\n",
"\n",
"**This is a fixed-text formatted version of a Jupyter notebook**\n",
"\n",
"- Try online [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.11?urlpath=lab/tree/fermi_lat.ipynb)\n",
"- You can contribute with your own notebooks in this\n",
"[GitHub repository](https://github.com/gammapy/gammapy/tree/master/tutorials).\n",
"- **Source files:**\n",
"[fermi_lat.ipynb](../_static/notebooks/fermi_lat.ipynb) |\n",
"[fermi_lat.py](../_static/notebooks/fermi_lat.py)\n",
"
\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fermi-LAT data with Gammapy\n",
"\n",
"## Introduction\n",
"\n",
"This tutorial will show you how to work with Fermi-LAT data with Gammapy. As an example, we will look at the Galactic center region using the high-energy dataset that was used for the 3FHL catalog, in the energy range 10 GeV to 2 TeV.\n",
"\n",
"We note that support for Fermi-LAT data analysis in Gammapy is very limited. For most tasks, we recommend you use \n",
"[Fermipy](http://fermipy.readthedocs.io/), which is based on the [Fermi Science Tools](https://fermi.gsfc.nasa.gov/ssc/data/analysis/software/) (Fermi ST).\n",
"\n",
"Using Gammapy with Fermi-LAT data could be an option for you if you want to do an analysis that is not easily possible with Fermipy and the Fermi Science Tools. For example a joint likelihood fit of Fermi-LAT data with data e.g. from H.E.S.S., MAGIC, VERITAS or some other instrument, or analysis of Fermi-LAT data with a complex spatial or spectral model that is not available in Fermipy or the Fermi ST.\n",
"\n",
"Besides Gammapy, you might want to look at are [Sherpa](http://cxc.harvard.edu/sherpa/) or [3ML](https://threeml.readthedocs.io/). Or just using Python to roll your own analyis using several existing analysis packages. E.g. it it possible to use Fermipy and the Fermi ST to evaluate the likelihood on Fermi-LAT data, and Gammapy to evaluate it e.g. for IACT data, and to do a joint likelihood fit using e.g. [iminuit](http://iminuit.readthedocs.io/) or [emcee](http://dfm.io/emcee).\n",
"\n",
"To use Fermi-LAT data with Gammapy, you first have to use the Fermi ST to prepare an event list (using ``gtselect`` and ``gtmktime``, exposure cube (using ``gtexpcube2`` and PSF (using ``gtpsf``). You can then use [gammapy.data.EventList](..\/api/gammapy.data.EventList.rst), [gammapy.maps](..\/maps/index.rst) and the [gammapy.irf.EnergyDependentTablePSF](..\/api/gammapy.irf.EnergyDependentTablePSF.rst) to read the Fermi-LAT maps and PSF, i.e. support for these high-level analysis products from the Fermi ST is built in. To do a 3D map analyis, you can use Fit for Fermi-LAT data in the same way that it's use for IACT data. This is illustrated in this notebook. A 1D region-based spectral analysis is also possible, this will be illustrated in a future tutorial. There you have to extract 1D counts, exposure and background vectors and then pass them to [SpectrumFit](..\/api/gammapy.spectrum.SpectrumFit.rst).\n",
"\n",
"## Setup\n",
"\n",
"**IMPORTANT**: For this notebook you have to get the prepared ``3fhl`` dataset provided in your $GAMMAPY_DATA.\n",
"\n",
"Note that the ``3fhl`` dataset is high-energy only, ranging from 10 GeV to 2 TeV."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"fermi_3fhl_events_selected.fits.gz\r\n",
"fermi_3fhl_exposure_cube_hpx.fits.gz\r\n",
"fermi_3fhl_psf_gc.fits.gz\r\n",
"gll_iem_v06_cutout.fits\r\n",
"iso_P8R2_SOURCE_V6_v06.txt\r\n"
]
}
],
"source": [
"# Check that you have the prepared Fermi-LAT dataset\n",
"# We will use diffuse models from here\n",
"!ls -1 $GAMMAPY_DATA/fermi_3fhl"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from astropy import units as u\n",
"from astropy.coordinates import SkyCoord\n",
"from astropy.table import Table\n",
"from astropy.visualization import simple_norm\n",
"from gammapy.data import EventList\n",
"from gammapy.irf import EnergyDependentTablePSF, EnergyDispersion\n",
"from gammapy.maps import Map, MapAxis, WcsNDMap, WcsGeom\n",
"from gammapy.spectrum.models import TableModel, PowerLaw, ConstantModel\n",
"from gammapy.image.models import SkyPointSource, SkyDiffuseConstant\n",
"from gammapy.cube.models import SkyModel, SkyDiffuseCube, BackgroundModel\n",
"from gammapy.cube import MapEvaluator, MapDataset, PSFKernel\n",
"from gammapy.utils.fitting import Fit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Events\n",
"\n",
"To load up the Fermi-LAT event list, use the [gammapy.data.EventList](..\/api/gammapy.data.EventList.rst) class:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EventList info:\n",
"- Number of events: 697317\n",
"- Median energy: 1.59e+04 MeV\n",
"\n"
]
}
],
"source": [
"events = EventList.read(\n",
" \"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_events_selected.fits.gz\"\n",
")\n",
"print(events)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The event data is stored in a [astropy.table.Table](http://docs.astropy.org/en/stable/api/astropy.table.Table.html) object. In case of the Fermi-LAT event list this contains all the additional information on positon, zenith angle, earth azimuth angle, event class, event type etc."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['ENERGY',\n",
" 'RA',\n",
" 'DEC',\n",
" 'L',\n",
" 'B',\n",
" 'THETA',\n",
" 'PHI',\n",
" 'ZENITH_ANGLE',\n",
" 'EARTH_AZIMUTH_ANGLE',\n",
" 'TIME',\n",
" 'EVENT_ID',\n",
" 'RUN_ID',\n",
" 'RECON_VERSION',\n",
" 'CALIB_VERSION',\n",
" 'EVENT_CLASS',\n",
" 'EVENT_TYPE',\n",
" 'CONVERSION_TYPE',\n",
" 'LIVETIME',\n",
" 'DIFRSP0',\n",
" 'DIFRSP1',\n",
" 'DIFRSP2',\n",
" 'DIFRSP3',\n",
" 'DIFRSP4']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events.table.colnames"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Table length=5\n",
"
\n",
"
ENERGY
RA
DEC
\n",
"
MeV
deg
deg
\n",
"
float32
float32
float32
\n",
"
12856.5205
139.64438
-9.93702
\n",
"
14773.319
177.04454
60.55275
\n",
"
23273.527
110.21325
37.002018
\n",
"
41866.125
334.85287
17.577398
\n",
"
42463.074
316.86676
48.152477
\n",
"
"
],
"text/plain": [
"
\n",
" ENERGY RA DEC \n",
" MeV deg deg \n",
" float32 float32 float32 \n",
"---------- --------- ---------\n",
"12856.5205 139.64438 -9.93702\n",
" 14773.319 177.04454 60.55275\n",
" 23273.527 110.21325 37.002018\n",
" 41866.125 334.85287 17.577398\n",
" 42463.074 316.86676 48.152477"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events.table[:5][[\"ENERGY\", \"RA\", \"DEC\"]]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2008-08-04 15:49:26.782\n",
"2015-07-30 11:00:41.226\n"
]
}
],
"source": [
"print(events.time[0].iso)\n",
"print(events.time[-1].iso)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean = 28905.451171875 MeV\n",
"std = 61051.7421875 MeV\n",
"min = 10000.03125 MeV\n",
"max = 1998482.75 MeV\n",
