{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "
\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/spectrum_analysis.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", "[spectrum_analysis.ipynb](../_static/notebooks/spectrum_analysis.ipynb) |\n", "[spectrum_analysis.py](../_static/notebooks/spectrum_analysis.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Spectral analysis with Gammapy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n", "This notebook explains in detail how to use the classes in [gammapy.spectrum](..\/spectrum/index.rst) and related ones. Note, that there is also [spectrum_pipe.ipynb](spectrum_pipe.ipynb) which explains how to do the analysis using a high-level interface. This notebook is aimed at advanced users who want to script taylor-made analysis pipelines and implement new methods.\n", "\n", "Based on a datasets of 4 Crab observations with H.E.S.S. (simulated events for now) we will perform a full region based spectral analysis, i.e. extracting source and background counts from certain \n", "regions, and fitting them using the forward-folding approach. We will use the following classes\n", "\n", "Data handling:\n", "\n", "* [gammapy.data.DataStore](..\/api/gammapy.data.DataStore.rst)\n", "* [gammapy.data.DataStoreObservation](..\/api/gammapy.data.DataStoreObservation.rst)\n", "* [gammapy.data.ObservationStats](..\/api/gammapy.data.ObservationStats.rst)\n", "* [gammapy.data.ObservationSummary](..\/api/gammapy.data.ObservationSummary.rst)\n", "\n", "To extract the 1-dim spectral information:\n", "\n", "* [gammapy.spectrum.SpectrumObservation](..\/api/gammapy.spectrum.SpectrumObservation.rst)\n", "* [gammapy.spectrum.SpectrumExtraction](..\/api/gammapy.spectrum.SpectrumExtraction.rst)\n", "* [gammapy.background.ReflectedRegionsBackgroundEstimator](..\/api/gammapy.background.ReflectedRegionsBackgroundEstimator.rst)\n", "\n", "\n", "For the global fit (using Sherpa and WSTAT in the background):\n", "\n", "* [gammapy.spectrum.SpectrumFit](..\/api/gammapy.spectrum.SpectrumFit.rst)\n", "* [gammapy.spectrum.models.PowerLaw](..\/api/gammapy.spectrum.models.PowerLaw.rst)\n", "* [gammapy.spectrum.models.ExponentialCutoffPowerLaw](..\/api/gammapy.spectrum.models.ExponentialCutoffPowerLaw.rst)\n", "* [gammapy.spectrum.models.LogParabola](..\/api/gammapy.spectrum.models.LogParabola.rst)\n", "\n", "To compute flux points (a.k.a. \"SED\" = \"spectral energy distribution\")\n", "\n", "* [gammapy.spectrum.SpectrumResult](..\/api/gammapy.spectrum.SpectrumResult.rst)\n", "* [gammapy.spectrum.FluxPoints](..\/api/gammapy.spectrum.FluxPoints.rst)\n", "* [gammapy.spectrum.SpectrumEnergyGroupMaker](..\/api/gammapy.spectrum.SpectrumEnergyGroupMaker.rst)\n", "* [gammapy.spectrum.FluxPointEstimator](..\/api/gammapy.spectrum.FluxPointEstimator.rst)\n", "\n", "Feedback welcome!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "\n", "As usual, we'll start with some setup ..." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "gammapy: 0.11\n", "numpy: 1.16.2\n", "astropy 3.1.1\n", "regions 0.3\n", "sherpa 4.10.2\n" ] } ], "source": [ "# Check package versions\n", "import gammapy\n", "import numpy as np\n", "import astropy\n", "import regions\n", "import sherpa\n", "\n", "print(\"gammapy:\", gammapy.__version__)\n", "print(\"numpy:\", np.__version__)\n", "print(\"astropy\", astropy.__version__)\n", "print(\"regions\", regions.__version__)\n", "print(\"sherpa\", sherpa.__version__)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import astropy.units as u\n", "from astropy.coordinates import SkyCoord, Angle\n", "from astropy.table import vstack as vstack_table\n", "from regions import CircleSkyRegion\n", "from gammapy.data import DataStore, Observations\n", "from gammapy.data import ObservationStats, ObservationSummary\n", "from gammapy.background.reflected import ReflectedRegionsBackgroundEstimator\n", "from gammapy.utils.energy import EnergyBounds\n", "from gammapy.spectrum import (\n", " SpectrumExtraction,\n", " SpectrumObservation,\n", " SpectrumFit,\n", " SpectrumResult,\n", ")\n", "from gammapy.spectrum.models import (\n", " PowerLaw,\n", " ExponentialCutoffPowerLaw,\n", " LogParabola,\n", ")\n", "from gammapy.spectrum import (\n", " FluxPoints,\n", " SpectrumEnergyGroupMaker,\n", " FluxPointEstimator,\n", ")\n", "from gammapy.maps import Map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Configure logger\n", "\n", "Most high level classes in gammapy have the possibility to turn on logging or debug output. We well configure the logger in the following. For more info see https://docs.python.org/2/howto/logging.html#logging-basic-tutorial" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Setup the logger\n", "import logging\n", "\n", "logging.basicConfig()\n", "logging.getLogger(\"gammapy.spectrum\").setLevel(\"WARNING\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Data\n", "\n", "First, we select and load some H.E.S.S. observations of the Crab nebula (simulated events for now).\n", "\n", "We will access the events, effective area, energy dispersion, livetime and PSF for containement correction." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "datastore = DataStore.from_dir(\"$GAMMAPY_DATA/hess-dl3-dr1/\")\n", "obs_ids = [23523, 23526, 23559, 23592]\n", "observations = datastore.get_observations(obs_ids)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Target Region\n", "\n", "The next step is to define a signal extraction region, also known as on region. In the simplest case this is just a [CircleSkyRegion](http://astropy-regions.readthedocs.io/en/latest/api/regions.CircleSkyRegion.html#regions.CircleSkyRegion), but here we will use the ``Target`` class in gammapy that is useful for book-keeping if you run several analysis in a script." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "target_position = SkyCoord(ra=83.63, dec=22.01, unit=\"deg\", frame=\"icrs\")\n", "on_region_radius = Angle(\"0.11 deg\")\n", "on_region = CircleSkyRegion(center=target_position, radius=on_region_radius)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Create exclusion mask\n", "\n", "We will use the reflected regions method to place off regions to estimate the background level in the on region.\n", "To make sure the off regions don't contain gamma-ray emission, we create an exclusion mask.\n", "\n", "Using http://gamma-sky.net/ we find that there's only one known gamma-ray source near the Crab nebula: the AGN called [RGB J0521+212](http://gamma-sky.net/#/cat/tev/23) at GLON = 183.604 deg and GLAT = -8.708 deg." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(
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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "exclusion_region = CircleSkyRegion(\n", " center=SkyCoord(183.604, -8.708, unit=\"deg\", frame=\"galactic\"),\n", " radius=0.5 * u.deg,\n", ")\n", "\n", "skydir = target_position.galactic\n", "exclusion_mask = Map.create(\n", " npix=(150, 150), binsz=0.05, skydir=skydir, proj=\"TAN\", coordsys=\"GAL\"\n", ")\n", "\n", "mask = exclusion_mask.geom.region_mask([exclusion_region], inside=False)\n", "exclusion_mask.data = mask\n", "exclusion_mask.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Estimate background\n", "\n", "Next we will manually perform a background estimate by placing [reflected regions](..\/background/reflected.rst) around the pointing position and looking at the source statistics. This will result in a [gammapy.background.BackgroundEstimate](..\/api/gammapy.background.BackgroundEstimate.rst) that serves as input for other classes in gammapy." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "background_estimator = ReflectedRegionsBackgroundEstimator(\n", " observations=observations,\n", " on_region=on_region,\n", " exclusion_mask=exclusion_mask,\n", ")\n", "\n", "background_estimator.run()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# print(background_estimator.result[0])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/adonath/software/anaconda3/envs/gammapy-dev/lib/python3.7/site-packages/matplotlib/patches.py:75: UserWarning: Setting the 'color' property will overridethe edgecolor or facecolor properties.\n", " warnings.warn(\"Setting the 'color' property will override\"\n" ] }, { "data": { "text/plain": [ "(
,\n", " ,\n", " None)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Y/nrFG/en07drMBMSYnjtu80M6BaKm4u+UbusqiK6uLacPai6zsx3mw84tCcc5upHemV+i+2C/Twx6DScKCojpBWlKxUuDQrLqljy6PUtN3SQ6xcsOef5jl72cdWqVfTq1Yveve0hkKeLbbQXZSlb4ZLjRFEZa3YdbbMon2bKwDg6e3vw76Xr2z2mWrOFn7YfYly/aLqF+pMQHcIHK3c06QVdY63DqGks2GcipWTJml3odRqG92g5UsGo0VNjNTs01ojOPhzPc172JwWF5nC07OOSJUvYs2cPX375JcnJyaxYsYKgoCD27t3L/v3761NnNseiRYuYPHly/e/btm2rX3p+6623GpV9TEhI4J133mnR1pEjRxBCMHHiRPr168e//vWvNj+LM1GEWeGSY2XyYcb1i25zbuvTCCGYf80oNuw7xvGTxe2y9cX6vUQH+xHXxR4Xfe3ovhSWVfHh6mRstobirFGpsdiszdqSUvL5+r2k5RYR5OvhkKOW2WZB42AaTm83F0qrqh1qq6DQHlpT9tHFxaW+7GPPnj1Zs2YN8+fPZ+PGjXh6Np93/lylGnfs2MGCBQvq94U3b97Mrl27+Omnn3j99dfZsGHDOW1ZLBY2bdrEkiVL2LRpE9988w0///xzu5+LIswKlxRmi5UtBzMY3ds5IT89I4OIC/PnqY9XOxR/3BTr9qSSfCSLORN+dyYx6DT87ZrRZBWUsuCznyko/b1uS2eDFzmmpl8EistNLPziFw4eP8mj145Bq1Y7FHucYypukGSkJVQOLO8pKLSXjl72MSQkhJEjR+Ln54fRaCQpKanJSletRRFmhUuKrIJSfD1c2z1bPpMJ/boR7OfB0/9bzZHsAof7Waw2vtq4j2827+eRa8fgftZ+sqtBx9+vG0vvyCAe+2AF7/20jWO5RYS5+pFaebK+nc0mOX6ymMUrd/DI+8vpGuzH47Mn4OFqwMfDSIEDzjqplXmEuzpWsjKvtBJfJVxK4QLQ0cs+Tpw4kX379mEymbBYLKxfv57u3bu3+7kozl8KlxTH80oID3B8ZugIEZ19SMnMY/qQHrzy9UYGdAtlUv9YOvu4N9neZpPsTsvh642/4elq4MnZE5p9UVCrVEwd1J1hPSL4ZV8ab/7wK4XlVeRFZ/HIwe/R1erIKSrHy82FId3DeOGWpAa2wgO86/NkN8fJ6lJOVpcS5xnc4r1KaX8JmD0uocW2CgrtpaOXffT29uaBBx6gf//+CCFISkpiypQp7X8uHS35llL2UeFc/LDlAJXVdVw3puWMWY5yLLeI93/aznNzJ1NeVcOP2w6yYd8xAn09iAr0JcjPE41aRVV1HcfziknJzMfbzYVJ/WMZFNfFIa/PMzHV1LHk6GZya0q5NnAYwX6euBp0TbY9kl3A2z9uYeHt05p1dPsg7ReMGh3XhA1p8dqn7b10x7RWj1uh43F2OcILHS51uXCxl31UUDivCJXAams6L3Rbsdpkveh5uBqYNaYfVw/vxeHsAtJPFpOaU4jVZsNFryWuSwDTBnVvV6iR0aBjdvehPLbnM0yuVbgaml+Cjg72Q6/VsC/9BH2iGs+IU8py2FtynGf7XOvQtVftPMKYvtFk5JWQkpnP8bxiyqrsISU+7i5EdPahZ0QgAd5NrxYodGwUEb04UIRZ4ZLC39ONlIyW43VbQ25xOf5ebg2O6bQaekYE0jMi0KnXOo1BrePWrmN548gqHomfTrCx6apqQgiuGt6Lj1fvJK5LAPozaisX1JTz9tHV3BQ1CjdNy3HYB46fZFtKBjmFZazaeZi+UcF07xKAl5sLAPmllaSdKOKrjb8R3tmHGUN7EBPi2L61goKC4yjCrHBJERHow+JVO5oNw2gLx3KLiOh8/suN2qSNvJoyjlcWUFJXhUTSy7sLT/22lPu6JdHdK6TJfgkxIWxNyeDDVcncljQQIQSpFSd5/chKrghJpI93eIvXzioo5dH3lxPg7c6fRvSiT1Rws0vjZouVXw8e59VvNjI4LoxrRvVBq2lcJ1pBQaFtKMKscEnh5+GKh9HAgYw8eoSfO/2kI5itVrYfyuQf149zwuiaptJcw9q8/azLO4AKQZhbJ/z07ggEtVYzSMl9OxfTxzuCu2MmEObWeJY6d/IAnv/kZ95auRn3GMm2oqPcGDmSRN+W04mm5RZy93+/oVdkIM/dPLlFkdVq1IzsFUW/6BDeWbaVfy9dz4NXj1TEWUHBSSjCrHBJIYRgfL8Yftp+yCnCvOVgBsF+ngT5Np/AoD1sL0zl4/QN9PYO495ukwl382+y3aHyHN48spq5W9+ir08EI/3jCHP1Q6/WUmu1kGUqxDPRzI/Ht+CZ7Mljw6YS7xt0zmvbbJJ1e1J