{
"cells": [
{
"cell_type": "markdown",
"id": "c80cd461",
"metadata": {},
"source": [
"(analysis-framework-tutorial)=\n",
"# Tutorial 3. Building custom analyses - the data analysis framework\n",
"\n",
"```{seealso}\n",
"\n",
"The complete source code of this tutorial can be found in\n",
"\n",
"{nb-download}`Tutorial 3. Building custom analyses - the data analysis framework.ipynb`\n",
"\n",
"```\n",
"\n",
"Quantify provides an analysis framework in the form of a {class}`~quantify_core.analysis.base_analysis.BaseAnalysis` class and several subclasses for simple cases (e.g., {class}`~quantify_core.analysis.base_analysis.BasicAnalysis`, {class}`~quantify_core.analysis.base_analysis.Basic2DAnalysis`, {class}`~quantify_core.analysis.spectroscopy_analysis.ResonatorSpectroscopyAnalysis`). The framework provides a structured, yet flexible, flow of the analysis steps. We encourage all users to adopt the framework by sub-classing the {class}`~quantify_core.analysis.base_analysis.BaseAnalysis`.\n",
"\n",
"To give insight into the concepts and ideas behind the analysis framework, we first write analysis scripts to *\"manually\"* analyze the data as if we had a new type of experiment in our hands.\n",
"Next, we encapsulate these steps into reusable functions packing everything together into a simple python class.\n",
"\n",
"We conclude by showing how the same class is implemented much more easily by extending the {class}`~quantify_core.analysis.base_analysis.BaseAnalysis` and making use of the quantify framework."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "114e888a",
"metadata": {
"mystnb": {
"code_prompt_show": "Imports and auxiliary utilities"
},
"tags": [
"hide-cell"
]
},
"outputs": [],
"source": [
"import json\n",
"import logging\n",
"from pathlib import Path\n",
"from typing import Tuple\n",
"\n",
"import lmfit\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import xarray as xr\n",
"\n",
"import quantify_core.visualization.pyqt_plotmon as pqm\n",
"from quantify_core.analysis.cosine_analysis import CosineAnalysis\n",
"from quantify_core.analysis.fitting_models import CosineModel, cos_func\n",
"from quantify_core.data.handling import (\n",
" default_datadir,\n",
" get_latest_tuid,\n",
" load_dataset,\n",
" locate_experiment_container,\n",
" set_datadir,\n",
")\n",
"from quantify_core.measurement import MeasurementControl\n",
"from quantify_core.utilities.examples_support import mk_cosine_instrument\n",
"from quantify_core.utilities.inspect_utils import display_source_code\n",
"from quantify_core.visualization.SI_utilities import set_xlabel, set_ylabel"
]
},
{
"cell_type": "markdown",
"id": "97036a87",
"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",
"----------------------------------------------------------------------------------------"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "efe3fa65",
"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": "markdown",
"id": "6795b2b8",
"metadata": {},
"source": [
"## Run an experiment\n",
"\n",
"We mock an experiment in order to generate a toy dataset to use in this tutorial."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "881bb888",
"metadata": {
"mystnb": {
"code_prompt_show": "Source code of a mock instrument"
},
"tags": [
"hide-cell"
]
},
"outputs": [
{
"data": {
"text/html": [
"
defmk_cosine_instrument()->Instrument:\n",
""""A container of parameters (mock instrument) providing a cosine model."""\n",
"\n",
" instr=Instrument("ParameterHolder")\n",
"\n",
" # ManualParameter's is a handy class that preserves the QCoDeS' Parameter\n",
" # structure without necessarily having a connection to the physical world\n",
" instr.add_parameter(\n",
" "amp",\n",
" initial_value=0.5,\n",
" unit="V",\n",
" label="Amplitude",\n",
" parameter_class=ManualParameter,\n",
" )\n",
" instr.add_parameter(\n",
" "freq",\n",
" initial_value=1,\n",
" unit="Hz",\n",
" label="Frequency",\n",
" parameter_class=ManualParameter,\n",
" )\n",
" instr.add_parameter(\n",
" "t",initial_value=1,unit="s",label="Time",parameter_class=ManualParameter\n",
" )\n",
" instr.add_parameter(\n",
" "phi",\n",
" initial_value=0,\n",
" unit="Rad",\n",
" label="Phase",\n",
" parameter_class=ManualParameter,\n",
" )\n",
" instr.add_parameter(\n",
" "noise_level",\n",
" initial_value=0.05,\n",
" unit="V",\n",
" label="Noise level",\n",
" parameter_class=ManualParameter,\n",
" )\n",
" instr.add_parameter(\n",
" "acq_delay",initial_value=0.02,unit="s",parameter_class=ManualParameter\n",
" )\n",
"\n",
" defcosine_model():\n",
" sleep(instr.acq_delay())# simulates the acquisition delay of an instrument\n",
" return(\n",
" cos_func(instr.t(),instr.freq(),instr.amp(),phase=instr.phi(),offset=0)\n",
" +np.random.randn()*instr.noise_level()\n",
" )\n",
"\n",
" # Wrap our function in a Parameter to be able to associate metadata to it, e.g. unit\n",
" instr.add_parameter(\n",
" name="sig",label="Signal level",unit="V",get_cmd=cosine_model\n",
" )\n",
"\n",
" returninstr\n",
"
\n"
],
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"\n",
" \\PY{c+c1}{\\PYZsh{} ManualParameter\\PYZsq{}s is a handy class that preserves the QCoDeS\\PYZsq{} Parameter}\n",
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" \\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{acq\\PYZus{}delay}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{initial\\PYZus{}value}\\PY{o}{=}\\PY{l+m+mf}{0.02}\\PY{p}{,} \\PY{n}{unit}\\PY{o}{=}\\PY{l+s+s2}{\\PYZdq{}}\\PY{l+s+s2}{s}\\PY{l+s+s2}{\\PYZdq{}}\\PY{p}{,} \\PY{n}{parameter\\PYZus{}class}\\PY{o}{=}\\PY{n}{ManualParameter}\n",
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" \\PY{k}{def} \\PY{n+nf}{cosine\\PYZus{}model}\\PY{p}{(}\\PY{p}{)}\\PY{p}{:}\n",
" \\PY{n}{sleep}\\PY{p}{(}\\PY{n}{instr}\\PY{o}{.}\\PY{n}{acq\\PYZus{}delay}\\PY{p}{(}\\PY{p}{)}\\PY{p}{)} \\PY{c+c1}{\\PYZsh{} simulates the acquisition delay of an instrument}\n",
" \\PY{k}{return} \\PY{p}{(}\n",
" \\PY{n}{cos\\PYZus{}func}\\PY{p}{(}\\PY{n}{instr}\\PY{o}{.}\\PY{n}{t}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,} \\PY{n}{instr}\\PY{o}{.}\\PY{n}{freq}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,} \\PY{n}{instr}\\PY{o}{.}\\PY{n}{amp}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,} \\PY{n}{phase}\\PY{o}{=}\\PY{n}{instr}\\PY{o}{.}\\PY{n}{phi}\\PY{p}{(}\\PY{p}{)}\\PY{p}{,} \\PY{n}{offset}\\PY{o}{=}\\PY{l+m+mi}{0}\\PY{p}{)}\n",
" \\PY{o}{+} \\PY{n}{np}\\PY{o}{.}\\PY{n}{random}\\PY{o}{.}\\PY{n}{randn}\\PY{p}{(}\\PY{p}{)} \\PY{o}{*} \\PY{n}{instr}\\PY{o}{.}\\PY{n}{noise\\PYZus{}level}\\PY{p}{(}\\PY{p}{)}\n",
" \\PY{p}{)}\n",
"\n",
" \\PY{c+c1}{\\PYZsh{} Wrap our function in a Parameter to be able to associate metadata to it, e.g. unit}\n",
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" \\PY{p}{)}\n",
"\n",
" \\PY{k}{return} \\PY{n}{instr}\n",
"\\end{Verbatim}\n"
],
"text/plain": [
"def mk_cosine_instrument() -> Instrument:\n",
" \"\"\"A container of parameters (mock instrument) providing a cosine model.\"\"\"\n",
"\n",
" instr = Instrument(\"ParameterHolder\")\n",
"\n",
" # ManualParameter's is a handy class that preserves the QCoDeS' Parameter\n",
" # structure without necessarily having a connection to the physical world\n",
" instr.add_parameter(\n",
" \"amp\",\n",
" initial_value=0.5,\n",
" unit=\"V\",\n",
" label=\"Amplitude\",\n",
" parameter_class=ManualParameter,\n",
" )\n",
" instr.add_parameter(\n",
" \"freq\",\n",
" initial_value=1,\n",
" unit=\"Hz\",\n",
" label=\"Frequency\",\n",
" parameter_class=ManualParameter,\n",
" )\n",
" instr.add_parameter(\n",
" \"t\", initial_value=1, unit=\"s\", label=\"Time\", parameter_class=ManualParameter\n",
" )\n",
" instr.add_parameter(\n",
" \"phi\",\n",
" initial_value=0,\n",
" unit=\"Rad\",\n",
" label=\"Phase\",\n",
" parameter_class=ManualParameter,\n",
" )\n",
" instr.add_parameter(\n",
" \"noise_level\",\n",
" initial_value=0.05,\n",
" unit=\"V\",\n",
" label=\"Noise level\",\n",
" parameter_class=ManualParameter,\n",
" )\n",
" instr.add_parameter(\n",
" \"acq_delay\", initial_value=0.02, unit=\"s\", parameter_class=ManualParameter\n",
" )\n",
"\n",
" def cosine_model():\n",
" sleep(instr.acq_delay()) # simulates the acquisition delay of an instrument\n",
" return (\n",
" cos_func(instr.t(), instr.freq(), instr.amp(), phase=instr.phi(), offset=0)\n",
" + np.random.randn() * instr.noise_level()\n",
" )\n",
"\n",
" # Wrap our function in a Parameter to be able to associate metadata to it, e.g. unit\n",
" instr.add_parameter(\n",
" name=\"sig\", label=\"Signal level\", unit=\"V\", get_cmd=cosine_model\n",
" )\n",
"\n",
" return instr"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display_source_code(mk_cosine_instrument)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "f58b3e02",
"metadata": {
"mystnb": {
"remove-output": true
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Starting iterative measurement...\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "0b934d21fd9140e8801071c62ff09e36",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Completed: 0%| [ elapsed time: 00:00 | time left: ? ] it"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"meas_ctrl = MeasurementControl(\"meas_ctrl\")\n",
