{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"
\n",
"\n",
"**This is a fixed-text formatted version of a Jupyter notebook**\n",
"\n",
"- Try online [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy-webpage/v0.13?urlpath=lab/tree/cta_1dc_introduction.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_1dc_introduction.ipynb](../_static/notebooks/cta_1dc_introduction.ipynb) |\n",
"[cta_1dc_introduction.py](../_static/notebooks/cta_1dc_introduction.py)\n",
"
\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![CTA first data challenge logo](images/cta-1dc.png)\n",
"\n",
"# CTA first data challenge (1DC) with Gammapy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"In 2017 and 2018, the [CTA observatory](https://www.cta-observatory.org/) did a first data challenge, called CTA DC-1, where CTA high-level data was simulated assuming a sky model and using CTA IRFs.\n",
"\n",
"The main page for CTA 1DC is here:\n",
"https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki (CTA internal)\n",
"\n",
"There you will find information on 1DC sky models, data access, data organisation, simulation and analysis tools, simulated observations, as well as contact information if you have any questions or comments.\n",
"\n",
"### This tutorial notebook\n",
"\n",
"This notebook shows you how to get started with CTA 1DC data and what it contains.\n",
"\n",
"You will learn how to use Astropy and Gammapy to:\n",
"\n",
"* access event data\n",
"* access instrument response functions (IRFs)\n",
"* use index files and the ``gammapy.data.DataStore`` to access all data\n",
"* use the observation index file to select the observations you're interested in\n",
"* read model XML files from Python (not integrated in Gammapy yet)\n",
"\n",
"This is to familiarise ourselves with the data files and to get an overview.\n",
"\n",
"### Further information\n",
"\n",
"How to analyse the CTA 1DC data with Gammapy (make an image and spectrum) is shown in a second notebook [cta_data_analysis.ipynb](cta_data_analysis.ipynb). If you prefer, you can of course just skim or skip this notebook and go straight to second one."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Notebook and Gammapy Setup\n",
"\n",
"Before we get started, please execcute the following code cells.\n",
"\n",
"The first one configures the notebooks so that plots are shown inline (if you don't do this, separate windows will pop up). The second cell imports and checks the version of the packages we will use below. If you're missing some packages, install them and then select \"Kernel -> Restart\" above to restart this notebook.\n",
"\n",
"In case you're new to Jupyter notebooks: to execute a cell, select it, then type \"SHIFT\" + \"ENTER\"."
]
},
{
"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": [
"numpy: 1.15.0\n",
"astropy: 3.2.1\n",
"gammapy: 0.13\n"
]
}
],
"source": [
"import numpy as np\n",
"import astropy\n",
"import gammapy\n",
"\n",
"print(\"numpy:\", np.__version__)\n",
"print(\"astropy:\", astropy.__version__)\n",
"print(\"gammapy:\", gammapy.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DC-1 data\n",
"\n",
"In this and other Gammapy tutorials we will only access a few data files from CTA DC-1 from `$GAMMAPY_DATA/cta-1dc` that you will have if you followed the \"Getting started with Gammapy\" instructions and executed `gammapy download tutorials`.\n",
"\n",
"Information how to download more or all of the DC-1 data (in total 20 GB) is here:\n",
"https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki#Data-access\n",
"\n",
"Working with that data with Gammapy is identical to what we show here, except that the recommended way to do it is to point `DataStore.read` at `$CTADATA/index/gps` or whatever dataset or files you'd like to access there."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/adonath/data/gammapy-data/cta-1dc\r\n"
]
}
],
"source": [
"!echo $GAMMAPY_DATA/cta-1dc"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"README.md \u001b[34mcaldb\u001b[m\u001b[m \u001b[34mdata\u001b[m\u001b[m \u001b[34mindex\u001b[m\u001b[m make.py\r\n"
]
}
],
"source": [
"!ls $GAMMAPY_DATA/cta-1dc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Let's have a look around at the directories and files in `$GAMMAPY_DATA`.\n",
"\n",
"We will look at the `data` folder with events, the `caldb` folder with the IRFs and the `index` folder with the index files. At the end, we will also mention what the `model` and `obs` folder contains, but they aren't used with Gammapy, at least not at the moment.\n",
"\n",
"## EVENT data\n",
"\n",
"First, the EVENT data (RA, DEC, ENERGY, TIME of each photon or hadronic background event) is in the `data` folder, with one observation per file. The \"baseline\" refers to the assumed CTA array that was used to simulate the observations. The number in the filename is the observation identifier `OBS_ID` of the observation. Observations are ~ 30 minutes, pointing at a fixed location on the sky."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gps_baseline_110380.fits\r\n",
"gps_baseline_111140.fits\r\n",
"gps_baseline_111159.fits\r\n",
"gps_baseline_111630.fits\r\n"
]
}
],
"source": [
"!ls -1 $GAMMAPY_DATA/cta-1dc/data/baseline/gps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's open up the first event list using the Gammapy `EventList` class, which contains the ``EVENTS`` table data via the ``table`` attribute as an Astropy `Table` object."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from gammapy.data import EventList\n",
"\n",
"path = \"$GAMMAPY_DATA/cta-1dc/data/baseline/gps/gps_baseline_110380.fits\"\n",
"events = EventList.read(path)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"gammapy.data.event_list.EventList"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(events)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"astropy.table.table.Table"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(events.table)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Row index=0 \n",
"\n",
"EVENT_ID TIME RA DEC ENERGY DETX DETY MC_ID \n",
"s deg deg TeV deg deg \n",
"uint32 float64 float32 float32 float32 float32 float32 int32 \n",
"1 664502403.0454683 -92.63541 -30.514854 0.03902182 -0.9077294 -0.2727693 2 \n",
"
"
],
"text/plain": [
"\n",
"EVENT_ID TIME RA DEC ENERGY DETX DETY MC_ID\n",
" s deg deg TeV deg deg \n",
" uint32 float64 float32 float32 float32 float32 float32 int32\n",
"-------- ----------------- --------- ---------- ---------- ---------- ---------- -----\n",
" 1 664502403.0454683 -92.63541 -30.514854 0.03902182 -0.9077294 -0.2727693 2"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# First event (using [] for \"indexing\")\n",
"events.table[0]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Table length=2 \n",
"\n",
"EVENT_ID TIME RA DEC ENERGY DETX DETY MC_ID \n",
"s deg deg TeV deg deg \n",
"uint32 float64 float32 float32 float32 float32 float32 int32 \n",
"1 664502403.0454683 -92.63541 -30.514854 0.03902182 -0.9077294 -0.2727693 2 \n",
"2 664502405.2579999 -92.64103 -28.262728 0.030796371 1.3443842 -0.2838398 2 \n",
"
"
],
"text/plain": [
"\n",
"EVENT_ID TIME RA DEC ... DETX DETY MC_ID\n",
" s deg deg ... deg deg \n",
" uint32 float64 float32 float32 ... float32 float32 int32\n",
"-------- ----------------- --------- ---------- ... ---------- ---------- -----\n",
" 1 664502403.0454683 -92.63541 -30.514854 ... -0.9077294 -0.2727693 2\n",
" 2 664502405.2579999 -92.64103 -28.262728 ... 1.3443842 -0.2838398 2"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# First few events (using [] for \"slicing\")\n",
"events.table[:2]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"astropy.time.core.Time"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Event times can be accessed as Astropy Time objects\n",
"type(events.time)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events.time[:2]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['2021-01-21T12:00:03.045' '2021-01-21T12:00:05.258']\n"
]
}
],
"source": [
"# Convert event time to more human-readable format\n",
"print(events.time[:2].fits)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Event positions can be accessed as Astropy SkyCoord objects\n",
"print(type(events.radec))\n",
"events.radec[:2]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events.galactic[:2]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# The event header information is stored\n",
"# in the `events.table.meta` dictionary\n",
"print(type(events.table.meta))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(267.68121338, -29.6075)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# E.g. to get the observation pointing position in degrees:\n",
"events.table.meta[\"RA_PNT\"], events.table.meta[\"DEC_PNT\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## EVENT analysis example\n",
"\n",
"As an example how to work with EVENT data, let's look at the spatial and energy distribution of the events for a single run.\n",
"\n",
"Note that EVENT data in Gammapy is just stored in an Astropy Table, which is basically a dictionary mapping column names to column data, where the column data is a Numpy array. This means you can efficienly process it from Python using any of the scientific Python tools you like (e.g. Numpy, Scipy, scikit-image, scikit-learn, ...)\n",
"\n",
"### EVENT spatial distribution\n",
"\n",
"To illustrate a bit how to work with EVENT table an header data,\n",
"let's plot the event positions as well as the pointing position."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Event positions\n",
"pos = events.galactic[::300] # sub-sample every 100th event\n",
"plt.scatter(pos.l.wrap_at(\"180 deg\").deg, pos.b.deg, s=10)\n",
"# Pointing position\n",
"pos_pnt = events.pointing_radec.galactic\n",
"plt.scatter(\n",
" pos_pnt.l.wrap_at(\"180 deg\").deg, pos_pnt.b.deg, marker=\"*\", s=400, c=\"red\"\n",
")\n",
"plt.xlabel(\"Galactic longitude (deg)\")\n",
"plt.ylabel(\"Galactic latitude (deg)\")\n",
"pos_pnt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### EVENT energy distribution\n",
"\n",
"Let's have a look at the event energy distribution."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"energy = events.table[\"ENERGY\"].data\n",
"energy_bins = np.logspace(-2, 2, num=100)\n",
"plt.hist(energy, bins=energy_bins)\n",
"plt.semilogx()\n",
"plt.xlabel(\"Event energy (TeV)\")\n",
"plt.ylabel(\"Number of events\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A double-peak, at ~ 30 GeV and ~ 100 GeV? .... let's try to find out what's going on ...\n",
"\n",
"### EVENT MC_ID\n",
"\n",
"One idea could be to split the data into gamma-ray and hadronic background events.\n",
"Now from looking at the FITS header, one can see that ``MC_ID == 1`` is the hadronic background, and the other values are for different gamma-ray emission components."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of events: 106217\n",
"Number of gammas: 8239\n",
"Number of hadrons: 97978\n"
]
}
],
"source": [
"is_gamma = events.table[\"MC_ID\"] != 1\n",
"print(\"Number of events: \", len(events.table))\n",
"print(\"Number of gammas: \", is_gamma.sum())\n",
"print(\"Number of hadrons: \", len(events.table) - is_gamma.sum())"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"energy = events.table[\"ENERGY\"].data\n",
"energy_bins = np.logspace(-2, 2, num=100)\n",
"opts = dict(bins=energy_bins, density=True, histtype=\"step\")\n",
"plt.hist(energy[~is_gamma], label=\"hadron\", **opts)\n",
"plt.hist(energy[is_gamma], label=\"gamma\", **opts)\n",
"plt.loglog()\n",
"plt.xlabel(\"Event energy (TeV)\")\n",
"plt.ylabel(\"Number of events\")\n",
"plt.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As expected, the energy spectra are roughly power-laws. \n",
"When plotting in log-log, one can see that the spectra are roughly power-laws (as expected), and the \"double peak\" we saw before looks more like a \"double energy threshold\" pattern.\n",
"It's there for gammas and background, and below 100 GeV the signal to background ratio is lower.\n",
"\n",
"What we're seeing here is the result of a mixed-array in CTA with LSTs, MSTs and SSTs, which have different energy thresholds:\n",
"\n",
"* ~ 30 GeV is the energy threshold of the LSTs\n",
"* ~ 100 GeV is the energy threshold of the MSTs\n",
"* the energy threshold of the SSTs is at a few TeV and doesn't show up as a clear feature in the gamma and background energy distribution (probably the rise in slope in gamma in the 1-10 TeV range is due to the SSTs).\n",
"\n",
"Let's look a little deeper and also check the event offset distribution in the field of view ...\n",
"\n",
"### EVENT FOV offset\n",
"\n",
"The event position and offset in the field of view (FOV) can be computed from the event RA, DEC position and the observation pointing RA, DEC position.\n",
