\n",
"\n",
"**This is a fixed-text formatted version of a Jupyter notebook**\n",
"\n",
"- Try online [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.14?urlpath=lab/tree/fermi_lat.ipynb)\n",
"- You can contribute with your own notebooks in this\n",
"[GitHub repository](https://github.com/gammapy/gammapy/tree/master/tutorials).\n",
"- **Source files:**\n",
"[fermi_lat.ipynb](../_static/notebooks/fermi_lat.ipynb) |\n",
"[fermi_lat.py](../_static/notebooks/fermi_lat.py)\n",
"
\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fermi-LAT data with Gammapy\n",
"\n",
"## Introduction\n",
"\n",
"This tutorial will show you how to work with Fermi-LAT data with Gammapy. As an example, we will look at the Galactic center region using the high-energy dataset that was used for the 3FHL catalog, in the energy range 10 GeV to 2 TeV.\n",
"\n",
"We note that support for Fermi-LAT data analysis in Gammapy is very limited. For most tasks, we recommend you use \n",
"[Fermipy](http://fermipy.readthedocs.io/), which is based on the [Fermi Science Tools](https://fermi.gsfc.nasa.gov/ssc/data/analysis/software/) (Fermi ST).\n",
"\n",
"Using Gammapy with Fermi-LAT data could be an option for you if you want to do an analysis that is not easily possible with Fermipy and the Fermi Science Tools. For example a joint likelihood fit of Fermi-LAT data with data e.g. from H.E.S.S., MAGIC, VERITAS or some other instrument, or analysis of Fermi-LAT data with a complex spatial or spectral model that is not available in Fermipy or the Fermi ST.\n",
"\n",
"Besides Gammapy, you might want to look at are [Sherpa](http://cxc.harvard.edu/sherpa/) or [3ML](https://threeml.readthedocs.io/). Or just using Python to roll your own analyis using several existing analysis packages. E.g. it it possible to use Fermipy and the Fermi ST to evaluate the likelihood on Fermi-LAT data, and Gammapy to evaluate it e.g. for IACT data, and to do a joint likelihood fit using e.g. [iminuit](http://iminuit.readthedocs.io/) or [emcee](http://dfm.io/emcee).\n",
"\n",
"To use Fermi-LAT data with Gammapy, you first have to use the Fermi ST to prepare an event list (using ``gtselect`` and ``gtmktime``, exposure cube (using ``gtexpcube2`` and PSF (using ``gtpsf``). You can then use [gammapy.data.EventList](..\/api/gammapy.data.EventList.rst), [gammapy.maps](..\/maps/index.rst) and the [gammapy.irf.EnergyDependentTablePSF](..\/api/gammapy.irf.EnergyDependentTablePSF.rst) to read the Fermi-LAT maps and PSF, i.e. support for these high-level analysis products from the Fermi ST is built in. To do a 3D map analyis, you can use Fit for Fermi-LAT data in the same way that it's use for IACT data. This is illustrated in this notebook. A 1D region-based spectral analysis is also possible, this will be illustrated in a future tutorial.\n",
"\n",
"## Setup\n",
"\n",
"**IMPORTANT**: For this notebook you have to get the prepared ``3fhl`` dataset provided in your $GAMMAPY_DATA.\n",
"\n",
"Note that the ``3fhl`` dataset is high-energy only, ranging from 10 GeV to 2 TeV."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"fermi_3fhl_events_selected.fits.gz\r\n",
"fermi_3fhl_exposure_cube_hpx.fits.gz\r\n",
"fermi_3fhl_psf_gc.fits.gz\r\n",
"gll_iem_v06_cutout.fits\r\n",
"iso_P8R2_SOURCE_V6_v06.txt\r\n"
]
}
],
"source": [
"# Check that you have the prepared Fermi-LAT dataset\n",
"# We will use diffuse models from here\n",
"!ls -1 $GAMMAPY_DATA/fermi_3fhl"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from astropy import units as u\n",
"from astropy.coordinates import SkyCoord\n",
"from gammapy.data import EventList\n",
"from gammapy.irf import EnergyDependentTablePSF, EnergyDispersion\n",
"from gammapy.maps import Map, MapAxis, WcsNDMap, WcsGeom\n",
"from gammapy.modeling.models import (\n",
" TemplateSpectralModel,\n",
" PowerLawSpectralModel,\n",
" PointSpatialModel,\n",
" ConstantSpatialModel,\n",
" SkyModel,\n",
" SkyDiffuseCube,\n",
" SkyModels,\n",
")\n",
"from gammapy.cube import MapDataset, PSFKernel, MapEvaluator\n",
"from gammapy.modeling import Fit"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Events\n",
"\n",
"To load up the Fermi-LAT event list, use the [gammapy.data.EventList](..\/api/gammapy.data.EventList.rst) class:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EventList info:\n",
"- Number of events: 697317\n",
"- Median energy: 1.59e+04 MeV\n",
"\n"
]
}
],
"source": [
"events = EventList.read(\n",
" \"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_events_selected.fits.gz\"\n",
")\n",
"print(events)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The event data is stored in a [astropy.table.Table](http://docs.astropy.org/en/stable/api/astropy.table.Table.html) object. In case of the Fermi-LAT event list this contains all the additional information on positon, zenith angle, earth azimuth angle, event class, event type etc."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['ENERGY',\n",
" 'RA',\n",
" 'DEC',\n",
" 'L',\n",
" 'B',\n",
" 'THETA',\n",
" 'PHI',\n",
" 'ZENITH_ANGLE',\n",
" 'EARTH_AZIMUTH_ANGLE',\n",
" 'TIME',\n",
" 'EVENT_ID',\n",
" 'RUN_ID',\n",
" 'RECON_VERSION',\n",
" 'CALIB_VERSION',\n",
" 'EVENT_CLASS',\n",
" 'EVENT_TYPE',\n",
" 'CONVERSION_TYPE',\n",
" 'LIVETIME',\n",
" 'DIFRSP0',\n",
" 'DIFRSP1',\n",
" 'DIFRSP2',\n",
" 'DIFRSP3',\n",
" 'DIFRSP4']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events.table.colnames"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Table length=5\n",
"
\n",
"
ENERGY
RA
DEC
\n",
"
MeV