"plotmon = pqm.PlotMonitor_pyqt(\"plotmon\")\n",
"meas_ctrl.instr_plotmon(plotmon.name)\n",
"pars = mk_cosine_instrument()\n",
"\n",
"meas_ctrl.settables(pars.t)\n",
"meas_ctrl.setpoints(np.linspace(0, 2, 30))\n",
"meas_ctrl.gettables(pars.sig)\n",
"dataset = meas_ctrl.run(\"Cosine experiment\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "0e3dbd26",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plotmon.main_QtPlot"
]
},
{
"cell_type": "markdown",
"id": "b2e180c6",
"metadata": {},
"source": [
"## Manual analysis steps\n",
"\n",
"### Loading the data\n",
"\n",
"The {class}`~xarray.Dataset` contains all the information required to perform a basic analysis of the experiment.\n",
"We can alternatively load the dataset from disk based on its {class}`~quantify_core.data.types.TUID`, a timestamp-based unique identifier. If you do not know the tuid of the experiment you can find the latest tuid containing a certain string in the experiment name using {meth}`~quantify_core.data.handling.get_latest_tuid`.\n",
"See the {ref}`data-storage` documentation for more details on the folder structure and files contained in the data directory."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "6210845e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
"
],
"text/plain": [
" Size: 480B\n",
"Dimensions: (dim_0: 30)\n",
"Coordinates:\n",
" x0 (dim_0) float64 240B 0.0 0.06897 0.1379 0.2069 ... 1.862 1.931 2.0\n",
"Dimensions without coordinates: dim_0\n",
"Data variables:\n",
" y0 (dim_0) float64 240B 0.4939 0.3932 0.4072 ... 0.342 0.5105 0.5098\n",
"Attributes:\n",
" tuid: 20241018-040908-183-249021\n",
" name: Cosine experiment\n",
" grid_2d: False\n",
" grid_2d_uniformly_spaced: False\n",
" 1d_2_settables_uniformly_spaced: False"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tuid = get_latest_tuid(contains=\"Cosine experiment\")\n",
"dataset = load_dataset(tuid)\n",
"dataset"
]
},
{
"cell_type": "markdown",
"id": "868ba095",
"metadata": {},
"source": [
"### Performing a fit\n",
"\n",
"We have a sinusoidal signal in the experiment dataset, the goal is to find the underlying parameters.\n",
"We extract these parameters by performing a fit to a model, a cosine function in this case.\n",
"For fitting we recommend using the lmfit library. See [the lmfit documentation](https://lmfit.github.io/lmfit-py/model.html) on how to fit data to a custom model."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e8f19380",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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XLT+pMvfv6+SdmxBacMMeJ935YPsprv/3ZgqqGhkQFURIgPUMjKIoTB0Sy9uLJ/Pf+6Zz5ahEFAVWHSzlZx/kYLbIe2DRD2yHEkbdYP2zB4mMquj4sKWIm09+SMOACZqd6pRTY65iKxCrKcKESpHBQLNOxwPxsfy8soo7auowhiTSED2JNMVARnyo1hEL4Xt60uNE4xNpTUYzjy49wPvbTwPWlZ6/3zSWiCC/C64dNSCCf94+geNldfxn/XHGp0Wi11lXuJpNZlYfLGX+yET7bUI425naMzy6+VG2Fm8F4KPDH3HnyDs1iUUSIVexFYh9eAd+KDxXUsaTMVF8EB7GUzFRnPYz8NCcJ/g08xIq61vsL0hNRjNvbM7nO1PTCfSTHihC9IRFtWC0GAnQO7jV7IY9Tjpy5mwD976zi30F1SgK/OKy4dw7cwi6bhKZwXGh/OWG0e1u+3x3Ib/6ZC9pMcF8/5IhXDc+RV5rhNNYVAvv573Ps7uepdHUSKA+kJ+M/wm3Zd6mWUySCLmSrUBs+YMYagr5TcVZUo0m/h4dyQfhYRQVr+Jvw+YTHx5sv8uLa4/yjzVH+b9tp/jTNaOYPjRWwy9ACM/RYGzgli9u4UT1CcL9w4kPjichOIH44HjiguPs/237iA6MRueGPU468r8DJewrqCYq2I/nbhnHJcPiev1YJotKVLAfJysa+PVn+3h21WEWTx/Et6ekEewvvyJE/zlZc5JHNj3CrtJdAExImMBjUx9jYPhATeOSYulu9FuxdFvnFZetUhp56Otf02xuZkT0CF6Y+wLxwfEA/O9AMb///ADFNU0AXDsuhd8sGEFsqBRTC9GVF3a/wH/2/sfh6w2KgdigWOKri4hvaSTeZCbZZOLqunoiLZbWqxTrFvf9+zTtUq2qKn9fcZhbLkplQFRw93foRkOLif/bdppXNh6nqNr6WjN5UDT/d8/F3a4yCdEds8XMOwff4YXdL9BkbiLIEMTPJvyMm4ffjE5xXqmyo7+/JRHqhlMSoQ7sLdvLj9f8mMqmShKCE/jnvH8yLGoYALVNRv6+4jBvbslHVSEiyI9fX5nJjRNS5UVKiA4U1RWxcMlCms3N/Gn6nxgRPYKyhjJKGkoobSilrPHcf5c2lFLRWGHr5nWBefUNPFNajpanxqobjTyz8jC/nD/cXgztDC0mC0tyCnh06QEaWsw8deMYbpgwwGnPJ7zf8erjPLLpEfaU7QHg4qSLeXTqo6SEpjj9uSUR6ieuSoQATtee5oerfkh+TT4hfiE8PfNppqac666553QVD3+6j9yiGgAWjE7ixdvGOzUmITzRr9b/iq/yv2JCwgRen/86itL1GwaTxUR5Y7k1SWooo+T4KooPLeWNQAVVUXi/oIiRgfHWRm8uToIOFtXwg3d2crKigevGpfD0zWOd/pz/Xn+Mr/YX88ersxk1IMLpzye8j8li4s0Db/LPnH/SYmkhxC+EX0z8BdcPvb7bn8f+IolQP3FlIgRQ3VzN/WvvZ0fJDvSKnt9d/DuuH3a9/fMms4XXN+Wz9lApv75yBNkp8iIlRFu7S3dzx1d3oKDwwVUfMCJmRO8eyGLm4f99j2Wl25gRPZJ/LnjX5dthS3YX8NCne2kyWkiJDOLf35rgksTEZLagUxRZcRa9cuTsEX636XccqDgAwLSUaTw65VESQxJdGockQv3E1YkQQIu5hd9v/j3Lji8D4O5Rd/PjcT926l6qEN7Aolq47YvbOFBxgOuGXscfpv6hT493quYUi5YswqyaefuKtxkbP7Z/Au2GyWzhj18c5I3N+QBcMiyO524eS1SIv0ue/3wWiypJkeiW0WLk1X2v8p+9/8FkMRHmH8avJv2Kq4dc7bJVoLakoaIH89f78+fpf+YHY34AwCv7XuHBDQ/SbG7u8Pomo/s0dxNCS/899l8OVBwgxC+EH4/7cZ8fb2D4QK7JuAaAf+z+R58fz1GvbTphT4J+MieD178zSZMkqKHFxF+X53H7K1uxSANG0YW8yjxu++I2Xsx5EZPFxKwBs1hy9RKuybhGkySoJyQRclOKovCjsT/ij9P+iEExsDx/OfesuIezTWft19Q3m3h8WS5znlpHXbNJw2iF0F6DsYHndj0HwPdGf4/YoP5pNfH90d/HT+fHtuJtbC3a2i+P2RWj2cIrG08A8PjVI3ngsuGaNTqsbjTyxuZ8thyv4NPdBZrEINybRbXwYs6L3LrsVvIq84gIiODJGU/y/Jzn7aef3Z0kQm7u6oyr+del/yLML4zdpbv51pff4mTNSQD89DpWHyyhsLqJ/6w/pnGkQmjrlX2vUNZYxoDQAXxrxLf67XGTQpO4cdiNADy/+3mcXU3gp9fxyb1T+f7Mwdx6kbb9VZIigvjJ3KEAPPnVQaobjd3cQ/iaz49+zr/3/BuTauLStEtZcvUSFgxe4ParQG1JIuQBLk66mLevfJvkkGRO1Z7iW19+i92lu/E36HjoikwAXt54nOLW/h9C+JrCukLePPAmAL+Y+Av89f27jXT3qLsJ1Aeyt2wvGws29utjdyQ1OpiHrxiBQa/9S/R3pw1iSFwI5XUtPLPysNbhCDeiqirv5b0HwA/G/ICnZz3dbyuxrqT9T5lwyJDIIby74F1GxoykqrmKu/93N6tOrmL+yEQmpkXRZLTw9xWHtA5TCE08vfNpWiwtTEqcxJyBc/r98eOC47g181bA2qjRolq6uUfvVNa3OOVx+8LfoOOxq7MBeGtLPrmFNRpHJNzF3vK95FXmEaAP6NdVWFeTRMiDxAbF8tr815idOpsWSwu/3fRbGk2N/GaB9Xjwx7vOyIuU8Dk7S3byv/z/oVN0PDjpQactyX83+7uE+IVwsPIgq0+t7vfHr6hr5pK/ruVH7+6itsm9tqCmZcSyYHQSFhUe+Xy/07cHhWf4IO8DAK4YdAURAZ7bykUSIQ8T7BfMM7OeITUslXpjPStOrmDcwCiuGp2EqsITXx3UOkQhXMaiWvjLtr8AcN3Q6xgePdxpzxUZGMm3s74NWFeFzJb+Pa35nw3HqWs2caqygVAndo/urd8uGEGwv54DhTUcKa3TOhyhsbNNZ1mevxyAm4ffrHE0fSOJkAfS6/T2I72fHfkMgF/Nz8RPr7Dz5FlOVzZoGJ0QrvP50c85WHmQUL9Q7ht7n9Of746sOwj3D+d49XG+PPFlvz1uaU0Tb7Yel//5ZcPcstA0KSKI524Zx+qfz2RYQpjW4QiNfXb0M4wWIyNjRpIdm611OH0iiZCHunrI1egUHbtKd5Ffnc/AmGCeu2Uc6345i9Tovg9hFMLd1RvreX7384D1iHtMUIzTnzPMP4y7su8C4F97/oXR0j9bWC+uPUqzycKEtChm9mGSvLNdmpVAcmSQ1mEIjZktZj489CHg+atBIImQx0oISWBa8jTAmpkDXDkqifiwQC3DEsJlXtn3CuWN5aSGpXLbiNtc9ry3Zd5GdGA0p2tP8/nRz/v8eGfONvDetlOA+64GdeSb4xUcKq7VOgyhgU2FmyioKyDcP5zLB12udTh9JomQB7tu6HUALD22FJOlfUPFzcfKaWiRJovCO52pPcNbB94CnHNcvivBfsHcPepuAP6959+ddnx31AtrjmI0q0wdEsPUIZ5x9PitLfnc8tI3/OazfVI47YM+OGQtkr4m4xqCDJ6/QiiJkAebOWAm0YHRlDeWs/HMud4mv12yj9te3srLG05oGJ0QzmM7Lj85aTKzU2e7/PlvGn4T8cHxlDSU8PHhj3v9OI0tZtYdKgOsq0Ge4tKsBIL99ew4eZZPd0nHaV9ypvaM/ffNTcNv0jia/iGJkAfz0/uxcPBCAD49+qn99osHW2sl/rPhGKU10mRReCiLGU5shH0fW/9sPaW1vXg7K0+uRKfo+NWkX2mylRSgD+D7o78PwMt7X6bR1Nirxwny17PmFzP5x63jmJAW3Z8hOlXbjtNPSMdpn/Lx4Y9RUZmaPJW08DStw+kXkgh5ONv22MYzGylrsL6zXDAqibGpkTS0mHlmlXSCFR4odyk8mw1vXgWfLLb++Ww25gNL+Nv2vwFww9AbGBal3SrKtRnXkhKaQkVTBf+X93+9fpxgfwMLxyT3Y2SuIR2nfU+LuYVPj1jfdHvLahBIIuTxBkcOZkzcGMyqmaXHlgLWga2/bW2y+MH20+0LGjt5ly2E28hdCh/eATWF7W+vKeLzr37EwcqDhPmF8aNxP9ImvlZ+ej/uHXMvAK/tf426lp711tmRX+nRE939DTr+sEg6TvuSFSdXcLb5LAnBCcwcMFPrcPqNJEJewLYqtOToEnvh4sT0aK7ITsTStsliJ++yyV2qVehCtGcxw/IHgQsThDoFno+ydq/9/ujvER2o/VbSgsELSA9Pp7q5mrcPvu3w/fYXVHPDv7ew8IWvaTJ67puR6UOl47QvsXWSvnHYjRh07tf0s7ckEfIC89PnE2QIIr8mn92lu+23P3h5JgadwrpDZRxc/W6n77L58A5JhoR7OLn5wn+jrV6ODKfCoCfNaOS2IPeoTTDoDPaVqbcOvEV1c7VD97NtJWXEhxLop3dafK7w2wUjGBwbwh1T07UORThRXmUeOWU5GBQD1w+7Xutw+pXHJUIvvvgi6enpBAYGMnnyZLZt2+bQ/d5//30UReGaa65xboAaCPELYX76fAD7/i1AemwI37o4jbQofwbvfIyO3mXbb1v+kGyTCe3VlXR482mDgbcjwgH4RUUVfg0VroyqS5elXcawqGHUGet4ff/r3V6/69RZVueVotcp/LS14NiTJUUEseqBmSwak+wxPZBEz9mOzM9Lm+eRE+a74lGJ0AcffMADDzzA73//e3bt2sWYMWOYP38+paWlXd4vPz+fX/ziF8yYMcNFkTpZB3U+tu2xFSdXtKtV+MX84ay83p+AhuIuHlCFmgLru3EhtBSa0OHNT0dHYlQUpjQ2MrOxsdPrtKBTdPbxHu/lvUd5Y3mX1z+9wroadP34FAbHhTo9PlfQ6c4lQC0mi4aRCGeobanli+NfAN7RSfp8HpUIPf3009xzzz3cddddZGVl8e9//5vg4GBee+21Tu9jNpu5/fbb+cMf/sDgwYNdGK2TdFLnM7bsFOnh6TSaGu2D8ABCAwz4N5Y59tidvBsXwmXSpkJ4MnDuF+v2wABWhQSjU1V+WVGNEp5ivc6NzEqdxajYUTSaGnl136udXrflWAVfHy3HT6/w4zmevxp0vk92nmHGX9dI4bSXWXpsKY2mRjIiM5iQMEHrcPqdxyRCLS0t7Ny5k3nz5tlv0+l0zJs3jy1btnR6v8cee4z4+HgWL17s0PM0NzdTU1PT7sNtdHGaRvnoTq4LzwTODWK1c/Tdsxu9yxY+SqeHy//S+hcFM/CX6CgAbqytY6jRCJc/ab3OjSiKwn3jrKtCHx76kOL6C1dgVVXl6ZWHALhl0kCvnAm4Jq+Ukppmfr9UCqe9haqq9m2xm4bf5JXbnx6TCJWXl2M2m0lIaP/LOiEhgeLijrd9vv76a1599VVefvllh5/niSeeICIiwv6Rmprap7j7TRenaWy3Ldz7BQbFwN7yvRw9e/Tcp9Om0hhkPUHWMQXc8F228FFZi+CmtyA8iaWhIRwK8CfMbOFHpmDr7VmLtI6wQ1OSpjAhYQItlhZe2vvSBZ+vaTTRbLIQYNBx35wMDSJ0vt8sGEGQn57t+WdZmSsrzN5ge/F2TlSfIMgQZG/g6208JhHqqdraWr797W/z8ssvExvreGHXww8/THV1tf3j9OnTToyyB7o4TWOlEltdwCXRI4H2nabR6Qm46q8oCh0kQ63ZvRu+yxY+LGsR3L+fzzMmA/DdIdcS9dN9bpsEgXVV6MfjfgxYV2VP17Z/7YgI9uPzH03jvz+eTkK4dw5HTo4M4o4p1hN9H+44o3E0oj/YVoMWDl5IqL931LSdz2MSodjYWPR6PSUl7d9llJSUkJiYeMH1x44dIz8/n4ULF2IwGDAYDLz11lssXboUg8HAsWPHOnyegIAAwsPD2324BQfrd66LzAJg2bFlGM3n2t7rRl7N4Zn/pJjzeq+EJ7v1u2zhu6paatldcxyAKybc6xGJ+oSECUxLnoZJNfHvPf++4POKojAsIUyDyFznhgkDAFh7qJSy2r4NpBXaKm0oZc2pNYB3dZI+n8ckQv7+/kyYMIHVq1fbb7NYLKxevZopU6ZccH1mZib79u0jJyfH/rFo0SJmz55NTk6O+2x5OcrB+p1pyVOJC4rjbPNZ1p1Z1+5zw2ffzqOD3+eWlt/yUtyv4c5lcL97v