"\n",
"But actually, the field of view position is stored as extra columns in the EVENT list: ``DETX`` and ``DETY`` (angles in degree, I think RA / DEC aligned field of view system).\n",
"\n",
"I presume (hope) this unnecessary information will be dropped from the CTA event lists in the future to save some space (make the CTA DL3 data ~25% smaller), but for now, let's use those columns to compute the field of view offset and look at the offset-energy distribution of the events."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Offset (deg)')"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"energy_bins = 10 ** np.linspace(-2, 2, 100)\n",
"offset_bins = np.arange(0, 5.5, 0.1)\n",
"\n",
"t = events.table\n",
"offset = np.sqrt(t[\"DETX\"] ** 2 + t[\"DETY\"] ** 2)\n",
"hist = np.histogram2d(\n",
" x=t[\"ENERGY\"], y=offset, bins=(energy_bins, offset_bins)\n",
")[0].T\n",
"\n",
"from matplotlib.colors import LogNorm\n",
"\n",
"plt.pcolormesh(energy_bins, offset_bins, hist, norm=LogNorm())\n",
"plt.semilogx()\n",
"plt.colorbar()\n",
"plt.xlabel(\"Energy (TeV)\")\n",
"plt.ylabel(\"Offset (deg)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So the CTA field of view increases with energy in steps. The energy distribution we saw before was the combination of the energy distribution at all offsets. Even at a single offset, the double energy-threshold at ~ 30 GeV and ~ 100 GeV is present.\n",
"\n",
"There is also a quick-look peek method you can use to get a peek at the contents of an event list:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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qMi+l05HpFcDZwMKI2JXybUl5ZgNXRsSuiHgSWAccKWkCMCYiVkZEAJcDJ1ezLtY83MtWOw6wBqHSw/cWdc5k1MhhDBeMGjmMjmnjKvI5ZmZmVh6ShktaDWwBbouIu4FDgQ9IulvSbyS9L2WfCDydc/umlDYxHeenF/q8eZK6JHVt3bq13NUxsyHwEMEBqsbwvbYpY7nijA5Wrd9Gx7RxHh5oZmZW59LwvhmS3gJcL+kIsu9ZY4EO4H3AMknTgELzqqKP9EKftxhYDNDe3l4wT6spNPSxN23t5h1F85iVmwOsASo0fK8SAVDblLEOrMzMzBpMRLwo6Q6yuVObgOvScL97JPUAb03pB+fcNgl4JqVPKpBuZg3EQwQHyMP3zMzMLJek8annCkmjgROAR4EbgONS+qHAG4DngeVAp6T9JB0CTAfuiYjNwE5JHWn1wNOBG6teoSZy1ZlHcdWZR3HYhDEcNmHMa+dmleQerAHy8D0zMzPLMwFYKmk42cPrZRFxk6Q3AJdKehh4BZiTerPWSFoGrAV2A+ekIYaQLYxxGdkKhCso8wqCZs2sXoaAVjTASk9zlgBHkI0h/hzwGHAVMBXYAHwyIran/OcDc4E9wBci4taU3sbexuYW4LzUQNWEh++ZWbOYOv/motc2LDypiiUxa1wR8SAws0D6K8Cni9yzAFhQIL2L7HtT0/K8KGt2lR4iuAj4eUS8E3gP8AgwH7g9IqYDt6fz/D0hZgEXpSdBsHdPiOnpNavC5TYzMzMzMxuwivVgSRoDfBD4LLz2FOcVSbOBY1K2pcAdwJfJ2RMCeFJS754QG0h7QqT37d0Twl3mZmZmZnWoWC8V7O2pcs+V5WuW3s1K9mBNA7YCP5R0v6Qlkt4EHJQmcZJ+Hpjye08IMzMzMzNraJWcgzUCeC9wbkTcLWkRaThgEQ27J0T3xu1e9MLMzMwscS+VDUaz/HdTyQBrE7Ap7WQOcA1ZgPWcpAkRsVnSBLIdz3vzN9yeEPkbD7/zbW/mlvM+WOtimZmZmVmdafTAwUpTsSGCEfEs8LSkP0pJx5MtR7ocmJPS5rB3f4eG3BMif+PhHS/vrm2BzMzMzKzhnHrJytfNVbPCGuH3VOl9sM4Frkj7QKwH/py0P4SkucBTwCkAEdGQe0Is6pzJaUtW8eruHkaOGMaizn1WaTUzMzOzMql070/ul/dGX2zBaqOiAVZErAbaC1w6vkj+htsTwhsPm5mZmVkzcCBZHpXuwWoJ3njYzMzMrDnkBhcOOGwwKr3RsJmZmZmZWctwgGVmZmZmZlYmDrDMzMzMzMzKxHOwzMzMzMwaSO/cMK9yOHiV/B26B8vMzMzMzKxM3IPVILo3bvdS8GYlkPQWYAnZ1g4BfA54DLgKmApsAD4ZEdtT/vOBucAe4AsRcWtKb2Pv/nu3AOdFRFSxKjU3df7NRa9tWHhSFUtiZma5entZ3HM1eJX8HTrAagAnLrqTR5/dSU/AMMHVZx3tIMusuEXAzyPiE2mT8zcCXwFuj4iFkuYD84EvSzoM6AQOB94O/FLSoWmT84uBecAqsgBrFnW0ybmZmVkja+bg0EMEG8COl3fTk56b9wSsWr+ttgUyq1OSxgAfBH4AEBGvRMSLwGxgacq2FDg5Hc8GroyIXRHxJLAOOFLSBGBMRKxMvVaX59xjZmZmOU69ZOVrAZM5wGoIizpnMmrkMIYLRo0cRse0cbUuklm9mgZsBX4o6X5JSyS9CTgoIjYDpJ8HpvwTgadz7t+U0iam4/z0fUiaJ6lLUtfWrVvLWxszMzNrOB4i2ADapozlijM6PAfLrH8jgPcC50bE3ZIWkQ0HLEYF0qKP9H0TIxYDiwHa29tbao6WmZmZ7csBVoNomzLWgZVZ/zYBmyLi7nR+DVmA9ZykCRGxOQ3/25KT/+Cc+ycBz6T0SQXSzczMzPo04CGCksZKenclCmNmrW2o7UtEPAs8LemPUtLxwFpgOTAnpc0BbkzHy4FOSftJOgSYDtyThhHulNQhScDpOfeYWYvwdx4zG4ySerAk3QF8LOVfDWyV9JuI+KsKls3MWkAF2pdzgSvSCoLrgT8ne5i0TNJc4CngFICIWCNpGVkQths4J60gCHA2e5dpX4FXEDRrCa36nccb19Yv/y0aT6lDBP8gInZIOgP4YURcIOnBShbMzFpGWduXiFgNtBe4dHyR/AuABQXSu8j20jKz1uLvPGY2JKUGWCPSvIVPAl+tYHnMrPW4fTGzejLgNknSKOBOYD+y71bXpMDsa8BfkK1uCvCViLhF0lTgEbJN0AFWRcRZ6b1qssm5N65tfrnLqLunsrJKDbC+DtwK3BUR90qaBjxeuWKZWQtx+2Jm9WQwbdIu4LiIeEnSSOAuSb3Dir8TEd8scM8TETGjQLo3OW9wDlas1ABrc0S8NskzItZL+naFymRmrcXti5nVkwG3SamH6aV0OjK9BtzrlLvJeTrv3eTcAZYNWW7g556ryip1FcHvlZhmZjZQbl/MrJ4Mqk2SNFzSarJtIG7L2S7i85IelHSppNz9Vg5JG6L/RtIHUpo3OTdrAn32YEk6CjgaGC8pd/WcMcDwShbMzJqb2xczqydDbZPSCqQzJL0FuF7SEWTD/f6erDfr74FvAZ8DNgOTI2JbmnN1g6TD8SbnVeP5SFZJ/fVgvQHYnywQe3POawfwicoWzcyanNsXM6snZWmTIuJF4A5gVkQ8FxF7IqIH+GfgyJRnV0RsS8fdwBPAoXiTc7Om0GcPVkT8BviNpMsiYmOVymRmLcDtS2ObOv/motc2LDypiiUxK4+htEmSxgOvRsSLkkYDJwDfkDQhbVwO8HHg4Zz8L0TEnrSIxnRgfUS8IGmnpA7gbrJNzms6ZLpZe3oGOx+p2H5hpd5v1VWrv1epi1zsJ2kxMDX3nog4rhKFMrOW4vbFzOrJYNqkCcBSScNJG5tHxE2SfiRpBtkwvw3AmSn/B4G/k7Qb2AOcFREvpGsV2+S8kl82myHoMiuXUgOsq4HvA0vIGgIzs3Jx+2Jm9WTAbVJEPAjMLJD+mSL5rwWuLXKtrjY598pzr1fv+4U1a4/jYNXq71VqgLU7Ii6uaEnMrFW5fTGzetK0bVK9BwdmzaLUAOtnkv4HcD3ZZnoA5HRnm5kNltsXM6snbpMajOdF7eUex/pQaoA1J/38m5y0AKaVtzhm1oLcvphZPXGbZGZDUlKAFRGHVLogZtaa3L6YWT1xm9R4Shn66F4cq6aSAixJpxdKj4jLy1scM2s1bl/MrJ64TTJrLPW4sEepQwTfl3M8CjgeuA9wY1Mj3Ru3s2r9NjqmjaNtythaF8dsKNy+mFk9cZtkZkNS6hDBc3PPJf0B8KOKlMj6deKiO3n02Z30BAwTXH3W0Q6yrGG5fTGzeuI2yayx1OPCHqX2YOX7Pdmu41YDO17eTU9kxz0Bq9Zvc4BlzcTti5nVE7dJFVLrL8FmlVLqHKyfka2gAzAceBewrFKFsr4t6pzJaUtW8eruHkaOGEbHtHG1LpLZoLl9MbN64jbJzIaq1B6sb+Yc7wY2RsSmCpTHStA2ZSxXnNHhOVjWLNy+NJmp828uem3DwpOqWBKzQXGbVEbupbLBaPT/bkqdg/UbSQexd+Ln45UrkpWibcpYB1bWFNy+mFk9cZtk5VaPq9xVUyvVtVepQwQ/CfwjcAcg4HuS/iYirqlg2cysBbh9MbN64jbJqqnVg69SNdrvqdQhgl8F3hcRWwAkjQd+CbixMbOhcvtiZvXEbZKVVV+r3OUGDrXUW4784AXqM4Cpd6UGWMN6G5pkGzCsAuUxs9bj9sXM6onbJKuaelxivB412u+p1ADr55JuBX6azk8FbinlRknDgS7gdxHxUUkHAFcBU4ENwCcjYnvKez4wF9gDfCEibk3pbcBlwOj0uedFRGBmzWDQ7YuZWQW4TbLX1POX+HLK71FrlXpXSp9PZCS9Q9L7I+JvgEuAdwPvAVYCi0v8jPOAR3LO5wO3R8R04PZ0jqTDgE7gcGAWcFEKzgAuBuaR7UMxPV03swZWpvbFzKws3CaZWbn01+X9T8BOgIi4LiL+KiL+kuxJzj/19+aSJgEnAUtykmcDS9PxUuDknPQrI2JXRDwJrAOOlDQBGBMRK1Ov1eU595hZ4xpS+2JmVmZuk8ysLPoLsKZGxIP5iRHRRTbErz//BPwt0JOTdlBEbE7vsxk4MKVPBJ7OybcppU1Mx/np+5A0T1KXpK6tW7eWUDwzq6Ghti9mZuXkNsnMyqK/AGtUH9dG93WjpI8CWyKiu8SyqEBa9JG+b2LE4ohoj4j28ePHl/ixZlYjg25fzMwqwG2SmZVFfwHWvZL+Ij9R0lygv8Dp/cDHJG0ArgSOk/Rj4Lk07I/0s3elnk3AwTn3TwKeSemTCqSbWWMbSvtiZlZubpPMrCz6W0Xwi8D1kk5jb+PSDrwB+HhfN0bE+cD5AJKOAb4UEZ+W9I/AHGBh+nljumU58BNJ3wbeTraYxT0RsUfSTkkdwN3A6cD3BlRLM6tHg25fzMwqwG2SNRyv9jd0lfgd9hlgRcRzwNGSjgWOSMk3R8SvhvCZC4Fl6YnQU8Ap6bPWSFoGrAV2A+dExJ50z9nsXaZ9RXqZWQOrUPtiZjYobpPMqqMVNjUuaR+siPg18OvBfkhE3AHckY63AccXybcAWFAgvYu9jZ2ZNZGhti9mZuXkNsmsNLmBUX6w1CyB0mCVutGwmZnZkE2df3PRaxsWnlTFkpiZWS20wqbGDrDMzMzMhkDSKOBOYD+y71bXRMQFkr4G/AXQu3fMVyLilnTP+cBcYA/whYi4NaW3sXdaxC3AeWkfULO6khsYNXOwNBgOsJpI98btrFq/jY5p42ibMrbWxTEzM2sVu4DjIuIlSSOBuyT1zhf/TkR8MzezpMOATuBwsoW9finp0DT3/GJgHrCKLMCaheeemzUUB1hN4NRLVrLz5Vd59Nmd9AQME1x91tEOsszMzKog9TC9lE5HpldfvU6zgSsjYhfwpKR1wJFpa5sxEbESQNLlwMk4wDJrKP3tg2UNYsfLu+lJTXlPwKr122pbILMakjRc0v2SbkrnB0i6TdLj6efYnLznS1on6TFJH8lJb5P0ULr2XUmFNj03MwNea3dWk+3veVtE3J0ufV7Sg5IuzWl7JgJP59y+KaVNTMf56YU+b56kLkldW7duLZTFGtypl6zk1EtWsnbzDtZu3vHaee7iElafHGA1gavOPIpFnTMZNXIYwwWjRg6jY9q4WhfLrJbOAx7JOZ8P3B4R04Hb03n+MJ1ZwEWShqd7eofpTE+vWdUpupk1oojYExEzgElkvVFHkLUjfwjMADYD30rZCz2wiT7SC33e4ohoj4j28ePHD7n8Zv1xcFc6DxFsEm1TxnLFGR2eg2UtT9Ik4CSyLR/+KiXPBo5Jx0vJto34Mh6mY2ZlFhEvSroDmJU790rSPwM3pdNNwME5t00CnknpkwqkV40XKagfrbDaXrNyD1YTaZsylnOOfYeDK2t1/wT8LdCTk3ZQRGwGSD8PTOlDHqZjZiZpvKS3pOPRwAnAo5Im5GT7OPBwOl4OdEraT9IhZL3k96T2aaekjjQs+XTgxqpVxMzKwj1YZtY0JH0U2BIR3ZKOKeWWAmkDGqYjaR7ZUEImT55cYknNrMlMAJamIcbDgGURcZOkH0maQdZ+bADOBIiINZKWAWuB3cA5aQVBgLPZu0z7CtxzbhXg3rDKcoBlZs3k/cDHJJ0IjALGSPox8JykCRGxOT1R3pLyD3mYTkQsBhYDtLe3e68asxYUEQ8CMwukf6aPexaQDWXOT+8CjihrAc2sqjxE0MyaRkScHxGTImIq2eIVv4qIT5MNx5mTss1h75AbD9MxMzOzsnIPlpm1goXAMklzgaeAU8DDdOrN1Pk3F722YeFJVSyJmdVaNRd28HA5KzcHWGbWlCLiDrLVAomIbcDxRfJ5mI6Z2SDkLtm9dvOO16U5aLFW5gDLzMzMzPrloGkv/y6sLw6wzMzMzGzAcoMM91xZPav2f5cOsMzMzMxaiIOg1uC/c+14FUEzMzMzM7MycYDVxLo3bufCX6+je+P2WhfFzMzMzKwleIhgkzpx0Z08+hWiIMsAACAASURBVOxOegKGCa4+62japoytdbHMzAbFS7ibWX88JM7qhXuwmtSOl3fTE9lxT8Cq9dtqWyAzMzMzsxbgHqwmtahzJqctWcWru3sYOWIYHdPG1bpIZmZmZmYVUy+9mA6wmlTblLFccUYHq9Zvo2PaOA8PNDMzMzOrAgdYTaxtylgHVmZmZmZmVeQ5WGZmZmZmZmXiHiwzMzMzMyubUy9Z+drx2s07XpdWL/OkKsk9WGZmZmZmZmXiHiwzMzMzsypohd4beH09W6nnqpcDLDMza2jehNjMzOqJAywzMzMzsxprpR6eoWiE35MDLDMzMzMza4jgpRF4kYsW0r1xOxf+eh3dG7fXuihmZmZmZk3JPVgt4sRFd/LoszvpCRgmuPqso70JsZmZmZlZmbkHq0XseHk3PZEd9wSsWr+ttgUyMzMzM2tC7sFqEYs6Z3LaklW8uruHkSOG0TFtXK2LZGZWcV5h0KpB0ijgTmA/su9W10TEBTnXvwT8IzA+Ip6XNBV4BHgsZVkVEWelvG3AZcBo4BbgvIiI6tTEzMrBAVaLaJsylivO6GDV+m10TBvn4YFmZmblsws4LiJekjQSuEvSiohYJelg4EPAU3n3PBERMwq818XAPGAVWYA1C1hRwbKbWZk5wGohbVPGOrAyMzMrs9TD9FI6HZlevb1O3wH+Frixv/eRNAEYExEr0/nlwMk4wDJrKJ6DZWZmZjZEkoZLWg1sAW6LiLslfQz4XUQ8UOCWQyTdL+k3kj6Q0iYCm3LybEpphT5vnqQuSV1bt24tZ1XMbIjcg9XCujdu95BBMzOzMoiIPcAMSW8Brpf0buCrwIcLZN8MTI6IbWnO1Q2SDgdU6K2LfN5iYDFAe3u752hZw2nmPbcqFmClMceXA28DeoDFEbFI0gHAVcBUYAPwyYjYnu45H5gL7AG+EBG3pnRP+CwzL9tuZmZWfhHxoqQ7gNnAIcADkgAmAfdJOjIiniWbt0VEdEt6AjiUrMdqUs7bTQKeqWLxzQalmYOlwajkEMHdwF9HxLuADuAcSYcB84HbI2I6cHs6J13rBA4nm9B5kaTh6b16J3xOT69ZFSx3S/Cy7WZmZuUhaXzquULSaOAE4P6IODAipkbEVLLg6b0R8WzKPzzln0b23WZ9RGwGdkrqUBaVnU4Jc7fMrL5UrAcrNRKb0/FOSY+QjSOeDRyTsi0F7gC+nNKvjIhdwJOS1gFHStqAJ3yWnZdtN7NW5yXcrYwmAEtT0DQMWBYRN/WR/4PA30naTTZq56yIeCFdO5u9o3ZW4O87Zg2nKnOw0n4PM4G7gYNS8EVEbJZ0YMo2kWxJ0l69EztfZQATPsl6upg8eXL5KtCEvGy7mZlZeUTEg2Tfc/rKMzXn+Frg2iL5uoAjylk+M6uuigdYkvYna0S+GBE70jjkglkLpEUf6fsmesLngHjZdjMzMzPry6mXrARg7eYdrzv3vKviKhpgpc32rgWuiIjrUvJzkiak3qsJZMuZQtYzdXDO7b0TOz3h08zMzMxakgOZxlPJVQQF/AB4JCK+nXNpOTAHWJh+3piT/hNJ3wbeTjbh856I2CNpp6QOsiGGpwPfq1S5zczMzMws0xvgueeqdJXswXo/8BngobTxHsBXyAKrZZLmAk8BpwBExBpJy4C1ZCsQnpP2lABP+DQzMzMzswZQyVUE76Lw/CmA44vcswBYUCDdEz6rwBsPm5llvMKgmZkNVlVWEbT6542HzczMzMyGzgGWAYU3HnaAZWZmZtXSO8cHvGKdNbZhtS6A1YdFnTMZNXIYwwWjRnrjYTMzMzOzwXAPlgHeeNiag6SDgcuBtwE9wOKIWCTpAOAqYCqwAfhkRGxP95wPzAX2AF+IiFtTeht7F9e5BTgvIry/nnl+llmF5PZSuefKGpkDLHuNNx62JrAb+OuIuE/Sm4FuSbcBnwVuj4iFkuYD84EvSzoM6AQOJ9se4peSDk0rmF4MzANWkQVYs/AKpmZmBTkQMtvLQwTNrGlExOaIuC8d7wQeASYCs4GlKdtS4OR0PBu4MiJ2RcSTwDrgyLQJ+piIWJl6rS7PucfMzMysKAdYZtaUJE0FZpJtUH5QRGyGLAgDDkzZJgJP59y2KaVNTMf56YU+Z56kLkldW7duLWcVzMzMrAE5wLKiujdu58Jfr6N74/ZaF8VsQCTtD1wLfDEidvSVtUBa9JG+b2LE4ohoj4j28ePHD7ywZmZm1lQ8B8sK8r5Y1qgkjSQLrq6IiOtS8nOSJkTE5jT8b0tK3wQcnHP7JOCZlD6pQLqZmZlZn9yDZQUV2hfLrN5JEvAD4JGI+HbOpeXAnHQ8B7gxJ71T0n6SDgGmA/ekYYQ7JXWk9zw95x4zMzOzotyDZQUt6pzJaUtW8eruHkaO8L5Y1jDeD3wGeEjS6pT2FWAhsEzSXOAp4BSAiFgjaRmwlmwFwnPSCoIAZ7N3mfYVeAVBK4GXcDczMwdYVpD3xbJGFBF3UXj+FMDxRe5ZACwokN4FHFG+0pmZmVkrcIBlRXlfLDMzMzOzgfEcLCuZVxU0MzMzM+ube7CsJF5V0MzMzMysf+7BspJ4VUEzMzMzs/45wLKSLOqcyaiRwxguGDXSqwqamZmZmRXiIYJWEq8qaGY2NF7C3cxa0VVnHlXrIlSdAywrmVcVNDMzMzPrm4cI2qB5VUEzMzOQNErSPZIekLRG0tfzrn9JUkh6a07a+ZLWSXpM0kdy0tskPZSufVdSsb39zKxOuQfLBiV/VcF3vu3N3HLeB2tdLDMzs1rYBRwXES9JGgncJWlFRKySdDDwIeCp3sySDgM6gcOBtwO/lHRoROwBLgbmAauAW4BZwIrqVsfMhsI9WDYo+asK7nh5d20LZGZmViOReSmdjkyv9H9JvgP8bc45wGzgyojYFRFPAuuAIyVNAMZExMqICOBy4OSqVMLMysYBlg1K/qqCizpn1rpIZmZmNSNpuKTVwBbgtoi4W9LHgN9FxAN52ScCT+ecb0ppE9Nxfnqhz5snqUtS19atW8tWDzMbOg8RtEEptqpg98btXmnQzGyAvMJg40vD+2ZIegtwvaR3A18FPlwge6F5VdFHeqHPWwwsBmhvby+Yx8xqwwGWDVr+qoL587KuPutoAAdcZmbWMiLiRUl3kA0DPAR4IK1TMQm4T9KRZD1TB+fcNgl4JqVPKpBuZg3EAZaVTf68rLN+3MW2l155XcDlIMvMzJqNpPHAqym4Gg2cAHwjIg7MybMBaI+I5yUtB34i6dtki1xMB+6JiD2SdkrqAO4GTge+V+36mNnQOMCyslnUOZPTlqzi1d09jBwxDMHrAq5V67c5wDIzGyAPH2wIE4ClkoaTzW9fFhE3FcscEWskLQPWAruBc9IQQ4CzgcuA0WSrB3oFQbMG4wDLyiZ/XhbwuoCrY9o4z9EyM7OmExEPAn2u9hQRU/POFwALCuTrAo4oZ/nMrLocYFlZ5c/Lyg24/tcND3mOlpmZmZk1NQdYVlG5AZfnaJmZmZlZs3OAZVVTyhwtcI+WmVmpPD/LzKz+OMCyqulvjtbVXU/zrV885iGEZmZmZtawHGBZVfU1R+u8K+/vdwghOOAyMzMzs/rlAMtqKjfg6m8IYbE5W/krE3qlQjMzDx80M6sVB1hWN/obQpgfcJ135f2MGTXidSsTTj7gjTz1wu+9cIaZmZlZGV115lG1LkLDcIBldaWvIYTw+oBrzKgR+6xM+MJ/vNLvwhnu8TIzMzOzSnGAZXWtr4CrNzjKDbrm/8m7+Lub1hRdOCO/h6tQjxd4npeZNTcPHzQzq5yGCbAkzQIWAcOBJRGxsMZFshrID7jyhxW2TRnLH73tzUUXzsjv4co/L2VhjUI9Xv31irmXzMwahYMvM7OhaYgAS9Jw4ELgQ8Am4F5JyyNibW1LZvWgUNBVbOGM/B6u/PP+FtbI7/F659veDNDnPLBSeskGE6CVO6hzEGhm/XHwZWbWv4YIsIAjgXURsR5A0pXAbMABlvWpvx6u/HPoe2GN/B6vHS/vfu24WJ7+esn6C8iqEdT9rxseet37eaikmZmZ2eA0SoA1EXg653wT8F9qVBZrMH31cBU672thjfwer0WdM/vN018vWX8BWTWCurfu/4aSlsQ3Myumr96tvrjny8yaTaMEWCqQFvtkkuYB8wAmT55c6TJZk+pvYY38HrBS8vTVS9ZfQFaNoO6Ew97GdfdtKpp/1fptDrDMzKxqvCS4NbJGCbA2AQfnnE8CnsnPFBGLgcUA7e3t+wRgZoPRX49XKXkGGrT1dz7Q94C+A7I/e+8k/uy9k4rm700zMys3z+sys2ajiPqPQySNAH4LHA/8DrgX+O8RsabYPe3t7dHV1VWlEprVv4EugjHYRS8kdUdEeznL3ihKbXcGO5TKrNWUGmC1crsD/s5jVivF2p6G6MGKiN2SPg/cSrZM+6V9BVdmtq9SeuL6ym9mZmZm/WuIAAsgIm4Bbql1OczMzMzMzIoZVusCmJmZmZmZNQsHWGZmZmZmZmXiAMvMrAhJsyQ9JmmdpPm1Lo+ZmZnVPwdYZmYFSBoOXAj8CXAY8ClJh9W2VGZmZlbvHGCZmRV2JLAuItZHxCvAlcDsGpfJzMzM6lzDrCI4UN3d3TskPV5C1j8A/n0A1/LT+jovdvxW4PkSylZMX2UuNZ/r5rr1Hg+1XvnvO2WI71UvJgJP55xvAv5LfiZJ84B56fQlSY+V8N7l+J03slauv+s+APpGyVmbpd0ZlO7u7uclbSwha7P/9+f6Nb5Gq2PhticimvIFLB5qvkLX8tP6Ou/juMt1c93qpW5DrddA6tZIL+AUYEnO+WeA75XpvYf8O2/kVyvX33WvfTla+dXsfwPXr/FfzVLHZh4i+LMy5Ct0LT+tr/Nix0PlurlufaXVc90aySbg4JzzScAzNSqLmZmZNQilaNGqSFJXRLTXuhyV4Lo1nmat11BJGgH8Fjge+B1wL/DfI2JNGd67pX/nrVx/1701614vmv1v4Po1vmapY9POwapzi2tdgApy3RpPs9ZrSCJit6TPA7cCw4FLyxFcJa3+O2/l+rvuVkvN/jdw/RpfU9TRPVhmZmZmZmZl0sxzsMzMzMzMzKrKAZaZmZmZmVmZOMAyM6siSbMkPSZpnaT5tS5PNUnaIOkhSaslddW6PJUm6VJJWyQ9nJN2gKTbJD2efo6tZRkrpUjdvybpd+nvv1rSibUsYytplnanUBvS178pSeenOj8m6SO1K3lxA20nitVJUlv63ayT9F1JqnZdChloW9Bo9SvGAZaZWZVIGg5cCPwJcBjwKUmH1bZUVXdsRMxohlWiSnAZMCsvbT5we0RMB25P583oMvatO8B30t9/RkTcUuUytaQmbHfy25CC/6ZSHTuBw8n+W7wo/S7qzWWU2E70U6eLyTa9n55ehf791cJllNgWNGj9CnKAVUckTZP0A0nX1Los5SDpTZKWSvpnSafVujzl1Gx/q1ySTk5/sxslfbjW5WkyRwLrImJ9RLwCXAnMrnGZrEIi4k7ghbzk2cDSdLwUOLmqhaqSInW32mj2dqfYv6nZwJURsSsingTWkf0u6soA24mCdZI0ARgTESsjW73ucuqkbRlgW9Bw9SvGAVaZFOoCTekld8unxm9uZUs6NAOs558C10TEXwAfq3phB2ggdWuEv1WuAdbthvQ3+yxwag2K28wmAk/nnG9Kaa0igF9I6pY0r9aFqZGDImIzQPp5YI3LU22fl/RgapOacnhkHWqmdqdQG1Ls31Qj13ugdZqYjvPT61mhtqBp6ucAq3wuI6+7sli3vKQ/lnRT3qtR/id7GSXWE5jE3n8oe6pYxsG6jNLr1mguY+B1+5/pupVPoTHjrbRXxvsj4r1k/82dI+mDtS6QVdXFwB8CM4DNwLdqW5yW0UztzkDakGaqd69idWq0uhZrC5qlfg6wyqVIF2jBbvmIeCgiPpr32lL1Qg/CQOpJ9oRhUspT9/+tDbBuDWUgdVPmG8CKiLiv2mVtcpuAg3POJwHP1KgsVRcRz6SfW4DrqcPhOlXwXBruQvrZEG1/OUTEcxGxJyJ6gH+mNf/+tdA07U6RNqTYv6lGrvdA65T7fSs3vS710RY0Rf2gAb70NrgBdU9LGifp+8BMSedXunBlVKye1wF/Juli4Ge1KFgZFKxbA/+tchX7u50LnAB8QtJZtShYE7sXmC7pEElvIJvMu7zGZaqKNCfzzb3HwIeBh/u+qyktB+ak4znAjTUsS1X1fmFMPk5r/v1roSnanT7akGL/ppYDnZL2k3QI2cII91S31IM2oDqlYYQ7JXWk1fVOp47blj7agqaoH8CIWhegyQ2oSzMitgGN+IW2YD0j4j+AP692YcqsWN0a9W+Vq1jdvgt8t9qFaQURsVvS54FbgeHApRGxpsbFqpaDgOvTyrojgJ9ExM9rW6TKkvRT4BjgrZI2ARcAC4FlkuYCTwGn1K6ElVOk7sdImkH2/8ENwJk1K2ALaaJ2p2AbIuleCvybiog1kpYBa4HdwDkRUXfTFQbSTvRTp7PJpgOMBlakV80NpC1oxPoV4wCrshq5e3ogmrmerpuVVVqOtuWWp46I9cB7al2OaoqITxW5dHxVC1IDRer+g6oXxIDmaHeKtSHpgWfBf1MRsQBYUOGiDclA24lidYqILuCIMhatLAbaFjRa/YrxEMHKaopu+RI0cz1dNzMzMzMrmQOsMkldoCuBP5K0SdLciNgN9HbLPwIsa9Bu+dc0cz1dt8asm5mZmVk9UbZfl5mZmZmZmQ2Ve7DMzMzMzMzKxAGWmZmZmZlZmTjAMjOzipA0SdKNkh6X9ISkRWlBld7rP5X0oKS/lPROSasl3S/pDwf4OZ+V9PYC6V+R9Iqkz+Sln5Y+90FJ/yap4OqGadPtX0kaU+Da1yR9aSDlLPAe+0vqkrQ+v/ySrpD0mKSHJV0qaWRK/6ikrw/lc83MrLIcYJmZWdmlzSCvA26IiOnAocD+pOV3Jb0NODoi3h0R3wFOBm6MiJkR8cQAP+6zQH6A8mngI8BhwF9L+lDO5SeB/xYR7wb+Hlhc5H1PBB6IiB0DLE+/JI0AlgE/Av4GuDEvkLsCeCfwx2T7vpyR0m8GPibpjeUuk5ntJWlceuizWtKzkn6Xc/5vFfrMmZKWlPH9Pi+p0fcjbUjeB8vMzCrhOODliPghQETskfSXwJOSLgB+ARwoaTVwPdkmknskfRD4KFnwMYlsY9S/j4irJLUB3yYL1J4nC6zeD7QDV0j6T+ColDYHODEi/kPSh4EbJG2NiNURkfvlaFX6nEJOIyf4kvRV4HTgaWAr0J3S/xC4EBgP/B74i4h4NKVfkeqwAviriNg/vd0lwIqI+F56jz3AlZJmR8Srad+i3s+9p7eMERGS7sj5HZlZBaT9tWZA1mMNvBQR36zwx34F+Icyvt+lwL8CPyzje1oJ3INlZmaVcDgpAOmVeoKeAt4BfAx4IiJmRMTXge8D34mIY4FZwDMR8Z6IOAL4eRoi9z3gExHRRvbFYUFEXAN0Aael9/rPiPhlRHwoIv4jfe6WiDg6IlYXKOdcsuCnkPezN4hqI9srbibwp8D7cvItBs5N5foScFFKXwQsioj3kbeJd0TM7Q2u0vkNEXFiRLyamy/V+zPAz3OSu4APFCmzmVWYpJfSz2Mk/UbSMkm/lbQwDUG+R9JDvcOdJY2XdK2ke9Pr/QXe883AuyPigXT+33J6zO5P15H0N+k9HswdLizp9JT2gKQfAUTE74ENko6s/G/FcrkHywpKT1Mfykm6MiIW1qo8vSRtAHYCfw58FTiE7Gn2eLJhPwD/I+8Jde+9Z5ANC/pMTtpBZPWcCPwY+BDwuYi4oYLVMGsFAgrtA1IsPddDwDclfQO4KSL+RdIRwBHAbdnoQ4YDm4dUQOlYsgDrvxbJckBE7EzHHwCuT19YkLQ8/dwfOBq4OpULYL/08yiyoY8APwEG8/T7IuDOiPiXnLQt5A2JNLOaeQ/wLuAFYD2wJCKOlHQecC7wRbKHLd+JiLskTSbbf/Jdee/TDjycc/4l4JyI+NfUzryceuOnA0eStaXLU6//NrLvRO+PiOclHZDzPr0PZO4pa62tTw6wrJj/jIgZ5XxDSSPS5rZDdWxEPA98PL3vMcCXIuKj/dx3LbBQ0qiIeDmlnUL2pelV4FRJPy5D+cwM1gB/lpuQ5hgdDDwBHFjsxoj4beoxOhH4P5J+QTaMcE1EHFWOwkl6N7AE+JM0FKiQ3ZKGRURPb9EK5BkGvFju9jKV8QKyh0dn5l0aBfxnuT/PzAbl3ojYDCDpCbLhz5A9KDo2HZ8AHJbzEGaMpDfnPMABmEA29LjXvwLflnQFcF1EbEoB1oeB+1Oe/ckCrvcA16TvRkTECznvs4VsPqdVkYcI2oBI2iDp65LuS93f70zpb0orXd2burJnp/TPSrpa0s+AX0gaJukiSWsk3STpFkmfkHS8pOtzPudDkq4bQjnfl7rtuyWtkHRQRGwH/g04KSdrJ/DTwX6OmRV1O/BGSacDSBoOfAu4rLcXqBhlK+r9PiJ+TNbr817gMWC8pKNSnpGSDk+37ATeXGrB0hPk64DPRMRv+8j6GDAtHd8JfFzS6DRU5/+D14Y9PinplPTe0t5VCVexN8jsLLV86X3OIFuk41M5AV6vQ3n9k24zq51dOcc9Oec97O3IGAYclYYxz4iIiXnBFWQPTUb1nqRRQ2eQLXKzKn3fEvB/ct7nHRHxA/oeGeAHMjXgAMuKGZ0z9ne1pFNzrj0fEe8FLibrwoasa/pXaa7BscA/SnpTunYUMCcijiObuzCVbGWsM9I1gF8B75I0Pp3/OYOclClpP7Lu+D9LcyJ+TLZSGGTBVGfKd3Aqy52D+RwzKy4igqyX+RRJjwO/BV4mm8Tdnz8G7lG2AMZXgX+IiFeATwDfkPQAsJpsaB7AZcD3U1s1uoT3/9/AOOCidE9XkXw3A8ek+twHXJU+91ogd8jeacDcVK41wOyU/kXgr5QtUjEB+PcSytbr+8BBwMpUxv+dc+3YVDYzawy/AD7feyKpUI/3I2TzU3vz/GFEPBQR3yAb5vdOsqGFn0tDBpE0UdKBZA+0PilpXErPHSLoBzI14CGCVkxfQwR7e5a6yQImyLqsP6a9+8KMAian49tyuqv/K3B1eiL7rKRfw2srY/0I+LSkH5IFXqcPsuzvIptg/8ucuRqb0rXlwHdT43QqsKzA02EzK4OIeJrU01Pg2gayOVW951/LOb6V7ItE/j2rgQ8WSL+WLOgptVxnsHfZ874sAS5PP4mIBaRl5vPe70myhTny/Q7oSO1bJ9mXpFLLWPD/z2ne6OiIeKjQdTOrS18ALpT0INl37zuBs3IzpJVH/yBn6OAX0zzRPcBaslVHd0l6F9mDF4CXgE9HxBpJC4DfpDn095OtsgrZYj3eO6/KHGDZYPR2f+9h739DIusxeiw3o6T/AvxHblIf7/tD4GdkT7mvHsJ8LQEPRsQ+q2ylJZt/SfaEuZNsaWgzs31ExGZJ/yxpzCD3wmoD/p+yb0IvAp8rQ7EmA39dhvcxsxLlPgBK5/unn3cAd+SkH5Nz/Nq1NDcqdyRQMZemfEsi4twiZVnE/8/eeYdZUpX5//Pe23kiE8hZQBRWBFkBRURXVNQVEy6IIibWNezPtGtCwSwGVlxExEUliChBBRZQDCCIZAEFFFiCQ3BgYFJPT0+H+/7+OHWn3zpV5/bt7jvTPd3v53n6mapTJ1Xdunfq1Pme7xtUOnH6mcCZNk1E9iasXV3WRNtOC3GJoNMqfgG8P3uQqH+py7gWeH22FmsLMvkNgKo+SrAyPo4g+RkvdwHbSGZLKiIdZq0GBJngfwDzVfWmCbTjOM40R1V/Ms7BFap6TWY1/yxVPUhV72tBf25K2M07jrPp823ya7omyiLgUy2sz2kSH2A5KeI1WKNZtH8OaAfuEJE/M7LmKeZCglzvz4RAmzeQX5fwQ2CJqt413o6r6jrCWo2TsjURfwT2M1muILwFPm+8bTiO4ziO47QSVe1X1bNbWN+VmRzb2chIWIfsOBsPEZmtqr3ZYswbCXEb/p4dOwX4Y+aKU1b2QWDfDTXdndm0X+BxsBzHcRzHcZzx4DNYzmRwaeYOdg3wOTO4ugV4FsH1L8UTwK9FZN9Wd0pEfkxYDNo/Wl7HcRzHcRzHKcNnsBzHcRzHcRzHcVqEz2A5juM4juM4juO0CB9gOY7jOI7jOI7jtAgfYDmO4ziO4ziO47QIH2A5juM4juM4juO0CB9gOY7jOI7jOI7jtAgfYDmO4ziO4ziO47QIH2A5jjNjEZGqiPxRRC4tOSYi8k0RuU9E7hCRfSajj47jOI7jbFr4AMtxnJnM/wPuThw7FNg1+zsW+PbG6pTjOI7jOJsuPsByHGdGIiLbAq8E/ieR5TDgLA1cD8wXka02Wgcdx3Ecx9kkaZvsDjiO40wS3wD+E5iTOL4NsMTsP5ylPWYzicixhBkuZs2a9Zzdd999wh37671/zydUpJBnYHaxnHTU8sUqWsijUZIUqy7kUYqZhHymWq0kT5Skg02+04vKyXAxi8bNlZxHdd0oZUrKleWplLRfGczvD3cU83SsyGfStmohj7bnr0ll7WAxT0m5mFpHVM/yNcU8m80qpFX78u3VOouPBbKqL7/fWXKyTbLrntuOu2yde265v5C2muXLVHXxhCt3HMdpAT7AchxnxiEirwIeV9VbROTgVLaStMKIRVVPB04H2HffffXmm2+ecP9e9LITc/tDPcUH7EdfUBysVLfPP1R3dRYf1geH8nV1tA8V8gwNV6P9krYq+cHcuv7iQ7dEeYYf7y7mGS5e5lpnvlzb6mL7Wm28DzDngXzdZYOgWnvjfYDOFcW0nqX5PvZuXezjtpfkB8pDi4pj+f4tunL7s+98opCnrFxM7w75nL5L+wAAIABJREFUazv7x9cX87xs/0La/Fsez/dnpwWFPG1X5u/ptu13HrU/KS6/+cTRM43CIZXDC2m/0gsemnDFzgZHRO4E3quqV02wnk8AO6vqO5vIK8D3gNcA96rqcyfStuM0g0sEHceZiTwfeLWIPAicB7xYRM6J8jwMbGf2twUe3TjdcxzHGRsi8qCIrBWRXvN3ygZu82AReThKO6Hk9xQAVd1jtMGViOwoIioiyUkAVf1iM4OrjAOBQ4BtfXDlbCx8gOU4zoxDVT+uqtuq6o7AEcBvVPXNUbaLgaMzN8H9gZWq+lhcl+M4zhTin1V1tvl732R3aAqwA/CgqhZ1s46zgfAB1hRERGb0j4CInD3ZfZhMZvrnP5mIyLtF5N3Z7mXA/cB9wHeB90xaxxzHccaJiHSKyAoR2dOkLc5muzbP9l8lIrdl+a4TkWeZvA+KyEeycBUrReTHItIlIrOAy4GtzYzZ1qP05UEReUm2/VwRuVlEVonIUhE5Kcv2u+zfFVmdB5TUs36WzMx4vVVE/iYiy0Tkk9mxdxCMjA7I6vpMlv6uLATHUyJy8Wj9dpyx4gMsZyryusnugDNzUNWrVPVV2fZpqnpatq2q+l5VfZqq/oOqTnxxleM4zkZGVdcBFwFHmuQ3Aler6uNZjL/vAf8KLAS+A1wsIp1R/pcDOwHPAo7JZoQOBR41M2ZjkVGfDJysqnOBpwE/ydIPyv6dn9X5hybrOxB4OvBPwKdF5BmqegbwbuAPWV3Hi8iLgS9l57QV8BBBKg6AiFwqIh8bw3k4TgEfYDmO4ziO40wPfpbNQtX/3pWln0t+gPWmLA3gXcB3VPUGVR1W1TOBdYB1Rfmmqj6qqk8BlwDPbkFfB4FdRGSRqvZm4TAmwmdUda2q3g7cDuyVyHcU8D1VvTUbfH6cMMO1I4CqvkpVvzzBvjgznGnrIlitVrWtLZyeNZeKjabsvh1tWn8oaxtmPani0WmZ5VijMnH++rGuSoVtOjvXN2vbt/2KnYMn0n5sjdaon2VlimbQo7dfNTvWznlWtcLOs0bOv2Yqr5kLYK2km26/Up4+bOqNPdVSdTdzvZs9Zj/XrkqFLbLPvxblS13zRuc/EVLfg3i/nm/BokWs6e/XJ5980l/eOI7jbHxeo6q/Kkn/DdAtIvsBfycMkH6aHdsBeKuIvN/k7wCsbM5aYvZFx8bLO4DPAn8RkQcIA6RLJ1Bf3MeSYBZA6Put9R1V7RWRJwlhOB6cQPuOs55pO8Cq1WoMDAwAMN+kxwa0dt+a5Q4ktu1TY/zN7aIcO4izZruxY7Ctr3tgpFU7kLJuwaui8m1NbPck2o8HazZainUttg/VNsRMSZiY9dhrZj+LuXNNH00n2+aAvermUrC617RvRkKpzwjyn0uPuQB2sNVr6l0alU8tiLLXz16jRhFrUl+4frPdC+tPOm7bmm6n7tF4UGZpZtRjB5j9ifS4zfq9+M4jjuDiq69uNMacUZTZSQNcWTu/cZ5D9t1QXXIcZwaiqjUR+QlhFmspcKmqrs4OLwG+oKpfGE/VE+jTvcCRIlIhLA24QEQWTqTOJnmUMKgEIFtLthB4ZAO368wgptwAS0S2A84CtiQ8K56uqidnCxDPAVYDR6lqb4NqHMdxNlniuEOPffp5hTzDc4rxq2ZFca/mdK0r5Bmu5YfZtZLIut1RbKyB4eJrg6rkh/JaUs/AunxQKVlY7E+tv+S/of6ojyWxqWpxUOWB4uuD/oX5/e7HC1noXpKv58l/KNbTubz4vDfckT/f7mXFPHH8qjjmVRllcahiuh54qpDW1kTdZbGx4ruotJZddubye8YWvyr1csGZVM4FfgY8CXzSpH8X+KmI/Aq4kfAu9mDgd2YQlmIpsFBE5qnqSpNeERF7O2kmx1uPiLwZ+IWqPiEi9ffHw8AThOe/nYF7xnKCTXIucJ6InAvcDXwRuEFVH9wAbTkzlCk3wCL83n9YVW8VkTnALSJyJXA08H7CF+7NwGmNKmljZHaq0Vv7/kS6LWNnKuwFazRTYWk2n/1vPiVrtDNQMSlZn302seXtucQzUPZYt5XVme2eRlMlhrUmny0yVHw+LLQB+ZkmKyu0fawktuPyqfQO8yF3DeTzNSMRTElNY2qJ7dTnFT/s2GOp87f/g8X3Xnti2z6W21mz3OcV1WWP1dv3qSvHcZxJ5RIRsf+lX6mqrwVQ1Rsyl9qtCe5/ZOk3Z2u1TgF2BdYC1zLi5pdEVf8iIj8C7heRKvDM7NCR5Nd8PUKIJWh5OXCSiPQQjCaOUNV+ABH5AvB7EWkHXt6C9Vm2z78WkU8BFwKbAdcRwnWQtX05cI2qfrFVbTozjyk3wMrizDyWba8WkbsJutgq4Zmuhj/HOY7jOI7jrCeL6zdanl0S6VcAVzRTr6qeEO2/PSpyQvbXsK6S2IM236eBTzc4foLZfpDouVBVDzbbPwB+EB0/jcSLelU9NNWu4zTLlBtgWTJHl72BGwjTxGcDKwnuN47jOI7jOI7jOFOKKTvAEpHZhOnbD6jqKsI6+oNGKbNe3VRhxFDBSpti+dZQYtsaTlgplQ0K0ezFa1rKZrbt/H7KxdAaUUDaaMH2OSd3NA3WIrlfTjLXVZ7PSuzaY8cOQ4/RYdryOWMLs90eXVhbxppU2D7aPCnpIeT7bNu0dc2LJIKplQ0pd8dGTo+2jP28U0YicfmU3LSRlM+SuhdSdaWMNCDf//o1agceeOCB3HdRVeNb1XEcx3EcZ9oyJa2UM83thcAPVfWiZsup6qz6X8maaMdxNgI77bRT7rs42f1xHMdxHMfZmEy5AZaICHAGcLeqnjTZ/XEcx3Ecx3Ecx2mWqSgRfD7wFuBPInJblvYJVb1sEvvkOI4zKvf++WEO3e2jo+ZrJo/jTCXieza2bW/Wlj3OZ2PClbVT1pazaSAixwDvVNUDJ6n9fyOYbcwCdlDVJyejH87MZMoNsFT1WlrgElhhxJLcrkmJncWbsWO3csPuRHpcd2qtla03XgNjj6Wswe0amvgi2fU5OWt2U3G3WVCUsi+H/Loruz4ptb6pK1qoZOseMMdseVtvTwP/+YF48U+GXUNl6+2NIqTZ8lVTptO0b/vSGZ3LsA1obOpaZ7bVWtGb7TjqT6OAzGV54vIpZ/xm1nPF5ZsJWtzou2OpX9YpNyU+BWnbZeeG+1CMhdRV8lgw+IziF2NoePRPoKMt/yUui4M1qz1f98r+7kKeODaWSPFXa/68fKjs5ctLFKPVYjntjGJsDRX7WF2bP9euZcU8legSDcbR4YGh7nw9PY8V8wzMLYkVtizfx8FZxTzLn5n/Yet+cvTYFkM95Sstr/npR9Zvv+C1Xyscb+uLfl3236uYZ1l5SCM7gCkbKJXdo80QD548NtbkIiI/AN4K7KeqN2ZpuwD3alkgu02YbKnJScD+qnr7ZPenTvYZPKyqx7WgrhcRnBb3AZbHbo+ZUdz3gf2AvwHvU9VfmeOLgZOBVxAeFy5T1aOyY18DDiPEpH0E+KKqnmXKng68kGDr//bMqbF+7DRCOKU67cCAquYDA05z/HnIcRzHcRxnZvAU8PnJ7sRYEZGxTghsQfBfunMDdGeqsAb4HvAfieM/Av4ILCQElr4gG1TVuQj4O7ADsDlg39ysAf4ZmEcYlJ8sIjbi/e3Ae4Bb40ZV9d2qOrv+l/Xj/DjfdMcHWI7jOI7jODODM4FnicgLyw6KyIMi8hKzf4KInJNt7ygiKiJvE5ElIrJcRN4tIv8oIneIyAoROaVYpfy3iKwUkb+IyD+ZA/NE5AwReUxEHhGRz2fBihGRY0Tk9yLyXyLyFCVxtUSkU0S+ISKPZn/fyNJ2A/6aZVshIr9JnOv+InJd1u/bReTgLP0IEbk5yvtBEbnYtPs1EfmbiCwVkdNEpDs7drCIPCwiHxaRx7Nze1t27FjgKOA/RaRXRC7J0j+anf9qEfmrvUaNUNUbVfVs4P6Sc9uNMLN1vKquVdULgT8Br8+OvxTYDvgPVV2pqoOq+kdT9/Gq+hdVranqDcA1wAHm+LdU9deA8YkuvcazsjbPbOacphNTTiLYKiqMWEenLLTr+epYyZ4k8qTKxvu2fDWRJ8YKPFJSQrsd12XPrcMcnGVUKtZOvT1hkw5pO3SLleXF5WPb97J8to3580e2Y+liX195XV1NSA8B+s3X39Yt1mY+Ydke171qVfm2zWPPvRb99KSs3S32fokt4m15K/Sy95stY2WAkP8lTNmx23obSVrLrOFT0lbHcRxnStAHfBH4AjDetVH7EaRhBwEXEwIUv4QgBfujiJyvqlebvBcAi4DXAReJyE6q+hThoXspsAthndSlwBLgO6bseYTZlTJz6E8C+wPPJvz383PgOFX9lIjsATwAzFfVwuIGEdkG+F/Cmv8rgH8CLhSR3bNz+q6I7Kqq92ZF3gR8Pds+Edg5a3cQOJcg1ft4dnxLwszPNsAhhJmjn6nq6dks0HqJoIg8HXgf8I+q+mgm66sPMg8ELlVV84TUNHsA96uq1QXfnqVDuG5/Bc4UkUMJg7SPmM/NXqtu4B+BU8fRj9cDTwC/G0fZTRqfwXIcx3Ecx5k5fAfYPnuwHg+fU9V+Vf0lQUr2I1V9XFUfIcx07G3yPg58I5sh+THhof6VIrIFcCgh1ukaVX0c+C/gCFP2UVX9b1UdUtW1Jf04Cvhs1vYTwGcIA6ZmeDNhzdFl2SzNlcDNwCtUtY8wWDsSQER2BXYHLs6crt8FfFBVn8oGMF+M+j2Y9WswM2jrBZ6e6McwYXn9M0WkXVUfVNX/g+BJMM7BFYRwriujtJVAfR3UtsBLgd8SBoRfB34uIotK6jqNMDj7xTj68VbgLFWdce9ffYDlOI7jOI6zCSMiR2Wys14RubxRXlVdB3wu+xuPucVSs722ZN/ayTwSPVw/BGxNWPfTDjyWSfRWEAZ+m5u8S0bpx9ZZfXHdzbADcHi97az9A4GtsuPnkg2wCLNXP8sGXosJHmq3mHJXZOl1noxmzfrIX5P1qOp9wAcIEsjHReQ8EWn2HBrRC8yN0uYC9RmttcCDqnpGNhA8j3C9n28LiMhXgT2BN451kCQi2xGMMM4aLe90ZNpKBIXmnM2akfVZGjm8WWx5e0faCx57gNm67Vy4lXz1JNz9IC9NSzn0tSWkcLOjr76V38XyvzopWRykZXm2TduGbT9uz+5bF7/UuTQrV7TYPjYqb9uxZdYYGeM6c+5d0WfUbuqy7oT2lzjlBhkzlNi2xK6FZbK+GHu/tSW24/L10/c3No7jOBsfVf0h8MMxFPk+8J/Aa6P0NYyYMEOY3ZgI24iImIfz7QkSvCWEx6hFZRK+jNEe6B8lDJTqRhbbZ2nNsAQ4W1XflTj+S2CRiDybMND6YJa+jDA42SObsRsrhXNS1XOBc0VkLmGQeSLNz8SluBPYWUTmGJngXoSBI8AdBBOLJCLyGcIs4wtVdVWjvAmOBq5T1cIasZnAtB1gOY7jOM504UUvM7GgElbu4+WQ543dVK4Zy3W3ZZ+6qOqQiJwAfDM6dBtwRDYLthfwBsIMzXjZHPh3ETkVeA3wDII070kR+SXwdRH5FGHGZSdg27J1QAl+BBwnIjcRBi6fBs5psuw5wE0i8jLgV4T3mfsD96nqw9n1uQD4KrAAuBJAVWsi8l3gv0Tkfar6eLaea09VbUZCt5SwfgtYvwZrG+D3hGXSa2nyXaWIVAjLo9vDrnQBNVUdUNV7JMSSPV5EjiMMlJ5FZnIB/BT4moi8NbsWrzX9QEQ+Tpi5O6gsfpiIdGT9FKA9a3tA1Qat4WjCYHFG4gMsx3GcFjE0u50nD9hiJMFuZ9QaTUtmrJuXV+2sfloxgtqOi5YX0raalX/JuKy/GHeqq5p/WdxRLdbdP5T/r2FxT28hT99Q/kQeG4jVKFCNYmNJyWND5+yiFmDd6s7cfhwXC6C6Ot/H/kXFl92zl+Sv4+wHi/W09efLrZtb7OTCPywtpD36ivyL/c2/eV0hz5P/+rzc/tqFoz831dqLiq159+f1DoWYV0DXA0+NWnfvHosLaV1LG5qAATB03/25mFapgdNY85TRTFDhsjrC0hhnjPyIYMxgA+99KktfDlxNmPFYUCzaNDcQDDGWEQYXbzAP7EcDXwbuIqwNup+xPZB/niB7uyPbP58mLehVdYmIHAZ8hXC+w8CNwL+ZbOcSzBlOjWbZPkoYzF2frVl6BPg2za1ROgM4P5MWXpXV82XCwHMQuA44FkBEXgBcnlmdl3EQYQ1VnbWEz+zgbP8I4AeEz/JvhGv/RHb+T4nIqwnGFd8C/gIcpqrLsrJfJAit7jXfrS+q6hez7V8S5H8AzwNOB16UnRMicgBhndeMs2evM20HWDbQsP0vNf5vKfUe0F4Y+7PdSG2WcgtsxpEwLm9lWl0JWV2j4L4piWCqfFxXSgpnt61EsD/6Pzolq7NSwFQw41j6aPNZR0HbRqNAxTlXvya2B1NiBfLywZyscMXIdq9Jj4MWWwbNM5OVGKaCFkM6UHBKLhjf3/aROPXlT7URU1Y+YTjpOI7jTDKqeky0XyOsr7Fp9xPc+8rKP0i0ZktVt43232y2f0B4wIfglBfXt5IwoPm3kmO2bCmq2g/8e/Y3al9L8tzAyCCh7Pg1ZXVk7X4i+4uPXUUYWNi0Hc32vQT3QctzG7SfGlzV20qeY3YNDm5w/BrgHxLHRrt2yXqz438gOEPOWHzJhOM4Mw4R6RKRG7PYJ3dmWvM4z8ESYrfclv19ejL66jiO4zjOpsW0ncFyHMdpwDrgxaraKyLtwLUicrmqXh/lu0ZVXzUJ/XMcx3EcZxNl2g6wKozMTVr1WqMTbsJsrtCGxa4asMowK5uqJrYhPw8831QwyxzoNumxLC4l5bOyPLudkgHG+yn5npXSxRJD27eUQ1/VbHcmZJCQlhta7DWqNghUvLa/PN+waWNdJHe0Usj2lEQw4Wg4EFlFxvt1Oq2jYAOJYo8pn5IF2hUt8f1u7zkrl7WrUGz5Ro6G9lTql6+1S+83HJmjVX1hUXv2N+PidDiO4ziO03pcIug4zoxERKqZy9LjwJWZHj/mgExGeLmI7JGo51gRuVlEbh7qX7NB++w4juM4ztTHB1iO48xIVHVYVZ9NWJD8XBHZM8pyK7CDqu4F/Dfws0Q9p6vqvqq6b1vXjF7T6zjODERErhKRd2bbR2X2644zo5m2EkFhRNJUNLQtxzqmpbRC9oLFBnHdiWNWOtio/Fwjq5trHI+7ErLAuUVX5PVYWV2qvJWyNZKl2XwpKV4jiaHFtmNleSnpHeTPJSXLs+cYO++lAiLbumz6cHQtUtJL22aZ/TQU3RXXJFwQraNgo6DHdr9mgxubPI1cAFNvVFLfESsLjI0arRywfsk2xR8UVV0hIlcBLwf+bNJXme3LRORUEVlkbGwdZ8bgMa02fUTkB8BbCXbcF5v0bwD/D3hb5t43bsYR8HjGISI7EgI970ewT3+fqv4qkfdFBCv3fYDl1pFwtLpE5JUEG/49CatlLgE+ZAIP23psLI4ewmNF/dHgX7PPtax/s4G/Ay9V1euiY98G5lhnyZnEpvg85DiOMyFEZDEwmA2uuoGXEMVfEZEtgaWqqiLyXML4tBBw0TLcCSt2Ne62JSPaoVn51zeVknV5Ws3nmbVNMQ7VbvOeKKQNar7BHWYXY2V1VPJvEFYOdo+ap6bFE1k9kI9VNaszscDQUKkWV7pqregGrMP5tDK/4KGe0ZfMrdkqvz9QEuNq1qP5euYsKQYnWPKaLQtpw9EbsuVvP6CQZ+F38rGx4rhYUIxxtXLn4kLTttX5PvVvUYz/MHTf/fmE/fcq5Jl9Z/GeiWNjNYgsMSZGi3flTBr3EAZZFwOISBtwOPB/k9mpqYSIVFW12Xfz4+FHwB+AV2R/F4jIrvUYVRFrgO9lZQq28KPUNY8QF+x3hHf95xICJ787rsTG2hKRB4F3pgZ9UbleEbmQENNs/Q9eFoj4jYRYXDMSlwg6jjMT2Qr4rYjcAdxEWIN1qYi8W0Tq//m8AfiziNwOfBM4IjPHcBzH2VS5BHi+iGyW7b+cEKj37zaTiLxdRO4WkeUi8gsR2cEcO0RE/pKFsTgF8w5ERI4RkWvN/skiskREVonILVnw3PqxE0TkJyJyloiszkJm7JvquIjsLiJXishTIvJXEXmjOfYDEfmWiPxvVtcNIvK0MZT9tohcJiJrgBeJyEIRuSTr900i8vn6eWXtfD3q2yUi8oHRLr6I7EaYjTpeVdeq6oXAn4DXl+VX1RtV9WxCEOYx1aWq56rqFarap6rLge8Czx+tj4l+V0XkUyJyv4gsE5Efisj87PCZwBtFxL51exUh8PGvx9PedMAHWI7jzDhU9Q5V3VtVn6Wqe6rqZ7P001T1tGz7FFXdQ1X3UtX9Y/mD4zjOJkg/YfaqPrNwNHCWzSAiryHMlrwOWAxcQ5gpQUQWARcCxwGLCDNfjR7abyIE1l1AmEE5X0TsROmrgfOA+Vm/TimrRERmAVdmdWwOHAmcGpkPHQl8BtgMuA/4whjKvinLPwe4FvgWYfZoS8KM31tN3jOBI0XC4oDsmvyTuUanisipieuxB3B/JNO7PUsfK2Ot6yDgznG0A/AfwEuBAwnrlgeB/8qO/RZYDfyzyf8W4JwsmPWMZFpLBJsZPdp1JDZ/LZFufxXmk2d2Il9HoiOx5bi1UE+tlbLrfuL1OTafPWa3O015a/neaA2WXXc1x67BStisA7SbfbumydqUDye+dvHXMWXTHp9/WRtxPnuN7Vonu1ZqXVS+I7HuKrWey64nG4yua49Rej31VHmb9nrF94i95rbMrMTntzbaN8u2kjbvdorGfhR2nVecr376DUO/O47jOFOBs4Cvisi5wAsJg4f3muP/CnxJVe8GEJEvAp/IZrFeCNylqhdkx74BfDjVkKqeY3a/LiLHAU8nDAQArlXVy7K6zgZSs0CvAh5U1e9n+7dm0rQ3MDJouEhVb8zq+iFw0hjK/lxVf5+VHSTMAu2pqn3AXSJyJnBwdk43ishKwqDqSsJg9SpVXZodf0/qehAeFVdGaSuBbRqUmXBdInII4XPebxztQLgn3qyqj2b1fQa4U0TensnozyYM1i8QkQUEueInx9nWtMBnsBzHcRzHcTZhMve+3uzv8kZ5VfVawszUccClqhq/i9sBOFlEVojICuApwvuzbYCtgSWmLrX7Jf36cCY1XJnVNY8w81XHShP7gK5sXVjMDsB+9T5ldR1FmGFK1TV7DGXtOSwmTEAsSRyHMItVN294M3B2SZ/L6AVii7K5hBmgsdJUXSKyP2H27g2qes9YGxERAbYDLjPX74+EMcTCLNuZwMuz9c1HALer6l1jbWs6Ma1nsBzHcRzHcaY743DvO4fgTveikmNLgC+UOceJyK6Eh+36vtj9KO8LgI8SZnruVNWaiCxnfGKHJcDVqnrIBiprRRlPEIQd2xJMQaB4jucQ1ujuBTyDRBiPEu4EdhaROUbatxdhADRWRq1LRPYmSC/frqrjWg+VzVA9ArxOVW9J5LlXRG4myC+PJJKdzkRm3ACrkW110bupmMdK/2LvLSsRTEn8rJQsln/ZMlaK1oz0Ly5vpYBWvteR2I7letaC3crqUhJF6Uj7iVeNlq67I+GZbmnk+d6E//u6vny9VjKYkvU1wsryZplrbOtq9LlYrCzTtm+v/5qiYdyodQ2aMva0rAQW8lJAq4S0dkn26tn88eWy+aqJPI7jOM6U5JuEtVW/Kzl2GvA5EblNVe8UkXkEG+7zgf8FThGR1xEe3N9LfibIMofw38gTQJuIfIzijEuzXAp8WUTeQlizBWFtV29dytiqsqo6LCIXASdIiO+1PUH+9jeT52ERuYkwc3VhySxgKap6j4QA98dncslDgWeRMLnI1nl1EKKmSLZ+raaqA6PVJSG24xXA+1X1kmb614DTCNfw7aq6REQ2B/aL6j2TYAu/FUGWOaOZcQMsx3Ecx3Faz6G7fXT9dsE2PsPt26cGqvoUCYc3Vf2phPhG52XrrlYS1hqdr6rLRORwwgDt+4QBxu8TzfwCuJwwC7SGYIqQlBOO0t/VIvJSwrqqkwjv824HPrSByr4P+AFBdvhXgoFF7HB4JuH8/59NFJG6UVLBDj3jiKzu5YRB2xvqFu3ZrN/lxjb9IIKJRJ21wNVk68Ea1UVYG7cYOENEzsjSHlLV8RhqfIXwXvU3koUwIZy7HWD9GPgGcJmqNgxpMhOQ6eo6vEBEX5Zt24X98QyW9ZQc6wzWZlG+eWZ7Q81g2e2eKPKrnWmaSjNYuameVNRfyyTMYPWZmyQODtzMDFavmUGydcXYY9bkYunjI9uNZrBsm7auVaaM7X7clRVm2zZjDSzsbFajGV57letlXvuhD/Gdq6/m5ptvnpF+F507bqtbHf/vDfPEP7lScqW65uVvwh0WPlXIs9f8Rwpp23Tm414tH5rVsC8Ajw/MKaQN1fJzn0+sm13IE9M/XHxft6I/P8+/sq8YZam/r/jLW1sRpbUV/5+q9ub7WO0rXsgonBdb/WH0WF2rt2svpNXai3V3rsj/zqzervjb1LM03+/OVeMz1Grry4fk6V9QvNazf3x9Ps9hxbXsXT+/oVj3Ljvn9psZGB3yvM8X61mWX0KysQdYInKLqiYtvh1nIojIicCWqvpWk3YQQSq440x2y3PKmdYzWPX/WxtJpuwFsP+lVxLb9nEh/m/YPrDb8YUdCNnBSjzAsmXsQ72VgqVcAONjcxKOhCm6ojvBDqTsdm4gZTvckQ86SiXxfF0zDxw5jZ35ZHqih0Lbjh2gDZhhgamrsyc/jO7sW7N+e3igVlYkR/y5pAbFw02MG2N3RvsZzY9tKDPss2XsaKgJWaJts2ZGVfELBXsr2PvaDsTso31bYjtmuIk8juM4jrMpICK7Ex4L/wT+9o0YAAAgAElEQVT8I/AO4J3meDth5up/fHDllOFLJhzHcRzHcRxnhDnARQRp40+ArwM/BxCRZxAEIVsRJHGOU8BfODuO4ziO4zhOhqreBOySOHY3MLr+2pnRTOsBVn16zsr6hqM8Vg3WlchnpYD2GxUr71JOclYWuGCBaTuSoqXWB9ny3abRaoOlSqm1VjaPmO1Z0clIl41ubK5gh7kajWz4kvo7IyXMaSqNRLAr0j5WzDEj98vnMXXFH0zXSJvVgUGzPSIxbO8YEcYNNwi6bLGnOJxYmxYHPbYLn6yoYG7CVyleD2bbtJfJ5us1er9Yxjo3sd2b2E5JZaHckTCW4DqO4ziO48w0XCLoOI7jOI7jOI7TInyA5TiO4ziO4ziO0yKmrUSwwojkz8qWYrPfjsS2lQja8la8FrvzWSmelfU1cg5MYeVfVj5m02OHulT7seKuTrvty9x5+YO5ThuhWaqyWMs2ZK5gTrto6zLSQysRbCg9jEWe9XptNOZIGGckgnkXQuOx1z8SI7A6FLVhywwZiaFJn9cxot3ThH075CWDttqUpDP+jK1zof34ck6V5qNoH92VOuSzfTHpZVbsddaU5JueQR8cx3GmByLyPODzBGe8GiHQ8EdV9a7s+MHAbxgxl11JcMo73tRxGPAZYGeCQvx24B2q+mCDdk8AdlHVN2f7mrWhhEghtwGnq+qPW3Sqtu3PAa8BngF8XlVPMMe2Ar5DiHG1FbCTPQ8R6QS+Dbwh6+9XVPWk7NgigvHF7oRHxbuBj6jq77PjAnwOeBswG/gj8F5VvTM7vgA4A3gpsAz4uKqe2+rzdyaHaTvAchzHcRynNZTFvhpXPZXDc/seeHjjISIHAL8EPgkcRlim+yHg9yLyHFWtBy97VFW3zcrsBFwjIn9U1Z+JyC7AWcDrCAOx2YQBwnisyvdS1fuygcqhwCkisruqfmYCp1nGfcB/AmWBf2vAFcCXgOtKjp8A7ArsAGwJ/FZE7lLVKwhLlt8O3EsYKB4GXCIim6vqEHB4dvxA4CHCwPZsYJ+s7m8RBqhbAM8G/ldEbq8PwJxNGx9gOY7jtIoKSLuZ6ysLItydj05WqRSfS3ZYkA8YvF3PikKep3U9Xkhb0JafMt2ybWUhz9+H8rPVPZXiNOd9fZvn9ue29xfyLOvPm2itHSwG6J3ftTa3v26o+F9O3+rirHh14brc/vCTnYU8Ek2pRjGWAahFioElLy5KCNoj35zuJ4r1VAaLc7NxYOHtfvb3Qp6hRfkgznEwXoDePRbn8/QVZ+nbrrw5tz97/72KbR2Sj7FbFlS4jFRA4Bwl7V153XFN1W+JB1fORucrwFmqerJJO05EnkMYSBwdF1DVB0TkOuCZwM8IA4EHVPXXWZbVwIUT6ZSqLgPOFpG1wDkicoqqPjmROqP6zwQQkaNKji0FThWR1PPw0cDbVHU5sFxEvgscA1yhqv3AX7O6KwShx2bAAuBxYCfg2vrAVUTOAT6Ybc8CXg/sqaq9wLUicjHwFuBjrThvZ3LxNViO4ziO4zjTGBHpAZ4HlE0Z/gQ4JFFuV+D5wPVZ0q3A7iLyXyLyIhGZXVZunPyc8OL/uYm+XCoiKxJ/l7awH/X2NgO2Jkgg69wO7BHluwPoBy4myCnrb7/OA3YRkd2ywMRvJcyWAewGDKvqPY3qdjZdpu0MljCylqTRSaZs2u07ZVt+rlnrEltr2+VJqTVQjZYX2Xzz55dvtzU4Gbt2Z47tm63Y+nzbhTvxGix7cj3Wpj2xnqnPeIPHxywp/3nb+djiPbeGy7RvbNZzFyYub9d91cyb6P782/VkedvOgLWWL7esF1O+rT//ejy1Bs+uraom7P4B7GRHuzlm7f/t5VoTfSx2fVjOQt5MUHSaj65WniXUZbbrczJu0+44jjMlWUB4qf5YybHHgEVmf2sRWZHlnwP8FLgWQFXvz9ZpfYgwMJsjIucB78tmYsaNqg6KyLKsr2XHXzWR+sdB/SHFSgFWEq7JelT1WSLSBbyW/GPlY8A1hFmuYWAJ8GJTdywxKNTtbLr4DJbjOI7jOM4mjIgcJSK92d/lJVmWE96ZbVVybCuCyUKdR1V1vqrOBeYDa4Ez6wdV9XpVfaOqLgZeABxEWNc10XNoBxYDT020rhZRHzDGISQLWl9V7VfVHwEfE5G6pvZ4gpnIdoR3+J8BfpPNJvZG9SbrdjZNfIDlOI7jOI6zCaOqP1TV2dnfoSXH1wB/IBgvxLwR+HVJOqq6EjgX+OfE8ZuAi4A9x9t3w2GEGPY3lh0UkcvNIDL+KxtUTohs3dVjgF2EuBfQyISineCuWM/7Y1V9WFWHVPUHhDVazwTuAdoyCWazdTubENNWIggjo8eUFTvkZYGp0WZPwjJ9VqQ87jSVd5oyVuZlJWKx47lVzHX3lOez27HcTDpMQz1mAbq1Ka9Uy9NnR7PSVhaYkvKlfMYhL7+z+ax+zbbRSOJnycn9THlbb39+gXyy/VSe2KY9hbV5tzbzDZpL2bHbfFbG1xldVntp7Hbq8sXtWzd9uz1s+mwlilbyNxipPu2PR1dJmuM4jjOl+BjwCxH5C/B9wk/2h4EDCDMtBbI1VkeQPfiLyIEEu/Ofq+rjIrI78GrMDNdYyezKDwVOAk5MGVyUDRybrL+d8N9ZhTCo6QIGVXU4O97FyH93nSLSlRlYQHBMPE5Ebia4/b2LYLuOiOxPuIY3ZuX/PctTd5i5CTg8k1A+ARxFGIDdp6prROQi4LMi8k6CechhhHVyzjTAZ7Acx5lxiEiXiNwoIreLyJ0iUrAFlsA3ReQ+EblDRPYpq8txHGdTQFWvBV5GsFh/jGAdvjdwoKrea7JuXZ8ZyvIsIAwOAFYQBlR/yo5fQVij9ZVxdOn2rI77gHcCH1TVT4+jntH4LkHmeCRByriW4NZXZy0jcsC/ZPt1jgf+j3Adrga+mlm0A3QSrNafBB4BXgG8UlUfzY6fSDCuuI1w3T4IvF5V67aw7wG6CY6DPwL+zS3apw/+wtlxnJnIOuDFqtqbvd28VkQuV9XrTZ5DCfFPdgX2IwSb3G/jd9Vxph5lcbHGY90+rrbd7n3cZIOsgxscv4oGL99V9c8k5IKjtHtCtF8SxGLDoKrHEKzVU8eTfVHVdYRYVm8vOXY1eflgfLwfeG/2V3b8KUIAZGcaMm0HWMLIfK9V4s2OfjZimV6dlNmezd8dle1MHGtPyMJsvZB2HrSSLdtGwfnPVjjbSgStFM+IvqyurC2KYZPTJVoXwYQNXizr6zP1WcmelfhZWWJOYhdJ9Maqi4uxsXcq5nc0505o+jWQj1OUcxHsMy+2rAzSWvINjZSPXQDjz3x98+ay2jKxOaNtxkr8rMKxM2EaWbZfx4oqqwkDyEbCyfqnt6lMiauqMvLGsj37i4MdHUaIGaPA9SIyX0S2UtUyFy4A2qrDLFg4YqTVu7YYv6lazX8IW88rxqpa1Jl3n9y2qxjkacv2Yrk5lbwz5orh4g23uC2/hvqJoaJp1U49+WBQf183r5Cnfzj/m7F6sHiucWysqhRvwGp7MW24L1+uMli8s2Y9kn8mWrt5IQu1yNayY1UxTxwHq62vGPNq7aLi89fi2/Pxwx46fMtCnm2+lI9bWvbV6opiZa3ctfiZVf9l/9z+7B9fX8hT+M+8JHZVWRwugMvvObE0vU5hQFNSdzN4UGHHcWYKm8rzkOM4TksRkaqI3EaQZ1ypqnFk1m0Itrp1Hs7S4nqOFZGbReTmoVV98WHHcZwZQQMTik9Mdt8cZ2MzbWewHMdxGpEtcH62iMwHfioie2bylzplspHC9Iaqng6cDjBr162K0x+O4zgzgPGaUDjOdGTaDrCEoPmBvBNarHCzSjhJzOd1J1z8OiOJ4Cyj7LCywJS7YNwXKw1LugWmohHH+9busKM8IG5uO5YIpoILp8rHWIdCKxG0UsBUBOWCRM9IcazGLeVoGOvyUi6CqUDFA3npD7WERrTfXKNVRnpjJI7Vtnx43kpCfmfvvZTrZIztpi0fS1ct9vLlZKhm4sWefaMp7uniIqiqK0TkKuDlgB1gPUyIX1JnW+BRHMdxHMdxGuASQcdxZhwisjibuUJEuoGXENyjLBcDR2dugvsDKxutv3Icx3Ecx4FN84Wz4zjORNkKOFNE6rFRfqKql4rIuwFU9TTgMoLt7n1AH1nsE8dxHMdxnEZM6wFWfXqu0aIIK8fKmcoZaVYqAPCcKNBwStZny9vtWGGXkoZVZ5uKFywc2bYyQEhLBHNugdZF0MgCG0U9bjaKrSVnd2eczWxduT6avgxFEsE4cHAdK320zoN9kS2Y7bPNZ6WDq4y9WH/eiS3X5yHjzthr2rEBnBvIKDsZcX4bNJeomigSy1ZT90hOntqkxHC+ad9+XKt6TXq6eKlEsOgjNzVR1TsI8V/i9NPMtpKw13Ucx3Ecx0kxrQdYjuM4juM0z6G7fXTcZWM799iWvSx+lVu3O44zHfEBluM4TotQhOHayLRjmQ1htZKP+zS/o7+Qp62SjzrWXuKOEse8AlhYydvED2txlrl/OD+tOadSbL+vls8zqNVCno6oTwu7ihb1T/bnYzpVK0U9QXtH8dw0jt/WXjyPlbtF9awq9nFWEyvmqtEEea29JE+jqdx6+2tKEuN4UdffXsgyNCffYOeqBjH9GtC2y865/f45JSdy/f3jrnvMsbI2IGWDMpGNFrfWcRxnVNzkwnEcx3Ecx3Ecp0VM2xksBervZe272Fr0cnA44frdY1682qVCNr2R5btdwrRoUXldsRt4vF/emTnljUDeWt2ux7LpKWv2eD1X+yK7Y7btW3P76jf/xj33Cjq+6HWqdsWOuRU7o/w9iTVYYvtl2o+vi8Wu77Jru+yHuWpknRSQv2Z2DZc9L2v5XjPrtGIqI29Z240dfbvpV1vbyI0Q32P2HqkmbP37zYREXzSpYJedDQ+Vp9vTzS1NI029K/7GxnEcx3GcmY4/DzmO4ziO4ziO47QIH2A5juM4juM4juO0iGktEawLuKzgbDhSn2nCdTznbG62Y9vsFCm5oXSNHOiITLBrCVVd1XYsp0OMpGjzNzPbCcv2dqs5sycTSQTZ1mwbiSErEtuRtbpdb1xce55hreHtuUQXWezqcStUs42YxfMla7tHjplr3m3kjvYax5LG1AdjdXVWbmhlhLFa0drkD1i54khf2hnxSa9U8vdI6r5MSfz6I11fyubdqipTTvyx3LDfdG18y/Idx3Ecx3GmHz6D5TiO4ziO4ziO0yKm5AyWiLwcOJkw9/E/qvplEdkaOAdYDRylqr2N6nAcx3EcpzXE8bFGs22HjWvd7jiOM5WYcgMsEakC3wIOAR4GbhKRi4GjgfcDOwNvBk5rVE+VEdGbFaJVozm7lOSqkpjbs+XjPHY/KSU0+itpy2fq7klZGhr53Nx5I9ubb5GvO2VXmHPrs3I/KxeMtWw2n5XvpcRgw4n0uIyNeWNkjNgYLrFc8YnEdlngGShqBK0LoXVBNOc4z0gMY4mgke/ltHgD5rp2mW3rVFiJYrNUrF7S9N+6EJobqRp9Q23ooWrifk2lx1QS97KVtNrvRNyXYaMQ7cv6VYxyNLNoqwyzsGfkc+2YU/xetEn+/qpI8arNb8/HuNqibVUhz5xK0V2zPap7blmMK+0spMV0SRTjqiTI01Atr/3ti+JrAcxpz/dxSV93Ic/QUPEm1Vr0vWkrubOiLFLy07R28/x+pcSptS2SvspwMZ7S/PuLn+Py3fLnW0tKoU1bUawqgP6efMGupcXPLI6VVVbP0H35GFddhRwwFMflKqFs4NRM8OFmggqX5XMcx5mOTLkBFvBc4D5VvR9ARM4DDiOMmWrZn0cUdBzHcRzHcRxnyjEVB1jbAEvM/sPAfsBXgbOBlcCbJqFfjuM4juM4juM4DZmKA6yy2SlV1YeAgxoWFDkbeB0E8deCTMGRkkIVyptj7QkXQOu2FgeBtXKqdrOdU5zZna5ILmNlZrNNQOHNF49sW+mfDToMeedAsTKgSmLbikhiWV1K1mfzWSlfLOEZSmxbt0ErEdzKbEfSx1w+2/5Ss20lPPEtZM+5lkg3H+z8vDyKPtOmdQi02zYib0cDCdZQIyllhg0A3ZHXPbUNjWiccoaGOdfJke34fk/JYNtS96uhM7rf7T3enl2yCvDAAw8gkrN+vEhV31Jeq+M4juM4zvSipS6CIrKDiLwk2+4WkTmjlSnhYWA7s78t8GgzBVX1Lao6S1VnLXARoeNMCjvttBP172H254Mrx3Ecx3FmDC0bYInIu4ALgO9kSdsCPxtHVTcBu4rITiLSARwBXNyaXjqO4ziO4ziO42w4WikRfC/BoOIGAFW9V0Q2b1ykiKoOicj7gF8QjC2+p6p3jr2ekaDCjWSBFiuB6rbxfI16zCr0rHQQIolgR3l6jq5ISmYrt3K/BQvKOxNL0cTK50Z3CssTd9JK2ayjmb2Yto1GoWbtsVSZlWY7tuOKbL7WYyWW1rU/dlez+ymfO9MviVwMZ9k+G4njgLEks3JPG3S4Ep1Lh/mMbNDomumXlQj25W/eqm3TBvpNxD+O5X72u2Alruusu5rZjgNzW+x93ZOdSsdUFB07juM4juNsRFr5OLROVQdEgjZPRNoYp2uzql4GXNbCvjmO4ziOU4K1YY/t3qHcFt5xHMdJ08oB1tUi8gmgW0QOAd4DXNLC+h3HcaY0FVFmt49MAc5tL8Y06q4O5vYrJQGc5rXl42B1VQYLeeJYVQDtUUytshhbXRIFgyqZ4e+M2muXojnLoI4uDXhk9bzcfk9H8TxWryrGxtLVedOdykCxrbY1+YW21eKlZjgKBhXHvALQaJK55FRZvW0xyFXb2vjaFhf+Ln9mJHMg3oc5S/LXpHeH4vXoeir/WZcNgiAfY6osDtXQTvsW637gqdK64vY8fpXjOE7ztHKA9THgHcCfgH8lzED9TwvrHxPDwMrs/6S5ifi9kJc5WcVWp9m2ZWyeuZGSzJbRlLQqFdkY0naHzWoc8w2ZbftwYJ8eaontGFtmMLEd9zFVn81npXsPm+2/R2Xsw0XKBdA+3MQSwZTDoL1GNphyo0DFRopoJZ72ZrAavf78g3Ly87dyzz5jwFeLnvb6R27GDuMoaJtMbUNeMmi3NZE+bMqvi57Lbb66cnG4CZNEx3Ecx3Gc6UzLBliqWgO+m/05juM4juM4juPMOCY8wBKRP9FgrZWqPmuibTiO4ziO4ziO42wKtGIG61XZv+/N/j07+/co0vZvjuM4k4aIbAecBWxJ0J2erqonR3kOBn4OPJAlXaSqn92Y/XQcx3EcZ9NjwgMsVX0IQESer6rPN4c+JiK/ByblgUTJuU2vp6srvS9mSU+7XR6TsFwv2K+bjGIXqNg1VNbOO7bwtvlS3u52TU68wCY3kVhJbKfWU8WLZwYSx2ybtr14Ebhdx5Tqiy1vbdbj84rWMa3HLqC3a6viNVSWlH19o3VuZk2UvWZ23VSP+VyHzHqs+HO066tS2HunP1pPZu6fqvn82wYaraEzXTOXdiBh827v935jHDAYfaHssb6s/MC4fEMnhSHgw6p6axYQ/RYRuVJV74ryXaOqryop7ziO4ziOU0rLAg0Ds0TkwPqOiDyPvHOA4zjOlEBVH1PVW7Pt1cDdwDaT2yvHcRzHcaYDrXQRfAfwPRGpTyusAN7ewvodx3FajojsCOxNFiQ94gARuR14FPhIWdBzETkWOBage4vZ8WHH2WiUWbNvkLr336s0z5XXHTf+Om09o9jNx3kcx3GmGq10EbwF2EtE5gKiqitbVfd4EKCudLIywNnR849VcKWs1a18KiWxAuhuMwkpC25r7R17xufydZenW4bimDIpmVjKmt1OYMaCymKMncZtxLdSrcGx0dJjrO7M1mv7HF3LJPac7XW1ssK4Lhu7xlzzTiNd7DLLDftMelsxfs56Boz8L76Zmimfs++vlSY36/Af34p1eo1yM1akDpmPov5JtnJKvFlEZDNga4KW9MHM0bTZsrOBC4EPqOqq6PCtwA6q2isirwB+Buwa16GqpwOnA2yzx3x9zvy/rT+2rla8xzsr+QtZLYmDNTsK6lQt+d6VxaHqiPLVtBibKY5ptapWvMd6owBSq4aLsZnieF59Q8XfqfldeXnviv5iPdX2ord/bV5ea6pPlvwG1vLnNjinqE+trMvniWNeAcx6NF9uqKd4zUo+RrqfzF/rNVsWKy8JX1Zg9XZ5SfNmdxWXLrctW53b7z+kGM+q7cqb8/tlwYGjPJD+pZ8oPgByHGcm07IBloh8OtoHwBeFO47TarKZ8vcCRxJGxE8AXcAWInI9cKqq/naUOtoJg6sfqupF8XE74FLVy0TkVBFZpKrLWngqjuM4juNMM1opEbSr97sI7oJ3t7B+x3GcOhcQXABfoKor7AEReQ7wFhHZWVXPKCss4Q3QGcDdqnpSIs+WwFJVVRF5LmGC7slWnoTjOI7jONOPVkoEv273ReRrwMWtqn+sVIC6GtDKAlNSqBgrC1xr1DpW+Rc7EnZbJVkzOq1KJEXpMFKRlDQsp1GM9CdrzRi3O+XiZ7ESl1gokpIS2lvGXoBYwmT3bT9tO0W5UHld1cS2xdYbS45qiW1blz0X+0HG+4lzmW2ufa+R8wxEXzF7A9mbrKbl6TEJvaq9xew93h7d720Jd0xbbZ9RKKWcBiHvtLm+/o2kEVTVQxocuwW4ZZQqng+8BfiTiNyWpX0C2D6r4zTgDcC/icgQQX54hKpuOj6JjuM4juNMCq2cwYrpAUpE4I7jOK1BRPYpSV4JPKSqyeUlqnoteW//sjynAKdMrIeO4ziO48w0WrkG60+MuBFUgcXA51pVv+M4TgmnAvsAdxAGTHtm2wtF5N2q+svJ7JzjOI7jODOPVs5g2WCcQ4S1CxvKoGhUqhWYnSm7rJQvlgimJFMWG2DVBleNjd+sd5lYzVbKhjAmJSW0ZXJBaPNOY6xaVZ6vzUoPzQmn+hhTtbeJtWG0srq4780Yudk8VtbXKGhxKlByqq5GpAIox9Zftj+zyrfb54xs95jPYSjqi3UYtO6QSavKqC/GeXDYZLMf6xwT5zi+R+29bJuxt1Lqdotvz2rJ7RqrXjcCDwLvqNuni8gzgf8gvNy5CPABluM4juM4G5VWrpj4vKo+lP09oqpDInJ2C+t3HMeJ2d3GplLVu4C9VfX+SeyT4ziO4zgzmFbOYO1hd0SkDXhOC+t3HMeJ+auIfBs4L9v/F+AeEemkOBXpOM4kcuhuH53sLjiO42wUJjzAEpGPE9y3ukWkro0SQhTY0yda/3hRylVvsczJSqvamrgajRR6a4z72uy2hBbLVtATycdyMrGEzM2eQBxouN8Erk0FJ64l6m0kEbRBj6spd8GYSmLbkpL4xRLB1Adjy6eCKcftr6McW9ea6JiVKNoP3daVuH6xJLTfSAQbXfM6A1F/ExJTq+LsNFLX2OnS7qeCZttbzOaP67Jug/UyzQY2biHHAO8BPkD43bkW+AjhA33Rxu5MZ2WQXbqWrt8fLgn021frbLgPxWDAC6q9hTwLSqLYDka+HXE9AINRYOH+WnshTyUKftxWUs+aoXzU9t7Bosa6ryQtRkuuUYwMF/MMLMz3qWtp0WE0juFcchqs2yxf99yHipmGuortr12Yv9nb+ooGkwNz8+W6lxW/8/POvj6333/YfoU8bXPyn1EcVBhgKAo+PAT89hf5Ac0hlcML5WD0oMCFctff3pJAwqXBkMeRx3EcZ6ox4QGWqn4J+JKIfElVP96CPjmO4zSFqq4VkVOBS1X1r9Hh4qjEcRzHcRxnAzPh980isnu2eb6I7BP/TbR+x3GcFCLyauA24Ips/9kiMmnx9xzHcRzHcVqxButDwLHA10uOKfDiFrThOI5TxvHAc4GrAFT1NhHZcRL74ziO4zjODKcVEsFjs81DVTW3KklEukqKbBS0BuuydSV2rVS8ziq17squJUk5m5e1uX57aGRHbBupNVCQX3eV8sfuaC9PHw9Nr7sy29ht236jdU+SSE/ljz+UtkS+1LWM12LUEttraQ67tsLe4saOXc1aqUafi73mdn2V/ezt2roGiwarlK/taxQJwK6jsuuu7D1eTaxLbPTdqbepxWUoG5ohVV0psvH94R3HcRzHccpo5ZL065pMcxzHaRV/FpE3AVUR2VVE/hv/3XEcx3EcZxJphYvglsA2BBfBvRmZrpgL9Ey0fsdxnAa8H/gkwdLxR8AvCEGGHcdxHMdxJoVWrMF6GcEqeVvgJJO+mmDfPmnUJXsDCcd0aM4p22IlVj3R8NFKpmy9OcFaWwOJn90fj0Yx57udkKx1pNqIrJqr1jp6ltkuWjpnFUT7kjhmr0YzcsG4LnssYUVPrJFL2bmntHRxurVKN8Z0g6tHtnP2++bGmG2vHXnpZZupayARsin+XGYba2zjk659I9LF4Qb3tL2VOoyDtpUFWqnrcEKpGu/XT39jSwRVtY8wwPrkxm3ZcaY+KVv28eZzHMdxmqMVa7DOBM4Ukder6oUt6JPjOE5DROQS8ovjcqjqqzdid9bTKUPs2jESB6uvVowD1a/5n91Vte5CnmZYVxI/qqeSvyQ1Kb5EWBEFg+ooCw4VUS2J89Y7lH/B0VYp5mmv5ute0dvcudYG8usoy17FdCzP54ljXgF0Ls/vD5U0PzA3v79ubrG1OJ4VwOJb8vHy+rcoLjnuXJXfj2NnAfT+y/65/dkPla8NvfK640rT68RBfIfuu7+QpyymVFm+0vZbEPeqrK3R6r38nhMn3K7jOM7GphUzWACo6oUi8kpgD6DLpH+2VW04juNkfC3793XAlsA52f6RwIOT0SHHcRzHcRxo4QBLRE4jrLl6EfA/wBuAG1tV/7j6lL0sTLmiQV4mZbGywlSeuK6Uwi9XQY95hdoRyb/aquljE6Fi3klfDWQAACAASURBVL5ayZk9AYnbS8kCuxLpzfa3mVsudgG0dQ9Qjs0TvyW2Ej9b3r7dt2/aY7meLWPqsjJMi73GHZGM0UoG+82baivvtC6ChRvW1Nc/0r5VKFqJXyyBtc3Ye7S9iY+lkUSw3s2JGls2i6peDSAin1PVg8yhS0TkdxunF47jOI7jOEVa+Tj0PFU9Gliuqp8BDgC2a2H9juM4MYtFZL3uSUR2AhZPYn8cx3Ecx5nhtGwGi5GgQn0isjXwJLBTC+t3HMeJ+SBwlYjUF3fsSAh87jiO4ziOMym0coB1qYjMB74K3EpYgP7dFtY/JkRGZE9VM08n0ZxdTplltlMBiK3kqpEjYa6dlGSsWT1VJRFEtdlAwXbbtpmLgBy53WHc6pKBftsTeWLsMXsuVpZnZYGxO2AquLA9f02kx2XsAvJUmViGaKSAmrjmuUDBiaDBkJZopm64mESg6kriHo8dBe09O5gwUbRdsdLBdQ2+O/Wg3kOj+yW0FFW9QkR2BXbPkv6iqgntpuM4juM4zoanlSYX9dgzF4rIpYTFOrs3KOI4jjMuRORAVb0WIBtQ3R4dnwtsr6p/noz+OY7jOI4zc2nlDNZ6sgeedSJyPrD9hmjDcZwZzetF5CvAFcAtwBOElzq7EIx2dgA+PHndc5zWMlqsqjIL9g3ZfmyvXta/Vli7O47jbIpskAGWIaFt2zjUFXQ5WVSk/hoyBn9WZmXjubabPLautSOxXYG8WaBVkkmz0YxrCfu3WpPRW3M2hlKeni7cYN/2P+W8F99KqTa1iTzx9aoltq2s0Erx4jgy9kNPadhqDfIkrn8u0q5p37oL9kdqNescmPy8G+hQ4/0MaRvpiza431LOgfbetcG0+8093kjFuC7r1tAYA3ePF1X9oIhsRnArPRzYivDB3w18pz67lUJEtgPOIli814DTVfXkKI8AJwOvAPqAY1T11kb1tqHMr4zcbx0l99uqWl4CO1zy/VwT5SmLVVWmHO6PYmM9PtxTyLM6iru1oiRPl+SlrU8MzCm2H30vegeLdquDw3lH0AVz+gp5nlherFuH8tek1lW8sYYivfdwMQwVw13569FEyK9SyuJn9e6QT2zrL/5ODEXtz1mSCChuuf720fOUUIgxtf9exUzLVhfTSmhm8DQefMDlOM5MYUMPsJocGTiO44wNVV1OWOc5nrWeQ8CHVfVWEZkD3CIiV6rqXSbPocCu2d9+wLezfx3HcRzHcZJMeIAlIpdQPpASYOFE63ccx2k1qvoY8Fi2vVpE7ga2AewA6zDgLFVV4HoRmS8iW2VlHcdxHMdxSmnFDNbXxnnMcRxn0hGRHYG9gRuiQ9sAS8z+w1maD7Acx3Ecx0ky4QGWql7dio60mppCf7YUos2sI+mKdPp2+UPKNbu7qzx/w/bNkoFqbm1UYrve6ToDRqvfH68pqpdvsMTN1l1t5mOO1/Yk2kwSr5Gwa0jGugYsXiiR8BOnuOajvLxd95Cqy7ZfjY6Z8mNdN9W3Jl+VXZPVjKd5vJ7KlkncS7UGa7BS66iaua/j26gjdfk3IiLSGduyl6U1KD8buBD4gKquig+XFCnM1ovIsWSxt7beJr53HMdxHMeZaTQ5XHAcx5mS/KHJtAIi0k4YXP1QVS8qyfIwsJ3Z3xZ4NM6kqqer6r6quu+CBf6T6jiO4zgznQ1tcuE4jtNyRGRLglyvW0T2ZmS2aS5QtMUrlhfgDOBuVT0pke1i4H0ich7B3GKlr79yHMdxHGc0pu0ASxkRdjVySbdqLmtJbdPbEnbWsURKUuq/lP4q7pi19+5N2OnaMj2z8seSOq9EupqTlFgSaMvY28Sqpqzcrj3RdoP2k3K9GKv2stfM1mvrirz4cxJBu52qKyYhxbRyvZQ1+0Bkyzw0RrlhLZIRJmzam8XK/JpRODb67uTu93rahHo3Jl4GHEOYVbIDpFXAJ5oo/3zgLcCfROS2LO0TZHH7VPU04DKCRft9BJv2t7Wi444zHWiVdbvjOM50pGUDLBG5EjhcVVdk+5sB56nqy1rVhuM4DoCqngmcKSKvV9ULx1H+WkYZD2buge8dS71tAosrI+uwVktxQDwQrfHrHy6uxZtbyb/wiONSAcwq6f3qaEBcFj8rpqtSrPuxdfNz+xUpiUOl+ZcSawY6C3nmdeXP4/He2YU8UinWLe35NC15AVJdMfpwPuoinU8W8/Qvzu+v2bpYb3tvsVzv1vnKF/05frEDaxfmXzy19Y++Rq+rJGDw0KIoVlhJrKxCoOFEzKvL7zkxt9/MQKnZuFge58pxHCfQygUDi+qDK1gfo2bzFtbvOI4T83sROUNELgcQkWeKyDsmu1OO4ziO48xcWikRrInI9qr6NwAR2YFJDDSsjHjJNXJIS0mjUjIpKxeM6x1OyA27rZNcm3mDGcu9OszbTvMWPKnTitNto6uMIVrObc62b95cd3Xn66qmXADbE+nxRba3li1j+zyU2G7kAmjbWZfYbuSAaN8gmzJq+iXxuZh9KwUcMG+srRTQ5ok/Y7ufKp+ztmwgvUy0kzOQbHDva8I1M/V90eh2s/f7ejluurkNxfezv09m+/cAPyasr3Icx3Ecx9notHKA9UngWhGp27YfRGZd7DiOs4FYpKo/EZGPA6jqkEgTujjHcRzHcZwNRMsGWKp6hYjsA+xPWNvwQVVd1qr6HcdxSlgjIgvJZstFZH9g5eR2yXEcx3GcmcyEB1gisruq/iUbXMFInJjtM8ngrRNtY2NRUIZlpKSDjbDqvY6OkULVroQULN63211GrtdRXEi+npwLYeJkUuXjvnRbyV0qCK8VhDUKDmzLWNVoKgBwvOjeHutIpNsF5rEy1bZvyxtJ5YDZbvS5WCmfdfhLBX0uOEUmAkjndKhWKhoHozZtmjLDTd6XwwkNn733rTtmKjAx5B0Ju7Py7RvRRjDjQwQ79aeJyO+BxcAbNnovHMdxHMdxMloxg/UhghTw6yXHFHhxC9pwHMcpoKq3isgLgacTZs7/qqpFWzzHcRzHcZyNxIQHWKpaX2d1qKr222Mi0jXR+h3HcVKIyOHAFap6p4gcB+wjIp/flGbOHWeqccjzPp9PKLGF31i4JbzjOJsirTS5uA7Yp4m0jUIF6ClJ7+uL8iWkUb0m7klXV3n+zmj42BkFHi7vWCoaMXmHQSsLtA5/XQ0kgqm6ra4xd8IN6rKTAGIldylZoJUUxvmaDehblh+aCw7cn8jTqE2jZ7PXJZb1WbfF3LU0n1cuArW5rm3RdWlLWPdZ6eBQwpEwal+HamXJOXPCWBK4rr88n01vFFzYUi357lRHD/PTaj6lqueLyIGE4MNfA74N7LfRewIIQrvRW1a1qJmcFcW0GiyxbVxTy38320t8O+KYVwD9GsXY0qILZX8tnxa3VcaQFj/YhZ1rcvu9g8UfwNXr8j+S/QPF/lSqRbPZSnt0viuK5Ya78uU6nxpdn7puYTGt64n8/uCcYp5aiZnnnIfy7T/1jOL5z30ofx7Ldy1ex2oUPqtraUkHgCuvO2799qG7fbRwfOi++3P7hbhYCeJ8hZhbCXyA4ziOk6YVa7C2BLYBukVkb0aeWudSPsZxHMdpFfUn2FcC31bVn4vICZPYH8dxHMdxZjitmMF6GXAMsC1hHVZ9gLUK+EQL6nccx0nxiIh8B3gJcKKIdNLaAOqO4ziO4zhjohVrsM4EzhSR16vqhS3oU0uoyIiarpELoHVCazOywJQUsJpQeAHUzL6VWVkpVneuA5E0x1bYM2tk28rPGkVNznXGdiDhXGilbIWgxWvL81X/f3v3HmVJWd57/Pv0bXpu3BxERSPDEpJo4g1EvCRBjR5wuYJGTdAEgzeiEZPjiUfNOSbG6FlR48kxCkqIATQa1CDxQkQl3g3eAAVEFGYxIDgIzIxz6Xvv7uf8sWtPP/VWvbt3z+y+7f591uo1VbXfqnqr9p6Zfnc97/PEdjF8Lf0oxdCYGAPTybS89BrjOXPhgh2m0atkKCyUwvWSmJ1c+F4U718uAyTAVFiPhaH7wr2Mx0rfl9n6sMAY+joeDjudXEpsF8Ng4yXHbsXlNLw2rk8XfcllKew2M9vq7tuB3wNOB97t7nvM7MHA/1yaXoiIiIhUdfOb3pPM7IjWipkdaWZvb7eDiMhBurz487PufoW73wbg7ve4+xeXsV8iIiKyxnUzycUZ7n4gJNDdf2Fmzwbe3GYfEZGD0WdmbwFONLP/kb7o7n+/DH0SERER6eoTrP5i/gMAZraeSgxce2b2B2Z2Y/FzjZk9Jrx2lpldb2b/vYt9FpHV6SyaqSMHgM01PyIiIiLLoptPsD4CfMnMLqFZYPhlwIcWeIztwG8VT7/OAC5iLt3yWcATgI+a2SZ3H8kdBMA9zAsJc1X6O7ziOPUl7u9t5pjkMqPHuSrrx0Jq4w0h/TpUJ3XVHaxdDu2BTI7smE58Ko6pQ19mk74MhbzEA2G5L5y/bVbkOPknnjPO24rztOLcrDTdcdw/V0M2tknvUTxnTK0cLsDafNeQm8TXCP1P5211In5g4ntX2p7khw7z8WbH5iZITWTmXaVzDnNzqqYy+0xn0r9Dea5X6+9Ipyneu+B0d3+nma1z979ZsrOKLJNYD6rTFOy1x0lqXB3sLwF19am6kbo9V/dKRGQ16doAy93fZWY3Ac+g+Zvr29z9Cws8xjVh9ds0MxO2tH4bdub51V5Eet5LgX8AngusmAGWYayzuUHxupr6VRuSbXtqjtNv5ZHqcM1xZjr4Z7CTNpM1RZ7SWll9VGtVzSbH7rNqm/6+8nU8+PB9lTY79hxePXYj+bJoqDpyH9xbbjO9uaae1mS5j2ntrOa2cpv+tKQfsHFHdb9U3X6N5NhH3lZ9H9M2tZJCv2nNq5Y4wKkUC85obNlcqrHVSWFfDYJERNrr5hMs3P0q4KouHe7lybGuAK4FPuLu+7t0DhFZnW4xszuAo83sxrDdAHf3Ry9Pt0RERGSt69oAy8xOBd4H/CrNGK9+YNTdDzuIYz2N5gDrqa1trXTw8+x3IOZtPeXQvgPHSb4IrWsD+WzoMYpvaCj/Wi6qrBSumB48xlfFNOk5aUhgTO2eS8ce5dK3Q/li4msWv9nuyyyn6zG2LIb45VKrp6ncY/hi/AY4vpnxo5yG6+VCDDNv8mz1W+a51zL592P69timkRwr7jM1Wd+uXZxdeC2G++UiFNNDNTIhf7nPa3/8GCWf9/ha6+od2L59e+nvortvpMvc/UVFkfMvAL/T7eOLiIiIHKxuJrk4H3gRcBvN34hfQXPA1ZaZvcbMflD8PMTMHg18EDjT3XctpAPuvrH1c8T8zUVkEWzdurX0d3GxzuPuP6c5R3MzsAm4193vdPc7F+ucIiIiIvPp5gALd98G9Lv7jLtfAjytg30ucPfHuvtjaT6GuAI4291v7WbfRKR3mNmAmb0LuIvmk+2PAHeZ2bvMrDqpSERERGSJdHMO1piZDQE/KH7xuQdY6LfXfwU8AHi/mQE03P3kg+1QK9JpKCZlS644F42Vi5DrVC4hYNuTT4SQsZGQ4S/GcsUDb0qyUcfXSpn/cvGOIcQwzVaX62cpDWPcJ83uVzPju9qBsJzJgNhWjIuLx2oTYhdjRBth/07f5BjKF8M4Y7hlDBdMY/f2hUn+Y2H/eKz4OYip/pLj5ZJLxuXpXBRmotNEldFMPGfx5/ypALrm72g+uTq+NSfTzA4D3l38/NnSdUVERERkTjefYJ1N87fk82jm/34Y8PyFHMDdX+HuR7aeaB3K4EpEetpzgFfGhDfuvg94NfDs+XY2s4vN7D4z+2Hm9dPMbG8IX/6rrvVcREREelo307S35j2MA2/t1nFFRGq4u1cemLn7jFlNvvCqS2nOG/1wmzbfcPfnHGT/REREZI065AFWUfsq+wvNcqVLnmUugGwghDKloXu5UL5c3dmYuW2kTanj4ZAIL56jFG2Xhn+VQvxCyFzMardhw9xyX1I/JRfblY35yhS3TfeJ2e4GY4a/GNaXxqKFPpdSJ8ZUdJnshkwk69OZ16Yzy6lMQeD4Zqb3Moohf9lCwzGsL4T7xcLSUA75i+fPhR4m2SSnM1GNuSLX/cnbGjMB5j77pUSHYTn9uE6G87Su8iDKLR+sH5nZS9y9NEAysz8Efjzfzu7+dTM7bpH6JrIsOq19tWjnT+pjpYWHF6s4sYjIStONJ1j6hldEltprgCvM7GXAdTS/5HkCzQymz+vSOZ5kZjcAO4DXu/vNdY3M7FzgXICHHdvPTJjnN2zVuYX7Kafu76/5fmrGyyPjPbPVLyMe0Jd+EQEzycO7PTMbKm3SbRNe/W9gtFE+3+Rstc2eqfWl9amZ6rWOTg1VtlXUfD3XN1C+R7MbaxolhYaZrX5JktZQ3vTTapvxLeX1db+onmryyOp+08kM45p6zcwOlN/Ho68brbSZOKZclmJgZ02Zx0ccz1W3vvPAam2h31MfU15PihPXtjlIdYOibhUf7mRQJiKy0h3yAEspkUVkqbn7z4AnmtnTgUfRLDB8lbt/qUunuB54uLuPmNmzgU8BJ2T6chFwEcBJj1m3hHk+REREZCVakYWGu2EWaEU0xe8Qh5JIthhOFSO2pjNFWEdDmFSaIG42E4qYCxEsHTh9sVSsNlaHDeFqE20y9cX9h8O30bm4snaFjacyH5ON8WKSWLQYFhjD54Zj0eK4QwzxS2Mv43lyhYajDnO3lLIAtskoGMP3GplQxFwWwqmkfak6cKY4ce69ByYzn8uY3HEgv3tJLlNmzA6YK0YMc2GBAK3v3DvJHdlN7v5l4MuLcNx9YflzZvZ+M9vi7ju7fS4RERHpLcteaFhEZKUxswdZUSvCzE6h+W/lggqfi4iIyNrUzTpYuPs2M+t39xngEjO7ppvHFxHpBjO7DDgN2GJmdwNvoSjs5u4XAi8AXm1mDZqZUc+qy1ooIiIiklpphYZFRBadu79ontfPp/lUXkRERGRBujnAOptmGM15wOs4iELD3eTMpYyO80Im2uSRjlNfYjrrXMr16WROSpyvMtlBvupcknIgmfcTluNkmalkxkt8bSAzG6aT3NypXA7weMM2pCm1MvOjSvnvZzPLSd/jhLh43Niv3AS4VLxncZ+JzPZU7vr7Qkaz3OSmdD2XCj/M05qZKvdlMkzhimnTc12eSbaXssxnluM54pSx9DMdZ5ctQ5p2ERERkRVpMQoNT6BCwyIiIl2z0mpciYhIXjcKDZ8JPNTdLyjWvwMcXbz8Bne//FDPISKyGhjGoM39szo+W806mT5s3NBXbTPl5RpPozV1sIatmiJyJslblK4DDNr8dbiOGCxXld7XGK606Utqbm0YqF7Hvr7y1Y5PVotFNaar9bNmR8rt+saqbWbWl88/MFqtVZWUE2PviTOVNht+Vj729OZKE6zmCfHQvuq21Oa7y+e7/6Rq1Pz6neWDT2w9qtJmYH/53jaeeXK1zdXXzt+hZXYwRYVViFhEVqNuPMF6A3BWWF9Hs+DnRuASYFkGWEYzTzzhzzq5yLiRkCl8X/iPdEOo0Tmc1NDMhVlFMULMp8q/INlYSBueC3kbqqlmWdcuiuFnpeVwrHTfUjr38ItLNkSvzfz/gfAOxPTt/fEmheOOJ8U4c+noc/diNvklqhSKl0mzHqWhl1EM1yyleQ/HHQv9T49VSsGeSbkfPpTtMvnnQvxKKdeT/Scy4X/x875799xyDENMk+fHd8mTP0VERETWqm4MsIbc/a6w/k133wXsMjMluRARERERkTWjG3Wwjowr7n5eWD0aERERERGRNaIbT7C+Y2avdPd/ihvN7I+B73bh+Ies3UWWIqhCuGAMjdqzZ245hgimclF9uQxv+5IY/g2NuaCrwcNCKFpMY1jKLtgmjWGUiyWLx00vLF5AI8alVecwANUwvhgWGFMyxlC64UwexRgqCfmQvdyNTTP3la5/un77bJtrzGX7i/2M4YIx3q7dtcR7FvaPmQM7Te4YxcuaSEJV94eu7Q2fv/hZHAmf/fgRLc/KaWazEREREZGybgywXgd8ysxeDFxfbDuJ5lys53bh+CIiIiIiIqvCIQ+w3P0+4Mlm9nTgUcXm/3D3Lx/qsUVERERERFaTbtbB+jKwYgZVs0Ar6Gpdsj2KOeVKOekyBV1juKAlIVqHHza3HEO7YrsYLTeWxFzFSLTD+uY6UMqCGA8wsZ6smMkuZviLoXsxRLBdWF0Mf+skUyGUCw/HfXKFftsdK5f5L25vVxy43WstpZR6+8uv5cIiYz9jm/jmjyUZEWOfM/ci3vo0RDB+ZuJrseh1rMs8kqT+Gw/7x0yX8VjxlNVE4HPq/l51s3K5iCwN1bgSEeku/T4kItIljjPtc8PSwfRbGGCGcr2m6bRYEzCdFJcYtuqXDPtnq7Wp0vpZ9zcOq7SZTtrsnN5UaXPPxOGl9d2T1cmno9PlOhVjyTrAxFS5lMLY/mqf+werX4D07y/30WtqbfSPl+9jX818xdnkf7ihPdUDpTWu+mumfG7cUS1AMHZM+fxH31DtwOiDytef1rwC2HRneZ7mwM79lTaNbbeX21S7WDHwiOOrG3fu56pb31naVDe4SmtPnXHiGytt0uOIiMicbmQRFBEREREREXr4CdYsc1F+MUoqveC4Hr9rLNXZjfV020SbWWafUr/C/jPJsWKY1ngI39o0FF5oFz+W69xAuLKYLTAW6k33jbFl8TwxrHCgTdHjeLzccjbrYZssfqV28V7EcLvk2+ZcceRYQDmGBcY40Hb754oOx+2dhCcCnrktaYhfzPaXZghsmcx8jqCaeLIl+9Gp31zRer5ibVuJiIiI9D49wRIREREREekSDbBERERERES6pGdDBI25cKWYxy2d4hynblenejflItxmknCrmUzd2pitLYaCDafzvdsUMa49cFrcty9MhI+hbJtCKF/MQhjbVDL3hTiz2RDy1tdhEFgs6DsVswhO1bcphd4lNzaXBTF3L2aTEMNcXFwUCwKnWQtzFaRzRYNzhZHTY4XleFsmMhks0/XSWxRvRdh/NAkxzO0Tl+M7HD+iaURiXG/dsc4CIkVERER6l55giYiIiIiIdEnPPsESERGR3qO6XSKy0mmAJSLSJbM4k6EO1iTVItVpksu6elZ7ZsrxwtNe/af6iP7RyrafTR9VWh+ZqR57b6NcoHy2JpAhrXv189HNlTZpjauJiWpW0dnp8rH7h6r3Y2asul9fEss9fH81NDm5jEo9K4ChfdVtqQ33lt+QjT+v1hzbe3y1xle6X53DbxurbLv6mjeX1p/55LeX1tOaV3Vqa1xRrk1VV7uqTlrzar7jiojI/Hp2gGXAumI5/heX/tcZb0BsV5qTEl4YDP/PpnOo4np/5s7GtNlplvI4DSnO5/LGXGcsTrBJ05nHA8QU7MPr5pZL6cQzac6hfAM2bKTWVJt5T7k89XF+U+xL/I0qPVa8UQOZG9vJPCso37PcfLBU7M9UWI7njNdSms+Vn9s2k8m4P5nJyp8q1bANb1ecp5XO4crO9QrL8e5nTtHsWzxuzTYRERGRtUhzsERERERERLpEAywRWXPM7GIzu8/Mfph53czsvWa2zcxuNLPHL3UfRUREZHXq2RBBpz5cqd2IMkbslcKhwoH6Q0z/YHL3chnQY1RbDDGcTWKuYvhWDO2KxxpuzMVyDQyUE2fbUDjRbJigEEPWSh0LYYRpKNtQCCscCjcgxqzFsLqBZB7FcDheDFeMF52GONa1gfKNyd3YTuXCAmPoYnovSingZ+vbjczNh/GwnIb4xfC/mL4/ho6Oh/e+krE+kwe9dFszbxfkQwTjJykux78vSUTrak/JfilwPvDhzOtnACcUP08EPlD8KSIiItKWnmCJyJrj7l8HdrdpcibwYW/6NnCEmT14aXonIiIiq5kGWCIiVccCd4X1u4ttFWZ2rplda2bX7tq1yp/riYiIyCHr2RDBWWCkZnuS2ZeYCDDejBi8FvPLTYQ4qZHkBDGLYAwX3LRpbrm/zZB2KnPsmUz4VyWLYYhltL4QvjYW0jkPhGx9G5JcyFHMihfjz2YzWfjSrIExxDBmMYxhfTFzYNy/EiI4Wd8uyvWxXbtcWOBEEiK4b294rT4N5PjIbG2TVC4MtJQcslG/Pd0n1y4ut4u2HImXEtrEz3vu78EaUM0L3ow8rm50vwi4COBxjxmaP3e3yCLpJDV7XQ2pTlK1i4hI53p2gCUicgjuBh4W1h8K7JhvJwem55mdts/LNZVGvVpjaaaD4IL7GodVts0m48K05hXAvVPl/X4xtaHSZnS63KfBvuo1jcyU+zgz1eYLm1abXesq24Z2V/ebXVcep9aU82JgvLzeX/PFRiOpMLHlpjZfvrT2qfniaWCsOm7unypvG95ejThtbKnWD0uNPLz8Hh2xs1rjqpPaWHXS+lW9UqC3bkBoVvediIjI8lCIoIhI1WeAlxTZBE8F9rr7PcvdKREREVn5evYJ1ixzhYPjl59JrrvS9719meV0n5a0UPD+ENYXQwQtE/2WJsGLr+WS5U20iaSL1vfFdHEh5G04Zv4L29N0c6WCvNP17dJse1EMEdwUvkbeEL4tjxeZC/2D/IXG87fLApjbv3QtmQLIAPvmUkdOjs0dazrsvj9kl0w/F7mu5DL6jYYwwHjctN1Cl6EcehojXOMdi9/LxzvX7rv/1sd9tfyDYmaXAacBW8