"n_bad = 0\n",
"length = 697317\n"
]
}
],
"source": [
"energy = events.energy\n",
"energy.info(\"stats\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a short analysis example we will count the number of events above a certain minimum energy: "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Events above 10 GeV: 697317\n",
"Events above 100 GeV: 23628\n",
"Events above 1000 GeV: 544\n"
]
}
],
"source": [
"for e_min in [10, 100, 1000] * u.GeV:\n",
" n = (events.energy > e_min).sum()\n",
" print(\"Events above {0:4.0f}: {1:5.0f}\".format(e_min, n))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Counts\n",
"\n",
"Let us start to prepare things for an 3D map analysis of the Galactic center region with Gammapy. The first thing we do is to define the map geometry. We chose a TAN projection centered on position ``(glon, glat) = (0, 0)`` with pixel size 0.1 deg, and four energy bins."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"gc_pos = SkyCoord(0, 0, unit=\"deg\", frame=\"galactic\")\n",
"energy_axis = MapAxis.from_edges(\n",
" [10, 30, 100, 300, 2000], name=\"energy\", unit=\"GeV\", interp=\"log\"\n",
")\n",
"counts = Map.create(\n",
" skydir=gc_pos,\n",
" npix=(100, 80),\n",
" proj=\"TAN\",\n",
" coordsys=\"GAL\",\n",
" binsz=0.1,\n",
" axes=[energy_axis],\n",
")\n",
"# We put this call into the same Jupyter cell as the Map.create\n",
"# because otherwise we could accidentally fill the counts\n",
"# multiple times when executing the ``fill_by_coord`` multiple times.\n",
"counts.fill_by_coord(\n",
" {\n",
" \"skycoord\": events.radec,\n",
" # The coord-based interface doesn't use Quantity,\n",
" # so we need to pass energy in the same unit as\n",
" # used for the map axis\n",
" \"energy\": events.energy,\n",
" }\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"MapAxis\n",
"\n",
"\tname : energy \n",
"\tunit : 'GeV' \n",
"\tnbins : 4 \n",
"\tnode type : edges \n",
"\tedges min : 1.0e+01 GeV\n",
"\tedges max : 2.0e+03 GeV\n",
"\tinterp : log "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"counts.geom.axes[0]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"counts.sum_over_axes().smooth(2).plot(stretch=\"sqrt\", vmax=30);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exposure\n",
"\n",
"The Fermi-LAT datatset contains the energy-dependent exposure for the whole sky as a HEALPix map computed with ``gtexpcube2``. This format is supported by ``gammapy.maps`` directly.\n",
"\n",
"Interpolating the exposure cube from the Fermi ST to get an exposure cube matching the spatial geometry and energy axis defined above with Gammapy is easy. The only point to watch out for is how exactly you want the energy axis and binning handled.\n",
"\n",
"Below we just use the default behaviour, which is linear interpolation in energy on the original exposure cube. Probably log interpolation would be better, but it doesn't matter much here, because the energy binning is fine. Finally, we just copy the counts map geometry, which contains an energy axis with `node_type=\"edges\"`. This is non-ideal for exposure cubes, but again, acceptable because exposure doesn't vary much from bin to bin, so the exact way interpolation occurs in later use of that exposure cube doesn't matter a lot. Of course you could define any energy axis for your exposure cube that you like."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"HpxGeom\n",
"\n",
"\taxes : skycoord, energy\n",
"\tshape : (49152, 18)\n",
"\tndim : 3\n",
"\tnside : 64\n",
"\tnested : False\n",
"\tcoordsys : CEL\n",
"\tprojection : HPX\n",
"\tcenter : 0.0 deg, 0.0 deg\n",
"\n",
"MapAxis\n",
"\n",
"\tname : energy \n",
"\tunit : '' \n",
"\tnbins : 18 \n",
"\tnode type : center \n",
"\tcenter min : 1.0e+04 \n",
"\tcenter max : 2.0e+06 \n",
"\tinterp : log \n",
"\n"
]
}
],
"source": [
"exposure_hpx = Map.read(\n",
" \"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_exposure_cube_hpx.fits.gz\"\n",
")\n",
"# Unit is not stored in the file, set it manually\n",
"exposure_hpx.unit = \"cm2 s\"\n",
"print(exposure_hpx.geom)\n",
"print(exposure_hpx.geom.axes[0])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Exposure varies very little with energy at these high energies\n",
"energy = np.logspace(1, 3, 10) * u.GeV\n",
"dnde = diffuse_galactic.interp_by_coord(\n",
" {\"skycoord\": gc_pos, \"energy\": energy}, interp=\"linear\", fill_value=None\n",
")\n",
"plt.plot(energy.value, dnde, \"*\")\n",
"plt.loglog()\n",
"plt.xlabel(\"Energy (GeV)\")\n",
"plt.ylabel(\"Flux (cm-2 s-1 MeV-1 sr-1)\")"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"# TODO: show how one can fix the extrapolate to high energy\n",
"# by computing and padding an extra plane e.g. at 1e3 TeV\n",
"# that corresponds to a linear extrapolation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Isotropic diffuse background\n",
"\n",
"To load the isotropic diffuse model with Gammapy, use the [gammapy.spectrum.models.TableModel](..\/api/gammapy.spectrum.models.TableModel.rst). We are using `'fill_value': 'extrapolate'` to extrapolate the model above 500 GeV:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"filename = \"$GAMMAPY_DATA/fermi_3fhl/iso_P8R2_SOURCE_V6_v06.txt\"\n",
"interp_kwargs = {\"fill_value\": None}\n",
"diffuse_iso = TableModel.read_fermi_isotropic_model(\n",
" filename=filename, interp_kwargs=interp_kwargs\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can plot the model in the energy range between 50 GeV and 2000 GeV:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"erange = [50, 2000] * u.GeV\n",
"diffuse_iso.plot(erange, flux_unit=\"1 / (cm2 MeV s sr)\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PSF\n",
"\n",
"Next we will tke a look at the PSF. It was computed using ``gtpsf``, in this case for the Galactic center position. Note that generally for Fermi-LAT, the PSF only varies little within a given regions of the sky, especially at high energies like what we have here. We use the [gammapy.irf.EnergyDependentTablePSF](..\/api/gammapy.irf.EnergyDependentTablePSF.rst) class to load the PSF and use some of it's methods to get some information about it."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EnergyDependentTablePSF\n",
"-----------------------\n",
"\n",
"Axis info:\n",
" rad : size = 300, min = 0.000 deg, max = 9.933 deg\n",
" energy : size = 17, min = 10.000 GeV, max = 2000.000 GeV\n",
"\n",
"Containment info:\n",
" 68.0% containment radius at 10 GeV: 0.16 deg\n",
" 68.0% containment radius at 100 GeV: 0.10 deg\n",
" 95.0% containment radius at 10 GeV: 0.71 deg\n",
" 95.0% containment radius at 100 GeV: 0.43 deg\n",
"\n"
]
}
],
"source": [
"psf = EnergyDependentTablePSF.read(\n",
" \"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_psf_gc.fits.gz\"\n",
")\n",