8sWvmtDwQYsqoVhUcNICU3ROhyH2WqFlh1fxvGq41gsKm9tyae2yTfmcQ1NCGNMaiRmi8rnOTKQ1ZN9cuQTTKqJ8fHjGR49XOtwnMZjEiGABx54gJdffpk333yTgwcPcu+991JfX89dd90FwB133MHDDz8MQGBgINnZ2e0+IiMjCQsLIzs7G39/fy2/lJ7r4DRNe9Y6H0P6DBYNsSY2bXsK2Tx45Ui2M5I/n85mq5rlEe+yhW9ad3odgCbT5fsiOzab2amzsagW/rnnn3y5v4hHPj/Ague/xuzBIzV64sbWVaGPd56RomkPZbQY+fjQx4B3Hplvy6MSoZtvvpmnnnqKRx55hLFjx5KTk8Py5cvtBdSnTp2iqKhI4yid5LzTNO21r/O5dui1AGwu3HzB6ZUhcaHcNNG6GvbO1lNODFiI3ms2N/N1wdeA5yVCAD8a+yMUFP6X/z/+ttran+uGCQPQ67zvxE1HFo5O5taLBvKna7O1DkX00vrT6yltLCU6MJp5afO6v4MH87hhIffddx/33Xdfh59bt25dl/d94403+j8gV7Kdpln+YPvC6fBkaxLUusWVFp7GhIQJ7CzZyedHP+f7Y77f7mFunzyQ+mYTN04c4MrohXDYtqJtNJoaiQ+KZ0TMCK3D6bHh0cOZkDCBHSU7KGzZRVTwLO6alq51WC4TEezHE9eN0joM0QfvH3ofsM6y9Nd72A5KD3lcIuTzshZB5gLrKbK6EmvtUNrUC7a4rht6HTtLdvLZ0c+4Z/Q96JRzi3/ZKRE8f+s4V0cuhMNs22IzU2e2+7frSaYlz2BHyQ4MoXn84OLFhAX6aR2SEA45Xn2crUVbUVC4cdiNWofjdJ75CuPrdHoYNANG3WD9s4M6n0vTLiXUL5SCugK2F2/XIEghekdVVY+tD2qroWoYAIaQ49wwMV7jaLSxv6Ca3y7Zx9q8rscgCffy0aGPAJg5YCbJockaR+N8kgh5qSBDEFcMugLouGga4HBJLU9+lUdRdaMrQxOiS7mVuZQ2lhJkCOKipIu0DqdXLBaV9zc3Y2mJBsXMngrffDPy372FvPPNKd7delLrUISDGowNfH70cwBuzvTuImkbSYS8mG0Q66qTq6hurr7g87/5bB//Xn+MpTldNWoUwrXWnloLwLTkaQToAzSOpndqmoyMGRCJX4u1r9eGMxs0jkgbN9p7CpVJTyEPsTx/ObXGWgaEDmBqsm9MG5BEyIuNjBnJ0KihtFha+OrEVxd8/tpx1hepz3ZLrw/hPuzbYgM9d1ssMtiff31rAs8uug2AjWc2+uQx8oz4MMZKTyGPoaoq7+dZi6RvHn6zx9bn9ZRvfJU+SlEUrsuwrgp1tD22YFQS/nodecW1HCxyo+GywmcV1hVy6OwhdIqOGSkztA6nz6YkX0SQIYjSxlLyKvO0DkcTtk7TH+2QnkLubl/5Pg5WHsRf5881GddoHY7LSCLk5RYMXoBBZ+Bg5cELXogjgv2YnRkHwBJZFRJuwLYaNDZuLFGBUZrG0lvHyuo4VlYHQIA+gMlJ1nlpvro9tnB0Mv4GHYdKajlQKG+43Jltrtjlgy4nMjBS22BcSBIhLxcVGMWc1DlAx6tCtu2xJTkFPtP1Vrivtaet9UFzBs7ROJLe+8fqI8z9+3r+tc46z/CSAZcA1pEhvigi2I/LsqxNbz/eKYNY3Y7FDCc2cnb3Wyw//iXg/Z2kzyeJkA+wFU1/cfwLms3tCxZnZ8YREeRHSU0z3xyv0CI8IQCoballR/EOAGalztI2mF5qbDGzItc6IHnyYOuAY9sW376yfVQ2VWoWm5ZumDCApIhAEiMCtQ5FtJW7FJ7NhjevYsm6X9OimhhhUhlVekLryFxKEiEfcHHSxSSGJFLTUsPqk6vbfS7AoGfB6CSigv0oqWnSKEIhYFPBJkyqiUERg0gLT9M6nF5ZdbCEhhYzqdFBjEuNBCAxJJHhUcNRUdlUsEnbADVyydA4vn5wDj+YOUTrUIRN7lL48A6oKcQCfBAWBsAtZ8+ifHSn9fM+QhIhH6DX6e2Fb58evXB77Ffzh7P11/O4bryM3BDaWXN6DeC5q0EAS/dYW1EsHJ2MopybK2bbHlt/Zr0mcWlNp1N8Zs6aR7CYraOasJZDbAoKpMDPQJjZwhX19dZrlj9kvc4HSCLkI67JuAYFha1FWzlT236fPjLYH3+D/FMQ2jFajHx9xjpk1VbT5mmqG42sP1QGwNVjU9p9zpYIbS7YjNFidHls7sJotrD6YAnlddJTSFMnN7ebV/lBuHU16Oq6OoJUFVChpsB6nQ+Q334+IiU0xX56ZcnRJR1eY7GoHCqudWFUQljtKtlFrbGW6MBoRsV65rDO/+0vpsVsYXhCGMMTw9p9blTsKKICoqg11pJTmqNNgG7ge2/tYPGbO+SUqtbqSuz/2aAobAqy1m7dUFvX6XXeTBIhH2Irmv782OeYz1vyrGkycsnf1nLl8xulA6xwOdux+UsGXIK+g9l5nmBFbjEAi8ZeOJtJr9MzLWUaYG2u6KvmjrCeHpOeQhoLTbD/547AAEyKQorRxGCjqdPrvJkkQj5kzsA5hPuHU1xfzDdF37T7XHigHzEh/pgtKsv2ysgN4TqqqtqPzXvykNUXbhvPv781nuvGp3T4efsxeh/tJwTSU8htpE2F8GRAYUvratDFjU2cq+JSIDzFep0PkETIhwToA1gweAHQWU8h6wu4LFsLVzpSdYSCugIC9AFcnHSx1uH0WqCfnsuzk0iKCOrw81OTp6JX9ByrPnZBnZ6viAj2Y/7IREB6CmlKp4fL/wLAN0HWf69TGm3Dt1vTocuftF7nAyQR8jG27bE1p9dwtulsu89dNSYZvU5hz5lqe2dcIZzNti12cdLFBPsFaxpLbzmyzRMREMHY+LGAb68K2UZuLMkpoNnkG6eS3FLWIkqveYGj/n4oqsrkptaSiPBkuOktyFqkbXwuJImQj8mMzmRE9AhMFhPL85e3+1xsaAAzh8nIDeFatmnznrotdqqigbl/X89zq450mxD5epdpgOkZsSSEB1DVYGRtXqnW4fi0b8LCARgRlkbktS/Dncvg/n0+lQSBJEI+6bL0ywDYXHjh0chrWrfHPttdgEVGbggnK20oZX/FfgBmps7UOJre+e/eQo6X17Mtv6Jd76COXJJiTYS2F22nwdjgivDcjl6n2HuWfXPcNzttu4tvCq21olPSL4VRN8CgGT6zHdaWJEI+yFaHsaN4ByZL+1MCl45IIDTAwJmzjew+fbajuwvRb2wNBkfHjiY2KFbjaHpnaY71cMGiMReeFjvfkMghJIck02JpYVvxNmeH5rbumJLGFz+ZzqOLRmodis9SVdV+aGZK8hSNo9GWJEI+aET0CML8w6gz1pFbkdvuc0H+eh5dNJKPfjCFcameOf1beA5bfdDsgZ65LZZXXMOhklr89AqXj0zq9npFUZgxwDp7zJfrhJIighiZHKF1GD7taNVRyhrLCNAH2GvXfJUkQj5Ir9MzKWESAFuLtl7w+RsmDGBSejQ6aYkvnKjB2GBfmp81YJa2wfSSbTVo1vB4IoL9HLrPzAHWLcANZzZILx2gocUk3wcNbCncAsCEhAkE6AM0jkZbkgj5KFuX6Y4SISFcYUvhFlosLQwIHcCQSM8bxqmqKv/d6/i2mM2kxEkE6gMpaSjh8NnDzgrP7amqysOf7mPC46vYXyA9hVzNvi2W5NvbYiCJkM+y1QntLt1Nk+nCqfOnKhr43ZL9PPTJXleHJnyErYnirNRZ3RYZu6Pdp6s4XdlIsL+eeSMc78AbaAi0vxHx5e0xRVGoazbRaDTz8c7TWofjU4xmIztKdgBSHwSSCPmsQRGDiAuKo8XSQk5ZzgWfbzCaePubk3y6q4CqhhbXByi8mtliticBcwZ65pDVsAADN04YwHXjUwjy79lJG+kybWXrKfT5nkLpKeRCOWU5NJoaiQ6MZmjUUK3D0ZwkQj5KURT7u9JtRReeXslMDCczMYwWs4Uv9hW5Ojzh5faU7eFs81nC/cM9tlBzaEIYf7txDH+8pudDYmekWAum95bvvaCxqS9p21NozUHpKeQqtvqgyUmT0SmSBsh3wId1VyckIzeEs9hOi80YMAM/nWNFxt4kKTSJYVHDsKgWvi74WutwNNO2p5CM3HAd22u+1AdZSSLkwyYnWhOh/RX7qW2pveDzV49NQVFge/5ZTlf6ZvM34RyePmT1v3sK2Xumqk+nnWzbY748jR7g+tZEaN3hMkprL6xXFP2rurna3sRU6oOsJBHyYUmhSQwMG4hFtbCjeMcFn0+MCGTqkBhAVoVE/zlRfYL8mnwMOgPTkqdpHU6PNZvM/PqzfSx6YRM7T/Z+W8uWCH1d+PUFjU19SUZ8KOMGRmK2qHy+u1DrcLze9uLtWFQL6eHpJIYkah2OW5BEyMfZt8eKO9ses75b+yynQHp9iH5h2xa7KPEiQv1DNY2lN9YfKqO2yURCeADjBva+6ejo2NFEBERQ21LLnrI9/Rih57l7+mB+cdkwFozuviml6BtbfZCsBp0jiZCP665OaP7IBAbHhnBZViLNJosrQxNeyt5N2kO3xZbusa5aLBydjL4PTUf1Or19RczXT48tGJ3EfXOGkhwZpHUoXk/6B11IEiEfd1HiRYC13Xp5Y/kFnw8L9GP1z2fy0BWZBPr53jA+0b8qmyrt7Rpmpc7SNJbeqG82sepgCQCLxjreRLEzbbtMC+FsBXUFnKo9hV7RMylxktbhuA1JhHxcVGAUmdGZQOerQp7Y7E64pw1nNmBRLYyIHuGR9Qkrc0toMlpIjwlmVErfZ2VNS5mGTtFxtOoohXW+XR9jMltYtreQH767U3oKOYltW2xU7CiP3JZ2FkmEhP30WFfTsE1mC2sPlbK/oNpVYQkvZNsW88TVIDi3LbZobErXbxAsZjixEfZ9bP3T0vEv9oiACMbGjQXOWxVy8P7eRFEUHl+Wy5f7iqWnkJNIfVDHJBESDs0de2rFYe56fTv/2XDcVWEJL9NsbmZz4WbAM+uDWkwW8oqsM7G6nC2WuxSezYY3r4JPFlv/fDbbensHLphG38P7e4u2PYU+kp5C/c6iWuyHYiQRak8SIcGEhAkYFAMFdQWcru145s8V2dZtjBUHiqltMroyPOElthZtpdHUSEJwgn071pP4G3RsfHAOH/9gChnxnWwr5C6FD++AmvO2uWqKrLd3kMzYjtFvK95G476Pe3x/b3L9eGsT141HyuR1pp8drDxIdXM1IX4hZMdmax2OW5FESBDsF8yoOOuYgM5WhUYPiGBwXAjNJgv/O1DiyvCEl/D0IatgXbWYmB7d8SctZlj+INBRm4nW25Y/dME219DIoSSFJNFsbmbbukd6fH9vkhEfxqDYEIxmlY1HLjy8IXrPti02KXGST3Zz74okQgLofntMURSuHWt9t/bZblm2Fj1jUS2sP70egDmpnjdktcloxmzppo/Wyc0XruS0o0JNgfW6NhRFOTeEla46uHd8f28zNzMegNVSJ9Svvim0Hpu/OOlijSNxP5IICaB9wbRF7bhf0DWts8c2H6uguFpa4QvH5VbkUtZYRohfCBMTJ2odTo+9881Jpjyxmre/Odn5RXUOrpR2cJ09EQoO6nA9qFfP46HmjLAmQmsPlXaffAqHNJoa2VW6C5D6oI5IIiQAGBM3hiBDEJVNlRw5e6TDa1Kjg5mUHoWqwuc5MnJDOG7NqTUATEuehr/eX+Noem7pnkJKa5uhq+7qoQmOPVgH101KnESAzo9ig4Ejft1sWzj6PB5qUno0kcF+DIwOpqKuWetwvMLukt0YLUYSghMYFD5I63DcjiRCAgA/vR/j48cDXZ8es60K7TlT5YqwhJdYd2YdALMHet5psRPl9ew9U41ep3DlqC5GQKRNhfBkoLP6JwXCU6zXnSfIEMRFrdvTG4I7667c+f29iZ9ex6YH57DkR9OIDw/UOhyvsKXo3LF5T63PcyZJhIRdd3PHABaOSWbZj6fz4m3jXRWW8HBnas9w5OwR9IqeGSkztA6nx5bmWOt+pmXEEhMa0PmFOj1c/pfWv5z/y6b175c/ab2uAzMHzAJsiVDP7+9NQgIMWofgVWyF0lIf1DFJhISdLRHaUbwDo6Xjo6vhgX5kp0TIuwrhsPVnrEXS4xPGExHQ927MrqSqKkv3WLeBr+6qd5BN1iK46S0IP2/lKDzZenvWok7vaqsT2hMYSFVEz+/vjaobjXKMvo8qGis4dPYQIIlQZyTtFnaZ0ZlEBERQ3VzNgfIDjI0f2+X1FouKrg9DJ4UPsJhZe/gzAGYHp1mPfnvQikZuUQ3HyuoJMOi4bKSDtTlZiyBzgfV0V12JtaYnbWq3X3dSaBIZkRkcrTrKpkV/ZYEuskf39zaPL8vlzc35PLIwizumpGsdjseylToMjxpOTFCMxtG4J1kREnY6RWcfwtpVnVBji5kfvbeLi/68ivpmk6vCE54mdyk1z2Wz82weALPWP+9xHZJt22JzMuMJC+xB7xWdHgbNgFE3WP90MImxnx4r+LpX9/cmcWEBmCyqHKPvo7b1QaJjkgiJduyJUBd1QoF+Ovadqaa8roWvj0rTM9GB1g7Lu1sqMSkKaUYjqSaTx3VIXjgmmbumpXPzpFSXPJ8tEdpUuAmTxbffZMxrPUa/5ViFvOHqJVVVpT7IAZIIiXZsdUI5pTk0mho7vEZRFOa0Nj1bmyfv1sR52nRY3hloPfUzscl2DNqzOiRnp0Tw+4UjmTU83iXPNyZuDOH+4VQ3V7O3bK9LntNdDYkLZWB0MC1mi7zh6qX8mnxKGkrw0/kxPkEOuHRGEiHRTnp4OvHB8RgtRnaX7u70utmZ55qeqV31VhG+p02H5V2B1lNW45va9oPxjQ7JvWHQGZiWMg04bxq9D1IUhbkjbF2mvbuJpLPYVoPGx48nyNBZWwYhiZBoR1EU+xJqV3VCkwdFE+yvp6SmmQOFNa4KT3iC1s7HjYrCgQBr88TxTR10InfzDsnPrjrM+sNlGM0dd1p3lnN1Qr6dCAHMzbQWqK/JK8MiXaZ7zFYfdHGybIt1RRIhcYHu5o4BBPrpmZYRC8Aa2R4TbbV2Pt4X4I9JUYg3mUgxdbAN5sYdkvPL63l21REWv7GdJqNrt/CmJ09Hp+g4cvYIRXVFLn1ud3PRoGhCAwyU1zWzt6Ba63A8itFiZHvxdgCmJEmhdFckERIXsM0dO1h5kOrmzl98bHVCkgiJdlo7LNvqgyY0NZ/XHtD9OySvO2T9Nz0pPbpnp8X6QWRgJKNjRwOwsWCjS5/b3fgbdPxodgZ/vCabgdHBWofjUfaX76feWE9EQASZ0Zlah+PWJBESF0gISSA9PB2LamFHyY5Or5s9PJ4xAyKYkxkvdULinNYOy7b6oAnt6oM8o0Py2kNlAMzOjNPk+W3bY76eCAHcO2sI37o4jegQz5tRpyXbtPnJiZPRu/HPmjuQREh0yJHtscSIQD6/bzo/mTtUOk2LdoyZV7AnJBw4r1DaAzokN7aY2XK8ArAm+1qw/fztLt2NRXVtjZLwDtI/yHHSWVp06OKki/ng0AddJkJCdCavIo9GSwvh/uEMufUFqC/zmA7JW46X02KykBIZREZ8qCYxjIgeQaA+kOrmak5Un2BI5BBN4nAXRdWNrMotYUBUsP3EquhcXUudvf2C9A/qnsetCL344oukp6cTGBjI5MmT2bZtW6fXvvzyy8yYMYOoqCiioqKYN29el9eLcyYlTkJB4Xj1cUobuq4Bqm0y8tW+IpefrhHua1fpLsB6bFc3eKZHdUi21bzNyYzXbKXTT+/H6DhrnZDte+nLPt1VwO8+P8Db35zUOhSPsL14O2bVTGpYKgPCBmgdjtvzqETogw8+4IEHHuD3v/89u3btYsyYMcyfP5/S0o5/Ua9bt45bb72VtWvXsmXLFlJTU7nssssoKChwceSep22BXVerQqqqMu/p9dz77i525J91VXjCze0s2QnAhIQJGkfSc4eKawHt6oNsxsWPA2BXiSRCtn5Cm46W09ji/o04tfZNkbU+SE6LOcajEqGnn36ae+65h7vuuousrCz+/e9/ExwczGuvvdbh9e+++y4//OEPGTt2LJmZmbzyyitYLBZWr17t4sg9kyP9hBRFYdoQ6zH6tYfk9JgAi2qxN+P0xG62H35/Cv+7/xKmtv671orte9dVY1NfMTwhjJTIIJpNFjZJl+luSX1Qz3hMItTS0sLOnTuZN2+e/TadTse8efPYsmWLQ4/R0NCA0WgkOjraWWF6FXvBdPHWLk+FzZHur6KNE9UnqGquIsgQxIiYEVqH02OKojA8MYxAP2238cbEjUGn6CioK6C4vljTWLTWrst0nrzOdKW4vpgT1SfQKTomJU7SOhyP4DGJUHl5OWazmYSE9k3YEhISKC527EXiwQcfJDk5uV0ydb7m5mZqamraffiqcfHjMOgMFNcXc7r2dKfXzRgah16ncKysnlMVDS6MULgj27bY6NjR+Olc24Onr9ypDUSIX4h9e1pWhWDuCOtr/+qDMtanK7axGiNjRhIREKFxNJ7BYxKhvnryySd5//33+eyzzwhsbfTWkSeeeIKIiAj7R2qqa6ZOu6Ngv2DGxI0Bzu05dyQiyI+JaVEArJF3az7PU+uDapuMTPrTan7yf7td3k26M+Pjrdtjtu+pL7ON9SmtbWZ/ge++Qe2O7bVaTos5zmMSodjYWPR6PSUl7X/RlpSUkJiY2OV9n3rqKZ588klWrFjB6NGju7z24Ycfprq62v5x+nTnKyG+wJF+QtCmy3RrIzrhu+wnxjysPmjT0XLK65rZX1Ct+baYja1gWlaErGN9ZgyNxaBTyCuWRKgjFtVyrlBa6oMc5jGJkL+/PxMmTGhX6GwrfJ4ypfP/4X/96195/PHHWb58ORMnTuz2eQICAggPD2/34cts7yq2FW/rsrGbLRH65niFnOrwYYV1hRTXF2NQDPbj355ibZ41iZ+lURPFjtiSySNnj1