{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"
\n",
"\n",
"**This is a fixed-text formatted version of a Jupyter notebook**\n",
"\n",
"- Try online [![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/gammapy/gammapy/v0.12?urlpath=lab/tree/cta_1dc_introduction.ipynb)\n",
"- You can contribute with your own notebooks in this\n",
"[GitHub repository](https://github.com/gammapy/gammapy/tree/master/tutorials).\n",
"- **Source files:**\n",
"[cta_1dc_introduction.ipynb](../_static/notebooks/cta_1dc_introduction.ipynb) |\n",
"[cta_1dc_introduction.py](../_static/notebooks/cta_1dc_introduction.py)\n",
"
\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![CTA first data challenge logo](images/cta-1dc.png)\n",
"\n",
"# CTA first data challenge (1DC) with Gammapy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction\n",
"\n",
"In 2017 and 2018, the [CTA observatory](https://www.cta-observatory.org/) did a first data challenge, called CTA DC-1, where CTA high-level data was simulated assuming a sky model and using CTA IRFs.\n",
"\n",
"The main page for CTA 1DC is here:\n",
"https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki (CTA internal)\n",
"\n",
"There you will find information on 1DC sky models, data access, data organisation, simulation and analysis tools, simulated observations, as well as contact information if you have any questions or comments.\n",
"\n",
"### This tutorial notebook\n",
"\n",
"This notebook shows you how to get started with CTA 1DC data and what it contains.\n",
"\n",
"You will learn how to use Astropy and Gammapy to:\n",
"\n",
"* access event data\n",
"* access instrument response functions (IRFs)\n",
"* use index files and the ``gammapy.data.DataStore`` to access all data\n",
"* use the observation index file to select the observations you're interested in\n",
"* read model XML files from Python (not integrated in Gammapy yet)\n",
"\n",
"This is to familiarise ourselves with the data files and to get an overview.\n",
"\n",
"### Further information\n",
"\n",
"How to analyse the CTA 1DC data with Gammapy (make an image and spectrum) is shown in a second notebook [cta_data_analysis.ipynb](cta_data_analysis.ipynb). If you prefer, you can of course just skim or skip this notebook and go straight to second one."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Notebook and Gammapy Setup\n",
"\n",
"Before we get started, please execcute the following code cells.\n",
"\n",
"The first one configures the notebooks so that plots are shown inline (if you don't do this, separate windows will pop up). The second cell imports and checks the version of the packages we will use below. If you're missing some packages, install them and then select \"Kernel -> Restart\" above to restart this notebook.\n",
"\n",
"In case you're new to Jupyter notebooks: to execute a cell, select it, then type \"SHIFT\" + \"ENTER\"."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"numpy: 1.16.2\n",
"astropy: 3.1.1\n",
"gammapy: 0.12\n"
]
}
],
"source": [
"import numpy as np\n",
"import astropy\n",
"import gammapy\n",
"\n",
"print(\"numpy:\", np.__version__)\n",
"print(\"astropy:\", astropy.__version__)\n",
"print(\"gammapy:\", gammapy.__version__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DC-1 data\n",
"\n",
"In this and other Gammapy tutorials we will only access a few data files from CTA DC-1 from `$GAMMAPY_DATA/cta-1dc` that you will have if you followed the \"Getting started with Gammapy\" instructions and executed `gammapy download tutorials`.\n",
"\n",
"Information how to download more or all of the DC-1 data (in total 20 GB) is here:\n",
"https://forge.in2p3.fr/projects/data-challenge-1-dc-1/wiki#Data-access\n",
"\n",
"Working with that data with Gammapy is identical to what we show here, except that the recommended way to do it is to point `DataStore.read` at `$CTADATA/index/gps` or whatever dataset or files you'd like to access there."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/Users/adonath/data/gammapy-datasets/cta-1dc\r\n"
]
}
],
"source": [
"!echo $GAMMAPY_DATA/cta-1dc"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"README.md \u001b[34mcaldb\u001b[m\u001b[m \u001b[34mdata\u001b[m\u001b[m \u001b[34mindex\u001b[m\u001b[m make.py\r\n"
]
}
],
"source": [
"!ls $GAMMAPY_DATA/cta-1dc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Let's have a look around at the directories and files in `$GAMMAPY_DATA`.\n",
"\n",
"We will look at the `data` folder with events, the `caldb` folder with the IRFs and the `index` folder with the index files. At the end, we will also mention what the `model` and `obs` folder contains, but they aren't used with Gammapy, at least not at the moment.\n",
"\n",
"## EVENT data\n",
"\n",
"First, the EVENT data (RA, DEC, ENERGY, TIME of each photon or hadronic background event) is in the `data` folder, with one observation per file. The \"baseline\" refers to the assumed CTA array that was used to simulate the observations. The number in the filename is the observation identifier `OBS_ID` of the observation. Observations are ~ 30 minutes, pointing at a fixed location on the sky."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gps_baseline_110380.fits\r\n",
"gps_baseline_111140.fits\r\n",
"gps_baseline_111159.fits\r\n",
"gps_baseline_111630.fits\r\n"
]
}
],
"source": [
"!ls -1 $GAMMAPY_DATA/cta-1dc/data/baseline/gps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's open up the first event list using the Gammapy `EventList` class, which contains the ``EVENTS`` table data via the ``table`` attribute as an Astropy `Table` object."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from gammapy.data import EventList\n",
"\n",
"path = \"$GAMMAPY_DATA/cta-1dc/data/baseline/gps/gps_baseline_110380.fits\"\n",
"events = EventList.read(path)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"gammapy.data.event_list.EventList"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(events)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"astropy.table.table.Table"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(events.table)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Row index=0 \n",
"\n",
"EVENT_ID TIME RA DEC ENERGY DETX DETY MC_ID \n",
"s deg deg TeV deg deg \n",
"uint32 float64 float32 float32 float32 float32 float32 int32 \n",
"1 664502403.0454683 -92.63541 -30.514854 0.03902182 -0.9077294 -0.2727693 2 \n",
"
"
],
"text/plain": [
"\n",
"EVENT_ID TIME RA DEC ENERGY DETX DETY MC_ID\n",
" s deg deg TeV deg deg \n",
" uint32 float64 float32 float32 float32 float32 float32 int32\n",
"-------- ----------------- --------- ---------- ---------- ---------- ---------- -----\n",
" 1 664502403.0454683 -92.63541 -30.514854 0.03902182 -0.9077294 -0.2727693 2"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# First event (using [] for \"indexing\")\n",
"events.table[0]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Table length=2 \n",
"\n",
"EVENT_ID TIME RA DEC ENERGY DETX DETY MC_ID \n",
"s deg deg TeV deg deg \n",
"uint32 float64 float32 float32 float32 float32 float32 int32 \n",
"1 664502403.0454683 -92.63541 -30.514854 0.03902182 -0.9077294 -0.2727693 2 \n",
"2 664502405.2579999 -92.64103 -28.262728 0.030796371 1.3443842 -0.2838398 2 \n",
"
"
],
"text/plain": [
"\n",
"EVENT_ID TIME RA DEC ... DETX DETY MC_ID\n",
" s deg deg ... deg deg \n",
" uint32 float64 float32 float32 ... float32 float32 int32\n",
"-------- ----------------- --------- ---------- ... ---------- ---------- -----\n",
" 1 664502403.0454683 -92.63541 -30.514854 ... -0.9077294 -0.2727693 2\n",
" 2 664502405.2579999 -92.64103 -28.262728 ... 1.3443842 -0.2838398 2"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# First few events (using [] for \"slicing\")\n",
"events.table[:2]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"astropy.time.core.Time"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Event times can be accessed as Astropy Time objects\n",
"type(events.time)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events.time[:2]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['2021-01-21T12:00:03.045' '2021-01-21T12:00:05.258']\n"
]
}
],
"source": [
"# Convert event time to more human-readable format\n",
"print(events.time[:2].fits)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"text/plain": [
""
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Event positions can be accessed as Astropy SkyCoord objects\n",
"print(type(events.radec))\n",
"events.radec[:2]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"events.galactic[:2]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"# The event header information is stored\n",
"# in the `events.table.meta` dictionary\n",
"print(type(events.table.meta))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(267.68121338, -29.6075)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# E.g. to get the observation pointing position in degrees:\n",
"events.table.meta[\"RA_PNT\"], events.table.meta[\"DEC_PNT\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## EVENT analysis example\n",
"\n",
"As an example how to work with EVENT data, let's look at the spatial and energy distribution of the events for a single run.\n",
"\n",
"Note that EVENT data in Gammapy is just stored in an Astropy Table, which is basically a dictionary mapping column names to column data, where the column data is a Numpy array. This means you can efficienly process it from Python using any of the scientific Python tools you like (e.g. Numpy, Scipy, scikit-image, scikit-learn, ...)\n",
"\n",
"### EVENT spatial distribution\n",
"\n",
"To illustrate a bit how to work with EVENT table an header data,\n",
"let's plot the event positions as well as the pointing position."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Event positions\n",
"pos = events.galactic[::300] # sub-sample every 100th event\n",
"plt.scatter(pos.l.wrap_at(\"180 deg\").deg, pos.b.deg, s=10)\n",
"# Pointing position\n",
"pos_pnt = events.pointing_radec.galactic\n",
"plt.scatter(\n",
" pos_pnt.l.wrap_at(\"180 deg\").deg, pos_pnt.b.deg, marker=\"*\", s=400, c=\"red\"\n",
")\n",
"plt.xlabel(\"Galactic longitude (deg)\")\n",
"plt.ylabel(\"Galactic latitude (deg)\")\n",
"pos_pnt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### EVENT energy distribution\n",
"\n",
"Let's have a look at the event energy distribution."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"energy = events.table[\"ENERGY\"].data\n",
"energy_bins = np.logspace(-2, 2, num=100)\n",
"plt.hist(energy, bins=energy_bins)\n",
"plt.semilogx()\n",
"plt.xlabel(\"Event energy (TeV)\")\n",
"plt.ylabel(\"Number of events\");"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A double-peak, at ~ 30 GeV and ~ 100 GeV? .... let's try to find out what's going on ...\n",
"\n",
"### EVENT MC_ID\n",
"\n",
"One idea could be to split the data into gamma-ray and hadronic background events.\n",