54fO1DIuP4Ik5E87Z/mzcXfTcN3M4b3z/K+8s28rd04e2qr+CgkLTKMKs0OGx2SRlphqkTeJi0DK8ZwQ/7TjEtpQMBp6juENLlFfV8Nm6Pdw3c7gTR2tHSskXGVvYWXKMe2OT6NpCcYlYj2BeTbyJQ2U5/OfQMtae3E9nF0/qbFZ0Kg3BRh8GB8Qwr9t4th3I5rUvtxDe2YdhPSLoGuSLv5cbQgjMFitZBaUczMjj591HKamoZnD3MB6fPb5N96FWqbhjyiD+vugnth3KPGfYloKCgmMowqzQISmrquGXvWnsTs0hM78Eg06DSqgw1dbh7W7Ez8PImz9sIaSTJ8F+rfeQNlutvPnjFob1CD8vDk7fZu/gQFk2j/e82iHHrNPEegbzSsKN/DvlR8Jd/bkmvHEI1Jg+XRneI4JthzLZlpLBZ+t2U26qRaNWYbXZCPLxICrIj9njEnjrxy3cN2NEu/bjdVoNt08ZxP99u4mE6BA0aiVvkYLjZGdnc/fdd3Pw4EFsNhtTp05l4cKF6HQ6Fi9eTHJyMq+99lqr7T788MP88MMP6HQ6oqKi+OCDD/Dy8mL79u3cfvvtgP0F+cknn6yPXw4PD8fd3R21Wo1Go6nPSvbkk0/y7rvv0qmT/bvg+eefJykpibq6Ou644w6Sk5NRqVS8+uqrjBo1qt3PRBFmhQ6F2WLlq437WLsnlf7dQrl6RC8iO/tgPBXna7XZyCksZ1/6CVKy8rnl31/yxOzxDO4e7vA1KqtreeP7X9Fp1Fwzqo/T7+FoRS5rTx7g2d7XtEqUT2PU6Lk/bgr/2PMZPb270N2zsVOYVqNmWI8Ihp0qcFFntmC1SbQadb1wfr3pNwbGhjklS1pMSCf8PF3ZdTSbAcqsucPywM6PKKytcJo9P707/0mY0+z5jlL2EeD+++/noYceanDs3XffBeC3334jPz+fyZMns2PHDlQO5qVvDkWYFToM+aWVvPTlLwT5erDw9ml4ujYWNbVKRRd/L7r4e5HUP463l23h0feXM65fDA9ePRIXffO1Um02ya7UbD5clUxCTAg3jE1A3c4PWKNrSBvvp65jTuQIPHVtF0Q3jYGbo0azKG0dL/a9HnULtZt12sYf9W0pGdyWNKjNYzibUb2i2JqSoQhzB6awtoKPhtztNHtzfn39nOc7StnH5jh48CBjx44F7Pm1vby8SE5OZsCAAW22CYowK3QQCkoreeZ/q5k2uDvj+8U49GFSqQTzpg1hdJ+uzH93GXNe/JRJ/WPpHhZAWIA3rgYdFquVnMJy0k4Usml/OnqthnnTBtM9rP2OY02xvzQbjUpFok9ku2319g7j+2wje4qPk+DbOns1dRYKyqoI6+y89KVdg/349tf9TrOncOnjaNnH/fv3YzQa6d+/P1OmTCEjI4OgoCCWLVsGQFlZ2Tmvs2jRogbCvW3bNubOnUtGRgYff/xxo7KPQgjuuOOO+iVvgNdee42PPvqIxMRE/v3vf+Pt7U3v3r357rvvuPbaa8nKymLnzp1kZWW1W5iVzSCFix6rzcZ/v91E0oA4JiR0a/UbbmyoP+8+8Ce83AyAZGtKBgu/WMf8d3/kiQ9X8f2WA5hqzdw5bQjPzZ183kQZYFNBCmMCejgtxnp0QDwb8lNa3S+vpAJ/Lze0aueFOAX6eFBUbsJsab5kpYLCmXSUso/z5s0jLS2NPXv2EBgYyIMPPgjA3LlzCQkJITExkfvuu48hQ4bUi3x7UGbMChc9y7alYNTrmNS/W5tt+Hu5MXfSQL7Z/BvPzZ3sVEFqDakVecwMHeg0e3GewXye8WurE6rUWazonBx3rFIJNGoVFpsNLUpMs0LLxMfH89VXXzU4dmbZx507d56z7OPy5ct59NFHmTBhAo8//ngj+6dLNf78888tln1MTExssuzjiBEjCAgIqO9z2223MXXqVAA0Gg0vv/xy/bkhQ4YQHR3d9gdyCmXGrHBRU2e2sHxbCjdNTGz3LHNQXBc8jAaSD2c7aXStw2SppcJSjb/Bw2k2fXRuWKWNMnN1q/oZdBpMtWanjQPsjnlWm/zDXnoUOh4dpexjbm5uff9vvvmm/rjJZKKqqqp+nBqNRin7qHDps+1QJpGBvgT6tF/MhBBMSIhh5c7DDO7e9vjmtmKy1uGq1qNqwVGrNQghcNMaqLbW4oXjzmSdfdwpKq+ips6CQeecr4GsglI6+7gr4VIKDtNRyj7+7W9/Y8+ePQghCA8P5+233wYgPz+fiRMnolKpCA4O5uOPP3bKc1GEWeGiZv/xkyR2C3Wavb5dg3nzh1+pM1ua9FQ+n2iEGrN0/v6r2WZFI1o3S9Wq1XTx9+ZIdgG9Ip1TiONwVgFRgb5OsaXwx+Cnd2/Rk7q19loiNDSUH374oclzN910EzfddFOj4xMnTmTixInntHvaeexsZs+ezezZsxsdj4yMZO/evU32aU5ww8PDOXz48DnH0RYUYVa4qDl+spjJ/WOdZk+rURPk60lGfinRwY1jFc8nHloDVpuNcnM1HloXp9isttRRaa7BR+/WcuOzGNYjgrV7jjpFmKWUrN1zlLmT2ueNqvDHcq6YY4ULh7LmpHBRU1JZg6+nq1Nt+nm6UlrZuj1ZZ6ASKsLcOpFWcdJpNtOr8gkx+rYYx9wUw3pEcCgzn4y8knaPY2tKJlq1mtjQpnN9KygoOI4izAoXORLnBBadZVXK82C1ZRJ9Itlc4Lylr035h+jvQAWppnDRa7luTF/e/nFLu0Kcyqpq+HhNMnMnDXBaGJiCwuWMIswKFzWergaKyk1OtVlUXoWXm3OWklvLsE6xHCjLJq+6tN22Cmsr2F2Szgj/uDbbGNEzkgAfd9744VcsVlur+1dW1/LSl78wtm80XS/w1oCCwqWKIswKFzURnX1JP1nsNHtmq5WcwjLCApyX8coRbNKGlBIXjY7pIYm8n7YOm2y9EJ5GSskHaeuYHNQXN23r822fRgjBXdOGUGe28uLnayksq3K47/GTxTzzv9V0C+nEzGE92zwGBQWFhijOXwoXNfFhASQfzWZU77Yt157NvmO5hAX4oD/PHtmFtRVsyDvI4YpcMqsKMFnqAPDRuxHu2okT1cV8mr6ZWRHD2rT8+232DiottUwJ7tfusWo1ah64egQ/bD3IPz74iYmJ3Rjdp2uzqwpHsgv4etM+ko9kkxgTirtRz67UHCI7++Dt3v6CGAoKlzuKMCtc1AyMC2PJ2t0UlFbSyav1nsdnsyr5COP6tT8zT3OU1Zn45Pgm9pVmMrRTN6YE9SXMtRMeWhckkvyactIr86m1mvn4+EYOlGfzRM+r0Kt1Dtm32Kx8mbmVvSXHeTR+RpucvppCrVJx5ZAe9I8JZcWOw/zt3R8J8vUkvLM3Pu5GrDYbvx07ya6j2ZRWVRMZ6MvExG74uBupqqnj511HeftEEaH+nkzoF0P/bl1QqZT9ZoWW6QhlH/fu3cudd95JZWUl4eHhLFmyBA8PD1avXs0jjzxCXV0dOp2OhQsXMmbMmHY/E/FHOcG0lcTERHn6YSlcHny1cR/pJ4t58OqR7XIuSj6SxSdrd/PirVPQOjkdJcC+kkzeTV3DsE6xTA/tj0HdfCUrgGOVefxz7+dUmGt4JH46g/2aL84hpSSlPIcl6Rvx1Xtwa9cxTgu5aorqOjPpucWknyzmZHE52w5lUmu2MGVgd6YOjKsvs3kmFquNnUey+GHrQVz0Wm5PGuSUlymF80dKSgpxcb/7KNhK7gDJPOdRAAAgAElEQVRrgfMuoO6EyvvtZk9LKRk4cCDz5s3j5ptvri/76OPjw8KFC9slzKtWrWLMmDH1ZR8BXnzxRUwmEzqdrkHZxxMnTqDRaAgPDyc5OblR2cf+/fvz0ksvMXLkSBYtWkR6ejrPPPMMu3fvJiAggKCgIPbv38/EiRPJyclpNJaznzNAYmIiycnJzX/gO9JPQkKCVLi8qLNY5CPvLZNrdh5ps43Cskp513+/kimZeU4c2e/sKjom79n+vjxUltOqfharRb58cJlMWrtA3pv8gfzs+Ga5reCoTC0/KdMqTsrthanyi+O/ykd2L5Hzdy2Rm/IPSZvNdl7uoSmy8kvkPf/3tfxy/R5ZZ7E41MditcofthyQ815dKlNzCs7zCBXaw8GDBxv8bi2Y4VT7Ldlbs2aNHD58eINjZWVl0sfHR1ZVVckPPvhAXnHFFXLixIkyJiZGPvnkk1JKKSsrK2VSUpLs1auXjI+Pl5999tk5r/P111/LWbNmNTp+7Ngx6e/vL81ms5RSyrCwMFlQ0Pj/rLu7e/3nLjMzU8bFxTVqY7PZpI+Pj6ypqWl07uznLKWUp7SsSZ1TlrIVLnq0ajV/nTGMZ5esQatVM6Jn60ocFpRV8sJn65g8IPa8xNnm1ZTxXtpaHoidSpR7QMsdzkCtUnNfXBLhbn5sKjiMTmjYXHCYUrMJkHhqXYlw68SNESPp5hF0QcORisqreOGzdVw3ug9De0Q43E+tUjF1UHcCfT3499L1/OP6cQT5Nl/9R+HypaOUfezRowfff/8906dP58svvyQrK6vRNb766iv69u2LXq9v+wM5hSLMCh2CQB8PHps1loVf/MKB4yeZPS4BN5dzfwCklGzcn86na3dz5ZAeTGxHdapzXeP91LVMC05otSifyRUhiRwsy8FFo+P+uClOHGHbkFLyzrJtjEuIbpUon0lCdAjF5Sbe+mELT8yZgFqlBIEoNES2ouwjUF/2MSkpiYceeoj58+czdepUhg8f3uw1zlX2MSUlhRtvvJHJkydjMBjYvHkzQUFB5OfnM378eGJjYxkxYgSLFi3ir3/9K08//TRXXHEFOl3DrZwDBw4wf/58Vq1a5YzHooRLKXQcgnw9ef6WJLRqNXf992te/mo9W1MyqK2z1LeRUpJfWsmaXUd49P3lrNh+iEeuHXNeRBngYFk2lZYaJgT2apcdlVBxU+QofszZhdlmabH9+WZrSiZVNbVMG9S+Sjlj+0aj12lYu7vpvMUKlzfx8fGc7TN0ZtlH4JxlH3v27Mmjjz7K008/3aT902UflyxZ0mLZR6DJso8AsbGxrFq1ip07d3LdddfVjw3szmszZszgo48+anC8PSgzZoUOQX5pJT/vOkry0WxKKkz4ehjZeTSHFTsOY6o14+lqwN/LDbVKhUGnISakEzeMSyA+LOC8Lv/+nLefcZ17tqli1G3/+RKAdx/4EwBBRm/CXP3YXpTG0E7n50XCUVYkH2LGsJ7tnuWqVIKrhvfiveXbGNcvWskMptCAsWPH8sgjj/DRRx8xZ86cc5Z9dHFx4dtvv2XRokWcOHECHx8fbrjhBtzc3Fi8eHEj26fLPq5fv75R2cfQ0FA0Gk2jso82mw13d/f6so+nazzn5+fj7++PzWbj2Wef5c477wSgtLSUKVOmsGDBAoYOHeq056IIs8JFTXWdmc/W7WZbSiYjekXy1yuHEdLJs4FgFJdXsSUlg43706k01TJv2mC6h3U+72OzSRv7S7OYGzXaaTYH+kWzryTjvAqztFWA5QhY0kGWASpQ+YMmCjSRnCypoajMRN+oYKdcr1tIJ1QqQeqJogteOETh4qajlH389NNPef11e9WtmTNncvPNNwPw2muvkZqayjPPPMMzzzwD2L3B/f3b58uihEspXLScKCrjX5//QvewAK4f2w/XJkJ0zmZ3ag6LVmxneM9I/jSi13mdoeWYinn50DJe6te4hJwjnD1jBsisKuT1Iyt5se/1zXVrM9KSiqz+Dup2g6YrQhMJKi/ACtaTSEsa2IpIK+rNukOx3D5tkuO2rXlg3gOWY0hbISBBeNqvoe3BB2sKCPL1YJITK4UptJ8/OlzqcqG14VLKjPkyxmazUZhdRPbRk9RU1aDVafAP60RQVABa3bljcJvCXGdmz9r9HN2VTuahbCpLqhBC4OXvSZe4ELoPjiFuUDQqB5ZHTxZX8Pwna/nTyF6M7OX4vk3frsE8P3cy//riF6xWG9eN6dvq+3CUotpK/A0eTrXpb/CkqLaiwTEpq8GSAdIEqEEdBCo/h186pKwD0yfI2g0Il5ngegdC1Ti+WADSmkNF1jtM7/E+stYNoR92btvmI8jqL+wzcG0CQtMVoesPqMBWDJY0ZM2PTIoR7MweACjCfDGjiOjFgSLMlyH5WYVs+HIL25bvwmDUExwdiNHdBXOdhZPpv1CQXUT8kFhG/nkw0f0iWxQAi9nC6o/W88vnmwmJCaLXiO4MmpqAh5+9SHpRTjGZKTn88NZKPnvxG5JuHcegqQnN2rVYbfzfd5u4Ykj3VonyadyNBuZfM5rHP1xJTGgnEqJDWm3DMSTCybWvVEIgAWmrhNpfkLU/gzUX1CGgcgdpAWs2IEE3EAyTEJrmvaalzYSseBZUXgivVxCqc79ICHUwu09MoVYMp5PpU7Bmgst1jf5WUprB9Cmydh3COAvc/4YQTa1ojANpo6RwGd18P8FWUYRwvROhcm/9w1FQuExQhPkywmK2sGLROjYs3cKQ6f2Z/9Ff8AvyadSuuqqG7ct388nzXxMYEcB1j87Aw7fpL9K8jALe//sSPP08ePC9efh36dSojV+QD936d2X8nJEc25fBZy9+w641+7jp6WtwbaLW8vLtKXgYDYzvF9Pme3Vz0XNb0kBe/+5X4kL9m8xU1V7ctAbK6pxb+aq0tpIBbtnI0ntA2xPheitouiHE7x9VKSXYCqB2PbL8OaS2J8L1lkazYCltyIoXEOpQ+yzZQQc1jVpFaU0IwvN5ZPnjCOEOLtPOsFuHrFgIyFNif+4YZSFUFNXEc/D4PKLDjyLL/wEeTyFUXo4/GAWFywglXOoyoarcxCt3vENmSjZ//+RerrxncpOiDODiamDknwbz2Kf3ERgZwIIbXiXzUOM0c7nH8nj5jrcZPnMQd71yc5OifDaRvcKY/+FfCAjrxMt3vE1lacNqRmaLlRU7DjF7XPMzakeJ6xJAt9BObPwtvV12miPU6MvJmlLqnBTeJKWFuvL/MMLjKMLjcVTuDyK08Q1EGewOM0LtjzD+CeH9GkIYkaX3IS2ZDQ3WfA/YWiXKAIG+7uQUliFUngj3x5DVSxvYlpVvARqE+yMtivJpcgrK6OzjY3+B0A1Glj9rn3U3g5TSvkxuWoqt4l/Yyh61/1T8C2n6Cmk++ofV1L7UUJ7j+aUtz1cR5suAulozr9+7iLDuIdz5nxvx6uTYl6lWp+WKuyby54en8/q9i8hNz6s/V11ZzRv3fcDMe6cw/KpBrRJRtUbNVfdPpcfQON7928fYbL+XP0w+kk1oJy+CfJ2zdzuuXzQ/7znqFFtno1VpCHfz57eSzJYbt4CUNmTlKxTX5pGhedDuNOUAQhgQbrchXOcgy59EWk/Y7dlKkNVfI9zuaZUoA0R29uXoiUK7fbU/wjgLWfWO3W7ddrAcQrjf1+iF4Vyk5hYS3vlUqU2Xa0DlDdVfNdlW1m5Blj2IrHwFZCVCNxhhnIMwzkboBoOsQFb+B1n2ELJ2a6vuTaEhBoOBoqIiRZzPE1JKioqKMBhaV5pVWcq+DPjxrVV4d/Lk6gentWkW2ndMTypLqvjwic95eNHdqDVqvn5lOXGDYhgwuW3OVUIIrrh7Iv+59S02fLmFUdfYYwAPZuTRz4l7wrGh/pRWVlNWVYOna9vrFjfH6IB4fs77jQTfxkLab97LDtnoN+9lxnZPY3i34zyf34e6bRv5u2Vz/fldb97fog2hHwGyClnxMni+ADVr7IKmbn3YWGSgLzV1Zo7lFhEZ6Av6cWD6Epv5GFS9j3D7K0I4nnbwZHEFJwrLiT8VwiaEALd5yNL7wDABobKv3EhbJbLqTbBkIFzngrZP0y8V+uFgnAPmPciqRci6Taf2rZWCGa0lJCSE7OxsCgqc6Imt0ACDwUBISOu+0xRhvsTJPnKCbct38Y/P7m/X0vCwmQPZs24/az/dRK8R3dm38SBPfv1wu8amUqm44fGr+fetbzLkygHo9FqO5xUzvGfbUkA2hRCC8AAfjp8spndUkNPsnmaAbxRfZ25jT3EavV3z7WJhSQNbPm/OyaSiWs/xIm8O5frxa2oXasyNvd29jSauSjjAs/sHYq50AUsbF7L0k6BuG9R8j6z9BeHesqA3hUolGNs3mmXbUvjLlcMQQg2GMUjTEhBeCG18q+wt357CiJ6RDSp6CZUP6IZAzWowXoO0Vdj3szVx4PWfZhzJfkcIFej6gbYHVC1Clj8OHk8r4txKtFotERHO+7wpOAdFmC9x1n26mXHXj8Ddu31fWEIIZvw1iTcfWExJXhlDruiPixNmoJ3D/QmPDyV55R6GXNGfsqoavN2cW87Q292F0qpqp9o8jUaouS9CS3nZPdSKXugNIxD60aDuzICBKrCV2EOGzDvBvA+hHwfGaxFCXx/H/M6ddWRXTSZG35kve1+Li6ZtjmpCCDDORZb9E6gDdeuKfZzJhIRu/H3RcnYezbZ7tWu6g+lzhNtdrbKTkpnHrqM5vHBrUuOThnHIyv8Dlz8hKxYgtH3BOLtVL5BC6JCudyBMHyErXrCLs5NqVCso/FEo/4MvYWpMtexe9xuDr0h0ir2QmCC8/D359bsdbV7CboqBUxLYt/4gAOcrH4jqPBiWtjJk+VMEq3aRq7mTpzP6UKYejdDGIlReCJUHQhOGMIxB5f4wwutVpK0IWXo/0nIMAIGNisofeTdLzbyY8W0W5dMITRdQuYIwtkugDDoNt08ZxPs/bSOroBQ04WDNA43jccj5pZW88f2vzJ3Uv+mCI2q7TVn9FaAC4w1tWtWxv5DMBiTULGt1fwWFiw1FmC9hsg7l0DnCHzevxiFJbSWiZxglJ0sJCG/ZA9tRwrqH1Ht9e7sZKSirdJptsAuEt7ux5YatQNrKkGX/RGgiEZ4vMDbkCgb6RfPEvi/YVdy0F7hQ+aByf8DuTFX+NAEeJyHoGOlVZVzf9Tqi3QOdMzh1V5BVLbdrgdhQf24Yl8CCT9dyMLMMqLHHUzvA0ZxCnl2yhiuH9GjWZ0AIDag6QfUXbXJSa2hLhXC7G1n9JdLm3BA2BYULjbKU3YGw2Wyk7TlO+m+ZZB85QXVFDWqtmoCwToTGBtN9cAxG99+XgbOP5BIa45x8x6dx93ZFrVE5lL3LUfyCfagoqsBcZyYy0IdjucVOy3Vts0ky80sJD/B2ij04HR+8EKEfiDD+njpzekgi3dwDeS9tLStP7GFM5x708gprMAuWUlJEb45Uj2fakPdYnR1PjPdQXDycuP+tCYfalU4xNaR7OG4GPR+tXMNTU21Ultfg10Ts+WlKKqtZti2FXw+kc/PEAfTvFnruC9iKQRPdJie1sxHqIKS2J9StB8PkBueklPbsZObd9tWKJtKGNutspqBwgVGEuQNgtVrZuHQr6z7bjM5FR7fEKOKHdMPoYcRitnAyPZ8dP+3msxe+oe/YniTdNg5vf09M5SbcvJ03WwbQG/VYLbaWG7YCIQQqtQqb1UbPiEC+2fQbU9tZbvA0e9JyCPL1aLF2c6uo+Qmwgct1jU7FegbzQp9Z7Cw+xi95B3kvbS3eOlc8tEaklOTVlCGAXt5h6DPjmeybiUHTvpKRjdB0BVnWbK3b1tIrMpCus+Kxlmq5/82l+Lh7EuTnSdcgX8I7+6BRqcgqKOVwdgGHs/IZFBfGglumOOYFb80Fo/Pyggv9aGT1t4gzhFnW7UCaPgNpsnuq60eD2p8GaUNNn4J8GwzT7dnUFIFW+ANRhPkiJy+jgA+f+ByDUc9NT19DeI8uzX7ZlhdV8Mvnv/LCDa8y469TUGvUmE21Th2PzqDFYnZuveAaUy0IgVavpXdkEItX7SDtRCFRQe2vRLRq5xHG9Yt2wijtSFmHrP4C4fFss1/eGpWagX7RDPSLxiptnKwupcJcjUoI/PQeeOtcEUIw78d8hk/4DWkrdGpiT6FyQ6K2p+7UtDBjbYHK6lo2/HaMssJvGBzuxrBYQVaJnoy8EnYczqSsqgY3g57eUUGM6h3FXdOG4KJ3LM+6zVYNshR0g9o1xgZoYsCSjpQ2oBZZ+TZYDiNcbwFtvyb+ZhGgS0AY/2wv8lH1PtRtBrf7EGrnbdcoKLQGRZgvYjIOZvHG/YuZcts4h5J4ePi6c8VdE+k3vhfvP7qETiG+aLTqc/ZpLZZauyhXV1bj4iTv6ezDJwju2rl+eXzG0J58sHIHT86ZiEbd9pnL9kOZFJRVMaR7uFPGCUDt5lOVmRwTPLVQEWxsOsOaxaYhJbcrYf67nDc+OJVbOxJq14Lmxjab2X4ok8WrkokP68Ts/rm4uY7j9gh3hMvvs1GbTbIv/QQrk4/ww9aDBPl6EBPioKDV7QBhRKVu+vm0BaHyQAod0pYLFf9FaELA62WEaHn2LjRdweM5qP4WWf6Y3cPbCUvsCgqtRVmvuUgpySvljfsXM+vRmYy4enCrliRDogO5/507yD56gt1r9zs1q09GSjYh3YI4tD3VaTZTth0lsldY/e8je0Xi5qJn6Ya9bbZZUFrJ4lXJ3DFlUIP42fYizckI/XCn2dt6vC9YM07N8JyEJQ30I5G1a5G2slZ3l1LyvzU7+Xz9Xu6bOZy7JmlwM3qAy9XImtUNxqpSCfpEBTP/mtFcM7I3r3y9kTW7jjh2odq19jrQTkcNlW8gNOHgeo9DonwaIVQI40yEy0xk+TNI6dwVJwUFR1CE+SJESsknz33NyD8Nofeo1iVzOI2Hjzt/ee1WCnOKSF65xynjqq2uZd+Gg