{ "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/v0.12?urlpath=lab/tree/cta_data_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", "[cta_data_analysis.ipynb](../_static/notebooks/cta_data_analysis.ipynb) |\n", "[cta_data_analysis.py](../_static/notebooks/cta_data_analysis.py)\n", "
\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![CTA first data challenge logo](images/cta-1dc.png)\n", "\n", "# CTA data analysis with Gammapy\n", "\n", "## Introduction\n", "\n", "**This notebook shows an example how to make a sky image and spectrum for simulated CTA data with Gammapy.**\n", "\n", "The dataset we will use is three observation runs on the Galactic center. This is a tiny (and thus quick to process and play with and learn) subset of the simulated CTA dataset that was produced for the first data challenge in August 2017.\n", "\n", "**This notebook can be considered part 2 of the introduction to CTA 1DC analysis. Part one is here: [cta_1dc_introduction.ipynb](cta_1dc_introduction.ipynb)**" ] }, { "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": [ "\r\n", "Gammapy package:\r\n", "\r\n", "\tpath : /Users/adonath/github/adonath/gammapy/gammapy \r\n", "\tversion : 0.12 \r\n", "\r\n", "\r\n", "Other packages:\r\n", "\r\n", "\tnumpy : 1.16.2 \r\n", "\tscipy : 1.2.1 \r\n", "\tmatplotlib : 3.0.2 \r\n", "\tcython : 0.29.4 \r\n", "\tastropy : 3.1.1 \r\n", "\tastropy_healpix : 0.4 \r\n", "\treproject : 0.4 \r\n", "\tsherpa : 4.10.2 \r\n", "\tpytest : 4.2.0 \r\n", "\tsphinx : 1.8.4 \r\n", "\thealpy : 1.12.8 \r\n", "\tregions : 0.3 \r\n", "\timinuit : 1.3.3 \r\n", "\tnaima : 0.8.3 \r\n", "\tuncertainties : 3.0.3 \r\n", "\r\n" ] } ], "source": [ "!gammapy info --no-envvar --no-system" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import astropy.units as u\n", "from astropy.coordinates import SkyCoord\n", "from astropy.convolution import Gaussian2DKernel\n", "from regions import CircleSkyRegion\n", "from gammapy.utils.energy import EnergyBounds\n", "from gammapy.utils.fitting import Fit\n", "from gammapy.data import DataStore\n", "from gammapy.spectrum import (\n", " SpectrumExtraction,\n", " models,\n", " FluxPointsEstimator,\n", " FluxPointsDataset,\n", ")\n", "from gammapy.maps import MapAxis, WcsNDMap, WcsGeom\n", "from gammapy.cube import MapMaker\n", "from gammapy.background import ReflectedRegionsBackgroundEstimator\n", "from gammapy.detect import TSMapEstimator, find_peaks" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Configure the logger, so that the spectral analysis\n", "# isn't so chatty about what it's doing.\n", "import logging\n", "\n", "logging.basicConfig()\n", "log = logging.getLogger(\"gammapy.spectrum\")\n", "log.setLevel(logging.ERROR)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Select observations\n", "\n", "Like explained in [cta_1dc_introduction.ipynb](cta_1dc_introduction.ipynb), a Gammapy analysis usually starts by creating a `DataStore` and selecting observations.\n", "\n", "This is shown in detail in the other notebook, here we just pick three observations near the galactic center." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data_store = DataStore.from_dir(\"$GAMMAPY_DATA/cta-1dc/index/gps\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "# Just as a reminder: this is how to select observations\n", "# from astropy.coordinates import SkyCoord\n", "# table = data_store.obs_table\n", "# pos_obs = SkyCoord(table['GLON_PNT'], table['GLAT_PNT'], frame='galactic', unit='deg')\n", "# pos_target = SkyCoord(0, 0, frame='galactic', unit='deg')\n", "# offset = pos_target.separation(pos_obs).deg\n", "# mask = (1 < offset) & (offset < 2)\n", "# table = table[mask]\n", "# table.show_in_browser(jsviewer=True)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "obs_id = [110380, 111140, 111159]\n", "observations = data_store.get_observations(obs_id)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "ObservationTable length=3\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
OBS_IDGLON_PNTGLAT_PNTLIVETIME
degdegs
int64float64float64float64
110380359.9999912037958-1.2999959379053661764.0
111140358.49998338300741.30000202119542841764.0
1111591.50000565682677411.2999404683352941764.0
" ], "text/plain": [ "\n", "OBS_ID GLON_PNT GLAT_PNT LIVETIME\n", " deg deg s \n", "int64 float64 float64 float64 \n", "------ ------------------ ------------------ --------\n", "110380 359.9999912037958 -1.299995937905366 1764.0\n", "111140 358.4999833830074 1.3000020211954284 1764.0\n", "111159 1.5000056568267741 1.299940468335294 1764.0" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "obs_cols = [\"OBS_ID\", \"GLON_PNT\", \"GLAT_PNT\", \"LIVETIME\"]\n", "data_store.obs_table.select_obs_id(obs_id)[obs_cols]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Make sky images\n", "\n", "### Define map geometry\n", "\n", "Select the target position and define an ON region for the spectral analysis" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "WcsGeom\n", "\n", "\taxes : lon, lat, energy\n", "\tshape : (500, 400, 9)\n", "\tndim : 3\n", "\tcoordsys : GAL\n", "\tprojection : CAR\n", "\tcenter : 0.0 deg, 0.0 deg\n", "\twidth : 10.0 deg x 8.0 deg" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "axis = MapAxis.from_edges(\n", " np.logspace(-1.0, 1.0, 10), unit=\"TeV\", name=\"energy\", interp=\"log\"\n", ")\n", "geom = WcsGeom.create(\n", " skydir=(0, 0), npix=(500, 400), binsz=0.02, coordsys=\"GAL\", axes=[axis]\n", ")\n", "geom" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compute images\n", "\n", "Exclusion mask currently unused. Remove here or move to later in the tutorial?" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "target_position = SkyCoord(0, 0, unit=\"deg\", frame=\"galactic\")\n", "on_radius = 0.2 * u.deg\n", "on_region = CircleSkyRegion(center=target_position, radius=on_radius)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "exclusion_mask = geom.to_image().region_mask([on_region], inside=False)\n", "exclusion_mask = WcsNDMap(geom.to_image(), exclusion_mask)\n", "exclusion_mask.plot();" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING: Tried to get polar motions for times after IERS data is valid. Defaulting to polar motion from the 50-yr mean for those. This may affect precision at the 10s of arcsec level [astropy.coordinates.builtin_frames.utils]\n", "WARNING:astropy:Tried to get polar motions for times after IERS data is valid. Defaulting to polar motion from the 50-yr mean for those. This may affect precision at the 10s of arcsec level\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['counts', 'exposure', 'background'])\n", "CPU times: user 6.4 s, sys: 479 ms, total: 6.88 s\n", "Wall time: 3.85 s\n" ] } ], "source": [ "%%time\n", "maker = MapMaker(geom, offset_max=\"2 deg\")\n", "maps = maker.run(observations)\n", "print(maps.keys())" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# The maps are cubes, with an energy axis.\n", "# Let's also make some images:\n", "images = maker.run_images()\n", "\n", "excess = images[\"counts\"].copy()\n", "excess.data -= images[\"background\"].data\n", "images[\"excess\"] = excess" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Show images\n", "\n", "Let's have a quick look at the images we computed ..." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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UWwKg8mGre0tdJlg+WEx4YuEADu4RMEVFc1ubMzz/myiq5kiZXVXtfXqmhjs4hnYs4UiEwTCezpSd4bpn1k0LqM585gViHZDc0+tl1GgfWyLjQL9gvIyLs3lndlMFo+Y3cHD2eowOfOWC4wH2ePi9ItFtlR4Mr1k8rMEfVp8wtbqpWe5pPLPjeH+ibn3A8nq0gS2qLo5bNmHLgF1XUf147G/uVfV7WqY6un1yTcuvX4qHo8BsYELu3AAMAQOAxO2WgIeDxFxdFfVxRSSWecfdpud2xaG67NKr/pzZ8b4QVLoIYNy/1M0Q4r7LyADZOd53/5x7XJZ5wm/IffiuiD0Y9vJExB0HqHYed+c2olqZvRAv58yMicNkw/HgMXTOdPBIwhRRl5gkIwWjcGYBu3KWIXWrq+CBdduYMw1nsD6ZmMiAgmeolGEhGP0BQo59UdILCpUe1sm5ZH8AcI8UDqGSGXn5/tLDXjon6JOr/34/XqzCHRNe19DbclXeF48yxKlcxNAsPA/ZS7i5c4wwIH5/SpDhhEPVdmbP7+850LdZXe7B8BoF9ZI9dXmYZ8peyWUpbz7BACEm6sTaJCSFScaERHVE3SIezZ0rUrCsbQVbICQFRsUE88Kii+Ke+My9yq5W1tY2khTl9lET3Qu8YedjU5woAyF2Qmxq7lnnHJgNtkBYFWPgzqjSa+x/ZdYNvxVpjwN1HUHDdjxRa08UxR1KRwdjRDtux/MxZHwnyiaKiYLFez1Lf348LrQMlmdB5v8+DpxHG+7EuY3Sg+E1CQ+1b9LOpAS4DtpXfzAXupge56o2LLMsryV1PYZSl824fdJVQs815hyOcy+uq13YuygucaoAYY71eEfPHAGkvTABKmupfpeOCRYHB3TA0lnzW7oItICthxfRTgmGzmYnxXE+jqWjCHbqhRVO7Phl7PNxnmu3DaMdDG18/B4csHmWvCiwpwCyGMC8y3Jm3i5j4kz2NkkPhtckqIKeIobHbqTupk0DZYAbSfqkLpaLlx3r4IHa6gDnMYBuq3NHgVeBAVS9zBSADbhhJ5S1iWPGnTxue4KVenYMzgC1/SREBGYJ24P1IB4qVCvKjvHePd7jgTSsc7sPlFVl7kF2v6iCHo9HP51JT9S1/XHvBMQDTidte0cKkEY9dcAq7XAOvKipXoHGIwKc1Tqo+qQGDFnkTtUVQBEPeblw8afiu9uoLvdgeA3CA+f5siW7cmM/7wmZ8QfePX4wCCkmYukVxLvptqvFkuOcGY3tGHfiXBYmUikyZtz254Z/qavqwd4AHBYLtzm6kwFwuKuuesg4uVeafo3H0mQgDWdZjT9UsDYfOyY/4MH3rq764kK/fEzK2D3vByDCvbjTw73VLiNFZXNCkWB5OJ+8gjZ9GVh7Q0VozpkdX44p5hpfMN1xIsUz/CS2YHgWpQfDaxB3nLgKAmOS8oPl+/L6pHZxVicFSylj4kongNv/fHL7w1+qi0wu6iQu1AUcZyJ8X7ZJ39wb7vfm6h8s8nHCfRDbSLuACv12j/vAEAY11+/B1VxeU/Fa2j3LRcL3MnGw9wXD7/dxcYB+n6UWgN3WnTVuG5ZCHacN+girx8testFljq9lWoSz2duoKvdguGIBCPeUs0Tw1q4pbFtlpP9AUTmE9Dcmrwdm4/RYUzedzNmbAypBzA5iAExZsRk26duMelybq8zYmhCfVNi15gr2iniqIdee6yJzU/t+px3HO0Npvb3JqpLOaqmpW1ZUZxZ1Qp9m+f84NSYKR5MvFO5QcVUS8CmB00NncFpgz/N2XRUux87bcfFF5Kj9/7H1x49noeKaeKy9IjfPzTIAo3++qJRAXnr7XXO4Tc6UHgxXLBjTeZh8v1sewjK9DpZV27nY5HwiNQp2VAKUe0ylsAG6UZ3r1cX3A2tnXV2AKIFU6novXf0DzErPKeBzYN95e0xqQBZv+n3l1MWNSWZ8VZX/Fu2MnI7b+1l07/vA+odDw6tEu/cX22kJZPQRhg3bcu8s5zrIlc4I2tlRsFsHn4W6Y4gpwJ0c2JfxYgNK2FqlLqCWGoY7erhHXwwd1F2LKU0mt02u9Z5SSp8l6X9R3qmxlvSDTdN8f0rpFUk/qmzf/uamaQ4vaebGiNsKy3g7JpKrhaW9a8vOxyjvoOkAyGs5cZk4hOQMldmesx+3g7l9a65gfLDQs6KtcqIPij8HDO8XaWOlXZN78KIF2M5ekrQ+lBaLDIbzedfetl5Lo2E+ZlhJa0PpyJCAhck3dJfCCVWaF5Dz9hVMvExlcxbu6qR78n28fMFYlg7H/z3IunRmsCBy/XKx9H65bdDtlw6i9LWy6/iiUNpAl6nzN1muG+ArSX+haZoPppS2Jf1ySukfSfqTkv4LSZ8j6U9I+oFr7tcTF2dXOwpg8wyHmbrxcVLXQ4gqTDyi1N1r2O1Q5WRC7XGVk9XeWZ7vscE13fnhNfXcaUH+rGfSEMtXTkr6xHtXqTmXNECAqrY22Dx9IGlWdUESE8JR+91GLQ2H0mgi1e1MdU8q2TzcCwxP6totuS8CmaXI2eZYGDb37ec6W3bQwO5api4uY2/YeMftGJRFE5wJ+nklM3dgpB4lGkkJpN6GbBxcLea5LlMdb7pcKxg2TfMJSZ9o//8opfTrkt6lcOjV6s6VGysOhLBCHkQeYtQxV6fcSC8FWPGAb6irTrrB3u1U2OmYAL41KLF+sBxnp15l24GOvrjXE1BESo9xyfYAVFRVNj3nz/OofaI7I/VJuj2WptMW9A4yIJ7W0mSRVeaztuPlRu/cC/uLMJZ+X5y3qW5dP9R2gG+rbYcxpO8E0Xuh3nLBcZbmbMxZ14lycHVZhJX+lnZArw60bC8U37YUKQOyeXb5P2PuAIvnv3RE3WR5aqp/Sundkr5Y0i9I+pCk9yvHx37T0+rTkxS3zTkYEj7DauyTEED0h57AYh58z+VdNiHc9uYMzVd++lc6U6SIo4NZsvpXxZ8//IAWYFjeE0JAL8Vh1xWsi3axkQ7U3aCIRQHAX1MOm1kbZqcJjhqOWa+ltUH0n+sAjOuKzZuc8XjfGetkx7mzhIVnU+GwKDeL8vEYFJ+5Coy4CYUFZFqc75rAZexszV5LL78z76RuGqbUZav+fJTmDle5b7o8FTBMKW1J+glJf75pmgNlG/fvu+T490v6ekkajUaPO+yZEpgXaXduvOfhdGM54qwA1ROwq4vXSfF+qJiYy9KypO7Ec2HiOWPkeLf9eb1EgJuJ5fuS+D0B7m57guWihhKjiPPkzNom8HiuWFzGg6wOS9mJUk50SZpM8t/hYXayHCgKNuB8SOrGVHqwM/dDFZ9DdYudYmYYKWL/1PZ9am2Vv6m03FOOicTZfmnqcCCkf0gJ6uVzg7idk8WJ+/Xc9VLcMeRtlffzLMtHPvIRpZSO7KMPNE3zXukpgGFKaaQMhH+naZoPXOWctrPvlaS7d+82+/v7K+zhpy+s2KiE2Lvc81uCjDMpjz2Uuiu5q7mwC9m5HmA7LM5lUj6OiciOkSKsA/D2PYpLj6SDCWwDQJbdu+c5Mz5SF9hhbNvt5y/ad2NlB8mofXLPKungIPeLMCAe6sUiA+Z4LNXzfP6RIl2wVtgd8a7DOgEasoMqdb3fMDJyjcsl2nOSHTxL2ypgSzoiJgqeEf/Nzu9LAXwALm0vFE4z3xoWEPO++O/l4gso1wd0YdUUtnDP901gh6+99prefPPNzWXfXbc3OUn6W5J+vWmav3Gd175OcbXIAZBSW66CAjIDRXFRAA17H0xPiuh/qRtS4SzQ37vdx1d17H1Ncey+4uH2/GKuzQSe2utU3X09ENpGNZUdw/0xiZyJMqko3EroAXbIjRY56zoDHtkZezvSaCydLqSjmfTWPNLfyt+B8WMsSkDwijxlcLwUv50XmvXqP7LjHbhYsPhuoHAceW1JTAUDdasWlYtZGUjuoOn35kDKNWjH0x6r4pV2cR4RG/tW+3eb5LqZ4VcqM7xfSyn9avvZ9zRN89PX3I+Vitt23JhOiIPbE5epGc4OfR9id4Iwydy4XdufM85h8crxhIjAADj3QF31TnYM/Z0Uf3jKPWQHUPPd3vDgApSo5EOFCl4a7UdDaXOaQ2kWi6z2MmYDSRsDaWtLevGlfOxsllXnh1WO1WIsT5WB2RkpY8rEd+bDvXiRAylUV1f73VQB4HvICkBOzjmmA35rL7jgzK9UhaX4bXCqAFgsPN5PnjccKhzrzwq/MbGdy5w664qMHBavx5lcbqpctzf553RLvMWPEyYy1WkAENif/7mKURbPvKMANIKrAVC3LfEZ1/GYPq+wQt+4LpPIDfVSd3J7tRMHOdgIQOY1Ah1UYX+epuYmAUJG3KtMWBGq61w5u2TShsrsL+Jexgqg3N2VtrekwSAfNx5Lo1nXLIFaDJN15w33TljDUfE5gORl0/iO8eN38m0Tyt8VEwLAw9hiT0Y8zKe0FbpqDHACwAC5B+LTZ34zimC4yaNWNx20rF5EyJPULdpx09Tky+SpeZNvq6DisPXntkLdRHjInJFJXScFD7s/hKz07MyGCgXDhIG6VxpbnD/45PZ6zKJnFTjz5J4oIEuxCVc9CePBeePqV5lRQV8AXWcsjIGz0JmkB7U03I9Qma2WCU6n0mmVwVLKzJHXRXtjZQqgFN7T7fZ9uSUme7/AzFx15F5gua7qI9guXRNgAZGNm7N4jvH75zcgNdMXktIui7pN+9yDFPZNKeywZaGFst6jL6T+23HfHh/LuNwUJ8rjpAfDFYjbY1ABy/zWUr2Sfe6ZBmXxBtQqds4j8NnVndJGeKZQcUrniat3ZU5zXfwxuZ3RcL0ypKS8ljtcvP2Fuvt6+B/fHyjHDuLc2NrKf5OJ9NZBVomPWtW4rjNAniwuTnhYqhdywIYLaHtVFx8LqWuW8GB1+kv/ZNdxld8XGw+H8rH28QKE3DRSjmX5DHl5Mb8Hfit39DDGPtZlu/5cuE24se9vCzvswfAJi3uJUY8RZwG8r