zsbuAtFH/V3f1C4HPAs4FtNP8peeny9FRERERWm9Xy+5CISNe4+4vmed2B1yxRd0RERKSHKERQRERERESkS3r2CZYxF7YUp2unwWIxgipmSYthgaVMg22GpNOZkK8Y/RaLy66rmbh94DzhncmFnKXhX7l26wlZBGNncpkCU3GfUuhguxDBcDdzoYBpceKcXOxkXI6ZEvclcXW5bIe5zIVJesgYFhgPletW7tTpPrniwKPhPgOj2QAAHR9JREFU9O0upVQzuVG/Pd0/TsPfH5bjtP74rsR/INKPV93fF00zFxERkbVOT7BERERERES6pGefYImIiKw0lVpVJz1w0c71zCe/fdGOLSIieRpgiYh0yaw7+0Oh672z1X9ip70cODAxWw2VvT+pcTXr1eDL4b5q6efdjU2l9V3TGyttRhvlGlf3jm2qtNk/Ua5XNTtbDXbw2aRPjWobGynXlPJ11VDktOYVwOD+8rE37qi2SS//8NvGKm3SGlON4ep9nN5Y3nb01++ttBn/zWMq2w6/bbyyLdXYXH5v62plbbqzfJxOal41tt0+b3HgusHVwCOqNbZERKT7enaAZcxdXLzINNV0TE4e/5uN06Oqv6I0pfNu4pyadH5US8xSPpT8rpHbp905o03h96Q4hyvOzxme2l/bJp2C1R9v2lC1EGrlwO1eK+UmD/O2hqpFR+dVmsMVfnkbC7+k7NlT2mVmbG72UP9Q2H9DeGcb07XtoZyOPXdZ8V4OD9e3gfL8qJnMHKz4OUjncOXmXU1n5mClv3LG9fhrXWmeYViOH8k4ZwvKf5da+1d/5RcRERFZWzQHS0REREREpEs0wBIREREREemSng0RdOZSssdAtvSCYwRXnC0QI/5DAnAGwg6WRMiVsoaHWKyZTCjZQJu7nwv5ivvEcMNUDB+LWcdjv9qFG8Ywt02b5jpgA5nU5unB4noMCyztM/8chso+uXOElPOTI+U3pnT/puaONRTCJeMpJpOwvJkOw/fqjjWaxOhNZ0L84vuSCxdM13Np3kfCsdIuxncpzk6Jn/2YfD+ePn236tp1+I6KiIiI9Cw9wRIREREREekSDbBERERERES6pKdDBFsZzdqNImMS3VxOu5g9rZSFMAkRjKFkfZlIuv6wPU3CF8PyYuK+eNy4PQ0xjKFtfdWMxbXnrOsvwGEhS3Q85xCZcL0kjM/6MuGDIZTPG3NtcuFu6Wu5sMYYUjnTZv9GJvSykckUmB57PNzXGK6X2z8N8eukXbu+5EIE47XEz2v6MYjvUjU5eFP8jMfTTybtRqnqIBGmiIiISE/r2QGWiIiIrG7P7Hth7fb56oCJiCwnDbBERLqkgbE7FA7up1og9+eNw0vr+2fXV9r8opGrvjdnd01h37Sw8N7p6rF3T5az44xP555lzhkbqz7fn5lMighXLxUGyxvX3Vv9L2cmU2Yvmh2sFggmubdpUWGA4d1tavUVNn382tJ649THVNqs31Xz5P7bN8y738DV5WPTSaHfmuOk5+rE1de8ecH7iIhId6yJAVYMW0ovOP5qEf+fj9nXZjLbQ3I+oBxCNRjDtzIZ4mJIIJQLBcflKJdQLzUejh1DCfsyIYrtMgrG12KfLWz3pF8DA42wXH/OXIjeWBLXFs+zLrxJuT6n9yhXEDhuj5kW9ydvbBpyWLd/DN2Lx2oXIhivP4YhTrbJVBj3j9kpx2NfQvtMosOKeMsmMstpEeFGzbIKDYuIiMhapyQXIiIiIiIiXaIBloiIiIiISJesyBBBM3sC8G3g99398mLbWcAbgA+7+3vmO8Ys9UVPk6i8UkhTLgNaDCOMN2wmbZg5VpypMBpitjZ3GL81lAmLa1eENhYh3hjCDdenN6CQZquLYXoxFC4eNxd6mOoklC/2PQ0RjKYzoY+546brg+FeDob94/n37Cnv75nrz4VbxhDBkSTcMBcWGUMEYzHitFBxDFeMIanxsx4/02lEaXwrcgWFc8vtjtVarpspIyIiIrKWrLgnWGbWD7wT+ELy0lnAE4BTzSwzQ0lERERERGT5rLgBFvBa4JPAfcn21pfjjr4oFxERERGRFWhFhQia2bHA84Cn03xaFV0BXAt8xN33p/uKiCyEmZ0O/APNKN4Puvs7ktdPAz4NbC82XeHuf7OknRTJyNWHilQrSkRkeayoARbwHuCN7j5jVn5I5e4fAj7UbmczG20t9wOt6SvxItMLHg3LcU5VLn17bJMeK05jivNV4pyYdtOu+vaF/TtILZ7ObYrzgw47rH6fKM5Bmk7mYMX5Qbm5QjFlem4+VCp3LXFuUqepzeP1t7svMc37YNg/9iVNhx7FY49nJurFeWPt5pONZVL2x3OMhf3T08VuxtfifKr4eWs3TzA3/zC3f4cVAti+fXvp76K7z1/UaYkVocgXAM8E7ga+Z2afcfcfJU2/4e7P6fS4M/Sxf3ZuouCU91fapHWvdjeqEc9jSXGokZlqHarBvuo7snOqfKy05hXA7rHyttHx6rEn9ifbampc9e0p18/qq8nR3z9R/nd8oGZ+pdV8SNNtA2PVDswm5bsGJqpthrfvLq1PbD2q0mbizCeW1jfdfH/1ODV1qAbSmlY793PVre8sbUoHQY1tt1cGPXUDpfkGRp0MrnqFBokishot+wDLzF4DvLJYPRz4WDG42gI828wa7v6pTo4Vf5HbaFZX9lJEFtnWrVvZtWvXihtUJU4Btrn77QBm9jHgTCAdYImIiIgsyLIPsNz9AprfJJeY2aXAlZ0OrkREFuBY4K6wfjfwxJp2TzKzG4AdwOvd/ea0gZmdC5wLcMxDlv2fVBEREVlma+63gTQSrF0IVUsMxMmFaEE5HXu8sUOZNmlEzd6w3AjpvWPEWzx/munj8NChXFjfTLiYmLI9F0YIsH5DfbvJzPmgHKYX98+ds935o1woXzx/Gq4Y1yczIX7x/J70JReiGEMBc6nZ26XSnwjHipcVo6jSy82lVs+F8nVaSiBXViD3mU7PswrTtNd1NX3qfT3wcHcfMbNnA58CTqjs5H4RcBHALz96WE/ORURE1riVmEUQAHc/p1UDS0Sky+4GHhbWH0rzKdUB7r7P3UeK5c8Bg2a2Zem6KCIiIqvRih1giYgsou8BJ5jZVjMbolln7zOxgZk9yIoJoWZ2Cs1/L3cteU9FRERkVenpEMFWCFMuux+UR5gxKVWM82lkltuFX8UZ/psz50vDv+J6DB+M+9SFZbV4iPOazmQBnAnLG6oJxuaOHQ4+GToWMwfGNmnoXnztiNDpeP40219OLvNgGpaY2x7D8mI/c2GJ7bIYRjEUcG/IANkudLIUbhjPyfzboRw+WJO0DSh/dtNLzGW3jPvEfxTi34k0H148VuYWrVju3jCz82gWNO8HLnb3m83sVcXrFwIvAF5tZg1gHDjL3RUCKCIiIm319ABLRCSnCPv7XLLtwrB8PnD+UvdLVq8zTnzjvG06SbF+MKnbO5E7t1Khi4h0lwZYIiJdMjk7yG1TxxxY3z+zvtJmOqmNtW3sgZU2e6fL+816NSdHw6uPgEemy/Wzpmdq6nCND5fWJ3ZV+5im+/CB6oO7oT3lPjU21rUpr/fXJKmpuQwe8KP50w+l9aoaWzZX2ow86ui2+0CzNlVpneqAo27glJ5vYOf+tv3tJg2IRERWtp4dYM0yF04Vf+VILzj+3x7DsWL4U1LP8oA0RCuGScVfNeI52v3aEH8VyiXV68ssV/oTOxPC12KI3KZQkzQN14vhc0PhBg4P1y+nBsM+uax6uXC/tC/rMueZyRTqTUP/4jXHQr+ljIihTZppMBfKGK9rT/hFMh63P/nAlfoZtsdfVePlpqF3uajK6q/RTe0+b/G1eJ4YhhiPm/76HPdp3b5OsnKKiIiI9DIluRAREREREekSDbBERERERES6pGdDBGEuBKtd5r243kkmtBgu2G50Go+VCz1MI99iONZMZjlXBDYVw7xiZ2ZG0pZNaRhcrnBvDAuMIYbtwgVzoXy57H5pWN3mTfWvdRoiGK9lIpPhbzxsbxciGI+9L9zLEIVZCvfbmHyoOi0ofKCPyXo8dvws5jIKpnKhp7nPa/xYtCs03IoIbfeZFBEREVkL9ARLRERERESkSzTAEhERERER6ZKeDRE05sKW2hVEzd2AToqopqPTmK2wk6xu7Ua3MeQrho/F4+ayG6bitcTr7TSsLobIxe0xI1+7QsO5gsS5Ar5puOJ02CeGK5aK9uZi7Chf20wmRm46k2kQoBH2ie/LeFhOdqltD/lQvIlMm3Zhq7ljRelnLFfEOC7Ha4n7lxOAlz9LrQjRTj+TIitZJ/WsVprFqp0lIiIL17MDLBGRpTY2O8QNI790YH2Gav2qeycOK63vmaxOYJxolIeqo1Pp8BaG+qtJ8Xf+YlNp3fqqtalmxsv/7A/dX/1vIK1NZTVfTPRPltcP215t09hQXj/mSz+vtJnYelRl285fK1/vg741Wmmz56Ry/bAjrruv0mZg88KH/AOPOL52+1W3vrO0XjcIS9usNBpwiYgsDYUIioiIiIiIdIkGWCIiIiIiIl2yJkIE40V2mlo9Lsf9hzLLabsozsOJ58+lzIb8/Jh4jjRAKB47N9cmtukLk20GkhuTzsmqE+dApfOp4jyqOG8qN+8rtknnYMXU6rmU6e36Etvl+hXncI0n1x7vXwy4yt2i3L1P98l93nLvfdpuKrO93ee9k/1z0uuNfZuq2SYiIiKyFukJloiIiIiISJdogCUiIiIiItIlPRsi6MyFLcWcXUlSq1KY1EhYjuFQ6T4t1Rxe84s5rdKwrHjOmDMrhhjG/av5yebEvuVSvsfl9Un817qw3AgNYyhdKX17sn8MORyuJkkDYDIcazB8EoeqCdMOmMjEoOXCBQGmwmtDmRDDsdCX9BS51Pr9me3xVqRp2uN67vPT7luPurA86KzcQCq3T+786bHGapZHEBEREVnbenaAJSIi0gtU40pEZHXp2QGWmTFYZDGIF5lecLYIb2Z7XE6LCeeKC0ft9rdMO89sb/fmxWPF/fs7WG73WilJRljpTx5vlAoNZ0rRxBI9fTHJRZvSNTVlfZpiIovk0V5/JslFPGd/OG679zFefzxN7n6lT33ieu4JZO5zkK538hlNzWaWO5G2H6xZHhjo2X9SOrJ/ah3/eeeJB9ZnZqrPA6fG0ke0NR/q0eQ+9lfb2GT12IN7y9sGq+Wj2LgjPVb12ONbyp/OTTuqn5ZNd46X1ieOqT6qPvxfvlNaHznzidXj3Hx/ZdtDk5pajW23V/f79vyDjHRgkkvmcjCDlbo+LScNuEREVo6e/W1ow1FHMXTccQDEepiTta2bcr8wd7p/N23fvp2tW7cu0dlWlrV87XBw1x/DDQ/1M5ob+KUDz8Nqlj/9pS+xffv23DBYREREpOf17ABr586d7aYorXhmNrpr166Ny92P5bCWrx10/SIiIiKrmbIIioiIiIiIdIkGWCIiIiIiIl2iAdYK5e5rOUTsiuXuwHJa4+/9kjGz083sJ2a2zczeVPO6mdl7i9dvNLPHL0c/RUREZHXRAEtWHHc/e7n7IL3NzPqBC4AzgEcCLzKzRybNzgBOKH7OBT6wpJ0UERGRVUkDLBFZi04Btrn77e4+BXwMODNpcybwYW/6NnCEmT14qTsqIiIiq0vPZhEUEWnjWOCusH43kBZpqmtzLHBPbGRm59J8wgUw8uPf/euftDnv4cDeDrYvZD0ubwF2tjl/p3L9XGjb8mufurzdPp1e44F1M+vK9ZpZrk91su3mOU5X3+PiXIdicd7j9ts7vcaD+Uw/vIM2IiJLw931s8w/wMOArwC3ADcDf1ZsfwjwZeDTwKbl7uciXv/pwE+AbcCbeu3agWHgu8ANxfv71mL7XwM/A35Q/Dy72D4IfAi4qfhM/EU41mnAtcC7lvu6VvMP8ELgg2H9bOB9SZv/AJ4a1r8EnHSI572ok+0LWU+Wr+3S/ant50Lbdnq9C7nGuN6t613INc/XTu/xob/Hi3G9+tGPfvSzlD96grUyNIA/d/frzWwzcJ2ZXQ28BHgtcDzwh8CFy9jHRRHmwjyT5hOC75nZZ+ita58Enu7uI2Y2CHzTzK4qXvt/7v7upP0LgXXu/utmtgH4kZld5u53AK8GfgN4u5n9irv/eKkuosfcTfOLjZaHAjsOos1CfbbD7QtZzx3zUCzkmO3adnq9ddvaXeNyXvN87fQe57d3eo2Lcb0iIktGc7BWAHe/x92vL5b303xqcSzQD8wWP6u6cHIbubkwPXPt3jRSrA4WP95uF2CjmQ0A64EpYF/xWl/x+qq/L8vse8AJZrbVzIaAs4DPJG0+A7ykyCZ4KrDX3e9JD7QQ7l77i2O6fSHruWMeioUcs13bTq+3blu7a1zOa56vnd7j/PZOr3ExrldEZClpgLXCmNlxwOOA7wDnA/8IvAr4yPL1alHl5rn01LWbWb+Z/QC4D7ja3b9TvHRekQL8YjM7sth2OTBKc67PT4F3u/vu4rUPAtcAfe5+yxJeQk9x9wZwHvAFml9ofMLdbzazV5nZq4pmnwNupxm6+k/AnyxLZxfmouXuwBJba9cLa++a19r1ikgPMPd2X6TLUjKzTcDXgP/j7muiFpSZvRD4b+7+imL9bOAUd3/t8vZscZjZEcC/0wx/vJ/m5G0H3gY82N1fZmZPofnL/DnAkcA3gDPc/fZl6bSIiIiIdExPsFaIYm7OJ4GPrpXBVWEx5rmsWO6+B/gqcLq73+vuM+4+S/MJySlFsxcDn3f3aXe/D/gv4ORl6bCIiIiILIgGWCuANfPt/jNwi7v//XL3Z4l1MhdmVTOzo4snV5jZeuC3gR8nNZWeB/ywWP4p8PRi7s9G4FRAySxEREREVgFlEVwZnkIzTfRNxTwdgP/l7p9bxj4tCXdvmFlrLkw/cLG737zM3eq2BwMfKjIm9tGc73Olmf2LmT2WZojgHcAfF+0vAC6hOeAy4BJ3v3Hpuy0iIiIiC6U5WCIiIiIiIl2iEEERkTXAzI43s382s8uXuy+Lxcw2mtmHzOyfzOwPlrs/i20tvKcpM3tu8f5+2syetdz9ERGpowGWiMgKV6Txv8/MfphsP93MfmJm28zsTe2OUdSae/ni9rT7Fnjtvwtc7u6vBH5nyTvbBQu53tX6nqYWeM2fKt7fc4DfX4buiojMSwMsEZGV71Lg9LihmNN3AXAG8EjgRWb2SDP7dTO7Mvl54NJ3uWsupcNrp5mFtFVXb2YJ+9hNl9L59faKS1n4Nb+5eF1EZMVRkgsRkRXO3b9eFCGPTgG2teqjmdnHgDPd/W+B5yxtDxfPQq6dZtmHhwI/YJV+gbjA6/3R0vZucSzkms3sFuAdwFXufv2SdlREpEOr8j8gERHhWOae1kBzcHFsrrGZPcDMLgQeZ2Z/sdidW2S5a78CeL6ZfQD47HJ0bJHUXm+Pvaep3Hv8WpqlLl5gZq9ajo6JiMxHT7BERFYnq9mWTQvr7ruAXvmFtPba3X0UeOlSd2YJ5K63l97TVO6a3wu8d6k7IyKyEHqC1cPM7Bgz+1czu93MrjOzb5nZ8+bZ57h0ovECzneOmT0krH+w03kCZnaamV15MOftlJldU/x5nJm9+CD2P8fMzu9+z0QOyt3Aw8L6Q4Edy9SXpbbWrn2tXS+szWsWkR6hAVaPMjMDPgV83d2Pd/eTgLNo/ie1WM4BDgyw3P0V7r5i5gi4+5OLxeOABQ+wRFaY7wEnmNlWMxui+ff7M8vcp6Wy1q59rV0vrM1rFpEeoQFW73o6MOXuF7Y2uPud7v4+OPAU5xtmdn3x8+T0AO3amNkbzOwmM7vBzN5hZi8ATgY+amY/MLP1ZvZVMzu5aH96cYwbzOxLnV6EmT3DzL5fnOtiM1tXbL/DzN5aHPMmM/uVYvvRZnZ1sf0fzexOM9tSvDZSHPYdwG8U/Xxd+mSqyLp2WrH8UjO71cy+BjwltDnazD5pZt8rfg68JtJtZnYZ8C3gl83sbjN7ubs3gPOALwC3AJ9w95uXs5+LYa1d+1q7Xlib1ywivc3csyH7soqZ2Z8CW939dZnXNwCz7j5hZicAl7n7yUUmpyvd/dfatDkD+Evgt919zMyOcvfdZvZV4PXufm1xjq8CrwfuBK4HftPdt7faJ/05rdj3OWHbMHAb8Ax3v9XMPgxc7+7vMbM7gP/r7u8zsz8BHu/urygGSj9z9781s9OBq4Cj3X2nmY24+6b0XGZ2DnCyu59XrF8JvBv4CfAd4CRgL/AV4Pvufp6Z/Svwfnf/ppn9EvAFd//VBb9RIiIiItJTlORijTCzC4Cn0nyq9QRgEDjfzB5Ls17MiTW75dr8NnCJu48BpIOlGqfSDFXc3mH7ll8Gtrv7rcX6h4DXAO8p1q8o/ryOZoFRimt8XnGez5vZLzo8V50nAl919/sBzOzjlO/BI5uRmAAcZmab3X3/IZxPRERERFY5DbB6183A81sr7v6aIlTu2mLT64B7gcfQDBWdqDlGro3RJltZjYW2j/u1M1n8OcPcZ3m+feo0KIfLDoflXL/7gCe5+/hBnE9EREREepTmYPWuLwPDZvbqsG1DWD4cuMfdZ4Gzgf6aY+TafBF4WRFCiJkdVWzfD2yuOc63gN8ys61J+/n8GDjOzB5RrJ8NfG2efb4J/F5xnmcBR9a0Sft5B/BYM+szs4fRLHAJzfDA06xZa2YQeGHY54s05wdQnOuxHV2RiIiIiPQ0DbB6lDcn1z2X5sBmu5l9l2aI3RuLJu8H/sjMvk0z7G205jC1bdz98zSzOV1rZj+gOc8K4FLgwlaSi9CX+4FzgSvM7Abg45luP6OY4Hy3md0NPI5mTZt/M7ObgFngwsy+LW8FnmVm1wNnAPfQHFBFNwKNIuHG64D/ArYDN9Gce3V90e97gL+mOUD8z9b2wp8CJ5vZjWb2I3q3Fo2IiIiILICSXEhPKbIMzrh7w8yeBHzA3fV0SURERESWhOZgSa/5JeATZtYHTAGvXOb+iIiIiMgaoidYIiKyIpjZDM1Q3ZaPufs7lqs/kZldDrwBuAxYBxwFrAd+VjR5rrvfUbPf22lGbf9l2HYycLG7P7qoC/i77r53kS9BRESWiAZYIiKyIrRq1XX5mANF0dpDOcajgLe7+/PCtnMI9fPm2fff3f3EsO3dwK6iXt/LgS3u/s5D6aOIiKwcSnIhIiIrmpndYWZvNbPrzewmM/uVYvtGM7vYzL5nZt83szOL7eeY2b+Z2WeBLxYZQt9vZjeb2ZVm9jkze4GZPcPM/j2c55lmdkVNF/4A+HQH/TzDzL5V9PPjZrbR3W8GJszspKKN0cxI+rFit08DLz6U+yMiIiuLBlgiIrJSrC+ykLZ+fj+8ttPdHw98gLnMpf8b+HJRPP1pwN+Z2cbitScBf+TuT6dZiPw44NeBVxSvQbOcxa+a2dHF+kuBS2r69RSaBc2zzOyBwJuAZxT9vBH4s+Lly4CzwrF2hMLrO4HNZnZEu+OLiMjqoSQXIiKyUoy3yfrZerJ0Hc0BE8CzgN8xs9aAa5hmohuAq919d7H8VODfipp+Pzezr0BzYpSZ/Qvwh2Z2Cc2B10tqzv1g4P55+v5k4JHANc2HVAzRrMsHzQHW18zsDTQHWpcl+95fnGPPPOcQEZFVQAMsERFZDSaLP2eY+7/LgOe7+09iQzN7IuXaftbmuJcAnwUmaA7C6uZrjdMcvLVjwOfd/ez0BXe/w8x2AL8BPA84KWkyXJxDRER6gEIERURktfoC8NpiXhNm9rhMu28Czy/mYh0DnNZ6wd13ADuAN9Msll7nFuAR8/TlGpqF3Y8v+rLRzE4Ir18GvBe4xd1/3tpYlJTYAtw1z/FFRGSV0ABLRERWinQO1nwp2t8GDAI3mtkPi/U6nwTuBn4I/CPwHSCmRf8ocJe7/yiz/38QBmV13P1e4OXAx83sBpoDrhNDk08Av8ZccouWU4BvuvtMu+OLiMjqoTTtIiLS88xsk7uPmNkDgO8CT2k9STKz84Hvu/s/Z/ZdD3yl2KerAyEzuwD4hLt/rZvHFRGR5aM5WCIishZcWWTqGwLeFgZX19Gcr/XnuR3dfdzM3gIcC/y0y/36vgZXIiK9RU+wREREREREukRzsERERERERLpEAywREREREZEu0QBLRERERESkSzTAEhERERER6RINsERERERERLrk/wMpW9IGxVAXJAAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"events.peek()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Stacking EVENTS tables\n",
"\n",
"As a final example of how to work with event lists, here's now to apply arbitrary event selections and how to stack events tables from several observations into a single event list. \n",
"\n",
"We will just use `astropy.table.Table` directly, not go via the `gammapy.data.EventList` class. Note that you can always make an `EventList` object from a `Table` object via `event_list = EventList(table)`. One point to keep in mind is that `Table.read` doesn't resolve environment variables in filenames, so we'll use the Python standard library `os` package to construct the filenames."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from astropy.table import Table\n",
"from astropy.table import vstack as table_vstack\n",
"\n",
"filename = os.path.join(\n",
" os.environ[\"GAMMAPY_DATA\"],\n",
" \"cta-1dc/data/baseline/gps/gps_baseline_110380.fits\",\n",
")\n",
"t1 = Table.read(filename, hdu=\"EVENTS\")\n",
"\n",
"filename = os.path.join(\n",
" os.environ[\"GAMMAPY_DATA\"],\n",
" \"cta-1dc/data/baseline/gps/gps_baseline_111140.fits\",\n",
")\n",
"t2 = Table.read(filename, hdu=\"EVENTS\")\n",
"tables = [t1, t2]\n",
"table = table_vstack(tables, metadata_conflicts=\"silent\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of events: 212603\n"
]
}
],
"source": [
"print(\"Number of events: \", len(table))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of events after selection: 215\n"
]
}
],
"source": [
"# Let's select gamma rays with energy above 10 TeV\n",
"mask_mc_id = table[\"MC_ID\"] != 1\n",
"mask_energy = table[\"ENERGY\"] > 10\n",
"mask = mask_mc_id & mask_energy\n",
"table2 = table[mask]\n",
"print(\"Number of events after selection:\", len(table2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When processing a lot or all of the 1DC events, you would write a for loop, and apply the event selection before putting the table in the list of tables, or you might run out of memory. An example is [here](https://github.com/gammasky/cta-dc/blob/master/data/cta_1dc_make_allsky_images.py).\n",
"\n",
"That's all for ``EVENTS``. You now know what every column in the event table contains, and how to work with event list tables using ``gammapy.data.EventList`` and ``astropy.table.Table``. \n",
"\n",
"Just in case that there is some observation parameter in the FITS EVENTS header that you're interested in, you can find the full description of the keys you can access via the ``events.table.meta`` dictionary [here](http://gamma-astro-data-formats.readthedocs.io/en/latest/events/events.html)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## IRFs\n",