0y4cRQblzonFaLVYuXX77YzZHr/+mNCCO6aNoSdR7NZumEvNlvrXixyi8t57pOfuXJovOOzN0expNm9np3Auw/8iafm3g1Ci6zb4BSbUtqQdZsRhlEI/dj6VJqt4bN1eziSXcBTcyYQHWRAVr2HcL0VoY0F4QrNjHVAbBeenDOBH7YeZP3etHOP05IOlqMgDE59cZRSgvUE2CrB9bY277ELwyTQRIPpf04bm4KCoyjCfBFyYPMhSgvKmHjTqHbZCYwIYNjMQSx+/HOnjGvrj7uI6hXGiKsHkZ9VyMGtDs6MzsG6zzYTGNmZoKiGS4Yergb+cf149h/P47lP13CyuKIZC79jtdlYmXyYJz9axYyhPZiQ0K3d4zsT+5d+vr0espMQQtidtaq/d47Buu2nPI27gvFasOYgTU3npG6K39Jz2ZKSwd+uGY2rgVPe5yMQ2u724hludyCrPkTaipvs7+/lxvxrRvPJut3kFpc32UZKM7Ly/+z7vsIFbPltutUmbVuzwVaIcL+3Vbm8m0K43oqsXY+05rXcWEHBiXQ4Yd61y8n7cRch67/YwtjrR6B2whLszc9eS97xfDZ+3b4Zbkl+GcvfXc3UeRPR6rTMeuwqPnnuK6rK2x4zmnssj5WL1zHr7zOaPO/pauDxG8bTr2sIj3+4kpe/2sD2Q5kUlVfVz7LqzBZScwr5dvN+7n/ze7YfzuSJ2RMY2bv1dZxb5vTMzsnJSlRBYCtC1m5uue05kDYT0rTIXh8Ze1Ys4f5P+5J21SKkrDtnf7PVyns/beOWyQNw1ZUhy58Ele+p5B12hKYrwjDFXh3K1vTLUpCvJ1cO6cEHK3Y0HqO02ItTqANBPxq03cC8v+03fTbV34A60OEiIOdCqNzsHtw1zgk9U1BwFMX56yLDVFFN2t7j3LZwdsuNHcDDx53hVw3m46e+JLpfJJ3DW7+nV1dTx6JHlzDqmqGERNuTYHQfFEPC+N68fu8i7vnvLQ3ipx0hP6uQ1+9dxNUPTMMv2LfZdiqVYMrAOMb07cqvB46zfl8ai1ftoLrWjFqtwmK1EezrSXSwH/dfNYKIzs5zJDobIVRIYQRZDsKxCl0OISvAeDOy6j3QRCDaMCOX0oaseh2h7YvQ9fl9zGpf8HweWfU2lD0MxhtAm9CkR3ny4WxCfDX0CtyBLPsaYZgOLlc2butyFUKakGWPgvt99tn5WUxIjGH59hQy8koIOxVDLq0FyMrXQOgR7g/ZVwv0o5GmpQjD2Fbfc5PUrACXpl/02oR+FLLiJYTrHOfZVFBoAUWYLzKyDuUQHB2IzsGQE0dImNCLqjIT/73rXW5fOIfweMdDaMqLK3j/kSV0CvVj4s2jG5y78i+T+erlH/nXTa8x54k/N3DgOhc7V+/li5e+Z9qdExiY1M+hPi46LWP7RjO2bzRSSmrMFmw2iV6raZfndqvRRIDlGOick5JU2kxgKzrlUGZFlj0OHo8hNI4XFpCyFln5BtgqwOPeRueFyhPcHoa6rfb0l1XvgLYPaKJAeAE2sJ6Eyp+5bUg+0jIY4fGMPb1nEwghkMbZCE0Esvw50A0GQ5LdA/oUapWKUb2j+GVvGnPGRULNamTNcoRhGrjMsBfGANAmgPwAWbcDoevf5PUcfg51O+zhV4ZJ7bLTAHUY2EqQtiqEyrk5ARQUmqPDCbO3t/MyOF2MnEzPJzAywKk2g6I6o9Gp+fPD03nzgcUMnpbIhBtHnXOWa7VaSV65l69fXcbQ6QOYeuf4Rtm+hBBc/cA0ovqE887fPqZbYhQj/jSYyF5hjZxurFYrBzYf5pfPNlNaUMad/76RiB5Nf/G3hBACF53zXlxadW1tHJh3O02YMe8FTcypCk5jQejtS8iGKeAy/ZzlFe3ZrA4gK9+0h3B5/KPZqkz22elghH4w0nIczAfBknpqOVoFqk5sTQukZ/wTqFxbXlWx2xsO2p5QswxZ/k+kyhOhiQJVZ0AwPDKLrJM7kaUCoRuC8HiugXjb7ajA7S5kxSvgFdfm6lDSVoGseNNe/UrtvGx3QqiQ6s5gywVV1/prUbsOad5vdwaUpfbGKh9Qd0XoeoFuJELl3DSwCpcPHU6YL/USZeY6CzqDc0VHZ9BirrPQZ3QPInuH8fUry3h8+ov0HhVPTEIkwTFBuHq4YK41k3ssn/TfMtm+Yje+nb2Y95+bWpxh9x3Tk279u7L1h2T+98xSKksqCe0WjIefOwCF2cXkpOYSGBXAyKsH0298L7TnS1hLSmDXLjhxwv57UBD06wfOeqHTj0WWPQzG61tVk7g5ZM1KhGF8/e9CPww0sfZEFyW3gX6UXfzUkaByB2m2Jw6xHEXW/gKyCmGcg9APdviaQhNuT9vJ77vlucXlZJSsw80BUW5gS+Vlz9zl8mewZtiFylYI0oaXRwSvrZD07XMnQt28SAltD9CPQFY8C+7/bPXMVNoqkeXPgH4E1C4/D1m7NCCtSFkDpiX2565NROhHgestoPIDJNgK7C87dVvB9AkYkuzL/i2UsFRQOJsOJ8yXOnqjnpoq58ZOVlfWoDfYvxw8fNy56elrKS+uIHnFHvZvPszKxeuoMdWh1qjoHOZPl7hg7nrl5vr9ZEcwurswZtZwxswaTnlRBZmHcqgorkSlEnhN8yQ0NrjV+9AOYzLBJ5/Am2/CkSPQpw90OTUbz8yEPXsgOhrmzYPrrwdj22cyQh1gz8dc/RWccrJqK7JuF9hOgm7gWdfwQ3jMt3sD165D1iwDy3GQJkAN6iCEJsru5OWk/M7VtWbcXNouIEJo7d7lZ+w364Dsks+ptWpxacmP0TgbYXofWf53cPtLk/vWTSEtqciK/yJ0vZEus6H2J6SsaVXscssXqURai6DyFdDEIrxes28PnI060O54ph+OtBbaQ9XKHgb3+W3yG1C4fFGE+SIjKCqAX7/b7lSbOUdzCep6VjiSjztjZjkvUUYD277u9BjqeHnAdrFuHdxyC8THw/PPw7hxoD5LBaxW+PlneO01WLAA3n8fRo9u2p4DCNdbkKUP2p2otG0LyZK2cmTlGwj3vza//KwOsNdubvNIHUclBNZWxou3hJQSq02icaDgiX3f+hZE3Xpk+bP2NJ2GpGb3uaUlA2qWI+u2I1xvBt1wVEJgU3cBSzpo45xzDzaTPS7a9DbC9XaEfqhD/YTaD9wfhdpV9vrYns8o4qzgMIowX2SExASSd7wAU0W102aYqbvT6T7YuTG9FwULF8Krr8Lbb8OUKc23U6thwgT7z/LlMHs23HMPPPJImy4rVD6n9kVfAI/HW+WoBadEufxphGE0QuvkAhZtxN/LjbySCmw2iUrlnFeBgrIqPIx6hzOv2fetR9kd02pWIMufQgo9aKIQKnuiGGkrOLWvW4swjEd4vWxfTj9tQxsL5n3OE2bzDrAVI9wfRegHttzh7PsxTLTbKX8evP6jLGsrOESHi2O+1NG76IkfGsvWH3c6xV55cQWHtqfSZ0wPp9i7aHj1VXjvPdi27dyifDZJSfY+H34IL7/c5ssLXX+E6+3I8ieQNT85nFJT1u1Flj2M0PYBl/YthTsTo0GHt7uR7MJSp9lMO1FEeBvC14TKC2G8FuH9LsL9EYS2H6jcQOWG0PazH/N+197mDFEGQD8GWbsGKa3OuYmK/wPdkFaL8pkIw0TQhIHpU+eMSeGSR5kxX4SMvnYoix77hCHT+2Mwts/BaNXiX+g3rtf529/9I9izB557DnbsgOA2eOAGB8OqVZCQAKNGQd+2eVgL/WBQh9hjc2t+tntS6wchRMNnLaUFzPuRNcvBcgzhdhdC51iY2IUkITqYzfuP02WMcxzlNu1PZ0BL9ZjPgRAq0HSx/zjaRxOBVAXYk4K4JLX52gC2mvVgzQSf99tlB0C43oYs/Ys9Lryp/WkFhTNQhPkPwmq1krL1KMf2ZpB1KLWYvo8AACAASURBVIeqChMqocIvxIcuscGExATxzavLue7RtidLSN2TTvKqvfzjs/udOPI/GCnh1lvty9hhjsVNN0loKLz0kn1/eudOaGtOZU0oeC4A805kzQqoehupDgBVAPX1fq2ZoA5BGCaA+wPOdUxyImP7RvPEhyu5Ykg8rob2LbmeKCoj9UQhf50xzEmjcxzheqe9OpSuL0LtuAPjmUhZDRX/ApfpqJwgpELlBboB8P/snXd4FNX6xz9nW7LZ9JBKOiSQ0HuVplRRRBHFgl57712v9VrutffeEfUq2EBQ6UWK9JKEEkgP6XWTzZY5vz8W+cElIclmaGE+z5PngezMO2cCmXfOOe/7/TYsBvOFbY6n0b7REvMJxuVysfTr1Sz5ehVB4QGkDk7mrGmD8Q20oLgUinNLyd6Zx97N+yjKLkWnF1zywAWtvk7h/iI+fvgrLn/0InwD25EwwvLlUF8PM1VQYrrySvjPf2DZsrYVgwkdmAYgTAOQ0uFOxK5SQAFdgFvNS5z6KxbhQX4M7BrLV4s3ccO5nnskK4rkg/nruHBYD7yMJ/4RIwzR4HMZsvrpg9aNrTMykbIeWf28O1YbK++PwDQMWT8XoSVmjWbQEvMJpCi7hM8e/wazxZubX7mKmC5HL8N27pPA0PMHMO3eySyZvYoPH5xF+ro9PPjl7Zh9WjbT2rYijdnPzWXqHZPocZY6RTCnDB9/DDfe6PEM9wiEcLdQffRRmxLzkSGNbkUtw/HQ6lYXl6KQX1pNbkklNrsDk0HP4NQ43vt5Dcu3ZnqkNy6l5NtlW9DrBWP7JR+HUbcM4T0epB1Z9RD43oIw9WvRedK5H1n7Buhj3X3jes+X4o/C0Bmc+5FSOQ691hrtCS0xnyDydhfw5u0fM+nasxlxcfP+ykaTkfFXj6bnqFSevuhlbu7zADe8dCUDxvduVJxDSknWjhwWf7WS3F0FXPf85XTu0w7FWFavhkceUS/e2LHuZfEziJziCv7YuIc16VkEWMzEhQVi9jLS4HCRV1JJeU0dL323nH2FZVw9fkCLrRMdLhffLt3C9qwDPHb5OapVd3uKMJ8Hhjhk7TvIhsUI70lg6Nbo/bjbrxYg7WsRPjORhs7g3Ntmh6ojxqPzRyLc/ejCM4UzjTMDoaYX6okgqWeqPP/Th2hQnBh1eqLMQST4hjEoJIkYS9NmCCeTmopanr/iDS6661z6je3V6vMddgfPX/465QeqMJr0xHePJaZLRywBPjgdTgr3FZG1MxepSIZfOIgR0wbjZW67KtUpR1WVu3CrquroXmVPURQIDITsbPXUwU5RGhxOvlu+lT/TshnbN4lRvTsT5Hv0ErvVZmfe2jQ+++0vgvx8eOLKsXSJaVoRTErJnvxSPv3tL4L9fLj5vCH4nkL//6SsB9tSZMNvoFS65Ut14YBw21e6MkG63Aps3uMRuiCkMwdZ8xK6oDdUHYtSPvOgQIm/qnE1Tj/69+/Phg0bGn17Pe0Sc4++veQfq5fjrTfhUJzk1pWRWXOA1SW7CPX257L44ST4tt5B6Xjy8cNfERQRyIV3tqKt53+wVtfx7IzXmHb3ZHR6Hfl7CqmvtaE36AmL7UBsSkc6JkUepWfdrsjOhrPOcqt5qUl8PCxZAolttwo8Vamoreff3ywhJjSQq8b1b1HirG+w88xXi1ibls3glDjO6pFIfEQQwX4+SAnFlbXsP1DO+owc6u0OLhzeg2Hd4ls8wz7RSCkPFuNlHlYDEAiGRNBFHLG8LJVKZOUdiKDPVbsfKRuQ5TMRwbMQwuhusXPlHZQxLXKPR/i7jVIMiadFXYKG5xwrMZ92S9leOiMR5v/vXQz19qdvcAJTYwaypnQ3L6fP4+yI7lwQ3fIluOPJvm3ZZKXlcuWT09sUx+Lvw8X3nsfCT5bw0Jd30Ht0O+tLbgkGAzgc6sd1ONyx2yl1NjsvfL2YQSlxTB3WvcW/F2YvE89dM4ll2zL56Nd1ZBaUsjY9myqrDZ1OEORnJiEihOmjetEjPvKkL103hxAC9CHur+aO1QUiMYJSDHqVTGWc+9171tKOtP2CtP0OCPcMXh8F6MGVj2xYCa588BoC3uc1qX6m0X5pN08jg07PWWEp9AiM5Y1dC6i013F14siTnpxXfL+GUdOHqWLj2GtUN358cwFZO3M9dmY6rQkPh5oa91J2gEq9oNXVUFnpNrtop8xavImk6FAuHN7Do/NH9exEbV0DmzPzeeLKcad8AlYNYyrYN7a5H/oQjk2g80dW3eXe6/a7r1FNcIF7xo5tEbL6cfAeB+aL3YWFGmcE7W7dM9Bk4YHU88mylvBb4daTOhZFUdi2PI1B56ojJqHT6Rg0qS+bF21XJd5ph8EAvXq5hUXUYuNG6Nmz3c6Y07KLSMsu4vKz2/Z/cNLAFFwuyfJtmSqN7NRHeI9HNvyGGtt9UjqQ1tng2I2w3IbO765jGnW41c+mIQJfQTr3Iaufce+Va5wRtLvEDOCtN3FL8jh+zttAYX3FSRtHUVYJ/iG+qvYRx/eIJSc9T7V4alPjqCetKo9N5fvZXplDWUONKg+2Q5x/Pnz1lXrxZs1yx2ynLPgrgylDu7XZv1qnE0wf2YuFf2Wo++95KmPoBnhBwx9tDuXui65HBL6GMLW8AFToghF+jyD0ociaf7dY+lXj9KZ9ThOAcO8AJnXsy4+5f3Fz8riTMoYDWcVExKtbiBaZGE5RTomqMdtKhb2WZUVprC7ZRY2jnhhLB3z0JuyKk/y6cgAGdujMmPDudPRpvXbyEVx7rdvC8bnnINIzVadDHDgAc+fCrl1ti3OKUmW1sSu3hFvPb5kjUnOkxIahHKzATo5unWjH6YgQAnxvQ1b/E4zdPXaHUhrWgu0XCPrE7TrV6nHokJZbofqfYJsP5vM8GofG6UO7TcwAo8JTuX/Tl1TZ6wgwHenBq0iFPTUHyKwpIreulAaXE4NOR6Q5iHhLKN0CYzDp2vbjcTkVjCrsLR+OwajH6VBJoL+NKFJhQcEW5uVvYlBIZ27vMpEYn2B0h1e3SklJQzUritN5YeePDAjpzPS4wXjrPZR87NDB7Qx1003w44+eC41I6Y5xyy0QdmpV8avF3oJSOkWF4G1S59dcCEG3+Aj2FpwZiRlwF175XHFQRexJhD6i+ZMOQzr3Q9WDYL4UncmzPX44qC7nexuy6kHwGo7Qte/WvjOddrmU/Te+Bm9SA2LYUZV76HtOxcXCgi08sPkrvty3gnJ7LSkB0QwJTaZ3UDwuqbCwcCt3bfiMr7NWU+uweXx9b4sX1mp194Xqa+rxtpz8HtF6p53/pP3Mtopsnu45nas7jSLO0uGIpAzuh3mYdwDTYgfzfJ/LsLnsPL7tv5TYqj2/+D//CVlZ8Mornsd4/XXIzITHH/c8xilOdlEFceHqPsATwoPJLjp520MnA+E9FmGeiqx6CGlb2qKlfCkVZP18t/KYLhjhd3vbx6GPRJiGgG1Rm2NpnNq06xkzQKJvGFm1xQwL7UJeXRnv71mEv9HMzUljSfQNb7Jqu9hWxYKCLTyy9WuuThxF3+DWq2h1TIokf08BUkrVqsNzdxXQsXMbl3DbiENx8nLGPDqag7kqccRRybgpfA3e3JB0Dr8XbuWFnT/yzx4XEWjyYP/dZIJffoGRI9262Q8/3HLBEUWBF16ADz6AFSvA6+S/5Bwv6mx2gvx8mj+wFfj6eGG12VWNeTrgtm5MQta+CbYF4D3RrY+uO1LBSyrVYF/jdhITPuA1CiHM6vkwe49D1ryE8LlYnXgapyTtPjFHeAeyu7qQ3dWFvLFrARfHDmZEWEqziTLMO4CrEkcyuEMS7+7+nUq7lTERresdDgz1R280cGB/MZGJ6vRC7tm4j4Qe6rRKuaRCQV0F1Y46AEK8/Ajz9m820c7NWY+fwbtVSflwxkX2otpRz0d7l3BvymTPXlpiY2HVKpgxAxYuhLffdldsH4tt2+DWW91/XrUKoqNbf92TTEllLSt37Gdvfik5xZXU2x0Y9Doig/1JiAhmYNcYusaEIYRAr9PhdKlbLOR0Kejbs4jNMRCGRAh4+aCT2G9g/RCpCwDdwb5opRiUOjD1RPhcA8YeyJpnwFuldisAfQIo1UilBqHzUy+uxilFu0/Mep2OGmc9b+xawI1J59AjsHVJrYt/FI90n8rzO3/E32imf8ixhf3rnXayrCXk1ZXhUFwETI/l+/mLuf6WaZ7vqx7EVtfAxj+28sjXnts4OhUXmyr2s/TATvbWHCDYy5cgk+XgXnANVqeNboExnB3endSA6KOSZnZtCatKdvGvXpd4lJT/5oLoATy5/TvWlO5maGgXz4J07Oh2hnrnHTj3XEhIgKlT3T7LsbHu/eecHNiwwb0fnZkJDz7o3qM+zZJLSVUtsxZtIiO3mGHd4hnTpzNxYUFYvE04XAr5pZXsyS/j4wXr0et1XHlOP8KCfNmTX6rqOArLq4kIOnMTwpFOYopbCERWAQJ0QUcriDmzQa+eopwQOqQhDlxZoPN8z1rj1KbdJ+Yqex27qwu5IuGsViflvwnzDuDW5PG8nrGAZP8o/I1HSuVJKdlRlcuiwu2kV+cT4xNCtE8IXnoDfr1DWPDHajb9+RF9whI4J6IHqQGtn6lZnQ18/e18/MdGkWOspKFetmh2ezh7aw7w0d7F+BnNnB3Rgzu7TjzqZaHGUc9fZZl8lbUKi8GL6zqNIfwwpbWFhVuY1LH3UcV0rcWg03NJ3FC+zf6TIR2SPV/q1+ncifbGG2HePFi8GL77DgoK3J9HRroT9V13wXnngfHEizQ4nC4Ky2tocDgxGfREBPu1yg5x9Y79fLloIxMGduWW84c2em6AJYLUuAjOH5LK5r35fDB/LXFhgRRW1Kh5K2QWlDE0NV7VmKcrQujAEAMcw4FK2kCn7nYCwuKOq9FuafeJeWVxBj4GE+Mie7YpTme/CAZ3SOLH3L+YmTji0Pcr7XV8vm8ZBfUVTIzqw83J4/DWH/nwT9rqw9Zv0kl9LJpPMpeS6BvGlQkj8DMeWwu31mljVXEGy4vTKKgoo6KsmCHj+vB74VYK6yuxOm30DU5gTER3OvtGHDO5LSzYwvz8TVyZMIKBHZoWNvAzmhkT0Z1R4an8XriNp7Z/z3Wdx9A3OBGrs4HN5VlcnnBWC39qx6ZbQDQNLif7rcUk+rZxqd9odM+Wp05VZWxtpc5mZ+WO/azasZ+8kkpCA33xNhpocLoorqghPMiPIanxjOrViQBL03aeizft4ac1O3nksnOIDQts8ri/EULQNymarjFhvPXTanblFrMnv4Skjm2voq6y2sjIKebGNng1n3EIA8gGUFP3WjoATQWsPdOuE7OUkr/K9nJNp9FtWnb9m4lRvXl06zeH2n3y6sp4Ke0XhoV24ZbkcRibaK86+4qzSF+7m8IPM/jXI5cwN289j2/7Lw+mTjlC9/vwca8u2cU32X/SPTCGKT69+OnRH7njrmn06/v/+6hV9jr+LN3Fe7v/IM4SylWJIxudyf5WsJUlRTt4oufFdPBq2TKkTuiYENWbZP9IXk2fj17o0QsdsZYO+Bpa5gvdkmt0D4xhd3Vh2xPzKYKUkqVbM/l22Ra6x0dwyajeJHXscMQs1+Fysb+wnBXb9nH/B/M4d1AKkwenHLV3m55TxNzV23niynGEBbbOJtDH28Td00aQWVjGq9+v4J07L2rzvS3atJsBXWJOKeeoUx59R7dRha75l6oW48pxx9Vot5xeG22tZE3pHhoUJ+d2VEcSM9jLlyS/SLZUZFNqq+bFtF+4JG4oF8cNaTIpg1tK84aXZlKWX87H98xikqUHF0QP4N9pP1Fhrz3iWKfi4v29i1hQsJn7U85jSH4UP97xA+fdMO4oy8gAkw8To/rwQp/LiDAH8tjWb9lXW3TEMftqi5iXv5EHUqe0OCkfTqJvOHd0nchHexeTXpVHnEXd/tUE3zCyrcdfMEVKSUleGRt+38qvHy3ml3d/448vl5O+bg/W6jpVrtHgcPLKnBUs2byHxy4/h9svGE73+Iijlp6Nej3J0aFcN2kQz14zgbTsAzwzaxE19Q2HjrHZnXwwfy3XThjY6qR8+HWenjmejLxi5q9Na9O95ZVU8vvG3Uwddgaap7QBYegMDvUEbKSrGHCBrvVCJRqnD+12xuySCl9nraJbQDQGnUrevUBnv3D21RSxtGgnYyN6MCQ0uUXneft4cdtb17Lwk6U8f/nrjLx4KP2GxfNp5jLu7nouQgiklHywdzF1zgYuc/ZjwRO/cmB/Ede9cDmdezfdrmXUGZgeN4TOfhG8mj6f+1PPJ9bSAUUqfLR3CZfFD/coKf9Nkl8kI8NTWXpgJxOjenscpzH8jWZq2tAr3hxOh5M1v2xk5Zw11JTXEt8tloiEMAxGPZVFVexYmU7u7gJSBiUzesawY/6cj4XD5eKV71fg7+PFkzPHY9C37J03NMCXhy4dwzdLt/D87MU8dvk5+HibWL41k7jwIPomta1yPDzYj6vHDeDtn/+kf5cYwj0o3Kqtb+DNn1YzfWQvQj18SThjMQ1B1r4N5qlHFIV5TMMShGkY4EQ60t2Wka58pHQihBcY4sGQBPpOJ93AR8Nz2m1inp+/CbviYnhoV1XjRvuEMGv/SvyNZiZ17NPi8/L2FLJh4Ray03JpqLcz+7m51FrrqL0zlN25mxkSkUxhbANbKSDuBxdzffYw4qLBXPPsDEzeLavm7hucQL3Lzju7f+eZXtNJq8rHqNMzuEOSp7d7iPOj+/NN1p9UOqxtjnU4ilRU2WZojNxd+Xzx1Hf4Bflywe2T6Dqwc6N+