deg
deg
\n",
"
float32
float32
float32
\n",
"
12856.5205
139.64438
-9.93702
\n",
"
14773.319
177.04454
60.55275
\n",
"
23273.527
110.21325
37.002018
\n",
"
41866.125
334.85287
17.577398
\n",
"
42463.074
316.86676
48.152477
\n",
"
"
],
"text/plain": [
"
\n",
" ENERGY RA DEC \n",
" MeV deg deg \n",
" float32 float32 float32 \n",
"---------- --------- ---------\n",
"12856.5205 139.64438 -9.93702\n",
" 14773.319 177.04454 60.55275\n",
" 23273.527 110.21325 37.002018\n",
" 41866.125 334.85287 17.577398\n",
" 42463.074 316.86676 48.152477"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events.table[:5][[\"ENERGY\", \"RA\", \"DEC\"]]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2008-08-04 15:49:26.782\n",
"2015-07-30 11:00:41.226\n"
]
}
],
"source": [
"print(events.time[0].iso)\n",
"print(events.time[-1].iso)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"mean = 28905.451171875 MeV\n",
"std = 61051.7421875 MeV\n",
"min = 10000.03125 MeV\n",
"max = 1998482.75 MeV\n",
"n_bad = 0\n",
"length = 697317\n"
]
}
],
"source": [
"energy = events.energy\n",
"energy.info(\"stats\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As a short analysis example we will count the number of events above a certain minimum energy: "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Events above 10 GeV: 697317\n",
"Events above 100 GeV: 23628\n",
"Events above 1000 GeV: 544\n"
]
}
],
"source": [
"for e_min in [10, 100, 1000] * u.GeV:\n",
" n = (events.energy > e_min).sum()\n",
" print(\"Events above {0:4.0f}: {1:5.0f}\".format(e_min, n))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Counts\n",
"\n",
"Let us start to prepare things for an 3D map analysis of the Galactic center region with Gammapy. The first thing we do is to define the map geometry. We chose a TAN projection centered on position ``(glon, glat) = (0, 0)`` with pixel size 0.1 deg, and four energy bins."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"gc_pos = SkyCoord(0, 0, unit=\"deg\", frame=\"galactic\")\n",
"energy_axis = MapAxis.from_edges(\n",
" [10, 30, 100, 300, 2000], name=\"energy\", unit=\"GeV\", interp=\"log\"\n",
")\n",
"counts = Map.create(\n",
" skydir=gc_pos,\n",
" npix=(100, 80),\n",
" proj=\"TAN\",\n",
" coordsys=\"GAL\",\n",
" binsz=0.1,\n",
" axes=[energy_axis],\n",
" dtype=float,\n",
")\n",
"# We put this call into the same Jupyter cell as the Map.create\n",
"# because otherwise we could accidentally fill the counts\n",
"# multiple times when executing the ``fill_by_coord`` multiple times.\n",
"counts.fill_by_coord({\"skycoord\": events.radec, \"energy\": events.energy})"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"MapAxis\n",
"\n",
"\tname : energy \n",
"\tunit : 'GeV' \n",
"\tnbins : 4 \n",
"\tnode type : edges \n",
"\tedges min : 1.0e+01 GeV\n",
"\tedges max : 2.0e+03 GeV\n",
"\tinterp : log "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"counts.geom.axes[0]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"counts.sum_over_axes().smooth(2).plot(stretch=\"sqrt\", vmax=30);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exposure\n",
"\n",
"The Fermi-LAT datatset contains the energy-dependent exposure for the whole sky as a HEALPix map computed with ``gtexpcube2``. This format is supported by ``gammapy.maps`` directly.\n",
"\n",
"Interpolating the exposure cube from the Fermi ST to get an exposure cube matching the spatial geometry and energy axis defined above with Gammapy is easy. The only point to watch out for is how exactly you want the energy axis and binning handled.\n",
"\n",
"Below we just use the default behaviour, which is linear interpolation in energy on the original exposure cube. Probably log interpolation would be better, but it doesn't matter much here, because the energy binning is fine. Finally, we just copy the counts map geometry, which contains an energy axis with `node_type=\"edges\"`. This is non-ideal for exposure cubes, but again, acceptable because exposure doesn't vary much from bin to bin, so the exact way interpolation occurs in later use of that exposure cube doesn't matter a lot. Of course you could define any energy axis for your exposure cube that you like."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"HpxGeom\n",
"\n",
"\taxes : ['skycoord', 'energy']\n",
"\tshape : (49152, 18)\n",
"\tndim : 3\n",
"\tnside : 64\n",
"\tnested : False\n",
"\tcoordsys : CEL\n",
"\tprojection : HPX\n",
"\tcenter : 0.0 deg, 0.0 deg\n",
"\n",
"MapAxis\n",
"\n",
"\tname : energy \n",
"\tunit : 'MeV' \n",
"\tnbins : 18 \n",
"\tnode type : center \n",
"\tcenter min : 1.0e+04 MeV\n",
"\tcenter max : 2.0e+06 MeV\n",
"\tinterp : log \n",
"\n"
]
}
],
"source": [
"exposure_hpx = Map.read(\n",
" \"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_exposure_cube_hpx.fits.gz\"\n",
")\n",
"# Unit is not stored in the file, set it manually\n",
"exposure_hpx.unit = \"cm2 s\"\n",
"print(exposure_hpx.geom)\n",