DTIr/8f7sgi12PXMqNE313pb4rR84eobKpkiBDkH01X3TPYxIhgAceeICXX36ZN998k4MHD3LvvfdSX1/PXXfdBcAdd9zBww8/bL/+L3/5C7/73e947bXXSE9Pp7i4mOLiYurq6rT6EjxOdkw2QYYgqpqrOHz2cKfXZcSH8tfrR7P6gZkE+bvHu2nherYtnKyYLIIMQRpH4zhVVe2nHrU+Nt9WbFAsA8MGoqKSU5qjdTiaS40OJtzFs988ia0+aELCBPz1MpLEUR6VCN1888089dRTPPLII4wdO5acnByWL19uL6A+deoURUXnpjX/61//oqWlhRtuuIGkpCT7x1NPPaXVl+Bx/PR+9lqP7o7R3zQplVQZjOjTbImQp22L5RbVUFrbTJCfnosGudepUjlG3zGTNHC9gPQP6h2PG7Fx3333cd9993X4uXXr1rX7e35+vvMD8gEXJ13M1wVf803RN9w58k6twxFuzFYf5GmF0utaa9umZcQSYHCvFc3x8eNZcnSJNFZstT2/kj9+cZD4sABevqP7cgdf0Wxutr8RkfqgnvGoFSGhDVvB9M6SnRjNxi6v/XjnGRa/sZ2DRVLP4GsqGis4UX0COFfk6ynW5rnftpiNbUVoX/k+ms3N3Vzt/UL8Dew5XcXGI2Vuc7rPHeSU5tBkbiI2KJaMyAytw/EokgiJbg2LGkZUQBSNpkb2V+zv8trl+4tZnVcqzRV9kG3rJiMyw6P6l6iqyoyhcWSnhLtVobTNwLCBRAdGY7QYOVB+QOtwNDciKYzkiECajBY2H5Mu0za2+qCLky7WbEaep5JESHSrbYfSrvoJQZtj9HkybsPXeGr/IEVR+Om8oSz78QxSIt2vwFtRFPv3VAawWr8f57rZy+uMjRyb7z1JhIRDHO0nZNta2H26isr6FqfHJdyHp9YHeQIZwNqercv0mjzpMg1Q1VRFbkUuII0Ue0MSIeEQ2w/XnrI9NBxdBfs+hhMbwdJ+jz4pIogRSeGoKqw/LO/WfEW9sZ68yjzgXDdkT2C2qKw4UExds0nrULpkqxPKKc3psp+Xr5gyOIYgPz1F1U3kSj2idR4kKkMihhAf7H7bu+5OEiHhkNSwVJL8IzFZTOz+5Hb4ZDG8eRU8mw25S9tdO6d1VWhNnnSZ9hW2X9ApoSkkhCR0fwc3sfdMFd97eycz/7oWi8V9VxaGRw0nyBBErbGWI2ePaB2O5gL99EwfGgvI9hjAjuIdwLmVe9EzkggJhygH/8tFFQUAbG07q62mCD68o10yNCfT+otw/aFS6fXhIzy1Pmht67H5yYOj0enct8DUoDPYOwVLPyGrq0YnsWBUEqMHeE5hvrPklOUAMC7Bs05rugtJhET3LGZY/iCTGxsB+Cao7dDa1nfRyx+yb5ONTY0kLiyArORwKqROyCfY54t50LYYwLrWbtLueFrsfLbtMSmYtrp6bAov3j7eI/7fOVO9sd7e9X9cnCRCveFxDRWFBk5uhppCJuutjeby/P2o1umIsNhWe1SoKbBeN2gGep3C1w/OdrvGdMI5Wswt7CvbB3jWilBZbTN7z1QDMGu4+/UPOp8tydxVsgtVVeWItACsdZsW1UJySLJHbUu7E1kREt2rs/YEijebGdxiRFUUtgcGdHodIEmQDzlQcYAWSwvRgdGkhadpHY7D1h+2bouNSokgPiywm6u1Nyp2FAbFQElDCUX1Rd3fwQeoqsrR0jqW7y/WOhTN2GbQjY0fq2kcnkwSIdG90HPvMiY3NgHnb49deJ1NRV0zNU1dd6MWnq1tfZAnrVLYh6x6wGoQQLBfMCNiRgDnvue+7mBRLfOeXs8DH+b4bJdpW83YOH1Yp6d5RdckERLdS5sK4cmAwuQmayLUrmAaBcJTrNe18dsl+5j4p1Us2V3guliFy9kHrXpQfZDJbGFj64rQrEzPqTGxfY+lYNpqRFIYCeEBNLSY2XqiUutwXM5kMbG3tbfUuHXPdnmaV3ROEiHRPZ0eLv8LAJOamlFUlXx/P8r1OqB1BeDyJ63XtZESGYyqSpdpb2a2mO1L855UH2TQ61h633Qev3okYwZEah2Ow2yngqSxopWiKPZTqr441ufIzldosLQQarGQYWyz8t7BaV7ROUmEhGOyFsFNbxEemmj/gcsJCLCuFN30lvXz57GN29h8rIKGFvduWCd650jVEeqMdYT6hTIsapjW4fRIemwI356Sjt6Nj82fz9Zh+lj1MaqaqrQNxk3MzTw3bsOnukxbzOze/jwAY5qaaf829MLTvKJzkggJx2Utgvv3My5tLgC7x98C9+/rMAkCGJYQSkpkEC0mC5uPVrgyUuEitm2xMfFj0OukQN7ZogOjGRQxCJDtMZtpGbEEGHQUVDVyqKRW63Bc5+RmclRrqcLY5uYOLmhzmld0SRIh0TM6PWOHXAFATnPZBdthbVmXrVuHsB6S7TFvZC+UjvecbbGi6kbueWsHH24/rXUovSJ1Qu0F+euZluGDXabrStjVenp3XFNHidC560TXJBESPWY7pplbmUuTqanLa22J0FoZjuh1VFW116p4Un3QukNlrMwt4f3tp7QOpVekseKF5rZOo994xHfG+hQZ/CgxGNCrKqOau2hc28FpXtGeNFQUPTYgdACxQbGUN5azv3w/ExMndnrtlCExBPrpKKpuIq+4lhFJ4S6MVDjTqdpTVDRV4K/zJzs2W+twHLY2z3Zs3nNOi7VlqxM6UHGAJlMTgQb374HkbPNHJpIcEcSUITFah+Iyu/2tq/HDW4wEd/gmU7HWcJ53mldcSFaERI8pimJ/MbbNuOlMoJ+en8wdyt9uGE1yZJALohOuYtsWy47Nxl/vr3E0jmk2mfn6aDkAsz3o2HxbA0IHEB8Uj8liYl/5Pq3DcQuxoQHMzown0M936tR2l+0BbNti5xf8d36aV1xIEiHRK2PjxgLnupp25YezMrhxYioRQX7ODUq4lCcOWt1+4iwNLWbrLDwPXZ1UFEWO0Qv7m9Cx4++B8KT2n+ziNK+4kGyNiV5puyJkUS3oFMmpfY3tl7CtZsUT2LpJzxoW59bT5rszPn48/8v/nxRMt9HYYua51UfYcryCD79/sVeP+Wk3aHXc3TD1IevpsLoSa01Q2lRZCeoBSYREr2TGZBKoD6S6uZr86nwGRw7u8vqTFfWsOFBCVnK4/YSH8Fwl9SWcqTuDTtHZVwc9gX2shodui9nYks+cshzMFrO0LgACDDo+2XWGstpmth6v5JJhnjE6pTdsg1ZTQlPODVodNEPboDyYvI0XveKn82Nk7EjAsWO8H2w/zZ++PMj7HnpkWbRnO7E0PGo4of6hGkfjmPpmEzEh/vgbdEwf6tnJ+NDIoYT6hbZbGfB1Op1inxtnG6jrrWTQav+SREj0mm17zJFEyHaMfv2hUkxmi1PjEs7nifVBIQEGPvrBVHb/7lLCAz27Xk2v0zMmfgwgx+jbmjnM+jqzwcsTIfug1bhxGkfiHSQREr3m6MkxgHEDo4gM9qOmycSuU1XODUw4ne2XryfVB9mEBHhHRYCtiaUUTJ8zLSMGRYEjpXUUVTdqHY5TmCwm9pbtBWRFqL9IIiR6bUyc9R3pyZqTVDZ1PflZr1OY2bpnL0NYPVt1czVHzx4FPGfifLPJTFVDF03nPFDbFVlpVmoVGezP6NYhuhsPl2sbjJMcPnuYBlMDoX6hZERmaB2OV5BESPRaREAEQyKGAI4do2/bZVp4rt2lu1FRSQ9PJybIMxrYbT5awfjHV/Kj97xn9SQ7NhuDzkBZYxlnas9oHY7bmNla/7XBS7tM27bFxsTJfL/+IomQ6BPb0qwjidDMYXHoFDhUUsuZsw3ODUw4jSeO1Vh7qBSLCpFe1Msq0BBIdoy1o7fUCZ0zY1gcsaEBxIYGaB2KU0ihdP+TREj0SU8KpiOD/ZmQFkWAQUdekQ9NifYyO0uthdKeUh+kqqp9O9ZTx2p0xt5YURIhuwkDo9j+m7k8umik1qE4hb1QOl4KpfuLd1QNCs3Y3pUcqDhAs7mZAH3X78L+fuNY4sICCPKXJV1P1GhqJLc8F/Cc+qBjZfWcOduIv17H1AzP2Mpz1IT4CbzO61Iw3YYnN8rsTlFdESUNJegVPaNiR2kdjteQFSHRJwPDBhIdGI3RYiS3Irf762OCJQnyYPvK9mFSTSQEJ5ASmqJ1OA5Z19pEcfLgaIL9veu9n+2NSH5NPhWNFdoG42YsFpUT5fVah9GvbKtBmdGZBPsFaxyN95BESPSJoij2zsI9bfdvschJF09j6x80PmE8iuIZ77zt3aS9bFsMrAcWbCeHHKnT8xVVDS1c9OfVXPr0emqbjFqH029kW8w5JBESfWbvJ+TgC/HSPYVc/uwGnl4pHXE9ja0+yNbDxt3VNZvYdsLa2sHTx2p0xrZFaft/I6z1iKEBekwWlS3HvGelzD5oVQql+5UkQqLPbD+Ue8r2ONTPxGiykFdc67XHW72V0WK0N3LzlEJpnQJ/vCab2ycPZFBsiNbhOIXt/8XuEhnA2pZt1pi3vM60G7QqK0L9ShIh0WdZMVn46/ypbKrkZM3Jbq+f0drnY19BNZX13tXkzpvlVeTRaGq09o+KHKJ1OA4J9jdw86SB/Ola7y0sta0IHaw8SINR2lLYzBhqTYQ2HvGOxoptB63GB3vn6qZWJBESfeav9yc71trPxJE6ofjwQDITw1BV+Pqod7xI+QJbfdC4+HHoFHnpcBdJoUkkhSRhVs3sLd+rdThuY8qQGAw6hZMVDZys8Pyiadtrq2yL9T95NRP9wjYA0pG5Y9Bm2drLhyN6E0+rDzpZUc8bm05wvKxO61Cczt7PS7bH7EIDDIxPiwK843VGBq06jyRCol/YfjgdPTl2iX3ZukzmJHkAi2qx/7/1lPqgFQdKePS/uTy2rPu2Dp5OCqY7NtNeJ+TZK88yaNW5vKuphtCM7YfzRPUJqpqqiAyM7PL6ielRBPrpKKlp5nBJHcMTw5wfpOi141XHqW6uJsgQxIiYEVqH4xBbkaytVsSb2ZLTvWV7MVqM+Om8Z5RIX8wbkUBNk5G5mQlah9Inh88eptHUKINWnURWhES/iAqMIj08HbAW9XUn0E/PlaOSuG5cCl7cCNZr2OqDRseN9ohfso0tZra2HpufOSxW42icb0jkEML9w2k0NXKo8pDW4biN4YlhPHzFCC4aFK11KH1iH7QaL4NWnUESIdFvejJ3DODpm8by9M1jGZogq0HuztPqg7blV9JispAUEciQuFCtw3E6naKz//zJuA3vY+vRJvVBziGJkOg3PU2EhGdQVdX+y9VT6oNsxbEzhsZ6TAfsvrInQjKAtZ1mk5m1h0p5acMxrUPpNeko7VxSIyT6TdsBrEazET9991soFotKblEN4YF+DIyR2TnuqLC+kJKGEgyKgdFxo7UOxyEbW+uDbKcTfcGEBOtq3e7S3aiq6jMJYHeqGozc9fp2FAVumJBKdIi/1iH1SNtBq7Y2JaJ/yYqQ6Dfp4elEBkTSbG4mt9Kxkzp/+O8BrvrH17z9Tb5zgxO9ZqsPyorNIsgQpHE03Ttb30J+eQOKAtOGeH99kE1PG5v6ioTwQIYnWPuWbfLAvmUyaNX5JBES/abtAFZH545NTLcWMXpL91dvZNsW85T6oKgQf3Y/cinv33MxUR727r8v/PX+jIqzdtCW7bH2LmktmPfEfkKyLeZ8kgiJfmXbHnO0Tmh6RiyKAnnFtZTUNDkxMtFbtl+qnvRCHBJgYPLgGK3DcDlbPyEpmG7P1kJhgwf2LZNBq84niZDoV20n0TvyghMV4s/olAhAVoXcUVVTFSeqTwCelQj5Klsxu6wItXfRoGgCDNa+ZUdKPafTeF1LnQxadQFJhES/Ghk7Ej+dHxVNFZypPePQfezv1jxw2drb2d6NDooY1G2TTHew70w1V/1jIy+sOaJ1KJoYEzcGBYXTtacpa5CfJ5tAP729l5Anvc7sLdsrg1ZdQBIh0a8C9AFkxWQBsLvMwXEbrSd7vj5ajsXiWcvW3s62smDbcnF36w+Xsr+ghr1nqrUORRNh/mEMixoGyKrQ+WzjNvYVeM6/DdtrqGyLOZckQqLf9bSf0LiBkYQGGKisb+FAYY0zQxM9ZCt695QXYttMKV86Nn8+2/aY9PNq75pxKax64BKevXms1qE4zD7fz0PeiHgqSYREv7P90nT05JifXseji0by3t2TGZbo/V2APUWzuZn95fsBz3ghrm0ysuvkWeDcUF9fJAXTHYsNDSAjPsxj+ivJoFXXkYaKot+NiRsDwNGqo1Q3VxMRENHtfW6YMMDZYYkeyq3IxWgxEh0YTWpYqtbhdOub45WYLCrpMcE+3ZzTtiJ76Owh6lrqCPWXNxeeyDZoNcwvTAatOlmPV4TuvPNONmzY4IxYhJeIDYplYNhAwLEBrMI9te1f4gnvos+N1fDd1SCAhJAEUkJTsKgW+fk7z+nKBn703i5ueWmL1qF0y/bzNzp+NDpFNm+cqcff3erqaubNm8fQoUP585//TEFBgTPiEh6up9tjAFuPV/CH/x7gm+MVzglK9MjuEs9q5OaLYzU6Y9sekzqh9kICDHy5r4hvjle6fd8yGbTqOj1OhJYsWUJBQQH33nsvH3zwAenp6VxxxRV8/PHHGI1GZ8QoPJC9n1Dr8WtHLN1TyOub8vlqX5GTohKOsqgW+/87T0iEmoxmxqRGkhAewMWDo7UOR3PjEmQAckeiQ/wZ5SF9y6SjtOv0ar0tLi6OBx54gD179rB161YyMjL49re/TXJyMj/72c84csR5PTxefPFF0tPTCQwMZPLkyWzbtq3L6z/66CMyMzMJDAxk1KhRfPnll06LTZxj++HdV7YPo8WxBNn2Tt7dX6B8QX51PlXNVQTqAxkRPULrcLoV6KfnuVvG8c3DcwkL7H7Yr7ezrQjtLdvr8M+fr7jEA/qWyaBV1+rTxmNRURErV65k5cqV6PV6rrzySvbt20dWVhbPPPNMf8Vo98EHH/DAAw/w+9//nl27djFmzBjmz59PaWlph9dv3ryZW2+9lcWLF7N7926uueYarrnmGvbv39/vsYn2BkUMItw/nCZzE4cqDzl0nylDYtDrFI6X13O6ssHJEYqu2N6NZsdm46f3nMTCE2qZXGFQxCAiAiJoMjeRV5GndThuZcZQ69wxd+5bZusBJYNWXaPHiZDRaOSTTz7hqquuIi0tjY8++oj777+fwsJC3nzzTVatWsWHH37IY4891u/BPv3009xzzz3cddddZGVl8e9//5vg4GBee+21Dq9/7rnnuPzyy/nlL3/JiBEjePzxxxk/fjwvvPBCv8cm2tMpuh7PHQsP9GP8wEhAVoW05knL8iazhbziGo+bIeVMOkVnry2RxortjU+LIsRf79Z9yzzp588b9DgRSkpK4p577iEtLY1t27axY8cOfvCDHxAeHm6/Zvbs2URGRvZnnLS0tLBz507mzZtnv02n0zFv3jy2bOn4BMCWLVvaXQ8wf/78Tq8HaG5upqampt2H6B3bJPqe1CnIuA334EkvxDmnq7j82Y1c8dxGSYbakDqhjvnpdUwZ0jqN/oh7vs7YC6U94OfPG/Q4EXrmmWcoLCzkxRdfZOzYsR1eExkZyYkTJ/oaWzvl5eWYzWYSEhLa3Z6QkEBxcXGH9ykuLu7R9QBPPPEEERER9o/UVPfvn+Ku2p4cc/QXlK1OaNOxckxmi7NCE10obyznVO0pFBTGxI/ROpxu2ZLmjPhQ2Rpro+3JMUkQ25udGceY1EjiwgK0DuUCdS11HKmy1tlKIuQaPU6Evv3tbxMYGOiMWNzCww8/THV1tf3j9OnTWofksbJjszEoBsoayyisL3ToPqNSIogM9iMswEBhlXsfb/VWtnejGVEZhPuHd32xG7CP1fDx/kHny4rJwl/nT2VTJadqT2kdjlu5fXIan/9oGjdNdL83um0HrcYFy79pV/CYztKxsbHo9XpKSkra3V5SUkJiYmKH90lMTOzR9QABAQEEBLjfuwRPFGQIYkTMCPaV72N36W5SQlO6vY9ep7Di/kuICwuQd/casW+LeUD/kqqGFvaeqQJgxrBYbYNxM/56f7Jjs9lVuotdJbtIC0/TOiThANugVVkNch2PaVfp7+/PhAkTWL16tf02i8XC6tWrmTJlSof3mTJlSrvrAVauXNnp9aL/9aaxYnx4oCRBGvKkQaubjlZgUWFYQihJEUFah+N2ZABr12qbjOQVu1cdqCfV53kLj0mEAB544AFefvll3nzzTQ4ePMi9995LfX09d911FwB33HEHDz/8sP36n/70pyxfvpy///3v5OXl8eijj7Jjxw7uu+8+rb4En9PTSfRtmS0qRqkTcqlGUyO5FbnAuV+i7szWTdrXx2p0pi8/f95u6/EKxj22knvfcZ9TdTJoVRselQjdfPPNPPXUUzzyyCOMHTuWnJwcli9fbi+IPnXqFEVF57oST506lffee4+XXnqJMWPG8PHHH7NkyRKys6VBlavYTo4dOXuE2pZah+/3xJcHmfDHlazKLen+YtFv9pfvx6SaiA+KJzkkWetwuqSqqr1QWsZqdGxM3BgUFPJr8qlolNE1bWUlh6MCJ9yob5kMWtWGRyVCAPfddx8nT56kubmZrVu3MnnyZPvn1q1bxxtvvNHu+htvvJFDhw7R3NzM/v37ufLKK10csW+LC44jJTQFFdX+TscRLWYLVQ1Gtz3e6q3sy/IJ7j9oVVXhLzeM5nuXDOaidBmr0ZGIgAiGRA4BerY97QvC2vQtW+8m7Tpk0Ko25DstnK43y/O2d/gbDpfL0V8XuqA+wWKGExth38fWPy1mDaNrT6dTmDE0jl9fOYIgf73W4bgt2zF6aax4IdtJw41u8obLkw4qeBNJhITT9WYA6+RB0fjrdRRUNXK8vN5JkYm2LKqFPaV7gNb6hNyl8Gw2vHkVfLLY+uez2dbbhfs6L3kdF2ftBSV1Qhea0fqGa/PRCs3rEVVVtf8/8oT6PG/iMcfnheeyFf3tLduLyWLCoOv+n12wv4FJg6LYdLSCjYfLGBIX6uQoxdGqo9QaawkyBDG8+DB8dBdw3mpcTRF8eAfc9BZkLdIkTrBOm3965WGmZ8QyPSMWnc69t/FcJncpLH8Qas717RofmQxRBg5WHKTB2CCzq9qw9S2rajCy53QVEzXcYi2qL6K0oRSDYpBBqy4mK0LC6TIiMwjzC6PR1Mjhs4cdvp993IbMHXMJWw3J6NjRGP73ay5IguDcbcsf0nSbbOfJs7y04Ti/+GgPbl7K5Dq5S61Jak375qVJVUUkmEyYVBP7y2XgdFt6ncL0jNZxGxrXCdlWgzKjMwkySCsIV5JESDidTtExOn400MM6odZEaMuxCppN7lOb4q1sNSTj/WMu+GXango1BXBys2sC64Dtl9aMoXFuX9TtEhazdSWog+RVQWV8UzMAu0p2ujgw93frRQN5/OqR3DRJ2y7TttdGOTbvepIICZewFf/15ORKZmIYM4fFcfeMQTSbpJ+Qs9kbKfo7uD1Qp11rA/tYDekmbXVyc5fJ67jWRCjn1DoXBeQ5pmXE8u0p6QyI0nbLUAatakdqhIRL2N7l9GRFSKdTePO7FzkpItFWSX0JBXUF6BQdY+IdLNQMTej+GicorW3iYJG1G7BtW8PndZOUjm9uTYSqj2K2mNHr5JSdO6ltqZVBqxqSFSHhEqNiR6FX9JQ0lFBUV9T9HYRL2eYbDY8aTsiQORCeDHS25aRAeAqkTXVZfG193boalJ0STkyozAUEuk1KM1qMhFos1Fta7L9wxTmltU28881J3v7mpCbPn1Oag0W1MDBsoAxa1YAkQsIlgv2CGR49HOjZMXqwzgNacaCYqoYWJ0Qm4Lz5Yjo9XP6X1s+cnwy1/v3yJ63XaaBtfZBolTa1y+RVj8IYk/Vzu0qkn9D5cgtr+O2S/fxr7VFN+pbtbK3dmpAwweXPLSQREi7U27lHt728le+9vdNtur96I9svR1vzPbIWWY/Ihye1vzA8WfOj84dL6oBzxfQCh5LX8YMuBaSfUEcmD4rB36CjsLqJY2V1Ln9+SYS0JYmQcJneTKIHmJoRA7hPG3xv02BssLc1aHdiJWsR3L8f7lwG179q/fP+fZomQQBf/GQ6y++fwYS0KE3jcDvdJK/jsm8DrEmvdGtvL8hfbx/Tsv6wa9t1NJoa2V9hbWsgiZA2pFhauIzt5Nihs4eoN9YT4hfi0P1mDo3jP+uPs/GIddyGHJfuX3vL92JWzSSHJJMYktj+kzo9DJqhTWCdUBSFzMRwrcNwT1mLIHOB9RRZXYm1dihtKuj0ZJsaMSgGShtLKawvJCU0Reto3colw2L5+mg5G4+UsXj6IJc9r63RbEJwgvw/0YisCAmXSQhJIDkkGYtq6dEA1gnpUQT56SmrbSav2PEJ9sIxu0s8p3+JrGQ4wJa8jrrB+mdrLVeQIYismCxA6oQ6Yptv+M3xCpqMrutb1nZbTN7kaUMSIeFSY+Ktc496sj0WYNBz8WDrsrW7DEf0JhcMWnVTDS0mpj65hp/8324aW6TBZm/0tk7PFwxPCCMhPIAmo4WtJypd9rxSH6Q9SYSES02It/6w7+xhh1v7uA0X7997O5PFxJ4y66BVd0+Eth6vpKi6iV2nzhLoJy9dvTEuQRKhziiKwuzh8eh1CkdLXVMwbTQb7T9/ExMmuuQ5xYWkRki41MTWX7Z7SnZiPLYWv0GXOHQM27ZsvS2/kiajmUA/aQjXH46cPUKDqYFQv1AyIjO0DqdLG47IWI2+siW7R6uOUt1cTURAhMYRuZf75w3j4StHEBHk55LnO1BxgGZzM1EBUQyKcF1dkmhP3lYJ18ldyuA3ribabKZJNbH/w5vg2WzrsMhuDIkL4a/Xj2blzy6RJKgf2eaLjYkf4/bdhm39g2bKWI1eiw6MJj08Hej56U1fkBgR6LIkCGBHyQ4AJiSMR8n/GvZ9DCc2ajrQ2BdJIiRco3UytlJTyITWuUc7AgOhpsg6MbubZEhRFG6alEpajGMnzYRj7PON4tx7W6ygqpFjZfXodQpThkgi1BfjE6y9omR7rGtmi/ML8+31QQdXwJtXwSeLrX86+AZR9A9JhITznTcZe0JTEwDbgwLst7H8IXkX5GKqqtpXhNy9Pmhj62rQ2NRIl75j90ZSMN21nSfPcs2Lm7jnrR1OfR6zxczuou0ATKgqbf9JB98giv4hiZBwvvMmY09sbB0AGRCAEQAVagqs13Xjva2n+N5bOyisanROrD6kqL6I0oZSDIqB7NhsrcPp0rn6IFkN6itb9/B95ftoNjdrHI37CQs0kHO6ik1Hy516jP5QxUHqLc2EWiwMazGe91l5g+hKkggJ5ztvMvZQo5EIs5lGnY7cAP9Or+vIRztPsyK3hHWH5Bh9X9lWBDKjMwn2C9Y4mq5lp0SQnRJuL5oXvZcalkpMYAxGi5Hcilytw3E7Q+NDSYkMotlkYcvxCqc9z868TwAY19RMx9V5jr9BFH0jiZBwvvMmY+ugTZ1QQKfXdWTO8HgA1uSVdnOl6I69f1CCe2+LAfxwVgbLfjyD8QNlrEZfKYpirxOSxooXUhSFmcOtCfc6J77O7LSN1WgtFeiUA28QRd9IIiScr4PJ2BPbFkyjQHiK9bpuzM60JkLOXrb2BZ7SSFH0P6kT6trs1jdcaw+VOaWbuaqq7Kw/DZx7U9gpB94gir6RREg4XweTsSe2vgvaHRiACeDyJx3qJzQyOZz4sAAajWa2ubD7q7epbanlyNkjgPsnQtvzK6lvNmkdhlex1QntLt2NRbVoHI37mTokBn+9jlOVDZwor+/3xz9efZwqUz2BqsrI5vPrg2wcf4Mo+kYSIeEa503GHtZiJMxsoV6n49CCPzs80dzW/RVke8xhFrO1N0mbHiV7yvagopIalkpskPsWINc0GbnlpW8Y+9gKymqlsLe/DIseRpAhiJqWGo5XHdc6HLcTEmDgokHWsT5rnVCPaDs2PyZ8CNYzkOc3CG39u4NvEEXfSGdp4TptJmPr60oYn/8J6yv3syM4hJE9eJjZmXF8sOM0aw+V8ns1S7oMdyV3qbV1QZtTe4Qnszt7HuD+q0Gbj1ZgtqikxYYQFxbQ/R2EQ/x0foyOHc3W4q3sKt1FRpR7dxXXwpWjkggPMjA0PrTfH9veSHHw5ZC5uMOfUS5/0uE3iKJvJBESrmWbjA1MVOqsiVDxDu4ceafDDzF9aByBfjoSwgOpazYRFih9ZTrU2sTSfhTXpqaI3ceXQ1Cg2ydC61v7B8lpsf43LmEcW4u3srt0NzcNv0nrcNzObZMHctvkgf3+uKqqth+0mnSR/Q0idSXWmqC0qbIS5EKSCAnNTEy0DhncWboTs8Xs8IiH0AADu393GUH+8kLRqfOaWLZlRGVfa9uCcbGjXRyY41RVZW3r9ues4ZII9TcpmNbGmboz1v5dOgOj4kZZb2zzBlG4ntQICc1kRmcS4hdCbUsth88e7tF9JQnqxnlNLNs65O9Pk05HuNnMoKoiFwfmuNyiGoprmgjy03Px4Bitw/E6Y+LGoFN0FNQVUFIvR7Q7oqoqR0vr2HS0vN8e07YalB2TTZAhqN8eV/SeJEJCMwadgbHxY4Fze+Y9VVnfIsfoO9JF75Fdrb2bxjW3oKt338aUaw5aV4OmD42VQbtOEOIXwvCo4QDsLpNVoY5sPFLOvKfX86uP9/bbMfp222LCLUgiJDQ1KWESADuKe54I/eT/djPhjyvtdSSijS56j+S0JkJjm5rdukfJ6tZtsbmtvaNE/7MPYC2RRKgjE9Oj8DfoWof+1vXLY0oi5H4kERKaalsn1NN+JtEh/qgqrDskx+gv0EETS7BWDO0KsCZC4/Vhbt2j5Nmbx/LIVVnMGSGJkLNInVDXgv0NTLYdo8/r+xuukvoSTteeRqfo7KvhQnuSCAlNZcVkEWQIorq5mqNVR3t0X1uX6bV5zun+6tE6aGIJcMZgoMKgx09VGTnncbc+mZIeG8J3pw8iPixQ61C8li0ROnT2EHUt/bPi4W1sfcvWHe77G65dpdaRJsOjhhPmH9bnxxP9QxIhoSk/nR9j48YCPd8emzwomiA/PcU1TeQW1TghOg93XhNLsHbyBsgKTSMg+3qtIhNuIj44ngGhA7CoFvaW7bXe2EEDTl9mO7G47UQldX3scC7bYu5JEiGhOdv2WE8LpgP99EzLsJ4mkmn0nchaBPfvhzuXwfWvsmuUtUHb+PS5GgfWuRaThfve28UH20/RYpLxD85mH8Bausvae+rZbHjzKvhksfXPZ7Ott/uoQbEhpMUEYzSrfT49ZkuEJiZM7I/QRD+RREhozvaisLNkZ4+3uGzbYzJuowu2HiWjbiCnsRjAresTtudXsmxvEX/732EMOuka7mz2OqETK60NOM9vu1BTZL3dR5MhRVGY1drQsy8HM842nbVv/49LcO9Gpr5GGioKzWXHZhOgD6CyqZIT1ScYHDnY4fvOat2/333qLGfrW4gK8XdWmB6vurmaY9XHAPdOhFa3HpufPTwOnSRCTmdLhPbWHMOIyoV92lVAgeUPWTsgu3FdmbPcOnkgM4fHMWVw7+fy2eqDhkQMITowur9CE/1AVoSE5vz1/oyJGwP0fHssJTKI704bxJPXjcbfIP+cu5JTmgNAeni6W78Qr8mz9kCaK6fFXGJQxCAiDCE0KQp5/p29kVChpsDaqNMHZSaGMyczoU+NXKU+yH3Jbw7hFmzbY73pJ/TIwixumpRKSIAscHbFdkTaneeLHS+rI7+iAT+9wvShMlbDFXSKjnEhKcC5Zpud6qJRp+iaJELuSxIh4RZsBdPbS7bLUXgn8YREyFbrdfHgGEIlsXWZcdFZwLlmm51y4waczlZU3chflufx8Kd7e3zfupY68irzgHPF6cJ9SCIk3MLouNH46/wpbyznZM3JHt//RHk9r2w8Tl6xHKPvSIu5hf3l+wH3ToRs9UFzpJu0S40fdg1gXRHq+G2IAuEpbt2A09laTBb+te4YH+04Q02TsUf3zSnLwaJaGBA6gMSQRCdFKHpLEiHhFgL0AfZJzL2ZO/b0ysP88YuD/HdPx4NGfV1uRS4tlhaiA6NJC0/TOpwOmS0qKio6RRIhV8uKG4W/YqBSr+eU4fxy6daC9cuf9MlCaZu0mBAGx4ZgsqhsOtKzY/SyLebeJBESbsNeJ9SLRGh2a9Oz/miD741s22Jj48aiKO55EkuvU3j/e1PY9btLSYsJ0Tocn+Kv9yc7bjQAu6LOS0LDk62NObMWaRCZe7GdUl3bw7E+kgi5N0mEhNuwN1Ys3tHjOqGZw+JQFMgtqqG4uskZ4Xk0T6gPsokMlhYIWrAPYB1zvb0BJ3cug/v3SRLUytZlet0hx8f6NJma2Fe+D5BGiu5KEiHhNsbEjcGgM1DSUMKZujM9um9MaABjBkQCMoT1fKqq2o/Ou2sjN7NF5Wx9i9Zh+DR7Y8WyHHsDTgbN8OntsPNd1DrWp7S22eGxPvvK92GymIgPimdA2AAnRyh6QxIh4TaCDEFkx2QDvTtGP0e6THfoRM0JzjafJUAfQFbr6SB3k3O6igl/XMndb27XOhSfNSZuDAoK+TX5VDRWaB2OWwr00zN1SM/G+ti2+ickTHDbbWlfJ4mQcCu9nTsG56ZEbzpaTrPJtwdFtrW1aCtgPZnnp7+wb7A7WJNXgkWFIH85Mq+ViIAIMqIygHPNN8WFZmXGE9ODDvZSH+T+JBESbqXt3LGeGpkcTlxYAEaLypGSuv4OzWNtLrB2A56a7L5Hn23H5ufKaTFNjY9vM4BVdOimiQPY/pt5/Gh2RrfXGs1G9pTuASQRcmeSCAm3Mi5+HHpFT0FdAYV1PTsKr9MpvHnXReQ8cinZKRFOitCzGM1GthVvA2Ba8jSNo+lYQVUjecW16BRr0bvQjm0Gna24XlwowKB3eAZebmUuTeYmIgMiezRDUbiWJELCrQT7BTMyZiTQu+2xrORwgmV7xS6nLIcGUwPRgdEMjx6udTgdstV0jR8YJUNzNWZbETpYcZAGY4PG0bg3VVUpqm7s8hrbyvb4+PHoFPl1667k/4xwOxMSrUvIvSmYbktGdcDmQuu22JTkKW77QrzmoHV+1RwZsqq5pJAkEoITMKkmeydycaGjpXVM/vNqFr2wCYul89cZqQ/yDO75yih8Wl8aKwJ8tOM0Vzy3kTc25/djVJ7Jlgi5a31QQ4uJTcesJ5TmZvruHCt3oSiKfVVItsc6NzA6mPpmE2VdHKM3W8zsLrF+D21v7oR7kkRIuB3bMvLp2tOU1Pd82nVVg5GDRTU+f4y+sqmSgxUHAZiSNEXjaDqmoPDYopHcNHEAwxJCtQ5HcK7XVG8OLPgKf4OOaRmxQOd9y45UHaHWWEuIXwjDo9xzW1pYSSIk3E6ofyiZ0ZlAL4/Rt5482nq8kvpmU7/G5km+KfwGFZVhUcOIC3bPIuQgfz23XDSQv94wRnqsuIlJCZMA68mxJpN0ae/MuXEbHfcTsiWSY+PHYtBJ3aI785hEqLKykttvv53w8HAiIyNZvHgxdXWdH5GurKzkxz/+McOHDycoKIiBAwfyk5/8hOrqahdGLXqrL9tjQ+JCSI0OosVsYdPRng1H9CabCjcB7ntaTLinIZFDSAxJpNnczPZiaXDZGdu4jd2nzlLVcGFXdFsiJGM13J/HJEK33347Bw4cYOXKlSxbtowNGzbwve99r9PrCwsLKSws5KmnnmL//v288cYbLF++nMWLF7swatFb9kSoFwXTiqIwp5t3a95OVVW2FG4BYGqKe9YHHS+r4/VNJzhVIaeT3ImiKExPmQ7A1wVfaxyN+0qODGJ4QhgWFTacN41eVVUplPYgHpEIHTx4kOXLl/PKK68wefJkpk+fzj/+8Q/ef/99Cgs77jWTnZ3NJ598wsKFCxkyZAhz5szhT3/6E//9738xmXx3u8RTjE8Yb2/3X97Y81Ud2/bYukOlPnl67EjVEcoaywjUB7rtoNUv9xXxh//m8tiyXK1DEeeRRMgxszJbh7CeV494ouYElU2VBOgD7O1AhPvyiERoy5YtREZGMnHiuSXGefPmodPp2Lp1q8OPU11dTXh4OAaD7Ne6u4iACHvfm95sj108OIZAPx1F1U3kFdf2d3huz7YaNDFxIgH6AI2j6djq1l8ec+XYvNu5OOliDDoDp2pPcarmlNbhuK0rs5O4Z8Ygbp08sN3tttWg0XGj8ddLbyx35xGJUHFxMfHx7V8sDQYD0dHRFBcXO/QY5eXlPP74411upwE0NzdTU1PT7kNooy/bY4F+ehaMSubacSnoHewC6002FVjrg9z12Hx5XTM5p6uAczPihPsI8QuxH6PfWLBR42jc15jUSH6zIItJ6dHtbpdtMc+iaSL00EMPoShKlx95eXl9fp6amhoWLFhAVlYWjz76aJfXPvHEE0RERNg/UlNT+/z8onf6MncM4O83jeGZm8cyLCGsP8Nye02mJvv3zF0LpdcdKkNVITslnMSIQK3DER2Q7bHeUVXV/uZNEiHPoOke0c9//nO+853vdHnN4MGDSUxMpLS0/R6syWSisrKSxMTELu9fW1vL5ZdfTlhYGJ999hl+fl1P33744Yd54IEH7H+vqamRZEgj4xOs70iPVh2lsqmS6MDobu4hwJo4tlhaSAhOYFDEIK3D6dCavNZu0rIa5Lamp0zn6Z1Ps714O02mJgINkrB2pMVk4ZvjFRwqruWeSwZTWF9ISUMJBsXA6NjRWocnHKBpIhQXF0dcXPf9TaZMmUJVVRU7d+5kwgRrhr1mzRosFguTJ0/u9H41NTXMnz+fgIAAli5dSmBg9z/IAQEBBAS4Z02Fr4kKjCIjMoOjVUfZWbKTS9Mu7fFjqKrKwaJaDHrFZ1aGbN2kp6VMc8vePC0mCxsOWwvg54yQbtLuKiMyg/jgeEobStlZspNpKe65uqi1sw0t3PHaNhQFrhufYl+NzYrNItgvWOPohCM8okZoxIgRXH755dxzzz1s27aNTZs2cd9993HLLbeQnJwMQEFBAZmZmWzbZp20XVNTw2WXXUZ9fT2vvvoqNTU1FBcXU1xcjNls1vLLET3QlzohgOdXH+XK5zfyr3XH+jMst9Z2vpg7OlxSS4vJQmyoP6NTIrQOR3RCURRmpMwAZHusKwnhgWQnhjBZyeX42jfZefQLQLbFPIlHJEIA7777LpmZmcydO5crr7yS6dOn89JLL9k/bzQaOXToEA0N1p4ku3btYuvWrezbt4+MjAySkpLsH6dPn9bqyxA9NDGxb3PHLh5s3U5bd6gUcxfDEb1FSX0JR6uOoqC47ViN7JQIdj1yKW/cdRE6Hyxk9yRSJ+SA3KW813AP7/v/kUk7f8nO0+sBmOjDXe09jcecI4+Ojua9997r9PPp6ent+sXMmjXLJ/vHeBvbu6ojZ49Q3VxNREDPVhAmpEURFmjgbIORnNNVTEiLckaYbsO2GpQdm93j75UrhQYYyJbVILd3cdLFGBQD+TX5nK49TWqY1Eu2k7sUPryDMKy/a8r0Ok76+aGoKmNXPQGRmZC1SOMgRXc8ZkVI+KbYoFgGRQxCRe3V6TGDXsclw1qbnnUyHNGbuPu0eXlz4llC/UMZGz8WkFWhC1jMsPxBQMW2rrmztQ51eIuRcIsKyx+yXifcmiRCwu3ZhkD2dnvMdjLJ26fRmy1mthS1jtVw00To1a9PcNU/NvLprjNahyIcJNtjnTi5GWraTzbYGWg9aDOhqRlQoabAep1wa5IICbdnrxPqZcH0zOFxKAocKKyhpMZ7p2kfrDxIdXM1oX6hjIobpXU4HVp9sJT9BTVUNxq1DkU4yJYIbSvaRrO5WeNo3EhdyQU3nUuEmrq8TrgXSYSE27OdHDt09hC1LT0flxEbGsDoAZGAd2+P2bbFLkq8CD9d1/2ytFDTZGR7fiUAczKlf5CnGBY1jPjgeJrMTews7l1zU68U2r71Q7VOxxF/6ziN8U3NnV4n3I8kQsLtxQXHkRaehkW1sLt0d68e4+