"Now from looking at the FITS header, one can see that ``MC_ID == 1`` is the hadronic background, and the other values are for different gamma-ray emission components."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of events: 106217\n",
"Number of gammas: 8239\n",
"Number of hadrons: 97978\n"
]
}
],
"source": [
"is_gamma = events.table[\"MC_ID\"] != 1\n",
"print(\"Number of events: \", len(events.table))\n",
"print(\"Number of gammas: \", is_gamma.sum())\n",
"print(\"Number of hadrons: \", len(events.table) - is_gamma.sum())"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"energy = events.table[\"ENERGY\"].data\n",
"energy_bins = np.logspace(-2, 2, num=100)\n",
"opts = dict(bins=energy_bins, density=True, histtype=\"step\")\n",
"plt.hist(energy[~is_gamma], label=\"hadron\", **opts)\n",
"plt.hist(energy[is_gamma], label=\"gamma\", **opts)\n",
"plt.loglog()\n",
"plt.xlabel(\"Event energy (TeV)\")\n",
"plt.ylabel(\"Number of events\")\n",
"plt.legend();"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As expected, the energy spectra are roughly power-laws. \n",
"When plotting in log-log, one can see that the spectra are roughly power-laws (as expected), and the \"double peak\" we saw before looks more like a \"double energy threshold\" pattern.\n",
"It's there for gammas and background, and below 100 GeV the signal to background ratio is lower.\n",
"\n",
"What we're seeing here is the result of a mixed-array in CTA with LSTs, MSTs and SSTs, which have different energy thresholds:\n",
"\n",
"* ~ 30 GeV is the energy threshold of the LSTs\n",
"* ~ 100 GeV is the energy threshold of the MSTs\n",
"* the energy threshold of the SSTs is at a few TeV and doesn't show up as a clear feature in the gamma and background energy distribution (probably the rise in slope in gamma in the 1-10 TeV range is due to the SSTs).\n",
"\n",
"Let's look a little deeper and also check the event offset distribution in the field of view ...\n",
"\n",
"### EVENT FOV offset\n",
"\n",
"The event position and offset in the field of view (FOV) can be computed from the event RA, DEC position and the observation pointing RA, DEC position.\n",
"\n",
"But actually, the field of view position is stored as extra columns in the EVENT list: ``DETX`` and ``DETY`` (angles in degree, I think RA / DEC aligned field of view system).\n",
"\n",
"I presume (hope) this unnecessary information will be dropped from the CTA event lists in the future to save some space (make the CTA DL3 data ~25% smaller), but for now, let's use those columns to compute the field of view offset and look at the offset-energy distribution of the events."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Offset (deg)')"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"energy_bins = 10 ** np.linspace(-2, 2, 100)\n",
"offset_bins = np.arange(0, 5.5, 0.1)\n",
"\n",
"t = events.table\n",
"offset = np.sqrt(t[\"DETX\"] ** 2 + t[\"DETY\"] ** 2)\n",
"hist = np.histogram2d(\n",
" x=t[\"ENERGY\"], y=offset, bins=(energy_bins, offset_bins)\n",
")[0].T\n",
"\n",
"from matplotlib.colors import LogNorm\n",
"\n",
"plt.pcolormesh(energy_bins, offset_bins, hist, norm=LogNorm())\n",
"plt.semilogx()\n",
"plt.colorbar()\n",
"plt.xlabel(\"Energy (TeV)\")\n",
"plt.ylabel(\"Offset (deg)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So the CTA field of view increases with energy in steps. The energy distribution we saw before was the combination of the energy distribution at all offsets. Even at a single offset, the double energy-threshold at ~ 30 GeV and ~ 100 GeV is present.\n",
"\n",
"There is also a quick-look peek method you can use to get a peek at the contents of an event list:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"events.peek()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Stacking EVENTS tables\n",
"\n",
"As a final example of how to work with event lists, here's now to apply arbitrary event selections and how to stack events tables from several observations into a single event list. \n",
"\n",
"We will just use `astropy.table.Table` directly, not go via the `gammapy.data.EventList` class. Note that you can always make an `EventList` object from a `Table` object via `event_list = EventList(table)`. One point to keep in mind is that `Table.read` doesn't resolve environment variables in filenames, so we'll use the Python standard library `os` package to construct the filenames."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from astropy.table import Table\n",
"from astropy.table import vstack as table_vstack\n",
"\n",
"filename = os.path.join(\n",
" os.environ[\"GAMMAPY_DATA\"],\n",
" \"cta-1dc/data/baseline/gps/gps_baseline_110380.fits\",\n",
")\n",
"t1 = Table.read(filename, hdu=\"EVENTS\")\n",
"\n",
"filename = os.path.join(\n",
" os.environ[\"GAMMAPY_DATA\"],\n",
" \"cta-1dc/data/baseline/gps/gps_baseline_111140.fits\",\n",
")\n",
"t2 = Table.read(filename, hdu=\"EVENTS\")\n",
"tables = [t1, t2]\n",
"table = table_vstack(tables, metadata_conflicts=\"silent\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of events: 212603\n"
]
}
],
"source": [
"print(\"Number of events: \", len(table))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of events after selection: 215\n"
]
}
],
"source": [
"# Let's select gamma rays with energy above 10 TeV\n",
"mask_mc_id = table[\"MC_ID\"] != 1\n",
"mask_energy = table[\"ENERGY\"] > 10\n",
"mask = mask_mc_id & mask_energy\n",
"table2 = table[mask]\n",
"print(\"Number of events after selection:\", len(table2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When processing a lot or all of the 1DC events, you would write a for loop, and apply the event selection before putting the table in the list of tables, or you might run out of memory. An example is [here](https://github.com/gammasky/cta-dc/blob/master/data/cta_1dc_make_allsky_images.py).\n",
"\n",
"That's all for ``EVENTS``. You now know what every column in the event table contains, and how to work with event list tables using ``gammapy.data.EventList`` and ``astropy.table.Table``. \n",
"\n",
"Just in case that there is some observation parameter in the FITS EVENTS header that you're interested in, you can find the full description of the keys you can access via the ``events.table.meta`` dictionary [here](http://gamma-astro-data-formats.readthedocs.io/en/latest/events/events.html)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## IRFs\n",
"\n",
"The CTA instrument reponse functions (IRFs) are given as FITS files in the `caldb` folder.\n",
"\n",
"Note that this is not realistic. Real CTA data at the DL3 level (what we have here, what users get) will mostly likely have per-observation or per-time interval IRFs, and the IRFs will not be stored in a separate CALDB folder, but distributed with the EVENTS (probably in the same file, or at least in the same folder, so that it's together).\n",
"\n",
"For now, the EVENT to IRF association (i.e. which IRF is the right one for given EVENTS) is done by index files. We will discuss those in the next section, but before we do, let's look at the CTA IRFs for one given configuration: `South_z20_50h`."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[01;34mcaldb\u001b[00m\r\n",
"└── \u001b[01;34mdata\u001b[00m\r\n",
" └── \u001b[01;34mcta\u001b[00m\r\n",
" └── \u001b[01;34m1dc\u001b[00m\r\n",
" └── \u001b[01;34mbcf\u001b[00m\r\n",
" └── \u001b[01;34mSouth_z20_50h\u001b[00m\r\n",
" └── irf_file.fits\r\n",
"\r\n",
"5 directories, 1 file\r\n"
]
}
],
"source": [
"!(cd $GAMMAPY_DATA/cta-1dc && tree caldb)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Filename: /Users/adonath/data/gammapy-datasets/cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits\n",
"No. Name Ver Type Cards Dimensions Format\n",
" 0 PRIMARY 1 PrimaryHDU 8 () \n",
" 1 EFFECTIVE AREA 1 BinTableHDU 53 1R x 5C [42E, 42E, 6E, 6E, 252E] \n",
" 2 POINT SPREAD FUNCTION 1 BinTableHDU 70 1R x 10C [25E, 25E, 6E, 6E, 150E, 150E, 150E, 150E, 150E, 150E] \n",
" 3 ENERGY DISPERSION 1 BinTableHDU 56 1R x 7C [500E, 500E, 300E, 300E, 6E, 6E, 900000E] \n",
" 4 BACKGROUND 1 BinTableHDU 59 1R x 7C [36E, 36E, 36E, 36E, 21E, 21E, 27216E] \n"
]
}
],
"source": [
"# Let's look at the content of one of the IRF FITS files.\n",
"# IRFs are stored in `BinTable` HDUs in a weird format\n",
"# that you don't need to care about because it's implemented in Gammapy\n",
"irf_filename = os.path.join(\n",
" os.environ[\"GAMMAPY_DATA\"],\n",
" \"cta-1dc/caldb/data/cta/1dc/bcf/South_z20_50h/irf_file.fits\",\n",
")\n",
"from astropy.io import fits\n",
"\n",
"hdu_list = fits.open(irf_filename)\n",
"hdu_list.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Effective area\n",
"\n",
"The effective area is given as a 2-dim array with energy and field of view offset axes."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"from gammapy.irf import EffectiveAreaTable2D\n",
"\n",
"aeff = EffectiveAreaTable2D.read(irf_filename, hdu=\"EFFECTIVE AREA\")"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"gammapy.irf.effective_area.EffectiveAreaTable2D"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(aeff)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"gammapy.utils.nddata.NDDataArray"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(aeff.data)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NDDataArray summary info\n",
"MapAxis\n",
"\n",
"\tname : energy \n",
"\tunit : 'TeV' \n",
"\tnbins : 42 \n",
"\tnode type : edges \n",
"\tedges min : 1.3e-02 TeV\n",
"\tedges max : 2.0e+02 TeV\n",
"\tinterp : log \n",
"MapAxis\n",
"\n",
"\tname : offset \n",
"\tunit : 'deg' \n",
"\tnbins : 6 \n",
"\tnode type : edges \n",
"\tedges min : 0.0e+00 deg\n",
"\tedges max : 6.0e+00 deg\n",
"\tinterp : lin \n",
"Data : size = 252, min = 0.000 m2, max = 5371581.000 m2\n",
"\n"
]
}
],
"source": [
"print(aeff.data)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"aeff.peek()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$$[3.783587] \\; \\mathrm{km^{2}}$$"
],
"text/plain": [
""
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# What is the on-axis effective area at 10 TeV?\n",
"aeff.data.evaluate(energy=\"10 TeV\", offset=\"0 deg\").to(\"km2\")"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This is how you slice out an `EffectiveAreaTable` object\n",
"# at a given field of view offset for analysis\n",
"aeff.to_effective_area_table(offset=\"1 deg\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Energy dispersion\n",
"\n",
"Let's have a look at the CTA energy dispersion with three axes: true energy, fov offset and migra = e_reco / e_true and has dP / dmigra as value.\n",
"\n",
"Similar to the event energy distribution above, we can see the mixed-telescope array reflected in the EDISP.\n",
"At low energies the events are only detected and reconstructed by the LSTs.\n",
"At ~100 GeV, the MSTs take over and EDISP is chaotic in the ~ 50 GeV to 100 GeV energy range.\n",
"So it can be useful to have quick access to IRFs like with Gammapy (e.g. for spectral line searches in this case), even if for 95% of science analyses users later won't have to look at the IRFs and just trust that everything is working."