"print(exposure_hpx.geom.axes[0])"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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9Nmnbzj2N1iAGWWA4EQMYrsf7rNUh1+dILNXiHmSU/UwA/ihuSxsBw6YQ3l/CNk0w8XCgvacXIusAcu+u00uORm2TgHjxaDj6X61TDyYLF9ctntttdzsEV+qukTmjBSYkBRk0U5khisc4QmVvr/Z9VKA9VFXAsy7KsQNe3t7G9gHgNz6yQtMA3/X5BXyBBXTxq9+9wqYjX5tNYrEEWNrlZU5WLi7a/VWVh19eArfbBlXVtG1u+zZWlxepYV5dJ+13igETWHtG/Ega8i7XhIEEzGpXVznQAKnhXgSgxQbnwNV0Dfri0bDbyrybq7bhcr8Hge6fIKmNfnvTlkO7xTzGfx1xZ7mvrpC5RqmNvWC2N8DFVQJTTaODUf1L7joH13feQT+LUAeseByQ5+qgjiGI63EH39I1OaMFkAmsA/baMdue5QrTffpu2me6Ls2xacd4n1214No0qa6vA6C93rbxeEkKvPs98A/+7ArbLfCv/trrDb6vLej+3e9oWcdZnVce2lraCQEWEHJap/3Dvo1DYFbWW1UAdlucX1Z5I6WkwEhXz3IwBjqNV4FXNF8eV6ClVEG7umr3dYIGrTmm4wAytyKgLc/lG/lNYaPV8OaQRr4Jtm+/nW5oD8gSj+EcxLl6lkCAAOzAwhcEy73dAhcGPHqNBEUf6a/WnfzyaOjip/c5AtftNgdIBdZe5lmll6Tn22v3wTHa9rplvxvpNTgYqw8ukK6D+xzc1Wf0VAH3kNJJnOPVtk+mv3UFXG0TISGw7vc5rq8rYCvSw34PHCxuVQGf+84VDg3w537z9QTf1w50P/utqYt3tkkgeVa3FQNoGa+C7F585Mlmd7tAOVAgth5mXTcAmn5/RY8EjbR7S3xxOcBxnVzNqnXaJ2Oi0/qlscvLN9rGub0GtgLM3m1Vx3iP8/bbQ5DlOYHEhjW+MuGepQr7zfI+Wj6HlnnxHA60ml5fCgRXv66Q+Xeyz9sYHqckQ2PPpJd7+FBXOXDSzU/zcGkpSqfHsjLISzc7ZrJD3zuQ+62A6yxYkVRnpHVhBEYGH5rEcPf7llxoNlXVym9HyUaBGEhpgLwdHfbAr3xbKzt83++8XuD7WoHuL39LC7gERw4Sn22AW5t8pG2BzJVtm4DL8ZN1B8wOxPyvqtat9+IiDz+rrocJttfAh75KQPfZkNESWD/w5Sns6dNcosjm3F8nUI60QgIdz5E17I6hqjzhjIrgeHUtYQH4suzKfr0rzHTb6yHQ9iDcDNnflQFUdMwBTgHVj1er9EJTeScD2+4Fzgq1Fa2XjJf7UbrBs7dnpuXRiQlAy1wBDCY+AOk+VVXe61DphgjY/d90GEyg7ZN0v9t9vt8Ds7FdZbdHOY2DtKpkv/wtK3z/P3p9gPe1AN2/+Q1tJV93V6uYstkk8K2qdp8VpWepe+AoIFvv8/ZyrBLgOhhrPkp+VhVws2vw6KLJwgAA/88XgT/1p/LGSu0za4hXSX4AEnhSplB7+m4rTTjo6v7Tpzm4ZwyK+u15nE6ZJ+M7WJL1qruTM+Cnz0RXtnM9lYkCV1fxgJkeB3K5pX+RiWsfy0W93MFOwXMwONql3wo4V1Wu7QLlgbesTM+GYSybm8sLwJAFA8CTLyFz+1IZpts+Cogqs/XfYZ8DKON7mEsOVdc2tGjKeJnuF7+xZb0/9IVXH3xfedD9G1+f2C27Ore7lt2e1Tk7paxA4Dw07YDa3kDW9zcdU64FrKuqWyNKALUKmPBh36YftLf/922cXVR5A2TjjgbFMi03aKiXj1q6rZowMOyuElwvA2bbNDnwKXNi2stHw0b+//1Rl+cb+TEHYDpA9/m9K/G5YltXLgX1KZb7+I02vjNdILHON/+4/VcNnf894zWGym0F4vpM0glI+yAbz11VqXdQciuLmDBtZ7o2wyh3qK4rqEhwBdogB8/oRwBVJqust2naet4EwHu7zwfZdrucFTPuL/zpFX74n7zawPvKgu5/988k7ZYPWa2qWsBb1zkjpYTAer43IAVykkVmzLbjEoL+r62tatmYn7qG7vcNNptmiBXbHVYXmzxsdxMzv96dqGN3b75V1nCj7ZLkQHv8OO+6utO+/j95ku973k+f5ZqkSyIK+pRT/Dp4s3Sm4JXFU6CLNFxnujymeQ9021UuRThIK/hmssN1CmeY/tP2pn8BuQdEBrq3+TPqkPG42+tu+JiiH+Vf3l4WRcNVlnAAB3IZgm1R4+llfuZrW9b7l/7pqwm+ryTo/vWvSoC7roa9LpcQ9k0Ongq8t/uUh7ax/X6YBmix7WwTSwvKqoFchnj6FL3rGuPcIlXQAZ52B3QiR+ij6hcPxJ4JEag2TfJwcFcubnN6aJSXgqszLqD1EIjyRNeAn7w7yOrscXuTbp7scN5Nlb15Ek3lFXvydkhygSFppf/pusqf3dlljdurfS4h9exWRlDp4RABrQMxl14sabz+5qft5fmo+eQKuXnHfTMAV24fRBrQ402TpAFNs6oSiLosYaftmSzB2vO53eVplCD99a9a4T/641cPeF8p0P3UV6apuwTOo13hSt7UCpoEV00PdIMBTQJI7ZJxgE3js3JFzNUbOP8PwXHfVzasttslsAaQO0eiBeWjtc2mAbBt121YX9RF8O0HVK52Ka7GUYCU5IPsrhJ4XlwMG6eutaL/7hzRNACetmV5dgW8cbUb4A7v2bMrDGxd5T0Rms44LP1X2wR8CWObbn+H620C6tZHu8nBWX86ANCfRBiwXpRXmt1NYsXNod1uDkOh1LZL7DYCTAdcNU/v5oAbDcopsJaAmuk+/cGW9X78T14d8H1lQPeTj1Z9YyhViKoC0LSNo64T4PaVcdM+cAXfpmPBFRL40r0sAlem1fnpBEXKD1l5ZFvZ7sqAAUjsWk3d2yIrNZyzfjmEPc43+THf7jW8bnaSA6f7ObOMUYNSgN1uh3G1wbnzQtMkZq8TV64NYEusluf0DkHUo+cLVV1zKwFtJaEZOAsTjkB4t2vrXlU1WS8pzSSuhgXyC+LqYU0DVE3cT++MHgT+XPU+KwByBqa/qNXIclmErGi7mP1qHTk0OVvWojcF1vzTb6zwo89eDeB96UH3r5237PZQqCRe2aLGwmME2APi8LNNHsbeJJDAlcDM8pxvchLiKzMByGbEuQShcQ9IDYRpFHAdkKOXTxM0tHe7tEW1wcBXVQEFYB7Xa9R8efxGGqDqgoxzXgDtQ5Mc9G+2CYAdIPSFdWMslkYwjQYxCTwuMRA0naiWwTb/7fe5EhHFBVqAzkBYb4LsH/fDB1wiHDzmXXxnuGf10H0SSINeCoj6ftjtyy9/rWs6COfnB5LUEZWPbf0TNy83+L7UoPuT66TdNk0aGFO73bcVqapShVfgUiDmMQVPptUZNVyzhayYb3wCuYKog7sDLTBwmcxA49yAlGmjCq5hrKxnkj6UGTrjy+NsM5QLon9uK5NWgFXpQBvdZjPd8G62w/N4/n7917mq0psOXtL8eSvIqq5+iyGrBaYB9hZtXeNxlaJYH+quXh27vHUWo5aTEkYC//wham/Ny6n3UEmJ43h0rwl8zpI1/X6feyWw96jni8b/uFol81Iwdo2XZdDy/9WzFf7K7csLvC8t6P7EqgVcb1PNvg1jxajrVCEdAHe7BNLaVerjNcOGoG9hNjCfRBZV+qpK51oHZQFyICZY3hi4MK2aVkgHaX/B6HVE2+zyz2W9CtAKwKV/lyLIbCPGo/m7jDHQsgvWfxOCjCzwyuL2rR3Xl597uDjAAgk4tc5di4RwqBLg8rgyaQd55sd7EXm/6D1zcNR6cdjHcbXOq+QQnQdI03wJxusqfT5T25kyYrfIu2FdpR6EAr1PtgBaQvQTqxV+8vhyAu9LCbr/2WqVrayqazdx+WvWFbKMwy41CLVm1zaGdT3s1gLDRsqGrt1ExokaaV2nNFyh7Fjn8ZVN03yChppfg9pVAPiR1q0N0pmt68BR+qjx8gUxF0T7dMJSnWmV9OEpHdst0sjdw0S3IxmKUoSvraHa7E2Tg7L2lvgjOClTVpbooN7LE026Dr2f0bVF4OkgSBap92Mv7NafxaFJ+bC8TZPrxnptSn44BhIBsb+snbxk04y7NAe0OPBfvYTA+9KB7o+uVj2gNmgv4Ij8YylrADt2edA+/F5n7Z5abQ0BaEFDG5MCK7o8WLFcYtvI58CUgSlDU21P0x4KAFsC1whUnJ16fF+ESvMuASQwBGSVb8ZkiJJ0MJASdnFcl4KitGPAy+N+T7VrH3mN6IAoX6guDURare5r3eKkHA9X8OUx94bxeApI2YvCrlvrp987j+Pde8+7KdQFBdCVvTQIxNpWFIiZPpocAQw1YH48gGSKvZcjWjz46ZcMeF8a0P2R1Qo10sfE6dLukhHjZNYA58ZO9C2vOqw2RK0wDCMrVnMgdRckbqvHhBsbpFqkh5WA2OM6QAJDKUDjjrFZ3ebou4dn2ttVy+70uM9i8rT89wHCSNMbG1mP7o/3RFTDpXlXHxiyXCCXiKoquRoqoPJcBFAeS54LMcutqjTdXHVgLVcEwFE9icyZK82n/2ocfX6rCj2x4LkjgOX1V8jBWPPVe0V5wutkr/MSzP16unQ/ulrhAOBnXhLwfSlA9+Odfstn1yAHVl0ZlYBcI11chfbB6bRcH0Tr2UqXhg9aGxTjq++u623MT3U7IIFHFYA2j7tFAOINrATC3nUssRdnF3OkBIIggdV9LoFppss0vH9n9RBgXRd02WcMYKMX22BNjToHSZoCKvf5LOvu/0bY7bFKg6r63I91Wm9Zz6Xp/EWclQ2pnmkZ9D5E0ojeq6heKZD6c+K2AijvQzQQ61KCA7qWY12lgUOVLChPaBu73ecrlNH2SODLfxKxj69W+NRLALwPHnQ/1r3F1BRkK6Q35UqONUhfs6L0cI7EAL1iOKPp668x3mglMSA1MJ2B43lz39luXbeNS21VAfvt0P/XTYE1YqC+Xddxg1RWwcWrGV4ahDkU5lUoIGqakq67rhNYjjnN+zXy2iMXJ7XzwFNEt/156rOr69zDIHqR9XJFlV5GzmyZnumU4bJOqidENtOxSYRhrD6UelG8FiAeAI3qQ2XXGi2EwzRHS9cTmSbP5xA9y30bL8q7B2EMP7zEU+4s7GOrFT79wIH3wYOutqcVWh3ngKTjHpC+iMVjamv536MD0z0GV676mdqhySdMAHEXz5mSNuiImUQMRI8PXueFJzXmrhPFUW+IqOGtZeKHhkfX4CtKqUWjztqYuN80MVNWyUPzLA3GeO9BWdJVAEb98p77cZlBGZgCsKbXPAlCHEBjufW6I//fwz5JCyrNKGg3DYCuzg0kNJR91dUitlpiw7xueih4/770YqdRjnCJQdMcpG7qS7QH+S7uGm0ZdCANaNtzg/JnPh+iPWjQ/YudrLDGcLDAv7uqYWdIMsMtYp2X7o4HxIMktGaXKrkzG42ny0MekEBAAVud4xkXyLWsOZptFEdn/yqbBXJZQMEsmqmk5dKwiBUD8UCbf1nA8/AXj0sOjFMC9eh+RY3a71l0XVHXnizUeytaRxRI1YNAAZpl5fNgmMoKej4gSWE6kBfpzbyUnZRfe1pqY/7Z2osrvUBVhvCXl58vki5ovXtZNUzjMgR7fg0SyWqQQLb77nUfh6RqD+CjqxU+84DZ7oMFXQIukDSbI1KB9XlWSICrcgOBdy9xCL77rttSAYO1FZyFALkv5qFJLmg8NlZx6Z+prA4YsjP6KvbXVQ0rKFDWLRVI9Lh327g835mBszYgXpvKC3qOiLGXZAX+R2AdgXJdD/15SzpmaVIEkGZXRfdVt/2aHGC1F6ASAJ85QZOyjPYEop4Q81ePBYZTy93t8xXw3CIpoRB1UDcZ5nEiMI7yUqAGUnsrsdmxXt26AlC3UppLIHpN1HIVgNH961gPQfkhA++DBN0f6gCXN7aSf95gQjK/JHXsjlHXbSSdxgFyDwgy3qpJ3TudigoksFRGwnyAHICBeBBNrQ83xhbM6syOl8wZnLIMbezOSg/WcF1SGGO8UQNWza4kT/g9cTZLwHXGFJWpL3fQSPV6uVYNj+nLzmeM9fkI281eHFUOpJF3wbrOZ0ICbV4qRWTMtdM2nTbNC2EAACAASURBVP1CJAed+Vh6aZReTDxWAt1oO7r/JfBkfD2nrlWtaSP/4UOT69t9edH2VBVkneEekGRGSHwA+OHVCr/wAIH3wYHuX5CBMx0U038gFZxaDoGVDFeBdod8kM1ZL61p2nzbrzokJqiVqfSBysxx3fx9nflEDHZKYpgzKl+SFTSds+HomG5r2YFcp63rfN9nIun/fh83LM0TaBlPlpZlRPfCVWYu9+AMga5Hhprvts/B8miafMAMyEEDyGUl1gu6EKr+qjKSfnuPLxSf8MExhtIEHu2N+ap2mo+Df3aOgpXkhWjAzdP4C97jqF9udDwqX8Z+jTmT5QKJ4UbYQBBu0OLJLz4w4H1QoPsDIilUSG+0NdLNZoH7QbHuX8E32vdBNjJhdo023X/TiF62GzrLu84XfaASSOxL00eDcmMAPJg9N8H4tttxPXcuKLsrkeq0ml+JzY4Ba8iCke55g8RggHziC/cZB2jrg8b39t+RyMGxSo6jS9/f26abCYZ07SvpZqluy/tGwFZXOppr+mTWgMgxrEddgVW20Geun43ScH0RjNWTEtiNMdlIAhqzCEjrOicWatGA7lHbIVJ7dnDlY+l7nRKPIP2DqxX+9gMC3gcDuj+wWmWajN5cbZTuzaDeCWsMG9atHK+QgJZx6y4fjYcm9/EFciarXUpgWMEzsOwagnaBo+mcnicQV1Kt0A6iwGA53QGLVHYK5O5gJXcyBADr5aG3gjbQzN0MxoKR33OdZcTjWh/68so/eyvenJhOu500f3krU9IeFutHs+/ya3L5qb3oHIBpKiM0DdolQwMJyuUGulCxcMpCM0kBeR1iXNdtp3TcKCzyOLkL4I4Zr9H1eo8z+EgsWvLE5qCSg9eNRraBVAceEvA+CND9N0fWUuDgmTYOZbh7tCxVG1+N1FgH3UjkwKt5aTxarZW4+9cK76wGyOeSR0y2lgtSZszpjpFF8+vZkL2LV9JPgXY6cPTCABKwlrRd7quzPMvFCSMOrPq/a3IG28i2uwLxud8Cgx4KkL9QNR1lJbIf9XIhprEXpbfUAfogx5sun6ZpwTerQ3UuITRN+mYegH4aMAugy9+S/dFNTOUGd01T64HYrt0n6ujzU0lCn/nY4GbUm3JpIQr3+PpP/dYJRQnYtd2uMOzJMM4ReZ0gFvDW36LFmb/zAID33kH3+zqGCwxvqHYv9PlroZ3dAmlAjcdrxD68asqENQ9tuBEA860dzTLTTwVp49FR/mjEN/RakEagabggCjBkNaWGplKDNxSPT0DXF0IGqgaw+ybv2vFYST4gy3XmQmAUHOrTK8PVZ6+SFOOqP2fvudL9s6fE505A9pc3G7ICcf8490nf5n312XLuKkiQ5vPhs9DnwjrgaZXR9uMDyJ+11/NDk/+rZwowPnvQtx2Mx1YTKxEIlZ/m+Bbry5nPiXUl0nVLL3GgxZvP3jPw3jvo0sfWtTsHU224QhyKpizAWa2ashae90yOQY7pDLe1AXBD9iIn8cE1mk+eiHS3EhsdC3cAjSQDnxU0tqZBprl2216ZdUDS2SGQALVG3gA0H7/PUT4E0HUQ3+sOLFzTEERZH9j8VsBAeqA1cg3cZ72p0d0b8wnmoOmtPOdG4pHZKuC6W5m+mClHRD2rgYYa3As1+vZOyQ0R4AL5GrraO1IGrFIKj2m65zEyXf57D0hf9M569YV+33avoPsRYblA3jCmTBs7G0DU+EqygZp3Q5UdRY3Z0+i2rm7Gwjmouqbreu6tNMw+mwk2CuQVnWG7q3ggjE7qTVN+uWn3Xe+3VmJ9BtrDUJYL5KxVAXtn8Xh+HRxRYIzGYlxqYBhfpvo8mY+zXU23k7Rr5GBP2Yov5/6aeX/FBxpI4Eng2WxakCYb9gkSka8zkAZsdeKExikNmE25l40BbxQnWhUsymNs9uAci9IpP+Xz8XoICXOwZZyPrFb43D2y3XsD3e81wAVi7Q5IjUVBVcHUWbHGc1cyT+MA7wDsYbrN9AQJB+h+TIQNQM/XgfG+6WbNBW8a9Z4A8q6cAmkf36QKHt7vky6uFVDZurIEZX0aX0+nz8qd06fYMONqngqCewujC6CWRctKi8YFODkGyAdHvb7ouRWI2XBZp7QcZL/66M7Q6b7GDoEcZPtyinarQKz/2vPRejwFuG4+acbj67+DuuY5JimULMqn6QgH621pEDB6qQL5846IAGR/LXGAFn8+f0/Ae2+gqzeRFVonL6g5ELJ7SjeviMU6W3PNduCji9x9KDpvlDYCaS3TDjlga5dXW/6uGT4M+qWGssg+B7hSeUraFgBskQ9SskgEpaj77q5ZDpzKdP3cHs9dudjtj8pbYsSwcEj5lQ2p3qvShTJZBWO9Zr48XY5Yy3ZmDbKJL0DSZ6squfapHqteIK4Pq/sYy0+JwD0Y9HxZkQKGym2XrLQ8pXU8NK5ulz7HHhmZeyR1ZGW0dEfkLmQR4z3avtbT5yTfL8zuBXS/x2acuUWFUm2Wxnqt4Bu5ja0svYJvxFAjcGZa8ejp4yqj07g0AnqJjTNNVBbNL3KPUpDzbpWeP8pTzQeqfPTX2b7OhWc6BWw9hw96RYyZ9yMaGHGmQ4bpUoO+uNUY18cMeA4FU2D4XBoJV5BW/dCf+RlEp90n0Ow13iYBK6eVR2sQ64pv4ToRaHtLzCf6vBStNB5Q9Bzo9qOBttLAWmRNIZ+oLCXTtRWmwNPbAs09G75ntcLfvwe2ey+g6+zMWQs1NQ0rMVQyGJUTnHl6enZXS8dU+4u03hI4shI4ILj8wBF7L6em53lodNaHhUXl0xeE5q1AMdYV83DtYnuFZ1wFwjHQdAkjkhD8BaCj1cxPfTZpZ4UwLROQXMe03jmguqfCDeKXUYPksuhGmSGaXRV9647hQMd2tznrHbP++2JNzn4ViF2bjcYAlMn2X2wosGQafbSj5R/HvBSmBvL6MCTA5b5aSWbYW1yXzO6L8Z4cdL9LZp1NsTHXy4AhgLHhkNHedmFcXQzIwY55ODv17THTc+qDdZ3Z40OOR/G0QTuol97HlC9KgwZAPJvHNVatkJG2HrFtvS5nIGMarcsXBDgFR+8+aln95cE8VYJg74Jh6v6n950vLgKx67r+jGHpOSuKLyUu2VEhyTeHfXIfdBmBngtca8F9eTV+NDsymiVJ9st9B2L9wGNkjTzIEgt2vbqUj0sOOsAW/fdeEEg+2hHRKo0RcFtf1FF95v93r1b4tROz3ZODbgS0GuaMN5rowO5gSctV0BrTcXnuMS1Xj7smCORsWMuo6/1GzJlljBgvzxHVadeqIfG8/MeROBoWabh08dKKrEzXBzaic7lG68vzwf4j3dm1XN+Onqkfi16AfC68Lh/0O0POyktlZH07oF3XmffFSWwlrlXOWrOlH7t4BFYCMlcfi645W/AcCWyb0k1jmSR+yTtmzIUMQObHHTFctTkyAtfg1ahRrw2FfT4jB1wnJffJdk8Kuv/yKuqUD29aJDWg+68xnKJbAl+XEqZ0XD3/QcJdx/Uy0lwXjMB0DkjQSm5sWg5Pp5XJtVAFwSkN18HwKP963LcdlJwBu2yhZS15PTj4aR6RpuwvrEgyiPRd1XZ9AJblVQ8G1gu+GG4wrGusS3uh/wTTvnxNmjBRk+k2aaYaYGDdMQGXDtTTQRdf5zmnpgVHoMr9CIh9OrmDLv1yV1We95jN8ePV9gnkzFZXGevzxBBwub1Hi0v/8IRs96Sg64CgYSXzBuTdef6iBqUsqwrCmUfEiNwiH1SaD6ypHSzMWTUQ34NDIRyIWSaZqbJRPa8DGs+r5/HuvpbNy+K6sTJYBdcoDsujcXwJP5cbPC3NXeGAYdm1Du3luPZgWD/cm0EHPB2Aee7Ie0Xlld66Qqz2aXJEI4X3QTCfEJEtaN6BavaiLngC6MSZEqN187BolTmPW2K6BGSfJKETKjLZAYkwEVAZxn96MEQ9LG57e2C4/05tJwPdbzWPBdX3aCUGyXhzb1DEgDtyMAiP0kbdXF8aUo2A7PFdOig9ZGWxY8e1jJ6f/yu787y8opZeADr4FbmM+bZPz1WdVZmjgrvn4/VCGaxfr+YPJObpAKusk8fIUD1MvShU1/X7wDTc3yANdupLQetKTwKCATQWoB98Q+6t4Aud0xqRFvxZhzKDHdd4Hu6L2kdrfEwBrscbKwfNmSztKMf4svN7HaVjPWD9iTwgvnW1wu+ciO2eDHQjwCmFqYYbFdCZcukiyF68O6ppS0wUFq4PVB90xIK0nBErjipGNL3VQTtiT1E1aeSYVy7Xb3luzT9ilQpOBFafRKGsOeoZ7C2OP3/v0fg1+XF3wwOGbmTO+llfdkGYX6uCsE6m0XvOMvC8DgIE4saPCcPr11JoplceA3INl4DJRZSOEuaLJEUeC7rkKE0XMuI+B+Zovj1gt81QclAvhzHTw0pIXBIs9QbVtUzro7PbiLScwk4Guuqi4w2oxG4j5qP/rsNGcfRfB8hKUoKCUaT9ntm+g1NJK2Y5tXurrMjBk/eopFeX7mE0dVeBccptjECv4VEXLmoYjBsBaQlgeQ+ily//KwzP6ywXFq+0z3ReN9RNUZ+pyldAe29ca28w7AWtJF3Exqjx+vKclBQGbl5dJvolCU3rGm6bGNnC7ARPBdTSJ3ZKKDQ2sKaeCmPmQKwfonRpgfXpNgjzcQNfkU5uQ7btdanU8Xi/7CSg+00iLehFu/bmVupuTzWukilLcUmAYHmLMrCyrFNg7KyQb+lo6qpaSfZwUIvWN/AyRoBNdzrNN8rP3XSYf+Rrq9KAn19fDlrGkg5MdjlghcjrTRQW1aUK+bOI9nVsykGX/5Hr4lGO+QsEFl+BOHsmDbAKWj4/3aQfu1QPh/4lIGl6d7Eq31c3MnUF03Q8h+5Hdhf3MQVVBeSDvByYRj9EGZ63fAgNyu2JFr3YI4nhm1Yr/N4JJIaTgG6J5YzdzBJb9QI74DoAjaWP4gGJ7ZVcjjx+6TrWheNRt7g0IOCsNDq3Mnht1BFTde8Evoh0oG3Mw8EBVeNp5XYwjdIoSOm5onz1WI34Onyf5yxpu9G//tir996JXivNwffW4pH5+sI5vtYugQoYMln+94vodMf8+24OuAxz7Zbh+h/ZlPbrvrjRedQyf1wB5mgAjfcqIiQ6zqADsXv79+0SFs0hbi/CTgK67nEwB2wjVqMNSOO6C1mULgJ6L4fmq1qvsuLI1ahU7oPFOQbh0uYG6SJGzeP+Po5Gah0YGFZaPcx9GoFhRXUAjSqrDlyMpXG2WhoIKzEU5j314lVABfJ6URX2I4aM4F+fD4FVBw39paDPZKXhTVqHoC93B0jqOtbfB4nL+hpNA2Y+yi5L04FLYSpbDL5b1gw