1fVWG+t/3cxn//yGrgOTuOjuczH7tm5P8MdVO9DrBDedN6TV7URCCC4d3Rurzc4XizZy47mD+WPTbq6dOLBVcZpixug+zF+Xzj8/+42HZ4whIaLlsqgllbW8OmcFvROjGNO76doEjSYwpIAwgf0v8BrUplBSsSLrF4BpIFTcCLpQhLELGJLdfdJKHTj3I+vnufe2vSeB11h1Xgg0TignNDELIUYBr+GuXCiVUo48+P1LgQeAL6SUr7X1OsuL0lhyYAdDQpPwNqjU2H8Qk9CTZS3myR7TW5RQ8nYX8N1LP1OSX87gc/txzpUjieocgbePiZoKKz/8uZIlwWks+3w9+Vd78w/LUMa+N5iQqGCP3niHdkhmY/k+fs7bSI61lLMjeqjy5mzSGegVFMtfZfvYfM1iAF5e+lSb4x6oryLUW/32m63LdzL72TlceOe5DJzU95g/A7PFm5EXD2HgpD7MfXU+L17zDne8fR2BoS2zlswuqmDp1r08e80kj3t8hRBcfk5fHv7oV5ZtzaTB4aRrjDpSoUaDnnH9ulDfYOfF/y5lVK/OnDsoBcsxXvjsDidLt2Qyd/V2LhjanQkDumgzMA8QQoDlWmTNa2DsdpQgSWuQNS+AcgAhFLc8aFM+zVKCczuy7ltoWAa+t3us861xcjhhiVkIEQi8A0yQUuYIIQ5/6lwKDAC+EkL4SilrGw3SDDaXnW+z17C1IpsHu01hQ9k+6pwNzZ/YCnLqylCkpHvgsZcYpZT8/vkyFn+1kgtum8igyX3R/486ldnXzHXTp7BzYyV9xg4lJyOfza9vJNjlw7irRnk0PiEEl8YN5alt3+OUCv/oNNqjOI0xpEMX3ty9ADXnTVnWYnoExqkYEXZtyGT2c3O55fVriEtp+VKw2eLN5Y9dxMJPlvDmbR9z/6e34u3TfKHTr+vTmTQwhaBWzrKPur7JyCWjejN7ySYSI0NUTYSJkcFk5Bbz7DWT+GbpZu5+9yf6Hazejg0PwmwyYnc6yS2pYk9+CWvTsukUFcLjV4ylYweVvK/PUISxO3gNQda+BH6PeKQCptS8BLY/IPB1RDMzbyEEGHuCf3ew/YqsehT8Hz2mzaTGqcWJnDFfBsyVUuYASCmLD/vs7yeQPOzPLabKXsfKkgwWFW6jW2AMT/eajq/Bm44+wSw+oK538daKLOItocecLUspmfvafHZvzOThr+4kKKzpB5te6OjiF8XS4p38a/Sl+A008u7dn2KtrGPqnZM8ejiHeQcQYQ5kT80BAtvYb3w4gzp05vmdDdQFKfhUtH15rN5pZ3tlDjPi1XE/Avey9JdP/ZernpzeqqR8OBOuGUNJbhk/vP4rMx4+dvtVbX0Dm/bkc8U5/Ty61v/Sv0s0r85ZTre41pklNEdEsB8rt+8nyNfMzecNpcpqY21aNjuzi1i4YRc2uwOTQU9UhwASIoJ5+uoJHhedaTSCz9VQ+xqy+hnwuxuha9l2gpQuZM3LUD8Xgt5DZ2p5jYcQOjBPBl0osvpZCPg3Qt8+DVvaGycyMScDRiHEMsAPeF1K+cXBz+YCG4BZUspjKiKUNtTwcvovmPVe2BUneXVlVDvqGRjSiTu6Tjyi7SbRN4wPa4txKM5jVk23ht01hc3u2a75eQPp63Zz9wc3YfFvPjEGe/lS77ITa+kAFrjz3Rt45fr3iEgIY+iUAR6NM943lIzqAo/ObQo/o5kAo5mibjYSVrU9MS8vTqNbQAxBJvUSwK8fLCJlUDKpQzxUEzvIRfdM5l+XvsqQnf2J79a0gERmQRnxEUH4qdRCZNS7k2NxlUeLRk2i1+lwHeblG2DxZvyAtv2MNFqOEHrwvRvqv0VW3gM+lx7U0W689VBKCc40ZO1H4NgEAS+3KikfcW2vQeDKQ9a+A/5PaFsSpwEnMjEbgH7A2YAZWCOEWCul3C2l/Bz4vCVBgkwWRod3w+ZyYNQZiDIHEWkObHQGG2iykOAbyl9lmZ7LPh5Gka3K7Ubl33QPYUVRJT++tYC73ruhRUkZwOq04Wf4/2VQi78P1zw7g9dv/pCUwUkEhbe+BzLGHEytU91qZykl/iYzBZHl1IY377BzLEobavglfyMPd7tApdG5JUvXztvAw1/d2eZYPn5mxswYzrJvV3P105c2edz+ovJWFVO1hJjQQPbkqdtCVllbr9rLg4ZnCKEDnxlgGuje/62bjTT2cy8x6yMBPchKcGYiHVvde8XCB3wuR+fdxlUl8wVgX+P+8hqqyv1oHD+Oa7meEOJWIcQWIcQWoABYKKW0SilLgRVAM64Dh+JY//7Kycqmb3AiQ0O7MCCkEx3/x//3fzknoifz8jfhVNruYfxz3gbiLaHH9AD+7bNlDJ0ygKhOLV+KrHU24GM48qHZsXMkQ6cM4PfPl3s01hhLKHVOG4pUz8Sg3F6Ln8FMzBod2cNdh3qwpXQhlVr3VwuuZ1ecvLv7d8ZH9iLaJ0S18W1bnkZiz3iCI9SxOhxyfn+2LU+job7pOoWaugaCfNWVXOwUGUxxpboz5qyiCuJVtoDU8Axh6ITO/xFEwEsIY1dw5SBtPyPr5yDt60EEICw3QsALoBQgzBe2/ZpC77autC1Q4Q401GD//v1H5LbDPzuuiVlK+baUsreUsjfwA3CWEMIghPABBgHpLYxj+fsrIaF1faZ9guIJ9fLnx7y/Wj3+w9lWkU16VT4jwlIoqCtv9BhbXQMbf9/CyOmteyOttFvxacTgYuT0oWz4bTO2utYXsHnrjRiEniJbVavPbYr9tcXEWUIJzNPTpbCe5fsfxlp2J7L8cmTlzciKG5HlV6JUPYasm4NUKo+KUeuw8XL6PMK8/ZmskvDL32TtyCGpn3qGARZ/H8LiOpC/50CTxwhA0rbVg/8lLNCPeruDcpWET6SUbN1XQBeVqrw11EHowxDe4xG+N6PzfxJdwNPo/O5H+FyEMKYiHJvBkITQqyQmYhoIrmyk0vjzS+PEkpCQcERuO/yzE9bgJqVMBxYC24D1wEdSyh3H+7pCCK7uNJJVxRn8WeKZAk92bQkf7l3M9Z3PJskvksz/Udf6m72b9hGdHHXMYq/GqLDXojRivh4UFkB0chSZW7JaPeYiWxVRPsGsLd3T6nObYl3pXvoFBTNpxhZuHvUXqQHRvJYTxqKGu3EFfoou5CtE0HsI8zRQDiArbkNaP0HKBhSpsL50L49s/ZoESyjXdz5b9f7l/D2FRCer61UdnRRF/p7CJj8P9vdRfXZbXlNHUscOLNmizr/dvsIyrPV21QvKNI4zzr0IQ4pq4YQwusVHnJmqxdQ4PpzQPmYp5YvAiyfymgBBJl/uTz2fF9N+pthWzeSOfVusBraudA+z9q9kZuJIUgI64lCcvL93EUW2KsK9j0zAOen5xKUew2mmEXKtZbikxOq04lRcR40rNiWanPQ8ug1t3R75/tpiBocksawojXM79sXUiuK3sbqjTdiVQD0JL3kx0G8Hf67x5+U7wnDYN6CEGlhwUT5PRs/GsN6KPrMBXZ4DYVOYU/EGDTXv46j6B5+XDKaecG5LHk+y//HpqWyw2TH7qqPj/Tc+fmZs1qZXLBIiglmXnqPqNfcVlnFO32R+XZfO6N6dCfG3NH9SEyiKZPbSzUwa1BWdTiv6OZ2QrlyE93hVYwp9HDhzwORZUanGieGMkYTp6BPM4z2msbfmAE9t/56/yjKPqFI9HCkle2oKeS3jV37IXc9dXScx4KAPs1FnYERoCgsLthx1XllBBWGxrVt2WlCwmbMjupPgG8bmiqyjPg+L7UBpfuuWnhSpsLZ0D6MiUuniH8mcnHWtOv9/kUDSfTquj03no6cjmfd5Bxx2938dXYkT83slmN8oBhc4RvpRf1841mc68vC2eXxQ1IPdjmHcEbWOp1KHHLekDGAwGHA0OFWNaW9wYDA1/VLTKTKEwvIaSlSaNdfbHezIKuKs7glMGNiVD+avxaV4XiewcEMGiiI5p0/LpGM1TiFkAwiVC/aECbCrG1NDddqtJGdjBHv5cm/KZP4qy+S3wq18sW8FSf4RxFlC8dGbsCtO8uvK2Vt7ACnh7Iju3NqIa9Skjn14dOs3DOmQdESicblc6FqokQywozKX9Op8rkg4aLMw8gAAIABJREFUi0hzEL8XbqV/cOIR7QxCJ1BcrXswb6vMwUtvoLNvBOEJgTy65RtSA6LpFdQyIY8/lO+O+PuSgmXEOF4gIfJzBm1P4d7RTwCtU/6SthRk7b+Rga96JLDQEsLjQyncX0znPp7pXTdGYeYBeo5MbfJzk9HAiB4J/LFpN5eNafue+Ypt+0iNCyPIz4fJg1LJyCnm/XlruXHy4Fariq3Yvo9f12Xw+JVjtdny6YjwcidnNZENINRX2tNQlzNmxvw3QggGdujMY90v5LEeFzIguBM2l53C+kqqHHUk+UdyS9I4/t3nMiZE9W60/9nPaOaqhJG8u+cPShv+v+3aN9BCTXnLZk5Ftio+3LuYazqNxsfgRf/gRGwuBytLMo44rrbCiiWg5VW/NpedL/et4OLYIQgh8DeaubPrRD7cu5i/ylq3tySlZEHBJox17xMeeAUGk+f7XcJ7DBgSoW62xzGaIzYlmv3bslWL57A7yN1dQGzXY1vsTRzYlRXb9pFbcnSxW2uoqKnjx9U7uGCo28HJoNdx90UjsNrsPDNrEQVl1S2KU2938Olv6/l+xTYenjFGEwo5TRH6WHBmqRpTurLA0LrtNo0TzxmXmA8n3DuAIaHJXBI3lJmJI7gsfjijw7sR7xvWbGFSv5BEJkT25vkdP5Bd6+45jekSRU5GfrPXzawp4vkdP3BBdH96BLr1bg06Pdd3Pptvs/8kx1p66NjcjHximkkMf6NIhU8yl9E1oOMRs+POfhHck3Iu32St5sO9i6lx1Dcbq9hWxYvpP7OvYjUDAn3x85/RojEcC2G5FtmwGKkcU0PGY3qNSmX7yjTqrer0b29ZupP41Bh8A4+9xxvib+GSUb155+c/qbN5tkzocLl4b94azu7TmfjD+qK9jAbunTaSod3ieOrL3/nw17XsKyxDUY4uFiyvdif2Bz6Yh93h4vlrJ2lymqczhs5IZ4saV1qElHZ34ZcmzXnKc0YtZavN+Khe+BvNvJj+C6PCU+nfPZa5b/yKw+7AaDIedXy1o54F+ZtZVZLBzMSRh/at/ybW0oGZCSN4Me0X7ug6gXivDuzetI8pt01odiwNLgef7ltGpd3KvSmTj/o80TecZ3tfync5a3lg8yz6BCXQOyieeN9QgkzuxFNsqybLWsK60r3sqSlkUlQfJgXsR2c4z61c1EaELhBp7A8NS8A8pc3x/pfA0ACS+3dm5fdrPdYa/xtFUVj81QrGX90yrfFRvTqRXVzBv79dyr3TRuJvaXkRWr3dwds/rcbbZGTq8B5Hfa7TCcb168KgrnEs3bqXt39aTaXVRlxYEBazCbvDRX5pFXani0FdY7nv4lHEaT3Lpz+mfmB9D+kqQujDmz++OexrQZ+I0Gn/N051tMTcRoaEJtM1IIrvstfyQvl8Gs418fGSBYwa0g8vvZEGl4PcujJ2VxeyoyqH/sGd+FevSwloQsN6UIckTDoDb2QsILbMl/DkMDp0bFqEQ0pJenU+n+1bRrwljHtTJuOlP/qlAMBbb+LKhBFMjR7IqpIMVpfsYnbWKqod9QghCDJZSLCE0S84gVuTx+GlN6KUvw0+l6nyswIQXkORtgWI45CYAabcNoEX//E2vUd3Iyw21OM4i2etxNvsRa9R3Vp0vBCCq8b25/sV23jo41+5amw/BnaNbVb+cEfWAT76dR3d4yO4evyAY+4jB1i8uWBody4Y2p2a+gZyiyuob3Bi0OuICvGnQ4BFk1tsRwjhBV6j3TrZvje3KZaUTmT9Dwhz0yp2GqcOWmJWgSCTLzcknUONo565tpX8vnwtByJsuITEpDMQ7RNC98AYruo0El9D8zOpPsEJPG4I4o43X8d4fhCfZi6jW2A08ZZQ/AxmnNJFYX0F+2qLWVWSgc3l4JK4oUfNwJvC1+jNhKjeTIg6tvauW4jABTrPE9xRGDq5JQelPC5JJCymA5NvHMf793/JPR/ciCWg9a1G6ev2sGjWcu7/7LZG/ZubQgjBxSN70atTFJ8sXM/3K7cxpncSXWJCiQkNxGjQ41IU8kur2JNXyrJtmdTUNXDVuP706dyy7Yq/8TN7kar1Jbd/zBcjK+8C+1aEqUVCiY1TPweEL1LWg/UTpOsAoIDwRRjiwdAVDJq156mClphVxM9o5qqR45CLyvH+zcSlD3quA734naWMt3VhysjJrC7exeriXXxdtxqrswG90BHmHUCCJZTpsUPpHhitulgHAEq524xdxV9WoQtGyjrAiduWW31GTBtMZXEVr9zwPte9cDmRCS1bBpRSsn7BZua+No/r/3MlHaI808BOjg7l+WsnkZFbzKod+1mxfR/5pVXodAJFkYQH+ZIYGcJFZ/WgZ0KUVjGt0SRC5wu+tyFrXz+2B/MxUOrnQe2HoO8AwtetJuaVCkIPSg249iFr3wahA+/J4HW2W9db46ShJebjwMX3n8/L173Lrx8uYuJ1Z7cqsUkp+fXDRezZvJ97PrwJi8mHydHqWAq2GumRC2cLcAtZHi+EEEy5dQLBkUG8cv17jL50OCOnDzmmqUj+3kJ+eec3SgvKuf2t64hOblu/tRCClNhwUmLdLwWKInG6XBj0ei0Ra7QKYeoNlquR1U+A7y2IFoqDKIoLap4B23ywXIPwuQKha6xVagz4SHDuRNbNgoYV4Hu7ZhF5EhGyESnIU5n+/fvLDRs2nOxhNEt1WQ1v3fExoR1DuOShC/APbr53sLq8hm9f+JGS/DJue+Na/ENObr+hdBUjqx5GF/yxejGVKmTFrYjgL4/rslnurnz2bNrP7g2Z7FiVTllBBR07R9C5byK9R3cnKDyAupp6cjMK2L0hk/KiSkZMG8LZlw9vtHBPQ+NkIx07kbVvgaEzwjwVYWhcF15KBWnfBDXPgaw+aBnZsmVwKRWw/Yisn4/wfxKhtVYdN/r378+GDRsafQhqifk44rA7mPfeH6z5ZQNDzuvPsKkDCY0OOSIhSSnJTs/j98+WsWXpDnqOSOWC2ycSHqfuErInSCmRFVchAl9rsbF7szHtm5D1P6ELaLk4SYtjS8nmJTv444tl1FZYSR3ShZiuUQR08MdaXcfWpTvZu3k/ebsL8Av2JalPAl0HJ5PQI5aUQUnoDW2vPNfQOJ5IaYP6eciG30D4IQxJoI8FYQRZh3RmgTMNlCq3ylfAO+j0rX/Bl7alyPrZiIBXmphla7QVLTGfZIpzSlg5Zx3rF24GoGOnCIRekJOWT9bOHJx2Fx2TIonrFo3Jy0jh/mJcThd9xvRgxMVDiE5S15ihNSg1/0EYe6mm2Str3wddEMJnuirx/qa20srsZ+dQlFPKBbdNpNuwLk0WbtnqGlg3fxO/frSI4VMHMen6s9HrtaSscfogpQLO3e6+ZFc+4ADhDfp4pLCA9V1EwMttcqaS1o+RSjU6v7vVG7jGIbTEfIogpaT8QAULP1nKyjlrSeqbyDlXjqD78K5HJYbyAxWsnbeJlXPWkDqkCxfdPRkfP/OJH7N9K7LuU0TAq22ewUvFiqy8WdUZOEBlSRWv3/wh3Yd15fxbx7d4KbqypIrPH/8WHz8z1zx3mTZj1mgXKNVPIkxD2vwyLWUDsvJOhO9dbt9oDVU5VmLWSu9OIA67k+9e/Jn8PYU8Mec+7v3oZnqN7NbobC04IohJ153NE3Pux2A08Pzlr3Mgq/jED9rYAzBBw+9tj1U3C2EapGpSdtgdvH3npwya1JeL7p7cqv3hwNAAbnn9H9gbHHz30s+qjUlD42QhXfngzHb3P7cRIbwQ3pOQtoUqjEyjNWiJ+QThcrn48P4vMXqbuPejm1vcwuPt48WMh6cy6fpzeOPWjygtOL4m51IqSGcu0r4Baf8LnBlguQ5ZNxvpzPM87t/xfK5ScbQw/4NFhEWHMP4fnj2IjCYj1zx3GTtWZ5C2dreqY9PQOOE0rEZ4naWeUYzXaLCvRUp1Xds0jo2WmE8Qi75cgdPh5OpnLvFoyXTIef0ZM2M4nz32DUobbAAbQ0qJdOxEqXkZWX4FsuY5pG0B0vYH0vo5VD8O0omsvBXFsav18e2bkbVvIvzuc/dlqkRZYQWrf1zPJQ9d0KZldrPFmxkPX8h3L/3M6ba1o6FxONK5Fwyt824/FkLn5xYYcuWqFlOjebQ+5hNAcU4Ji79ayYNf3N6mIqMxlw1n2/I0Vn6/lpHTh6oyNukqQ1rfBVchwnsSWG44qgpTShc4092FW+VXoPhchvC9s1kRAintUPcNsmEpwu8h1fepVs1dx8CJfVvUitYcqUOSMRj17PprL10HJqkwOg2Nk4Ar312lrSaGWHDlgUE9O1WNY6PNmE8AS7/5k+FTBxES2TbxeJ1Ox4V3ncuir1aoMmuWjjRk1X0IQxIi8HWE+dxGWyOE0COM3dEFvQkBL0P9z8jSySj1vyBdB46YZbqXwnOQdd8gK25GKkWIwFcRRs8tI5ti4x9bGXJ+f1ViCSEYcl5/Nv6+TZV4GhonB7u7TUpVTCAdKsfUOBbajPk447A72PDbZh6ZfZcq8eK7xWAJ8CFj/V5SByd7HEc6diNr/o3wvadVGrw67xEopvlQ9SDUfYus/wmwI3VBbqUwpcTdDmXsg/B/wiMJwZZgra6jtsJKVCcVXHcOktAzjrXzN6oWT0PjxGMG2byta6uQdSC81I2pcUy0xHycKdh7gMDwQILCA1WL2W1IFzK3ZHmcmKWsR9a+grDc4pEwvk7ngwz8D7LqIYR5mrtyW6kCBOiCVd1HboqirBIiEsJaZTLRHFGdwinKKlEtnobGCccQC64sMMSrF9OVrW48jWbRlrKPMzkZBcR2aZ1zUHPEpkSTm5HveYC6r8GQivAa5HEIIcwI39uR1k8AA8IQhzDEnpCkDOBocGA0qfteafQy4rA7tQIwjdMWYUgGx07V4klXCUgr6E6eyNGZiJaYjzPWSit+IeomK79gX6zVdR6dKxWruxjLckWbxyEMncHYCxqWtjlWa/Hy8aLealM1ps3agJfZdNKlUDU0PMZrONK+Fql49nw4ioY/EKYRmtvUCUb7aR9ndHodikvd9ibFpSA8dX2yrwJjT9VEPoT3BKRNBfGRVhKZGEZRdilOh3r9lXm7C+jYWZsZaJy+CF0wGPuAre2COVKpRNr+AO8JKoxMozVoifk4ExwZRElumaoxi3NL6dDRs8QqHekIY1/1BmNIBqUcqdSoF7MFeJm9CIsJIWunev2VezftJ66b5qajcXojfK5C2hYinfs9jiGlRFo/QHiN1hymTgJaYj7OxKZ0JDs9T9V9y5z0fGJTPNy3du2HJuziPEEInbu/0blPtZgtZeCkvqz+Yb0qsRRFYfVP6xl0roovLRoaJwGhD0FYrkPWPI90HWj1+VJKqJ8NriKkeSpSqUHKhuMwUo2m0BLzcSY0OgSTt5HMrVmqxHM6nGxesp3UoR6q+yjVoAtQZSyH0AWAPLEzZoCh5/dnx+oMVTTE1/6ykeDwQGK7qluop6FxMhBewxHmC5FVjyLtm1t8nuKqQVY9hLTOAqUSKq5DVt6KLJ+JUnETSs1LSPtfbncrjeOG1i51nBFCMGLaEJbOXkXn3m1Xztn4xzaiEsOJiA/zMILO3W+sKpLj9Y5nq2tg06JtZG7OImdXPtZKK0InCI4IIjYlmt6ju/P5k99y38e3eKyqVlFUyY9vLeCOd65XefQaGicP4T0BdJFI6zvIhhSEebK7YLMRpKx3d1jUzQJdBPjejjD2BH0UQujcidhV4FYArPse5MdguR5h6neC7+rMQEvMJ4ChUwaw9JtV7FiVTvfhnitg1VZa+fGNXzln5ki+e/kXctLzKM0vQ3Ep+Pj7EJ0cRWLPOAZO7I0lwNJ4EH0YKAWgD/F4HEfhKnDHVRF7g4MFHy1m1dy1dOqdQLdhXRlx8WD8O/ijuBRK88rITsvjr9+2kLFuD/+Z+RYPfHFbq5NzTUUt79z1KWNnjjypvtcaGscDYeoFxtegfgGy5kUkRjAkIfQdQehBqUE69oJjFaADv4fQmaccHUfowBANhmiE91i3Haz1XbCvd8v4Cs0yVU00P+YTxK4NmXz++Dfc89HNdIhqfeGWy+ni2Rmvkbe7gJiuHRkwoQ/x3aIJjw9Db9BRW2ElNyOf9HV72LEqg35jezHltglHeThL68duERDzVFXuS0obsvxqRPAXqjna5O0u4ONHZhOdFMnUOycRHNG0lKmUkrQ1u/j3zLewBPjw+Jx7iYxvmRrY/h05fP7Et/Q9uyfn3TxOa5PSaNe4Z71Z4MwEVyGgIPEBx3oQZoT/IwjRcs93KeuRNS+CsCB879ZaqlrJsfyYtcR8Aln+3z9ZNGsFt7z2DyITWy4lWV1ew6PnPk9VSTUPfH4b3YZ2OWYSqamoZf77f7BtZTpXP30Jyf06HfpM2rcg675ABLysSiKStiVI+2p0/v9scyyA7LRc3rnrU6bdcx4DJvRp8Xn1VhvPX/46u9bv5YrHL2bUJUPxC2q8f7xwfxHLv/2TzUt3MP2+8+k3tvXqZxoa7QFZ9z3SsQXh/7hHL9ZS2pHVTyFM/RDmC4/DCNsvWmI+hVg3fyNzXpvPOVeMYPSMYRhNxiaPlVKydflOXrzqbSISwnj65wew+DexRN0IaWt38/nj3zDzyUvodrBYTEoFWXkbwvdWhLFbm+5FSgVZdT/CZwbC1HYzieryGp677HUue+RCeo5I9WA8kg8f/JIdKzMweBno2DmS2JRoAjr4obgUSvLKyMnIp6a8lmFTBjDykqGqOFNpaJyOSGcOsvqfiICXEPpQz+O4ipBVDyD8/6W1VrUCLTGfYpQWlPPdiz+zf0cOg8/tR1K/RKK7RGHxN+OwOyncV8T+7Tmsm7+R/dtySB7Qifs/vdUjXeh927J5957PuP+TWwiLdf/yyYbVyLpvEIEvt2n5WdbPQ9r/dP9CqrCM9eGDs+jQMZipd0zyOIbT4eQ/V7/FsAsGEh4XSm5GAbWVVoQQBEcGEpsSTXRyJAajVl6hcWaj1LzultFVYVtL1s0BpQDhe7sKIzsz0BLzKUpxbinr5m8ia0cOeXsKsdXa0Bv1RMSHEdu1I77Bvqybv5HHvrkbL7Pn7i5LZq9k85Id3P3Bjeh0Ord4QO1LCOEDlls8WtJ2u1M9iwh4HqGP8nhsf5OdlsuHD87iiTn3HXMVoaWx3r/vC575+SH0Bq0oRUPjf5FKDbLyFkTgO41avbY+XjWy8lZE4LsnTC//dOdYiVmbNpxEwmI6cN5N4xr9TErJvy59lRkPTW1TUgYYdekw1i/YzI5VGQeXiCVYbkJW/wthfQ9pubZVM2fp2I6seRnhe7sqSRlgxfdrGTFtSJuTMkBcagwhUcFsW5FGnzE9VBidhkY7w7ETDMmqJGUAofNHGpLAmQ6mAarEPJPRyuhOUfZu3o+UkpQ2eC7/jRBOplxvwV76AkrFTciyaVBxNTj3I23zkGUXo9iWNxtHKrVI68fImtfcVZgq7CuD+yVk24o0BkxsebFXcwyc2IftK9JVi6eh0a5wZTbZ0+wpwtDJXfGt0Wa0GfMpyvaV6QwY37tNldNSSmj4HVn3DUndo/n4KQM9JzyE0RzrFg1QapHOfVA/F6oeQKkJQvG6DJ33AIShI6AHWe3+ZXNsQdrXIUyDEIGvN7tcJaWksqSavF3uPV6dXkdIZBDRXaLw9jlyBaCssAKDUU9QmHqKZHHdYlj+3Z+qxdPQaFco5WBofYHlMdGFgXO3ujHPULTEfIqSk5bHuKtHeXy+VGqRtS+BYkX4P4HBEE9Z6Wvk79OR0P3gQomwsD/dn3Xze5K7y4+eA5bRb9grlBVb8DIbMfsa8QkIw69Dd4QpBRF4BUIXeMzr1lZaWf3jelb/uB6btYGYLlH4BfsiFUlJXhkFmQeIT41hxMVD6DW6G3q9nrL8csJiOnh8r40RFtuBkrxyVWNqaLQXpJSe+tMdA4FbBVCjrWiJ+RSltKCcsDjPWhikUousfgJhTAW/qw+p8oTHhVGaX05C91iydubyzQs/YKtrYNiUgQw5rx8dk+/AqM8npOoZyivHk7YphjU//0V9rY3p96fQbWjTSVlKyfpfNzH39fl0H57Ctc9dRmxK9FEzfqfDydZlO1kyexW/f7GMmU9Mx+VSVC/S0hvUt9vU0GgvCF2ge9asJkopCJV1+M9QtMR8iqIoEr2+9SUA7orrNxGGLuBzzRGJUa/XoThd/PrhIpZ/v4Zp95xHv7E9/6cNKx5d4DOEiEcZMfVRRky7nYz1e5n93BxSBiVzyQNTjkqiLpeL2c/OJSc9j9vevJaYLk0bQRiMBvqN7UXfc3ry509/8dpNHzBmxjBqq6ytvtdjUVtZh49/y1WMNDTOKAyJyIZVqs6apXMfwmuUihHPXLTir1MUi7+ZmgoPkpV9JSgHwHL1UbPV6vIa1i/czPZV6Tw6+y4GjO/daG+00EchLNcga98EHKQMSuLRr++msriKjx/+CpfLdehYKSVfP/cDFQcque+TW46ZlI+4hhAMu2Agd7x9HUu+Xs2+7Tk47I7W328T5GbkE9vCsWhonHEYU8GZhpT1qoSTSh04M8DouReAxv+jJeZTlI5JkeRm5LfqHCkVZN23CMsNR7U/SSnZsXoXxbll3P7WdfiHNNMmYRoOIhDs7gIqbx8vrv/PFdisDSz4aMmhwzb+sY1927O58eWZHrV1RSdHcdPLV1FTVsvmJTtafX5TpK/dQ0LPONXiaWi0J4QuGAzdoKH5bowW0bAMjD0QuqZ17TVajpaYT1E69U4gfW0rKxwd20GYGq223PlnBuUF5dz4nyuPMrZoDCEEwnwu0vbboe8ZTUZmPjWdlXPWkLsrn3qrje9e/pmZT0xvU691Ys84Bk3qw5dP/tfjGIdjq2tgw2+bGXKeZkmnodEUwjwVWfdfpFLdpjhSqULWf6eaMY6GlphPWfqP60nG+r1Ulbbil8axGWEa1miL1eznfqD3mO5EJ7dCEMTYD5xZSOX/l9QDQwOYeO3ZLPx4CevnbyKpTwLx3dquj3vtC5eTv/cAmxZta3OshZ8soeugZILCj11BrqFxJiOMyQivs5C177qdpzxASgVZ+w7Ca7TqfdFnMlpiPkUx+5oZPLkfP7/zW/MHH0Q6M8HQ6ajvZ6XlkL52N5c/Nq1VYxBCD4Z4cO074vuDz+vP7o2ZLP12FWddNLhVMZsiIMSfsy4axAcPfEm91eZxnH3bslnzywYuvu88VcalodGu8bkcZC1Y30ZKV/PHH4aUTmTtWyDrwefS4zTAMxMtMZ/CTL5pHLs3ZrJtRVrLTlBKQX+knaS9wcEH939JYs84ErrHtn4QughwlR7xLW8fLzr3TSQ3o4DOfRJaH7MJRl86HG+LN+/e9ZlHyTlvdwEfPPAlVzw2TXON0tBoAUKYEP6PurWuqx5COnNadJ505iAr7wNXMViuO86jPPPQEvMpjLePF/94Zgaznvme3RtbInUnOfyf1GF38NGDszAaDfQf1xbP4aOXuXwDLXiZTar2H8d07YjZ15uoTuG8ePVb7N/RwoeElKycu47Xb/mQ6fdPocdZWmWohkZLEcIb4fcIwvscZPVjKDX/Qdo3uyutD0MqdSj1v6CUzUCWXeyuwlZKoOZfyPKZKJX3IK1fIF0HTtKdtB+0PuZTnMSecVz7/OV8/PBXjJ5xFmNnjkCvbyIZCn+3aIA+grw9hXz51H8Jjwul+/AUAsM9bPyXFaA7+lwfPzNqG5P5BvpQV1PPpQ9NZeMfW3nv3s/pOqAzZ00bTGLPuKNauxx2B1uW7GDZf/9EcSnc88FNRCaGNxFdQ0OjKYQQ4D0eTGeBfTmy/r9uLX1dIAhvUKrAuQswgNcY8H8KYeh0SLxISjs4s8D+J7LqAbeRhc8/NKcpD9ES82lAl/6deODz2/nqme/5a+FmRl0yjP7je2G2eB95oCGRkqy/+G32TnasTmfKbRMZcl5/5rw6zzNrRynBuQ8MiUd9ptOrr6zlciqHRFX6je1F10FJrJu3kdnPzqGqtIaY5Cj8QvyQikJJXhkHskro1DOOsVeOpMeIlKZfWDQ0NFqE0PmA90SE90T3nrNShGxYC/X/Bd97EeYLDiXjI84TJjAmu798LgHrl8jKu8D/Ia0ozAO0xHyaEBIZxO1vX0fG+r2s+G4N37/yCyFRQYTHhqI36KitsGL22kTv4UVEJNzD1Dsn4RtoAcA3yJfq0prWX9S5C4Sfu+fxf9DpdTTU29t6W0dQlF1CaEzIob9b/H0Yc9lZjLnsLGoqasndVYC10orQ6QiOCCQ6ORKTd8vtKjU0NFqOEHqkcz/Y5rt91w0tqycRwgy+N0BDT2T1v8D/n27nKY0WoyXm0wghBCmDkkgZlITT4eTA/mKKc8tQXAo+/maiky7GV9yLCEhC6C2HzovtGsXvny1r9fWk7TeEd+N+0ZXFVRhMBqpKqwno4O/pLR1B9s5cYrtGN/qZX5AvqSpYYGpoaLQM6SpAWt9H+D+NMMS3+nzhNRhQkDUvQuCr7oSt0SK04q/TFIPRQHRyFH3P7kH/cb1IHZyMf0gwwvtct2fyYRvA8d1jyd1dgLUVetTSsQscW8Dr7KM+czldpK/dTe/R3Vj/62ZV7gdg/YLN9BihFW5paJxs3Jr7byHM0z1Kyn8jvIYijN3AOku9wZ0BaIm5vWG+AJQKaPj//mcfPzM9R6Ty508bWhRCKla3EYblukaLN7YuT6NDxxAm3ziOFXPWqKJxnbk1i5ryWroP79rmWBoaGm3EuROUWvCe1PZYPv9A2lcglcq2xzpD0BJzO0MIA8LvXrfU3mE6uGdfMYJFX61oVklMKrXImn8hjL0QXsOO+txus/Pz2wsYd9VI4rvFENulI/M/WNSmMTvsDmY/N5cpt01s1FRDQ0PjxOLexpqAEG3/fRQ6X4RpMNiWNH+wBqAl5naJ0HdE+D+JrPvqoNww6BGqAAAgAElEQVRePdFJkQy/YCCznvn+CHeow5GObciq+xCGJLBce/TnUjLn1fnEd4ul+3D3kvMlD13A2nkbWy6C0kjMb174kYi4UPqN7elRDA0NDfWQUoJjG5gGqRfUNAjpbLvc7pmClpjbGVLakPZNYF8Hhq7IhtXI0ikoVU8wcWYILqeLL5/8DpfTnZylUodsWIdS/dTB5esbEJZrjnpTllLyy7u/s29bFhfff/6h7/sH+3HTy1fx1b++Z92vm1o1VrvNzpdPfUdRVglXPjndo5YuDQ0NlVFKAT1CH9LsoS3G0Amc+46ofdH4P/buOz6yuurj+OfcqZlJ2V06LAu7C0iT3kGlF8VHUB7FAhYEVFDB8lhRFBFFVFCwIqCAPorYH+kCggiIVAWUsrAgfWE3deo9zx93FrJLsplk7iSTyff9es1rk0nmzC93kzlzf/f3O2d0da3KNrMNgI3d/WqLltYl3X0C+2+kWby6BAq/xot/hsQ8LLkxltoSUtvg1UVQvJ6gcBzHfS7B4w9184/LfsbG265BtqMfkhth2X0hvdvL2kUCvPDMMn76pUsZ6B3kQ985mnx3boWvb7jF+nzw7Pdy3md+yt3X38uhH34tq6/78i1WL47VnftvfZCfn/4b5m26HseffRTZ3MS7U4lIjMIXIIgxKQMWzK71fq4AqVhjt6MxE7OZHQ0cA8wBFgJzge8BL1+uK1PCC9figz/GMntjPV/HEmus8HUDyB+FexkK17L+Ky4m9XiK752UZ/Z6e7LbIbuyYKt5pOylP5hqpcpj/3qCm377N+645m72euurOOBde45agnPuJuvyqYs/zOU/+hNfPfLbzN9yHlvs9grmbbYeXXM6Cashzz7+PI/e+zi3XXknZsYhxx/ENntt2cQjIyKtQ2fL9bKxphbM7E5gJ+AWd9+2dt897v7KSRjfy+ywww5+2231rS5ud+4OgxfjpVuwro/UXQDAvQiDF1IZ/Ds3X/96/vLbB3ly0TOsvt4cOvJZSsUSzyx+jtXWncP2+23N7ofsOK69yqVCiTuuuYcH73yEx/71HwaWDWJmrLbObOZtNpct99iUjbadr6lrkRbk1efwZR/HZp8X29+oh8/jS0/AZv9Yf/c1O+ywA7fddtuIB6Oeqeyiu5eWH0wzS6K3Pq2h8Ae8fBvWcyoW1J84zTKQfy9Jm8Xu+17BHm86nUo54OlHnqU4VCKZTrLWBmtMeHo5nU2z8+u2Z+fXbT+hx4vIFApWA7xWdz+mKe1aS1ol5frUk5ivN7NPAx1mth/wAeD3zR2WjMUri/GhX2I9p48rKQ9nucPw6qMw+FNS+Xczd5N1Yx6liEw3ZoanXgmlm6HjdfEELd2CpbTrol71rMr+JPAscA9wLPBH4LPNHJSMzQfPw3KHY4nGuilZ/mi8+Ge88lhMIxOR6c6yB+KFy3FvvFGNh71RI4zM3jGMbGYYMzG7e+juP3T3/3b3w2ofayp7Cnnlcag8OmK5zPGyoDtakT2sUpiIzHDJzSHohsL/NRzKB87HsntiI7SPlZGNOpVtZvewimvJ7q55ialSuhHLvGbErU0TktkPX/YxyB2la0AiEr0OdB6PL/sEpLbARmj9Wg8v3giV+2HWN2IeYXtb1TXmg2v/Hlf798Lav28HBps2IhmTVx7AsgfEFs8Sa+KkIXwGGpwaF5H2YIl1IP/+WuvGT4+7r7IX/4IP/Ajr/pw6S43TqInZ3R8FMLPd3X140eRPmtlfgC82e3AyiupjkNgg3pjJDaC6WIlZRF5kmV2BAO89BbKvh45DiDbmjM7DQRj8MV6+I0rKdW7jlJfUsyo7b2Z7uPuNAGa2G5Af4zHSTF4Ei7lSlmXBS/HGFJFpzzI7Q3IBPvA9eOFyyO4P6R0gsT5WK0oUhoNRGeDidVG72NTW0HUyltROj4moJzEfBZxnZsuv3C8F3tO8IcmYLANeiDemFyCua9Yi0lYssQbWfRJeWQSFK/H+s6D6NO5A+Cz4C2Cd0cxbalsghN5PEloOy+wN2f2wYPZU/xjTxpiJ2d3/DmxtZt1ElcKWNX9YskqJ9aNV2Ym144tZeRQS8+KLJyJtx5LzofNYCJfi/d+FygPQcRRkDyBYqRiJu0N1ERSuwJd+GDreAtmDYmkl2e7qqZX9uZU+B8DddY15ilhyY6jcB5l42rJ59WmgBMGascQTkfbllYfx3lOxzGug66Oj7g4xM0gugM73Q/W/8P6zoXwXdH0svh0lbaqety4Dw25V4CBgwyaOScaSeRVevB6P65pw8era9ittlRKR0XllMd57CpZ/L5Y/su4EG/WIPwUsg/edjvvIPeElUk+Bka8Pu50K7Ams1/SRyagssV50LadwdcOxPOzFC1dDJr7tVyLSftwreP+ZWO6ttdXa42OWxDo/HC1eLfyuCSNsHxOZ7M8BE9ttLrGx3FH40M9r09AT5wM/xDKvxpJzYxqZiLSlod9BMAcy+004RJScj8eHfo1Xn4pxcO1lzMRsZveY2d212z+BfwHfav7QZFUsuT7WcRje92U87J1QDB/8ZbR3Off2mEcnIu3EvYwX/oDljmz4kpcl1sIy+0LhsphG137q2S518LCPK8DT7l5p0nhkPLIHY96LL/s0dJ2IJRfW9TD3Igz8BC/fjfV8QQsxRGTVSrdDYl0sGdPOjewB+LKPQ+5IzBLxxGwj9Uxlf8ndH63d/uPuFTO7cOyHSbOZGZZ7e9S+sfcUfOB8vPrMqN/vXsKLN+BLT8S9H+s5DQvmTOKIRWRaqtyHpbaNLZwl1gLrhurjscVsJ/WcMW8x/BOL6rFt35zhyISk9wA68KFLYPBiPJgFwbqQ3ASS62NexasPQfleSGwQrahMbzfVoxaRacIri7COQ+INmlwAlYejhayyglV1l/oU8Gmgw8yWX8Q0oAT8YBLGJmPwsA8Kf8ALV0EwG0tuhKd3gepTUH0EStfD0HN4Yl3IHoD1HIHFWZRERGYG74vaQMbIgh7w/lhjtotVNbE4DTjNzE5z909N4pikDl66Fe//PpbeAes++cVrPysvy3AvQPF6fOhS8AE8904syE3+gEVkGgvAw3hDesjENga1v1WdMW/q7vcDl5jZy+Y93f32po5MRuVDv8YLV2BdH8VSm6/ye82ykD0gmu4ePA/v/Sx0f15Ny0Wkfom1IHwC2Di2kB7+B0vHd926nazqGvNHgGOAr4/wNQf2bsqIZJW8cBVeuArrPhVbqTbtqliQx/PHY4MXRy3cer6s1dgiUhdLLoTKg5B5TSzx3MPa9eX6dpLMNKuayj6m9uFB7iu2MjKzbFNHJSPy6lP44IVY95fHlZSXMzM893boexwGfwH5dzRhlCLSdlI74L1fgNw7x+zHXJfynRCsqY5To6hngv+mOu+TJvOB87COQxuq0mVmWOexePFqvPpEjKMTkXZlyXmQWAdKf2k4lrvjhd9j2YNiGFl7GjUxm9naZrY90arsbc1su9ptT6KynDKJvPo0VO6H7GsbjmXB7KhHauHyGEYmIjOB5d6GD/wk2g3SiNINED4PmVfFM7A2tKo5iQOAdwFzgW8Mu7+PaBuVTKbiDbUOUJl44mX3x5d9EnLvVlcpERmTpTaHzO54/1nQ9ckJTWl7ZXE089f9Wa1xWYVRz5jd/cfuvhfwLnffa9jtv9z9V5M4RgG88m9IbhZbvGg/cwLC0SuFiYisIHckEEStG8OBcT3Uy//Ge7+A5d+FJTdqzvjaxJhvedz9UjN7HVEFsOyw+7/YzIHJSqqLIblhvDGTG0ZxE2vFG1dE2pJZEro+DgPn4ctOgPwxkNoes9GXK3k4AEO/xovXYJ3vx9I7TeKIp6cxE7OZfY/omvJewLnAYcCtTR6XrMyLYB3xxrSOKK6ISJ3MUtB5LJR2wQfPB78AMntBcmNIzMUpQ/gcVJ6ItliV/wqp7bBZ39Aq7DrVc5FgN3ffyszudvcvmNnXAU1lTzZLxZ9EvQSk4o0p0164JNpGF6x20RSPRFqZpbeG1Dfx8r144VIY/AlUnwHKQAosA5aD9LZYasv4TyzaWD2Jeaj276CZrQssAeaP94nM7OPA8sa/SWAzYA13f97MDgf+B/iJu5853tgzQmJ9qD4Gcda6ri6G5PrxxRORmaVyHwx8DyyNdR4HqS0hWOfFqW0Pn4fyv/DitTB4IeTeBpn9Vzn1LfUl5j+Y2Szga8DtRFW/fjjeJ3L3r9ViYGavB0509+drXz4c2BG42Mw63VXZfGWW3AjK90F6x1jiefg8hP0QqKmFiIyPu8PQT/HCn7D8MZDeacTdHRbMgcyuWGbXaEV2/9lQuhk6P44F+SkY+fQw5tsWdz/F3Ze6+6XABsCmwP81+LxvBX427PPl/6POy/swCEB6d7x4Pe6VeOIVrsEyu+udq4iMi7tHdfdLd2Gzvolldq5ry6Ul59V6wK+L934B96ExHzNTjetV2d2L7r4MuGSiT2hmOeBA4NJhd/8KuA24zd0b3L3enqLKO+tC8dqGY3k4gBcuh+yBMYxMRGaU0vV4+W6s+3PYOFtBmiUgf3S0SKx/3BOvM8ZET5caOat9PfCXYdPYy/dMb+vuIzXMwMwGlt8WLVrUwFNPb5Z/Dz54MV5d0ligwQuidpHJcS8VEJEZzMOl+MAFWOcHsaBzQjGissBHQ+VevPT3mEc4fSxatGiF3Db8axNNzF7PN5nZcWZ2Z+22bu3uw1lxGnvsJ3PPL7/Nnz9zk4kl52PZ1+N9p+HhxC7D+9Af8fI/IffOmEcnIm2vcBmW3rXhAiFmHVjunfjQL2Ma2PQzf/78FXLb8K+tqh/z7xk5ARtQV2sjdz8HOGdYzB7gNYDaGk1UxxsxX4r3fg46P1J3Qwv3Mgz9Ai/egHV/AQtU7lxE6uderbWcPTmegOmdomvVlUWavVvJqlZlnzHBr63KocCV7j6+Wm7yoqh143uw4uV476ejphbZg7CgZ8Tvdw+hfAc+eDEEc7CeL0crJWXGCp/aJJbvC9b+dxzDkemiuhgsF613iYFZAlI7QfluUGJewar6MV8f95O5+wXABXHHnWnMDLIHQWpbGPolvvR4PLkpBGvC8sLyXgEfiDpSWSfWcSik91DDChGZmMpDEHeN6+RCvHyntuKsJIaO1zJVLLE2nn0teBmK10WVvCxDdLXBwQsQrI117AepVyopCzD2ma4qf8mIwhewxBrxxkysAcXnx/6+GUaJeZrysB8f+BGU78GyB8Ls87DEipf+3UOoPgyFK/ClH4LcWyFzgPYui8gE1LXmd5x0sjASJeZpyCuL8b4vYakdYfa3sVFq0JoF0dRT50ZQ+a9a1Z07oOtj6oUqIuMTzMLL98WbSqvPQjArzohtYcxTJzO7qlaSc/nns83siuYOS0bj1aeinqa5t2GdR4+alFdmyfWxnlPBMnjfV3GvNnmkItJWkgujGbg4VR/GkgvjjdkG6pnTXN3dly7/xN1fANZs3pBkNO4h3n8m1nEoltlz3I83S2KdJ0TXpAu/jX+AItK+EhtA2ItXn4glnHuIl/4GqVfGEq+d1JOYQzN7cX28mW1Acy42yFgKfwTS0RapCTJLYJ3H4UO/xatPxjc2EWlrZkksszcULosnYPnvYF0NFytpR/Uk5s8AN5rZhWZ2IfBn4FPNHZaszD3EC7/D8kc2vHjLEmth2f1riV5EpE7Z1+LFP+OVxQ2FcS9FpT07DotpYO2lnu5SlwPbAT8HfgFs7+66xjzZyneC9cT37jKzf61bVTmeeCLS9iyxGpZ7O95/5oS7Q7k7DJwPyQVYZueYR9geRk3MZrZp7d/tgHnAE8B/gHm1+2QyVe7D0tvHFs4Sa0AwO6rmIzJMsNpF2sMso8vshyUX4r2n4uH4iji6hzB4IV65H8sf26QBTn+r2i71EeAYYKSOTw7s3ZQRyYi88jCWPSDeoIkFUHk4Wm0pIlIHM8Pz78cGz8eXfQTyH8DSW4/5OK8+hfefA1Sx7i9OuDvVTLCqkpzH1D48yN0Lw79mZtmmjkpeLuwDG7ke9kRZMAvU/lpExsksgPxRkNoWHzgHH1ojWreS3DKqyV+rMujhAFQexIvXQPkOrONNkP0vFTkaQz0FRm4iusY81n3STJYAwpiDVoFEzDFFZMZIvgKyh0DxT3jvlyBcChhuuVp54DSktsAye0D+GJ0l12lVbR/XBtYDOsxsW16qndYNqGfgZAvWgOqTkNostpBefQpLxhdPRGYG9yEY/BlevBZSW2HZg6DzONx6wJ+HymKoPgblu8CXQdADlh87sACrPmM+AHgXMJfoOvPyxNwLfLq5w5KVWXIhVB4grkv77g6VB6PpKBGROnn5Abz/65DcDJt11gptZKMkMefFLlTR68x9+MD3oXgTdH5QveDrMOpEv7v/2N33At7l7nu7+1612xvc/VeTOEYBSG2Pl26Ob3tT5R8QdEWtIkVE6uDle/G+