{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "71d00eeb",
   "metadata": {},
   "source": [
    "# Tutorial 2. Advanced capabilities of the MeasurementControl\n",
    "\n",
    "```{seealso}\n",
    "The complete source code of this tutorial can be found in\n",
    "\n",
    "{nb-download}`Tutorial 2. Advanced capabilities of the MeasurementControl.ipynb`\n",
    "```\n",
    "\n",
    "Following this Tutorial requires familiarity with the **core concepts** of Quantify, we **highly recommended** to consult the (short) {ref}`User guide` before proceeding (see Quantify documentation). If you have some difficulties following the tutorial it might be worth reviewing the {ref}`User guide`!\n",
    "\n",
    "We **highly recommended** beginning with {ref}`Tutorial 1. Controlling a basic experiment using MeasurementControl` before proceeding.\n",
    "\n",
    "In this tutorial, we will explore the more advanced features of Quantify. By the end of this tutorial, we will have covered:\n",
    "\n",
    "- Using hardware to drive experiments\n",
    "- Software averaging\n",
    "- Interrupting an experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f5bfac56",
   "metadata": {
    "mystnb": {
     "code_prompt_show": "Imports and auxiliary utilities"
    },
    "tags": [
     "hide-cell"
    ]
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import signal\n",
    "import sys\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "from lmfit import Model\n",
    "from qcodes import ManualParameter\n",
    "\n",
    "import quantify_core.visualization.pyqt_plotmon as pqm\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\n",
    "\n",
    "rng = np.random.default_rng(seed=222222)  # random number generator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b88a56",
   "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 sticking 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",
    "----------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "21867a1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data will be saved in:\n",
      "/root/quantify-data\n"
     ]
    }
   ],
   "source": [
    "set_datadir(default_datadir())  # change me!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d236c77d",
   "metadata": {
    "mystnb": {
     "remove-output": true
    }
   },
   "outputs": [],
   "source": [
    "meas_ctrl = MeasurementControl(\"meas_ctrl\")\n",
    "plotmon = pqm.PlotMonitor_pyqt(\"plotmon_meas_ctrl\")\n",
    "meas_ctrl.instr_plotmon(plotmon.name)\n",
    "insmon = InstrumentMonitor(\"InstrumentMonitor\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75bac5f5",
   "metadata": {},
   "source": [
    "## A 1D Batched loop: Resonator Spectroscopy\n",
    "\n",
    "### Defining a simple model\n",
    "\n",
    "In this example, we want to find the resonance of some devices. We expect to find its resonance somewhere in the low 6 GHz range, but manufacturing imperfections make it impossible to know exactly without inspection.\n",
    "\n",
    "We first create `freq`: a {class}`.Settable` with a {class}`~qcodes.parameters.Parameter` to represent the frequency of the signal probing the resonator, followed by a custom {class}`.Gettable` to mock (i.e. emulate) the resonator.\n",
    "The {class}`!Resonator` will return a Lorentzian shape centered on the resonant frequency. Our {class}`.Gettable` will read the setpoints from `freq`, in this case a 1D array.\n",
    "\n",
    "```{note}\n",
    "The `Resonator` {class}`.Gettable` has a new attribute `.batched` set to `True`. This property informs the {class}`.MeasurementControl` that it will not be in charge of iterating over the setpoints, instead the `Resonator` manages its own data acquisition. Similarly, the `freq` {class}`.Settable` must have a `.batched=True` so that the {class}`.MeasurementControl` hands over the setpoints correctly.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5c4c4fe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that in an actual experimental setup `freq` will be a QCoDeS parameter\n",
    "# contained in a QCoDeS Instrument\n",
    "freq = ManualParameter(name=\"frequency\", unit=\"Hz\", label=\"Frequency\")\n",
    "freq.batched = True  # Tells meas_ctrl that the setpoints are to be passed in batches\n",
    "\n",
    "\n",
    "def lorenz(amplitude: float, fwhm: float, x: int, x_0: float):\n",
    "    \"\"\"Model of the frequency response.\"\"\"\n",
    "    return amplitude * ((fwhm / 2.0) ** 2) / ((x - x_0) ** 2 + (fwhm / 2.0) ** 2)\n",
    "\n",
    "\n",
    "class Resonator:\n",
    "    \"\"\"\n",
    "    Note that the Resonator is a valid Gettable not because of inheritance,\n",
    "    but because it has the expected attributes and methods.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self) -> None:\n",
    "        self.name = \"resonator\"\n",
    "        self.unit = \"V\"\n",
    "        self.label = \"Amplitude\"\n",
    "        self.batched = True\n",
    "        self.delay = 0.0\n",
    "\n",
    "        # hidden variables specifying the resonance\n",
    "        self._test_resonance = 6.0001048e9  # in Hz\n",
    "        self._test_width = 300  # FWHM in Hz\n",
    "\n",
    "    def get(self) -> float:\n",
    "        \"\"\"Emulation of the frequency response.\"\"\"\n",
    "        time.sleep(self.delay)\n",
    "        _lorenz = lambda x: lorenz(1, self._test_width, x, self._test_resonance)\n",
    "        return 1 - np.array(list(map(_lorenz, freq())))\n",
    "\n",
    "    def prepare(self) -> None:\n",
    "        \"\"\"Adding this print statement is not required but added for illustrative\n",
    "        purposes.\"\"\"\n",
    "        print(\"\\nPrepared Resonator...\")\n",
    "\n",
    "    def finish(self) -> None:\n",
    "        \"\"\"Adding this print statement is not required but added for illustrative\n",
    "        purposes.\"\"\"\n",
    "        print(\"\\nFinished Resonator...\")\n",
    "\n",
    "\n",
    "gettable_res = Resonator()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eba38ef",
   "metadata": {},
   "source": [
    "### Running the experiment\n",
    "\n",
    "Just like our Iterative 1D loop, our complete experiment is expressed in just four lines of code.\n",
    "\n",
    "The main difference is defining the `batched` property of our {class}`.Gettable` to `True`.\n",
    "The {class}`.MeasurementControl` will detect these settings and run in the appropriate mode.\n",
    "\n",
    "At this point the `freq` parameter is empty:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "98c44659",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n"
     ]
    }
   ],
   "source": [
    "print(freq())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fbdd9d47",
   "metadata": {
    "mystnb": {
     "remove-output": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t --- (None) --- \n",
      "Batched settable(s):\n",
      "\t frequency \n",
      "Batch size limit: 2000\n",
      "\n",
      "\n",
      "Prepared Resonator...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b9db4c3b21c4c8ba1eb5aa7cb5dbc18",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Finished Resonator...\n"
     ]
    }
   ],
   "source": [
    "meas_ctrl.settables(freq)\n",
    "meas_ctrl.setpoints(np.arange(6.0001e9, 6.00011e9, 5))\n",
    "meas_ctrl.gettables(gettable_res)\n",
    "dset = meas_ctrl.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ecc5e58d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7f1d35ede040>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotmon.main_QtPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f04a96a0",
   "metadata": {},
   "source": [
    "As expected, we find a Lorentzian spike in the readout at the resonant frequency, finding the peak of which is trivial.\n",
    "\n",
    "#### Memory-limited Settables/Gettables\n",
    "\n",
    "Instruments (either physical or virtual) operating in `batched` mode have an upper limit on how many datapoints can be processed at once.\n",