eXDuPduydz7bgB/Ryd+7CN1XDXfi8bD5djsqgMiQshLSZE63CEg9pOo5dxG22kTYXwZGitENrVuho0qMVIjMVivT08xXqdcGuSCAmPYFsV2l68vVf3v2RYHNMyYvE3eOc/+bqWOvaW7QXct3/Q6tZu0nOliaLHkTqhDuj0cPlfWv+isK3NtpiKYj1HdvmT1uuEW/PO3wrC69iO0fe2TsjbbSvehkk1MTBsoFsecTZbVNYdKgNkW8wTtT1Gf6ZWCt3tshbBTW+hhiexJtjaRXp6YxPFajTHZ/9Tjs57CEmEhEeYlGg9OXaw8iD1xvpePcah4loe+28u/1nvfV2m3b2b9J4zVVTWtxAeaPD6Xk7eKMw/jDHxYwBZFbpA1iLy7viYQj8DgYqBfbGPMa35Od6uHqN1ZMJBkggJj5AYkkhKaApm1dzrOqH8inpe23SC1zfle12Xaft8MTedNh9o0HP12GQWjknGTy8vO55Itsc6t+r0GgCmpc5kzNRrsKDji31FXvc6463kFUl4jEm2uWP73oETG3vcqGzW8DjCAw0U1zSx9USFM0LUxOma05yuPY1BMXBR0kVah9OhrORwnrtlHH+61j2P9Yvu2Y/RF8sx+vOtOWVNhOYOnMu0jFgig/0oq232qtcZbyaJkPAMuUuZuOdTAHacXANvXgXPZltb3DsowKDnylFJACzNKezmas9hWw0aEz+GED85jSWcY3jUcOKC4mg0Nfaqy7u3OllzkqNVRzEoBi4ZcAn+Bh2Xj0wE4L97ijSOTjhCEiHh/lrn+Uw8WwzAgQB/GhQFaorgwzt6lAxdPTYFgC/3FdFs8o7W9+6+Lba/oJpDxbUyXsPDKYpib80g22PnrD61GrDWMdrm+y0ckwzAV/uLMJotmsUmHCOJkHBvbeb5pJjMDDQaMSkKG4KDoHXQYU/m+UweFE1ieCA1TSbW5pU5LWxXMVqMbC3eCrjvWI2nVx5m/rMbeH1TvtahiD6SOqELrT5pTYTmDpxrv+3iwTHEhgZQ1WBk09FyrUITDpJESLi38+b5XFrfAMCKkODWW3o2z0enU1g01vpu7fOcgn4NVQv7yvZRb6wnMiCSETEjtA7nAo0tZvsvgqkZMRpHI/pqSvIU9IqeE9UnKKjz/J+fviqpL2FvubV/1+yBs+2363UKi6cP4v55QxmaEKZVeMJBkggJ93benJ7LWhOhjUGB1u2xTq7rytVjk4kPC2BQrOfX02wqtHaTnpI0BZ3ifj/OK3KLaTZZSI0OYrj8QvB44f7hjImzHgu3dTL3ZWtPrwVgTNwY4oPb98e6d9YQ7p83jJTIIC1CEz3gfq+cQrR13pyeES1GUo1GmnS61u2xjq/rSlZSOFsensuvLs/sryg1s6Wwddp8intui326y7pqcO24AShtE1fhsWTcxjm2+qC222LC80giJNzbefN8FM6tClm3x3o+z0dRFPQ6z/+lXNVUxf7y/YB1RcjdlNY0sfGItQ7runEpGkcj+ostEdpatJUWc4vG0Winurna3um+s0SoyWjmq31FvPr1CVeGJnpIEiHh3s6b5wMdbI/1cp6P2aKy8UgZ5XWe2RPlm6JvUFHJiMwgIcT95nctySnAosKEtCjSvWAbUlhlRmcSGxRLo6mRXaW7tA5HM+vPrMekmsiIzGBg+MAOrzlUXMu97+7iqf8doqHF5OIIhaMkERLur3WeD+HWHkAjWowMsG2Pzf1Fr+f5fP/tnXz71W0s2e2ZRZ+2Y/PuelrMdirvuvGyGuRNFEWxt2r4+ozvnh7r6LTY+UYPiGBgdDCNRjOrD5a6KjTRQ5IICc+QtQju3w93LkO5/lUuG7wAgBWm3ndunTksFoDPPbC5oqqq9kJpd+0f9Nbii3jtOxO5alSy1qGIfjZ9gG8fo280NdrfiMxLm9fpdYqicNVo6xu4/+7xvNcZXyGJkPAcOj0MmgGjbmD+mMUAbDyzkQZjQ68ebsHoZAw6hX0F1Rwrq+vPSJ3uePVxShtKCdAHMD5hvNbhdMhPr2NOZgIRwX5ahyL6me2U4rHqYxTV+V735M0Fm2kyN5ESmsLwqOFdXmtrrrjuUBk1TUZXhCd6SBIh4ZFGRI9gQOgAmsxNvT69Eh3iz4yhratCHrY9Zju6PCFhAoGGQI2jac9iUbHIsEmvFhEQYT9G74unx1adWgXAnIFzuj0NmZkYRkZ8KC1mCysOON7mQ7iOJELCIymKwmXplwGwIn9Frx/nmtbTTEtyCj1qBMTmIvetD1p/uIzpf1nDf9Yf0zoU4UT2OiEf2x4zWoysP7MecOzYvKIoLBxtXRVatle2x9yRJELCY9kSoQ1nNvR6e+zSrASC/fWcqmxg9+mqfozOeZrNzewstg69dMdE6JNdZyisbqK4pknrUIQT2eqEthZtxWj2nS2f7cXbqW2pJTowmrFxYx26z1VjrHVC1Y1GTDJ7zO1IIiQ8VlZ0FimhKX3aHgv2N3BZlvXo+bpDnjF7bFfJLprMTcQHxZMRmaF1OO1UNxpZkWtd/r9+/ACNoxHONCJ6BNGB0TSYGnzqGP2aU2sAmJ06G72DbTuGxIWy8Vez+eyH0zDo5deuu5H/I8JjKYrC/PT5QN+2x+6dlcHnP5rGz+YN7a/QnMp2WmVK8hS369b85b4iWkwWhiWEMjI5XOtwhBPpFJ3PDWG1qBZ7ItTTbtKp0cHdXyQ0IYmQ8Gi27bGNBb0/PTY8MYwxqZFul1R0xp37B3266wxgXQ3ylO+n6D1fS4T2lu2lrLGMEL8QJidN7tVj1DYZqWuW5oruRBIh4dFs22ONpsZ+Ob3i7qedyhrKOHz2MAoKU5Lda6zGyYp6tuefRaecK0IX3m1q8lR0io6jVUcpri/WOhyns60GXZJyCf56/x7f/6n/HWLCH1fx8Y7T/R2a6ANJhIRH66/TY/XNJn718R6mPrmGxhZzf4XX77YUWYesZsVkERUYpXE07dkGrE7LiCUh3L2O9AvniAiIYFTsKMD7j9GrqnpuyGpa74asRof402Ky8N+9vtd7yZ1JIiQ83vw0a53QxoKNNJoae/UYwf56thyvoLimiZUH3bfXh61/kDtui12alcC3Lh7I7ZM7nrskvJN9e8zLx20crTrKqdpT+Ov8mZEyo1ePsWB0EooCO0+e5czZ3m3li/4niZDweFkxbbbHzvTuXamiKFw9xrqd467NFS2qhW+KvgHcMxHKTongj9eM4vLsJK1DES5kSwq2Fnv3MXrbatCU5CkE+/Wu8DkhPJCL0qMB+EJWhdyGJELC47XdHvtf/v96/TjXjLM2PVt/uIzK+pZ+ia0/5VXmUdlUSbAh2N7VVwitjYixHqOvN9aTU5ajdThOY98W6+FpsfPZRm4sk0TIbUgiJLxCf2yPZcSHMTI5HJNF5Yt97vciZTstdlHSRfjp3Wd+V5PRzO8/38+O/EqP6s4t+odO0dm7THtrndCZ2jPkVeahU3TMSp3Vp8e6IjsRfeuMwxPl9f0ToOgTSYSEV+iP7TGAa8a67/aYux6bX3WwhDe3nOSn7+cgeZBv8vZj9LbTYhMSJvT5kEJMaABTh8QAsEwm0rsFg9YBCNEfFEXhsrTLeP3A66w4ucK+VdZTC8ck8+evDrLj5FlOVza4TRO0BmMDu0t3A+dmPGnFbDZjNJ6rBVlzoICUMD23T0yipaVZw8gEgL+/Pzqda9/jTk2eioLCkbNHKK4vJjEk0aXP72z9tS1mc9e0dGYOi+Oq1hlkQluSCAmvMT99Pq8feJ0NZzbQaGokyBDU48dIjAjkhvEDSIoMItDPsfb5rrC9eDsmi4mU0BRSw1I1iUFVVYqLi6mqqrLfZraoXJmm54q0eBLCLZw4cUKT2MQ5Op2OQYMG4e/f8z43vRUZGMmo2FHsLd/LpoJNXD/sepc9t7OVN5bb34TMSZ3TL485JzOBOZn98lCiH0giJLyGbXusoK6AjWc29npV6G83ulkhssXMpryPAJgWNhhFtYDi+iTNlgTFx8cTHByMoihU1rdgDmki0E9PWkyIy2MS7VksFgoLCykqKmLgwIEu7e49PWU6e8v38vWRJVzfrEJoAqRNBQfncbmrdafXoaKSFZNFUqiciPRGkggJr9Ff22NuJXcpxuUPsioCMBiYmvMp7F8Ll/8Fsha5LAyz2WxPgmJiYuy311cbUQz+xEYEERgY4LJ4ROfi4uIoLCzEZDLh5+e6ovrpLRb+CXxTugvj9qX4AYQnu/zfan+zbYvNGzivXx+3yWhm6Z5Ctp2o5G83jJaRNBqSYmnhVWzJj217rLeaTWaW7y9miZZF07lL4cM7WG6posxgIM5k4pKGRqgpgg/vsH7eRWw1QcHB52qmGlvMNBrNKIpCRJD7nGLzdbYtMbPZhR3Sc5cy8svfEGU2U6fTkWNLijX4t9qf6lrq2Fq0Fei/+iAbs0Xlkc/38/HOMxworOnXxxY9I4mQ8CojY0baT4/15QTL2rxSfvDOTv66PE+b+WMWMyx/EBWVt8OtU9xvramzvsumNZ7lD1mvc6G271pNFgv+eh3hgQYMenkpcRcuX1lo/beqQ2VqYxMAXwfZRqxo92+1P2ws2IjRYiQ9PJ3BkYP79bFDAgzMzUwA4L9yekxT8uolvIptewz61lxx1vB4wgINFFY3sS2/sr/Cc9zJzVBTyM7AAA4G+BNosXBjbV2bC1SoKbBep5GwQD+GJ4aREtnzonThRVr/rQJMb7Cuwn4d1PbfhPb/Vntr1clVQP+vBtksHGOtOVq2t0h6cGlIEiHhdfpjeyzQT88V2dYjwJ/naPBurc467+zt8DAAFtbVE2mxdHqdVhRFcfvVoFmzZnH//fc7fH1+fj6KopCTk9Ovj7tu3ToURWl36u58xcXFXHrppYSEhBAZGQlYv8dLlixx+Hlcrs2/wWmNTSiqyuEAf0r0+k6vc0sWM5zYCPs+hhMbaTY22FeV56X1b32Qzazh8YQGGCioamTXqSqnPIfonhRLC69j2x4rqCvg64KvuTTt0l49zjVjU/hwxxm+3FfEHxaNxN/gwl/4oQmcNhhYG2x9Z/2tmtpOr9NCY4uJAD89Og8o8Pz00097VDScmppKUVERsbGxgDWBmT17NmfPnrUnJ715XEc888wzFBUVkZOTQ0REBABFRUVERVmb+OXn5zNo0CB2797N2LFj+/W5e63Nv8Eoi4VRzS3sDQxgWWgwi6trO7zO7eQuheUP2le2AL6JSaEhXE9CcAIjY0Y65WkD/fRclpXAp7sL+O+eQiak9a1Zo+gd934rJ0QvKIpiT35W5K/o9eNMHhxDQngA1Y1G1h0q7a/wHJM2lfdiE1EVhWkNjQw2ms67QIHwFOvxZBczWywcK6snr6gWo6mDVSo3Ex0dTVhYmMPX6/V6EhMTMRi6fp/Y08d1xLFjx5gwYQJDhw4lPj4egMTERAIC3PhEXtpU6+kwrEnxTa1buG+Hh9OkKGj5b9UhrYcS2iZBAKsV62rynNBBTq27uqp1e+yLfUWYtahHFJIICe9kqxNaf2Z9r7fH9DqFRa0DEl29PVZrauDTEGvB6R0XrAa1vihf/qQmPVqqG01YVBW9TsGgV2hoMXX60WRsXyDbH9f21PlbWOnp6fz5z3/mu9/9LmFhYQwcOJCXXnrJ/vm2W2P5+fnMnj0bgKioKBRF4Tvf+U6Hj/v2228zceJEwsLCSExM5LbbbqO01PEEOj09nU8++YS33nqr3fO03RobNGgQAOPGjUNRFGbNmtXj70e/0+mtR+QBULiyrp5ko4kKg57PQkOtN2v0b7VbrYXe9qLuViawr8bOPbLJqYXe0zPiiA7xJyMulIp66cyuBdkaE14pOzab5JBkCusL+7Q9dvXYFF7eeIKSmiYsFhWdzjVbQZ8e+ZQGSwtDAuOZ4meCxjaJWHiy9ReLRr1Zzja0ABAV4oeiKGQ90nlR+uzhcbx+10X2v094fBWNxo5/qUweFM0H359i//v0v6ylsr7lguvyn1zQ29Dt/v73v/P444/z61//mo8//ph7772XmTNnMnz48HbXpaam8sknn3D99ddz6NAhwsPDCQrquDjcaDTy+OOPM3z4cEpLS3nggQf4zne+w5dffulQTNu3b+eOO+4gPDyc5557rsPn2bZtGxdddBGrVq1i5MiRLu0e3aWsRXDTW7D8QfxqCrmruoY/xUbzelQkN8z/B37u2keoTaF3W7sDA6jS64kwm5lQ2VroPWiGU0LwN+jY+KvZhATIr2OtyHdeeCVFUbgs/TLeOPAGK/JX9DoRGpkcztpfzGJQrOu6JpssJt47+B4A3xp3L8qN11pfiOtKNO/W22IyU99sXZWJDHKTX8K9cOWVV/LDH/4QgAcffJBnnnmGtWvXXpAI6fV6oqOjAYiPj29XI3S+7373u/b/Hjx4MM8//zyTJk2irq6OUNvKSBfi4uIICAggKCiIxMSOZ3XFxcUBEBMT0+k1mslaBJkL4ORmrqk5w78PvEARNXwRqHCN1rF1ppMC7tWt/bJmNTRaf0k6udBbkiBtyXdfeK3L0qyJkG17rDezxxRFcWkSBLD29FoK6wuJCojiqsFXWZMeJ70b7amaJmsSFBpgsBeP5z42v9Przy+m3vm7zk/fnH/t1w/O7m2Y3Ro9erT9vxVFITExsUfbWB3ZuXMnjz76KHv27OHs2bNYWk/5nTp1iqysrD49tsdo/bcaCNyhb+SZnc/w6r5XWTh4IXp33BrroIBbBdaEtG6LtbYDcFWhd0FVI6crG7h4cEz3F4t+IzVCwmvZtsf62lzRprbJSF1zz2tUeurt3LcBuHH4jQQaAru52rVqG60dpqOCz60GBfsbOv04f3Btf1zbH84/7aUoij1x6Y36+nrmz59PeHg47777Ltu3b+ezzz4DoKXlwu09X3Dz8JsJ8w8jvyafVadWaR1Ox84r9AbI9fejyGAgyGJhSmOzywq9txyrYNbf1vKzD3JoNnle80lPJomQ8Fr9dXoM4OmVh5nwx1V8uP10f4TWqf3l+9lduhuDzsAtw29x6nP1VIvJQovZgk5RCPehkRqOjKzIy8ujoqKCJ598khkzZpCZmdnnFabexuIuQvxCuH3E7QC8su8V92wYeF6hN8DqEOu22PTGJgJV1WWF3uMGRhId4k9RdRMf7jjj9OcT50giJLza/HTrts36M+tpMjX1+nFiQvxpMVn4PMe5s8dsq0FXpF9BXHCcU5+rp/wNOtKiQxgQFYTeRUXj7iAtLQ1FUVi2bBllZWXU1dVdcM3AgQPx9/fnH//4B8ePH2fp0qU8/vjj/R5LfHw8QUFBLF++nJKSEqqrq/v9OfrT7Zm3E2QIIq8yj40FG7UOp2O2Qu9w6zF2W33QXEug9XYXFXoH+un54awMAP619qisCrmQJELCq/XX9tiC0UnodQp7zlRzsKgXAxLP61rb0XHckvoS+8rVt7O+3etYnSnQX09ksOcWSfdGSkoKf/jDH3jooYdISEjgvvvuu+CauLg43njjDT766COysrJ48skneeqpp/o9FoPBwPPPP89//vMfkpOTufrqq/v9OfpTZGAkNw27CYCX977snqtCYE127t/PiRtf5bi/HwZFzyV3b3L5ycybJ6WSEB5AYXUTH8mqkMsoqtv+y3QPNTU1REREUF1dTXjr8EvhWZ7a/hRv5r7JFelX8NeZf+314/zo3V18sa+IeSPieeXOSY7fsYOutdYj8H9p90L77M5neXX/q0xMmMjrl7/e6zidobGx0d7VODDQveqWRHtNTU2cOHHCbf5flTWUMf+T+RgtRl6b/xqTEnvws+Nir+x7hed2Pce05Gn8+9J/axLDG5tO8Oh/c0mOCGTtL2cRYHDDInMP4ejvb1kREl7PNnts3Zl1fdoee+CyYeh1CqsOlrLD0UGsnXStpabIenvuUgAajA18dPgjAL6V9a1ex+gsf/wil8r6Fpo76QEkRGfiguO4NuNawJpouLPVJ1cDMGfgHM1iuOWigcSHWVeFPt4pq0KuIImQ8HqjYkeRFJLU5+2xIXGh3DRxAAB/WZ7X/TJ/J11rrVpvW/4QWMwsO76MmpYaBoQOYNaAWb2O0RnK65rZeKSchhZJgkTv3JV9F3pFz+bCzewv3691OB3aX76f/RX7UVA0TYQC/fTcO2sIwf56GuVnziU8JhGqrKzk9ttvJzw8nMjISBYvXtxh0WJHVFXliiuucP8pzsIpFEWxj9zo6+mxn84dRoBBx8GiWk5VNnR9cSdda89RoaYAS/7X9iLpb2V9y+36rSzNKcRsUfE3KAT4uVdswjMMCBvAlYOuBNxzVchkMfGHLX8A4MrBVxIbFKtpPLdeNJCvH5zD3TMGaxqHr/CYROj222/nwIEDrFy5kmXLlrFhwwa+973vOXTfZ5991qlD84T766/tscSIQP55+3jW/3IWaTHdNFp0sBvt14Wbya/JJ9QvlGsyrul1bM7Q2GLmlY3HAfqth4/wTXePuhsFhdWnVnP07FGtw2nnndx3yKvMIyIggl9O/KXW4RDopyc6xLcOJWjJIxKhgwcPsnz5cl555RUmT57M9OnT+cc//sH7779PYWHXwzBzcnL4+9//zmuvveaiaIU76q/tMYC5IxKICXVgGriD3WjfrtgFwHVDryPEz7VdrLvz0objFFY3ER8WSIi/rAaJ3hscOZi5A+cC8Or+VzWO5pwztWd4MedFAH4+4efEBLlXV+dvjlewJs+5Iz58nUckQlu2bCEyMpKJEyfab5s3bx46nY6tW7d2er+GhgZuu+02XnzxRYfn8jQ3N1NTU9PuQ3g+RVG4dKB1vMNb25/GfHx9v0yU3nysvPN+Hx10rT0vKo5EpfBNVR46RcdtI27rczz9qbCqkX+tt75z/97MwbKqKvrs7tF3A/DVia84Xevc5qSOUFWVP279I03mJiYlTnK7Fdn/7inklpe+4XdLDtBi6n3nc9E1j0iEiouLiY+Pb3ebwWAgOjqa4uLiTu/3s5/9jKlTp/ao18YTTzxBRESE/SM1NbXXcQs3kruUWze