1YMZz2UE13sE5h0F9uAKxe9tEXjJRHlEd6cs6cv73w07GdPnvjTRqHNExtxJb0fNNsRM/V2NxxhgzkGuDESsu6UvRm9v1SYJ+pJF6BYqYbiQb6OwwIHfbYt6le+ng6C+ZEtCWKrdfE/ejhW0wEubh+qKMeiPRMx2TGJwl+7gWX3ZksQq4ynCdNGj5ztCy1hr5IFnvt4s0wOYfrlRZgR4M6yrpwpEHQ8kH1495WDSVl/qtfvap5MXgeSrLBcrgCsRkjfc8aiPOdmHH/Ycg3vtl7zvofr19+wwY3iCg3D30MGclkH3Yf/SQ9CaPgaqf39mP58d00YCYewp4GMMRxIWFa8Pm+RxMo8pTCpsCwRLo+TaBpJSP7mt98Hvszyd6Sfu+A77WEd9nHJUN9hjWK01bIV8PQ+UN5qVrY/D5cp8A3DNStLPXBu2iEaCtbB9I32cTYHZArZBkhzHXSDX1mKA3g1tpcMwH0yL5wlkugTZbm3ekfLpimPvu7uVY1K40f5cYIqD++tUKf/A+67rvO+iWLlItAq8GqatZYrwRgJbA01lulJc33FI8FOJE5/BrikAZhfDo0UfAVjKPF3WxpoAuiqPpIxCt7FgUpkwyyg/I71upHAzb2370IlbWurc4znZriacgq0zXz+Wyga7pUGGo80bbaBKwAvmCN96rikyBb058IJcHVD6YGmSL2LOuGeGAS/PF1AmWPpCrYMr2MFbf3Rr79/rk9fxUdhLQ5X/pwrzBc9u7iIwbsd3S8QigIlYdnRuF+KVyV3bM0029QOaE3SVuCSRLYRGwzQFd3S4B7H4kfqncpX8vl8dxkI3SeR3SuD54pnKCgrACterv6oHhvbEG+ZRk9ZbhdtUkmUDBTNfTnctiGX/MIhcxBe/IHc0H6fg/NjAXuZaVALX0wuA983oUrYSXnRvDOqjHGDZGzF6UnVzTdXOgGksfgeMYs4WFeZ7RuaNuqh/38o9pwA7e0YukBOoe5m9qDdewKF4pzCtvxC7ds0DTObCV9v3c0XkjoHe7ywtM43i9iM7hZdLGoYN80YAfJ1I44LonDFmwEwOmb5D0XQRAR1PgjQbJfP2FkjdBpPn6+RRMxz7542WIFjTnz+UKnenn6+E2cq0aFkkLjf3marol7Hk/7GTywthxtahRlBik51NiNRHwzGHDY8e1vM6sPG50DRHw+fk9fsTqx/7HwhyAorKVKqQPiPk5HKSjOL7tLmBzGsIcVlIC9KhO+XN0cC3dQ23g9MXlojicqcjBTmViBzlWyTaAgeYJJLbbAyByJlj6AsPYVGDGiUDV8/C4elyNGvHUFyR0AE3B1QHTr5MvrpUd0x5EBLQIwj3OKcD3pKD7flyQNhgHkhJbitI/z/GIYUaDOkD5gUZlBmJAGbuPY2ERY/cylcCpFCcCnShNdB4Pi8o/9vL0ayqZlsV7SXqv9f67D3VJIii9kMh2gRYAND+fcu5LXVawCS8CStHXnll2lyLeixVdupp42+PogjglgPYBtEhaaBCzXyCXEoBcC2b8qD7N+Z3CTs50owsrMdk5N8Eblodr/nOAeA5THWO2c5hrVHa3uWFz4pbAeqxiero528CQ5Xre0blKYDgG5FH5o/RT8TXvaNKK5qUDaH4+vR5dL4D7uj5vxMYUkPvjTc4wVSooSQ0KvqUBsCnm6zZn4gSAcAUyxnEA5mQIYLgQvIIrkINpJCVA/iv5RWA6xoJ9+/2yky9iHlkEjro/ldZZjIarjTXuUr5RGUsPaQ77is7xIgH2vYSNgXMEfqW0JbCdijOW79i229RLdQx8fbtUNxWEfa3lsa+G6H3k4kcVkiuhartM4G5ckVtWheRSFn2eZ8oX1/ObMteJXQop5ZFdB4ZA28ezfZUSeL88HWdWRmDbl2ti/xR2Uu+FuXGBoaZa6l7eBehK6UtxNG7p+F2OTbFizWPOS+H9CBsDtqgilwBW98euZQzox9JEchJm7N8lTmNxovriZQVyVz+VGMaeZ2m1uArDGV9T02wZBygAdJB2Tp6edwSspaUfSzbXCyOagVmy5wHSMaB+0XZypjvFQqL4U8AYATQszPOfAtepsup5o3J4+qluTWRTzK6Ufu4bPmJ0ejw6/xyQ9f1mZvwo/6hsY+UpxZsDtAznvRl7aXoe/nIh2I6t29EgngBDBkxXsqYZTu91uQFI4Oxsd+qLEdEki5JFcUpfDfZ0jcSLnn1p8ST+N0hsVtcLoblnyKAMtn0KgI3s3l3GxtKUwKuS7ShNBLKe59h55qSL4vt1zgX5uz789wK2Glb6L22PAdwYaI/JEqU0YzYWJwJRDY9elDyujDSqS1NEMIrjIAzZ17i+xkb03T1aBLhjNhXvLvruXfXg0bwQA62G+7Z6LvjMQHU1K9XP+wRb2swOxWlsLhO8K3hH/3c979jDKh2besB3rQB3Pc8YEHqcMXYxdnxOXmN5lxpJVO7naTARwEZ5+4uhlGbqOOMoaE4tPRiZAg2Qd9vnygBZmUZunH9ZWJeILMXvy/mc7Lj0cgKG9Sj6QMCUzY3/XurW89qDAl1guiHfhYHp8anGMxfwx2wKlJ/3oU4B/l3CS3HuUkk97RiY34XNv4hnUMrXnwHrxF17OaV6VIpToWVgvkZzlJeCS7aCXZU03TGtVm0OGEb5+DEHYf+PVjWbxb4njuu98IkRmseYN0P0rB8C4D2EMmTmFXZMR5uTnjbFUE51I94LU3uveY2ljfIauydzn8scBj52jhcFvFF5/aUTSVeRlFXZz70XtAustpJ//4KJf1+PedH0G2l9OQIwVBvzeCixWQ8vbes+y6Yvhln6sPxzW5e7pI19MNbj6PPyZ1qq46cG5JOcJ6q4d03/XsDXyxA1trnlGDs2J5/n6SaV0twFFD3sRT346GU2dS/ucj0vopxTeZTYrjdg3/Y8PB7Zmn9Hz78gUtm2n0e/IDHH1lX6RekiII3AduyY5+eMtwTofZkwvFb9lJQvxl8CTH4+Se+3k60S4boPaUHPf7ITTFXaUro5eU015gj8x95+c2xOg36vLx1NW7oHcwFrDuCNpSu9zNQUvKbSzX1RPe8LY6zOjNULvpxLTEgXzvfja8tj7FwR4Gr6EkONWK4vJK7HfL/EkseOl86trHZVAG/GI/sm0K2Rs1t9QUG2x5752GI3Udvw+snfK6PpvtWtTRk1nDHA0HhTYc97EWMNsBRP96fSvIgyRnnMAU4NGwO8EphFceb+l0zzKunJY/XByzJVb8YA18/t+ZbqZlSG6KdAqiDKUXc/v7NiGj/FM8VyCbjKOOdKEaWBtKqSlc2CNFz3d0zf9TTOdN17obSSra+3sB45VhrjmVM/33pVvpGmDa30RpkCrikQGwOAsXxo0WDbHDCdG1evW8Oj8/qxqful8TyvsXNGg11j6UvnKVVsjTdW+T3f0j3xskR5uUX5RaDp+3OA1eM0ADZdmIJojRws+H+GmOX2+QpIjrHNMcB1RjoGvNF2lEc0OYLH1lXuDlca4Ku6G6aL1AA5cOoLaAwKfS0GfeZj9S8aFD6FnQx0pxpUlKYUHgHsGHDMzX8s3Rzgfp7855xzzvnmxBsDqhLAO8BFQDr2EpmKE9WLqKF4HqUylF6eY3VmDuiW5IQ6iK+AS0Z2Jsch4RFj6wGoGuqyJZeu0kBblNbjRftu6jUR+eqGbmFVfpxM94D0eZ8sD/vv80EuO0RrNmgenk+Ur4PwXNx4EXYvTNe3x9KU9t0it5CokXpYCYheBGh6nLnsrGQRwET5ld7qEfBNlWkue55zvFSGuecdOxY1nCnw9TrhAMr9OgjTPBQUFHQVfB24ozL0MkQXSFmhxEKd4SogzhkEi/Iq2boazjiLgHu/HwKrM+N1BRxZzi6tsl1ltdHMMz2mz1zvsy5GFbV9Nwfv99NOArp/dDzia1bzXcTHALB0A8du8Jxj0fG5ksNdjznwlNKWXlJjb+YSSM6JO/VSvOs5EYRHNvdl7AMgc0DW71sEmL4/NkgWHadUAAxnTAH5YFFlcTTP/rgBZgSc/tn1krdBabDMQbakx3JK8aGRcsnxsRXM1nULvA7OtwGKRvLBwY43KHsyqM0BT6/3DVqcOoWdbO2FUuOeSjMXXKf+x7ZL5fTzKzDMyS8CBX+jRuV0cJkq51ybK/HMeT5z4ui1jgH22JKKU/lPHS+9VEug60y2lh9sm8dVhz3rtrmI+aYQZy1xM5Zcx2Cq4KmDa2S1JZCNpAhlyM6MgSFTbgR4tVxT04v9c+1ehgoAmlQHom/MkQH7AKTG0/ilbxC6ad18nrb0XuzkoDvGaiJdz9OPHSulHTs2B4ijvKJ8/AGXbu4csND/5wHJu7L0qAxRhS09o7kvotK5xmxsUGQsTy/TGOgq06yDsAax/KANfy3p9VwRs3VvhQroNc+pQbNSHN13MFZmW9KJ+3tXYTBIVlVA1bTh2XrBAryepgdWswGAI32005+tz0BjmMfTfZUXagA7DOtPRHxOZScD3S8ej/ja1Wr0DTMGlqX4c0B0CpSnzhF17aP0Y6Dvcaceeglox84/FXbXwYLohVh6fiVAvMsLZoqVzAXnqGzRvgJsBJYMc5bLY2Sr7hLmIFzSeDOgJkgVANXZqR7vrys4NsezQbcVrEuzyhQ013X8eXZqu/vSQxUrvdyj7UhymMpP6w//FYwbtPh0Kjvp0o7eeMYaWSmssu3o+FiaqXQlQJlKH9ncYyUgjgBpLM8pxjsH4KJyRnHngOkUU55K53mM1YuS5h3Vi9r+FXBLAAvZVq3WQZW/KY2X8RmmDJYASD/YyFe3pPUyPpd5LA2yTQ2uRdKBu4yhKq/xUFVJu/V86o6KUjqouzx8JTYgv29H5JKDfhZJ/+ear1J2Kjsp6P7B8Yg/vVrdqcFPAWspfAqkx9KV4pXKOCdszErlo0WDQSUgLL3lddtZ61i6EsuPBrVK5dC8dH/OdUzdGwTH/do8H/0BsZeCAmyk59b2AxKIKqNV/dan/VYYgqr/IjAG8jgKpGS2PqlhaqDNl4t0ySD6CkXpyxS0s7oF3ujzPSVTCWGuqdvdbTFWbBWAf3JClgvcwyLmdwGkEuj6filOCXAjAJuTd1Q+BOnveo20EqubAzhjYOvhERCX7sXY/RkDzqmyRcfH4irY+wIznifD3MtAu5Z+vMR0le0SLDVNXQjTvCNm2wNz1YITkM/wmgu4TAeMM2Ju82vCPlDmefnXgfmvXyPm8Vv9MN4MG5MuImswlBWAMrOldBCeG/er5wL3ALpfOB7xDTPcx0qgN7YfsZgojv+XbvwcwJ+KV8p3aqBrhfG3vYNeFYSX0swNnzq3h1XIu2tjIE6Phbkgr8fHyuXbGuYAyuNT0oIC5ZlsE4SjMIJsScetgeLAmXowRANlGu4DZFOeDMAQeCNdVmUKDXMwpmdD5KnAuNGAWl0DVdMCdoUkMehiQLyHc2QDbysRLuyCY//4xCwXuAfQBaaBaQpoPSxyOvc4Ufyx880Fz7F9NQfRaLm6I/Kpj7rtle+IvBHrp7v1