFwGUMG2EhFxBxCgr3NIKZUMINlmmzw0k5yVmm6Bh7ubq5tBREREVOrkJ6h/HlpAHiEi5/Rl1QAcAfCYs5UopW4CuGm7zCagDwbQRSkVZbfZbmg35d6rlDqgL+sK7RuCvSAiIiIiKoXyHNQrpc6JyD0AugOory8+ppTaXJCG6AH999CGtewLwE1ErHny0UqpFKXUMREJAzBfRJ6FNqTlpwCWc+QbIiIiIiqt8jVOvVJKAdikPwpLEID++u+H7dZ1AbBN//1RaIH8L7g1+dS4QmwH5UO6RTGtiYiIiMhF8hXUi0g3ABNwa/KpYwBmFqS3XikVAe3G2NzKRYMTTRU71xNS4O9pdHUziIiIiEqlPI9+IyLPAQiDlg8/S3/EQpsAakzhNo9Kiuj4FAT4cOQbIiIiIlfIT0/9qwAmKKU+tVn2iYj8pq/7rFBaRiVKVHwKAjmcJREREZFL5Gecen9oPfX2NkIbA55KIW2Meg9XN4OIiIioVMpPUL8GwAAHyx8EsK5gzaGSij31RERERK7jVPqNiNiOLvM3gNdEpDO0ceMBoA2A9gA+KszGUckRHZeCJkH8ooaIiIjIFZzNqZ9g9zwGQAP9YXUdwJMA3i14s6ikiY5PRgB76omIiIhcwqmgXilVq6gbQiVbVHwKAjn6DREREZFL5CenniiLpNR0eJnyNe0BERERERVQfiefqgpt9tfqADJ1zyqlJhZCu4iIiIiIyEl5Dur12WTXADgFoD6AIwBqQpsN9mBhNo6IiIiIiHKXn/Sb9wFMV0o1BpAEYCCAagC2A1hZiG2jEkIp5eomEBEREZVq+Qnq7wbwtf57GgBPpVQcgDcBvFxYDaOSIzYxDb5mo6ubQURERFRq5Seoj8etPPpLAGrbrCtX4BZRiRPF4SyJiIiIXCo/N8ruAdABwDEA6wF8JCKNATysr6NSJjo+BQEczpKIiIjIZfIT1E8E4KP/PlX/fQiAcH0dlTJR8SkIZE89ERERkcvkKagXETcAVQH8CQBKqXgAzxZBu6gEiY5PQTkfD1c3g4iIiKjUylNOvVIqHcBGAGWLpjlUEkXHpzCnnoiIiMiF8nOj7BEAdxV2Q6jkiopj+g0RERGRK+UnqH8dwHQR6SsilUXE1/ZR2A2k4i86Ppk3yhIRERG5UH5ulF2v/1wDwHbWIdGfuxW0UVSyxCWnoYxHft5KRERERFQY8hOJdSn0VlCJJyKubgIRERFRqZXnoF4ptb0oGkJERERERPmT75wJEfECUB23ZpcFACil/ixoo6jkUErlXoiIiIiIilSeg3oRKQ/gKwD3Z1OEOfWlSHxKOrxMzKcnIiIicqX8jH4zE4A/gNYAEgH0BjAC2oyy/QurYVQyRMdxjHoiIiIiV8tPF2tXAA8qpfaLiAXAGaXUJhGJBfAKgJ8KtYVUrEXFJ3OMeiIiIiIXy09PvTeAK/rvMQDK67//BeCewmgUlRzR8Skco56IiIjIxfIT1P8LoJ7++x8ARolIEIBnAVwqrIZRyRAVz9lkiYiIiFwtP+k3swBU1n9/G0AYgEcBpAAIKZxmUUkRHZ+CmoHerm4GERERUamWn3Hql9j8fkBEagCoD+CsUupaYTaOir/oeN4oS0RERORqBRqLULRpRBOVUgcLqT1UwkTFMf2GiIiIyNXyk1MPEXlKRI4ASAKQJCJHROTpwm0alQQ3ElPh52l0dTOIiIiISrX8TD71DoCJAGYD2K0vbgvgYxGprpR6sxDbR8WegsEgrm4EERERUamWn/Sb0QBGKqWW2SxbIyJ/Qgv0GdQTEREREd1G+Um/MQLY72D5ARQwR5+IiIiIiPIuP0H9N9B66+09A2BpwZpDJUlSajo83N1c3QwiIiKiUi+/PetPiUhPAHv0560BVAfwtYjMsBZSSk0sYPuoGIvicJZERERExUJ+gvpGAKxDWNbWf17TH41syqkCtItKgOg4BvVERERExUF+Jp/qUhQNAQAReQvAUADVoM1QewDAa0qpvTZlAqDdkNsPgAXADwDGK6Xiiqpd5FhUfDICfRjUExEREblavsapL0LHATwPoDGADgAiAGwUkfI2ZZYCaAigB4C+ADoBmHd7m0kAZ5MlIiIiKi6KVVCvlPpWKbVZKXVKKXUU2nj4vgCaAICI3A2gN4CnlVJ7lVK/AhgLYKiIVHFZw0spBvVERERExUOxCuptiYgJ2og6NwD8oS9uC+C6Usp2SM3N0NJwWudQl4eI+FofAMoUUbNLlaj4FAR6e7i6GURERESlXrEL6kWkr4jEAUgCMAFAD6XUNX11JQBXbMsrpdIAROvrsvMKtIsD6+N8Ybe7NOKNskRERETFg8uCehF5VETibB4d9VVbATQD0A5AGIDvRKRCAXf3PgA/m0fVAtZX6qWmW/B7RDTGLTuIGZuOIzXd4uomEREREZVarpwBdg2AvTbPLwCAUioewAn9sUdEwgE8BS0wvwwgU4AvIu4AAvR1DimlkgEk22xTOEdQis3ecgKnrsXj1LV47DkVDQCY2KOui1tFREREVDq5LKhXSt0EcNOJogYA1sTt3QD8ReRepdQBfVlXvcxeRxtT0dh3OirjdwVgf0S06xpDREREVMq5sqc+ExHxBvAatB78SwDKARgDIAjASgBQSh0TkTAA80XkWQBGAJ8CWK6UuuiSht9hUtMtmL3lBPZHRKNFzQCM7VoHRresWVqV/TwzfhcALWoG3MZWEhEREZGtYhPUA0gHUB/ACGgBfRSA3wF01Ie3tHoUWiD/C25NPjXu9jb1zjV7ywnM/iUcCsDuk1pvvKO0Gj+zEY+1ro5T1+Izgn8iIiIico1iE9QrpZIAPOxEuWgAw4u+RaXT/ohoKP337NJqlFI4eS0OXz/ZivcnEBERERUDxW5IS3KtFjUDYA3Ts0urOXUtHneV82ZAT0RERFRMFJueeioexnatg78v3sDJq/FwN4jDtJqt/1xB17sruqB1REREROQIe+opE6ObAdUDvPHliBaoXd4H8clpWcrsPhmF1rV4YywRERFRccGgnrI4ExWPmoHe6Ne0Ctb+kXlQodikVHgYDTAb3VzUOiIiIiKyx6CeMlFKwaIU3AyCbndXwOZjVzKt33n8GjoFl3dR64iIiIjIEQb1lMmlG0mopI9Bbza6IaisJ05ejctYv+WfK+hSv0J2mxMRERGRCzCop0yOXYpFg8plMp4PvKcqVh08DwBItyhcjUtGRV+zq5pHRERERA4wqKdMjl2KRYMqvhnP76nuj8PnrsNiUfjj/HU0q+rnwtYRERERkSMM6imTY5dvol6lW0G9iKBNrUDsORWFLcc4lCURERFRccRx6imThOQ0+Hhkflv0a1oZo5cexKXrSRABGlbxhdGN14NERERExQWDesqQkJIGT1PWoSpXHbqIY5duAgA+3XICIoKJPere7uYRERERUTbY3UoZ/rl8E/VtUm+s9kdEZ/yu7J4TERERkesxqKcMxy7F4u7KWYP6FjUDIPrvoj8nIiIiouKD6TeU4e+Lsbivc9aJpcZ2rQNA66FvUTMg4zkRERERFQ8M6inDxeuJCPL3zLLc6GZgDj0RERFRMcb0GwIAWCwKIgIRyb0wERERERUrDOoJAHA2OgHVA7xc3QwiIiIiygcG9QQA+PtSLBo4uEmWiIiIiIo/BvUEIPuRb4iIiIio+GNQTwCA45E3EVzRx9XNICIiIqJ8YFBPAICUNAvMxqyzyRIRERFR8cegnnA9IQV+nkZXN4OIiIiI8olBfSmXmm7BO+v+xqGz1zFj03Gkpltc3SQiIiIiyiNOPlXKzd5yAqsOXtB+/yUcADjRFBEREVEJw576Um5/RHTG78ruORERERGVDAzqS7kWNcpm/C4AWtQMcF1jiIiIiChfmH5TyvVpXAk7wq/By+SGFjUDMLZrHVc3iYiIiIjyiEF9Kbf/zHWM7lwbvRpWcnVTiIiIiCifmH5Tyv0eEY1WTLkhIiIiKtEY1JdiSilcT0hFWW+Tq5tCRERERAXAoL4Ui4hKQM1AL1c3g4iIiIgKiEF9Kbb3VBTa3BXo6mYQERERUQExqC/F9p6ORqtazKcnIiIiKukY1JdSSilExacg0MfD1U0hIiIiogJiUF9KnYtORPUAT1c3g4iIiIgKAYP6UmrP6Si0rsV8eiIiIqI7AYP6Umrf6Wi0vov59ERERER3Agb1pdTVm8moUMbs6mYQERERUSFgUF8KXbieiCr+DOiJiIiI7hTFNqgXkS9ERInIC3bLA0RkqYjEish1EVkgIj4uamaJtPcU8+mJiIiI7iTFMqgXkQEA2gC46GD1UgANAfQA0BdAJwDzbl/rSq7UdAtmbDqOD37+B3+ev47UdIurm0REREREhcDd1Q2wJyJBAGYD6AXgJ7t1dwPoDaClUmq/vmwsgPUiMkkp5egiACLiAcB2QPYyRdH24m72lhOY/Us4FICvfouAj9mIiT3qurpZRERERFRAxaqnXkQMAL4B8D+l1FEHRdoCuG4N6HWbAVgAtM6h6lcA3LB5nC+cFpcs+yOiofTflf6ciIiIiEq+YhXUA3gZQBqAT7JZXwnAFdsFSqk0ANH6uuy8D8DP5lG1wC0tgVrUvDWEpdg9JyIiIqKSy2XpNyLyKIC5NoseADAewD1KKeV4q/xRSiUDSLbZd2FWX2KM6VwboQfPo1qAF1rUDMDYrnVc3SQiIiIiKgSuzKlfA2CvzfNBACoAOPv/7d17lJ1lfejx7y/3kgAxhFu4BQl3kUsSkLtAUXparXDskUKt2C49WgGpsupde+TYYlWwBnqWIEWOZS04lYtQEZREWwUMhHCNBBIkXDIkgQy5Qchl5nf+eN8ZdjaTMJPsy+w9389as2a/z/Ps9/3tJ2vt/ObZv/fZFUn3cOA7EXFRZk4GlpRjekXECGBC2actmPvcCj44dS8+/Yf7NzsUSZIk1VDTkvrMXA2s7jmOiKuA26uG3UVRY39teXwfMD4ipmbmg2XbqRRlRLPRFt089wUuPM2EXpIkqd0Mmt1vMnM5sLyyLSI2AEsy88lyzBMRcSdwdUR8AhgJXAHcsLmdb1R4bf1Glr+6nr0mbNfsUCRJklRjg+1G2f44F5gPzATuAH4DfLypEbWAu+Yt4YxDt3QvsSRJklrVoFmp70tZR1/d1gmc0/hoWttPH13C5R86vNlhSJIkqQ5acaVeA/TiyrXsMGYE248Z2exQJEmSVAcm9UPALQ8t5gNH7tHsMCRJklQnJvVtLjO5d+Fyjp8ysdmhSJIkqU5M6tvYhq5uPn/TY/z+5TX888wFbOjqbnZIkiRJqoNBfaOsts2MWQu5cc7zxeOZCwD4zOkHNDMkSZIk1YEr9W3s/mfe2PY/gTmLOpsXjCRJkurGpL6N7TR2dO/jAKZNntC8YCRJklQ3lt+0se5MPnbivszrWMW0yRO44NQpzQ5JkiRJdWBS36Y6X11PBHzpjw9pdiiSJEmqM8tv2tTtj3Tw/sMnNTsMSZIkNYBJfZuaOX8Zpxy0S7PDkCRJUgOY1LehBUtXs8+E7Rg9YnizQ5EkSVIDmNS3oZvmLuaso/ZodhiSJElqEJP6NtPVnTy2eAVH7DW+2aFIkiSpQUzq28y9T7/McftNJCKaHYokSZIaxKS+TWzo6uayXzzFZ258mOVr1rGhq7vZIUmSJKlB3Ke+TcyYtZAZMxeQwLX3LGLcmJF85vQDmh2WJEmSGsCV+jYxZ1EnWT7O8liSJElDg0l9m5g2eULv46g6liRJUnuz/KZNXHDqFH7y0GImjf8Dpu87gQtOndLskCRJktQgJvVtYt3Gbg6ZtAP/5y+mNjsUSZIkNZhJfQvY0NXNjFkLmbOok2mTi1X4kcM3rZzq6ZMkSdLQY1LfAip3trnv6eUAb9rZZvYznfzxYbs3ITpJkiQ1mzfKtoD+7Gwz/8VVHLz7Dg2NS5IkSYODSX0L2G7U8N7Hfe1s89r6jYweMZzhw/wWWUmSpKHIpH6QW7hsNd2ZXHDqFPbbeSzvOXTXN+1sM/fZFRy1z/jmBChJkqSmM6kfxF7f0MXXbpvHpWe9k8++50Bu+dTxdCdvukl29jPLOWbfnZoUpSRJkprNpL7JNnR1c9kvnuKcq3/LZb94ig1d3b1tJ3/rl0wcN5q3jR0FwA5jRjJ21HCWrHx9k3PM61jFoZOsp5ckSRqq3P2myap3tlm7fiMAV//6GQBue7iDfXYa