"\n",
"The CTA instrument reponse functions (IRFs) are given as FITS files in the `caldb` folder.\n",
"\n",
"Note that this is not realistic. Real CTA data at the DL3 level (what we have here, what users get) will mostly likely have per-observation or per-time interval IRFs, and the IRFs will not be stored in a separate CALDB folder, but distributed with the EVENTS (probably in the same file, or at least in the same folder, so that it's together).\n",
"\n",
"For now, the EVENT to IRF association (i.e. which IRF is the right one for given EVENTS) is done by index files. We will discuss those in the next section, but before we do, let's look at the CTA IRFs for one given configuration: `South_z20_50h`."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[01;34mcaldb\u001b[00m\r\n",
"└── \u001b[01;34mdata\u001b[00m\r\n",
" └── \u001b[01;34mcta\u001b[00m\r\n",
" └── \u001b[01;34m1dc\u001b[00m\r\n",
" └── \u001b[01;34mbcf\u001b[00m\r\n",
" └── \u001b[01;34mSouth_z20_50h\u001b[00m\r\n",
" └── irf_file.fits\r\n",
"\r\n",
"5 directories, 1 file\r\n"
]
}
],
"source": [
"!(cd $GAMMAPY_DATA/cta-1dc && tree caldb)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Filename: /Users/adonath/data/gammapy-data/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits\n",
"No. Name Ver Type Cards Dimensions Format\n",
" 0 PRIMARY 1 PrimaryHDU 8 () \n",
" 1 EFFECTIVE AREA 1 BinTableHDU 53 1R x 5C [42E, 42E, 6E, 6E, 252E] \n",
" 2 POINT SPREAD FUNCTION 1 BinTableHDU 70 1R x 10C [25E, 25E, 6E, 6E, 150E, 150E, 150E, 150E, 150E, 150E] \n",
" 3 ENERGY DISPERSION 1 BinTableHDU 56 1R x 7C [500E, 500E, 300E, 300E, 6E, 6E, 900000E] \n",
" 4 BACKGROUND 1 BinTableHDU 59 1R x 7C [36E, 36E, 36E, 36E, 21E, 21E, 27216E] \n"
]
}
],
"source": [
"# Let's look at the content of one of the IRF FITS files.\n",
"# IRFs are stored in `BinTable` HDUs in a weird format\n",
"# that you don't need to care about because it's implemented in Gammapy\n",
"irf_filename = os.path.join(\n",
" os.environ[\"GAMMAPY_DATA\"],\n",
" \"cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits\",\n",
")\n",
"from astropy.io import fits\n",
"\n",
"hdu_list = fits.open(irf_filename)\n",
"hdu_list.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Effective area\n",
"\n",
"The effective area is given as a 2-dim array with energy and field of view offset axes."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"from gammapy.irf import EffectiveAreaTable2D\n",
"\n",
"aeff = EffectiveAreaTable2D.read(irf_filename, hdu=\"EFFECTIVE AREA\")"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"gammapy.irf.effective_area.EffectiveAreaTable2D"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(aeff)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"gammapy.utils.nddata.NDDataArray"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(aeff.data)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NDDataArray summary info\n",
"MapAxis\n",
"\n",
"\tname : energy \n",
"\tunit : 'TeV' \n",
"\tnbins : 42 \n",
"\tnode type : edges \n",
"\tedges min : 1.3e-02 TeV\n",
"\tedges max : 2.0e+02 TeV\n",
"\tinterp : log \n",
"MapAxis\n",
"\n",
"\tname : offset \n",
"\tunit : 'deg' \n",
"\tnbins : 6 \n",
"\tnode type : edges \n",
"\tedges min : 0.0e+00 deg\n",
"\tedges max : 6.0e+00 deg\n",
"\tinterp : lin \n",
"Data : size = 252, min = 0.000 m2, max = 5371581.000 m2\n",
"\n"
]
}
],
"source": [
"print(aeff.data)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"aeff.peek()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$$[3.783587] \\; \\mathrm{km^{2}}$$"
],
"text/plain": [
""
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# What is the on-axis effective area at 10 TeV?\n",
"aeff.data.evaluate(energy=\"10 TeV\", offset=\"0 deg\").to(\"km2\")"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This is how you slice out an `EffectiveAreaTable` object\n",
"# at a given field of view offset for analysis\n",
"aeff.to_effective_area_table(offset=\"1 deg\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Energy dispersion\n",
"\n",
"Let's have a look at the CTA energy dispersion with three axes: true energy, fov offset and migra = e_reco / e_true and has dP / dmigra as value.\n",
"\n",
"Similar to the event energy distribution above, we can see the mixed-telescope array reflected in the EDISP.\n",
"At low energies the events are only detected and reconstructed by the LSTs.\n",
"At ~100 GeV, the MSTs take over and EDISP is chaotic in the ~ 50 GeV to 100 GeV energy range.\n",
"So it can be useful to have quick access to IRFs like with Gammapy (e.g. for spectral line searches in this case), even if for 95% of science analyses users later won't have to look at the IRFs and just trust that everything is working."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n"
]
}
],
"source": [
"from gammapy.irf import EnergyDispersion2D\n",
"\n",
"edisp = EnergyDispersion2D.read(irf_filename, hdu=\"ENERGY DISPERSION\")\n",
"print(type(edisp))\n",
"print(type(edisp.data))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NDDataArray summary info\n",
"MapAxis\n",
"\n",
"\tname : e_true \n",
"\tunit : 'TeV' \n",
"\tnbins : 500 \n",
"\tnode type : edges \n",
"\tedges min : 5.0e-03 TeV\n",
"\tedges max : 5.0e+02 TeV\n",
"\tinterp : log \n",
"MapAxis\n",
"\n",
"\tname : migra \n",
"\tunit : '' \n",
"\tnbins : 300 \n",
"\tnode type : edges \n",
"\tedges min : 1.2e-38 \n",
"\tedges max : 3.0e+00 \n",
"\tinterp : log \n",
"MapAxis\n",
"\n",
"\tname : offset \n",
"\tunit : 'deg' \n",
"\tnbins : 6 \n",
"\tnode type : edges \n",
"\tedges min : 0.0e+00 deg\n",
"\tedges max : 6.0e+00 deg\n",
"\tinterp : lin \n",
"Data : size = 900000, min = 0.000, max = 10595.855\n",
"\n"
]
}
],
"source": [
"print(edisp.data)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"edisp.peek()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This is how for analysis you could slice out an `EnergyDispersion`\n",
"# object at a given offset:\n",
"edisp.to_energy_dispersion(offset=\"0 deg\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Point spread function\n",
"\n",
"The point spread function (PSF) in this case is given as an analytical Gaussian model."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Summary PSF info\n",
"----------------\n",
"Theta : size = 6, min = 0.000 deg, max = 5.000 deg\n",
"Energy hi : size = 25, min = 0.020 TeV, max = 1258.925 TeV\n",
"Energy lo : size = 25, min = 0.013 TeV, max = 794.328 TeV\n",
"Safe energy threshold lo: 0.100 TeV\n",
"Safe energy threshold hi: 100.000 TeV\n",
"68% containment radius at theta = 0.0 deg and E = 1.0 TeV: 0.05099999 deg\n",
"68% containment radius at theta = 0.0 deg and E = 10.0 TeV: 0.03700000 deg\n",
"95% containment radius at theta = 0.0 deg and E = 1.0 TeV: 0.08269457 deg\n",
"95% containment radius at theta = 0.0 deg and E = 10.0 TeV: 0.05999410 deg\n",
"\n"
]
}
],
"source": [
"from gammapy.irf import EnergyDependentMultiGaussPSF\n",
"\n",
"psf = EnergyDependentMultiGaussPSF.read(\n",
" irf_filename, hdu=\"POINT SPREAD FUNCTION\"\n",
")\n",
"print(psf.info())"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"psf.peek()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This is how for analysis you could slice out the PSF\n",
"# at a given field of view offset\n",
"psf.to_energy_dependent_table_psf(\"1 deg\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Background\n",
"\n",
"The hadronic background for CTA DC-1 is given as a template model with an absolute rate that depends on `energy`, `detx` and `dety`. The coordinates `detx` and `dety` are angles in the \"field of view\" coordinate frame.\n",
"\n",
"Note that really the background model for DC-1 and most CTA IRFs produced so far are radially symmetric, i.e. only depend on the FOV offset. The background model here was rotated to fill the FOV in a rotationally symmetric way, for no good reason."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background3D\n",
"NDDataArray summary info\n",
"MapAxis\n",
"\n",
"\tname : energy \n",
"\tunit : 'TeV' \n",
"\tnbins : 21 \n",
"\tnode type : edges \n",
"\tedges min : 1.3e-02 TeV\n",
"\tedges max : 2.0e+02 TeV\n",
"\tinterp : log \n",
"MapAxis\n",
"\n",
"\tname : fov_lon \n",
"\tunit : 'deg' \n",
"\tnbins : 36 \n",
"\tnode type : edges \n",
"\tedges min : -6.0e+00 deg\n",
"\tedges max : 6.0e+00 deg\n",
"\tinterp : lin \n",
"MapAxis\n",
"\n",
"\tname : fov_lat \n",
"\tunit : 'deg' \n",
"\tnbins : 36 \n",
"\tnode type : edges \n",
"\tedges min : -6.0e+00 deg\n",
"\tedges max : 6.0e+00 deg\n",
"\tinterp : lin \n",
"Data : size = 27216, min = 0.000 1 / (MeV s sr), max = 0.421 1 / (MeV s sr)\n",
"\n"
]
}
],
"source": [
"from gammapy.irf import Background3D\n",
"\n",
"bkg = Background3D.read(irf_filename, hdu=\"BACKGROUND\")\n",
"print(bkg)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"# TODO: implement a peek method for Background3D\n",
"# bkg.peek()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$$[1.2053319 \\times 10^{-5}] \\; \\mathrm{\\frac{1}{MeV\\,s\\,sr}}$$"
],
"text/plain": [
""
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bkg.data.evaluate(energy=\"3 TeV\", fov_lon=\"1 deg\", fov_lat=\"0 deg\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Index files and DataStore\n",
"\n",
"As we saw, you can access all of the CTA data using Astropy and Gammapy.\n",
"\n",
"But wouldn't it be nice if there were a better, simpler way?\n",
"\n",
"Imagine what life could be like if you had a butler that knows where all the files and HDUs are, and hands you the 1DC data on a silver platter, you just have to ask for it.\n",
"\n",
"![gammapy.data.DataStore - your butler for CTA data](images/gammapy_datastore_butler.png)\n",
"\n",
"Well, the butler exists. He's called `gammapy.data.DataStore` and he keeps track of the data using index files.\n",
"\n",
"### Index files\n",
"\n",
"The files with in the `index` folder with names `obs-index.fits.gz` and `hdu-index.fits.gz` are so called \"observation index files\" and \"HDU index files\".\n",
"\n",
"* The purpose of observation index files is to get a table of available observations, with the most relevant parameters commonly used for observation selection (e.g. pointing position or observation time). Their format is described in detail [here](http://gamma-astro-data-formats.readthedocs.io/en/latest/data_storage/obs_index/index.html).\n",
"* The purpose of HDU index files is to locate all FITS header data units (HDUs) for a given observation. At the moment, for each observation, there are six relevant HDUs: EVENTS, GTI, AEFF, EDISP, PSF and BKG. The format is described in detail [here](http://gamma-astro-data-formats.readthedocs.io/en/latest/data_storage/hdu_index/index.html).\n",
"\n",
"For 1DC there is one set of index files per simulated dataset, as well as a set of index files listing all available data in the all directory."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[01;34mindex\u001b[00m\r\n",
"└── \u001b[01;34mgps\u001b[00m\r\n",
" ├── \u001b[01;31mhdu-index.fits.gz\u001b[00m\r\n",
" └── \u001b[01;31mobs-index.fits.gz\u001b[00m\r\n",
"\r\n",
"1 directory, 2 files\r\n"
]
}
],
"source": [
"!(cd $GAMMAPY_DATA/cta-1dc && tree index)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Gammapy DataStore\n",
"\n",
"If you want to access data and IRFs from the CTA 1DC GPS dataset, just create a `DataStore` by pointing at a folder where the index files are located."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"from gammapy.data import DataStore\n",
"\n",
"data_store = DataStore.from_dir(\"$GAMMAPY_DATA/cta-1dc/index/gps\")\n",
"\n",
"# If you want to access all CTA DC-1 data,\n",
"# set the CTADATA env var and use this:\n",
"# data_store = DataStore.from_dir(\"$CTADATA/index/gps\")\n",
"# Or point at the directly with the index files directly"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data store:\n",
"HDU index table:\n",
"BASE_DIR: /Users/adonath/data/gammapy-data/cta-1dc/index/gps\n",
"Rows: 24\n",
"OBS_ID: 110380 -- 111630\n",
"HDU_TYPE: ['aeff', 'bkg', 'edisp', 'events', 'gti', 'psf']\n",
"HDU_CLASS: ['aeff_2d', 'bkg_3d', 'edisp_2d', 'events', 'gti', 'psf_3gauss']\n",
"\n",
"Observation table:\n",
"Observatory name: 'N/A'\n",
"Number of observations: 4\n"
]
}
],
"source": [
"# Print out some basic information about the available data:\n",
"data_store.info()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of observations: 4\n",
"['OBS_ID', 'RA_PNT', 'DEC_PNT', 'GLON_PNT', 'GLAT_PNT', 'ZEN_PNT', 'ALT_PNT', 'AZ_PNT', 'ONTIME', 'LIVETIME', 'DEADC', 'TSTART', 'TSTOP', 'DATE-OBS', 'TIME-OBS', 'DATE-END', 'TIME-END', 'N_TELS', 'OBJECT', 'CALDB', 'IRF', 'EVENTS_FILENAME', 'EVENT_COUNT']\n",
"2.0\n"
]
}
],
"source": [
"# The observation index is loaded as a table\n",
"print(\"Number of observations: \", len(data_store.obs_table))\n",
"print(data_store.obs_table.colnames)\n",
"# Compute and print total obs time in hours\n",
"print(data_store.obs_table[\"ONTIME\"].sum() / 3600)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"24\n",
"['OBS_ID', 'HDU_TYPE', 'HDU_CLASS', 'FILE_DIR', 'FILE_NAME', 'HDU_NAME']\n"
]
}
],
"source": [
"# The HDU index is loaded as a table\n",
"print(len(data_store.hdu_table))\n",
"print(data_store.hdu_table.colnames)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"# Of course, you can look at the tables if you like\n",
"# data_store.obs_table[:10].show_in_browser(jsviewer=True)\n",
"# data_store.hdu_table[:10].show_in_browser(jsviewer=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Select observations\n",
"\n",
"With ``data_store.obs_table`` you have a table with the most common per-observation parameters that are used for observation selection. Using Python / Table methods it's easy to apply any selection you like, always with the goal of making a list or array of `OBS_ID`, which is then the input to analysis.\n",
"\n",
"For the current 1DC dataset it's pretty simple, because the only quantities useful for selection are:\n",
"* pointing position\n",
"* which irf (i.e. array / zenith angle)\n",
"\n",
"With real data, there will be more parameters of interest, such as data quality, observation duration, zenith angle, time of observation, ...\n",
"\n",
"Let's look at one example: select observations that are at offset 1 to 2 deg from the Galactic center."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of selected observations: 3\n"
]
}
],
"source": [
"from astropy.coordinates import SkyCoord\n",
"\n",
"table = data_store.obs_table\n",
"pos_obs = SkyCoord(\n",
" table[\"GLON_PNT\"], table[\"GLAT_PNT\"], frame=\"galactic\", unit=\"deg\"\n",
")\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",
"print(\"Number of selected observations: \", len(table))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"ObservationTable length=3 \n",
"\n",
"OBS_ID GLON_PNT GLAT_PNT IRF \n",
"deg deg \n",
"int64 float64 float64 bytes13 \n",
"110380 359.9999912037958 -1.299995937905366 South_z20_50h \n",
"111140 358.4999833830074 1.3000020211954284 South_z20_50h \n",
"111159 1.5000056568267741 1.299940468335294 South_z20_50h \n",
"
"
],
"text/plain": [
"\n",
"OBS_ID GLON_PNT GLAT_PNT IRF \n",
" deg deg \n",
"int64 float64 float64 bytes13 \n",
"------ ------------------ ------------------ -------------\n",
"110380 359.9999912037958 -1.299995937905366 South_z20_50h\n",
"111140 358.4999833830074 1.3000020211954284 South_z20_50h\n",
"111159 1.5000056568267741 1.299940468335294 South_z20_50h"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Look at the first few\n",
"table[[\"OBS_ID\", \"GLON_PNT\", \"GLAT_PNT\", \"IRF\"]][:5]"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'South_z20_50h'}"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check which IRFs were used ... it's all south and 20 deg zenith angle\n",
"set(table[\"IRF\"])"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Galactic latitude (deg)')"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Check the pointing positions\n",
"# The grid pointing positions at GLAT = +- 1.2 deg are visible\n",
"from astropy.coordinates import Angle\n",
"\n",
"plt.scatter(\n",
" Angle(table[\"GLON_PNT\"], unit=\"deg\").wrap_at(\"180 deg\"), table[\"GLAT_PNT\"]\n",
")\n",
"plt.xlabel(\"Galactic longitude (deg)\")\n",
"plt.ylabel(\"Galactic latitude (deg)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load data\n",
"\n",
"Once you have selected the observations of interest, use the `DataStore` to load the data and IRF for those observations. Let's say we're interested in `OBS_ID=110380`."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"obs = data_store.obs(obs_id=110380)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Info for OBS_ID = 110380\n",
"- Start time: 59235.50\n",
"- Pointing pos: RA 267.68 deg / Dec -29.61 deg\n",
"- Observation duration: 1800.0 s\n",
"- Dead-time fraction: 2.000 %\n",
"\n"
]
}
],
"source": [
"print(obs)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.events"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Table length=3 \n",
"\n",
"EVENT_ID TIME RA DEC ENERGY DETX DETY MC_ID \n",
"s deg deg TeV deg deg \n",
"uint32 float64 float32 float32 float32 float32 float32 int32 \n",
"1 664502403.0454683 -92.63541 -30.514854 0.03902182 -0.9077294 -0.2727693 2 \n",
"2 664502405.2579999 -92.64103 -28.262728 0.030796371 1.3443842 -0.2838398 2 \n",
"3 664502408.8205513 -93.20372 -28.599625 0.04009629 1.0049409 -0.7769775 2 \n",
"
"
],
"text/plain": [
"\n",
"EVENT_ID TIME RA DEC ... DETX DETY MC_ID\n",
" s deg deg ... deg deg \n",
" uint32 float64 float32 float32 ... float32 float32 int32\n",
"-------- ----------------- --------- ---------- ... ---------- ---------- -----\n",
" 1 664502403.0454683 -92.63541 -30.514854 ... -0.9077294 -0.2727693 2\n",
" 2 664502405.2579999 -92.64103 -28.262728 ... 1.3443842 -0.2838398 2\n",
" 3 664502408.8205513 -93.20372 -28.599625 ... 1.0049409 -0.7769775 2"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.events.table[:3]"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.gti"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.aeff"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.edisp"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.psf"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
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"source": [
"Here's an example how to loop over many observations and analyse them: [cta_1dc_make_allsky_images.py](https://github.com/gammasky/cta-dc/blob/master/data/cta_1dc_make_allsky_images.py)"
]
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"source": [
"## Model XML files\n",
"\n",
"The 1DC sky model is distributed as a set of XML files, which in turn link to a ton of other FITS and text files."
]
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"# TODO: copy an example XML file for tutorials\n",
"# This one is no good: $CTADATA/models/models_gps.xml\n",
"# Too large, private in CTA, loads large diffuse model\n",
"# We need to prepare something custom.\n",
"# !ls $CTADATA/models/*.xml | xargs -n 1 basename"
]
},
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"cell_type": "code",
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"source": [
"# This is what the XML file looks like\n",
"# !tail -n 20 $CTADATA/models/models_gps.xml"
]
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"At the moment, you cannot read and write these sky model XML files with Gammapy.\n",
"\n",
"There are multiple reasons why this XML serialisation format isn't implemented in Gammapy yet (all variants of \"it sucks\"):\n",
"\n",
"* XML is tedious to read and write for humans.\n",
"* The format is too strict in places: there are many use cases where \"min\", \"max\", \"free\" and \"error\" aren't needed, so it should be possible to omit them (see e.g. dummy values above)\n",
"* The parameter \"scale\" is an implementation detail that very few optimisers need. There's no reason to bother all gamma-ray astronomers with separating value and scale in model input and result files.\n",
"* The \"unit\" is missing. Pretty important piece of information, no?\n",
" All people working on IACTs use \"TeV\", why should CTA be forced to use \"MeV\"?\n",
"* Ad-hoc extensions that keep being added and many models can't be put in one file (see extra ASCII and FITS files with tables or other info, e.g. pulsar models added for CTA 1DC). Admittedly complex model serialisation is an intrinsically hard problem, there is not simple and flexible solution.\n",
"\n",
"Also, to be honest, I also want to say / admit:\n",
"\n",
"* A model serialisation format is useful, even if it will never cover all use cases (e.g. energy-dependent morphology or an AGN with a variable spectrum, or complex FOV background models).\n",
"* The Gammapy model package isn't well-implemented or well-developed yet.\n",
"* So far users were happy to write a few lines of Python to define a model instead of XML.\n",
"* Clearly with CTA 1DC now using the XML format there's an important reason to support it, there is the legacy of Fermi-LAT using it and ctools using / extending it for CTA.\n",
"\n",
"So what now?\n",
"\n",
"* In Gammapy, we have started to implement a modeling package in `gammapy.utils.modeling`, with the goal of supporting this XML format as one of the serialisation formats. It's not very far along, will be a main focus for us, help is welcome.\n",
"* In addition we would like to support a second more human-friendly model format that looks something like [this](https://github.com/gammapy/gamma-cat/blob/b651de8d1d793e924764ffb13c8ec189bce9ea7d/input/data/2006/2006A%2526A...457..899A/tev-000025.yaml#L11)\n",
"* For now, you could use Gammalib to read the XML files, or you could read them directly with Python. The Python standard library contains [ElementTree](https://docs.python.org/3/library/xml.etree.elementtree.html) and there's [xmltodict](https://github.com/martinblech/xmltodict) which simply hands you back the XML file contents as a Python dictionary (containing a very nested hierarchical structure of Python dict and list objects and strings and numbers.\n",
"\n",
"As an example, here's how you can read an XML sky model and access the spectral parameters of one source (the last, \"Arp200\" visible above in the XML printout) and create a [gammapy.spectrum.models.PowerLaw](..\/api/gammapy.spectrum.models.PowerLaw.rst) object."
]
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"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"# Read XML file and access spectrum parameters\n",
"# from gammapy.extern import xmltodict\n",
"\n",
"# filename = os.path.join(os.environ[\"CTADATA\"], \"models/models_gps.xml\")\n",
"# data = xmltodict.parse(open(filename).read())\n",
"# data = data[\"source_library\"][\"source\"][-1]\n",
"# data = data[\"spectrum\"][\"parameter\"]\n",
"# data"
]
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"cell_type": "code",
"execution_count": 68,
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"source": [
"# Create a spectral model the the right units\n",
"# from astropy import units as u\n",
"# from gammapy.spectrum.models import PowerLaw\n",
"\n",
"# par_to_val = lambda par: float(par[\"@value\"]) * float(par[\"@scale\"])\n",
"# spec = PowerLaw(\n",
"# amplitude=par_to_val(data[0]) * u.Unit(\"cm-2 s-1 MeV-1\"),\n",
"# index=par_to_val(data[1]),\n",
"# reference=par_to_val(data[2]) * u.Unit(\"MeV\"),\n",
"# )\n",
"# print(spec)"
]
},
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"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises\n",
"\n",
"* Easy: Go over this notebook again, and change the data / code a little bit:\n",
" * Pick another run (any run you like)\n",
" * Plot the energy-offset histogram of the events separately for gammas and background\n",
"\n",
"* Medium difficulty: Find all runs within 1 deg of the Crab nebula.\n",
" * Load the \"all\" index file via the `DataStore`, then access the ``.obs_table``, then get an array-valued ``SkyCoord`` with all the observation pointing positions.\n",
" * You can get the Crab nebula position with `astropy.coordinates.SkyCoord` via ``SkyCoord.from_name('crab')`` \n",
" * Note that to compute the angular separation between two ``SkyCoord`` objects can be computed via ``separation = coord1.separation(coord2)``.\n",
"\n",
"* Hard: Find the PeVatrons in the 1DC data, i.e. the ~ 10 sources that are brightest at high energies (e.g. above 10 TeV).\n",
" * This is difficult, because it's note clear how to best do this, and also because it's time-consuming to crunch through all relevant data for any given method.\n",
" * One idea could be to go brute-force through **all** events, select the ones above 10 TeV and stack them all into one table. Then make an all-sky image and run a peak finder, or use an event cluster-finding method e.g. from scikit-learn.\n",
" * Another idea could be to first make a list of targets of interest, either from the CTA 1DC sky model or from gamma-cat, compute the model integral flux above 10 TeV and pick candidates that way, then run analyses."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"# start typing here ..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What next?\n",
"\n",
"* This notebook gave you an overview of the 1DC files and showed you have to access and work with them using Gammapy.\n",
"* To see how to do analysis, i.e. make a sky image and spectrum, see [cta_data_analysis.ipynb](cta_data_analysis.ipynb) next."
]
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