    "When an experiment is comprised of more datapoints than the instrument can handle, the {class}`.MeasurementControl` takes care of fulfilling the measurement of all the requested setpoints by running and an internal loop.\n",
    "\n",
    "By default the {class}`.MeasurementControl` assumes no limitations and passes all setpoints to the `batched` settable. However, as a best practice, the instrument limitation must be reflected by the `.batch_size` attribute of the `batched` settables. This is illustrated below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "11f48755",
   "metadata": {
    "mystnb": {
     "remove-output": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t --- (None) --- \n",
      "Batched settable(s):\n",
      "\t frequency \n",
      "Batch size limit: 256\n",
      "\n",
      "\n",
      "Prepared Resonator...\n",
      "\n",
      "Prepared Resonator...\n",
      "\n",
      "Prepared Resonator...\n",
      "\n",
      "Prepared Resonator...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b0d1ef48ac7e468f9db9095a3dd9c9ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Prepared Resonator...\n",
      "\n",
      "Prepared Resonator...\n",
      "\n",
      "Prepared Resonator...\n",
      "\n",
      "Prepared Resonator...\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Finished Resonator...\n"
     ]
    }
   ],
   "source": [
    "# Tells meas_ctrl that only 256 datapoints can be processed at once\n",
    "freq.batch_size = 256\n",
    "\n",
    "gettable_res.delay = 0.05  # short delay for plotting\n",
    "meas_ctrl.settables(freq)\n",
    "meas_ctrl.setpoints(np.arange(6.0001e9, 6.00011e9, 5))\n",
    "meas_ctrl.gettables(gettable_res)\n",
    "dset = meas_ctrl.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bd0cbdc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7f1d35ede040>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotmon.main_QtPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1a6c5b0",
   "metadata": {},
   "source": [
    "## Software Averaging: T1 Experiment\n",
    "\n",
    "In many cases it is desirable to run an experiment many times and average the result, such as when filtering noise on instruments or measuring probability.\n",
    "For this purpose, the {meth}`.MeasurementControl.run` provides the `soft_avg` argument.\n",
    "If set to *x*, the experiment will run *x* times whilst performing a running average over each setpoint.\n",
    "\n",
    "In this example, we want to find the relaxation time (aka T1) of a Qubit. As before, we define a {class}`.Settable` and {class}`.Gettable`, representing the varying timescales we will probe through and a mock Qubit emulated in software.\n",
    "The mock Qubit returns the expected decay sweep but with a small amount of noise (simulating the variable qubit characteristics). We set the qubit's T1 to 60 ms - obviously in a real experiment we would be trying to determine this, but for this illustration purposes in this tutorial we set it to a known value to verify our fit later on.\n",
    "\n",
    "Note that in this example meas_ctrl is still running in Batched mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a8fd9739",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decay(t, tau):\n",
    "    \"\"\"T1 experiment decay model.\"\"\"\n",
    "    return np.exp(-t / tau)\n",
    "\n",
    "\n",
    "time_par = ManualParameter(name=\"time\", unit=\"s\", label=\"Measurement Time\")\n",
    "# Tells meas_ctrl that the setpoints are to be passed in batches\n",
    "time_par.batched = True\n",
    "\n",
    "\n",
    "class MockQubit:\n",
    "    \"\"\"A mock qubit.\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        self.name = \"qubit\"\n",
    "        self.unit = \"%\"\n",
    "        self.label = \"High V\"\n",
    "        self.batched = True\n",
    "\n",
    "        self.delay = 0.01  # sleep time in secs\n",
    "        self.test_relaxation_time = 60e-6\n",
    "\n",
    "    def get(self):\n",
    "        \"\"\"Adds a delay to be able to appreciate the data acquisition.\"\"\"\n",
    "        time.sleep(self.delay)\n",
    "        rel_time = self.test_relaxation_time\n",
    "        _func = lambda x: decay(x, rel_time) + rng.uniform(-0.1, 0.1)\n",
    "        return np.array(list(map(_func, time_par())))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0ad9691",
   "metadata": {},
   "source": [
    "We will then sweep through 0 to 300 ms, getting our data from the mock Qubit. Let's first observe what a single run looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a04da706",
   "metadata": {
    "mystnb": "remove-output:true"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t --- (None) --- \n",
      "Batched settable(s):\n",
      "\t time \n",
      "Batch size limit: 300\n",
      "\n"
     ]
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      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
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       "Dimensions:  (dim_0: 300)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 0.0 1.003e-06 2.007e-06 ... 0.000299 0.0003\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 0.9972 1.063 1.065 ... -0.04871 0.07343 0.01665\n",
       "Attributes:\n",
       "    tuid:                             20231210-040916-850-323b3f\n",
       "    name:                             noisy\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  False</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a4c547cd-d15b-45c3-9928-9c24bbeee85c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a4c547cd-d15b-45c3-9928-9c24bbeee85c' 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>: 300</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-25159399-f713-4af0-ac97-4771c42d8357' class='xr-section-summary-in' type='checkbox'  checked><label for='section-25159399-f713-4af0-ac97-4771c42d8357' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>x0</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.003e-06 ... 0.000299 0.0003</div><input id='attrs-090eef18-1f3c-4730-8b6d-1ca33ea2e07a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-090eef18-1f3c-4730-8b6d-1ca33ea2e07a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b38b5211-c950-4ec7-9f93-531b201e711c' class='xr-var-data-in' type='checkbox'><label for='data-b38b5211-c950-4ec7-9f93-531b201e711c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Measurement Time</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>batched :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><pre>array([0.00000000e+00, 1.00334448e-06, 2.00668896e-06, 3.01003344e-06,\n",
       "       4.01337793e-06, 5.01672241e-06, 6.02006689e-06, 7.02341137e-06,\n",
       "       8.02675585e-06, 9.03010033e-06, 1.00334448e-05, 1.10367893e-05,\n",
       "       1.20401338e-05, 1.30434783e-05, 1.40468227e-05, 1.50501672e-05,\n",
       "       1.60535117e-05, 1.70568562e-05, 1.80602007e-05, 1.90635452e-05,\n",
       "       2.00668896e-05, 2.10702341e-05, 2.20735786e-05, 2.30769231e-05,\n",
       "       2.40802676e-05, 2.50836120e-05, 2.60869565e-05, 2.70903010e-05,\n",
       "       2.80936455e-05, 2.90969900e-05, 3.01003344e-05, 3.11036789e-05,\n",
       "       3.21070234e-05, 3.31103679e-05, 3.41137124e-05, 3.51170569e-05,\n",
       "       3.61204013e-05, 3.71237458e-05, 3.81270903e-05, 3.91304348e-05,\n",
       "       4.01337793e-05, 4.11371237e-05, 4.21404682e-05, 4.31438127e-05,\n",
       "       4.41471572e-05, 4.51505017e-05, 4.61538462e-05, 4.71571906e-05,\n",
       "       4.81605351e-05, 4.91638796e-05, 5.01672241e-05, 5.11705686e-05,\n",
       "       5.21739130e-05, 5.31772575e-05, 5.41806020e-05, 5.51839465e-05,\n",
       "       5.61872910e-05, 5.71906355e-05, 5.81939799e-05, 5.91973244e-05,\n",
       "       6.02006689e-05, 6.12040134e-05, 6.22073579e-05, 6.32107023e-05,\n",
       "       6.42140468e-05, 6.52173913e-05, 6.62207358e-05, 6.72240803e-05,\n",
       "       6.82274247e-05, 6.92307692e-05, 7.02341137e-05, 7.12374582e-05,\n",
       "       7.22408027e-05, 7.32441472e-05, 7.42474916e-05, 7.52508361e-05,\n",
       "       7.62541806e-05, 7.72575251e-05, 7.82608696e-05, 7.92642140e-05,\n",
       "...\n",