27vbzQeO3INbH17MJ07eDyhW80cMC0YM9+8zSZKkocpMsMmqd7b5ycMd/OThjt7+6t1uTpgykd8seJnM4lkPPbeCI/Ye37B4JUmSNPiY1DfZtH3e1vs4gLOP3puzj96bqGir3O1mxPBhHDJpBx5fvAqwnl6SJEmW3zTdiftP5J6nlzN6xLDeb4vtUfkNspXOOmoPbrj/eQ7bc0ceX7yST50ypfq0kiRJGkJM6pvstkde5NKzDmP/XbffpL36G2MrHbTbDjz90hpeXbcRiDfthiNJkqShxWywidZt7OLZztfelND3x0n778yHr5nNk0tX9e6aI0mSpKHJlfommvXEMk47aJeteu6y1a8z97kVAMyYuQDY8uq+JEmS2pcr9U10+6MdvO/wSVv13Hkdq3ofV++QI0mSpKHFpL5Jlq9ZRxBMKL9YaqCmTZ6w2R1yJEmSNLRYftMktz3SwfuP2LpVeqB3R5zN7ZAjSZKkoWNQJfUR8UPgI1XNd2XmGRVjJgAzgPcB3cBNwKczc02j4qyFWfOXcc1Hpm/180cOH2YNvSRJkoBBltSX7gQ+WnG8rqr/emB34HRgJHAtcBVwTkOiq4Enl6zm7RPHMmqE1U+SJEnadoMxqV+XmUv66oiIg4EzgOmZOadsuwC4IyIuzsyOBsa5iQ1d3cyYtXCTcpjq/eN7xvz4wec5af+d2dDV7R7zkiRJ2maDMal/d0QsA14BZgFfzszlZd+xwIqehL50N0UZzjHALX2dMCJGA6Mrmga+MfxbmDFrITNmLiCB+54uwq0uj6kcc+MDz7PLDmMsoZEkSdI2G2zLxHcCfwmcBnwOOBn4WUQML/t3A5ZVPiEzNwKdZd/mfAFYWfHzQm3DLm5YzZ6Y6HuLyf6MkSRJkgaqaUl9RJwbEWsqfk7MzBsy87bMfCwzbwX+BJgOvHsbL/ePwI4VP3tu4/nepHKLyZ7jvsb0cBtKSZIk1Uozy29uA2ZXHC+uHpCZv4+Il4EpwExgCbDJV7BGxAhgQtnXp8xcR8UNtxGxuaFbrWdLyfufWc6Sla/3ucXkecftw3880sFuO45xG0pJkiTVTNOS+sxcDaze0piI2BPYCXixbLoPGB8RUzPzwbLtVIpPHGb3cYqGqdxi8vM3PcriV9YyeeLYTcbc/btlXHDaFM48suYfFEiSJGkIGzQ19RExLiK+FRHviojJEXEa8BNgIXAXQGY+QVF3f3VEHB0RxwNXADc0c+ebamcdtSc3P/SmDx64a94S3nvolkr/JUmSpIEbNEk90AW8k6Is5yngGuBB4MSyfKbHucB8inKcO4DfAB9vbKhbNn3y23jw2U66u7O37fnO19hp3Ci2GzUYNxySJElSKxs0GWZmrgXe249xnQzyL5qKCKbtM4H7F3XyrrfvBMDNcxdbdiNJkqS6GEwr9W3lvx+1J7fMLUpwMpMHFnVyzL7udiNJkqTaGzQr9e1m7522Y9nq11m7vot5HSs5cu/xDBtW+113JEmSJJP6Onrvobvx898t4be/7+TjJ7292eFIkiSpTVl+U0enH7orl/38KX722Ivc8tBiNnR1NzskSZIktSFX6uvounuf5dnO1wCYMXMBQO9e9pIkSVKtuFJfR3MWdfY+zqpjSZIkqVZM6uto2uQJ9NwaG+WxJEmSVGuW39TRBadOAYoV+mmTJ/QeS5IkSbVkUl9HI4cPs4ZekiRJdWf5jSRJktTiTOolSZKkFmdSL0mSJLU4k3pJkiSpxZnUS5IkSS3OpF6SJElqcSb1kiRJUoszqZckSZJanEm9JEmS1OJM6iVJkqQWZ1IvSZIktTiTekmSJKnFjWh2AM20atWqZocgSZL6yf+3pc2LzGx2DA0XEXsALzQ7DkmStFX2zMzFzQ5CGkyGalIfwCRgdY1PvT3FHwt71uHc2pRz3VjOd+M4143jXDdOLed6e6Ajh2ICI23BkCy/Kd8Iav4XfvG3AgCrM9PPCOvIuW4s57txnOvGca4bp8Zz7b+V1AdvlJUkSZJanEm9JEmS1OJM6mtrHfC/yt+qL+e6sZzvxnGuG8e5bhznWqqzIXmjrCRJktROXKmXJEmSWpxJvSRJktTiTOolSZKkFmdSL0mSJLU4k/oaiohPRcSiiHg9ImZHxNHNjqnVRcQXIuKBiFgdEcsi4taIOLBqzJiIuDIilkfEmoi4KSJ2bVbM7SAiPh8RGRHfrWhznmsoIvaIiH8r53NtRDwWEdMq+iMivh4RL5b9d0fE/s2MuRVFxPCIuCQininn8emI+EpUfBuSc711IuKkiLg9IjrK94sPVPW/5bxGxISIuD4iVkXEioi4JiLGNfSFSG3CpL5GIuJDwGUUW3YdBTwC3BURuzQ1sNZ3MnAl8C7gdGAk8POIGFsx5nLgfcCfleMnATc3OM62ERHTgf8JPFrV5TzXSES8DbgH2AD8EXAI8FnglYphfwdcCHwCOAZ4leI9ZUxjo215nwM+CZwPHFwe/x1wQcUY53rrjKX4v+5Tm+nvz7xeDxxK8f7+J8BJwFX1ClhqZ25pWSMRMRt4IDPPL4+HAc8DMzLz0qYG10YiYmdgGXByZv5XROwIvASck5k/LsccBDwBHJuZv21etK2nXCGbC/wN8GXg4cy8yHmurYi4FDg+M0/cTH8AHcB3MvPbZduOwFLgvMy8oWHBtriI+A9gaWb+dUXbTcDazPwL57o2IiKBMzPz1vL4Lec1Ig4GfgdMz8w55ZgzgDuAPTOzo/GvRGpdrtTXQESMAqYCd/e0ZWZ3eXxss+JqUzuWvzvL31MpVu8r534+8BzO/da4EvhpZt5d1e4819b7gTkR8e9lWdlDEfGxiv59gd3YdL5XArNxvgfqXuC0iDgAICIOB04Aflb2O9f10Z95PRZY0ZPQl+4GuilW9iUNwIhmB9AmJgLDKVYgKi0FDmp8OO2p/PTju8A9mfl42bwbsD4zV1QNX1r2qZ8i4myK0rHpfXQ7z7X1doqSkMuAf6CY8+9FxPrMvI435rSv9xTne2AuBXYA5kdEF8V79Zcy8/qy37muj/7M624Un7z2ysyNEdGJcy8NmEm9WsmVwDsoVtlUQxGxF/DPwOmZ+Xqz4xkChgFzMvOL5fFDEfEOitrj65oXVlv6H8C5wDnAPOAI4LsR0VH+ASVJbcHym9p4GegCqncC2RVY0vhw2k9EXEFxE9UpmflCRdcSYFREjK96inM/MFOBXYC5EbExIjZS3Ax7Yfl4Kc5zLb1IUUtc6Qlg7/Jxz5z6nrLtvgVcmpk3ZOZjmfkjipu+v1D2O9f10Z95XULxvtMrIkYAE3DupQEzqa+BzFwPPAic1tNWloqcBtzXrLjaQbkl2hXAmcCpmflM1ZAHKXYQqZz7AymSI+e+/2YCh1GsYvb8zKHYmaLnsfNcO/cAB1a1HQA8Wz5+hiKpqZzvHSjqjJ3vgdmOoka7Uhdv/P/nXNdHf+b1PmB8REyteN6pFP82sxsUp9Q2LL+pncuA6yJiDnA/cBHFdl/XNjOoNnAlxcfmfwqsjoieOsuVmbk2M1dGxDXAZWUd5ipgBnCfO7L0X2auBh6vbIuIV4HlPfcvOM81dTlwb0R8Efh/wNHAx8sfMrPnOwK+HBELKBKkSyh2E7m1GQG3sNuBL0XEcxTlN0cCnwH+FZzrbVHuljWlomnfiDgC6MzM595qXjPziYi4E7g6Ij5BcTP+FcAN7nwjDZxJfY1k5o3ldotfp7jB52HgjMysvklIA/PJ8vevqto/CvywfPy3FCtxNwGjgbsotmRUbTnPNZKZD0TEmcA/Al+lSHguqrh5E+CfKBYGrgLGA7+heE/xnoeBuYAimfwXilKPDuD7FO/VPZzrrTMN+GXF8WXl7+uA8+jfvJ5LkcjP5I33lwvrGbTUrtynXpIkSWpx1tRLkiRJLc6kXpIkSWpxJvWSJElSizOplyRJklqcSb0kSZLU4kzqJUmSpBZnUi9JkiS1OJN6SZIkqcWZ1EvqFRG/Kr/afVCJiIyID9T5Gn8fEQ/X4DyLIuKibY9IkqT+M6mXVOks4Cs9B41OULeQWO8O/KzOl/82cFqdryFJUl2MaHYAkgaPzOysx3kjYlRmrt/a52fmklrGs5lrrAHW1Ps6kiTVgyv1knpVlt9ExK+AfYDLy/KXrBh3QkT8OiLWRsTzEfG9iBhb0b8oIr4SEf83IlYBV5Xt34yIpyLitYj4fURcEhEjy77zgK8Bh/dcr2x7U/lNRBwWEbPK6y+PiKsiYlxF/w8j4taIuDgiXizHXNlzrc289k0+JejPOSJil4i4vYzjmYg4t4/zjo+IH0TESxGxqoz78LJv54hYEhFfrBh/XESsjwg/NZAk9ZtJvaTNOQt4AfgqRfnL7gARsR9wJ3AT8E7gQ8AJwBVVz78YeAQ4ErikbFsNnAccAnwa+Bjwt2XfjcB3gHkV17uxOqjyj4e7gFeA6cCfAX/Yx/VPAfYrf3+kvO55/X71/TvHD4G9yv4PAn8D7FJ1jn8v2/4ImArMBWZGxITMfAn4K+DvI2JaRGwP/Ai4IjNnDjBWSdIQZvmNpD5lZmdEdAGrq8pfvgBcn5nfLY8XRMSFwH9GxCcz8/WyfVZmfqfqnP+74nBRRHwbOBv4p8xcGxFrgI1vUW5zDjAG+MvMfBUgIs4Hbo+Iz2Xm0nLcK8D5mdkFzI+In1LUzF89gGnY7Dki4gCKRP3ozHygjOOvgSd6nhwRJwBHA7tk5rqy+eLyU4cPAldl5h0RcTVwPTAHeJVijiVJ6jeTekkDdTjwzqpSk6D45G9f3khq51Q/MSI+BFxIsfo9juI9aNUAr38w8EhPQl+6p7z+gUBPUj+vTMZ7vAgcNsBrbekcBwMbgQd7OjNzfkSsqBh/OMXrXB4Rlef9A4o56HEx8DjFpw5TK/4AkCSpX0zqJQ3UOOD7wPf66Huu4nFl0k1EHEuxGv01ivKZlRSr9J+tT5hsqDpOBl5yuK3nGEfxh8C7++hbUfF4P2BSee7JwGMDuIYkSSb1krZoPTC8qm0ucEhmLhzguY4Dns3Mb/Q0RMQ+/bhetSeA8yJibMVq/fFAN/DkAGPaFvMp3kOnAj3lNwcC4yvGzAV2oygpWtTXSSJiFPBvFPcPPAn8ICIOy8xldYtcktR2vFFW0pYsAk6KiD0iYmLZ9k3guIi4IiKOiIj9I+JPI6L6RtVqC4C9I+LsiNivrMM/s4/r7Vued2JEjO7jPNcDrwPXRcQ7IuIUYAbwo4p6+rrLzCcpbhj+fkQcExFTgR8AayuG3Q3cB9waEe+JiMnl7jbfiIhp5ZhvADtSlCV9E3gK+NdGvQ5JUnswqZe0JV+lKAd5GngJIDMfBU4GDgB+DTwEfB3o2NKJMvM24HKKXWoepli5v6Rq2E0UifIvy+v9eR/neQ14LzCBYoX8x8BM4PwBv7pt91GK1/2fwM0UW3f2rrBnZgL/Dfgv4FqKhP0Giq1Cl0bEu4GLgA9n5qrM7AY+DJwYEZ9s2KuQJLW8KP7PkSRJktSqXKmXJEmSWpxJvSRJktTiTOolSZKkFmdSL0mSJLU4k3pJkiSpxZnUS5IkSS3OpF6SJElqcSb1kiRJUoszqZckSZJanEm9JEmS1OJM6iVJkqQW9/8B2siFEjP07eIAAAAASUVORK5CYII=",
      "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 140266250386496>"
      ]
     },
     "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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       "  --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:                             20240829-164603-980-1123d1\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-489f4d12-61fc-4ed4-9892-e20b3142e5af' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-489f4d12-61fc-4ed4-9892-e20b3142e5af' 