{ "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/v0.10?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: 0.10\n", "numpy: 1.16.0\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/jer/anaconda/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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ce++9FBcXs3r16ovaPiqKwv333899990HQHJyMh9//DE33HAD77///iVbO3744YdMnDjxspMyyDNmMQJ8eHofOkXLD8cuvWQiVBSFyaY4Hhq3jM/Lj+LoYxnacA9/MuoKnB6/p/oEaf4x7Kg65tR4q8NGXnM5UZ6mPsV1uq2GVL9R3Sbl8wxaN+4ePZcNZUdwyDNnMYQMl7aPb7zxBi+//DKTJ0+mubkZN7eudQdycnJ4/PHHO2toXy5JzGJYM9utbK86xp1xc3pd2xvvHUaiTwRZ9af79B4GrZ7ClmqqzU29jq3taOZwfSHfipvLlopsmqztve6zpSKbOGMwJW21OFu7vtHSRlFLFddGTO59MGeflxv1HmQ2/Odrb7S0kVlfTEZtPieaymSimBhww6Xt47hx49i4cSMHDx7k1ltv7YwNzk5eW7FiBW+99VaX1y+HJGYxLNhVB83Wdtptli7Ja29NHmN9wgnsZSLXeatiZnCssZQmS5tT4wtaKrGrKqtipvO73E+pt7R0O7bB0sbvcz/luoh0xvtGMjt4HC8e+4TGHt5rd9Vx1p05zA/GLkOraMhtcm7iyIayI/i7eRHvE+bUeIBUv1EUNFdS0FLJKyc/56dH3mZD+RF2V5/gH4W7eOTgW3xUcoBWW4fTxxTicgyXto9VVVUAOBwOnn76ab773e8C0NDQwPLly3n22WeZOXPmFftc5BmzGNLymyvZUpHFgdp8dBotNtWOr96TBSHJzAlO5HRrDYk+Pd/KvdBYnwiMegPryg5za0zvP0gbyo6yIDSZpeETsToc/CLzfRaHTmBOyHh89B4ANFvb2VmVy6byTOaFJLEsPA2Ab0RNQ6toeOLIO8wKHsfc4PEEG3ywOuxkN5awpSKb2o5mfpp0A6EefiwLT+Odwt08mbyy2xKdAFXmRjZXZhHRx1vfblo9WfXFbKvMYXnEJL4VNw+vC4qUFLfWsKHsME9lfcBj46/vdda6EJdruLR9fPfdd3n55ZcBWLlyZedktT/96U+cOnWKp556iqeeegqAjRs3EhwcfHmfi7R9FEORQ3WwunAXR+uLuTo0hTnBiRj1BlRVpaClii0VWWQ1nCbK00S6aTQLnFwzDPDAvtdwoPJfYxYyxdT9radPzxxid9Vx/jvlG51VtgrPvXdGbQFG/dma2C1WM5MCYlkYmnLJCWLV5ia2Vmaztyavs4lFjFcQC0KTSQ+I65xJraoqqwt3kd9SwXfjFxHm4d/lOKqqktdczp/zNjE/OIkN5Uf445R7nZ7I9nzOWo7UF/HipDt7XMr1aelB9tSc4BcpN2PQ6p06thievtqOcKCXS7kKafsoRoTVhbs401bHM6mrulw9KorCaO8QRnuHkNNQwi+zPsBD5+50Ym6zdWDDwSPjruOVvM/JbihhQWgy0V5n1y2qqsqJpjI2lmdSbq7nsfHXdSl9GWsM5r/GLOSO2Nk0nLtF7efm2Vnn+lKCDD7cEj2DW6Jn9BiboijcGTubDeVH+E32GqK8TKQHjMZD60ajtY0vqk/SZu/g1uiZTA0cw9GGYg7XFZLewy8X51kcNrZX5vDI+Ot6XV+9PGISJ5sr+LL6BPP78AuPGP4kiQ4NkpjFkFPQUsmR+iJ+k3prj7d0k/yiuDt2Ln/K+5zvxl/tVHnKPdUnSPOLYZxvOE9NuIVtlTm8lPspOo0WT607TdY2DFo3FoYmc1/8wm4TrkHrRqhH97F9XYqisOxcQZD9tfkcbzxDh8OKp9adm0ZN61I+dGFoMp+VHWFSQGyvE9/+Vfwl7lo9C0KSnIphcdgE/lm0h3khSbL2WYgBJolZDDlbKrK5OjSlx6R83rKINP5yajPvn97LbTGzehxrtlvYVJHJPXHzgbONJ26MmsJ1kZOpaG+gw2HFS+tOsMF30JORXqNjZtBYZgaN7XbMVNMYdlTl8mbBDu6Om9ttcs6sL+bj0gyWhU90uivVeN8IGqxt1FtanS56IoS4MmRWthhSHKqD/TWnmB2c2PtgzpanXBE1lX+f3k9GbX6348x2C/974jMSvMMY5xPeZZtW0RDhGUCcMYQQD79BT8rO0mm0PDR2GVXmRp7JXsO+mjxsDnvn9uLWGt7I38Zrp7YwIyih83a9MzSKBqPOnXa7BYfqIKehhC0VWWyuyCKzvliWVo0gw22e0XDzdT5fuWIWQ0qb3YJW0XTOeHbGWJ8wyttjWV24iy9qTrIwJJnxvpEoitI5Y3prRTbj/SK5O27esEm8zvDUufPY+Os5WFvA5oos/i9/Oz5uHljsNhRFYX5IEk+nrmJTRWaflkGpqkqLtYMvqk+wr/YUHlo34owhKMCX1Sd5I38b80OTuSY8TTpcDWMGg4Ha2lpMJtOI+rkYKlRVpba2FoPB0Kf95CdKDLjzs4u3VGRT2Nq12cRU0xisqr3bikCXYlMdBBl8+HHicvZUn+Dtot1UtDeg02hxqA6mmsbwYMIS4ozBI/Lko1U0TA0cw9TAMTRZ22mxmnHT6ghw8+q8dZ3sG8WbBTtYGTXVqc8gt/EMZeY6TrVU8kD8orNJ+YL9iltr+Pfpfbxw7BN+nLi8x8lvYuiKjIyktLSU6uorOBNbdGEwGIiMjOzTPpKYxYCqt7TwxxMbaLGZWRiSwvWR6bidazaxs+oYa0sOYFftnGqpIN7bueIZOQ0lxBlDzk3aSmFhaApWhw2bw4G7Vuf0c9WRwEfvccm7DWN9wlEUONZYSpJfVK/H+Z8T6xnlGchjidd1aYxxXrRXIA+NW8br+dv4a94Wfjhu2RWJXwwsvV5PbKxzDV3EwHGdM5YYdI2WNp7JXkOqfwzPp93OkvBUIjwDCDL4kOgbwf3xi3gqdRWo8OrJTU4fM7OhmFnB47q8rtfo8NC5uVRS7omiKKyImsrr+duo6+i+ehmcXb9d0FLFz1NuumRSPk+jaLgnbh4FLZUUt9Zc2YCFcGFy1hID5u2iXUwJGM0Nkend3k4NMvjwVOoqjjYUs6U8q8fjnS1CspPpgWO7VLASlzbVNIbFYRN4KvtD9n5lohhAXUcL/yzaw+qCnayInILJidnYOo2WeSFJbK3I7q+whXA5citbDIgGSxtZDaf53aS7eh0b5WXi5qireDnvcww6N6YHxl905dtoaeOtwp202MzcH391f4U94iwNTyPcw591ZYd5u3A3yX6R6DU66jpayG+pZHpgAmO8Q/tUSW2KaTR/OL6+H6MWwrVIYhYDYk/1cdJNo7tU0erJLTEz2Ft7kk3lmXxwei9zghMJNvhiddg51lhCVsNpZgSNdbqwiPiPCf7RTPCP5kxbHQUtVVgdNpL9ovj+2CUYtG787Mi7ePThDoSnzg2z3dqPEQvhWuSMJgZEtbmJUZ7Or6P1c/PCW+/JD8cuo8naxpc1eWQ1nEanaIj3DruoAYPouwjPgEuW5/TQutHiRLvK85qsZrzOFYNRVZUOhxU3jWtNuhPiSpLELAbE5SxTijEGE2O8vG4twnkTA2L5suakU7O3Ab6oOk6ERwCvnNzIwboCFM4uYYszBrMwNIWpptFyV0OIPpCfFtEv6jpaqLe0oCgKwQZfQgy+FLU6v1ayrqPl3Ppm5wuNiCtjTnAiPzn0D24eNR1fN88exzZb23i3+AtijcEsC0/jrrg5GHUG7KqDo/VFbKnI5qOSAzySeC2hHn4D9BUIMbxJYhZXjKqqHKorZHNFFsWt1QQZfFFVleqORsb5RHC4rpBbY2Zi1PVeBWdbZQ4zghJ6XK4j+oeP3oOrw1L43xOf8Wjidd3WLDfbLTy4/3VCPfx4cdIdXa6KtYqGSQFxTAqIY1tFNs/lfMR/p9wkdbeFcIIkZnFF2Bx2/pa/ldLWWpZHTCLdtLzzRN1iM7O76jjbK4/xXM5HPJ26qsdjVZub2F6Zw0+TbhyI0MUlrIyaymrbLn6d/QE3RE7p0jf6/NXw6/nbsKp2Xku/u8db1fNDk6m1tPCv01/y3fhFA/UlCDFsSWIWV8TfC3fQYjXz3xNuwu0rJ2mjzsDS8DTG+0by3f2v8Wz2Gh5PuuGSk4Mq2ht4MfcTro9M77VvsOg/GkXDXbFzyKg7W4P77cJdRHmZUFA401aHyd0bnXK2iYYz5TiXhqfx6MHVNFraer09LoSrk8QsLltJay1H64v57cTbL0rKFxrlFcjvJ93Fo4dW8+ODb7EgNJkUv1GdJTl3VB0jt/EM34yeznwn+gaL/qUoClNMo5liGk1lewOV5iZAxeTujYfWjf939J9MMY1x6lhGnYGJATEcqMvn6tCU/g1ciGFOErO4bFsrs5kfkuTUldM43wjmhoxntDGEanMTr+dvxXauicW0wDHcN2ahNEQYgkI8/Ai5YPJWQUvl2avmPswBCDb40mBp7Y/whBhRJDGLy5ZRW8DPU1Y6PX5m0FgO1Obz0Lhr+jEq0Z90irbPPZntqgOdIpP5hOiNVAAQl63VZsbfzcvp8X5uXn3qDSyGnmCDD/WWlj5dAec2niHK09SPUQkxMkhiFpfNXaujvQ8lGc12a4/PosXQZ9C6MdUUz/bKY06NL26tocbchLfeg701eeyvOSUdqYTohpwdxWWL9w7ncF0hc0PGOzX+cF0hCT7O9VoWQ9eisBSey1nLFNPoHmfQm+0WfpuzllZ7B6/nbyXS04SKSlFLNd56DxaHTWBGYMJlVYcTYiSRxCz6TFVVStvqqLO0oFEU0k1xbCrPZE5wYq8n1xabmQO1+Tw/8fYBilb0l0hPE6uiZ/DbYx/zQPwixvqEX/TvX9neyI8OvolG0fBk0o0k+kZ2jnGoDjIbTvPh6X0cayzl26PnS31tIZDELPrArjrYXXWczRVZtNk6CPXww6GqFLdWU93RzJ/zNvJgwpJu97c57LyWt4XpQQmylnWEmBU8Dk+dG3/L34qn1p1ZwWPxdzPSYbdyqL6QzeVZ55bJ3YmbVt9lX42iIc0/hnE+EbyU+ynvn97LLdEzBukrEWLokMQsnGJx2Pjjic8w263cPGo6yX6RnVc3FoeNLRVZ/O/xzyhqqeaJpBvx/0rpxdK2Wt4t2oNW0XB7zKzB+BJEP5kUEEeafwzZDSUcqM3neGMZeo2WMIMfo7xMvDT5rh4rgxm0er4/dik/OfwPloalyS9twuVJYhZO+UveZjy0bjw09pqL1q66aXQsC59Iqn8Mjxx8i+/u/xszg8cS7uGPQ1XJay6nytzEwtBklkdMQiu3K0ccjaLp7PN83lsFO1kQkuxUZylvvQeTA+LYVZXLtZGT+zNUIYY8OUOKXhW0VFLUUsV3xizssaBEuIc/L03+Fn5uniT5RKJTtBi0epaEpfK7SXdyfWS6JGUXktlQzLTAeKfHTw9M4GhDcT9GJMTwIFfMoldbKrJZEOrclU+ohx9jfSLQa3XcGDZlAKITQ1W73eJUJ7HzvHTutNst/RiREMODXL6IXh2uK2R6YILT42cEJXC4rrAfIxLDgYfWjRab2enxrbYOKccqBJKYhRPa7BZ89B5Oj/fWGWiTyl4uL9k3iv21p5wev6/2FBP8RvVjREIMD5KYRa8MGj1tfbjF2Ga34C5XPi5vQWgy2yuPYXXYeh17dn37KeYEO1ekRoiRTBKz6NV430gyavOdHp9Rm89434h+jEgMB6O8AknwDuO1U1t7bHhhcdh4MecTAty8WV92iA9O7yWjNr/PTTKEGCkkMYteLQxNZnNFFg4nTpQNljYyG4qZFTxuACITQ9134hfSajPzu9xPyGsuR1XVzm0O1cHWimzu2PNHDtefLdPqp/dCp2j5rOwIjxx8iw1lR7rsI4QrkFnZoltttg4KW6ow2y1YHTbeLdrDbTGzui27aXHY+PPJz5kfktyn2bhi5HLT6Hh43HI2V2Tx17wtuGm052plQ3bDaQpbqlgansZ9YxbioXPv3O/GqCkUt9bw94LtlLTVSrlO4VKU4fbbaHp6upqRkTHYYYxo5e31bCg7yv7aPKI8AzFo3ajpaOpsPvG9+CUk+IZ3jldVlRNNZbxX/CWhHr58Z8xCOYmKizhUB3nNFdR0NGO2W3i3aA8Pxi9mkimu23067FZ+e+xjJgbEcm3EpAGMVoj+lZ6eTkZGxiWvcuSKWXSRWX+av57azNWhKTybdjup/bUEAAAgAElEQVR+F5RHLGyu4uW8z/l+xhtMNY0h3icMu+rovEW5OCyV+SFJ0iVIXJJG0TDWJ5yxwLozh5hqGtNjUgZw1+r59ugFPJuzhqVhqT0WuBFipJDELDoVt1Tzl1ObeGjsNZdsyxjrHcyLk+5ka0U2bxfuZmrgGIw6AzMCEy7ZWUiISzn/bLmnhicXCvf0J8IzgIy6Aq7qQyUxIYYrScyi00elB7gxckqvvZIXhCZT1FqNXXWwIDR5gKITI0Wz1Uy73UKcMdjpfdL8YzjVXCGJWbgEeRAoAKjtaOZ4UxmznZxNvSh0Atsrj2Fz2Ps5MjHSWBw23LX6Pt1hcdPosDixHlqIkUASswAgs76Yif4xTpdEjPIyYdS5U9xa08+RiZHGqDPQYjP3KdHWWVrwlpn+wkVIYhbA2WpdRn3fTnw+ek9pOiD6zEPnxljvcPbXOFeu06462FN9gqlyG1u4CEnMAjjbrL7d1rck22bvwF0r0xRE3y0ITebz8qNOPQrZX3OKADcj0V6BAxCZEINPErMAzpbdPFJf5FRdY4BKcyPV5iaivYL6OTIxEqX5R+Pn5snr+Vt7TM55zeX8o2gXq2JmDGB0QgwuScwCgDAPfyI9TRxwsib2topsZgcn4uZEj2YhvkqjaPhewhKarWaezv43e2