       "       2.20735786e-04, 2.21739130e-04, 2.22742475e-04, 2.23745819e-04,\n",
       "       2.24749164e-04, 2.25752508e-04, 2.26755853e-04, 2.27759197e-04,\n",
       "       2.28762542e-04, 2.29765886e-04, 2.30769231e-04, 2.31772575e-04,\n",
       "       2.32775920e-04, 2.33779264e-04, 2.34782609e-04, 2.35785953e-04,\n",
       "       2.36789298e-04, 2.37792642e-04, 2.38795987e-04, 2.39799331e-04,\n",
       "       2.40802676e-04, 2.41806020e-04, 2.42809365e-04, 2.43812709e-04,\n",
       "       2.44816054e-04, 2.45819398e-04, 2.46822742e-04, 2.47826087e-04,\n",
       "       2.48829431e-04, 2.49832776e-04, 2.50836120e-04, 2.51839465e-04,\n",
       "       2.52842809e-04, 2.53846154e-04, 2.54849498e-04, 2.55852843e-04,\n",
       "       2.56856187e-04, 2.57859532e-04, 2.58862876e-04, 2.59866221e-04,\n",
       "       2.60869565e-04, 2.61872910e-04, 2.62876254e-04, 2.63879599e-04,\n",
       "       2.64882943e-04, 2.65886288e-04, 2.66889632e-04, 2.67892977e-04,\n",
       "       2.68896321e-04, 2.69899666e-04, 2.70903010e-04, 2.71906355e-04,\n",
       "       2.72909699e-04, 2.73913043e-04, 2.74916388e-04, 2.75919732e-04,\n",
       "       2.76923077e-04, 2.77926421e-04, 2.78929766e-04, 2.79933110e-04,\n",
       "       2.80936455e-04, 2.81939799e-04, 2.82943144e-04, 2.83946488e-04,\n",
       "       2.84949833e-04, 2.85953177e-04, 2.86956522e-04, 2.87959866e-04,\n",
       "       2.88963211e-04, 2.89966555e-04, 2.90969900e-04, 2.91973244e-04,\n",
       "       2.92976589e-04, 2.93979933e-04, 2.94983278e-04, 2.95986622e-04,\n",
       "       2.96989967e-04, 2.97993311e-04, 2.98996656e-04, 3.00000000e-04])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-89810d80-04f7-44db-84ce-cd596ba64230' class='xr-section-summary-in' type='checkbox'  checked><label for='section-89810d80-04f7-44db-84ce-cd596ba64230' 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'>0.9972 1.063 ... 0.07343 0.01665</div><input id='attrs-74575410-98a4-4f55-8236-f07e109b1105' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74575410-98a4-4f55-8236-f07e109b1105' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-245bfc8f-4c80-443b-a080-a02f285b4a4b' class='xr-var-data-in' type='checkbox'><label for='data-245bfc8f-4c80-443b-a080-a02f285b4a4b' 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>qubit</dd><dt><span>long_name :</span></dt><dd>High V</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>batched :</span></dt><dd>True</dd></dl></div><div class='xr-var-data'><pre>array([ 9.97233014e-01,  1.06318945e+00,  1.06455158e+00,  8.83793899e-01,\n",
       "        8.54829507e-01,  9.88824590e-01,  8.59848089e-01,  8.63993368e-01,\n",
       "        8.66778567e-01,  8.37008024e-01,  8.06025533e-01,  7.41484630e-01,\n",
       "        8.46138450e-01,  8.81818090e-01,  8.21757097e-01,  6.85518511e-01,\n",
       "        6.77180395e-01,  7.58038769e-01,  8.32173346e-01,  8.09036911e-01,\n",
       "        8.08151483e-01,  7.30401084e-01,  6.79787321e-01,  6.35216418e-01,\n",
       "        6.21462118e-01,  6.74769273e-01,  6.85184552e-01,  6.75744828e-01,\n",
       "        7.04377623e-01,  6.19177287e-01,  5.97234026e-01,  5.07610440e-01,\n",
       "        6.50901787e-01,  5.09120535e-01,  5.83673947e-01,  4.57201858e-01,\n",
       "        5.84567728e-01,  5.77278894e-01,  4.73231736e-01,  4.27799388e-01,\n",
       "        5.78058588e-01,  5.51680750e-01,  5.78029923e-01,  4.18137096e-01,\n",
       "        4.11911450e-01,  4.03883917e-01,  4.48900719e-01,  5.28552840e-01,\n",
       "        4.73869815e-01,  3.70490854e-01,  5.22947828e-01,  3.61286103e-01,\n",
       "        5.14261530e-01,  4.98071218e-01,  4.50247260e-01,  4.80092186e-01,\n",
       "        4.49971181e-01,  2.94803112e-01,  4.24198776e-01,  3.66737841e-01,\n",
       "        3.41820967e-01,  3.04630949e-01,  3.82957211e-01,  3.82565819e-01,\n",
       "        3.21770660e-01,  2.44865232e-01,  2.37730253e-01,  3.49009836e-01,\n",
       "        2.76030232e-01,  3.13826895e-01,  2.12045390e-01,  3.69873311e-01,\n",
       "        2.21918296e-01,  3.52387047e-01,  2.36441581e-01,  2.91823407e-01,\n",
       "        2.03581456e-01,  3.37557190e-01,  2.36679497e-01,  3.43210183e-01,\n",
       "...\n",
       "       -4.61657876e-02, -6.83193110e-02, -6.93639006e-02,  2.18082367e-02,\n",
       "       -6.81647069e-02, -7.64109642e-02,  4.16576630e-02,  2.35153147e-02,\n",
       "       -5.69305264e-02,  9.79578354e-02, -3.31327779e-03,  7.28614896e-02,\n",
       "        7.61512947e-03,  7.18063579e-02,  2.44945400e-02, -3.43585728e-02,\n",
       "       -2.65995496e-02, -4.39182302e-02,  1.12579746e-01,  1.00128339e-01,\n",
       "        7.66104633e-02, -2.65458351e-03,  1.06969853e-01, -3.54225244e-02,\n",
       "       -2.69185161e-02, -5.81867142e-02,  3.68301790e-02,  6.57066623e-02,\n",
       "        3.67318576e-02,  3.53448321e-02,  1.05504322e-01,  1.06860498e-01,\n",
       "        1.54140346e-02,  5.44167177e-02,  1.11359192e-01, -6.79992311e-02,\n",
       "       -6.95409544e-02, -2.13099346e-02, -1.21397148e-02, -7.37210756e-02,\n",
       "        1.12387545e-01,  1.05818471e-01,  3.82974502e-02, -5.29576181e-02,\n",
       "       -1.97436354e-02,  4.10315200e-02, -8.30792914e-02, -2.56467620e-02,\n",
       "       -5.27878109e-02,  9.54954450e-02,  2.30129755e-02, -2.17952174e-02,\n",
       "       -1.78294604e-02, -4.52961073e-02, -2.87813661e-02, -7.93521559e-02,\n",
       "       -6.80826340e-02, -3.70492161e-02,  4.45122126e-02,  2.68997858e-02,\n",
       "        7.70401831e-02, -2.50338925e-02,  7.87982867e-02,  6.46034769e-02,\n",
       "        3.14973938e-02,  1.05457720e-01, -1.96987589e-02, -7.21829777e-02,\n",
       "        1.22799884e-02,  1.69386660e-02, -6.04139585e-02,  4.03635435e-02,\n",
       "       -2.40656312e-02, -8.53643450e-02,  9.97848250e-02, -3.39919183e-02,\n",
       "       -3.49335894e-02, -4.87097756e-02,  7.34301353e-02,  1.66534679e-02])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d6bd8322-fca9-47b2-9fcb-db25f49425c8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d6bd8322-fca9-47b2-9fcb-db25f49425c8' 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-832ea87c-454c-471e-b20f-e4eaf7c7d02b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-832ea87c-454c-471e-b20f-e4eaf7c7d02b' 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>20231210-040916-850-323b3f</dd><dt><span>name :</span></dt><dd>noisy</dd><dt><span>grid_2d :</span></dt><dd>False</dd><dt><span>grid_2d_uniformly_spaced :</span></dt><dd>False</dd><dt><span>1d_2_settables_uniformly_spaced :</span></dt><dd>False</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (dim_0: 300)\n",
       "Coordinates:\n",
       "    x0       (dim_0) float64 0.0 1.003e-06 2.007e-06 ... 0.000299 0.0003\n",
       "Dimensions without coordinates: dim_0\n",
       "Data variables:\n",
       "    y0       (dim_0) float64 0.9972 1.063 1.065 ... -0.04871 0.07343 0.01665\n",
       "Attributes:\n",
       "    tuid:                             20231210-040916-850-323b3f\n",
       "    name:                             noisy\n",
       "    grid_2d:                          False\n",
       "    grid_2d_uniformly_spaced:         False\n",
       "    1d_2_settables_uniformly_spaced:  False"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas_ctrl.settables(time_par)\n",
    "meas_ctrl.setpoints(np.linspace(0.0, 300.0e-6, 300))\n",
    "meas_ctrl.gettables(MockQubit())\n",
    "meas_ctrl.run(\"noisy\")  # by default `.run` uses `soft_avg=1`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5e34df94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7f1d35ede040>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotmon.main_QtPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cd212ae",
   "metadata": {},
   "source": [
    "Alas, the noise in the signal has made this result unusable! Let's set the `soft_avg` argument of the {meth}`.MeasurementControl.run` to 100, averaging the results and hopefully filtering out the noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cace9ef8",
   "metadata": {
    "mystnb": {