3UROFAXmRBkIszb9nYUFZ7LIgp10c5CsZo72zxSV9V2o74zoWX18nzjIoq6OuvhvvDKOiCstX131sjnhHggXtMwKbOaXWVGOB5n9nd0KM3mMeaMibOXobU3V2ynwKSn9qCDlS9AJUDD4FG//RpeuMG96UNrz0GUxcFtsaWt1vsjO9/tqrL7daCi33jQy4IZDgAO+vw2W9YGzyILF88K5pay2MdNkx4MVyQ8wO4QKMHqcbY9ZxIeY0Z4B8Z4Hk634bi66VkcPjHY+8JZE4DpXkTug+OwEZYTyW1RbmPCGSHFTnRSMCjvP+1g8zpRqH5clz4dHraxg4tQh1EnsZHtDKRRHfGLzpIO7DhfnNw0AEhhGoB941Hnd/VMjdKhUBZhGCozen47XxzdyYWDhQXQ1XPEgYj7oj0PFZL1/VBRTV3Fcb6g+nhwn9O2X9uKLBtUf1Ty0txw06QHwxWIP0QYwd1uU3p2Xc1A3RoW38nac6eHq8Qb6gZBe6yasw9Xt9x76JO2XPn53kNxaAf26BO/se/PFHbTSt3iqYhvGUA4zF1lFgkYMYb7h9H/SavDPVoU4zvMbewvskcZ8N1TmCwwHXjANBOde4Yd8TnjXwZT89s5w162URcLgW+RikOG84lNlSJsyOMBF/bqDhIHY19gUbUd5Hglx9yfA85HWFDXFQ4mHFGMa7mg30S56f1/JsW9jR4bVtqExsVnfO6TAWBzYOLhA6BmCseGe0kdYJl4TFz3hkpdYHI7kOx7V8XdIUPQMbYpGA2qvtQtEeYqOIsF/YE9bio2PCIdjusypgQXV1VkfaDGUdqLzbbUtsUWAY3C67qrbtmzmbIZwR073K/bNhk/Z5P0z1myH+vecnegEe7jNmKeBfqBPdAXt9Lm6wzRAc4XQAfFR8V5fu2y0hLsvlIUz0D8WbupqnIPhk9YeIg8UBY10gFtQxc3dOdBYrUG1NwuiC2Hh50JXHp4vU1CPQArNphaph46WMFUfNtRjneVCnubTybfMY5zq6INvwcpCjXsKrPCFxVqvTMbQDYpq8kA/FjSsD2oqmNvGCn2KSFDo3Si0Lb/dgA48ZNuPvD7dVsjaqgvXKVzintJ1p5nDLnM2zZZAJ3k1yjAAAAgAElEQVSl++/vIATown5d3S77JWvPbbveZ5it7LsyYH+ZmeCmSQ+GT1gGxR8PHXmffO6ZCj7REWx40sUd4kovHjFqHurigIYatCz7w/+8/w6mj7NP1fa9VzzxPpWT1EHXA9BhP6S/3ZG03T6dMD8VbXnWxrl5oO6On9dhdPPEsmo7biP0UmQ+/stsvg50yxiWM0AfB//NS0bIcSyAMGjZMaUDxRcXH2t3ivn5blLx364ENr8nf45wpHW2WSiuc5PkSmCYUvpsSZ/XNM3/nlLakDRsmubRart288RZ2pa6e45gZ3GhiksZouGbPdEm7fuDBhhS/cUT891mKAVwoCKjzqIi8SBgC/O8Vr+eq2r01xmr276csWwrGC5Bxh4kTn9QFzeUg6kl6dSs/u5Fxb5WKwouOEjWyuo2RR4o/sA9e0wkgM49uN12GYi5rZBruJdZdozb4cqFRnYM56X2Dw+tt8ciU3qY+b8zeW/bF1ApmB/M25mm24z9N+IznjW0m8bau8nyKcEwpfStkv6ssu3531Ouvv4Dkv7gart2MwWD+13lh/JA2WZVVrX2vU1mumiUpy1WYsCFyQzoJMUkLMMtmHi07WxtoEg5K4s9jNXN3wVAAXPCdKRu/OREEbJCIj8Lghet4HxX3fibK9vrPilpdJDVXo9n3FA3rpLrj5TT8hZ1ntwn7TU3FWYCD5+Rut5xzxM/kvSmuql67uSSveLtLpmrZwW5ndZL9ksRPkR7C4VayzgR3gLLBcTx3i5joW7ioB88X5zjOcm+cLkW4rGafu9eBd3Z6E2WqzDDb5P0Zcp1B9U0zb9OKb200l7dUPGV/lixujPRnbmxymLf8Zg72BYPGKxJ6m41SjuogwCk7xvsnky3KVYKD6+Di6eQuRrLJH5LwST4nkIDnvECGHIvALWzJM/icNb1SBGyMaoDTKdjnZf79/vaHGYWORxmlbo6CObn5gTu4aj985zohwoAOtFyduemDxYiPKxuevB8YlelYdaeetco4jF5HlDNKQEn+018YSxtfm5C8ZAfKRa5if0e7lGXteG/mxTPld+P73ktO+8mA+JVwPCkaZpFLjgjpZSGupjh1EsrgBxeQ8BmmerhNh03tA+L9soV3+14gBJsEaZ4Zm2TF01MH0HXh3YNN5bD8obF30JdkJMu2g3dJsdk8/HgPnCClPZHGI1kID+QdnZy1snBQQZD2j+VNG0HLA0yII4V5cYcvHbbqja/8yCqXDsbPSn6UjoR/PdyZuhjWG4byne+sDnT9bhMaibyzLgmMFA33RKVG2aKONP0PjtbHCtMNLC7I3XLk3E/LCKlB9sXPm//JjPEq4DhP00pfY+kjZTSV0v6zyX9/dV262aKPwzutSP4mFi1ufKDDfCgUvnEoz1iuZhk7lV2tS+pWyKMc93z66ywZD5u//P7cXWOicjG5h4wzcTzqplI6WmmPVfrmeAeskGRirU6M77ZLIPhg/ZcgFlzaWORM1OkYJywqW1J40EGws2pdDyVprP4jc4UGT1I6QzCTub2Q/pYOh1K1dU/I0OFsKJKF9m02xCpIMT1PbwJ1XdbkQXEboBlgQhAn2fEwQ52XgLnWF1Gy2e06fv5OAu9qXIVMPyLkr5F0q9J+nOSflrSD6+yUzdZyknvth53Vrj9cJnKw3GoUFJUReG40rOM4wCAIryFB7V0vlTqirOiMuyDSQxrKJ0DHIvKgBPA++FMpQQV+uQqJGwpSRocZvshajpt4URZr3OYTVV1K+zALsftgM/n+RhXVzeVFydfSFz1RN0GPJO17aBUqpulED7jZhOYvRRqu/+mDo6lbZDrkPbHOBMr6b+n1A3Wh8WWqn/5R4wnwrU97fDEvrvJgPgpwbBpmlrSD7V/vVwi5UPjagjfu+eVY6iegs3KY7pghUw8wJE85TLGy8EK+xXXcdudn1Oqq8vux9PPPPWu9Dg7q3UDPu2VOb2cg+olXQTEI2U74qAOG6tnqpyXm1pEwDRtjJTLfA2H0slcOjiMFD2PzWSM3ZPv5gt3oPAbnClYmI8NpgnZNRiDZaqzm0e8FBegdmDn8HvS1kLZ2UOQOBWJ/Fo8E6TNoX6z2EqPV3kbdavZePyss0+esZssj+1/SunXdIltsGmaL1pJj26w8AC551YKEPD9NtxQLYUh3j2tTEpnQpQHAxR44HHC4LF0byEPr28MTtswCbIfOB6PJuITHtXSCxH4/TtbIn3LwZkaiG47LW2fqK1el2/Ufo632otPDAct+5vHPTL2J5XUzHKVG+6FhaVW2E8dqMtiCtjN+PPcYbehjXUxH9qBpVRF6Qf/920QhsqLAKo44VosjGW+NOPtaquHO5WhV+4tdpCmYjc54q6pnKhrxuAZcdPLTZXLwPzr2tdva1/f375+s2Kx6MXEV0kmvdt5mDA4ENz25/X1ygwRDwD2iQ4QuFda6no4HdA87arMb6aSMelvnOsMjocfICAkRQqw8s2ovMiDs591dYGB7xeKqjxSZOiwkEwV8Yql53LYsr/JRBq0s3JWZzCRpM269UhPpMm8m51CP3083bOP+k4WD32TutseNMpeaVihs0HuFQbm7H2kLhhJXa9y6WQqzQ0wPd/7xJ1g/CZui5Ti2fAFmt/JzTieKUX6HuBaAuBNdZ5Il4Bh0zT/VpJSSl/ZNM1X2ld/MaX0zyT9lVV37qaJr7ioh84CCcWAPfJgu+cRhgZIue3H815dtfVJh2d0mSOnrFCClLakqbXnKh7nnTsu1AVYGA6vzhwAOB8rn8iEuDiA4vGUuimG68M2dnCcAZCirlSwGY/zZ/vKzhYpLzCTWXaklEUWaNfBvGSzLBYcP1M3rvKhMlB8Ul0V3Jky9kAPoi8954wnZoDazuN3L3/bLWXGvKsAefpd/na+iJQFH/jcY2JdW1DRD7dtyu73pspV1PzNlNLvbUv2K6X0FYrNvHpZIsseCFRLD1/YUHj4yD+VusZzKdQSHlrfTN2B1SfL2NoqWcegOJ/yS0wsd8BIF21CeBGXxah5P/08t0/5mLjzCJUNu98LiknN+IwkrQ2kwUDamMbmUA8PQlXcaj9ztoPRfziUposuc10owKjcgIsAZ363Uv0tq3uXC4qHUNGnWlEJp6y9yELizpUthapM+w6GqOBvqWv/hPny3JVsGkHt5/4WyguJax0eZsPv6p7l0nt9E+UqYPgtkv52SulO+35f0p9eXZduvjgYwm54SJxRkY1B+X08gzzM2MXcjlTmqgIWzgAp5SV1J7Hs/w5iPrFqBTNFHU12jk+QZeJB1A52yxwIDpKeDQGjLFW286KibSOD1i4wn0cg+DnQjHN17Im1syad77VcL7rjAitj0fItTf3V791/H9R/Sm6VNlh3evikg/2tFe+9XyULcxUbJx3jxvWkboGJMvzFwRuV2h0jvoj4/33BcHOLq+43Va7iTf5lSb8rpbQjKTVNc9t2CHyiggp1pMwayFt1+6AUcWE+2fAIlk4RAINUNFRnWBypZlRceVEBnM50mGiAlBu9AS0P8nU10plAWY6LVynshSVYunrJ/WPblCI8Q+qq15NxtgOuz3I5/7mked1ed5ZV37mk326P35S0s8gB2vcWMcHPWVWrWleVdFp39/JgUSmdTa5uMukdsLYVTHld3S1DKVLr5gSABYA6VXiPMZt4cdySjbpdetzes3/ued/cTxmsXaq3ZDbhQOLPFyUpnFb+jDjDvtXMMKX0l4r3kqSmaXqbYSE84AeK0AseutIu4+IMr9yjQooHtMwFLXfFKx0uPoGwecFW3LFCH/jjPjxez+1UDuCARgmeKo4tAaRkSPSBezvfp7iS6jrbB5taelR3VTLYmReqOK6laZ2Bb7yI0mCTSdgTKfHlmRdSxOoB+B5byL7B7ol9U23Yj33unuUybxkmyVg5I/ay/VI3NMtVab4rzSqMq+w97I37KLNanE3yPbnQ/Gaek46J5kwB4Dy7N9leKF1NTfakgomyl/nXV9Odmy9MEsAJZrbMSI2n01mZgyGBy0d2vhQqsmeHuKPF2R5qDfY2aTnToO/YJMswHL/+1M6DdZT2SF5pg+980jhg0wb3xSROtVQfBliQTuZpbSOF8wD19OCwW9h1pMwI2Ur0VF01kj4RQ7ipbpC8b1ngtSm5B88MGihS4ko7Kb8NrNi3UfAMGMwNHtvmquh2e09zZVshv60Dnhea4PMdXaxK7YscgC11wdk1G7dTOuO96XIVNfn7/H1K6a9L+nsr69ENFzfMYwOUukZqhAnjsXSukqAmUVDBV/GxLoo/1A5OsEKPO6SvtFWCtceuOQsF0JjEvHcGgwCGDriAsu+3Api4bfVA2UO7qTzx/Xzi3QCKbUm7Y50XeqX/b7Zjd163r8pgOJ3m1L5B3fXYMiZuG2OBYjMlfiMvVODnAhSEJjXtsZhL6J87T8gn9sra/N6MM+MFGJYOEvcgs2CQ+cTY7iliVFHDd+y+iGF0EPVqRfQb5x8qPE6vW68mL5GppM950h25bcIk50EDCAEXChhIAUQUL+CBJsAYgHTGx8PuTMCdM/ywMBNseXg6nUG6Gu/qqxvP8TS7eudFPrmWF5ZFYCK0T/A0/QJ0/RgCyNckfYYyiC3qLggwDlstwLHR/GIhzRdddXOobHM8PAx1uZ5HnrhnjyCwW49txKZHpofnQAP8/JaMIWFD/lu6swYG6OPmzw/MjD65OeKOuoHY2I1rBQBzXRYFX7B8WwG/b17HyqybbRJg314n0sN2brJcxWbomShryvb5v7rKTt0W8YfbDdPSRa+hHwsgYnMcFOcCpm5HlH3n8V4EN7NqLwtzKVdzN7APl5xTTtpl9+1quPcX8Q3Pyz5zftX2H6dHPesWBmBiV3W2A9a1dFZLZ1V8D0AlZZvjYhGqslf2KVV974c7UmDnMCs/Vkv+70zP7YUc5/ZRv94y8f65NxlAwzbqnmLfswXQlPWd4Hn/jRprA0cfbNOFhem5UZMVmShS/k1/u2mam74IrEyYxO419litcXGci6dGoUZxrKu+UjdmsVRP3FZFyhjsi3OXPcRuXIfNOjCWnmKf6JcZ8KWLtjEP1+B4D+UAoOaSZovM/taGUlNFUVrY1pkyGOIxR4WGfWGbq+u8x/LhPDzt/CaMfZl9MVOYKzx2kHqISREHWKbw+T17hZ/SnMHCUNkfbXCue3o51mMTATXS53xMUfnpoxTskv1lYLqEVXG+252xHXP/aBekMt50QLwKGP63TdO81z9IKb2//KyXEH+opa5qhK1F6hYcWMbOXC2DLZZgK11Un1nJpbA3OvtyB49nQxBisczL6HnV7vUdKMpCuQNhoz0fAHX74MzaKT3sXrkF58DHJL00z3uiMLmxo3kBC/oCSOzZOPKdM2XYHQuWg7o7eT6psIsSEwoQY0vFtugAhZDmuK4AMc9HxuzgAAmQweRcPfaxBMzcbMHxLKiwO7+nSlEAg98M27LbRPldPRIBBxdFJJ4nMPxCf9MWd33Parpze8QBEYYyVtSeI3wDz+1CkVvK915QwHOAWZFn6oISk5J6g5XC8O9eWqpZY1+SYoK63c/tXAQlewC0q3e0CaAw2bEJljapcmLzOiy+P1W26SVJa1W3HBoAdXeQ4wrn87x/Mqxte5gzVGazbG90UwGB1TChMk3S1U0m/Ng+Yxz4jcaKGMOZImXR2RW/CeMnXXRYeNgUjht3dAGWAJpvCu9j6wvaqcLuiZnhRPFs8CwRrL9j48/z4NXTYbkz+7vpzhPpEjBMKX23pO9RLurKmEMQfvAa+nZjxSe3FHYhVupkn0vxIG0rvKfHuhiewSR1tU4Kx4a36Su8e2ul7t69O207b6nLjnxC+eSkvx73xmRyFQx7J4zEJwqAtqZgNu5Nh2URlsS4rSmX4xpUWWXemGT739ZWdoo8eJAdJ9P2vra3sr1xfZLtiAcHOQZxvb3OkcIccGZ/iIOfbAy80KurtTBNd5xRxAEHDA4IbHkzO8+dZAi/90TB/nguPIjfbcpl9pGPO22y0HjModfDJMPEPckEkBNqVIbn3HR5LBg2TfO9kr43pfS9TdN89zX26cYLq/dY0j11vXqk36GqerHSLUX2ApVsvDad2xg9hs/DPIgTJKZsW91yWahXrjqdKKtLgNGWXdcf8rKCtNRlpdLFybLTXpu8WdT9XQV79BjJkaJ6D/uAvCzplan0UrvzzmyWHSpbWxkMiR+sqlzNeq/K2wQMWnQYjwM0Dw8zkJ7MpbPDPA4sBEftPZ4oWBygvqEIuPaKPgCJL37+f16JNaU9wIgFBYB2Wyljuav8DBGCQ6SBL0iYDqiYI3UnN8HS24r8YtqkXYCY58nNKM5eAXaPQLjVYJhS+oKmaf6VpB9LKX1J+X3TNB9cac9ugTDxN9VVb2FeMBEeKFgDGyx5tWhWfWxLpSp3nrerADW3VS4zzqMylcG5UjA0b89jCwFmtyF6f3wM/B7coO8B4QCEp7Khfm5I2tvLf1UV3uWNaS7aUFXZSzwaS9uto2Vzmh0mx/MMhINBZoj8f22QQVV15BP7NgncW7I+y34vdwx5/CD36Wy4KY7nOaA9aXl0gJsQuA7brMKsiUXEJOELlau0HvriZdqkWADcQ8/CB0h7RhKs9LaE1CCX2Qy/U3mL0O9b8l0j6atW0qNbIDzsgAxFF47b7zG8u+NDCseCB+t6BRKva4h640DjsYte8oqJ5mEieIdhBYARbZKDuqXuJCLrAcO7s2C3xblxnwnmDJJxYTywacFYpQCBkTKQDYd506fpNAPi1lYGPDaJWhtIo4m0uyttbmX2NzjQebD1+jgfc1bnz6o6t39HEdjtKXaVYtsB+kN/3XYKoMOo8LD6PidHilhGZ35l2TPG2G1xh8oLwvlYKEwjLBxSZrj0k8VszT6rFV52jzbAprujCNM5Un5eaQcZKuINsaXeBnuhdLma/Gfb/35t0zQd80NKqUym6KUQBuzjigcFcGRlhY2grmDDIv8R9uiMz4UfD7CFddTKD/JDdWPpSnWmtIfxGeobtfo43yerF2f1fgHeAAv2Sbe/OUMmbGWr/dtor31g7dWSThcZ8FCJpQyEBFBPJtLDh8ESdajzbBMq1cxm0v5+/nvjYTiIdicZPEfDfO7+vvSwjnQ+B30e/KEC7CiigRZABR3GDNurxxiygACOvuB4RhLsC5YGGNImNkdUZNRegFf2e1G5+pFiUfOiH/fa+zpQJACwOAP2mEA493lhhsjPSyrV5GWf9WLCQ/RQwbA8tpAMC+xzVKwpc32ZhO5N9PhAVzE5jsm7b9/5A7vMszuy47wPewo2cKrw1PoudniS6YsUcWj0kc3cCS2atP+HRU4k3ZtmVXg2kz7+MI8dMplI9+61mSMts8N2uLub/yat53g+V6fQq+ckp/aVIq/jcde+SHtbLajO5l27mofmHLT/h1G5k8QLWPB7wMBJz2MxVHvOWdGGs3h+G84/VQY17JwexO+hN2gErmXgnOP3R8vgWqQhytqC9XrKntugb4NcZjN8WdK7lL3JX6zQFnYUv2Evl0ilCJ/BWeFhNqXnFLUGGw0gRXzaMrXUAQiDP6qtq2q1HcfrMuO9P+Cen0pgsVdk9ocHRunqGKC3qQyqU4W6t9WyseNZZI9sTDMwjcfB7irlyta7u9L9+9ku+MmH+ZyTRdgAd3YyC6zrLgM8PAwGuTHN7awN8nu+m8+lo5bCbrTq+GSS25sudL7rnqTz2okPdBEEGL99hckDIHMw8zzvE4VKjMCqAcuRQmtAPT9pr3Ns59EmrBAgPFKAOCo+4IgJxRc6fmeeLfY9wW55pGCPt4UVSpczw6+R9Kck3Zf0N+zzR8ohN71cQTzsZFdh23NVR+o+xG6TQE10wz2gyYNIoLSHt7gDBCAEwDie8leIswpYKIDJBEbc0E97D9RVi7eVq1V7bOVkHOC1vRM711WVdGcngxD7mewcZsCTMiA9eJDPJaVudzc7SkbjYHgAnxTA6GXA7rTXHAyCQR4cRKVvNo6iH6dVtDmd5v6s7UvjKsCHhcPT3WBWbqt1zztmiyMFiy5DkCaKhY3YxaFiwcJ259dhoQQ0TxWxiqW91uMHpdgugufCA8cxa2DCIALiNsllNsP3SXpfSukbmqb5iWvs060TAMaZoWeT8FC5t7aM4eJzD3fwmLSxQpX147kegDiy84mFKxmOX9/VL08p47hTXaxYw3XZwMmZkBThMAAhaux43FVrJWk4b4OpD6XXX89q7sYkO1TWxxkIYXN13RaAnWcvclXl47nmYJDfD4dhZxwMWsBtfwSO5xype7yUrzupuo4Fj890Ly7jUhZDgHUfKYLnPVXTgZMxdRMJx7sd2jUG3pe/F78ZAO4guWbHc647xHhWPPrgNsmntBk2TfMTKaU/opyJMrHP/8oqO3ZbxD3LlcJmhqNgrrzKEojrdiIeXg/x2FBWO3mQywokhFGgzhKsS6Cu1HV88EAzkSjx5BtTMQGkrufU769q74sc121lL+3eoAswAAvgRbhLXcfnxy0Awtzm86zS/tZvZg/yu98d529Msp2xrrNafDyX3nwQzhQpmODxPB8jZRC9dy8fM5lIaw+yl3lzGsyQc9fH+RqA486O9PIsxhNGTsqas0JAzPOm3TmCNxbvsKvVI0VWEmxSCsdL6XwhrnVD3YIWqLqe6khcIY68sYINbqtb75HfnT2UD/ScgmFK6QeUF/c/IOmHJf1xSb+44n7dGgFI9tWtAOIgBlASTsMkI0MAYCQMZEvBCpk4BONOFas5MWm+faervlKXeeDogU1iJEedw7bo9QvxLA6UzQCf2fYRlXXc0p1Zqz9jj+MzZ2iLhfTxj+fPZ7P8vq5Djd5f6HzfE0JqplPphZcGGlR1jj00FVfSeTD2bJYB9cGDDKh397osdDjMdsO1QZQDO610Hty9s5PbOWy91NstOqFWHrdji43YgcJjBB0MXc0kXAogmynAk0XNVVfYHcA4aMf9ngIk/RmBzXkEAsH9eMQBS0wkvmczZhkP+bltchVv8lc0TfNFKaV/0TTNX04pfZ+kD6y6Y7dJfJJsKex/nvyODcgrhBBviLcPR4aX+4cdApbkyHoojTMUGMlA3YrVBN3y4DPpAEMcOUNl0AMs3qy6cW2fvSu9cC8zrKNZTolbG0q/84bOt/R0mx4sbDJpbXeHud2qyp5cKYe+jMc573g2l946yGxzMMjgNh7X5wCGnFY5Ba+q8jE4VaQMcGctQJ7Vmf1ttRSsqnJ/J5O2uMI4O14A9Y4aze8yyAx4bZAB+3VlICmrcbNIuTrNK0xvW6FJADrszQx7Y5HCAcIOfQT3e9457xcKrcKzVSiAi7DwHasL6vQJ59BtY4XS1cCQsZqllF5RHr/XVtel2yeokuyN4sUUpO5q7Ku01FVl8fgBYNiHCPsA7KgYw6SRujGIXBPVx+1OTDLi0Ejv2lPkztbKnuCmls4eRojMRBkI8dguFu3GTPey4wLVlwKshLHA5FBrd+oAMEnnqXQ4PY5nGaRe2Iuq1YeHkaO8PpFevJcB8XiWVea67rbz8GE+R+p6ok8WwQSHwwjRkfL7zTbr5WQhrR2EzXOEwe8g50cPFGl+MDN3rpzbTxWgCCOnyg2/Fxkhbv/jXDQHNAovEMHzNS8+882q8FS7PdMzUjDTAIKEE91GuQoY/mRKaVfSfy/pg8rP/Q+ttFe3UAh1GShUUR5kXj1bQ+o+5KzYgBSb8QztXIKdYZdS2KrIOvACCrTlxnnPuU326ltDPlLO/x2PpVdeycxJyiDx0ksZWLDzoSp/7ueGbfDNh9muJwUQ4vDY2urGEsIed3akz3g5Z5XMZvH5yy9nJke4TV1HhooknQ7zuVIA7sFBBsOTRQZVvq/blQJnC3bJR4cZ+CeTANzhMLPe+TyDbtP296yOfUY2FNksLCo76sZoEnokRchKpTCpeD64xyny+wGgHqXAMWW0wFzZ4+92w1N1ARgzzkDdmEqYqqf73Ta5igOFqtY/kVL6SeXx+oKV9uqWij9Y2G/cGeGCysuDig0QYPOS9u4E8YfcvX7ukOE8GAITzj2eyb6nfxjbzxS2PAKesfmttyrtWaEC7+1J69OMMJPJQk0b34dT4qylG7AsPMvuWCGdDpaH/XE8zuejhsMwUbfHraqL7bBqP28M/Lg27x2Mz9oAbPo0GOh8iwEyXrBvNnUO6F4f5PAbfou3FBoAmSgsNtQRPFLUXIS5w/hphzY8pc5/LxZZficvoYbNzxdczsW2TBuEhMEC3ZN9W+UqzPBcmqY5kXSSUvoxSa+upku3V/xhJLuCfT48l1gKoPQH08MxGjuG1Rz2xkNMetagPRf1zAGRWETUuDNry1kH6t6o7fPWVga4+/ezGixlmxxAI2WQvLsnvfiStP4578on3Lunuw8e6O7/+2v6rd8MlodKPZlkIB0Msiq+1na2qiIdb1yAZRoPdeeVdd2ZH+t0Xms2y2wRtomDhpS+8TjUaymAHbvheJxZbRpEIQiAESFEx0GT162tzJDrWhp+NBg4C5w7vEilxFOLI4bfB0ZYRgAAer4fB5/dG+RxP120weRVsLqHClvgeakze3YqhV16X12nyW1mhdLbBEOTZWTmaiem9B9L+n7l3++Hm6b571JKX6jsqf6QpP+saZpbO+Y8XFJXZcbhAcC5usvkwfbothx+QEJ1YHkcw+TDbsSEkYIJDuwzwndQkVwFG6ktp/Wy9OqrGexeeSW/AgSHh60TYiANx9nGtrUzyCe88plZX37wQJodaevhh89tci+9FLa/4xbMPut+C2qzaJfrjMetA+eh9BnDSqP796W9PY0Wp7rz8X8nHRzoZF7p6LCr/u7sZOY5nWbwPq0ifAcWe76PSiWd1MFEZ7OsYqMWA8zn9kLpvCjEcBjseHueU7kcWNwzTFD0gbqAs6kIo+J3d+3Caw3iSNHBvxkAACAASURBVDtVtrmOhmG3rar4/d2BhpOFXe5YPFHLybS5rUHWpbxTMGw+9SEXJaW0JulvSvpq5Wru/zyl9PeUK+T8UUnfKOkPSfoH77Bfz7ygasyUV2k8ulKAz3khUzvPPczYlFCxKwWjqBU/DnYnJuHQ2h0qJg+eSK5NFoxPmm1lL/LnfW6O8/uMlzIDevnlzMyaRZ4q43GojjC8nE6ynRFv41XplXVpstGxCe7uZi8xTHFzmtueTLLN7hMfz4BF7nFVZTvhyTyH07ywc0f60i+VhiPpw/9G+uAHVX/8jXP1FYaI4+fhw7BVnrW2vuEwg