{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"\n",
"
\n",
"**This is a fixed-text formatted version of a Jupyter notebook.**\n",
"\n",
" You can contribute with your own notebooks in this\n",
" [GitHub repository](https://github.com/gammapy/gammapy-extra/tree/master/notebooks).\n",
"\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\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"The [CTA observatory](https://www.cta-observatory.org/) has started a first data challenge (\"CTA 1DC\"), focusing on high-level data analysis. Gammapy is a prototype for the CTA science tools (see [Gammapy proceeding from ICRC 2017](https://github.com/gammapy/icrc2017-gammapy-poster/blob/master/proceeding/gammapy-icrc2017.pdf)), and while many things are work in progress (most importantly: source and background modeling and cube analysis), you can use it already to analyse the simulated CTA data.\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.\n",
"\n",
"More tutorial notebooks for Gammapy are [here](../index.ipynb),\n",
"the Gammapy Sphinx docs are at at http://docs.gammapy.org\n",
"If you have a Gammapy-related question, please send an email to to Gammapy mailing list at http://groups.google.com/group/gammapy (registration required) or if you have an issue or feature request, file an issue here: https://github.com/gammapy/gammapy/issues/new (free Github account required, takes 1 min to set up).\n",
"\n",
"Please note that the 1DC data isn't public and results should be shared on CTA Redmine, as described here: https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki#Sharing-of-analysis-results. Unfortunately there's no good way yet to share Jupyter notebooks in CTA, as there is e.g. on Github and nbviewer which can show any public noteook."
]
},
{
"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: 1.13.3\n",
"astropy: 3.0.dev20763\n",
"gammapy: 0.7.dev5344\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": [
"## Getting the 1DC data\n",
"\n",
"You can find infos how to download the 1DC data here: \n",
"https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki#Data-access\n",
"\n",
"Overall it's quite large (20 GB) and will take a while to download. You can just download a subset of the \"gps\", \"gc\", \"egal\" or \"agn\" datasets, the ones you're interested in.\n",
"\n",
"**In this tutorial, we will only use the \"gps\" (Galactic center survey) dataset, so maybe you could start by downloading that first.**\n",
"\n",
"While you wait, we strongly recommend you go over some [CTA basics](https://www.youtube.com/playlist?list=PLq3ItKoU0hy7ED6m1eve6WIFs7ibcv3YR) as well as some [Python basics](https://www.youtube.com/playlist?list=PL-Qryc-SVnnu0tPV623TFnrAEQLykkZF5) to prepare yourself for this tutorial.\n",
"\n",
"Got the data?\n",
"\n",
"Assuming you've followed the instructions, you should have the ``CTADATA`` environment variable pointing to the folder where all data is located. (Gammapy doesn't need or use the ``CALDB`` environment variable.)\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/deil/work/cta-dc/data/1dc/1dc\r\n"
]
}
],
"source": [
"!echo $CTADATA"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[34mcaldb\u001b[m\u001b[m \u001b[34mdata\u001b[m\u001b[m \u001b[34mindex\u001b[m\u001b[m \u001b[34mmodels\u001b[m\u001b[m \u001b[34mobs\u001b[m\u001b[m\r\n"
]
}
],
"source": [
"!ls $CTADATA"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can find a description of the directories and files here:\n",
"https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki/Data_organisation\n",
"\n",
"A very detailed specification of the data formats is here:\n",
"http://gamma-astro-data-formats.readthedocs.io/\n",
"\n",
"But actually, instead of reading those pages, let's just explore the data and see how to load it with Gammapy ...\n",
"\n",
"Before we start, just in case the commands above show that you don't have `CTADATA` set:\n",
"\n",
"* you either have to exit the \"jupyter notebook\" command on your terminal, set the environment variable (I'm using bash and added the command `export CTADATA=/Users/deil/work/cta-dc/data/1dc/1dc` to my `~/.profile` file and then did `source ~/.profile`), then re-start Jupyter and this notebook.\n",
"* or you can set the environment variable by uncommentting the code in the following cell, setting the correct path, then executing it."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# import os\n",
"# os.environ['CTADATA'] = '/Users/deil/work/cta-dc/data/1dc/1dc'\n",
"# !echo $CTADATA\n",
"# !ls $CTADATA"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Let's have a look around at the directories and files in `$CTADATA`.\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/baseline` 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": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[34mcaldb\u001b[m\u001b[m \u001b[34mdata\u001b[m\u001b[m \u001b[34mindex\u001b[m\u001b[m \u001b[34mmodels\u001b[m\u001b[m \u001b[34mobs\u001b[m\u001b[m\r\n"
]
}
],
"source": [
"!ls $CTADATA"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[34magn\u001b[m\u001b[m \u001b[34megal\u001b[m\u001b[m \u001b[34mgc\u001b[m\u001b[m \u001b[34mgps\u001b[m\u001b[m\r\n"
]
}
],
"source": [
"!ls $CTADATA/data/baseline"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gps_baseline_110000.fits\r\n",
"gps_baseline_110001.fits\r\n",
"gps_baseline_110002.fits\r\n"
]
}
],
"source": [
"!ls $CTADATA/data/baseline/gps | head -n3"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" 3270\n",
" 11G\t/Users/deil/work/cta-dc/data/1dc/1dc/data/baseline/gps\n"
]
}
],
"source": [
"# There's 3270 observations and 11 GB of event data for the gps dataset\n",
"!ls $CTADATA/data/baseline/gps | wc -l\n",
"!du -hs $CTADATA/data/baseline/gps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's open up the first event list using the Gammapy `EventList` class, which contains the ``EVENTS`` table data via the ``table`` attribute as an Astropy `Table` object."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n"
]
}
],
"source": [
"from gammapy.data import EventList\n",
"events = EventList.read('$CTADATA/data/baseline/gps/gps_baseline_110000.fits')\n",
"print(type(events))\n",
"print(type(events.table))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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 662774403.255 -173.287 -62.4099 0.0486149 1.60795 0.258016 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 662774403.255 -173.287 -62.4099 0.0486149 1.60795 0.258016 2"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# First event (using [] for \"indexing\")\n",
"events.table[0]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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 662774403.255 -173.287 -62.4099 0.0486149 1.60795 0.258016 2 \n",
"2 662774459.184 -172.62 -63.0346 0.0611035 0.979058 0.555166 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 662774403.255 -173.287 -62.4099 0.0486149 1.60795 0.258016 2\n",
" 2 662774459.184 -172.62 -63.0346 0.0611035 0.979058 0.555166 2"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# First few events (using [] for \"slicing\")\n",
"events.table[:2]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# Event times can be accessed as Astropy Time objects\n",
"print(type(events.time))"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events.time[:2]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['2021-01-01T12:00:03.255(TT)' '2021-01-01T12:00:59.184(TT)']\n"
]
}
],
"source": [
"# Convert event time to more human-readable format\n",
"print(events.time[:2].fits)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 16,
"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": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events.galactic[:2]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"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": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(186.15609741, -64.019)"
]
},
"execution_count": 19,
"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": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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2/aYtqcJ82yVpKK6f4H3uyVqUzO1QllkuboZwUV4HFZELcdZY2Aw8DrxPVZ/J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\n",
"text/plain": [
""
]
},
"metadata": {},
"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(pos_pnt.l.wrap_at('180 deg').deg, pos_pnt.b.deg,\n",
" marker='*', s=400, c='red')\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": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0,0.5,'Number of events')"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {},
"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": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of events: 107301\n",
"Number of gammas: 1112\n",
"Number of hadrons: 106189\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": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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L68NjF00jzayqCcBngPHJ41Nuq25mOdM0Yx+WmTSD4wuAKymsFu9zZ1ozsx4e+2h/aRLH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\nZlYVJw4zM6uKE4e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"text/plain": [
""
]
},
"metadata": {},
"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, normed=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": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0,0.5,'Offset (deg)')"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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7ACzBrQwxDMMYbLaSDMLMqwDeFfwYhmFsHfrEEEehUT7rz6HBW7HVIIZhDDSM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\nZ6jDqOd8DCuPVa9klm+vl4lPG9N+NJ7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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"energy_bins = 10 ** np.linspace(-2, 2, 100)\n",
"offset_bins = np.arange(0, 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,\n",
" bins=(energy_bins, offset_bins),\n",
")[0].T\n",
"\n",
"from matplotlib.colors import LogNorm\n",
"plt.pcolormesh(energy_bins, offset_bins,\n",
" 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",
"### 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": 25,
"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(os.environ['CTADATA'], 'data/baseline/gps/gps_baseline_110000.fits')\n",
"t1 = Table.read(filename, hdu='EVENTS')\n",
"\n",
"filename = os.path.join(os.environ['CTADATA'], 'data/baseline/gps/gps_baseline_110001.fits')\n",
"t2 = Table.read(filename, hdu='EVENTS')\n",
"tables = [t1, t2]\n",
"table = table_vstack(tables, metadata_conflicts='silent')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of events: 214675\n"
]
}
],
"source": [
"print('Number of events: ', len(table))"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of events after selection: 45\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": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"caldb\r\n",
"└── data\r\n",
" └── cta\r\n",
" └── 1dc\r\n",
" ├── bcf\r\n",
" │ ├── North_z20_50h\r\n",
" │ │ └── irf_file.fits\r\n",
" │ ├── North_z40_50h\r\n",
" │ │ └── irf_file.fits\r\n",
" │ ├── South_z20_50h\r\n",
" │ │ └── irf_file.fits\r\n",
" │ └── South_z40_50h\r\n",
" │ └── irf_file.fits\r\n",
" └── caldb.indx\r\n",
"\r\n",
"8 directories, 5 files\r\n"
]
}
],
"source": [
"!(cd $CTADATA && tree caldb)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Filename: /Users/deil/work/cta-dc/data/1dc/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(os.environ['CTADATA'], 'caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits')\n",
"from astropy.io import fits\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": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n"
]
}
],
"source": [
"from gammapy.irf import EffectiveAreaTable2D\n",
"aeff = EffectiveAreaTable2D.read(irf_filename, hdu='EFFECTIVE AREA')\n",
"print(type(aeff))\n",
"print(type(aeff.data))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NDDataArray summary info\n",
"energy : size = 42, min = 0.014 TeV, max = 177.828 TeV\n",
"offset : size = 6, min = 0.500 deg, max = 5.500 deg\n",
"Data : size = 252, min = 0.000 m2, max = 5371581.000 m2\n",
"\n"
]
}
],
"source": [
"print(aeff.data)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
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58ssv7xkQyc/ChQsZMWIEt2/fplu3bnTr1g2AsLAwwLhY6MqVK1m4cCF2dnY4OzsTHh5u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\nyva9nOn6udCpPySF/pvS3fm7W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mqVQGZuIX9tP2B1KNVHSCSc/wzsXQVLp1gdzSmLDolm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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"aeff.peek()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$3.7835869 \\; \\mathrm{km^{2}}$"
],
"text/plain": [
""
]
},
"execution_count": 33,
"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": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 34,
"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": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n"
]
}
],
"source": [
"from gammapy.irf import EnergyDispersion2D\n",
"edisp = EnergyDispersion2D.read(irf_filename, hdu='ENERGY DISPERSION')\n",
"print(type(edisp))\n",
"print(type(edisp.data))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NDDataArray summary info\n",
"e_true : size = 500, min = 0.005 TeV, max = 495.450 TeV\n",
"migra : size = 300, min = 0.005, max = 2.995\n",
"offset : size = 6, min = 0.500 deg, max = 5.500 deg\n",
"Data : size = 900000, min = 0.000, max = 10595.855\n",
"\n"
]
}
],
"source": [
"print(edisp.data)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/deil/Library/Python/3.6/lib/python/site-packages/astropy/units/quantity.py:635: RuntimeWarning: invalid value encountered in true_divide\n",
" result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n"
]
},
{
"data": {