     "remove-output": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting batched measurement...\n",
      "Iterative settable(s) [outer loop(s)]:\n",
      "\t --- (None) --- \n",
      "Batched settable(s):\n",
      "\t time \n",
      "Batch size limit: 300\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f14e4e5bf45749a6ab02098b0fa70840",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dset = meas_ctrl.run(\"averaged\", soft_avg=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5aa93cbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<quantify_core.visualization.pyqt_plotmon.QtPlotObjForJupyter at 0x7f1d35ede040>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plotmon.main_QtPlot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7ffa625",
   "metadata": {},
   "source": [
    "Success! We now have a smooth decay curve based on the characteristics of our qubit. All that remains is to run a fit against the expected values and we can solve for T1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bd8301b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'tau': 5.994542423605272e-05}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z7I4tdlTgqCU2JiIcS2VsicNJWUXNk+m9Y0vLnZSV1/y8UTYLYRbzgdha2hBlPRBbVuGktJbnjbRaCK+MdVS4KK2lvfbwA7HlThel5TXH2sIsWMPqH1vhdFFay89mtZg9sU6XQUktzxtuMWELs3hia2tDmFesy2XUeh0s5oOLNQyj1vfCbMYntrb32GQ6EAvU+rM1VayjwoURcHGJm3dsudNV4+cU4HmP6xtb4XTV0gI8v7/BxIaZTZ7PCKfLwKilERavWJertqsAZhMHHVsbk1dsc9Si9ulZs2YNQ4cO9Tk2bNgwJk2aVOM5ZWVllJUd+MOen5/fsI3KGAUXvchH70/mpvgIepY5eHl3FiYgx4hmyhvf854rAoBrBh3OHWceBcDO/SUMemR1jU87fkBn7j+nJwD7isroP3VljbEXnXAYD19wHADF5U761RJ7dq80nhxzPAAuA/r9u+bY045qzwuX/cVz/+SHVtX4h3bg4Um8cuWJnvtDH/2Y/cXlAWN7p8ez9LqTPPfPevxTducFTqh6pMTw3k2neu6f/+wX/La3KGBsp8RIPrltiOf+pS98yaadgd/v5Ggb39x14HfpHwu+5uvtuQFjo6wWfnhguOf+9a98yyc/7w0YazbBb9PO8ty/ZdF3rPghM2AswE8PDici3P0hfPfSTfzv2501xn579+mepO7/lv3IK2t31Bj7xR1/pUO8HYCZ72/hP59tqzH2w8mD6N4+GoCnVv3KEx/9WmPs29efzLGHxQEw9/NtPLxiS42xi64eQL+uiQC8unYH9729ucbY+RP+wuAe7QH437d/cvuSjTXGPnPp8Zx5bBoA727K5IZXv60x9tGLjuO84w8D4OMte/nHi9/UGPt/o3vy9xM7A7B2Ww6X/mdtjbH/GnE0V57aDYDv/tzPeU/XvFhh8ulHcsNpRwDwy54Chs/+tMZY78+IP3KLg/6MyC4qq/XfsvdnRJHDSc97a+4d9/+MOOruFTXG+n9G9Lr//aA/I06ctjLoz4ghM1YH/Rkx/LFPgv6MOPfpz4P+jBgz58ugPyMuX/BN0J8R17/ybdCfEbcs/i7oz4i73twU9GfE1OU/Bv0ZMevDn4P+jGiOWtRE5szMTFJSfOfHpKSkkJ+fT0lJScBzpk2bRlxcnOeWnp7eKG07piAXl8nEJpuV/Wb3ZY2nkGfCZzPC8jVhZhNmv+Q3zGyq8eafKVvMphpv5kOINZtqvvm3wURVFh/4drCxIiIiTcFk1NZX1oRMJhP/+9//GD16dI0xRx55JBMmTGDKlCmeY8uXL+ess86iuLgYu91e7ZxAPT3p6enk5eURGxt76A13OWF2T8jfxXkdU/nFauWRPdkMLyqu+snc834mbVRZimbEMAyfpK6ufwbBxhoGniEoqF9XcGN1R9enS7y+sTUNS4J7uLHqWjhdBs5arkWYOfjYqgS+KramIUz385p9YstrGMKsakPVcFx9Yl0uA0ctsRazyTOcUVes2WTyDJO4XLUPLVnMvrH1Gd6qbTjObDJ5ehYMw6DYUfvzVsUCFJVV1Pq8duuB2MJaYyHSGuYTW9PvpdlkIsp2ILaorKLG30uTyUS0zfd5a4zFPfTrHVvb72XVFIOqNlTUFGvgM22gqKyi1uHyWHuY599csaOC8oqaY2Miwg5qCLzYUVHrsHas3Xe4vLbhxpiIMM+/jYaWn59PXFzcIf39blHDW6mpqWRl+S7/zsrKIjY2NmDCA2Cz2bDZbI3XKK+9ek4qLuUXq5XP7BFeSY/XXj1dT2m8dki9VOvFqkf3U22x/g95J0B1aaxY94dVcPH1jbXUJzbINtc/Nrj/TDRWrNlsIqKRYr2ThLpivZOE2phM9Yv1TijqUp/Y6EaKbWltqE9spDUMrHXHVcVGNkKs3WoJ+veyOWpRw1sDBgxg5UrfcesPPviAAQMGhKhF+OzBc1LlENvndnv1/4Vrrx4REZGQCmnSU1hYyIYNG9iwYQPgXpK+YcMGduxwT76aMmUK48aN88Rfc801/Pbbb9x222389NNPPP300yxatIibbropFM1389qD5/jSMuwuF9lhFn62htcYJyIiIk0vpEnPN998Q58+fejTpw8AkydPpk+fPtxzzz0A7N6925MAAXTt2pVly5bxwQcfcNxxxzFz5kz+85//hGa5ehWvvXqsQL9S9/yhz+wRlQHaq0dERKQ5aDYTmZtKQ0yEqqaq0jrwakwUU5MT6VdSyguZe9yPX7AAeo5umNcSERFpgxri73eLmtPTbFXu1UNsGieXuPeSWB9ho6hqVuv7U9yJkYiIiISMkp6GkjEKhk0jvaKCTuXlVJhMfFU1xJW/290TpMRHREQkZJT0NBSXE95z7x80sLK353PPvB7DfVtxhztOREREmpySnobitV/PycXupeuf+S9dr9qvR0RERJqckp6G4rUPz19Kywg3DHaGh/F7mN/GU1uWN3HDREREBJT0NByvfXgiDYPjK5eufx4Z4Rv3/SINcYmIiISAkp6G0nkgRCZ57lYNcX3uXx6jOFtDXCIiIiGgpKehmC3Q62LP3ZMqJzN/HWGjzL+MkEpSiIiINDklPQ2pxwjPt93Ly2lfUUGp2cy6CL8hLpWkEBERaXJKehqSV0kKE3g2KvxUJSlERERCTklPQzJbYPhDlXdMnFo5r+fjSDsGlWNcw6e740RERKRJKelpaF4lKU4sKSXcMPgjPJzt0QkweAocdVaoWygiItImKelpDBmjYNImogZN4QSHe3n6J+ZyWD0VZvdUOQoREZEQUNLTWH5aBqunMagwH4BPIiuXrqsOl4iISEgo6WkMLiesuB0wOLX4QNX1ApMJ1eESEREJDSU9jcGrDld6RQVdHe6q61/YvZauqw6XiIhIk1LS0xj8Nh8cVLmKyzPEVUV1uERERJqMkp7G4Lf54KkllVXXI+34DGipDpeIiEiTUdLTGPzqcPUuLSPG6SLHYmGTzXogTnW4REREmoySnsbgV4crHBhY2dvziX8BUtXhEhERaRJKehqLVx0ugEElNczrUR0uERGRJqGkp7F41eECOKm4FJNh8JPNSpbFgupwiYiINC0lPY3Frw5XostFrzIHAJ9ERgAGnDFVdbhERESaiJKexuRVhwvwFCD1zOt5f4p2ZhYREWkiSnoaW8YoGDYNODCv50t7BCUmk0pSiIiINCElPY3N5YT3pgBwpKOcDuUVlJrNfGmvHOJSSQoREZEmoaSnsXmVpDABQyqHuFZ5r+JSSQoREZFGp6SnsfntwzOkuBiAj/13Z1ZJChERkUalpKex+e3Dc3xpGbFOJzkWC9/ZbAceUEkKERGRRqWkp7H5laQIB04tKQXgoyivIS6VpBAREWlUSnoam19JCoAhRe4hrlWRdgzvB1SSQkREpNEo6WkKfiUpTiopxeoy2BEezm/hYQceUEkKERGRRqOkpyn4laSIMgz6l1YOcUVGopIUIiIijU9JT1PwK0kB8NfiA0NcKkkhIiLS+JT0NBW/khSDi0swGQYbI2zssVhg+WTYtDS0bRQREWnFlPQ0Ja+SFMlOF8dWFiBdHWmH4n3w+nh4/+5QtlBERKTVUtLTlLxKUsCBIa6PvHdn/uJx2PRGU7dMRESk1VPS05S8SlLAgZIUa+0RFJpMB+KWXAE/LG3ixomIiLRuSnqakt8+PN3KK+jiKKfCZOIz794ewwWLx6v6uoiISANS0tOUAuzDUzXE9aF30lNF1ddFREQajJKepuRXkgLg9CL3ENcnkXZKvYe4QNXXRUREGpCSnqZktsCIR30OHeNwkFZRQYnZzOf2iOrnqDSFiIhIg1DS09R6joaBN3jumoDTK2txfRAVWT1+39amaZeIiEgrp6QnFM54EC6YR9XuzFVJz8eRdhz+sesXaF6PiIhIA1DSEyo9z4NBtwPQq8xB+4oKCs1mvvQf4tK8HhERkQahpCeUko8A3G/CaZUTmgMOcWlej4iIyCFT0hNKXkvYT/fanbm8ljgRERE5OEp6QqnzQIjtAJg4vrSMRKeTfIuFryO8hrhiOrjjRERE5JAo6QklswWGPwSABROneVZxeW1UWFEKPy0LRetERERaFSU9oZYxCi56EezxDK2c1/NRVCSe9VolubBonEpSiIiIHCIlPc3BUWdBuJ2/lJYS53SSY7GwPsJW+aDh/qKSFCIiIodESU9zUFl9PRz4a2Xl9fd9VnEZWrouIiJyiJT0NAdeS9KHVs7rWRlpp1q/jpaui4iIHDQlPc2B15L0ASWlxDhd7A0L8xriqh4nIiIi9RPypOepp56iS5cuRERE0L9/f7766qta42fPnk2PHj2w2+2kp6dz0003UVpa2kStbSSepesQDgyt3LNnhf9GhcX7mrhhIiIirUdIk56FCxcyefJk7r33XtavX89xxx3HsGHD2LNnT8D4V155hTvuuIN7772XH3/8kRdeeIGFCxdy5513NnHLG5jZAmdM89wdXnigAGmFd9x7d2oys4iIyEEKadLz6KOPcuWVVzJhwgQyMjJ49tlniYyMZO7cuQHjv/jiC0466STGjBlDly5dOOOMM7jkkkvq7B1qEaKSPN/2Ky0l0ekk12LhK+9aXJrMLCIictBClvQ4HA7WrVvH0KFDDzTGbGbo0KGsWbMm4DkDBw5k3bp1niTnt99+Y/ny5YwYMaJJ2tyovCYph3Gg8vq7/kNcmswsIiJyUMJC9cLZ2dk4nU5SUnwn56akpPDTTz8FPGfMmDFkZ2dz8sknYxgGFRUVXHPNNbUOb5WVlVFWVua5n5+f3zA/QEPzm6Q8vKiYhbExrIyM5G5ysNYQJyIiIsEJ+UTm+li9ejVTp07l6aefZv369bzxxhssW7aMBx98sMZzpk2bRlxcnOeWnp7ehC2uB686XADHl5bRvqKCAouZzyMry1KYzJrMLCIicpBClvQkJydjsVjIyvIdrsnKyiI1NTXgOXfffTdjx47lH//4B8ceeyznnnsuU6dOZdq0abhcroDnTJkyhby8PM/tjz/+aPCfpUF41eEC9xszrMhvFZfhgsWXqSSFiIjIQQhZ0mO1Wunbty8rV670HHO5XKxcuZIBAwYEPKe4uBiz2bfJFosFAMMwAp5js9mIjY31uTVbGaPggvnuHh3cQ1wAqyLtlJhMB+JUkkJERKTeQjq8NXnyZObMmcOCBQv48ccfmThxIkVFRUyYMAGAcePGMWXKFE/8yJEjeeaZZ3jttdfYtm0bH3zwAXfffTcjR470JD8tXlSSu0cHOLbMQcfyCkrMZj7xrOJSSQoREZGDEbKJzAAXX3wxe/fu5Z577iEzM5PevXuzYsUKz+TmHTt2+PTs3HXXXZhMJu666y527txJu3btGDlyJP/+979D9SM0PK/VWSZgeFERL8THsSI6imGVdbn840RERKRuJqOmcaFWKj8/n7i4OPLy8prnUNe2T2HB2Z67W6zhXNAxDavL4OMdfxJd9XYNugOGTKnhSURERFqXhvj73aJWb7UJnQdCTJrn7pGOcro6ynGYTayKsh+I++Rh+GFp07dPRESkhVLS09yYLdB3gueuCTizqAiAZVFRB+IMFywer5VcIiIiQVLS0xwlHe5z96zKWlxr7BHstfi9ZVrJJSIiEhQlPc2R367LnSoqOK60DJfJxLvevT2glVwiIiJBUtLTHHl2Zz7g7EL3ENc70VHV47WSS0REpE5Kepojv92Zwb07c5hh8KPNytZwv50GVI9LRESkTkp6mquMUXDBAs/uzAkuFydX7tPj09tjjYH0/qFooYiISIuipKc56zkaLpjnuVs1xLUsOgpPpTFHAczsoVVcIiIidVDS09wdPRLsiQAMLikh2uVid1gY6yJsB2JKcmDROCU+IiIitVDS09z9/oU7qQFsBpxRWYR0WbUJzYaWr4uIiNRCSU9z57cyq2qI6/3ISMpMfrFavi4iIlIjJT3Nnd/KrL6lZaRWVFBgMfOx3V49XsvXRUREAlLS09z57dljBs6qbc8eLV8XEREJSElPcxdgz56RlUnPp5F2csxVb6EJYju6kyQRERGpRklPS5AxCi56CewJABxeXkFGWRkVJlPlhObKyT3Dp7uTJBEREalGSU9LkTEKbt0Kg+8EewLnFrh7e5ZGR2HYYqDf1e6kSKu3REREAlLS05KYLTD4djh7Nme6IrC6DH62WfnRKIGvnoUFZ8PsntqvR0REJAAlPS3N5rdg8WXEFWXz12L3nj1Lo6MPPJ6/SxsVioiIBKCkpyVxOWHF7YABwOjKCc3LoyNx+ARqo0IRERF/Snpakt+/cPfkVDqxpJSUigryLBZWRfrt2aONCkVERHwo6WlJ/DYetACjKnt7lsZEV4/fsrwJGiUiItIyKOlpSQJsPHhO5SquL+wRZFn8lqt/v0hDXCIiIpWU9LQkfrszA3SuqOD40lJcJhNv++/QXJytIS4REZFKSnpaEs/uzL6VRkdX7dkTE1U5xdmLanGJiIgASnpanoxRcNGLEJnkOXRGUTF2l4vfw8PZYLP6xv/6kYa4REREUNLTMmWMgsk/QWQyAFGGwRlF7j173vCf0Pzdy/BId+3bIyIibZ6SnpYqzApnz/LcPb+gEID3oiIpMPkOf1GSow0LRUSkzVPS05JljHLX4gJ6lzk43OGgxGxmuf+EZkAbFoqISFunpKelSzoccE9tvqByQvPimOjqE5pBGxaKiEibpqSnpfPau2dkYRFWl8EWm5UfrNbA8VrNJSIibZSSnpbOa++eOJeLMyqLkL4eG2CHZgi4waGIiEhboKSnpfPs3eN2Qb57QvPyqEgKfSY0myC2oztJEhERaYOU9LQGGaPgopfAnsDxZWV0dZQHmNBswPDp7iRJRESkDVLS01pkjIJbt2IafCcXFDsAvwnN9sRQtUxERKRZUNLTmpgt0P5oRuXtx+oy+MlmZXPVhOaSHFg0FjYtDWkTRUREQkVJT2vicsKK24l3OTm9ckLzYv8dmpdMgB+WNn3bREREQkxJT2vy+xeQvwuACyp3aF