6QUMY44Wbgv1GyCxM8r51QR1L1YZJDGFDCR9NlDaVZJ/0bdtEYWtUrdkCdCZbYUoIRpxYETL/OptXuoDNrYTfndUct5drBt+iKLs+QtZa/4vgIIby1DaeWxYJhS+vtaDnpJOcvqnciXSfqNpmk+3F7j70r6YwrHZq1Pg3XeJAGgvJCDxxdiH6LEEo4PVFcqmGD/cZaIDBWhPNiDkroeZilYIAUAvGAEMYVTSfdfyWD4yithq6sqqZpXqqqw4QFW55kkVZVp2Mc/kdHm4EBanJwfT1vrLWM7rTKzIQVuMskq30FbymaxiG0/h8MMUC/UtfSZnyfpXdKLXyJNp3rx4Kd1sphrfz8cK3i6YXUAt9s/CcdBLUfNPjgI1oeTBnsj9k/aPGkHGUCaTqWz/fiN1+x1XVlVxkbr4VQsXDD0oUKFPdRF5k7QO9deG0p7825BBw/p8SIPLMLEOR7q+QFC6XJm+Nff4XeXybsk/Za9/5ikL1dWm39K0r+W9APvsO0bJ9j1ZsosAHXFY7rKFC08fuyNi+oCqyNUhgeYqtdeceS4/f+uYuKUxvGhfT9UjvV797uld93PQHVwkAEG1ZiwFbzIi0V4liVJH/5wBsOPflQaJOnhQ00mWcUcDUMVpfIMbA4HCPY8YgClKLywtaX2n98v6Uvzl//BH5de/h7d//Efkz74hh61NQ/39zPr3NvLudFnVajP02n+Pyo59wAQYtckXGg+D4CWwhbpThVJ58UgTtQWqrWxdgcaHl2C7wlnkcIuTGD0hroqL+fekbQ5DDWdbVNH7e85UvZus4A6Q/VtD3gOnxcglC4Bw6Zp/ukKrreM9TVN0/yKMiguPyml90v6ekkajUaPO+zGiduAPK+3THVzZgADIOwC8X02ans9sXPwSDOByELAWw3LJGzjjgIsCSu5sxMTHVvbfJ5BijjA2Syrn3t74aE9j6ieH0vjdWl+cs6sUuvcIKSF/Y8Bw7M62ObaIIPgqGVn/Gm6IendNiI70otfJr38c9qYvKEjszmOLNxGymr2nb2BNN3M/TvMS4yzR1Tj6TSPw3Qa9388D5MAcYc4hdxeiQmirAUJCAGCmDAW7e9FUQ/YJCwO27JrBeee/9ZWeLLICyKaxkQROIzdkucKVdmB8LbJRz7yEaWUjuyjD7DT5zu1Gb5T+Zikz7L395Xrn14qbWffK0l3795t9kkluAWyUDBAAptxoqDOesiDFJ5DmEKlSPNCCLPx1DzftpP2sSGx+sMY9iR9/ktRTms6zUxqey/P9MWi1sFBZlqLhbQzCUAcDKIIA2rieakZKOVgoLWtSQZGSaPZkUaL6vx8Ut8A1zcf5jb39qR7uxkQN9vCretbQ2nBPnr3bBR+t3T/vnZ3/59z+x+gRpcWi9wuOn2zqHRwcJGd4oXe3e3aFR8+zGNwVoX6PRxGLCNjPZnkkJvtOspgYecjHhAPM6CI46xcKAFQIg08PvWRpHn7I6PysiUoixyACThjI6YsFyrybfQev/baa3rzzTc3l3133WD4zyV9XkrpNUn/Ttlh8k3X3IdnSjCGe1gDO9bB4HbUTaYfKQCQ/FVS92AWbpti8sD+YJm+nwoebZjBms4jYc5ZxulCOtyvtLU71N5efZ7JIXVT7IbDKNE1mUja2paGa61L9lgnD4+0PlFmYouTc32btLijmXS6H4AEON27l/v0GS9n++J5yf+PV7r3f/yfSi9/t/SFX628W8VC0j+T5sda27ujlwdvndctJEaQv9lM+uTrC43GCx3PsiMHVkcfvDAsAPmoNQesj6V6GDZEz79mQavmLRi2HhDqEwKEgBlB0NiGz+x7D6bntyVsSsog9rqC0fHM0A880PzuXm2IohEHipCa28gML5NrBcOmaaqU0rdL+ofKv+Xfbprm/7vOPjyLgu3Qwxs8dAZQlLqZJG74trTczmY+eBqxTbEVpNS1L/K9s8/TKgdR4/N48CCDwauvVue5yVL+nhCY2Uznmy8BWGt1LS0yopwdHOn11zPo7e4endsXpUiDm8+7mSZSqJ3Hc+noMIPJ/r70sY8FO/2cX/xxvfrqj+szv+S/yd7r+Yl08JY0P1YatPbJRQY1jxtcLMK7PJvl+yC3GVuhlMGuqWMrgdNFsEWCxdmelPS+11/P6Xm1wvGyNsvnYiMm1AZgc1twba9s1TlRnOvViKRgih6mhWo+V+zMCNgRDH6o7MzzeNbnTT4lGKaU/pGk/7Rpmv32/V1Jf7dpmq95JxdsmuanJf30Ozn3NgveZVZw1BcYG8G3pG+hMpVhOI1CrRortupcKMAQmxQeag/EJsNlpAxIx7OI6Xt0EGC3tRWFV8etE+TU7GUn89CI9+pH57UGjw6D6UkRKjOfh/0Pz68UTBOH9HyefTBS7s9HP5qLIywk/csP5Qraez/5CY3Hn9DeXvZ8v/RSbmN/P9/PZJI3jnIVGEcNQiGHBw8i+4QNqaoq+klq3sY02x4BSqrueHziwFBrXd3agSf2f7QCKVRb38qTSVsCFnZFtAPiSxf2OUIIDcBIYH/pSHue5CrM8B5AKElN03wypfTSCvv03IqzAf5PnCB/VEYmhtD3tmDCHBZtYGzHjqj2/2SToG5JGQg3Jd2x4gnYAJnYqMajcVZbpfAEY+d7dJhZ3At7ATKer0yOMCrofJ6BR4riDZ7VQbofIS+c89uKoOXfljSaS5OP5/vc+qh0/0PS539+bpOMFYD5ZBFZKfv7wejO6m6KHXbANIgKN3tWJIIgcEJ36J/UDb8BfGetU8OzjviN+AzzhW8YhheYZ6VM7SxLs53ZcSyumEcAXbcNPq8giFwFDOuU0qtN0/ymJKWUPlvvMOi6l6uJh9fg5PDP3c4HkBGQ7WpSrVCTvDSY1FWjeC9FzrIXO5C6nlXAifJW5O7iAcbLuj6W5tOIweNzD052wDt3aLQA4mBFit9iIR3MuoHKbm/jlQo/OpSmH8ttvPxyMM7Ttm281fSNmEOutz7JjhGAdDqNkB7GB1WfQrVSAJ+U73Uyifuj76XXloXNFy3MIM78+f3LYH1PzeN3hSH68YCt9+N5B0LpamD4X0n6uZQSoTa/T9lC3cuKhEni/3enCCl21LLDfuQMkAlFVsJQ0t32GEAWMARApGAQ5AmPx11AACwknVduIdaQ/Ythk3WdHRYPHui8HBZpb3WrMmN3pL3ZLNc+xENL1gdAcniYK8Ew8fGqThTAgmcUQJy9Ib3whvTFkr7gC6J24tEsgBybqBTB16NxLgWGiixlJ87ubmaG43G+n7cOIsiaOo4ni643fX2s8+BvFihunZ0T/TdUex9oAQA+4IVmwKIpRVC+e58dBNkqFNvgbdrz+EnIpwTDpmn+QUrpSyT9HuX59x1N0zxYec966cR9MYHwMkpZPWSCEJLDXses+tgJAUWvk8i5yKA9n6yP0Tg7NIirA/QohCB12Y8HTuNRbeoMFhQ8WCy6mzTBpMg2qarsdHh0GOE5eI3fPAw7F8JeL8R2AxpSsKk3lM/TvwxwwjuOuv7ggfTGQeuZb2Mkt7ciVpL7xFTwsY/le6bkGNFei0UGWNRpFgXf83lr0mYcGYt8uMggz5axgBi74HmNS4o7ECztCyV5xb73MgsEkQuU7b+NoTOfjjwWDFNKX9A0zb9qgVCKeMBXW7X5g6vvXi88yNh4qCiC5xkP5K6yswTHCgBIG1Ko0YeKbSGlANGppJeUHQ6TSZu9sMT2lVoVmUrTZGFgD/TjRsNgfx6a4hVjeMVDXdcZqE7mEbd4cJhjsWbqFkeVwgPKhliwRcBwX5Fidvyr2emyV9gy69oKZrT9XRuGWk1A+e88yH2TcsWez3ylzY2+14YetWz3tMqLwFGb20y6H2BKm5gDqofSUTsWOLFg99wzZbbY99htVR6itbBzsRmiJZTbx/YSchkz/E5ldfj7lnzXSPqqlfSolwvijhX+KPDAQ++sEdbnD3ytKBzKK6DCLnYvSnr1fp60eEBhZQcHoT6SP3y6yCzu9dfzNQhB8XxetaC61nqcz8ZhK8TZIoWHFjW7rjMAMpFnik3TYcKwwUX7OYJXldJZxNUdSPoNSQ/3pZf2pb1xOD5gvxQ4WCwykL3xRqjIOIDwPDuz22xr8wOapwudpwCiLm9thUrumSnn9kl19yUBzDBlEP5EuBUaAyBehtXACg/tu9uaWfIk5LJ0POyCX9s0jWsmSilNlpzSy4oFexFhOICf72nsajTCe5gh5yITZWa5Nwn2QjXqUkUEOAaDKI5KcDL2RP6oJygFUPIe1ZGiDGzxeXAg/fYbeW+VfYU6CNMbKMfGkaLmD2IZlMyCgBoJeKIm7i+kyULaO5R2d8JxI3ULL4zGUYHmrPUcsy3A6SLCdVCDKT6xbdsJwAAPD7M3fm2Y2fXWVms+qKOv68rA704tKXa4czvgUN08dXekEapFfcueDV4uV3Gg/LykL7nCZ71cg5QBuUwMVn0mPjFlsKSmOAdGIRV7rrSNU8DAq0mfLLLqB9NJgzypAYFBgcJNHbX/CGmRMrj4lqEOQAcH0sMql6zyIqSkIA4VjoC5Im0NkMABUamrRjImfMe52OdOD6SNVo1fG0o7i4gZXBtIVdv/tUGA5Fq7UJwsAvAwA7DRVKc4bGsvnUmaVFm9hoV6DcqhIsuI36d8BlzYGgKHmEcjuN2wl8vlMpvhy8pVZjZSSl+smF87ikW2l6cgZYiNsx9UZn6ssqgDDIkfsVZmImfKYPTGGxFGsrub7YfEEr7+eq4h2NRR6gpHgVeBRq1EpZRCHcZLfaoAj7rOKunhofTRw6zKUgyGuDiKTVCSjFL6Jwp7mi8UZeXwWgGOzpSPJX2yHb9R3e5pXUnrbUbK1laX7RJIzT2yrelZHaBfVV0VWTKP9cMMhlN1Vee19t6I/cQJRL4yIO735s8B79m0qY8ffPtyGTP8Gkl/SrmYwvcp5teBpO9Zbbd6uYq4DXFfOt9WkrJb1EncVEww7E9TRUVrNraXQp2lSg3ZJ0eHEZj84EFmiKeLyM7YbAHxrM7qH8UKcBbQ7skit/PmQ+kRjgplEJgpM0JCP9yJcKzItQXUPK7OvaOwJClClABA33nON0PnHIqlNjPp4UdzX3d3s7NkZycyScbjzPqOF127J+BIiiDxmoQNzdpr4Y3HuTQeSFvtIkRQNjnCVBIqSz7hDKPmoNsN+5CZty+X2QzfJ+l9KaVvaJrmJ66xT728TcFTyORAnRwratjdseMp3nCsyHMdKhiPxxhWlVQdRs3C8TjH0T16PUJkUCcXi6hcgypMqt1x3Xq4DzOj21cA3ETdrVNLm6fH0gGIrk5S3Zkd6PhciuIVI/ucWEvAEFMD7RKDN1E4W3C0eIwlhWxhxb7z3mwWdQ+xC+7vS8P91rNdSc1+BJu7ms0YUNh3aPcPK4b94R12dtiD4DuTq9gM35NS+sdFbvJfaJrmv15t13p5O+LeR89hJcCaStoes7ih8FBWkgY2MaWYwHh+TyxoGk8w4SajYXhYx+N8zIMH0m/OIrQFEJ4pPLwY+BcKQNxQgBN2PeLkmOiePUNWBl5UnCpnCiD0bRNk7WFqYAxdhebYU0mDD4WnGZB31ouazLid1RkEX3kls+bjWTY37D6QDufS+jDbDM+97mo90Qud70KIaryviBJgzFCD+awHwE9frgKGX9s0zbla3OYm/2FJPRg+o8LE8IwMz0yBje0qs8ItBVg8agsn7O5G0VXAcF8ZXLYlTSlRtWjte5U0nHcZyr7CEYJzg2KyFCogVAZWN27b31Y4eAibwQkCuNFnB7WBghUCmLw/VgZe1HLP/HA104OYK2UwfF3Sw9dj8bjz4bCnsq+KFAwP7zMbSJ22nnbiFqmsvTaUakKYZnm8PF6yVINLO2EvT06uAoZrKaX1dptQpZTcxNTLMy6ls0WKiTRRnngzOz6pndj7Oi8d39RR9PO82Og8VEqCePH0en08WJ8Uqh9xcWd2zcraoW9U5/bKPLBb7IWlp9TBkM2uSFd8pKgItK7uLoCe7eMMkTYpggFQNpLqNzIrJByJrUWPZgGEDx5k9uebSZ1W3Tzuw0PprXl25LidkGuWDLBXhVcjVwHDH5X0j1NKP6L8DPxpSe9baa96eaJSTh4m1kSZda0rauo1ymlhD2ppuoiwjlNlcDtpzzlQqLA4Bcbqxri5Ed8BC9DD/odjAPAk02Jo5/vG67RXt/06UTcQmzLGx+r231VfHE2o7nwOEPouggDjiSKMZ65s+3xjX7q7L03bMJr5IoNuI+nhLJxOBJE/bM9980HkmO/bX6Vgv4wlr72sVq6Sm/zXUkq/JukPKv9Of7Vpmn+48p71sjJxYMSR4JOe8IxlAdx4owEPAMqrxSzUza91MMbZQ4oZDh7PmKBfFK91RgQo0tZJ21ccKlwHkAbcON+3ZkUoagu4Eo5E0VzGhnusFez33ENfBbBVyvbPmaT9FoFh6KjA2AF9vHF+YfN0u2gvq5crjXXTND8j6WdW3JderkkADi8eW9nn7oyQAmCoaEP9RFTjEjQ9+FmKrIpGXVAc2HcAjufRntr5AB1qNm3AmAgnGSgz17ky4NR2Ptur0majrgq/VvwfT/WBurUA2V2QtlgcqJLjzpoHCrbnVYVYNJw9e2YJ//dFqpfVyqcEw5TS75H0P0r69xUl9I6aptm59MRenmlBpdxWBgXCMzzUpARDVzkBEYoiwA6lUGVJHyN+j3PXrE13cnC+s07S8NaUmVNStvU5KGNT41hU4hJoAF3PzKC/a3YdqoADbo0iSJ1FANY4UBRdpThGsjY432sVwlRR8cmu8cWFcXGA7GW1chVm+D8pb9z0Y8qb0v5JSZ+7yk71snrxOD2pm7bljMR3a8Px4A4XnBvr6tbHczsc4FWmzrkXF+Z2bG1Q8w9gwYEhhbqMSutOFzJq8DTXdh798nqOeIgBS18cKkV2CxtwuZOGtEDPGGFvai/Z7zUjS/XXt+x0k4B7yBmvXlYnV1WTfyOltNY0zZmkH0kp/fyK+9XLCsVtbrxH9fS8Z/eqev5yWY1ZivzYcsKW732j8rIfydrxP08lBKDdDukVvNnrwyv4wGIBXQDR1W1nX26nxJwwsu94LbfxxJZ4Whzvtk/Ua++v74Dn5bfK7JleVitXAcNZSmks6VdTSn9N0icUDrtebqgAEqfKTgw8wR6/5hPS1VpeKQ4718UtBdx7zHEAG04IVxf5HpXRr8Wm556pgpcV4PFd4gCYpC7g4vBYxrAAsrIG5FQ5eycpdpajLfrJK8HQHvpTjinH0i9YIvtez+xzjmdf496jvFq5Chi+V/n5+nZJ36G8Cfw3rLJTvaxenMXB+pwhuVopBQDMFHYtwj8eKcAIdduzYABMvi+LCwCssvey72kLB47a63k4UKVuIQbAjfNgZgDvRNleSnvsGsgCUXqsG3W3ceV7t1NyPfrvKYIIgO+VZpATBaPlHibKgOwpd72sRq4SWvNv2/8eS/rLq+1OL9chbqT3itilWlYX7z1HmBAVB78yywW1FEAE2LwAKbGGKj5zBulxkQCgO05crYRB4cTZVNcr7DGOeJfduw7gIUnhnQYI8SL6jnacs9eOzVzdDd0ZB8DTUwg3FCW7GEPup1The1mdPBYM29jCx+6C1zTNF62kR72sVHziw4rKDIcyU0X2nsldMh7OA2A8bIRtCABNJv2WsooOqEoZGPAcA4beB3emAGxUmqENL+oghRPIw29gfOxXvK1wlHibUt5D5dDGjc+xZQKGA2WVmo2a3BEF6Dt4onKjCnsNQ8a0Z4LXJ5cxw6+7tl70cm3CxKQ6NsyrzHeFcVX2x7lDO2dRfE6epqu4Q2ufEJiJtYOXGXA+VNQzHCrbCdW+bivUWLYvAFBkfeJcgMcZKezV9xVuFCE5CPGOXmSWvjhLpp2kAFgCqBFn12zo5OE92wp7KSDrZgRsuj04rk4eC4amHvdyy6QM5l3mAXaGOCjOcy9steR4V6/9GIDIg70pnOAeYlRVj7ejSjVhLW7XkwJYuabb9bAV+v3zeVmggTAi+uxqM6BdnuvibJFFolaX5ZWB1YQPsb3BQMF6/Tj+3wPiaqQPun6OpAQ32JIzGGdPgJHUVfdQsTnXixzgbJB9V4ap7LWfs9+s11R8S5EeSEUQ32OYQhAOrMQxIiXoOPsChOmbMzwP3YFNsjWpB1cDSL7tqhSqOPZOLx3mx7Gg+ELBOEhR5MKzdNyW2MtqpA+6fs6kZEwAAiqzOwmceY11kaHg6XTb25mdU3pdAUOAEyfGprL9EI/wWBkwX1I4G6h+7dWpUa03i/appOPq8kRR4JbgcMDJ4w4n6gZd00+PWXRQHygDOMe6/RXQJbtlWVyn7Fi2+vQMn6Gd08tqpQ+6fg6lVBNdhYOFkGrmqW6l13egCCL2DY34zGMJedA8Z5h0QA99IXXPQ14obuA7vanoT60o+CCFgwUvNPfMtbhXL4wgdT3eztg87g+2RuaNV8Ohb84yvf4i94E6DXP0UmiyNobq2eB1SR90/ZwJoEM6mpfRd1siHl7AcKGuJ1Xqqm7LbGeNHce1qZLD8ZvWBs4YNrB6oABqjncw47x9ZSBx54azR7bMrJW917LzPdZv2eLgMX8IjpJ9xUQg0BubJqq/O5LKKjyl88ftqt4371MPjKuTqwZdD9QHXd94gamhIk4VO7JJETcHaDk74jMEpkU8H+d53CGTGabnjhYA+VjdPOIzhUcaVRfVlD9sdYTw7KurFjsQyv4PsG8o7I7ci8czLjMTuFOG6x6r63BxwH6xfaU016xomz5iH91Sd8vJZcC3DJh7eXLydoKu5+qDrm+NoAqX9f0Ig5EuekNln7saCANcFH/uCaYdbJLOxlzVBqTKsJ/y+mWqXZmD7MDhQOdFJlzcCz2019LJARgzTiXgMiZuLz2zNrxtV+lhiGVloD7r5PrksqDrPybpftM0f7N9/wvKC54kfVfTND9+Df3rZQUCW/O9k7Fh4Q3Fpsc+IbCqqaIyDCo2jAh2Vnqe2e/E7XZjO4f+eEjLQN1Nj3zbUO6hsu/L72BZbmNcKBwwnoVDf50ZSl3VFLBiAeG6czvXw4uoZuOs1RcErr+lbpD4liLrxZlxD4irl8uY4Xcpe5GRdUm/W9lM8iOSejC8oeKs6VDhAYbhMfHWFDYx1FyYpNsIvXy/T1rfEe9YEY5CgLF7fT2MB1DyIgduy/T4PA/i9v4Q+uPnuaqNAEoDZQADzMprj9uxoI4i4+eB0CwAZ8r7mZCb7I4k2B5OmS11TQy0xXGlU6WX1cllYDhumua37P3PNU3zpqQ3U0q9A+UGC0BDiXopJqc7O1xl3VTsbHds5w3sXDyrsnMrawvvq2+I7moggdWAgj+cZUA3wrW9OOpW+92BgoVJkc0Cs3P1H48178v0RC9MwZ+r3h6MThEJQni4Nu2WsYOVHY+aTP96G+H1yWVgeNffNE3z7fb2RfVyI8WdCR4D6Kqr5++SIeKhLmxd6cUHakVAtIenSF32BXs8tTZwStAXAMHVztLbrbYtmBXCviruMX8cOwR08TbDMstJ4d71Q/usFLeF1sVxtMFYs481m0xxvBcDKL31PSiuVi4Dw19IKX1r0zQ/5B+mlP6cpF9cbbd6WaW455M8ZSqnlB5U1LVHiskJWJLfjN3QMzIAGBw1OEqY3MTblUHJFEggP7nMXqGQQ2VtSsE8KeLgJbW8PuKaAoB4+FFnXQV3eyLAxT3RJuA3sVcv40WoT2l7dJbriwILkjt9OLb0kPfy5OUyMPwOSf9rSumbJH2w/ew9yprOf7LqjvWyOlk2McvyRO6hLdVFV98cWEtV1q/jHmIHXFgh7cFCS+eEA89a2y9sfPSdneoAQi/k6uzUS2OVITXOVnHE4GQqizj4PXCOb3rFlqfsdeKpjnjZvQ36497nZQy0l9XIY8GwaZo3JH1FSumrJH1h+/FPNU3zs9fSs15WJoAKmRkwOQz7qINuN6uKc/kM9jZU1P3zeDrZNcr0Ng/6BigAMlRJ4h09lAeVWrroJCEt0FV1jw9cV7eytouro8RG7iiYahluw72V6rs7l+izx26WsYwed1myQleVe1a4WrlKnOHPSuoB8BYJKi5hMx6jN7L3A4UzwsvOO4jgJBiqW+4K9dvtbQBUaXdzO+ORooJLyRrZbximNrJ2nGXhDOGagBNbAWwq1GlnvG6XqxRsjjZ9YXDHE84VWKDa444VWxY4M5a1i4qNCu4A6cUkygyfXp68XCk3uZfbI4AOm8R7XUNYl9vBsNFNFWxxpm6GBpN4WVxeUtdLO1jyfw9RoeKLM0sP/fHAac9W8W0DnH2xfwhto0q73ZG+elocIS3sclcGQLt90bNiAHZXvekHTLeMjfQFw50s9INx72W10oPhcyhz5SowgCHZH2wBgHNh2H4Go/ICo2VcoNvYZN/BwMrKzzA5L6OF2g5jcxudF4Tlmg6cDkBci42u3BZ3UBzLvbIooK46k5UusmJXX91zDmi5rdKzfSbK7JaAcrevlnGUrjL3KvLqpQfD51BKVU/qxrVJ3Rg4xI36zopoUwrvMEUKLqvO7KX43XFRCoCCU8S3EHDgph/L4vPcHDAvzmFzKOII3Wbnlb1LUCozYPBc7wAAGDVJREFUVuir118EjGGM2Gq9AIabKbyfPQBer/TOqudUnJHgAS0dJWvFsY8TZzEAoavRA/u/A0rp1fb3nOdpc5TzcuZWMrDSkeFOi2Xeb44v94JeFpvofZa999fSoeL2Tr93v69yLFzV79Xj65OeGT7HwmQbKZwSMC3ADIcAjowSzJzZeRVsavc5mABGc0WaX1V8TruuEpOZQj1D2XcAjhdu4JV9VPwz/u/B4jhu1tVlm84MN9pzZ0vaoG/OUpcxvJnCETNVLDaPbCwcyKnJCGvsZbVybcwwpfTNKaV/0f79fErpd9l335hS+mBK6c9fV3+edyltXThP1tUN9YBZEdQM2A2L46rinLnC1riu7DzYsvNKNuq1E51RAVSEtuwo2wK3FWWv3PEBgPnnzkzdMeN9ZrsBBzS3T0oBcr5VwFSRYuhsmHtyZw4MUYoq35y7o1w5e9fGqVeXr1eukxl+RNJ/1DTNJ1NKXyvpByV9efvdNyoXgfg7KaWtpmnKnSh7ecJSgqEUtrjyOLyapOQN7DvYkVehPlIwJsDQWVhl7cKw/DNnng4qO7pYVRhQBWSGiuKqyzZfd9ZWxvu5A4i9XLh+OVGWeYx9gVjmNPJ7pg0WG8bR2+b/vVyPXBsYNk3jWwX835Lu23uci426IWO9rEhK+xkxcl74daTYm8QnqJRBhpARYvTczoXaPLb3vpGT1AURgNZV5tLW6N5ljvVjHHzxkMPw3EkxsfceygLjdSBESoeKB677lgdMKN83BdBjHEpmzb0AiuV99XI98rRsht8i6Wfs/Qck/ZKkH22a5tHyU3p5ksLkmynbrLZ0kS3uqLvRvHuGXXUETHxyO/tZqOutRoUl/hBP8sSOl8L2OFZUjC693oT5AN4jZUD0/rp9UIpgcALEueZAwXABXp8ggF2pSrNpVa0A4w1F9ZqyNJeDnfcNR86aHVc6bHpZnVw7GKaU/oAyGP5ePmua5n2S3nfJOe+X9PWSNBqNHndYL29TYCxe5sq/K9XDyzyn0sU0NI71WDm345Eax/vSGQFojhQA5fF+5TVwSHh+cMmuABwHXg+Eli56n92h5O0tlMEPpucLBHbRYXG8VwQqx8830vIx6+XJyUc+8hGllI7sow80TfNeacVgmFL6Nknf2r79w5LuSfphSV/b1ka8krSdfa8k3b17t9nf33/SXX0upVJmRzATwIgJ78wKYXKiAhOf90gX4+5cFS2DiQEZT1EDELEBsh8yucFlBohvDgWwA3YjZWYL23NGNpH0Qtv3maQ3FLUdy1hH95KXNRg9TY59n/F8A2iugmO/xDbp6r2n83k9xD605snKa6+9pjfffHNpPdaVgmG7ZQDbBryqrA6/t2maD63yur1cTQAlipNiK/RwmDI0xpnZUBFcTcjIoDgWsHKbHDnMk+I6Y/vOz2PPEcJ/SgcM9wKTRbXmXrwAAkxwoOy5nVo/YYduZ6yVQXNbGaQ8jQ8hFTApsnio7ch4eHEIj+HENklAOdW/H6rLVntZvVynmvyXlBfk/zmlJElV0zRfeo3X72WJAIZS2AdhiO4soJw9+cZ4ujxjQ+qGtLgXFdbENT2bhUBt9v4AuI6UQQVgKhkb4E2/h8VfyQoB6bfac15ur7nbHvuWoiyYs7s1dfc65l48SHuuyMP24re+ZzM2Wgdd7oV2YYReJbuX65Hr9Cb/GUl/5rqu18vVxJ0HUtf+5TFyMB3sbXieH7bnlVWtnf14xRfE4/LOFJVs8ACXMYiD4rPyWnVxPt5nCi6gHvP577TH+97GtOk7/qkYC6/Ec2bfcw2KYFSKeMHSCSNro7wf9/L39sLrlT4DpZeO1xLQIE4P9e1YXVsiYDgvzscZw253R+oGHXs4jhQg6zF5UhcQSjvk/9/e2cdoXl11/POdWXaXZXeBytpQFrLbtLRSEytCWwrGDTQNjW8FqTZEFIxtqvQNY1rbP4zEmNJopKZrpQltF7WNrZaArgap0m3VUlpAYHmpiCytxI0twXbfWJbZOf5x79nfee48szszzDwvs+eT/DK/1/u795l5vnPuveec613qtfS6yXj5bk3GlftaDlIWbfJYam+Tj9/FVFxebhw7jELbfpZxaMHrTChjBWU809144ix9puwaHimGCdB96V3I1lHGNLwbvZdu3Ct+eVc351wg19PlG4xje1FQ/YvvZcVlSdsED9FqivRzdp6md9H6KGbu2uO8ELY22iO6Grkl5/6VbQbwKILrw2fZusf4+Ql6hw28zXvIFfGGRYphcoT4pfW8ht419ljjftEjrauIl+V/XHFsLc7CxgmC2NVtRamNUoFO+E6gN6FE7Eq3fo9eTnRhiVlwWiu0FbI4+xvrHD+LNtQvEj8Dj9v2d7tfY1yiNBksKYYJ0H1x99HFEUffvrhFYYkRF15OtJxi+a1YRXFwQYhC069+3vV1gZ0tqSv0iqrf7zHBnorfZ3/jJEucUV7PTD/EabqJojbsLi5e7/f65xWF/zBdV9uzf2emmuGSYpgAnSB5BuzoRBxD7GazCOMKd4fpup79LDPo7fa2kwgxGiYKpV+Ps91xRb4omDHON7bRJ3pcBFvxmaAbi/Q6t93XOKnjwwLtEqMuunH96Jho9mD42c9vMRk8KYbJEfzL+X0668otndgFbv0O3eKCLi74BcqkS4z/7TdBErukTmtJtWIZ72v9/vpNXvRz+HZ/vjhO6q4wp9FZfe4y48sLxC5+/Ifh7/L2+jOxDRNNGa0YZtd4uKQYJj1EJ2y3juLYmN8Tu7xtktIomu2421RznXDdxWR183z0HVwbyold1yh6sxHHFtvxSt98Jn26uc9Tbe2ld8bX7/d/BnECBzrH6ziM0Ip1O5GUDIcUw2QGLjT76O8i0nZJfaW9eN6FJQqdi2gUntbXzmldVNbSCZJHeMT8g4T72wmQ2P2MYunWYJs66wf0ug75cydRutc+zudi6HkV3R0HOl9MwvO+joyPc/qaLNk9Hg1SDJMZRCst+u/BTMsrCtbhcD7mYWt9BuPESVtW212coIvbdfGaDNdb95n4XHS3id1532I27ejY/RwzZ3X9p78/Cq632+sVLdSYCsxn6H2sMfppJsMnxTDpi89wehfPw97c2ouzyB5T68IQ10FuZ3jbmeYYutf+MbqouEV4It2Y5AqKaMVZ2zjB4xM6cQ0Uf6dbc+vrsSeZ8LpBrxBOMXOt57g06kG6MUXCMz7x4u/1Lv5hSuTOAcr4bAriaJBimPTFv+QxasLdSTw2Gbp43Ofotc7iBIlbdNDbxfTr3mWOkzLtMppT9R0utLG77WXEHICexssjXuL4X5y9Fb3uOXEc0Nvhz/l60YQyp+hmjONnd4hOrH0M0d+7B3iG3uzgyfBJMUxmxQXARctFInZHp+ldtCiKofvjuWXntAlOfVsZynOrdDXd7LSLVhxnjGIdu9jRhSem+oqLTkWr8iS6pKp+zuu4rp6LrkJeD3/vPrqhAZ9F9q63hza665Kvm5xjhaNFimFyVGLXMyZXjWOAMft063/oY2wemjdJ6S7GqJTYPY2TLX7N3Xv8/VFAXSzbcbwJukzafm9MnBB9Dn3d5Ek6a86veU5Fj8JprUsfl/TPYDVdl7m1Xt1tKbvGo0mKYXJMXDj20AkQdCIQM9bEsUG3iFwIpimzwH69FQMvZyVlPM/zE/rWLh8Q1yBxvz+FsmJyWq+XW7bRKltBFx4XLc4VFIvRo148iYN/Jj42Gccro2XqvoreNX6WFMJRJsUwmRPerXRhiTO//Rymo4i59dQvBG+6KdPdT9qyFM5BbyTK4T5ltb6QcZwReusdx/3aZQii+EFvai8Xw7a7T2iHi647ecfPIBktUgyTOeFf6DjJ4MRJEI80if53EbfwojC4gPhssYuQu6p4yqzosOxxw2vqtZhsgVCPNiXWynAtWoKexdpX1YPO4vSxQp8sim460+F4BV36/zij7rPKmbB1tEkxTOaMz8j6l9xdblycYmbsU4GT6fL1QWdlwcyY4JhgtrWcDlLyIh6kN+Ijro88SemCugh5Etq2bOjGIV2Qpyhi6uI7Wet9qL7Xu+sT9K7F4r6KsR3eRhfAZ+lNxJCMLimGybyIlpBbajFszwViXT3+AWVSwru57Vog0AmNZ8lZ3VxvM8P4pIwnW/B9d6dx8YoRLu1YYMw442OfCmXEZ2I2mja5Q78QQE+GG63BFMLRJ8UwmTf9IkmisPmExEE6/0PonJdjdzeut+yJU+OMr9NGi3gewmipRpcdX6PErVcXPKe1TKco1mCc4Z5uyohrSMflBOJn4uefoYhhTpaMDymGyYKI0RfxnFtK++nG12JIXJzh9dXuoDfRQpzccDHyLDAuRB6mBzPFyaNi9tbrvrpe9DeMC8XH1FrtOGacmInl+wz5FL3C6gK4J1xPxoMUw+RF4ULn0So+hhhFLU6YROfsdmY1usBEPDO16I15jhZg7Aa3Powrm+da0Yv5G/vNcBPq3YqbtylGtcQQwGR8SDFMXjTtGKKn4XJRcissRnW07i5rmGmduQXorja+pOgL9IpfrAf0ZtfxhaOi0EYRji400+HZ2F334yjm3u3e17QtZs1OxosUw2RR8PEy6LJlt8t4xjjiOPMc3XLcsorJV9dQZqYnw/MHge/WclfSKz7tBEh0fYnvj3U/geLaE897We5PCJ2FeyBsMcSv9btMxocUw2TRcGsOeoXO8xFGKw16LTWPAHGrL1ppjouYW18HmmvR99GfdWvNfR+j2EVrbiVdfHLMvBNjs2MXeB+9C8In40+KYbKouFUUU3e5u4375sXIjDiB4jPPMclD7Ca3Vlf8441xwF5enO2OcdNx0iO+n3o95mX0sUQX3wNhP4VweZFimCwZsdsY3Ux8Frkdn/NZWujcWXzyBDqrzBd7X0vvbHXsfkfxi2V6veKxX38h/IzJXb1b7NbgIVIIlyMphsmSEx21Pc19GxHifoQx0UOMbPEynm+e8fL9uocKtqLo9zn9/vDbyZHovxjHBzO+eHmSYpgMhENha602d8mZpte5OuZRdGEyuiQIPgvsFp6viUJTDuF5JyaRgN5Jm0P0WoDRVSYnSJYvKYbJQPGuZcxL6McHKaLoGbU9NG8y3ANFEN3SbPMjRqvzQHPsY5Y+DhhFLYpezDDTji0my5cUw2RoRCsrOizHVflW0puRul3JrnWTcYH1ctrIFJ/NjiIXI2Tivr8nOT5IMUxGguhO464w3p3eE45j6FwM/4sRLX7sYuisoHPkjmLYb0uOP1IMk5EjClK7dnG/LDETzXN+TxsSFyNIppufSZJimIw8rb9gZKLPfbPlRUyrLzkaKYbJWNFP4I51T5LMhX5JQpIkSY47UgyTJElIMUySJAFSDJMkSYAUwyRJEiDFMEmSBEgxTJIkAVIMkyRJgBTDJEkSIMUwSZIESDFMkiQBUgyTJEmAIYihpPMlHZZ0RTh3naT7Jf3SoOuTJEkCAxZDSZPAR4F/DOfWAucDrwOuHGR9kiRJnEFbhu8Bvgh8N5xT/WkDrkuSJMkRBpbPUNIZwGXAxRRLEAAz2ytpJ3Av8IfHKmfVqlWce+65S1bPJEmOTwaZ3PVjwAfN7LCkngtm9hHgI7M9KOkvgMuhiOHGjRuXsp497Nq1i82bNw/sfYMi2zV+LNe2DbJdO3fuRNL+cOpWM7sKQGZL1zuVdC3wjnp4Ml2X+DTK2jzvNLPblqwCi4Ck/WZ20rDrsdhku8aP5dq2UWnXkorhrC+VtgHbzexvBv7yeTIqv6jFJts1fizXto1Ku9LPMEmShCEtCGVmVw/jvQvk1mFXYInIdo0fy7VtI9GuoXSTkyRJRo3sJidJkpBimCRJAqQYzkDSpKR/l7S9Hr9G0t2SbpE0lp+XpDMlfVnSY5IekfS+ev5lku6SdHsNixw7JF0q6T8kPSHpd+q5sfidSVot6RuSHqy/l+vr+W2Sdkl6oG6vredPlvR34f5rQlkjFd8/37bVa1vquUckfSWcf3tt2/uXtNJmllvYgN8CPkdx/QH4FLCBEkp46bDrt8A2nQ6cW/fXAY8D5wA3AK8BfhZ417DruYB2TQL/BbwcWAk8WNs1Fr8zit/t2rp/AnAP8AZgG3BFn/s/DHy07m8Anq3tXlv/ZlcAtw+7XQts2ynAo8BZ9fiHw7Xb6u/6r7zMpdhG9r/mMJC0Efhp4OZwepISNz1N5zQ+VpjZbjO7v+7vBR4DzqC0bZrxbdvrgCfM7EkzO0T5svw8Y/I7s8K+enhC3Y42o2nAOpUQrrUUMZxiBOP7F9C2KynRIN+pz8+Wv2DJfp8phr18DPgA5Uvk/Anw98AFwJ3DqNRiImkT8OOU/9RbgU8C7wL+cni1WjBnAP8djp+u58bmd1aHZR6gJC/5kpndUy/9gaSHJN0oaVU9txX4EeB/gJ3A+8xsuv6D8/j+zw+4CbMyz7adDZwqaYek+yT9SijqVkrb7q1tXRqGbU6Pygb8DPCJur+F2k1eThvFmrgPuHzYdVmk9rwNuDkcXwV8fNj1WmBbTgG+DPwoZVhDwCrgFuB36z1XADfWa68AdgHrh133RWrbVuDrwEmUcN3/BM4eZD3TMuy4EPg5SU9RulsXSxpHa6kvkk6gpE/7rJmNhJPrIvA0cGY43kixmsYOM/s+sIMyxrnbCs8Dn6EMBwBcQ+lKmpk9QRHDVw+lwvNgjm17GrjDzPab2TPAV4EfG2Q9UwwrZvYhM9toZpuAtwN3mdkvD7lai0IdY/oU8JiZ/fGw67OIfBN4paTNklZSfm9/O+Q6zRlJGySdUvdPBN4EfEvS6fWcgLcCD9dHvgNcUq+9FHgV8OSg6z0XFtC224GflLRC0hrg9ZSx7YExlHC8ZOBcSOlC7qxjOAAfNrN/GGKdXjRmNiXp3ZTM6ZPAp83skSFXaz6cDtxSM8BPAF8ws+3V3WkDpTv5AGVMF+D3gW01/6coKfGeGUbF58C82mZmj0m6A3iIMmZ/s5k9PEvZS0KG4yVJkpDd5CRJEiDFMEmSBEgxTJIkAVIMkyRJgBTDJEkSIMUwSZIESDFMZkHSSyV9TtKTNVb0bkmXHeOZTZIW5Bsm6WpJLwvHN0s6Z47PbvGUa0uFpK/Vn5skXbmA56+WtHXxa5YsFimGyQxqdMBtwFfN7OVm9hOU6I6lXLD6auCIGJrZr5vZo0v4vnlhZm+su5soGVaSZUaKYdKPi4FDZnaTnzCzb5vZx+GIdfQvNeHm/ZLe2BZwtHskfUDSzpr48wZJVwDnAZ+tyT1PrNlLzqv3X1rLeFDSP8+1EZIuUUnUu1PSpz1DiqSnJF1fy9wp6dX1/AZJX6rnPynp25JOq9c8HdUNlLCxB1QSqvZYfJK2S9pS96+R9HhNVHphuGeDpC9K+mbdjlxLhsiwM1rkNnob8F7gxqNcXwOsrvuvpKRWgmI1PXyMe94CfA1YU49fUn/uAM4L79hBEcgNlDRdm+P9TX220GQZAlbX586ux38OvL/uPwW8p+7/JjXzDSVzyofq/qWU/Hmn1eN9/d5FsWi3huPt9Z7TKbHEGygJWP/N76MkYr2o7p9FiRkf+u/9eN8yNjk5JpL+FLiIYi2eT0nUuVUlZfthSi66ltnueRPwGTM7AGBmzx7j9W+gdNd3zfF+51XALjN7vB7fAlxLyVkJ3fKU9wGX1/2LgMvqe+6Q9H9zfFc/Xg/sMLPvAUj6PL2fwTllNAKA9ZLW2VLm6kuOSYph0o9HgF/wAzO7tnYX762nrgP+l5JiaQI42KeM2e4R88vIPN/743NH4/n68zDd92AhWZSn6B1uWh32Z6v3BHCBmT23gPclS0SOGSb9uAtYLek3wrk1Yf9kYLeZTVOy4Uz2KWO2e+4Efq2maULSS+r5vZT1WVruBn5K0ubm/mPxLWCTpFfU46uArxzlfoB/BX6xvufNwKl97mnr+RTwWkkTks6ky893D7BF0g+p5JJ8W3jmTuDdfqCwKFIyPFIMkxlYGcx6K0WEdkn6BqWb+cF6yyeAX5X0dUrXb3+fYvreY2Z3UHIO3lvTif12vX8bcJNPoIS6fA94J3CrpAeZPa39JZKe9o2ytME1wF/XlFfTwE2zPOtcD7xZ0v2Usc3dFPGLPARM1cmc6yhjgbsoaff/CPC1ZnYDv0cR83/y85X3AueppL5/lC5FVzJEMoVXklTqbPNhK3kSLwD+zMzSajtOyDHDJOk4C/iCylrLh4B3DLk+yQBJyzBJkoQcM0ySJAFSDJMkSYAUwyRJEiDFMEmSBEgxTJIkAeD/AcdTxT4/bWaCAAAAAElFTkSuQmCC\n", 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\n", 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aP95Nth8WMHxMApjx8EW1Z6708Mb84xg/NiQ8uIAkjoyTrm23JUaW6PnFUp+VoKI62A0NTKkPDD7QvF3/jr9dKlvv6j4qroOKB0/7Pv6gD8XgeZqdM8v4QogvD377NXLbJOwelusA65kwnoqHcP+GaiA6yEewR/z618oqMs4xj22N20dzQmSmN1EKGF6hOPB4mIzbmeJbmOKhUn+ge9CxM01/YH1GNx7qRbc8ln6i6gptcgwP9PbMGCY7miszV9gQ5+IDKaq2OH4iGPNAwo5b9SdSiteQuUk8i8TPaaIUtuIz8w0Nfr6jRz46JTy2j/8+OZSUAcrrQcI8H9rHPbYAaIwaiM4OP7YH4+N4A2Dd1utq/Ex9ZohdOYZYcf02wvKbLAUMr1h4aIfeuh5SwoOLHVDqM0hnHK5G+4RKgBKACDjGgF5XS3kgYBAT9cEgZoYslMN0tpXB0FP+XDUeYli+jdvYvACFMyAPpWFbZ2R8troPntJF2NbZbgRAZ12RYcUP3uAYtkNaHYzvUCk4/lDZC+9q9ZB4H9zDO7K2EX8pOJv154z7D2hyDp4a6cegFmZk2+ex+OssBQwfg6woA6KHuOwrD0TeyNixoi3HwYOHmLe+q3juefUYNk87iyEgK7aNOxWc7UXVF68p4Bnr8tVh++jRnVi7rrp5EVWcQiyX9YPjS/kFQbqhOzccNJ0tRfYXX1TOsDxOT8r3MgJE9DYTC+g22wjMsnZcxee6SP2YRgfU81RpnFIwR89dj+YKP5dGfbMB2401fL7XXQoYPgZxtueDGJsSD/CKMovwogkA07Gy+ujgR4EHL7rgYBfZQ6MExLApBw9/8B0MXJ1yQW135uYFTOOk9M44keiZxqkRWZSDlwcl839dWYV0YT39iQ6scdjP+3YeAJzYN+wTIOHcD5QrB0U7nLMtv87r6k9T6n2R+mFK0X7oL1auj3vIpbOg5pEB7p2nfQ8Mv2lyU8/riRMHE8DCPabY69xWh/GchxWAq5XDIYhPcxsQYAiADDk6HHDczjazY3tYRWXLom3J1Thv3z3GnKdPSB/ZEODlToYIjFI/ZY2XBwDuzId2Ff5Hh1HMyHCV0VXjaLPD5CH1zQ9RpWQb6h8OhcxEVghg31K2zWLr9VqSkaHyDEXbKecylPOMiu8T2OOdd/COdt6bJgUMr0Dc4B/tTRj2GcCuNg0BmMfMEZrj4BKdA35898S6OEvz2MBooxsaAG7gd8YX28ch4uq1G/l5EL0wrdQHIAakl79f6OysdR4zd2zbDTkhhlTToXN0thzF84LdBMG+zjCd3b+RqhnVbO4FkQEeEyn1HTXRBss1j7GTlXJKosuQGh5fJDdNChguUYacBAxcz9uFVRHywgDH8I9K59No8mB7iISrM26Q96IM6o7n5bBQuxX2weYW81bjOTpzdbWN9XOdzaOFuU5tH77XNAzgYyUnzY6dCyznRGfBHoAiti/mHMd+DrE/2bHcqcK9pBAGAOil16Ld0T3V7mWOzhv6Bci6k4e++rPkUutsbUe3MXomz9CLS+oHtzvQ+nPk7d8UKWC4JDkPCBEvox/L8AMMrZLqSPVjBw9/uKU+G/ES/vTFWYgXDXDbkIMOYOh5zx7QvbBtYziIC+o76xzkWB/Zqg9wZ41e/BS1zpmgsyFeFuQqO+uNfXXW6GAebaWRJbuZgfvilcn9GP4dnRAOws7kAPlj5YDyxvblhdeG7aWzYVZDmkKt7LX3yuQecuPmEn+GhlT76y5XCoZVVb1T0t+T9LzStfz+tm2/t6qqFyT9iFL41de3bbt/lf1almAvcoYAW3HhIXRAA3x2lACRCYMYeAz4xvaLAOXM06ef9CBsN4g3GgY41CimqkRl9G1dZY/qqDMMvLy0AUD5gIvXkOP4frLj+fWEdcO43BM/pB5LZ/s519lURvrnhVlxBvmsdm5yiOAf/8cXwnn2Sc6TlyUxnrHQRmRqkW0OXV9nnTxHHgngeeyy5Q7uQ6aX6yhXzQwXkv5K27Yfq6rqlqRfqqrqpyX9OUn/taT3SPoGSd93xf16pOJqDMn5ngvrAwiA9AfZB/tIyYgu289DW9y2IzsOKtSa+oMcIY+V2nrs5+pQZGtu84wDzB02Q4PTQc0Hsce0uQfXAUx2PLb1SZO8hiE1A3mwOU/641VyXP33a0h/z7su6voW5xZxQHXW7oDFfyTaRofCe3wfP9Y8LPN+nvdS8fP2Y8frXessELo55SbaD68UDNu2fUXSK93vh1VV/Zqkd6hvWx8qBnKtBEZI4C3ZHZ75AXhI+YFzRsMA4o2NNzdWmvYH2W2FbtQ/Vg7zkHJ+Kv3yUBNiBcmqiOW3htSxIedDVLEYOF6qys9Rtj0A54zXPd2o7yNlsEONBIxgkVwvv66upiscn+vjZftp80R9ACJ/nD5xL5iXOoI54tfJQc8dSrJzX7NtHTAjkDqLnYR1ftw27OMvoqGXwhD7LGD4CKWqqndL+jxJvyjpNyR9WNLrkr7ucfXpUQgPhwdWOxgAIs4c2M8ZjDsBZl1bcdDy8NYDH47B4HEDvHQ242GIuQ0VTfXQEXeKnMcEXT30OZ/diURffR+33UVDvuw3VXNkfXCHRzwGQqzn0OAGZAFaKTuBotffwQzVGYBvNAwa8T/9jKq6HyOqwLQT24yMciiPPd4rnxdliCFGMIwvlFrD/btu8ljAsKqqLUk/Jum/bduW2RD/xAXbf1jSV0vSZDUGATx5woMCM4xzAFOWyjMfXH3zh+xA+YEm9suzUHwguVrHgIr2LB7koTCYyBIAPAoYHCgb131bAI9B5WAGw/T1sn28n2zvjM63cVOA5/0SHsI5+Png9JGyp9fzqeN5E3hOfN9E+UXi3vxa/XlFOC8HH38Jun3QxdXRsfqzGEaVGRli3fE8iMOMnmW36fr2BHafqO8Rjyya/kRVe+jcnkT5+Mc/rqqqvFraR9q2/aD0GMCwqqpVJSD8+23bfuQy+3Sd/aAkvf2ZZ1rt7i6xh79/GVKJXJ09sf/+oS6dZ3gc2/54RlH73MbmTprGto+eYl/PYItpfgww79tM6Y1FyqBnaTiQy9p0duGBx1Kuq+eTq0uZiY6UwmhOlBxIR8rqL6AlZQcKM8Z5SS7AHMbGOUgJ8LbtmrnTKdYHpI8Aoqx9zBEejAwYRcD1a+OgtKpckZv7y7kNsX5X5zfVr2zEy6ft+kooE+L3nfsSc73Zjhcs5+TA7aFRkWk+yfLSSy/p/v37m0PrrtqbXEn63yX9Wtu2f/sqj31V4oZ+7HR4cpkzxIHH1RIGJB5fL9aK8BA6CA4Z1aN32lXW6B30GEXOgTQ/Bi2hPzhwAMNbyizK2SRTFjgLYcAAXl6y349Hfxl4t+3aORhQc1BK4EbqWqMEKruh32RU0L7HzTEQ/EXgqXVcF+8f15f7CJCuqR9+wzVw0PHr4tN4RrOB25wdQLkHmzqbckj/ETcdRCYHWNbqTygVNQfuN+maB+prNv6yva5y1czwS5QY3q9WVfUr3bLvbNv2J6+4H0sTHgYGOkwipmlJfU+k7DdeyphlQC7yLGwf2Wd8QN0j6t5Gn+WOPriTxdt1dY9Bsa4EhFSrkTKgwZgcnFHbGJyE9jhj9YBlljN7HC8WtuN8mPb09khaHUnTWQb4e8pxhqi50VOOwJQAUynFeuG8iU4K73MEVK/Q7YHW0QPt/Riax8Rtt66yAk5DXmNPraOvnDMvFNcKZP/Zz19ezvD9RermC1fVr6tctTf553QDvMVvJLzta2Vvruek+nY8qP7Wl87Od8IyAqmxezEIYyydq37xeOvKITeI2yndNueOFo4HGKIORoDAcST1gRnVncGDVxlwwCYK23LbHsdCzfaqP+tKQHh7W6pqqekaO+rao1yW2+DOy6QgoyTa7DgXV6sVriHbxr6yLwLAsXyurD14vB/te8ktrh198vqM3q6XJUPVJ2+Zlwr7xheqPz/O9lyr8XAmB8vrLI/Nm3wThQfH7TA8OFL/je8MwI3b/vATb+QTgzNdJwwS76xnKbjtKhYcqJQZij/4McYv2oE8XGND/dnzPGTF5aIgbK4XjofK1sHC9pXDZm4rxxVi06Ms1Yqk44W039G5h/P08mFSoxiUHlXFqMoDys6sIvNZhG/MA4AIZdAwY0QnihdF8DRK9yKz7dS2jf3x++3B9Ifqmz484J5jDdmum9DeSP0X3nH4jnbM6ywFDJckHoLhEoEvgo4PFAZnDF3xLAs+rfoDtrZ1UgZkwBOQjTZF+uDA5QBCmyu2fsiAHkM6hmycsnPzgG6pDwp8t0ovAlcRPTvjeJ628dS1IeCDRTtTh4266ufqXxwoETxQv/14hFdFoIBROQgpbOfHc3Wb8/Dr4i9hlkebH8/VeRJDamCFALuzzhUNq+7XHRALGD5C8YePt7KXYo82PR9MQ06N+JC7wT6CHeKAGAeAT+t5pMQe/DjRThQHlKuXCO07k+FFgGc3buOhN943QI7zhJ2hOjdKKrGDJiqt10k8Vnbe+DUCAHft/Py689uBIZoZhkDMbbHYRXFMRAfMsa0DnP1YXuxX1hc/Xuy7nwMM0M8HW+GxbcOx/eXkpoG5MgOX8jO4Zu16bnisk3gdpYDhI5QIdj4IolrsD04cBDxshEmMlcM6yEseq++1dFD1QRZtTAxWt4uxT7R/RRWyVv88pH5hWrd/+dwrCtfDB69sOW15QYV4LniFj5QzegjxYJoEnwjL50IeWTvMqxwrsbiDorE2+O81Fp2h0VccR/TVK9p4OJK/EEZ2LM+ckc5O3uX3xOM9owOH+EruG+cci+QOhUU5IJL1Q2QDZgqff8VNNPEFep2kgOEjFlfBXNVy5iT12QgPpVeTwckBEHrg9cKWu23H582NzMXBxz3bQ4Zzd2oMDfiYExvTDb0CypH6Uxd4f3xgY//kOnh6m/fnUNJ99TNZGLSeYROZtoeqyM6TZQxw5vzwCkJUHY/3wO8tfSXX2/sU++dTmMYXooOhsy2/XrQH8MRBjF14zZa53dHNA67iulYRGTX7E+zuGTo+058/J9dNChg+IhlSEXzARbuKX/ix+m9pHuRocI+eRtrBEUGVaMSZR1SDediHHl7fh+W0FY3tPrDYHk8lQBj77XY2XgZ4yUf2m0Bkz0TxDBgyfDhnTAo4hxjEqNnuZHA2To1EPOSo6AC7V+ke8pL7/wPll4GzQrdVOhP1YG+Axr3xfr0im3ag5H66uuwg6vPI+P7xnsQXBP0A8DjGtvopp+cV/71OUsDwEQsPkjOr+KA6OMbQBunsQJuHD206i/NS+LE/HiLjYOhAFx0pPjBk3/QtejgX1geE/66q+QCN7I3iEcQNcr576s/rImvPC0V4UQkH4PNMEh5IvCXpjlJMoyR9pjsmYTneFu3I/jtYT8O2rHcGGRnZSP2YM7bzor9uRnFnjYNrrb5NUso21MjMPVBeYRnATF+G0gNhr9wHhfaumxQwfEQSQcPBIxq5nb3U6qudxG/BcobmDPGHN76V24HtYll8z1eNtkYfVEPnRz/5jp5OqZ9p4elxXtDWWYm37bY+JlOKDMtZTXSCSH1nSmTl/mKKjiifNxqG6AHMEViHXhhD3vV4rPiMuNBn2LWrtR73h3liiDXK+n7e9fDzjrZLv2ZS/5wwz8Rn8ibIpcCwqqrPlvQ5bdv+31VVrUsatW37cLldu37iKoYn7ruqxUMLYAwF02Jjc+eIv+mdTWArlM7a62izVX9OZGxagG9Un