"print(psf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To get an idea of the size of the PSF we check how the containment radii of the Fermi-LAT PSF vari with energy and different containment fractions:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8, 5))\n",
"psf.plot_containment_vs_energy(linewidth=2, fractions=[0.68, 0.95])\n",
"plt.xlim(50, 2000)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In addition we can check how the actual shape of the PSF varies with energy and compare it against the mean PSF between 50 GeV and 2000 GeV:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"psf_kernel.psf_kernel_map.sum_over_axes().plot(stretch=\"log\", add_cbar=True);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Energy Dispersion\n",
"For simplicity we assume a diagonal energy dispersion:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"e_true = exposure.geom.axes[0].edges * exposure.geom.axes[0].unit\n",
"e_reco = counts.geom.axes[0].edges * counts.geom.axes[0].unit\n",
"edisp = EnergyDispersion.from_diagonal_response(e_true=e_true, e_reco=e_reco)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Background\n",
"\n",
"Let's compute a background cube, with predicted number of background events per pixel from the diffuse Galactic and isotropic model components. For this, we use the use the [gammapy.cube.MapEvaluator](..\/api/gammapy.cube.MapEvaluator.rst) to multiply with the exposure and apply the PSF. The Fermi-LAT energy dispersion at high energies is small, we neglect it here."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background counts from Galactic diffuse: 13331.893410079298\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
\n",
" name value error unit min max frozen\n",
" str9 float64 float64 str14 float64 float64 bool \n",
"--------- ---------- ------- -------------- ---------- --------- ------\n",
" lon_0 -2.497e-02 nan deg -1.800e+02 1.800e+02 False\n",
" lat_0 -3.975e-02 nan deg -9.000e+01 9.000e+01 False\n",
" index 2.683e+00 nan nan nan False\n",
"amplitude 6.033e-10 nan cm-2 s-1 TeV-1 nan nan False\n",
"reference 1.000e+02 nan GeV nan nan True\n",
" norm 3.585e+00 nan 0.000e+00 nan False\n",
" tilt 0.000e+00 nan nan nan True\n",
"reference 1.000e+00 nan TeV nan nan True\n",
" norm 1.019e+00 nan 0.000e+00 nan False\n",
" tilt 0.000e+00 nan nan nan True\n",
"reference 1.000e+00 nan TeV nan nan True"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.parameters.to_table()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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BWqKxVAHfY4KoFtbQz4XxweEqae4BAIPcLSdGwxbJHoU0833S+OW7ywpygPrzWNjFe7SkpiIyA8ke6qISThmm1L+zpUwt6YvKpfYrgTGIkvACDwgICDiWCCGUFcFArdmGt2rY4qgna5ylFpMlyE2NBWydDcpFXWhjtOR6lyyVmtXhDSBOdjWNWvsirFRFnCAijWlvjTMfFyTGlHuN6GIJB9h91d1gtTJpavorydEcy3nizM0VsFXY8Jzp5lT3OW5QA2hqaivDrpaIZTG44W2NO+0TY2wJNkFz6a/xiDjxBd3KsbfGOenFpfoNnyTk7vEZtUTreH75llGre2vG2ufu83ZLx9TFopgWoDrk7PlUZJUuE+7i8xZOdy/RZF+30oSpeHEsjFYrP/fa5QOr9y1fCyEEsNhVm6x5EW9L6F7nuommdcssHMYNqLntXunnraDrPiAxqrKR1M5pr2Vp5AwA19t3zZeF087ezirgYuDBAg8ICAg4lggslICAgIDjCBMaOqwQFrEvjRf1MU5gcThEQihchmwSKvmGaB2Tgh95+uJqcjJvWku2kV61uJpctk/TK6GLYUwJFoqGyHYbeGa+Lp0xN9eHc7jknFzZyLfUisiV53kRlcKi1hqM2mQtSVZxCELKm4cNSuzRvLLbHvsQCHOT66Xeviw/3qvkuvDjp4QudZVPfcKxyNRV5vJrmYOSJphDLLJb1pJmnvg83EF53SymhLQP2dW05VN160sq65c5GpWLIRhAwwlc/l7XyJYQBnGnJ1o+Lu0EWY3RUphu0DyxMFYOOcqy3UOtUMI5fF+3Kw2jLStlT2kuuO5CyvJ3C123lVMi3IcneXydmJKU/r7h0By3DZz51ojpku71twJjgCgJDR0CAgICjiWCBR4QEBBwLGFCEnNViGyF7uQaAO1Ezhnw3ebJ+fLQOlduRKXDzAYQt32vdlMSAjAsSk/XMSa3X1xQVgPkDvPLFOS4ycFcDbCh3+ukyrmWEAE3pOjm6r5GqRPhn/C5UpcAUS5kVTke09S3nasrDC4yR2pt4IiZ0KQ2ZJEP19TUCFmtzrvjUURhA7Jw4iWaiFxSLbICe3HapVUez/+y9mmkkFBjWUi4gluDcVhBrkFnpmXc3MgjTxZL6bnxQU1x0pe6871U0n2xjPfPPOciciEtZl5we7k5I4SmtB5m88ek/fM9IG37lpXE7wWeizYtR1npx0SKnrmGQIQRU+O50yWOltwXzNip5rIFKw53HOFKzKP5sxIQEBBwZOAs8IP83XBPxtxjjPmAMeYRY8ynjTF/91ZGduwscGNLNMfO8hHRmr14n3OBJrJ4ljVRrfFuKUEiv+QlJ+hqybb9kyVsjUuSM6PqQrYKRQxqa0ZCPsliYi4zLIBFFrwfI48v5iSmPxc+JieurN+OC85YIzuDO25rpgksk+mxikRvJUlCstWaLUksceKUrSY5l0ZE50q2hiSuOAnLVrtY3txUmc9b7wtFSu3bRDs9pepM9jyiwidhSSeeNeMLEquSg3CSssFc/cTNZwKda07EizfB5z9IteWZeFy1BtN03YRrz/Nb18R3sHs8I7KeP+fK5an3BiaJJi7Z80stt3dz64tCj8/Wdi+W+g4SGas1Ifc8cbrHWdNdktbMPV8ZzMos8ALA/2StfdgY0wPwJ8aY37XWfuZmdnbsXuABAQEBh4lVslCstZcAXPLLu8aYRwDcDSC8wAMCAgJWjtvEAzfGXADwegAfu9l9HMMXuEEVu2GLW8tJG9b+bi0JrbBLJm5hSvrLOblfw9K5hct4ucDzeMp+Hxy24BCECGOxq2/ppqjiaOFYLEDU9eGEtNKwQlzp/ruFS2iyK1sTexKBqHixdZj73JevUzKPXVlJGM91yVHnXi/jEbcicm/pXOZuf82VXwxTcad11siO5yEQHSufq1xj1q02FLaQOeAkbBcaGlobPOX2GS+GCgC9byrqZM/LlsIton3dTZWzLyEaAFjbfdqNtdBwzbSp/PSdlmvbJxrmQP285d7l5HZida4K/wwwZ59L2eW6LXsuAMxrLuKKBK4odCdc+h0S+2I9+GWt9kq6V7qUKO7HTiKAhcXqwlc+0c7l9SRbsOx9sCq8ABbKSWPMJ+j/77LWvmthf8Z0AbwHwA9bSwpxLxDH8AUeEBAQcLh4ARb4FWvtG/fdlzEp3Mv7/7HW/tqtjCu8wAMCAgL2wSobOhjXXfpfA3jEWvuzt7q/Y/cCtybCpOm4r6Lgx63J2L1aplHMrqK48LxNytxgz6Zg9565qPkSHjK36WpgsVScQyTMFhBXkZkTrNYnZcitmXpb/UfVU6uuOW3q6qUPzdddP/nShe3Z1Z5RCEQYMzVdaWKOWM+C2I425+t2cw3XTIjnvJb5UnoOFy3J4i8LmwDq1o+p/LxBYkKpZ2wwC4U54RIGSuNFjXM+L25fxyXh6djNcd4iDXBiXoiLzq46l7KzxnXbutBMM9cQTWuoOuLpVRdCQUwhoJ7Oe+lDMwmFxjjEICqKptRzKVLlVo8awlih1mR0D8occoCgFkLxoROen4J47mM/Lq5p4OeOjyX3CDNy+H6Teg4O1zCnXJZjUpHkcE6cu3lJd69htTDA6gp5vhbA2wF80hjzp37dj1tr33czOzt2L/CAgICAQ4UBonhlLJQPuj2uBsfuBW6NmSfSxHIuKLHGfF3puJJH1BCWOLZS8cY8cBZN6kTOumnRry8nKdnqE6uDkyqM2CezIrIYWCDIRIuNfll4SrS52SKZPfql+fLlT3wWAHA33WjtrlalJpm3WslqHGQqTDW2kljU8bU4yepttGGh1t21sS4zWj5JWNJYmA/c9qtZiKievHXL7I1w1ahUSCZ0LaZUYSpWpYidAUBm1Woz1WKSa1nii9eJGBignZ7ijLrkkI57RNt1x87abu6oSFk01nGZkbfME7Xg41TPu5247TPSA09yTYgmQ18NSvfVePMeGoubowbXD8SL1mSN502e3yx190WPvNxaJaafFub8s8gaJ6oj0/X717nkrkwN/xy3LOnY0/0u16NmdRckcrbjPZtnLi6c360hqBEGBAQEHE8YrDKEslKEF3hAQEDADRAs8BVC3EJpV8UJsmahJc0Nn+Apm5p4qzXS9e4dJ8Na9EMrIRjmW0d7cExT35SY3e5xQ/m8MzhXtM6xJc61Lwk2sW4/pNZRgpwSVJ1775svnxh70aG2bpNSefc88UPCXwXxnLNokfPOSUhxm1ngKo2Xz4XMJwtrcZZMwhkmWkwoA4D1Cbd8Rq48hUhkjDGVYQ9z/Vx0xnvxcj1xCanV2pCRW15mbo6nDU1i7lpd3pn55rqUWFxvqrBVe6YJPwl32IhVmVgI3oVOLIVN+PN44sIJMVOFlySEWQOcz0XOu0Pc82RJcrdWqk+t6oTrX5I8AOudCyd90+o5d3Y1ScvbmaZrf1ZrFk3XTeoODJEK+FgD/xxzqX4v13nv+7mKsuVtA28WBgbGBAs8ICAg4PjBACY0dAgICAg4ngghlBUhKnP0rz8OABisu2z7KKUy3lhDDHYJW4cz6MLyGENdfXYvhTnC7n9NN5lcvcyzTzhswSqJw6jvt19krgBAo3CudoO6r3PLNeFsj+lcswdeN19Oz90PAJim5LJSuCTbftatq1iDW8c36zX9cZils6gQyKX2vYz4uNyB3s9xXutqr9+Vc81ISY7DGTLfW0a5y7OC2rv5cAGXZM9K7upeLYyf9cTlHJlxk1EbsMqHO7ZT0pZn3Wp/3zCHmUM0zEQqUnfckphSKatMZm7eq4wYPRwCGfgQAbFAiv4pPa/1uwEAeaLbj1PSC59LOCxnR81VLJeU5wMaIuHQHc/l/L6gMCPztDncw/M1PxaFwaRGgaUIxiRhsF06TjsztSiaglbbfd5saQhnJXCVPKvd54pw7F7gAQEBAYeNYIGvCKbIkTzhOM9tb22O1pcnm2LfQLhWHUkCTWKhsegUWx8z+M4v9DNfa85L/PGy5aaykS52jgFUo7qq8awXE64xiRqBjLLdeKM2JgDY6t6lY2mfXhhfk7S72zNn7cZDqjic6udZ2x23jPWWYJ62WNP16kkWk1rUkGZ7q+ZtTF1CLSKOb0kWXtl0Y2hR02FuNi1iVGy9cXVf03e5aUDnUqx+QC1/tiTZA6i8hc/bb6TbOgB/O5yYPKX7n+jneabWPFuu