ftxFQ/WwOWCscRRMI0YRSRMaNna+Un5TgB29gDVMAdeZsUsHOmimPeYhcgAAIABJREFUrmEKvtGqV+oe1q+EVQ3XLnBGOyURjM1Y0+1IfnC/3LqO9VkCZjTl18t6aJC9dandAtMDaiX9H0j3OZIOPB1nrPH5lRjvfdq9gO7vH4/4pkDbHRssisLqIHwMcCMwnQLqSBecstKnV4AWOEvrg3r+Dk6ejuEHOWcUp3SuSJcdu/cOjHpf5kgMc0DVn1GpKzhW1tIz9B9QnnWmbNc9EWBpIl3XwzyeeipEkkIJcCPgjOJqGjLZEuBqGjdOiqgk3tgUYD9+VykhKxPiF7WCKePMmXrll/h798BygXsCXSBv6NGxUhpNG8WfYjlR/nPy07AxUFWLADb6gkDJtFx+ryKQKtVtlsO/y1UCN89/TJIoleuuA2XRfvTCK4X7NUTPtOR3OyYvRBqv73t4JCXowJmCpLLdaECtL38Qh+F63NP4wJqm4bZP6400X/ayIvcwtRLAroLKMPkpeNmukPf0ShjhpvX0odi9ge7vHo/4lk7bjRiuNxq3CDhLae8KyrpfeoNGQBs18jnm5/BFrkvnHLsHQAKoCOgViClVTJkD35wyMH4p3tighgPq2Etz7BzVyG8KcMdA12dEAbHMwLAK+XqzCpQEX4b31yDxSwNrCuIlndfzze5TFe8rKKpv7hh7vQuzrapY0piTfIzZ+pc3KgzHGv7RPbFc4B5BF2gv/FtkwoQ2xDEwZBzXcudsR2Du8chQ+Gbltn8SJDI9dtcPEk59liRizl5GGoF0bftTQMy4kPi+DQnDSNwImLW76Bqwh2uaKgjX/GoMzxfJCpD4EejynilD9cEwYOgO5lqvygx9vnXrqeBs16UGhrtXAsPnMGEN9/85XxGOpgazLFxPYepjl57WwXyw7ON4kdo8kCQ11W8PciyKr5f7O/cIuMA9gy7Qzk3XBqo2F0ynQLQEvvqvy/DRFJj0RkXrfE4BbIml3kVeYRlK4OeVTtlsNt0Uw4G9SDNWOYLp5oBwBL4OqlGeHg5LU7pOmj4jL2tlx5WxRqCrjBcYB1d9uSmQK0irp8IYe3XXrYjdlrZL0oNv14VWP6XNAt0COJ1GzHOr58Khy1/D+rRyPxh+aO6u+c4BZ31RT7xfTm73Drq/fTzi22x6sLIWYBxUS/vRjXagjdY0jfKaIzF4ulL328N0e8xNTI9pmmiRD1rEIh2ImV6BmCDHeFyYO2LaEWhq+Bj7YJo5Hg8Own5tsHjeE6osjYJjjbwuqJTAZ++SQWXxnBVnHgvW/aeU4LLAHECeYrclYM7ukez7pAgPj8xlhoj1RkyXVtfAbpfHnQu8UzPIXC7zbH/7nlku8ABAF2hvxHeIN8NYYyptR0A7Bsb+sUCNE7E+YBxkNdxZs7uCRTYmLfDNPuUV4WGRVuuSDIGYaQmsfl+98nJK5tGOO6COdfdKA2WQ7ei5RhKEH1eWWiHPx6WFMcDVxcfPJB0kXhXE7cG3jic/jK2zoPquH3NQBsYnUHA7MsaZmvI7BYpzAFP1231hnd0pG4PLNWL3MK1nv/kAABd4IKALtDeEwDsHcKN6FDXE6ALdvYrpNHxsoKwEuCVgZcO8nci31BVyludpIPlG4BUBcgRICoa6ktSqEEfzryRuVP5IYojAuhSmFi1QTXOgLgEuf2SkrAMOogrIlcTRvHTNBcatkSY5OGByFlo0gKaDYCVQLk2IiPRbzatkzNMXKeexKVCco+82TZt/Kd7YKVySUv12hZg4eLrfeCCACzwg0AVa4P2uO67NUNrW/YghRo32iKEuWGr4zHdq8EvTsXF7WR1IR9rHaPe+lC4CbdXFHLD9/jEdWbAz6MqO+3lLjBcWHt3riEFHldbTlq5FQVe9ChRQHUyBIQOGxBlMfAAGGq6Ca6TREkiVFfOYxxtzGfNttamvQxB4x2SDko3FbZo0Vbjo9YChVttgXE6glKB5MFz3f/0BAS7wwEAXaG/Qd3fAOweMpi5AgdHTVxIHyAFD810XtktlGqvbpfjPIz9Ql90E4X5t0Uf73HTGj5Yp8mZwn0n/abzo5VWSIKJy7eVYBMhNcFz/lf0qwBIogaE/rYIpEEsOqvH2kkU19MOtC6yWYWNrLPiA2qEZ5lGSFZwR98sx2k1Wty0H3ruYD5jt97HWO5XvAbGkpvXR6wzj3yIH4l97YIALPEDQBdob9T0GvLSIgSlzUZsCSL94b9Cap2u/fp4p3TaaQ34XluzWoDzRIgJWBVQvL6sl83NwdIarA1mQ/z2mpQgU8vPyK8PZoAzMe9v37Qb5F3p5nQqUPKbAqwNnmAh3wI0GySLgdcAtzSzzOMpEI3Zb+hTPmVR498GlcQYapYZI8y0t59g07bFjU/5qhAMuz0OwjHo2/XktjC5jjaTVSUB//wECLvBAQRdob9j3yMI4JZZK83UKNE4Ut8R+ndVql3kM4HkjfSDKbQxI5xwH4oE6P+75nGNYoZ21Egjdt9fZqcdXIHawdmDVl4XrwnodZ8F1MY2mc5ZPI3DzeMRQ+eLQfWXAY0xXwyPAdZYaDYY5IwZyUI4GyhSwneFGH6osWcmDwSdB7AOfXH54stmnwbbnYrRMWyoj2sEx1iXWI7Y9JTGs8wTv//WBAi7wgEEXaIH3z6xWg69+uvmIvJqzydJ02Ai0gbyBjZkC/F0Bd84kiinvhtKkCdoRwxdK9FJSljkGrs5ytQzu2uZx/BlEssNY+ug+RosD6UBYafALtu/AGmnADtoKuNGAmbJSZ75jnglTg2Qet79vhW230ld91RQ8Ca6HJv6E+lhaTR/JDmStQGKttxgyXkoPXk/IeNcAfvUBAy7wwEEXyNlmNBFhyo1Lu9GMez5yPh/silhtdG61ZuTYXfJRGwN+VkwHT5aFpsDoi+T4gAQBh8zSAZjbem6VGRyAI+P5N5J/1K2klQA3Wn1K46r2Gt0nZbUKrPryiMAaKDNc7eKrruuAWxsol2afcV/D536KR/cjv1xfrhFIM8/GmGxkkfTgC5lHLJigGckHCqpAeo50EyP7nUOOHoI9eND91eMRH1mt+psfNciIMamjulsEipFvqh+HxYmMEwxcmojKG4V7pVPQiiZPRIOADqC8L3zYJWbpoMz0CsCahnm7jFAjB2zNH4i13Ehy8DLp9ek1+EvU7wmvgWWLGGvEboEcsF1yoEuYg666hI0x3AikS+zWB8ymPsFeYrEOys5E+3s8IQ2UjG5h+30M1oO4yEGT2w1iIFbdV3tLZMmff+AsF3gJQBcAPtcB75y3mV8QG2cEsmPpXDeNWGRpOUXG8bLqvgPo3AeheZTqc+SX6+GRN4MCq4Oy9jZKmi/9kMkY15ZPFeTHfGj+8ojAW6/HX25aHh0opJeHvkR820EYsq/arYIqMATQEsAyrh+bs7pYSbMtSQmlqb600spiJW22NJCmTHjKv1eBuNeNkfeQ3HOhQluvHJwZX8P/3ksAuMBLArpAC7wA8K91g2suNYwB5NikBTWPNwa0tIgBT3kxOBiX9OqpF4yDODD0NR4bpIiO+6I3Gn+MIR+RBusU1CPtF1JGlQYj3+GobLQz+/e0ei4CbGmxcQXbEvCWwBUYhnFbJ0i4jgvEg2klfZdWAmbdLzHdSErwfQ6UVcjBOPJIKA2YKcCS/WrcnhEj7ylRr2WYkgD9TLsy4v/5JQFb2ksDurS/dzziz8sEipJDfonZloBMR7fVPH7EqMZMB1ycRXqeHlZaQazUFVfTtM44o3istrXF8xdCBLYKoC5RlGQZ5lNLGSKPBX2B8bqP3blKlVfz1rUTgPx+KAhHjFfL7CuElQCV3xFTButgW5qpBuT/U59dL0kHwDymGwEvkDRYZ7ElgFX2GsVdVUCzz4GY6+tWGMoIvAzqteoOxqm+rDMvG+ACLyHoAsCvHI/4vo7xOkgAOUj46LWaSwgEGE2vnhERAFUWJ7KSxOGmoK/5AnH5o/MRZCIW7zPJvMvv+To79WOelgA65b3gxxRMYWkwElaqvN7r8N6FhkfsVheqiXxoqcGqblsCWgVhlxIi5hvNOIt8cqN/pldTbXZqDVyVCKLvmgExIHteLiOoz6/mAeSSgfrqHiTM6w8Z7995CQEXeElBFwA+ezzi+zvgVb2xRpn9erffma3H88ZaB8fVHLTHbu7cGz8XbL0cUdwSy9U4eo+cnWoenpZMJGLKOqPMBwebwna0fJ/65DrT1Z6vPz8HX32hsRfCsBoxg6WXQUkuGJMRPJ3rvFyFrDS9N5oSTHA9Nvm+m89KcxAuSRF781zgT6UHjUdwdeYc5bOugN0+lhHcbYy9PG7zuf/ySwq4wEsMukB7439wtcoajrPRqC6WXMamBs+mGK37wUZ5AEPw8y7wmPEa66AQYywHyBuE67MRWNLc6wDBv6djGgXKg/yPseg6OMbzq3lPwF0JyZQi8GX8cyQGSuZKMHIZocReS9sR63WZAZgnNdBUcjg0ZRnBpQAf/Brb1skRtAhcFax53EGW0oLnz/LpUqIkTTcsT/e/RgJoAPjbLzHgAi856ALpAfw7q1WmfY759DZA2NX37j1tjG3OYaIOxmNMNQLT7Hg93B9zyyk1vE2VwqLpoRrXV5/ST3brYAiQ9FBg+Cx4afSF3gO4sLgaX70paMz7Ful5OThrj0U9Fgi4nDhRGzBG4OkAfLaZBtIS4+X02rEvQZQ0XYZl1ylM2k3j6kwzlw2qKq1tS/DUdKrR0nYBe9VvoLm0oPk2Vpf85Uvw1XDWh7/1koMt7aUHXdrfOh7xw91i6MBQlFdjWObkLtvR4I3b1MBbdGNDgJaGO/a1VsaNtiO2w0q92QzDdP+izo/xv47CDOB7EC4AtF4Go0Qa/A5DgG0Qa71q5xJXgRZIXWCXFTabIbDu90OvBPeh1S/vanpdiGYMbFX/5dTa6NtmvjBNadCM9rzAq70g/dID0xx4Y5HnQZa63w/Zb1ROBe39XpiyZD/CGQC0z/AXXxHABV4h0AWAX+gezF8U8I0mFOhFKwsChgw4qus+OMNzjMkEFVJ30i1akKRPJ409MgfCzWY4KyiSGbQs0TqqCribzRCAud00OUBrHJbjrB6m1TLWIzIIkDfKi+76Nvs233UFXO0C2UNAzBmkA+blZQpTQFSw5D0oAakCuDNe/202OesF8rg18jIr0xwD2JJngeZT+sBklN41WT8WnSsaQIviqSnLdfv5Vwhsaa8U6NJ+/njER2WQTTVC7V4COchGYFmaclzbv4dHldK7idogSqCqgEdTlpSdux52BWmah6aNwPTQdANeTWLKDqRsUKXjjFMC8igtAGy3aTs6niICjy5SPGeEu10CrouLHByBHFwdRCk1AENdlp+aKUkKFxfDvJ3JahjjsuwlPdeBN7ovHq4DZQ6qznZL5p/W4QpiDq5jEhfvtwJxgzRoxjZZIem5APCZVxBwgVcUdIH0wP6yfI1CB9y4r+ZYVZIQSrPZnMkSZNn1jPRY/R+cRwZaVHIoAW7JGgHPktwwBqZjQMo4CqQOrgTSMcbM4zxvia3rdXD7VnRI3qfLy/a/qoDzC+B2lwOkywQEPwK0Hqf84MyY+Udr4kbgHIEv0MZTECxpubf7ofQU3ScHcxqvU9muPwPNJ5IG/OV9iL6RYxYxZSCXGIAEwj/3ioIt7ZUFXdrPHY/42Go1YLl82CWmSmssLALiBu2gjDYktTE2q2HqffBGBxo6sKEaIBuDAjPQNgwyQK/oJeboUoKH6T/L4OE3u6GMwIZNANzthmB71p1PQXW7be/5mB6t13PRXS/vMxdr4bGqau+Jlt1lgcePc/CN1lXgOUoarUsXPPduN9Rv9Ud2rNfAf3/+PF8fBotXJQljux2yXdVveR6No/fZ2fEcZuwDcR4G5AwXQOaZ8N++4oALvAagCwCf7h7kf9q5l1GTjaYSE5QVO0uMWAFWR79LXXztPjJNFI8gpSB8tsnjrJHAlbaxOH5soKUF7LYkIxA0PJxA/ahp2aSGRYC92w3L4uzp8jJ1aftBJQDncn03u7RPYMu66RdJOz3YwJUC6X6f4p3VicXqc2TczSaWDs4v2rRkohGoRsCrjJZxfMovXyjK5s826V7rfeM943kuL4Grq/w+9/dh33kGmPTAbb1vU9KB5q/PDMg9I4DW86FCPoh2APCp1wBsaa8F6NJ+5njEj8kUYn34Kj2UBtP4H8kIqgNGjNYZKTAESWVJx07/quvEmErpCBhqymi8POqh4McjhhsBaNPkzJFZRGx3jaFbmuqEvDfOpKamsup9IHAC7T0mmBKAXUYgG9SXpcoIymR5Dwiw7q2g+SlQ67U97tI7MNOYXp+Zv4x0ny+cm10eR3sTTZPAXO99ZBG4jhEINWXI/I+WclTGy6m/FYCffo0AF3jNQBcAPtk94E90A2260hXbsA60Ach8Z/0DgkBq0Dr44nZuQKmDP7QLY66M53G0IfhxZcTVLjE0tWhQLWKe2tAVKB0kGefRZT7QEuXPf2Wzfk5eJxvpoRnev3WdM11lu1XV5k+JYL9PDJIvMAVIP8YXCuM583W91+NFkgPQnue8qnDcN20dcS2hqnDGm1fX6eXfZdbX1X3T37NHcp9YbgKvslfq6nodu10+u+yutg/WyfXjNGXqDdre5SdfM7ClvXagS/tr3QP/CXEv43+NfAIAkLMu7aoCQ8bl5mAagescwL3rvr4Yxrwd1EqAy/y1YbFxOxNWDRfI2a6eb123XVi/1+7doGFA+2Kp6/afXWCGR6txqfdC0yBbvEYZsMoOvFYNd+B1NzGVDdYb67ML+q42lkgvMqpA9qBWdXtzea3rOg0UKrvVgbDLS+B6m8sJBGMC72aTvwgZzpeZezLofQYSsPdThavhs2sA/ORrCra01xZ0aawAf/Vs1XfnD4VGr+wIKMsJmm4O4Co7XVveQGJSaqoPjnXDI+BSD4Fs/domz0v1W9V0VefVLwycbXLvBjbSaAIG76WzYgf6233svwy04c52VVZQLwHmo4CpTFdZrrJWDWdalRB6gHURuC/UGmi6YaJqLV2U8/xCHXxLtu+We2FXoIt7dgEcdokBE0BVNnjUgfGhSQRDAfrQABfCig/NcBCs12Y78KUUpt4STYPscz70mPjE7esNtrTXHnRpf6WrEJ98tMqYWzT5QO3x42FeTdNqf1F8ApVaJD2U9kvg61aKR+YGpIdfkgJqpOvXMHZje4AMNGFOL6Ycolpl75zfDM/JbS2vyyHKQDVtVbXAf1aX9VugvCYCdeFoim6v41cV1k57+8jrQvgZ0HSAU61SOAA0q+AmrIc3hcaLB4BNB+jdTVpvKmDXZC+P230uH/C0Kiso3h+Qnq0PgvH+P32aF2ngnSCeGPs98ImbBWzVFtA1+7HrtoJ8+oOrARgBcQ9Q9xVQv0wAOWKrmtYHXiLG6+mjwTlPU/IN1m0FQtrBmIteh4YpABMIIwBV4I3A1plSqfweXgUAyf26TrouXwKqyRIbVW7QvAa6a0Zz10PNAWhBtTrL3xyMDyTWq1bZ0K3GGawc3gCbM2AvcTbrlgE3DdabpPkCyauCVUQlhJKsgA3w7pP2+vWl2zStN4SCsLu6UT+vAHzsrQVsI1tAt2CsMD/3Nat+IMe7x2Sz7G7rIFfETl0bddY7xnjVY4LxIu+EaDtjt/bEfSyHcSLgVNapC+Wwa9q/BICBVFNitdpYo562lz8Kc71WvQP65yFgC7T3nWGax6oOpIL6LI9UrVrmSta6Obc06xTfWS/qPExvSP+GEk2AgKpxm2MLvE3TuQE0bRm7eKsaqGYC73aXF4dSA2UHpmM46+zNNnlOKDOuKuAv/9ECtmO2gO6EsQL9ja9vG5jPmnKgrKp2FJ9WchFjY+dU2zEvBGVhaqoPj4HtmCub5zsGjB43ewkVjhFsNa7qtPoi4gi3z8zS9Gu5FtWZoy84KPvlC8BdwepagDaSCrhfr4eZKkP1m6P5ORNuJG7VeY03xxRPWTIBFWhBte82CNPt05wBuxugqkLg3e3SQjVA3rsgqOpqYXWdT7CgEVypETPOX/qnC9jOsQV0Z9q//wepQv3SN68G7JVtJAJWD4/0VmV5dZ0WQ1FwUqY2xm7ZqAjKPijnjNIJGcNceyaAkr2qBAHkU1l9Gmp2febipOf0+GPWj0nZS0F7Ebx/ZLs5I3aquxaAPbb/jtAcGKvPhjcMSOxXmbAy5abBQGRlWNMxaO3TZ12Qjs1WVdty94eUvjlkWo1/lVflI70cnVChGq56pjBcXQarpgXbH/y9BWjvagvoPof9wO+2Fe1Xvm3Vj9gD+WANkNrA48dtpT/skzsVbe6gWclbIWKvyoAVM7gfTR3eFGQH31ccoOTSywsVekBx5qryiK/Hq3nz5/cxYtmu4+r9UPm1l3G6iP1AmMsGQAuUVdXeEJULsrjqDC0DY1ooL3jEYMO3XXcM9uCYRwS8QGLdPZrus9Pq/VEpZ10Du6vUc9hdxS9g1et3O+DP//YCts9rC+i+B2PF+9x3rnowdeZbC8CdFwCXgHReAFkFLGVu2qAiCcE/1eLpe1DCMIwWNUDHFDZKHuexc+sNaHkVCHoXvTp3SdPy05+XpouBZ9diL6RVhSECq+ZKZusXR6DNJAfRcqtqeMP0PNxX05ujcUMxu9NuNQ213SyNfpQcLQVtGqzqCtW+6U/FhWkoL+i3zgjEdLdTZsv1dzeb1mvhI7+xgO17tQV0X4BpRfz1711lWq+zth6E7c7rxAFluFWFwfoHDOd/lCcHTxRk3egap+kqATxnqE6b1nXa94VrShMeHNAH8qX0rDW+T3pw3FLpheXPbs4csPXj/fZK9u0++ENR0G4OGHgm6DFNpzepB+4OeGlkubxBZLn7fZIXOk2BvrO6pKK+GKnxMjnD1Yuh7mSE7/zcArQv0hbQfcH2XZ9PFfR3vq/tepaAl9tT3gelbbZx/+IAgZbAH0kQ/ToFBM6u4a9kfxV1gaN9WsewCB4rpHjrQKStmgzDBzJBxJA1rssLmYtXpNX2+yvLYAbYagE9HhCDrPvtZhcr8fxC+c/BNYbt9915hOX2/r/rQT4spi9aTiDmqRl+K+5e3/rZBWjfL1tA9300rbi/+wOJASux8YkYtDnbzgLPjSETkPULBQPw1Exdd4yANdIZIxpbiifhKzR92VnGsanXaswqvK5se52HRezW06pOG92D7HwCnnXh405RN8OPN83wzaJpB2xWtmWCBJqmnw1GecAXHqetJGsA+Od/eQHaU9gCuieyb/6lVKG/8EPJ/YxGB/0SK/Zwdvtd441sVQtwRJqjhhOIsu6vsLjmkLND7qt5/9/DA+BvXZWabEaYA7G+QCoIs3Wwj9hrJhEY4A5YawFgszgj7mJzjPfXRe4eSI+xAD43+yZnuUA+z4J67jf8zQVoT20L6N6DaUX/w4+u8Pgy13OVgEbhCrg++k8czQCpBLRqztIUtPqwAFwizVKB2PXKaL9wjExMB9SisLw8zwOAzfx0PVs/DOWDqXRTx6I4HEDrwbgDZGe+TYNjN3AWSQhAW4eeXQFf95kFaO/TFtC9Z/MG8KUfaVlwycdXsTMasfeBsZzBOoOLBnkK4FFigTxHlG805XUqbQA8EdMdza90DaVjHq73zM01XIZFeYRlEIarcZzhqnSQxTsW79mqAvb2PTMA+PKfTXXsK+NSLXZCW0D3gZk2kOsfW4Uj+5F8mQ2M8aBbltgGfsYAJsrH4w2A4DCMW9ju15dF/OViLXpV5V80WNcdy2tERtEEnMgASh7R9RzQT72tqjYup2WF12b5lJiqv3RKUgKQg21/LGeyfRwyX6DXDI7iHrbfA48+ubDZh2oL6D5g04Zz++M5Ax4MICkqs+WVtMoeaNdlYB3rLuvAj4NSyU1K43NfDlV5UHEBHP+yQrMTGWbXLvjSn4t+ZPvbFnibpgVXyHEvW/9/bAulS5V5+QnqvJcRs4/YrB8bY7cRA+YzkNXLmwZY/2RbX4K7v9gDsgV0XxI7+yljLj91lgOeAi5Q/mDaGLMthZe6zhHgAjkTAwZfK9SpwkD+UctokWygXd3q8rJ92VxftetbXF+1s/32+3ZK6uUl0Mh5q/2+9XDg7LLdLk3X25zb4gM3nVZzlgrRr1l5SF4PO0F5B+HifQmY8Biz9fu2PwzjNA3w42khnAVoXx5bQPdlNWlwAIBPPurYnEkGAwZnADpHh/RjEdAyXOaLHvet3666MHHGEwd5rrsPJ15LthHw7ve5M//NrpUZiJu7Xf65Htr5BbDe7hPJ321xdik9gc15+5LYnAM4pMw2m5YV9yy5ygvj9y9i/dn9O+b3LgPcgNkyjbLfH7uO817spbIFdF8Vixrkz37FtDsUgDS9dcYIvoJLYVDouG3RcbcD0H3N4OnTlom+804blcsC8qsTulCPSgj6/S//vLeSTfqhcmCRa0EwTD/6eNatS/DoAqi2O6wev4HjO1/C6vEbwFM5CcF3f5tLFCxcVSWGTBkjul++7S+qgQRxBH7kneIjWOzltgV0X2WLGu6nvjIWSzkhAEA29bTUTaZDPk22d7skB1xdpc+/6Jx+EmJ12i+BalSUiFQOdOBuzQGCL9cWIEM+dPvYPmsTbZ/h4gItCL/9DlaXsnJQc0S/PBqQQJjbmd+yzjabMZj28T/BYq+PLaD7ulmpgX/6g7Ij3VvvPlOvJbvb7XC7a9deuOXC2B3IXl3lUsJxBFgVYDmLSj0a3E2sd/rfDwm6f+KH4Mtj/q8ud4cGeNS0INw82eKMGW3OgSfP2v+6blvO1VX7slJK7oXSgnzsLSy22AK6i7U2BQg/+xXStT5LOmRV9R9FJNgCSXel6YcKgZzZRmDrU4Ldlay3kkdXnYM5gfV2n5acdNJZ18B+234VgQvLX7/TXsSjxx0tbg7AtmO5dQ3sbtO6tosksNgMW0B3sXk2AShrAI//8xWePk1MkzPm2O13eUCtadKnu4Gyv66n7dfxlXjUePWPbcO5AAAA/UlEQVScPshWdeei1ECySnDebtv9sw3wxiWAn5gx0WOxxWbYArqLvTBb/ZdHfNlzpv3H/+4qG6NziBsb4vOPaqrnlgLvN/7iMmFgsfu3BXQXexD2z/0PCyAu9nrYDB+hxRZbbLHFXpQtoLvYYostdkJbQHexxRZb7IS2gO5iiy222AltAd3FFltssRPaArqLLbbYYie0BXQXW2yxxU5oq+Ox7B/5oQ996PjhD3/4dKVZbLHFFnsF7Atf+MLxrbfeCkntKOgutthiiy32Ym2RFxZbbLHFTmgL6C622GKLndAW0F1sscUWO6EtoLvYYostdkJbQHexxRZb7IT2/wO8z/kKRPiAhwAAAABJRU5ErkJggg==\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Exposure varies very little with energy at these high energies\n",