90D7gPewTmyW1RXgs1x/sTzlp0P6YpUWz5Stj9SPgyblDsMIoh6X7xqs5QYn4OUF85w7zhgOFSN3K+pD3z3Ivt1iPZXZ/CwUq6zq7NS/1nxyZv8PLmX/kLj+PQLacJyP18veeLn4UzePev+fFNJif6fF0p2neQNwbCqqm+S9JeUtIg/IOlFpeKrX77crl1PQX1AxfBZ7KSzrCAWX+ChrMNvWBuOBc8+kc4yFpa5iscAhA2419cBNjI1mBIsbYhJ0hcPvfD+xEml2BdwnSupcqzzlwmZN3guvRiuA4zCerdHurMhshmAH9viQlk1J3NoiA1yvmvqMyW/dt7PbeU4yRX1WT/nHadSxW5XK+dew86ksznl/qKI9xQg9Wl7hbGIAAAgAElEQVRq/YU4xAb9pcox5sovLq4ffb/OTPEyzPCbJX2hUt1BtW37m1VVPbfUXt0A8eKn52ViSP0UtIktd9XO7Tiy5bK2GPheXAAVnDAS1GWYp0/GFNV4KauNbh/y0IzINKJtNNoi+fbpNZ19OPukrwxoCgRQSgwgp29bOsvkpAziDsQ+BSYgjHMEFRUb21D//dzRAojDi8DhYIiTy+8j3mfAxB1NvAQALI8h5AXj6ZIcc+i3O2h8vRe/iEDGM+SA7Cya+8V+saTXdZPLgOFR27bzVHBGqqqKF3qRIAwCt5+hogJ07giJKsh5jpZxWB/Dd6Qc6kBoiDsqZtYuYAPzwQ7k6Xiou3hr3ebn/fKP1GcmUvYmE2gcB8qQ+g9b8nhBr2zNAMae1ih7nyPj4UOZKwCTl4R7xJuB/WTXzZe72QBBtfWg8SFg4H643dNfUlwzXn6cGy8B9ld3rQBYL84abaxcE8RfdoB/vBc8d5F583L2wrfuxLrOchkw/OdVVX2npPWqqr5C0n8l6R8vt1vXV/xtK6UH75byYPHSRwBRDPyV8kNHzKEzOvJOvXqNF1H1bA5nXj6R1NT2d8O5ezq9aCoANer6E4VgY45Bv/y6uCoZwcJDTHxQezuVUhWZ+8oDHtbC+ceJ3FnHtWy668e14TrHTCGpD/pSn515BXAps09XfSOrJH6RF0S0Y3oOOkIbQwDHdeOlxcRhrytl2cQwLCmBKmwfQIumEQdC2vYCvZ7J5C8TzvW6ssPLgOFflfSNkn5V0l+W9JOSfnCZnbop4mXuiYfzElbu0Igls6Q8GHkQYU4+MbqUswSIYfN93Ua2sP2c5Ujnv9m9OGijbD8cmp6SvsWYM0CCvkRAdHuVh+Z4P2GKFJgFHGr1a0ZGGxzigMR/t9H6eTT27fcoqvoAlwMYLw/OLzqOPAOIfrq6i7rtzinsu9F76y8wt7OSmeQCaPEMOdvnmG5jdHV4SKjPCbDTznWWNwTDtm0bST/QfYpcQvBKAoReFp4Kzzz8I+V5kaW+Xc7Zog8Sr4kXB7/bd2CHUt/T6QyNbfnIvilnRduobiNrA29wdEjQZ2cfDr6RPcn6yhzRblKgX9hYPRMHluwlz9zW6uePw4p9cWZwjkN5wJgNsD96TCdA7NcPVdYDrB1s3akFOLmDQ9Ye6nFkhP4y2e/adnPC0EsOGzF2Vq5Ra7+ls6mezoi5xp526TbNIa3gusi5YFhV1a/qAttg27bvX0qPrrm4l9HtTTAXz5ZwVZiS9Qxacm+ls+EasbT93NYvlEtbuTgIDoWIeAwbqhCqcQynQa30toeOxUCnbWdAbi8FPGbKqW9UxSHo2k0KDHpUTE9HdBsi5xnnkXFWWdlyt+NGkIbduzqPecLrFi5CG87MuZZS/yXEeTkj5LnxnF8HOb/fvLAId4khNs4eMWNw/pgL3KzgL1G3+WJKcDNG9F5fZ7mIGX5V9/3N3feHu++vVw6KLxLEi46iSlGyXuobqB1EvHCrP2hUl/Gy9wCFP4BznQW4IducMyWFdR50LPUdH66Sx76jBsd5iL1AAmEhAGK0kXIObnOM5zZWTplzlc4LB3Bd3GM+11lPPGDj3n7ai9fRVUb/zTn49A5uy43nJmXQI28Yxunefqk/1WsMb1FY5iDqZg3PAIIVzmx/TAyuQvPsSvm+SmdNIpHdOgu/rnIuGLZt+zuSVFXVl7Rt+yW26q9WVfUvJP2NZXfuuoobpd2WJWUw83p8xMYx05qUA1sbJacBlUF4e7u9bWz/ndU4qESvKADDAAEEfDY5BzYpMzgGLucq9ec1YR/3VrMNjI5+u/3L2wd43BFECM1t6+eQB9gLOOyqD5hbYT/EgcDVZJZFG96J8nQEhDTFkJ5oLwQ4cEqgcp83P7WzyPPUTjeH+HXmOeM8o8mB58ivnb983CF1orPX2K+Zg2Hc5jrJZRwom1VV/fGuZL+qqvpi5RTOIkG8oKeruTFOjAeYSY1gFgCl1Lfl8Yk2NACDN7WHQ8BSXR119QrgdNBi8ANGTWgH8PQ4QGQx0DaDzJcjbk/zcKGZ0gPGZEfYBTm3VfstJTWYsJMhZ5SbBmJ1cO6L53f7Ncaj7nUKpTxwbofrUCtXpeF+RZOAlMFmzf47QLMt80JH9ZntuNce3sQ3rBI7qceI+rbRboxG4kHUvt6zp5ChvPjrJpcBw2+U9ENVVd3u/u9K+gvL69L1Fze0YnzHGwgguko2V67u4suH7IQ+YIacHx5cO7I2z3OayNb5QDvPiyjlbJkT9dW6CLhx4Lm6zbWJg5vfrfovBfe2uo1OyoMdQPfrO7LfHn6EZ1/KIUOYCOKg9m0BGVisT6FA/yIgS/1robActT4ez8877oMNkfN1LzbiYOhmFL8m/kKN5okhlucOJALx6bN0vQHxMt7kX5L0H1ZVtS2patv29eV36/oKb1WYDA8aKtSxslrEwDy29QCfe+ykbKNy+04sscSybTs2jhgPgXBQiuCEtLaPe4ud7UlnH6AhOyXLveiAD0AHfX4TwkMamxdKfRi2Z9InHAIrSqySyjc4VTaVnUucN4A+Vo7T8yBkFy/awD07Ud+jzUtr3X7Ddv3FxjX2UCS3z/HS5Hx4CThIcr5eu5CXr6xPe8o2aH9RevWeddtvyFbMiwuPMbU2uQeRdV5HeUMwrKrqr4X/kqS2bYvNMIjb4qT0wKyH9QwA9yr7pEjScLaGx8e5VxUwjX1wYaBg62I72bYe9+jqanS0xG93FjijZRnfvp+z0uis4NvNDSyHXXsOMQNxOrCdMysv4NrYdh40zX2IXlufxsBfLJg2FPrjtRkBHlcjo+d1oX4WSpwXhlAjjzP0GMnI4t0b7844HFCU83dm6MVF/BwjS4y21LGt40V3XUHxMmrygf2eKHmZf2053bne4rYvf1v6QAAMyEmV8gMXJ36CRTAgI7PCkTEEXv6Ax7JUtBVZYWNtRJufDxx/aJwFOlt09lgP/I4AGT2jBBq39j+W/3IPrYNDo1zUdWrLAMc4F4jH9jGDnNvhnMV6MVOyiYZsgl6xB8eYM2BnVZyb2zA5L9Z5oHY0AbhqGnOgPSTHi9jy7DjQc20x2dBXV8G55hHYfbvrKpdRk7/H/1dV9bck/aOl9egai9vKHJi8/DoPDYAk9T3Oo+4/+7hh3N/wzphY77Fkvg51jSwBBnscwO5MAWAZdFRbgYkA8O7pxDYZbYbOtCKQewUaxNtwx4bvzznjKYYlc20pzDrt+u5tcj28YEQsWYbK7B5+gAlAXFPf7OBMdWz7u72X/nvgtgOhaw30FyCMsYvRUcf1gQ1jOgHUtpTA0EOTvN/+kuHZhE3TT54JWOF5mTjXUS7DDKNsSHrPo+7ITZCo/i2UYgSxSzEoEMAoeoixb7kDIR7Hp6B0ddIHHMIg9rQ+Z1MeSuLfkaUyAHyAxf5FNckZDOs9m8ON+666Y99aKHlsGfSRZQKGm8qOjAP1g6tp2+2yUvYoo0q6quyFF7xij7OwIY+6rD2A1z3HI/XZups5KEARnUkxZ9pB3UNqiBPlhdWobyf16ST82ZH6xRYA2xUlAN1WBk4PHcLkMPQCvI5yGZuhZ6KsSHqbpO9aZqdugjjbkvIDCyCxDYPRA1il/GBLZ3NHh+xtvs6BztUZjh9tTK62xhCNkfpMLg58xBmCdBYg/b+DztA5OdMaKRvtVwa2cWbKsjgfNB+YTaX+teOF5MHjkW3Jlnnc6NA5+LkMXS/sob4/x3ZAAQjdvuzfUlbZAWXuESFE3r9R2DfaQ2X9co0gZke5acHT8aI9+brJZZjhV9nvhaRPtW0bHW1FOom2MGdWXirLVUQGr08L6aWZpL7dyge2lAHPHSvO+ty2FvsaPzG4mGXONlyi0d/PN4KgvyDwqktZ7XQDPeqmlAOxZfu7h3Zs68liQZ3z6zFXqugiZSa0am1S19CvE0ySa+4ZRX7dI4BHOxoAGAEpXqPorXcg5H5yfTiee4GlHAdK4VkPyfKcYq7VibKzz4PFcbjgGecZ9WwW2oI1X1cglC4Hhv9j27Yf9AVVVX04LisyzAz4z6DcUWZGM6UwEWxv0fEQWYHb+aKnl8GyqVwyrFV/AnZv13NTo9oJUA95mmkHZsC3l83yfaIXkvWEqXhhhOgY8BcEajPHHwJaBih2z23184kXSip0BHgG+pCaB+gCJgDAkZ0n6qqrjC5RNXZxVdjvEX12psZ6XgbYhj2PnW39415fzokQJcKUtpXDazDR0OeVkdQ2Ut2cnQqV6+Evh+sqlwHD9/mfrrjrFyynOzdDGHiutq4re/Owl1HmngcKEIgpY26/qpXfzNEDO1aOr6MPM9tWOqti0WZc7zOsDanjUt9LzuCgXfrjzgkHsiGVtw770u8924519MXzlfetLWyJeE5h4LAgbyfOHufiLNAdHs5ynVHX6gO8F54dGmz0yyvXSP0MG9m6k65PAOKe+kDo+dNuKrmlzG7dhursmkLEG5K2amk8lkZdh+ZzqZnn4sHYNs/LsLmOci4YVlX1HZK+U6mo6x6Llc77+6+gb9dSAAovkUSMG5451nl84MjWk+uId5EHHEDhzYx4Gh2DDk8fqnUERI85A0AY6B7jN2QYpx1AgqrTC9sH1in1WSPLPf4yekoBgci0atvfbbCb6rOSaDcFEGBgI2WVULbvoTJIIDGv3Fl0tO359eHF4GE9fs/d5ible+Xi4O/VfhygvRKSlK+je7G5rzx3BJlvKN8b7KjnsbuTpn8ePGfXPZzG5VwwbNv2uyV9d1VV39227XdcYZ+utfAGJ07NJwCCNSDEqXl2gDsXaE86n6H5YIlsa00ZYDkOAyxmNngMm9dfjCo0ABXznaWzjMQ/znJvqZ/cXlvf+A8z5DwBbmdB7gxiEPtLxe2BCADhecEL5TAZUiM9vMXZKoAA23UHBm3Fexavk3tu6ZOntnlGEkUdOPdYVNdB3u+j999rUm4pXX8pv1TI3qGdmaSmkUazvi3W7ZT04bqH07hcxAw/t23bX5f0o1VVfX5c37btx5bas2suDDi31/BWZ8DwwHusmrNBL4s1ZFtysFmxfY5tPUZ7SWcAVcppd652r9k329CWe0Kjw2Rk31L/WOy7at+Ao3vLFfalPYozuGMiFll15gjYc628PT+GmzKc8UbPONcYIJTt6xECsHAHxKFc58Z+R2Bs1H9O6Ke/oHguHJxwHEn9a7qiPsP1PGQPP2KZp4Fybv780RbbXHdbIXKRzfDblKYI/Z6Bda2kL1tKj26Q+EDH4M1Dhz1N6ucce2CrP8i84X2QwAY9jILt4kxqrONT6+xDLVvuVapZ7rX4fHY5KYOFi6ereYEKKQOOXwN3utC2M22v+xdjFWFTXAv66vY4zv1I+V7QNvF4OFm4hn6tqfKM84fjbagPMKjfOH8IsPf7gCrrAzCCCkA1UZ5mNDpEeK78XriTK4ZyOdOlrzBHzoNnKKZa+gvLHUkxkuC6ykVq8l/qfn6gbduZr6uqajKwS5FOeEildIF5sAgGrpQHH/XwSB876b7ZdxzaXIR1sKZo33PW4AZ9qc9ECMaOc50QhOzZCoAvaihtxyoyhJ8AAJ7FAaMhnAgAAOwoeuDsCscQXvLIAG8pA5p7tKM6Sn8jk0S15sWzpXQ/AFipryZPlLNcuC44rQA4zyWeKzt3YPAxFtRzpKPKPXjfa+mw6RewYJIvMmBk5+cFJo7sNzZUAHesfsC6T1Ph18Cv5U0AQuly3uSflxTV5KFlRdQPX0CtWFU/dxhVakUpu4LsAKkfGuK2uBg8DQh6MVa36VA5Bxbk4BI9wgAMIAiIOetx4zuZCZ4z6zFn9MPT2bxSSlTfsb1tKwHaTP15XnYk3ZX0THdsru1I0uZI2tyQnpe0t5fLeZGX7KqwmxwAsVhqy50rroISBO/hO/thP/rr18C/YXleyIF7C/DEuZPpm79cRpJmTQqL2VWeR5pZExfqv0RdtT3UWVurp2hiBsC2OgRyMSPmpshFNsPnJb1DyZv8ecpa17by/SwyID4A4ls/qn1eDt8DkZ1ZMpi9yGdMyGd/d9AAUK7qwYKik8YLGLjtDHaIiuaAiBp6qBTMzFwjHjfJIKOPnpKGigsY3pV0504K49jcl+5167aUwG5nR1ospOlUWjSJHd3elp65kwBxPpdmM+nePem1fenTXZ84rpc2c6DjugHc7mnmpTSuU5jJbJ6Xc60BMq7dULUfZ6zYJz3ez6d2YB3Xjnu7Z/sslIBwV2dfnh6E7gyQ/64mY0bwawAY+iRl0QYKq78prFC6mBl+paQ/L+lFSX/blj9UCrkp8gbiRmlXOd3jyRt6VUndQ50kzMNTwNwz6x5k2sGZMJSr7GrZuvqeVNiRq3D0iQ/7rIyk1VGOP1sspJVZnqvF2SwsyAOIAXDi2ObzDGzPPZcAr2mktQfS1r0MMiujDHaLJvV3YyNt/7a70uaWdLJI6yXpcCbtLRIwwUx3JN3ZkSaT1Nbre9LRIs3D7Pa9saS6zucopb6Ox9LJnlQv+oUdsLvCOrlXhC1JfYa6UGaVqNBu72Mf4iS98AKsv1UCehiwlMNlPHyLF7OH9PCC4PiydR4ug/mEfrE92sN1zziJcpHN8EOSPlRV1de0bftjV9inGykAzSR8n4RtRkqD1wN9pcy23Ita2/dqaAu10B0fgCHFZWPRAdah3roKuaIESGtdIK4DBf3wdDMvIuADjHWjUQ7qXSzS78kkfZpGWu9+13Np3kjHiwRcvbJdo7zPegdwi0UCMs7J+7Xabb+xkY7XNNLKVNqf94G87tqmnbqW1iZp/9VRAkOuO/fGw3fcjiud9SjDSk+UjuvOHG/DQ6C8kvR56qu/gGC2rqpz7WnHc7VjEL/sG3ETwFC1oesub2gzbNv2x6qq+tNKmSgTW/43ltmx6y4e0sFvjPyoj6iZ/tam3L07PKT+4PAH3cvMO5Pk90R9+1xMsEcYvO4Zc3VqIWmjzmACUBxMpdFUGjW5Kkq0E3ruMmAf2xmN0n8pAVrT5A/OJ2xjxCneVR/cFou8/8kinwOq+Uqdjjcep31Oj3lPGs1zGf/NUe5bVacXwNZWag92CsPFJuzXzGshco09dtA9wKi+nklDOwTquy2Se9UoV6gBxLYlPafEgAHMY2XvuJRfkjGTxuNGeSm73RvxWMynDgyrqvo+pXvypZJ+UNJ/JulfLblfN0IAQR6cGKDqwcv+Rga8/OGvlVTVHSUgwDblqrjb4uLERUfKA8CdMQ6SABhqGX3DOwyD297OzEqSDval0Sw5g1CnAZO6TvvNZmn7ugPU1XFmg840ZzNpd1d68EDanWd73KFyHjcgMJ9nNojUdWp7MpFuzfK1GynbGzkm35I03kvrRqMEfFtbuV8bG+lzNE/bzGbSrWkOv3HbL9c8OhjcQ+xefnfmEFgu25dvD59C3Au+KulZJdvq7Y7BLxbdOTdn2SdAvNddX2eB2HI9xdJDom4iK5Qu503+4rZt319V1b9p2/avV1X1PZI+suyO3RThYWcAUIGF8cs36q+UVTsv0kB4yaakrU6VHM3PMkgYSyycQF9QmZz1uVHcwRXvMv1rmgRggMUpGE4TKEnZmQEYjsfpezZLA3M+z/Y42vJtZrOkEs/m/ThFZ8uo4oDb/n5mmKvjZEc8nkuTvdTefJ76WtXpm77S/9EoA7yUfu/sZLPAZJLWzWbSw85RszPNpgy30cmumd8bz2rBS++e5U2ll4nH+S2U7YFuF8YGyAtyq2uTKta3tlKfpXQtx93LSsqOoMVCWu3O/6H6MaIAtQdxewjQTco6cbkMGFJPYFpV1QtK9uaXltelmycAkL9l3XHinjtUawdEB7G50mCs6v5b3oHMPYXuyY6M0D2rPPhepQU26eqzA2JdJ8CQks2urpM3GDveyigNTIClbTLgTbtRvrGRzqVtEqg1TRqoh1NpNu3Ht91Vv9rOre086Gez3JdbW8m2t7OTjwdLUnf9OBZ9gAXWdfq/vZ2ZI0A7Hqdj1nXqf72fy2V5GS0fVNwzL8bhqZOEEMH8uM9SLovlec6ek4wZBKC83fVxZZRfMPVcOhrl5wqp6gSG/uxFB4qLe95vYliNdDkw/PGqqnYk/c+SPqZ0H35gqb26QcJDAxh6DFf0Bks5W6JWnjcDZrnbtTFtpEnTN+C7URumR66qFyzF2YJK7NkUsENsWFIGTtLEYHeombCog/3EQjY7YKnqBEh370rPvue29MLbO+9Im2JfXv49vb6b7YOAo3tx69eyao7ayvYj2r6bjillcJOyp/l4kVgiLPLhflLD9/aSY2akDIQAyHic1fi6zkyyrhPQrnUmgvG95JGeLvL14+XBtAExtModHO79VXev9tS3Q8LuCajmfnhYFbbazU7Fv72d7a+H3YunUZc11Egn82z6AGixZzvDdQ83AHhTWaF0OQfKd3U/f6yqqh9Xul6fu9Re3UABoIgX9Dc1TDF6lj3aP3rwPCQGAGxtO97g7AOgwWKIYaNQg/fHC5JG5uqqpavM062sArtTZHtb0nNvk977OVkXffUVaWNdtz/5SR3vzXQ0z+pr03mOt7YSozuaJ1Bd38ggRNs7O+mzuZEZJetPmsQ2pcwKsV8+7NRl7ge2TJgu/ceBc7xIbY1Gaf2oyex3Nktebl46XGsAjjCYQ+UXkccPTiRtdGaP2byflcNL0acI9bAXT8XEW45zCDNGXUu7o8wwvb4l9xuzA6YTslDGyqFN9Ocmy2WY4am0bXsk6aiqqh+V9K7ldOnmiasUsDQ3jDvgDRnMYQUH3fqHyozAU6Ua9YtuehaGezlRMzH6+/GlPMg8LGZT0tZGdiZsdervrec3pY11aX4s7b2uw2kGn8lEWn3X26X3v1/6D75U0guSnpM++3el9/6C9C9/Qau//dtanR5qbXzQY2dtBziotjHUZTxOcYnEJq5Mug2aRiezhaZmx1wsEqCtb2SmN5slVbmqMxsF4CaTdPyjeVbt6YOUmen6Rlb5yfA4UWJ7t7rzOOn2RfWNdQ1PWdg8Z7WwLeDES40wmFiN+9SB1jl77txJH9jw7m7a53VJrypPd0nsZbzXHo7lL2l/Pm+ivCkwNHnLZcyqqvpPJH2v0nX+wbZt/6eqqt6n5Kn+DUn/Rdu2N+6aR++gFyhA/WEQ8DC6HZGQBradKU+ChJok5VzhE2snZkP4rGqwE47DAHQGMqoT+N29K73wgvTii+lT3dmRXnxnWjk/kh480Pq9++n3fC5tbErPf5b0nvdI+iOSPkfSHUn70rON9N570uxI2n+o1cmanp3sqq4bLRYJpO7eTX1gUOMMATRhQSvP3ZHe+17phXdIoxWt7O3p1qufkl7+PWl3V8fTRXIYmENkMkke68Uig8j2dgK41VFmg9gbTwwcYaErdW5r3Mvez8L1Rb3EXELOr9TZNZXMIOSnY8bwFDlshaRYutNjrKQScz6EAgHyaAAEajvgnfZD2WQSy8s5Y72p8lbB8C2ZDqqqWpH0dyV9haRPSvrXVVX9I6UKOX9G0tdK+pOS/slb7NcTLdGzG0MXXG0+VgI7fqOC4U3FHuXrpez0kLJ9ijc6AIh6jDAwCK/B2YGzZDJJwPSudyVce+e76wSC29tJBZ6sJwCUkk1wdihND9OI3L4tbe4ozSN2pzvilqR3JPqysyNN1hJwjte0PX9F83m363YazEdz6f69ZGrc68oMnzRZDdRzz0lf/CXS7S9XcrMsJP2K9P/+C+lXflmrn/w9rc4ONR4vtLOTzuXuXelTr0lHHYBsbknP3skgsr+fGSHq93wuzbs3y3icArGJb5zNstNJSiovJguYHF5iCjwc27dndai7x8QdwgLdDELqI+aQsaTRIoO3s92NDWm9TnZmnoc6tB1TMcnvPrDjSjebHZ4LhlVV/WMNg16lFNL0VuQLJf27tm1/qzvGP5D0Z9WvVHWTiudeKICb537CAGED5KX6AzxSjueLmSQe+gDwxfg03/a8dVUtjS3GDjB853vHiR5u35bGq4nZzY87z8pxNrQxGkcrUnssVZ9WCgsGrA6y4W++Lm0kGFnZ39f2/GHK+rizKU3WtLo40eooTeWEmgs7XNsaSe9+t3T7KyR9wM7gS6Q/+MekOz8k/ezPSru7Wtnd1Upda03SM3cP9PzzzamdcmtLWr+TDYH792Y6nHaOli6ecTpNIEwmi5TXz2aJHcbcZTc/eCpbLKQgZaeZ1K9Y3nT3EWCFWR7YvqcgZ/ZOqc96d3b7FW3cWUJ+MkJkQQyuvqlAKF3MDP/WW1x3kbxD0u/a/09K+iIltfknJP2mpO97i21fS3E7nRcPRUXxaSsplY/nz71+/saGPSCetwybaNSxQGU7InavFSWnxNZWUhs3N7KKrBdf7ComnCT6NP105/k4SSNwfz8b63Bp7j+Ubv2mpAeS3q40jF9JQOnVFzpKONl7mAbzqSdmRWuTHM4jZZuh3v1u6Q/9IfWBEPl86W2H0r//aem3fiux16ZJTHZnR7eeW6R+j9eyN6ZppHuf1tboFW1ND7S11ZzaF4mJnEzSdTmcZva82iEM6YTTaaosw8uOewpzrO2DeOl/qvNIOcPE1W3uMeCGZsH1QWWGKY7HyT6IZuHxrbTnwstX56y/iXIuGLZt+8+XcLwh1te2bfvLSqA4vFNVfVjSV0vSZPU8LnP9BdDjwT62ZV7xxYN1AUEPvMaz7DFpUl/NctXHH/pTFbsLm9nsQknWN6S1jTqps5P1DHp7r2d9UlI7nZ2qaac2rfmxEhDiCjjUaamCyZq0GEmL49MYl2pUp7iXU9W70fE8xwCuTVKfqjs7SdW+dfeCq/o+6bnPSjr2aDW1WdfpuPCwyXpC++3tpOLv7yfWuxhrPJ7ppAO8qs4BzWuT7K12FjYaJfY476iVRwLwgqp0FoQW6qthaAjHYTv2B0i596ehUR0AHnSB6Muz1DsAACAASURBVGTNLBbZHOKs1HOs3S54U+2DH//4x1VV1YEt+ggzfb5Vm+FblU9Keqf9f1HSy2+0U9fZD0rS2595ptXu7nJ69xgENSk+fI3ytIx4lokDQ/V1D6bUr0CCeJAsahvfOE56oTNKQHPnTlKLb20nR8HOjhIQbm0l7/H0IKHA3p4O95tTrfhwlj3BK3g5mkZqH0jVnqRXUq+OOgDd2Myq9cZ6+ozHWkxnWuzN1TTzNLinOW5x5bk70tYt6c4z0vNvV57VY0h2pGffLb2wK33iE9LuceeOXsvxNLjHx6sJDOed6j+fJ090p0qvdamI1WQsjVbV7B6chv+s1FI9ymT4eJ7i+XBAeKC0O0O8EjhhOEQbxKK5LmgMPCeVUpTBvXtp/Wuvdfcz3aKkznf7eogOTjOeC0CV5+amyUsvvaT79+9vDq27ajD815I+p6qqlyT9npLD5OuuuA9PjPBWJ8fYJzDydLpN9QN2vcgDThEGDt5BPMLMZzIkDNIVJaCREpChCqI5Sl1YyewwjarT9AaCChuiWiR1AdeTjn1N1tPOUKXpNAOp1wGbG/Xb2FC9Nzu1x/G9vd1lmWzdSqA5GqW4kU/9W+mz/o2k959/sSdrndf7OIGe1LmFuyv34EFqb+916d49HT94qGlnM5xOs11xNpPW62REhHGtjaWVHYur7Jre3UseYqk/lUJt95Z7JvVNHT6/SAyal/ILzFXwPUkvN9KD17LpgygFbJWUDPOspSZsE0v5Pw0qsnTFYNi27aKqqm+R9E+V7ucPtW37b6+yD0+SxPhC3sTO3qR+ZohXKUZgjwixaKjUPoBwmnih2FpdaapuwxXDqJMmsZ7ZTLr/6kLPjv6/BIp1nYBlNNJksjjd/jQoisbm8wx+i+OuAsNutt2xvGNialppvJbAdK85VTnJUPnMA+mZ8StpXw76G78hfeEr0ud9g1IB9vd0nfiUpJ+WPvWb6bhNK9UdJ2pOuioPJ9Lew7RekqYHau890L17SVt+8CAfe3s7Af3mLOM7nnbPyJF0WjNx9nLyHrtexssPcwX3zysORVaGFuCA6iAq5TxmljuA0iaOF4BxpPSCJfQGkESeFiCULgGGVVX9tKT/vG3b3e7/M5L+Qdu2X/lWDti27U9K+sm3su9NFPc0OhhGLyTbzpUT8wHAsfKDj4rj9QnXbH8H4FM7ZJ2LFNR1Aj/ydyGBi0VKPTuczfSO6W+p2rmdgGW0qmpDUtOons5PA65Hewut37uX0KGuExubHyc9bvczXcfXupiVo+SVJoZlltLhR6PUFykD4nQqbb481+pobml8B7rz8/+nXvrcn0r2wfe+NzHB6WEC4tlROsZ+B8oTdV6Ow1Mb4clsoaZJKu9Bl7K3vy99+l5ylIDtW1upT1tbOcB5u0t/g003jWWnzKWHe9KDeT/3GxsixXgxfURA8jJozv79+XGniKvdDmSoxgRvE5QvJRD12pVPEwC6XIYZ3gUIJalt289UVfXcEvv01EkTvj0DQcrqy77SQ7uulJS/rQxoDKToOPE8Vq9lyBQD67JcYMvGkHJICU6B43kCifWJ9Oz4ILGzjfXUynwuTee9QgzP6YHW6zqB0Xg1sbDd3eRdRk1eLDrP9EMdTjMTjYHWqKw4Z1DLKeE1Gkkf+9hc4/HvamPjd09T53Z2pM96LjmAtraUQoJAtv19aXqg+681evAg+4GaJhd4aJuc8bJqqW63npvo1mKhra2FZrMcfF1t35IWx9qeznTnTqrCvT6R6nvS67N8bwBEXkpkrkQhf7lRAi13iDXhg9Beo/5zgOrsgnqsc9Y/TXIZMGyqqnpX27afkKSqqj5bNztf+7FIfBvX9u1sDluTz8kc22HbI+VMFpgHNiSYwqlqdg4dqAJArphtUKOVrK52CzH/Ybhf39tL2wCGeJ+JReyo5P5+LvaAHE6zN5RYPnNcnx6WfbyUl5SLORzNEnt7+wvS1kbHPpsTaXaoo2lzqg7v7+f9aJtq3JNJ8iSTi63tbWmx0HqzK6lJ3uZRna5Jkx1KhNpsdKl7OD38fg15bj20xb3HsZo52zog+nYwRCpkx/ZieM/TLJcBw/9e0s9VVUWozZ9Qmk+5yJJkCAixGTbqF3VdV/YI8mDjKYSBeBVsSv77AIBdUdWZklbjca5NSJ5w0ySWpfGaMUNJs8OcpdG1t78vTV6b6/bi905Z4PGsOZPWhnp6OM2ZH7ThNQv397MzQ+rnKzeNdEBwdJPtZzufTJ7VO3fSMf7g+ECrncq+v7vQwz3p5Zel3/7ttF3TJDb5/PMJ9ChJRlGKZ+7WuVSP0nVYn+x1oUGd4WJxfEp8t7YSQ+Ulsb+f40T9xRbtwNgUqb59XszfEJg16k8VixqtbjmFQshHLoCY5A3BsG3bf1JV1edL+qNK4+hb27a9t/SePeVyGkSr4Tf4TCnxHs8fs9d12WSn4Fnbb1QmVCOcKSuS1ppUjcXnOCH3d3u7y86oV9Kgr+sEgqSDzI+l/YPT0EPACnVTDxKn5T+ACShWdVaNYWmuErOfp8hRqBVAhCEuOiCc2nlOX5a2X9Vp0Yd3TpPVByfJa68lQPzkftrnhVlilAR239o2B1NdJ4Y7PZS2NhNabm3m7Jv50anHperY4ZpdR4KhmTLAVSwYnDvOZkohM5Rz8215TnguiODk/vPSbNQPtF7YskYFDJGL0vE+t23bX++AUMrxgO/q1OaPLb97T7c4II7Dfyl5BQFDKdkQV5UA0QeDlMCwS+s9zW5p1C8nNVK2fUlp4GIPWx+t5qKCjnb37uvkweu6dy85Gw72s3rpZbmkrthBB2oP99NvtGUqY89mCQh3d/v2wflc2p8mkBspATddOG17kdkWDJlc4PuNdPjrCac+9WoKmj6e59Cdw5mFNM1zvyaTXC9xNpOOdhdaqR9qfXaYsnGee06a3O1o7HHq+PRQTTM/raPI9djeTi+Ak3udD8fuzVQJ9AhtwebHC006m0kkW9coASETW7m5ROqzQNnyq46te5LlomvxbUrq8PcMrGslfdlSelSkJ7y1AS7EVSAp57ESSI03mfUU8uyygk9jFDeU1ewNK6JKDN1qF0KysfEwqZeTNWm8npwi0wMdvvq6PvGJxK5e30sTMVHhGtub2+CO5jlDwtkfkzrhMd7fz0xuPO6mAlCe5nKkdCwYE44JGA/XzYsNHEs6/kRihF7mfzZL57nVHcPLfB1Mk+OkbbLtsqqlt9ULrc2PE1vesDCfxUIar/ZMADDY8TgzzKrJ4NbYOXiQtoMa8aA8Ewj33U0pQ8HS0aZYmOBZuSgdD7vgB9q27Zmtq6qaDOxSZIkSH17UXymHSnicWXzzL5RL+/u8GmxfKbMw7HdSAgICjp8fPUj2SOxlTaPpNKm1r72WHRBjQ+1JeFLcrojKjN0PVvjpe9KDhQH2LIeFoDoCIl5rD8FGyjKcCDOlOSsWU+kz01TJxVV0dddh0QHf63spLGbF+uex4unCHieV+fTkjpMabdeTyjuo+nuNTivNeKA9H4DQbcfniceKDtkV67C+gOD5chmW/PNKkaxvtKzIFYuDHgPJ85l9u1iyC08ymSqtMlCNmlxhum2SCspAvjXZT84TSVqcpIrRTWZAkk5T2CgSIOV12PecNUm5fuD+vvRwkVVGzoUiBQCgV/pZCd8XCWlvUmevbPJyWNlcnUo8yx5srANUwx6NlAPPdZBPZnogzQ5Pz5vzbe0aOruNAMVvzvWyIIZTDDvgGzlZipyVi2yGzytVmVmvqurzlMfTtoZDoopcofiDz00kR9WLwrpN0EvHe47zSrfvw7k02s3MbrPzIq+O0kB++WVpe3+uu/uvaHWcAW+lziEoUmJbi0Uyn/kcJLCklVECGjI7PK7x3m4yTr9m53agXMjWverk0nJ+MSgZpxHLWH6iXFUaJ0Zj+88lbXYuQirlYD5YGaW4wZWNLhp9eiDtLXJg996e7r+60P0HyQ4p5QiivT3pnlKK3i2u/UgaL/rTMHgcIqDJp1G/yIPXsvR7HtVi/y4yLBcxw6+U9OeViil8j/I92JP0ncvtVpE3kqj+SP1BVCsPJGxTFBklPtFtkADBdJoHPyWrpJwfvLubPsRL7+3lmeaqriMeNE0mB2zMJzpa0JfuePN5erjuK9W4AQxnyvUbpTzIOT/O/diW+/XQwPYEsWNicEfViRJAHb2agfqFF5J1YL2rWHPKjufHKYh8d1fHs0b37kmvvtpl/zVdIHYtrVJSSzn7pFVOX8ShwvwjbgeVzJQR7tl5cxgPsc0iF8tFNsMPSfpQVVVf07btj11hn4pcUqKNaK5+IDUpe2SbMJg8DhEh5W8hnWZckH+LfY9J1t1TjKNlaK6S3V3p1f0Ebs7a3NO5IWm9kXb2+jX82K5RngMGdkRVbp+fgzL1nm3BdYFF+/YUNsCzrm6bNWVb7IGk+pM6LZBKvOFooS6fuuvtdKrD/WQ/vXcvh+wwT8x4nMJrPv2atNGpyXGCLS+8ADt0jzLn7WYC1tPf81TuIpeTy9gMv6Cqqn8WcpP/Stu2/8Nyu1bkzYirRV6TDmDE48h2rjJ7mfGZcsDzwTTb/V7fy04V7GddBl6vgMBql0980iQnyCtKaiEDNlajxIN6osyONtWvsAyLJHMGWbH1h8rFcKUMDIBMrX7pLFIcPQ/cHS44bCaStl5LYEgudApAn+nZ2SvSaKR2vjh9afDigBXu7GQgfdtz0tteTgc8ZehjqV5I8yazRlfdPV0Odk2tw6gGF/n9yWXA8ANt256qxV1u8p+SVMDwCZIhVUlKLASwkrJ9CfCCYY1sG82k49eSzRDWt9upsKhy2K+Ii5O6aQg61DtWAsF7ygUBamUV3fOuAT4cz6vKs6VImRnCcL2oQKwg7ROrO0t2EwJCEYSRMrjwYY6ZY6X4xk+9lkDu3r0EYJ++x5wpi9PQH2yjCI4ilq2N05zGxw/y5FSEEx3PE6AT9uTOEwDRYwSHbIJFfn9yGTBcqapqrZsmVFVVufmmyBMoERiP1AcFyjr5NAMsp5rJaCGd7GXb1UPlOngAWq3+BFQwM5gXpaI848HZHYPd520GlD3InI/bzWiDKiyttelFCWiDc0QNx2bqgcgxPs/3P57n2EeY8cmCGMxu/0UCRRzL83li14tF8s4fdQcfdeE87lmHIXtRVT8+9zR+F3l0chkw/BFJ/6yqqh9Weub+gqQPLbVXRR6J+MBxm50DoqvSM/tMlZlaowRsZDcAiM5UENQ82Jyvg+EMAZIzVrzD/vH5hmGwXopqoWR/9Emw3EsM2EyVC1dQ7rhVTuEjRXFdfdDHdICD6fW95CQhTRk76sP9bEf1LJr5PLHK6TSpxI1lp+wq2VV3lV9c2Ag5zxguU+TRy2Vyk/9mVVW/KunLlZ7D72rb9p8uvWdFHonEeDMP5uW3258AQ8qFRdUMACVNDGaF3dEFQPIAcQCRoONt5WwZV1Gls7P/udor68dUWX1v1WeqnBsxmPTVbYm0TT9HyqE89IvKOHh/qzrZUGezFEKzNs42Rfwqs1n2KuNsejg3L3GTfu8pMe+Z9WOk7EzxeNIiy5NLpSa2bftTkn5qyX0psmRxIJT6QLewb9a5HTGyMw/u9jhGb9vtdwAdzgC3dzFhOkA5s98OWAi2zlo5m4b22a9VKmQBAHI+XiHmWH2TAX1w1Zn+LhbZBCClYO2T7mJ5DUavTkaBCUwJhNO40wa26k4oDwHycyu2weXKG4JhVVV/VNL/IunfU45qOGjbdnvJfSuyBGFQeQZEpT4YeiC3p3utqB+W41kgMEafOwPg4KGR+t5k4vpQCSmsgPrLfl6YlvAXvLEwQ8JPauUsE+yJtR0DBnvYrXf7In3CMSRlcI6GckJ1qGZ9suh7fmkHYHcHj18/AsO9nL/Ut3MWVng1chlm+L8qTdz0o5L+sKQ/J+m9y+xUkeUKoOclvIZm2PPQG5ghcYu04wPVw0HmynGPI2X7I86V+ODV6hefRb1lOwcKDyRfV6rSM9dZT7V7jj04m745SLLfaWqi9YvYRTcDYEJYdM4SnB/YVbl2OETc4+5TdXoAObZN7oc7n/w6FVmOXFZN/ndVVa20bXsi6Yerqvr5JferyBLFmRtqIgG9b9Y+5dVVaBv7HJ7jWHgg7h/biP0EqGMIA+DkKWuuYhLE7NNvSn0wbNTP7HAvNcf09LcITNj23EmDqi1lldg9+kOTOvm18OITDsoFCJcrlwHDaVVVY0m/UlXV31SKox2cd7TIky0RsGBWXr1mPrA9ThBPD3PWNVW2h6HmoqKO7LeU4xLdo0xID4LTQuqr7W6zhDkNTZLkbJSsFkDVAStOmSDbDgAFuJxRyvblJUK7HvOIuk7B1lqJxfr0rZyrM/P4UihAeDVyGTD8oNIz9S2SvlVpEvivWWaniixPPG4PAMMeCDBFDzK/PfDXg7hRe+N0k9GpIeUQFvaBofmAjyEl2A+pXcjyA+UgZa9oA5BJ2R5Iv1A9CQA/by5qt636jHIeJ+kquXu8V5VtmYT9NHY82bZurwTc34gRFhvicuQyoTW/0/08lPTXl9udIssWH+hun+PbxQdljHWLAxQmNqRmA2iNsrODPnhwNMHWOGq8xp/UT0NbKE97wPEAV7zQ1AtEPfUgcGIR2Y5sFLdXco6k+qHqrioDrwdq18qphFIuBBEDzZuB/SbKLNf7UOTq5KISXr+qC2bBa9v2/UvpUZGliatmABAB0sf2cfYYGV4UlgGEfI8G2vBBDgBt6GzxBY8J9IwSQA0bHCDFenc2jJTnl/a++rwgXuWGa4OH3MNv9rtrhJNHti3TLnjuNU6mCPZeMccBF8cPrJF+edB6UZOXLxcxw6+6sl4UuTJxldSrR8NgUAWRWMSAQR3jEvmP97k55+ODH9WUfWBcRzqryo6VagBOlG1y3ia2OwdcPMzSWaYFW8WGOVOeAoH+nCixyT1bL/VrJ3qRCxcye4ix9BeEAzJ9Qb2GfTgTL0B4NXJRCa/fOW9dkesprqoRMoKq5xVcYnUY39/j8vCaXsRemvDthUpRGT2rhJCfxrbxYGmv2O19oiqP1AfzIe8vAMN2bh7wUCOAiwwX2jpR/7zdAVKH/z7AatvXBc++dDbLxvctavNypQRdP6UCi3IAcVB09Y7P2D60AbsCCD0djv3cRomtjm3Zz+cCob1tZbuf5xl79ZmxEmPbsvUAHecI+G4rOWFwpvh0ojBiPNBt1489JTUZIMWZQ2iMz8XSqO9A8vNCfIoCBz0Pd4qhRu5EeSvhT0UuJyXo+ikVL5oQbYQx5o7fDog8OGSBRDbJthEM2c+zUyicAMCNlABuR1nNnSoBE57o6KFdV7Y3Skm9BazJHtlSznKhtJl7vj3bhnMCMGGChCQB7F5TEVsmwB3znx3g3E4pZUDGbgsb9UK1RZYrJej6KRS3rXll6Vj6Suo7JWBcDHLCcWBMzlYIG5HtK/WdB9joYnYJAAfrWiiB10Mlp0n09noJMp8Dxs+vVQJFCjC0ymEv3jf66i8J1HC/Lm7n9Jxswnom6oMh15u+RmeUs3JntNgwXQorXI6UoOunSNzG5awjej3ZxqtBs4xtyAZxEHUnhQ9YYhBx1OzZ+i1r00GXGooUZSW0ZaE+y2y67Sj8KtsedjVXVu3xFrtTg3lHYuqbn69/UKMpCOEeeMB8q2sXBxVtowJ7OTQ/nstQjGGR5cllg65rlaDray8OVLUSM1tTPxZPygDkAdFD4TWowj5nB4C1sPUe+gKDHApKdscD8YJz9VmnszTfbk99WyasEFW37r53uvNuQpsKyxz8/HxJ9wPQPJjbr+tO1x9nsw64/CcY3b3mSPQmF0a4XHkzQdczlaDrGyHO7ibq59+SQuYFRt1z7EwSJwCe6aECDtFW6MAo9QsmyPbDbuaA4dkatO1xjENebYCH3+5NjmDo5cuGBJXePeFMW0AWDNfLbaOeq+32WYX/ZNlIfTZZQPBq5KKg6z8r6cW2bf9u9/8XJb2tW/3tbdv+H1fQvyKPUBjAhKps6mzFapjZinKamjOUtW4/1rntEIaIzc3V0JHtv6U+AAF2MaRlTxkMcapEMIufqPLTlrM38pI9zMe967FNqW/LhL0xI99x2K7p1rVKU55OlR02fi9c1ec63erW++x9kQ0XWY5cxAy/XcmLjKxJ+iNKY+GHJRUwvIbiAyrW35NyeAcq7patd8cJ3zBLgAhm6dN2et1DHBie6+vqt8f24clF5Y6MCjAB0Mfh/9Ta8338WkQw9OBzd2QA/ByDeomk+Klbvqas3u8rA3pMQfSgc3fisI3nQzsbL7I8uej6jtu2/V37/3Nt296XdL+qquJAuaYCmDCYpbPpcIg7DKjD5zF8rIMpHXfturPE2dCqfZi97sjaco+y2+ycXUaHz0gZYD2t7UTZMeL92FD2YjsADbFC9vF0QIpVYPP0wrJS395KjOIQa/WwI3f0AKyAYWSoRZYnF4HhM/6nbdtvsb9vU5FrKz7oiZcjGNodEw56Hg6yUAKyNeX4Pil7WQlYhl25isfvVv2wFC+u6o4QQIrg6g1l5ufMDjWa7agAQ/6zOzdcRefjFWm8ba4PGS6Am6vdEagcyDhv/wbc3azgbdI3hX2LLFcuAsNfrKrqm9q2/QFfWFXVX5b0r5bbrSJXJUNqGwPcZ7KT+sxsopwtwYAm6NjVXGd7AIqnsnFMZ59SriIDW6LwghdfIHB7KD7Sz8/b9PJgACFe7Ub5xQDgOpP0cJ1YFMKvH2YGziECnUsEUv47Ky0hNlcjF4Hht0r6h1VVfZ2kj3XLvkDpmf1Pl92xIssRZygwl6FKzx6UHdVFGFylfoFV8oqjysn/ytqK3uYV+++A5UwNux02Sjy4nu/Lcu+DH8OBmO3dpudeb2ejvBz8fGTbDnm63evsts6Y/x3XDV2fIsuXc8GwbdvXJH1xVVVfJul93eKfaNv2Z66kZ0UeubiqijTKaWw+H7HH+fnghTXx4Lxube0rxdURa+jHBJhwviBup4yq+2bXVxwghJ7glAGECV/huPTdayfi3PCqN4vwccYHEG53+zCNp+dwO0h7iqKfhztIHOwc9P36LGwfqZ+OFx1ARR6tXCbO8GckFQC8IeJ2NikPOlRdn9TJPbRDbE/K2R9SrjyNOuhhO7RHXJ6rut4uubk+57I/pEe2rZfOgpVyLjg4ItMijS4yNg/ZoT13aBBCE68DL4ZR2NdBz00C9MXBDnaKbdSvuzN4qQDiMqV4659CAfg8zk3hO9qqPBTGB7vXP3SV2gfzeSqr1J9LmOPO1PdqD/XfAcVntIvpbw4iRxoGdu8r4mE/FGud2jKF7R2U/Tp4+A9FLVzdjiE9Uj+uMIYSFVmeFDB8isTDNChswOREXq0ZGxz2RKmv5kVA8N9UmXEgjEyIY0hn0/eGQkgIEvf2AD0HiTh9gU9DABgSID2zfV31d2BygPZ9os3RPdF+jf2Fs6HsPaf6jtsrYxxikauXct2fQvEQEgYgaqZPv+kTng95ab0Nqe9gwa53GPZzoPCQluhl9mOgEjuLjKoosX+0OcR8PVzIgQf7JMHg+6EP3u8I1kNg76zaf3ugerRV+ic6WIpcjRQwfMrlvAHndjXfNm4f1TjAACY3G9jvvMEf+xS/AUJPqzvP2xrDX+LxIlBxvpQei6Etb2S3cyZ40fnSpnut40tFA9vHa1Hk0UsBw6dQ4oDy2oOoz7X993g+nAAODB4/R1s4QMgMifYvt5V5LKF/FLaPAMc2kQWOwrbRMRKvgTM72o3qPvvGog/R1hidUxw7Oo7W1S/s4F7n6HQqcjVyZWBYVdXXS/rvur/7kv7Ltm3/n27d1yrlQv+9tm3/zlX16WkWH2RMxFSF5Xh2pTyoAcOhLA4AwsvWoxYScD1UWdvtdACRh5ewnE+c/D3aLWOgc1SLG9vHwZEUwQhw8XpFFdtVcQc2B8Noq121NhTakM6CeOxDkUcvV8kMPy7pP2rb9jNVVX1A0vdL+qJu3dcqFYH4+1VVbbVtu39eI0UevTgLjCxJ6qtsPrB9cDookh/sBR3qsG8EjyG1MoKhV8CO4mwWp4+XAaPdeM7eB2d1PgXokBrP9niihxw/fi785oXi54CDhbYdyFlfgHD5cmVg2LatTxXwLyW9aP+9nF6scl5kicLAI63OPaM+0KOx30EMEPFAbabp9Mo1HhdIu1Htju1yrKieDm3vMXpSDoz2UBZnY5FNYjP00vxxilFKmnkKXjQxyNpz+ybtRHskv/3/sS4G2SKPXh6XzfAbJf2U/f+IpI9K+pG2bR8+ni49XQKoEWoSwZDBiVc5DmSpDzy06SEisVhBZEbY5cbqA2xse6ZkVyGrJDoqyBYh3Y42vKKOAyEl+Ukl5Bp4gLWH0TgL9P65s0jKLBgHzFj9EBoPAmfgOaAOOUriTHlFlidXDoZVVX2pEhj+cZa1bfshSR+6YJ8PS/pqSZqsxskXi7xVYYC5ehxDPTxn2PdzLyzpfBc5JaQ+2DmDQuJseg60Pp2nt+XxjOQsw9xw4hDTyHIq2cD8vIhstDXSLwcqB3icIn4+I2WGSRiN1GeHCHZa3w/1iGK3RR6dfPzjH1dVVQe26CNt235QWjIYVlX1zZK+qfv7pyTdlfSDkj7Q1Ua8lHSd/aAkvf2ZZ1rt7j7qrj6VEtPron2L3w6IzsbcNjhUkcXj5YYeNJwW7lTwtnCksMyzQtxrTP99LmXAC0A9srY2lB5EKec931M/5tHVeT/PWIgC9T+Cs3uOKQrrQh+dTbpTykG8yKOTl156Sffv3x+sx7pUMOymDGDagHcpqcMfbNv2N5Z53CKXFw9vQWL8HQPVJ30HDPFEx5i4+pzlABgz1gEIgIKr7djwvPJ2DOUZcsDUytVtPLBbynbLkVJ4izNEAM6Byx03MNCY27xQZnIAJcUghuI1Xdz84O0dqajI+61XxgAAEvxJREFUVy1XqSb/NUnPSvrfqqqSpEXbtn/4Co9f5ByJtr7oGOEh8Wo1gM9cuYYhQAKbcsYZPaIRJCkM6+AMmxsCQFelx6EdF2yhyEK5fiFq+ZZSdZqhLBi3CXp/aZu+AbZuG4T5cs18xkG2b2y/GK5UQPBq5Sq9yX9R0l+8quMVuZxEg/15QIj4gGVqUHc4wKKoAeisM4bT0E6lnGrnZbia8Imxe66qy9qbK6u0c/UL0DZKQHhPWZV1YF5TDjJ3myp2POIDYbHRxoqzhxeDvwiGXjjS2Yo1BQQfj5QMlKdc3FMqZW/rtlK+LqDi3lVqCQI8gGH0BGM7k/pAiAyFrgAoXrwgAqLbLb3AAvY/WOWq7ePHWCjVYXSVmEnqAb3WtnWwlLI6HIvfDjmNYuC19xsni59XAcTHJwUMn2Jx+587HLaVJkHfUgY+AHGIDbnjwNt2tZXBzncMLJb6DApAjECJKuvHoP2pbe/qqW8bGRnHcBXW4xA94DoCmwdkO0DHjBTagiXiNaaKTWzDr2ORq5MChkUGvcQOkLA7z8hA4gPkg1/KzEfh29PwPFQFwLzIcdAMrI8McKjd6BhytgnDi4DkYHseWEU7K9vHc477xeOXIOvHKwUMn3KJA5/0HwcsApPjXCFRxXZb44l9IouMAOR2tMjmHPR8W5eh2EDpLBi6uuuMzCe+Qk3FDnnc/ffCrh4gXVubqL/u9Bli0bRLuuDMto/B10WuTgoYFpGUB1+rNEClfiqZx+DFgUqmigcfS2dV3cisImNkGwDJi7U6CEfAiMHiQ84fP6aflzNdMll2lHOFF8r1DX1mvChRTaY9P4afJ32Yh/2LPD4pYFhEUl+9dMYEy/FS+r69G/4dCHBGDAGhM8ihfGcHMrczDh3HQcWZ35DtjnPxia44Lh7wHUl3uv9s6xW1/aWAuCPH1XFS/rx/0Suugf2KPB4pYFikJz5gXa0d8uy6msxARs1eUb/iRrSdRWdDBMTIsgCbobmd3RETbYND9sVoq3QV1+dWbpSzWshAgbHCnsdKjqZ1Wx/V/WPlMBumVHAWG+2RBRAfjxQwLHKuxJARB0dnXKSbuYqMk6IO+zsAYV+LbbI/drsN235V2avNXCLO1ByMaBf1NrLPcfgtZTse9k5+k9FCNgxgOlGe3N5Dcrh+PleLp98Ru8i8LN7/Io9HChgW6Yk7Gvx7yHHhnlnEA5Y988MZJ/+j82WoL4At85QAKGSRDDknvL3orAFUI6Nle0KIAENXv9eUi7/C7obA16csJTTJUwr9WABjAcHHLwUMi5wRBxJnbFH9ZHBL2dvc2H+fTN7tg/PQloPikBq+qcy+UMVrJTCE9cXYQ/+4E8Zzjbe79T69KJ5dmBtAFq+Plx2TzhZVYDKsffWdTj7Jlhdj8JdQkccjBQyLvKFE0HJ1ELtdnLAdRoa66wAa7ZJSLvpwor7qW6ufAyz11c6hsvvubeY3nl0/J2Qon1ndeVIWzL3tUi4PRirhTDlVz6+Jg/VIeU5oL3YbTQRFHo8UMCxyrkS10728nrnBZ2j/LWUm5qAyNAGUF0TwfGbAykGST8xNpp9eXxCbo28DW2R7VGAXKvbEijRUrgE0YY8zayfaAQHJOK9MkSdHChgWuVBq+4alYVdjjmFUQakfVoJndl39wqsxvk62PBZJYNmxpANlNZZjwQwdyDwFkO04lntwCR2ilBehMM5AvXzXsfI80K5Kz60tZrzza+be5fPYaZHHLwUMi1xKXE0kD3im7NF1VRAwxM4XWVcMp3GWJiWG6BMkOZA4iMaMj6HwGfeCR1CcheV+njDSuF623o/FMRbKXm9n0kPe8iJPlhQwLHIpifa+oU+0LcKqSH/ztuJ+/F5RDmHxNmM4jx9D6jtvvFLOEAjF9lBfx2G7yDA9he+83GN38PBd1OHrIQUMi1xaFuFb6oeVEHM3Ucri2FEORp6prwJHlgf4MIkTqiziQdIb3TGorNNIeqikRjvweBtRjYfFobJ7NR7vo+cOk5rnTNYdQ1JOS2y67yOdDf0pgdVPphQwLHIpiewKZoZzApDaVp5j5Fa3/ZDDIAZKu5oc54s9UgZD2OCGshqOdxknDlkerqLy7WExDsrUQKSaTSz6QB/3bXuPG4ShrlobCvvzXYDwyZQChkXetAACHnwsJUC4pRwXuKrsXDhQv0pL9LR6MYhj9TM1PDd4qEgDoOZxgd5HKTtH/v/2zj3GrqKO459vl7plKU9ZSaGQlvASTEQsb4wNEFLjC7AoIaKtEYLyNgaEP4zEGCAawVAREh6tChGUBrSaCgoFlWepbZdSRGQLNjYKQZPSUpp2f/4xM72zZ8/dF/fec+/290lO7pw5c86Z2bv73ZnfzO83uUjmvca05CUP3Jr36JLoTSrcm9tK088irR/Mh/WUpJ32wsXQGTP5pEL+C5R6bWmYuIXa8DX1qNL+x6l8LmzFqNH5rGxK53a8tNRma3xPvvdIXr8eapuyk91fHBK/Q23YW4zZWNzGNO89pvJlwS1c/DoHF0NnXEwq+UzrBNOau62E8PobCbPOaSFy6k2l4WXxl7DMCyUtn0kTK3kcwjSrvSV7dhKjVKckovmmU3l8QrLzvEdXFLMkjPmGV7n9c3NWtui54rQ3LobOe6YoGqnXlIIQJBHM4xIWZ1lz3958eUsihcnP7Y/pSDbFssg6yf83HwLXm2FOw/WUl38W3e2KdSyuofRZ5M7DxdBpCEnEcjezzYQeWzG6TC40+RKZ1PNLvbY05Ew9yBQZ5514fRO1Xmi+Qx+F+9IkT+ol5mJYXKKTrhd9p2HwZEse97FoS8zPXRA7BxdD5z1TtKNBTczSpEjRKyUPiFrc5S7ZG4sbNKWFzCmqzEbKg6fmApTSZV4hZHlpI/sehgpY6hXmYpgvtM73inYR7FxcDJ2GMFD4zHtN+eLrYtis4oRFMYR/IrdJpg3n08x02bKVoiDm4lWsdyqbXPLy8sXgEHl98niIeWxGpzNxMXQaRrE3lEQiX4aTRCOfAEkCmCY+koim4W0PQwPHpriCMHhoWuz55RMwFJ5RrHcKyJDPWCcvkjQzvEvh/nzRuYthZ+Ni6DSFXCRgcCgtGDoLW1zLN4XgltdNbc0i1CZQuqkNabeUPKesF1r0QU52wmSLzOML5ktvkndMWsxd9iwXwc7HxdBpKvVmZYvD6pTOxbOb2rrFbmoLq1PPMNka86UySZhyYS2zERbXR6b3pxno5K9ctqlVvTY6nY2LodM0yuxz6bM4pC7O8CYf5eJMb7ItpsmO3O6YR5XOF0MXbYr5+sZ0LgZPxOR2y3yGvKxdzsTAxdBpCWUzvLlnSTpPQpNc67qoDY1z+2Ii2SF7GOpVki+1gaGRdJLLX+4xku9bkupRb4bamVi4GDotpWyhdU4+G5yGqWnyYhuDfZXz3lsSxdydL7cl5j7KifTsfAOnYk+wbDjvTExcDJ1KyIe3ZRSX2kBNtJJQFSPL5F4suf2xbFhbtoFT/mwXwZ0PF0OnUoazvyV737ZCuTKbIwz+Zc79mtO1PNgCDBVEHwrv3LgYOm1NmR9zGWWiWgy7lfccR3qns/PhYui0PfXEqZ69Mb+v3gSNC55TxMXQ6WiGG2b7EhhnLPjvi+M4Di6GjuM4gIuh4zgO4GLoOI4DuBg6juMALoaO4ziAi6HjOA7gYug4jgO4GDqO4wAuho7jOICLoeM4DlCBGEo6VtJ2SXOzvCslrZD0hVbXx3EcB1oshpK6gBuB32d5U4FjgeOA81pZH8dxnESre4aXAg8A/8nyFD+txXVxHMfZQctCeEk6ADgLOJXQEwTAzDZK6gOWA98f6TmTu7s5/JhjmlZPx3EmLquHudbKeIY3A1eb2XZJgy6Y2fXA9fVulPQz4GyA7u5uVk+f3sx6DqK/v5+ZM2e27H2twtvVeUzUtrWyXX19fUjalGUtNrPzAWTWvNGppIuBC+LpntSGxPsCm4ELzezBplWgAUjaZGa7VV2PRuPt6jwmatvapV1NFcO6L5UWAkvM7Fctf/kYaZcvqtF4uzqPidq2dmmXrzN0HMehoj1QzGxeFe8dJ4urrkCT8HZ1HhO1bW3RrkqGyY7jOO2GD5Mdx3FwMXQcxwFcDIcgqUvSXyUtiedHSXpK0iJJHfnzknSgpMckrZW0RtLlMX9/SY9Keii6RXYckuZI+pukVyR9K+Z1xHcmaYqkZyWtit/LdTF/oaR+SSvjcXTM31PSb7Ly87NntZV//1jbFq/NjnlrJD2e5Z8b23ZFUyttZn5kB/AN4F7C0h+AO4FegivhnKrrN842TQOOiendgZeBI4EbgKOATwMXVV3PcbSrC/gHcDDwPmBVbFdHfGeEdbdTY3oy8AxwArAQmFtS/lrgxpjuBd6K7Z4af2d3AR6qul3jbNtewIvAQfH8A9m1B+N3/Yv0zGYcbftfswokTQc+CdyRZXcR/KYHqC0a7yjMbIOZrYjpjcBa4ABC2wbo3LYdB7xiZq+a2VbCH8tn6ZDvzAJvx9PJ8RhuRtOA3RVcuKYSxHAbbejfP462nUfwBnk93l8vfkHTvk8Xw8HcDFxF+CNK/Aj4LXAi8HAVlWokkmYAHyH8p14A3A5cBPy8ulqNmwOAf2bn62Nex3xn0SyzkhC85BEzeyZe+p6k1ZJuktQd8xYAHwT+BfQBl5vZQPwHl/z772txE+oyxrYdBuwtaZmk5yV9KXvUYkLblse2Noequ9PtcgCfAm6N6dnEYfJEOgi9ieeBs6uuS4Pacw5wR3Z+PnBL1fUaZ1v2Ah4DPkQwawjoBhYB345l5gI3xWuHAP3AHlXXvUFtWwA8DexGcNf9O3BYK+vpPcMaJwOfkbSOMNw6VVIn9pZKkTSZED7tHjNri0WuDWA9cGB2Pp3Qa+o4zOx/wDKCjXODBd4F7iaYAwDmE4aSZmavEMTwiEoqPAZG2bb1wFIz22RmbwJPAB9uZT1dDCNmdo2ZTTezGcC5wKNm9sWKq9UQoo3pTmCtmf2w6vo0kOeAQyXNlPQ+wvf264rrNGok9UraK6Z3BU4HXpI0LeYJOBN4Id7yOnBavLYfcDjwaqvrPRrG0baHgI9J2kVSD3A8wbbdMipxx3NazsmEIWRftOEAXGtmv6uwTu8ZM9sm6RJC5PQu4C4zW1NxtcbCNGBRjAA/CbjfzJbE5U69hOHkSoJNF+C7wMIY/1OEkHhvVlHxUTCmtpnZWklLCSEHBwjmjxfqPLspuDue4zgOPkx2HMcBXAwdx3EAF0PHcRzAxdBxHAdwMXQcxwFcDB3HcQAXQ6cOkvaTdK+kV6Ov6FOSzhrhnhmSxrU2TNI8Sftn53dIOnKU985OIdeahaQn4+cMSeeN4/55khY0vmZOo3AxdIYQvQMeBJ4ws4PN7KME745mblg9D9ghhmb2VTN7sYnvGxNmdlJMziBEWHEmGC6GThmnAlvN7LaUYWavmdktsKN39KcYcHOFpJOKDxiujKSrJPXFwJ83SJoLzALuicE9d43RS2bF8nPiM1ZJ+uNoGyHpNIVAvX2S7koRUiStk3RdfGafpCNifq+kR2L+7ZJek7RvvJbCUd1AcBtbqRBQdVCPT9ISSbNjer6kl2Og0pOzMr2SHpD0XDx2XHMqpOqIFn603wFcBtw0zPUeYEpMH0oIrQSh1/TCCGU+ATwJ9MTzfeLnMmBW9o5lBIHsJYTpmpmXL9RnNoUoQ8CUeN9h8fynwBUxvQ64NKa/Tox8Q4icck1MzyHEz9s3nr9d9i5Cj3ZBdr4klplG8CXuJQRg/UsqRwjEekpMH0TwGa/8e9/ZD/dNdkZE0o+BUwi9xWMJgToXKIRs306IRVekXpnTgbvNbDOAmb01wutPIAzX+0dZPnE40G9mL8fzRcDFhJiVUNue8nng7Jg+BTgrvmeppP+O8l1lHA8sM7M3ACTdx+CfwZHBGgHAHpJ2t2bG6nNGxMXQKWMN8Ll0YmYXx+Hi8ph1JfBvQoilScCWkmfUKyPGFpF5rOXz+4bj3fi5ndrfwXiiKG9jsLlpSpauV+9JwIlm9s443uc0CbcZOmU8CkyR9LUsrydL7wlsMLMBQjScrpJn1CvzMPCVGKYJSfvE/I2E/VmKPAV8XNLMQvmReAmYIemQeH4+8Pgw5QH+DHw+vucMYO+SMsV6rgOOljRJ0oHU4vM9A8yW9H6FWJLnZPc8DFySTpRtiuRUh4uhMwQLxqwzCSLUL+lZwjDz6ljkVuDLkp4mDP02lTymtIyZLSXEHFwew4l9M5ZfCNyWJlCyurwBXAgslrSK+mHtT5O0Ph2ErQ3mA7+MIa8GgNvq3Ju4DjhD0gqCbXMDQfxyVgPb4mTOlQRbYD8h7P4PgLTXzAbgOwQx/0PKj1wGzFIIff8itRBdToV4CC/HicTZ5u0W4iSeCPzEzLzXtpPgNkPHqXEQcL/CXstbgQsqro/TQrxn6DiOg9sMHcdxABdDx3EcwMXQcRwHcDF0HMcBXAwdx3EA+D8BAAj+fcTn1AAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "images[\"excess\"].smooth(3).plot(vmax=2);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Source Detection\n", "\n", "Use the class [gammapy.detect.TSMapEstimator](..\/api/gammapy.detect.TSMapEstimator.rst) and [gammapy.detect.find_peaks](..\/api/gammapy.detect.find_peaks.rst) to detect sources on the images:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "kernel = Gaussian2DKernel(1, mode=\"oversample\").array\n", "plt.imshow(kernel);" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys(['ts', 'sqrt_ts', 'flux', 'flux_err', 'flux_ul', 'niter'])\n", "CPU times: user 1.03 s, sys: 137 ms, total: 1.17 s\n", "Wall time: 9.96 s\n" ] } ], "source": [ "%%time\n", "ts_image_estimator = TSMapEstimator()\n", "images_ts = ts_image_estimator.run(images, kernel)\n", "print(images_ts.keys())" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=2\n", "\n", "\n", "\n", "\n", "\n", "\n", "
valuexyradec
degdeg
float32int64int64float64float64
21.387252197266.42400-29.00490
10.282207204266.82019-28.16314
" ], "text/plain": [ "\n", " value x y ra dec \n", " deg deg \n", "float32 int64 int64 float64 float64 \n", "------- ----- ----- --------- ---------\n", " 21.387 252 197 266.42400 -29.00490\n", " 10.282 207 204 266.82019 -28.16314" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sources = find_peaks(images_ts[\"sqrt_ts\"], threshold=8)\n", "sources" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "source_pos = SkyCoord(sources[\"ra\"], sources[\"dec\"])\n", "source_pos" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot sources on top of significance sky image\n", "images_ts[\"sqrt_ts\"].plot(add_cbar=True)\n", "\n", "plt.gca().scatter(\n", " source_pos.ra.deg,\n", " source_pos.dec.deg,\n", " transform=plt.gca().get_transform(\"icrs\"),\n", " color=\"none\",\n", " edgecolor=\"white\",\n", " marker=\"o\",\n", " s=200,\n", " lw=1.5,\n", ");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Spatial analysis\n", "\n", "See other notebooks for how to run a 3D cube or 2D image based analysis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Spectrum\n", "\n", "We'll run a spectral analysis using the classical reflected regions background estimation method,\n", "and using the on-off (often called WSTAT) likelihood function.\n", "\n", "### Extraction\n", "\n", "The first step is to \"extract\" the spectrum, i.e. 1-dimensional counts and exposure and background vectors, as well as an energy dispersion matrix from the data and IRFs." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 5.6 s, sys: 206 ms, total: 5.81 s\n", "Wall time: 2.94 s\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%%time\n", "bkg_estimator = ReflectedRegionsBackgroundEstimator(\n", " observations=observations,\n", " on_region=on_region,\n", " exclusion_mask=exclusion_mask,\n", ")\n", "bkg_estimator.run()\n", "bkg_estimate = bkg_estimator.result\n", "bkg_estimator.plot();" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.33 s, sys: 10.3 ms, total: 1.34 s\n", "Wall time: 773 ms\n" ] } ], "source": [ "%%time\n", "extract = SpectrumExtraction(\n", " observations=observations, bkg_estimate=bkg_estimate\n", ")\n", "extract.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Model fit\n", "\n", "The next step is to fit a spectral model, using all data (i.e. a \"global\" fit, using all energies)." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OptimizeResult\n", "\n", "\tbackend : minuit\n", "\tmethod : minuit\n", "\tsuccess : True\n", "\tmessage : Optimization terminated successfully.\n", "\tnfev : 75\n", "\ttotal stat : 224.78\n", "\n", "CPU times: user 785 ms, sys: 4.12 ms, total: 789 ms\n", "Wall time: 399 ms\n" ] } ], "source": [ "%%time\n", "model = models.PowerLaw(\n", " index=2, amplitude=1e-11 * u.Unit(\"cm-2 s-1 TeV-1\"), reference=1 * u.TeV\n", ")\n", "\n", "for dataset in extract.spectrum_observations:\n", " dataset.model = model\n", "\n", "fit = Fit(extract.spectrum_observations)\n", "result = fit.run()\n", "print(result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Spectral points\n", "\n", "Finally, let's compute spectral points. The method used is to first choose an energy binning, and then to do a 1-dim likelihood fit / profile to compute the flux and flux error." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "# Flux points are computed on stacked observation\n", "from gammapy.spectrum import SpectrumDatasetOnOffStacker\n", "\n", "stacker = SpectrumDatasetOnOffStacker(extract.spectrum_observations)\n", "stacked_obs = stacker.run()\n", "\n", "print(stacked_obs)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "Table length=4\n", "
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e_refe_mine_maxref_dnderef_fluxref_efluxref_e2dndenormloglikenorm_errcounts [1]norm_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)
float64float64float64float64float64float64float64float64float64float64int64float64float64float64float64float64float64float64float64float64float64float64float64
1.5651.0002.4481.112e-121.637e-122.442e-122.722e-120.9242.4080.191370.2020.1801.3497.49456.1640.200 .. 5.00028.183813660262814 .. 151.16742430364361.027e-121.499e-122.122e-132.243e-132.006e-13
3.8312.4485.9951.516e-135.467e-131.996e-122.226e-120.8462.0960.205230.2190.1911.3146.95648.3850.200 .. 5.00020.95673034885535 .. 120.809431163008181.283e-131.992e-133.105e-143.322e-142.895e-14
10.0005.99516.6811.794e-141.958e-131.840e-121.794e-120.55412.8550.23980.2670.2141.1483.52912.4540.200 .. 5.00016.23702428487214 .. 86.253178901348589.938e-152.059e-144.293e-154.781e-153.830e-15
26.10216.68140.8422.122e-155.211e-141.297e-121.445e-120.4744.8590.37420.4680.2891.6122.2655.1280.200 .. 5.0005.72235477846527 .. 29.952304201669291.006e-153.421e-157.931e-169.923e-166.142e-16
" ], "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", " 1.565 1.000 2.448 ... 2.122e-13 2.243e-13 2.006e-13\n", " 3.831 2.448 5.995 ... 3.105e-14 3.322e-14 2.895e-14\n", " 10.000 5.995 16.681 ... 4.293e-15 4.781e-15 3.830e-15\n", " 26.102 16.681 40.842 ... 7.931e-16 9.923e-16 6.142e-16" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ebounds = EnergyBounds.equal_log_spacing(1, 40, 4, unit=u.TeV)\n", "\n", "stacked_obs.model = model\n", "\n", "fpe = FluxPointsEstimator(datasets=[dataset], e_edges=ebounds)\n", "flux_points = fpe.run()\n", "flux_points.table_formatted" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot\n", "\n", "Let's plot the spectral model and points. You could do it directly, but there is a helper class.\n", "Note that a spectral uncertainty band, a \"butterfly\" is drawn, but it is very thin, i.e. barely visible." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "model.parameters.covariance = result.parameters.covariance\n", "flux_points_dataset = FluxPointsDataset(data=flux_points, model=model)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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UO5TKmJjIXl650usvlGL0cNDNkE4Q9m+XDmwP9kWnX85aONmCMC+ygNOsBfVt0o+u25CujXD8NUkCimNmm929v5hzW7pFwN03StrY399/Vb1jAVpKKpW9/sH42OSqjce/Hq3N+IO0jlnSwucFW9TEWDjdMVoPYbu05YeZlQ07u6eOQ+hdJc1dVtmqirlkrNsQk3Vxp3RrAos7oTQtnQgAjaSvt6veIVRfW7vUNmfqYkZjI9kThGpOb4xLtU9+wOuVk/vdpcPPTJ3uODQgPRqtqtgh9fRljkHoXRVUaYwPyKyWQos7ZUx/jNZJIElAbjkTATO7r4jr97r7+RWMB2hZjTYmoKbS6y909Uzuq9f0xjgzqXthsPW9LPPYsUORqorhtu8JaVusquLsJVmKJq2aWlK6mvK2JLTFpj9GXrO4U+Ll+xfQJul38hw3SRsqGw6AxMg3vTFaVrna5ZXzmTFHWvLCYIsaH5EO7IzUQwgHLe6+L3Oa4Yy5U8cg9K6S5iyp4nTHLPIt7sQKkImX72/5ancfzHexmf1RheMBkHSptqCfPj6qvx7llXNp65TmnxRsUT4RTHfcH21FGJS2/Y909KbM63tXZCYH88JuhrYaDwgsmCTECiilux1YAbJl5EwE3P0nhS4u5hwAqIhs5ZXdg2JE8fEHWYv/1IClgqqIc5ZJK8/OPHb0wOQshnSCsOch6fHbdXysRPr6bGszxMdd1IKPB6s/5loBcsoy0SQJzShvu4+ZnSvp7ZJeJWmZpCOSHpB0k6T/cPcDeS4HgOoykzq6gi3aw9CI0xu7eqSlZwRb1NjRyHTHSJKwcyBWVXF+lm6GlWFVxTpMdyy0THS8ymJ6bAJJQsPJN1jw+5J2SbpR0t9L2iOpS9LzJL1W0o1m9kl3Z5wAgMYyremNR+rTvZDW3iUtWB1sURPj0qHdmWMQhgeDgkkjkQ/fjpnhYk2xokk9fbWZ7phNoSQhowUhMjaBJKEu8v0r+QN3fzq271lJvwi3fzGzhVWLDAAqLef0xjqs3lhIqi0YM9CzXFr1isn97tKRfbEEYbu0+17psVsnz7O2zOmO0VaEeIJUSxNj2ZeIlsJlorMt7tTFMtFVlG+MwNOSZGbdko64+4SZPU/SqZK+7+6jWRIFAGg+2VZvbMTuBSnoBpi1INhOODPz2MjhzO6F9NfBTZlFnboXxeohhAnCzPn1raqYXiY6m3SSEC3FnC6qRJJQlmLaje6S9CozmyfpR5IGJF0u6W3VDAwA6qqZuhfSOmdJi08NtqiJMengzsyBisOD0qPfD5Kb49fPDmcvrMxMEOYsq3+zfTpJyNaakOrIPruBJKEoxSQC5u6HzexKSZ9x9382s19WOzAAaEjN1L2Qlmqf7B6Icpee2xsruzwo7fhZkCSktXVIPStiZZfDLd6SUg/Hk4Qsx9o6s89uIEk4rqhEIJw98DZJV07jOgBIjmzdC+5Tk4NG6F5IM5NmLw625bH1adJVFaNll59+VNp6V6T1w6Q5S2PTHcPX0SqS9TQ+Emx5k4Qssxvq2UVSY8V8oP+ppA9I+o67P2hmJ0u6vbphAUALMGvcxZkKyVVVcexY2M0wmDkOYdcvM8tDd/VMtkLMiyziNHtxbasq5nM8STg09Vg8SYjObmixJKFgIuDudykYJ5B+/4Sk91czKABoafkWZxo93JjdC2ntM6T5JwdblE9Ih57KTA72D0pb75R+fTByfVfQzRAdg5Ce7ljrqor5FEoSck2BbMIkgSZ+AGgU6cWZohq9eyHNUsFSzXOXSSvPyTx2ZDhzDMLwoPTk/UFNhIzrT8gcg5BOFjpn1/Z7KSSdJExhkTEIkSmQ6aShQZMEEgEAaGTT6l5okNkLcTN7g23ZizP3jx4JqirGl4DecXfmIlOzFmRJEFYF+xvqw9WD8tZZS1xblhaExkgSSAQAoBk14+yFuI6Z0sJTgi1qYkw69GRkHEK4RsOWWzOrFXZ0R5KDSDfD3BPqV1Uxp0iSMCVPyJYkRAYwVjlJKLTWwG9LWi7pR+6+LbL/D939uqpGBgCYvmacvRCXap+sqqjzJve7S4efmVowaedmacstmdfP7Zs6ULF35dRlrxtCgSTheFdDdZKEfGsN/IOkVyooJ/w3Zvav7v6Z8PD7JJEIAEicy9dtkiRdf/W5dY5kGlqhe0EKvo/uhcHW99LMYyPPZVZV3D8o7d8qDf4k8/uZvSRzkGK6q6Grt8G6GdJ8ssJloSQhOoBxGvK1CFwk6Ux3HzOzayX9p5md7O5/FjwdQLGa6kMDydEK3Qtpnd3S4hcEW9T4aGS6Y6Ql4dc3Bd9X2ow5U5d+7l0VJA71rqqYU+4k4Zy+tqKbPvIlAu3uPiZJ7j5sZhdJWm9m/yWpgeZ4AAAqqpnWXiikrUOad2KwRfmE9OyeSBdDuG3fJD1yc+T6Tql3RZbVHZc3RlXFHKbTuJEvEXjczF7t7ndKkruPS7rSzD4u6U1lRQgAaC5Fr70QJgmN2r2QZqmgKuKcpdKKszOPHT0Q62bYLu19RHriDk22iliwBsO8SBdDehGneAtLg8uXCFyWbae7f8jMPleleAAAzSRv98LhzPEHWafVNaCuHmnp6cEWNXZMOrAjVnp5ezBYcTzSMjJzXuY4hHRLQvfihhyHkG8Z4uNLUpnZGZJOjJ3/7eqFBQBoaunuhWhP9cRE9sGJ0ZoBjax9hrRgdbBFTYxLzz45dW2Gx2/PXC2xvSt7gjC3L+jCqJOCEy3N7DpJZ0h6UFK6rcdFIgAAmI5UKhjU19mduX98NPv0xkYenBiVags+zOf2SSsjA4PdpSP7Y9Mdt0u778tSVbFv6kDF3pVT/6yqoJiKC+e4+2lVjwQAkExtHeFvxHMn97kHTfHx8QdZS/uW7ppv3y9JWnvp6QXOLIGZNGt+sJ3wksxjo4el4R2TYxDSycL2n2UuPtW9MJIYRGY1zJxfsW6GYhKBTWZ2mrs/VJEnAgBQiJnU0RVsGd0L49lbDxpp5cZidMySFj0/2KImxqSDuyJrM4QJwqO3BMlDWmd3ZoKQbkmYs2za0x2LSQS+qiAZeFLBTEWT5O5+xrSeBABAuVJt0ozZwRY1NpJ9aedm6V5IS7VPjiOIcpcOP505SHF4UNrxc+nRH0Su7wgrMhavmETgOkl/IOl+TY4RAACgcaRXbuzqmdznPrXlYOxoxbsXasJM6l4UbMv7M48dO5RZC2H/oKQHir51MYnAdnffMK2AAQCoN7NgbYH4+gKR7oVDNqhOjQQD9hq99kEuM+ZIS14YbGkfubXoy4tJBH5tZv8paaMiRQzdnVkDAIDmE+leeLp9SbBv2Yubs7RyBRSTCMxUkAD8VmQf0wcBAK2lmNLKo0eaq/ZBEQomAu7+7loEUg3h+ggXrV69uuC5AABMka+08ujhWOtBE5RWziJV6AQz+6qZ9UbezwuLDDU8d9/o7mt6enoKnwwAQLHa2qWuudLsxcHUvUXPl5aeIS16QbDA0eylwcDFtsZdmCitmK6BM9x9OP3G3feb2ZlVjAkAgOaTs/bBxGRBpAasfVBMIpAys3nuvl+SzGx+kdcBAIBcpZUbpPZBMR/o/yLpp2Z2g4Lo3iLp76saFQAAra7Y2gejR6SJ0dz3KTeMQie4+9fMbEDS6xRUFbyUcsMAAFRBrtoH42PZV26swODEnImAmc1292clKfzgn/LhHz0HAABUSVu71DYnKB4UFa99MHpEGj+W/R455GsRuNHM7pV0o6TN7v6cJJnZyZJeq6CL4AuSbpjWEwEAQGXkqH1w/57is4GciYC7n29mvyPpaknnhYMERyU9IukmSe909ydLiRsAAFRJKqVnR4pfGyjvGAF3v1nSzWUHBQAAGlLBgkIAAKB1kQgAAJBgJAIAACRYzkTAzAbM7FNmdqGZddUyKAAAUBv5WgTOkfQdSa+RdKeZ3Wxmf2pmz6tJZAAAoOryTR8ck3RHuMnMlkl6vaSPm9lqST9z9z+qQYwAAKBKil48yN13S7pO0nVmlpJ0btWiAoAGNrT/cL1DACqmpFUE3X1C0v9UOBYAaAo7h4/WOwSgYlhOGEBZLl+3qd4h1NRDuw9KSt73LUnXX01DcCsiEQCAIgztP5zREnD31n2SpL7eLi2fN6teYQFlK5gImNmns+w+IGnA3W+sfEgAmknSfku8fN0m3b11n7atfUO9QwEqopiCQl2SXiJpS7idIWm+pCvN7F+rGBsAAKiyYroGVkt6XTidUGb2OUk/lHSBpPurGBsANKS+XmqsoXUU0yLQJ6k78r5b0gnuPi6p6PWOAaBVMCYAraSYFoF/lnSvmd0hyST9L0n/YGbdkm6rYmwAAKDK8iYCZmYKugFulnSWgkTgb9x9V3jKX1Y3PAAAUE15EwF3dzP7rru/TBIzBAAAaDHFjBH4mZm9vOqRAACAmitmjMBrJb3XzLZJek5B94C7+xnVDAwAAFRfMYnA66seBQAAqIuCXQPuPihphYJaAoOSDhdzHQAAaHwFP9DN7COS/lrSB8JdHZL+o5pBAQCA2ijmN/s3SrpYwfgAhVMH51QzKAAAUBvFJAIj7u6SXJLCQkIAAKAFFJMIfMvM1knqNbOrFFQT/EJ1wwIAALVQcNaAu3/CzC6QdFDS8yV92N1vrXpkAACg6oqZPqjwg58PfwAAWkzOrgEz+16hi4s5BwAANK58LQKvNLMNeY6bpNMqHA8AAKihfInAJUVcP1KpQAAAQO3lTATc/c5aBgIAAGqPUsEAACQYiQAAAAlWzFoDi7Pse351wgEAALVUTIvAf5vZW9JvzOzPJX2neiEBAIBaKaag0GskrTezyyQtkfSwpLOqGRQAAKiNgi0C7r5b0g8knSvpRElfc/dnqxwXAACogYItAmZ2q6Tdkl4kabmk68zsLnf/i2oHBwAAqquYMQKfdfd3uPuwuz8g6RWSDlQ5LgAAamJo/+F6h1BXxaw++N3Y+zFJH6taRAAA1NDO4aP1DqGuiukaOCTJw7edkjokPevuPdUMDADQWC5ft6neIVTcQ7sPSmrN761YxbQIzIm+N7PfE7MGAABNbGj/4YyWgLu37pMk9fV2afm8WfUKqy6KmT6Ywd2/a2bXVCMYAEDjuv7qc+sdQsVdvm6T7t66T9vWvqHeoVTUt95b/LnFdA1cGnmbktSvya4CAADQxIppEbgo8npM0jYVt0QxAAANr6+3q94h1FUxYwTeXYtAAACoh6SNCYjLmQiY2WeUpwvA3d9flYimxnGypA9K6nH3N4f7fk/SGyQtVlDn4Ie1iAUAgFaTr0VgoNybm9l1kn5X0h53f1Fk/4WSPiWpTdIX3X1trnu4+xOSrjSzGyL7vivpu2Y2T9InJJEIAABQgnyJwNfD4kHl+Iqkf5P0tfQOM2uT9FlJF0gaknSPmW1QkBT8Y+z6P3T3PXnu/6HwXgAAoAT5Sgz/PP0i7CaYNne/S9K+2O6zJD3m7k+4+4ikb0q6xN3vd/ffjW1ZkwAL/JOk77v7L3Kcs8bMBsxsYO/evaWEDwBAy8uXCFjk9XkVfGafpB2R90PhvuxBmC0ws89LOtPMPhDu/hNJvynpzWaWdbaku693935371+0aFGFQgcAoLXk6xqoVq0Ay7Iv36DEZyS9N7bv05I+XeG4AKCgViyqg2TLlwicamb3Kfjg/o3wtcL37u5nlPjMIUkrIu+XS9pV4r0AAEAZ8iUCL6jSM++RdIqZnSRpp6QrJL21Ss8CAAB55EwE3H2w3Jub2TckvUbSQjMbkvQRd/+Smb1P0i0KZgpc5+4PlvssAAAwfdNedGg63P33c+y/WdLN1Xw2AAAoLN+sAQAA0OLyJgJmdkb49fTahAMAAGqpUIvAH5rZKZKurEUwlWZmF5nZ+gMHDtQ7FAAAGlLORMDMPhIe/5mklJl9uGZRVYi7b3T3NT09PfUOBQCAhpRv1sBHzezi8Jzb3H1D7cICAAC1UKhr4Gx3/yNJL69FMAAAoLbyJgLu/sHw69/WJhwAAFBLTB8EACDBSAQAAEgwEgEAABIs3/TBNjO72sw+ZmbnxY59qPqhAQCAasvXIrBO0qslPSPp02b2ycixS6saFQAAqIl8icBZ7v5Wd/9XSWdLmm1m3zazGZJVmHuCAAAS6klEQVSsNuGVh8qCAADkly8R6Ey/cPcxd18j6V5JP5Y0u9qBVQKVBQEAyC9fIjBgZhdGd7j730n6sqQTqxkUAACojZyJgLu/3d1/kGX/F929o7phAQCAWsg3a+CvIq8vix37h2oGBQAAaiNf18AVkdcfiB27UAAAoOnlSwQsx+ts7wEAQBPKlwh4jtfZ3gMAgCbUnufYi83soILf/meGrxW+76p6ZAAAoOpyJgLu3lbLQAAAQO2x6BAAAAnW0okAJYYBAMivpRMBSgwDAJBfSycCAAAgPxIBAAASjEQAAIAEIxEAACDBSAQAAEgwEgEAABKMRAAAgAQjEQAAIMFIBAAASDASAQAAEoxEAACABGvpRIBFhwAAyK+lEwEWHQIAIL+WTgQAAEB+JAIAACQYiQAAAAlGIgAAQIKRCAAAkGAkAgAAJBiJAAAACUYiAABAgpEIAACQYCQCAAAkGIkAAAAJRiIAAECCkQgAAJBgJAIAACRYSycCZnaRma0/cOBAvUMBAKAhtXQi4O4b3X1NT09PvUMBAKAhtXQiAAAA8iMRAAAgwUgEAABIMBIBAAASjEQAAIAEIxEAACDBSAQAAEgwEgEAABKMRAAAgAQjEQAAIMFIBAAASLD2egcAAEC9XH/1ufUOoe5oEQAAIMFIBAAASDASAQAAEoxEAACABCMRAAAgwVo6ETCzi8xs/YEDB+odCgAADamlEwF33+jua3p6euodCgAADamlEwEAAJAfiQAAAAlGIgAAQIKZu9c7hqozs72SBusdR4vokdTsoy8b6XuoZSzVelal7lvufUq9vpH+PSRVs/8dNFL86VhWufuiYi5IRCKAyjGz9e6+pt5xlKORvodaxlKtZ1XqvuXep9TrG+nfQ1I1+99BI8VfSix0DWC6NtY7gApopO+hlrFU61mVum+59yn1+kb695BUzf530EjxTzsWWgQAAEiwhmoRMLPrzGyPmT2Q47iZ2afN7DEzu8/MXlrrGAEAaCUNlQhI+oqkC/Mcf72kU8JtjaTP1SAmAABaVkMlAu5+l6R9eU65RNLXPPAzSb1mtqw20QEA0Hra6x3ANPVJ2hF5PxTu2x0/0czWKGg1UHd398tOPfXUmgQIAEC9bd68+elipw82WyJgWfZlHe3o7uslrZek/v5+HxgYqGZcAAA0DDMrunZOQ3UNFGFI0orI++WSdtUpFgAAml6zJQIbJL0jnD1wjqQD7j6lWwAAABSnoboGzOwbkl4jaaGZDUn6iKQOSXL3z0u6WdLvSHpM0mFJ765PpAAAtIaGSgTc/fcLHHdJf1yjcAAAaHnN1jUAAAAqiEQAAIAEIxEAACDBSAQAAEgwEgEAABKMRAAAgAQjEQAAIMFIBAAASDASAQAAEoxEAACABCMRAAAgwUgEAABIMBIBAAASrOESATO70MweMbPHzOyaLMffZWZ7zezecHtPPeIEAKAVNNQyxGbWJumzki6QNCTpHjPb4O4PxU693t3fV/MAAQBoMY3WInCWpMfc/Ql3H5H0TUmX1DkmAABaVqMlAn2SdkTeD4X74t5kZveZ2Q1mtiLbjcxsjZkNmNnA3r17qxErAABNr+REwMzOM7Pu8PXbzeyTZraqzHgsyz6Pvd8o6UR3P0PSbZK+mu1G7r7e3fvdvX/RokVlhgUAQGsqp0Xgc5IOm9mLJf2VpEFJXyszniFJ0d/wl0vaFT3B3Z9x92Ph2y9IelmZzwQAILHKSQTG3N0V9OF/yt0/JWlOmfHcI+kUMzvJzDolXSFpQ/QEM1sWeXuxpIfLfCYAAIlVzqyBQ2b2AUlvl/S/whH/HeUE4+5jZvY+SbdIapN0nbs/aGZ/J2nA3TdIer+ZXSxpTNI+Se8q55kAACSZBb/Ul3Ch2VJJb5V0j7v/t5mtlPQady+3e6Di+vv7fWBgoN5hAABQE2a22d37izm35BYBd39S0icj77er/DECAACghqadCJjZIU0dyS8FI/7d3eeWHRUAAKiJaScC7l7ugEAAANAgyi4xbGaLJXWl34ddBAAAoAmUU1DoYjPbImmrpDslbZP0/QrFBQAAaqCcOgIfk3SOpEfd/SRJ50v6n4pEBQAAaqKcRGDU3Z+RlDKzlLvfLuklFYoLAADUQDljBIbNbLakuyR93cz2KCjyAwAAmkQ5LQKXSDoi6c8k/UDS45IuqkRQAACgNsopKPRc5G3WFQABAEBjKzkRiBUW6lSwzsBzFBQCAKB5lNMikFFYyMx+T9JZZUeEoly+bpMk6fqrz61zJACAZlbOGIEM7v5dSa+r1P0AAED1ldM1cGnkbUpSv7KvQTDd+14o6VMKliH+oruvjR2foWBxo5dJekbS5e6+rdznAtVECw6ARlXO9MHoDIExBZUFLyknGDNrk/RZSRdIGpJ0j5ltcPeHIqddKWm/u682sysk/ZOky8t5LgAASVXOGIF3VzKQ0FmSHnP3JyTJzL6pILmIJgKXSLo2fH2DpH8zM3P3slsjAABImlKWIf6M8nQBuPv7y4inT9KOyPshSWfnOsfdx8zsgKQFkp6OxblG0hpJWrBgga699toywmo8DwwEf0zX7r6lzpGgGPx9AWhUpbQIDIRfz5N0mqTrw/eXSdpcZjyWZV886SjmHLn7eknrJam/v99bLRF4eN0mDe0/rGuvOb/eoaAID4djBK5ljACAGvjoRz9a9LnTTgTc/auSZGbvkvRadx8N339e0g+ne7+YIUkrIu+XS9qV45whM2uX1CNpX5nPbUo7h4/WOwQAQJMrZ7DgCZLmaPJDeHa4rxz3SDrFzE6StFPSFZLeGjtng6R3Stok6c2SflxofMATe587Pmq7VTy0+6Aktdz3JTGyHkDtMKOnvERgraRfmtnt4ftXa3IQX0nCPv/3SbpFwfTB69z9QTP7O0kD7r5B0pck/buZPaYgCbminGc2m6H9hzNaAu7eGuRhfb1dWj5vVr3CQhGG9h+udwgAMEU5swa+bGbf1+Rgvmvc/clyA3L3myXdHNv34cjrowrGIxTt5EXdLZftXb5uk+7euk/b1r6h3qGgSHTlAGhEpcwaONXdf21mLw13pUf5n2BmJ7j7LyoXHpKoFbs76MoB0KhKaRH43wqm5f1LlmMuygzXTF9vV71DQAF05QBodKXMGlgTfn1t5cPBdLTqB0kr/oZJV07rYHAZWk3Jiw6Z2WVmNid8/SEz+7aZnVm50AAAQLWVs/rg37r7ITN7paTflvRVSZ+vTFhA66ErB0AjKmf64Hj49Q2SPufuN5rZteWHhGLQLNl8WrUrB0BzK6dFYKeZrZP0Fkk3h8sDl3M/AABQY+V8cL9FQeGfC919WNJ8SX9ZkagAAEBNlJwIuPthSXskvTLcNSZpSyWCAgAAtVHyGAEz+4ikfknPl/RlSR2S/kPBqoQAIhjTAaBRldM18EZJF0t6TpLcfZeCRYgAAECTKCcRGAlX/XNJMrPuyoQEAI2NBaTQSspJBL4VzhroNbOrJN0m6YuVCQsAGhcLSKGVlLP64CfM7AJJBxWME/iwu99a6v3MbL6k6yWdKGmbpLe4+/4s541Luj98u93dLy71mQDK14oLKeXTygtIFcJYl9ZUTkEhhR/8t0qSmbWZ2dvc/esl3u4aST9y97Vmdk34/q+znHfE3V9S4jMAoCQsINW6kt7VU8oyxHMl/bGkPkkbFCQCf6yghsC9kkpNBC6R9Jrw9Vcl3aHsiQCABpK03xJZQKr1JL2rp5QWgX+XtF/SJknvUZAAdEq6xN3vLSOWJe6+W5LcfbeZLc5xXpeZDSioW7DW3b+b7SQzW6NguWStXLmyjLAAAFJrdockuasnrZRE4GR3P12SzOyLkp6WtNLdDxW60Mxuk7Q0y6EPTuP5K919l5mdLOnHZna/uz8eP8nd10taL0n9/f0+jfsDQF4sINX86OqZVEoiMJp+4e7jZra1mCQgPP83cx0zs6fMbFnYGrBMQdXCbPfYFX59wszukHSmpCmJAABUS9I+KNJasRuoVbt6vvXe4s8tZfrgi83sYLgdknRG+rWZHSzhfmkbJL0zfP1OSTfGTzCzeeHiRjKzhQqqGD5UxjMBAEi0abcIuHtbNQKRtFZBbYIrJW2XdJkkmVm/pPe6+3skvUDSOjObUJDErHV3EgEAQMmS3tVT1vTBSnL3ZySdn2X/gIJBiXL3n0o6vcahAQBaWFK7etLKqSwIAACaXMO0CABAM2jFAXNINloEAABIMBIBAAASjEQAAIAEIxEAACDBSAQAAEgwEgEAABKM6YMAgMRiOigtAgAAJBqJAAAACUYiAABAgpEIAACQYA2TCJjZZWb2oJlNhEsP5zrvQjN7xMweM7NrahkjAACtpmESAUkPSLpU0l25TjCzNkmflfR6SadJ+n0zO6024QEA0HoaZvqguz8sSWaW77SzJD3m7k+E535T0iWSHqp6gAAAtKCGSQSK1CdpR+T9kKSzs51oZmskrQnfHjOzB6ocW1L0SDpQ7yDK1EjfQy1jqdazKnXfcu9T6vWlXLdQ0tMlPAvZNdL/yVI0UvzpWFYVe0FNEwEzu03S0iyHPujuNxZziyz7PNuJ7r5e0vrwuQPunnPcAYpnZuvdfU3hMxtXI30PtYylWs+q1H3LvU+p15dyHT9TKquR/k+WopHiLyWWmiYC7v6bZd5iSNKKyPvlknaVeU9Mz8Z6B1ABjfQ91DKWaj2rUvct9z6lXt9I/x6Sqtn/Dhop/mnHYu5Zf6GuGzO7Q9JfuPtAlmPtkh6VdL6knZLukfRWd3+wwD3J3gFUDD9T0EoaZtaAmb3RzIYknSvpJjO7Jdx/gpndLEnuPibpfZJukfSwpG8VSgJC66sUNoBk4mcKWkbDtQgAAIDaaZgWAQAAUHskAgAAJBiJAAAACUYiAABAgiUyETCzbjP7qpl9wczeVu94ADQ3MzvZzL5kZjfUOxZgulomETCz68xsT7yUcI7VCi+VdIO7XyXp4poHC6DhTednirs/4e5X1idSoDwtkwhI+oqkC6M78qxWuFyTaxaM1zBGAM3jKyr+ZwrQtFomEXD3uyTti+0+vlqhu49ISq9WOKQgGZBa6M8AQOVM82cK0LRa/UMw22qFfZK+LelNZvY5NVaNaACNLevPFDNbYGafl3SmmX2gPqEBpWm2ZYinK+tqhe7+nKR31zoYAE0v18+UZyS9t9bBAJXQ6i0CrFYIoJL4mYKW0+qJwD2STjGzk8ysU9IVkjbUOSYAzYufKWg5LZMImNk3JG2S9HwzGzKzK8tYrRBAwvEzBUnB6oMAACRYy7QIAACA6SMRAAAgwUgEAABIMBIBAAASjEQAAIAEIxEAACDBSASAFmdm42Z2b2S7pvBVtWFmN5jZyWZ2dxjbdjPbG4n1xBzXfdzMPhbb129m94Wvf2RmPdX/DoDmRx0BoMWZ2bPuPrvC92wPi+uUc48XSvq4u78xsu9dkvrd/X1FXPsdd39eZN8nJD3j7v9oZldKWuju/1ROjEAS0CIAJJSZbTOzj5rZL8zsfjM7NdzfbWbXmdk9ZvZLM7sk3P8uM/svM9so6YdmljKz/2tmD5rZ98zsZjN7s5mdb2bfiTznAjP7dpYQ3ibpxiLifL2ZbQrjvN7MusNqfkfN7GXhOSbpMgXLAiu871vL+fMBkoJEAGh9M2NdA5dHjj3t7i+V9DlJfxHu+6CkH7v7yyW9VtL/MbPu8Ni5kt7p7q+TdKmkEyWdLuk94TFJ+rGkF5jZovD9uyV9OUtc50nanC9wM1ss6RpJ54dx3ifpT8PD31BQ6z99r13uvlWS3P1pSXPMrDff/QG0/jLEAKQj7v6SHMfSv6lvVvDBLkm/JeliM0snBl2SVoavb3X3feHrV0r6L3efkPSkmd0uBWvymtm/S3q7mX1ZQYLwjizPXiZpb4HYXyHpNEk/DX7pV6ekn4THviHpTjP7KwUJwTdi1+4NnzFc4BlAopEIAMl2LPw6rsmfBybpTe7+SPREMztb0nPRXXnu+2VJGyUdVZAsZBtPcERBkpGPSfqBu/9B/IC7bzOzXZJeJemNkl4WO6UrfAaAPOgaABB3i6Q/CfvdZWZn5jjvJ5LeFI4VWCLpNekD7r5L0i5JH5L0lRzXPyxpdYFYfirp1WZ2chhLt5mdEjn+DUmflvSwuz+Z3mlmKUkLJe0ocH8g8UgEgNYXHyOwtsD5H5PUIek+M3sgfJ/N/5M0JOkBSesk3S3pQOT41yXtcPeHclx/kyLJQzbu/pSkKyVdb2a/UpAYPC9yyrckvUiTgwTTzpL0E3cfz3d/AEwfBFAGM5vt7s+a2QJJP5d0Xvo3czP7N0m/dPcv5bh2pqTbw2sq+oFtZp+V9C13v7OS9wVaEWMEAJTje+HI/E5JH4skAZsVjCf481wXuvsRM/uIpD5J2ysc1y9JAoDi0CIAAECCMUYAAIAEIxEAACDBSAQAAEgwEgEAABKMRAAAgAT7/ywuPFfgHP+PAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8, 6))\n", "flux_points_dataset.peek();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exercises\n", "\n", "* Re-run the analysis above, varying some analysis parameters, e.g.\n", " * Select a few other observations\n", " * Change the energy band for the map\n", " * Change the spectral model for the fit\n", " * Change the energy binning for the spectral points\n", "* Change the target. Make a sky image and spectrum for your favourite source.\n", " * If you don't know any, the Crab nebula is the \"hello world!\" analysis of gamma-ray astronomy." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "# print('hello world')\n", "# SkyCoord.from_name('crab')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What next?\n", "\n", "* This notebook showed an example of a first CTA analysis with Gammapy, using simulated 1DC data.\n", "* This was part 2 for CTA 1DC turorial, the first part was here: [cta_1dc_introduction.ipynb](cta_1dc_introduction.ipynb)\n", "* Let us know if you have any question or issues!" ] } ], "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": "3.7.0" }, "nbsphinx": { "orphan": true } }, "nbformat": 4, "nbformat_minor": 2 }