4e7Teh2XDB4vHanVlERNocJT2tidcePH1Ly+hWOaH5rUA7NGt3ZhERaWOU9LQmXnvwmICL8wsAWBgbU32HZu3OLCIibYySntbEa6NCgFGFRUS6XPxmDeerCFv1eO3OLCIibYiSntbEb6PCaMNgZKG7HtersTHV47U7s4iItCFKelqbjFFwwQIwud/av1Xu0Lwq0k6mxW9jwuJ9Td06ERGRkFHS0xr1HA3nvwBA9/Jy/lJSistkYpF/Pa737tRkZhERaTOU9LRWUe08315SOaF5SUw0Du8YTWYWEZE2RElPa+U1SXlIcQntKyrIsVh4PyrSN27L8iZumIiISGgo6WmtvCYphwEXVm5W+Jr/hOYvn9ZGhSIi0iYo6Wmt/JavX1BQSJhh8F2Ejc3WcK9AkzYqFBGRNkFJT2vlt3w92eni9CJ3Pa5XfHp7DM3tERGRNkFJT2uWMQpOvNZz99LKCc3Lo6PItvi99dqoUEREWjklPa1djxGeb48rc9CrtIxyk4mFMX5zeyKTm7hhIiIiTUtJT2vnmdvjrrQ+trK3Z1FsNGVexdd5c6ImNIuISKumpKe188ztcZccHVpUTFrl8vVlUV7V1/N3waKxSnxERKTVUtLTFhx1FtgTAffy9TF57t6el+ICVF9/+0at5BIRkVZJSU9b8PsXUJLjuXteYSF2l4tfrVbWRET4xpbkwMcPN3EDRUREGp+SnrbAb2VWrMtgdIG7+vpLcQGqr3/8EPywtAkaJiIi0nRCnvQ89dRTdOnShYiICPr3789XX31Va/z+/fu57rrrSEtLw2azceSRR7J8uUop1Mprd+Yqf88vwGQYfBZp57fwML9HDVg8XvN7RESkVQlp0rNw4UImT57Mvffey/r16znuuOMYNmwYe/bsCRjvcDg4/fTT2b59O6+//jpbtmxhzpw5dOzYsYlb3sL47c4M0KmigkHFJQC87F+aoop2ahYRkVYkpEnPo48+ypVXXsmECRPIyMjg2WefJTIykrlz5waMnzt3Ljk5OSxdupSTTjqJLl26MGjQII477rgmbnkL41nBZfI5PK5y+fpb0VHkmgP8KminZhERaUVClvQ4HA7WrVvH0KFDDzTGbGbo0KGsWbMm4DlvvfUWAwYM4LrrriMlJYWePXsydepUnE71RtQpYxRc9CLEpHkOnVBaxtFlDkrNZl6LjQ58nnZqFhGRViJkSU92djZOp5OUFN/5JikpKWRmZgY857fffuP111/H6XSyfPly7r77bmbOnMn//d//1fg6ZWVl5Ofn+9zarIxRcNMPMPhOwN3vc3me+3q8EhtDsclU/ZwA84FERERaopBPZK4Pl8tF+/btef755+nbty8XX3wx//rXv3j22WdrPGfatGnExcV5bunp6U3Y4mbIbIHBt8MFC8BkZmhRMYeVl7PfYuF/MVG+sfZE93wgERGRViBkSU9ycjIWi4WsLN/hk6ysLFJTUwOek5aWxpFHHonFYvEcO/roo8nMzMThcAQ8Z8qUKeTl5Xluf/zxR8P9EC1Zz9FwwTzCgMsqNyt8MTaWCu+Ykhz4aVkIGiciItLwQpb0WK1W+vbty8qVKz3HXC4XK1euZMCAAQHPOemkk/j1119xuVyeYz///DNpaWlYrdaA59hsNmJjY31uUunokWBP5JzCIhKdTnaFh/FeVKRXgEkruEREpNUI6fDW5MmTmTNnDgsWLODHH39k4sSJFBUVMWHCBADGjRvHlClTPPETJ04kJyeHG2+8kZ9//plly5YxdepUrrvuulD9CC1b5U7NEYbBmMqVXPPiYr1KUxhawSUiIq2G/650Teriiy9m79693HPPPWRmZtK7d29WrFjhmdy8Y8cOzF5LqdPT03nvvfe46aab6NWrFx07duTGG2/k9ttvD9WP0LJ5rcz6W34hL8TFssVm5Qt7BCeVlB6I27Icup4SggaKiIg0HJNhGNVqTrZm+fn5xMXFkZeXp6GubZ/CgrM9dx9KjOe/cbH0KynlhUy/DSIvesm9+ktERCQEGuLvd4tavSUNzG+n5vF5BYQZBl/ZI9jkP0dK1ddFRKSFU9LTlnl2anZLdToZUeguRPqfeL8sWtXXRUSkhVPS09ZljIITr/XcvTwvH5NhsDIqkl/Cw31jVX1dRERaMCU9Aj1GeL49vLyCoZWFSOf49/ao+rqIiLRgSnrEPbfHHu+5e9X+PABWREWyLTzAAj/t3SMiIi2Qkh5xz+3pf2CI6yhHOYOLijFMJv4TF2CGvPbuERGRFkhJj7ideou71lalq/a7C5Eui47ijzBL9fgty5uqZSIiIg1CSY+4mS0w8jHP3WMdDgYWl+A0mXghLq56/PeLNMQlIiItipIeOSBjlLv6OiYArq7s7XkzJordFr/enuJsDXGJiEiLoqRHfPUcDf2vBuD4sjL+UlJKhcnE3GorufApYyEiItLcKemR6o46UJri6sqVXG9ER7PHv7cnOqUpWyUiInJIlPRIdZ7yFCb6lZbRp7QUh9nku2+PPQnS+4esiSIiIvWlpEeq8ypPYcLEdbnu3p4lMdEH5vaU7IOZPbRRoYiItBhKeiSwjFFw0Ytgj6d/aRn9SkopN5l4Lt5rJVdJDiwaq8RHRERaBCU9UrOjzoKwCACuz90PwNKYKP4I89ulWTs0i4hIC6CkR2r2+xdQsBuAPmUOTqrct+dZ/5Vc2qFZRERaACU9UjO/JelVc3veiY7iN/+aXFq+LiIizZySHqmZ35L0Yx0OBhcV4zKZeDbeb5fmyOQmbJiIiEj9KemRmnUeCDFpPoeu96rA/kt4+IEH3pyoCc0iItKsKemRmpktcObDPod6OMo5vbIC+5MJXr09+btg0TglPiIi0mwp6ZHaZYyCi14Ce4Ln0PW5+zEbBh9FRbLBZvUKNrSSS0REmi0lPVK3jFFw889gc6/a6lZewbmFRQDMSozH8I7VSi4REWmm6p30jB8/nk8++aQx2iLN2R9roSzfc3dibh42l4v1ERF8bLf7xmoll4iINEP1Tnry8vIYOnQoRxxxBFOnTmXnzp2N0S5pbvwSmRSnk7/nFwAwOzEOnwGt7F+arl0iIiJBqnfSs3TpUnbu3MnEiRNZuHAhXbp04cwzz+T111+nvLy8MdoozUGAiuqX5+UT63Sy1WrlreioAw98PB0WT9DcHhERaVYOak5Pu3btmDx5Mt999x1r166le/fujB07lg4dOnDTTTfxyy/6n36r46m8fkCsy+Cq/e4hr6cS4ig1mQ48+MMb8MjhWs0lIiLNxiFNZN69ezcffPABH3zwARaLhREjRrBx40YyMjKYNWtWQ7VRmgNP5XWTz+G/FRSQWlFBVlgYr8ZG+55Tkqtl7CIi0mzUO+kpLy9nyZIlnH322XTu3JnFixczadIkdu3axYIFC/jwww9ZtGgRDzzwQGO0V0LJq/J6FZsB11eWp5gTF0ee2f9XSsvYRUSkeQirO8RXWloaLpeLSy65hK+++orevXtXixkyZAjx8fEN0DxpdjJGQUQcvDjKc+jswiIWxMXwi9XKs/Gx3J6z3/ecqmXsXU9p2raKiIh4qXdPz6xZs9i1axdPPfVUwIQHID4+nm3bth1q26S56nIyRCZ57lqAW/btB+C12Bi2+RcjBS1jFxGRkKt30jN27FgiIiIaoy3SUpgtMOJRn0MDS0s5tbiECpOJRxPiq58TYPWXiIhIU9KOzHJweo6GgTf4HLo5J5cww2B1VCRfRtgOPGBPdK/+EhERCSElPXLwzngQLlwA1hjAXZ7iovxCAB5JTDiwYWFJDvy0LDRtFBERqaSkRw7NMaPhtt/A5k58Ju7PI9bp5Geblf/FeG1YuPQ62PAabPtUK7lERCQklPTIoftjLZS5S1LEu1xcU7lh4RMJ8RRWbVjoyIelV8OCs2F2T+3dIyIiTU5Jjxw6v5VZf8svoHN5OTkWC3PiY6vH5+/SpoUiItLklPTIofNbmRXOgSXsL8bFsj2shu2gtGmhiIg0ISU9cugC1OUaVFLCyZVL2KcnJWBUO8k4sGmhiIhIE1DSI4fOU5frABNwx75cwg2DzyPtrIq0Bz5XmxaKiEgTUdIjDSNjFAy+0+dQ54oKLstzT2p+ODHBtwp7FW1aKCIiTURJjzScU2+BmDSfQ//Yn09qRQU7w8N4Ic5vUnNsR21aKCIiTUZJjzQcswXOfNjnUKRhcOu+XADmxsXyR5jlwIMpPd1zejSZWUREmoCSHmlYGaPgopfAnuA5dHpxCf1LSnGYTTyceOA4v7zn3rfnkW6w+iElPyIi0qiU9EjDyxgFt26FS98AazQm4M59OZ66XNUmNZfsh9VT4ZHu2rtHREQajZIeaRxmC4RZweGuxdWtvIJxlZOapyYlUBxoUnNJjjYtFBGRRqOkRxqP33L0a/bn07G8gsywMJ5MiKvhJEObFoqISKNQ0iONx285ut0wuGtfDgAvx8aw2Roe+DxtWigiIo1ASY80ngA7NZ9cUsqZhUW4TCbuT06kxv4cbVooIiINTEmPNJ4AOzUD3JaTS4zTxWabjVdjYwKfq00LRUSkgSnpkcaVMQouWACmA79qyU4XN+W69+55IiGOTIvF9xx7ojYtFBGRBqekRxpfz9FwwTyfQ+cXFNGntJRis5l/+xckLcmBn5Y1ZQtFRKQNUNIjTeOY0e5NCyvn+JiBe7JzPXv3LI+K9Ao2aQWXiIg0OCU90nQyRsGkTTBsKgDdy8u5Zn8eANOSEsg2V/06GlrBJSIiDU5JjzQts8VnkvLl+/M5qsxBnsXC1ORE39jPH4Ntn6rHR0REGoSSHml6XklPOPBg9j7CDIMPoiJ537tExa8fuGtzze6pXZpFROSQNYuk56mnnqJLly5ERETQv39/vvrqq6DOe+211zCZTIwePbpxGygNy2//nqMc5Vyx312i4t/JieSa/X4t83erPIWIiByykCc9CxcuZPLkydx7772sX7+e4447jmHDhrFnz55az9u+fTu33HILp5xyShO1VBpMgP17rtqfR3eHgxyLhWlJCX4nVK7t0uRmERE5BCFPeh599FGuvPJKJkyYQEZGBs8++yyRkZHMnTu3xnOcTieXXnop999/P926dWvC1kqDyRgFJ17ruWsFHtybg9kweDc6ipX+ldg1uVlERA5RSJMeh8PBunXrGDp0qOeY2Wxm6NChrFmzpsbzHnjgAdq3b88VV1xR52uUlZWRn5/vc5NmoscIn7s9HQ4mVFZivz85kWxLgF9PlacQEZGDFNKkJzs7G6fTSUqKb8mBlJQUMjMzA57z2Wef8cILLzBnzpygXmPatGnExcV5bunp6YfcbmkgnQeCPd7n0LW5efQoc5BrsXBfcpLvpoWg8hQiInLQQj68VR8FBQWMHTuWOXPmkJycHNQ5U6ZMIS8vz3P7448/GrmVEjSzBfpf63PICkzbu49ww+DjSDtLYqJ8zyne13TtExGRViUslC+enJyMxWIhK8t3yCIrK4vU1NRq8Vu3bmX79u2MHDnSc8zlcgEQFhbGli1bOPzww33Osdls2Gy2Rmi9NIhTb4G1z7pLT1Q6orycG3L2MzMpgYcTE+hfUkZ6RYX7wffuhKNHuhMmERGReghpT4/VaqVv376sXLnSc8zlcrFy5UoGDBhQLf6oo45i48aNbNiwwXMbNWoUQ4YMYcOGDRq6aonMFhj5GGDyOTw2v4ATSkopMZu5s10SnjVb+TvdGxaKiIjUU8iHtyZPnsycOXNYsGABP/74IxMnTqSoqIgJEyYAMG7cOKZMmQJAREQEPXv29LnFx8cTExNDz549sVqtofxR5GBljIKLXvSZ32MB/p29jyiXiw0RNubGxR6IXzhGe/aIiEi9hTzpufjii5kxYwb33HMPvXv3ZsOGDaxYscIzuXnHjh3s3r07xK2URpcxCi580edQhwonU/blAvB0Qhzf2yqTWkcRLBqrxEdEROrFZBhGtQUyrVl+fj5xcXHk5eURGxtb9wnSdFxOd8mJ/F2eQwZwa7sk3ouOomN5BYt37iam6lc2tiNM2qj5PSIibUBD/P0OeU+PiEeAnZpNwD37cuhYXsHO8DAeTE48sIxd83tERKQelPRI8+K3UzNArMtg+t5sLJW7NS+N9lrGrvk9IiISJCU90vz47dQM0LvMwfW5eQBMS0pgW3jlbgua3yMiIkFS0iPNT+eBEJNW7fCEvHz6Vy5jv61dMmXeq9zfvV3FSEVEpFZKeqT5MVvgzIerHbYAU/fuI8Hp5CeblUcSvaqxF+yCj6ufIyIiUkVJjzRPGaPgopcg3LcMRXunk6l792EyDBbGxvBOVOSBBz+eDu/f3cQNFRGRlkJJjzRfGaPgklerHT65pJSr9rursT+QnMjWcK9qKl88Dj8sbaIGiohIS6KkR5q3LicHnN8zcX+eZ37PTe3bUWzymuDzzmTN7xERkWqU9EjzVsv8nof2ZNO+ooJt1nDu896/p2QffDKjKVspIiItgJIeaf6q5vdYo30OJ7lczNhzYP+e12K8Hl89FVY/pB4fERHxUNIjLUPGKLhtG9h8tx7vU+bgppz9ADyclMB6m+3Ag6unwowjYNPSpmuniIg0W0p6pOUIs8LIJ6odHpdfwLDCIipMJia3TybT4lWLq3gfvD5eq7pERERJj7QwPUfDwBt8DpmAB7JzOMLhYF+YhZtS/DYuBPeqrlXTNNwlItKGKemRlueMB2HQHT6HIg2Dx7P2Eud0sslm44Ekr4nNVT6e7q7irpIVIiJtkpIeaZkG3QaxHXwOHVbhZMaebMyGwVsx0bwSG139vPxdsGicEh8RkTZISY+0TGYLnDGt2uETS8u4uXJi8yOJCXwZYasWA8CKOzTUJSLSxijpkZYrKing4bH5BYwsKMJpMjG5fTt+896xGQAD8nfC7180fhtFRKTZUNIjLVdhVsDDJuDeffvoXVpGgcXM9SntyDUH+FXfsrxx2yciIs2Kkh5puaJTanzIZsDsrL10LK/gj/BwJqUk4/AP+vJpze0REWlDlPRIy9V5YOVkZv/16W5JLhdPZe0h2uVifUQE9ycHWNGluT0iIm2Gkh5pucwWGP5Q5Z3Aic/h5RXMrCxV8VZMNHPifHd01tweEZG2Q0mPtGwZo+CiFyG2eiX2KgNLSpmyLxeAJxLjeTs60jfgp3cas4UiItJMKOmRli9jFEzaBOPfgVNvDRhycUEhl+3PB+Ce5CS+iIg48ODa52DTG03RUhERCSElPdI6mC3Q9RQYPKXGeT435e7nzMoaXTelJPOjNbzyEQNen6D6XCIirZySHmldapnnYwb+b+8++peUUmw2c21Ke/4M8ypO+sXj8NFUTWwWEWmllPRI61PLPB8rMCtrL0eWOcgOszAxpb3vHj6fPASPHK6l7CIirZCSHmmdqub5jH0T7Ak+D8UYBs9k7SWtooLt1nCuSW1HocmrV6gkFxaNhY9UlV1EpDVR0iOtl9kChw+GkY9Xe6i908mzmXtIcDrZbLNxXWo7Skx+84A+ma5eHxGRVkRJj7R+GaPgggX4z/HpVl7Bc5l7iHG6Ny+8qX0y5f7nVvX6KPEREWnxlPRI29BzNFwwt9rhox3lPJW1B7vLxeeRdm5vn0xFoPO1c7OISIunpEfajp7nwcAbqh3uU+ZgdlY24YbBB1GR3JeciMs/SDs3i4i0eEp6pG0540G4YB7+Q10DS0t5pLJcxZsx0TycmFC9Tpd2bhYRadGU9Ejb0/M8uHB+tcOnFZfw4N59ALwcF8OshHjfxGfts9rAUESkBVPSI23TMaPhopcgIt7n8MiiYu7OzgFgXnwsj/onPl88rpIVIiItlJIeabsyRsFtv8GgO3wOX1RQyF2Vic/8+FgeSfRLfJZcAT8sbbJmiohIw1DSI22b2QJDprh7fcKjPIcvLij09Pi8FBfLw96Jj+GCxeO1jF1EpIVR0iMC7l6fS171OXRRQSH3Zrvn+Pw3Lpbp/pOb375Ry9hFRFoQJT0iVbqcDJFJPocuKCji/r37MBkGr8TF8O8kr8SnJAfevE6Jj4hIC6GkR6SK2QIjHq12+LzCIu7PzsFkGCyMjeHe5MQDGxh+9yo83A1WPwQbX4dtnyoJEhFppsJC3QCRZqXnaNh1g3uVlpdzC4uwAHcnJ/K/mGgKzGYe2pONFaB0P6yeeiA4tgMMf8g9ZCYiIs2GenpE/NWwgeGowiIe3ePeufnDqEiuTW1PkX+RUoD83bBonCY6i4g0M0p6RALpeR4Mur3a4dOKS3gmcw+RLhdr7RH8I609+83+/4wqZ/2oXpeISLOipEekJoNuA3titcP9S8t4Yfce4p1ONtlsjE9LIdNi8YsyVK9LRKSZUdIjUhOzBUY+FvChng4HC3ZnkVJRwW/WcMZ1SGFbeIApcvk73ZObNclZRCTkTIZhVKur2Jrl5+cTFxdHXl4esbGxoW6OtASblsLrl0H1EqTstli4KrU9263hxDqdPLYnmxNKy7wiTL7naZKziMhBaYi/3+rpEalLz9EBC5QCpDmdLNidRa/SMvIrE6BlUZFeEX6JUv4uTXIWEQkRJT0iwaihQClAosvFC5l7GFpUTLnJxB3tk3k+LjZAv1AVQ5OcRURCQEmPSLBqKFAKEGEYzNyTzfi8fACeSIzn3uREymt6rvydsGqa5vmIiDQhzekRORib34K3/wkl+6s99GpMNNOTEnCZTAwoKWFmVjYxtf0z0zwfEZE6aU6PSKhkjIILXwz40CUFhTyetRe7y8Uau50xHVLZHlbL5ufazFBEpEko6RE5WF1OdvfSBDCopJT5lUvat1vDGdMhlU/tETU8keG+vXMTVDgarbkiIm2dkh6Rg2W2uIelCFCKAshwlPParkz6lJZSYDFzXUo7XoiLqXmCc3E2PHq0enxERBqJkh6RQ5ExCi56scYen2Snixd27+GC/AIMk4nZiQnc3i6JkkA1u8Cd+Cwaq8RHRKQRaCKzSENwOd0lJ35aBhtehrL8aiGLYqKZlpRAhcnE0WUOZu/ZS4eKGlZu2RPh1l/dvUkiIqKJzCLNhtkCXU+BM6fD7dth/Dtwyi0+IRcVFDIncw+JTic/2qxc2CGVj2ua51OSA5/MaPx2i4i0Ic0i6Xnqqafo0qULERER9O/fn6+++qrG2Dlz5nDKKaeQkJBAQkICQ4cOrTVepMlVJUBD7gR7vM9DJ5SW8drOTHqWuXdwvj61PbMS4qgI9DxfPqM9fEREGlDIk56FCxcyefJk7r33XtavX89xxx3HsGHD2LNnT8D41atXc8kll7Bq1SrWrFlDeno6Z5xxBjt37mzilovUwWyB/tdWO5zmdPLiriwuzSsAYG58HFektSfLv1J7aS4sngBbV8NvH6toqYjIIQr5nJ7+/fvzl7/8hSeffBIAl8tFeno6//znP7njjuo73/pzOp0kJCTw5JNPMm7cuDrjNadHmpTLCY90dw9XBfB+pJ172iVRZDaT6HQybc8+BpaW1v6c2sxQRNqgFj+nx+FwsG7dOoYOHeo5ZjabGTp0KGvWrAnqOYqLiykvLycxMTHg42VlZeTn5/vcRJqM2QIjH6OmZe1nFJewaGcmR5U5yLFYuCa1HY8nxNVcvgK0maGIyEEKadKTnZ2N0+kkJSXF53hKSgqZmZlBPcftt99Ohw4dfBInb9OmTSMuLs5zS09PP+R2i9RLHcvaO1VU8NLuLM+y9jnxcYzrkMLvNe7iXLmZoYqWiojUS8jn9ByK6dOn89prr/G///2PiIjAq2CmTJlCXl6e5/bHH380cStFcCc+kza5V3Wd/wKMfdOncGmEYXDvvlxmZO0l1ulkk83GhR1TWRIdVfNmhvk73XN8REQkKLUUBGp8ycnJWCwWsrKyfI5nZWWRmppa67kzZsxg+vTpfPjhh/Tq1avGOJvNhs1ma5D2ihySqlVdVQ4fDCYzrJ7qOTSsuITjdmbyr3ZJfGWP4L52SXwSaee+7BwSXK7qz7lwDIx+VvN7RESCENKeHqvVSt++fVm5cqXnmMvlYuXKlQwYMKDG8x5++GEefPBBVqxYwQknnNAUTRVpHKfeUm1Ze6rTyZzMPUzOySXMMPgoKpLzO9ZQu8tRpPk9IiJBCvnw1uTJk5kzZw4LFizgxx9/ZOLEiRQVFTFhwgQAxo0bx5QpUzzxDz30EHfffTdz586lS5cuZGZmkpmZSWFhYah+BJGDV8OydjMwIa+AV3Zl0s1Rzt6wMK5Nbc/dyYnkm/0nRRuw9Fr4dZXm+IiI1CLkSc/FF1/MjBkzuOeee+jduzcbNmxgxYoVnsnNO3bsYPfu3Z74Z555BofDwQUXXEBaWprnNmOGdq+VFurUW9xlJwI4urJo6d/z8jEZBktjojm3Yxqf+Pf6OArgv6NhWjqsfkjJj4hIACHfp6epaZ8eaZY2v+Uepqp52jLf2qzc3S6J38PDARhVUMhtObnEuQKcY090L5XXXB8RaSVa/D49IlKpjmXtAH3KHCzemcn4yl6ftyp7fVZG2qunSiU5B+b6uJzuVV7a0VlE2jj19Ig0J1XV2rcsh+8XQXF2wLANNit3Jyex3eru9RlcVMyUnNzqVdvDI8Ec5lv1XTs6i0gL1BB/v5X0iDRX3gnQ13PB6VueotRk4vn4WObFxVJhMmF3uZi4P4+/5xUQXueTm9w9S0p8RKSFUNJzEIK9aE6nk/LyWosBSAhZrVbM5jY0Ovvbx/Bi4ARla3gYDyQnsr5yg84jHA7uyc6hd5mj9ueM7QiTNrpXkImINHMNkfSEdHPC5sgwDDIzM9m/f3+omyK1MJvNdO3aFavVGuqmNI0uJ7uHpfJ3VXvo8PIK5u3ew5vRUcxMjOcXq5WxHVI5r6CQG3L2kxRoU0Nw7+j8+xe+GyaKiLRi6unxs3v3bvbv30/79u2JjIzEZApcKFJCx+VysWvXLsLDw+nUqVPbeY+CWOGVazYzMzGeN2OiAYh2ubgmN48x+TUMeZ16K7Q7CqJToPNA9fqISLOl4a2DUNtFczqd/Pzzz7Rv356kpKQQtVCCkZeXx65du+jevTvh4XXPYGk1Nr8FK24P2OPj7VublWlJifxoc/eEdXGUc1tOLqeUlNZ8UmwHOGMaRCVBYZYSIRFpVpT0HITaLlppaSnbtm2jS5cu2O32ELVQglFSUsL27dvp2rVrjcVmW62qCc4Fu2HHGvhmbsAwJ/BmdBSPJcaTY3EnLqcUl3BrTi5dyyuCey2t9BKRZkL79DSSNjNc0oK16feoqnBpr4tgxIwa9/axAOcVFvHOH7sYn5dPmGHwaaSdczum8UBSAnstQfzzz9+t2l4i0moo6RFpycwWd09MLWIMg1ty9vPGzt0MLirGaTKxODaGsw7rwBPxcRTWmkAa7tvbN8CvH7lXkWmTQxFpoZT0tBKDBw9m0qRJQcdv374dk8nEhg0bGvR5V69ejclkqnX1W2ZmJqeffjpRUVHEx8cD7p6bpUuXBv064iVjFFz0EtgTag3rWl7BE3uymb8ri16lZZSYzTyfEMeI9A68HBtNrRs0lOTCf891L5tfcgUsOBtm91QPkIi0KFqy3kq88cYb9ZrQm56ezu7du0lOTgbcycqQIUPIzc31JCIH87zBmDVrFrt372bDhg3ExcUB7lVzCQnuP9pVc3W+/fZbevfu3aCv3WpljIKjzoJPZsDaZ9xJSg36lpXx391ZrIy081hCPNut4UxPSuTF2Fiu3p/HyMKiIDY3xD2ZetFYuGAB9BzdUD+JiEijUdLTSiQmBq7SXROLxUJqamqDP28wtm7dSt++fTniiCM8x4Jpi9TBbIHBt7urtv/+hXsFVmQyLLkcivf5hJqAocUlDCou4X8x0TwTH8eu8DDubZfEnPhYrtmfz1mFRcF9QLx+GTAXep7X8D+TiEgD0vBWkIodFTXeSsudDR5bX/7DUF26dGHq1KlcfvnlxMTE0KlTJ55//nnP497DW9u3b2fIkCEAJCQkYDKZuOyyywI+70svvcQJJ5xATEwMqampjBkzhj179gTdzi5durBkyRJefPFFn9fxHt7q2rUrAH369MFkMjF48OB6X482rWqi87EXwOGDYcSjNYaGAxcVFLL8z13csi+XRKeTP8PDuatdEqMPS+OdqEjqnrljwOsTYPEEzfMRkWZNPT1ByrjnvRofG9KjHfMm9PPc7/vgh5SUB/7w7981kYVXD/DcP/mhVeQUVS8XsH36WYfQWreZM2fy4IMPcuedd/L6668zceJEBg0aRI8ePXzi0tPTWbJkCeeffz5btmwhNja2xiX75eXlPPjgg/To0YM9e/YwefJkLrvsMpYvXx5Um77++mvGjRtHbGwsjz32WMDX+eqrr+jXrx8ffvghxxxzTNvZdbmx9BwNu26ALx6vMcRuGIzPL+DCgkJei41mXlwsv4eHM6V9Ms+WlzNhfz4jC4uo9Z344Q34bRWMfNx3iXvVEnvt/SMiIaakpxUbMWIE1157LQC33347s2bNYtWqVdWSHovF4hnGat++vc+cHn+XX3655/tu3brx+OOP85e//IXCwkKio6PrbFO7du2w2WzY7fYah7TatWsHQFJSkoa9GsoZD0LHvrDs5hortwNEGgaX5xVwcX4hr8bGMD8uht/Dw7mvXRJPJ8QxLq+ACwoKiappe6+SXPc8nxOvhR4j3MNq703x3UxRe/+ISIgo6QnS5geG1fiY2W/J77q7hwYd+9ntQw6tYbXo1auX53uTyURqamq9hqICWbduHffddx/fffcdubm5uCrrOu3YsYOMjIxDem5pZMeMhqNHHtjY8LvXYOvKgKFRhsE/8vIZk1/A6zHRLIiLYU9YGDOSEng+PpZL8gsZk19AYk11vb582n0LpGrvH1V5F5EmpqQnSJHW4C9VY8XWl/+qK5PJ5ElSDkZRURHDhg1j2LBhvPzyy7Rr144dO3YwbNgwHI46KnpL81A13weg5/nwSHcoyakxPNIwGJdfwN/yC1gWHcXcuFi2W8N5LiGOBXExnFdQxN/zC0ivqM88NAMwwYo73CvONNQlIk1EE5kFwDNvxumseSLqTz/9xL59+5g+fTqnnHIKRx111CH3HB1sW6QBmC0w8jHca7lqZwXOLSxi6c7dPJq1l4yyMkrNZl6Ji+Gsw9L4Z/tkvoyw1VIK1Z/hrvK+5mlNfhaRJqOkRwDo3LkzJpOJd955h71791JYWFgtplOnTlitVp544gl+++033nrrLR588MEGb0v79u2x2+2sWLGCrKws8vLyGvw1pFLGKPcwUw2lLPxZgNOLS3htVxbP787i5OISDJOJ1VGRXJmWwnkdU3k9JoqSYMuEfHAXTO8MK6Zol2cRaXRKegSAjh07cv/993PHHXeQkpLC9ddfXy2mXbt2zJ8/n8WLF5ORkcH06dOZMWNGg7clLCyMxx9/nOeee44OHTpwzjnnNPhriJeMUTBpE4x/B85/AQbfibv3p+bExQQMKC3jmay9vPXnLv6WX4Dd5eJXq5X7k5M4Pb0DsxLi2G0JYujKUeCe/1O1y/Ompe4ESOUuRKSBqcq6l6oq622ycncLo/eqkW1+C1bc7rvqqg75ZhP/i47m1dgYdoa756qZDIOTS0q5oKCQU4tLDm4SoVZ7iQgNU2VdSY8X/SFtOfReNQHv/XUik8FkgqK97r12Op4AM4+AsoJqpzmBjyPtvBIbw1r7gfemXUUFowuLOL+gkI4V9em9qexx0movkTatIZIerd4SkcC8V3oFcs7T7j15/FiAvxaX8NfiEn4PC2NJTDRvxkSxNyyMOfFx/CculoElpZxfUMjg4pIg6nxV/r/snZvgyOEQps0qReTgaE6PiBycIKq7d66oYHLufj7csZMZWXsZUOKe+Px5pJ3JKe34a6eO/Dspge9t1rpXfhVnw8Pd4KNpmu8jIgdFw1teNGTScui9akZczqCqu1f5IyyMN2KieDPa3ftTpYujnLOLiji7sCj44S97PPS/1l1kVfv9iLRqmtNzEJT0tA56r5ohlxPWPgvv3RlUuBNYa4/g7egoVkbaKTEf6Hg+oaSUs4qKGFpUQnwwG2raE917Dh11VvU6X6DaXyKtgOb0iEjzYbZA/2tgzZPuUhN1DFhZgIElpQwsKaXIZOLDqEjejo7iqwgb39gj+MYewb+TDPqXlDKsqJi/FpcQV1MCVJLjnl9kjXEvga9iTwBMvrtOx6RB3wmQdLiSIJE2RkmPiDQcs8W9vHzRONyrroLrSI4yDM4pLOKcwiIyLRbeiY7ivahIfrJZ+TzSzueRdh4wDE4sKWV4UTFDiouJdQV4boffarJAw20Fu2H11AP3tSRepM3QRGYRaVieXZ7TDur0VKeTf+Tls3hXJm//sYt/5uznyDIHFSYTn0XauatdEoM6HcY1Ke1YGBNNVjAbINYmf5e7l+ijaZoYLdLKaU6Pl9Y2T2Tw4MH07t2b2bNnh7opDa61vVetUk37/OzbCqunEWwvUJXfwsN4LyqS96Mi+dXqu2y9Z1kZQ4pKGFJcQvfy8iCqidXAngAjH1evj0gzpDk9zZn3B34LmDewevVqhgwZQm5uLvHx8aFujrQGte3z0/5oePc291BTkLqVVzBxfz4T9+fzW3gYqyLtrIqM5HublU02G5tsNp5IjCe9vJxBxSWcUlJK39JSbPXJrUpy3b0+p94Bg29r1v9mRaT+lPQ0hkBb+GvegMgBGaPcK60+meE7vyZI3cor6JZXwBV5BWRbzKyuTIC+jIjgj/Bw/hsXzn/jYolwufhLaRknlZRwcnEpnSsqgnuBT6bD57PhyGHwlyugy8nuBKiF/WdGRHxpTk9D2/yWexKnf82i/N3u45vfapSXLSoqYty4cURHR5OWlsbMmTN9Hn/ppZc44YQTiImJITU1lTFjxrBnzx4Atm/fzpAhQwBISEjAZDJx2WWXAbBixQpOPvlk4uPjSUpK4uyzz2br1q2N8jNIG2O2wODb69zgsC7JThcXFBTxVNZePt3xJ7Oy9nJeQSHtKyooNZv5NNLO9KREzk7vwIjD0vh3UgIf2yMorqsSvLMUfnwTXhzlrgT/ykUw40h3YdQlV6hAqkgLpDk9Xg55nojL6f4QrLFIo8nd4zNpY4P/7/Daa69l2bJlzJ07l/bt23PnnXfy8ccfc/nllzN79mzmzp1LWloaPXr0YM+ePUyePJn4+HiWL1+O0+nkzTff5Pzzz2fLli3ExsZit9uJi4tjyZIlmEwmevXqRWFhIffccw/bt29nw4YNmM2hy5k1p6eVcTndCcPvn8Hen91fi/cd0lMawC/h4XweGcHndjvrImxUeCU6YYbBsWVl9Cspo19pKceVldVvKKwmkUnQ62LoMQLS+8Mfa9UzJNIAtDnhQWjUpGfbp+7//dVl/Du11zSqp8LCQpKSkvjvf//LhRdeCEBOTg6HHXYYV111VcCJzN988w1/+ctfKCgoIDo6Oug5PdnZ2bRr146NGzfSs2fPBvsZ6ktJTyvnP4xUvA/em1Kvqu/+ikwmvrJH8Lk9gs/sdk8l+CpWl0HvsjL+UlpK/5JSepY5gqgLVgeTGQyvvYU0zC1y0DSRubkpzGrYuCBt3boVh8NB//79PccSExPp0aOH5/66deu47777+O6778jNzcVVucnbjh07yMjIqPG5f/nlF+655x7Wrl1Ldna2z3mhTHqklQs0Cfrokb6J0M/vwZongn7KKMNgSLF7hZdBLn+GWfg6IoK19gi+jrCxNyyMr+wRfGWP4KkEsLtc9C4ro0+p+9arzEFkff+PaPhtplg1zO1dMV7zhESajJKehhSd0rBxDaSoqIhhw4YxbNgwXn75Zdq1a8eOHTsYNmwYDoej1nNHjhxJ586dmTNnDh06dMDlctGzZ886zxNpcP6JUNdT4LATYNnN7mKkHnVvimgC0iucpBcWcV5hEQawLTyMryMi+CrCxtf2CHItFtbY7ayx2wGwGAZHOso5vrTMkwylOOs7f6eyXW9PgopSyNkG6+f79mBZo+Dwob4TqEWkQSjpaUidB7q7r2vcgr9yTk9VPaAGcvjhhxMeHs7atWvp1KkTALm5ufz8888MGjSIn376iX379jF9+nTS09MB9/CWN2vlvidOrw/xffv2sWXLFubMmcMpp7j/2Hz22WcN2naRQ3LM6Oo9QMX7YPFl1GcfIBOVK8LKC7m4oBAX8Gt4OOsjbKyPsLEhwsbusDB+tFn50WblZWIA6FBewXFlZfQsc9CzzMHRDgf2YHqDSvbBG1cGfsxR5J5A/eOb7rIax491zw9SD5DIIVPS05Bq3YK/cgLl8OkN/sEVHR3NFVdcwa233kpSUhLt27fnX//6l2eicadOnbBarTzxxBNcc801bNq0iQcffNDnOTp37ozJZOKdd95hxIgR2O12EhISSEpK4vnnnyctLY0dO3Zwxx13NGjbRQ5ZoKEw04vVt40I5OjR8OPS6k8JHFlezpHl5fytoBCATIuFDRE21tvcSdAWazi7wsPYFR7Gu9FR7vMMg8PLyz1J0DFlZRzpKD/4uUGOAvjyaffNe4J0QyRAGlaTNkhJT0Or2oI/4D490xttAuMjjzxCYWEhI0eOJCYmhptvvpm8vDwA2rVrx/z587nzzjt5/PHHOf7445kxYwajRh1oS8eOHbn//vu54447mDBhAuPGjWP+/Pm89tpr3HDDDfTs2ZMePXrw+OOPM3jw4Eb5GUQaTNU+QL9/AVuWw/eLfIfAYjse+Pe4+S14+4bAdbq8pDqdDC8qZnhRMeCeGP29zcr3ETY2Wa38YLOyNyyMX6xWfrFa+Z+7Mwiry6CHw8ExDgcZZQ6OdDjoXl5e/5VixfsOJECBJkQHk8RUxQS6Jg2dVIk0Q1q95aVBVwTpf1GNSqu3pF7q+vdYtWR+3Vz4aTm4yg/qZbIsFjbZ3AnQJpuVTVYbBZbqWztYDIMu5eX0cJTTw+HwfE121lBFvibnz4PodvDTMtjwMpTlH3jMHg/9r4VTb3H/rIE2Ta2JVplJM6Ql6wehLdXeas30Xkmj+e1j94aEDcAA/ggLcydANitbrFa2WMPJq6FIaqLT6U6Cyso5vLycwx3ldCsvJ+pQPqatMdBloHu1W9Aqh+cH3wlJhzfOf9z0H0OpJy1ZFxFpaF1OrmNBQvBMQKeKCjpVVDCicljMwN0j9LM1nC1WKz9Zw/nZauX38DBy/FaMVUmtqPAkQFVfu5WXE+cKon2OgnomPFWtxLdESLDDX8EkMyrVIyGinh4v6j1oOfReSaOqKidziElPfRSbTPxqDWdLZRK0LTycreHhZIfV3PuRXOGkS3k5nSsq6FReTufyCjqVV5BeUUFEY36022LhuDFw9NkHVqPWOH/KL5mp69pesAB6jm68tntTb1OLouGtg6Ckp3XQeyWNrj5zYBpRntnkSYC2WsP5rfJrZljNHfUmwyDV6aRTeQWdy8srv1bQqaKcw8orsDZkA+0JgAlKcmqPu+gl9+TyR7rXHmsywwXz3NsRNCb1NrU4SnoOgpKe1kHvlTSJ2lY7xXRwbzBYkktT9ghVKTS5k6Ht4WHsCA/n9/AwdoSHsSMsPODkaW/tKyroWFFBhwonHSoqOKy8gg6Vx1IrnIdefiOQiAToMRy+ezW4+MF3HpiEHaxge25q7G2q3FqkasfshuoJUo9Sg1DScxCU9LQOeq+kyQX6w/XTsso/nlB9Xy4DBt0BXz1X53L4hmQAOWYzO8LD+D083P017EBiVFJHoWCzYdDe6aRjuTsJSqtwkuKsIKXCSUqFk1Snk1iXizpq1DeMyCQY8Wj14a6a3gv/nhv/FWwuJ2xdDYvHgaOw5te1J0GXk2DrR75xMWnQd0L9JnfX1qNUta2CkqGgKOk5CEp6Wge9V9JsBPyj5rcPUMDEKAB7IvT5O2xcDAW7G7ypBpBrNrMzLIyd4WHsDLOwKyzMfT8sjF1hYTjMdaczES4XKU4nqRVOUioqSHE6PUlRVYIU73JRe3pVD93PgMMHQ1S7wKU77Am1J5ZVK9i2f+Ge2N1Q6hoOq7VHyXC/395DfXUlQ22850lJz0FQ0tM66L2SZqWuPyJ1zQ+yJ0D/ib49Ep/M8F091QRcQI7FzJ+VCdDOsDAywyxkhYWRZbGQFWYht4bl9v7CDINEp5Nkp5Nkp4tkp5Mkp5PkigPHkiofjzSMpuk5anB+w2Fw4Hchfycsv9V376Sgnq+GZKjnBbDpdd/foYPZULIFz2VS0nMQlPS0DnqvpMXxTowik8FkgqK9dc89aQaTqb2VmkzstVjIDLOQaalMiMIsnqQoyxLGvlpWnAVid1UlQO7kKMHp7ilKdLqIdzpJcLm/JjpdxLtcwdU3a0rRaXDec7Dl3eqbRDYV/6E8qJ6M11WXrtclENcRMB34naztd7SJe4yU9ByE1pr0DB48mN69ezN79uxQN4WlS5dyyy23sG3bNv75z3/Su3dvJk2axP79+xvsNVryeyVSL96Tqb98OtStCUo5sM9iYZ/FQrbFTLbFQnaYhX3myq9VxywWiuuYYxRIhMud/CQ4XZ4EKcHpIsHlJNbpItbld6s81igTtJsbeyKMfMz9vX/CbDKDUc9dv6v4D70F2gW8kXuMtDmhNJnVq1czZMgQcnNziY+PrzX26quvZsKECdxwww3ExMQQFhbGiBEjPI/fd999LF26lA0bNjRuo0Vag6qCql1PgU4Dau75qZpHBHUPpfW72v2/8p9XVF+V1gDCcdcqS3U664wtNpl8kyOLhf0WM7lmC7kWM7kWC/vNZs/35SYTpWYzmWYzmfX8C2YPkAgFSo6iXQZRhovoqu9d7u8bdKl/YynJgUVjAz92sAkPuH+fFo2F8EgoL6495tQ7YPBtzXKekJIeaVCFhYXs2bOHYcOG0aFDB89xu98OsyJyELwLqRbsdg89RLVzryryHlrwnghb21Bat0Fwxv+5Y9c8cRA7Nx+6SMMgsqKC9Iq6Yw3cSVKuV1K032IhtzIp2m+xkGc2k+99s5gprOxNKjGbKTGbyTrItoYbBtEuV2USVJkMGe6vUX7JUpTLHRtpGNgrh+TsLgO7YRBZeb/5pQRBqCnh8fbJdPj6ORj5eLObJ6ThLS8techk8ODB9OzZE4CXXnqJ8PBwJk6cyAMPPIDJ5J5sV1ZWxr/+9S9effVV9u/fT8+ePXnooYc8VdN///13rr/+ej777DMcDgddunThkUceISMjg65du/q83vjx45k/f77PsareIG+rVq1i+/btnuGt+fPnM2HCBJ+YefPmcdlll9Xr523J75VIs/XDUlh2s1/19WTodREcMcydPP20zL0MvwWpAAr9EqH8AMlR1fdFZhOFpsqvlYlSY7C6DOyGd0Lk8iRG9spkKdLrscjKxyIMA5vLhc0Am+EiwjCwVh33ukW4DMIhtJPEG3CH7VYzvPXUU0/xyCOPkJmZyXHHHccTTzxBv379aoxfvHgxd999N9u3b+eII47goYce8hk+aUiGYVBSUdIoz10Xe5jdk7AEY8GCBVxxxRV89dVXfPPNN1x11VV06tSJK6+8EoDrr7+ezZs389prr9GhQwf+97//MXz4cDZu3MgRRxzBddddh8Ph4JNPPiEqKorNmzcTHR1Neno6S5Ys4fzzz2fLli3ExsYG7LkZOHAgW7ZsoUePHixZsoSBAweSmJjI9u3bPTEXX3wxmzZtYsWKFXz44YcAxMXFHdqFEpGGccxoOHpk7ZNTuw2CMBt88XjImllfYUB85Tygg+EEiswmikzuXqMis4kis5lCU+VX84EEqep41ffFJhMlZhMlJnPlVxOuys91h9mEAwt5DfejVmPyS4T8bxGu6gmT1XPD8324YRBO9WPeceEcOBbpMkh0uWDJBHfW1dg7bAcp5EnPwoULmTx5Ms8++yz9+/dn9uzZDBs2jC1bttC+fftq8V988QWXXHIJ06ZN4+yzz+aVV15h9OjRrF+/3tPT0ZBKKkro/0r/Bn/eYKwds5bI8Mig49PT05k1axYmk4kePXqwceNGZs2axZVXXsmOHTuYN28eO3bs8Aw73XLLLaxYsYJ58+YxdepUduzYwfnnn8+xxx4LQLdu3TzPnZiYCED79u1rnNNjtVo971liYiKpqanVYux2O9HR0YSFhQV8XERCrGoOUW3OeBA69q3eK+TNnghOR+2bANa0RLuZsQCxLoNYnBDEPKXaGECZyZ38VCVB3glRsdlcecw3WXInT+7HykwmSk0myszu76vuO7y+NyoTK6PyfumhX4Z6Oba0jFd2Z7nnES0eD6aXmsVQV8iTnkcffZQrr7zSM+Tx7LPPsmzZMubOncsdd9xRLf6xxx5j+PDh3HrrrQA8+OCDfPDBBzz55JM8++yzTdr25ubEE0/06RkaMGAAM2fOxOl0snHjRpxOJ0ceeaTPOWVlZSQlJQFwww03MHHiRN5//32GDh3K+eefT69evZr0ZxCRFsK/VyjQ3CGAbZ/C75/B3p/dX4v3HXiO2A7uyddVc5B+fAu+ej4kP07NKhOzBny2iMqelYRDmFdcGwP3kF5NiVGp6cCxMrP/cTNlJnCYTJRX3hwmEw4OHHN43corYz33cR+z+c+cWXGH+30O8eTmkCY9DoeDdevWMWXKFM8xs9nM0KFDWbNmTcBz1qxZw+TJk32ODRs2jKVLlwaMLysro6yszHM/P79++yfYw+ysHbO2Xuc0FHtYw03+LSwsxGKxsG7dOix+m4tFR0cD8I9//INhw4axbNky3n//faZNm8bMmTP55z//2WDtEJFWJJheocMHu29Q+74uVc/TYElPAyUrF8yH7C2w9pkmLSdyKEy4V9CFGwYxzmYybTd/p/u9r+v3pZGFNOnJzs7G6XSSkpLiczwlJYWffvop4DmZmZkB4zMzMwPGT5s2jfvvv/+g22gymeo1xBRKa9f6JmdffvklRxxxBBaLhT59+uB0OtmzZw+nnFLzL116ejrXXHMN11xzDVOmTGHOnDn885//xGp1L9Z0HmLXLriHwRrieUSkhakrSeo80N37k7+bGhOWiHg49VbI+9NdrsN/0vWImWA2H/qmjlX73VQNyZx6C6x9Ft678+Cfs60rPNh1cw0n5MNbjW3KlCk+PUP5+fmkp6eHsEWNZ8eOHUyePJmrr76a9evX88QTTzBz5kwAjjzySC699FLGjRvHzJkz6dOnD3v37mXlypX06tWLs846i0mTJnHmmWdy5JFHkpuby6pVqzj66KMB6Ny5MyaTiXfeeYcRI0Z45uYcjC5durBt2zY2bNjAYYcdRkxMDDabrcGug4i0UGaLe3O7ReOo3lNTOXQ/6okDiciwf9fcc1Tbsv3iffDeFL/6XUnQ5WRIPtKdmHU52XcoxmyB/tfAmidrT8p8BNnbZA4HV3kQz9fCRafUHdPIQpr0JCcnY7FYyMryzf6ysrJqnOSamppar3ibzdZm/qCOGzeOkpIS+vXrh8Vi4cYbb+Sqq67yPD5v3jz+7//+j5tvvpmdO3eSnJzMiSeeyNlnnw24e3Guu+46/vzzT2JjYxk+fDizZs0CoGPHjtx///3ccccdTJgwgXHjxlVbsh6s888/nzfeeIMhQ4awf//+g1qyLiKtVMYody2rgPWhpvtOhq2t56iuXqW6VqnV9Jw1JmWVatsRObYjnDHVXTLi98/cp1clWD++XfvE8KbQf6K7NwsIKlmzxUL6ie7rWF7HhPXYDgfmeYVQyPfp6d+/P/369eOJJ54AwOVy0alTJ66//vqAE5kvvvhiiouLefvttz3HBg4cSK9evYKayNxa9+lpa/ReibRyzbkSeKCaaP5FY6H+P0Og+mw/r6gsP1JDknXMefDbqoaZbzT+Hffz1DY0aIuF48bA0Wf7Vn//+GH4eHqAEwIUZT1IrWKfnsmTJzN+/HhOOOEE+vXrx+zZsykqKvKs5ho3bhwdO3Zk2rRpANx4440MGjSImTNnctZZZ/Haa6/xzTff8PzzzW3Gv4iIHLRgJkmHivfO2LUlNPX9GQLFdxsUuPxIVdmRjFHupOOTGbVPtq617pZXT4zZEvyO3t7tHjIFUo4JrocuhEKe9Fx88cXs3buXe+65h8zMTHr37s2KFSs8k5V37NiB2Ws3zIEDB/LKK69w1113ceedd3LEEUewdOnSRtmjR0REJKCmTMrqSrLMFhh8u7uXqbZ5TIsvq3zCAHOlhk/3fb6D+dmCTQZDKOTDW01Nw1utg94rEZF6CjQs591j1My1iuEtERERaQItoCemsSnpERERaSua81ypJtA4pWNbuDY24tci6T0SEZH6UtLjJTw8HIDi4uIQt0Tq4nA4AKqV1BAREamJhre8WCwW4uPj2bNnDwCRkZE+BTyleXC5XOzdu5fIyEjCwvQrLCIiwdFfDD9VOztXJT7SPJnNZjp16qSkVEREgqakx4/JZCItLY327dtTXt4GaqG0UFar1Wf/JhERkboo6amBxWLRfBEREZFWRP9VFhERkTZBSY+IiIi0CUp6REREpE1oc3N6qja1y8/PD3FLREREJFhVf7cPZXPaNpf0FBQUAJCenh7iloiIiEh9FRQUEBcXd1Dntrkq6y6Xi127dhETE9Pge7zk5+eTnp7OH3/8cdAVYNsKXavg6VoFT9cqeLpW9aPrFbzGulaGYVBQUECHDh0OesuSNtfTYzabOeywwxr1NWJjY/WPIki6VsHTtQqerlXwdK3qR9creI1xrQ62h6eKJjKLiIhIm6CkR0RERNoEJT0NyGazce+992Kz2ULdlGZP1yp4ulbB07UKnq5V/eh6Ba85X6s2N5FZRERE2ib19IiIiEiboKRHRERE2gQlPSIiItImKOkRERGRNqHNJD1PPfUUXbp0ISIigv79+/PVV1/VGr948WKOOuooIiIiOPbYY1m+fLnP44ZhcM8995CWlobdbmfo0KH88ssvPjE5OTlceumlxMbGEh8fzxVXXEFhYaFPzPfff88pp5xCREQE6enpPPzww/VuS0Nrqddq/vz5mEwmn1tERMQhXIngNMfrVVpaymWXXcaxxx5LWFgYo0ePDtiW1atXc/zxx2Oz2ejevTvz588/qGsQrJZ6rVavXl3td8tkMpGZmXnwF6MOzfFarV69mnPOOYe0tDSioqLo3bs3L7/8cr3b0tBa6rUKxWdWc7xWW7ZsYciQIaSkpBAREUG3bt246667KC8vr1dbgmK0Aa+99pphtVqNuXPnGj/88INx5ZVXGvHx8UZWVlbA+M8//9ywWCzGww8/bGzevNm46667jPDwcGPjxo2emOnTpxtxcXHG0qVLje+++84YNWqU0bVrV6OkpMQTM3z4cOO4444zvvzyS+PTTz81unfvblxyySWex/Py8oyUlBTj0ksvNTZt2mS8+uqrht1uN5577rl6taUhteRrNW/ePCM2NtbYvXu355aZmdkIV+mA5nq9CgsLjWuuucZ4/vnnjWHDhhnnnHNOtbb89ttvRmRkpDF58mRj8+bNxhNPPGFYLBZjxYoVDXeBvLTka7Vq1SoDMLZs2eLz++V0OhvuAnlprtfq3//+t3HXXXcZn3/+ufHrr78as2fPNsxms/H222/Xqy0NqSVfq6b+zGqu12rr1q3G3LlzjQ0bNhjbt2833nzzTaN9+/bGlClT6tWWYLSJpKdfv37Gdddd57nvdDqNDh06GNOmTQsYf9FFFxlnnXWWz7H+/fsbV199tWEYhuFyuYzU1FTjkUce8Ty+f/9+w2azGa+++qphGIaxefNmAzC+/vprT8y7775rmEwmY+fOnYZhGMbTTz9tJCQkGGVlZZ6Y22+/3ejRo0fQbWloLflazZs3z4iLizvIn/zgNNfr5W38+PEB/5DfdtttxjHHHONz7OKLLzaGDRtWx099cFrytapKenJzc4P+eQ9FS7hWVUaMGGFMmDAh6LY0tJZ8rZr6M6slXaubbrrJOPnkk4NuS7Ba/fCWw+Fg3bp1DB061HPMbDYzdOhQ1qxZE/CcNWvW+MQDDBs2zBO/bds2MjMzfWLi4uLo37+/J2bNmjXEx8dzwgkneGKGDh2K2Wxm7dq1nphTTz0Vq9Xq8zpbtmwhNzc3qLY0pJZ+rQAKCwvp3Lkz6enpnHPOOfzwww8Heznq1JyvVzD0uxX8tarSu3dv0tLSOP300/n888/rfX4wWtq1ysvLIzExMei2NKSWfq2g6T6zWtK1+vXXX1mxYgWDBg0Kui3BavVJT3Z2Nk6nk5SUFJ/jKSkpNY7HZ2Zm1hpf9bWumPbt2/s8HhYWRmJiok9MoOfwfo262tKQWvq16tGjB3PnzuXNN9/kv//9Ly6Xi4EDB/Lnn38GdwHqqTlfr2DU1Jb8/HxKSkqCfp5gtPRrlZaWxrPPPsuSJUtYsmQJ6enpDB48mPXr1wf9HMFqSddq0aJFfP3110yYMCHotjSkln6tmvIzqyVcq4EDBxIREcERRxzBKaecwgMPPBB0W4LV5qqsS+s1YMAABgwY4Lk/cOBAjj76aJ577jkefPDBELZMWroePXrQo0cPz/2BAweydetWZs2axUsvvRTCloXOqlWrmDBhAnPmzOGYY44