"energy = np.logspace(1, 3, 10) * u.GeV\n",
"dnde = diffuse_galactic.interp_by_coord(\n",
" {\"skycoord\": gc_pos, \"energy\": energy}, interp=\"linear\", fill_value=None\n",
")\n",
"plt.plot(energy.value, dnde, \"*\")\n",
"plt.loglog()\n",
"plt.xlabel(\"Energy (GeV)\")\n",
"plt.ylabel(\"Flux (cm-2 s-1 MeV-1 sr-1)\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# TODO: show how one can fix the extrapolate to high energy\n",
"# by computing and padding an extra plane e.g. at 1e3 TeV\n",
"# that corresponds to a linear extrapolation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Isotropic diffuse background\n",
"\n",
"To load the isotropic diffuse model with Gammapy, use the [gammapy.modeling.models.TemplateSpectralModel](..\/api/gammapy.modeling.models.TemplateSpectralModel.rst). We are using `'fill_value': 'extrapolate'` to extrapolate the model above 500 GeV:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"diffuse_iso = TemplateSpectralModel.read_fermi_isotropic_model(\n",
" filename=\"$GAMMAPY_DATA/fermi_3fhl/iso_P8R2_SOURCE_V6_v06.txt\",\n",
" interp_kwargs={\"fill_value\": None},\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can plot the model in the energy range between 50 GeV and 2000 GeV:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"erange = [50, 2000] * u.GeV\n",
"diffuse_iso.plot(erange, flux_unit=\"1 / (cm2 MeV s sr)\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PSF\n",
"\n",
"Next we will tke a look at the PSF. It was computed using ``gtpsf``, in this case for the Galactic center position. Note that generally for Fermi-LAT, the PSF only varies little within a given regions of the sky, especially at high energies like what we have here. We use the [gammapy.irf.EnergyDependentTablePSF](..\/api/gammapy.irf.EnergyDependentTablePSF.rst) class to load the PSF and use some of it's methods to get some information about it."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"EnergyDependentTablePSF\n",
"-----------------------\n",
"\n",
"Axis info:\n",
" rad : size = 300, min = 0.000 deg, max = 9.933 deg\n",
" energy : size = 17, min = 10.000 GeV, max = 2000.000 GeV\n",
"\n",
"Containment info:\n",
" 68.0% containment radius at 10 GeV: 0.16 deg\n",
" 68.0% containment radius at 100 GeV: 0.10 deg\n",
" 95.0% containment radius at 10 GeV: 0.71 deg\n",
" 95.0% containment radius at 100 GeV: 0.43 deg\n",
"\n"
]
}
],
"source": [
"psf = EnergyDependentTablePSF.read(\n",
" \"$GAMMAPY_DATA/fermi_3fhl/fermi_3fhl_psf_gc.fits.gz\"\n",
")\n",
"print(psf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To get an idea of the size of the PSF we check how the containment radii of the Fermi-LAT PSF vari with energy and different containment fractions:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(8, 5))\n",
"psf.plot_containment_vs_energy(linewidth=2, fractions=[0.68, 0.95])\n",
"plt.xlim(50, 2000)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In addition we can check how the actual shape of the PSF varies with energy and compare it against the mean PSF between 50 GeV and 2000 GeV:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"psf_kernel.psf_kernel_map.sum_over_axes().plot(stretch=\"log\", add_cbar=True);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Energy Dispersion\n",
"For simplicity we assume a diagonal energy dispersion:"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"e_true = exposure.geom.axes[0].edges\n",
"e_reco = counts.geom.axes[0].edges\n",
"edisp = EnergyDispersion.from_diagonal_response(e_true=e_true, e_reco=e_reco)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Background\n",
"\n",
"Let's compute a background cube, with predicted number of background events per pixel from the diffuse Galactic and isotropic model components. For this, we use the use the [gammapy.cube.MapEvaluator](..\/api/gammapy.cube.MapEvaluator.rst) to multiply with the exposure and apply the PSF. The Fermi-LAT energy dispersion at high energies is small, we neglect it here."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background counts from Galactic diffuse: 13361.304230271548\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"
\n",
" name value error unit min max frozen\n",
" str9 float64 float64 str14 float64 float64 bool \n",
"--------- ---------- ------- -------------- ---------- --------- ------\n",
" lon_0 -2.501e-02 nan deg nan nan False\n",
" lat_0 -3.980e-02 nan deg -9.000e+01 9.000e+01 False\n",
" index 2.711e+00 nan nan nan False\n",
"amplitude 5.852e-10 nan cm-2 s-1 TeV-1 nan nan False\n",
"reference 1.000e+02 nan GeV nan nan True\n",
" norm 1.025e+00 nan nan nan False\n",
" tilt 0.000e+00 nan nan nan True\n",
"reference 1.000e+00 nan TeV nan nan True\n",
" value 1.000e+00 nan nan nan True\n",
" norm 3.425e+00 nan nan nan False\n",
" tilt 0.000e+00 nan nan nan True\n",
"reference 1.000e+00 nan TeV nan nan True"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dataset.parameters.to_table()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
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