vyuiTo4tZqfp35IQ9lvInFbuOveVt4KXcdR+uLnSoNK8RwJmdV0en6yHReOfk5MV7BhHv6dzsus76YPdUn+MWEmwcwOjHSGLRuPJy4nIN1BWypyObvBdvx1XtRbW6kuLWGeJ8wfjL+BhJ9I1BVlZPN5fy7ZB9vF+3iobHXEOEZMNhfghD9YsATs6IoU4C9wC2qqn5w7rWHgTuB51VVfW+gYxJQbW6itK2WaK8gHjn0Ft+Imsb1UeldrohbrGa2VebweflRHhq3jEB370GMWIwEWkXDVNMYpprG0Ghp44vqE6wtzeDPU/+L+K+spw/18GNOcCK7q47zXM5HPJG0osdfIIUYrgY0MSuKogWeBz6/4DUjMAWYCnwISGIeQMUt1XxYsp9TzeVMDogjxhiERlFYXbST1/O3MS1wDLHGYBqtbRxrKGViQAxPJq8gzENOiOLK0ioaPj5zkCeSbiTa2P3chVnB47A6bPzl1GZ+JXdtxAg00FfMP+Bs8p1ywWvnqwwMr24aI0BOQwmv5G1kZdRUvpewGHetvnPbj8Zdw7bKHP5esBNvvYGrAhO4O24ePnqPwQtYjGi7qnNJ84/uMSmfNzdkPJ+eOURBSyVxxpABiE6IgTNgk78URYkAVgCvXvi6qqrNQBaQgVwtD5hKcyN/ztvEDxKWsjA0pUtShrOTcxaGpvD7yXdR2laHl85dkrLoVzsqc5kf4lyJV42iYX5oMjsqc/s5KiEG3kDOyv4D8LiqqhetjVBV9VlVVSeqqvrOAMbj0jaXZzIvZDzjfCN6HBfo7s3tMbP4pPTgAEUmXFV1R1Of1iqP8gykytzYjxEJMTj6NTErivI9RVGOKIpyBEgH/qkoShFwE/CKoig3Onmc1vN/CgsL+zFi12C2W9ldfYJ5IUlOjZ8YEEttR7OU3xRDjjQ0E8NVYWFhl9x24bZ+Tcyqqr6sqmrauT+xqqrGqKoaA3wAPKiq6kdOHsfr/J/Y2Nj+DNkllLTVEGzwdXpWtVbRkOYfw8mmsn6OTLiyYIMPRa3VTo8vaq0i2N23HyMSov/ExsZ2yW0XbpMCIy7IYrdh6GNhEA+tGx3S3Uf0o7nB49lSke3UWIfqYFtlDvNCxvdzVEIMvEFJzKqq3n1+DbMYeEadgUZrW5/2abC24qVz76eIhDi7DCqr4TT5zZW9jt1SkY2/mxcxfejpLMRwIVfMLijKy4TVYaegpfcTIEC7zcKR+iLS/KP7OTLhyow6A98Zs5A/HF/HyabyS45RVZVtFdl8cuYg98cvGuAIhRgYUpLTBWkUDQtCk9lQdpQHExb3On5H1TGSfKPwdzMOQHTClU0KiEWraPjjic+I9DQxPzSJCI8AzHYrG8uPsrv6OKiwODwVu0NqZouRSRKzi5ofksSvsj5g/ZnDXBMxsdtxmfWn+fTMIZ5IcmoCvRCXLdU/mt9PvosDtflsq8jmWGMpleYmAt29mRcynmjPIGoszTybs4YIzwBujZklLSHFiCKJ2UV56tx5bPz1PJ+zltymMywNm8B43yiUc+tPKtob2FKRzd6ak/xw7FJpGCAGlF6j46rAeLIaThPvE85v0m676Hvwm6Oms7v6BC8c+5gfjl1Kgk/4IEUrxJUlidkF2VUHh+oK2VqRTXXH2eYVn505jEHnRoxXIN56D5qtZmYHj+OXE27GJM0qxCD49MwhqjuaeXz89egvsYpAp9EyL2Q8JncjfzyxgadTV+Hr5jkIkQpxZUlidjEtNjN/OL4eh+pgUegEfpy4HL1Gh81hY1vlMbZXHqPB2srPklbIVbIYNFaHjU3lmfwsecUlk/KFUvxGkeofzc6qXK6LnDxAEQrRf2RWtguxOGz87tinxHoF8f+SVzI9KKHzpKfT6FgUNoFn0lZxY+QUfp/7KY2Wvi2pEuJKyagtINLT5HQXs4WhKWytzMahyoQwMfxJYnYh2ypy8NZ7cFvMLDRK9//0i8ImMCkglrWlGQMYnRD/UdBSyQT/UU6PjzUGY3PYabK292NUQgwMScwuQlVVtlRmcW3ExM4JXj1ZFj6RL2tOYrZbBiA6IbqyOOy49bE6nV6jwyLV6cQIIInZRRSfq0Ec7x3m1PgAdyPx3qEcqS/uz7CEuCRvvYGajmanx1scNppt7Rh1hn6MSoiBIYnZRTRa2wly93Hqavm8QHcfmvpYulOIK2GaKZ4vqk9ic1zUJfaS9tXkMc4nHE8pGytGAEnMLkKnaLA6eZI7z6ba0SvafopIiO5FeZkINvjwZU1er2NtDjuflx9lYWjKAEQmRP+TxOwiIj1NlLTV0GrrcGq8Q3WQ01BCtDGonyMT4tJujZnJP4v3cKyxtNsxNoedv57ajMnNmwl+zk8WE2Iok8TsInzdPEnxG8We6uNOjc9uKMGoNxDrJd17xOCIM4bwvYQlvHJyI2/mb6ektbZzm9VhY3fVcX6V9QEddhsPJizucaWBEMOJFBhxIUvCUvnD8c9I9Y8hxNB9g/kWm5l3inZzY9TUPj2TFuJKG+8bydOpt7C98hjP53xEnaWVRmsb7XYLRp2BRN8IZgePQ6eRRy5i5JDE7EJGe4eyctRUns1ew/3xixjnE35R4i1preXVvI1M9I/lqsD4QYpUiP/wc/PC180Tm+pgimk0U01jiDEGYdDqOd5Uxvqyw7xX/CU/GneNVKsTI4IkZhczPyQJb52BV/M2YbZbiPcOxd/dSJC7D1kNp6kyN3Fd5GQWhiQPdqjD3nf3vQbAq9O+M8iRDG9bK7JZX3aYX064mVAPvy7bZgaNZWbQWHZVHee5nLU8mbziojFCDDeSmF3MyaZydlYdx2K3Eubhz+nWWnIaS2mwtDIxIJYnxt9IqKec2MTQUNPRzPun9/LLCTf3+PhldvA4zHYLr+dv5cnklQMYoRBXniRmF7Kn+gT/LNrDN0ZdxfcSFuOu1Xdua7C0sbkik2dy1vBI4nJijDLpSwy+7ZU5zAwa22NSPm9+SBKfnjlESWstUV6mAYhOiP4h0xhdxLHGUv5Z9AVPJK1gXsj4LkkZwM/Nk5tGXcWdcbN56fh6GqSBhRgCdlTmMj8kyamxOo2WucGJ7KzK7eeohOhfkphdxEclB7g9dhbhnj1365lqGsOkgFi2VGQNUGRCXJrNYafF1k64kx2mACK9TNT2oZSnEEORJGYXcKatjvL2BtID4pwaf3VoCtsrjzldDlGI/qJ+nR1khZ8Y5iQxu4DshhImB8Q6vdYzwjMAb72B0rba3gcL0U90Gi0mNyNF5xqwOCO/pZIwg0xeFMObJGYXYHZY8epjcX9PnTtmu7WfIhLCOfNCkthake3UWIvDxu7q48wJGd/PUQnRv2RWtgvw0Or7/NytxdqOh3Tq6dHUDT+7IuP2L/3NlQhnRJoTnMhPj7zDwpbkXlcKfFJ6kNHGUKdmcAsxlMkVswuY4BfNobpCrE42kS9uqabDYSNSqiiJQebr5sm9o+fzu9x1nGwqv+QYh+rg49IMvqg+wb2j5w9whEJceXLF7AJCPfyI9gpkb00es4MTex2/qSKT+SFJaKUpQI96u9KVyl9XxhTTaHSKhj+e2ECYwZfJptH4uHmgUzSUtdWzveoYQe4+/L+Ulfi5eQ52uEJcNknMLmJF1FT+cHw9UZ6mHm8J7qzKJbfxDLdEzxjA6IToWaJvBNdFTOLDkn18WXu2R7NddeCj9+C6iMmsjJqGh85tkKMU4sqQxOwixniHck/cPF7M/ZRl4WnMDR6PUW/o3F7R3sDG8kwO1xfyWOL1eOs9Bi1WIS5UY27ixdxPCfXw44djlzHeNxJFUVBVlRNNZWyuyOLnme/xaOJ1UidbjAiSmF3IZFMcQQYfNpQd4UcH30SnaLGqNtptVqyqjSVhqfwi5Wa5HSiGjBabmd/mfsz8kCSWhU/ssk1RFMb5RjDON4JtFdn89tjH/HfKTfL9K4Y9Scwuxs/NC7PDioJCrDGYIIMPBq0eVPii5iT1llbuiZtHoMFnsEMVgs/LjpLgHXZRUv6q+aHJnGmvZ92ZQ9weO2uAohOif0hidiGNljaeyfk3k/3juG/KPRi0XZ/J3Rx9FRvLM3k6+988kXQjIXJbUAwim8PO9spj/DTpBqfGLwlL5ReZ/+KmUdMuqgUvxHAi025dyJ/zNjLNFM8tMTMuSsoAeo2O5RGTuCEynT+cWI9DdQxClEKcdbK5nEB3byKcXLYXZPAh2iuInMbSfo5MiP4lidlFFLfWUNHewI1RU3odOz80GTeNnuyGkgGITIhLa7a2E+Bu7NM+Ae5Gmq3t/RSREANDbmW7iK0V2cwPTXZ6bfLC0GQ2V2QzwT+6nyMbuWT98uXRa3ROF8U5z2K34aaR05oY3uSK2UWcbq1hvG+E0+PH+0Rwug/NA4S40mKNQeQ1V2C2W5wab3XYyG06Q1wvpTuFGOokMbsIm2pHpzjXXQpAp9Fhl2fMYhD5uxlJ9I1gT/UJp8bvr80n2itQJi2KYU8Ss4vw1XtSZW50enyVuRFfvawHFYNrWXgaH5VkUNHe0OO4GnMT7xd/yTW9LKsSYjiQxOwiZgQlsKPqmNPjd1QdY3pQQj9GJETv4r3DuDn6Kn6Ts4bDdYUXrRRQVZXshhKeyVnD8ohJJPlFDVKkQlw5MkvCRUwxjebdoj0UtFQSZwzpcWyVuZFDdQWsmnjHAEUnRPdmBo2loq2e3+euo8NhZYx3KON8wvHUuXOgNh+douH2mFmkm0YPdqhCXBGSmF2EXqPjrri5/M/xz3g08TqivEyXHHe2LvEnfGPUVVIvWwwqVVXZXJHFujOHMLl7syp6Bg3WVnIbz7DuzGE8dW6sip7B4rBUFEUZ7HCFuGIkMbuQKabR2Bx2fpOzhumBCSwMTe4s3pDfXMmWiiwy6gpYGTmVq0NTBjla4cpUVeWdot3kNp3h4cRrifYK7LLdoTo4WFfIWwU7MLl7y9WyGFEkMbuY6UEJjPUJZ3tlDs/lfESluZFGaxs2h51ggy/eOgPbqnJAgdnBiXjp3Ac7ZOGCzrcf/VnyCjwv8T2oUTRMMY0m2N2HF3I/IcIzgDAP/0GIVIgrTxKzCwpwN7IgNIXDdUWEefixMDSF9IA4tBotqqqS11zO5oosNpZn8kjitU6XRBTiSlBVlfVlh/n26PmXTMoXijYGMT8kiU3lWdwVN2eAIhSif8msbBdktlt4Mfdj0gJieCJpBdMC49Fqzq5xVhSFBJ9wHkxYwoqoqbxw7GNqO5oHOWLhSnKbzqBVNMR7hzk1fn5IEl/WnHS6EIkQQ50kZhe0pSKbYIMvK6Om9jhpZnbwOGYGjWVtacYARidcXUFLFRP8Rjk9oSvA3YjJ3djrWmchhgtJzC7GoTrYVpnD8vCJTp34FoWlcqD2FK22jgGIToizpTX1fax3rVd0WFV7P0UkxMCSxOxiilqr0SnaXtcyn+fn5slYnwiO1hf1b2DCZTW2mimsqKO4sp6W9g589B7UdDQ5vb9DdVBnacFHJ8v7xMggk79cTIvVjMnd2Kd1nyZ3I03SSk84obiynuKqemx2B75eBibEhqHXXVyjXVVVMgvK2XToJCdLqwn08UIFahpbSRwTxFH/PO6MndPr5C+A7IYS/Nw8CTb49sNXJMTAk8TsYnQaLVZH3275Wew23KWVnujBvtxi1h84Tn1zO4mjgtFqNFQ2NPO39fuYOyGO66Yn4WVwA8DhUHlz4wFyiiu57qrx/ODGWbjrz35/tbR3sDOrgB2nsvid43N+Pvf6Ht/XoTpYX3aYhaEpUmREjBhytnUxozwDKWmrocVqxqg39DrerjrIbiyRgiPiklRV5b3tR8g4Wcqt8yeSNiYcreY/T8jK65r45Mtj/Hr1Rn5660L8jR68u+0wZ2oaefrupXi467scz+jhzjVTExkd688Pdv+dP+/14YGr5l3yvR2qg9WFu7CpDqYHxvfnlynEgJJnzC7GqDcw0T+WnVW5To0/Wl+Ev5sX0cagfo5MDEebDp3kSH4Zv7xrMZMTIrskZYCwAB/uW34V0xKjefH97ZTXNrErq4CHvzHnoqR8IYOnjpUJ6bxbsZNHM1ZzoCYfVVUBsDns7K3J45nsNZxpq+Phccv7PFlMiKFMvptd0JKwVF7I/YRU/+gei4c0Wtp4p2gPq6JnDGB0Yriw2u18tCebn926EKNHz8+CV8xMJruwgjc3ZTA7Ja7b8Zn1xawtzaC2o5k0/xgSlFHUNrfx5NF3cdfqCXX3o6GtHT+HN4n6UcwKH4tB032CF2I4kitmFxRtDOL2mFk8l7OWQ3UFl2yll9VQzM8z3yPNP4ZU/+hBilQMZRknSokM9CMyyK/XsYqisGhyAruzC5mdEnfJMZsrsngjfxtLw9P43eS7uHv0PO4bu4BxVQm8lvYgsU1RnCqvIbE9jms8phKpBLN+73Ee/vPHrNuXi8OhXukvUYhBIVfMLmp6UALeeg/eP/0lbxfuPvt3nQdFrVVsrcihztJCpEcAB+sK2FN9gplBY1kYmiz1iF1QfXMbVQ0tAAT6emHy8QIgq7CcaeNGOX2c9LGRNLWa8fG8+Go5s76YT0sP8mTySoIMPp2vB3h7UlXfzAvv7GB+WjJ3j53OG8XbmJUyr3NcUUUdf9+UQXFlPd+9djoajUwCE8ObJGYXluwXRbJfFAUtlRysLWBD2REqzA0sDkvhluiZnZPDasxNbK86xtPZ/2ZV9AxmBycOcuRiIGQWlLPx4AlOltYQbjqbBMvrmogLM3H1pHjaLVY8z820doZeq0WrUWhsNeNn7LrmeG1pBrfHzu6SlAGa2zvILani8VvmM3fC2Q5See3lbCzP5PbYWQDEhAbws1sX8tx7W1mzJ4tvzJ5wOV+2EINOErMgzhjCgZp8AtyNPJ226qKOUoEGH24adRUzAsfy22NrcdfomRo4ZpCiFf1NVVXe2XqYQ3mlXD89qctyJovVxr7jp3lv+xFa2jsYFxns9HEtVhtuOh25pyuJDvnPnZfTrTXUdjQzKSD2on3WfpFNRKBvZ1IGWBCSzC8y/8VNo6bhrj37fFmv0/Lda6fz5BsbWD5tPAY3ObWJ4UueMQsq2xvYVX2chxOX99jmMdzTnx+OW8bbRbux9XEttBg+PtqTTe7pSn71rSXMTR3dmZQB3PQ6ZqfE8au7lqDVaHhn2yGnj7v3+GkmxUew7cipzhnWACeaykjzj0GrdD0ddVht7M4u4vaFk7q8HmTwIcjgQ0lbbdfXfY2MjQriy2NFffhqhRh6nErMiqJEK4py9bn/91AUxbt/wxIDaWtlDrODx2HU9b6uOc4YQojBl0P1hQMQmRhozW1mPjtwnEduntfjTGsPdz1P3bOU4soGDued6fW4DofKpoMnuWnOBDzc3fhwV2bntg67FQ+t20Xj/7puLzqtwpzkiyeLGTR6OuzWi16fNCaCk6XVvcYjxFDWa2JWFOU7wAfAX869FAl81J9BiYG1ryaPOX14bjw7OJF9Naf6MSIxWHZkFjApPhJ/Y+91p/28PJg7IY7n/7WV+ua2bsedvTV+CL1Wy6QxkTz8jTnsP1HC/32+n/qWdrx07jRYWzvHl9c18T9rdlHT2EpEoN8lJ3M1WtsueXfH3U2HxSZ3c8Tw5swV8/eAmUATgKqqeYDzD5bEkNdsM2Nyd/4miMndSLPUzh6RMk6WMiv54me93bnz6sm46XT84q2N7DiaT4fV1rlNVVVOnanhpQ93crK0mh/fNAeNRsHXy8B/37EIhwMef+1T9n1RzqbCHN7dcZDn/rmVX6/eRLjJhyduXYDN7qC9o+uVcUFLJVaHnVFegRfFU9fchtHD+QlpQgxFzsyQ6FBV1XK+Dq2iKDpAFgyOIHpFi8Vhw83J6klWhx295uLGBGL4azV34OfV+yON8/yMHvh6Gbhv+VV8tv8472w7zJhwEzqtlsr6ZjqsNhZOjGfR5IQuz6qNHu58e9lUbp2fxv4TJZRWl3PKUcbCCSlMSYjqbHwxcUw4u7MLWTQ5oXPfjeWZzA9JQvOVZ9KqqrIrq5A7Fk6ipLqB9g4rBjcd4SZfdFqZTiOGD2fOxDsURfkZ4KEoyiLgQeCT/g1LDKQ472Ay64uZETTWqfFH64udbhsphhc3vY52i633gee0W6y463Ukx4SSHBNKdUMLJdUN2OwO/IwejAkP7HFdsafBjTkTYolq8eL3uevwi9B16Ua1eHICb27MYF7aaPRaLZ+XHaWgpZI7YmdfdKwDJ0oorW7gb+v3odNp8fZwp9XcQXuHjflpo7l6UgK+ffilQ4jB4kxi/inwbSALuB9YD/ytP4MSA2thSArryw47lZjNdgtf1pzkmdRVAxCZGGiJo0I4mFdKfMTFt4kv5eDJUsaN+s+TrSA/I0F+xl73U1WVUy0VbK3IZn9tPhpFodnaziMH32Jm0FjujptHpJeJxFEhhAX48NynGwkYp6G4rZrHEq+/aKLiobxSnnh9PbOTY7lrcTpxYabObaerGth08CRPvvEZj31zXpelWkIMRb0mZlVVHcBr5/6IESgtIIb3T+9lc0VWj12kHKqDtwp2kuYfTYB77ydfMfwsnDiGp/6xiW/MSrlkH+UL2R0OthzO49Gb5/XpPWwOO2/kb+NkczlXh6Zwe+xsjDoDDtXB7qoTvFO0m3v2vsIEvxhM7kbqY1rJK68m4HAAd6XMwF//n++9uqY21u3P5f8+P8BNs1N48PqZF73fqGA/vr1sKvtyi/ntv7bxy7sWE+Qr379i6FIuXE/YZYOiZNHDs2RVVQelvE56erqakZExGG89olWZG3k25yPmBCeyNCwND13XCTTl7XX8IXc9+S2VhHv4o9VoCffwZ27IeCb5x6KTZ87DUkl1A1sO5XEkv4y2c89k28wWRoebePyWBd3ehlZVlf/7/AB1zW19SsyqqvJq3iba7Ra+l7Cks0DIV+2qyuXvBTv4VtxcRnkGEuERwIETpWw+fJLS6kYCfbyw2e3Ut5jx8tATHezPj1bO6fX9399xlKa2Dr69bKrTMQvRH9LT08nIyLjkD1hPifl854Lvnfvv6nP/vR1oU1X111c0SidJYu4/dR0tvF20m2ONJUwxjSHcwx8VlV1Vx9lXk0e8dyjfGXM1McYgHKrKqeYKtlZmU9PRzA/HLiPWKJP1hwurzc5r6/eRU1zBgrQxTB8fjbeHOy1mC3tyCnn9swMEeHvw1N1Lu9wWhrPLmT7YmUlNYys/XbWgx/aNX5VZX8y7xXv41YRv9jrZcP2Zw5xoKuPhxOVdXq9raqOhtR2dVou/0YPH//Yp/+/2RZ1lQ3vS0NLOT15bxx8euL5P5USFuNJ6Sszd/mSoqloMoCjKTFVVL7w/9FNFUfYAg5KYRf8JcDfyg7FLqeto4cuak1R3NFHWVkdxSw1/mnIvib6RXcYHGXyYHpTAgdp8fpf7KY+Nv57oSyxhEUOL3eHgf9bsQqfV8NJ3r8ftgtnS3p4GbpqdyjVTE/np39bx4P/+m5lJMYwOP5uciysbOF1Vz9zU0dy3/KouM62dsaUim2XhE51aAbAgNJlPzhykpqOZwAuW8wX4eBLg4wlATlEFQb5Gp5IynJ1FHh8RSFZRRZ8acAgxkJxZQ+ClKMqs839RFGUG4NV/IYnBFuBuZHnEJL4RNY2i1hqeTrvloqR8oSmm0dweM4tX8zbS3R0YMXRsP5pPu8XK92+Y1SUpX8jT3Y0/PHAj81NH4+tlwMfTgLeHO/PTRvO/37uRVfPS+pyUW2xmjjedYZrJuTrrBq2eqwLj2VeT1/0xzZaLGmL0xt/oQUt7R5/2EWIgOZOYvw28rChKkaIoRcArwL39GpUYEvZUnyDFL8qpW9RXBcYDZ+sei6FLVc+WxlwxM6XXtb0ajcLNc1MpKK/jmqmJXDc9iasSo3udFNadJks7vm6e3T5XvpQQgy8NltZut7vrtZgtF5fm7Mn5JV5CDFW9JmZVVQ+qqpoKTABSVVVNU1XV+cr1YtjaUXWM+SHJTo1VFIUFIcnsqDrWz1GJy1FYUYfV7iAp2rl16HFhJrw83Mg9XXnZ763TaLA5HH3ax+awo1O6/0VgdJiJwoo6mp28ArZYbeQUVTA2MqhPcQgxkHr9tVFRlP/+yt8BGKzJX2Lg1JibifIy9T7wnChPE/tqpYb2UFbT2EpkoG/nz7EzIgN9qWns/qrVWf5uXlgcNirNjYQYfJ3aJ7fpDDN7WF/v7Wlg4pgIdmTmc+208b0e78tjxYwOD3RqrbUQg8WZW9mtF/yxA8uAmH6MSQwRfTh3A2fX1mno405iQCmK8isHieYAACAASURBVLXmASg9VO9yll6jY3bQOLZVZDs1vqK9geLWGqaYRvc47pqpiazbm0tRRV2P48rrmnhvx1Guvcr5hi1CDAZnCoz87sK/K4ryIvBxv0Ukhoxggx8FzVVM8Hdu9mphSxXBBudmx4rBERbgTWFFHXaHA62m99/LVVUlv6yWqycl9Dr2Uhyqg6P1xRxvKsNst56tZ12dyzTTGGK9u7+dbnPYWV24kwUhSeh7mcEdHeLPPUum8Px727htwcSLnoPb7A6+zCnkrc2H+OacCSSOknKyYmj7OjMgPIGLG6SKEWdeyHi2VmY7lZgdqoOtldk8EL9oACITX1dkkB+Bvl4cPnWG9ISoXscfK65Ep9UwJtz5Rxrnba88xselGXjrPZgSEEeQwYcmSxtaRcODB17nO2OuZkXUlItuqzdYWvnbqa3oFC03RE1x6r2mjhuFn9GDD3dl8u62w6QnROHlrie3pIq9uaf/P3vnHR5Vlf7xz5k+6QlJSCMkJAFCAoEQOtKrCoKIBQHX3ldRd137uvoTFbGtoqy9YEHAQgfpHekQCDVACJDeM33O748JJZByg9Lv53nyJHPn3HPPnSTzzjnnfb9fnC43kcF+fLdkM6t2HqJ/agIdW0afcyKbisr5RMke8+kKYFogBHjlfA5K5dKgS3Bzph9eR0ZJNi39I+tsu/h4OlaXg6U5O5l/bCsmrYHWAdG0C4xRVcEuMQa0b8G05dtIignDbKg9Q9rucDJ1+Vb6t2/eoD1pgGmH17Kh4AAPNx9I3Bkz4znTDmEKdDOZhcw7tplhUR1pZPTB7naytegQ6SVZ9AxtxU3RndEK5a5QzaNCeOa2vhwtKGXVjkx+XZOOj8nA34d3Z2BaC7QaDS63m017s1mwcTez1+3iH7f0VuQ9raJyIVEyY77+tJ+dQI6UUrn9jMpli0mr58GE/nywZz4PJPQnOeDsGZaUkkl7FvDrkT9oHxRHtHcwPjoT5U4rC45t5dvMFQxr0oHejZMuwh2o1ETnxGh2HsrhjR+WMG5Ejxodl8otNv77y0pC/H3onaKs7vgEa/P3sr5gHy8kj8BXf3bQEwiCi0J46eZbeGHbj+wpPYpGo8Go0dHCL4K74nrjpTOe8/35eRlZv/swN/Voww1dkqp9qNBqNHRo0YS05lHMWLmd179fxEtjBqgqYCqXFEoC86tSyjGnHxBCfHPmMZUrk6SAJjzcfCCT9y6kkdGXPmHJRJmDcOFmf1kO32auoMBWxmspt5EWXD1Jp394Gw5V5DNpz3xK7JUMU7gsqXJ+EULwtwFpTJ69hnvenkqzsEZ0aNGENs3CsdqdrMs4xPqMLHq2acaoPql12jaeiZSS2dmbGB1zTY1B+XSivBsxIrozxy1F3BPf98/e1knmrs8gPiKYYV1rL/UTQnBj99bkFpcz948MRlxzUaT/VVRqRElgrjbVEULogPbnZzgqlyKJ/pG8lTqGzUUHWZmbwVx7GRo0aITApNHzTddHCTTWLAbX1DuYZ5OG88qO6TT1DqZdUOwFHr3K6UgpWZV+kLl/ZFBhsdM9KYasvBKmLN7E/+bYiQr259qOibx1/5Bz8i4+UJ6DxWWvcXWlJnqEJvL05m+51dntLCtHJUjnIXBlA27QhOAUcSzZup/nb68/0AshuL5zK974YQk3dE2uV3BFReVCUWtgFkI8AzwLmIUQpScOA3bgfxdgbCqXEDqNlg6N4qqVrryR/it3xPWsNSifwN/gxU3RnZl3bKsamOvB7XZTVliOzWLH298L7ypN6NqQUrJvcybHM3NxOV0EhPqT1K0F+hr2jqWUfLngD/YcyefWXm1pHRtebTZ8OLeYX1ZtZ+uBowzqUL83d00cKM+ldUA0GoV7w356MxHmILIrC2nhF3FynHXtaUspwb4SaZ0N7gLQJQACnIcpr7DQu2Uc4UFDazjPDvbVSNsycBcBOqLMTWkZ7k1GVg7JMeHncssqKn85dZlYjAfGCyHGSymfuYBjUrkMyLEUc6SyoN4a0xOkBTVjSuYKjlYWEeGlGtWfSUVpJetmbWT59LVUllowehmoKK6kSYsIrrmpC+36JKM9LYNYSsnKn9ez5IeVCCGIS4lBo9WwZUk634+fQZchHRh4V2/Mp816f12TzsHjRbw4pn+NSV/RoQE8Oqw7UxZt4p0Zy3l+VL8GJ3053C70DUz202sEeudm3KWfgSMdpB2p8QZ9B4RpMEKfcNp9u6FiMtK5B+E1CvSpiCplMCklmccX0DLsZ2TZ/4HvMwjh2TuWtnXIio9BF4Mw9gdtOOACxw4Gt/oObzYjXf9GaBuefa6i8ldT14y5pZQyA/hJCJF65vOqLOfVzaHKfBJ8w+utMT2BTqOlhV8Ehyvz1cB8Bkf2HmPS418Q3zaGsS/dTGzraIQQuJwuti5NZ/F3K1k2dTUPvH0H3n5euN1uvnn5J44fzOW2fw0nvl1stQCaeziPOZ8u4p37PubRD+7BN9AHi93BnHUZvHbX4DozsYUQjOqTyrOfzyH9UA7JMWENuhc/vZl95ccVt3e7CuhnnkKIOxJhGA4+T4AwgywG2zJk+USkLgHh86gnyFp+QLoOI/xfQ4jqe9hCCGyyJSv330ZS7EZk+XsI338gbauQFZ8jfJ+tFuQB0MXz264Ahrbdhyx9DvxfQ2iCqjWRzkPgzABpA+EDhlSEJqBBr4uKSkOoa73piarvE2v4eus8j0vlEsfldqNTIFBxOnqNFqfbdZ5GdHmSn13AB49+xo2PXcdd/zeKZm2angyyWp2W1H5teOLTB2jSIoKPHv8Sh93BrI8XUnisiHGT7ychtdlZs9rQ6BDuePkWWnVuweQnv8btdrN6x0ESo0MJ9q/fGE6jEfRv35yFG/c0+H7aBsWwq+QIJfbKettKdznlhU9z0B6Dd9C7CFNvhMYbITQITRDCPBwR8D7gQpa9hdtVirTMQfj+86ygfIKYsEAysvJxmR4Ex27c9i3IiskIvxfODsp4SsJ2Z+Xj2+hWhLEXsnzSqfHZN+EueRZZ+h9w7gd3HtKxCVn0CO6yd5Eu1bBF5fxQ11L2fVU/DpZSWk9/TgjR8CwNlSsKf4MXOdbS+hueRo61hG4hLc/TiC5PZn60gF43dyVtQEqtbTQaDTc9OYQPHv2cFdPWsnzaGl6Y+gSGOkp8hBDc8Mggxo9+j91/7Gd79jE6JSr3H+7UMpopixq+KOajM7F+1XEe3vY8vfyP0tivHAHkl3uxbHcM27LCkFWyre9NeRBtmI0v18bx2tH3zupr00fjPLNkn3HIkueh4iMwdEBoal9xCQ/yo0lIAOv35NA1diCy4hOEoQtCF1Nj+3UZh2kW3ojQAB+kvBGK7kW6joN9A9L6K8LrTjB0xJPz6kmyke5ysM5DljwHfs8hdA0rJ1NRqQ8lU57VCo+pXEW08IugxF7BoYp8Re2PVBaQbyvD4XbybeYK/rf3d74+sJzNhZm4ZcMch64USgvLSF+9mx4ju9TbVqPR0G9MD36dNI/Ezgn4B9cvfSqE4JoRXVg2dTUWmxNvo/JaXS+jAbvThdvdMF1t6TzM/7Vdxdi0bey1+vHVqnZ8sbIdW7PCGJGWzoRb5tEspBCjzk6X1oeYebQZ8njdNctC6BHmG8G2BGHsVu8Yru2YyNRlWyhxpoF9I5gG1diusLSSn5ZvY3DHllXXMSCMfZDln3qCst9rCGPXk0H55Hg0PgivmxA+DyFLX0O669boVlFpKHXtMYcBkXiystvBSXcCPzyynCpXMVqhoVfjJGZnb+TBhAH1ZtF+vGch+bYyZmStp3NwAtHewZQ7rPxyZAPfZK5geJOOXBN65c2mpZQczsgm/0gBUkJIVBDRiVEIIdi6dCfJ3Vri5atMeaplx3gKjhYRk6R85pvaN5mf35tN0+7NFFsjgkdgxKjXNayG2XkYWfoSA7v8g6OutsxzzaZXx1D6hbU+mXHttq8hOOgllhZF4fKLY9qol/C7Q8H9G9JAliOltV6blJS4CPq2S2DCT2t5+VorWm1M9XFKyd7sfCb9tpoBac1pHXtaNrYuHiw/Ivz+g9DWbQ0pDB3AsBmsC8Dr1tP6t4PzEGDx7ElrYxANUDBTUakrc2cg8DcgCnj7tONleMqoVK5yBkWk8OqOGUzPWseIJp1qDM5SSl7dMZ1NRZm83vZ2Wgc0qdbu2sh27C87zsd7f6fYXsGQqCujRN7tdrN25kaWTl2FrdJOVHNPYMreexS9yUDPkV0oKywjKFx5IpxGo8Fg1ONyKd+nN/uasVnspDQLZ83OQ3RPVlautmbnIdrGRSi+jpQSWT4R4TUGYepDJPBqyi2sytvNlweWUeqoxKw1UO604l2QwqNxa2gScie6ekRITiCEFil8wXVMUfuhXZII9i7C6nDx94k/khAVQmSQPwF+ZtbvOky5xc7IHm3odsbrIV3ZIO2gPyvftWZMg5ClL4N5hKcEyzoHaVsMmhBPEpu7BLCDcSCYBiA0qt2kSv3Utcf8FfCVEGKElHL6BRyTymWCSWvgH4lDeSdjNrtKsukX1poOjeLQVSV5bSg8wE+H1pBRms2kDncT4xNaYz9xvmE8l3wjr2yfRrR3MCmBTS/wnfy1uJwuvnzhBwqOFzHs0Wtp2TEeTVWinNvtZvcf+5k9eSFFuSWk9GqYVKnQCOwWu+L2ZYXlePma6NIqhu+XbOF4YRlhQb51j9/tZuGmPdxzbSflA3Ns83w39j55yEtnpH94G/qFtabUYcHmduClMzLu/ZlURuxA4z6kvH8ATSA4tgMj62xmsTv4bXU6ttLZNE/2Iy0OdmWXsnlvNm4J/VITuGdQJ3S6Gmax9j9Al6B4hit00Uhh9tRGV05BGHsj/N9EaE9ls0vnPqRlNpQ8DX4vVHtORaUmlNg+ThdCXIdHAcx02vH/nM+BqVwe+Bu8eKH1CDYXZrLo+A4+2bcIo1aHzeWkuV84WqHhH62G1hqUTxBg8GJk0y7MPbr5sg/MP02cibXCxrjJ958l9KHRaEjslEB8uxgm/O1DFn69lJFPDlFUL1yUW4JWp+XANuUBbf3czSR3T8So1zG8WzLvzljOc6P64utVc/6m2y35fN56Ggf60CKq7qXc05G23xGmQTXehxACf0P13a+tWYkkRq6tV0zkZP/uCkCCcw/SXYLQ+NfYrtxiY/wPi4kM8ubOzjkYvUby4IByhPetJy0sv1+6mQ9+W8kjN3SvpvYlpQMcO8A8RPF9V50IFf9D+D6HMJydxCd08Qjfx5CW2Z4Mb/831ZmzSp3U+7FQCPExcAvwKJ595pHA5f3OqfKXohUa0hrF8XTSDfyv031MaDeGyZ3u5e64PlS4bHQNUaYilRbUjOzKQo5WFp3nEZ8/Co4VsXHBFu4aP6pG9a0T6A16xn3yAMW5JWxYsFVR36t/WU/Pm7tyZM8xcg7l1dve6XCyYvrak8llA9Na0C4+kn9/vYBV6QdxOE8tiUspycjKZcJPSzleVMYjw7o3TFzEdbRKgUsZm7KSwV0Gzn3KTrAtBUMawnQtsmwCUp69Xy6l5L2fV5DYJJgH+mzBaE5AeN2OtC1Huo4ihCA+Mph/3dIHu8PFd4vPyDq3zgVNCALlAilSSnDtBuN1NQbl0xHm60DXAqzzFfevcnWiZL2mq5RyLFAkpXwZ6AIoE8JVuerQabT46E3oNToOVeQR7xOGoQEiJC39IjlcqSzT+1Jk1c/r6Tg4tZriVm2YvU10vj6Nz56ZQmWZpc62WbuzWT5tDX1Gdee6e/vxv39+Q2lhWa3tXS4XX/97Kk2aRxCT5Pl3FUJwS6+23N43leXb9vPYpF9466elvDN9OU9/OptP56wjpVk4/7qlT50iJDUjod60rFO40YAmBFn5hSdZqq6eXQVIy88I07XgNRo0ociS55D2TR4lsCp2H8nFwAFGdZgH7nyE7ziENgThNRpZ+h+k8wgAep2Wh4Z2ZeWOTEoqrJ79cetCpOVX8H0SaV9b75hOjs2+1vMBw2uEovbCfD3SNr/auFVUzkTJO+aJd4xKIUQEUACogscq9eKU7nOQZ9TicJ9yFVW61HmpkL46g1v+OUxx+xHjruP1sR/wzn0fM+alm4luWd332u12s3VpOj+88Qu3/ms44bGNCY9tTHlxBRPu/JDBd/clbUDKyZpmt9tNxrq9zP9iCQaTgXvfHH3W65eaEEVqQhTHC8vILijB5XIT5OtFXESjc3+tNSGeTGSdMonWUJ8C0CeBphGy9DXwfbxGNS3pPIQsewNhHoLQJ3oO+jwKtiXIyikgP0HqEgANlGxkTGcQhpvAdC1CeD5cCNMAQIssfQapb4sw9cfbEEHPZG927f2OjjE7QdoR/q8gtBG4dU3BtgJMChyvKr8FQ3s0CpemhS4OKczgOgC6+Krl850gywA96OIQ2mBFfalcuSgJzLOEEAHABGATno/Gn5zXUalcEfjpzeTZap/V1USerZR4dxhTMlewNn8vpQ4LOo2WWJ9Q+jZOJq1RM8UyoBcDa4UNnwDl1YTe/t4ERwZyzYjOTH7qKwJC/UnpmYTRy0hpfinr523GJ8Cbu/5vFC3STgW96+7rT7OUGJb+sIqf359Dk+YRaLQacg7nYfYx0eOmLnQZklZNX/tMwoJ8qyeC5eTAjBme70YjtG8P/fqBAoU3YeqHrJyGMPVRdN+p0TsQptFg7AOV3yKLH0Xq2yEMnUB4gbsYaVsKrsMIr9GI04KkEAJMfTyJZq79Hncp6WbGJg0P3HAXwnx2kBSmvmDoBLalyIpvwF3IdYkudh8zI7zuBX3bkwlfwjwKWfYaaKNrVAs7gbT86skSr6VOuvYXKwDpygP7eqT1d9A2Bk0jTzZ4xYdIXSLCfANC36ph/apcMShJ/nql6sfpQohZeBLArryCU5W/nBMiJIcr8on2rn8WkF1ZwJbCgxytLKJ3WBIvtB5BsNEXh9vFjuIsFuVsZ9rhtTyReD2RXkH19ncxMHkbqSipX47yBJWllZh9zPS4qQvdhndkx8oM9m3KpOBoEd7+Xtwz/naatqp55yixUwKJnRIoyinm+MG8k+5SkfFhDZv5HjwIzz4Lc+fCkCEQGwvFxfCvf0FpKTzxBDz4INTVp749yE+R9g0IQ1qdl/vfo62RZTPBeI0nGHqPhSoBEWlbAVhB+CBM/cDQ6aQRxZkIITx1x1XKW/tyczGbaxcrERofMF+PMF8PQG5lHnN3baZTh+qlUULfHHweQZa9CqbBVWVOp/7ePFnWszz74143I9wNzIlwF1apmHVE+L2M0J36/UppBdtyZNlET9+mgQ3rW+WKoEFTD+nJuLAJIX4ClKscqFyVnBAhmXN0M/fH1+9U9Eb6b/jqzbyRens1b16jVkP7Rs1o36gZq/J283r6rzzf+kYam2rOzL2YJHZuzsaF24htrSw/cuPCbbTq3BwArVZLSs8kUno2rIQqsHEAgY3P0VQhPR0GDIAHHoAPP4TA0+qqX38d1q+Hhx+GLVtg8uRag7MQGo90Ztl48HkcYWhXYzvp2O1ZmvZ5DCFOBVFP0ByCaGhG9Gl4mwyUlFsxBynbHy8ut+BlrLmtMKSB3/+BdSay+DGkNgqE0VOrLC0I40DwvgfchZ46Zuk8SyGsJtzOLE9Jlu9TCK+bzr6uMIFpAOjbeGRINUEeIROVq4pzlaO5fDb9VC4qA8NTOFyRzy9H/vBksNaAlJJP9y1mb9kxxrcbVS0on0m3kBZcG9mWbw4sP19DbjB2q50je46SueMwrbq2YN2cTVgr61fZsllsrJ21ke43NqBe+K+krAyuuw7eeANeeKF6UAZPEO7UCZYuhW3bYMKEOrsT+pYI36eR5f/FXfpvpG2tJ3HLXegxhCh9DVn2fwifhxGGv15IJjUhklXpmYrbr0o/SPuEqFqfF7oohM+DiICPq5bThyK8H6l6PMIjzamLBm0Y2Ncru2j5x6BrhqaGoFzt2towhM/DyMrvav2/UblyOdfNOvUvRUURZp2Bf7YaysRds8goyaZfeBtSg2LRCg0u6WZr0UEWHtvOpqJM7orvrWgW3DesNbOyN5FrLSH0Is6acw/nsWzqGtbP3YR/iB96o57C48WUF5Xz2qh3efa7xzDVUi/sdDj54vkfaH1NIiFRF8kD+NtvPfvIo0fX3c7HB776Cnr0gMce8+w/14LQt4LAj8G+GmmdVaXU5QZtY4SxD/g+wfnywOnbrjlv/riE6zu3wqiv+60tr7icXYdzeWBI/TrlQuMNmtpXMYT5JmT5h1WJW41rbee27wD7EvB7td5rAqBP8ew7OzPgROKbylWBqO3TmBBiJjUHYAH0kVLW7x93HkhLS5MbNmy4GJdW+RM43E42FBxgUc4ODpbnYtYasbrsNPFuRM/QVnyTuYJ329+Bj17Zm/aUzBV460wMa3Jxlvm2Lk3nu9em0314J7oN70hQmGe26Xa72bZsJ5Me/wKn3cnj/7uf1t0TTy7jSynZu+kAsycvxOxj4p43RqOrJ4icF6SENm3gvfegj7KELfr3hzvvhFGjzu/YGkBhaSXLtx8gO78Et5TszsolOMCH527ri76WxLfSCivjf1jMNa1jubbjXxPwpHWeJ/nN++4qN6pT15bSAtYlyMpvQVoRjaYpVhaTld8DboTX7X/JOFUuHdLS0tiwYUONq891vSPU5bms+jGrNAi9RkeXkOZ0CWmO1WWn0mnHrDVg1hkosJXhrTMqDsoA4eZADlbkUem0sSpvN8tzd5Fv89hQhpoC6BWaSJeQ5pi0yh2VlLJn436+Gz+Dh967i6aJ1ZdCNRoNbXsnM2njG7wx5gPeunMScW2b0qSFpwzqyN5jCCHocVMXrhnR6aRU5wWntBQyM6F37/rbnmDoUFi58pIIzOUWG1/M/4Ptmcfp0iqalLgIhBAE+3szZdEmRr7yNQ8N7Ua/dgknjThsDidrdx7il9U76NIqhsEd/rocVmEa5KmvtkyDis88phvCC+kuBscG0CV5yrwqv22YoUUD9MFVrhzq0spediEHonL1YNIaqgVMrdA22PrRJd3kWIp5ctM3JPlHcVtMt5OZ35nluSw+voNpWet4MGEAyQF/rR7OjPdmc+vTw84Kyqej0+l4ZsrfefNvH5DSM4lGEUFIKel1SzdiW0df/NrsigrPEnVDxuHjA5XKM87PF2UWG69+u5Dk2HDee/iGamIo3ZJiGNG9NR/PWsNbPy3lp2VbaBbeCJdbkpVXTHxEMH8b2IGUZsoNOpQiDKkIQyrSeRAc6SBtCH1j8BqF0AZ79ttlecNq82U5EhPY//CUVrmPe45rQj1bA2fMzlWuDC7dglCVqwbfqpny0coiIryUuS2tyN3FvrIcXm17C818qu/rtQ6IpnVANLtLj/Lf3fN4qPkAWvnXHkQbwqGdWVSUVCoyn9BoNPS57RrWztzA4LsViFVcSAIDoaQELBYwK3N44uhRCLr4ZWofz1xDSlwEo/rU7ABl0Ov4+/BraJ8QxTeLNjK4Y0v0Wi1hQb408jv/O3BCFwO6mLOf0ASBJgCc20Hfpt5+pJRIy3zAjnRu88zKdfGAAFcm0jrTMzv3ffKU+IrKFYFqEqpy0dEKDT1CE1mSs0NR+xxLMavydvNc8rCzgvLptPCL4KHmA/h470KcbuVWiXWxefEOOg5OVbwE3a5vMpk7DmMpr1ty81yQUpJRks3s7E38nLWehce2UWgrV3ay2exZxv7xR6UXg6+/hhtvPPcB/wUcLSgh83gBI3vUrUsN0C05lshgf2x2J0kxYRckKNeFEAJhGoS0zFHUXlrngisDvB9CEzABYeqL0DVF6KIRxp5o/F9D+DyELHsd6dh5nkevciFRA7PKJUHvxkmsytvNoYq6dbKllLybMYdmPo1JCqi/lL6VfxRhpgA2FZ4qoym2VzA7exNfHVjGVweWMe/oFsocygJnRUklASF+itqCx6zCy89MRelfG5hX5e3m2a3fV3kdW3BLSWZ5Ls9t/Z73d8/lmEWB6MXDD8P774PTWX/bOXM82djduv35wf8JFm/eR6+U+FoTu86kf2pzFm1WaJRxITD2AldWvcHZ7cyBslfB6240ptpfc2FIRfg8jiyb6JH3VLkiUOIutbBKkvPE40AhhGqPovKXEmzy429xvXhr50x2lhypsY3FaeeTfYvYW3ace+OVLw33bpzE8txdlNgrmbRnPs9s+Y4cawkR5kAizIEcrsjnn5u/5ZN9i6hw1l1/bDAZsDXAD1lKid1ix2BqqClE7Uw9tIaZRzYwJrYH49vexm0x3RgR3Yn7EvrxduodxPuE8dqOnzlQnlN3R4MGQWgo3HcfuOpYUdi+He6+G956q2F70ueBQ7lFtGpa+yrJmbSKbszBnEvHrUwIM8LvBaT1N2T5J0jX8WrPS2lHWpdA8SOgbYrG5576+zS0A20U2Nedr2GrXGCU7DEHSymLTzyQUhYJIeo211VROQc6NorHpNHz2b7F+OjN9AhtSYjRD4fbxfbiw6wr2Eu7wFgivQKJrcff+XQivYI4ZinilR3T6dAojompY/HSVa/FLXdYmZG1nld3TOe5pBtrzBB3SzdNUqJYPXUt/Ub3UHTt/VsP4h3gjW9g/SYHVpedPwr2U2ArRyME4eZA2gXGoDvNCGR57i42FWbyfPKIGsdo1hm4NrIdYeYA3s2Yw6spt+Knr2UPWauFn37yLE/36gVPPQXXX+85DpCdDZ98ApMmwQcfeBTCLgDHC8tYtHkve7PzsTuc+HoZ6dCiCd2SY3G63NU8lOtDp9Xgcl9aTk5CGwb+r4PlV2TJ00htNGiCAbvHD1oXD7qmnj1lpX2aBiKtCxDG7udv4CoXDCWB2S2EiJZSHgYQQjRFFRhROU+0CWzKhNTRbC/OYm3+HjYVZqITHhOL19reRqDBhyc3fo27AX+CbulmX/lx7o7rw9ComnWcffQmxjbrwY8H8GU7nAAAIABJREFUV/PR3gX8o9VQwJP9vaXwIItytrOzJBudt4aDXbOpWDuVG1p0IjmgCZpayl9yrSV8v2Ah4bfEs7v0KM39wmtsW+60MuPwetbk7yHRL5IIr0DcbsnCY9v4JnM5fRonc31kKhoh+O3IBh5I6F9vaVlqUCybCzNZlrOTIVF1qGz5+nqWqb//HsaPh/vvh6ZNwWqFrCy47TZYtgwS/1xyUWFpJWUWG3qdlpAAb/Tas5eiLXYHn85ZR/rB4/RsE8etvdti1OsoLKtkxfYDTF22DS+TnpzCMlo2UfbBLKe4nAAFFpwXGqEJAO87wOs2cGwFd6lH8tNrNEIbjrt4nGKnLsDT1q2WVV0pKAnMzwErhRAnyqd6APedvyGpXO1ohIaUwKakBNasNx1uDmRf2XHFWtnLc3dhEDqGRNYvA3lT0848ufFrDlXk46/34p2M2WiFoF9Ya8a1vA69Rsf8zKXM/nU9P9zkwsdo4u8tB1eTEd1efJh5R7ewPSuTSlchXVuG8+3BFVQ67fRunET/8DaYtJ6l7WJ7Ja+n/0KifyT/l3IrQcbqM+vsykK+P7iKdzPm0KdxMruWZDDpyb28veQ/9d5L37Bk3ts9l+si29X64QEAvR7GjvV8ZWaecpdKSPCUSJ0jDpeLdbsOs3DTHnIKywjwMWN3urDanfROiaNvuwSC/DxOXHaHkzd/XEJYoC/vPzwMw2miK7FhQbSvsql87ou5fLVwIz1TlAWtpVv30TUp5pzv4XwjhAFq1MI+l7mPOl+6UlDiLjVPCJEKdMaj+jVOSnn5OtmrXPb0rjLG6BbSot62UkpmZW9icGQ7RbWjJ4w35h/dwsGKPNoHNePGJh2rnTvgtp4UZRVz4J2DeD8Uy8Sds3gmeRgGjY6ZRzbye/Y24g54E/ZNKX9/936imntqZg+U5zArexPj03/mqcQheOuMvJcxh46N4rgxuma97EivIMYlXseHu+cz5eAK/LM0CIVS9TE+oUgpKbCVE2JSmLAWG+v5AqyVNv6YsY7jB3JwudwEhPrTcXDbkypndVFusfHO9OUgYGiXJNrFRZ4U+sjOL+H3TXt5/su5PHJDd1o1bcz0FdsJ8vXi3ms7n2x3JmFBvrx+z7Xc+uq3LNy4m/7t6/79F5RWsGbnIV6/+zpl934poWkMzkzQxSpr78z0nKNyRVDrx2ghRMuq76l4nKSOAtlAdNWxBiOE6CWE2CKESD9tBo4Q4lYhxCYhxOPn0q/K1UXboBiK7RWsy99bb9sVeRmUO60MDm+ruP/WAdGszt9DjHfIWUEZwO1yk9C+GXaLgz/GLGDtd+t4/KWJvP7p10xZ/jv85yDe2xw8NfnBk0EZoJlPYx5tPohEv0jezZhDenEWdreD4U061jkerdBwT3wfMstzkaJhsyKTVo/drSDr+jQcdgfT3p7JC0PGk7F2D40iAgmLCaEkr5Txt7/Hx09+RcGx2hOqHC4X70xfTpPQAJ67rR/tE6KqBdvIYH/uGJDGw0O78f7PK9hzJI+l2/Zza6+2tQblE4T4+3BTjza8/8tKDhwrqLVdYWklb/64hKFdk07Oyi8nhKkf0qo8x1Za54A2Enfpv3EX3o278G+4i59EWmYi3QpL6FQuGeqaMT+BZ8l6Yg3PSUChwK6HqszuScAgKeXhMxLIbgU6AFOEED5SSvUvSaVWtELD31tey4Sdv+GUbroGNz87eEo3y3J2MiNrPU28gjFqlWdFC+C4pZghUe3P6nfDgq3MeHcWwZGNGDC2FyPG+bBmVzq/Orexq3QHXTaF8eSnj9EovGYhDiEENzftwn+2T+eHQ6vpF9Za0UzeS2ckzjeM9OgiIrYouw+3dFPisOCtq9104kzsVjsf/v1zfIN8ePb7cQSGVt8uGPboIJb8sJqJd0/isY/uo3HTkLP6WLfrMAgY2y+tzkCbFBPGyJ4pTPptFfERjQgJULZsPqZfe5Zt289r3y+ic2JT+qc2p2ljzyw+r6ScxZv3sXTrfq7t2JLr/iIt7AuOvj3Iz5D29QhD3R/c3Nb5YJ0Pxt4I87XgnQRCA65spHUBWKaC110IUwPkV1UuKnVJcp7YRx4spbSe/pw4N3uYUcCME0lkUsrc07s8cdnTflZRqZWm3sH8s9VQPtq7gJlHNtInLIlorypJzopcFh9Px6TV82zycD7cM588W+lZ+7e1saHwAH56M+Hm6ku2K39ex9zPFnHfhLHEJJ2S+Uzu1pKN64vwQk90tuCH13/hgYl3oK2l1lYjNPQPb8P4HT+fTDJTwsDwNqyN2Kl41ryl6BDh5gACDMqFNX5881cCGwcw9uWbaxRRMZqNDLqzN35BPnw07gue//GJs0w4ft+0lyFdWtU7+wXo0boZH/66ipS4SMVj9DEbaRoayNj+aew9ms/EacsorbSiEQKdVkOP1s14YXR/Ihoprze/1PD4Wz+JLHsVfEStnsxu60IoeR58HkXjPbb6k5pAhD4Z6Tzs6QeJMDVoPqVykVCS/LUaOHPpuqZj9dEc0AshlgK+wHtSyq+rnpsBbAC+lVKWNbBflauUaO9gXku5jd2lR1mWu5P1BfsBaGzy576EvsT7hCGEoHNwc5bn7qKFnzJ95HX5+0jwDat27MjeY8z8eAFPfvogoU2Cqz0nhMAlXbQIiuHeN/vw8bivmPf5Yq67r3+t1+jYKI5ypxVdAwwNmngHo3FCUWz9gVlKyfxjW+kb1lpx/8V5JWxdms4rM/9Vr7JZ1xs6sH7OJrYuTad9/1MqXIWllRwvLKWdwkCr12mJCQviUE6h4nGC5zX3MRsZ1jWZG7okYXe6cEuJSa+7+DrkfxFCnwC+zyHL3kRqf6mS5IwDBDgzPbNh20Lwuf/soHx6P7po8H0RWfoMGNohNMpkb1UuHrUGZiFEGBAJmIUQ7Tg1k/UDzmXTRge0B/oCZmCNEGKtlHKPlPIr4KtaxiGACqpSDoMuAa1elUsHIQQt/SNp6V97IOgRmsg/N33L8CYdCTb61tlfdmUhxyxFdAtpXu34sqmr6XVz17OC8gnsbie+ehNarZYRT1zPO/dPZuCdvWu1dNRrdOiEllxrKT611RmfQZG9At9jgmMdXGQVLyDKWKX0pA0HfduTrkVSSr4/tAqn20XHRspLblb/8gft+6dgVlhe1GNkF5ZNXV0tMJdZbPj7mBXNlk/QONC3zv3iM7HYHRSWVRLk63ndhBD1+i9frgh98yp/6z+QtoVQ+R0gQRPm8Wh2HUV43VV/P7ooMHQF6+/gNfL8D1ylXjIzMxFCVFQ9FIC3rPJhrutj8UA89o5RePaZT3yNA55VcmEhxMNVyV5b8CSPzZNSVlRldS8H6hW8lR68pJTeUkrv2FiFWYoqKlX46c0Ma9KBibtmUmSvPX0hz1rKOxmzuTG6E/vKc046XlkrbWxetJ1uw2re67O7nZQ6LHjpPI5ZYTGhRDRrzNal6bVey+l2YdYZ2FC4X/F9rMnbwXCfTN5osYrD+ZPZVrCGCtsBpGUqsvgB3JXT2FF0kLd2zWRv6XEeb3kteo3ygHVo5xFadW1ef8MqWnVtwaFd1VXaDHotdkfDks2ahPiTW1RGsUI98dU7DtKqaWN8vS69+uTzgRA6hLELGr8X0QR+hCbwYzT+/0a4ixCmgcptJE0DkbZF53ewKoqJjY3lRFyrinEnl8Lq2mP+CvhKCDFCSjn9XC4spfwQ+BBACJEIfCCE0AEGoBPwzrn0q6LSUAaGp+Bwu3hx60/0DUumV+NWJ/deC2xlLMlJZ2nOTm6ISqN/eBvSi7PYUnSI1KBYCo8V4R/ih1+jmmfb6/L3Ee8bRkbJUajaeo5vF8uxA7k1tgfPPnZKYAwrcjMYGpV20gazv6bm2YyuiYb7vs7HmW/l7QmNOFJmwHFNDo6O5WiKXDTxcTC882Q0zo+Y/3okcxZ/jqEBQRnAYXOgNypPktMbdTjtzmo2hiH+PtgcLo7kFRMVElBPD57ZffrBHNo3b8LPq7Zz58C6E50qrHZmr9/F3YPqbnc1IN15CEPNgjk1om0K7jykdDfME1rlgqPkt9O+Bq3sVxt6ISnlLmAesA1YD3wqpVRmJ6Si8icRQjAkqj1PtRpCkb2CpzdP4bENX/L3DV/w/NYfsbjsPJM0jP7hHju+QREpTD20hnKnFZfThaYWGchieyU/Z61ndMw1HLUUk1XhWZLVaDW4nDXrT0vpUfUaGtWe9kHNeH/3XGyu2g0I3P5aRn1UTPFWyScvhXM004gm34nx52K8XzyKcUoBuZ/b+XRMI+zLbdzRKbPBQRnAN8iHgqPKdaWLjhfjHeBdbU9Xp9XQp20cv2+uv5QNYP/RAiptdv4+rDsZh3OZumwrp00cqlFWaeXNqUtoFx9JUkxYjW2uLgQNExVxV51zZezBX8ko+e8dLKU8uXRdpZV9LfB8Qy8mpZwATGjoeSoqfxVNvYO5M64Xo2O7U+qwIBD46c3V9KgBOgUncKA8l9fTf+GO0O4UHS/GbnNgOG1Geaginw/3zKNX41a0DYrhBnsak/bO59mk4eQczCO2zdnKZVJKfjnyBw63i9TAWNoHNeOzfYv5z/ZpXBuZyhzn9yeXn8udVlbmZrA+ZwkDwlYTOvg7dvzs+Uw8ccnLNd6flHZk0X1I13GPJnMDSO3fhgVfLeWaG2sWOzmT1b9uIK3/2btRfdom8PyXc0lr3oTkOgJohdXOp3PXcX3nVvh5m3h2VD/enbGcf/xvFv1SE0hNiDopybl82wFWpR+kb7t4RZaPVwNCGwHOvbUoh9WAcy9owq+Y5LgrGSWBWSuEMEopbQBCCDOgvDBSReUSRK/R0aieRLBbm3Zl/rGtvHdkASU3m/ny97m065xEmcPC2vy9HLcWc2OTTvRq3AqAvo2TKbZX8O/NUynO2s/wJ6orTmVXFjI7exOZFbk83eqGkx8G7o3vy+aiTBYd38GUzBWEmPyRUpJnK6FNQFMejHYTbB6BEPUvMwthAGMfT12r9x0Nek1aX5PIT2/9xr7NmcS3qzuXo6K0klW/rueJ/z1w1nNBfl48Oqw77/+8kpt6tKFH62bVbBqllOw7WsBnc9eRHBtOn7bxAPh7m3hxdH92H8nj9017mbM+A4fThY/ZQMcW0Yy/e/BF91S+pDD2Q5b+G8wjFf1tSOt8hOnCGJGo/DmUBOZvgUVCiC/wrJvcRS0Z1CoqVxJCCAZFtKVvWDI/li9h3rq1lDcV+BjN9AtrTWpQbLWZthCCm6I7c2RhJvm9fXlx/3Ra+IV7sq9tpeRYiunZuBWjY6+p5m4lhCA1qBmpQc0otJVTZC9HCEGo0R8fvQl30Sww3Kh84IZOyIpPGrxgqdFouPkfN/DZs1N45L93ExkfXmM7S7mFj5/4ig4D29YoMAKQGN2Yf93ahx+XbmHa8m10SowmyNcLu8PJ5n1HsdgdXN+5Fb1T4qrN4IQQtGwSqtik4mpG6KI9zlSW38BrRJ1t3fZdYFuOlA6kfT0ILUIbDaaBCK3yGnKVC4MSrew3hRDb8ZQ5CeAVKaXqx6xy1aDX6Li9Vz+cSwoon5zL2DdGY/KqedFoyQ+rKPj1EO9//hClZgdZlfk4pZvO+gRa+UedtWR+JkFGnxqEUCwgGlChKLyguiaQYtr0aIXNYuftez+mbe9ket7cleiWnjfuyjILa2dtZMkPK0nulsiNj9etQd20cSD/vKU3xwvL2LAni7JKG3q9lpt7ptA6NrxBJVUqNSN8HvXUJwsNmG6oManLbfkNSl8DfUuEoTVoIgAXOHchS55H6uIQPo8iNMpMYVTOP4oyRKSUc4G553ksKiqXLEIIxrw4kh9e/4X/jJxI9+Gd6DKkPf4hfljKrSz4cgnr52zGbnPw4Dt/wz/YD3+giXejv+DiPh5bQK1CkwJZ2rBAXoXNYmP93C2smL4Gu9XB0h9XMf3dWZh9TDSKCEKv19G6RyJ3vHwL8W2Vly2GBflyfedWDR6PSv0IbSPwew1ZPtGzfWEaCLpEEDpwHUFW/gD2TeDzBMLrlur7y4ZUMI8Ey0/IkmfBfzxCc/mqpV1J1BuYhRCdgf8CiXjKnLRAhZRS/Q2qXFVodVpuf34ER/YcZfm0tbx6yzvkHMqjvKSCwNAAElJjCQz1Z/KTXxGXEkPf0T1ISG32p68rDGlgXw76BGUn2JbVq698JrlZ+Ux67HPCYkIZ/vfraNEhDo1GUxWsN7NyxjoAbnpyCH5Bde/Nq1xYhDYY4T8e6dgLtvlI21rABcIXXMch8FM0hjY1nysM4HU7SBey/EOE3zMXdvAqNaJkxvwBHpOJn4A0YCwQfz4HpaJyKRPVPILB9/Rl35ZMWvdIZMAdvYiIO5V9bLPY2DB/K188/z0D/9abnjd3/XMXNA5AljwJ5lvqbSrdJUj7GkTAfxV3X1pQxn8f/pT+Y3vS46Yu1S9tNnLNjZ3pPrwTv34wj0mPfcHjk++vdSlf5eIh9AmgTzhlPGCZhdT41BqUq+F1KxTdi3TlIJSuzKicN5QuZe8TQmillC7gCyHE6vM8LhWVSxa7zcGkxz6n46B2DLrrbFMAo9lIt2EdadExnnfu+xi/YF/a9VGuWS2lBOcOpHURuHOqjmqRJc+g0/vidNScgSvdlciy1xGmQQhN/eIeJ5j72WJSeiWdFZRPRwjBDY8MIvefeaycsY5+o3so7l/l4iBtCxHe99XfkBPZ/L3BuhC8R5/nkanUhxKBkUohhAHYIoR4UwgxDlBrFlSuWjYu2IpfI18G3lm3jV5wRBBjX7qZmR/Nr1U040ykMxNZ8jiy4lOErjnCawzCawyYhoFzL+O/Xc2EOe2R8pTspZR2pG0FsvQZhDYGzLcpvhdrpY0N8zfTV0GgFULQf2xPVkxfi9vtVnwNlQuPlG5wHQFdS+Un6VogXUfqb6dy3lEyYx6DZ1/5ETw62U2AunPzVVSuYJZPW8N19/ZTJNTQPC0OodGwZ+MBWqTVbSghnfuRpa8gvO8CwzXVy4j0rXCbroeKSVA2AVn5A1LXHJDgOgzapgjzbWDo1CABifRVGcQkR5/lu1wbMcnR6Aw6Du/KrmZ9qXKpceKDYAMy34UOjzqYysVGSbnUoaofLUDNckMqKlcJFSUV5BzKo1XXForaCyFIG5BC+qqMOgOzlC5k2ZsI7/sQxpr3pDUaLfg+itS3Qlp+BPMwBAK0jRus8nWCssJygiOVZ44LIQhqHEBZYe1mICoXHyG0SBEArmzQKfwA5cpCaP6CKgKVP01dto/bqUOIVUqpIKNAReXKwlJhw9vPq16/4tPxCfCm8Hhx3Y3s60HTqNagXA1jH7D+hhAahP7P/RvqDDrsVnuDznHYneiNV6bN4pWEMPb2+DXr6reFlNKNtC5E+DxxAUamUh91/Xddf8FGoaJymWDyMlJZZsHtdisOzhUllfV6HEvbQoRpoKL+hBBgGoy0LvjTgblpqyjmfrZI8f1UllnI2p1NRLxqInHJYxqALHkKTNcjtPUoqdmWgfAGnVpwcylQ63+ilPJQXV8XcpAqKpcK3v5eBEcGkbFOmXuSlJKNv28jsUs9Pseuo6BTtjwOgK6555xzREqJtdJGZEI4/iF+7FiZoei8tbM20qpLC7WW+TJAaEMR5puRpS8jXTm1tnNblyLLJoI2DCq/RFrnId3qVsXFRBUYUVFpAEIIetzUhaU/rqZVl/oD6f6tB7GWW8k/UsCsLQfR6jSEN2tM62sS0epOl+d00TA7PlF1jnKklOzZeIDlP61h+4qdCI0Gl8OJf7Afnz07hX/P+AeBobWXWeUezmP+l0t4YGLDzDFULh7C7Fn4lCX/AEM3MA0AbQRIJ9KxAco/AOdBMA1GVM2WpSMdKqeAoSt4jUVo1CKcC40qMKKi0kA6DGrLoinLWfzdCvqMuqbWdlm7s/m/W99Fb9KzZ8N+GseE4rA5WTRlBVMn/EqPkV3pP7YHWq0WNCFV2dUKxR1cWaBRbvRgs9j44rnvyT1SQM+RXRn94k2YvU24nC62Ld/J1y//xIPtn+aZbx4lpVdytXOllGSs38c3//mJGx4aRGxytOLrqlx8hPl6MHYF6wJk2ZvgLgQpwZ0Hhk4Q8BEabfCp9oB0F0Pl98jS58HvFYTmTP12lfOJqK++UgixQUqZJoTYdiLhSwixWkr5J+WMzo20tDS5YcOGi3FpFZWT5B8t5INHPqV5Wjz9xvQgtMmpNzaH3cHyaWv4+ImvSe3Xhoffv/Ospd/sfceY/vYsDGYD974xGo1zCdK+Ho3fs2deqkbcJc8hTNcjjLWLgpzA5XTx0bgv8Qn0ZsyLI8+YqZ/i4ye/YvF3K0jplUTHwamYfEyUF5Xzx/wtHoGRhwfRpoeqeX25I6VElj6P0CchvEbV2Y7KL5CubDR+L1zAEV4dpKWlsWHDhhqXyZTMmKsJjADHUAVGVK5ygiOCeOrzh1n49TLeumsS4bGhBIUH4rQ72bNxP8czcxnywADG/vvmGs+PjA/noffuZPKTX/Pz+3MYMW4AVH6NdOxG6OteIpf2rR4NZEMHRWNdP3czdquDMS+N9MzOa+GBiXcQHBnE4YxsSvJLyT2cj9nXxG3/Gk58u9gG1UerXMI4d3tmzeZb62wmhEB63QFF9yOdmQidcuMSlT+HkhlzUyAHz/7yOMAfmCSl3Hf+h3c26oxZ5VLDYXewe/0+SgvL0Rv1lBWWs2nhNp76/KF6zy0rKuffN77JKzPGYjJuA8tP4PciGn1ije2lIx1ZNgHh+xRCn1xjmzN5447/ct29/UjuXnOfp1OSX8orN7/NK789jdnHrKh/lcsLd9l7CF0MwnyDovay8kdwFyJ8HjzPI7u6+FMz5tMysK2oAiMqKmehN+irBb33H/qEnjfXv8QspQUf01IeeXUb5Uf+halJLCCgcCw22ZIy60h0Xqn4NfJBuA4grfPAuQfhM+6soJx7OI/sfcdxOlz4B/sS1zYGrVZL7uE8SvLLFAui+Af7kZDajG3LdtLpuvYNeh1ULhOce8B8o/L2hvbI8kkNSk1U+XPUJTByAxAlpfyw6vE6IKTq6X9KKaddgPGpqFx27N96kPveGltnG+nKR5a9ApoIrPJvLPiqhDEv3czaORvYuWoOXfpuIj7pRYqyzRTtMWH2j6VRzM3oAp9AiFM10TtWZbB4ygqy9x+nWeum6Aw68rLyKSsq55obO9OkZQTBEUENEkQJjQ6mOK/0nO9f5VLHAcLQgPYGkA0ToVH5c9Q1Y/4nnmzsExiBDnj2l78A1MCsonIGbrcbp8OFwVSzAxSccIF6BWHsiTDfiFuzm+K8BYy//T1ikqMZfN/9NGvTFBwb8A3+kL3772bOJ3spzs/goXe7EBjqCcxzPl3Empl/MPTBgbTtk4zecOqahzOymf/FElbMWIeXf8OWpJ0OF3qDqux1xSL8wF2gvALAnQ8atTr2QlLXx2iDlDLrtMcrpZQFUsrDqMlfKio1otFoMPuYKC0oq72RbT5ooxFVy4nZ+46Rvmo3197bj7tfG0VcSgxCCIShA8JrKAmJG3jovTtJ7duaDx79DEu5hZU/r+OPeZv5xxcP02FQu2pBGSC6ZST3vH47bXoksnVJOmXFygQjPLXO+1VlrysYYewKtsWK20vbYoSh23kckcqZ1BWYA09/IKV85LSHIaioqNRISq8k1s7aVONzHk3i+QjT0JPHfv3vPDoMakuXIWlnn2AcAI5NIEsYdFcfIuLCWDp1NbP/t5C7XxtVpwKXEIKRTw0lIMSPaRNnKRp75vbDOGwOmtfjhKVyGWPsi7StRboK6m0qXcfBsRWMvc7/uFROUldgXieEuPfMg0KI+4H1529IKiqXNz1GdmHF9DU1m0O4MgHdSU3i3Rv2cSwzh1HP1ZyMIzQ+oG8H9g0IIeg3pgdzP1lESFQjoppH1DsWjUbDkAcGsPDrpeRn1/1GbLfamfb2THrf2r1Be9IqlxdC44/wGoEse8UjJFIL0lWALH0VYb5NVf+6wNT13zcOuFMIsUQIMbHqaynwN+DxCzE4FZXLkaaJUTRvH8dnz3yHw+6o/qS73OMiJQT52QX895HPaNOjFY3Cg2rtT2iCQJad7NtSbqVJYqTi8Qy6uw96o5537p/MgW01y9wXHCvivw9/SnizxvS4qbPivlUuU0zDEIbOyOKnkJafke5TyX5uVwHu0gnIwlGARyFMOnZSX2mtyl9HrRkeUspcoKsQog+QVHV4tpRS+eaEispVyu3Pj+Crl6Yy4c4P6T+2F217J3n2gYURh62MZV8tZfH3K0nu1pKAUP+6O5MW4JT8pr6OxLKa0Bv0NAoP5Lp7+/HF89/jG+RDp2tT8Qv2xVJuZdvSdPZvPUjvW7sz6O4+qpDIVYAQArxuBUMaWOciix5CCh+PTKfrMGgjwTwCoWsK7hJk+SQQWo92tkEtozvfKKljXgyowVhFpQHo9DrufPVWti3byfKf1jB1wq80ighEo7ExbMwfFOe05uH37iQr4yj7txystR8pXUjHJoRp0MljAnDZlRtYOOwO7FY7na5vT+chaexYmcHWpelYyiwYzAaSr0nkzv+7DaPZ+CfuWOVyROjiwedR8L4HWfEZOLaB/6tnC9yYR4JjM7L8Q/AajTD1vjgDvkpQayJUVM4TGo2Gtr2Tads7maLcEkryStHqNDQO8SKhixnhFYlGq2HW5AW4nK6aNaztG0ATfFIOsaK0EqfDRdZu5ZaPmxftoHla/Ek5zjY9Wqma1yrVsW8Ax26E/9s1GlYIIcCQCn4vI0tfAF1ThK7ZRRjo1YGa4aGicgEIDPUnJqkJTVpEovcfjrTORToyiIwPp1FEEFuX7TzrHOkqQFZ+drKsCmDtzI10ur49BceKOLr/eL3XlVKy7KfV9BxZvxKZytWJlBJp+RnhfVe9LlJCF4UwD0ValGX5q5wbamBWUbnACG0EwucxZNl4pPV3Bt68ZTGCAAAgAElEQVTRjZ/fm01xXglQVVJl34QsfdbjIFVlVnHsQA4Lvl76/+3dd7xcZbX/8c+afnpCByEmiBBClRBCBwndcvGC4kv0iooKIioq5aJSvQQF5fIDQRSBe+0FREEIoVdBAelwAQ1FQzMkOXXqXr8/9iScJKfM4Nkz+5zzfb9e88qcmWfvWcNmZs1+nmc/i30/uicHHzWPH53yM3qX9Q37Ou7OtZcsxANn6z1mNuS9yThUfjacx5Derrb22XlQ+jMejHCtvvxL1JUt0kAedEPhFjx/KwRL8e5TmbWFc8wZm3LXT45jp4O2Zd31nwNrw1o/gWV3JggCHrvrKX4+/7cc+qX3Mm3m25g2820sf72b8z75Pd7/uQPZbu+tVukK/8dzL7PwyttZ/LdXOe6iT41YVUomufJzWHo7zGo7T7NEJ57cJJwklthq9A2kbkrMIg3ihbvxvh9AejbW/nlIzQAcL9zHBumfM+8DT/HAnXl++9A72WTWXrS0l+hZuoAHFj5M+5Q2/uP0DzFr581X7u99xxzAtFkbc+tP7+LX3/k979huOql0itf/sYSlryxjt0N24sP/+QFa2nLDByVS99rZEK6fXRq9mbwlSswiDeCFe/G+K7DOM7HU9FWes9xekNuLlvZn2P2983n7dnN55B5nyeKltHS0cNT8I3j7rE2G3O92e23FdnttxcuLXuUfz75CpVRm7jqz2Xz2pkNPJhNZXaILLz1Vc/Uod4fgNUiMcpmfvGVKzCIRcx/A+76PdZ6+RlIezNKbQ8cJbLLJuUw75geY1X698oYz1mfDGTUWJRAZLL0j9F6GB8uwxJTR25efBJKQnB51ZJOWJn+JRK1wJ6Rm1XR5iaVnQXITKN7fgMBEwmVfLbsrDFw7attwBvc1WO5ALUQTISVmkYh54XYst3/N7S23P164LcKIRFbTcjhevBPP3zxsE/cA+q+EYDnk9m1cbJOQurJFolZ5HZIb194+uXFYA1ekQSy5NnSejnefhRcfDFeaS2+LmREEA1C4HQo3AWms8xuYaZW4KCkxi0TNUkBQxwYVQBO3pLEs+TaYcj4U7sD7L4fyYtx7wvWzrQ0S64LloP8neO7AlavRydhTYhaJWnJjKD0FyQ1qa196OhxnFmkwsxbIHYg7UPkJ5OZB9iAS6bcDhGUi8zfj3d+E3AHQ8kGNNUdAY8wiEbPcAXh+QU1t3R3PL6hrTFpkLHn+Nshfg005j0T70SuTMoAlpmCth2FTzsOL90D+miZGOnEpMYtELT0bvAcv3D5628ICsDSkVGRCGs89j/dfgXWcgo3Qw2OJqVjHqfjA1eFZtIwpJWaRiJklsI6T8b7/wfMLw9mtq3Gv4APX4f2/wTpOVPegNEfhbkjNxFLTRm1qybWxzM6Qv6UBgU0uGmMWaQBLTYPOM/Gec6DvMjy9A6TeCcm3Y5VFeGEhJNbFus7GklooRJrDC3evUvt7VNl54eI5rYdGF9QkpMQs0gBe/DM+cC0E/ZB6B5Qeg8Kt4EU8vS20f3nN4vQijebLoZ4fhsn1wuuaZUwpMYtEyN1h4Od44S6s9QjIzF1lqU0vP4cPXA19l+Odp2KJjiZGK5IGL9Te3ItvoQCGjEZjzCJRKizAi/djXd/Csruvsf61pTbD2k/A0lviPecMOf4s0iiW2gxKD9e+QenhsAdIxpQSs0hE3It4/6+w9q9gic5h25kZtB4ZnqnU86UoawiWfJRgyUebHcb4lTsAz9+Ee3nUpuGlfTfUNyYtNVFiFolK8f5wclctM1wtgeUOqvl6Z5EoWOrtkJoO/T8Oh2FGkr8GSENqm0aENqkoMYtExIsPYdndat8guxuUHhr9C1EkQtZ+PF56BPp+gAc9azzvQT/e/1M8fyPW8Z+6tC8CmvwlEhXvB6t9MpdZDicJFIBcZGGJjMQS7dD5Tei/El/2OTz1LrAseG9YXKWyKJzE2DUfS0xtdrgTkhKzSFSsNfwyq5F7gbCAhWa5SnNZoh1v/STQCvmrgQwkOsBawNohWAKlJ8NeHhlzSswiEbHM9njhNqzW2rWFeyC9PWYaYZLm8mAZ3n1GOEdiysWrzJNwr4RDLn3/C+VnofXj6s4eY/oGEIlKZhcov4CX/z5qU/dAM1wlFtwDvPtsLD0ba//iGpMXzZJYZg7WNR8vPQr565oU6cSlxCwSEbMM1nIo3nvekJNoVnB36P9JWLc5vUMDIxQZQunB8N/WI0Y8E7ZEO9bxVXzgKtyLDQpuclBXtkiUcu/BgqX48pOh9aOQmYPZmx87Ly8KV/6qvIx1nqpu7FEEr2w+Ju0SGzwzFuFMSJ6/AWs5uKbuaUtuhKc2heK9kN07+uAmCSVmkQiZGbR9DFLvxPO/g55v44l2cIA8kMVa3gftnwuL1Is0kXsApUeh4+Sat7HMrnjxEUyJecwoMYtEzD2AyvNQWRxWlEqsB2bhOsOlZ/DS41hmJ0jNaHaosTfame6KVb8Sa/+kEeFMQCUgidWz/rW1g/dFFtFkpMQsEiH3AO89D4L+cL3s1YrPu5egcEc4A7bjRCw9q0mRikB4qV6Aex6zGq+l926wtkijmmw0oCUSpYFfhkm582trJGUAszSW2xdr/yLecy4edDchSJGQmUH6XVC4u+ZtvHA3lpkdYVSTjxKzSETcC+FEmvZj1qgqtTrLvKv6hXhLg6ITGZrlDsTz19dU6czLL0Dl75CZ24DIJg8lZpGoFO6G1BZYjYXnreVgPH+jSj9Kc6W3D7um+y8fcd12D5bhPedirYeP+sNT6qPELBKV8l+x9PY1N7fUZuH62nUs4yky1swSWMdJePk5vGc+Xvq/VRK0exEv3I4vPxnL7oHlDmhitBOTJn+JRKYI9cxuBSANXookGpFaWaId7zgjrDC17EtAgFsnJLrA85DeHGv7DJbRgjhRUGIWiYp1QfBazc3dB8LLThLtEQYlMjovPQq9l4WX9bUdDeQheAPKz0PleSy5EaRVhzkqSswiUcnujnf/F7R8GLPk6O0Ld0JmB8yy0cc2Qen65X+dF/+M916MtX8e0jussQKYB8vx3u9Dz9nQcYrGlyOgMWaRiFhqBiTXheJdo7Z1L+L567GsxuukebzyOt57YXh5X2b2kMtyWqIL6zgBSEP/zxsf5CSgxCwSIWv7FN53BV56bNg27kW857uQ3ATS2zYwOpHVFBZi2b3CiYgjMEtgbUfhhZtxzzcouMlDiVkkQpbaLKzA0/MdvPcSvLxo5XPueYKBhfjyE8CSWPsXVNdWmsa9gudvhhp7bSy5HqRm1rUYidRGY8wiEbP0NjDlfMjfjHd/Ew9eh2BpeFmUtUBiY0jNDh+r8ZpnkTEXLAMMS21c8yaW3gYqL0QX0ySlxCzSAJaYimd2gIEFYXd1ds9qCch2qLwMhZvDM+fc+6HlUJ05SxOUw5rgdUnq8r4IKDGLNICXX8S7z8Laj8ayO6/6ZGoapD4JLYfg3WdiOLR+sDmByuSV6ISgFw/6sURrbdsEL0NirWjjmoQ0xizSAN53MdZ6xJpJeRBLrIV1norn/4CX/97A6EQI64Fn3gXFO2pqH64AdmfY+yNjSolZJGJe/isESyC7z6htLbEWlt0XCjc2IDKRVVnuQHzgunCxm9Hkb4TUZkNWTZN/jRKzSNQKd2DZebUtMgKQ2z9ci3iEAgIikUhtjaW3xLu/NWJy9sI9+MDVWNunGhjc5KHELBIxD/4ZXqNcI0uuB14ECtEFJTIEM8NbjwbL4m98gqDnQoLySwC4B3jxLwTdZ+P9V2Kdp4ZLc8qY0+QvkcglgUrNrcMz5Qr63SyN5F6Ewm2QXwA+EF66V7gF+q8gsLZwclhqJpY7ELJfxizX7JAnLCVmkYhZchMoPVX7JJnys5BYF6u7MpXIW+NBH94zH0hhrUdCehvMwh+GQaUHCgshfx2W2x/L7dfUWCcD/SQXiVp2Hl68Gw/6a2ru+Ruw3P4RByUScg/wnnOx5NvC7unMdiuTMkAi2UGi9VCsaz7e/2u88McmRjs5KDGLRMySa0P6XdD/01EndHnpKSg9CNl5DYpOJr3SX8B7oO2zqyTk1VlyA6zjeLz/f3EPGhjg5KPELNIA1vYZvPwE9F+O+5qTutwdLz6I93wLa/8SluhsQpQyGXl+AZY7eMSkvFJqFlgrlB6JPrBJTGPMIg1giXbo/CbedzEs/TRk3w3prXFPQOl+GLgWvB9SW+CFu8CykJqlpTklUu7lMMl2fLWm9mYG2b2geH+4GIlEQolZpEEs0Y51nIhXXoX8TXj/L6H4YHgG0vIByOwZfvGVn8V7LwHLQMdXsOTbmh26TFQ+AJbDLFv7NokpePlZ9JMxOkrMIg1myfXxzBwo3IR1zYfM3FXPjNNbQO491cIW34Cus5ScJRqWBc/jHtTWlQ3g/bpUKmIaYxZpMPdiOAu2/Tgsu/OQ3dVmhuX2w1o/gvecq1XAJBJmGUjOCCcc1siL90F6qwijEiVmkUYr3g/JjbHMjqO3zc4DD6D8ZPRxyaRkuQPx/PU1tfXKP6D8AmR2jTiqyU2JWaTBPL8Qyx1QU9vwzPkAPL8w4qhk0sruBpXX8YE/jNjMg36853ys5d+0+E3ElJhFGq2yGFKb194+9c5wG5Ex5h5A+RnI7oH3/Yhg+ZkElddXa+N46VG8+2tYagvI/VuTop08NPlLpOHqHC+2RP3biIzAPYD8DWEXtmWw1OZ4dg8o3An/PJggvRVkdscIkzKWwHLvh+w+uoSvAZSYRRotsS5UXoTk2rW1L78QbiMyBtzLeO93IViOtR8HqS3CIROAjuMJik9B30VQuA1vORRrPwZSWyohN5C6skUazHL74Pkba27v+Rux3D4RRiSTSt8V4EWs83QsPXONhJvIbIlNuRDL7g7lp7G0FrppNCVmkUbL7gnlJ/HSs6M29eKfwbshPbsBgclE58EbePHOcNlXSw/bziwBbZ+Bygt4+bkGRiigxCzScGYtWPtxeM/8EZNzULgf7/kWZPeD8tNhvVyRf0X+JiyzW7hE7CjMklj2gLA+szSUxphFmsAyc6AdvOcsPLVVePlU6u2A44U/Qf+VUP4rZHYMz66L90HwT8juAy3vwRJrNfstyDjkpcewlg/WvkF2F7z7NC2/2WBKzCJNYpk5MPVSKNyJ9/8UgtfB+6DycpiAu75DIvXmUpxeWQz56/FlJ0Ln17HU9OYFL+OT5yHRVnt7awu3kYZSYhZpIrMWyB0QLiJS+Qe+/GvhxJshKvdYciNoOyqsQNV9FnR9O6z1LFIra4Wgu/b2wbJwG2kojTGLxIT3/wbLvW/IpDyYZffAsrtC/roGRSYThWV2xAt31r5B8a6wZ0caSolZJAY86IbinyG3f20b5N6DF27VhDCpT3YfKD6AB2+M2tQ9j+dvhmxty8fK2FFiFomD0iOQ3hpLdNTU3JIbQHIjKD8dcWAyEbgP4IU/QvFeSG+BLz8ND/pGaF/Ee76LZWZjqY0bGKmAxphF4sH7sERXfdtYJ4zw5SrilSUwcBVevBtSm1Vn87eEl98tOQRvPRJr/dDKa5rdAyg9hA/8BhJrQdtnm/sGJiklZpE4sBzu/fVdluL9YC1RRSTjnJdfxLvPxLJ7Yl3nr5woaEAQHAd9l0PfpfjAL/HMbCABlZfAurDcQZDdO1xoRBpOiVkkDlJbQd/luOcxy43a3IOlUHk+rDwlshoPusOk3PYxLLvXGs8nElnoOAZvPQRfdgIkN8fS74TEelhqWhMilsH0c0gkBiy5LqRmQuGO2jbI34RldsXquSZVJo/8Qiyz/ZBJeTBLbhgWsig/gmV2VFKOCSVmkZiwlsPw/l/g5edHbBcUH8H7f4ZXXiXoPpOg59v4wPV40N+YQCXW3AO8sBByB9W2QWYOBG/g5b9GG5jUTIlZJCYsvTnWdhTefTqevwn3wirPe9BP0D0flh4FyY3D65lz78Eyu+DlJ/Blnw0TtgdNegcSC5V/AEks9Y6ampslsMzO4ZUBEgsaYxaJEcvuBom18YGrof/HeHo2Zq24d0P+ZqACU/6bRHa31bbbA68swXvPh8qF0P4FleqbrDwPNnqRilVYO/hANPFI3XTGLBIzlp5JovMUrOtcLL01JDeExNqQWBdb6+drJOWV2yXXxjq/AZXFUKi93rNMMNYC3oO7176N92jpzRhRYhaJKUuuj+XmYS3vheA1rO2IUdfGNstibR/HB66t74tZJo7kRoBBefR63wDuZbxwr2p+x0jDErOZdZnZtWb2iJk9YWafGPTc8Wb2kJkd3qh4RMYLD96A0uOQ3bO2DVJbgqWh/Hi0gUlseNCHF+7GB66Hwm2Q2RHP/6G2jYv3QXJDzciOkUaOMR8LPOnu7zOzdYH/M7OfAhlgDrATcBXwywbGJBJ/lX9AclpYiaoGZgapWVB+CdLbRBycNFO4stev8OK9kJqJJdYJJw2WHoTyCwSWJdH+ueG3L7+I9/0I6/hKA6OW0TQyMTvQYeGMlHbgDaAMZAc9LyKr8wBI1reNJYFKFNFITHj5Jbz7jHBlrykXVJfbDFf2ci/j+euhZz5B6Wms8yQsOai2d9ALhdvwgauwtqPCuQwSG41MzBcBvwcWAx3A4R5e19FjZo8BDwDnNjAekfEhMRWCl3EPal4i0SuLsdTMiAOTZvGgF+/5Jtb6ESy3zxrPm6WwlvcTpLeGpV/Al34eT88M/1/yfDj+nH4X1vl1LLVZE96BjKSRifkA4GFgH+AdwE1mdpe7d7v7fGB+A2MRGT+Sm4QFK0oPQ2aHUZt75VUoP4u3HQXFR4BS+IWc3FSXUE0UhdsgtcWQSXmwRGpTfMp8vPcHkDscow8sWy1oMaVBwUq9Ik3MZnYs8Onqn0uBUz2cKvqcmS0CZgJ/GmUfBvRR7epea621ogtYJIbMDHIHhRV/0ttiNvLH1nsvCbu/l5+EJzcNu7UrrwBJyB0IuX0xyzQmeBlz7gGeXxAupVmL1NZgCSyRwtJDX2onjbdo0SLMbEV5OAPaqvkx2lnZ7v49d9/e3bcHngbmAZjZ+sAWwN9q2Ie7e6u7t7l724wZM6IMWSSesnuDteK9F+BeGrKJuxMsPwUKC6H1MGzqpSS6TifR+Q1sykVY+2fx0gN49xkj1uKVmAuWhN3RqS1qam5mWGYXrewVMzNmzGBFXqvmuJXzrBp5HfNZwK7V8eRbgJPc/Z8NfH2RccssiXWcAFTwZV/EB36PB8txd9wH8Pxt+NKPQ+FumPpjEi2HrDKL28yw9FZYx9ex5MZ4z7launO88gGw1vqGJaw1TOYyLjRsjNndFwP7N+r1RCYasyy0nwDl/8PzN0L/L6rLL6bDspHBAEy9jER6+DWSzRJ422dh+Veh9Chktm/gO5AxYW3g3XVNBiRYHm4n44LWyhYZR8wM0jOxdDjjesWXsxfuxxkgkd68hn0kwjHr/A2YEnOsrbysqfQkkAfrgPRcsKk1/7ByD/DiPVjHV6MPWMaEluQUGcdWnjGV/oxl9qh9w+weUHpQ3dkx5V7B+67Elx2Dl/+KZXfHcu/D0ttC4QaoLML7Lq1t2dXSA2CduixqHNEZs8gE4N6PJbpqbm+Ww0lXu8JVvCBO3AO89zvgA9iUC9e4rMly+xKUnoKln8a7T8O6zhx+X+UX8d7vY+1fjDpsGUNKzCITQg68v+bW4czuIlgWD5ZXL6dySKyDJdeJLEqpQf53EHRjnadhlh6ySSK9JcGUy2DZJwmWO9Z25Cr1l8Mu8Fvxgd+Gz2W2a1T0MgaUmEUmAEtvgxfvxnL71bZB8U+Q2BDv/W44VpnYECwBlZfx5AwsdyBk5mpBkgZzr+D567GOU4ZNyiskMjMJ2r4YjkH3nINbByTWAS9A5W+Q3rG6stfwkwElnpSYRSaC7G7Qf2W4FGdyoxGbujveexFQwlIHQ9uxWKK1+lwRivfhA78Ik3f7sZjVuU63vHWlh8Jei1Rt6zVYy8F4/lrouhgLFod1lclAanpdQxsSL5r8JTIBmGWwlkPwnu/iwchd2t59GlQWw5RLsJaDVybllfvJ7ol1nQPBG9D3o6hDbwgzW+MWS+UXsdSWNTe3RAckN8D8NSw9E8vMwTLbKSmPc0rMIhNF7hAsPRPvPgUv/mWNGddeeZWg90LIL4Spl5BIrjvsrsxyWMeJePFevLI46sgnHfcBvHA73n8VPnANXnyoerwq1cpg9UiCl6MIU5pEXdkiE4SZ4a2fwgq34/0/Bv8hpLcGknjwCpT/Go4l5w4mkZ41+v4SrZDdB/ILoO2T0b+BScCDHuj/BV68K6yfnNwYvIIX7gG/FJLTgSS1ns+7lyB4FRKqITCRKDGLTCBhwYt3h2trl5+ByvPglfD6144d8J5vYdk6FuDLvhvv+S8sZol5LLqia9lHTdcJ18iDN/Dlp2LpbbGu87Hk2m/GAnj5Obz3h1C8j6DtkyRqmR1f/BMkN8aS641ZnNJ8SswiE1C4QtgW4W0QD3rCEpC1SkyFoCfctvwSFG7GKy+Hr5FcD7Lzap6oNJm5B3j32Vh2L6z1g0O2sdRm0HUW/sYR0P0NmHrJKPsshpdDtXwgipCliTTGLDKZWK6+YgaeBxIEy08PJ41ZBsvtG16WZZ14z9kEy/9T49CjKT0IGLQcNmIzswx0fQeKDxL0/3zYM3b3It7zXUiuB5ldIghYmklnzCKTiKVnhd2fNS444fmbIFiMpT8AnV9b9drazBxoORQKC/HlX4fOM7DUJhFFPjbd12P1mvV2cXt+AZY7uKb3kEhNI2g5BAauxkuPhjW0MzsRLiKzFAq3h0VMUlti7Z+rvZCFjBtKzCKTSXZ/fPnx4B9dpSzkUIKgAn2XQusHsdZ/H7KNWRJyBwE5vOccmHKhEsVQSk9A+5drbm7ZvXHAsvPw/A3QezF4KSz3mN0Z6zhBa19PYErMIpOIJdeGzFy890Jo/8qwi4e4O/SeH/7RevTo+829G89fH3bZZuaMXbwxvd54cFyjnT27O3gRRvkhtOoLtIIXsexcLDu3up86yjzKuKbELDLZtH0aes7Be86G1o9jqWmrPO2V12DgV1C4DdqPJpGo7bpayx2M5xdimTm4F6BwF164PVyohBSkpmG5AyC1dWwTbhTMDE+0gy8Dq/GypmApJFatn6ykPHkoMYtMMmYZ6DglHMPsPgNPboCltgAMr7wE5aex7F6QeieW2bH2HadnwcAv8MI9eN+l4XW6Lf8GybeFC2CUn8D7LgOS0HEiltwgqrcYO5bZCQq3Q8vQQwKr88IdWG5etEFJbCkxi0xCZilo/VCYKIoPQLAYPMCyu0LHl8OykKXjqe/CjRRUXsf7rsA6T8dSm6729DTIHgj5G8LJYl1nD3n97Xg7m66pWzt3IN5zLuTeG/4wGoGXn4fKC5CZO4ZRyniixCwyiZmlILvz0E8m1grX1F49wQ7Dy09B8Ao25b/X6B5/8/UMWg4GCnjv97CuM95i5OOLpTbDUzPxnvOrP3yGrhzllSXhIjCtHx01gcvEpUELERmSZfcOL5eqVd+VkN5p2KS8itx7ofJSuGjJJGHtxwLgy7+GF/6Ie2Xlcx704gPX4ctPwnIHqht7ktMZs4gMLTMX+i7HS09h6ZErHnn5xbCu81pX1rRrszTk5kHhFkgdOe66r4czUrd2OLZ/AhTvCUs19v0QT24AXgmHEtLbYx1fxdIzGx22xIwSs4gMySwD7ceGY6Odpwx73ayXX8K7z4REF5aqI6kkNw0nOY1RvOOBWQKye2DZPfDKKxAsAZKQ3FClGmUlJWYRGZZldoT2o/HuM/H0bCx3EKTeAVg4QSm/AC/+EVr/A/pGXttZVmXJDWASzUyX2ikxi8iILLMTTPkeFG7Bey+A4JXwicQ6WHYeNuUCLDGVYODXYbKutahF5W9Ycv3oAhcZp5SYRWRUluiAlkOwlkNwD8LHVlvwwrLzwtrN7ceMuj/3Ep6/Bes8PYpwRcY1zcoWkbqYJYZehSq3H178YzgRbDT5P0Byk9pmcItMMkrMIjImLDEVazsqHI8uLxqyjbvjA9fjA9dh7cdiZhNmRvbqVry3ifr+JDrqyhaRMWPZPYEk3n0anpoZ1m1esSRn6XG8cCOQxLq+OeSqXyKixCwiY8yyu0FmdljEYuB3g4pYbIK1HTXpiliI1EuJWUTGnFkOcvuFZ8wiUheNMYuIiMSIErOIiEiMKDGLiIjEiBKziIhIjCgxi4iIxIgSs4iISIwoMYuIiMSIErOIiEiMKDGLiIjEiBKziIhIjCgxi4iIxIgSs4iISIwoMYuIiMSIqkuJiETA3ZsdgoxTOmMWERGJESVmERGRGFFiFpGmcXd1+YqsRolZREQkRpSYRUREYkSJWUREJEaUmEVERGJEiVlERCRGtMCIiDTd4JnZZtbESP41mmEuY0FnzCIiIjGixCwiIhIj6soWkVgZb93a6r6WsaYzZhERkRhRYhYREYkRdWWLSGzFtVtb3dcSJZ0xi4iIxIgSs4iISIyoK