JdXOatZqulT6zfC1cuJCCggK+++47br31VmbMmMFtt93WoK/R6nt6kpOTsVgsZGVl+RzPysoiNTU14Dmpqam1xld9rStmz549Po9XVFSQk5PjExPoObxfo662NKSWfq38hYeH06dPH3799dfAP/Ahas7XKxg1tSU2Nha73R708wSjpV+rQPr169cov1st4Vp9/PHHjBw5klmzZjFu3Lh6taUhtfRr5a8xP7NawrVKT08nIyODSy65hOnTp3PffffhdDqDakuwWn3SY7Va6du3LytXrvQcc7lcrFy50ifD9jZgwACfeIAPPvjAE9+1a1dSU1N9YvLz81m7dq0nZsCAAezfv59169Z5Yj766CNcLhf9+/f3xHzyySc+y/I++OADevToQUJCQlBtaUgt/Vr5czqdbNy4kbS0tPpchqA15+sVDP1uBX+tAtmwYUOj/G4192u1evVqzjrrLB566CGuuuqqerelIbX0a+WvMT+zmvu18udyuSgvL8flcgXVlqDVa9pzC/Xaa68ZNpvNmD9/vrF582bjqquuMuLj4z1LA8eOHWvccccdnvjPP//cCAsLM2bMmGH8+OOPxr333htwmV58fLzx5ptvGt9//71xzjnnBFym16dPH2Pt2rXGZ599ZhxxxBE+y/T2799vpKSkGGPHjjU2bdpkvPbaa0ZkZGS1Jet1tUXXyu3+++833nvvPWPr1q3GunXrjL/97W9GRESE8cMPPzTKtWrO18swDOOHH34wvv32W2PkyJHG4MGDjW+//db49ttvPY9XLVm/9dZbjR9//NF46qmnGn3Jeku9VrNmzTKWLl1q/PLLL8bGjRuNG2+80TCbzcaHH37Ypq7VRx99ZERGRhpTpkzxWWa9b9++erVF18qtqT+zmuu1+u9//2ssXLjQ2Lx5s7F161Zj4cKFRocOHYxLL720Xm0JRptIegzDMJ544gmjU6dOhtVqNfr162d8+eWXnscGDRpkjB8/3id+0aJFxpFHHmlYrVbjmGOOMZYtW+bzuMvlMu6++24jJSXFsNlsxmmnnWZs2bLFJ2bfvn3GJZdcYkRHRxuxsbHGhAkTjIKCAp+Y7777zjj55JMNm81mdOzY0Zg+fXq1ttfVlobWUq/VpEmTPO1OSUkxRowYYaxfv74Brkjtmuv16ty5swFUu3lbtWqV0bt3b8NqtRrdunUz5s2bd+gXpBYt9Vo99NBDxuGHH25EREQYiYmJxuDBg42PPvqoga5KYM3xWo0fPz7gdRo0aFC92tLQWuq1CsVnVnO8Vq+99ppx/PHHG9HR0UZUVJSRkZFhTJ061SdxCqYtwTAZhmHUr29IREREpOVp9XN6REREREBJj4iIiLQRSnpERESkTVDSIyIiIm2Ckh4RERFpE5T0iIiISJugpEdERETaBCU9IiIi0iYo6RGRVmPw4MFMmjQp1M0QkWZKSY+IiIi0CSpDISKtwmWXXcaCBQt8jm3bto0uXbqEpkEi0uwo6RGRViEvL48zzzyTnj178sADDwDQrl07LBZLiFsmIs1FWKgbICLSEOLi4rBarURGRpKamhrq5ohIM6Q5PSIiItImKOkRERGRNkFJj4i0GlarFafTGepmiEgzpaRHRFqNLl26sHbtWrZv3052djYulyvUTRKRZkRJj4i0GrfccgsWi4WMjAzatWvHjh07Qt0kEWlGtGRdRERE2gT19IiIiEiboKRHRERE2gQlPSIiItImKOkRERGRNkFJj4iIiLQJSnpERESkTVDSIyIiIm2Ckh4RERFpE5T0iIiISJugpEdERETaBCU9IiIi0iYo6REREZE24f8BV68F4JVPwQwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = Model(decay, independent_vars=[\"t\"])\n",
    "fit_res = model.fit(dset[\"y0\"].values, t=dset[\"x0\"].values, tau=1)\n",
    "\n",
    "fit_res.plot_fit(show_init=True)\n",
    "fit_res.values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a36dec",
   "metadata": {},
   "source": [
    "## Interrupting\n",
    "Sometimes experiments, unfortunately, do not go as planned and it is desirable to interrupt and restart them with new parameters. In the following example, we have a long-running experiment where our Gettable is taking a long time to return data (maybe due to misconfiguration).\n",
    "Rather than waiting for this experiment to complete, instead we can interrupt any {class}`.MeasurementControl` loop using the standard interrupt signal.\n",
    "In a terminal environment this is usually achieved with a `ctrl` + `c` press on the keyboard or equivalent, whilst in a Jupyter environment interrupting the kernel (stop button) will cause the same result.\n",
    "\n",
    "When the {class}`.MeasurementControl` is interrupted, it will wait to obtain the results of the current iteration (or batch) and perform a final save of the data it has gathered, calling the `finish()` method on Settables & Gettables (if it exists) and return the partially completed dataset.\n",
    "\n",
    "```{note}\n",
    "The exact means of triggering an interrupt will differ depending on your platform and environment; the important part is to cause a `KeyboardInterrupt` exception to be raised in the Python process.\n",
    "```\n",
    "\n",
    "In case the current iteration is taking too long to complete (e.g. instruments not responding), you may force the execution of any python code to stop by signaling the same interrupt 5 times (e.g. pressing 5 times `ctrl` + `c`). Mind that performing this too fast might result in the `KeyboardInterrupt` not being properly handled and corrupting the dataset!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e1b46c8b",
   "metadata": {
    "tags": [
     "raises-exception"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting iterative measurement...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "73a1678ff36d447cb17c8f60f99250ad",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Completed:   0%|           [ elapsed time: 00:00 | time left: ? ] it"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "[!!!] 1 interruption(s) signaled. Stopping after this iteration/batch.\n",
      "[Send 4 more interruptions to forcestop (not safe!)].\n",
      "\n",
      "\n",
      "Interrupt signaled, exiting gracefully...\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "Measurement interrupted",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[16], line 27\u001b[0m\n\u001b[1;32m     25\u001b[0m meas_ctrl\u001b[38;5;241m.\u001b[39mgettables(SlowGettable())\n\u001b[1;32m     26\u001b[0m \u001b[38;5;66;03m# Try interrupting me!\u001b[39;00m\n\u001b[0;32m---> 27\u001b[0m dset \u001b[38;5;241m=\u001b[39m \u001b[43mmeas_ctrl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mslow\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/quantify_core/measurement/control.py:388\u001b[0m, in \u001b[0;36mMeasurementControl.run\u001b[0;34m(self, name, soft_avg, lazy_set, save_data)\u001b[0m\n\u001b[1;32m    385\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_finish()\n\u001b[1;32m    386\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset_post()\n\u001b[0;32m--> 388\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_interrupt\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Propagate interruption\u001b[39;00m\n\u001b[1;32m    390\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset\n",
      "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/quantify_core/measurement/control.py:666\u001b[0m, in \u001b[0;36mMeasurementControl._check_interrupt\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    658\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    659\u001b[0m \u001b[38;5;124;03mVerifies if the user has signaled the interruption of the experiment.\u001b[39;00m\n\u001b[1;32m    660\u001b[0m \n\u001b[1;32m    661\u001b[0m \u001b[38;5;124;03mIntended to be used after each iteration or after each batch of data.\u001b[39;00m\n\u001b[1;32m    662\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    663\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_thread_data\u001b[38;5;241m.\u001b[39mevents_num \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    664\u001b[0m     \u001b[38;5;66;03m# It is safe to raise the KeyboardInterrupt here because we are guaranteed\u001b[39;00m\n\u001b[1;32m    665\u001b[0m     \u001b[38;5;66;03m# To be running MC code. The exception can be handled in a try-except\u001b[39;00m\n\u001b[0;32m--> 666\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyboardInterrupt\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMeasurement interrupted\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: Measurement interrupted"
     ]
    }
   ],
   "source": [
    "class SlowGettable:\n",
    "    \"\"\"A mock slow gettables.\"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        self.name = \"slow\"\n",
    "        self.label = \"Amplitude\"\n",
    "        self.unit = \"V\"\n",
    "\n",
    "    def get(self):\n",
    "        \"\"\"Get method.\"\"\"\n",
    "        time.sleep(1.0)\n",
    "        if time_par() == 4:\n",
    "            # This same exception rises when pressing `ctrl` + `c`\n",
    "            # or the \"Stop kernel\" button is pressed in a Jupyter(Lab) notebook\n",
    "            if sys.platform == \"win32\":\n",
    "                # Emulating the kernel interrupt on windows might have side effects\n",
    "                raise KeyboardInterrupt\n",
    "            os.kill(os.getpid(), signal.SIGINT)\n",
    "        return time_par()\n",
    "\n",
    "\n",
    "time_par.batched = False\n",
    "meas_ctrl.settables(time_par)\n",
    "meas_ctrl.setpoints(np.arange(10))\n",
    "meas_ctrl.gettables(SlowGettable())\n",
    "# Try interrupting me!\n",
    "dset = meas_ctrl.run(\"slow\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a33155c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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