83OhhGMiwmx7iGHJdw1Z9eOOegY7TWeNs4AVW8NT444/qEiMi72luYjZ8gbTMge1+oYl4eBZrF5s3FS98oqSt3Lvs6BaO1YvrD+7snAs5t/LPcRj5XMppUI0X21TYzeoo2mBH+qo9upGYYy5yxjzfmPMr3uVroCAgIAjAWPMgf8OG4dtgS/tRgHgewH8EIAHAPx3AH7ukMcVEBAQsCcCCwX7dqOI4TzuCgfRCfAJhcgL+HAykduYibu/Rg1321ym69to1fnOujypnCtcK/elEAy3NxMMM3If2cGpFrdvkcCRhE7MHqX4ksRk95bLwyWJxiGUilz1oqWt2gQlJc7EVd2rea3MZWWXXx7WgJbtmF8/jDSs0PNhhRaVn1sKp4jwUy/TEJO16phJ2bboRwOAIc63iFS1KKzRGCtfWDDqnpkvMye+6bfrTTVEIu37AA13tLb0cwbreevtTMlz4kbLMpfic7xo0nKhs9q1pNCc1CqcePbT83XR41+YL2/e/0oAwNNnXj9ft1vovSCJXEs6+I2YNOf9XLJAFz9vHSvt56hUn8bK2wlYuoIF0+ZzYDghzk22RQd+uXb5vNR+pPfNShCSmIt4XjeKzwP4JQDbAL7nxRpTQEBAwFKEJKZiSTeKHQB/6cUYS0BAQMCNECoxPW6mG4UxZu4Tbaz1UZ5/CQBg1nSME24z1oqVOSB60hwiaVCpfRQ7V5D52lwKLy3BuDUYq+o1SBVOXMWJVWYDc76ltVaHSqJryoLZYu42p8y+cI9TysD3Jsp3bYxciKAkjjG3BJPyag6lTIgtIONvlHpOzLKpfBa+1tUei9xvQHnYzDxhtoCEAPYaq6Bp9FoOIw33SGiGZRM4BCEa2rWwA7GDIs/SEPkDORsarDtOmS9s48bq95/S9aFlZp40Zk5FsDGg8nIKXc18uT7vP56S2qC/xzi0xseSeYsuPjpf99R/+uB8eeMlbv1df0Xn/7lTr5ovD6y7H2rKmvS8iIwES0jwtZZwCs8112JIJ3pAw5vMxGKu/tjfj6y9vlspq2xU+OeZwohtet7lulVTXffYY4/V3h/W2kV9ihvB4Mha4IfNQrmpbhTW2o78XTh/1403CAgICABw//33194fN7cXAxNFB/o7bBy2BX7L3SiqOMX2pqs6FO4ud1bpWk1ciYUtiR4AyKakxZz5ij2y/lpj7ebRvPKEW3j2ad2mpxZBfvb++fL1Dbc8KNWSZmtVuK+NRK0+ttREYIm9CeYud73YUc0DGF6dL6eX3ViTBlmFXRXxkkq/UUer+HayE/NlsZAbVo/JFu7MJ9sK4uUas9wqE342J2y5Ea8pvaVE8z5kwTFvbbMYFlttMq/jdDExC6hnUBN14s4unv/OfOMZVWLWk5AO3OVm5q155o5zYpIbS7eHz7njX3pMj9+i98hpFSQTRNxh6rrXEd8lgayeek5ouLHYsV63tE2dlgZufXvr8nxda131wif+XLiZ96QiganEfc5dgEbE9J0Yd41q3ZFKvkfIs/Fgb4y7QkW+bqPW7NouvhSXdRQCgMp7dPHJ0wvb3BIMah2TjhIOm4Wy0m4UAQEBAbcfZqkC5FHAsavEDAgICDhsvBjhkYPgGL7AzTy5JwnHlNy7BrnK1nco5oasCZWqi8vFTZGbV7Ud0+773w8A+NxvfHK+7v6//OB8+cS3qSucrDlRIea4cghFeNIjCsOVyWKZMWtkN3IN97QLl/zkhCsnw1JfPlw++fh8XdTRY0X3ucTvdE1d9lGpib1O7MIlnKxiKQApq+fWZh0KBzWTxWbPLDqUkqsr4aJpS4+/XRF/vvR634ZL5Znr7/a/W1IIheW27SKXvkXcamkAnDd0+0G6vrANg+diErlxc7KNS76Hlc5704esGl0NvYEExSS5ykns3oySqwN33asdTX5HOel5++tVjXU2mJqEAAAgAElEQVSbE1/1svlyvOnCZMXGWTobPX6vcsnvNNYxX8t1Lp4tFyUaRlNKPvtwSDfVZ4glDhgSOmE5BrtkmUvluS1ew883j2W9eE4/94l8293/Wr5gGAQeeEBAQMDxhDmyLJTwAg8ICAjYB8YAJiQxV4OoKrA2cCXM1rMgI2KhMN9XWBysusft1yaZc6GZ+511td1T85RjRnTPqHuZ9XT/3PoqqtwY2omyAUxKGXJpA0YcW862z7ehsdSYE37/w4aOb9hWRklj3S0XX9RO9fnTyjzoefZMdlZDSHG86OouCxUAwGDqW8IRg4CVGznEIeXZGZVJ8+e7cCyP3VzndVxoaKjt1QT75JZHS9q3MR+4JIm84RK2WNagsIS/B7bbWkrP7KFm5I47TUhZ0hCzQ5gTZJQxm2JS6vJW4kMo514+X5eQZvyg5ZQFJawEAF1SJjSeVRSf2oNZ4cMx8Qn9vOrpPTLpuhDKqKWMIw7DSZhO+OoAUDT1tfDsxG03LZe/wNqpu1bdlBhToOeNJmniW+UVS3TmgXpIT5DVlELdsSScCADdrSf18yv+vbDyl20opQ8ICAg4vggslICAgIBjisBCWQ2MLdHYcaGBouXCAnlTM/xTKshIvEJaVKgrXzSoYMML+tc6xfe10rP/hjcBAF62pvuPutSxu6/C+tLqjF1xdhXFLWf1NT6ulPtzURJDSqn5c3bxd86+zI9ZwwrpY5/XY3mFtu6OFiUV61S8ATcvHDbhsICwT5IlbbEAoKBCkHbk3OnNXWXEzMX2ATyeunDCswOSHaiIjeDJNRm1t5N9AkDbu+tc9m9LKg7xbAUO93BRT+7HUpM6IFd9rXRl7yx1YEllUi4bSzSwQl4/0XBEy7p550KlhBgnoiLJBVwltcWDLxaT7vQAEF3X0JhtusmantGiskFXGScTzy7ZLrQ4aZpzuMqF1NYjVWvk0Fjbh7FiUhVsUUOGfuLCGd1cmV7c3IKRtk/UxgQAo0rvgak0DaFCHVb0rHwiMaFS/WlXn0EJP+6l6HnTOMJqhAcalTHmPmPMN/rlltfyDggICPjKQGQO9nfIuKEFboz52wDeAWATwIMAzsM1XHjr7R3aHrAA/C+siPqwdTM2VMZsnNWSEt+XY1liwbElPKPmsDunHHeaGLy1dk3cGmt+/EKtJ+ZMp5nnHpNVW9LvpyT8am2+Grq9JJk4scnf3W64JJa9S7dZY0tky5XdRzMqlaeWa7bhxsJl0sOCyscL38iX7hgudeeSZtFcT6aarOMGyjwvgoz0vCUJWlQ6Px1o4qo3uCQH1bFSQlfmslbeTfM+t9TIam5Rg+X26MrCmGNqFNyZOmuVLfQxCYO1pmqBZp6bPO5qkjEnb6Tp28txI+BZRnrdPvnYz1WsivWujb8HWZaAOeliwY4K/fzyQK+rWLPjjt6366neFyIOZ+3yay1eCOt+zxrL7TuRHtirVH5Z2Tx7UdPSi6DRc1e1WCfdy01wW8JVYcmzfhRwEAv8B+A0THYAwFr7BQArFhsICAgIOKIwxsXAD/J3w12ZXzDGXDbGfGoVQzvIC3xqrSooGWMS1LQ3AwICAu5wGHOwvxvj/wbwLasa1kGSmH9ojPlxAC1jzDcB+B8A/MdVDeAFoyoRXfdJpo5vN0WqcNL9HQBmnrtbdNV9zErSjfauZi3BRXzfMnWfN3uaKGFMmsq3Fd1jdi9Lcv+EG8ytx7jkuOldVXZ/+ee1ARdCEb45UG83ZX35Oic2x6c0sZV1vNbyktZqAJB5bexJpttPyO2WEEqW6Jhz0pCOqFR9J3LHmp18zXwdXxf4HNjdfXXVWedcklk94tR3SE87G7hw0Kyr3OZRooGuYenCaMwTZyXRuVwB5WN7kYZoSs+F57AI87Q3Bo5rnwxJ+XKTusJT6CgZuBBKk91+auUmSph7Jd5EJ7wWrjupSUpJpE9IQZET3e3St6ej+ohRQ0NDqefXsyyClNcDGl6cmnPzdYOctM99OKwR6X0V0z2SGF7OZaf6Xfo88zIU/AxlpGgpeuA5hSmzlObdq0Ayz31lWFES01r7n303spXgIKP6MQDPAfgkgL8D4H0AfmJVAwgICAg40nhhIZSTxphP0N87bufQbmiBW2srAD/v/wICAgK+8nDwJOYVa+0bb+dQGHu+wI0xn8Q+sW5r7UO3ZUQ3QlWh2nGc09i7qhwWiWJSmMu90hupxrFLJu5hA8oAYGbC3BWl8EBOHN6tVJkPUy+Cz257k3jMcqxa93YKmUnDg4pKwmvtqLwLzONjHnMO990pNKyz0yFmRM/NEXOnmYWS+tZdMfGdmfEtoZP1TLfvxctLpiWEcWmi88OMkrXMjeVU9Ox8HSsIXo9dWCAhPjJPVtF2Yxy3NbTFyoQS+uHy7kak11jCOcyG4K7qsWcx8DkxT1vCImaoYZe0ubzZS+lDViNioWynxF324YJOouEYvi6inFhx+7l1kgDwdQtbsc41z1uj8m0BiWVzV0fnug/3LPWGfC30vt3uOpVNljrg0KCEAdciUvys9BmrhUv8frnhBd/D8mxyCCXGYt0BP0MDLr/3l7ND4bDV4HjqgX+r//cH/L+/5P/9GwBGi18PCAgIuANhcPwqMa21jwOAMeZrrbVfSx/9mDHmQwD+l9s9uKUwBqbtfsHld5r1vBsJWVqUeBJw6yhBLdECSopIldxQkzrchmvaeGC+vDNzlgAnJqXKDVDLfFpRo2GyTmZ20YLn6sZx4iy5WiUmJQa3Zs4zGM50/zFpi2823b5OGBKIWqKbXWtuS5ZQL3NzwVZ3d6bzwjzgSeT12sn8Ym9EuMXsbcwiTYxJSy62quOuVsiKSNm1SK3aUa7n3feW97mptjFr7aqFOWs5C35IFnwj18RjnLvxsY58TvUB+ZrXyCYPx5KFzM2QZ23nEV1Ozs/XXRlrwlWszfVMrdKNFrX1y3W+59uQVSrJ1WFJ2vDkuYkIWAKd/05FYlAD17KNeeTXu/fOl58r3Bzx/J5o6ly9/PIHAAA779X+5OVMz//Em98wX67ucXUV0QlNrttMX4xjiOY8JVmpAncjdV7KOOZz1fPqly653b+uFcCrgEVdBO9WYIz5twC+Di5WfhHAP7DW/uub3d9BWCgdY8xbfDs0GGPeDOAmm4MGBAQEHDesrpTeWvvXV7Ijj4O8wL8fwC8YY4SntAXg+1Y5iICAgIAjjSOqhXIQFsqfAPgqY0wfgLHWLleqOSwkCewZ545a77ZnlPRpU5mynF1M7iMnTaQUnPnMM3LfIt8mrM/uMQljcenvztS5sq1UjxVH6uq2IueWc6d21o2WlmXMi+VwioROOCzBx5/4JNPuRM8liTkEEvsxEY+cwkEludCCDoWjZFx8fNYOZy6+JAfrPHc9b0myDayGSMZFk5bdWIYzvRa71BW+m7q53J7oOha+2rCOM97aeUaPeU2X49iJQTVbT83XWdLgjnxyMiY+d+5lFQDgyoZrq8ed2tujq/PllEIo6dTx91ttTSLmpYZeBlNfi0CJwaRJ18jfgzHtMyl0X5JcbVKSlgWg5Fq0K3pGxhqiSSZufOO+cst3rYZ45B5tJXr886Vqzn/hp/45AODi72uIivHg0xpmu//7XTip2dbz5/MqW45rzqHBjAgGrZkP/WTK/+dnW0In9iPvXzqWm4YxS2UzjgIOooXy95/3fwCAtfbFiYEHBAQEHDaOIQtFMKTlJhw75ZHbM5yAgICAI4jjxkIRWGt/hv9vjPmnAP7DbRvRDaHujGT746lmqpMGdZg37renM9Iy7CTX726vuWz7ZatlwqzaJq5oZ0M7uRtibjAfdZK77+bUiZ5ZJmXq279RWGRZ13rWWl4jbnDLh4m4VJ4hXcHLJvGliXtd+pLnWaqhipTCEqK8xyEcYYsA2kmcWSqW1BCZUy087DHPBRYtmPo2+t2R16ue5Kxap9vLDDJLZjPTEEFn6MIZZYPGd04ZQ+muux/Mc5fm60yh825zt2zuUjaG6L0DwNXCufCnYu2InlKbtCgnNTy/vDah9nYUApgUS8JNkW6feoYVh64S6lrfgguHbLZItiHRWgVhGrUnGspoXr84X5bzzmiu+k2972zm9stsj/YVDUc9/Z/1vJahc1pL/Mu+O+9ZY7l+v+jPc9d6Ds1ZvyzhxufjjJ+rrS8/vfTzm4dZGQtl1biZhg5tAA/c8FsBAQEBdwIMjm8S83kVmTGAUwB+6nYOal8UOaJLnue54RvCdrT6cJzpL74IU3WMJpgS4nR3fHVj2uHKOLIEvSV5MVYLPCKrbzhr0Hp/zIISm6RBPfAJub1ug9Qnq5LG8qbHo8x3/Kk08cdJzM3McZY3UrUU2FIRy5qTuNcy5VbPPOd8RpZwQbrdyzry9FLiTnPC1SdvW8Sp5ySjcJ+5ow1b09KdJ0v08z55Vh3vbbTIUl0v1BqWSkqu1LyeavVia82N+2RDI4HJ5Sd0fL5R8HRDPTPhQwPAzszN60aHPAxKcpXU9cnYxWLmFiWHT3fc56cyvUc3hppcldgr89DR1vtd+Pd5rJ4VJzFT646VjZXTLmJwAADfADjqUi1FoV5qy3cHElEsAKioY9Bf/OlvAwD84Q//+nzd+bfqXJ/6NhXee+7MqwEAO4ZEwog0sDVxz+Mk13t02tDl014fX64PUPfscp8c3XjdK/X8PqQJ11uBPa4vcGhFJgAUAJ61do++XwEBAQF3HI4uC+UgPyv/yFr7uP97ylpbGGN+6cabBQQEBNwhWJ0e+EpxEAv81fwf39DhDXt89/bDGBjv9omLe23twvzjbeLYSthgt60l1/xLOskWtbE5iShhg0FObdLK