/TrDFwu7607yy9FvwbLb91FZvPPBBDre9vJX3tp7q+IIOutaeY/37OxnWyexzB84lJTSl17E4w0sbjtNktHBRejSzhrlXc0fhmUbGjGRa8jTMqpnX92vfIuKrE1+xqWAT/jp/Hrn4EbdL9i/NSiAuLICCqkY+3SUnyJxF00TooYceQlGULj/y8vJ69dhLly5lzZo1PPvssz2638MPP0x1dbX94/Rp7d+1iD5qPcI+oKqQ31ScBeBfkRHkNFe0O8LeUxPTrVPJX1hztPMZZOd1rbULT6bi2n+yrHIfAHdk3dGrGJzpoSsy+cVlw3hkYZbb/YIQnuvuUdZVoSVHl1Da0P9jSBxV3VzNX7Zb36jcM/oe0iPSNYulM4F+en4wcwgAL6w9itHsXatCTUYzP/6/3b1rUtuPNE2Efv7zn3Pw4MEuPwYPHtzhZGiTyURlZWWnW15r1qzh2LFjREZGYjAYMBishZ7XX389s2bN6jSmgIAAwsPD230ID3beEfaFdfVcUVePWVF4KC6GWkWxH2HvqRsnDmBQbAgV9S32guIOtXat5c5lcP2r1j/v38dH1NBiaSE7JpsxcWN6+QU6T6CfnvvmDCU7JULrUPrdrFmzuP/++7UOwydNTJzI+PjxGC1G3jzwpmZxPL3zaSqbKhkSMYTF2Ys1i6M7t08eSGxoAGfOet+qkJ9ex8S0KH72QQ4mDZM8TROhuLg4MjMzu/zw9/dnypQpVFVVsXPnTvt916xZg8ViYfLkyR0+9kMPPcTevXvJycmxfwA888wzvP669kuywkXOO8KuAL8rryTFaKLAz8DjsVGoNQXW63rIT6/jF5cNB+DlDccpr2vu/GKdHgbNgFE3wKAZtKhm3s97H7CO03CnFZfjZXWYLU5sOO/AuBF3sm7dOhRFoaqqSutQvIZtVeijwx9R1VTl8uffXrydT498CsAjUx7BT+++Q4Stq0LWY/T/WONdq0J6ncKdU9P54iczMOi1S0c8okZoxIgRXH755dxzzz1s27aNTZs2cd9993HLLbeQnJwMQEFBAZmZmWzbtg2AxMREsrOz232AdTjioEGDNPtahIt1cIQ9TFV5sqwcvaryVWgIy0KDHT7qfr4rRyUyKiWC+hYzL6xx/EjwVye+oqKpgvjgeC5Nv7RXz+0Mdc0mbnnpGxY8v5HT3fVJ6o3cpdbarDevgk8WW//sY62W8DzTU6YzInoEjaZG3jn4jkufu8XcwmNbHgPgxmE3Mj5hvEufvzdun5zmVatCFotKU5su9VoPcfaIRAjg3XffJTMzk7lz53LllVcyffp0XnrpJfvnjUYjhw4doqHBCS/ewnN1coR9bHMLP6iyTu7+Y0w0p3v5bkRRFB68PBOAd7eedCh5UFXV3kDx1sxb8dO5z7vRf649SmltM41GM/HhDrQI6AkHx430t/r6eu644w5CQ0NJSkri73//e7vPv/3220ycOJGwsDASExO57bbb7Fvx+fn5zJ49G4CoqCgUReE73/kOAMuXL2f69OlERkYSExPDVVddxbFjx5zyNXgbRVHsq0Lv5b1HXYtjzXH7wyv7XiG/Jp/YoFjun3C/y563L4L8ratCaTHBRAR5fn+hD3ac5rJnNrDxSJnWoQAelAhFR0fz3nvvUVtbS3V1Na+99hqhoaH2z6enp6Oqapf1P6qqcs011zg/WOE+ujjCfk9VDeObmmjQ6Xjw+EcYLcZePcX0obFMz4glJTKI0toutsda7SjZwaGzhwgyBHHjsBt79ZzOcLqygVe+PgHAb64c0b/DHnswbqS//fKXv2T9+vV8/vnnrFixgnXr1rFr1y77541GI48//jh79uxhyZIl5Ofn25Od1NRUPvnkEwAOHTpEUVERzz33HGBNsB544AF27NjB6tWr0el0XHvttVgs3rN14Uzz0uYxKGIQtS21fHDoA5c85/Gq4/bO1g9d9BDh/p5TA/rtKWmsfmAml2c71grGXZXVNvPElwc5VdnA4RLXJcBdkVaxwrvZjrB/eAfWZOjcL2I9Ck+WVnL9oMHsq9jPP3P+yU/H/7RXT/P0zWOICvbHz4GVpbdy3wJg0ZBFRAS4TyHyn788SIvJwrSMGC7NcqwZpMMcHDfCyc3WWqp+UldXx6uvvso777zD3LnWZn5vvvkmAwYMsF/z3e9+1/7fgwcP5vnnn2fSpEnU1dURGhpKdLT1dGB8fDyRkZH2a6+//vp2z/Xaa68RFxdHbm6ufStedE6n6FicvZjfbvotb+W+xe0jbifQEOi057OoFv6w5Q8YLUYuGXCJfeyOp/CWKfR//CKXmiYT2Snh3DklTetwAA9aERKi17o4wp50/es8Ov1PALy671W2FW3r1VPEhwU6lASdqjnF+tPrAbh9xO29ei5n2HKsgq/2F6NT4JGrRvZ/8bajNVi9rNXqzLFjx2hpaWl3qCI6Oprhw4fb/75z504WLlzIwIEDCQsLY+bMmQCcOtVJf6hWR44c4dZbb2Xw4MGEh4eTnp7u0P3EOVcOvpLkkGQqmyrtxcvO8tmRz9hVuosgQxC/mfwbtzqg0BMtJgvvbT3FV/uKtA6lx9YfLuPznEJ0Cjxx7WhNC6Tbco8ohHC2To6wk7WIy9Iv4/qh16Oi8vDXD/fpFEuLycJbW/LZefJsh59/9+C7qKjMSJnBoAj3KNo3W1QeW5YLWIsyhyeG9f+TODhuxOHr+kl9fT3z588nPDycd999l+3bt/PZZ58B0NLS0uV9Fy5cSGVlJS+//DJbt261d7nv7n7iHD+dH3dl3wXA6wdex2ju3fZ0d8oby/n7Tmtt2H1j7yM5NNkpz+MK728/xa8/28cTX+V51Amy/2/vzqOavNa+Af+SkDATJpkHQQUVwakFAalaUVBq8X1tq611qvOHbanVHs9pLaXailWL51hrh6VgtUq1KvbDoQ4YtYBDES2IIiCIiggiSBAQSPb7RyTHyBiakEDuay2W8mTnyb7ZeZI7O3uorZfgk0TZummzA9zg7aQ9veGUCBHd8dwUdjyzy/tHL36E3ma9UVpTiqjUKDDWuenjG0/cwKcHr2LN4WvNzlFVX4UDebI32RkDZ3Q+DhV7+LgexgIehIZ8LB3noZ4H6cB2IzBzlJVToT59+oDP5ytsxVNRUYEbN24AAK5fv47y8nLExMQgKCgI/fv3b7ZmmUAgG5wqkfx3/FJ5eTlycnLwySefYOzYsRgwYAAqKlpOfknb/qff/8DKwAolj0uQdDNJLY/x1YWvIK4XY6DVQK3bykZZrw13gpWxAEUPa5CYcVfT1emwTcm5uP2wFvZCAywdr6bXmU6iRIgQAEZ8I6x9aS30uHpIvp2MvTf2duo8swJ6w4DPxZ+3KnDymuIb6v4b+1HbWIt+Fv0wwn6EKqqtEr1M9bF3kT8ORgTCQl07XndguxGExigkp6pgYmKCuXPnYvny5UhOTkZWVhZmz54NLlf20ufi4gKBQIBNmzbh5s2b+O2337Bq1SqFc7i6uoLD4SApKQllZWWorq6GhYUFrKys8MMPPyAvLw/JyclYunSpSuuuK/R5+pjlNQsAsC1rGyQqHjB/9s5ZHCk8Ai6Hiyj/KOhxu/fQWCOBHhY+XVfom1N5Gl2IsKMYYygsfwwAiH7VCyb62tUGlAgR8tRAq4GIHBYJAFh3cR3yK5WfCm1rZoA5gbKvvL76/bp8YcIGaQN2Xd8FAJgxQLsWUARk05l7Wxur90HaGKuFN36S3a4G69atQ1BQECZNmoTg4GCMHDkSw4cPByBb1DU+Ph579+7FwIEDERMTg/Xr1yvc39HREdHR0VixYgVsbW2xZMkScLlcJCQkID09HYMGDcIHH3yAdevWqaX+uuANzzdgJjBDYVUhjhcdV9l5axpqsPrcagCy626g1UCVnVuT3h7hCitjAW6V1yDxcluTELQDh8PBt9OHY99if4z30r5ZbxzW2e8AdERVVRWEQiEePXpE223oACmTYvGJxUgtToWHhQd2he2CPk+59XQe1TQg6KtkVNU14qvXvKBvfkW+domlgSWOvXZM6XOqQ35ZNfZcvI2Il/vCzKDttYzq6upQUFAANzc3GBj8zZk9Uolsdlj1fdmYINcAlfcE6TKVtlUX+vbyt9hyZQs8LTyxd9JelXxYWH9xPbZnb4eDsQMOhB+AEd9IBTXVDt+fzseaI9fhamWEk0tHac3AY23S0fdv+ssR8gwuh4svRn4BSwNL3Ki4gY3pG5U+h9CIj4WjXcE3P4/VV2bjk5RPUFhVCDOBGT4d8alWJEEA8MWha/j+zE1EHbzatQ/cxlgtorve6v8WDPUMkVORg7N3zyp/gue2bskuy8KOa7KFSz8e8XGPSoIA2bpClk97hQ5qaa9QqbgO/zqQifK2th/SAtr1RR0hWsDa0BqrAlch4mQEdl7bCX8Hf7zk9FKH7lvXWId9ufuwvywOBvb3wQAYcoVYOGQOpvWfBmO+mr9+6iBRTimSr5eCz+Pg3Zf7aro6hMDcwBxTPaci/mo8VpxdgZGOIxHoEIhAx0BYG1q3fefs32QLdj5dq6oRQLSzM6R6HIT2Du3w9dudGAn0sOAld6Tml6OfrUn7d9CAz/9/NpL+uofbD2uwY27L+4JqA0qECGnBS04vYfqA6fj52s9YmbIS+17d1+aLcU1DDfbk7EH81XiU15UDAEz0LCGsH48vxszDcJeunRbelgaJFKueTpef5d8b7r2080WU6J5ZXrOQXJSMInERjhQcwZGCIwAATwtPBDoGItAhEENthipuktq0dcszi6XuNjNFth4HphIp/mE+pGuD6EILgtyxaFQfTVejRadySpH01z1wOZBvQ6StKBEipBUfDP8AF0ouILciFx//8TG2BG8Bl6P4bbK4XoyE6wn4KfsnVD6pBADYG9tj7qC5CO8brtaVcjtr57lbyC97DCtjAd4d20/T1SFEztrQGomTE/FX2V9IuZuClOIUZJdnI6ciBzkVOdiWtQ2Geobws/NDgGMAAu1GwOW5rVvu8XjYZCFbo2ZpRSWsT34BeE/rkV/BcjW8WWlrauslWJmYBQB4J9ANgxy1Z82gllAiREgr9Hn6WPfSOkxNmorU4lTs/ONzzDQfBJjY4pGdF3bm7MbP136GuF4MAHA2dcZ87/l4xf0VxU+sWuTh43rEHpetofPheE8IDbWznkR38bl8DLcdjuG2w/HesPfwsO4h0orTkHI3BanFqSivK4fojgiiOyIAgLMZQwDfAiNr6uBbV4fV1pao5XIxrK4O/yuuBlCt8q1btE2puA7fn76JPr1M8Jafi6arg3+fzMWdilo4CA3wgbrWJlMhSoQIaUMf8z74yCkEq279htj8X9H3/hacN9BHgtAMNU9ntbgL3THfZz5Ce4e2uEbJg+on+CY5D3weBx+HaXb67n9O5qKqrhED7M0w9UVnjdaFkI6wNLBEmHsYwtzDIGVS5DzMQUqxLCnKKEnHbT4fv/D5+MXMFHqMoZHDgR5jiHrw8L+zgVS8dYu2OXDpLrY+3TBZXNeABS+5a2yJjmv3qvDj2ZsAgM/DB8FYy9YMaon215AQTcr+Da+LNuMPGyucMjbCQjsb+U2eT+qxYOAsBAeuaPaV2bNulIgRn1oIPo+DGSN6w8VKc7NXFo/uA3FdI14b7gSelnarEx3WztIKXA4XA6wGYIDVAMzznofHucdx4cAMpBgaIMXQAHf4sh7O+ZVVcG9o/O95u3jrlq42L8gdxZW12J52C2uOXEdheQ0+D/fq0P6Hqvb18RuQSBkmDLJDsKo3b1YTWkeoHbSOkA6TSoCNg4CqYlRwuXjN0Q6lenrwevIECyurMLqmDhwzB9meZe2MP5ix9TzO5j7AMBdzfPPWMDiYG3ZREKrRXdem0UXdtq2em/kFQLbYZuja1hfblF+j9wAwFOnp4Q5fDyNq6572BnFk5+jANdoTxKUU4POkbDAGBPWzxubpw9pdI0zVHtU2IPb4DSwa1Qd2Qs0+/2gdIUL+rlup8hdlC6kUv9wtQcLdEuwuvo8xNbXggAFVd2Xl2vGviQNgJODhUlElQjeeQdJfXbvux6Na9WxmSYhKNM38qnruuqi6Jzue/VvL93tu6xaXxkYEPJsEAWrZukVbzQl0ww8zXoAhn4ezuQ/w2pZU3Kmo6dI6CA35+OxVL40nQcqgRIiQ1jw3rsBaKoVXfX3zbUM7MP5ggL0ZDr0XhMFOQlTVNWLJrgws/eUyxHXqT1DuVNTg1W/+wMIdf6JMrN0Lm6nD6NGjERkZqelqAAASExPRt29f8Hg8REZGIj4+Hubm5pqulmZJJbKeILT05cTTY0dXyMq1RENbt2ircQNtsXeRP2xM9VHyqA51Dardu601afnlnd6sWtNojBAhrenouIIOlnOzNsaviwOw6WQuvjmVh4u3Hv6NyrXvUU0DvhXlIS61EPWNUtTWS2Ak0I1Pxl1JJBJhzJgxqKioaDepWbhwIebMmYP33nsPpqam0NPTw8SJE+W3f/bZZ0hMTMTly5fVW2lt8kzPa8ue6XltbebXwFeB/mG0dctTgxyFSIwIRHFlLframKr98ZKv38c78X/iJY9eiJv9Yrcbf0iJECGtcQ2Qfap8Ov6guafjD1wDOnxKPo+LpeM9McqzF7gcDkyffn/PGEOjlKlkcOOTRgl2pN3CN6fyUFkj63Hyd7fC5+Fe3WIGR09VXV2N0tJShISEwMHBQX7c0LB7jRdTuY7O6GqvXNPWLQQA4GBuqDAWMTXvAbLvVWHuSDeVzijLK63GykTZNj397Uy7XRIE0FdjhLTuufEHiv7e+IPhrpYY6mIh/33nuVv4329TkVda3bm6PnX7YQ2Cvz6N1YeuobKmAR62Joib/SJ2zfdDP1v1fzLUVo2NjViyZAmEQiGsra2xcuVKhW78J0+eYNmyZXB0dISxsTH8/PwgEonkt9+6dQuTJk2ChYUFjI2N4eXlhcOHD6OwsBBjxowBAFhYWIDD4WD27NnNHl8kEsHUVPb3f/nll8HhcCASiRS+GouPj0d0dDSuXLkCDocDDoeD+Ph4df1JtIeKe15Jc6VVdVi4Mx2rD13Dx4lZaJRI/9b5ausl2Jd+B69/l4rgr0/jbmUtHM0NERncPRdopY+HhLSlafxBi7NZYlQy/uBJowTfivJx71EdXtl0Fh+HDcTbfi6d+tTmYG4II74ebEz18eF4D0wZ5qS2XakZY6htrFXLudtjqGeo1N9n+/btmDt3Li5cuIA///wTCxYsgIuLC+bPnw8AWLJkCbKzs5GQkAAHBwccOHAAoaGhyMzMRL9+/RAREYH6+nqcOXMGxsbGyM7OhomJCZydnbFv3z5MmTIFOTk5MDMza7GHJyAgADk5OfD09MS+ffsQEBAAS0tLFBYWystMnToVWVlZOHr0KE6cOAEAEAq1e0VelVBDzytR1MtUH5HBHlh9KBu7zhfhTkUtNr81VN4jrYxHNQ0I+ioZVXWy5Qm4HODl/jb4KLQ/jATdM6XonrUmpCupefyBvh4PB/5fIJbtvYI/8h5gZWIWTl0vxdopPuhl2vZO9bn3xfjhzE2smjwIBnweeFwOtrw9DHZCA7W/KNU21sJvl2Y2Ujz/1nmldhN3dnZGbGwsOBwOPD09kZmZidjYWMyfPx9FRUWIi4tDUVGR/CurZcuW4ejRo4iLi8OXX36JoqIiTJkyBd7e3gAAd3d3+bktLS0BADY2Nq2OERIIBLCxsZGXt7Oza1bG0NAQJiYm0NPTa/H2Hqup53XPTMh6Wp9NhnRv5pc6cDgczB3pBmcLQ7yfcBlnbpTh9e/SsHX2i3BsZykPcV0DLhVVYpRHLwCA0IiPgQ5muFtZi6kvOOO14c7daoZYSygRIqQj1Dz+wE5ogJ/e8UV8aiFijl5H8vVShG48g7VTfFpclKy0qg6xJ27gl4u3IWWAey8TLB4t23yRNlFtbsSIEQo9SP7+/tiwYQMkEgkyMzMhkUjg4aG4FcCTJ09gZWUFAHjvvfewePFiHDt2DMHBwZgyZQp8fHy6NIYerQt6Xgkw3ssOexb6Y+72i7heIsbkzSnYOusF+DiZK5RjjOFSUSUSLhQh6a97aJBIkfbPsfIPZt+8NQyWRgKt3etMWZQIEaIluFwO3hnphsC+1ng/IQPXS8RYuDMdomWj4Wwp6/14/KQRP5y5iR/P3kRNvWxa7PiBthingRVcDfUMcf6t813+uE2PrSrV1dXg8XhIT08Hj6fY62BiIksq582bh5CQEBw6dAjHjh3DmjVrsGHDBrz77rsqq4fOo5lfXcLbSTaj7J14WTK0+8JteSJU8bge+zPu4peLRbhx/7/jFfv0MkZxZa08EbI2abunuruhRIgQLeNpZ4qDSwKx4dgNGAv04GxpBMYYdl0oQuzxXDyolq0FNMTZHP+aOAC+bpYaqSeHw1Hq6ylNOn9eMWE7d+4c+vXrBx6Ph6FDh0IikaC0tBRBQa33+jk7O2PRokVYtGgR/vnPf+LHH3/Eu+++C4FAAACQSP7+ei0CgUAl5+m2aOZXl3AwN8TeRf747nQ+3h8r6wkV5ZRiwU/pqH86kNqAz0WYtwPe9HXGcFcLje1d1hUoESJEC+nr8fCviQPkv3M4HIhyyvCg+glcrYzwUUh/TPS269EvTqpUVFSEpUuXYuHChbh06RI2bdqEDRs2AAA8PDwwffp0zJw5Exs2bMDQoUNRVlaGkydPwsfHB2FhYYiMjMSECRPg4eGBiooKnDp1CgMGyNrH1dUVHA4HSUlJmDhxonysT2f07t0bBQUFuHz5MpycnGBqagp9/Z716ZtoB1MDPpaH9Jf/PsTZHOAAXg5mmObrgvAhDl2+PYemUCJESDexYkJ/BPSxwnQ/Vwj0aOULZcycORO1tbXw9fUFj8fD+++/jwULFshvj4uLw+rVq/Hhhx/i7t27sLa2xogRI/DKK68AkPX2RERE4M6dOzAzM0NoaChiY2MBAI6OjoiOjsaKFSswZ84czJw5s9PT3qdMmYL9+/djzJgxqKysRFxcXIvT8QlRNXMjAZI/HAUni+7Ry6tKtOlqO2jTVUK68UaeOojaihAZ2nSVEEIIIaQdlAgRQgghRGdRIkQIIYQQnUWJECGEEEJ0FiVChBBCCNFZlAgRQjqMJplqP2ojQpRDiRAhpF18vmxhtZqaGg3XhLSnvr4eAJptF0IIaRktqEgIaRePx4O5uTlKS0sBAEZGRrSqtRaSSqUoKyuDkZER9PTo5Z2QjqArhRDSIXZ2dgAgT4aIduJyuXBxcaFElZAOokSIENIhHA4H9vb2sLGxQUNDg6arQ1ohEAjA5dKoB0I6ihIhQohSeDwejT8hhPQY9LGBEEIIITqLEiFCCCGE6CxKhAghhBCis2iMUDuaFierqqrScE0IIYQQ0lFN79vtLTJKiVA7xGIxAMDZ2VnDNSGEEEKIssRiMYRCYau3cxitx94mqVSK4uJimJqaqnRdjqqqKjg7O+P27dswMzNT2Xm1SU+PkeLr/np6jD09PqDnx0jxdR5jDGKxGA4ODm0uKUE9Qu3gcrlwcnJS2/nNzMx65JP7WT09Roqv++vpMfb0+ICeHyPF1zlt9QQ1ocHShBBCCNFZlAgRQgghRGdRIqQh+vr6iIqKgr6+vqarojY9PUaKr/vr6TH29PiAnh8jxad+NFiaEEIIITqLeoQIIYQQorMoESKEEEKIzqJEiBBCCCE6ixIhQgghhOgsSoRUaPPmzejduzcMDAzg5+eHCxcutFl+79696N+/PwwMDODt7Y3Dhw8r3M4Yw6effgp7e3sYGhoiODgYubm56gyhTcrE9+OPPyIoKAgWFhawsLBAcHBws/KzZ88Gh8NR+AkNDVV3GG1SJsb4+Phm9TcwMFAo053bcPTo0c3i43A4CAsLk5fRpjY8c+YMJk2aBAcHB3A4HCQmJrZ7H5FIhGHDhkFfXx99+/ZFfHx8szLKXtfqomx8+/fvx7hx49CrVy+YmZnB398fv//+u0KZzz77rFn79e/fX41RtE3ZGEUiUYvP0ZKSEoVy3bUNW7q+OBwOvLy85GW0qQ3XrFmDF198EaamprCxscHkyZORk5PT7v00/V5IiZCK/PLLL1i6dCmioqJw6dIlDB48GCEhISgtLW2xfGpqKt58803MnTsXGRkZmDx5MiZPnoysrCx5ma+++gr/+c9/8N133+H8+fMwNjZGSEgI6urquiosOWXjE4lEePPNN3Hq1CmkpaXB2dkZ48ePx927dxXKhYaG4t69e/Kf3bt3d0U4LVI2RkC2Guqz9b9165bC7d25Dffv368QW1ZWFng8Hl5//XWFctrSho8fP8bgwYOxefPmDpUvKChAWFgYxowZg8uXLyMyMhLz5s1TSBY685xQF2XjO3PmDMaNG4fDhw8jPT0dY8aMwaRJk5CRkaFQzsvLS6H9/vjjD3VUv0OUjbFJTk6OQgw2Njby27pzG/773/9WiOv27duwtLRsdg1qSxuePn0aEREROHfuHI4fP46GhgaMHz8ejx8/bvU+WvFeyIhK+Pr6soiICPnvEomEOTg4sDVr1rRY/o033mBhYWEKx/z8/NjChQsZY4xJpVJmZ2fH1q1bJ7+9srKS6evrs927d6shgrYpG9/zGhsbmampKdu+fbv82KxZs1h4eLiqq9ppysYYFxfHhEJhq+fraW0YGxvLTE1NWXV1tfyYtrVhEwDswIEDbZb56KOPmJeXl8KxqVOnspCQEPnvf/dvpi4dia8lAwcOZNHR0fLfo6Ki2ODBg1VXMRXqSIynTp1iAFhFRUWrZXpSGx44cIBxOBxWWFgoP6bNbVhaWsoAsNOnT7daRhveC6lHSAXq6+uRnp6O4OBg+TEul4vg4GCkpaW1eJ+0tDSF8gAQEhIiL19QUICSkhKFMkKhEH5+fq2eU106E9/zampq0NDQAEtLS4XjIpEINjY28PT0xOLFi1FeXq7SundUZ2Osrq6Gq6srnJ2dER4ejqtXr8pv62ltuHXrVkybNg3GxsYKx7WlDZXV3jWoir+ZNpFKpRCLxc2uwdzcXDg4OMDd3R3Tp09HUVGRhmrYeUOGDIG9vT3GjRuHlJQU+fGe1oZbt25FcHAwXF1dFY5raxs+evQIAJo9556lDe+FlAipwIMHDyCRSGBra6tw3NbWttl31U1KSkraLN/0rzLnVJfOxPe8f/zjH3BwcFB4MoeGhuKnn37CyZMnsXbtWpw+fRoTJkyARCJRaf07ojMxenp6Ytu2bTh48CB27twJqVSKgIAA3LlzB0DPasMLFy4gKysL8+bNUziuTW2orNauwaqqKtTW1qrkea9N1q9fj+rqarzxxhvyY35+foiPj8fRo0exZcsWFBQUICgoCGKxWIM17Th7e3t899132LdvH/bt2wdnZ2eMHj0aly5dAqCa1y5tUVxcjCNHjjS7BrW1DaVSKSIjIxEYGIhBgwa1Wk4b3gtp93midjExMUhISIBIJFIYTDxt2jT5/729veHj44M+ffpAJBJh7NixmqiqUvz9/eHv7y//PSAgAAMGDMD333+PVatWabBmqrd161Z4e3vD19dX4Xh3b0NdsWvXLkRHR+PgwYMK42cmTJgg/7+Pjw/8/Pzg6uqKPXv2YO7cuZqoqlI8PT3h6ekp/z0gIAD5+fmIjY3Fjh07NFgz1du+fTvMzc0xefJkhePa2oYRERHIysrS6JizjqIeIRWwtrYGj8fD/fv3FY7fv38fdnZ2Ld7Hzs6uzfJN/ypzTnXpTHxN1q9fj5iYGBw7dgw+Pj5tlnV3d4e1tTXy8vL+dp2V9XdibMLn8zF06FB5/XtKGz5+/BgJCQkdelHVZBsqq7Vr0MzMDIaGhip5TmiDhIQEzJs3D3v27Gn2FcTzzM3N4eHh0S3arzW+vr7y+veUNmSMYdu2bZgxYwYEAkGbZbWhDZcsWYKkpCScOnUKTk5ObZbVhvdCSoRUQCAQYPjw4Th58qT8mFQqxcmTJxV6DJ7l7++vUB4Ajh8/Li/v5uYGOzs7hTJVVVU4f/58q+dUl87EB8hG+q9atQpHjx7FCy+80O7j3LlzB+Xl5bC3t1dJvZXR2RifJZFIkJmZKa9/T2hDQDa19cmTJ3j77bfbfRxNtqGy2rsGVfGc0LTdu3djzpw52L17t8KyB62prq5Gfn5+t2i/1ly+fFle/57QhoBsNlZeXl6HPoxosg0ZY1iyZAkOHDiA5ORkuLm5tXsfrXgvVMmQa8ISEhKYvr4+i4+PZ9nZ2WzBggXM3NyclZSUMMYYmzFjBluxYoW8fEpKCtPT02Pr169n165dY1FRUYzP57PMzEx5mZiYGGZubs4OHjzI/vrrLxYeHs7c3NxYbW2t1scXExPDBAIB+/XXX9m9e/fkP2KxmDHGmFgsZsuWLWNpaWmsoKCAnThxgg0bNoz169eP1dXVdXl8nYkxOjqa/f777yw/P5+lp6ezadOmMQMDA3b16lV5me7chk1GjhzJpk6d2uy4trWhWCxmGRkZLCMjgwFgX3/9NcvIyGC3bt1ijDG2YsUKNmPGDHn5mzdvMiMjI7Z8+XJ27do1tnnzZsbj8djRo0flZdr7m2lzfD///DPT09NjmzdvVrgGKysr5WU+/PBDJhKJWEFBAUtJSWHBwcHM2tqalZaWdnl8jCkfY2xsLEtMTGS5ubksMzOTvf/++4zL5bITJ07Iy3TnNmzy9ttvMz8/vxbPqU1tuHjxYiYUCplIJFJ4ztXU1MjLaON7ISVCKrRp0ybm4uLCBAIB8/X1ZefOnZPfNmrUKDZr1iyF8nv27GEeHh5MIBAwLy8vdujQIYXbpVIpW7lyJbO1tWX6+vps7NixLCcnpytCaZEy8bm6ujIAzX6ioqIYY4zV1NSw8ePHs169ejE+n89cXV3Z/PnzNfLi9CxlYoyMjJSXtbW1ZRMnTmSXLl1SOF93bkPGGLt+/ToDwI4dO9bsXNrWhk1TqZ//aYpp1qxZbNSoUc3uM2TIECYQCJi7uzuLi4trdt62/mZdSdn4Ro0a1WZ5xmTLBdjb2zOBQMAcHR3Z1KlTWV5eXtcG9gxlY1y7di3r06cPMzAwYJaWlmz06NEsOTm52Xm7axsyJpsqbmhoyH744YcWz6lNbdhSbAAUrittfC/kPK08IYQQQojOoTFChBBCCNFZlAgRQgghRGdRIkQIIYQQnUWJECGEEEJ0FiVChBBCCNFZlAgRQgghRGdRIkQIIYQQnUWJECGEEEJ0FiVChBBCCNFZlAgRQgghRGdRIkQI0SllZWWws7PDl19+KT+WmpoKgUDQbBdsQkjPR3uNEUJ0zuHDhzF58mSkpqbC09MTQ4YMQXh4OL7++mtNV40Q0sUoESKE6KSIiAicOHECL7zwAjIzM3Hx4kXo6+trulqEkC5GiRAhRCfV1tZi0KBBuH37NtLT0+Ht7a3pKhFCNIDGCBFCdFJ+fj6Ki4shlUpRWFio6eoQQjSEeoQIITqnvr4evr6+GDJkCDw9PbFx40ZkZmbCxsZG01UjhHQxSoQIITpn+fLl+PXXX3HlyhWYmJhg1KhREAqFSEpK0nTVCCFdjL4aI4ToFJFIhI0bN2LHjh0wMzMDl8vFjh07cPbsWWzZskXT1SOEdDHqESKEEEKIzqIeIUIIIYToLEqECCGEEKKzKBEihBBCiM6iRIgQQgghOosSIUIIIYToLEqECCGEEKKzKBEihBBCiM6iRIgQQgghOosSIUIIIYToLEqECCGEEKKzKBEihBBCiM6iRIgQQgghOuv/ANHJwB2FgAX0AAAAAElFTkSuQmCC",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# create a fitting model based on a cosine function\n",
"fitting_model = lmfit.Model(cos_func)\n",
"\n",
"# specify initial guesses for each parameter\n",
"fitting_model.set_param_hint(\"amplitude\", value=0.5, min=0.1, max=2, vary=True)\n",
"fitting_model.set_param_hint(\"frequency\", value=0.8, vary=True)\n",
"fitting_model.set_param_hint(\"phase\", value=0)\n",
"fitting_model.set_param_hint(\"offset\", value=0)\n",
"params = fitting_model.make_params()\n",
"\n",
"# here we run the fit\n",
"fit_result = fitting_model.fit(dataset.y0.values, x=dataset.x0.values, params=params)\n",
"\n",
"# It is possible to get a quick visualization of our fit using a build-in method of lmfit\n",
"_ = fit_result.plot_fit(show_init=True)"
]
},
{
"cell_type": "markdown",
"id": "488679bd",
"metadata": {},
"source": [
"The summary of the fit result can be nicely printed in a Jupyter-like notebook:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "e6f191c1",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
Fit Result
Model: Model(cos_func)
Fit Statistics
fitting method
leastsq
# function evals
41
# data points
30
# variables
4
chi-square
0.05401459
reduced chi-square
0.00207748
Akaike info crit.
-181.590952
Bayesian info crit.
-175.986162
R-squared
0.98500943
Parameters
name
value
standard error
relative error
initial value
min
max
vary
frequency
1.00672220
0.00772124
(0.77%)
0.8
-inf
inf
True
amplitude
0.47789092
0.01162976
(2.43%)
0.5
0.10000000
2.00000000
True
offset
0.01084505
0.00899208
(82.91%)
0
-inf
inf
True
phase
-0.01828517
0.05450434
(298.08%)
0
-inf
inf
True
Correlations (unreported values are < 0.100)
Parameter1
Parameter 2
Correlation
frequency
phase
-0.8874
frequency
offset
-0.3755
offset
phase
+0.3337
frequency
amplitude
-0.1078
"
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fit_result"
]
},
{
"cell_type": "markdown",
"id": "3a6641e6",
"metadata": {},
"source": [
"### Analyzing the fit result and saving key quantities"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "4c8a7ea6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'amplitude': np.float64(0.4778909161594739),\n",
" 'frequency': np.float64(1.006722202788316)}"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"quantities_of_interest = {\n",
" \"amplitude\": fit_result.params[\"amplitude\"].value,\n",
" \"frequency\": fit_result.params[\"frequency\"].value,\n",
"}\n",
"quantities_of_interest"
]
},
{
"cell_type": "markdown",
"id": "54821380",
"metadata": {},
"source": [
"Now that we have the relevant quantities, we want to store them in the same\n",
"`experiment directory` where the raw dataset is stored.\n",
"\n",
"First, we determine the experiment directory on the file system."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "2084197a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PosixPath('/root/quantify-data/20241018/20241018-040908-183-249021-Cosine experiment')"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# the experiment folder is retrieved with a convenience function\n",
"exp_folder = Path(locate_experiment_container(dataset.tuid))\n",
"exp_folder"
]
},
{
"cell_type": "markdown",
"id": "033c7543",
"metadata": {},
"source": [
"Then, we save the quantities of interest to disk in the human-readable JSON format."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "57d7ca8f",
"metadata": {},
"outputs": [],
"source": [
"with open(exp_folder / \"quantities_of_interest.json\", \"w\", encoding=\"utf-8\") as file:\n",
" json.dump(quantities_of_interest, file)"
]
},
{
"cell_type": "markdown",
"id": "9054cdd5",
"metadata": {},
"source": [
"### Plotting and saving figures\n",
"\n",
"We would like to save a plot of our data and the fit in our lab logbook but the figure above is not fully satisfactory: there are no units and no reference to the original dataset.\n",
"\n",
"Below we create our own plot for full control over the appearance and we store it on disk in the same `experiment directory`.\n",
"For plotting, we use the ubiquitous matplotlib and some visualization utilities."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "81af206d",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
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