U7HcOwm6Pjxmb3czw1KbYz1fw4JuvPfzeDg4SaOdvuq5Ar/9CLWyv9TEMckILDkXEvOgeGMs8XzoD1j2QLWCFJG6eHUJ3vc1rPMELLPruB5rlob8+6JtVv1nRmfSMqp6EvNBI9TKnnhNSJkwy70VH7wQD3sbiuPFW6LrPxnteBORsbk7PvA9LHsAlt5mQjHMDPLvgfBpKF0f8wjbSz2JOWFmmeWfWNTOKLOK75cmsdSmWOZVeP9ZuFcmFMOrT+ED38c6j2fYf6uIyOgq90H1P9DxpobCmKWx/AfwwYujYiMyonoS80XANWZ2lJm9B7gK+HFzhyWjyh0BJPC+08d9rcYri/Dek7DcW7DU5s0Zn4i0HS9cjmUPwizVcCxLvQJsTtTEQkZUT63s04FTgc2ALYBTavfJFDBLYl0fx4LZ+LIT8NJtY16vcS/ig5fgvSdjuSPjryAmIm3LPYTS3yHzqthiWubVULo1tnjtpp4CI7j7ZUBMvb6kUWYp6Hw/lO7EB88H/wlk94TkJpCYC6Tx6tNQvhXK90d7CdNbYz1fxxKrT/XwRWQ6CZ+EoDOqFBiX5EZ48U9o6enIxkzMZrYL8G2iM+Y0UamoAXfvbvLYZAyW3gZSZ0LlXijehBfPg/K9ED4PhFFRksTqkNwQKg/hyz4JmT0gewCWWGeqhy8i00H1aYj79SKxHlSfijdmG6nnjPls4HDgEmAH4EhAna1bhJnhyc2iYiGlG7CON0B2P0gsWKEerbtD+AQUrqkl6D0h9/ZoG4OIyKiq1Dm5Wj9L1OLKSOqdyn7QzBLuXgXON7ObmjwuqZOHg3jfV4Eq1nM6llh7xO8zs+hdav5I6DgEH/ghLPs4dJ2k6W0RGZ11Qbgs3pjhsqjAkYyonlXZgxadVt1pZqeb2YmAip62APcS3vclLLF21EZtlKS8Mgu6Cbo+imX2xHs/h4dLx36QiMxMyQ2g+tiEt2iOqPIQJObHF6/N1JOYo/05cDwwAKwPNLaZTeIx+L9RK8j8sRNqo2Ydh2LpXfD+76oSj4iMyKwjmm0r/zO+oOW7sBgb8rSberZLPeruQ+7e6+5fcPePuPuDkzE4GZ1XHsSL12Gd72ust2nucAifgpKuTojIyCy7L168IpZYHvbjpb9G61xkRKtq+3gPMOpplLtv1ZQRSV186PfRGW/Q01AcszTkjsAHf45ldo9pdCLSVjJ7wuAlePl+LLVpY7GGfoGld8KC2bEMrR2tavHXwZM2ChkXD/ujDf/5o+MJmNoO/Fy8sghL6rqPiKzIrAPyR+P934ZZZ0SfT4CX/4EX/4LNOjPmEbaXUROzuz86mQORcaj8G5ILsKAzlnBmAaS2jvZAKzGLyAgssyuU78J7T4Xuz4w7OXv5Abzv61F3Kq3IXqUxL06a2S5m9jcz6zezkplVzayx9kbSmMoiLLkg3pjJBVB9ON6YItJe8sdgifXwpZ/Ay/+u6yHuIT70B7zvFKzzA1h66yYPcvpTgZHpyHsh7uszwWw87FOJPBEZlVlAmDsWipfhvSfhyY0he3B0zXilRajuhagiYeGPYGms5ytYYt0pGvn0ogIj01ICiLllmldBaVlERuDuUL4bL14J5XuAJATrRBUHl52AewVPzIXUZmCzovra1acgtQWWOxxS2zW2e2SGqScxr1BgBHgSFRiZWsHqUH0k3pjhU1hizXhjisi055XFeP/ZQBHLHgT5o7Bgzktfd8cri6DwByjdCMFqkH83ltpGJX8nqN4CIwEqMNI6kgvxSrxbyb3yICQXxhpTRKY3L1wb9XDP7oP1nIllD1whKUNU7jdILSDo+hA256dYZh/oPyfegiQzzJhnzMNWZxeALzR3OFKX5AIIl+KVx7Dk+g2H83AAyndD/qgYBici7cAL1+KDF2Pdp2LJuXU9xiwJucMgtSXe9xXoPFGLvSZg1DNmM3uDmR037PNbzOzh2u2wyRmejMQshWX3gcL/xROweA2ktn7ZO2ERmZm88hg+eD7W/fm6k/JwltoU6/o43n+mavFPwKqmsv8H+N2wzzPAjsCewPubOCapR/ZgvHQLXv5XQ2G8+iw+dCnW8eaYBiYi05m74wPnYLm3NTQjZ6ktsMxe+MC5MY5uZlhVYk67+2PDPr/R3Ze4+2K0+GvKWdCD5d/b0DtS9xLe/00s+3osOS/mEYrItFT5J4SDkNm/8Vi5w6F8D159qvFYM8iqEvMKG2Xd/fhhn67RnOHIeFhmdyzzGnzZ5/Dq0+N6rIf9UQWfYE3oeGOTRigi040XrsSyB8SyvcksjWX2hsKVMYxs5ljVkb/FzF5WjNnMjgVubd6QZDwsdziW3R9f9j944fIxe6a6h3jxr/jSD2PJDbHOD2l/oYi8pPxPSO8QX7z0Dnjl3vjizQCrWpV9IvAbM3sbcHvtvu2JrjUf0uyBSf2s42BIbRVdyxm8BLJ7Q3JzSG4Ilgcv4uV7ofB7KN0MXoBgDl64Csp34okFWHp7SO+MWWqqfxwRmSIevgCUo5m0uCQXQOUR3EOdBNRpVU0sngF2M7O9gS1qd/+fu/9pUkYm42LJeVjPF/HKYihehw/9CqqPRteKwueBAiQ3gdxRkNkTS8wGD6NezJUH8MKVMPAjyL0FMvvrD0hkJgqXQTAbs/iqAJp14CTAB8HiabzT7urZx/wnQMl4mrDkPEgeiQFevg/v/waW3Rdyb395RxcDggVRp6rsAXjlEbz/u1C8Cbo+2nCvZxGZbppVltepr56VgI5U2/LyPXjfV7H8+7DO99XVZs2SG2I9p2HJjfFlJ+HhskkYqYi0jGA1qD6Le3y1+D3sBQwsG1vMdqfE3Ia8+lzU97TrY9G143EwC7D8EVh6R7zvjFj/QEWktVnQCUEPVP8TX9DKQ5BcqMtj46Aj1Wai4gDfxbKvxVJbTjxQ7u1AGQqXxzY2EWl9ltoGSjE2ECz9JYopdVNibjeV+6J2aw3uTY7OnD+AD12CeymmwYlIy8segBeuiuXv3sNevHRLtFNE6qbE3Ga8cDmWPSgqJt8gS86DxAZQuiWGkYnIdGDJDSG1CQz+ouFYPvAjLLMXFsxqfGAziBJzG4mamd8BmT1ii2mZPfDS7WN/o4i0DcsfgxevwUt3TTiGF66FygO1y2IyHkrM7SR8Ciwb77vT5EKIufeziLQ2C2ZhXR/D+78xoTfmXvgTPngh1vVJzDJNGGF7a3y+U1pH9RkI1ok3ZmIdCJ+JN6aItCR3h/DpaCV1+Dykd8F7v4Snd4X8+wkSqy4Q4uFSfOBHUHkY6z5ZzXEmSIm5rTRjE39Qiysi7crDAShegxcuBy9G25sSa2GWwdM7Rj3bh35BmN4JOt6MpbfFgu7aY5+HykN48SYo34Zl9oFZH8QsPcU/1fSlxNxOgi7wmJuSh8vAxi5OIiLTk5dux/u/A6nNsM4PQfIVK5TkjD76FGHpbhj4PvR+Frcu3DqjL1o2SuSprSH/nrqKGcmqKTG3k8T6UH0K91J871YrD0VF6EWk7fjgL/HilVjXh7DUVqv83iC9FaTPqZXu/Va0YyN/LGaZWGtrixZ/tRWzdJREyxNfSfky5Tuw5GbxxRORluBDf8CL12M9XxkzKQ8Xle79MoRLsMGfKCk3gRJzm7HMftF1ohh42I+XboLsXrHEE5HW4JVH8aFLsO6TsGDOuB9vlsW6P4WX78BLf2vCCGc2JeZ2k9kdqo/hpTsbjzX0v1h6ZyyY3XgsEWkJUdne72G5d2CJifddNuvAOo/H+7+v6oAxU2JuM2bpqJTmwHdqXV0mxkt34cW/Qu5d8Q1ORKZe5UEIX4DMPg2HstQWkJwbb21tUWJuR5beBsu8Bu/94oSSs5fvxfu/iXWdGHWbEZH2Ubway+wfW7cnyxyIF/4USyyJaFV2u+p4Gwb4so9B/jgsvfWYD3Gv4gM/hKHfQmoLfPBiHAebjSUXQOqVL9tKISLTi1cewPL7xhcwtSX0fwv3UK0dY6LE3KbMLKpRm9wCHzgHL6yPZQ+C1FYv20rlYR8+cAEMXQoYdByCpbeBYM3o8/A5qDyM938bLAUdh0F6dyVokWnGvRr1Wk5uEFtMCzrxoDsqCZxYN7a4M5kSc5uz9DaQOhuKN+JDl0LfGXhiXQhmgYfRH2nl32Bp6Hw/ZA8jCBIvD5TZA3JHQPlufPA8KN4InR94sfqPiEwDXgSC+KtyWSf4YLwxZzAl5hnALA3ZvbHs3tHqycqj4H24D8HAT6HjMKzzaMyyY8QxSG8Nqa/B4EX4ss9CzxfV0k1kurAkUG3CtHMZpZP46ILADGOWxlIbQ2obKF4VLRTrPH7MpPyyGPn3YJnd8N5Tca80ccQiEhezNAQ9UaOKmLiXofo0JNaOLeZMp8Q8UxWvhHAI8kdN/Fpxx1sg6ISh38Q7NhFpnsRG0eWruFQfhcRa43pzL6umxDwDuRfxwZ9incc1NJ1lZtGe6cJv8bAvxhGKSLNYZme8eG18AQvXYuld44snSstGBagAACAASURBVMwzUvEGSG4aS69US6wBqVpbOBFpfendoPIoXlnUcCgPl+KlGyCzXwwDk+WUmGcgL/0Vy8RX/9qye0W9WEWk5Zmlsdw78P6zG1ofEpX2/AGW2RdLrBbjCEWJeYZx96gkX3KT+IImN4LqYi0CE2lx7iU8HMDTr4JgDgz8MHpNmIjC76PtlrnD4x2kaH37jOMD4JXojzImZh14MAvCZyGxTmxxRaQx7hUo3RJ1ias8BOHzRC/7FQjWxEu3QvUJvOvTBEFHnTGrMHQJXrwO6/5S/HuiRYl55imDpZtQtSsZJXwRmXLuDqW/4APnQ2IdLLNPVAkwWBuzIErY1cfw0h0w8EN4bn/CzhOwjkNHXRAazbbdjw+cB5bHer48oZaRMjYl5pnGOsCH4i8w4ANgufjiiciEuJei8rmVR7Guj2OpTV/2PWZJSM7HkvPxjkPxwfOh/xv44AV49k1YaiMI1gIzqC6B6kN46TbwAtbxRsjso5K8TaTEPMOYZfFgDlQfhxhWZQN4+DxQjXV6XETGz72E930ZrAubdUZd08zRtsf34B1vxJd9Esp34tWnwJcADsEsLLkAy70TUq9Uo4pJoMQ8EyVfAeV/xJaYo1ib6B20yFQbvBDIYJ0njjuBWjALes7Aez+DZXfBMns2ZYgyNr31mYEssydevGLiqzFX4oUrsczescQSkYnx8r148aZaid2JvbRb0Il1fggfuKA2EyZTQYl5JkptFf1banzvsZfuilZjp3dqOJaITJwPXYLl3o4FXQ3FseRCLLMHDP0xppHJeCkxz0AvltIcOBcPl044jocD+MA5WP594AW8+gRefRL3QoyjFZGxePVJqCyK2rPGIXsQXrwmalAhk07XmGcoS70CsgfivadA98njfpftPhQtFPEqPvADCJdGPZ4BwhfwxFpYamvIHoipebpIc5XvxtI7xLan2BLr4cHsqEFFcqNYYkr9lJhnso43Y16MEmznh6JkXYew+Dfo/TSQgNx7scxOkJj74nWtqA3cYij+FV/2GTz1Siz/XizobuIPIzKDVR6C5MJYQ1pyQa1KoBLzZFNinsHMDPJHQnIh3vcVSO8A2YOiP8gReGVxdHZcuBw6DoXOTxAEL/8VMktFLxLJhZD7bxi8GF96AnT9z4h7KkWkMR4uwYKd4w0arAXhknhjSl2UmAXL7A6pV0LhSrz3y7glIbkQC9YEDA+fhcrDUH0SfAhm/5AgvW19sS0D+fdAapso+Xd9qu4zcxGplwNxb1c0iGnnhoyPFn8JABZ0Y7nDsNk/wLo+g6V3hKAbgk4stR3k3x2V4Zv9nbqT8grx09thncfhfV/Dw/4m/AQiM5j11Opgxyhc8tK6EZlUOmOWFZgFkFw/utW4O/SehOXejCXnTzx2ekdI3w6DF0Dn8TGMVkQg2uJE5SFg39hieuUhLPOq2OJJ/XTGLGOr3AfhC5B9XeOxckfgpVvxqq5dicQmtQVe/jvuYSzhvLoEwqdiX1Am9VFiljF54Uose2AsNXItyGHpPaB4VQwjExEgmsmy2VC+LZ6Axauw9B7RGhGZdJrKlrGV74HcW+OLl945qlIUX0SRGcPDpdFizPAZ8DBaC5JcAB2H4gM/htQ2De1n9urTeOGPWM9XYhy1jMekJWYz6wEuAubVnvcMdz+/9rUTgSOAr7r7zydrTDK2qF5uGYI14wuaXAiVh+NvPSnSptxLULwBL1wG4dOQWIgl1gICvLw0al5R64fu/d+Bzg9PqKlM1DLyW1FfZhUGmjKTecZ8HHCvu7/ezNYA/mVmFwNpYEdgJ+BSQIm5lYRLIVg91s5RFnTiOFAEOmKLK9KOvPzvqL9yYg0s97baGfFLb2iX/2V6ZTE+9DsYuhivPot3n0IQ1P/GN2oZeQZYN2TfEPNPIeMxmacrDnRZ9ArfCTwPVBj2ezWJY5G6Gc35r2nGvkuR9uKFa/C+07Dc4QTdn4u2HY4yy2TJeQRdx8Psc6F0Mzz/JsLKk/U9T+VBfNnHwXJY10c0kzXFJvOM+Wzgd8ATQBfwFo+WEPaZ2T3AbcDXJnE8Uo9gNoRLcPfYzpo97AUSRJMlIjISL96ID/4M6zl1XNPKQWoLwtX/AEvfB8+/jbDjjVh2H0hutMK1Zw8HoHIvXrgKKg9g+fdAeg/1VW8Bk5mYDwDuBPYGFgJXmdkN7t7r7qcBp03iWKROFszCSdeua60dT9BaXV+9KxcZmVefwwd+iHWfPKFrvUHQgc/+Ab70RPB+fOBcqD6BB6uDpaMKfuFSSC7AMntB10cwyzbhJ5GJaGpiNrPjgKNrn74AfM7dHXjQzBYBmwK3jhHDgBdLRc2ZM6dJo5VRpbaKpsY6DoknXulmbHlPaBF5GR84F8se3FhBH8tA10fw3lOxWWeDpaD6FFAG64BgLcwS8Q1axmXRokWY2cCwuzpr+bG515jd/Rx338bdtwHuB/YBMLO1gFcAD9cRw909v/w2f/7Ef1FlYix7IF64Avdqw7E87MdLN0F2Hzxcipf+jg/9AR/6LV64OrrW5aUYRi0yPXn1qaioT0fjC7AsuRGktoDi9ZilseQ8LLkQS6yrpDzF5s+fz/Dctjwpw+ROZZ8CXFC7nmzAJ9z9uUl8fpmo5MaQWAuGfgO5NzUUygfOg8R6eN/pUH28tu1jPbAEHj4Chd9DdUnU8D37Wiw5L56fQWS6KF6LZV4TX2/l7AH4wPlYx2tjiSfNN2mJ2d2fAPafrOeT+ETtIT8QrdpMvRJLbTKhOOHgr2HoEkjvhuXeCKntV3jX/uLy/PD5Wqerz0F2P+j479hepERanZf/hXUcHF/A5KbR9WUv6DryNKHVN1IXS6yJdX4Q7zsNL98/7seHAxdA35chfyzW8xUsvdOoU2kWzMFyh2OzvoFXHsV7T45WkIrMBNVFUSWvmJilIDEXKotjiynNpcQsdbP0DrXk/BV88Gd1XQv2cIBw2eeh/7vQczpB/h11b8ewYA7W9UkssQHedyru5UZ/BJHW5wNgnfHGDDqjuDItKDHLuFh6u9qZ7CP4C+/DB3+Klx9YIUm7D+Hle/GBc/EXjoHSX2HWWQTZvcb/fBZA/miwHAxdGuePItKikkDjCy1X4BXQYq9pQ00sZNwsmIN1fwqvLIbiVfjAd6NrWFYrr+lDkJiHpbeB9E7RatDMLhN/Pgsg/3582Uch82rV8JX2llgXKo9DauNYwrl7tNAyWC+WeNJ8SswyYZacB8mjoqKdXgLvBwysC7NkdF146ftg1lmNP1diNcjsC4XLIf+ehuOJtCpLLoDKv2NLzITPAAaBakBMF0rMEguzNNhKf/ilm6JV3HG9IGT3x5d9DHLvUtUwmZa8ugSK1+OVB6D6CHghKvyRWC/ac5zeDdK74IM/xzpeF8+TFq+rLbZUqc3pQolZmqfy71grfFliTdzyUP0PJNePLa5Is3n1OXzwfCjfg6V3wzK7Q+LtEOTAS1B9DMr/wntPhWANqD6Fl+/FUps39rw+hBeuxLo/G9NPIpNBiVmaxiuPYJl94g2aXBCdaSgxyzThxRtqJTZfC53HYzZCq9PE2pDeEXJvheKf8f5v4cs+hc/5OUHQwN7jgYsgtVVDpT1l8mk+UJrHB2Pf9mHWGcUVmQa8cDk+eGHUjCL3lpGT8jBmCSy7FzbnPCCEF95LGFYm9tzFG/DyrVj+qAk9XqaOErM0jyWJWm7HJ9qWpYkeaX1eugsfvATr/uK4z1gtmA1zLooWbi39AO5D9T+vO164LCrD2XUSFsS8J1qaTolZmidYN9qmEafq49F2kpW4V4nae4tMPfchfOA7WOdx2ATbpQZBD8z+EVQfwl84Fi/dOubvuFcW431fxAvXYD1fUq35aUqnHtI0llwYbfvI7BFLPF++SCY5H68+AYWr8cr9UHkEKII7nlgz6vWc3jGqya0a2zIVCpdDchMsvV1DYYLk+njnh/Hi9fjgJeDnRSu3kxtBYh0giPoqVx7Cy7dD9cmoznb29Zjp5X260v+cNE96Z7z385A7IqrX26jijZBciPd9AyoPYJm9sI63RIk46IzOJqpPQPVBvHg9DFwAucMhs7+2V8mkcQ+jldCdH4knYGZfGLoEm3VW1Hmt/He8eF1tf7JHdQOSC7DswZDeQQm5Deh/UJrGkuvjiblQ/DNkG1ud7V7FB84H78dyb4Ouj73sbNgsgORcSM7FMntG03r950DxZuj6KBZ0NTQGeUm9e2KHtZidOaqPAxad1cbAghye2hZKd2DZfSC1MdqR3N50GiFNZfkj8cGL8PCFhuJ431ehuhh6vo7l6msDacl5WM9pWHI+3nsSHvY3NAaRulQehORGsRb0sOTGUHkotnjS2pSYpaksuRGW3R/vOwP34oRihEOXwdCvYdaZBKnxtcMzCyB3JJZ6Jd5/1sw8g5PJFT4dfz33xDp4+HS8MaVlKTFL83W8BYI18d4vRCUJxyEsXAd9p0D+gwTpbSf09GYGuSMhXALF6yYUQ6R+IRB3J6ckeMwdp6Rl6RqzNJ1ZAJ0fhKFLog5RubdCZq9VTkd79Ql88GIo3Q6p7bH8EQ2OIQX5Y/D+MyHzGi0Gk3Fx92iHQfkOvPIwhM8RLbzqiZpOpDaH1HbR75XlozeBsQ5gWdRTWWYEJWaZFNGU8lsgvWOUcAd/CuldILkJJNaLipGEfVB5GC/fA9VHsewBeOJ5rOP18VyvS74iqkRWvgPS2zceT2YEL/0NH/xf8MFaneu9ILEWYBC+EG1VGrwE/AeQfQMebAClW+NdoFV5GEuM7zKOTF9KzDKpLLkA6z4Jrz4JpduiJFy4AqiCdWLJDbHsflHdYKow9JvaxzE8txlkdofynTMyMU9Fd6GJPGerrANwH8L7fwCV+6OylsvPiFcwH9LbYbn/xisP1nYOlKDyKB72x1J1y93x8p1Y/tiGY8n0oMQsU8IS60DH61d5VuHleyGxfjx7oJdLLsQH/6btJrJK7kN47xchWAeb9U3Mxm4kYcmNoPsUKPwWL9+BD/0Kyx/Z+GAq90XXl5ObNR5LpgUlZmld4XNR1504BWtD+OyIX3IvRFWUll87DHLxPrdMC+6O930TS8yD/PvGddZvFkDHoXjYC/3fIcweSJBYs4GxhFETjLgu58i0oMQsLcwh9nNbg2FTpV55DIpX4uW7ofo0BLOi7wmX4sEcLLUlZPePyotOE9P9BXzKi5cUr4veFHZ9fMLHMsi/k7B4LSz9CD7nwon/nxR+CyQgs9/EHi/TkhKztC7rgfD5eGOGL0DQg4dL8YFzofxPLLsv1vlhSMx7sZxhVN7zcSjdivd9BU+sj+XfjyXWiHc80lLcy/jgRVj3pxq/hNLzdVjyX9FOgM4Pj3sngBeuxYf+D+s5VbsIZhglZmldyQVQWYR7GN8LU/UhsE586YnR6trZHxpx21ZU3nNedOs4BIZ+gy/7WNToPqbFaNKCSrdCYt3oenGDgsRqhLl3RjMyff2Q/wCWWG3Mx7kXYOBCvPw3rPvzWGKthsci04vehknLsqAbEqtHi19i4oWroHwH1nkClj+yvtKelsRyh0Wryfu/g5f+Ftt4pLV46WYss2ds8Sx7IASrYcmN8WUn4gPnR3v0R5iG93ApPvRrfOmHcB/Eer6BJdePbSwyfeiMWVqaZfbDC5djqS0ajhWWH4LiDTDrbCy99fjHktwIuj8TrdbtOQNrYFGPtKjKg9Dx3/HFS8yN9udnD45maApX4Ms+A4An50fFSLwYtTMNl2GZXbGu/4nljF2mLyVmaW2ZvWDo13j5Xiy1+YTDuDss+ySkdyHI7DzhOJbcKCoiMfAdrPvkCceR1uMeRq0UE3Nji2kW4Il1oz7JqY0hfyTkjoh2BlQfBS8AyajITmKuriULoMQsLc6CfK2U5tnQc/qECzb40KUQPgZzzmt8UB2HwNKr8MqDLXFmM91XYU/UaD/3xFdrVwCLPzlaqha79qkZJNaMbiIj0NszaXmW2RlL74D3fnFCrRu9+BcYOBc63kwQQ09mswSW3R8Kl6/6eb2Mh/1RsYoWqWYlq5ICErgPxRs27I2mrEXqpDNmmR5y78YGL4yaYOQ/UNc1YvchGLgIL90CiYVY9oD4xpPeGe89ZYVd1lGjgweheDVe+RdUnyR6sa+AdeDJhVh6J8i8uq5KUjK5zAxPbAiVhyGGNQ1Q+x0Ml0DcbSClrSkxy7RgZtH1udRW+MA5+NC6UaJNbb1ChS53h/BJKF4frcBObR3tJ116bLT9Ki7BOhD24WEfFnThlcX