5Yk1cfenlHi7PqAyYe/tN4hu3SHKejZPclJJOYVIhCfMLuN6ps1z16YumZTmmuCacrLIJz9z6AB2C01cFdXirTCicMrWKK2NAwDO9HV8/ZpwlON8t0nPm0W0Ch/a4vZ3OV0XlgCY759EtPpmsRSBk7vGh052M22198xYww6T3Ak/nd7UsMjd61+eL0sz7K2WcqOv7epYBxM31kskILXT17lmTrY0SC4illDQOWzF7rsbI028ta5pOKfy+uy2r2GJnRYdt3Qhw9FM7xsWm5J7pEttzpKujrXyIlzDtbvn656JtOx/6kN/aao8/iHpmWdf950AgG/895oWsw0dy3MX/sJ8+VLpntc2Jcf7sZb1D70swNUpS2Do8pmGuy9OlsTpp7Z2wmmPTq64YdhxbGpsjHmnMWYXwEPGmB3/twvgWQC/vtd2AQEBAXcSRAvlIH+HjT1f4Nbaf2yt7QH4aWtt3//1rLUnrLXvPMQxBgQEBLy4MNHB/g4Z++mBv8Ja+1kAv2qM+ernf26tffi2jmwP2CTD5IJr1XW979ghu5W6hNzxXFpnjSN1f4ct+q5dPP31RLP1ks3fKpTZMiKXjpWKxS2+PtB9XrlGGtfXnavX76srHZ1aDMdcIwXCYUoty/y59JvqMjZIWVDYDsxTT6g7907iwgk7hbrSHDZpJm6/iWG2iDIfJqkb69aQWCwjcnWJnt7y++pEylJhvW1pecUKesLsAIDh1LdvIykA5iFLV/iKFBCLJVIAHG7amejnFy+7/W+vabjNnFKmUc9zykeVnhSzi4YTt3yRQlCzQu+xRqrjPt3f8Oei4x8Qe6mRuPUbTeWGt6svz5fjbceuyTKdHyk5B+pMJcEOhSAkDLdOjJy0R3rZPnTFapJjalk28CqEfeJrFwlJHMTu/IYvpxCV0Xl5dqrnlZduLKc6yoI5dfXz8+Vs3YX/qt4F3VdB19g/G3yPZ1M9F5mr/PEvY7UwNT76UcJ+MfD/EcA7APzMks8sgG+4LSMKCAgIOGo4ojHw/fTApZfb26y1E/7MGMq0HTLKKJknLYUTXezR5Sbxwk0NUEUh/ZIaLOqFNwtNDKbegm0S37loUtNjakB8vXDWXEwa5FGkg0kSdwNUxAteQhHGaKrbCB8aANba/lyoYo87l0hFW05iXtzo9+mxs8CuDnR8Md2T7cw316X5E4uJ0czU75jM9PNLuVqI7cxrm/dIeIt0n8WyfWZXvYFnt4jz7i9Xh6jPfWowHHuxp16pnH4WaBLObpoub+A8K3wXHOLcn7HKve7sOEtuxkludcIAeG9mTFWCdC2HE53Ey/4e6zQWdegBoJ+568lNk8ebmkQUL2qnr0lG5v/fl38OAJBN1BJ9ZuMV8+WR71jD2u95ixog+/23x8pDP9tTC3orcZa1dIwCgJzqGwrvAUzpWRJtfAC4Pqbnwf87bGml6IlYn6He0CUnN6hZ9ed2NWH7aOmSky/pkwVOFbDCaS9HmshfCczq9MBXjYP8rHz4gOsCAgIC7jhYGFgTHejvsLFfDPwsgLsBtIwxrwfmAcU+ahHPgICAgDscR9QC3y8G/s0A/iaA8wB+ltbvAvjx2zimfRHbEmve1Zq1nXtWUCijQQ1dm8a5hy1u3ktaylKezE1mWQioM3KudPeqtuYyMw3H5OvKEx6tvc59Tte53Ypo2Y2VeeAJ5UUK7xWyK85l+6ztLdilhOQ4avltNCywNdFzeWbLhVaubOsA+Vj9jhcSonXbAypv9+Prd3V7DsEMyWvttHwSkprXDtPFcBUngXtt0i5fYh4MSBv8ycq50qdaGi7aaBPP2tsa7PZy8vf0hpurM129L/pbyr1Odl04oUEWVXqCwmi9rwIAlKybTeGkvCCetw/TrLUoictNgX1yk0XCrvVUREswrHQuYwpnNAYuIZiSFMAZOm+RE+Ak5ZWuJmwl+V5rszbRcBT8FHN9BRMFGr5uoiZ2lehcTzM9bulDP9zCMCZhLtGnb/eV88/3mDwPrBGeUN1D5VvcNV9JpfT4CFaBY1dKb619N4B3G2O+y1r7nkMcU0BAQMARwvFkoQAArLXvMcb8VbiKzCatDw0dAgIC7nwYHMsQCgDAGPNzcDHvrwfwrwD81wD++DaPa+/xzCZof/KPAACbr3Nu8yxTl5DdM+Fxi5IfAHSYQzpzjJOM2AozauckbpN5hkqbt7WMO34l9Xb2iX0OS/CyePtcPl9RDGHXe4I7uxQCSUgX2XNg0zUd/4A6hY9mXi1wSRm6297reZfEXJnqcr/jzpVd1u1tPdYzT7twQ39dqSFnz+hctUmPuu/DIaOZ3l7c6u10z4WhznaV8cMt14Q/zjxubp1l/XWVdmJAnWcuPOEWtTmDRgAAOBXGhFreDfvKrS42LgCoM0M4HDOdumsxnupkFaV+XmdCublokETDhDTnr47dPXSN0krNROd9veHmiEMU41JvosunnNLFBjFLKmoPJ3NRGT2XksI112fu2ZiQTvw5fJm297UUxA1nNcUzxZPu/IiPnRNnfL2jcgYT687x1ITCVde1LL5cc4yTVqGhrfvXNZyTRW4O+yNt38ba63nbzUGVLNYE3BrM/J47ajjIqN5srf1eANettT8J4GsA3HN7hxUQEBBwNHCUS+kPImYlWYKRMeYuAFcB3H/7hrQ/qukUWw9/EgDQv/ByAEDjjCYT61ab59gSb3WUqSnW9UmTVq6/+I1IEyjNq65zyZf+zfvm6778PhUdevNPakXZ2e9wiaer62rNl6THPfJGy67uHkQph/XG4MUn1SotcrXE2i3Hxz1LfGQWznriWbc8mapVee9ZnYsTPWe9jKdqnbGFvTN0v+VNyjXmue5rNnPfvX6VNMBj3f9LH9AN79twXgrzlbk7z+mGs6palpoGk7XbnyxJHl9WnrZpOwtPLDagnmSKB95qe04ttZMvV29jd81ZnSwq9VSkt/TnL7t75PJ1/fz0BiVZW15bvVr+wLaIXy5WfkmJv4K2E2Gsaa7j7zT1unSlGpdMretT4mlP3T22QxXGvVjvZ2mGHZMCdL9Uz2TqrVWupRg19BmZ+qhpQs2w1yPl3689+Rk/KNVjb2V6LXttTaQXXWeNczNtS8ltEZrr7ugzlrY0SbndciJaExJpi8nzinzD8fQ57Ti0Khy7JCbhN4wx6wB+GsDDcD9IP39bRxUQEBBwhLCsJeBRwEGSmNJ95z3GmN+AS2S+Yp9NAgICAu4gGFRHtKHDC+qJaa2dApgaY34VwCJZ9RBQ5iW2vuy0r9efdi72qY6W3hbUqNf4uMQsJVEiaj018AJCSaUJtO5Im7Tmn/pTAPWwCePD/+AD8+W3PnABAPDgGzWEkq+pRvLT15yrWlListuiEIUXS5qMdCxN4jZvdD0HNlb3c0z89+HI7evxxzTJOp2qq/nAPS6EcX1HQwGPfkFDQF/yBPDzFzTplKTqNjZbbvsrT6v7zGN9+YOaBHyg+CwAoPPsF/VkrZ6rfdolsexU3V+8akEvDdUjf6bn8oy66NnbnAb179hvnq9ba2mS8GsHvwwAGH3x0fm69lktTz/Zc4kz1ugWrWpAS+GHI53r7YwkGIyEPXSsnabO60Zb56WVuC+tpRoaO5FROMPrdHNTZ06upj5xx6JVrEMvYSDmZqckcpaVLsnXmOh9kVLZfbzmxlKrhYA+Q5nf171W5793SduUVY+6Uv5iV8+Ps/NVruca+/hccre28jOkTZ5c9U2mqdYiXdNne3CfW37OUMK5q9fwxHPuvquu7t9o+YXC4uha4Dcb2LnpszHGfIsx5nPGmC8aY37Mr3u1MeYjxph3G3NEg00BAQFfmTBHt5T+Zo+4nKt2AxhnuvwLAG8D8CoAf90Y8yo45cNvA/AJAH/lJscUEBAQcFtgYQ70dyMsM2BvBftpofxHLH9RGwAnlqw/CP48gC9aax/1x/gVAN8O1+3ewlVX7zsLxajAl37dcU9PvvrTAIDummoRSzktABiflW5w1/q2hgikgz3/ckpbJgCYbZFu8g0wu+Tc8iaV7XMp/EbPuePrVNI9ou7bX1Bq7BxnziqfVpgP7CpzSXbp+d3b10idjZAk7lyHQ+JTU9389hV3rimp5p083V347ohoNDHpld9zQt3ezp/9AQDg6d/50HzdtceU+fDsh6lU2+Ml3/mx+XL/vLu9nvyohkCufVrn9aHv/iEAwM/+zx9c2A8AvPt//3o3jt/+PR3TSLc3/rbm8vJ2ouN/8KybgzPr6p4b4mFf3nbb7Qx1/k6uadjgdEs15UWzvZvruuZEw1AbnjPNaoHZUO+7UceFNi4nGgJaaygzQ+4xaWMH1BknEjpp7ijfOhroWNaGbrm5oWGJQVdZXW3PpW9uKQuIu9rnU3dcaXMIACYhDe+xhnMGX3TPbY/CKo0HlP1jhwO/PSk3EutK2vI1YtJaoDdUPHLnml/T+V0VVmFdkwH7TQAuAvi4MeY/WGs/c7P73C8G/k9v8rP9cDeAJ+n/FwH8BQD/DMBvAvgCgJ+7yX0HBAQErBx2daX0exmwq3+BW2v/8GZ3ug+WWdfWWvv/wb3IAwICAo4cVpTE3MuAvWm8IBbKCnAR9SrO8wCWUzwIxphfAvDXAKCfNQC4kEg1c65Y2dOITkmtp5LRYvfyhLpYN3PvsllWR6NCnrtc4cCr/3tlTT7yy5+bL/deqq5c3HHLzHhpkzLiiabvDh5riObRgtzWgTsXZp6s9fVXf1o4F31C5fNc/BH5Rg+dnrJsuj0tkmg2jd+nbn/P3Xr8Z55zRTGPP6ru9Zc+o5dGQigzcokbTXV1u5muzy87FsDn36tshXLE2oOL+OJ7NYYUty/uu83wH/6oX1remvXKzIXJztynbIf8zIX58qOlYwdd3dV7ZbOlIZSmVwisMmJ70AO869vKDYc6vkaiy11ouEbuN0MsnIravwk9LZuQYub1S/Pltt9uY02v62nqr5L4MKEUuQDABBp6ayZuv2VT2VGWKHHxZTfXjef0mOl5ZU+JHoQp9F62TXrG1l2MQ4qrAKA4e0GPX+p27cd9+7SMQlNNfV6qLRf6iNY15Dk6oWS3qHLX5fSUCrxoXkUplJkvjz32GIwxHFf8NWvt2/EC8QKqLE8aYz5B/3+XtfZdMsRlu36hY2Ec9gv84wBeaoy5H8BTAL4bwPfcaCM/4W8HgFdtnrY4eGg6ICDgKxj3338/rl692rnxN/cHV+3eAFestW/c47ObMmD3w6G+wK21hTHmBwH8Nlzi8hestZ9+IfswqUH3pc4a6T7ofp1LEq9JB5ogk19kS58n9EsqcS35ZX8+ovNOJOvct6l107/39Hx5dFmTJXHH3SPdoZZvX2gTZzt2FhDrOrNVd+a0s2Y3NqjknMq3pTvb9mh5MuXkpjuX9FXqjWyuUSm9b0NVsxQz9UbOn3TW+mSsltpTX9DEVekFwYupbnP5oiazntxSDeaX+e/cyOo+82ZNKJ//c5rM2n7CcY7ZKmd8/ledBfY7H1C+78Obb5svf9Wf/Uu3cEq5zU9svma+/CePuOTslWtqHV44r9dFbpFdsttYYuD61uL9MiFvaACSU/D32CzS6xpl1KzZ87zXunr8DnlxRewOzGJdLNLFSff5+Kjp8Xx7al1mG6Qt7pNz5XW6lzfVQiq85n1Fnm367OPz5XzLeblpn8rveypxwLHjphfM4ueRxahinwgd3fXy+brLHb0v2taLz031nJNcPWabuf1zYnQ1euArE7O6KQN2P9xwVMaY3/Wl9PL/DWPMb9/sAa2177PWvsxa+6C19n+92f0EBAQEHAakkOdWaYTW2gKAGLCPAPh3L9SAfT4OYoGftNbOA6PW2uvGmNP7bRAQEBBwJ6FakZystfZ9AN53wy8eEAd5gVfGmHuttU8AgDHmPtxi4P1WkLUzvPxbnTsc+cRJ/KQmFvOnNaQUtZ3bF9+nbtysrQkSKaVnDi4rpSVt53gkI+L1nla3PMrULa4mvmT50x+dr2tSYmfwMpdsvpJoqIHL4l+mNN85Wom66rteRbCgTvHc6PxE34Ur+m21ApJYQxgiHMjbj3LdQeq/e/4ujRVcpHDR1UsuNBUT37cqdfxfvqTHbV5QffZl2HitC/RXbYwAACAASURBVGG89of/2/m64py6veuPOlbVeEvv86c+sFge/YGv/wk95pmfnC9f+Q6XdN78bt3/tVxdfGlld3JTz5+1269uubm4ckXDRRsb6vZ3u24HNQ30poYC2pQzy+ElFCiUYOjx6UYuyZgWGiJhNUBJ0iWVhnukTRqAedF7c1fnZ42Olfm6hOyaPhdVW8vXi7PuWsUd5fyD9cR9ElJqKgCguqpl9bPrPoTygL5KIiIKtLY1pCiKktVZTUxO+xqetF4S47m23j9jUvQ0Xlt90rswX9cplKgggas0WnVk+GBFOi8GDnKmfw/AB40xQiv8SwDecfuGFBAQEHC08AKSmIeKg6gR/idjzFcDeBMcDeZHrLVXbrBZQEBAwB2BoyxmtV8p/SustZ/1L29A6S73+pDKw7d/eEvGFcdon3NZbjtzbmc1pkw0ufVRw2WlC+KJX+sqi2endK7krNRpuNrWDHrs27Od6Sj3fv0udVWjzmL79ILKeA0ptLVOOb7t5l3KEDBcJ+y9Vlai4199aQJwkpgl3YaGWFK/3S41bBjP1JVOfLiG6azc8kyq6qkfBV7ySp2LJI1r/wJ1Hvh4rON+5I3fDwB467/V717/8Mfny+uvexUA4PJrVU1wR/PkuKvlll/6nRq6OvuQXoORL/t/4kPKUunepeGqk29xLK7tk8pnPpHpdfmal7iwALc2250Rk8mzJMpS1/W7Gno6e8Kd67mehkpeVn1qvtz5+B/Nl6tdN9aoSyEKLssWHvNJLV8vSQ5CWBplU7cf9TTsIIiHOletG0ifMg+88syQmGQV7FPK34+b7nNWDbRN5aQ3TjomUdWlMRP3G49/Qcf9mLte7XV9Hsena73uAAAp1U/EFGacVO642zO91ieo6/36xCsjfkZVLFeFY/cChxOYegeAn1nymQXwDbdlRAEBAQFHDMfuBW6tlTj326yl0i8Axpjmkk0OBSZJkJ2/2//HWTIRJdai8xfmy+NzLwUAPNt5cL5ue0aWkKyb6uk8t6NW14437M9tqsXwmtfo8sZYq9caI2fhpc9olZidkGfgk6O9mfLU83Sx+epmrqJDKXFcR02XfJ11SKu5pHZTkbOEJoVyq6mPLc52nCXIbc4+u6sJ3ScuOUuwQRzldkvn9cQpN2/dHukvby6/fR5+wo2hfOj75utefr/ysCeJm+/rpInGLcemTeeZtDZ0fGlPuc89n719yTfrdWudUkuuvOASxdNELbX+TLdv+WrY3Yz2H+u89jw//t6TOqaTDeVGn6hcYi6ihHeLxJ4Gn3pkvvypX/kTAEC+q9899ZBeo3OvvwAAWHuIkuc9rcqUe9wyj5ss+NLPpXCg3XfJM/LibbavVanZVPffuuSqI8vHVLt9TNrrrXPO2o/PqOdabWhyO+m4ubAj9TYbJJY1vaz7SnrueuQknDVK1LLvT91310kQjhO6W9bdFyOqRt6katl4y21/6WO3xMxbgIVBaY+myvVBRvXhA64LCAgIuCOxKjnZVWO/GPhZOPGVljHm9dA6/j6AxeBvQEBAwJ0IezxZKN8M4G/C1ev/DPQFvgPgx2/vsPZBnAAnnTtohp4DSiEUCZsAwOMtlyy7PubSYQ0RtBKXBO1RSfk1Knl+9nLh/9XDD4njfPemupJnN10I5RyVCWcDddtnXRcuYPe3V2liLStcuKT/GU2ATR/VcMypl78MQF2UqaKO3vm6LKt7zt3Pp74rvKHzL9RrR1G49Z22ji+le1baq2Xksp47oYnLMz0N50iYJos0GTVrqqssSa5NatPVmmmIonPZ6YDn5H4PL+lcCnr3ajKv9aAmLMc+Ccpl3NzTcJm283qifOJ1H8ZqDvT46ec1sSe1BiLkBACmo9nfyXUNAQy/TG3jPHaf1nDD2de5OeTkd0JiUdh051hQ13lL5zXLfC0Ead6Xsd4X09SFvvKIhM3GpMe+68MdNCedBy/osR5w4ajnzmoIrKRWficaLvSSUXm9Je317JyGS3DCPS+7LR1rVml0VrTLYyqvHzbpfi7deUubOgBog6QE/Dk011dvXx7HGPi7AbzbGPNd1tr3HOKYAgICAo4QzJG1wA8SA3/DEi2Uf3QbxxQQEBBwZGDhvMqD/B02DlKJ+TZr7Txk4rVQ/isAP7HPNrcPxqBqOBdTVAQ5A7/b1rDG7sx9j7t4M89awimtVPm8p/s6Jc/4sATrPk802oLtMascel3kviqpZWuL7nMrV5evM9awgF3C3eWWbk3/+WBd2QDTRF3FiW37c1Fu+KTQfT6141zpTkPjJmsdXX7pfe7mW2urezoiHnmeu7loUAil3yI1wwZpWM+cWz6FMn6mqYaxUuNc5N5At2k+rk1JZk+oOy5ondAQws6Tro6M23jhJHGjPdk9qXR8rDiZCSOIpAbWr2u4Kv6Mk3OePqOxs9G2XreLH3Vhg8sPa9jj9FdrWODSBxfDPYzhkxoisD6OxfULJtIXgQS84pmGqAypXI58W8AZ3QsSjgN0DlgugkMUxofh4pNanm/PqK7D9mkXutsGtS2kF1W65u7HdSq1Z7kF1uuWc+k9o9IXnQaFi7yeQUnhIg79iATBWqbPa5/uIfjQ3PorSc/8I3pdbwX762q+eDjICzw2xjSstVMAMMa0ADRusE1AQEDAHYOjGkI5yAv83wD4fWPML8L9iH4fgHff1lHtgyqKMV5ziZHIi98IbxgArlRqgUvT35is7qJS62FYuN+hnKxf5nueWnfbb1JnnBM9teT61KBYkhyXJsptZodKEqYnSYAqLdSqmmQuCRY/8JDuP6Mk5Sln6QxTPdfdkrqs+OOzdcJNj68O3L640fK5nlqVwsOeEh+bqzbvO+fWW1J9io3OK1t47V0vYERVnVuZWshp4uZtPdfzB3dW8QdO79OEMScMd59yFu7kKjUKJgGjUcslvq4btSpNQsnryB13fayc++qPfme+/Ic/5tSSU7ruL/12rSUYPue2Z73zG1ndjLvfovdo735n7abn1eq1VPUoVmVEFjh3lcqt8wJZg7xHzaalETBbwgXrgW+4OWLu+LSn44v9sU5VKoY1S6gjj9cpN1x9eVU9l2JIourey7CUPY976qVFp1w1ak4JWVYBbMTuGD1Dnik1a66uuWtgssX6ilvBi0URPAgOooXyT4wxnwTwVjgmyk9Za29aDzwgICDguOE4W+Cw1v4WgN+6zWMJCAgIOJI4tha4MeZNAP4PAK8EkMG1Qhtaa/v7bnibYE00L6+tms7F3bZabjugsnhJUibk6g+oDHecu9OvtZsiXeiz6949JP3mTqru63qqrtzUuhDFzkyTSXzRZXlQqatbUal6Yd0Y8r6GTdbu07EKdzkCtWkr9LsSLhE3050LaXSnvjluUxNcm4km4QaVc2ULEgpivfLMJ854flJKAk6Mnrc0lk5I49pS8lMSrpMWaXTfo8nf+JSTStg+cUGPRc2mz0lshwYzOqnhlidLpzd9cUvdczl/AHjpukt8ta5fnK974iOaRBXkOySMlui1OvMaF2LY+dyXF7bZC696+8t0/G9903zZ3uNCM7O2hogq1uP2oY+IkoQi2wAAa57z/UysyW1+Htb8sDtT3aaiWofd065uYpLSfUk88+7EhSWaAw1VdDnc5RPC8Y6GkHLitFczHXd6woW2onXldtuW3m9V210vbvocUfpQQl+SJAcAQyX8s+tufb6z2GbuVmBtPRx5lHAQC/z/hOvd9qsA3gjgewG85HYOKiAgIOAo4biHUL5ojImttSWAXzTGBC2UgICArxiw53mUcJAX+MgYkwH4U2PMPwFwCUDnBtvcNkRljs3nHI90tOZK6gcNdf+E7cHgUAYzJ0a+NLci5kUjJZ50w7lsqVHmCWfFWbe4CffdKtXPOcQhYRgeC7NIZpUP50TUkbyrrq6UHAvrAADSWMcVeXbNkI55faTflRBCLybVuFLDEtPIhZ5aCTFr6KbNInesLm3P4Rwe127HsQmYj8yYVu67u7GyDVQKQMNFW5V+HlEI5Mwb3RwZGuBTqXJ/n7jiXPHL13WuLxBN/NTI8cyjp5QjfPKVpBP/tAuNmZjKy8+p27/2gJur8ZYyQy7+vrYOY9z3LY4xde6b/+J83e5rv26+LPx4Lu8vKMRR+keUQwm9yWI/FdGuBzQcByg7qEmd7Lls/nrHhauuFxp2aUb6DEW+biDNlU3SoPZsmLn7supoCCh5xav08x0Nd4gswPDUhYXxA0Dqu80bYjTxPZr5Zyeb6T1o2xomSzfcOYyeWmy/d2swqI5oDPwgpUNvh4t7/yCAIYB7AHzX7RxUQEBAwFGBhQuhHOTvsHEQGqGUxY0B/OR+3z0UTCewD7sITvc1rwMAFOfUemtRMmZqnaW0k6vDkJO1LRMeUTKuTUnKRjTz26hFtDWlJqtNtQA3jbOKErLWud6p8Ba2iEoB9abCkpdrUOKwSPT3tZ24fTWNVtGdilVsyXiu+5P27vm6yUy3z2LxALjKT284EQVqxWppcfJWKvraAxVCKhJNGF9uqAUr/OtGRo16K/UsZA7Yaowj6rzir9uEOuIwl3/QcFz73Or8sWBZUbrzIul0nO5SdyTfXNe09Avdt7xlvvzqe5zVvPPZR+frsr5aetndzvO75y2kBka4+jkSpmr4RyzRR61IFr0N5mmzNyPzVlgSbKPqxcy469IhUacGeT5pXpPyB1DnlHdnbqwzFkYjC35oHFehQcnvLKH7LnfHL4i7PTuhCeXGus6F9Td5SR5GTJx2HpeAOe+l5/oPqWvWgCqvT4g++6c+v7CfW0V13GLgnvu9Z+THWvvQXp8FBAQE3DGw2nLwqGE/C/xbD20UAQEBAUcUEkI5ithPTnZRUegIwEQRYt9M2PifReYbm2Qxv8riO2XF3GgvatTQ7ddI2EpCDBw2uTIgV5PCMbnnpPOxkkjDKbKv3Ynua5wT39Y3KOYkbDdRl7JpnAsbkasdV5pElXBHSqEILpsf+XDKlakmm2xD56JjXeKuSe43J5MKzzcekPvKfGFx5f1g9oXMG4emYgo9NYybgxnxgVuRhgKkldyMhI74vE/13VhqnHX6fPu045zHJ+7X49NctnxibINa6dWEs/xytqbhuvV7NcnJaK77+3Gq17VFetyjljtGES0v/xbusyTJgeeFW3zfPBZJa400yVl4vfCcE/1jrV/oXXVhomhD5/9aQzW8K7hz5fL5yZp+nrTd/ZJnFKak71adRWErSxoNCbUNNF5wzFCrOg6x5P4evALNSEvyHwDW2+6VlY8WiQy3iqPKQrlhEtMY8yZjzMeNMQNjzMwYUxpDYgQBAQEBdzgqz0S50d9hIxTyBAQEBNwAR9UCP3aFPLbRwviN3wRAXXjWmp5add8km84Mh3amrrKU2ncTdc+b5KoPS8/RpV/WZqbuK4djdrz2OJefszKgcMmrBvHIqWS67fnXbRrLekkdvX2YaLepCnvb2mdjHqPj7u7dJqswus85xDOjEEYUuXOdkbY6t7sStsQU+nlKYZP+jLnJ7lisVz41Gu5IzGK4gNUMI39dmQXD12ASu7GOy0XZBABYb4wWtuFw1+PWhU7SRI+5GStPWpQhUwqxdK4/oeN7elFjundeQ0vtUxqmapx07AzTVRZLTGXx48Qdi2sCmDogvPs+lbLz22Sn68IZs5i60nd0LNPYXYNmQTzuVL9r/Fgy6gTfTfW6yTPGIaYZhWOsl6FgHntN+ZAZN/568HeTVMMdonnOyoZ8X8i5jKe6z4jqOkRTv0vXAh+/9Uiwtab2rN8uGGP+GwD/EE625M9baz9xo22OXSFPQEBAwGHjkCzwTwH4awD+r4NucJAX+NvhYuU/COBH8CIX8hQmwbMtZxnF8B15yNKaETdYLLhmrBYPC1vJrzc335V9AppAWmvoNLWJz8sCN2LZ8v6Z5yzxsYS8gV5GFWeecy4JvOdDmvIy95mFswp/fLZE2duY+apTbmrMCaDSdvz49PxNpN8Vazyj8UWUbKpIj1sScmxpMX+99MJQ7O0w33e+jhKbzE2WSk62qrljipwXJ3jYUhsVix5Au0WNcP1u00LHF40W0z7pBbXQ0weovoD0qKuO85KmTbXAuftSYt014qpdPq+o4cbdIc177i409rbUqCKNbrqGLeOriam6ckJ639JgmsfUJGtcPo/IAmdhLUk4Tjua8C0o+Sx64YDOJyc5Z9nivPDzPGjqfoeVO1e+1g16norU7bf9KqoEfe8NjdgD4TDUCK21jwCqh38QvJBCngmOQiFPQEBAwCHC9cR8sUexHPsV8nw7gPPW2n/h//8xABJc+lFr7f97COMLCAgIeNHxAkIoJ40xbPa/y1r7LvmPMeb3AJxdst3fs9b++gsd134W+I/CsU8EDQB/Di7+/YsAXpQXuEWEYelcpdILOLF7nJH72IgWwxEsACVhDS5TLg21o4Jz+brEAR6AwhbkikuSMCL3p6gWp7eklm7MTZYkJ4dwmBts/Lg4LNMm4SkpT+cQSe24B+yYzaJJy/TMORkmvFwAGCX9he+mlrSgabm5pIFzTkk4SYLx9SssCzy5cymIhz4tKITj55gbPGd0j+RlVPseABRN3Vc7dwJMraEmZrlxdnnh1QCAUVeTZbWwAJ2LlMXzXHYqDcdIko4T7QVdq4wE0wScJJREcmGpaTRlQdulhkME40yvVWwXw5CMDJ6nza30SGAqnrj9c5NcDrGkM2qp5mHben48b5I8lmQlUJdgKP1zymFCpu5JQ3Nz/2uXnsut4AW8wK9Ya9+4937sN65kQB77vcAza+2T9P8PWmuvArhqjAlJzICAgK8IWItDYaHcDPYzyzb4P9baH6T/nkJAQEDAVwisPdjfrcAY853GmIsAvgbAbxpjbth7eD8L/GPGmL9trf355x3k7wD441sb6s2jsgaD3Dlslf9VbCXqKnP5edsutlaaRlTK7jP341IdQOZJt2Np3aXb80ViFoq0F2sm6vIyGyDxoRHWE2d9BWHPsCubUQhBWCCtkvS4E2JWeB3xksJBMYWDJMzEnPcOKQ9KiMLUcvwKCedkpW6fEsOAy+oFomD4/M/n/P2K1AaJP9+q3DnWJBBoWXTYc7Oo5ggAsWfPcIhkUmhYgyUMdP/E/PDMipIV+ja0a7zICYzJEc2hY+H7Ra77mFgiQwp3SJiIZQ+4LV7iz5W1z7m9XNe4snxWE+QwnNQPxHtwqwXjhrJchhF1S4wdi4ZDc82OHn/juuPEJwNVHYypVJ+1x8vWYhdGZreU0eLriPX7iyVvyIIYO8PY7T9qL1eJvBUcRhLTWvteAO99Idvs9wL/EQD/3hjzPQAe9uveABfu+o6bGmFAQEDAMcNxFbO6DODNxphvAPBqv/o3rbXvP5SRBQQEBBwFrCA8crtwEB74+wEcmZe2gUXTM0kK754Ve5SHi