1tExoXhuo+j7OJWl7U0g86YRUREYkSJWUREJEbUlS0i45q6m2Wi0RmziIhIjCgxi4iIxIgSs4iISIwoMYuIiMSIErOIiEiMKDGLiIjEiBKziIhIjCgxi4iIxIgSs4iISIwoMYuIiMSIErOIiEiMKDGLiIjEiBKziIhIjCgxi4iIxIgSs4iISIwoMYuIiMSIErOIiEiMKDGLiIjEiBKziIhIjJi7NzuGuqyzzjo+ffr0MdvfokWLmDFjxpjtT5pHx3Li0LGcOHQsh7Zo0SJfsmTJkCfH4y4xjzUz63P3tmbHIf86HcuJQ8dy4tCxrJ+6skVERGJEiRms2QGIiIisoK5sM/PJ/h9BRERiY9InZhERkThRV7aIiEiMTNjEbGaXm9lrZvb4oMe2N7P7zOxhM3vAzHaqPt5lZtea2SNm9oSZfWLQNseb2UNmdngz3qWfJCEAAAbfSURBVMdkV89xrD63d/XxJ8zsjkGPf7h6HL/U6PcgoXqPZfX5OWZWMbPDBj2mz2ST1fn9eoSZPVq93Wtm2w3aRp/Lobj7hLwBewI7AI8PemwhcFD1/sHA7dX7pwDfqt5fF3gDyADtwM+AFPC7Zr+nyXir8zhOAZ4EplX/Xm/QNtcASeAXQHuz39dkvNVzLKt/J4FbgeuBw6qP6TMZg1udn8tdganV+wcB9w/aRp/LIW4T9ozZ3e8kTLCrPAx0Vu93AYsHPd5hZkb4wX8DKPPmjG0NxDdJncfxI8DV7v5iddvXBm0z+FhqJn4T1HksAY4DrgKGO47SJPUcS3e/192XVh+/D9h40Db6XA4h1ewAGuxLwI1mdh5hN/6u1ccvAn5P+D9SB3C4uwdAj5k9BjwAnNuEeGVowx3HzYG0md1OeBwvcPf/rT53NeFx/Im79zQ4XhnekMfSzN4GfADYB5izorG76zMZX8N9Lgf7FHDDoL/1uRxKs0/Zo7wB01m1q+X/AYdW738IuLl6/zDgfMJfbJsBi4DOZsevW93H8SLCX+RtwDrAs8DmzY5ft7d0LH8N7Fy9fyXVrmzd4nOr9VgOev7dwFPA2s2OPe63CX25lJlNB65z962rfy8Hpri7V7utl7t7p5n9ATjH3e+qtrsVONnd/9Sk0GWQOo7jyUDO3U+vtvsRsMDdf92cyGV1dRzLRbzZtbkO0A98xt2vaULYMoRaj2X1uW2B3xKOQT/TpJDHjQk7xjyMxcBe1fv7EJ5RAbwIzAMws/WBLYC/NTw6qdVwx/F3wB5mljKzVmAu4S90ia8hj6W7z3D36e4+HfgN8Dkl5dgb8lia2TTCLuuPKSnXZsKOMZvZz4G9gXXM7O/AacCngQvMLAXkgc9Um58FXFkduzLgJHf/Z+OjltXVcxzd/SkzWwA8CgTAZe7++JA7loar8zMpMVbnsTwVWBu4ODyRpuzuOzY86HFkQndli4iIjDeTrStbREQk1pSYRUREYkSJWUREJEaUmEVERGJEiVlERCRGlJhFRERiRIlZJEJmtr6Z/czM/mZmD5rZH83sA6NsM31wOb06X+9IM9to0N+XmdmsGrfd28yueyuvWyszu7f673Qz+8hb2P5IM7to7CMTiQ8lZpGIVJclvAa40903dffZwIdZtbrOWDsSWJmY3f0od38ywteri7uvKGwwnbAamIisRolZJDr7AEV3//6KB9z9BXe/EFaeNd5VLRT/kJmtUY1npDZmdqKZPWZmj5jZOWZ2GLAj8NNqsfoWM7vdzHastj+wuo9HzOyWWt+Emc0zs79UX+tyM8tWH3/ezM6o7vMxM5tZfXxdM7up+vilZvaCma1Tfa63uttzCJdPfdjMjl/9TNjMrjOzvav3P2Fmz5jZHcBug9qsa2ZXmdmfq7eVz4mMZ0rMItHZCnhohOdfA/Zz9x2Awwmr89TUxswOAg4B5rr7dsC33f03hCX0jnD37d19YMVOzGxd4IeE1X+2Az5YyxswsxxhdafD3X0bwmV8jxnU5J/V2C4Bvlp97DTg1urjvwWmDbHrk4G7qnGeP8LrbwicQZiQ9wMGd8tfAJzv7nOAQ4HLanlPInE3YdfKFokbM/sesDvhWfQcIA1cZGbbAxXCetKrG67NvsAV7t4P4O6rF61f3c6EXeqLamy/whbAokHFB/4HOBb47+rfV1f/fRD49+r93QlrKePuC8xsaY2vNZS5wO3u/jqAmf2SVf8bzKquvwzQaWYdrrq+Ms4pMYtE5wnCMzkA3P3YapfuA9WHjgdeBbYj7L3KD7GP4doYUM9C9/W2H7zdSArVfyu8+X0y2jZDKbNqD15u0P3h4k4AuwzuGRCZCNSVLRKdW4GcmQ3u+m0ddL8LeNndA+BjQHKIfQzXZiHwyWp5S8xsrerjPUDHEPv5I7CXmc1Yrf1ongamm9lm1b8/BtwxyjZ3Ax+qvs7+wNQh2qwe5/PA9maWMLNNgJ2qj98P7G1ma5tZmlW74BcCn1/xR7VXQWTcU2IWiYiHpdsOIUyIi8zsT4RdwSdVm1wMfNzM7iPsnu0bYjdDtnH3BcDvgQfM7GHeHN+9Evj+islfg2J5nbAM39Vm9gjwy2HCnmdmf19xA94FfAL4dbUsagB8f5htVzgD2N/MHgIOAl4mTMSDPQqUqxPRjgfuARYBjwHnUR2bd/eXgdMJf1jczKpj9l8AdjSzR83sSeDoUeISGRdU9lFExlR11nbF3ctmtgtwibvrbFakRhpjFpGxNg34lZklgCLw6SbHIzKu6IxZREQkRjTGLCIiEiNKzCIiIjGixCwiIhIjSswiIiIxosQsIiISI0rMIiIiMfL/Af2q5UYq28eKAAAAAElFTkSuQmCC\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 7.100e-02 nan nan False\n", "\tamplitude 2.912e-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 5.042e-03 7.189e-14 0.000e+00\n", "\tamplitude 7.189e-14 3.176e-24 0.000e+00\n", "\treference 0.000e+00 0.000e+00 0.000e+00 \n", "\n", "Statistic: 42.226 (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
0.7740.6810.8805.755e-111.149e-118.838e-123.450e-110.9362.0420.1470.1540.1401.26012.360152.7690.200 .. 5.00056.82736077344509 .. 227.660830865326665.390e-117.252e-118.466e-128.877e-128.063e-12
1.0000.8801.1362.912e-117.506e-127.458e-122.912e-110.9230.3190.1000.1030.0961.13617.742314.7750.200 .. 5.000114.08743116543133 .. 487.950845101761442.687e-113.308e-112.905e-123.004e-122.808e-12
1.2921.1361.4681.473e-114.904e-126.294e-122.457e-110.8970.0270.1160.1200.1111.14714.971224.1240.200 .. 5.00078.24025777805605 .. 358.718082989308471.322e-111.690e-111.704e-121.773e-121.636e-12
1.6681.4681.8967.452e-123.204e-125.312e-122.074e-111.1601.3970.1510.1570.1451.48714.582212.6320.200 .. 5.00096.25330907994987 .. 228.794779506126648.646e-121.108e-111.125e-121.171e-121.080e-12
2.2971.8962.7833.180e-122.850e-126.454e-121.677e-111.1460.1620.1440.1500.1381.45715.219231.6310.200 .. 5.000101.33520129148684 .. 249.879783331533563.644e-124.632e-124.575e-134.757e-134.398e-13
3.1622.7833.5941.357e-121.106e-123.475e-121.357e-111.2131.3380.2180.2310.2061.70010.563111.5820.200 .. 5.00052.63650430715782 .. 110.877597159866711.645e-122.306e-122.962e-133.131e-132.798e-13
4.0843.5944.6426.863e-137.226e-132.933e-121.145e-110.7740.1690.2080.2250.1921.2596.45141.6210.200 .. 5.00014.68576252786226 .. 107.494615854146745.310e-138.642e-131.428e-131.545e-131.316e-13
5.2754.6425.9953.472e-134.721e-132.475e-129.662e-121.0461.3030.2830.3080.2591.7116.76445.7510.200 .. 5.00021.000351801111176 .. 65.136039521872183.632e-135.940e-139.814e-141.071e-138.981e-14
6.8135.9957.7431.757e-133.085e-132.089e-128.153e-121.3720.5370.3820.4170.3492.2788.03164.4970.200 .. 5.00025.594608964059326 .. 36.241240731949762.411e-134.001e-136.709e-147.319e-146.125e-14
8.7997.74310.0008.887e-142.016e-131.762e-126.881e-120.9291.8800.3900.4450.3411.9224.21517.7680.200 .. 5.0009.045165002963646 .. 34.667941351558578.259e-141.708e-133.466e-143.951e-143.029e-14
11.36510.00012.9154.496e-141.317e-131.487e-125.807e-121.0010.0260.4870.5620.4172.2843.94415.5520.200 .. 5.0005.956190625912367 .. 21.3622360878516774.503e-141.027e-132.188e-142.525e-141.876e-14
14.67812.91516.6812.274e-148.606e-141.255e-124.900e-120.2581.1830.3270.4420.2311.3981.1931.4220.200 .. 5.0001.218046461095414 .. 24.869917457151975.860e-153.180e-147.430e-151.006e-145.257e-15
18.95716.68121.5441.151e-145.623e-141.059e-124.135e-120.8630.0490.6810.8550.5292.9382.3035.3020.200 .. 5.0001.962892508344067 .. 10.5243314949586579.928e-153.380e-147.833e-159.843e-156.092e-15
26.10221.54431.6234.910e-155.002e-141.287e-123.345e-120.0000.5910.0000.2640.0001.0590.0000.0000.200 .. 5.0001.3479592650749823 .. 19.5074164930217157.631e-305.198e-151.993e-221.298e-157.631e-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 8.877e-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 ... 4.575e-13 4.757e-13 4.398e-13\n", " 3.162 2.783 3.594 ... 2.962e-13 3.131e-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 ... 9.814e-14 1.071e-13 8.981e-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 3.951e-14 3.029e-14\n", " 11.365 10.000 12.915 ... 2.188e-14 2.525e-14 1.876e-14\n", " 14.678 12.915 16.681 ... 7.430e-15 1.006e-14 5.257e-15\n", " 18.957 16.681 21.544 ... 7.833e-15 9.843e-15 6.092e-15\n", " 26.102 21.544 31.623 ... 1.993e-22 1.298e-15 7.631e-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": [ "FitResult\n", "\n", "\tbackend : minuit\n", "\tmethod : minuit\n", "\tsuccess : True\n", "\tnfev : 31\n", "\ttotal stat : 30.31\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 2.667e+00 7.147e-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 5.108e-03 7.241e-14 0.000e+00\n", "\tamplitude 7.241e-14 3.183e-24 0.000e+00\n", "\treference 0.000e+00 0.000e+00 0.000e+00 \n", "\n", "Statistic: 30.311 (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 0.0715 2.91e-11 1.78e-12\n", " joint 2.67 0.0715 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 }