4wHchfAHL7h/V+07MwyxdG9MSqDyAF6+FwYug482Qfa2uKbYYS70CyvfElpgp/wOSC17cHy9SD/22yLRi6W0gdTaU/ooX/gj9Z+HBahB0g1eis9SgA0vtiHWfjCXn4WEfbmnMOuIbhwXR84Yv4KUb8cH/xXJvg8x+L0u20TXF1SGxOpbZFa/+B+8/B0o3Q9cnsDqn12fqteTxWvk4jesyQmZvvPcU6DgslmTqhSuxzD4Nx5GZRYlZph2zdNRTOfMa3KtQ/Q94P5CAxNpY0LPyI4jKe8bN8eI1UPob1vNVrM663pZYD7q/BIMX4b0nQfeXYulCJI2z5IZ4Ym60fqDj4IZiefneaFq862MxjU5mCs2jybRmlsCS87DU5ljqFSMkZcBy4BU8HIjteaOSnYuhcD3W/cW6k/KLQ7IAckdgqS3x/m9pcVgLsc5j8aFf4NUnJhwjahl5NpY/GrNMjKOTmUCJWdpeVF5zQ6guii2mVx+G8Fms64NYYvUJjssgdySET0Ppz7GNTRpjiXWx3JF47yl49ZlxP969gPd+BUu9Esvs0oQRSrtTYpYZwVJbRdd04zJwUbS4K719Q2HM0lj+GHzwEp01txDL7otlX4cv+9S4SrB65RF82aejN2v5Y5s4QmlnSswyM2T2w4t/xsPBhkO5l6F4FeTeGcPAgOTmYAko3x1PPImFdRyMdZ2ID5xH2HsaXr571DdPXnkU7/8e3nsyln0d5I/XinuZMC3+khnBEqtDemcYvBA6GzuT8cGLgETUoCCOsZlBelco3wUr7c/WSuzGNVIhzFJbwqxvQvE6fOBHUZ/u5EIsWAsIcH8BKg+BV7Dsftisb2LB7Jh/AplplJhl5si9E196ApS2m3DrRi/fB0O/h/Ru8SbN5MKo9258ESUmZlnIHgiZA6L+4NWHofos4FiwGeTeAcHaOkOW2Cgxy4xhQSd0fQLvOxU6jxt3cvbyvXjf6ZDdHyOMd3DBWtGLvrSsaD/6atFNpIn0Fk9mFEttjHV9Bu//Pt7/g7rqIruX8MGL8b6vYZ0nYsn50JRz25iTvYhMSzpjlhnHUhvDrDNh8Hz8hWMhs2d0jTc5/8Ua1u4lqDwK5VvxwtWQ2gyb9Q0smI2Xinj4XLypOXwegllxRhSRaUqJWWYkCzqh84NQfRoKV+ED50H1Mdw6AAMfhMS6WGpLrPsULDmsR29iAVQext3ju85ceQiLs5a3iExbSswyo1liLci/A+Md0TYo7wccrDMq/TmSYDWwLFQegNQmsYzDy7dhHW+MJZaITG+6xixSY5bCgtlYMGf0pEy0CMiy++OFy2J5Xq88DOFzkNoulngiMr0pMYtMRGY/KN+Fl+9vKIx7iA+ci2UPwSwR0+BEZDpTYhaZAAs6sfx78f5v42H/xAMN/Tr6N3tQPAMTkWlPiVlkgiyzG5beHu/9Ah72jfvxPvRHvHgF1nkiQZCIpshXuknz6DhLq1JiFmlE7t1Yait86Yl46fa6HuLhMsK+M/DCZdGK78QaTR6kiEwnWpUt0gAzg/wRkN4a7/8OPrQalj0AUlthw/Ylv7gvungdXroBy+wNsz60ykVmIjIzKTGLxMBSW8Gss6H0N7x4NQz8ECcNQRd4JVp1nVgLS++M9XwTU1lHERmFErNITMySkNkVy+wadS4KnwUfABJRUrbMVA9RRKYBJWaRJogaHqw51cMQkWlIi79ERERaiBKziIhIC1FiFhERaSFKzCIiIi1EiVlERKSFKDGLiIi0ECVmERGRFqLELCIi0kKUmEVERFqIErOIiEgLUWIWERFpIUrMIiIiLUSJWUREpIUoMYuIiLQQJWYREZEWosQsIiLSQpSYRUREWogSs4iISAtRYhYREWkhSswiIiItRIlZRESkhSgxi4iItBAlZhERkRaSnOoBiAi4OwBmNsUjmTmWH3ORVqMzZhERkRaixCwiItJCNJUt0kJWnl7V1LbIzKMzZhERkRaixCwiItJClJhFRERaiBKziIhIC1FiFhERaSFKzCIiIi1EiVlERKSFTFpiNrPZZvZrM7vbzG41sy2Hfe1wM7vdzE6YrPGIiIi0osk8Y/40cKe7bwUcCZw17GuHAzsCu5hZ5ySOSUREpKVMZmLeHLgGwN3vBzY0s7VqX1te3siHfSwy47n7y24yPiMdQx1HaWWTmZjvAt4IYGY7ARsAc2tf+xVwG3Cbu/dN4phERERaymTWyv4KcJaZ3QncA9wBVADc/cfAjydxLCIiIi2pqYnZzI4Djq59+lp3f3ftfgMW1W5jxTBggGiamzlz5jRnsCIiIpNk0aJFmNlA7VMD8l67xtLUqWx3P8fdt3H3bYBBM0vXvvRe4M/u3ltHDHf3nLvn3T0/f/78Zg5ZRESk6ebPn8/yvFbLcS8ufJjMqezNgJ+YWRW4FzhqEp9bRERkWpi0xOzufwU2nqznE2lX6tm8alpxLdOdKn+JiIi0ECVmERGRFqLELCIi0kImc/GXiDTBaNdU2/3as64lS7vSGbOIiEgLUWIWERFpIUrMIiIiLUSJWUREpIUoMYuIiLQQrcoWaVP1rlputdXbWm0tM53OmEVERFqIErOIiEgL0VS2yAynqWOR1qIzZhERkRaixCwiItJClJhFRERaiBKziIhIC1FiFhERaSFKzCIiIi1EiVlERKSFKDGLiIi0ECVmERGRFqLELCIi0kKUmEVERFqITbc6uauvvrpvuOGGLFq0iPnz50/1cGYsHf+po2M/dXTsp1Y7Hf9Fixb5kiVLRjw5nnaJeTkzG3D3/FSPY6bS8Z86OvZTR8d+as2U46+pbBERkRaixCwiItJCpu1UtoiISDvSGbOIiEgLUWIWERFpIS2VmM3sPDN7xsz+Mey+bczsZjO708xuM7Odhn1tz9r9/zSz64fdf7iZ3W5mJ0z2zzCdjef41479str9d5rZ54Y9Rsd/nMb7u1/7+o5mVjWzw4bdd2Lt2L9lMsc/3Y3zd/8NZnb3sPv3GPYYHf9xGuexf3vt2N9tZjeZ2dbDHtM+rzvu3jI34NXAdsA/ht13JXBQ7ePXAtfVPp4F3AvMq32+5rDH/AZIAP8LdE71zzVdbuM8/nsCfxgljo5/E4997fME8Cfgj8Bhtfs6gZ8CSeC3U/0zTafbOH/3O3lpfc5WwP06/pN27HcDZtc+Pgi4Zdhj2uZ1p6XOmN39z8DzK98NdNc+7gGeqH38NuBX7r649thnhj3Ghj3WkLqM8/ivio7/OE3g2H8QuBQY7fdexmE8x9/d+72WCYA8Lx1vHf8JGOexv8ndX6jdfzMwd9hj2uZ1JznVA6jDCcAVZnYG0dT7brX7NwFSZnYd0AWc5e4/qX3tV8BtwEXu3jfJ4203ox1/gF3N7C6iP5qPufs/a/fr+MdjxGNvZusBhwJ7Azsu/2Z37zOze4iO/dcmf7htZ9TffTM7FDgNWBN4Hej4x2xVrzvLHQVcNuzz9nndmepT9hGmNTZkxSmNbwFvqn38ZuDq2sdnE71jygOrAw8Am0z1+Kf7bRzHv5vadBHRVNMDUz326X4bx7G/BNil9vEF1KaydZuc47/SY1490v26NffYA3sB9wGrTfXYm3FruX3MZrYh0bXLLWufLwNmububmQHL3L3bzD4JZN395Nr3/Qi43N0vmZqRt4d6j/8Ij3sE2MHdn5vE4baVcfzuL+KlqbrVgUHgGHf/zRQMu2008Lu/CNhRv/sTN55jb2ZbAb8mugb97ykaclO11DXmUTwBvKb28d5EZ8YAvwVeZWZJM8sBOxO9g5J4jXj8zWzt2h8MtRWTAbBkSkbYvkY89u4+3903dPcNgV8CH1BSborRfvc3Gva7vx2QRr/7cRvt2M8jmrI+ol2TMrTYNWYz+xnRat/Vzexx4PPA0cBZZpYECsAxAO5+n5ldDtwNhMC57v6P/2/v/kKkKsM4jn9/G9IWWJQtUZBsERRGrNWaSUJLpuSdlLFSWGt0kRjBQlRXlXcSgUWmRtKfiwL7YxFeiJVoVlqpuS5JdNEqBEvZXX8sW3u6eN9Zz06zuzPLTpx1fx8Y5sw773nPO4dlnznPGd6n5sBWl0bOP7AcWC1pCDgJrIiypV+mkAbPvU2yBs//3cD9kv4m/e13+29/4ho8908Bs4CN+bvRUER0/u+TbrLSpbLNzMyms6mQyjYzM5s2HJjNzMxKxIHZzMysRByYzczMSsSB2czMrEQcmM3MzErEgdmsiSRdKuktST9IOihpX15neax92osl8Bo8Xo+kywuvt0iaU+e+XZK2T+S49ZL0RX5ul3TvBPbvkbRh8mdmVh4OzGZNkleH+gD4NCKuioibgBWMrIgz2XqA4cAcEQ9FxNEmHq8hEVEpRtBOqhBnZlUcmM2a53bgVERsrjRExPGIeBGGrxr35uLuhyT9p4LOWH0kPS6pX1KfpHWSlgOdwJu5wPx5knZL6sz978xj9En6pN4PIWmRpG/ysV6VdG5uPyZpbR6zX9K1ub1N0ke5/WVJxyVdkt/7LQ+7jrSk7mFJvdVXwpK2S+rK26skfS9pD3BroU+bpPckfZ0fw++ZTWUOzGbNcx1waIz3fwYWR8SNQDepok5dfSQtBZYB8yOiA3g2It4llb27LyLmRsTJyiCS2oBXSBV7OoB76vkAklpJFay6I+J60jK+qwtdfslz2wQ8eA5zUAAAAkJJREFUltueBnbl9veB2TWGfhLYm+e5fozjXwasJQXkxUAxLf8CsD4i5pGWydxSz2cyK7tSrZVtdjaT9BKwkHQVPQ+YAWyQNBc4TaoxXm20PncAr0XEHwARUV1ovtotpJT6QJ39K64BBgoFA94A1gDP59fb8vNB4K68vZBUL5qI2CGpUth+IuYDuyPiBICkrYw8B3PymskAF0iaGVO9Fq9New7MZs3zLelKDoCIWJNTugdyUy/wE9BByl79WWOM0foIaGSh+0b7F/cby1/5+TRn/p+Mt08tQ4zM4LUWtkebdwuwoJgZMDsbOJVt1jy7gFZJxdTv+YXtC4HBiPgHWAmcU2OM0frsBB7MJU+RdHFu/xWYWWOcfcBtkq6s6j+e74B2SVfn1yuBPePs8xmpuD2SlgAX1ehTPc9jwFxJLZKuAG7O7V8CXZJmSZrByBT8TuCRyoucVTCb8hyYzZoklwJcRgqIA5K+IqWCn8hdNgIPSNpPSs/+XmOYmn0iYgfwIXBA0mHO3N99Hdhc+fFXYS4nSKXztknqA7aOMu1Fkn6sPIAbgFXAO5L6SSVWN4+yb8VaYImkQ8BSYJAUiIuOAEP5h2i9wOfAANAPPEe+Nx8Rg8AzpC8WHzPynv2jQKekI5KOAg+PMy+zKcFlH81sUuVfbZ+OiCFJC4BNEeGrWbM6+R6zmU222cDbklqAU6Si92ZWJ18xm5mZlYjvMZuZmZWIA7OZmVmJODCbmZmViAOzmZlZiTgwm5mZlYgDs5mZWYn8C5r/iUf3JCNpAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 8))\n", "background_estimator.plot(add_legend=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Source statistic\n", "\n", "Next we're going to look at the overall source statistics in our signal region. For more info about what debug plots you can create check out the [ObservationSummary](..\/api/gammapy.data.ObservationSummary.rst#gammapy.data.ObservationSummary) class." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "*** Observation summary report ***\n", "Observation Id: 23526\n", "Livetime: 0.437 h\n", "On events: 201\n", "Off events: 225\n", "Alpha: 0.083\n", "Bkg events in On region: 18.75\n", "Excess: 182.25\n", "Excess / Background: 9.72\n", "Gamma rate: 6.95 1 / min\n", "Bkg rate: 0.72 1 / min\n", "Sigma: 21.86\n", "\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "stats = []\n", "for obs, bkg in zip(observations, background_estimator.result):\n", " stats.append(ObservationStats.from_observation(obs, bkg))\n", "\n", "print(stats[1])\n", "\n", "obs_summary = ObservationSummary(stats)\n", "fig = plt.figure(figsize=(10, 6))\n", "ax1 = fig.add_subplot(121)\n", "\n", "obs_summary.plot_excess_vs_livetime(ax=ax1)\n", "ax2 = fig.add_subplot(122)\n", "obs_summary.plot_significance_vs_livetime(ax=ax2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extract spectrum\n", "\n", "Now, we're going to extract a spectrum using the [SpectrumExtraction](..\/api/gammapy.spectrum.SpectrumExtraction.rst) class. We provide the reconstructed energy binning we want to use. It is expected to be a Quantity with unit energy, i.e. an array with an energy unit. We use a utility function to create it. We also provide the true energy binning to use." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "e_reco = EnergyBounds.equal_log_spacing(0.1, 40, 40, unit=\"TeV\")\n", "e_true = EnergyBounds.equal_log_spacing(0.05, 100.0, 200, unit=\"TeV\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate a [SpectrumExtraction](..\/api/gammapy.spectrum.SpectrumExtraction.rst) object that will do the extraction. The containment_correction parameter is there to allow for PSF leakage correction if one is working with full enclosure IRFs. We also compute a threshold energy and store the result in OGIP compliant files (pha, rmf, arf). This last step might be omitted though." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "*** Observation summary report ***\n", "Observation Id: 23523\n", "Livetime: 0.439 h\n", "On events: 125\n", "Off events: 98\n", "Alpha: 0.083\n", "Bkg events in On region: 8.17\n", "Excess: 116.83\n", "Excess / Background: 14.31\n", "Gamma rate: 4.43 1 / min\n", "Bkg rate: 0.01 1 / min\n", "Sigma: 18.74\n", "energy range: 0.88 TeV - 100.00 TeV\n" ] } ], "source": [ "ANALYSIS_DIR = \"crab_analysis\"\n", "\n", "extraction = SpectrumExtraction(\n", " observations=observations,\n", " bkg_estimate=background_estimator.result,\n", " containment_correction=False,\n", ")\n", "extraction.run()\n", "\n", "# Add a condition on correct energy range in case it is not set by default\n", "extraction.compute_energy_threshold(method_lo=\"area_max\", area_percent_lo=10.0)\n", "\n", "print(extraction.spectrum_observations[0])\n", "# Write output in the form of OGIP files: PHA, ARF, RMF, BKG\n", "# extraction.run(observations=observations, bkg_estimate=background_estimator.result, outdir=ANALYSIS_DIR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Look at observations\n", "\n", "Now we will look at the files we just created. We will use the [SpectrumObservation](..\/api/gammapy.spectrum.SpectrumObservation.rst) object that are still in memory from the extraction step. Note, however, that you could also read them from disk if you have written them in the step above. The ``ANALYSIS_DIR`` folder contains 4 ``FITS`` files for each observation. These files are described in detail [here](https://gamma-astro-data-formats.readthedocs.io/en/latest/spectra/ogip/index.html). In short, they correspond to the on vector, the off vector, the effectie area, and the energy dispersion." ] }, { "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": [ "# filename = ANALYSIS_DIR + '/ogip_data/pha_obs23523.fits'\n", "# obs = SpectrumObservation.read(filename)\n", "\n", "# Requires IPython widgets\n", "# _ = extraction.spectrum_observations.peek()\n", "\n", "extraction.spectrum_observations[0].peek()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit spectrum\n", "\n", "Now we'll fit a global model to the spectrum. First we do a joint likelihood fit to all observations. If you want to stack the observations see below. We will also produce a debug plot in order to show how the global fit matches one of the individual observations." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "model = PowerLaw(\n", " index=2, amplitude=2e-11 * u.Unit(\"cm-2 s-1 TeV-1\"), reference=1 * u.TeV\n", ")\n", "\n", "joint_fit = SpectrumFit(obs_list=extraction.spectrum_observations, model=model)\n", "joint_fit.run()\n", "joint_result = joint_fit.result" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Fit result info \n", "--------------- \n", "Model: PowerLaw\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max frozen\n", "\t--------- --------- --------- -------------- --- --- ------\n", "\t index 2.663e+00 7.092e-02 nan nan False\n", "\tamplitude 2.911e-11 1.782e-12 cm-2 s-1 TeV-1 nan nan False\n", "\treference 1.000e+00 0.000e+00 TeV nan nan True\n", "\n", "Covariance: \n", "\n", "\t name index amplitude reference\n", "\t--------- --------- --------- ---------\n", "\t index 5.030e-03 7.184e-14 0.000e+00\n", "\tamplitude 7.184e-14 3.176e-24 0.000e+00\n", "\treference 0.000e+00 0.000e+00 0.000e+00 \n", "\n", "Statistic: 42.231 (wstat)\n", "Fit Range: [ 0.87992254 100. ] TeV\n", "\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax0, ax1 = joint_result[0].plot(figsize=(8, 8))\n", "ax0.set_ylim(0, 20)\n", "print(joint_result[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Compute Flux Points\n", "\n", "To round up our analysis we can compute flux points by fitting the norm of the global model in energy bands. We'll use a fixed energy binning for now." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SpectrumEnergyGroups:\n", "energy_group_idx bin_idx_min bin_idx_max bin_type energy_min energy_max \n", " TeV TeV \n", "---------------- ----------- ----------- --------- ------------------ ------------------\n", " 0 0 32 underflow 0.01 0.6812920690579611\n", " 1 33 34 normal 0.6812920690579611 0.8799225435691069\n", " 2 35 36 normal 0.8799225435691069 1.1364636663857242\n", " 3 37 38 normal 1.1364636663857242 1.467799267622069\n", " 4 39 40 normal 1.467799267622069 1.8957356524063753\n", " 5 41 43 normal 1.8957356524063753 2.782559402207123\n", " 6 44 45 normal 2.782559402207123 3.593813663804626\n", " 7 46 47 normal 3.593813663804626 4.6415888336127775\n", " 8 48 49 normal 4.6415888336127775 5.994842503189409\n", " 9 50 51 normal 5.994842503189409 7.742636826811269\n", " 10 52 53 normal 7.742636826811269 10.0\n", " 11 54 55 normal 10.0 12.915496650148826\n", " 12 56 57 normal 12.915496650148826 16.681005372000573\n", " 13 58 59 normal 16.681005372000573 21.54434690031882\n", " 14 60 62 normal 21.54434690031882 31.622776601683793\n", " 15 63 71 overflow 31.622776601683793 100.0\n", "\n" ] } ], "source": [ "# Define energy binning\n", "stacked_obs = extraction.spectrum_observations.stack()\n", "\n", "e_min, e_max = stacked_obs.lo_threshold.to_value(\"TeV\"), 30\n", "ebounds = np.logspace(np.log10(e_min), np.log10(e_max), 15) * u.TeV\n", "\n", "\n", "seg = SpectrumEnergyGroupMaker(obs=stacked_obs)\n", "seg.compute_groups_fixed(ebounds=ebounds)\n", "\n", "print(seg.groups)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "fpe = FluxPointEstimator(\n", " obs=stacked_obs, groups=seg.groups, model=joint_result[0].model\n", ")\n", "flux_points = fpe.run()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=14\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
e_refe_mine_maxref_dnderef_fluxref_efluxref_e2dndenormloglikenorm_errnorm_errpnorm_errnnorm_ulsqrt_tstsnorm_scan [11]dloglike_scan [11]dndednde_uldnde_errdnde_errpdnde_errn
TeVTeVTeV1 / (cm2 s TeV)1 / (cm2 s)TeV / (cm2 s)TeV / (cm2 s)1 / (cm2 s TeV)1 / (cm2 s TeV)1 / (cm2 s TeV)1 / (cm2 s TeV)1 / (cm2 s TeV)
float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64float64
0.7740.6810.8805.754e-111.148e-118.836e-123.449e-110.9372.0430.1470.1540.1401.26012.360152.7680.200 .. 5.00056.84123283322248 .. 227.564294091070545.390e-117.252e-118.466e-128.878e-128.063e-12
1.0000.8801.1362.911e-117.505e-127.458e-122.911e-110.9230.3130.1000.1030.0961.13617.748314.9760.200 .. 5.000114.10827174442738 .. 487.86359879137842.687e-113.308e-112.905e-123.004e-122.808e-12
1.2921.1361.4681.473e-114.905e-126.295e-122.457e-110.8970.0280.1160.1200.1111.14814.987224.5970.200 .. 5.00078.37352113849738 .. 358.71538480769981.322e-111.691e-111.704e-121.773e-121.636e-12
1.6681.4681.8967.454e-123.205e-125.313e-122.074e-111.1601.3980.1510.1570.1451.48714.582212.6310.200 .. 5.00096.23812605916038 .. 228.886316421928778.646e-121.108e-111.125e-121.171e-121.080e-12
2.2971.8962.7833.181e-122.852e-126.457e-121.678e-111.1460.1620.1440.1500.1381.45615.219231.6310.200 .. 5.000101.3026355684861 .. 250.069994363909023.644e-124.632e-124.576e-134.757e-134.398e-13
3.1622.7833.5941.358e-121.107e-123.478e-121.358e-111.2121.3380.2180.2310.2061.69910.563111.5820.200 .. 5.00052.6130037801357 .. 111.009085725779951.646e-122.306e-122.962e-133.132e-132.798e-13
4.0843.5944.6426.870e-137.232e-132.935e-121.146e-110.7730.1690.2080.2250.1921.2586.45141.6210.200 .. 5.00014.673263320314994 .. 107.631448081940525.310e-138.643e-131.428e-131.545e-131.316e-13
5.2754.6425.9953.476e-134.726e-132.477e-129.672e-121.0451.3030.2820.3080.2581.7096.76445.7500.200 .. 5.00020.98476725592205 .. 65.247074146176063.632e-135.940e-139.815e-141.070e-138.984e-14
6.8135.9957.7431.759e-133.089e-132.091e-128.164e-121.3730.5270.3810.4160.3482.2788.06965.1080.200 .. 5.00025.852281091320584 .. 36.2802137410563652.415e-134.006e-136.709e-147.319e-146.125e-14
8.7997.74310.0008.900e-142.019e-131.765e-126.891e-120.9281.8790.3890.4440.3401.9194.21517.7690.200 .. 5.0009.03652347498847 .. 34.741052915788838.258e-141.708e-133.466e-143.952e-143.028e-14
11.36510.00012.9154.503e-141.319e-131.490e-125.816e-121.0050.0220.4860.5610.4162.2854.07516.6040.200 .. 5.0006.108419580636659 .. 21.3779366678426084.526e-141.029e-132.188e-142.525e-141.875e-14
14.67812.91516.6812.279e-148.621e-141.257e-124.909e-120.2571.1830.3260.4410.2311.3961.1931.4220.200 .. 5.0001.2176598223007438 .. 24.9194860830341555.860e-153.180e-147.431e-151.006e-145.257e-15
18.95716.68121.5441.153e-145.634e-141.061e-124.143e-120.8730.0410.6800.8540.5292.9442.3605.5710.200 .. 5.0002.0491770958125324 .. 10.5092059271209431.006e-143.394e-147.839e-159.842e-156.102e-15
26.10221.54431.6234.920e-155.013e-141.290e-123.352e-120.0000.5910.0000.2640.0001.0470.0000.0000.200 .. 5.0001.349562401383848 .. 19.547494900743367.648e-305.151e-151.995e-221.298e-157.648e-30
" ], "text/plain": [ "\n", " e_ref e_min e_max ... dnde_err dnde_errp dnde_errn \n", " TeV TeV TeV ... 1 / (cm2 s TeV) 1 / (cm2 s TeV) 1 / (cm2 s TeV)\n", "float64 float64 float64 ... float64 float64 float64 \n", "------- ------- ------- ... --------------- --------------- ---------------\n", " 0.774 0.681 0.880 ... 8.466e-12 8.878e-12 8.063e-12\n", " 1.000 0.880 1.136 ... 2.905e-12 3.004e-12 2.808e-12\n", " 1.292 1.136 1.468 ... 1.704e-12 1.773e-12 1.636e-12\n", " 1.668 1.468 1.896 ... 1.125e-12 1.171e-12 1.080e-12\n", " 2.297 1.896 2.783 ... 4.576e-13 4.757e-13 4.398e-13\n", " 3.162 2.783 3.594 ... 2.962e-13 3.132e-13 2.798e-13\n", " 4.084 3.594 4.642 ... 1.428e-13 1.545e-13 1.316e-13\n", " 5.275 4.642 5.995 ... 9.815e-14 1.070e-13 8.984e-14\n", " 6.813 5.995 7.743 ... 6.709e-14 7.319e-14 6.125e-14\n", " 8.799 7.743 10.000 ... 3.466e-14 3.952e-14 3.028e-14\n", " 11.365 10.000 12.915 ... 2.188e-14 2.525e-14 1.875e-14\n", " 14.678 12.915 16.681 ... 7.431e-15 1.006e-14 5.257e-15\n", " 18.957 16.681 21.544 ... 7.839e-15 9.842e-15 6.102e-15\n", " 26.102 21.544 31.623 ... 1.995e-22 1.298e-15 7.648e-30" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flux_points.table_formatted" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we plot the flux points and their likelihood profiles. For the plotting of upper limits we choose a threshold of TS < 4." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "flux_points.table[\"is_ul\"] = flux_points.table[\"ts\"] < 4\n", "ax = flux_points.plot(\n", " energy_power=2, flux_unit=\"erg-1 cm-2 s-1\", color=\"darkorange\"\n", ")\n", "flux_points.to_sed_type(\"e2dnde\").plot_likelihood(ax=ax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The final plot with the best fit model, flux points and residuals can be quickly made like this" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.4, 50)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "spectrum_result = SpectrumResult(\n", " points=flux_points, model=joint_result[0].model\n", ")\n", "ax0, ax1 = spectrum_result.plot(\n", " energy_range=joint_fit.result[0].fit_range,\n", " energy_power=2,\n", " flux_unit=\"erg-1 cm-2 s-1\",\n", " fig_kwargs=dict(figsize=(8, 8)),\n", ")\n", "\n", "ax0.set_xlim(0.4, 50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Stack observations\n", "\n", "And alternative approach to fitting the spectrum is stacking all observations first and the fitting a model to the stacked observation. This works as follows. A comparison to the joint likelihood fit is also printed." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : minuit\n", "\tsuccess : True\n", "\tnfev : 31\n", "\ttotal stat : 30.35\n", "\tmessage : Optimization terminated successfully." ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stacked_obs = extraction.spectrum_observations.stack()\n", "\n", "stacked_fit = SpectrumFit(obs_list=stacked_obs, model=model)\n", "stacked_fit.run()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Fit result info \n", "--------------- \n", "Model: PowerLaw\n", "\n", "Parameters: \n", "\n", "\t name value error unit min max frozen\n", "\t--------- --------- --------- -------------- --- --- ------\n", "\t index 2.666e+00 7.139e-02 nan nan False\n", "\tamplitude 2.909e-11 1.784e-12 cm-2 s-1 TeV-1 nan nan False\n", "\treference 1.000e+00 0.000e+00 TeV nan nan True\n", "\n", "Covariance: \n", "\n", "\t name index amplitude reference\n", "\t--------- --------- --------- ---------\n", "\t index 5.097e-03 7.235e-14 0.000e+00\n", "\tamplitude 7.235e-14 3.183e-24 0.000e+00\n", "\treference 0.000e+00 0.000e+00 0.000e+00 \n", "\n", "Statistic: 30.349 (wstat)\n", "Fit Range: [ 0.68129207 100. ] TeV\n", "\n" ] } ], "source": [ "stacked_result = stacked_fit.result\n", "print(stacked_result[0])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " method index index_err amplitude amplitude_err \n", " 1 / (cm2 s TeV) 1 / (cm2 s TeV)\n", "------- ----- --------- --------------- ---------------\n", "stacked 2.67 0.0714 2.91e-11 1.78e-12\n", " joint 2.67 0.0714 2.91e-11 1.78e-12\n" ] } ], "source": [ "stacked_table = stacked_result[0].to_table(format=\".3g\")\n", "stacked_table[\"method\"] = \"stacked\"\n", "joint_table = joint_result[0].to_table(format=\".3g\")\n", "joint_table[\"method\"] = \"joint\"\n", "total_table = vstack_table([stacked_table, joint_table])\n", "print(\n", " total_table[\"method\", \"index\", \"index_err\", \"amplitude\", \"amplitude_err\"]\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "\n", "Some things we might do:\n", "\n", "- Fit a different spectral model (ECPL or CPL or ...)\n", "- Use different method or parameters to compute the flux points\n", "- Do a chi^2 fit to the flux points and compare\n", "\n", "TODO: give pointers how to do this (and maybe write a notebook with solutions)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# Start exercises here" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 2 }