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-657c3603-5d74-4922-bb27-6aa76032f1eb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-657c3603-5d74-4922-bb27-6aa76032f1eb' 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-374bc264-6e42-4d23-a378-6c7738c343c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-374bc264-6e42-4d23-a378-6c7738c343c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22486cd2-5cbd-4ec8-bd32-84978da2f6a6' class='xr-var-data-in' type='checkbox'><label for='data-22486cd2-5cbd-4ec8-bd32-84978da2f6a6' 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-ebd767d6-da38-4008-ad6a-cda4a1ed5419' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ebd767d6-da38-4008-ad6a-cda4a1ed5419' 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-62cb0f3d-94ee-4651-8dc0-20251d6b565c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62cb0f3d-94ee-4651-8dc0-20251d6b565c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa1d3e16-b306-4b92-a219-f049a1e43215' class='xr-var-data-in' type='checkbox'><label for='data-fa1d3e16-b306-4b92-a219-f049a1e43215' 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-7a909007-a3c3-4ef8-a667-6e5450655c96' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7a909007-a3c3-4ef8-a667-6e5450655c96' 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-bad257aa-0702-410e-aaa0-43f0330686da' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bad257aa-0702-410e-aaa0-43f0330686da' 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>20240829-164603-980-1123d1</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:                             20240829-164603-980-1123d1\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 0x7f924969ebb0>"
      ]
     },
     "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."
   ]
  }
 ],
 "metadata": {
  "file_format": "mystnb",
  "jupytext": {
   "text_representation": {
    "extension": ".md",
    "format_name": "myst"
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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": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}