0u2zGVksKvO4RR2q5d9t0PKgf3Euf3CXAHqqmvSGotZJCWI/bLkWMkNGAgc7mBXc759xM0bfGstKp5pF+rqStl7RWPilmmigsjHr4hNUlA4Q1p6mYiYJeSCT32jBi4kyqjQp+3bf+VdvRYzaLhl5pkdXGq/2dDQkszxoFAGw/aUy+7dv92mhnjasYYFSi9xUCRaRDJOteBkWLlltsp4LO2IWBqe5bFT6fbXJzqudurOJaGGFbNS52XHN1Rok35cQgVGUsoeE+NmanXepl6dc2OoJeXJQGUDyrYLkeTUnCOJeV/SXEP3OTH63WjdzXWrqeERVhOs3aO+uUTr+sX5uorK+oe+ReI41rni51mYVhy6bC0JN3EB2SpgAZTLI4svOg6khRIQEBDwlYxja4EfNcSmwmbqeLrSemt7pkmhASWrTOoSM8xtriixJVYTJ4245Zks848vc7e5RF9KzDnZlJPVWHqrtNqDjy1jTGst2RTS6JZ56tx6S7jsiWFvg/TCo8U7sC5A5D7nBJjobgNaBs36zOOmimmxBzFbwn3OaCwiG8BWKy/HPjnanmlDXJPRvHoxLD4mc6ubU8dN3mlpyXhslFQl23Uo4d0ptun4bqzTjNuM6VxPKrd+WdNnoO5ZifgXl8pPCiq7F8483Vf1Jtzec5oQd5xaj1lv+Zb0KLPOei5iUuQ5WRJRG3ecONooI514On7De0ZxxPUTVBdh3Ha7bZ1fFjnrzlT7vDNxXlK8rR5AlFDj7pbbV5FqncuUzkWeHU7Er0V6j0hSnZ+RVeHYVWIGBAQEBOB4x8ADAgICvpJhAVQhBr4aGFRo2pH8BwCwa0gDnHih4rZyR3NWHpRkU2HZ/VStY+FsJ9ydfYmaIaAuNvOBLVVvSRKPk5jsqkoIoaopK6rbLokp3obHLQk1DpXUE5vOBU4pMVrr+O3dTuZrc4il9ApzJSnNsas6vybQsnAOOzShnzd8KTaXTLNbPmltLIxvbfDUfDn3+u9Seg0AKfPTZ85V71KSlXXYl4WxWLZAysczavc1bbYXtpktkUpwIB10HwKYlqRAyCqPnv+eRMvlIHqxCwdxYrAgZUNpdccJZ4bcI6OutiGzPW2JdjVx62clKQhSGK9vXGipP7k8X1dSCGY7dZz4UUWhy4jvO71fp10Xrpl1lrefk+vJypOsby/3laiEAnVpByEN3A7lwKNqga82XbsPjDF/wxjzX/zfh40xX0Wffbcx5mFjzA8f1ngCAgICDgJXSn+wv8PGob3AATwG4C9bax8C8FMA3kWffTecUNabjDHdZRsHBAQEvFiw1h7o77BxaCEUay23YfsogPP0f/F5LHBj/0e4tcL84Aw+u1xzV5l+GSNy70RMn9kizO2V5gvCmgDqYRNuUiD7Yr7zMjU/DkvUwyG+vLzGQ19sGMHuPx9LQjMcVpmw+7mEJ84131L2PoupoziFU2aerzuxi6EEoN7oQUInJdkHXFbfHHvmgB6qFk4RhTopAweAhJgXlWfkML+fG0pMWs5Fn6TLbQGZq5rSHYeDIhe24JL1VqKqfuuJO68xyTIs4/EDWiLPIRIWRpKahm5KPPQao6fhz0kZPzwvwt+vcb+JSVVaNwd5wutYxdGt57BNRiySpu9239p9dr6OQzhXUsfd5iYZLPEwyJSdIiFJVpZk1pOwVzJSrmwlfA+761arKaC2fXI/sMTDqvAVH0J5Hr4fwG/R/38NwCcAfMJau6h/GRAQEPAioqoO9nfYOPQkpjHm6+Fe4G+RddbadwN49z7bzDMVG2tr80o9sUrYesg5cSX/ktXKFurUV5SNKQHDSVCxkBOyRLnlmVjdgDZ9zegncWbYmk78v5RMo99P0Q5n62lZyzDWQm4RD70bLzbynebc7FkqQdV6MtSgeZnVItxzACggVXCLolKAWooA5nrtbLU0qVFu6pvisoY4by+J2Jy24ea4k9QnuyJqlEzXovRt76QFF1D3zCRhzElI9uJGmbuvUtKqTgv1AE5MXIPjaUPHP0yVR82Q8xrRvTArF+0mrt7kuRDPjM+Vn9qhr9TcnelYee/ybNQaPNMz0Irddd+wqn2e5iSi5jn1hjS+q4hrJdy4WI+dm1bHRDCQKuA2tGpWLHwAaA+clZ9TVed2W5OvkuhmD2JCbpwkOdkze+yxx2rvD2vtC5bCXoXS4O3CbbXAjTE/YIz5U/93lzHmIQD/CsC3e23xA8Fa25G/C/fcffsGHBAQcEfh/vvvr70/bnY/lT3Y32Hjtlrgvh2btGS7Fy5U8nZr7edv53EDAgICVomqPJom+GGGUP4+gBMA/qXvulzs13pob9i5mM88McjuIYcdKukETy2YliQB2X2NUg0rTJaILrF7yImvKvLHoqRMRa6euMJcMs5g7fBlkIQmh1BqnHafOuDEY55QYm9+LuT+knsbe+4vj4/nVUr9ea7SSl3tGQlXjX35eEEOHuthVz7ONK24VJ1Kpv1cTlLdxtK+Rj5EU1AoQkIBANDAYjiIr6GcI0shsMiXhIvGkRps/URLwhtTV9Zu7HJOPy9LyIkT0nmhyxMvXMWhPU7sGX89J8QtH8XKfx8UEq6ixGiq59+NXYiCeeKJ0X1JqXxnV0MokdXjR+XifcmJ+NTfg51U77sWibwtE09LSYwrm2nKK911c2wpDBo39fjWi7Px/PJ9JWSDzg3E614o7ItkXR8Eh8lC+VsA/tZhHS8gICBgVTiqMfBjV4kZEBAQcNiojqgJfuxe4NbEGHVc+e6cA0oeU1T7rl+3lAOtYYFeoe2geuQeXkucmh1rhLP7NqEQgPBgudSeXfTYLLp1rJIo3Fsb6/4zyvZLiIG1ojkcJO4lhzXEfXb7b/j9cDhosaw+ItU6ZqHMww7k8kbUpmuWUX7IR5mYZ10PKyR+TMRsoCsnzIaCQlS8/SAXhT/9fJbqcte787WwRo3f786xE2kZdneqOfWZv68GIA1vc1LH33XHmlAoYlQu58dL6Ibbu6WJvgxk/W6px2rFGmKI/LyL8iZQVzaU/fN9x6XwUjPRylXNsGWoLZ9nT9W6x/M90HDXtegrG4QZQV3PmefQY0z3NYfkSn8/2ZS70ut5CatnSvx9nmN5XqI95CwkTJTmyhhaBVxHnpXucmU4di/wgICAgEOFtSiDBb4a/P/tXXuQJWdV/53uvo+ZnX0AWWAhYELxkmCBCPK0iASoUCIQHkKhQEKphQWCWBYQKMEURRWIZUQijxggqRJKUALRYMUEeaq8QkxYQiIiG+JCyoRKzO7O48693cc/vu/c79d7+87O7N7MTC/nVzW1Pff24/u+7uk9j9/5nUoyLEaerlltfWr+W9L/7va/Jif+OFlofFK2NIuKdavDeev6yHQtSlKOk6fkAvC1rGqTLU22oMdVZqyLTVa76XVrQ3UmkDjnGR2TUzJKuOmufU/JsrmVUB3JTWaZ52yJu94dqbMLN6zt7EoWKuywlO9t5Db3aH3mhYSj4lzYklulJOcoJqeHI5o/Jb4WiXNtYM8srRpQQAAAHQ9JREFUF/M2JtcXIGExWt+7R9TAuLBK0GNXblgl5q5usqp30bpYIr3eLDvNpR+9BNbYXgatZbTgO0VzQtU6JbEwWHc5aWiPQWJZ1uUHAFb6oZKSOxL1RulemZjUqJh8voC6RygNXZ1YROxIbKDMiUnm73djkpITowtZ8ias2nfWHXmAmlOyrdC6F7jD4XBsJkIIxS1wh8PhaB/U9cDvEYxDACR/NZczJzy4dRyKYPfLXOXFLIUKspxEh2Kyjduc8blq542iRPPU7mlPlRJjhbV74jJkEuIZNYQ4aknKMQ+chLvI1V4sY7KJzl9hci1qbczIqsjIxV43KGxRC6fEJNKu6o50fUpW3ZWHJDSLGu3QlGSz9l2sJz6gJOFq1Nbmvyku8bdwVlXTY6ehimmrJ6GlxV4KEdhac+IxbxAOY/edwS3VbL07JNCUU5jK3H0riQdSiAkAulUMvRG/n8WcbLuezKNahigTsdQjMSzSObey+JrOO20f7oQ1Kin5PU/t60xiYLlI3HTGwgq1T4vPWC2EQnIJS/1wjhXSJm/Sbufk93zJY4n1IXyzZwS3wB0Oh6OFUHghj8PhcLQT6qX0M4WxB6yUncMC7F6V0e1kNyzDZDCLW5dx13pjkdTZEIkBwOe1Um52q+cPJ/exE1XdVknXeaVIbruFQGpjJbd4LBvALBQwIyWq1o2aNaptjXo5qfYRz9ramHGIh8dn3NweMVOYb8shGOMU94Zp/sYnBoDl3cFVZrYBMwe6sT1aSWyIUUP7sj4xL+Y7zLmPaoP0eHMpu20vjvheprEYpzqbwum3ll6scc4hDi5bN5ZGfyXVGuSk7DeKcgGrCynENKKxWEhsQJ+tkPb2MIaTVuleZsTCMR75Yp7umy5QaC2qHHIpf01OQU2RMz03FYXOVmN7u5qaJP298POUxxAHt4djmH58jZ1FKpEWLmLt805ND/yeC1Rv10KerdIDdzgcjlZgvd14TjROLiLvFZGbY9vJz4jInmMd0zoLXKBjy6fKgyXAfN4Od2mJVh03Gm4Sk5pW2WXcbT6GNaaZ023a2JzAqgrSRT4ULLGMLFG2WixxwxY2Wx/ZuCkyV29yF5XYXYgsqcXRZLPneiPlZGGbwNSoxrdOc+lEq26llyyeeepSM7+cxJ6qKB6mlBhc7iW9bEtO8lw4sZVXYV6cUO7kaa4L3Ulhrx2d5A3YGvJacoq4X4Tnp5b4pApXs/RYI5wb6RYIa80CXpxwZQtcYtWl9OhedtL2kX7oHnT3KK0PJ6rtGbl7mNby0AoRyRvA99j0wAckwjaUlLwty0mRNU7092D1C+lZOJSnpsRmDa+Sjj13sOLG003JRU6YmqfLYlhN1bQrFc2lSmNZidY41zfMCpvEA78GwPmqOhKR9wA4H8Cb1zrALXCHw+E4BirVdf2cCFT1atVx9d3RbScb0ToL3OFwODYbW0AjfDWATx5rp1a+wC1kwqXiTWBRH0M9hBAbztYaq5LGdhQAqijBNsi5/Ro1io0uJifmjvTvk8ayYCXH6XtO9piry8k2Hqtx0edz0uMmbnGvXIpjpcRlkcrbLZzCCaKlEYcArOVasyiShQVq5fu0FtKfTM4eqSgJSi62TZsTqhyOsPvL67MrT+GanZHrz2vN57d1q/GlMbldUKiigW5cC2VwaM3CCU0cZQAY0Z/VYQ3Jw6UpDZbvXg2fLw5TWKBf8LoUE9+PiN/eiWGmXj5lrHGMrIfO62563lnD+gAp0c0hItNjB9K6c7iP6yY4jGXNqjl5XgvdIDzDQg2gWRDN5sCa/0VD+LNJNuJEoAqU62ehnCIi19LvF6vqxfaLiHwewP0bjnubql4R93kbgBGAjx/rYq18gTscDsdmQtfPQvnpWo1qVPWZax0sIq8C8FwAZ+k6zH5/gTscDsca0BnEt9cDETkbIWn5dFVdOtb+QCtf4IJhdN1Nga+u9UzuW3SluV0Xb3O4xMBhGSsT5rBEnrOaIbUcE+PLpu+5K/3hhX3heyqjnlPSYI6XYI4rc5+tfdqO0d3jz5rK4jns0KM2Y6N80v3k8w8iC4OZFzuKNL75KoQwhjlro1Noifi4h8rYKX1IPO/aWoUx1qQCSALB1pjXqqNpLmPWEVXyDylcY251h+bf1CqOr19kk6Ejring4419VC9fJ243PWOr0Z3ncBtrui8Nw+dcts8KfIjP83yHyu+ZZRJDJ3u6pPFN41ou+3F8XCuRzmWt9HjMde32qNhJNQX83I/iGvbonD0hRhA/I9n8xPmZsTM+pkFCAkhhRg5t8fNqqqQ81llhAxb4ieAiAD0A18S2k19X1desdUALX+AOh8OxudiMF7iqPnSjx7TuBV5BsBT5y6MGbnMXyRIwa5T52lyJWTQ18qX/8Vej0E6tUTCaLRXjvu4uU3NYtkqXssCHnZNk1VrFIQCUsaMMV4U2oaZ1TORUu9YAKRm4UnLCdTLxVqtya0jIsaXTG4Zx19Yio0rGhkepnvibPP+ILS22yuK8uiP2IplDHO7XgiRvpOhMel7MB+YqPfOMuIsOe07WuJm9sSGJm5fjhC+JnJElyRaoVTCaqBQAjIQt/yhGRZ4j86B3Fku18wDAXE7dj44aM3AUdzo2iGZu987VVCFr3iwLujUJpnFXKu4IZIli9vY6JXl+pPdt6zmtgtW263Ml77rJY6Z1N+1xFnSbBTaYxNxUtO4F7nA4HJuLE6+yvKfgL3CHw+FYC7p9tVBa9wJXZOPEjIzLy5vFccZazOTq1lqWHcOlM74qC/LUk3GT3Nl6sowSOJacpEhAVdC14q0opySLTLiKS845LGDjHlCj5UFD4oy50XMZJbPiNoc6WODIwknsEtfEsmrnDSGEXjetK5f4NwlTcQhgfhBafnWpgXJF98DavrEo0hw1VTbt69U8JVErXvcYuuExdxpqBlaQQiwrDY2ta2Ou0li7pLdt6zbM1ta4Zj5zrVXfWJM+rSWHQ4xzzk2P+fw2r90rt48/23HXrWl8C6FWYbiTwiJkbVr5O5+Tn8vdnTCW3VWSUuAQCof8esOQaM3ovrFO+Xi/ikTSMn7ey9qYgPrfmIGT37OCW+AOh8PRQig2jYWyYfgL3OFwONaC+gt8dtDkwnVj6IQ7xTeWzzNvlaZsjA3WnWZealaGbVY7ZPeVMYgKaXfm9x1/xnzecUkzsRl42/adxmE1Hi+Hc4YU7hlEF5rDEzzvFG6isEHG3N2whsyy4bU6kgW1PHavuyBudjXJBihG6ftBJ4U4VoqwzeGmhdXkgncHhybOyeGSYjl+fzhpbBe7kmzB4JTAUqqzjxKMHdPBpFQAkMrGD1En+sUhaVBH9k63l8IaBYUNOsPENBq7+0Qu4nBE031hrv5Y7oD+UpllYqXs3IaMYfzwmhIg3cNiNYQrmBHFYTJjbXH5PY9/Pg/Hz62k1majfDLcxBh2UmhrWSYZNcyOqh0fN1nFk/9eCqq7mC0UZbk9m2K27wXucDgcmwjvSu9wOBxthbNQZgcRHXd+NxVBZqGwapq1aKq3IZssHuFMNhdnSCyyYGYKg9XsjLHBCn8Ma8nF5dkcrrAQSk0VLkvb5ioekST8XzWUPNcU9KghQh6/P5Yh0aFCqJIeD1Pg42IKZhsU3FItShDwxYS6j5tb3h+l8u/+cgqHZGVsTtFLzQCGdLw54N0jJCtAbcosdDOP5NZziMNU8WoSCcRcSAVYyb1nxoqFE3itVqloi0MITW3/OBxhe/K9HFHLNFPgyyhEwuEWC5ktDyl0VmvoELvSdxPbo7szqVRaezdrogEAyzk1+ojhEg63VTkVIsXnga85oiYNzEKxdam16iPWFP+djq9V+9ssp+4HpL+BpqKxE4XHwB0Oh6OV8EKemSFDiT1VKAU2PeoVZc3gyTLaLlkPnVqpfWz9xdZDTYgoWBKjki1RSkA1CCSxKFJNZCtaDzuqQxPHAMBytEpK4kszd9rEkAaUDWPuspXyZ9KcADIRLxbzqnF7o6VYawdW04gO8+LSZbbaGMMiJlTJEuVSbZujFNQyjZolmzXMbdgsiQoAK7vDWu0h644TnpZ07hOPXMrJsXJCli1wsxq5kTHztHcXwfI3DXagLvI11B10XKw1oOeuk3Gz5Zi8npJ8Hmtck9HJ1jCLt42vX016jIvUPk8WHjje7g8nRcq47N/04WvNwrOG5Dx5SKwTz7B1bdLkB6hpMbVMWyZvxNail08+93yuaRb68UIVKEfNtSZbjda9wB0Oh2Oz4Ra4w+FwtBGqHgOfFUQr9FZj8it6V0pJlZxcSnO5utUU7nYWklmrFSeNqHVVdA851MAKe126lrnKTe2sAKAvwd3m7u2c5MvnwvHGkQ5IfNlSrHs46WZTuGQOgXtsLjGQlOaAFMJg97XWHi2GaDJK7HGIxubHYYNilNaVub1cwm6QGs85ln9XqTs6d7u3teIkKisrLsd1Wd6V1qpLtQAmkdAfpiRpB6RRHee4WpCaIs3beNBZRSEeDsNFnjurJfLxA6H7BgsLUDhplL43HXC+F0PSBu/mFhYYf1RLnluJfd6dVBDk8y7T+g2QxjLXMc48qXBSwjTdNwqxUFijjHIQRUE6+WhO+ld5XHdt5onbWAcUslwt07m6cTNrCF0yOCwzC3glpsPhcLQYlXohj8PhcLQPXko/S8g4m2184xrHl8IG1TjsoBOfAYlxwswMhpVMC3etJ1d6joT7jaXBzIYqY55x7DpPHFn+nsuXDcwIMeU7DkUwI2RuEJgRc4upocTqHHWKn9sxMb6a6l1kLuTEounkkyqOrIbI4+ewiUkUFFMaHhhLoNapnmD895IYP8z+MWVFPr5P62pyB1WXQlg0VmPcHM5SCIdDDKbyOE3l0hpZcMu2moolj7UK67JI7eWWR+nPzlql5cSvL4fUiKM0Rky6PjeqsPvSpHDIc+Cu9IOKWVWx+QWF/oYNEhArU8IahoWcnnW61xxOMcXEWsiyZKZVbB4xan4uLGTJf4NNIZimsMqJQKGovJTe4XA4WggFqspf4DNDsvzC/7Rc+bVC7abMwpyW1GgSjuLWUAsICUEWs6pxu6n60HjEzEc2PjQALBehqvCuuX2NYxmfB5MJqjCGauL8tdZZ0SupCkrCckVgnOs0vfFhQ3u6gVBFXWzNpQVZN0WqlFyqUhLSLCHm3zeBedb1alprwEyVqg3CXHxf6+3bIreaKl0HeRqfWeCcTFwisapu9Dys4heot+JbjlZ1SVYnV9XyuEfjpsCk0U0VsjuK4MVxwpjvkSU557hBM3lhi1V43rmBNHPWx1XL9BkLW1lCkJsq81qaR8YW+HCU9l2K4mqH8/QsdJmbLZN/e6yj3iVO/KHVhYl92TPgcRnY82jSeZ8VPITicDgcLYRCoZ7EdDgcjhbCk5izhI5DFyZKtETu22JJAkTRPeRyWxar6sLKz8mlReIO7zpyW/ieyqyX55MQEJcMdyInmrnRnLAcC2c1hAKAVGo9v5pK7XMK0ZR5TLjSNVcpLJDlIQTRobCNUOhnzJOmXFdB7qt51cw3bnJFa4nPKWEDW89Sm5OQdj/6oE7qwmX/Yd0sbAMAWUMHew5rcDglXTeNiedSNoTOOqRBbWJTXUosckLZ2uNx2IgTf7VWc3FcRS3Ek2Bl41WtUz0lr2MIxNrUAXUetYVO+Pq9zmQtRC0hS89A2VB2zqEtC+2MqP3fYMRJzhjG5BARJxEb+OsLVRIh4/Bk1ckmzsWwJCbr/69gspXcrEvpAX+BOxwORyuhqihL10JxOByOVkKdhTIryJh1MooZ8GkcWHPV55VU6ShGYMcz86M7oo7YFjoh957DJkuSQjdW1t8jvjErvJm7zyGcpo7a7HNya67MOLSsO01qhYjhlA4xJzrDNJd+Hs5VkxrgVnHxvMawAIAOa3/H7aJK7nWXy+7zScZJRRzgASvkxeW0knSgzugxXj+3URuQNviRIvDbWTmSudEGDo0VSiqUMZyykKcQjrB2enT1m1qfAUAvtpIzNkuYEoWTyNtuYuJw+zMrG68oXNSjY3Zk4b7xWg2JHWRhhR1F+v5eWZJrsNDMSpaeG5AyojF+eK6sPGhcfg6r9Dtp3r0Y7tlZpLVk9hTr8xsjh/XG+b6V5SRrjLXP7b5MrS8Qqe03M3gM3OFwONoKZ6HMDJVkONK9d+0zTmb1s2SJWJKMrReumMuMo0sWNncL0YVs4phFSbrVKyRQVGZRF7k7mVQJ22ZdpLFy02BLvFRdTrBxMibydbMpfON4rk4x2SQWSNWDPBcWvupYU1taW7bwx4lDToxSI9wuVaVaVSk3rK1rrue1/YC6Bd4/fHv47PZbx5/N7U5Ni7HvDADAYpE0wntVur51CuIkMFeQms54RsnCTq2aN6xVSQJObOlZwpnP2SHPjK3OnoQ1klrqchdtR+EssjTZ2rfkKa+1FuQZRE31OU3e2u7DPx5vr/SDt1J2071kb8R42Ox5NXmG7M00J3wHE8cAdU17q5xdoqpXtrZZE73puvYM1ZtVExEgeiPzktZiFlB4SzWHw+FoJ1RRbUJDBxF5J4DnIxCVbgdwrqr+ZK1jWvcCFxH0unXdkKqmmUHyk9GC6pSshUL/e0drVqjLDdPFOjFxUbNahahpFNe1Litd1l3hSsnxuCguS2M1C7xH8c0im5Rl7RZcXck0wHDdLlHMioJkN/NJC7yguKhVcvaokjOn8RVxjTuUzMm5i0xtXWNcldYKtFZ5XJau0Fhz6qizI1jIsntvmt9CsrY7vbAuPaJpdii3UUT9EB4f5z460UvKKIZdVETvjJ4Bx7UL7s0Yt9kCF+F1S8d1TCOHnos5um9ZjPuyBd6hXqi2Rkwt1ILlWMO1+L7nZcoXFFGmt35fJ/9elMZUMKXR9qPvuSemaczwvWDwcXk5We3bRP+suNqZ1qKfh3P1pqy13Y0ueU6zwiaFUN6rqn8MACLyegBvB/CatQ5o3Qv84MGDOOec52/KtQ4cOIDTTz99U661mTgZ53Uyzgnwec3gOice+9ikJKaqHqJfd6DGom+GbNdWQdsBIrKoqs1B5RbjZJzXyTgnwOe1HSAiVwE45Zg7BvQBcAeZi1X14g1c610AXgngbgC/qqp3rLm/v8Cno00P2UZwMs7rZJwT4PM62SAinwdw/4av3qaqV9B+5wPoq+o71jyfv8Cn42R9yE7GeZ2McwJ8Xj+rEJGfA/A5VX30Wvs1V8A4DJdv9QDuIZyM8zoZ5wT4vH5mICIPo1+fB+DmYx7jFrjD4XBsPUTk0wAegUCo+RGA16jqj9c8xl/gDofD0U54COUoiEguIv8hIlfG388Qka+JyGUi0rr1EpEHicgXReQmEblRRN4QP3+AiHxBRK4QkclWKNscInK2iPyniPxARN4SP2vFvRKRvoh8U0RuiPfkgvj5pSJyQESujz+PjZ/vFpF/pP3Po3O9UUSuE5GXbtV84jg2NKf43ZnxsxtF5Mv0+cvinP5gK+bSKqiq/9APgD8E8AkAV8bfPwJgL4DfB3D2Vo/vOOazD8Dj4vZOAN8H8CgA7wZwBoBfR3DVtnysG5hTDuC/ATwEoRb9hjinVtwrBI2EhbjdAfANAE8CcCmAFzfs/1YA74nbewHcGee9EJ/VAsAVLZvTHgDfA/Dg+Pt96bvPxnv8t3ZO/2n+2bZWylZARE4F8GsALqGPc0Q5BNTFSVoBVb1NVa+L24cB3ATggQjzqtDOef0ygB+o6g9VdRXhD/35aMm90gDrHNKJP2vFMhXAThERhJf2nQBGSHPc8jjocczp5QAuV9Vb4/G303c8r217H7cD/AVex18AeBPqTVPeB+BzAJ4M4OqtGNSsICKnAfhFBOvoIgAfRijV/ZutG9Vx4YEA/od+Pxg/a829iqG66xE0L65R1W/Er94lIt8RkQtFxloDFwH4eQA/AbAfwBtUtYr/Ie8HcC2AT27yFCawwTk9HMC9RORLIvJtEXklnepyhDldG+fomIatdgG2yw+A5wL4QNw+EzGEcrL8IFhu3wbwwq0eywzm8hIAl9DvrwDw/q0e13HOZQ+ALwJ4NEK4SwD0AFwG4O1xnxcDuDB+91AABwDs2uqxn+CcLgLwdYSS8VMA/BeAh2/12Nv24xZ4wlMBPE9EbkFwyZ8hIm2zTBshIh0AnwbwcVU9Gfi3BwE8iH4/FcE6bR1U9f8AfAkhZn+bBgwAfAwhVAQA5yGEG1RVf4DwAn/klgx4HVjnnA4CuEpVF1X1pwC+AuAxWzLgFsNf4BGqer6qnqqqpwF4GYAvqOpvbfGwThgxbvoRADep6p9v9XhmhG8BeJiInC5BBvBlAP5hi8e0bojIXhHZE7fnADwTwM0isi9+JgBeAOC78ZBbAZwVv7sfAlf4h5s97rVwHHO6AsCviEghIvMAnoiQn3FsAK1TI3RsGE9FCDHsj/FJAHirqv7TFo7phKCqIxF5HYB/RkhcflRVb9ziYW0E+wBcJiI5ghH1KVW9MtI69yKEHK5HkhJ9J4BLRWR//O7N0WrdTtjQnFT1JgkiUd9ByDldoqrfnXJuxxR4IY/D4XC0FB5CcTgcjpbCX+AOh8PRUvgL3OFwOFoKf4E7HA5HS+EvcIfD4Wgp/AXucDgcLYW/wB01iMj9ROQTIvLDqFHxNRE55xjHnCYix8XhFZFzReQB9PslIvKodR57psn+3lMQkX+P/54mIi8/juPPFZGLZj8yh8Nf4A5CrJb7LICvqOpDVPWXEKocT70HL3sugPELXFV/W1W/dw9eb0NQ1afEzdMQFPQcjm0Df4E7GM8AsKqqH7IPVPVHqvp+YGyFfjWK7V8nIk85+gRr7SMibxKR/VH0/90i8mIAjwfw8SjsPxfV6R4f9z87nuMGEfmX9U5CRM6S0JRjv4h81BTwROQWEbkgnnO/iDwyfr5XRK6Jn39YRH4kIqfE70wi9d0Ipd/XS2iiULOsReRKETkzbp8nIt+PTQqeSvvsFZFPi8i34s/4O4fjeOAvcAfjDADXrfH97QCepaqPA/BSAH+53n1E5DkIWhhPVNXHAPhTVf17BNnQ31TVx6rqsp0kll//NYAXxf1fsp4JiEgfoYnAS1X1FxDkIn6PdvlpHNsHAfxR/OwdCNo3jwPwGQAPbjj1WwB8NY7zwjWuvw/ABQgv7mchNJowvA/Ahar6BAAvQl133uHYMFwLxTEVIvJXAJ6GYJU/AUGk/yIJbbFKBE3nozFtn2cC+JiqLgGAqt55jMs/CSGUc2Cd+xseAeCAqn4//n4ZgNciaL0DqRv6twG8MG4/DcA58TpXichd67xWE54I4EuqegcAiMgnUV+DR4VIFQBgl4jsVNe8dhwn/AXuYNyIYBkCAFT1tTGUcG386I0A/hdB9jMDsNJwjmn7CDbWOWaj+/Nxa2EQ/y2Rnv/j6foyQt2D7dP2tHFnAJ7MnobDcSLwEIqD8QUAfRHhkMM8be8GcJuqVggKh3nDOabtczWAV0fpUIjIvePnhxF6dR6NrwF4uoicftT+x8LNAE4TkYfG318B4Mtr7A8A/wrgN+J1ng3gXg37HD3OWwA8VkQyEXkQks71NwCcKSL3kaDDzqGfqwG8zn4RavDrcBwP/AXuGEODNOULEF6cB0TkmwghiDfHXT4A4FUi8nWEsMBiw2ka91HVqxA0u6+NsrYWf74UwIcsiUljuQPA7wK4XERuwPSWYWeJyEH7QWgZdx6Av4vyqxWAD0051nABgGeLyHUAngPgNoQXNuM7AEYxofpGAP+G0FhhP4A/Q8wdqOptAP4E4T+gz6OeU3g9gMdLaC/2PSS5WIfjuOByso6feUSWShl1xp8M4IOq6taxY9vDY+AOR2CdfEpEMgCrAH5ni8fjcKwLboE7HA5HS+ExcIfD4Wgp/AXucDgcLYW/wB0Oh6Ol8Be4w+FwtBT+Anc4HI6Wwl/gDofD0VL8P1/0FySbvzclAAAAAElFTkSuQmCC\n",
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