"image/png": 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ud9PSaUKIS6qlR6Jaa71Caz1Oa32f1rrwko9KCNEtlfsrqAvWorVmf41GAXn9\n7NTF8zyGo2EUiihRQtEgLnMSNaG2l8I2dspX3kkj/27U5ThldFzOGL1z9/YO77c6WAuQSEzSsNan\nwbnbjdWG6ki2yI2eEOIshzm1RGud09XjuFRycnJ0cXFxk3379+9nxIgRXTSi3umLk7U4rGa+1yf2\nBMUbDPPl6XqGpDtJdlhaPS8QCGAYBmazmV27dnH//fd3WP4O+T4Q3YlSqlv+bI4vIdnUkMRTKfV9\nYJnWenp8+1EArfXT7exvNjD76muG/mLvF590ypiFEF3vjL+S+lAdJmWiPh6QqAj4+EeVYuqVydQE\nPfS3p6PR2Aw7Axz9CEaC1IU9JBn2blOIor33zb0uB0Z9yNMkIUljZmUQ1hE84ViZGLOp6ZfHYmr9\nxs+izv+lbFw/tzX+SKDLE6MIIUSn2PwInPy8Y/u8YjT80zMd2+dlTuumj20bcmGc74HFkSNHmDdv\nHtFoFKvVyssvv9yJoxRCtMNHwHCl1FCgDFgA/Gt7T9Zavwm8OS5nzC86aXxCiC5WGagmyeygMnCG\nUDTECa8Hq8lEqs3OjAwXAxwDcFnqEstIBzj6AWA1rAQCFbjNLf9d3J11agBDKTWYWDblK4Ao8JLW\n+g/ntLkF2AB8E9/1N631k501plglkZa5LE6qg7WE4tVHzi0R01oJVaBdkatwNBwrgnXeNhLAEEII\ncWGinE3cCWeDGeebbzl8+HA++USe0grRFZRS/y9wC9BXKXUMWKq1Xq2UegB4i9gd5H9orfd24TCF\nEN2M3bBRFaiiPuTHGw5x3AsmFSHdrghFQ1QFqgFNH1sa0XPuBKwmG56wF6thbbX/2mAdydbutcqg\ns2dghIFfa60/Vkq5gRKl1NuNyj81eF9rXdDJYxFCCNGVZKbEBTtw4AB33303H3/8Mb/73e946KGH\nWmz3zTffsOD2H1FXU834nHH853/+J8oUi5xHNaxZs4Y//CH2HGHfvn1cd911GIbBjBkzeOaZ5v8+\ndXV1DBkyhMOHD+NyuRL7CwoK+NnPfsbcuXM74WqF6H201gtb2f934O8X0mejJSQXMzQhRDdSHaxF\ncfbBern/DFEdwRMO4bJYGOyK4jAsDHVfzQnvCa50DiQQCeAN+9CcTdAZjARxW5x4wz4AqoI1pFlT\nmn1edwteQCcn8dRan2go76S1rgP2E8ueLIQQQly2wuHwJf28Pn36sGLFilYDFw1+85vf8NNFv2Rn\nyeekpaWxevVqVHwOhkZz9913JyqhDBo0iG3btlFaWtpi8AJiFVYmTZrEhg0bEvuqqqrYvXs3M2fO\n7LgLFEJ0OK31m1rre1JTm/8skI/EAAAgAElEQVRRIoS4PCUZdqI6SlWwhvqQhyP1p/lH1XEsJhM2\nkxmHYaavPZmojpJiTQXAZthwmB30saU26ctm2BIBDUO1HBaoDbY/yeelcsmqkMQTE40Bdrdw+PtK\nqU+VUpuVUiNbOf8epVSxUqq4vLyiE0cqhBCiJ/nLX/7C+PHjyc7O5t577yUSiQDgcrl47LHHuOGG\nG8jPz+fUqVMAlJeXc/vtt5Obm0tubi47d+4EYNmyZdxzzz1MmzaNO+64A6/Xy7x588jKymL+/Pnk\n5eVRXFzM6tWrWbJkSeLzX375ZR588MGLuob+/fuTm5uLxdJ6LiatNe+99x5TZv0QpeDOO+/kjTfe\nSCwnOV/O7vr6+kTFljFjxvDmm28CsHDhQoqKihLtXn/9dWbNmoXd3nZOJyGEEEJ0nMpANVXBGjRg\nYOKU7yRVAT92wyAUjVITCjDYOQCbYcdlceK0xIITtcE67IaN6mAt9fGKJFbDSiASwB8NALRajKKt\n5SVd5ZIEMJRSLuB14P/UWteec/hj4Cqt9Q3A/wO80VIfUs9aCCHEd7V//37WrVvHzp07KS0txTAM\n/vrXvwLg8XjIz8/n008/5eabb04krVy8eDFLlizho48+4vXXX2fRokWJ/kpKStiwYQNr165l1apV\npKWl8dlnn/Hb3/6WkpISABYsWMDGjRsJhWI5l9asWcPdd9/dbGzz588nOzu72euVV165oGutqKgg\nNTUVwzBQSpGRkUFZWVm7AxhPPvkkM2bMYM+ePbz33nv8+te/xu/3M2vWLD788EOqqqoAKCoqYuHC\nFme7CyGEEKIT1Abr8Ed8uC0uaoLVHK4/xu7yWjKcbswmE26LlSGuQRgmM26Lm2A0lCge0bAMxGZY\nmxSzsBk27Ka2cy92x+ISnV6FRCllIRa8+KvW+m/nHm8c0NBa/10ptUop1VdrfaYzxtNWJZGzbWJf\nloZqJA1Tay5WQ1WTtqqRnFv5RAghxIV79913KSkpITc3FwCfz0f//v0BsFqtFBTE0i+NGzeOt99+\nG4B33nmHffvOpmqqra2lri42hXLOnDk4HLF1pzt27GDx4sUAjBo1iqysLACcTieTJk1i06ZNjBgx\nglAoxOjRo5uNbd26dR16rY2rjDQ8nVBKYYovIQmEImitE1VJzrV161Y2b96cWE7i9/s5cuQI1157\nLbNmzeJvf/sbBQUF7N27l8mTJ3fo2IUQHU9yYAjRcyRb3Rgmgwp/BYfrz+C2WBjgiBKIRuhvT2KA\nY2CsGIQGb9iDCYUffyJ4URuqa3GWRXecYXE+nV2FRAGrgf1a6+dbaXMFcEprrZVS44ndd3XaGpHW\nSqg21hCwqAmeO1nk4jQELSLRSKvVSLpjlEsIIS5XWmvuvPNOnn766WbHLBZL4o95wzASeS2i0Si7\ndu1KBCoaczrP/g5pqyzpokWL+P3vf8/111/f4uwLiM3A+OKLL5rtf/DBB7njjjvavrAW9O3bl+rq\nasLhMErBsWPHGDRoUGIGRqU3iMtuJjWp5ZsVrTVvvPEG11xzTbNjCxcu5LnnnsPn8zF37lzMZgm2\nC9HdSRlVIXqOcn8F9aE6TvuqOeaJkGoNMjw5HZMyUCpWydJmtmE3WTniOUqaLQ1/JJA4PxQJwfmf\n418WOnsJyU3AT4FJSqnS+GumUuo+pdR98Tb/AvxDKfUpsAJYoM9XrF4IIYRoh8mTJ/Paa69x+vRp\nACorKzl8+HCb50ybNo2VK1cmtktLS1tsN2HCBF599VUgVtHj888/TxzLy8vj6NGjrF27ttXlFuvW\nrUsk1Gz8upDgBcRmW9xyyy28/d8bUCj+/Oc/88Mf/hClFEP7xgIv4Ujrv16nT5/OihUrEtuNS6pO\nmTKFvXv3UlhYKMtHhBBCiEukKlhDVaCaM/5yTvuqOe33cn2KHaUUhskgqiOkWfuQYkvGF/YSioYZ\n7ByMw7CTZk0hEA9iNFQt6Qk6uwrJDq210lpnaa2z46+/a60LtdaF8TYrtdYjtdY3aK3ztdYfdOaY\nhBBC9B6ZmZk89dRTTJs2jaysLKZOncqJEyfaPGfFihUUFxeTlZVFZmYmhYWFLbb75S9/SXl5OVlZ\nWTz77LNkZWWRknI22/+8efO46aabSEtLu+jrOHnyJBkZGTz//PM89dRTZGRkUFsbmyU4c+ZMjh8/\nDsDvnn6G/3x5FeOzM6moqODnP/85AE5bbMZEtI3nA0uXLsXr9TJ69GhGjhzJsmXLEscMw+C2226j\ntraWm2666aKvRwghhBBtK/dXYGCiNlRLddDHca+XwU43hkkxKMlJKBpmQNJAPGEPvrAfm8lGstVN\nWMdmlAYjQWpCsSWwjh4UwFCX42SHcTlj9M7d2zul7+pgLanWZODsEpKU+HZHqQ952rWURQhxeXOY\nU0u01jldPY5LJScnRxcXFzfZt3//fkaMGNFFI+pckUiEUCiE3W7nq6++YvLkyRw8eBCrNbZEo6Cg\ngCVLllzSfBHBcJQDJ2vJSHPQx3l2SaLWmn+U1dLPbeOKlEtfPaQnfx+Iy49Sqlf8bG6UA+MXe7/4\n5LzthRDdR1hHqApUYzaZqfRXsKf8NClWTZrNzpVJfakO1tDP3g+H2UEkGsZkMrCbrJhNZupDHpwW\nJ96wL/F37eWgvffNl6yMaldpKBVzvmNVwRrgbAJPiCXUNJvMiWSe3rAPb9jXYl+t7W+JYWqaAMMX\n8TfZ9kcCbY5bCCFE1/N6vUyYMIEbbriB2267jT/+8Y9YrVaqq6u59tprcTgclzzZpSb2UELRNFGn\nUgqTansGhhCiZ9Fav6m1vic1NeX8jYUQ3Yo35MVqsnDGV84Zfy0jUp24LVZsJgOryYbLnERYh0ky\nO0i39yHNmkIoPvMizZYKcFkFL76LHp+FKxgNtXqs4R8ZYlNsoGnFkXOTeYaioWY3hQ3Cjfo6n3Mr\nkJyb1DMcDTcZmxBCiO7H7XZz7owTgNTUVA4ePNgFIzpbKrWlQiPKpCSAIYQQQnRzZZ4TKKU46T1N\nmbceBWQ43QxxX0EkGsZmWLGZ0zEw4Y/4sTaqstlQcdPajsqbEPsb+HKrRNLjZ2AIIYQQvUVbAYzY\nDIxLOx4hhBBCtF9FoIpQNEh1oJITPg8V/lggwm5Y8ITqsBsOXBYnFmUm2eomEo0kHtif+5C8PS63\n4AVIAEMIIYToMVpbQgJgUoqoRDCE6DWUUrOVUi9VV9d09VCEEOdRE6ylPuTBH/ZRFazh67pqfGFI\nt4cY4krGathwW1KwmMxUBWpwWZzUBuuI6CieeOoBSztnXVzuJIAhhBBC9BBtz8CQJSRC9CaSA0OI\n7q8hj6LFZKEuVE9NsJoKfyw/4pVOO9el9MNtTSbdlo5SirCO0N/RF4Bkq5u+9j5YTJZelT9RAhhC\nCCFED9FQWazlGRhnAxxCCCGE6HpJZgf+SIDKQCX1oToqAj6OeyEYhRSLDdA4jCQCkSBOcxJmZTQL\nVrgszl5V4bLHBzDam8CkYf1PQ8WRxu/N8cokFpMl8R6aViUxKzO1oTp8YR++dlYkaag+cm5VErPJ\njEXFPscfCeCPBNrVnxBCiPZbsWIFI0aM4Mc//jGBQIApU6aQnZ3NunXrvlM/27dv54MPPuiQMT32\n2GMMHjwYl8vVZrunn36aYcOGcd111/HWW28l9jfEJ86dgfGnP/2JZY/8WmZgCCGEEN1AWEeAWCXM\nk96T1ASr+aLmNP5ImD62MNcku3Fa3LgsyaTZUki2ukmxJpNkdhCIxopPhNooVtGT9fgqJO2NRqVZ\nY9PrQtGz1T/C8fcN1UiSzI4m54QbtU0yO6jwVyaqiTRt2bKG6iPnJlyxGzYwbLE28W9uIYQQHWvV\nqlVs3ryZoUOH8uGHHxIKhSgtLf3O/Wzfvh2Xy8WNN9540WOaPXs2DzzwAMOHD2+1zb59+ygqKmLv\n3r0cP36cKVOmcPDgQQzDaLsKiVKSxFMIIYToBvxhP2aTmVPeE/giQaoCAcwmE31sdlwWBxaThTRr\nrBxqbbAOk8nAbthwmB04zA48YW+7H9T3ND1+BoYQQoje7fnnn2fUqFGMGjWK5cuXA3Dffffx9ddf\nM2fOHJ599ll+8pOfUFpaSnZ2Nl999RWPPPIImZmZZGVl8dBDDwFQXl7O7bffTm5uLrm5uezcuZNv\nv/2WwsJCXnjhBbKzs3n//fcvaqz5+fkMHDiwzTYbNmxgwYIF2Gw2hg4dyrBhw9izZw/QdAnJmjVr\nuPbaa5k4cSI7d+5EAVGtW7yOhuubOnUqY8eO5d577+Wqq67izJkzF3U9QgghhGjOYjJzxn+GcDTM\n4fo6POEQNsPAbXHgMBw4zS7COkKSORbMcBqxx+O+sI/qYC1a62ZJO3vLjIwePwNDCCFE71VSUsKa\nNWvYvXs3Wmvy8vKYOHEihYWFbNmyhW3bttG3b1/y8vJ47rnn2LRpE5WVlaxfv54DBw6glKK6uhqA\nxYsXs2TJEiZMmMCRI0eYPn06+/fv57777sPlciUCHY1t27aNJUuWNNuflJR0wctOysrKyM/PT2xn\nZGRQVlYGnF1CcvLkCZYuXUpJSQkpKSnceuutXJs5mqjWrV7HE088waRJk3j00UfZsmULL7300gWN\nTwjRPSilZgOzr75maFcPRQjRyGnfGaI6Sl2ojsqAH7thENGagQ43LkusNCrEAhI1oTpsJmuTcqfO\neFDjXL2lCokEMIQQQvRYO3bs4LbbbsPpjC0nnDt3Lu+//z5jxoxp9Zzk5GTsdjuLFi1i1qxZFBQU\nAPDOO++wb9++RLva2lrq6ura/Pxbb731gpaltEW3kMdCxdeMNBwq/mgPt9xyC/369QNg/vz5fPKP\nfUR169exY8cO1q9fD8CMGTNIS0vr0HELIS4trfWbwJvjcsb8oqvHIoSIqQ3W4Yt4qQnW8nVdDXbD\nINliJdVqx1BmApEA6bZ0kq1uIJYP0W7YqA95cFmcOMwOKgJVpNvSqAvV47a0nTOrJ+rVAQyLMiey\nuLoszjbLz3jCXpzmpETSzgYRHeGMvzJWwsawJJJvtse5yTtbbKPO30YIIUTLWvpj/3zMZjN79uzh\n3XffpaioiJUrV/Lee+8RjUbZtWsXDkd7shzFdMYMjIyMDI4ePZrYPnbsGIMGDQJAc/Z61TmJMBSx\nr0dr13EhXyshhBBCnF9YRzArg3J/OREdocxTjy9s8D1nEm5LEsFoELvZgaFMiaIRVcEaTCjshq3J\n7/SG3Bf2eM7E3qZX58BwWZyEdJiQjiXjDLeRMLMhYWc4Gk68ACI6SiBeTSTZ4k4kVmmPc5N3tsRu\n2HrtN6cQQlysm2++mTfeeAOv14vH42H9+vX84Ac/aPOc+vp6ampqmDlzJsuXL0/MoJg2bRorV65M\ntGvY73a7W52J0TAD49zXxVQtmTNnDkVFRQQCAb755hsOHTrE+PHjgbMzMPLy8ti+fTsVFRWEQiH+\n67/+K1FaderUqS1ex4QJE3j11VcB2Lp1K1VVVRc8RiGEEELE1Ic81IXqOek7jS/s5Yuacsym2O9k\nh9mK3ezAaXaSak1hUNJAfBE//kiANGtK4mG205yUqHTZMOuitywZOVevDmAIIYTo2caOHctdd93F\n+PHjycvLY9GiRW0uHwGoq6ujoKCArKwsJk6cyAsvvADEyq4WFxeTlZVFZmYmhYWFQKxyyPr16zsk\niefDDz9MRkYGXq+XjIwMli1bBsDGjRt5/PHHARg5ciTz5s0jMzOTGTNm8OKLL8YrkGh8oVggftCg\ngSxbtozvf//7TJkyhbFjxyYqkzy//A8tXsfSpUvZunUrY8eOZfPmzQwcOBC3231R1yOEEEL0dhpN\nhf8MZZ4THKqtpZ/dQT97EuP6pmMxmeln70uyNQWFwhP2kmJNJqqjABjq7J/r7X1I3tOpy3HK6Lic\nMXrn7u0d0ldVsAaIlVGtDtYCkGpNBqAmWEvKOe9rg02fsgWjIQIRP1c6B3XIeIQQPYfDnFqitc7p\n6nFcKjk5Obq4uLjJvv379zNixIguGlHvUuMLcbgithRy1KAUTKamS0iqPEGOVnm5boAbm6X58sRA\nIIBhGJjNZnbt2sX999/fYfk75PtAdCdKqV71s7kj75uFEO0XjoYxm8wc85RRE4wlBC8+48FpjjC6\nTzpJZidJ5iSiOorNsKK1xmqyYDGsRKJhbL1sFn5775t7dQ4MIYQQoqcIR2JPa4akO5sFLwBM8Yc4\n0VaeWxw5coR58+YRjUaxWq28/PLLnTVUIYQQoseqCtaQZk3BE/YSjoapCVbzWWUdFpNmqMvCwKQ+\nGCYDqylWWcRh2PFHAjgtSTgMO2EdQSlZKNEaCWAIIYS4JJ7d8ywHKg90aJ/X97me34z/TYf2eblq\niEskWVtO/myKryGJao03GOZ0bQCH1WBAciwf0/Dhw/nkk08uxVCFEEKIHsuizNQEa6kKVHHKV8VR\nj4+rXDbC0SiDnGlYTFasJitWkyWR6zDVlkIoEsQT9gKxnBeiZb0+tGNR5kTlEHM8SUrDN47ZZMYT\n9ia2PWFvIitsw3GryYKtUTLO2lBsiYnvnGolANXBWnzxhJ8N/22LPxK4kEsSQgiUUoZS6hOl1Kb4\n9lCl1G6l1CGl1DqllDW+3xbf/jJ+fEijPh6N7/9CKTW9a65EtFfDktBzq480aAhg1PnDHKnwUusP\ncbpWfs8I0VMppWYrpV6qrq7p6qEI0aM1/K3YwGVx4gl78EW8mBSYlKY2FMRqGFhMVmyGDbvZjlIm\nLIaVKBqzMrAYVmwmqwQvzqPXz8BwWZxN3lcHawnFK4w4zUnUxPNiQGwdU+M8GEktJFIJRUJggZAO\nc+7RYCQYC5IYEIlG4DwVUsPRMPSytU9CiA6zGNgPJMe3nwVe0FoXKaUKgZ8Df4z/t0prPUwptSDe\nbr5SKhNYAIwEBgHvKKWu1bqNck3nITMlLtyBAwe4++67+fjjj/nd737HQw89lDi2ZcsWFi9eTDAU\nZs78n/DC75c1Oz8QCHDXT3/K7j3FpKT14d//+B9cP+waav0holHN3r3/4Kc//SkQW0qSkpJCSkoK\nffv25Z133mlxTBMmTOCJJ55g8uTJiX3PPfccR44cYcWKFR37BRBCfGda6zeBN8fljPlFV49FiJ6s\nIeAQjobxhn34In7qQnV8U1fN13V2vufUjEgdhDfsxW7YUUqhUNhMFkLREP6wH6c5iXA0LNUn26HX\nz8AQQoieRimVAcwC/nd8WwGTgNfiTf4M/HP8/Q/j28SPT463/yFQpLUOaK2/Ab4Exl+aK+j+wuHw\nJf28Pn36sGLFiiaBC4BIJMKvfvUrNm/ezP/s+YQtG15n/759zc5fvXo16X368OWXX/J//fpBVj//\nFC577BlGRGtGjx6dKPE6Z84c/v3f/53S0tJWgxcACxcupKioqMm+oqIiFi5c2AFXLIQQQlwefGEf\nYR3BE/ZSFazmpO8k+6rOYFYmbugTJTPtiljAwrBhN2ykWVPQ6MTM/oaH4g3bMgu/bRLAEEKInmc5\n8DAQjW+nA9Va64a/uo8BV8bfXwkcBYgfr4m3T+xv4ZwEpdQ9SqlipVRxeXl5R19Hh/jLX/7C+PHj\nyc7O5t577yUSiU0icblcPPbYY9xwww3k5+dz6tQpAMrLy7n99tvJzc0lNzeXnTt3ArBs2TLuuece\npk2bxh133IHX62XevHlkZWUxf/588vLyKC4uZvXq1SxZsiTx+S+//DIPPvjgRV1D//79yc3NxWJp\nWvN9z549DBs2jKuvvhqLxcKMH97Oxo0bm52/YcMG7rzzTqxmE/+6YB7b3nuPhjyf0dayejbyzDPP\nMH78eLKysnjyyScB+NGPfsTGjRsJhUIAfPnll1RUVJCfn39R1yqEEEJcLmqDdVgNK/6wn/qQh2Ak\nQCgSxVCaqqDmCkcKFpMFq8lGktmJUgpfxI+hTPgifiwmCw11zqPR2P2JzMJomwQwhBCiB1FKFQCn\ntdYljXe30FSf51hb55zdofVLWuscrXVOv379vvN4O9v+/ftZt24dO3fupLS0FMMw+Otf/wqAx+Mh\nPz+fTz/9lJtvvjlRdWPx4sUsWbKEjz76iNdff51FixYl+ispKWHDhg2sXbuWVatWkZaWxmeffcZv\nf/tbSkpiX/IFCxY0+cN+zZo13H333c3GNn/+fLKzs5u9XnnllXZfX1lZGYMHDwZAa7jiikGUlZW1\n2c5sNpOSkkJNZSUQm4HRlr///e8cOXKE3bt3U1paygcffMAHH3xA//79yc7OZuvWrUBs9sWCBQta\nzcEhhBBC9DRJZgd1IQ++iJ/qYBXFZ85wqNZHktnC6LRknBYXydYUlIrNwEBDssWdeAGo+C2X1bB2\n5aVcNnp1Doz6kCfxviEXhlkZTW6+zCYz/kggkeDT2yg5pzfsa5IHwxf2YTIZiQSevrAPR6PjVsOK\nYWo58UVLU4UaTyNqiMQFIoFeVxNYCPGd3ATMUUrNBOzEcmAsB1KVUub4LIsM4Hi8/TFgMHBMKWUG\nUoDKRvsbND7nsvHuu+9SUlJCbm4uAD6fj/79+wNgtVopKCgAYNy4cbz99tsAvPPOO+xrtAyjtraW\nurpY7qM5c+bgcMR+ru/YsYPFixcDMGrUKLKysgBwOp1MmjSJTZs2MWLECEKhEKNHj242tnXr1l30\n9elGwQetYw9xWgog6BaCFIZhgihEzjMDY+vWrWzevJkxY8YAUF9fz8GDB7nxxhsTy0hmzZpFUVER\na9euvcgrEkIIIS4PDbkSqwNV+CI+jtTXMjDJjKEU/R1u3JZktNZEohHcFhfhaLhZkELrqMy4+I56\ndQAjpJuvYW6c1BNiSVk8YW8igBGOnj2n8fuG/gxlIqTDKBThc3LdpVqTac25fTUeS6RRP2EdQb7F\nhRCt0Vo/CjwKoJS6BXhIa/1jpdR/Af8CFAF3Ahvip2yMb++KH39Pa62VUhuBtUqp54kl8RwO7LmU\n19IRtNbceeedPP30082OWSyWxB/7hmEk8lpEo1F27dqVCFQ05nSe/R3RUlCgwaJFi/j973/P9ddf\n3+LsC4jNwPjiiy+a7X/wwQe544472r6wuIyMDI4ePZoYz6mTxxk0aFCr7TIyMgiHw9TU1NCvbzrV\np+vPG8DQWvNv//Zv/PznP292bO7cuTz88MMUFxcTjUYTQRwhhBCiJ4voCMFoiEDEzxl/NSalqA0B\nhLki6WwVEYthwVAmVPy9L+JvErBo615CtEyWkAghRO/wG+BBpdSXxHJcrI7vXw2kx/c/CDwCoLXe\nC7wK7AO2AL+6mAokXWXy5Mm89tprnD59GoDKykoOHz7c5jnTpk1j5cqVie3S0tIW202YMIFXX30V\ngH379vH5558njuXl5XH06FHWrl3balLLdevWJRJnNn61N3gBkJuby6FDh/jmm28IBINs3vA35syZ\n06zdnDlz+POfY7laX3vtNSZNmoRhit0CnG8JyfTp01m9ejUeT2zW4rFjxzhz5gwAycnJTJgwgUWL\nFvGv//qv7R63EEIIcbkK6wi1oXoCET+nfRV8W+/luLceiynKFUlJOM120qx9SLa4sSgzNsOGxbBi\nMVlwGHYAfBE/IMtGLkSvnoEhhBA9mdZ6O7A9/v5rWqgiorX2Az9q5fzfAb/rvBF2vszMTJ566imm\nTZtGNBrFYrHw4osvctVVV7V6zooVK/jVr35FVlYW4XCYm2++mcLCwmbtfvnLX3LnnXeSlZXFmDFj\nyMrKIiUlJXF83rx5lJaWkpaWdtHXcfLkSXJycqitrcVkMrF8+XL27dtHcnIyK1euZPr06QRCYebO\n/wkjR44E4PHHHycnJ4c5c+bw85//nJ/+9KcMGzaMPn36UFRUhGFqSBrWdgBj5syZHDhwIJGc0+12\ns3btWvr27QvEqpHMmzeP1157ra1uhBBCiB7BG/ZSH6rnmOc0lQE/VzisVAUDjErrS7I1FQCTMsVm\nXhhWzCYzZmUQ1hF0QzoxmXlxwdTlOG1lXM4YvXP39ovupypYk3ifZk1ptd1p/xlsplh0TJ2T1y7Z\n6k68rw3VJd43tHNbXC32WR/yNFmu0jgfR4OG456wN1FfuPF7IUT35jCnlmitc7p6HJdKTk6OLi4u\nbrJv//79jBgxootG1LkikQihUAi73c5XX33F5MmTOXjwIFZr7PdFQUEBS5YsYfLkyZdkPN+e8RCM\nRLl2gPv8jYlNW/1HWQ393HauSLF36th68veBuPwopXrVz+aOum8WQsQc957kjP8MgUiYb+t9OM2Q\narXR35GMWVlwW5OxKDOWeD7DkA4nEnb6In4chp2wjiRSFIiY9t4398oZGA3BAosyN9sHZwMHNcFa\nzCYzRvyby2wy///s3Xl4ZNlZ5/nve85dIhSSUllZi6tcVbjKNga3GVO4MIt7pjEzNMZQbA0NZh3a\nD2Z4zNLD9PRMA/PQQwM9zANmbMCm3V6hbYPB7oECA81mPOzYxoDxQhsDpuxyLZmpLZZ77znnnT/u\njVBIqUwpM6WUlPl+6tGj0FVE6GRJCkW897y/l5jiLFxzp+n9NRpmP5DjMJ6Nxun73my7UNQ4+wGe\nylxGSOGC+/dzP9z2g26MMcfDaDTiuc99Lk3ToKq84hWvoCgKVldXefazn80zn/nMa1a8gHZEjLuM\nASAignOyZwuJMeZkEpEHgAfuffI9R70UY64LVax4ePRx1psN/mZ9nTo5TheOe5bOALCcr+DFseB7\nIEKTmraNZK4Dd/raz17TXbkbsoDRpHa03elyZXbsfL12we6KKtXA1g/YIFtgvd7YNnlk3nTiSNNs\nzC5vNJtbgwc9xG6+b9LUXp772e35ks0ULkiinf/YJpAYY8zxsLS0xM4dJwArKyv89V//9TVfj6pe\n8HdsL97Jni0kxpiTSVUfBB581v33ffNRr8WYkyxpYqPZZK1eYxg2+cvzQ5ZzuLmXc2t/Ce88XjLK\nLs+in/WpY02/23URUvrRPgUAACAASURBVCDzVrA4KDdkAcMYY8y1o6q7jvY0B2s6RvVyeJE9p5Bc\nrZPYqmqMMcZAG9gZUmCzGfLo+BxRlUwUARayjIVsQCYZIoICS91O/vlwzlnuRXd/tvvi6lgBwxhj\nzKHp9XqcPXuWM2fOWBHjkCmKk8sbLubc4RYwVJWzZ8/S6x1uxoYxxhhz0NbqdapU43A8OnmMRyYj\nqqjc0st50tJteMno+R65y3EiOByqyiiOWeh2YRTd9BFzcKyAYYwx5tDceeedPPTQQzz22GNHvZTr\n3iPrEzInTB7bf6vh2c2KcZM4+9GMpd7hPMHq9Xrceeedh3LfxhhjzEGaxGqWRxg1MQljNpp1PrA6\n4paeByJPWjzDQjag9CWZeOrUsFKcpk5Ne9vuhM1uI1Jt98XVuyELGDurYJvNcFugJ7TTPkpXbAvU\nHIUxmctYrzdmxxeyPqMwJmiY3cf8fWXit+3p9a79oY1dmMs4TmbBoNMg0UmsLsjB2O2YMcYcd3me\nc889FiB3Lbz4R9/OJ9++zE9+zf6nffzOBx7lm1/3pzzrE07zlm/97ENcnTHGGHP8FS6nihVRE+Mw\nYrVeY62uiCo8Nkk886ZTeJcTU8R7hxePEGa3ne66mGdFi4N1QxYw5seXQjs1ZOcY1SYFVorlbcfW\n6nVOFcs8Njm77XjQQBObWSDndEwObAV7zj7ukmenYZ4xRZrUsJgPOB8n5NJOImFHsSLOpdcaY4wx\nO9UhUfrLayF57ifdyj/5xFtYHdWHtCpjjDHm5NgMQ2KKrDcbnK9W+fDGBk1yrBSJT155AgDL3ZjU\nXtZjEiYs54skTThxu+66qFNDYW0kB+bynukYY4wx5lhqYiK/zAIGQO4ddbSgTWOMMTeuOtYkTagq\n5+vzPDY+x5+fG3NzWXBTKTx95QkIMMgXQZXMZWTiKVzOJFbtfXSTLney4sXBuiF3YBhjjDHXmzok\niuzyCxhFJoSYDmFFxhhjzPFXxYrSl0xixSRWVHFC4T1LeWAYHE9YWMC7nMVsQCRR+hLV9u9m4QtS\nF6JdXGaQtrkyVsDorNbrrBTLrNbr5F2+xTCMGGQLs+vMZ2dkrm31eHxyDoDC5zSxwXUZF+MwBi5s\nIZnyzlPHGi+e3OWs1xt4HFWqZxkaq/X6LPfC77N3avoLaIwx5sZypQWM3DsaK2AYY4y5QU1ixWYY\nUcea1fo8H1g9T50cdyyU3FT2KX3JcrFM0sRytkTPlzTdbouo0XIKrzErE3WqbutPFSua1AaxTN9P\nLcwVI6aXJ3FMSIHlfIlGA6nLtmg0EC6RW9H3PVJXuVvMB+2IHnE0qSGSZmuJGi/rFyOqPQk1xpgb\nURP1iltIGmshMcYYc4OZvs7KXMZGvc7Do0d41+NrnCpylnO4tb/ESnGa0vfajEJVQmpf4+Vd2Ce0\n7Sfm2rEdGMYYY8wJp6rU8cp3YNS2A8MYY8wNZhwmOHGcq85zvl6jSpGkwjgG7lgYULiChXwBVWWQ\n9WlSIHcZdazJsj5RE6V4vLcpI9eSFTCMMcaYE25agCivqIAh1kJijDHmhlHFioRSp4ZJHLNer/LB\ntRF1dNzSSzzt1G2IOE4VKxQuR1VRwIlDYbYzfiHr06RmW8yAOXyH2kIiIneJyO+IyPtF5K9E5Dt3\nuY6IyMtE5EMi8hci8mmHuSZjjDHmejNtAcm9XPZtc+9oghUwjDHGXP+SJhSoYs3ZyeOs1Wu89/wG\nZ0rh9gXlKcs3U/oey/kyucsYZAss5gMy8Thx29r6rXhxNA47AyMA/4uqfjLwmcCLReTpO67zBcBT\nu7cXAa845DVts9kMAbYFX8YU2jeNrNXrrNXrDMOItXq9va7bmu9buJLMZaw3G5S+JPfbf4hXu9sA\njONk23vXJdWO46QdxeMyMslmIZ65y/Hi9x3gCeCvIP22Ts1Fx/4YY4w5/uowTUO/wgyMZBkYxhhj\nrm/TnRdJE8NmkzrVDJuKJglRldv6AxayBXJXsJC14Z3TzMKkaTboIXQ5iVa8OBqH2kKiqg8DD3eX\nN0Tk/cATgffNXe1LgJ9WVQX+SERWROT27raHrtH2B3ClWJ4dmw/CjHNBnNMX+cvF0uzYtArXxIYz\nvZtmx4X2LNg03AW6H3YPMUXwW5NFYorkktHzJeM4YTEfAO3M4MtNtb2SCSRb/0b7JTTGmJNoVsDI\nLr8Pt+haSFQVkcvfwWGMubZE5F7ge4BTqvoVR70eY06K3OWsNRuMw5iHR4/xNxtjmiTcVCaesnwL\nuStnxYugEVFHUGWSKpbzJepYEwiz11u2A+NoXLMpJCLyJOA+4I93fOqJwD/MffxQd2zn7V8kIu8U\nkXc+9tjZw1qmMcYYc+JMMyyutIVEFaLtwjDmyIjIa0TkURF5747jzxORD3at1v87gKp+WFVfeDQr\nNebkUVVGYcxas8H5yVk+PnqUj4+H3Nr3nC4TT1m+mYVskZ7vMcgXKFzOUr6IaqLwBct5e/K68MW2\nk8VWvDga16SAISKLwFuAf6mq6zs/vctNLngWpaqvVNX7VfX+W245cxjLNMYYY06karYD4/L/rGdd\n24mNUjXmSL0OeN78ARHxwE/Stls/HXjBLq3YxphLqGNNnWoE2KjXmcQJSZVHJzlNTDxxYZHSlxSu\nIKSAIDjX7mbsZ/2jXbzZ1aFPIRGRnLZ48QZVfesuV3kIuGvu4zuBjx32uowxxpjrxbSF5EqnkEA7\nyaSPjYIz5iio6ju63crzng18SFU/DCAiP0vbev0+9iAiL6LNluOuu+/a49rGXL9EhJAiq/Uaq/Ua\nH1zbQIA7FoR7l29GcJwpz6AohcvJxIPq7qfYzbFw2FNIBHg18H5VfclFrvZLwDd000g+E1i7FvkX\n5+s1APIuNHOzGbLZDMnEU/iCJjWzsM7QZV80qWG93pjdR+ayWS5G7nPWu54qAO02kThxrNbrbDSb\ns2wN7zybzZAmNXjnaeYCND2OzWbIerNB5tp1TeZyNOYzNeaP7XYc2qrjXi43KNQYY8zxstVCcvl/\n1qe7NmyUqjHHzq5t1iJyRkR+CrhPRP7Nbje0ncvmRjeJFeMwZrVe55HxIzw6fowPrm2wUjh6Xrln\n8TSL+TJL+TJBI5nLKH1B5jKSJurUzAI8p6Gd5ng47B0YzwG+HvhLEXlPd+y7gbsBVPWngLcBzwc+\nBIyAbzrkNQFbhYBpYOb5eg1B8C5jpVhmrV7jVLHMo5PH29BN2ukkVdoqCCxkfdbrDQqXs5wvcXZy\nDjzMbzZy4qhjTea2/lf3fY/zYZVGA6d9j81mSN4FaDpxNBqIKbKcL7EaK0S3SoBBIztjOudDR3ea\nDyG9mML6t4wx5kSr45W3kEyLHsFaSIw5bnZts1bVs8D/dK0XY8xJUcea0hWICKv1OqMwZLWuyETJ\nnOPmXp/S98glwzlP35fkLidoxIkjcxkhBQKRovvYHB+HPYXk99hjA043feTFh7kOY4w5aUTkR4DX\nqupfHfVazPGWkvK6P/g74MrHqILtwDDmGLqqNmsReQB44N4n33PQ6zLmWIsaGdYj1pt1Hp+c56PD\nTTZCxh0LOXcsnKJ0JYO8zb7w4kgoTWqoUj07sVv4YnZ/SRNOrtnsC7MHKycZY8zx9AHglSKSAa8F\n3qSqa0e8JnMMfeDjG/zKX7Sdl3esXH7g2HwGhjHm0kSkoN05/N8CdwBj4L3A21T1Awf85f4UeKqI\n3AN8FPhq4Gv2e2NVfRB48Fn33/fNB7wuY46lOtYoyjhWnKvOcnaywUdHI/res5xHbu0tMsgGFK6k\n100TEXE4hMIXs6kiCSWlgKI2aeQYumFLSaUvZzkYj0/OAZCJJ6bAo+PHySTj0cnj1LFCRBiG0azy\ntl5vcK5aZdTlXSBwvloFoIkN4zC+oK3DicOJYxwnQDt2Z5qxkYmn0TDL1/Ddt2WzGeJEZvkUwzCa\n9WDN5154cXhxl8zCMMacLKr6KlV9DvANwJOAvxCRN4rIc492Zea4GTft34VXfcP93HXTwmXf3nZg\nGLM/IvK9wB8DzwX+HHg9bZZbBvyYiPyaiDzjCu/7TcAfAk8TkYdE5IWqGoBvA34deD/wZtuVZ8zF\n1amhSYFhs0kVJ3gRxrF9HXXXYIl+tkDuCnKfIwgiQs+Xs9yL0LXeqyYyl82KF7b74ni5YXdgnC5O\n8fHxowBM4piBW2QxHzAMIyZxQt/3mYQxUSOlL6ljRdFV6qpUE1JoU2o7VazJXUajgUZDG/oy93nf\n/eDHFMFvZW9AW8wYxwmBNh9j+ksSNJKJn1UIQwqzX6zp+xJm84hnBRVjzHWhG6H3Sd3b47RPmL9L\nRL5FVb/6SBdnjo3pCNWF8srCmGcFjGAZGMbs4S9V9Qcu8rn/W0RuZ3vLx76p6gsucvxttHlxl81a\nSMyNZNKdyJ3ECefrVd63uokAT+g77h6cofAlfd9nIeuTu6x9fTc3SAG2cgFtuMHxZuUkY4w5hkTk\nJbRtJM8HfkhVn6WqP6yqDwD3He3qzHFyNSNUYauFpEm2A8OYPYiIXHQ/uao+rKp/ci0XdCmq+qCq\nvmhl5dRRL8WYA7Wz8DCJFcMwYjNs8tjkLO9f3eRMKQyyxF2D0xS+ZJANWMj6OHGUvpxNgQwpzHbK\nT6eO2I6L482+O8YYczy9F3imqn7LLk+In30UCzLH01YB48rOGBWzHRhWwDBmDy8EHhKR14jI54nY\nqxxjjsL8BMWkiXGcsNlssFqvMYmB1G0ovHOwROlLFrMBg3wBPzdRRFXJxG+bMGLjUk8Ge+A1xpjj\n6WtVdTR/QER+C8DCPM28qxmhCpBn0wwMayEx5lK6HXBPA34f+NfAP4jIj4vIZx/tyoy5MdWxZq1e\nZ71eZ7Ve4wOrQ/5+s+L2hYx7ls5wqlhiOV+m9GXX3i+g7d+6whcX7LSYnzxijq8bJgNjsxnSpIbT\n5crs2DQ7womnjjWbzXD2A174AgRCakia8F3AZ+5yUhfsMt2+FFObR+Gcp8QziRW5y8nEU9Pevum2\nJ2Xi2WyGLOaD2fv50TzTy4XLSbS/YJO5YM5pxXGavzEf2jnN2ahjPfsFnPZwzR8zxhxfItIDFoCb\nReQ0W6Ool2kT743ZZroD40pGqAJkrmshsRBPY/akqqvAq4FXi8itwFcCPyUiS6p6rMImLAPDXM+G\nYcQ4TFhv1jg7WePvN8fcVDoE4bb+IoNsiV7WA1W883jxs9diSRNRo00YOaFumB0YTWqoYr3t2Omi\n7Qn04gmpIWhkpVimn/VZKZa5tXczg6wtMHiXEbvgzKSJ0hWz3qnpxBEvjtPFKZrY4MXRz9pxdolE\nSGHWVzUtfGzdPs56ryLtdZaLpdk6o8bZ117KF4G2+FL6kqhp9jY9Ng34hK1KYpw7Zow51r4FeBdt\ncOe7u8vvAn4R+MkjXJc5pmYFjCvOwJgWv62AYcx+icgp4AuBLwHOAL9ytCu6kGVgmOtZ0sQoDHls\nvEYVI1VyZM6xlBcsZAvtyeDu9ZGqzj6G9nWR7Tk8ufa1A0NEBPha4F5V/X4RuRt4wnEKKjLGmOuB\nqr4UeKmIfLuq/vhRr8ccf1fbQlJkNkbVmP0QkQXagsULgM+gLVr8CPCbqmq/QMYcMlVlEicArNfr\nPDo+x99uTlCF23qOOxZOMcgGLOfL5C5DAREhl3ZMaiae0O2ohzbzYj4D41Lmd8ybo7XfFpKXAwn4\nXOD7gQ3gLcCnH9K6jDHmhiQin6uqvw18VES+fOfnVfWtR7Asc4wd1A6MYBkYxuzlI8BvAa8FvkJV\n6z2ub4w5IHVqmMQJURObzSYfGz7GR4ZjbutnNDHxxMEyy/mpWXGi8AUhBQRBu/+mbSRT+y1egE0m\nOU72+137DFX9NBH5MwBVPS8iJypQ4WI9TufrtTYnovuZXK3XiSkwDCPW6jVK39t2H8Nmc3bMiUO7\nIJjcZTSx4byuzX7Ax2FM4XJiiiRN5Dt+SWa5FygOIWlCVVFRzlWr7TgfEh5P1Ei7EWYr96L05Sz3\nYl7WZXrMZ17YPGNjTox/Avw28MAun1PAChhmm+oqMzCmY1SthcSYPT1JVTcBRKQQkaeo6oeOelEX\nYxkY5iRT1dlrnyY1qCYEYbPeoNGGLr6JXBwr/XZMauYyFrI+SRN1auj7Hk1qyCQjaNj19dDl7MIw\nx8N+v1uNiHjaJ8+IyC3AiXqms5gPdj1exYrSl2Td/4oqVghCkwKjMCSbK3wUvmSjWZ8VMLx4grbj\ndnKfMwpjiAERQRCCRpbyRerUEGJgKV9ksxnO7m/6SzTdkjQNlBEVokYWsgWSKl4gqc4KINOMi5Kt\nINJ5hS8Yh/EFx4wxx5+qfl/3/puOei3mZLjaEM/pDgxrITHm0uaKF18IvAQogHtE5FOB71PVLzvK\n9e2kqg8CDz7r/vu++ajXYsyVUFWCBkIK1KnhfHWexyerTGLg7zYDdyxk3NpfovQ9Bvni7ORw5jKc\nOJrUzE5i57L7yWwrXpw8+3228zLgPwO3isgPAr8H/NChrcoYY25wIvKdIrIsrVeJyLtF5J8e9brM\n8VPHRO4FNz0ddZlmBYxgBQxj9un7aTMwVgFU9T3AU450RcZcZ0SEoG3hYrVe53x1nscm5/mH4ZDH\nqwm39uDuxZsYZN2oVFdQumI2GGG6u91cf/ZVclLVN4jIu4D/nnak35eq6vsPdWXGGHNj+xeq+lIR\n+XzgVuCbaPuu/8vRLsscN3VIV7z7ArZaSJqojOvIP3vFH/D4ZsVSL+PN3/JZnFm8cKefMTe4RlVX\np9vbOxYiY8wBme6cCBoJKVDFCUEbqhgZBs+ZUjnT65G7vJ0yQtuan7kMVLsJj9muO9XNybevZzzd\n1JER8CDwS8CwO3bilb4kl4xMPDEFMvGz8aaFKxk1Q5w4hs0mkzAmaaJJF2Y2jZrx7Ha5tHWhOjVs\nNJvtMZfz+OQc3nkKl7NWrwMwjhNKV7DZbBJSwOHagBkcTgTX/XF0c/kX2Y7+rSpWszeAOtYX9HjV\nsZ69Hbbp/wdjzFWZPjN+PvBaVf3zuWPGzNQhXXGAJ2wfo/q3jw9538PrrCzk/M1jQz5ybnRQyzTm\nevJ+EfnngBORe0Tk/wH+6KgXtZOIPCAir1xdXTvqpRhz2cZxQpMaHp88zmOT8zw6XufRScVtfXji\nYJHb+jezXJyin/VYzBZmrfYJpXTFRVtDpq345uTa7zOeXwF+uXv/W8CHgV89rEVdS6eLUyzmAxbz\nAUEj3mWzXIte1qdKFU4cozimTjVBE2GXF+hVqohpKw8DmPVrAfR8ySSO6fsei/mAqiuCxBRZLpao\n4pigAScO7zwiMitmwFZeRtB4QTUxapq9Ta+zM/Miapy9HTbbrmXMgXiXiPwX2gLGr4vIEicse8hc\nGwdVwAhReWSjHU/3xc+8A2h3ZRhjLvBtwLNoH5PfCkyAf3mkK9qFqj6oqi9aWTl11EsxZl+a1NCk\nhjo1bDZDHp88znqzyUPDEf91PXC6zLl78TSnixUW8yV6viST9nWTiKNw+Z7TQnaeCDYnz35bSD5l\n/mMR+TTgWw5lRcYYYwBeCHwq8GFVHYnIGdo2EmO2qePVFTC8E7wTmph4dL0tYNx5egGwYE9j5onI\nD6nqd6vqEPjfujdjzAFJmhBxVLFmEsaE1LaNANw1EE7lJQvZYHZi13UbUwXZKkyo4pyNPL2eXVHs\nqqq+W0Q+/aAXY4wxpqWqSUQeAZ4uIhaRbS7qajMwALKugPHIetuKePupdtqWjVY1ZpvnAd991Isw\n5nrUpIYq1QjCanWO1XqTKkU+vFFzez/nTK/HYjZop0dKRu4ylHZHRTY3qdGJI2rspkVG23FxHdrX\nk2IR+a65Dx3wacBjh7Kia+x83fYF5nOvD7JphkWsKFxBkxr6vsSLp+xaM9aadRyOoA2ZW2zvo9u2\n1MQG5zyFL6hjTeYyoiYKVzKOk9nX8679hVqvNyhciRfXtq+ktmWkTg1OXNd+Us16u6CtUA7DiEw8\nSRO5y2hSuCAjo441hS92zcSAwxmvutfWLWPM3kTkh4GvAt4HTHu/FHjHkS3KHEtVSBTZ1T1BK7yj\njonN9QlnBgWDsv17Y5NJjNnGi8hpLpJHpKrnrvF6jLkuNKkhamp3XsQJHx+v8rFRhRflCf2MOwcr\nFK5gMV9iIevPXus450F1VqjIxG/77bTixfVpv2f1luYuB9osjLcc/HKuvWnwpfitn/bpvOA61fR8\nj1EY0fftdtrSlSSNTMKYwhfbAj17vj1j1WggV+FMeZqPjR4mIyNpop/1iCnOvka/u/5mM6SfLRA1\nkjQSka3ih3h6xTLDZnhBASNpQlw7Iqj0JVWsiZpYyPqz600zLy7IxDjEVvrp/z9jzFX5UuBpqlod\n9ULM8Xa1LSQAeeZmOzBuXe5tjVa1DAxj5n0S8C52L2AocO+1XY4x1wcnjkmsmIQxVaq6k7rCSuE5\nXfTo+z6FLyl8vi3zIhNPItnJ0xvMngUMEfHAoqr+r9dgPcYYY1ofBnLgsgoYItKj3aVR0j7G/4Kq\nfp+I3AP8LHAT8G7g61W1FpES+GnaQLqzwFep6t919/VvaLM4IvAdqvrrB/EPMwerDpHyKltIci+E\nqDy6MeHWpXI2WjUk24FhzJz3qep9R72I/RKRB4AH7n3yPUe9FGN21aSGKtYEjazVqzw8OosX4eHx\nkFt6OStFyZneCgv5gAXfw4snd3l70jdF8J6E7nsqhbk+7Pn9VtVI2zJijDHm2hkB7xGR/yAiL5u+\n7eN2FfC5qvpM2hDQ54nIZwI/DPyYqj4VOE9bmKB7f15VnwL8WHc9ROTpwFcD/4i27/vlXUHbHDPV\nVU4hgXYSSR0Tj6xPuG253Bqtai0kxpxYNoXEHCeqWzv6mtTMpjWOwpiNZoNz1SoPj8b8w3BEz3vu\nHtzE7Qu3MsgW2+KFyyh8QaTdhT7dXW5tIjee/baQvEdEfgn4eWA4Paiqbz2UVV0jm81wNpI0E88w\nNbMRoHWsUdUuXyInkWhSTVIlpAYRhyQoXW92+0mc4F1GLhlVnHC+XqPu2jocDpcPZnsOvXjGcTJr\nKZmSbnRqTJGogUw8ZyfncOJmuRWDbGEWVuPFwS5zjqfZFzsvTz/2h1CrbLqRsdZCYsyB+KXu7bJo\n+wxhs/sw794U+Fzga7rjrwf+LfAK4Eu6ywC/APyEiEh3/Ge7Fpa/FZEPAc8G/vAK/i3mENUhcXrh\n6h7Ti8zx4J9/jCYqty33ZgURayExZpuXHvUCjDmpokYyyQgpkDQRNBJSYBg2iRqZxIiIMvAZTxws\ndhl+bVhn7ovZazSPI/NWtLiR7beAcRPt1uLPnTumtLOvT6xGA6eLrar02eosaCCTnKQJ7Sp8ucup\nY00VawQYx4bSeUBZKW4CwEvGJI4BYaHos9Fs4CWjThVOA148fd3KppgWKaYv+qdEBO98W/hIERxM\nYsUgH7QFjK4CMsgWLvlvm2Zf7Lw8/bg/l5NxUKYPLMaYq6eqrxeRPnC3qn7wcm7b7ZR4F/AU4CeB\nvwFWVTV0V3kIeGJ3+YnAP3RfM4jIGnCmO/5Hc3c7fxtzjBzEFJJ//flP448+fA7vhK981l1zGRj2\nuG7MnOeIyLtU9S93fkJEBrTBy5WqvuHaL82Y4y3bkeVXp4a1apXz1Tp1ijwyHrGY5ZwuSxbzJVaK\nFRJK3/faooXzhBRmgwmCRjyO9pyLuZHst4DxKlX9/fkDIvKcQ1iPMcYYZr3LPwIUwD0i8qnA96vq\nF+91267171NFZAX4z8An73a16Ze6yOcudnznOl8EvAjg7rvv3mtp5hAcRIjn855xO897xu2zjzcm\nbXHdChjGbPOTwP8hIp8CvJd2Il8PeCqwDLwGsOKFMRfR7rgYtYGdccJjk/M8Nhmz0QiLuePuxRVy\nV7BcnCJzGU5cO+FRAznZtiJIJn5bW8peklrY5/VivwWMH+fCHIzdjhljjDkY/5a2ZePtAKr6ni6I\nc99UdVVE3g58JrAiIlm3C+NO4GPd1R4C7gIeEpEMOAWcmzs+NX+b+a/xSuCVAPfff7/1GxyB+gAy\nMHaaZWBYAcOYGVV9D/DPRWQRuB+4HRgD77/cnXLG3GjqWJNQxnHCOIyoU80wBOqUWM49ty8s0vcL\neNcWJjLx7VkTEQrJ2lGptLsvokZ8N41kvyzs8/pxyQKGiHwW8NnALSLyXXOfWgauq+ajzWbY9mVp\nIHUjRp14nDhGYTRr9XDi6PucoIm+KxmHUfsL0VUIE4m1ao2goc3EkIye7yHiGIcxSkIQapexmA3I\nXU6IkTpWiAhOfJfIO+l+Mdv7jand+e1wDMOIOjWUriBqRLoTpdOqZB1rqtTQpz/bZrUfdWoodsmv\nuNjxnayqacyBCqq6tuOP854FAhG5BWi64kUf+B9ogzl/B/gK2kkk3wj8YneTX+o+/sPu87+tqtrl\nHr1RRF4C3EF7hvFPDuRfZg7UYRYwmmA1KWN2UtVNuuKyMWZvTWqoU0OVakbNJqv1JuMYeGQcWMod\nT1hY4KZyhUG+iAD9rI+Iw3ejUqcFDWiLF6oKslXI2A8L+7x+7LUDowAWu+stzR1fp32ie91oNJC5\nnBDDLMshk3brUhUnVDFS+gwnnsIVrHcBoJM47nIyCgpXEjUwjuP2TlPVbYNaBuDs5Cypy6PINIds\nwGI+YBwnNKnBu/b+Qwo0qSHLcpx0oZ7dmkSEkAJVrHDI7DjATeUKozAmaJwVPObDO/fSZmVcWKjY\nb7aFhXcac6DeKyJfA3gReSrwHcAf7ON2twOv73IwHPBmVf1lEXkf8LMi8gPAnwGv7q7/auBnupDO\nc7STR1DVvxKRNwPvAwLw4q41xRwzdUiUB1zA8E5wYmNUjTHG7J+qbtsV0aQGVWUYx90J2oq1ZpOP\njjaponBLL+P2hSX6vk/P9yl9gRffnqQlbWsTCRrJxLcFi+5LXM6JWnP9uGQBQ1V/F/hdEXmdqv79\nxa4nIj+uqt9+zyYRJAAAIABJREFU4Kszxpgb17cD30M7FvVNwK8D/26vG6nqXwD37XL8w7QtKTuP\nT4CvvMh9/SDwg5e1anPNVQeQgbGb6WhVY8zJ1GUpPXDvky+r+9CYKxY0kMvWCc2k7UAEj2OtGVKn\nmipGnAjLueO2/gJ936fwBbnL8OLbE7Sk2cTEaSHDq+30Nq19/SRcqnjRsUBPY4w5QKo6UtXvUdVP\nV9X7u8uTo16XOV5Utd2BcZVTSHZTeGctJMbsQkSecdRr2A9VfVBVX7SycmrvKxtzAOZ3Y7ct7TWj\nOOFsdZbHJuc5W23y8GhM6Ty39PsMskV6WZ/FbJGFrN+25Ds/a/eY7roAbNqImdlviOd1KZetf35M\n7S9Iz/cRhHHXGlLFCiee1E0fHMcJhctRlDrVNCngEDKXA9rOK5YMEUfUttVjo9lkEkYgQiLhaNtC\nqljNRpzmLiehJI3UsSZ0o1frWFOnisKVNKkmaUZ0kdKX7RxllNIVJLRtH0mBni/xXR7GZjNkMR9c\n8G8fxwkeRyTR9z2g3ebV971Z5kU9l/sxCmOcOHq+3PX/ZZOa2YPWNC/EWkqMuXwi8iCXyLrYzxQS\nc+NoYvujcig7MDJnU0iM2d1PiUgBvA54o6quHvF6jDk2pqNOR3HCJE4YNZucrzd4eDQkqjDIPE8c\nLNPzPRbzJUpfULgcQbZl7onILLTTmHk39F6c+Rf2USPeZdzWv4Vb+zcDzIoUXhzavZ7YbBqq2JBU\naVJD0xURptulvPPkrqDsXug3qaaKE9abIao6y5NowzrDrEhQdNdv5yJXhBTIxNOkmtDlWTSpIWhD\nSA2ni1M0Goga2zFDtNkYUSOFLyinxYSu8LJTSIFIIqatlvbp5WlRZbrtq3A5QcNsHbuZz8mY3s4Y\nc0V+BPhR4G9p0+3/Y/e2STu2z5iZaYvH4bSQiBUwjNmFqv5j4GtpJzW9U0TeKCKfd8TLMubITWKF\nojQaUJRJGFOlmum00zNlwa39BUpX0M8WKFxO5jIUZq+1LuVyxqaa69dB7cCwPT3GGHMAuuwhROTf\nqep/N/epB0XkHUe0LHNMvemPPwK07R4HzTIwjLk4Vf2vIvK9wDuBlwH3SbvH/btV9a1HuzpjjoYA\n4zAhauRs9Thr9ZAmJT42GrOUZywVBafLFQbZAC+O3Od4HCIym6o43zZywf1bG4nhKgoYIpKpzk7v\nv/SA1mOMMaZ1i4jc24VvIiL3ALcc8ZrMMdLExA++7f0APPW2pT2uffly7wjRznYZs5OI/DfANwFf\nCPwG8ICqvltE7qAdSW0FDHNDaVJD1ESTGoZhxDiO2GhGfHw8oorKcp5x52CZ0vdYzAb0s35bwOha\n6D1bu91t3KnZyyVP2YjI781d/pkdn/6T6QVVfd3BLuvam44b3WyGDMMIJ44mNQiCAlWMbDY1pfMU\nrptDPGupUMZxRNDAKAyJXY5Fq60oLub92cdBA5vNxrbrjcMIhyA4VBUvbT7FtBWjTtW29W40m5S+\nxOEYhzGR9nrT3IxGA3Vs21/a+x/PrQkyl+FxeOe3ZV1ML09Njwmy7fPNLtebvzz9eP56l2pBMcZc\n4H8G3i4ibxeRtwO/A3zn0S7JHCeTpm33+57nfzLPecrNB37/1kJizEX9BO046meq6otV9d0Aqvox\n4HuPdGXGXGMhBVR19rpmEseoKlWMjAOsFDm39Pv0swGDbJHMZWTiKVwxa1uPJFJXwIg2sd3sYa8d\nGPPpj/9ox+euqz08p4tTrNbrBI2g4GhDOHOXE1WpUqROiZt7fXJXMI4NjUYEIalSxQm5K6hTRelK\nEtOsC0FEWM5W2GjWcSI0qQ337PkeiURBe7uFrP3frV0YqKrO7mfni/86NZwpT/PY5Cx1aijFgbSF\niaCRmCJR4iyodGcWxjS4E9pAT8jx4i940ChcziRWswLGtKASNTEf0Tkf2Dl/eWc2hjFmf1T110Tk\nqcAndYc+oKrVpW5jbiyTpn1M7eWHE2eVewvxNGY3O9r7dn5u5wk/Y65b2mUCRk1EjWyGTdbqDZoU\n+dhozEqRcWuvzyBfZDEb4Jyn50ucOIIGpDvhOb/rwtsODLOHvQoYl9o7avtKjTHmEHUFiz8/6nWY\n42m6A6OXH86TvTYDw/7UG7OTiPwlFz4PXqPNw/gBVT177VdlzLU1iRWqiUmsqFJNFSvOVav83cYm\nCpwqMu5YWKLvF1jIB5S+bHeAd5MSBSHb5eSpMXvZq4CxIiJfRttqsiIiX94dF+C6Giq92Qxn1b8q\nVjSpxktGJjmjsNHupEAYh0CThkxipFAld546NgzykjrV7QQO2mkjUSOl75FJxigMCd2o0dzlTOJ0\n1GiBQ+j7BWKKVKkd26oo4zAiakRJgHSTRtpdFpMw4bysAVD6ksLlVKnGaVvJnG7DqlND0wRCijjX\ntpsAW+0kItSxJqa4LRhn2ioy314CoDptVdl+xu9io1N3tpZcqUsF+lyuqNGqu8aYE++wCxiFdzTB\ndmAYs4tfBSLwxu7jr+7er9OOVn3gCNZkzDUltFW8pIlxGFHFCSElCi8sZTkrZY9BtkjhS3LJZq0j\nYDsuzNXZq4Dxu8AXz12ef0C+rtLwGw2cLtqazCiMaVJD5jIKXzAeh65KKFQxsBnSLCk3c45xDCxk\nOU1XQSy6calVDNxULiIIm2GTJjVdQSNHdQRstXKcKk7x+ORxqjih5/tEDVRxAiKopvbFvzhUwakj\naaCKHi+em8oVAIbjUdsyIjCdVzRtPYkkcrJZIWCamYHSjoKVRC/baiuZtntsjX3dXhjZWai4WHvI\n/PUyd+VDb5ImOKAHuKTJHiyNMSfeVgvJIe3AyISqsQKGMbt4jqo+Z+7jvxSR31fV54jI1x3ZqnYQ\nkQeAB+598j1HvRRznRjHCT1XMgyjLiMPVutV1ptNkip/vzlmMReWi5LlfIlB3r4O6mdtFmBCKWTr\n9cBBnqA0N45LvqJU1W+6VgsxxhizRUTeArwG+FVVC5AxFxp3OzD6h9hCslnZ1l5jdrEoIp+hqn8M\nICLPBha7zx2bxHJVfRB48Fn33/fNR70Wc3KpKorixOFxjOOERJoFdq43m3x0uElQ5Uyv4K7BTWSS\n0cv6lK6g8AWqqQ3tZJql1+6GnraQ2IlFczmu/JS4McaYw/QK2jF9LxORnwdep6ofOOI1mWNkq4Xk\nEEM8rYXEmN28EHitiEyLFhvAC0VkAPz7o1uWMQcvaCBqwotrhx0ASZVJHBO0ISZlEoWbewWny5Lc\n5WSSU/oS301udOLarIuuXX0+QMaKF+ZyHWoBQ0ReA3wR8KiqPmOXz38O8IvA33aH3qqq33+Ya7qY\nfG47kxdHr5tPDFB6jxchc471uh0CUKfIIMuZxEiTEutNRc9nRE3UUnd9Ycpms9EG1oijASZx3E4Y\nQWm0Ya1ea1tVXEnSCAiTOCFqonQ5Doc4RxXrdvKH87M5yeMwou8XOFetztYeNSK4dh2podGAE0cm\n27/VIYVZS4d3fvbvV1Uyl3X30z7I7MyumMSKni9pUjO7n+mEkiY1JE2UvgTadpyFrL/t68KF7STz\n69nN1eRnHOZ9GXNYVPU3gd8UkVPAC4DfEJF/AP4j8J9UtbnkHZjr3vjQQzxtjKoxO0k7NuFeVf2U\n7vFZVHV17ipvPqKlGXPgVLXN76N9Tp+60M5xHHO+XkMVPjba5KayYCnPWcoX6fsFvPMULsfTPuf2\n4hFklrdnbSPmalyygCEit6vqw1dx/6+jnZX905e4zv+nql90FV/jQCzmWxNjvctYcksIMAxDSp9T\n+vaXb1rAaFIid471pqZOkdQohfM0KQENpfOotgWLXItZFsQ41hQuotq+aA9dLoYCidR9zZqkUBY5\nXhxOPOtxxCQGej6DTNoqaGooXEHVpcS31c2Ep815CCkQUwQHmZ97oBAhpjT75ueSsZgP2i1hmihc\nzjhubRsuduRdDEOb35E0ETTi1FH6kipWXXjp1hPeneNfL5aVsdeI1YN8oLNKrzkpROQM8HXA1wN/\nBrwB+MfANwKfc3QrM8fBtZhCYgUMY7ZT1SQi3wa8WVXXjno9xhwGVUVECNoNAkC6IQeB9WaNURhz\nrpowDoHFvOCJC8vkvmAxX6LnSnLfFi+mr3+U6f1Z5oW5envtwHiNiJwG3g78GvB7qrrv3j5VfYeI\nPOmKV2eMMTcoEXkr8EnAzwAPzBWTf05E3nl0KzPHRTUL8TzEFhIbo2rMbn5DRP4V8HPAcHpQVc8d\n3ZKMOTiNBgrJySRrBxOkmkmsWK3PU8WGh0dDMteeUL19YYnFfJnMZfR8SSYZgpC7nKCBrNt5Drbz\nwhyMvUI8v0BEerRn+r4M+BER+QhtMePXVPUjB7CGzxKRPwc+BvwrVf2r3a4kIi8CXgRw1913HcCX\nvdBqvb7tF6uKNXWscQgJJWqg8J5hFcjEsdZUZOJIqjSaGMeAF6FOiWHTICIkVbI8o0ntxz1XAIrr\ntlB58e3oVU0IjkYjUZWkStRIiJHcZfR8Rubar0U3plUQmtTgnYK24TriBVUlkYgkRGRby4R2E1Jy\nl+FxRNoHJR89MUWctOE8TWxmLR3zbSCbTft3ehLbnSiZ+G33n7oKa9ONjHXiZpdhK7TnYq0k19pe\nwUEWLGSO0KtU9W3zB0SkVNVKVe8/qkWZ4+PwW0gcte3AMGY3/6J7/+K5YwrcewRrMebASfd+MwxR\nFFWlihOq2FDFyDAoy7lwS6/fZl64dkyqw9H3vdlrEI+fvZ/u6jDmau356lFVJ3QFCwARuQf4AuAn\nROQJqvrsq/j67wY+QVU3ReT5wP8LPPUi63gl8EqAZ91/36GcEqpiBV12A9BmPGhbeECVJkVK7wma\n6HnPRl1zc69P6tJ5JzEwyHKaGFlrakrnEWCQJ1BFEEpX0KRmVsBw4lESqm37SBNTV7xQgqZuaxYU\nrqAQmITJLA3Yi6dONW1JRHE4ci2IGtGuKDJNDJ5KKKKJpbzNnRrHCU1qiKm9nXdtIaPRgNNudOpc\ne0edGjK3lRjcn8u3gLZA4rs8jPbf51Dd+nbFboTpXi0j14qqbj1KX8HnjTlEPwC8bcexPwQ+7QjW\nYo6hySFPISm8EKyAYcwFVNXmkprrzrTA0KQ2YquKFUEjKUWGYch6s8HZasw4BAaZ4+7FU/R8n8V8\nqc0P9CWyI6xzepJzeuLVmINw2ae/VfVvgZcDLxeR4mq+uKquz11+m4i8XERuVtXHr+Z+jTHmpBKR\nJwBPBPoich9bJbRlYOHIFmaOncmsheQwMzCshcSYnURkAfgu4G5VfZGIPBV4mqr+8hEvzZgrFjSQ\nS97utkg1ijIOI8ZhzEYz5OPjIYXzeHHcu3wTS/kymWQUvm018d2u7Pnd1dMTnhagbw7SVe3fV9X6\nam7fPVF/RFW1m6HtgLNXc5/GGHPCfT7wPwJ3Ai+ZO74BfPdRLMgcT+MmUniHd4dzVivPrIXEmIt4\nLfAu4LO7jx8Cfh6wAoY5saYTC6cTDCexYhSGjMKESQyMQoIMbip7FK7Ai6fwBYUrENqpho7thQpr\nwzaH4bDHqL6JNj/jZhF5CPg+IAdQ1Z8CvgL4VhEJwBj4ap3vN7gGppkOi/lgNvpzqh3549CuJcMB\nVUos5QUhJZwI601N6JbczkGOFM6xlBc46LZitTkWZ6sxty+UDEOFoqzXI5wIIpDlGa77JY+q9H37\nrQmamMSmHVOaEpMYyb2iCoUD1YDrsjacOEqXqFOFAhk5IQWcdzht21gSimpio9nEiaNOzezBJWjE\npzhr71CUqKkbBzvs+tt82+UpdBNTRrO2EURm+RrTkape2pyNcZwAzEauwlZrSkjhgsrsQWVkTOdV\nXyw0aK9ePOvVM9eaqr4eeL2I/DNVfctRr8ccX5MmUh5SgCdA7toxqta3bMwFnqyqXyUiLwBQ1bHY\nL4k54ZrUtNl4sSakwDiOGIYxHx1uUqdEzzs+YfEUfb9A6XsMsnZcqtC+ZvLiucYv48wNat+vDrt2\nkU/sPvygqjZ73UZVX7DH53+CdszqkWnmhqqsFMus1rOuFpxzbeGA1L5mF0edEitFyblqghfHRqOA\nw0ubWUGEvs9YKXKaFAlddkbuMlbritsXlFFoyJ1nvanInceLsJQnvLRBl0ET/SwnaiKmRAXkLlDH\nSJUiIpBUEWkzM9rCh5JUSERCahBxiAhRA163gkIBEqCp7UubD+uMGok6V8DogkQFoSFAarM4JrFi\nWg4IKZA0oS5rA3po50VPR6qWvpzNjYbt42rr2G7gSZoo/PZupMTBPADqNGvjIgWMvSrDVjk215qI\nfJ2q/ifgSSLyXTs/r6ov2eVm5gZUhXho7SPQtpCotsX5zNtrM2Pm1CLSpz2lg4g8GaiOdknGXLlJ\nbE+uTl8vrDdrVLHi7zfWWcwLFjPhtoVlFrIBC75P4Qu8a4sW84H+Vscz18K+Chgi8jnA64G/oz3x\nfpeIfKOqvuPwlmaMMTekaZVv8UhXYY69cR0PLcAT2hYSgCYqmdVyjZn3fbTh9neJyBuA59C2/hlz\nIgkQUmx3iDdrrNdD6hSJKoxj4Pb+gMVskczl5C5vd1zgENoToja1z1xL+92B8aPAP1XVDwKIyCcC\nbwKedVgLu1ZyybbtwsikHfczbEbUqdtF4EpGYUQCvAjD0LBWN6yUOZshEFXwomTiCJrYaGqq6Igo\np4se4xjQpqJ0ns1m3H5d58ica1tRnKOK9WxXRS4O1UTURO49w2Zrs0tSZa2uKb0nabueNPegUcea\nhJJSQ92NOo0pElPAu4yYArkrCBrw0u4waVtFIuhWO4eqkkRnE0Om1522dkx3a7S3324cJziE2LWR\nTO9fkNnt56eQRI2EFMhc1u7oQLuHxIsLGvecJR00Il1FeP76B/Ugu5817Nd0YsxJY1vLD56q/ofu\n/f951Gsxx9ukSfQOs4XEt/ddx0Qfe2JqzJSq/oaIvBv4TNrXft9pAfTmpBqGEVEjm82QmAJr9QaP\njEcMg3Jbv+TW/hKlK/Eua9tGxJG7HERmz9eteGGupf0WMPJp8QJAVf9apOt3OOEW8wHn67VtHwOs\nVmvdi+9EPxuwEYbtNlpxbDQT1uqcJyxkQCAkoXDgnVAHZaMJ5K7dRnV7P28LGhrJvWOjqRGgcI5c\nHJU2ZDgmselGqQqZa4sfMSmFc5yNoWvN8F1ltGZFSpK0WRlNSuRZTtJE0Kbd8tsVBdqqaCBqpOxG\nI3nJSCSgndmcNHVtMkrQSNEVNRQll4xaEw43y8yAtpCiXevHzpGoMUXc9H617Z9OmmYZGbC9iBA1\n4WftLW2xo/C9S37f2vu79IOlamofYGn7+qbXT90o16u1nzXslx5Qy8y1pqiNxTpgIvKyS31eVb/j\nWq3FHG+TQ24hKbq2kcc2JpzqXxd/8o05SD3gPO1z6aeLCLYz2ZwE05NPIQWCRmKK1KlhFIasVps8\nMhmxkGXkTrm1t8hitkTucpbywdbJNhGy7iSnMdfafgsY7xSRVwM/0338tbTpy8YYYw6WPbaafRnX\nh1vAWOy1TxE+78fewYd+8PmHNu3EmJNGRH4Y+Crgr2ijxaDNw7gmBQwRGQAvB2rg7ar6hmvxdc31\nYTYutTtpGElsNhsMw4hGE0KbfXSm16PwJbnLWcj6szzAKWsbMUdlvwWMbwVeDHwH7Va5d9A+cF4X\n8m5s0GYzJGhkpVgmd3kbgNn9YvZcwWaasBFqqhjJPQybhnHw9LNInRwbTcNanXNrL81CKB8dbwIw\nCg2LWc4kBgRhMzSICIXz1DEyFoEMqhjbyR1JqVN7ufQe100zmYqaCKkN/GxbMxL9LMOpR7udD6lr\n8yh8SUwVdRQSacfuhzirpkYNFK5sW2oUUoo00u60SCTqtDU11yHErnobu+prO7WlfYLbdLs/grb9\ndK4LFZ3usHAIdWraNgyXkVDqWHe30W0tJZnLLpgmMr3vqWxuksql2jpC9++d3u9OsbtPL37b5d1c\nbsvHpdpErnYXwzT1+UrbOa709vPrtnaSg9FNITFmT5OQDnVnxBc843be+Mcf4U//7jyTJjIoD3Vw\nmTEnyZcCT1PVAwvuFJHXAF8EPKqqz5g7/jzgpYAHXqWq/xfw5cAvqOqDIvJzgBUwzL5FTZAaRmFM\nnRrW6lU2miEfH43YDMpiJjxxsETP91nIBrPAzlyy2c6L9rn8yWt9NteH/f7kZcBLVfXLVfXLgJfB\n9dMQO20bCRqputyIwhd4yehlfQBK3yOpMmwaJlEoXPr/2Xv3aNm2vK7v85tzrrWqau/zuPf27Qf9\noBvoAbSCNjRoJFEQMTxsQQwJaDLkEVpHJEJeihiDQxhiHAIyhhliCzQNQREGGmlDgj0YNjQhEBQ6\n4WEwiMhtuu+rz9mvqlprzccvf8y5Vq197jnnnn3v3Xfvc8763nFuVa1aj1m1a9de87u+DzbB00ab\nq0uj4aivuNnX7LndSd6zXU8C1gGMCG1pEtmEMFpJ+hTz8vKckfzl0qdE0ERjBgIjLwPwKdGlyNp7\nTnzPQd/Rp1iyM+JIOoTkC5MasyVGlUSakBZxnITGQpjkHIpUMiwCykCY7AiDXKMqhNJaMhA204rU\nTMRkAsNK/uIbxje83yEFFrbJLDAJZ1xOQS77G28nLSrAKFvLFpXdcn0eKZsW+8idJG+qOk7mp/dv\nh7PmX9ytWeXF5l+k8t8LxWAZOiumhMX9aoO5bBCRv1Vu3yMiP3rrv4se34zLg7aPLNz5nUAuKssf\n+eSPysfy8XnWnjHjocJvAC81e/i9wOdOF4iIBf4n4POAtwBfJiJvAV4HPFFWm385ZzwvQgr0pR61\nKY2C29hy1B9y2J/wwfUJzgjXa8NH71/lanWdPbfP0i5oTIVBxnO+WXkx46Jxr5dTfgL4Q8BJebwE\n/hnw+85jUDNmzJjxEGOw6v3NCx3FjEuPNkSW9fmeRDaFIOnC7HOeMWOCDfABEfkJJvWpLyajSFV/\nSkTeeMviTwd+XVV/A0BEfhD4QuCDZBLjA9zhYqSIvAN4B8Dr3/D6FzqsGfcpbs0oc8ahqrSxxZMV\n0G3YEkq2XUigBl65XLFyeyxczqKrbIUgWONQyM0jhciYVbczLgr3SmAsVHUgL1DVExFZndOYZsyY\nMeOhhar+y3L7kyJSA59A9lb/mqr2d914xkOD73r/b/DvPrLh977psXM9zpCxMRMYM2acwo+Wf+eN\n17JTWkAmLn4PWQn9t0XkC4D33G5DVX0n8E6AT33bW2eJ5EOOoaUwasJr4KC7yUF/REzKBzcnXKkq\nXr9/ndrUXKmvUpe6VCMmN42o5jB+EnYiwn+h9uH7tX1vxuXAvRIYaxH5FFX9BQAR+VRge37DuhjE\nFHBiWYcNlXFYsfSxwyePIFTGklQBoYuGPhlWLteoGlE2wbGwkZPgiSlhyi90SImlVTYhoKpYk1s+\ntjFgRIq1JN9vrKWLkaCKIedsGBH6FOlTwpCp9soYSImTAM4kDvrI9VrpxLMJHicGrxtWtmEd1mxj\nT2UslXEkrfCpp7I1mpQ+9aSSVdHFPrehSG48Uc3WghRTzrAo4T6+2EJg6I7Or80ZSxwyOOLOihGz\ncQVBSOXxmFuRci7IYEFQ3dWomkn16vSLLmgEVYyxd7SNTCtUdXI73dfQqmLFjNaS8/pCNci57f/F\n+hBvzeB4IX+Q5jaSlxblxPQ7gX9D/jV7k4j8aVX93y52ZDMuA/7+z/0WAH/4d7zqXI8zKDBmC8mM\nGTuo6rtFZAm8YdrSdw643R9WVdU18BXneNwZ9zmG8+rh3CyhtLGjDS2bcMKN7pgnt2uCKivreO3e\nVa5UOQOwNtWYE1eJG8//0+Tc/8ViJi9mvBjc66fn64AfFpH3i8j7gX8IfM35DetiEDRijcOnwJ5b\nYYzBJ48v4ZWVsQTNXwRdtBx7x8pFggoi0CXDykY2AYLKOFn2KbGwjnWIJDL5oAptjPiYcGLYRqGL\nESeGLkW2ISAinIQhMyMfM5MlQiUGK0KXDCEZDvsKr4kuBtbB06fIQd+hKGvfsgmeLoYxH8Mnj8GQ\nNBGLL04BnzJDO3zxJXIQqE++1LSGko3hxywLRIga6VOfyYsUx8dDoGhMcUdslHyOunxJJk0jYTFg\n+OIcAj7hdObE8EV6txyK4TktORk6ycsY9j+teoW751S8WBgx57Z/EXlRUr5bt3+xeRgzXhJ8K/BZ\nqvqZqvoHgM8Cvv2CxzTjkqALiT/+Ka/jsz/xfAmMWYExY8ZzISJvJ9s3/vfy+HefU0bRB4Gp/+N1\nwIfudWMRebuIvPPg4PAlH9iMyw1FxwuAm7BFyrKTcEzQMJ7nXa8bXrlc0ZS2kbqoLgTBGUcs+Wp3\nOueez/1mXATuSYGhqj8vIp8AfDyZDf5/VdWf68hmzJgx4+HG06r665PHvwE8fVGDmXG50PrIojr/\nK1hjBsaswJgxY4q/Qs6neB+Aqn5ARN50Dsf5eeDNZd+/DXwp8CfudWNVfQ/wnk9921u/+hzGNuMS\nI6ZYWgoD29hixPD09ilO/JY+JZ5ttzTGcq2u2XNLrlXXqGy2jQwqCyd2vMB31uD6GTPOE/dEYJS8\ni/8a+GhV/WoRebOIfLyq/tPzHd7Li8Y2OLGICOuwwYplaVf41BM00MXAvrOI5JpQn7KNpIsGVaEy\niXVwLF3k6bZh3wW20XK1CliJdMnyWKP4lEiqbAJEVUISolqEiJXcJmJEOOw7BDjoDVYUVfLxUlZK\n9Mmw9hWm8lQm8dS242pV08Z8oikIh31L0ESfIrWxxBTpaLHGsg4n+OQBJWnKTSsomhQ/KDBKa8eO\nyVVSYVtVNW8v+YvSSFas2GIVMWIwYogpM70+eWpTF0UG9PissNA02lXakoUVUsBrYGkXpbkkWy+m\nqgpTWlCiJqSoQaSMYUBlKkSyvmO47ZPHijllVRlg2FW9Dq9fVE7Vzg6YLns+Sd1gHbn1eJcVsx3k\n4iAiX1xJPwpfAAAgAElEQVTu/oqI/BjwQ+QMjC8hn8zOmMHWx1EdcZ5oCknSzgqMGTOmCKp6eMvV\n5xclsRSRfwB8JvAKEfkg8I2q+t0i8jXAj5Pb/75HVX/lxRxnxoMNRYkpZqs1+Xy6iy197DnutzzT\nbgma1d8fvX+dytasbK5KdWIzgVGCOueq1BmXFfeagfEu4F8C/155/EHgh4EHisC4Xl8d7x/0R1ix\nPLZ6hKPDA5JmK8deVeHEsA5rqqRsgmUTHM4kKlFuesejKhx0NSEZNsFiYCy4fO0Kjn1PVGXta7bR\n0iXDwuYvmqgJMFyplENvueICR33FngsYURKwCZY14FVyjauNNDbx9HaBlQ6fACILazn22f4SNLG0\niaCGmHoaqlKNGsrIlNo046R9GLCiiJhsvyABWib0DhUlqEfUkDQikplea2oMQiUOI4aeXN8aNaIm\n17iiYDRi1RRbi5bXn9+HVL6AsYyEQtIEA4EBoydvag2pTEU3vqYMJxavabwNGu+YR2HE7AiV8vOw\nqqML9VSt6rDsHj5biZxpcr94/mZJ4IXi7ZP7TwF/oNx/Bnjk5R/OjMsGVaX1keXLQWC4YiGZFRgz\nZkzxyyLyJwArIm8G/hzwMy9mh6r6ZXdY/mPAj72QfRary9s/5mPPQxwy4zJiyJUb8vu61NPGli7m\nC4RdilypKq5VDZWtqU09khfDhcLhXHc4154x47LhXgmMj1XV/0REvgxAVbcyf6JnzJgx4yWHqs7B\nbDPuCh+VpLwsFpLhGHMGxowZp/BfAn+JXKH698kKiW++0BHdBrOF5OHBENhZmYpt2KIofey52d/g\nxG9ZB89H2i37ruKResGjzTX2qn0qU7GyC4yxo/ZWUdDZNjLj8uJeCYy+pC0rgIh8LJPe6wcRlXG0\nsePJzdM0dsGJP8GJsI6RG74FwKdsG6lN4tmu5vFFhzM6Bn0qYEURURpRNtGyDZ4Tb4gqLF3EmYQt\nCg4ryol3PNL0OLG4so2U24VJ2TYSLI3NbSBrn3+EbbAsXBy/fJJmq4pPKTeWAP0kJLRuHDH50jZh\nCJqIGokasgLBVDhxGLHEomgIGqhMhQJKoo8diYRQbCaam0ra2NHYZrRyRI04cQT1hJT3n9+/ciyK\nz65oGZIm2pjf403Y5oDJ8sr65KlNlV9kacoYGkbyxoz7N2JGxjm/xpjVJCng1Wd7y7BMEynuFBn5\ny1vH+yGF3KFdRmmQU9aR4fimWGeGMWTlhZyyjkzHd941UoOaxEx6u19o5dV54dau8nt97kGHiCyA\nrwJ+B7AYlqvqV17YoGZcCmyLGuJlsZAUBcbcQjJjxg6quiETGH/poscy4+FGtmiHcd5iyOfhbezo\nYsthv+ZG19LGyMI6Xrd3lYVdsnJ7LO0CK6acZzNpF3w4z7tm3D+415nTN5KTll8vIj8A/ATw589t\nVJcAe25F1Mg2bli6FV0MWDGElHhyWwM5j8IZpbGRm9saAzhRYhJElKiCNXkSXNuEqrCNiWNfcewr\n9lziWh3ZqwK1STijnASHE8UZQ2USm5C/TLYhN5Csg+UkOLqYSZCT3nHQVfz20XJcVxiyNRKbYIhl\nIt6lSNDE2udGkaGuVciER9SAT4E+hdHK4cQS1JM0EgrhAYyNJYO1QyaT9txkkq0avtSrDnaPmLJd\nZGg06ZMnaBj3CRBJ43YDCTJM8odjwK4xZLC97BpOdHwulvyMwWbiSlVqHNbTBGU803yLqVVETx2L\n0a4ytY5Mjz2MLaGZGEFPkRTT8Z1n68mw/6EKd/d6Llkd/N2Gc8mG+jLj+4FXA/8h8JPk9PnjCx3R\njEuB7uUkMGYFxowZ9y3mFpIHHzFlW3RMObttOC9fh2P61BOS0sbII3XD48sle9UVKlPjjMuNfCJj\ncOdw7vtim+1mzDhv3BOBoarvBb4Y+HLgHwBvU9X3nd+wZsyYMeOhx8ep6l8G1qr6buALgE+64DHN\nuARoc9DRy6rAmAmMGTPuP6jqe1T1HdevX7voocw4B6hqrjrVyDa2tKFlE7Yc+2MOujU3uhOeaTcs\nrOVq3fBoc53GNqyqFQvb4IyjEjfJw3t+TC8kzphxUbhXCwmq+hHgfwUQkY8XkW9R1QfWUze0kFix\n9LGjLjaMpXNYiRhg5SJr7/DFMuJVaGxuIOmiLXIs5WZXo+oRUT7S1UW5kQiqbItqoou2BHjCM22D\nM56jvqKN2Urik+HIO7bREqJhg0Mk+6DXvSMp3NzWfKhpWLnAykUOvCWpMCRy7leJE+9pY+DJ7ZpK\nDJW1OBH6mKikJxTlRmUiPnnauM1SMpNtGFEjIXUkTblRJEUCWSXhk8cmky0d5cswpjC2kyRNREKx\notRkl0bCimOrbWaCE9m6UYIzh2DQqGkUtK3DZrReDC0pw/GSZMWBEVOsLrvHURN92OT1SGjK9gSZ\nrDsoPgZbCVAUKokudqN9BCCliJesPhlwaplmrYOo0qvfyfNUxzGgSiCOyhC4fdDnoO7IPd47X+L0\nD8lof9EclGolfwZ1Yh3Jr0dGtcnAuE+Z9tvZWu6laWWKO9lUbrWEKLuA1Nut+5CrGIc6nQMR+Z3A\nk8AbL244My4L2jAoMF6+GtXZQjJjxowZlwchhazi1XzeFlJgEzZswobDfs0T6zW1FVau4qNWV1i6\nFXtun9pUVLbK52lkpXJVrNn3orq4X8LoZzzYuOunUEQ+WUT+mYj8soh8s4i8SkR+hGwh+dWXZ4gX\nA59CzqEwLk/Mi9Rq5VzJtRCWNmdTdLHkICRDYxIf2TZ0wRBUiCrc7Go20WJFudHV+CQYwCfDJmQO\nqS+VrEAhORLHwY0ER1Dh2Fe03hKSsA2Wo7YiJmHdOVSFw23Fk9sFJ8ERVTjoa7pkOPGOI+/oo3IS\nPNtoebZNrENgGwJBFZ8i2xjwKdKnWCwevtSwlqpShKiBNnYc9tsiVQulUtbTp44udSVHI5I04tXn\nbI1SpRo14lOfbQ2qZd+RPvWjjWNq2TCYsS0llryMkAJB42hDWdhm94Mrdok80ddxMj/YTEIZB5pz\nKxLFXlLWHS0n6HgrpVo1aMykhgiI7CwoE0TSSEQNORqnLC4lRUMHi0mxmQzr38neMayX37d0evkt\n26WJMUUm+RvDeiIyvv/jOG851nOP/xLh1h3pXbyWd3vu4cA7ReQR4C8DP0r+zv0fL3ZIMy4Dtn0m\nE+65hSQl+M7/AL7lDfCb/8eZjjUQGLMCY8aMHUTkdSLyj0XkGRF5SkR+RERed9HjmvFwYbBHD+eu\n67AmaMBrztW7VtW8YrGkNjWVqahthTMOi6EyLl+UEzPbRWbcd3g+Gu3vkdOV/zi5wu8XgN8gS5u/\n/ZzHNmPGjBkPLVT1u1T1pqr+pKp+jKq+UlX/7kWPa8bFoz1rBobfwJP/D3SH+fYMEBEaZ+jCrMCY\nMWOCd5GJ5dcArwXeU5ZdKswZGA8OhkD5MfC+XBzblsDOm90NjvoNJ77l2XbLlbpm5SquVldYuj32\n3B6VyQRGbXOW33ChdsaM+w3P96ltVPV7y/1fE5H/Fvh6VX2gzmRO/Jr9au85y51xNHZBFzsqUyEI\nx77lShWojMGnNNpEVnXMCoqQ7Rxbn5UaiypSl8aQTXA8u2lYukhXRaIKdQnqFFFCMoSUWdBtsFQm\nsbARK8q6z/uLg10lCgfbmtom1r1lIE+fOm54fNHxka6mi4YumjFI1IpiACNKGy1WehKBdfBsY8h2\nB8BrtppkS0ViGzzgiapUxmBEcGJow5agihNhG1sUpULwY9tI/pioKH3qcy+1DkqLsFNcSJav+dQj\nGIyYoriINKYelQpeA45sFzFiiCSsGE78evxCb1NPY+rcOlJsJQaDjx5kJwAY2kFUlUhuT8mWjnEF\nEgmjhi71ExVD6dguoaRGDJLy6wwad2GjqdwXJkqH6fKyPTLeV1WQbO+QiWoiW0F2j4fA1OE1CzIq\nSIZWFmDcz9BCkl9WuYo6YdulvA+DMmN4PKhPhnTqAbfaQ273eLr81POTHU0tIrdtG5GHvoXkMeCv\nAJ9B/vi8H/imYueb8RCjDUMGxj1Kefv17n53cubjNc7Q+VmBMWPGBI+r6pSw+F4R+boLG80dMNeo\nPniIKdKWhr1N3NLHjjZuWYctN/uWqMpjzYJXLK5Qmzq3jbgFVmy2ipRzNCczcTHj/sXzfXoXIvJW\ndtOOE+CTpcxGVPUXznNwLxe83j68Zs+t2HMrjvpDGpNtCn1KXKksVgx9jIgoVpQ9F+hiJiCSCied\no3ERZ5V6UpF6c1PT1YG+tIi8etmyCY6ljfTFcmJE2Zba1KWLtNGw6S2LashIUPpgONxWXF141p3g\nrMEYZXPSoI/DUV8RVeijoTK5yhXyD3LfBY69Y2EjSqQxgTbG8ryQVNmS7SSmTDx9SvQpVzBdrxuM\nCMfBl/epYh08tTE4a2ljoLGBmALOVGObyGDHoWQwDCRG0kRt60wMIJih+STm3IXR8pECxki2s5SM\nCieWPnmcsWWcnn23GskRKzbXqaZ+nMRDnhjnDI2SF4Etk/adhWK0n5SQpAFRI6I7QiGJjsudWBKZ\nFDGFeEgaco4GSoJCAFm6FMA4nNicGo1idOgqZhwDsqtcTShVWX8gBoY62OF1DJvr9HbMwcj7NcUW\nQ3kuFYvQ7n05TZBM8y9ul2Nxt8dTnFquE7/lMNhb1h1e/0OKHwR+iqyAA/iTwD8E/tCFjWjGpcBg\nIblnBUY/IS26ozMfb1HZWYExY8ZpPCsi/yk52B7gy4CZXJ5xrhgunI325pRtI7l1JF9ovF43XK0a\nGrsYFRZOLI1t8kWxcj4bSTjOPwh6xozzwPMRGB8Gvm3y+MnJYwX+4HkM6uVGdRsWsppMWKsyCQdo\njGVLoE+RdbA5e8I7TrxjYVNWN3iLj8Kmd1xtAl6FI+/YeMtjex2VVVShMomT4PBJSJqzNNpY/MbR\n8tiiI3phEy2NSySFPgx+ZMuiijx7ZEgpEXyibhztpueJ4xXW5DpXVfBGcUbH6XsXTVZg9Moi5nrU\nqJbgIgtr2MZEowkrQkjK2nusMdQmf2ke9B1WBCPCNgRiSnhNdFHoUyKp0oQtkCf1Q4DmQI4IShu3\nZQJugEQbtiCCE0fUXKGaJNHHPk/SB2VGycwY8hG8FsXCRDyxjS1AmYRDKsGcg8pBjEBRXpgiywg6\nhCHpqM4IGkeFxKDmEMlVVRQSImjAqMk92gi+jN0aS0yDymQXijmoOvryfuQQpqLKKAqMKZKmU8Gi\nqI45IfDcjIhQVC9Dte2QnRHJShQKKZOzMXaYKjwG8kLKf3BaZTFVbEzHuVNr3J1xGEiJgZgqOz29\nzkCCPLzkBcCjqvpNk8ffLCJfdGGjmXFp0IUXQ2CcvYm3qWYFxowZt+Argb8NfDv5L9nPlGUzZryk\n6GM/nkNGEpvQ0sesZj70B9zo1hiEG13LnquojGHpVqzcCiOGhW1G0mMaQ+bOEMw+Y8Zlw10JDFX9\nrHvZiYh8TqlavS9xO/vInluN92vb0IW23M92DR8TJ6FGgaOuYhssduERFbbe0kfDSZ+bQkKwdMHS\nesOr91uSCmvvaGyijQZVoVNhUwI6AQ6C5Wrt6VRYh0xgHHc51FNViElYVJEnbvYsVhUpKa4ybLee\nJ48XXF/0Rc0BglK73clnHw1JISksnSEkgxUlqeBMVmdEjew78Clyo6vZrwLLxqAKJ6GnMoY9V7EJ\nPltpBFIJA7ViOBZPJQaRrOJQpVQ9ZWJkGzxGhNpk5UMPGLFYa3cTaBUCIU+8BUSzzSNpQlKe3Jok\npd0k3yI5gBUocoOs/qgkhxUNig8ATRFrqqyYmNo2NKsqclOHQTCEYr2wWPxAWtis4KBYUKyY8dhW\nDbHsd9qrPT2+EVPCTeU5xMWAhCJFGVJNWkYG3JoGnVtZ8rikWGa0bKOyCw91hXAZMCo0Jv+f7nuq\nqpgqNobnxv0UEmP6/K3hUMPPdrqtuTWOpygyHlb7SME/F5EvBX6oPP6PKE1Qd4OIvB74PuDV5F6f\nd6rqd4jIo2QFxxuB3wT+Y1W9WRR13wF8PrABvnxQ14nInwL++7Lrby51rjMuGGfOwDhlITk7gbFw\ndmw+mTFjBqjqbwF/9KLHMePBhk8+N/lpyBfPgC52dLFlHdbc7Dbc6FqsGF65WHG9WVGZij23n7Mu\nTDVekE2a5ryLGQ8MXqounDkZf8aMGTNeAojIsYgcAX+aHKLcl38/CPxX97CLAPw3qvqJwO8F/qyI\nvAX4euAnVPXN5Capry/rfx7w5vLvHcDfKeN4FPhG4PcAnw58Y2lFmXHBGC0k7oVkYMwKjBkzXixE\n5N0icn3y+BER+Z6LHNPtMId43t8IGnHGZWVvUSFnu8gJQrYkWzFcq2uuVDWNaahNjYiwMDmoM19Y\nMmOW34wZDwJeKirugfyNOOx3XuHGLfCxz9WaCl0Urteeg75iVQI5D7dVtm6osFdHllXkZgnatKL0\ncVebGpLQRTNmFHTBEJNQWSVEYa8ORM3KhT4a1n2+0mZF2QRLFwzaWxYrpdv60X2QovLMjUi6XiMC\ne3WgC446JA7L9lGFVRVJKoRS33q1CnR9nfM7Qq5ljRrpYsWhr0oWR2ATIKqhMgmfOrpU7CGiqErJ\n0oic+J69qmLf1Wxin6/4l0/JJkRCShgRgslX6q3mx3EShAk5ZMiKBVECfrRRUOwHSSJMlBMheRq7\nyDkOxRKS19upIVJKGExRbqTxan9KaQydHFQS+fk0WiOGTAwVJZa62Z6EU4ct4x6sJAYzhmGOCg89\nHQKaQ0QVWywtg60mljwQLYMburqFwR4iY6DoCJFcqVVCQsubMv5yBo3l8c5GMlaqDgoLVVSG48WS\nSbL7eYzqlWI1mdpMbsVUtTEc4441scVWMuZe8Fxlx637vt3y2z13u0yOu+37oqGqV17k9h8m2/9Q\n1WMR+VfklPwvBD6zrPZu4H3AXyjLv0/zG/+zInJdRF5T1n2vqt4AEJH3Ap/LzvM944IwhHgu6zNa\nSFaPnbaT3CMaZ+ca1RkzTuOTVfVgeFDUbG+9yAHdDnOI5/2H4bxusASHFNiGLX3yJE0c9Dc58h0x\nJU6C52pds1/V1LahtjVOKpZ2AWQ1eSj5bDNmPEh4qQiM289K7nN0sR+pmVcuXsGHNk8SU56Edcnw\neANrryxdZBssT21rjCgpwSN7ntomnl03vGKvw9pCYPR2zKfwydDYTCQMoZ4rG1E17NVhtJN0wXLc\nVVxpPNYoPgpdr/gusNqrObxZ8iZSPsE8uLHBuX2MERbVLlB00xusEYxRKqN0AUIUWm9Y2shxX2XC\nJJmc5ZEMPhmOvONVS2ijcqNrqE3Cmzy2pII1OchUFbYxf0mqComOhXWceD+2lyysZRMCSRVrhKia\ng0JTaUAxiaVzjPGTZjfZDBowCLaw0TlDIWdRlL6TEhZa7fx+Zb1s3YiF+1BSCQFNJSfEGotXjy2k\nQ0JwUpW8CcXhUMlkyEhgFDuIT55kEpW4LM+TnBItRjIBkt+QMXVCNWKxRGImOYqtI1tXwGjK24vk\nphR2zSOVcYUcyWRFGj6gIqPVRHVceguJkAodMuR87BpMhsfZ3pH3N7WTMNnfsFTRO0q4BrJnwHD/\n1iyNYZ+jrWTaPKKndngad8jOuO1ztwkIveu+LxFE5I8Cv788fJ+q/tMzbv9G4K3AzwGvKuQGqvph\nEXllWe21wBOTzT5Ylt1p+a3HeAdZucEb3vCGswxvxgvEaCFxZ7SQXHnNCwzxNOMxZ8yYAYARkUdU\n9SaMirVZnz/jBWM498kZajGfE6dAJBI1FdvICUe+52bXIgj7VcUrF1epTMXCLVnaBbWpcntdOdca\nzyMv88nOjBlnxEtlIZkxY8aMGS8hROSvA18L/Gr597Vl2b1uvw/8CPB1qnq3WeudaKC70UO7Barv\nVNW3qerbHn/88Xsd3owXga2P1NZgzD2ekI4ExqtfmIVkVmDMmHErvhX4GRH5JhH5q+QQz79xwWOa\ncR9jJBh0l9c2tPj51HPij+lTZBM8TgxXqoprdUNlKqxx1KamMhWVzdaRIe9iaJYbFB0zZjwIeKnY\n4t98ifZzqdDYevyF/0h3E9WsDjjyhqX1RDVjPakqXGk8G5/f0o3PV8YOt5YuLFkVS0lUoZaEWHhm\n3bDfZKmYs8rVyuMkW0hCMrTesl8HKpNoXGTrLVFzTau1QrQG3xdbRFQ2654QEqtVxdMfPuLaoyue\nMTUxBHojBO/H1xZiQ+3ylXpr8muorHLsK7pgWPeOyiW6YKlt4jeP92hsCRAyuS52YRNRhaBCbRIL\nG0eLjBHlmbZhYXuydTpxs2tZuYo+RbYh22qsMaOPrzKGqEqf4nh/3ylBBC8WITdxhJiDQUMKORi0\n1JUKQtSQW1VSONUiIjq0kihGLDF2IyPtjMPHrLSIlJoqDKoeinpDhxrawWKShtrRVNaJBLKSJL/a\nrA6Z2lGYhncWGwhk1j2kwNDKMahH8nhNOWb+WYUUSrhoVlRQVBpMKmCHBpJsQymHHm91EvSZRpXK\nOOayzmBlUbKVZBo+mtNByzayY/eHIM4xpHRiobndbDiRxm2mto/hj/hgcxlfwOC3ug1OXV0o600r\nWJ9z9eH+uBDx+cDvVs1vqIi8G/hFdtkVd4SIVGTy4gdU9R+VxU+JyGuK+uI1wNNl+QeB1082fx3w\nobL8M29Z/r4X/GpmvGTofGJRneH6w2AbufJq+NAvnvl4jTNzjeqMGROo6veJyL8gt/EJ8MWq+qsX\nPKwZ9ykGi+9wvhTJDXbD+deJP87B8CmrlpeV42pVc6W+xsIuqG3NwtSZvFDFmDwHiRqxYrMVe8aM\nBwh3JTBE5M+r6t8o979EVX948txfU9VvAFDVLz7fYV4MrtVXxxyMddgUCX9NZVoaKyWTYKjHFK40\ngW3IXxKtt1RGOTnuOAFWezUf98qemLKFw6B8ZF2TNOdSXFt6Hqk9QYVNsMQkHHeOvTrgrFLbxEGf\nWdWUBGeVYIS29WhSYkocH3W0XaCqLMfHHc2ywncB6wbrQsmJCIm2Dezt16SUZ4QxLXh8vyMpbL0l\nRMOiimy85VX7Hb99sqRxkWtLT20SR13FtaaM1zuWLvJI09NGQ1KhMomjvuJ6nYmepEobDUF7rAjH\nheipbc4UaWyisVpaPaAylqS5LSOqUpmIMwY7sR9so2dpU+60LtYEVSVIGEkBKwZTvriHLIrGGPrU\nA5k4MWoIyRdrSiJqABxaJtBBA3aYhGupM006TuRznoWSsxN3NomUct1p0F2exTCJjoMnscxBVOM4\nPx8m87G0qhg1p60bmk0fcSBaRMbn82exZGqUP1w7QkJOPZ9QjJamEyYcgQ4tJMOxBttM3r+ZWD9y\n+Ird1dNOxm9ksOPkKleZZIQM79OQezGMAXYk0ZT0GCwmgxXlTq0lw/6H9Ya2k1sZlPtISnkduFHu\nX7uXDUqryHcD/0pVpzXYPwr8KeCvl9t/Mln+NSLyg+TAzsNCcvw48NcmwZ1/GPiLL+bFzHhp0Pp4\n7w0kkBUYxuUMjO7sGRiLytLOIZ4zZtyKR4G1qr5LRB4XkTep6r+96EHNuH8wnC/Z0rbXpZ7G1mz7\nLV3s2cYtPnVsYsdB32ERrlUNV+s9GrtgaZcsbAPsFBeIjJkXM3Ex40HF8ykwvpSdJO4vAj88ee5z\ngW84j0FdBqzD5o7PWREqMSRRVi6yDikHbzaRVRU56RwLm+iCwZgsAVs1OctiWUXWvaNx+Xp3TIK1\nSkiGZ7saA5x0DmeULli2wbKwiaRCVMEWJYAPeQLorKHvIyEkjBXqynJwc8t2G3j6yRNWexXW5LDG\nqrYEH3HlxPf4sMVYQ9cFYlxQuwpncpbFSe+oeotPhpSkvBZHFyz7TcgkRxL6YFByjsam1MdmZUUe\n5zNtQ2MSXoUuWvqUa2O3MatJKpMwotig1CZRm8R+lfBpUCsIToQuBhrrSgWoUBlbAkNBg6e2Nmc6\niBDCGlvUA0kFpacyNVGzeiFOmW4VWm0xIqRCagyKi7F3W0NRSCScqUoIaE51PpXVIBYVMvGhhiSp\nTKaHKtVME0TZXck0SccJ9zApH8aeFRa50nVcv5Aag0okFYZ+OiG3YoglFWQaujmtXw3EXeAnU2GD\njEoM0XSK+FBNp8iBcV9D0NREnpg07fapE/KF3RUGYMwIEU4Hgw7HGAJLd4GmjK9nIDpkSuAo4/qT\nl8TuaX0OmXSJ8S3AL4rIPye/it/PvREInwH8Z8AvicgHyrJvIBMXPyQiXwX8FvAl5bkfI6s9fp1c\no/oVAKp6Q0S+Cfj5st5fHQI9Z1wsti+EwKj3oLkCsYPQgWvuefPGGY5bz/t+7Wne8lFXeeWVxQsY\n9YwZDw5E5BuBtwEfD7wLqID/mfz9e2kgIm8H3v4xH/umix7KjNtgqEZN5fzHieXYn4znn13cFgVs\nPom5UtcsbY0Th8HgjKMybneup+lU8PqMGQ8qno/AkDvcv93jBwp+Ir+/FVYMzmRVwdJFnEmEJFSi\nXGkCTx4vcDaxHQM7Ya8J9MFwbeE5amus6EhKKIKPwqbPjSWb3rGoIl3MNpJrtS/qCcar4MHnQEoz\nJTBMJimefmqNep8bLmI+0TRG2L9S07WB/Sv5xLXtI64yHB91aFIWyys4a6hsYrOJZXKotK1BjKBJ\naZusAtl6y6bPbSiV3U0so8rYuuJs4iNVzdUqK1P6lK0nobyILlpElGUJMq1MwhnFSIcVxYgCnqVz\ntCFPdUNKOGNYWmhjQFXpU2JPK4zkn02XIkvrsroC6FNiv7L4FOhTADwLW5FKQ4dqoDJVVjyQVSBq\nKIGeZHuIxjKBN2OYqBE7hi0NfzBSmaZDwqiMbSgxBazYTFToriVkUCEkjROliB0n5EmzWqesjIop\nAaBpnJhHjVizm8zc2uQxbRoZno8jqTJBaR/RkfSQEu55OuhzICR2Vo9hnTQuT8XyMpAFWb2i47sz\nkK1wtR4AACAASURBVCHjvobQ1UFpMh3/5DdxRwNly0okB6IOTw7ry+T9PUV+TK0ol/gbrKgofppc\ng/pp5NH+BVV98vm2VdWf5s6v7rNvs74Cf/YO+/oe4NJVAz7saH1keSYC4wTqfWiu5sfdyZkIjMf2\na25uPF/+rp/nsz7+cd71FZ9+xhHPmPHA4Y+RA5J/AUBVPyQiL6pB6jwwt5Bcbjjj8Gln725jh4+e\nPnmO/CHb0AFw7HPuRWMctW1YuT2ccTS35F1Upnr5X8SMGReA56Pp9A73b/d4xowZM2a8BCikwv+i\nqh9W1R9V1X9yL+TFjAcfP/wvnuDHf+WpM2ZgTBQYcOYmkj/32W/mH/8Xv49PecN1bqz7M207Y8YD\nir58TyuAiOxd8Hhm3Gfokx+Vq9uwzXWpsWUbt2zDmjZ2nPieo75jYS2PL/ZY2AX71RUqW9HYGlUt\njSNZjTFmlRXcqbp+xoz7Hc+nwPhdInJEvpq3LPcpjx9oDWlldm9NSIGFXRBNJKHsVx5nhGPvebZL\nVKI4ozy7qRHJGRVHbUVlEtYZks9hmNs+Wyx8zMoLyLYSgD4YbLFv5KrUnDRwo+wzqhR1QlZFqELf\nB2JIOQMjJnyfiCmhJayzPWm5cjVfZatry9Fhh7WGrg34UolXVZYYEm3refrDxyxXFXv7Dd5Hgo9U\ntSOlnFORj+N4KjWklAg+YZ2gCYwVljV0wUADJ20+uT7cVrzx0Q0bb/PF9kW2zYgoz5w0VFZZVjFb\nScyQ/ZDrZK0oziRWLmdLbGPPiXfsVz2V6YmqbCWMNhMAXwIqN8FztcoZI4qyCW35WSZy7IcnFfVG\nXQJDQ0rjFXyfcqinEcHHRG0tISWgxWuiEoNIwKotyo2Sp1HCRncSPsGKHe0riuKk1MDKTrGA5DpX\nJZEkYgZVQbGLDFYJw65yVUXHIM8U/RjoqXI6PyKrOCa2CtkpHIZ9GTGI6q72tTyLKnGwXQy5GYPF\nZaKYSGSFRQ4ojbvXNcGtf0jTrfyollwR9NT6g0rluQzq8Hk5baF5ju3klg3vpO64FZfAZvKzIvJp\nqvrzz7/qjIcFP/mvnwHgKz7jDJLw7iQTGPV+ftyfLQejcZa3vuERXn1twb9+6uwZGjNmPID4IRH5\nu8B1Eflq4CuB77rgMc24j2DIWRXr2GWFblGmtrEtOWlZ6b1yFUvnaOwCZyoqcTn3QjWTFihjlL2c\nPmfJoe1zDsaMBw93JTBU9aH91O+51Xj/sD/i0eY6AM+0EJOnNjVtPOLYO5Y24qzy1MmC/ToQk7Ld\nBq5fMbjK4n3Owzg6atlWlqo29LHCGs0TfsBHYa+OJM3BlhvvMKLc3DhEwJmEs3lymYkOpW8DfR+J\nSQk+0XWBEBJ4D1UFJ8fEkK+4WSscHXZcvZbJieOjHlcZQsiNGu02cHLcc/VqM34Btm3AWIPvAiFm\nEiMlZbvxGCNst569/RrfR4wRzCMrutbjrOPoYEvwiQMrXFtWbHqHNbtsDAE+sm5wNrFXR/bqgBGl\nL7aZR5Y9tcnZH0sXWdhInwyHfcUj0WNFM0FklJWNuJIrcRIi+85yEiK1sePEP6qnNgaf8rQ5xRx8\nuo2BlavwKeduDI0okK0nlTF0MfKIacZ1giaaYtmobMLHHfFhjWDFjJ5EgZybMUzqS25G1AhmZ9ew\npgJy8KYRgzNlPj8SBwmH2xEXmerI7Ds2T+JV0GJZobSIwC4wdLBp2NIOMjaqTNpRhj+gwLj/nM2h\nIzGQH+nYfDL8QAfSJA0T/9KeAhT7i4z3882OwNFCZxglb8eOhBlICEVPezsHbmKS0wG7wM88ptN2\nl/H56YAvLz4L+DMi8pvAmjFjVT/5Qkc140LhY+ITXn2FL3rra+99o35dLCRFgbF+FmIAe7YismXl\n2PZzG8mMGar6N0Xkc4Ajcg7G/6Cq773gYc24xBjUFk4sXexQoAs9fezpUk9InrU/4Wa/KWH3nsoY\nKmuoTI0zFSu3Gs+DhnO2u7WMzOTFjAcVL1WN6owZM2bMeGnxeRc9gBmXD31IVPaMIW39CVx9LSwz\nEc/3fxEsrsPX/t+7ZfeAvcay7sPZjj1jxgOKQli8F0BErIj8SVX9gQse1oxLCieWkEJuppPcftfG\njpACbdiyjRu2wRNV2YRAbQyvXObcoj23x8ItqUwO76zEjYrXW0mKdJsgz0ugKJ0x4yXFTGDcA5xx\nbMIWnzxWLEHzCZwAjzXZD/xI0/Osq9l6y6JSVC1R4dG9wBMn+Urz/pUFKeWr2NbkEE9VONoI+4ts\nDRkCMa0oB11FSsqmt7z6qmfduRwWaoudwBooYYyLZQ7uadtAnyK0ATRxctJz7fqC46OeGBOHBy1V\nbbEllPPwoGVvv6LrAr5PrFYVRwdbjDUEH0kxV67Wtc3Wky6/dmPzVfzjw7ZUhg5X1aFrA10XUIXY\nJT58mNUslYOQshXGSu6z7ntl21nWzWC9UNZFrQG5ReRK43Hi2IRcTbsOltLYiRWltZEbXVapCI5j\nD6oVC9vTRcWZ0hxTFBi1sYRy9b+PkZAUKzIuy5YUQ9CET/lAB303WhJCSniTVRdV3P2REJEc3EnC\nm1gsIcrKKT4lnJRQT0nFFpLGKtasdigP0656dZAVAuT21vyHSSbxNWp27R2nLBMaszJBS9tHUYTo\n0GqiIENTikaMpokpZFeXOrVgaLGZDGqMsaUkuzyIGkYFRkzhVNCnDLYVMaNCJJVjmmFIxUEyKCu0\nvK9TDMNJxeKSlz33dqommdpmdvvR56gwbtd2Mix/uSAiC+DPAB8H/BLw3ao6zxpnANDHRO3OSmCU\nDIxX/y74gm+D3/pZ+KUfgqMPnYnAWNaWzazAmPEQQ0SukkOPX0uuoH5vefzfAR8AZgJjxilM29wG\ny6svtmJU6WPHNm6BfP7Zp8jVqsaZ/D1fmQprHI2piRqpXT3ORYbQzimRcbsWkpm8mPGgYSYw7gF7\nbsVhf0SfPLWpCKVRQkR4tKnZBM9jTc8TLvFs59irI4olRMPj+xv+3Yfyl8qjez1HbUWIgpVETIKq\nsD5uubrKFarOpDEjom2zhH/bG/bqwLpzY/WoiGDLSawqLJfZXpBSZm7pO3CO/niNfWzJ0WFLCIm0\n3dJWFdcf3UMV1ocblsurxKi0bSAl5fiow5WAuL6PbDaeK1cbNhtPX05eh8aT4COLRUXbemJIVLWl\n70K2lFjDduM5uLHBVZamcbRGqGuHsynbUbaevg20q/wlXNf5fd2rHf2EHFCFo7bi8f0OcIjk6lln\nEm00bKOlNolrteeor1i5yNJHTnyFM4mFTVjJhMR+FdgEQ20SfbJsI1gptbalqrYxnkQmPpwYtqGn\ntpaoWkiQYnkgEyMiFP8hWIQu5j9EoUyagyq1MTijoMX2Y7K1QzWVCq1Y8jISVg1GLGqUkHLOh2gh\nBcRgxY7HFy25DwyVqGOuGEbz+zkQIlJMHgZTiAMZrSVpfHZo7titD4x1pUOjieiE4BCgtI8MYxAY\nbSyZrCk/z8E2U8iZfGxKk8nUnjIQCbf/0zslJMbtxv2czgGZEhPD3p7visStZMbLiHcDHng/WYXx\nFuBrL2IgMy4ffFAqe4aT0fd/G9z4N/DGfx+MgU/7KnjkozOBccYwz73a0YdEiAl3VhXIjBkPBr4f\nuAn8n8B/TiYuauALVfUDd9twxsMJRYkpZ+gp4JOnSz1tyWY7CcesfY8zOb+tNpaFtazcito21KZh\n5ZY448bWtUQayQtFZ6vIjIcOM4FxRghCbRtCyiGRT/TH9CnlCZgor9jrubmpSCosqsgHD5ZAj2oO\ntFTNV619MkWFAau9msbFUrkqOKt0Qbi6twv59NEQVdirA300qAopFsWAkTGfwvtyVb9Z5CyM42Oe\nfmpZalh3k7GDm1llYeqKmwctsffYuuKpJ08wVlg0DleZcb/txtNuA64yWCvEqCySEkIixt2tHit1\n44gx5fDRqNx4dsNqryKuaqwztK2nqizt1nPzxgZnDeuTjuVeTQyOlJQnNK9bOWHd7RQZN7dVVnCU\n9wqgcVm9cFgeh2S42ZtRPWEEmhIKqpoDOttS4TqQRs4oa++oTUJE6cRgi1LBGSWqpUtDaGQmHXIF\nrlKZHJrqJAco2fJvmBz3KYerhpSwIhgRoio2ZTIKwA5Eh+TXgikRmSkHdSYtYZxiEE2ncytSAgQj\nu3rSIQdDRRm6d61YIhFTqmAFydVbxZeZA0CHCtaxkBQdAjE1jaqMsVa1kBqicooYyLdDPWwJ/RwV\nIDIqNKZkhYpBdLfv4fcNTVm1AadUFafIBd2Fi47hoKqonCYiptuM5EVhOqZqkrLCReEtqvpJACLy\n3cD/dWEjmXHp0MXEtfoeq/JU4af/Vr7/iW/fLV8U1UV7eKZjr+r892jjI1dnAmPGw4mPmXw/fxfw\nLPAGVT2+2GHdHiLyduDtH/OxZwj9nfGSIKQw1psmFJ98Pv/XgI+eqIFNWNOnQBsDGouye7GiNg2V\nbVhOMy8oQfGT88sZMx5WzATGC0BjF/jUs3Qr2nhAF22+cm+UxxY9Tx4tMEZpXOK3ntzJbdcnnmZZ\nFaWEoa4SMcHVPVi4hKBEFRYmEyKPLHsO2pqUsvUiphz0GbZlMlcm1NYIKWVFQ4wpX2Wr60xgrI9J\nTyV45LE8CBFICocfIVUVzeOP0j1zA9ot8fFXsblxE/b3CauG5bIixERdWdo20LYBFw11Zen6UCbX\n0Hf5NfZdpG0Dr3h8xXbrc6OKFTbr/KWtCq4ypKg0C5fVGTdb9vdrTk56HiuTZ9/HTGgsK1Z7NX0f\nqWvLYlWPL8GWpo2kQuMilVFOeseiShiUbbCciGNZxUJgZELCitInQxtN3tYmGhupNXHQV1ypdkr9\n2gzKg4G4EKrSAgP5Z9JFS2NLMJPJqoPGxpHMEMAmyYoLTTgxOGMIKeFK+0lWKuQ2lBCznSWpFqvN\nYPFIiOwY96QxKzEwJM2KjEH7YDBjAOd0wp4Dn2J5LTFbdrQoMTAkySGaY4uH7mgMM6grxIxEQdSI\nwRXFhj6n+SOV/4ZlA0GQ376suhCRieIjL89kRyEwyvN2fC0yEgtjEGl5fbtjn1ZNjIGdnCYwzESx\ncqeTgQs6SRhL4VU13GqhmfFww4dEfa/kweET0B3CF3wrvPlzdsub7KumPZsCY1XnU4ZNF7m6uEcS\nZcaMBwvT7+coIv/2spIXAKr6HuA9n/q2t371RY/lYULQOJIXoVhFhKyGzWqMyIk/xhlHTPlcZmUd\nzhisuGwbkZx1kRUWBmMsTuyoBh9wu/OU2+VgzJjxIGEmMO4Rocj7hxRhK7nh4kpV4STQxsSqym0Z\nyzqSUp5k7+3XxJDwIWc+dFtPiIm9/YbjrZQIBGGvFja+Yr8Oo1LDltpUjHDcVuw3gS4YnE0sFjUh\nJEJIuMogIiwWjvVJvyMvYsiTue0WHpMs4e877NUlcVNBVdN1Ma/r+/xvsYBYJr+qiMB26zE2t440\nyRJ8KgqLPH6ApnEcH3Wo6pinoapQW2JMrNd9sbxkZUfXBoKPNLXN+zeSt/ORGLMVpSrPadKRrInR\nUVdCmNiwfSWsqvyerztb2l0szqQxK6OP2W4CsHSR476iLq0uXTRUpfHk2LtRFdGnYYKb58xWlK7c\n9ikRkqFPptgfIKT881Iy+eFTtqNEzec7IUFtM5ERUqJWO06oLZm0APBJscYQNZMcw8R8qCmljEpK\ndkaOjUiFjNhBxjUVI5akcax8HfIlBkVDIBRbiRkJCEWHGhAQW1QdqbSLDKRO/kGoyJhjgZwmCqa2\nEx1fw47oGC0pEzJiGP/tlBM61q3usjIAIvE5f8hVlSS7atiprSQxUXNMsjRuZxt5mYmMob46H3pX\nYT20kFx9OQcz43IhZ2Dc4+fxyV/Ot6/6pNPLF9fybXtwpmOPCow5yHPGw4v5+3nG88KWc7WQAkkT\nIYVd80jsOAnHdCngNdLGgAC1MSzcgoVdYsSw5/ZAYGWX+RxFFa878mKae3HrhZiZvJjxoONcCQwR\n+R7gjwBPq+rvvM3zAnwH8PnABvhyVf2F8xzTC0XUSGNq+jTYBRyJxLWqQRBOQmTpIisX2a8DW29x\nNnFtldh6y2YTy+Q+0nWBxaJiu/VUzuBDIl41HG2EVQVHnWNVxTHsUoB1b3l0r+dwmzMdlnXELxx9\nF6hqSwxZ1WCsQN3AZgMhZLVFuwZy8GYKgaa2bKoamjqTG32X1/Ue9vbz4wIjwnYbcE5gu6VjCZM8\nQWtz/sNiAe2mw9YVvk/0fSQlxRjB+4T3ieWywhqhaRzdtqfvI83CcfLshtVeJlNizEqSK8YQQyKG\nlMNKQ56EhphgVbNZ9zl3QoTYOKxki826dziT61ib8ukuOZnUTvDRoI1w0jkWLmKbXGUrAs4mQrAj\nseFMQlXoS9bGUOtqRYuaQfDJYERxo90kr4ODTbBYKQGeJis/lIiTRJ8MIglilhY6MZhCGCXNioMo\nQsr8FUPVaGVOh4ZmWwmQlFTqVYc0UFuIglRCoyJFuVG2HwiMIcl6mL7fmhcxrV6NooiasQI1oeMb\nLGKLmmOaZTFYQtJ40IGsoBx/ilRsKjL84CZjzePJx9wFjE7GnHtnT/0RH9fV3Ti1KGMGymKw3Jii\n1LiI4M4pHub66hnPj/4sCoynBgLjLaeXDwTGGTMwdgTGHOQ54+HE/P08424Y1BZeQ7nQmehTT1Il\naKSLLZuwJqRAHxM+RRbWcaVeYsWwtHvUtqY2Vb4waReEFKhsjcWU875djtiA2VIy42HDeVN03wt8\n7l2e/zzgzeXfO4C/c87jmTFjxowZM+5b+HgPNar/33vh7/1B+LnvhEfeBM2V089XC7D1C8jAyKzw\nupsVGDNmzJhxK9zQBFJst0Fz7tigwmhjmy8alQslV+qapSt2EVMjItSmwoodba7WuHLxZpd9Niss\nZjzsOFcFhqr+lIi88S6rfCHwfZr12z8rItdF5DWq+uHzHNcLQWNy/sL0S6U2DUGPqYxhz3les9py\n0NcsqsjBtiKkiuOtYMxOBaAKde3+f/be7ueyLL/r+/x+a6299znPS1X19MwYj+0EI0twEVnGFr5I\nLqygKCDFwAWWghTJCShWIvgDQJFi5SLhhlwkAQU5igPOBVwkQrIlK8hCQkQQJBMhgYkE2Ajw+IWZ\n8fR0VT3POWfvvdbKxW+ttfc59VR3VVd3V/fM/raefp5zzn47+3nq7L2+6/vCPEXG40zuHDePBt4/\nQBcsrDM4UxCMUQkucT95pqScZqX3kcNkM/vBO24fDziv3N+NHO5H5ilBiqakONybNMJ7OB6R2xsY\nBqYpml0EbFlRW+ZwMBXGPHMSRVXPsggIJluzClVrKLm+ccUOklDv8U4shwNwXhgnU2LkXAJGmZtV\nQou/Y9iZkkS0WGqSNaKoM7VGKnaVcZwR8a3KNcWM88I8Re5HOzbBtWwMFbOS9D4Ss9hXkmKnMFvJ\ns5M/+91MyZQcSuZUfs9aLCnRWQbGlLSoMCzHYYwKzqwcrZmjhK/mXJUGFrQacw0WzYwRnLda1uCL\nZUTs3NbYhpgtIaIqBeZ1NSixKEFKVkfJxajIJWsCauXouUXCFAuLaiORWiinos0KsuRT1O2ajaQG\ne1Z1Q91uqvvJNYwzNetIU2OUDcrKjrLOrrDa1VWO5kqNsQ4SXZpVXkSzgqy2/9D7gXO7jT2WM7/J\nhzWWbNjwaWGcX6FG9Z/9TfitfwTf/2Pn4Z1rDI9ePwOjX0I8N2zYsGGDoTaNgN2/VLvumCamODGm\nE8+mZ0wlvPzZODI4jxdHUE/vdnTa0bmutRxWoiLlVBrqls/dLeNiw3c63nYGxleAX189/mp57gUC\nQ0R+ClNp8L3f972fysGt8ai75f3xqQXplA+Nvb8i5a/TqWPnPN+9z3z9aHkMx8kGrM+fHtlfdTbo\nLwP5rveMo1lJUs58zy7ytW8mnjxyjFHpXOJ+cpxm+/n5CeYonGblpp95dgo4zQSXeGc/cz8EvumV\n06HUnMZEdzUwfgNrIwkRxhEVSP3ANNrjBqeQHZwO8LQ8HwKnvjtrL9EukFLGObM2xPsj3g08nxLj\nFPFecU6ZS4aGU8u6qOPHeTaLyDhGs5N4IcXMMHi+eX/AqTWcWJtKZDwJwWshTDLzFAnBcTrNbeCb\nc2aOGSnPgVW7qlM6Z6SPFtJhLrWsMQudS0xRmaKy72am8toYbfnaciIC+xAZozInQUPkUGwmNeSz\nNsoIQFJQy8eo4Z612CJlaV9W4ar0OTMl2COlPpVG8BgBsAzyVeprlSgQgi5mCAC/GmSv2zwsgJM2\niD9HJTGgJGyuX2r1qXXT1W5hx/Xiv5VqL8liDSqWq1FQDjeJVblKCeXM+fyoqh3kBWFkITIEsLJi\nWdjBy+VWuRYii+2lLXsRjnlJiGykxYbPIiwD40NuXOcDXL0L/8n/8fJl+tuP3kJy2giMDRs2bLhs\nGkmlJa6GdcacStPIiSnN3M0TnVqY+00YUHEMfkevPZ3rEGjbq3bamqextoxs5MWG73S87X8BD40O\nHhgSQc75Z3LOP5Jz/pEvfvELn/BhPYyggaCB3vXs/BX38x1aKjOtWSLzbj8Ss/DO1czQwc2jobRx\nKL5Uh6aU8cFxfdPz6PGOu9HRDZ6YhTlJy1k4lln83iduhpnj5Hh28tz0U2vhqAPrmDLOK33vIQTm\nOZkKwzlTVzx7yjwbWQBY0GfOprjw3lQYLphio+9sPWAYPM5bRkRKma4zZtiXrI27uxFyZp6sNnWa\noqlAsPyMrvf0gycEx3iKdh68EoLiVJFSAevU9uG80vWOoaxTW1tEwIdSHyXSjiPFjJZciJqJ4YNj\n12OVtJrb+XSajXhwqYWkihhRkfNCXtQ2kasusg+RnLGmE2cVq1a7WnIyyuNaudppQqGEdwqJRd2h\npQWlBn32zrIpnGTLsij/HLzq2T9MKeeyVrDW/1RKzGU25YSKtvPTlin/Vyk/ibu48Nl7l0JI2N6M\nVGj5FIVgaKoJFvKonfiCSkQshMVyHAtJsuy9KjXq+6yEwfIeHsB63+Xxel9LNeuHo+hD2jHn1X91\ngfwQS7Nhw1vCK2VgTEfwwwcvMzx67QyMq9pCsoV4btiwYUML9jeyIrb7OGsaycx55pROxGz3hnWC\n6lFn5IVThxdPUE/K1jRXSYvOdW/pXW3Y8NnH2yYwvgqs5RTfA/zmWzqWD8Xe79j7He/0j/ld+y8z\nphNeFKeCF5tB/9LOFAfvXp0YfOKdq0iKic4nus7UAykmQlCub3se3wiHQ2ToYI6mEshYE+ppVjLC\nECJXnTWQPDsFboe5VXlOUZijFmWE0veOrnPW2nF9bYNLdXD3nBxnIzBygr63NzWeIJQPyRBMsdH1\n4OxGdRg8rlZ55IwvZIZzRkJMdwfbzBgRKYGd8xLMOAyevnP0vWMcI94LzgnOK+qkESPO2QBa1dpU\n+sHTdY6+9+RSMVWJFIBQEjrP1nWKCnhnqok5CZ1PxCx4Z6RD5xO9rwRLVTVIOZfVb2iEyVWYuSq1\nqoNLhFKzUdUXCQhqz1dCqVMjOaQQGM1CUoiKGsqastCrw4ngVEmFpMnkVsFa4Qp5IQiOpfnFlR9i\nUQoYSaELYSGCiF1Oq9pBRBqLL1LPZ14RDS1C8ywUs4VwsrSDVEWDrP67pA0aMSKsiAo53wfFonRh\nE2lBnhdKibQmGGQdOFofP4xLdcXaWrJGbt+X/zYlxobPAnLOr6jAOELYffAyw+srMHZbiOeGDRs2\nAPZ57FctIDs3EHPkbr7nEA+c4pH3Tt/kMJ+AzPN5wosS1OHE0bueTjsG19O7nr3fNduIijKlCSfu\nTHmxYcMGw9u2kPw88GdE5K8DPwq8/1nMv7jE/XzgGE902iNyR6ee34lHjtFyFw6T43aY8M4Y166o\nHq52wuMne4ZdaLkVqgMiM3NURDK55DT0PjFHh5DL7HomOJu9F8nsu8hptryHwwS7XSCnzLDzOKec\nxpnxFBkP3lQWISCFlFDvbTiqzl5LyaoubDRvigwV0jhxPHpUBeeU1GX63nM4TI1sIJg9JgRlvw/4\nYDfWfeeJMdkxDZ5pTm07u33AOSXGxDAEnJq9Rp0QgkNUuLpe7CvXtzaTGLrqCzQCpR98q48Fy6oI\nGo2z0cxNPzOESM6CSGZXml3GqFz3cyumACMzUk44zfTe1qmExD5EIyoAX0iMoAmfMoOLxcqR8GK2\nHkl1cC4EZ1Wug0ZEMp0udoqgRn5JssG1VyWoWg5GWcqVNhGRSgYoAbNnLKoKXakylHwR9JRZiIxW\nySraZgpEtCSHFLJhlU9RB+4q2rJfEDGnSbVz1HaPYi1RUcuwoLZ9rHIlyvleFB/l/K+UF2vFwzqb\nomZutGVZtpXr8a9sI3UZiwJZyBbKcyIvkhKm9nng+fV72LDhLSGmTM58eIjn/IoKjKevd7ldFBgb\ngbFhw4bvXMxpbsTCMZ5KTepIzJEpjUxpKtYRs42cYsSLch16vHp6HejdQF8yL+wex+7HvPp2P7dh\nw4aH8UnXqP414MeAd0Xkq8BPAwEg5/yXgV/EKlR/FatR/c8+yeP5uFD7nPf+CpX3COo5pcgx2kDz\nODm+sB8JxaIwFIHDTT9zemfftnN/NxKCQ1WYYyoqDCMwOpe4x5WBqw2MO2fz4ArsfORYshlyzlzt\nBHU9u11gGDLhqNzJxFgCPI1oKBYFr8x4/KCMd6s/AXVANPWFOjgdOYbAbucJQcnZ0fWO43FG1AaA\nLjhcISaurju6KTbLzP3daPaLwaNThN7hg3J13TNNlofRF7XFHBPeKd1gAadX1z2ng1XW3j623I7Q\nuTZZ7l2GIdD5xODn5kwwgscsHPs+sgtGMMxJuOoiXhPjoeNRP3GYreo2F4uJL7kUe2ehnxkImgm6\nbH/njIhQjMToNHGKjiCZ4GPJxdCW+tA7q1XdeT0jI3IGXy0hagPw3jmCmDcyNltIUV6ssh+czgiW\nSwAAIABJREFUuJZD4dS1XIxKWMRcyQ+HiiOTWrhnHdC71YWxBnba64uioak2iq6j5kgoQpJlMF8f\nW5Wq2kU9x6beWNQS5wTAGRlQTkpN526qh7zKryjqikowaD5f1ypSV5tcvZ+8eq6qLypPUc9ltZDU\nkM+FPOEF68uGDW8DYwlI/lAFxvQqCoxHr63AGIJ9fm0Wkg0bPh8QkR8Hfvz7f8/vftuH8m2DOc3M\nOeLVE1PEiyNrZooTc5pb7oUpNJROFRVhcAEnHiceFWfrl4DOSoa4VWhn/b5hw4YX8Um3kPyJD3k9\nA3/6kzyGDRs2bNiw4dsB01xtbK8Q4jk8+uBl+lt4/tsQ52YZ/DCICPvgNgXGhg2fE+ScfwH4hR/+\nkR/6z9/2sXyesbaSevWM88SYJuZsWWb38wFBOKUT99NzYoo49Twbj3gVdi7g1NO5rmTpDfhiJVHq\n5FZpvkOJOTYSY1NibNjwIt62heRzCa+e3vUIcOWHFtxTsxDevTotM/pRS0ikcD857u9GvFeur9Ta\nSdRyI3Y7ByzZCVPJb6gWhONk9Z5OMr0zS8MpKn5WTr1ynBzBZ7745Wvunlud6tVV4HjYM3U9qBCf\nPuPgLURznhMxZbi+sRyM/ZXdxNab2ZzAe9SZRUNE2O+V8RS5urLK0kOaUV0CNc26YU0iMVpeRg3j\nzBn6vlaLZm4fDYxjpO8tJNQsJErXmwJjtw+E4HBeuR4yJ+/pXKLkddL7uVTOJgafSmCnWUC8Zryz\n38VatRI0oZK56ma8JvYhtzaQTu08+7JOVSr0Llq+xUqlUUM+fRL6koehYtvvnRCSzU4qS/jmfjVA\nUBGy0OwitRq1hsEGdfgsrUZrqSA1dr5GYkrRGKwTquVCWWHqisVsUfdffSJOfLNtNEWFmvIiEm3b\nK/UH5SLbjDBtvdxyN2xZaZaTtQrj0oLRHuUlh0NWS1brC/U7K1vIZfvIA/kXayvJQ/aP+r7X+Rxn\nx3W+MbJseRgb3h5O0YiD8CoKjOvv+uBl9u/Y97/878Gf/vuvfAy7zvPNu5H37kaeXG0hcxs2bPj2\nhxESrl3/nShzmlGEu3jgOB8A4fn0lETi2TzhZbb7P9/jxNFpR68DQWt9qoXUVwVGtd6uM8c28mLD\nhoexERgfATXM8+n4jL2/4pun93AidBoJCl/an7ifHZ1P3E+enM0icp8dx/sD3eDpH3murntyzhzu\nJzrnmEttZ84W6OnLYDy4xPOTt7GamK2h08gxOoLLzGnm+VG4HjLf/V09v/VvhLvnJ4Z94HSMxJR4\nOt/Ce7/Dcbfn9vGAjGL5FNcDx2fZqlRFjcxwHlIENXvIsAtm9+gd3/j6PV/80hVxTtZ0Auz2Vq8K\n4L3gvSPGmdA5dleW9xFy5uqm53ScSTFze+2YYiC4xL6L5GRy5/1OGOeOfRdhEJwmrrqZ+9HjXWLn\nLeMiuMQ4K95lep+YohCc5Vf0q1aQWoe682YliVl41E2IwCCJnYs8mz1DISrmpCWrwvJLfAnpTJnW\naLJzERXhEIWdy+ByIReEnffMaWlhSTnTq2PvAzHnZg0xcsThRAkoQRxjikZqqCcXUkNELe8Cy7bw\nsvonW4iH1hmOlu9GGGi1cwCJiLAQHbVWtdZ1xRwbkVFnBCwTxC/1oyyNKJRBf1pZT2oWh2VsLG0o\ntX2kPV+XwQiK2kTiKnmwsozY/rS0m+QzgsbeV26EhuWRnu/jDC2r43x9XVWsiqxCTFfmky0DY8Nn\nAWP5zO1fRYERPiQD4/f/JPy9/wm+8U9f6xhud56/8Q9/g7/xD3+Dv/ATP8gf/+Hvea31N2zYsOGz\njClNRi6sVBfrnw/xaJM8pYEk5YSIMMbRls0Wxr73wawi4nHqSpOh3XN1GiDnorxY7onqvdt2r7Fh\nwwdjIzDeADFHVJTBddx0E3M+oiI87kbu5h23vc3Ej1GZo3DVj5yOPTHaB1UfMscRhl2wmlDNjFEY\np0zvhXevTni1QbXXRHKWszknoXeZvZ8RcQxB+cK1KT2maIqIq+ueOCd8UPKY6a8HTnwBdcrVlQVk\nTmNkGDw5D4xjsoyMsKfvHNMUURXLrxBQr0WFEYhzwnll2Bmp4ouqowZ47vadNZRMidvbAVdmC/ve\nN6VG5424EMkMPvHFR3A/OnpviopdiEVNkbjqIo8Hy8NoDRya2/ntS2aFYJkTNVCzhaKqcBMm4iog\nIWahK2oKaxKxthLxRmR05bEpXqSoIGxQ3ztfFBOJoK6RTk6FwXkmjOBwqoUQsFwL+8fm2mA4iDaC\nwNh5cOqNiFC1bRblhZELihayog6lF3KjDtx1VdxRQz0zimvLIUvuRVM8NKVFbnkXjflf5VMAZ/uj\nXLiVtXrhxe/14l/DO9choGuFidSvVYDoeXtJe+flWFZBous/kIK67Hn2BkuextlyhjUpoqt92cGs\nVtqw4VPGFMtnh/+QP8LpCP5DMjCu3oUf/S/hb/93jbB+Ffz3P/GD/MpvvM9/8wv/H7/6teevtM6G\nDRs2fF5QJ3YeIi+mNOFQxjQZYSHCFCcO84GUrSPt/elEXz5PVZTO9agoO7/Hl+pUy1jzq3ufJbTz\n1UrgN2z4zsZGYLwBrNfZ0bsdj8LM/TwhCE/6ma/e2Uy/k8zzkyd64Qv7kafHK+7vY2m7SBxHZbdz\nqNhzY1TmKTL3jnd2I6ekHE8e7zIiFgg5ZxswXhVVwc5H9jeRf/O85zh7Qu+5vum5e34iBMc8Jfb7\njtA5TseZq+uOlDL3QNdbiGjOI6pC6Bz7feB0nBERQqeIWvUpwNV1x+k0G7FR5G/1ebCB5M3tQNc5\nDvcTt4/tJrrrHEWkYYGdmnmyGxtJ8GiY+MZdhwr0PjY1RecSVz6yc5E5C4fZoUVdcR1mTtGxc6WH\nOwt9+bmSE6eopCzchtnqVNUUBFNKpZoUNNj2jjExOAvODOoZow3Oe+eKJQLmbE0iADtfL3IGC2ny\nKELnlCDahICuWCCcemKaGwkRWoiTIlnPLpxZclNcGPEgreWjNYaUC13MiVafSm0CSe27K/qGWkF6\nZvcQC5hSzNpSSQIv7syu0cIzS8jU2i6yNKDkRkish1j1NaGQArKEZtbjSaumkDq/nFf/r4oUe2qp\nX60kUUxx1YCyIlAuVBuVRHnhFqH8PeScyav3tq5S3SpVN7xNVAVG5z6EbHgVBQYsQZ/TAfrrVzqG\nH/q+J/zQ9z3hf/hbv8r7h/GV1tmwYcOGzype1j4GFthZcYonEpnjfMSJ45RGIPN8es6cbJLt2TTS\nqXIVBhSldz1eA712ZhtxgZginQtFgQquTCgtbWsvP54NGzYYNgLjDeBEca4jYYPm79pd8f54wovw\nheHEzkWmJOxCZE7KaVaCJm6ulFMJY9M2016rUZXHN2r1n5iVxBpOc6n6FO4nz5WPXPuZOVtjSUzC\nvBcGn/gdOnLqiDFxcxMJQYlz4u4OZCfE2ewgNbPC++VmuFadaqkydU5aq0iMif0+MAxWrarOZvtT\nyuyvOmLJ1egHj/eWZ3G1E7zLBJ04zcrukQMct8OpVZx6l7gJMyLLkLJzib2PxCTs/UyvyWwXau7A\nUCpLr/1staolOySokTyDKy0gamTB4DxzsuaJwXmmFAmlwaNN3hMbKRFUcWI2kuBcGxDvZNW+USwi\nawWGFyE7pVNvhEUZYHu13BBfFRZlHS1NIaboWAbplTiwjAq7wFpbhjZ1wrry1FQVrqkrau2piOAo\nMwF1qxeKhvpaU0aU7dUl3GrAvt5vzY+oswb1HCW5ECmIIDUr44IYaOvnvKyzPo5qGykZGdVasz6e\nLOc3IC+97K/yMhYhxaLcWO//wdU38mLDW8b0qi0k8+nDa1ThIxEYFU/2gW/dT6+1zoYNGzZ81jDl\nmU5CezynmaDhbJk5R2KOxBTbOl48h3hoiolDnBi8w5f7L1eUrL3rSyucTTB1rrO6VHG2TRIO3QI7\nN2x4DWwExhvAieO2uyEe7abyC8MXGdNvA/BdO5PWfmvMDCExx8zd6FGFR/3I15/3dC7hXWaOZqvY\nhchz53m8qz46q/90mksCgg2G7yfH1AuDj0xZ2PvIcXY4tYDKcVbAE2NHTpmu94ynmdMY8cECPIed\nhWvOcyJ0rtSkZuY5472gKlxfd4hKq3uNMXF105vXb5zpe486ZTrN7PYd4zgzT4m+E7S3sMmbfmbf\nGYP97BS46Se8Zm56C9IULPzy3f7E4ExRcj879oWgeToF9qWe1PIQZqak7H0i5szgjIQIqszJ8hti\nzux8aHkTDrNzjCmScuY2dJxSZHCOOdl2THGg7L1nSomgVn0lCE6lbcerZy4KChXHlOaVMsNkgX2x\nhtQ8hkymU1OoePVEiViGhP3dBA3twrW+gNV91AyImGcU17apoi3RslWhZpq6oiodFGlVobZIMpXC\nasBfQzeTpEYu2Pq0MM5EamqLNaq6olaeWoVqDQZdSA8LBi2alMLg1NcbOVBVEGUZyXX7tu/UdBuL\nFURbvkYlQuSFfTTqopITOaPiSJJW1hV5IVNjk3Ju+CzhVBQYwX0AkZazERIfVqMKEEqt93QHfPG1\njuXxPvDe/abA2LBhw+cbQXzLvQDa92M8Mbie59MdYLbxWqF6ikcO830hNhLPppFHoSOSCRroXGfW\nYNejmKK1Vt3XT++cc7vnqxNCbZJow4YNH4iNwHgDePXcF9/b4AZimhlc4Ol4AuzDaSoph5WImKMw\nl2YS32UgMUfX8itUMvejRyTz3jG0wdRtHznMppp4shvpnbWQOMk8HQNdCa70krkteRFzsg/hborc\n3wmPc26T0FfXPSrC6WTkggjWMHLtLWTzkcMHRz/Y42EfCCVbAyy4U0RwXkmDZ7dz+KL0uB3MOuM0\nsw8R7yxI88luZAixqSt6tQaPoInBJVKOTWmyc7bcYxm58TOdc2USXphT4ioEU3s4x5RSIyOkDICD\nuhbBWC0fvtgLnAo9FqCJGuGQybhoSo2g2TIdpCYyLKFKXv3SjAE415PJhDIgrnCizY6RhRamKSiu\nCCcWa4OsSIeFpW9qCizIE3x7XvLS8lGlh5XQMPWFnA3MXV2PmnJRwjjL30Ozh1AzM3QlTiihmHkJ\ntFzbRcof+xK62UiO88F/JUXq+5azc0BTSDTyAs6sHGdKjotttz1WacvqmJaF5Oy5/AA9sVhPViTJ\nxXvYsOFtoVlIPkiBEU3W/NoKjNfEo13HV9+7f+31NmzYsOGzgjFNhGLVHdNEp4EpTSWI0zHGkYwF\nsB/jkankXrw3fotQ7uueF9uIiLDTDq+hkRde/GINrkHiIvhyr7VWerT7qQ0bNnwoNgLjDbD3O55O\nz0g5sffXjOlEpz1Tspu6BEzJhoRjaRWZk6kqYlzqOA+T5TdYAwYcZoeTzBSVR7uJmITBGYGhZJ50\nU6v/7DXxzdnR9xEvFkj5ZDeSsW06dYyzfUAOg7WFHA8TNzc9ziuH+4lptMDOeUrc3AwcjxO7XSBn\n2O0C85zY7QOqVv8KEFzmONl7cioMYeLkHXNUHg0nepcQgX3JnjjNjptuQrCgzZ2LXIWZK29ZFCln\nVGaCmh1kcHajfhOEnesaQaEiHGPkNnRMKdGptXdc+8CYLOHBq+LKQHlKkd55y1dwwpxjCVFKlv0A\ndK4j5YSXRFBPKOqIGphpmRWWE+El4AthEPNM73ZMaSx2jSW54TLI0rZlzyieTMJJbQCZC8VguRdV\namhbytSGkcrSA6aUqFYSLNvCFyuKtY9YyNRa/WDHoa2iy4kyE88UFU6UuSRrOz3vIJd8bvew4720\nlyiVIkmr3At7LzULRBo9UBtTFnUFS5OIqNX5wkpJslhYqlqivlb3siYxpB3bOeWxbhhZ/57OLDIP\nvL4RGBveJpqF5INaSCoZ8UoERlVgvD4R8WQf+Ce/uVlINmzY8PlEJS9EhCCBQzwCgaCBOc1kMsd0\nQhDGNJJWNpJOPTFFjtGsx4OzxhEnHi+ewe2I2bIuYLn3MWWtnt2XVWzKiw0bXh0bgfGmyEtP8zEe\ninfOPHCQuA4zqbRfTEn58s2J46zc7jO7YBkP3iWOk0PI3PQTz0fPaXZ4l1pFaMxC7yM2PDRy5BiL\n1SLMjRT4zfsdnSbe2Y1MUY1kcJZRkWJmniLjOOO8tlaQvLcqV+fV2kWGQDcUxliFR4939ppTrroJ\nkUxKAgGuuyW74vEwkTErzN7HNoPdaaLrErdhJgGDJrwmbkO2rIgy3tyVv0Ynlk+RMwSndOpKJoXi\nxOwcXqu/sNg71DFUKwNi6gpAnZaO7VKrmtdEA0WJUNQRYi0fdfDd1AJN6CBNSVEf2/H6sh0LZLJW\nESMpWuDmShpoA/jlYlb/GS4qjUWZkKmEhpj3sqhJXF41hLBWPSy4vBhW6WILzCzESA3BXGwbdlwp\nJyMGijWkSR/XF96cynNLuGa1rixqCR44uuXY149f2D4LCZE4V1SsyYlGZlxseyFLONt2eqCOtZ7H\npkopr212kg2fFbySAmM+2vfXDfF8TWwWkg0bNnxeUdUWY5pa/oWj3sufbHIxnshkTtFIjCnN3M1m\nJxHg+Txx7S2M04knaLBmQr8zNYazz2Bf2ka6YhdOq3uJmOPZfeWGDRteDRuB8TGgDuwO8cQUE51T\neufMihEmpqT0LvJsCjzqJ/71t/Y82Y0WvpmFJz7yr97bIwKPdhNzUg6jI2hm5yM5O1IW+mITiVkI\nwCkqXjI3wUIudx5+4x6CZm7DyN3oef8YSDlzPZiN4+gD/m40AsNHem/BnSIdu33gdJoJnaPv7U8j\npcwXHsFpht7P3PQTTq1ZpTaJJIT70fG7ru3G+RiV287ed1We9C7ypB85RWVw9vF9EzpUFmvCdeg4\nxcje22B0ToneuYWkKIqJ4JQgrlSNJqSSGaqknIk5ErQzAgFtA3WgqQ8A6+DWos4gY/YOd96IUVjz\nVHIdqpJCUDQ7Eqk1hdRGjCRVvSD4wu6nEj65JgHqhasqK7oiJYykZgHJObXarZhTkzoiduFb20TO\nRvAiaNazQfdZyGWuwZ2OSLR2lBwLSWNkRsoJqSoMlkpX6s85Ey9UIMs5A2oY6boxxE48i02jeViM\n8FgFdrbjLiRELmqQGtx5RnOsmkPqe7ykG+rfgP2e4vlyeTn29Tmr2R6L7WTDhreHVwrxbAqMV8jA\n6K7O13kNPN53HKfEcYoMYbsB37Bhw+cH9X5LoOVfxBy5nw+ICPfzkZQTc5oZ04mUE6d4xCzFjvfH\nA1602Yq9OLx4dt5UbYPrbULT+TYBNOeIwybiKip5sTWPbNjwetgIjDfA/Xxo3jYEvDiyQqeO3s2k\nnNn7mWdT4hjrYD7x7tWJKSnvHQI3/cy3joHgLDPifvQMPnLdm+ICbOykkq2pxC81ob1L7H0qAY/W\nFnIbZgZnWRJf2p8Yo7WfeJd5dvJMsePqpidns5h88erEYXYMQ2Ca4XqvaCFJ5qioZpzMPNmZ8qIe\n500/07nEdTeb6sJb40rQbIqL0gZy202EkmshwKMuomWm3aupHKZsErzeBXrnOcapvEf7YLd2D1My\nePX4onCo4Y5Q2jxKbsOcZ7z6ckHQs8GsK2aDtQrDiUPz0jSyqCTKQFs9LrtCYFT1Ql7WFV1m7bXW\nejrIddC+2EBquKasFB6ZGqtZWjlYVBVZFuuIv2DpndScj7zkYbD4LJ0oMdfzoy3Msz6u5EdVWbTc\njHLOUlFXrOtSK/lQz2dVW6xbROr5zSm+aDFZDvKsFeSMcFgRLfls3XWuxwWZUP6mzn5vD6RcGCGx\nbLM9t8rFWKs2lm3X7W7Y8PYwxhri+XErMF7fQvJ4bwOA9w/TRmBs2LDhM4tTPNG7/sHXQlFhwGLF\nvY9Hck7EHEkkIzLy1CZ57qYTt6Fv5EMQT9Cu3e/1rkNY2kYEu/ec8wzlHnKtusg5vxi4tWHDhg/E\nRmC8AeY0c9vdtMdeHGgmaMeuEBhDsgaNY1TmKEQvfGl/4hvHjvcPe667mafHwBAiTixX4sl+QgSc\nZqQQF24VbjkmNQJDLbxSRbibJlKGJ/3I4DKnKHxlf2BKwv3s6V0EBp6fPFdXntOYmaPy7tXIN+46\n5qDc4/juRzYT9/4xcJxcqzp9Zz8yJyG4hApch8TOR3pnrzvJfOPUETQS1MJEvWQed1OrNhVMdZHJ\neFFCsYBMKbJz1pPtNAB3JXvBXvN1EE9Jhy7KCSduVS9qNhIAl6yaFKmMtuVYsCIPqmpiLlVYlQio\nSolU/oOFpKjBlU2xIbSAppgjKafzQCax44qFTLCQTTESRUz9UIMw61BbBBymeliTHimnpQGlkCyV\n6Ig5tfceU1wIkEJgONGmJKmjd9cIjIVEcaXSq5IruTSStKFSaQVZkw/193KJtTKirtu+r2pc2/te\n0RtKUay0sM+FBKodJOt16/7qNlqbC3quuFmtuxzn8v6arPOCHaltLGTO1t2w4dNGbSH5wAyMSmC8\nigLjTSwkO2tW+tb9xJdvX4Es2bBhw4a3gIfIi2M8mYK3KaiP5Jw55ZGUIqdkYchjHHk+PSNoIAPH\nODE4Z+uJFOtIR18yh4YS7A5FXZEz3oVGWqyr7WGzkGzY8FGxERgfEw7zgcHvmNNcBrO12jPRu8hV\nFvadkRRTslDLISSCs9aQXYj03oZQ7/Qjd86Rs9C72AZYOx/xajewCWHvZ55Pkevg8GVfXpZZYq8w\nuITXicPs2PnIF69OPDsFnCjeRWsACYnMTO+jMcWa6VwiaCa4xJyEfZg5lHrT2hoCECQjRQ0ikgli\n63SarCZWMnvnTXUh4GTJpwjOLB+3XW+1o7IMvKvVwjkHmIWkGDdQLVkTUAiB9dw/S/XoarDqNRS1\nxkoJIQppGcyvLySZhGZtx9KaQQBxy4x83ZbHEdcD42p5qaTFKt/iEi2kk8XqUgmJ3JQRl7kQF3kX\n1DyOJRuj2kG0EBi5kDOXqMqMtDpf59YOFuLhhSaPMrYXOTvCvLaBrNQZa9XFWTZF2dcaD6khml2G\n83PZ9iznJMX6/T6orrg4D/VNPRTauZEXG942Xs1C8joKjI8e4lkVGFsOxoYNGz5vyGVSqFo3Uo5M\naWIu30/xiBfPId4zpoTTzDFOOFGC61CUoAGnnsENZRLNmuKChlZb7zUQa1YYL06ibOTFhg0fDRuB\n8TFhyjPX/popz7w/fotMplPHnIXBJZxMPJs8cxbGZLaOnbeq0DBYWKcTayl5dzjhx2DZGWqKhzkJ\n137Gq0V4xiRch5m72bHzC1nitQyIJePESA4B7mdH7xNX3ZGM4J1VsHYuse/mZlGZkmVWdG45pvvJ\niIuchb2P7JwFhz6dPEHtg/02RG5DJOZMp1pUB5AyXIVAp9oaRAbnS56DY8yRK78/oyBcCdPMObcL\nDCyWiU57xnRqmQh12To4bTkWFDVFTlaNlef2+6q93KLLjH3r48YsNBYWav9EvLg2WO8ktHVi8TSi\nINlIgipDVK2kgjQ1wEMD6PUFrKo5qh+zrlPrYdvA/CUWjHV+B4Xp10KGTHlRZ6xhmRCpnU9YyCFT\nYpglJGWjaKriZW3v4OKiXEM1LTCTZoN5SAFRv0tRo5xVq8pCFNV1q0JmvY11j3rO56RDyyZhUWU8\nVJFa20cuj23Dhs8KxldSYLxGBsYbhngC/Nz/8y/54X/ryQfbWjZs2PCxQkS+H/ivgEc55z/+to/n\ns4B1jsTaNjLGkc51Z8/1rkdFuZvvcTiO87FZSeq9xJhGYorsvCkornzfFKNOizKXZZLLF4UF0KzI\nkYRbq0EfuvnYsGHDa2O743gDePXczwfu5wNBPFoCD3vt2fuOTpWb4NgXccGXd0dcsVa8e3Xiql8a\nPK5KQ0dwiTEqU1JSsWbchImbMHM/+/a5Z60jSlAbTKoInXPsnKdXx84pXpR9IUmufOS2m6y1pJ/5\n8vWJx7sJJ5lrP/POMFqIaD+xDzNXXeTd3YnbfuJ2MBIkqKlJeme2mGtvxIeWwergXCMqrkOgd469\nD0U9gR2jKr3z7FqwkRTCwRG0K2nNrlkzapWpXCgZvIaW7FyDMqUQDk4diPkSa12VVYs6XNm2qmtj\n1UVtYOSJX+0fVgqFKjeUal/Ii0KD2ohSgkBXgaBVVVFSL9px1HUrlrDKvFg0VgP8SlA4Of9nW+u5\n7L0Y2eBYyJRGziDNSlIH/U2pgb4wYH+ZWuEhBcSLy7zsZ1llXLyojhAp5MQDIZ51uXojcLZ+teHk\nfLbs+rUPJSQeEGZc1q1u2PA28YkpMMbXV2B89yMjP37xH/82f+/Xfue119+w4TsVIvKzIvI1EfmV\ni+f/kIj8UxH5VRH5sx+0jZzzv8g5/6lP9kg/v1jbRqq1t3c9h2hEhYrydHpGTJFTPJmFhMycJjKJ\nu/mOmCNePWOa291D0ECnHb32XIcbOtcxuJ5Q7hmDeLuHVLMmV1LDlZr7Lahzw4aPB5sC4w2w9zvu\n52XmatCO+3yPV8/ODYxp5HHXkzJ4OeFUOMWJ57PHa8TvT3Qu8Wzy3PiZUzRCYs5C0FQUDzOH6EqG\nBexcxImy9xDU8U5v+QqDc+y9DdFSzowpEVSJeWTOif3VPaekzElIZftjUgYX6YLJ25xmnnQjMQv3\nPvK4GxmTchuMaKnKi0cdgBK0ZkQoO98xOMecbHZ77z05UzI6tAy+haSZTjtcUUiEFRGh5QO+1779\n7NRxisdWderVQ7YLUUrWwuFEWz2qtlyNGS/W3KFlsN7V4yjbjTmy6kjFY+RHIxyKmkGaqqEoK1Yq\nh+ppzDmbaiLFdk6q3SKtBuc5L6THpYIi5tTCN826IU2BUBUWS3YFLSCzBmlqppEqtVnjjJYothxR\nV9pNPFO2rnNV94K9o4V6li8uZhZqc4jlU5y/n6qIqARJXa+e/0Ru56PubwnqXN7v2e+BJffCcW4R\nWh/3JeGxfn59vi9xucyy7vqcbJzvhk8Xv/b15/yxv/R3cSr8kR/8buDDQjxfQ4HhAmizkBAhAAAg\nAElEQVT4SBaSJ1cdP/9n/l3+yF/8u3zz7vTa62/Y8B2MvwL8ReDn6hMi4oC/BPwHwFeBXxaRnwcc\n8Ocv1v+TOeevfTqH+tnDyxo7HnruMB9w4uhcx9PxGfuw5zAfGOOIIJzSyJhORmSU+6HDfCj30pE5\nzXhRnFpNahCzjfRqGUC7Yh8Rde2+rga7X9pDNvJiw4aPDxuB8YbYl5vEp9MzbsMNnL7FnBargiDs\nvEPFOqOf9CPPZ89hdtx2E70mTlGRosyobuKbMDMVQuNrx0DvIl/Zn+jVIxIJ6hhT5HHXE3NudaMx\nZSKZlCM757nXiZyEfei4myemZAqKKVkFa5DM4BJTEt7pxtZyUm+PFVOHzEnoS7bFbRg4xlhaVgKD\nc4RSYerKcUBRZLiOoB0xx5LgnBCB4HrmNNG7AYotZCryveojdOIIJbjSqbV6BPFErE50VlOexBqe\nWUaaQTxRik2ghGUuNoxqSRAQBznSYiRXg+068A4ayoy/kkUIpXa1EjAt2JPEoAMpp2aDyPk8QHLd\nAgLLxUxFcTgOJXzPFBw1sJI26LdB/UVVKSwZEyuCJCZLy1Z1eHFNhZHIBHHMdf8pl/wKzjIrlnOw\nhGO2n1fnse0Ts4osyhElSlVKsDxXLTEs+yC/6AmNpeaUVTq3hZKajWVdTVvPZVVaNPJk9W/wUkmx\nzrj4oJyMtT1m02FseBv4ta8959nRrin/+9//VwAE9wE3wlWB4R9O3X8BYf+RLCQAX3ls17/376eP\ntP6GDd+JyDn/HRH5ty+e/gPAr+ac/wWAiPx14I/mnP888B99lP2IyE8BPwXwvd/3vR/5eD9rOMYj\nu5cQtDXLrNpFVLRliu3Dvt2jzXkmlmrU3g2MnIgxMqaxTRSd0kRXAuO9hsUugk0yVXVFBnybqNN2\nv7Fkqr2CAnTDhg2vhY3AeEOsFRiH0h9t1gPzjexKm8YpzuQMO6fchKmFcz6bq00BbruJLqZGFExR\niVnYucjezwzOwjrHZFkTAIc4cxs6cobOOVDhGCcedR29c1yHjlM0MmNOprS4CXYz3BXyxEnGOct9\n6J3J8HfeAkejZlNriOVx3ATPIc5mE/GBzrlScVpqUUXoVrVUwjKQNxtKV9QV1uCRcjyzUSBL00h9\n3qkREHOeCRLahWBtnYgprppCilKh1JmuB6M15yCWlo/6WGQZqIPN8Df7SVYQCwCNK1JCLypZUyEN\nLgfLVQFSVQvrStNKVExpWth5WYiOF3MaFCkWk3rhXBMvNfMDaOcv5XR2PHVwH3NspIWu1A2rX8Vy\nzlbvc3m9toOcZ1YAxIv2jzUeem2tbjirQS0KFFOxPBQA+oDtZUWIrJd7Wf7Gy5QXLxIbGzZ8+rgb\n7fP6K493/Ma3DvZZ9UEzefWaFF5BgVGX+wgKDICbwaTZT4/zhyy5YcOGD8FXgF9fPf4q8KMvW1hE\nvgD8t8APicifK0THGXLOPwP8DMAP/8gPfdtw8C8jLwCmNHPMJ0JpmvPqiSlyzCemMrkY08yYJlKO\nHOORUzoxxhPvj0f6MiF3jDOh2MJrzppzfgnwrJNpmHWkTajoYhn+oIaRl6lINmzY8GrYCIw3RFNb\niAV51kF00IDGJRwSTmQyO++5CRMCvD8Gnk2+BHVmHoeZU4z0mpa2kQyDiyU8s0dFuBeraLVKp8i7\ng2OKqbDEnjlHdm4g58SjINzJyN4HphQRgdsw06ujc4Hfup9xkgkKc04EdcScuZLU1A1OBJVIp8qj\nruNrxwM3PnAdOpwKKWdrXSkyu53blYBLU1ssQnyhdztymYVXdTZwXw1cHY6+KDZslj2b2qLUlBbK\nAGCxiIhySiOhkAQZW2fMUwsBXQ/qY1Fd1ByISGzM+nq56mFUEeYSgFn3B7SMDAGc+BZcaW/m3E7h\n1JNKxWkNezrFE5RtxBSLyiQvNaKrAfZisaCpHtbqgEU9sRAUleA4U31wng3RiJTy3tfkSyUvXFFw\nKOekwEPbbI8v61PL8Ug+f21tFakX8xcIiHx+PtufEy8O5FqFag0jvahsXQd11iyT9dtZE1DrYFBg\ns49seCt4fjI10p/9w7+X/+X//hf8vu+6ffnCz34b/q8/Zz/7V6w2DbuPrMDovLILjqeHTYGxYcMb\n4mFn40uQc/4d4L/45A7n84WquIg54otl5DAfbEIoJ45F4Rpz4pROkDOndGJKE0EDz6YjKtCp4xit\nrt6JEtSzc3sQYe92y4SbwKCmcsvYxNtc9r22GNuir2Z32bBhw6tjIzDeEOtBLJh9YcozYxyZkhEa\np3hCEPY+EFNi53L5kIx8eTDv8JQUjaaCiFk4Tp5H3UTMQtBIysKUE77M+19531o9oCjxsYG6bx+w\nNljzqsw54VQZRBhjIhbSwd6DfZheO49X5X6ecKr0zpGzZWoMzhebCNyGjqBqVpDS5FFZ6koQdNqR\ncsSLzdAlxHyE4op6pJIQvh17px1OXWuJaIN5UXwdtJdmjVSSne1tmurFl4tFDatcKx3axaJsow5G\nbQBdlRtLLkOVHNoyK2VGzYqQ9WBe2vEkUdxqX3qhTjCFQGKubRvF9uDU2fuR8ntbX/BWhITts2xr\nPRhvu1jZO1bPt1rTvDzfMibKUlURkorKo7V/XBAKZ2oPlraRZs0B8spOsoZQWk2olFZ9/GIexvr4\naxZIIxEEJMvZLMZaZXFJTNTX2ym9UHM8dINxeTybDHTD28D9yUjyP/j7vsSPlwyMl+Kf/U2II3zx\n90J/82o7CPuPrMAAeLQLvL8RGBs2vCm+Cqx9Ht8D/OabblREfhz48e//Pb/7TTf1mYVZQiJxPrRr\ndMyRnd/xdHqGYG0gKSUSkVgmHgVrWrufD6gIOxc4xIm970tYut1bJhJBQrMr14kdKPdPOZMkt4D1\ntSp2U1ps2PDJYJtSfEPs/Y6937ErX8HZgH1OEzHPOHEc44SKsPcep2oqjK6jd4nv3p94p7ewzDGZ\nZSRleDYFbjtP7zI7DzELc0pGRIhwHTqe9DuGQjJoYZlrBsLZoFtKxapIsaHYwDM2tYEteR0CO2fE\nSFDl2gduSpvI467ntuvIZG67jqEsZ8FGnYVximVXxDzTuZ7gOpx6+xJP54ygaNYNjLWWEsjYlzTn\nmmVQlQGK0GtHV6R762pVq8Y024pX3xpHTBXhWtNGPSd10OyLjUVKq8flbHuVHwJNKdCeF1nCMVft\nF0hl7BebS7OOYIPxanGp763qHToNOLVZg5dd6ta/U0XOpIm12USKcqQSJ5frrLdV0UiWvJzvJXh1\nIXYuL8Lrx3UbzfZTPKJQMzyW41EKgbRWN6x8ow+936ryqMdRn0+XdpQlcfMBpYi8uOylZ2a17KUa\nZWsk2fA2cHeaEYFdeFiKfIbTU/v+p34J9BWWhzdSYADc7jxPjxuBsWHDG+KXgR8Qkd8tIh3wHwM/\n/6YbzTn/Qs75px4/fvTGB/g2cJg/+LPpfj4wphEvjsH1DK5nSjaJ+Hy6gwynOLYq9TnNFs6ZJ+Y8\nczefiDnRl8/LK9+3e8dOOwa349pf4zXYPWi5xxIWe3DnurN2uHZ/UqzKGzZs+PixKTA+RhxqnWoJ\nDXKYb27vu6ZQuKcyvzAn4YDVk+5cxJdmkKCZvZ+5n2dyzsScuQ1GVHhRomREaGGZobRKQAnAzInO\ndcXGEc3aIcphnvCqhXywXIzcT1bfWp4XgasU8Kp0hRyZUsJp3Ze272uGubaGaAmvrMejqGVXqMdL\nQBG8hDYY9WKtIuYXTEgyz6BjNQAVY9NNWSElRNMG7NUeoauBcCUj6gXFiSMWNQxlu8020TQUtMyL\nmpuxWC3KILkMnl3WRmrUdXMhNUw6mMq2z1UOCSnNKfV9LSoQ+15IKJZMirpcHcBrzcAQWYIuy+8g\nYefdim1Xg35WFpPVvtYWk8ttVqKhkjprUglY1C+cqyfO93HOD7Sq0/JftXBUy8lLSYKitqjbqBt+\ngZC4fJhfrIFdW0rq8nW5tbIEHs7C2BQYGz5tPD9Frjr/ajfCp2f2vbt+9R10Hz3EE0yB8fSwZWBs\n2PCqEJG/BvwY8K6IfBX46Zzz/yoifwb4m1jzyM/mnP/JWzzMzwQu8y6O8cRQKlJjji1If4wjz2cj\nLG67m1aRajWoI7ncD89p4ul0x851PJ+P5X5YGUvbiIV1Cp3r8RrMDi5KwCYnq1pW1CZSqtUYVpNB\nBW1ibCMxNmz42LERGB8zdn7Hs/muffCNcWTn9ozJ+kU6VWLJmdh5C/Lc+4CW/hGrUKWEaJpyAuBL\nux3fPB3xqvhCHjgxy4cXR8TYXrNuLAP8tZwO7AP1SSfczTM3oaNTx1SUG0EdTpRH3eL9i9mUG2Bq\nhc57wIgHp46UI66oFVRcC89EBJcL0YLiJZTlE0E9NXzSCI2ean+JQOesnmrdaJFyKmGeECkWDxFC\n+ROuwZDVPrK+XgRxHEqPtwV8uqY2kJzIUgf52lKl5zSv5v8XYsSJI0suORSlorUw9ymnRhTU30m1\nSJQTCKutVlKj1YnKMkCWtizNT1kVCVKtMSWzQkojSma1PvUUVlUFbRv1fLnSCgIsFpjyuJIlFU3p\nUH631aKhZZ31jENdfh3KucaaKFjbMy5VMGfLl19eJjcyY22hucSHWj5kWWZ9TGtWqu5n4yw2vE3c\nnWau+ldUU5yeQXfTguReCWEPX/t/YR7Bd699fLdD4LfeP772ehs2fKci5/wnXvL8LwK/+HHu69vF\nQlKJi0pe3M8HUk5chyvGOJLIXPk9Yxw5RatFRYRTHNsEWM1m23vLygil+nRKkUG7NqlWLchAa8MD\nu69bSAl7vd5brxWx6xyu7f5hw4ZPBpuF5GNEZYorgdAGnmVWP+aIL9kSU0rchMDjrmfnPZ1zBHXc\nBvug3HvP485CO6tqog59r3xAxPI3gpqKYD3IBdoAfB+u6N2AitIXmVvQQOe0feDa9iyoUkqGg4oR\nJCaPswuGL6RIqH3Y2uElNMvEWkLXaUdwXSFGwlLjWRQGddn6Qd+5rmVE1OcTuakeOtcVi4ieB3OW\n12qVaCrqDy+ufS+/hHaehMX+0LmuVW1pYdNNgbG8XynERdCw/LJX+/diVaWNFBFt3+sxV+VCp6F9\nVXtLU3w0+085ZJaLYmXxq2JhbT9ZXx+rxaKud5b/wTJDkFfNJLUJZb1eOxZ5cZBflR2LiqJ6PlcB\noatjqdtZ7D1yZnFZf28qk/JfCyDNyz5fauPIq6+L87E+Bw8SH42rkGVbxiGdkSCbhWTDp43n48xV\n/4pzDaenMHxAyOdDGB7B3dfh//yTr39wwO0ubBaSDRs+o/h2sZBU4qJi73eNWKgh70+n5yDS2gEP\n8z3HeEBRDtGey5gSw7LdOgRh5wZ6N9BpT9BA73r2/oorvy92Y7MxS1X3lsk0V+4NLwO+15M/m2pz\nw4ZPBhuB8QmgDohhba+w51POhazQVXZFZkqJUzImt1fHYZ5bQKcX5TDPXJc8CsuzcGXb0louggam\nNBLUsies1UPJpFXIpqN3A3s/IKIMzswAg1vUGtaSEcgkXFElmIzO1odlIG3ZF6EMOhUtfdkVphbQ\ns6DNVk/KYm2IKbbqUtuSnP2cC9FRv7ycz0jKKmvi0s4wlyorVxQisIxfY452MSsD51r/2ggWcrOu\ngBFDCxFyfmnSGsqZE3OOC8nBef5ERW32aI0jIi+oLWquSVVprJ+/3P8a9RxcXkjXKo7z55f16rlZ\nH0tVd9T8ifVx1PcmDxxRzvnsy85peoEIWDetVKtJPW8vy8UoT7ywnYceZ3IjJKra5cxOkpflG0mS\nz7fx0PvbsOGTxt1p5vpVCYzj01cP76z4g/+1fX/vX77eegVmIdkIjA0bNnz8WFtIjvHU7ktq68gp\nWhC+L/d399M9x3hqCg1BOcR7Uo6MaWSMpnZ2JefLq2dwA04ce3/F4HfsnDU4CWKWkabG0BbcvyYt\nmop2w4YNnxo2AuMTwJPu0WoAHNss/uB2RmCox5cZdKsqzUwJjtEG5Z1z3M8wxoh57JRDnLkNPYML\nTIXAMFLCAhGdKMF1zHmmd337cFa1HItcJP1ajmPv9q3uVUTp1dsfQ7amB1NN5EZIVB9gtaXUwWYo\nyoiq1vDi6QqBUWfa66A3iC8Bk9KOpQ6Kq0XEQj4XZYIUJUMiWzVWVTC4c6mzK/uBBwaxObX1+sri\n14aJOrDOuXV5A01tkXNqqgz7fRpBsahDln9C1QuZC4mxzoy49EbWbZ2FZK6OvWV5rAf2Zfn6fD3H\nFXK2bXsfa6JnrUY4C/RcJ2aTl2DSuo9mIZIlGLU8XltN6rGtz+/6WNrvo/2vHveieljbOtprDyz7\n0Ps/IyvOdrZs+1Lx8YKE5WJba3xeyAsR+VkR+ZqI/MrquXdE5JdE5J+X70/K8yIi/6OI/KqI/CMR\n+f2rdX6yLP/PReQn38Z72VAsJN2rKjCevT6B8eh74N/5CTg9f/2DA24Hz7PTTErbDfyGDZ81iMiP\ni8jPfOtb77/tQ/lYUCeZwAiNOUcEm6iaUyzNaEv9eyI1VW0qKmO/ahQJauH0XZl4rPd6Qb3dx6/a\n5Jolt6pT89nNyUZibNjwKWIjMD4hhDKQ7wuT68UzpYmbYGyyV2sS6dTUGDfB8W6vXAUbOO+8qSIG\n79i1CtNETBEVmPNMzLOxzmokQ82jsFlxhxfPYT6QScXq0eHEnw2s3UoVkSiKCbFsCy3qiblcMFQW\nhUVVVViiczrL27D3Z4P+2kxSB8F1m/WxKySGU9c+/Kc0FVneyvqAcCoXq/ZcuZBU60u1eiwD7PPv\ndd21v1FksSnMaUaRpso4WzfNxZaz7Hdt96iQZpfQllXRAkZfMgCWB/5bqx/qxfLsPbCQEbbt5b9c\nSKj68zqI83Kfa/XE8h6W44AasHmuplgTAZcX7aomOVM4FFTljvES+dy6cWnZWC1zmWdxqdS4PI8P\noZFLaxvKSyMyHnjh88FfAPwV4A9dPPdngb+Vc/4B4G+VxwB/GPiB8vVTwP8MRngAPw38KPAHgJ+u\npMeGTxd3p/gaFpKPQGCArVMDQF8Tt7tAzvDstAV5btjwWcPn3kISj+1eYnA9c6lANeuy3WsnMlOy\nVpGaY3Yf7xjTiZhmm3Qq91R9acO7CTcoSq9DCez0dC60ibLWROK6NqHVrNYs97MV9Z5sIzE2bPh0\nsBEYnxB2fsdNuOYLvd3zG3mg3HaP6bXnyvdFUeG48oGb0LH3Hi+CYjaSzjmedANBlb0PHGMsVgwt\nXr2OvlaLikdQdn5noYpScwYyQs296Jpn0InSa9/COFUcvQYGNxQLjGvqDKhEh1/ZSYzFrgPPViVV\nAi2t9jQwlAtDzcGoaoOa+wC2bq/LusJSi1oJgIVBT20Qf6lq8OLOLjYVtUUk58SUJmoGRmLNnhdC\nRc8VC+5iH1W10mloGRlnr5cBtC8ETG06aeGWqyyQy+aUl6H+DtbLVPXKmSLmMueBlVJBpLSULI/X\nygs7P7nNXlwe0+X7bKqMlWJive3LytazHA7OL/prEuPSpnL5/GrFctAvHtMZ0fAgD3GxXD5/vhFD\nl7kYL9neZw05578DfPPi6T8K/NXy818F/tjq+Z/Lhr8PPBaR3wX8h8Av5Zy/mXN+D/glXiRFNnwK\nuBtfM8TzIxMYT19/PYzAAPiH//o9vv7s9JG2sWHDhg0PYecGRIRjsYqs87OmNDNlm1yacySIZ0xT\nmYxSC/LEJsW8eLpi4wZw5XEo97Z2T+eLEti3e6BY7tfqfeulffkSW+PIhg2fDjYC41OCirL3VyjK\nzl/Rub4NpHvnSTlziDOHeW7S/JQzvfPFMqLEnNuA9aa75Spct0rSVKwOnfZt5j3mSK+9qRtapoAr\nA+xFHZKLvL6GGHXaW981RoD48sG+KAEsgdmpkRmVjBhcT2iNJEZKhKLw6DQUZUMqCgEpx5haeGb1\nHe5Xnkevvn3VC9clGVADOy+RV8tVtCDRYhupx7IO8Lz8Wh/H5UD+Emk1u5/awN4sJ5dfl6TFOn9h\nXflaj/Py4rkmgJZ9Lr3j1bqxkBkfnBFR91W3WbG2kDwYiHmxrfrz5bLrtpJq6YAXbSNn+1yRCmev\n19mOB97PGRFx8fOauHmImHhBxfHtM5ny5ZzzbwGU718qz38F+PXVcl8tz73s+RcgIj8lIv9ARP7B\n17/+9Y/9wL/TYS0kn7QC4xbiCPPrExBfvDFb3n/6v/0y//5f+NvEzUqyYcOGjxm9mm3YiamRpzQR\n1HOMJw7zkeN8IObElEbmPJtd2u+IObaMuJwznXYMboeIWGtfud9yRbmhZQKNojRd8ti24dKGDZ8l\nbP8iP2Ec5gO+2jpEmdLEmE4l88EG5cc4EVTp1LHzvrWO1A9qVwZqYVWNd4pHphJGVAfo1fM3JauN\nWsvcgpoyQVXbV1VOhJJxkclMaSQRUcyaolho0ZznIp3TZiGpNot160oiNwVEvTDUQaMvnkK3smBo\nkeRVrAmCKhWseKhj+9IussalckFW+y1PlG/yWqx5tZOs0WpqV4PfZs9YB5OuvmBJz4aXhGBeHF96\ngKipgZ7VOlKh6APbyy9IHM+2+YAq42z9nB98vh7/pcVkvbyyEC0LP/CAreP/b+/M4yO5qnv/PVXV\nm6TZN+8e7MHGNpsXsIkhGGMIkBgTTIhZkpjNEJaQhCRAQl54wEvghZCEJYDZTMIeFmODwQHM8sxi\nbGPjfYx3j7fZRyOp1d1Vdd4ft6pUanVLLU1L6pbOdz76qJZbVedWj6pvnXvO79BZtY+8NsgkG5h8\nvmkrl+RsyZ93phSTJUSrHrZRBml9E1X1QlU9RVVP2bBhQ1eNM2BkNiKetWEozSFUvJRULplDGsnT\ntqznolc8iRedfBj7ayFjdUslMYxeod81MMajGqpKNRpnLKxS9IrZGK6URFD4nhOnjzWmFtWScfU4\n49E4ilLySoQaUvSKiQjnxNghnWhLxxO++DTiRjZ2TaveWXqIYfQW5sBYANISoPWoTloRJH1QunQE\npxMxGBQYCIpU/ICy7zNUcI6FwYIr7TRUqCTlUJ2ehivLGiC4FJXUSeJept0D1/OcDkWaL1jxy5S9\nEj6pcySg5Jez6I00miPVuyh4xSTVpJClj6RlpkgkDNIqHYpOpE0kUQ5pWB9JOdWiN5HiEYhPQZz9\nqUMgFQBNq33kSbU68utpREKaHtLcHlyuZKhRZn/qaU+FP9NIiyjnSAEmaWG0I90/oRU5ObIijRDJ\nO07S5VbOiDQ1JMXJQkjLyI/MSZBcM42+yJwIOcdHpmPRog95QUyfCbGq6ZgqlDqRwjJFryLnKEo7\nlU8dyZ10qqMh12aKTdomAqTZ9Fxp1ZZ6Hbnfzce0ig7pUx5JUkNIfm9Ptm8DDs+1Owx4cJrtxgIS\nRjHjjbgzEc84gvrI3CIw0tKr47N/yQl8jzOO3chJR7h0ybH69M9MwzAWjn7UwKhG45PWa3GdgaQ6\nSCNusL8xQj1uMNIYpRbVUZR6XCPUkLJfoRHXKSZjV088GtpIxs5lyn4lGfeWKCUOjXSM5Sfj57zu\nWDpOa07TNQxjcTEHxjxTCSpOb8IvUo9r+BJMSk3wJWAgGCDUmKLn5uUDSXUUAir+ABW/QsmvUPbL\nWTRFqgsRJOVNK0El8TBPvKC783uZcyHUiBWFocTb7EqXps/jWGMGgsqkEqiaVObwPJ+BwgDlxNGB\nKkW/kOgETHiogayiSEoURxT9YpI6MpEukkZt+IntzS/zzc6LlHb5h5HGRC0cAhN9mbyvOUUk7e/k\nY6ZGK8BEOgm0flfOUl3QKf3Ir6eVVSalizA50iSvFdK8r51uRpz8m2zX5NSKPM3aD60qkGRtW3yB\nt6tY0vJ3Lsokr22RRY4061HQwkHRtN4q3aM5mmU2ToiWzpX+5xIgrSTyJ8A3c9v/OKlGchqwL0kx\nuRx4toisScQ7n51sMxaQ0cQZ0JEGRj2pIjJXDQyYs5AnTNg4amKehmEcAGWvNGm9lEzQ1eM6oUaU\nvCINDZ2wexwmY7V0wsFNzE1MTgV4uOp7oTrR+aJfdJNvmcabT9kvJRGvacnUiYknc1wYRu/RYVyq\ncSAE4icv+s454CeRDUTj2UO27AdZuNpYWCNSpRrVKSeOCY3VlUZ11TtRoBZPHO8qdwSMx7WsLJTT\nhtAkV3DCMdFIoh1SoU+noRDQ0DB57XMP8KxUqU5oOhREQHziXH5gqNGEzkYSfB4Tg5KlzfiJmKWg\nWbRFWkEkXYaJyIt2FTvStvW4kUVruHs68TKfRmKkfW7EDSS51nQCTJ28rEcaTXJ6CEyK0sgfkfYh\njWjJb2teTqNyWto1w9v0RMpK6gghi6SY2mbqsXkRzlQfYroIi2abUmdB2s80lSTb38mXf4smrSqP\nNO/TyV6LdOeUc4vOcgDSLomiDxCRLwJnAOtFZBuumsh7ga+IyKuA+4A/SJpfBjwPuAMYA14BoKq7\nReTdwNVJu3eparMwqDHPpM6AjlJIUufDYjkwkiiR0ZpFYBiGMXvSiayxsIrv+dk0hKKMhdUstaNO\ngyiOqEZVPIRqNOYq7MUx41EVEY+iV2Q8Hs+iMGKNCKSQ0wbzMmHzAD+rQiciRHGE7/tEcZRNuCma\npUpPJ7puGMbCYA6MBaCSiFKGQchIYxQ/EaxMX8jGoyoVv5g8IANKfkQgQoRmL8ehhkiSCpKup+rI\n6cO07JdBJypppBUwgExcsxpWqcX1LKXEy1Xd8PCIJaboFbOX0tQxICJ46lGQwAklaT2raKIaU0jK\ns05UP3E4p4t78KORi4QQnxil6AWEketfGtGQ0rw+aZ/41JsiKporj6SEGqFAQfxJWhutaE7TaCfY\nGSXnTKuQREkd8uZjmkVH89ta7W/3pZimm+TTTpqXIR/l0N5pkK9nnqWY5F74m0uTtmNSVRGZOe0k\nH6GTj5xIHRHtnDTNkSOZk6zFpfIpNVOOT7NXWtyXvB25jS1tmsmZ1Auo6kva7FcsQ3EAACAASURB\nVHpmi7YKvKHNeT4NfLqLphmzJNWT6EjEczypIjJXEU+YcyUSgIE0AsM0MAzD6IBYY8bCKpFGrCq6\nZ5AnHkOFQUYaoy66F7I29bhB2S85bbkkGpjE4YBMjCuKXpFGXKfklZBE5NxPKowUvQL1uEHJLyaR\nzn4WJZyOq4p+cZK2W14o3UvGtO3GCIZhLAzmwFggqtE4KwpD1KI6MTFhHE68QEbO6RCrE9Gs+APU\npZbpWgQSEEuEJ372QNfYvXClgpqpCGekkYu4kICGNhDEhd8RZ86HQHxKfhGSh/R4VAOFgh8QEOCJ\nK08VJREL6Qtq4AdJhEScKTWnkRAFL6ARhxQ8d42BoEItqiUOlIkXSj/pQxzF1BMR0jCeCOsDpo2+\nSCm2cVjAVGdGZv8MFUQ6Ia+VkUZjzFSZpFkTYzrvfTe8+81pMemLf9sIj7yOhOacC7moi7yAVbo8\nXWRFXnujE5rP186JkjojpqSPTHfuNm3b3ZO8+GerVBQbtBgLxUgSzdBRBMb9V7nfqTNiNnQ1AsMc\nGIbRK4jI2cDZRx39qMU2ZQqj4RgrCkNEGjEWVt03a+KQKAdO7yIdH6YOhdFwzP1ujFD0S0RxSMkv\nJW0iykGFRtyg7FdyEz2JaHocZlXxUnH9FA8hisNs8iwd5+TLtmY0OS8sKsMwFh77i1sg0rSJ9eW1\nAESJEyOOYwYLQ1n4fz0OMw9v0SuiabgbroxTnGpJ5AQh0xd/VSWOXepGGp0RaeRC4rJKJR5l3+UX\nVhJNjUijLKxudXFldp18FETqdImJE40Nb9LMf8kvJQ6UeNKXQqYjkZRMTW11Hm8XkRFrTJRcv5Vu\nxIGQF/rsFmk507lY2apvee9+uxf3ZgHQ5uX215v4LNqVP82TRtC0K1E6k53trpMXFG3WwWh5vpyI\nZstrTBO1MSXVpU3ERrNmhmH0GqkzYKA4wzNs153wrT93y0Mbp2/bigOoQpKSRomMmoinYfQMvSri\n2YgbrCgMAVCLnEgnIlT8crY/TU1NU4t9cVXxatE4Rb/oUqS1QS2qJWMXlwqSVipJJ5sKXpGiX6Qc\nlLPUFEWT1Gc3Xg7V6bWlumPTOSWaxwvmvDCMhcf+6haIvDOg5BUpecVJL1p+UqFjMBjAT1JFGnHD\nlcJMSo9mJZ0AL535V6hHru51s3BjIVFa9phI/cjEQ5kQKgokyOwZbuzPamL7SQnVCcFKzcqnplES\n6T6nc5GcO5fCkr24imTbsz7nxUyZ0IjoJAJjMUm/FNtZ2aq6SEq7vrWthtHi3NOdvzniIXWMNZc1\n7YRmW1o5HqZrP51dHVx8Wg2KWVUG6ea5DGMBGal1mEKy/2H3+5n/AAc9bvYXOoAqJCmpiOeYRWAY\nhjENw/UJR+loOOacFwkjjVHAjZXCOEwiewtEceQcDXGDWGOqYZVGXCdOqoZ4iRZcXtMrnWxLU0KA\npJJegIdzlqT7il4h07qA1gLiVkbVMHoHc2AsEKlXGWBVcWXm6YWkUodXZCAYZDDxSPtJGoSSVNAQ\nF96WRmCgiu8FWTh9HMd4icMhnZkPvIA1xVVJZRJvyova/sYIRa9A0StQDspJ5IVrU/ZLFP2Jetse\n3uTqJp4/RRQz1cQIki+SdDn/O0/RL2bXSCMz8g4TmIhc6UVmSh1pxXQ6F9PtT5nLy/ZM6R7NbVNa\niZpOKYnKRJTNgSh1t6qMMpeoiHyUSCfnanU/JfevebthLCQdi3imkRNHnQFz+TsMSuAXDygCYyBJ\nIRkxB4ZhGNOgaE5kPcz0yUpekaHCIJKkMY+Eoy7dWaAR1zOx+lJSES/WCJJU6mo4StEvEiSV/tLn\noNN7c5N/XpIam594i+MoiaidKMMea5xELqeTVU06XIZhLDqmgbFI1KM6Zb+UhcZBqgUREXgF4jjG\n9wLqUd2VddIYL3UGJGVL08gKBMajGh5OeTnV1/DFz3QmUq2K5uodMBEx4Xs+nk7WawiTnMCiX6To\nFZxeBpMrbKTOiULiwW4Wl/QTjY4wiiY5JKYT6mxF3mEyU0WRudJcZaQVs9G8aOZAcyUFmfb4mZwI\neW2KbpUG60QXYka7cqkkrbQq8m2al1sYlDn2pm2XCnsyWSMkP0Axp4WxmIx2GoGRim/ORf8ipbTy\nADUwkggMSyExDKMNe2p7WVNazUhjlIGgkk18jYVVYo2pxfWkUp+LDA7jkJHGCL4X0IjriQ6Wi86I\n1E3+xRpR8ss4Z4YTiVeNqQSDQCImD9kkXJoCrTgh+9SBkYmbq7Qda9mYwDB6A4vAWCRijVlVXEnJ\nL1FJvMmhhijKYDBA0SsC4jzNuTrWRb9E0S/he0H2IBbxiDQkipMqH7gX1KHCIJFGicaFUgkqKJpF\nPqT5h2l0SMUvZ7PsYRy6LwjcC3fq9Ej1M9LzeeJlAkoAJX+ifnfq0EijKyp+OXNw5NMgpkuJmKTD\n0eExB8J8hwgeqL7HbJ0f+aiJPG1FMufo1OhanfR2WhUd3rZJg4sZjpkxysMmWoxFpmMRz9oBVCBJ\nKa+E6z4H7z8Gbr981ocHvkcp8EzE0zCMjHQsmSKJNsVQYTArH+8hDASVbIIsikPqUZ3xqEYtrifC\nnPUsRaQWjROpE+90FfkiNzmEZFXhBgI3/vU9P0mLTlKxEdCJCQs/SYsW3FgpP7Y0Z4Vh9C7mwFgk\nJsLnGtkLvavsUcr2OzEhJ2uZPvQbSURFIMEkkaKi51I+Mr0MXMnUSONE+2KiEsdoOJZFZqT44lOP\nG9kLfIQT8UyjHMajWvblojpR7WS61If8fnBRJ2l6y3Qv4u3SRlqVIO02s30RT+9/p8yk7zFfjplm\nDvSLeVKqySzP1SrNI3ey1ttmcwlp+t3m2rMNBbXQUWOhGa2FeALlwgzPuzRy4kAcGGf+PZz4chjZ\nDtuumdMpBkuBlVE1jB5CRM4WkQv37p27vs2Bsrc+UZ55qDDIaDhGpBH7w1HqcYPxqEYjKW3aiBvU\n4waRxlTDUaIk0iJNY0412AqemxjzxafoFYkSHbhAAgIpUI2qBLkyqC7SkkkC9VFWVW+yVlg6Dpwk\nSG/f/4bRU8y7A0NEniMiW0XkDhF5W4v954vIDhG5Pvl59Xzb1AsMFQapRuOT0i088Sn7JRoaEni+\nC2dLPqJIQ9LXr4K4h29el2KoMJgJYEryct/QMPM6F8Slo6R1rduRpp4E4jMQVJwIqHiTnAq+eNkD\nP3XEFLwCtTS9pKlsaJ5U4yIfWdGpMyKfMjIf6SMwN12LPDM5IBZCrbqTKJKuRUzM5VzTRFS0Ewed\n75mQ5vSUtFzrdO0MY74ZrYcMloKZ/8Zq+8ELoFCZvt10PPaF8HsfgMIANMbmdIrBks9YzVJIDKNX\n6IUqJGn1PUg10kKq4TgDfpkgcUo0EsFOVWUgqFCP6xS8IooyHlazyYf0O9hVsXMDCZfKnI5Fna7a\nQDCA7wWU/VJWDjV9jkYa4Xk+hSQ6OO+wSJ0dMFEhzsQ7DaP3mFcNDBHxgY8AzwK2AVeLyCWqektT\n0y+r6hvn05ZeJU258PAIc8JCEZ7zDIuHT0AgTlQofUin5UwD8YmIaWhIPW6wolCinkQ+eHhUEnXn\naliduGbiuc4TaUTFLzMWhkREWXpJqnsRJF8EAJWkznYhp6WRinY2k0+ZaPfyHnjBAWljLARppEWz\ng6MbDo8DjSxZjBrkB+QAWUQfQCt9jWYNjE6PNYz5ZrQWMljs4Hk4PuyiL7rhmCwOQH10TocOFgMT\n8TQMg3rcQHCTW7tqe/DEY01xshNlPKpR9AqMhlUXNRxVETxGGiPEOD2K8WicQAKq4RjFZAzaiBsu\nPdYr4EuQfTMXvCKRRhS8Ah6CLx71uEFB3Pi1HjcoeEE20SS5yThFs3FUfkzVbjLDMIzFZb7fep4M\n3KGqd6lqHfgScM48X7NvqPhlhgqD+J6fRSV44qGqmYPCF5+SX2JlcRXpU3QsrGZVRgDiOKIe1bMy\nUIH4+J6flVWthtVJL9qt0h6mexFPH+b1JidFmlKSkte/SOn1kqjdphfqgXczumI+mE1ExVzCNjst\nSdvpeQxjsRitRVl50mmp7T+w9JE8xcE5R2AMFH0T8TSMZczd++8BYEd1J7vG97iNqtTC2sTYU9M0\nTpzzQoRQI8p+BSV2GhdxmAjXu/FwkIxvx6MqA8Fg4rhwUceKS6OONc602dJIioIE2fkVzXQ3fPFR\njSdVGsmW24yhLI3EMHqH+X7bOhS4P7e+LdnWzLkicoOIfFVEDm91IhG5QESuEZFrduzYNR+2LjjV\naBxwjoyyX8JP8/BEKHiFLI1jIKjgiRBIMCmULRXZTI/JojlypU+btS4AopywZuqUSEU6B4IKFb88\nyVmRfylvF2mR7ksjMzzxst8p00VWtEot6SVSZ1K36YbDoxecJv1I6khpF31hgxVjsRmphTMLeELi\nwDiACiR5CoNzj8AoWQSGYSxnHrViMwCHDh7MQQMb2VXbw7ryWg4a2AjAcH0/o+Eo++rDmeBmLRpH\nVRmPXCWSKJk0c2PKkDAZdzbiOmW/Qi2uZVVEYtRV31MXeRzFYfadno5zBUETnTkRyfTjnLinG9el\nE3XN4wGrSmYYvcl8v/m0+mtvfiu4FNisqo8Hvg98ttWJVPVCVT1FVU/ZsGFdl81cHKJ48kxV3vFQ\nkADfCxgMBpxzI4mqACj4EwKgrmSqR5DoYgwVBrObnlYhqQQVV24qqT7i515424lQNms5FL0CRa9A\nlGwveIUspSRtn68uktfGMIy5YgMGYzkzWgtnLqEKrgpJ1yIwDiyFZMxEPA1j2XHV9uuphlXu2X8v\n948+wENjj7gJL1V21fawfXwnURwxEo7ii5usUlX2N/ZTj+qE2qAe1ahFzjkRa0wtqlLyS/gSEGmU\nRGE4Ec+SX0I1zsaYJa/kxO+9IJn081y1EXGpJL4XZKVRXXqKi9CIiTONDHTqmMPGIIbRm8z3dPc2\nIB9RcRjwYL6BqubDKT4BvG+ebeoZUocETKRjBF6An6gp57eDEz+KvIiCBK42iTg15TRyI3Ue5CMF\nfPEz8c6WNrTYnmpopFEY+Rl+v81sv0UBLB/SWun9SDqbYoMSox8YqYUcPjgwc8Pafhja2J2LHoCI\n50DJZ8f+Gl+46j7ASXIEnvDsEw5iVcWc2YaxVHns2mMyzbVN5Q2QRBLvb+yn6Bfx8NkxvpNQQ0AI\nIxdZ4YtPLa5R9iuMM57J+NSicQKvkOmjFbwChSSNJNaYKHaTcrFGlLxEbF4jN05OJgcbOuFM1Syq\nI6SUiIM2R2H267jGMJYj8+3AuBp4tIg8CngAOA94ab6BiBysqg8lq88Hbp1nm3qGil+etJ6KWboU\nDpf6EcYhgRfQiEMKiUaG07lwOX6B+JlXuRbVGAurDOSEO1s5KPLbil5hShpJfnuscSZ+1NwmT1qF\nJA3762bkRXoPsvXkS8owukE7Z4Y5OYzFZrTeaQrJMKzb0p2LFgdhbPecDt28bpA9Yw3+9hs3Ttq+\na7TO655+dDesMwxjFojI2cDZRx39qHm7xta9Wzl29bEAbF5xJOAm36rROEW/RCNuEIgSakjRKyEI\n++p7CLyCE+MExqNqUkq1xHB9hIrvqolUwzEKXoFII+LIRVz44iqI+OJnhdHTCnsFf0ILLg33FvGy\ntJR0DJtOulmFEcPoT+bVgaGqoYi8Ebgc8IFPq+rNIvIu4BpVvQT4MxF5PhACu4Hz59OmXiVNxxhJ\nvM0eHjET6R2p2JAvPmEcJlVGxD2wvQK+eFn51JHGaOKNDl2NbL/IcGN/dq40L7AdqfMi79xISbcD\nbR0VM5US7SXSe2p0zkLPUnQzaiKdbcnO3cfRJMbSZ2wxRDwLA9CYWwrJm87cwnlPOtxVSk7+zJ7z\n7z9h2565RXQYhnFgqOqlwKUnn3Liaxbieukk2u7aHtaUVgOKACsKK3io+lA2IZU6Njx1KdCRRhS9\nIrWoxsrCIFEi2FkOysQaJzoXLmpjoDCUiXF64lHyi4SJpgWaVhabEJF31VCCrOJIQ0MCfGI0mwTM\n7lcHFckMw1h85l0xUVUvAy5r2va/cstvB94+33b0C0OFwaxsKbErl5rqV4RxyHhcw0se2uC8zgNB\nhVpUo+KXaCQOkFhjCjLx8apqpndRj+oU/WL2u11UBUxEXKS/mx0aeUq5ElfdpFnY06IvDMNYDox0\nWka1q1VIBqA+N4eDiLBx5eTIwoNXVXh433g3LDMMY5H49v0/53cPfwoAL/zmPXz9nM3sqe9jTXEV\ncW5SYOf4Tlf61AvYXq0TSEAtqrF9/BEGgkFIquw5TQunS6EiVBujFP0iBS8gTMaqZd9FExc952Qo\neEXCuEGsEUXPjTc1+ReIn+hd+DTUCXlGKD4u+kLEy6qR+In+hY90PImxGOXqDcNoj/019hjD9f2T\n1tPogDQ8LnVCDAYDSWWMfIUQ57wYKgxSTMLsIp0osdpO76IV7Zwa+e3t0kT6SbjToi96n9mUXe30\nfNmyRV8YPUoYxdTCeGYRz7AO4Xj3qpAUh+Ys4tmKg1eVecgcGIbR16TOi13ju/n6OZuz7T966Bo2\nr9jMaDhGNayyvryewWDQpRTjUYtr1OMagbhxYTWq8vDYbiKNqIbVTLxTRAjjEF8CJ9QpkkVYhBpm\nle1KfpmSV3LODxGCJLIindhKK4sE4rvxqghFr5B963tJCVWRRPuizRggjSROU0zMeWEYvYX9RfYI\nqVhnRJyJFsGEoyIt+VSQYNKDdDSsAi76waWTBJNKp+YdIMUkZzBNIZkplaSZ6aIvDMMwjO6gqrzy\ns9cATO/AUIX/PMctdzuFpEu54QetKlsEhmEsEdaV12bLa4qrOOPgU6hFde4evovvbLsOgDuHH2Qs\nrLG7vodqWEVVqUXj7BrfzXB9jHXlFUlaR4FG3CBSVwmk6Bepx3UKXjGLsHATdQEFv0ggAQWvgOf5\neCKUPBdBXPDc9jRat5gIfqaTfLHGWfpIqiMXaoQgRBpN0sFIJzjScbZNchhGb2IOjB6j4pcJvICy\nX2KoMAiQlVF1eYPF7CHtJyFxY2GVWlSjlDgk6nGD8biWlU3NOyqmOC2mGaR222GRd8wc8LnalH81\njLzGxVz2G8ZiM1aP+MntOwA48zHTVBcZ3wf3/Qy8Ahz7nO5cvDgAGkNYm7ltBxy8ssyu0TrjDXtm\nG0a/s2t8ssDvz7dfxw27bmd9eSPHr17LtTtv4cT1j+GYVUcz2mhQCSpEKEOFFYi4799YI3wJGGlU\n8SXAT0TpVZ3+m5/oUjiBT8+llcQhRb9AkERXFCRwySOqNOKQOEklaWg4VYdN3Lmi3Lgx1cfw8MxJ\nYRh9yLxrYBidkYp4pr9b7R8NXV5yID61JGJjwC9nqSNpOknqYc4TNb3wV6PxrApKXphzOvLinoZh\nGMb8sHvURdH93xc9nketH2zfcHSn+33OR2DN5u5cPHGc0xiDQnn6th1w0Cp3jkeGxzly3TR9MQyj\nr9hT38dTNp7I7tpeQg05cuhIlHvZMb6DRlRnTanCvvp+VhQGeWhsByuKFYQ6ICgxK4uDjDTGWFta\nTaghgQQ04gZFv4iqi76oJDoYALEqkjg7EEFUmiqLBE6Y0wsybYtWacIzpYOYuLdh9D4WgdFDpGkk\nzdvS7annGVwEQikpb+qJRxiHRInTYiCoUPZK1KM61bA6KaWkmenSSDp1VHQq2tksxnkgmJCn0Y6Z\n9DJMYdzodXYlDoy1AzOk+Y26KA0G13fv4sUB97tLOhgHr3IvIDc/OMy+MUtDNIx+Y83zPpYtpykk\nP3vkV6wprgJchY+N5fXcP7qNKA4peWUeqe6nHocMBGUE4cgVh+GLz8rCCkp+ifHQpZUNFSqMR+MU\nvCIiHoOFIYpeiYJfoOiViFGXCuJN6Fp4ifhmwQvcuFLECYmKUJAJEdDmibtONc/MeWEYvY85MHqQ\nvNMifQCPRzVKfin7SUkVlZ1w0cTH2fzgrgSVyev+xMzabCIqLPqiP7Fa54bRP+xJHRhDnTowNnTv\n4oXuOjAOW+O+e17/+V9x0nu+ZyVVDaPPuPpLz8qW/+XGXwMQxjHX77oRgK1772Q0HENRCn4RTzyO\nGNrAimCQXeMjDDdGuG3vPZT8MtWoShSHDBaGkvKpMSsKKxkLJ3R3XDlU5xgZDNzzqJCkmgCQiHfG\nTFTXE8gqnICr4OdN84pjYyLD6G/MgdFDtEof8cVvuT0fgVBLIixKTe2KfpFKUJm1WOds6aeqI0uJ\nKXmes8S+wA2jN0kjMNYNdujAGJpGJ2O2FNMUku44MDavH+QTf3wKf3rG0USxct8uc2AYRj+xZeXR\n2fJbHvcEAI5dfSQPjI1w1fbrOXb10Vy74xYAGlGdm/fcy3BjP574DBYKlP0ihw6uZbi+zwl3aujE\nOMVjKHDiw2uKayn5ZQriInVXFAYRXGUShKQMqtOrqPhlUKXil/EQgqRKH0nlEj/V0WiKpGgW7IQD\nH0cZhrE4mAOjByn7pSmaGM1OjNRZUfKKRInHeSysZtsrQWVK6shM68bSxUIiDaN/2D3qIvDWdurA\nGFjXvYunDox69xwNzzp+E+eedCgAO0a6Iw5qGMbi8d1td/P0gx6PL8K1O2/joIFV3DXsBD63rNzE\n6uIqPPFYEQwykEZReAUGgkFWFFbiiU8lGAAUT7xs3Fv0ixS8gEbsSqcGXkA5qUiSinc24kY2cZam\nhZT8Ujaxl453mp0VWfnUnMaFlUc1jP7E/nJ7nHw6SSsGggolrzjpIVyP6m2dE9PtM/qLA/3iNaeG\nYfQOD+6t8uWr72Prw/vZNVqn4AtD05VQBefAqKwBv4tRcHkRzy6ybtC9hOwase8fw1goRORsEblw\n7959B3SeNa+4eNL60St87h25h/tHR3niumNQlC0r13PE0JHExFSCAcK4wVBhJYJQ8IqsKKx024JB\nyn7ZOSL8IpWgjIgQJhESgmSaaYH4KIowES0hSDZxlyfSaNK4qN0YxyqRGUb/Yw6MPqHZiVHLrad5\ngX7TC21z6shM69D90qmGYRjGzLz/f7by1q/dyBu/8Cv2jNZZO1ic2ck4ugMGu5g+AhMinvsfgvHh\nrp12VaWA7wm7Ri0CwzAWClW9VFUvWL16VcfH3DF855Rtez7zAtac9e8AnPSBa3nqQSc7cc6hIVYX\nVwJw2OCh3DdyLxvLGxGESjBIlJRMDSRARFhRXMl4VKMRN6j4ZVYVVrjqIghlv0TBCyj6RXzxKPlF\nYo0RBMVFcASeK5+aRlukjg53HT9bng6LujCM/sfKqPY4aepIqyiMWk7YE1wKSS2qZVUW6lG9I6eF\nYRiGsbg8uLcKwG+2jzBUDlg72Lqk9iRGd3ZXwBOgnLzoXPpm+PZb4E3XdqVEq+cJ6waLFoFhGH3K\nnu+/GYBf/eXJABw8eGhWieTYVcdwx/CdrK9sZMf4DnzxOWLocPbVhxERPDxqcR1UGUhE5X3xqMcN\nCl5A2S8Ra4yq4otHmERb5MesaUpIs+5aqBF+bj6202ojhmH0L+aG7BPaaWDkaY7AaKaT1BGrMjI3\nZvL4G4ZhTMf24RqHrHLVoa67b+/MAp4PXAv3/rS7JVQBVh4CL/4vOP3PIQ5h5x1dO/W6oRI7TQPD\nMHqavGhnM7vGd2fLD40+wEhjlHtH7gfg0IFDQOGgyiZ8CRiu72dV0aWQNOIGKwtDFLwClcA950p+\niYJXINKIetxAVdGkbKonHoFMnmNtF5Hm41lKrGEsM8yB0cekTow0nSSNxij6RYu0MAzD6CMeGR7n\nmcdtolxwX8trZnJgfONP3e+Nx3ffmOOfDyef75ZHHunaadcPFdlpERiG0besK6/Nlo9f4549qjGj\nodPMOWhgIyPhGANBhZXFFeyp7UVR1pRWAy59wxefSlDOiXeWM4HNdFwbSECo4ZTrh/HUbea8MIzl\nhzkwliDthDrzTg0T8uwuFrJoGMZcGamFjNYjDltT4Y+fspmjNwxy1nHTaFvEMey5G574cnj638yP\nUWlp1i46MNYNFk0DwzD6iDXP/+S0+4cKg2xecSSDwQCVoML26k42lNcRowzX9xN4ASsLrlSqq0KS\npo/4mShn0StQ9AqTUkNEZEqqCExoXoBLKbHoV8NYnpgGRp+T6mDk11MNDIvCMAzD6H22D48DsHFl\nidc+/Wj+9nnHTX/AyMMQ1eHQE2GG2cdbdt3CrbtuZcPABtaU1nDcuuMmvQS0pTgIxRUwsr3TbszI\nuqGSaWAYRh9x5Wef0nL7/sYIKwpDbN27lWNXH5ttX1V0zooVhSFGwzHWFNqLhzbiBp54LR0VnSAi\neGrzsIaxHDEHxhIiTSVpdlyYmKdhGEbv8siwe3ZvWlHu7IA997rfqzdP20xVedMVb2L72IQT4vHr\nH88TNj6BteW1vPKxr5xekX9oY3cjMIaKjNUj/uLL1yMCpx+9nnNPPqxr5zcMo3vcvOdmTlhzQst9\nKwpDAJOcF9WwSiWJsCh6BYrFqc6LUKOsgkgrLbfZYukjhrE8MQdGn9P8BdCNLwRw5VRN0NMwDGP+\n2b4/jcDo0IGx9z73e/UR0za7fc/tbB/bzuuf+HpOO/g0frPnN3z81x/nN7f/hmpY5eRNJ3PixhPb\nn2BoU1cjME591DqOWj/I1ffsZt9Yg5/cvpMXnnSovYQYRg/SznnRjtR5MR2BpdsahtEFzIGxhGjl\nvKiG1UyfoVUkhmEYhrGwqCo/3Lqdeqg8+/hNPJKkkGxa2aEDem8agdHagRHGIdv2b+O793wXgHMf\nfS4bBzZy4sYTefGxL2ZfbR9P+9LT+PmDP5/BgbEBHrml437NxMlHruGKvzoDgIt+ejfvvPQWHh4e\n5+BVM7/4GIZhGIZhgIl4LkusnKphGMbicdvD+3nlRdfwus9dy7dufIgbHxhm9UCBoVIHcwpju+H6\nz7voiELriI0PXfchzr74bD554yc5ds2xbByYLAi6qrSKE9adwMV3XMzuYbVv8gAAHo9JREFUXFnE\nKXQ5AiPP4w5zVQlu3LZvXs5vGMb8ccfwnYttgmEYyxhzYCxxKkEli7qw6AvDMIzF5/ZH9mfLH/3R\nnVx+08O84IkdplJ8562w5x7Y8JiWu1WV7937PR6//vG872nv4wNnfKBlu9MPPZ2HRh/i3EvOzaoB\nTGFoI9T2wT1XzmzXLDn+4JV4Ar+4azcP7xtHVbt+DcMw5octK49ebBMMw1jGmANjGWKODMMwjMXj\nrh2jiMBfnHUMtz40TD2Keemp0+tZZDxwLaw/Fl78ny1337DzBu7ffz9nH302zzvqeRyxsvV5X/nY\nV3LWEWexs7qT+4bva32tdVvc74t+F+pjndnXIZWiz7EHreTTP72b0/7pB3zsx3d19fyGYRiGYSxN\nzIGxDOkkhcQwDMOYH+7aOcqhqyu89ulH8cGXnMjnXnUqx2xaMfOBtf2w+0543IugsnrK7u/c/R1e\nftnLAfjtw3572lMNFAZ43RNeB8BNu25q3ej4F8Az/s4t723j5DgA/v28J/K+cx/HppUlbnrAUkkM\nwzAMw5gZc2AYhmEYxgJQCyN27K9x144RjtowRLng8/wnHMJTH72+sxM8nDgaDnr8lF31qM4nb/wk\nm1du5sNnfphDhg6Z8XRHrz6asl/m5p03t24gAked4ZbnwYFxzKYV/OGTjuAxB63kvt3djfAwDMMw\nDGNpYlVIliGWQmIYhrHwPP9DP2Vron/xR6cdObuD994H33itWz54sgPj7n1386JLXkQ9rvOu33oX\nTz/86R2dMvACjlt3HJ+79XOsKa/hgsdfMLXR6sTOtPLJPHDE2gGuv3/vvJ3fMJYaIvIC4HeBjcBH\nVPV/FtkkwzCMBcMiMIy21OMG9bix2GYYhmH0PTtHamx9ZD9rB50D+dSj1s7uBD94t3MiHPYkWHHw\npF1fu/1r1OM6bz7pzZx99NmzOu35J5zPltVbuPCGC9lXa5HGMbQRgrITDp0njlg7wL5qg31j9n1j\nLH1E5NMisl1Ebmra/hwR2Soid4jI26Y7h6perKqvAc4H/nAezTUMw+g5LALDMAzDMOaZGxONh/94\n2UmceMRqin6H8wfVvXDLxXDzN+DUP4XnvjfbFcYhH/31R/nsLZ/lzMPP5NWPe/Ws7TrziDM5ePBg\nXvytF/OeX7yHLau3cM6Wczho8CDXQARWHzG/ERjrBgC4b/cYjxtY1bbd8HiDvaPOyVEueGxc2bqM\nrGH0OBcBHwYyJV4R8YGPAM8CtgFXi8glgA/8U9Pxr1TVtL7xO5LjDMMwlg3mwDDaUvQKi22CYRhG\nX7NrpMYPt+7gitseQQROOGQlpcDv7OBHboGLngfVPeAX4cmvyXb97MGf8ZWtX+EH9/2Ao1YdxZ+c\n8CdztvG4dcdx6kGn8t17vgvAvcP38o9P+8eJBquPgB23uwooKeLDphPAP/DviSPWOgfGxdc/wM6R\nGk8/ZgOeN7mk7Fg95Ix//hG7RydEqL/4mtN4ytHrDvj6hrGQqOpPRGRz0+YnA3eo6l0AIvIl4BxV\n/Sfg95rPIa7m8nuB76jqr1pdR0QuAC4AOPyIw7tmv2EYxmJjDgzDMAyjLSLyHODfcTOBn1TV985w\nyJJitBby8R/fyTknHsrRG4Zatvny1fdx7b17eOFJh3H8ISt5/+VbGSoFvOEZW/ijT/2SWx4aBmCw\n6LOi3MELfxzB5X/noi78Irz6CthwDJRW0IgaXP3w1bzhijegqrzisa/gL0/+ywPu5yee/QkU5R+v\n+ke+8Ztv8HtH/R7rB9ZzzJpjYP0xcMf34RNnTj7oGX8HT/+bA772kesGGCj6fOrKu/nUlXfz9uc+\nhtc+/ehJbb7164fYPVrnr3/nWA5aWeY9376F//z5PebAMJYKhwL359a3AadO0/5NwFnAKhHZoqof\na26gqhcCFwKcfMqJ2kVbDcMwFhVzYBiGYRgtaRfWrKq3LK5l88cXrrqPX969i/ee+3hU4aWf+AW/\n3raPH9++g2+8/nR2j9UpBR6DxYAH9la57eH9vPVrN1IueHztVw/w1C3r+fHtOwC44rbt3Pbwft7z\ngsdy7b17eOyh7dMjMsIaXP8FuOqjcPip8Oz3wGEns2NsBz/c+h2+cOsXuHPfnawrr+Picy5mdXlq\nOdW5ICIIwouPfTFf2foVXvt9Jxj696f9Pb//239F4ahnALl3oCv/Fa79LDztLVAbdk6Xke2w+y63\nf+NxsO7oqRdqwUAx4KdvPZM9Y3X+z7dv5f3/s5X/+sXklJXdo3W2bBzi9WccjYhw28PDfOrKuzn9\nvVdwzKYhPvrykykXOoxsyTE83mD/eAjAUDFg1cCEg6kWRlnER+B5bFhRmvX5DaNDpMW2tk4HVf0g\n8MH5M8cwDKN3EdX+c8qefMqJ+tOrfrTYZhiGYUxLJVh9raqesth2zBUReQrwTlX9nWT97QBJWPMU\nTjnlFL3mmmtmdY2PfP2vaYQ1ADQ3Xp/um0nb7J32eG3dLk+kyvX37yVWpeB7KEoYxRyxdoB7d4+x\neqDA3rEGnicMlQKGq06PoVLyOeOYDfzsjl3sq9ZZN1SiUvSJ9m5jc6XKhi0ngTd5viBvQ6M2zPju\nuyhX91Ko7kXjkEdWHczYY57Lw2OP0Igb/GbPb9hb28vq0mrefNKbeeqhT53QqegyW3dvZV9tHxfe\neCFXPXQVJb/ECetOIPACDhk6hC2rtyAP3wi//hKIBxpPOYeIQGWt27/xMTA01dZWb2zjYczWh/cT\nxlPPeeTaQTYMlZJ2Ebc+NEwYwbY9Y2xeP8i6wdYVtqTllaDaiLjtoWHiZBwkIhy7aQWDpYBYldse\nHqZaj7KzHL5mgE0r2zsx2l1nNnR0jhmadGLFgtk64zkO/DoveeZb2bTu0NldV2RRn81JCsm3VPWx\nyfqsnrWzuM7ZwNlHHf2o19y89boDstkwDGO+6XTcbBEYhmEYRjtmDGvO51kfccQRs77AF/ZexnCn\ngpYLwcapm24D2JSsrMjtWDmxeP32ZD23jQ3J74d/1Nm1y0DZXaDiw+AD/49NA5soB2VO3Hgir3vC\n69iyesu8l8I+du2xADxm3WO49M5LuWffPdy+53bCOOT7936fi++42DVct2aGM8XuZ/gm99MpHq1r\npA0nPyl+8rMJtgKMd36JjKbP+1bNnaepe7fN9RrGvPLUh184awdGD3I18GgReRTwAHAe8NIDPamq\nXgpcevIpJ75mxsaGYRh9gjkwDMMwjHbMGNacz7M+5ZRTZh3S96Hf+hjaYrbdXVxaLoMrjtHKzMnH\nNJ9w5narKgWKgYeqi5IIPB8/aVBtxAwUfBSoNSIqRX/K+UbqIUPFJA2htAIpr4ThB1r3IVn3Sysp\nrzqMelynEbmojoHCAJ4srmNnZXElLzvuZZO2hXFINaxOe9yUCJeRXdAYmdxmxv8pM/9XSq+jwCPD\ntZbnbBdtA+6/w8YVpexTUWDHSI0oCbpYN5j8X0jOsa/aoFpv/X91uuuk556Jmc7RwU3rznU6ufcd\nXGjme9KdCODjjuqvIDcR+SJwBrBeRLYB/6CqnxKRNwKX49xyn1bVmxfRTMMwjJ7FHBiGYRhGO7YB\nefn6w4AHu3mBkx7ztG6ebtFpqXJRWdvRsSW/RMnvbZ2FwAtYUVwxc8M8a1fO3OYAWdOlbJrpJEFN\nLtToBqr6kjb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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"edisp.peek()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/deil/Library/Python/3.6/lib/python/site-packages/astropy/units/quantity.py:635: RuntimeWarning: invalid value encountered in true_divide\n",
" result = super().__array_ufunc__(function, method, *arrays, **kwargs)\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 38,
"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": 39,
"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",
"psf = EnergyDependentMultiGaussPSF.read(irf_filename, hdu='POINT SPREAD FUNCTION')\n",
"print(psf.info())"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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b8vLyfNGiRbXdjcOSvzrxvrllNcmsl1S5do2bJN12vUhydWZGkps51CDSIMl2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\nkqTO+19ju32eNHalAAAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"psf.peek()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 41,
"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": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background3D\n",
"NDDataArray summary info\n",
"energy : size = 21, min = 0.016 TeV, max = 158.489 TeV\n",
"detx : size = 36, min = -5.833 deg, max = 5.833 deg\n",
"dety : size = 36, min = -5.833 deg, max = 5.833 deg\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",
"bkg = Background3D.read(irf_filename, hdu='BACKGROUND')\n",
"print(bkg)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"# TODO: implement a peek method for Background3D\n",
"# bkg.peek()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$1.3316669 \\times 10^{-5} \\; \\mathrm{\\frac{1}{MeV\\,s\\,sr}}$"
],
"text/plain": [
""
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bkg.data.evaluate(energy='3 TeV', detx='1 deg', dety='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.\n",
"\n",
"Side comment: if you have data, but no index files, you should write a Python script to make the index files. As an example, the one I used to make the index files for 1DC is [here](https://github.com/gammasky/cta-dc/blob/master/data/cta_1dc_make_data_index_files.py)."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"index\r\n",
"├── README.md\r\n",
"├── agn\r\n",
"│ ├── hdu-index.fits.gz\r\n",
"│ └── obs-index.fits.gz\r\n",
"├── all\r\n",
"│ ├── hdu-index.fits.gz\r\n",
"│ └── obs-index.fits.gz\r\n",
"├── egal\r\n",
"│ ├── hdu-index.fits.gz\r\n",
"│ └── obs-index.fits.gz\r\n",
"├── gc\r\n",
"│ ├── hdu-index.fits.gz\r\n",
"│ └── obs-index.fits.gz\r\n",
"└── gps\r\n",
" ├── hdu-index.fits.gz\r\n",
" └── obs-index.fits.gz\r\n",
"\r\n",
"5 directories, 11 files\r\n"
]
}
],
"source": [
"!(cd $CTADATA && tree index)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Gammapy DataStore\n",
"\n",
"If you want to access data and IRFs from the CTA 1DC GPS dataset, just create a `DataStore` by pointing at a folder where the index files are located."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"from gammapy.data import DataStore\n",
"data_store = DataStore.from_dir('$CTADATA/index/gps')"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Data store summary info:\n",
"name: noname\n",
"\n",
"HDU index table:\n",
"BASE_DIR: /Users/deil/work/cta-dc/data/1dc/1dc/index/gps\n",
"Rows: 19620\n",
"OBS_ID: 110000 -- 113269\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",
"Number of observations: 3270\n"
]
}
],
"source": [
"# Print out some basic information about the available data:\n",
"data_store.info()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of observations: 3270\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",
"Total observation time (hours): 1635.0\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",
"print('Total observation time (hours): ', data_store.obs_table['ONTIME'].sum() / 3600)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"19620\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": 50,
"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": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of selected observations: 57\n"
]
}
],
"source": [
"from astropy.coordinates import SkyCoord\n",
"table = data_store.obs_table\n",
"pos_obs = SkyCoord(table['GLON_PNT'], table['GLAT_PNT'], frame='galactic', unit='deg')\n",
"pos_target = SkyCoord(0, 0, frame='galactic', unit='deg')\n",
"offset = pos_target.separation(pos_obs).deg\n",
"mask = (1 < offset) & (offset < 2)\n",
"table = table[mask]\n",
"print('Number of selected observations: ', len(table))"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"ObservationTable length=5 \n",
"\n",
"OBS_ID GLON_PNT GLAT_PNT IRF \n",
"int64 float64 float64 bytes13 \n",
"110380 359.999991204 -1.29999593791 South_z20_50h \n",
"110381 359.999991204 -1.29999593791 South_z20_50h \n",
"110382 359.999991204 -1.29999593791 South_z20_50h \n",
"110383 359.999991204 -1.29999593791 South_z20_50h \n",
"110384 359.999991204 -1.29999593791 South_z20_50h \n",
"
"
],
"text/plain": [
"\n",
"OBS_ID GLON_PNT GLAT_PNT IRF \n",
"int64 float64 float64 bytes13 \n",
"------ ------------- -------------- -------------\n",
"110380 359.999991204 -1.29999593791 South_z20_50h\n",
"110381 359.999991204 -1.29999593791 South_z20_50h\n",
"110382 359.999991204 -1.29999593791 South_z20_50h\n",
"110383 359.999991204 -1.29999593791 South_z20_50h\n",
"110384 359.999991204 -1.29999593791 South_z20_50h"
]
},
"execution_count": 52,
"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": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'South_z20_50h'}"
]
},
"execution_count": 53,
"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": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0,0.5,'Galactic latitude (deg)')"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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JWoGGDVh4E/D4JE8GHtVv/nRVnbskkUmSJk7LcxrnAiYKSVLTE+GSJAEmDUnSFjBpSJKa\nmTQkSc1MGpKkZiYNSVIzk4YkqZlJQ5LUzKQhSWpm0pAkNTNpSJKamTQkSc1MGpKkZiYNSVKzsSSN\nJEcluTzJXUmmhpR7WpKrkmxMcvJSxihJuqdxXWlcBvwJcP5cBZJsD7wdOAzYDzgmyX5LE54kaTbz\nTsI0ClV1JUCSYcUOAjZW1dV92Y8A64ArRh6gJGlWk9ynsTtw/cD6pn7bPSQ5Psn6JOs3b968JMFJ\n0ko0siuNJF8EHjrLrldV1Rkth5hlW81WsKpOA04DmJqamrWMJGnhRpY0quopCzzEJmDPgfU9gBsW\neExJ0gJMcvPUhcA+SfZKsgNwNHDmmGOSpBVtXLfcPivJJuBxwKeTnN1vf1iSzwBU1R3AicDZwJXA\nP1XV5eOIV5LUGdfdU58APjHL9huAwwfWPwN8ZglDkyQNMcnNU5KkCWPSkCQ1M2lIkpqZNCRJzUwa\nkqRmJg1JUjOThiSpmUlDktTMpCFJambSkCQ1M2lIkpqZNCRJzUwakqRmJg1JUrOxDI0urTRrT/70\nPbZd+4anjyESaWG80pBGbLaEMWy7NMlMGpKkZiYNSVIzk4YkqZlJQ5LUzKQhjdhcd0l595S2Rd5y\nKy0BE4SWC680JEnNTBqSpGYmDUlSM5OGJKmZSUOS1MykIUlqlqoadwyLKslm4LoFHmZX4AeLEM64\nWY/Js1zqslzqAcunLgutx8OravV8hZZd0lgMSdZX1dS441go6zF5lktdlks9YPnUZanqYfOUJKmZ\nSUOS1MykMbvTxh3AIrEek2e51GW51AOWT12WpB72aUiSmnmlIUlqtuKTRpKjklye5K4kc955kOTa\nJBuSXJJk/VLG2GoL6vK0JFcl2Zjk5KWMsUWSByb5QpLv9D8fMEe5O/vfxyVJzlzqOIeZ7zNOsmOS\nj/b7v55k7dJHOb+GehybZPPA7+FF44hzPknel+SmJJfNsT9J3tLX85tJDljqGFs01OOQJLcM/D7+\netGDqKoV/QIeCfw2cB4wNaTctcCu4453oXUBtge+C+wN7ABcCuw37thnxHgqcHK/fDLwxjnK3Tru\nWLf2Mwb+C/Cufvlo4KPjjnsr63Es8LZxx9pQlycBBwCXzbH/cOCzQICDga+PO+atrMchwKdGGcOK\nv9Koqiur6qpxx7EYGutyELCxqq6uql8AHwHWjT66LbIOOL1fPh344zHGsjVaPuPBOn4c+MMkWcIY\nW2wL/1aaVNX5wA+HFFkHfLA6FwD3T7Lb0kTXrqEeI7fik8YWKODzSS5Kcvy4g1mA3YHrB9Y39dsm\nyUOq6kaA/ueD5yi3Ksn6JBckmaTE0vIZ/6pMVd0B3AI8aEmia9f6b+XZfZPOx5PsuTShLbpt4f9F\nq8cluTTJZ5M8arEPviJm7kvyReChs+x6VVWd0XiYJ1TVDUkeDHwhybf6rL+kFqEus32bXfJb6IbV\nYwsOs6b/newNnJtkQ1V9d3EiXJCWz3gifg/zaInxLODDVXV7khPorp6ePPLIFt+28PtocTHdcCC3\nJjkc+CSwz2KeYEUkjap6yiIc44b+501JPkF36b7kSWMR6rIJGPw2uAdwwwKPucWG1SPJ95PsVlU3\n9k0EN81xjOnfydVJzgP2p2uDH7eWz3i6zKYk9wLux5ibHWYxbz2q6uaB1fcAb1yCuEZhIv5fLFRV\n/WRg+TNJ3pFk16patLG1bJ5qkOQ3ktx3ehn4I2DWuxe2ARcC+yTZK8kOdJ2wE3XnEV08z++Xnw/c\n4woqyQOS7Ngv7wo8AbhiySIcruUzHqzjkcC51fdkTpB56zGj3f8I4MoljG8xnQk8r7+L6mDglukm\n0m1JkodO940lOYjub/zNw9+1hcZ9N8C4X8Cz6L5l3A58Hzi73/4w4DP98t50d45cClxO1xQ09ti3\npi79+uHAt+m+lU9cXeja9s8BvtP/fGC/fQp4b7/8eGBD/zvZABw37rhn1OEenzHwWuCIfnkV8DFg\nI/ANYO9xx7yV9Xh9/3/iUuBLwL7jjnmOenwYuBH4Zf9/5DjgBOCEfn+At/f13MCQOyknvB4nDvw+\nLgAev9gx+ES4JKmZzVOSpGYmDUlSM5OGJKmZSUOS1MykIUlqZtLQWCV5SJJ/THJ1P0TL15I8a573\nrJ1rlM+G8x2b5GED6+9Nsl/jew9J8qmtOe88x31tkqf0yy9PsvNWHOPWLSyfJOcm2WWWfack+cst\njaF/7+okn9ua92rbYNLQ2PQPIX0SOL+q9q6qA+keINtjhKc9lu65FQCq6kVVNdaHAqvqr6vqi/3q\ny4EtThpb4XDg0hp4gngxVNVm4MYkT1jM42pymDQ0Tk8GflFV75reUFXXVdVb4VdXFF9OcnH/evzM\nAwwrk+SV6eZAuTTJG5IcSfeA4D/0cw3slOS89HOPpJs74uK+/DnDAk8358cn+4H6Lkjy6H77Kf2c\nB+f1V08vHXjPXyX5Vro5Qj48/W0+yQeSHNmXfRjwpSRf6vfdOvD+I5N8oF/eq78quzDJ62bE9t/7\n7d9M8po5qvDnDDxpn+RV6ebN+CLd8PrT2x+R5HP9VeCXk+w7sP2C/jyvnXGl88n++FqOxv2Eo6+V\n+wJeCvztkP07A6v65X2A9f3yWvr5BIaUOQz4KrBzvz79VPl5DDztO70OrKYb5XSvwfIz4jmEfq4C\n4K3Aq/vlJwOX9Mun9OfdEdiVbgiHe/fnuATYCbgv3dPuf9m/5wPAkf3ytQzM28LAnCF0w418oF8+\nE3hev/yS6XJ0Q9ycRveE83bAp4AnzVKX64D79ssH0j0FvTOwC91T6tOxnQPs0y8/lm64E/rjHtMv\nnzAjzt2BDeP+9+VrNK8VMWChtg1J3g48ke7q4/fo/ti+LcljgDuB35rlbXOVeQrw/qr6GUBVzTcY\n4MF0zWTXNJZ/IvDsvuy5SR6U5H79vk9X1e3A7UluAh7Slz+jqm7r63rWPMefzxOmzw98iLsHCvyj\n/vX/+vX70CXTmYNrPrCqftov/z7wienPKv0siEnuQzdcy8dy91QfO/Y/H8fd85z8I/CmgWPfxEAT\noJYXk4bG6XLu/sNHVb2kH3xwejrdk+jG0Ppdum/NP5/lGHOVCVs2tPXWlJ9p+v23D2y7k+7/2dZO\nsDQY06oh+wbjen1VvXue496RZLuqumvIsbYDflxVj2kL9dfivG0L36NthH0aGqdz6SZSevHAtsFO\n4PsBN/Z/2J5LN/3oTHOV+Tzwwuk7kZI8sN/+U7rmoZm+BvxBkr1mlJ/L+fTt9kkOAX5QwzuVvwI8\nM8mq/hv80+coNzO+7yd5ZJLt6AaknPZ/6W4agF/vPzibrt736WPbPd0cMDNdRTcQ53RdntX38dwX\neCb8apjta5Ic1R8rSX63f88F3J3wj+bX/Rbb7ijQmodJQ2NTVUXXxPEHSa5J8g26SXz+R1/kHcDz\nk1xA94fo32c5zKxlqupzdO3+65NcAkzfQvoB4F3THeEDsWwGjgf+JcmlwEfnCf8UYCrJN4E3cPcw\n53PV9cI+nkuBf6G7mrpllqKnAZ+d7ginmyP9U3QJdnCo7pcBL0lyIV3inD7P5+mai76WZAPdVLKz\nJclP0/XRUFUX09X3EuCfgS8PlPtz4Lj+M7mcu6d7fTnwiv53ttuMuhzaH1/LkKPcSkskyX2qm1Ft\nZ7pv98f3f7DHEctudHNiP3Ur378zcFtVVZKj6TrF1/X7zgfWVdWPFi9iTQr7NKSlc1q6BwlXAaeP\nK2FAN/d6kvck2WWeZrW5HEh3A0KAHwMvhO7hPuB/mzCWL680JEnN7NOQJDUzaUiSmpk0JEnNTBqS\npGYmDUlSM5OGJKnZ/wdzeY9oTv4FDQAAAABJRU5ErkJggg==\n",
"text/plain": [
""
]
},
"metadata": {},
"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",
"plt.scatter(Angle(table['GLON_PNT'], unit='deg').wrap_at('180 deg'), table['GLAT_PNT'])\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=110000`."
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [],
"source": [
"obs = data_store.obs(obs_id=110000)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Info for OBS_ID = 110000\n",
"- Start time: 59215.50\n",
"- Pointing pos: RA 186.16 deg / Dec -64.02 deg\n",
"- Observation duration: 1800.0 s\n",
"- Dead-time fraction: 2.000 %\n",
"\n"
]
}
],
"source": [
"print(obs)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.events"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Table length=5 \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 662774403.255 -173.287 -62.4099 0.0486149 1.60795 0.258016 2 \n",
"2 662774459.184 -172.62 -63.0346 0.0611035 0.979058 0.555166 2 \n",
"3 662774479.309 -170.593 -63.4715 0.046142 0.510549 1.45144 2 \n",
"4 662774490.724 -175.637 -63.6409 0.0493567 0.36688 -0.795908 2 \n",
"5 662774506.865 -172.124 -63.4013 0.0505401 0.607364 0.7702 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 662774403.255 -173.287 -62.4099 0.0486149 1.60795 0.258016 2\n",
" 2 662774459.184 -172.62 -63.0346 0.0611035 0.979058 0.555166 2\n",
" 3 662774479.309 -170.593 -63.4715 0.046142 0.510549 1.45144 2\n",
" 4 662774490.724 -175.637 -63.6409 0.0493567 0.36688 -0.795908 2\n",
" 5 662774506.865 -172.124 -63.4013 0.0505401 0.607364 0.7702 2"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.events.table[:5]"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.aeff"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
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{
"data": {
"text/plain": [
""
]
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"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.edisp"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"obs.psf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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."
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"model_Arp220.xml\n",
"model_agn.xml\n",
"model_bkg.xml\n",
"model_dm_dSphs.xml\n",
"model_dm_gc.xml\n",
"model_fermi_bubbles.xml\n",
"model_galactic_binaries.xml\n",
"model_galactic_bright.xml\n",
"model_galactic_composite.xml\n",
"model_galactic_extended.xml\n",
"model_galactic_pulsars.xml\n",
"model_galactic_pwn.xml\n",
"model_galactic_snr.xml\n",
"model_iem.xml\n",
"models_agn.xml\n",
"models_egal.xml\n",
"models_gc.xml\n",
"models_gps.xml\n"
]
}
],
"source": [
"!ls $CTADATA/models/*.xml | xargs -n 1 basename"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" \r\n",
" \r\n",
" \r\n",
" \r\n",
" \r\n",
" \r\n",
" \r\n",
" \r\n",
" \r\n",
" \r\n",
" \r\n",
" \r\n",
" \r\n",
" \r\n",
" \r\n",
" \r\n",
" \r\n",
" \r\n",
" \r\n",
"\r\n"
]
}
],
"source": [
"# This is what the XML file looks like\n",
"!tail -n 20 $CTADATA/models/models_gps.xml"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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](http://docs.gammapy.org/dev/api/gammapy.spectrum.models.PowerLaw.html) object."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[OrderedDict([('@name', 'Prefactor'),\n",
" ('@value', '6'),\n",
" ('@error', '0'),\n",
" ('@scale', '1e-20'),\n",
" ('@min', '0'),\n",
" ('@free', '1')]),\n",
" OrderedDict([('@name', 'Index'),\n",
" ('@value', '2.2'),\n",
" ('@error', '-0'),\n",
" ('@scale', '-1'),\n",
" ('@min', '-4.54545'),\n",
" ('@max', '4.54545'),\n",
" ('@free', '1')]),\n",
" OrderedDict([('@name', 'Scale'),\n",
" ('@value', '1'),\n",
" ('@scale', '1000000'),\n",
" ('@free', '0')])]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Read XML file and access spectrum parameters\n",
"from gammapy.extern import xmltodict\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"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"PowerLaw\n",
"\n",
"Parameters: \n",
"\n",
"\t name value error unit min max frozen\n",
"\t--------- ---------- ----- --------------- --- --- ------\n",
"\t index -2.200e+00 nan nan nan False\n",
"\tamplitude 6.000e-20 nan 1 / (cm2 MeV s) nan nan False\n",
"\treference 1.000e+06 nan MeV nan nan True\n"
]
}
],
"source": [
"# Create a spectral model the the right units\n",
"from astropy import units as u\n",
"from gammapy.spectrum.models import PowerLaw\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)"
]
},
{
"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": 66,
"metadata": {},
"outputs": [],
"source": [
"# start typing here ..."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"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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