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n"
]
}
],
"source": [
"from gammapy.irf import EnergyDispersion2D\n",
"\n",
"edisp = EnergyDispersion2D.read(irf_filename, hdu=\"ENERGY DISPERSION\")\n",
"print(type(edisp))\n",
"print(type(edisp.data))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"NDDataArray summary info\n",
"MapAxis\n",
"\n",
"\tname : e_true \n",
"\tunit : 'TeV' \n",
"\tnbins : 500 \n",
"\tnode type : edges \n",
"\tedges min : 5.0e-03 TeV\n",
"\tedges max : 5.0e+02 TeV\n",
"\tinterp : log \n",
"MapAxis\n",
"\n",
"\tname : migra \n",
"\tunit : '' \n",
"\tnbins : 300 \n",
"\tnode type : edges \n",
"\tedges min : 1.2e-38 \n",
"\tedges max : 3.0e+00 \n",
"\tinterp : log \n",
"MapAxis\n",
"\n",
"\tname : offset \n",
"\tunit : 'deg' \n",
"\tnbins : 6 \n",
"\tnode type : edges \n",
"\tedges min : 0.0e+00 deg\n",
"\tedges max : 6.0e+00 deg\n",
"\tinterp : lin \n",
"Data : size = 900000, min = 0.000, max = 10595.855\n",
"\n"
]
}
],
"source": [
"print(edisp.data)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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pZ2xruX66ne3NN2j9EvASQE5OTo8rF/nBBx9QUlJCbm4uAD6fj4EDo7Ebm81GQUEBAOPGjeO9994D4P3336esrCx+jrq6OjweDwCzZ8/G6XQCsGXLFhYvXgzA6NGjycrKAsDlcjF58mTeeustRo4ciWEYXHXVVS3mtmrVqjO+j9YCENHyJe2znEUGxsaNG9mwYUN8OYnf7+fgwYNcfvnlzJw5k3/9618UFBSwa9cupkyZcsZzF0IIIUTP9b3vOBc7o++N9td9w8GGSmwmMyOS0/AYDdjNFo56j3LtRSM45jvGRc5UTMqE05LAEe8RjvtOMNCZxgBHfzxGPYlW92muePYkgCGEEL2Y1vpI7M/jSqnVRGtYHFNKpWutj8aWiByPDT8MNG3YnQEciW3/8SnbN5/nqXc6rTULFizgiSeeaLHParXGgwBmszle1yISibBt27Z4oKIpl+tk0ef2shoWLVrEn/70J6688spWsy8gmoHx1Vdftdh+3333ceuttzbblpGRwaFDJxNiDh8+zKBBg9q8fqOzycDQWrNmzRouu+yyFvvmz5/P008/jc/nY86cOVgs8lZCCCGE6A1MsQUahxrK+a6+kmAkTE3AIByJkNX/cj6r/IqLnW4ucg6gIRRdLtLYXTMj4ZJmX5JUBarPSwBDlpDEVAdrqTcazvn4U9uini2zyYzZJC1IhBCdRynlUkolNj4GpgH/DawDFsSGLQAaqzKuA25VUflAbWyJybvANKVUaqx457TYtgvKlClT+Oc//8nx49F4TVVVFd999127x0ybNo3ly5fHn5eWlrY6bsKECbzxxhtAtKPHl19+Gd+Xl5fHoUOHeP3119tcbrFq1ap4Qc2mP6cGLyCa+fHqq6+iteaTTz4hOTm5Wa2LtpxaxLM9N9xwA8uWLYs//+yzz+KPp06dyq5du1ixYoUsHxFCCCF6kepAdOlHOBIi0WrFG4qQ1e9ifuAeQHnDIS5PuoQrU67kqPcYlyYOY0jiDxiUkI7T7KC/ox9pjn7xcw1xD27rMh0iAYyYQDiAoc+9knyCpeW3c2fDaXa0aK0qhBAddBGwRSn1ObAD+H+11u8ATwLXK6X2AdfHngO8DXwDfA28DPwaQGtdBfxfwKexn8di2y4omZmZPP7440ybNo2srCyuv/76VotfNrVs2TKKi4vJysoiMzOTFStWtDru17/+NRUVFWRlZfHUU0+RlZVFcvLJteZz587l2muvJTW1481bZsyYwaWXXsrw4cO54447eOGFF+L7srOz448feOABMjIy8Hq9XJ87imef+iNwZktIlixZgtfr5aqrrmLUqFEsXbo0vs9sNnPTTTdRV1fHtdde2+H7EUIIIUT3OeL9nm883wKQbE9l89FiPqssJ8nqJD3BSVWgjgiReFeRg/WHSU+4CACF4oj3aIsv4uuCnvM2X3UmqaQ92bicMXrr9s0dPs/3vuPYzXZSbVLcSAjROZyWlJImrUv7lJycHF1cXNxs2+7duxk5cmQ3zej8CofDGIaBw+Fg//79TJkyhb1792Kz2QAoKCigsLCwW+pFVNYHKK/xcUmKk/IaHyMGunHaum/ZR29+HYgLg1Kqz/5u7qz3zUKI3sMfDuAw2znqPcYnx/czxO3GabERCBuYlOIi50WEIiHqjFpAMzRxWPyLdx37WuSEv4oBjv4dmseZvm+WhatCCCFEB3m9XiZNmoRhGGitefHFF7HZbNTU1DB+/HiuvvrqLg9ehCOaCk+AhmA0u/BslpAIIYQQom+oClRzpOEYOytDDHaZ+Ph4mMuTakm22bk8ZRjfe4/Qz57GYNdgqoM1eIL1OJ3RAEZjVobb6mrvEp1KAhhCCCFEByUmJnJqxglASkoKe/fu7YYZgTcY4rjHj0kpnFbzyQBGt8xGCCGEED2JPxzgmO84X9cdJaI1uQOcfO81mDLIQXVAY1YKI2xgNzu4yDmAeqMBu9ne6huJeqOhy8ohSAAjxm62Y1Xy1yGEEKJ3iMTeYFw2wIXTZqE+EM3EuNCXjgohhBCiYyoD1fS3p1ITrMGqTLhsVo75vAxPSiPBksDIlJFU+CvRWnNp4jB8YT+1wVoucbXe9ayjDS3Ohnxij5HaF0IIIXqTxkBFY3tY1bi9m+YjhBBCiO5XbzSQYHZQWvklGo3NbKYq4GdQghuTMhEI+ylvOEpIhxjiHkwgHMBj1JMc+7zsC/tbZFt05Wdp6UIihBBC9EKNgYrGwEVsBYnUwBBCCCH6MG/YR7n3CN96PFQHAqQ5EnGYLfS3R1ugpidEW7MPcQ+mOliL3WxnoCMtXueitaUiFf7KLpu/BDCEEEKIXqgxUBHPwFCNRTwlgiGEEEL0JVWBGgDKqsuoNzxsrzhBZkp/rCYT3pAft9WKSZlJc6RRHazlElc6x30nWs2s8IX9LbZ1tAPJ2ZAAhhBCiD5v2bJljBw5kp///OcEAgGmTp1KdnY2q1atOqvzbN68mY8//rhT5vTQQw8xePBg3G53u+OeeOIJhg8fzhVXXMG7774b335yCUn0eWMmxmt/e5V77723U+YohBBCiJ6v8T2A05JAXbCey5Ps1Bk+TEphAhKtLlLsySTbkgA45qtgoDON733HW5zLE6zvuom3QmpgCCGE6PNeeOEFNmzYwLBhw/jkk08wDIPS0tKzPs/mzZtxu91cc801HZ7TrFmzuPfeexkxYkSbY8rKyigqKmLXrl0cOXKEqVOnsnfvXsxmc4slJI2BjEiHZyaEEEKIC0UwHEQDe2v3ccBThVJQG4yQ5rByaeJFRHSEoYlD+KbuGy5NuhStNRclDADgYudAAI77T5BgduK2uki0tf/FyvkmGRhtqDcamj1vCHmbPfeGfG0e62tnnxBCiO7zzDPPMHr0aEaPHs1zzz0HwN13380333zD7Nmzeeqpp/jFL35BaWkp2dnZ7N+/n9///vdkZmaSlZXF/fffD0BFRQU333wzubm55ObmsnXrVg4cOMCKFSt49tlnyc7O5qOPPurQXPPz80lPT293zNq1a5k3bx52u51hw4YxfPhwduzYATRfQrJy5UpGZY7kVz+dyfZtJzNEWruPxu3XX389Y8eO5a677mLIkCGcOHGiQ/cjhOh8SqmfKKVeVkqtVUpN6+75CCF6lhP+Ko76jrGv9gDHfHU4zGaqAprs/gMYnpROojWRfvZ+HPNV4I5lX1ziir73ONRQHj/PQEcaLmsC0HoNjK4kGRhtCOlw8+eRULPnRsQAnK0ea+hQG3uEEEJ0l5KSElauXMn27dvRWpOXl8fEiRNZsWIF77zzDps2bSItLY28vDyefvpp3nrrLaqqqli9ejV79uxBKUVNTXQN6eLFiyksLGTChAkcPHiQG264gd27d3P33XfjdrvjgY6mNm3aRGFhYYvtCQkJ57zspLy8nPz8/PjzjIwMysujbzgal5B8//1RlixZwvYdn3LEq7jn325kfM64du/j0UcfZfLkyTz44IO88847vPTSS+c0PyHE2VNK/S+gADiutR7dZPt04M+AGfh/tNZPaq3XAGuUUqnA08DG7pizEKLnOdxQjkmZCYT9RNCc8AcY4naTn3gR1YEarCYbqfYUDtYf4gfuwXxXf7DZ8am2lGbPVTyns3tJAEMIIUSfsGXLFm666SZcrmgV7Tlz5vDRRx8xZsyYNo9JSkrC4XCwaNEiZs6cSUFBAQDvv/8+ZWVl8XF1dXV4PJ52rz9p0qRzWpbSntYKcsaLdcae79i+nR//+McMHDiAiiN1/GTOzzhy8Bug7fvYsmULq1evBmD69OmkpqZ26ryFEO36C7AceLVxg1LKDDwPXA8cBj5VSq3TWjf+D/yH2H4hRB9VG6yL17CoCtTgMDupDdZQGain0u/DaoJaI8BlSck4LU4GOtIASLDEuotYohkWJ/xVpDn6xbuOQLRwp9fw0t/Rr4vvqiUJYAghhOgTzqX7hsViYceOHXzwwQcUFRWxfPlyPvzwQyKRCNu2bcPpPPN8u/ORgZGRkcGhQ4fizw8fPsygQYOA6P0qFErFfmgMbJz8e2jrPqRTiRDdR2v9/ymlhp6yeTzwtdb6GwClVBFwo1JqN/AksEFrvbO18yml7gTuBBj8g8Hna9pCiG5mUiaqAzVEdIRAJMCRhuMc93upMxRj+qcR0RHMykyl/wQZrgyO+08w0JFGWiwo0RjQcFlavrdxmh3dvnSkkdTAEEII0Sdcd911rFmzBq/XS0NDA6tXr+ZHP/pRu8fU19dTW1vLjBkzeO655+IZFNOmTWP58uXxcY3bExMT28zEaMzAOPWnI11LZs+eTVFREYFAgG+//ZZ9+/Yxfvx4IJqBoRTk5eWxefNmqqoqMQyD9Wv+FT++rfuYMGECb7zxBgAbN26kurr6nOcohOgUlwCHmjw/HNv2P4GpwE+VUne3dqDW+iWtdY7WOmfAgK5rdSiE6Fpaa2qDtZwInKA2WIM/HGJQgpt+tuiXGOFICJvJRppzADazjYGONCr8lS3O42lSC9IfDnTlLZwRCWAIIYToE8aOHcttt93G+PHjycvLY9GiRe0uHwHweDwUFBSQlZXFxIkTefbZZ4Fo29Xi4mKysrLIzMxkxYoVQLRzyOrVqzuliOcDDzxARkYGXq+XjIwMli5dCsC6det45JFHABg1ahRz584lMzOT6dOn8/zzz2M2m4FoEU+lID09naVLl3LNNddw1/yfMDorO36Ntu5jyZIlbNy4kbFjx7JhwwbS09NJTEzs0P0IITqktcXnWmu9TGs9Tmt9t9Z6RZfPSgjRY4SJENIG/nAAhSLV7uCE38fQxH5EdBiHxUGCxUWSNfrvuT8cIByJ1n2sDtTEz5PYZOmIw2zv2ps4A+pCTxMdlzNGb92++ZyOrTcamq3tOXWfUgojEsJqiq60ccXWBUG0C0lCK+k1EO1C4mxjX3xM2N9uGo4/HOiRLxghxJlzWlJKtNY53T2P7pCTk6OLi4ubbdu9ezcjR47sphn1PeXVXmp9ITIHJcW3/Xd5Lf3dNtKT2/83KhAIYDabsVgsbNu2jXvuuafT6nfI60B0N6VUj//dHFtC8lZjEU+l1A+BpVrrG2LPHwTQWj9xhuebBcy69LJhd+z66rPzMmchRPcIRgx8IR/HfMc45qvjG0+Yy5OtAKQ7+2FSJjSQYElggONkFtbhhnIyXJd006xbOtP3zV1WA0MpNZhoMaKLibahf0lr/edTxvwYWAt8G9v0L631Y+drTtFOIq1zW13UBOsIhoMApNiSmu1vK3gBnDZ4AbGuJubT7ZcAhhCil9rwe/j+y84958VXwf94snPPeQFrzMBoSnGyvWp7Dh48yNy5c4lEIthsNl5++eXzMkchxBn7FBihlBoGlAPzgH8704O11uuB9eNyxtxxnuYnhOgG1YEa7GY7x33HqTd8fOsJkxH7fr6/3U0gEqCfvT8uSwKVgapmx2a4LqE6UEOqPaWVM0f1xC/Vu7KIZwj4rdZ6p1IqEShRSr3XpHpyo4+01gVdOC8hhBCi12msgdGUUuqMAhgjRozgs8/kW1ohuoNS6r+AHwNpSqnDwBKt9StKqXuBd4l+Bfa/tNa7unGaQogewG11ccx3nPqQl4aQwQ/cJuqCAdIcTszKzADnAIJhg2DEwNRK9YgI7b8p6GnBC+jCAIbW+ihwNPbYE6uafAlwagBDCCFEbyeZEudsz549LFy4kJ07d/LHP/6R+++/v9Vx3x34lt8sWoDXU8vYsWP529/+hlInu5CsXLmSP/85mghZVlbGFVdcgdlsZvr06Tz5ZMv/Ph6Ph6FDh/Ldd9/hdrvj2wsKCvjVr37FnDlzzsPdCtH3aK3nt7H9beDtczlnkyUkHZmaEKKHMCIGIR3GaXZwuOE4ABaTiYvsLi5yhkm2pWJEgvjDAQY60qj0V3GJK73FefrbL7w26d1SxDO2rm8MsL2V3T9USn2ulNqglBrVpRMTQgghzlIoFOrS6/Xr149ly5a1Gbho9MSjD3PbXb9h3759pKam8sorrzRbQrJw4cJ4J5RBgwaxadMmSktLWw1eQLTDyuTJk1m7dm18W3V1Ndu3b2fGjBmddXtCiPNAa71ea31nSkpyd09FCNFBtcE6PEYD+2q/prTyS469bPE6AAAgAElEQVT7vBiRCCk2Jy6Lmx+4hxAI+xnoHIg31lGkf6xVaqM6w9PszwtJlwcwlFJu4E3g/9Ba152yeycwRGt9NfB/A2vaOMedSqlipVRxRUXL1i9CCCFEa/7+978zfvx4srOzueuuuwiHo9W33W43Dz30EFdffTX5+fkcO3YMgIqKCm6++WZyc3PJzc1l69atACxdupQ777yTadOmceutt+L1epk7dy5ZWVnccsst5OXlUVxczCuvvEJhYWH8+i+//DL33Xdfh+5h4MCB5ObmYrVa2xyjtWbblv/N9Fk/AWDBggWsWbPmjJeQ1NfXxzu2jBkzhvXr1wMwf/58ioqK4uPefPNNZs6cicPRM3rDCyGEEL2dL+zHF/LhMNs41OAhyWanMuDHarJgRAxclgQUKtqNRJmoCUY/cjdtmdrYiaSxC8mFpEsDGEopK9HgxWta63+dul9rXae1ro89fhuwKqXSWhnXKf2sraa23/w1spltWE0WGkLec75OaywmS/TFF/a3uV8IIUTn2b17N6tWrWLr1q2UlpZiNpt57bXXAGhoaCA/P5/PP/+c6667Ll60cvHixRQWFvLpp5/y5ptvsmjRovj5SkpKWLt2La+//jovvPACqampfPHFFzz88MOUlJQAMG/ePNatW4dhRItGr1y5koULF7aY2y233EJ2dnaLn1dfffWc7rWyspLEpBSslui/cxkZGZSXlzdbQtKexx57jOnTp7Njxw4+/PBDfvvb3+L3+5k5cyaffPIJ1dXVABQVFTF/fqvZ7kKIHkQpNUsp9VJNTW13T0UIcQ5CkRA1wTpqg3UY4SDH/RXsrKwirDUNIYPMlAGYMJGecDHBcJChiUNItSWTZEvCH4p+3hzg6N8i48JsaqerRA/VlV1IFPAKsFtr/UwbYy4GjmmttVJqPNEAy3lLsWirhWpTjd1HaoOnJot0jNPsoD6W0tNaN5KeWDBFCCEuZB988AElJSXk5uYC4PP5GDhwIAA2m42Cgmj96HHjxvHee+8B8P7771NWdrJUU11dHR5P9B//2bNn43RGu05t2bKFxYsXAzB69GiysrIAcLlcTJ48mbfeeouRI0diGAZXXXVVi7mtWrWqU++1sUV60yKeSikUUOszqG4IkuqytXn8xo0b2bBhQ3w5id/v5+DBg1x++eXMnDmTf/3rXxQUFLBr1y6mTJnSqXMXQnQ+6UIixIUrFAlRH/JiRAxO+Cuo9DdwoD7EZUl2rMpEss2Fy+omGA4Q0RGcFidHvcdwWKLZkRcnDKTSX0V/R7945kWjU59fCLrya/5rgV8CXyqlGpvJ/5/ADwC01iuAnwL3KKVCgA+Yp/WZJLsKIYQQ7dNas2DBAp544okW+6xWKyr2ad9sNsfrWkQiEbZt2xYPVDTlcp0Mgrf3T9WiRYv405/+xJVXXtlq9gVEMzC++uqrFtvvu+8+br311vZvrBVpaWl46moIx+7j8OHDDBo0iLREO4eqvNQHQu0GMLTWrFmzhssuu6zFvvnz5/P000/j8/mYM2cOFotkDAohhBDniyfUgC/kJRQJoYDvfUH62SEQDuOyW3FYnFhMFrTWWEwWguEgiVY3bquL6mA066o3Zfd3ZReSLURb0Lc3ZjmwvGtmJIQQoi+ZMmUKN954I4WFhQwcOJCqqio8Hg9Dhgxp85hp06axfPlyfve73wFQWlpKdnZ2i3ETJkzgjTfeYNKkSZSVlfHll1/G9+Xl5XHo0CF27tzJF1980ep1OjsDQylF3jXX8e5ba7j8jtv461//yo033khqgo2KugCR03w3cMMNN7Bs2bJ4l5LPPvuMMWPGADB16lQWLlzI0aNHWbFiRafOWwghhBBRIR0mEqtR0WA04LK6+MZTTardzMVON0YkTILFgd1sx4QJi8mC1WQlGA7iC/txW12YUNQE6+KrCnqDbulCIoQQQnS1zMxMHn/8caZNm0ZWVhbXX389R48ebfeYZcuWUVxcTFZWFpmZmW1+YP/1r39NRUUFWVlZPPXUU2RlZZGcfLLa/9y5c7n22mtJTe14u7Lvv/+ejIwMnnnmGR5//HEyMjKoq4suc5wxYwZHjhwB4P6HH+WVF5czfPhwKisruf322wEwmSBymtzGJUuW4PV6ueqqqxg1ahRLly6N7zObzdx0003U1dVx7bXXdvh+hBDnn9TAEOLCUxWoptbw0GDU4w37+N9Hv8NqMuE0W0i0JtLfnkqqvT82UzSj8iLnAIyIgc1sY4CjPzXBOpJtSb0qeAGgLvQVGuNyxuit2zef0dh6o6HNuhdN9zVGqRpCXlyWBIB4Ec/G57WxF8SpvCEfCZaWqcataSzg6TQ74s8bH5/JnIUQPZvTklKitc7p7nl0h5ycHF1cXNxs2+7duxk5cmQ3zej8CofDGIaBw+Fg//79TJkyhb1792KzRd9UFBQUUFhY2KX1Ir763oPTauYH/ROabd9fUQ8aLhvo7rK5NNWbXwfiwqCU6rO/m8/mfbMQovsEw0GqgjXYzXaOe7/HGw7wdV0DAxxWLklIQaNxmp1cnHARNcG6+GdIjSbR6iYYMfCH/CTZEqkzPBdErYszfd/cexbDnIFgxGhzn6FD8ceBcAA4GaxofNy0kGdb5wo1Oc/pnBqsCEfCLQp6GmdxPiGEEN3D6/UyadIkDMNAa82LL76IzWajpqaG8ePHc/XVV3d5sUutdbMino1MShGKRLp0LkIIIYQ4c96wH2+ogbpgLYcaagnpCD9wOTErhdPixKTMKBRWkxWryUqESMsCnbbo8/aCFxfil+V9KoAhhBBCnA+JiYmcmnECkJKSwt69e7thRqChjQDG6ZeQCCGEEKJ7VAaqqTc8VPnrOOb3kmCxMMDuwm6y0t8xALvJhtPioCHkw2PUo3UEi6l5B0ubyXpG17rQghcgNTCEEEKIXklrUK3UzjYp1W7XFCGEEEJ0j3qjAX/Ix8H6Cg42NOALQarNgcvixGa24wt5sZgsVAdqMSIGiVY3YR054xIGvYEEMIQQQoheqL0lJKfrQiKE6F2kiKcQPZ8/HMAfDuAL+6gNBkhPcOK2KlJsSZiVBac5AbvZQYLFSViH49kTjX+GIn2j9IAEMIQQQoheqM0lJGfQhUQI0btorddrre9MSUk+/WAhxHkT1uFWt/vDAeqNBqoDVRz11uAwW0ixOfmBKwmnJQGryYo5VvcCiBbqDAcBcJijy0cspmh1iJomdRt7oz5VA6OttUD1RgNWdfKvwmaOVo0/tQtJ44vi1HN5Q774Y4uy4Av5MHSIJGsivpAP52lSehq7j5hN5hb7ms7LHw7EX6BCCCFEe7TWbS4hiWgdy9BoJcIhhBBCiPPCrE5+3gvpMBZlpjpYS22ghiPeSqoCfmwmMy6rFYfZES/W6TA7cVtd8QBIIBKkv/1ka/amn1t7W9vUU/WpDIy2ipQYOtRsX6otGp02mqThhCKhZl1JmrZQDUVC8Z8EixNDhzDCRvzcpxOORF+Ip3YlOXXObUXshBBCdMyyZcsYOXIkP//5zwkEAkydOpXs7GxWrVp1VufZvHkzH3/8cafM6aGHHmLw4MG43e23O33iiScYPnw4V1xxBe+++y4QDV60lYHxxut/409/+J1kYQghhBDdyBvyEoqE8BoN1Bke6g2DE34rlQGN3WTGZXWTYEkgrMOEdZjaYF38c6Pb0rxFut1k645b6BZ9KgNDCCGEaM0LL7zAhg0bGDZsGJ988gmGYVBaWnrW59m8eTNut5trrrmmw3OaNWsW9957LyNGjGhzTFlZGUVFRezatYsjR44wdepU9u7di1LR7ydaC2A0bopojbmVDA0hhBBCnD9GxMBqsmI32TjmO051oIpvPLU4zGaGJMIlCf1wWlwoFGZlxmaxEowYJFsTMXSI6mAtCoW9SWZ+05UCvV2fysAQQgjRtz3zzDOMHj2a0aNH89xzzwFw991388033zB79myeeuopfvGLX1BaWkp2djb79+/n97//PZmZmWRlZXH//fcDUFFRwc0330xubi65ubls3bqVAwcOsGLFCp599lmys7P56KOPOjTX/Px80tPT2x2zdu1a5s2bh91uZ9iwYQwfPpwdO3YQzb842YVk5cqVXH755UycOJEd2z8Bolkard1H4/1df/31jB07lrvuuoshQ4Zw4sSJDt2PEKL7SBFPIXqOQDjIcd8JqgO1fFf/PVUBP3aTmR+4kkh3JuK2JpJgdmI32TAiBsm2JOxmGzazDZclgVRbMq4+1HXkVH0nVCOEEKJPKykpYeXKlWzfvh2tNXl5eUycOJEVK1bwzjvvsGnTJtLS0sjLy+Ppp5/mrbfeoqqqitWrV7Nnzx6UUtTU1ACwePFiCgsLmTBhAgcPHuSGG25g9+7d3H333bjd7nigo6lNmzZRWFjYYntCQsI5LzspLy8nPz8//jwjI4Py8nIam4woBUePHmXJkiWUlJSQnJzMjyb+mGFXjCKi276PRx99lMmTJ/Pggw/yzjvv8NJLL53T/IQQPYPWej2wflzOmDu6ey5C9GVhHcZsMhOMBPCH/XhDIY544YpkBw6Lk1AkRFhHUMqE2WTGqq3UBOuwKDOhSAhDh3CaHUR0pM1rNK2H0RtJAIOThTLrjQbcVhf1RkObYxtCXgBcloRmxTsh+oKsDtREC22am5+7Pa0V72x1nDqzcUIIIVrasmULN910Ey5XtLbQnDlz+OijjxgzZkybxyQlJeFwOFi0aBEzZ86koKAAgPfff5+ysrL4uLq6OjweT7vXnzRp0jktS2mPbqUdqlKqWQBj+/bt/PjHP2bAgAEA3PzTn7HzyzIiEd3mfWzZsoXVq1cDMH36dFJTU1tcRwghhBBnLhT7rOgJ1hLSYfbXVdIQ0oxOdZFojQYcnJYEzMqEzWTFhEIphdVkwWVJaBaYCEaMZktImurNwQuQAAZwslBmY8HNUDvFMkOnFPZsKqwjhCIhUu0pNCb1nK4DCbRevLM10oFECCHOXWsf9k/HYrGwY8cOPvjgA4qKili+fDkffvghkUiEbdu24XSeeQrn+cjAyMjI4NChQ/Hnhw8fZtCgQS2WkDTtNtK0BkZb93Euf1dCCCGEaKkh5MWIhNBoKv0VnPA3UB0M4DCbGep24rTYcFicRHSYFFsyidaTxbudOJt9gd6o6Zi+RmpgCCGE6BOuu+461qxZg9frpaGhgdWrV/OjH/2o3WPq6+upra1lxowZPPfcc/EMimnTprF8+fL4uMbtiYmJbWZiNGZgnPrTka4ls2fPpqioiEAgwLfffsu+ffsYP348oViLEZOCvLw8Nm/eTGVlJYZhsGb1mwBEdNv3MWHCBN544w0ANm7cSHV19TnPUQghhOjLbCYroUiIQCiAQnHUF6QhZMIXCuEwm9E6gs1kw2qyxpeK+MJ+AHwhX5/qMHImJIAhhBCiTxg7diy33XYb48ePJy8vj0WLFrW7fATA4/FQUFBAVlYWEydO5NlnnwWibVeLi4vJysoiMzOTFStWANHOIatXr+6UIp4PPPAAGRkZeL1eMjIyWLp0KQDr1q3jkUceAWDUqFHMnTuXzMxMpk+fzvPPP49SJr4+Xg+ASSnS09NZunQpP/zhD6PtYceMBaIZGG3dx5IlS9i4cSNjx45lw4YNpKenk5iY2KH7EUIIIfqaUCREXdCDL+SlMlDBrppKHGaNRWmuSBlAP0d/LkpIx6RMOMxOghEDI2LEM/TPJJu/r1EXeprouJwxeuv2zZ1yrupgLam2ZGqCdfFtKbYkAGqDdSTbkqiN7Uu2JVEXbP4tWzBiEIqEuDhhYKfMRwhxYXNaUkq01jndPY/ukJOTo4uLi5tt2717NyNHjuymGfUdRjjC7qN1uGwWhqW5MJmat0oNhsLs+d5DRmoC/Vytf6sTCAQwm81YLBa2bdvGPffc02n1O+R1ILqbUqrP/m7uzPfNQoi2hXUYszJTG6yj3mhgX105R70h0hMsJFqtJFldmE1m+tv7E9Jh7CYbgXCABGvCGZcX6G3O9H2z1MAQQgghepHGLyZSXbYWwQuIZmVANAPDCEc4XufHZFJcnOSI18o4ePAgc+fOJRKJYLPZePnll7vuBoQQQogLXCAcxIgYVAeqOeGvwRcKMcRtw2oykZ4wECMSwmayoYGEWMCiv6MfvpCv13cR6SgJYDTR2DHEEuv2EQgH4vssJku8gErTbaFICIvp5F+jWZ1clVNneLAqS4vUH49Rj0Lhtrrwhf2njbL5wwEp4CmEOGdKKTNQDJRrrQuUUsOAIqAfsBP4pdY6qJSyA68C44BK4Bat9YHYOR4EbgfCwL9rrd/tyJye2vEUe6r2dOQULVzZ70r+Y/x/dOo5L0Sx8he0EruIbY/u8AXD1PkM6gPRgtT9XXZslui+ESNG8Nlnn533uQohuoZSahYw69LLhnX3VITolU4NOvjDAfxhH76wl1ojQFhHqDOCDHYlEYqEsJvt2M12gpEgbqsr/rnTaraB1i0+Y4qTpAZGE43dSNxWF26ri3CT/rouS0KLriMJscBEgsVJgsVJki2RVHtKfL8RNuKdTZoyIiGCEQOAcKTtjieNTr2uEEKcpcXA7ibPnwKe1VqPAKqJBiaI/VmttR4OPBsbh1IqE5gHjAKmAy/EgiKiB4q3UG1jv1JgVopqb5D6QAi3PfoGKXyBLykVQrRNa71ea31nSkpyd09FiF7JZUnAF/bHa17UGx48hodvPTUoFMMSkxmZMgiXxYXL6sakTCgUAx1pGBEDfyhatLMxcNE0eOEN+brrtnokCesIIUQvppTKAGYCfwTuU9E1ApOBf4sN+SuwFHgRuDH2GOCfwPLY+BuBIq11APhWKfU1MB7Ydq7zkkyJc7dnzx4WLlzIzp07+eMf/8j9998f3/fOO+/w7/++GH/Q4Fe3387Shx9qcXwwGGRp4R3sLNlJv/79WPm318A1gEgsdePLL7/kl7/8JRBdSpKcnExycjJpaWm8//77rc5pwoQJPProo0yZMiW+7emnn+bgwYMsW7asM29fCCGE6HF8sSBDQ9hLTbCWCl8VhxoacFnMOC0WrCYbCkWaM41wJEyS1U19k+z+xi/GW8u6SJBCns1IBoYQQvRuzwEPAI0pZf2BGq3j6WGHgUtijy8BDgHE9tfGxse3t3JMnFLqTqVUsVKquKKiorPvo8cKhbo2S65fv34sW7asWeACIBwO85vf/IZ/rV3P6g8/4c1/rKKsrKzF8a+88gr9+/Vj//6v+e1997H0D9EgRzgWwLjqqqviLV5nz57Nf/7nf1JaWtpm8AJg/vz5FBUVNdtWVFTE/PnzO3q7QgghRI9WF/RgM9swKzP1RgPBcIBjfi9mpakOavrZXdhMNmwmOxEdwWKy4Av746UHrCYrKIUv7CdyBtn5fZ0EMIQQopdSShUAx7XWJU03tzJUn2Zfe8ec3KD1S1rrHK11zoABA856vl3h73//O+PHjyc7O5u77rqLcDj6RsHtdvPQQw9x9dVXk5+fz7FjxwCoqKjg5ptvJjc3l9zcXLZu3QrA0qVLufPOO5k2bRq33norXq+XuXPnkpWVxS233EJeXh7FxcW88sorFBYWxq//8ssvc99993XoHgYOHEhubi5Wq7XZ9h07djB8+HCGDhuG1Wbj5p/dwtq1a1scv3btWhYsWADAT3/6UzZv+hCt9RkvIXnyyScZP348WVlZPPbYYwD87Gc/Y926dRhGdHnk119/TWVlJfn5+R25VSGEEKLHS7A48RgNnAhUUROspvjEidh2K1elJpFgTsBtTUQphcVkiZYesCaSZD3ZnlyhcJod2MytdwcTJ0kAI6beaKDeaGi2rb0XUNO1SK2tS/KFfFjN1vjjpqwmC9Z2irJEi74E4o8bU4n8TYqKNi0wKoQQbbgWmK2UOkC0aOdkohkZKUqpxl9CGcCR2OPDwGCA2P5koKrp9laOuWDs3r2bVatWsXXrVkpLSzGbzbz22msANDQ0kJ+fz+eff851110X77qxePFiCgsL+fTTT3nzzTdZtGhR/HwlJSWsXbuW119/nRdeeIHU1FS++OILHn74YUpKojGjefPmNftgv3LlShYuXNhibrfccgvZ2dktfl599dUzvr/y8nIGDx4cT7XJuOQSysvL2xwHYLFYSE5Opqa6Kp6B0Z63336bgwcPsn37dkpLS/n444/5+OOPGThwINnZ2WzcuBGIZl/Mmzcv3tVECCGE6K0aQl5qAtUcaTjGwfo60hMspNmdDHYlk2Lvh9vqxmVxkmh1t1r/UOuINGw4C1IDI6a1YpsptqQ2xzctrNlakU1Dh0iyJlJneAjp5i/URKu73bnEz2e2E4qE4sVFw03OE9Jh5GUuhGiP1vpB4EEApdSPgfu11j9XSv0D+CnRoMYCoPFr+nWx59ti+z/UWmul1DrgdaXUM8AgYASwoyvvpTN88MEHlJSUkJubC4DP52PgwIEA2Gw2CgoKABg3bhzvvfceAO+//36zZRh1dXV4PB4AZs+ejdMZXZe6ZcsWFi9eDMDo0aPJysoCwOVyMXnyZN566y1GjhyJYRhcddVVLea2atWqDt9fY/vUeBFPRasBBN1KpoVSKl4Doz0bN25kw4YNjBkzBoD6+nr27t3LNddcE19GMnPmTIqKinj99dc7cDdCCCFEzxbWYRoMLx7Dwwl/DYe99QTCZiCE2xJdFgInO0oqaDVQ0dq/y6JtEsAQQoi+5z+AIqXU48BnwCux7a8Af4sV6awi2nkErfUupdQbQBkQAn6jtb7gFmlqrVmwYAFPPPFEi31WqzX+Yd9sNsfrWkQiEbZt2xYPVDTlcrmanbstixYt4k9/+hNXXnllq9kXEM3A+Oqrr1psv++++7j11lvbv7GYjIwMDh06FJ/LkfJyBg0a1Oa4jIwMQqEQtbW1pKb2O6MlJFpr/vCHP3D77be32DdnzhweeOABiouLiUQi8SCOEEII0ZuEdRgNeIx6vEYDx32VHKj3kmS1ENFhLk5wc0lC9N/fBIsTpRROi5OQDke7jMQ+gvvCflk2cg4kgCGEEH2A1nozsDn2+BuiXUROHeMHftbG8X8k2snkgjVlyhRuvPFGCgsLGThwIFVVVXg8HoYMGdLmMdOmTWP58uX87ne/A6C0tJTs7OwW4yZMmMAbb7zBpEmTKCsr48svv4zvy8vL49ChQ+zcuZMvvvii1et0RgZGbm4u+/bt48C33xJ2pvKPN97gv/6rZRbE7Nmz+etf/8oPf/hD/vnPfzJ58mQsZtMZLSG54YYbePzxx5k3bx4ul4vDhw/jcDhIS0sjKSmJCRMmsGjRIv7t3/7ttOcSQgghLkTBcBBDh6g36qn0V3HU18DFThsaGJqYSpIthQgRLMqCWZlwNnYYUWYsZjO+kC+6TTIvzokEMIQQQvQJmZmZPP7440ybNo1IJILVauX5559vN4CxbNkyfvOb35CVlUUoFOK6665jxYoVLcb9+te/ZsGCBWRlZTFmzBiysrJITk6O7587dy6lpaWkpqZ2+D6+//57cnJyqKurw2Qy8dxzz1FWVkZSUhLLly/npz8pIBAMccei2xk1ahQAjzzyCDk5OcyePZvbb7+dX/7ylwwfPpx+/fpRVFSEodQZBTBmzJjBnj174sU5ExMTef3110lLSwOi3Ujmzp3LP//5zw7fpxBCCNETRdDUGw1UB6qpDvrxhkAR5AeuREzKhD/kI82RRkRHq1LFAxYx1ljGhVUyL86JutDX3IzLGaO3bt98zsfXGw24ra4WBTwbNdafqA3WYTFZokU1lRmFIsmWGC/geWp/3lMLdzotzug2Fa0wC9G0ocZCLo3XaSzU2Vj7onHNVOO+xseBcAC7FHsRokdzWlJKtNY53T2P7pCTk6OLi4ubbdu9ezcjR47sphmdX+FwGMMwcDgc7N+/nylTprB3715stuibk4KCAgoLC5kyZcp5n0tlfYDyGh8j05Owms+sVvfXx+sxKbh0QPs1mjpDb34diAuDUqrP/W5WSs0CZl162bA7dn31WXdPR4gLViAc4Lv6g/jDAfbX1eGymAEYltifJFsyCoVZmUixJxOMGBgRo1m3EdG2M33f3OczMIxItDJ8YwChOliLaqVjYCASJNmWREPIi0WZ49tPDVw0clqc1BmeZi/YkA5HGw/GDm8MXjTOAU4GLupjhTybFnpp+liCF0II0XN4vV4mTZqEYRhorXnxxRex2WzU1NQwfvx4rr766i4JXgBEmhTxPFNmkyIciZx+oBDigqS1Xg+sH5cz5o7unosQF6KIjuAx6qkN1nLEW0u5N0ySFSwmE5e4kjGbzER0JP7Z0KzMmAnjbCV4EQwHpe5FB/T5AIYQQgjRUYmJiZyacQKQkpLC3r17u3QummgEw9RKML4tZqUISvxCCCH+f/buPMqy7Krv/Hefc4f3YsqpqjSVCpUKmdEGIZnB9GozGQSmwI1NW6LBmKYt06YZTNvLgLHBDF5mLYZGBoFlJjFJ0EA3kpcMNs1ktwGDhMASErZQC6kGVVUOMb3h3nvO2f3HHfJFZGRWZGSMmfuTK1a8eOOJzIwX7+539m8bs8MkTFFVvPNsNxPW6w2uVTXncnjueJlzxTIiwqXyEkrbXjL2oxvaRhZZ8eLOWAHDGGPMsVHVPUd7msOjB9iB4Rz7ysC4U2e9bdUYY8y9JaRAnRokCk/Pn+GJ6YTcwVKWc3G0RiYZ42wJBQTIJCMTz7zbTW+7LQ7f/ppjjTHGmDs0Go24cuWKHcQeMVVFkNsqFHknhJT4syt750Ed1rquXLnCaDQ6sscwxhhj7tQ8Vu3IU41ETczClKdmT/Lu9Sml87xoZY0XrVxiKVum9CNGviR21y98QeayIZ7AiheH757fgZG7/IbzFjMuJmEKQOmK4XPmMkIKTMOMkMIQ5rmUjZmGGdVC2OYN97vwgtK79nGixmEOcNI0BIrO97ifvXqCqEcAACAASURBVM4zxpiz4MEHH+Sxxx7jmWeeOeml3NU2Zg2TKvCurb23ru4lxMRTmxWXBabn93+72zUajXjwwQeP7P6NMcaYwzAPc+rUcLW6zOX5FpfnM563VPC8pXbXxVK2wpIfk1Ay8eRZTu4y/MJxpDka93wBo6+O3ey89XoTgPPFGgDnus8b9SYhBapUA+32IoCggSrWQ3Fi0e4+qH4aSUyxDfT0bTEjksilLZKwq1gRNR7o+zTGmJOW5zkPP/zwSS/jrvctb3onv/i2J/mjb/ms27rd//lL7+BNf/gEb/+nn3lEKzPGGGNOvzrVxBTZarbYbmY8MZ3RJAfUZJKRu5wyK3HiEJSoiZHLCSngfXsMaK0jR+eeL2AYY4wxd5MqJIrs9t8Byr0jRGvvMcYYc2+qY03mMlSVa/U1rs43eed6xcMrBVWKPLL6XACW8xVQJfMZI19Sx5p5rFjJl6lTQ+FyK14cIStgGGOMMXeROiTK7PYjrnLvqKONIjHGGHPv6dv0N+stnpg+wZX5hGt1W7xYznOel59nlC1RuIIlP8KJo0mB6DIKXwwFi2KPeAJzuCzEcw9920j/Ga5nYfRylxMW2jmiRi7Pr5JJ1v6Hjg0AszAbPvbL46hSTaNhWMdms8U8Vvvuq6pite/HM8YYc/eoY6I4QAGj8EITk4WsGmOMuedUseJKdW045ntmXjENDhFh5HNEhHE2RkRoUqD0JaUv2iKGtfgfKytg7KE/+F8sAjRdxkVvKRvv+M8aNTGPbZCnFzcUHxoNQ4rtvoh0Fb2mzcXo1tHEhqhx3wGeUe1dNGOMuRc1IZH72x9Vm3uH6vGMUzXGGGNOg6iRqJHMZWzVmzw5fYq3Xt7gXJGzlsMD41XOFxco/ajNJ1RlrVglaNwxDKKO9U3fQN73caDZF2shMcYYY+4iB92Bkfn2Nk1UDhChYYwxxpwpSRObzTYA1+ZXeGq+weX5jOeOM547XqbwBWv5eUZZSZMCF4pzOGl/V6YUwfthhOqtMi8ym0xyqKyAYYwxxtxF6pAo/EEyMNpdG3VMjLEXW8YYY+5eVaxIKKrKPM6YxRl/tl1Rx4xslBj5ESKOrBuN6pyjTg1JE0vZmMxdP4xOmmx86jE6thYSEXmhiPy6iLxLRN4pIl+zx3VERF4jIu8RkT8SkY87rvUZY4wxd4M6JvIDFDD6XRvBgjyNMcbcxZImFKhizZX5ZTbqDd5xbYtLpfC8JeVD1+6j9CPW8jVyl7GcLbGSL5OJH3ZgLMotuPNYHecOjAD876r6NhFZBd4qIv9eVf944TqfDbyk+/gE4Ae7z8diu5mQS0b/xlPSROyzL0TYqDc5V6wx7QI5S3d9q5AXR+FKpmGGE0fpyva+uN73tNVss5qvADCL8+u3dZ6YIrM4x9NW+jLJ8K5dSO5ynLjbquz5PX64nk2d2uBRS881xpizqw6J1dHt/3rPF1pIjDHGmLtR0sQ0zEiaeHr2QZ6Zb/OBSc1zxxn3lSPG2RLL+Up7nCeCINSxRkTIXU5mDQwn7th2YKjqk6r6tu70FvAu4AW7rvb5wE9o63eA8yLyvONaY6OBlXyZC8U5AJJGoqbuI1Klur1eamhSw1qxylqxCoCXtgcqaMA7z4XiHONszDgbD/ffFwgAQgrEFIkpMvYjAGKKOHGMfIl3fiiAFC4n6+5/v8rbuG6vD7Exxhhzdt3JGFWAxnZgGHNmiMiLReRHROTnT3otxpwVjQa2w4TNesZjk5qQhGloGGUjMpfjcG2xQvzQKtK/+VzH9nhwr50Y5nicyN+8iLwIeCnwu7suegHwgYWvH+PGIgci8moR+X0R+f1nnrlyVMs0xhhjzpyDhnguZmAYY06OiPyoiDwtIu/Ydf4rRORPulbrrwdQ1feq6pefzEqNOVumYcZGs8W1+RU+OH2aD84mPDD2XCgTH7p2H0vZCiM/YjlfIncZq/kKqqkL82zftL5VWKc5HsdewBCRFeAXgK9V1c3dF+9xkxv2sqrq61T15ar68vvvv3QUyzTGGGPOpOaAGRi2A8OYU+PHgVcsniEiHvgB2nbrjwReJSIfefxLM+bsSZqoYsUszrlWXeWxyVWemG6TOccDo2VevHqe54yfw0rW5lyM/Yik7e/Cxd305nQ41gKGiOS0xYufVtVf3OMqjwEvXPj6QeCJo17XtXoDYGjZ2G4mbDcTRn5E1EiTGkpXkFLiSnUNaLcR9VkY0LZf1Kkh6+5js9lqr9ddJ3Y/BFvN9rAFqc+4uFattzkYGocfFo/rRvtskbmMRGK+MFt4rznDVaxuOn+43+50K168JegaY8wZd/ApJH2Ip2VgGHOSVPW3gKu7zv544D3djosaeCNt6/Wzsp3L5l623UzYaLbYDlPmYcZTs3UuVzWF8zx/aYWVfIULZfuGuHeecTYmaLyhRaQ/RjMn7zinkAjwI8C7VPV7bnK1NwF/q5tG8onAhqo+edRr6w/6V/JloO2LChq5NLpI0kTQwLlijUSk6ooPTawJfcAnbYEipMBSV6VrYjPcV3t5my3RpDDcrs++qGI9VPr66zlxRBJ1bBj5kqS6I58i7JFV0ed17GU/2RaFyy3A0xhjzrg6WAuJMXehPdusReSSiPwQ8FIR+Ya9bmg7l829qo41XhwhBabNhGfmV3hsMmc199QpUrqc0o9wOBS6oQmOTHwb3tmNTQUrYJwmxxmj+snAlwD/RUTe3p33jcBDAKr6Q8BbgM8B3gNMgS87xvUZY4wxZ96Bx6j2LSTBXqQZcwrt2WatqleArzjuxRhz2kWNJJQqNUzDlGvVOu/f3uR8kXHfaEzhPBdGl9od+CJk3U70pAknjsIXO4oWfZinOXnH9i+hqv+RvZ98F6+jwFcez4qMMeZsEJHvAn5MVd950msxp9s/f8u72JqHg00hyWyMqjGn2B21WYvIo8CjL37k4cNelzGnjqoSNbWZF/MrPD3f4MnpjLUiZznLOZevkLuCXDJylzPO2p3wCsPUxzrWZC4bWknq1NhO9VPC5r/scnnethxm4lmvNwkayCTj6fllQgokTUzCFCeOzGVs1ltMw2xoC7lWrRNT266x2Wzd0NLR364/3ztP2aXZ5i7Hi2ezbvMzFjM5kqYd+RSL7St9C4zvtj3dKgvDGHMmvRt4nYj8roh8hYicO+kFmdPpl97+OACf/edvfwK5hXgac6r9HvASEXlYRArglbSt1/uiqm9W1VefP2+/Pszdr041282kaxvZ5InpjEnwTENgNS8ofEnpR8OuiiYFVJXC5cOuC+fa3RhNamMBMssJPDWsgLHLPLahmyv5MlWsiBrIXc48zIgaUVXqWCHiWMrGVKnNwgja/ueuYj38x29iQ0o7sye8uJ3Jtn7EhfL88JhOHFWqh6+hzbtQTUNFsD9v9+nSl5S+vGUWhjHm7FHVH1bVTwb+FvAi4I9E5GdE5FNPdmXmtAlR+eJPfIiPfeH5276tZWAYczqIyBuA3wY+TEQeE5EvV9UA/G/ArwDvAn7OduUZs1OTmnYYQ5hyef406/U6T8+nrOUZl0rlz527jwvlRZayZVbyZVbzFQpfMPIlIm3mRS+lSObaHRrADaGe5uRYM48xxpwB3Qi9D+8+LgN/CHydiPxdVX3liS7OnBrtBJKDvUtkU0iMOR1U9VU3Of8ttHlxt81aSMzdRlVpZ0S0mtQwCTNUE1v1Jk/NtnhqVvOccclylrNWLDH2S1wo2gL/MB5V2995+a72kKLbIW9OHyslGWPMKSci30PbRvI5wD9X1Zep6neq6qPAS092deY0qeLBJpCAtZAYczezFhJzt+knPfZiN81xs9lkvd7kiWlDlRwbdcVKPiJ3bdtI7LIuoC16ILKjNd+mjZx+tgPDGGNOv3cA36Sq0z0u+/jjXow5nVT1wCNUwVpIjDHGnB19oGaTGoJGYops1OvMwpR3b0x4YOSpYuTh1YuMs2WWs+WhVaTPs9i968KcDffkDoztZsJ2Mxm+LheyJaBNnd1uJmTiGfulrjdqjBePd+2IHdXEZr1F0kTmMlR3brl1rv3BiCQy8RQuJ6RAkwJbzfaOdfRrmcV5e1txJE1sN5MdabfzWDGPFZMw3XF+Jn4I7qxiNYR51rEertMHgNax3nG+MeZM+J92Fy9E5P8BUNWNk1mSOW366SEHmUACC2NUrYBhzF1HRB4Vkdetr9uvDHP3aI99aqpYc3l+mSvzDf50c537R45LoxEPr55nNV9jJV8h7yaKOITdjZKLx0aWdXH63ZP/Qk1qhkRZgAvFzu10oavkeZfxvKXncL5Y44HxfWQuI3M53mUkTVSpDexcysYkdr7g891//qSJcTZmNV8hahiKGNCO46kX1tJPL3HiiCQaDawVq8N9Ro1EjYQUWM1XhvMXgzujpiHMczHos+/j6u/DGHP6ichIRC4C94nIBRG52H28CHj+ya7OnDZVaJ/b+0LE7RpaSIIVMIy521gLibkbBY3MY8VWvckz82v82faM9TpHtd2hUbiCpXwZVMldjhOHd9kwbaQ/BrO8i7PFWkiMMeb0+rvA19IWK962cP4m8AMnsiJzatVd4eHALSTd7UKyEE9jjDGnU50akiZyl1HHmqdnH2SjqXjfVsV9I0fpAw8uX+R8cRHnHCnFIbCzcHmXe9HuTPc2GvVMsgKGMcacUqr6fcD3ichXqeq/POn1mNOtz644aAEjc5aBYYwx5pRTpU41kzBlo7rG5fmUx6eB5y1lrOUF58tVlvwyS9mYwhc4cagmClfQpOaOxqKGFMicHT6ftHvyX2CvwJZr9UZ3WTG0WNSx/eEA2OguX0ymnYYphSuYhtlQwctcRtQEsSGScOKYhRnjbEzpRzSx6R4no0lhaDUB8M4zDTPg+migq9U6ThzSXS9qHEYGVbEiaGQ5W9pxP8P9dTkYi9uirNJozNkhIp+mqr8GPC4iX7D7clX9xRNYljmlhh0Yd9xCYjswjLnb2BhVc9Y13c6LOjXUsWGz2eBKtclT84b7Ro5zRcGl8gKFK1krVkndVJKRL2lS0x4/3eTX234LE1a8OB3uyQyMlXyZlXx5x3l9AGbhi+Egv0k1TZdZMQ1t0GZayI+outDNkAJe2v/Qhc9JXX5F0oTHDVkUl8oLQ1ZGn2HhxQ+PN+4KHEkTQQOqynwh2LN9fB2uH7o8DGDIvVgMJG2zMXbmXRS+sD4vY86Ov9x9fnSPj889qUWZ0+lOW0i8E7wTC/E05i5kGRjmrOpzKhS63IrAPM6YhRmPTaZcLD1OhOVsROlH7XAF2jeCC5ejXf4FgCB7PoYVJs4W+9cyxphTSlW/ufv8ZSe9FnP6VXdYwIB2lKoVMIwxxpwWucupU0NMgUmYcbW6zDRUvG97k7Ui5/7REkvZiLX8PKUvKLsdF6UrEGTYuQ5WqLhb3JM7MIwx5iwRka8RkTVp/bCIvE1EPvOk12VOlzvNwIC2jaS/n19/99P8xG+/j59/62NW1DDGGHOs+p0XszhnFmZMwoz1+ipPz7Z4//YmpfOcL0rOFWus5efJXDbstChdQSTtaP03dw8rQ3X61otcMqJEYgo7/tMXrmQeZvgu8XbkR9SxovE1mVtDSQie7WYbJ54mNSz5Mblvq4ZbzTZ1FxxTx5pZnFN0P2RRI7M4Z+xHJBKa2jaRRMLhcCK4rtbkFvIvMvEkub7GKlY7vp861jdkXvSZGLuzMQ5b/6SzV96IMea2/c+q+n0i8lnAA8CXAT8G/LuTXZY5TfoWkvKAGRjQ5meEqEyqwJe//vfoB5I8//yIv/TIfYexTGOMMeZZZZIxCVOiRqpYM4tTNuoJT85qonoKTSxlJaVr20aWsjHa/QFh7EeEFAgayXYdDyVNBwrxNKeD/ct1LhTnuFCcYyVfJnMZQSNpIelllI2pUkWTGqaxDdrcDg0hNSxl46HY0RcRYgrkPmctXyWkQJ0aqjhn5EuCNoQUhhyOpImY+qwKJWjAiSNpQrriRX/dxfyL0pc7fviipuGjv87uIkWfibE7G+OwJbWqpzGHqN//+DnAj6nqHy6cZwxw5xkYAFnXQvLU5pyk8EWf8BAAs/pof2cYY46WiDwqIq9bX9846aUYsy9BAyEF5rFis1nnWrXBe7em3D/KWcuVj7pwH5fK+7pjN0/pCnLJGLmShFLHmsxlNxQv4GATSMzpYf96xhhz+r1VRP4dbQHjV0RkFbAKodnhMAoYfQvJU5ttMf6R+9vA6SbaZBJjzjIL8TRnRZOabvf6hFmYs15d5fJ8i3dvTLhUtqNSH1m7xGq+RuELctcWLaLGbhpkxCE2tOAuZi0kxhhz+n058LHAe1V1KiKXaNtIjBkcRgZG4R1NVJ7eaidgPXhhDGAZGMYYY47NPM6JGpnGCdNQcbWakwkUznGuWKHw5RDImVDGvkS1LbQ7cYgIqrrjs7l7WAFjwbV6gwvFuWE0aSbX/3rqWOGlDYcZ+3Zr0lre5mY8PnmSOtVEiWSSkTQN2Q9Pzy/vqABGTWSSk7mMWTciNXc53nlmcY4bWkQCSSOFK6lTw9VqnaVszDxWjBbGo2bimYRpm4ehidxlNCnsyMNo11/vGBG7mI1xFHkYtjXLmMOjqklEngI+UkTsedvsacjAyG7cLrtfuXc0oW0hAXjBeStgGGOMOXp9fl4Va5rYcHV+mXmqeffGhPOFcLH03DdaZjlfoXTttJGosc26WMi5SJrw4oeihRUv7j72QnjBkF/RFQcWAyjrVJO7nNzlOBxRI0vZMkkj0zAhkUgSWcnWiBoY+VF7n2HOAyv3sdVsA+0PVeELxn7EdjMBGPIttpvJUFhIGtsfQOepY03SxMXyPJNmwqhYYxKmQBvWOa83ESckTZS+pIr1kIPR67+nvlCxo6hyBDvRLbzTmMMjIt8J/E3gj4HrgTnwWye2KHPqHEoLSdZmYDy9WbFUeC4st78rrIBhTEtELu7jaklV1498McbcRZw4pmFG1MS1+hrr9YwPTGbcV3ouliOWsoIL5SXG2QgvntxlFNL+jlrMubBRqXc/+xc2xpjT768BH6aq1bNe09yzqr6F5A6mkOTe0STlqa2K56yNhvuqLQPDmN4T3cet3tb1wEPHsxxjzr5paAcktJMaZ2w1E56ZT9Hux2w5H7Hkl9od3sqwc305WzrJZZsTYgUMY4w5/d4L5MBtFTBEZES7S6Okfb7/eVX9ZhF5GHgjcBF4G/AlqlqLSAn8BPAy4ArwN1X1fd19fQNtFkcEvlpVf+UwvjFzeKqm22l3Jzsw3PUWkgdWy6GA0QTbgWFM512q+tJbXUFE/uC4FrNfIvIo8OiLH3n4pJdizA59aGdIgavVZbaaGR+YbLOcZazknodWLrGSrZL7nMLllK5AUZazpR0t8ebecaBXOSLyNSLyr7vT/+Rwl3QytpsJpS+Hr0NqaFJDTIE61qgqThxNakgkZnFKnSrmcU4i4cVRuhGJSNTIPM5pYoOIcK3eYKvZImpiFmaEFNioN4kaqVPDLM7ZbiZdBkb7TyI4vGTEFIkaEIQr86s4cVyr1qljPaw1cxle3J5bpvrrefE7brPjskMeRtP3sBljDs0UeLuI/CsReU3/sY/bVcCnqerH0IaAvkJEPhH4TuB7VfUlwDXawgTd52uq+qHA93bXQ0Q+Engl8FHAK4DXitirhtOmD/Es77CF5E+f2ea/PrXFc9ZG5Fn77pe1kBgz+KRDus6xsikk5jTogzYBQgpMwpSgkZgC6/U1rlQTHp9ss+Qznr+0wvOXzrGcrVD6Ai+OXDIylyHirHhxDzvoq5xHgA90p1cPaS0nqtHAheL6k3rQQNCGqIkmNSgJQdrTqmw3VbfNqSFpxIlnnC2hqkSNNKmh0YDgqGPdFjo0UqeKoGHIqQgaiKm9fh3r6wUMEXKXEzUSU0REmMcKEdeG22gY1rqcLVH6kqVsfMP3tZh90Z/e67LDlNRe6BpzyN4EfBvwn4C3Lnzckra2uy/z7kOBTwN+vjv/9bQtKgCf331Nd/mnS5t+9fnAG1W1UtX/D3gP8PF3+k2ZwzVkYNxBC8nLHrrAvImowl965BJ5d18hWQuJMZ3vEpFPvtUVVHV+XIsx5ixZDNRMmtpxqfU2z8yf5vJ8k6dmU0Y+49JoxEq+yrni/JAdOPIjCl8QUhgyL4LGHUURc284aAuJAmMR+Wjg+Ye4HmOMMbuo6utFZAw8pKp/cju37XZKvBX4UOAHgD8F1lWHKuhjwAu60y+gK06rahCRDeBSd/7vLNzt4m0WH+vVwKsBHnrI2r+PWx0SmROcO3ji+td95ofxdZ/5YcPX/QvD2lpIjOn9N9oixvOAnwXeoKpvP+E1GXOm1LGm7na7z+KMaai5Ws1ZzjJC0nZUqitRFC+OhFJ0Q9gWd5z7brBCZgPa7ikHfZvmu2nDi74E+MbDW44xxpjdut7ltwO/3H39sSLypv3cVlWjqn4s8CDtromP2Otq/UPd5LKbnb/7sV6nqi9X1Zfff//9+1meOUR1SHeUf7EXESH3Yi0kxnRU9ftU9ZOAvwxcBX5MRN4lIv9URP7cCS/PmFMtpMA8VlSpZruZcK26ypX5NR6fbiMI943GfOjafSx1u8uXsyUKV5C5jKCR0O0ej9q27IvIbU0dCbt2o5uz6UDlKlV9P/D1h7yWE7PdTMh3Ve4yydo2kC7zwokfcjEaGpaynMzlOAk48SSNzMKUJtVk3QjROlaE1BA0J2mkdCXiHKpKow1NqkkopSvIXU7m2gCbviUFoIpzvHgy8SRJxBTwziMIkzClTg2la9tDpDvG6H+QvThmsWLMuPt6f71idWoo9hiDerPzd+vbYIwxh+ZbaIsPvwGgqm/vgjj3TVXXReQ3gE8EzotI1u3CeJA2UR/anRUvBB4TkQw4R/sCvT+/t3gbc0rU8fALGNBNJrEChjE7qOqf0eYEfaeIvBT4UeCbaSeQGGN2aVLbmj8JU5ImNpt1Ls+3eWY+41xRcL4ouVRewEvG2I8ZZ2ME8C5rW0YWjmO8eEIKIG0xY7/HOJnlZtwVDhri+QYR+RkReZOI/OZhL+q4NRpYyZd3nJe5tugQNJA0tQUNDW3oZqjJXUHpSkZ+hMMRNVGlOXWq8eLxXQGkSjVNqtr7cDmr+QpK6nIw5jSxvWwlX6boMy8WMiSa1LRBNS7DiydqInc5IkJIgSpWXaGlXVudmiELo/QlMV3Pythv1sXurIzefrMt8n0UOYwxtyWo6sau85616VNE7heR893pMfAZwLuAXwf+Rne1LwV+qTv9pu5rust/TdsegjcBrxSRsiucvAT4z3fw/ZgjUId0R/kXN9MWMKzH2JhFIpKLyKMi8tPAvwX+K/DXT3hZxpxKdayHN4erWDFptpmEOet1hQNCSiz5ksKXjHy58Gas33H8sZh30Rct3CEPIzCn30F3YLyqPy0iX3t4yzHGGLOHd4jIFwFeRF4CfDVtoOezeR7w+i4HwwE/p6r/RkT+GHijiHw78AfAj3TX/xHgJ0XkPbQ7L14JoKrvFJGfA/4YCMBXqto+zNPmKFpIoC1g1LYDwxgAROSvAK8C/iptIfeNwKtVdXKiC3sWNkbVnJR250Vku55Sx5rtZotr1YQr1YxcHEXm+JCVC6zkq5SuwDtP6Qqc83gcmQhB4w27J/pA0MVgUHNvOFABQ0Q+Z+H2H3d4yzHGGLOHrwL+Me1Y1DcAv0I7leSWVPWPgJfucf572WOKSJec/4U3ua/vAL7jtlZtjlV1RC0khRcaC/E0pveNwM8A/0BVr570YvZLVd8MvPllL3/p3znptZh7S0iBaZwTUmCjWedqNeHJ6YzzRc6FsmTsC1byVVa6cakAzvkdk0b601asMHDwKSR9OlsF/KNDWsuJWcy/2G7aAnomnnHWtpXUqR62LyVVkipNaghde4fQtlckcfgh/0HJXLZwu8Q8zpg0W0MbCLStKo0GNupN4Po2qKSRuhuz2m+fqlNF4cq2kpki0UVyl7e5GV2WRkKZhln7ZBFm+G4L1nYzuaFNBtotXQCRxNiPgLZSOvajIfOiTk27NnFMwwwnjpEvb7ivprte7vLhdP+1MebgVHVKW8D4xye9FnN6HVkLSeZsjKoxHVX9VABpfTHwYlX9VhF5CHiuqlp7nbmnqSoiQh1rokaaFGhSw3azyTTMeXwyJ6qjTpHlbNyGdEqOd22RQmmni/T35a1FxOxy2wUMaUtf51T1NUewnhOxeGDfdJMFvct43ugi6/UmdV2jXbu5KihKnQJVjIx8Ru4cQROiibzLnwDIJEedErQhaaKKFdMwJxMZCh25K4gpUtOQiR8qi0kTSdMw61i704UriSkStMElx3KxxFaz3T6eywgpEFJocywSlF3xoNHrWRiLIm2BJaY4xE7FdD3hF/KhCDPyJfM4x6mDPQoYiz1q+83LMMbcnIi8mVtkXajq5x3jcswpV4dEeQQ7MDIn1kJizI1eCyTg04BvBbaAXwD+4kkuypiTFjR088uEKjbM45xr1VVmoebx6TYPjAoS8Pylc6wV58gkI+uy/qB9w3Rxp8XuXRd9gcTcu267gKGqKiJ/UUReBWx0573l0FdmjDHmu7rPXwA8F/ip7utXAe87iQWZ0+nqpOY/vucyH/fQ+UO/79w7ayEx5kafoKofJyJ/AKCq10Rkf2npxtzFkiYUmIUZVaxZr6+yUc94Zj4jd47VomA5G7Oar1F2I1Jzl+PFtRMVFzIv9ipUWPHCHGQHxsPArwIF11tJjDHGHDJV/U0AEfk2Vf3vFy56s4j81gkty5xC3/5v/piYlIvLh3/8VGQ2RtWYPTRdQLJCO/UJsB8Uc0/qd0W0u8DTMBlxs9lgGmquVRXT4Lhv5FjKRm3bSFe4yF1G1EThchLWMmKe3UEyMP6hqv69Q1/JKZFLRpS2haLPw3DiqOK8rQwKVCHiRCicp3CezaZiNS+JmkgaiRpRPJaengAAIABJREFUlJACrmsVUUAQVvIxquDEU6eKSdgidyVJE1E8UQNtqoagRLy4oc0DoE5VW53szMKc0pc0sWEWZkNirxdP5trRrz7W3X221VAvfhipOjxJOIbMCyduyL3o9ecJMpwuuqyLPuPCidtx/d5iNkZIYRiNZIzZt/tF5MVd+GZfSLYCshlcm9YUmeO7vvBjDv2+bYyqMXt6DfB/AQ+IyHfQjp7+ppNdkjEnI2hAVEiamMU5dayZhgnzWPH+yRZrecH9Hl64fIFzxXm884x8yZIfX8/7W+iYTSQ8/haPaO5lBzmS/GgR2VHAUNXXHtJ6TtxKvkzopgP2nx2OqIHcjXEiVClSqmfsPbkrmIYJ54pxV8BIRI1D6Gbp+qyItjK5lp+jThWxu9481ixlAk5JGgkaKLt8CU2Kl2zHzOOQArIQFlqniuePnscz8yvUqaEUB9LmYSxlY65V60SJw3ar3VkYfSEDYBbnQI4X3+VfLFzP5cxjNRQw+oyLqIk+onMxrHPxdBWr4bRlYxhzIH8f+A0ReW/39YuAV5/ccsxpM28SH/PgOVZHhx+anHvLwDCmJyKZqgZV/WkReSvw6YAAf01V33XCyzPmxLTjUhMxRa5Wl5mGhmfmM1aynAdGYwpfMs7G5D4nE49D2sKHOFTTMFRBVYc8DGP2cpACxjXgHYA1IBljzDFQ1V8WkZcAH96d9W5VrW51G3NvmTWR1dHR7G7LvWO72jsI2ph70H8GPg5AVd8NvPtkl2PM8dorRDNqokoNMQVmcUpU5fHphJAco1IofEnpS0Z+TCaevNvx3b9puliwsIwL82ye9dWOiHyUqr5z4awnVPWu7r1WlFwygkZmYUaTarxkQyuEE0EQGk1UzYSR98QUSKrduKCGqBFBSLQjV133w9pup5oNXzcpElNgKVvG4fBkxBSoUkXSCAizMEVRogZAyOV6K0bUyLV6A4DSlxQup0p1OykEhu1YDsdms0VIEXHCLMyG77eONfTjjlLc8cTRt5IsjlIF0G4nhd+jVQR27sC4WWvJQSzOgr4Tu58sjTntuoLFH570OszpNG8i96/eOB3qMLQtJLYDw5iOHV0ZQ7srHNrX1FWsqGPNJGyzXm/ywemUi0XJPEZesLTGWnGOsR+RuYzSdbu/pd3V7cTt2G1uzLPZz9s1P0lXaRaR/4V2KzPd10uqOj2itZ2olXy5HaGaaprUkLmMKs4BhgyKmJSNpuJCMaJOCRFoUkJoaDQy8iWgVDGwlJXkLmer2WK7qRhnGav5GlPmBI2M/QhoD/wvzy8Pj5W7nCrOSSgC3Q95W7hw6mhSQxUrvHgulm36/GQ2HbZh0T0heHFUoSKSEG3X3xcCIgkUmhRIkhhlo+HvoW/56D/3BYi+MLJYqLhZe8jide40/yJpgkMoPCRNVsAwxtw1qpAY5UfznJZ7IVgGhjG9+0Xk6252oap+z3EuZr9E5FHg0Rc/8vBJL8WcQYuZdyLCLM7JxFPFmro7FtlqNpmEGU9OJ+TOsVaUPDcrWcvPU7iCwuUo7TGEQ3a8IWm7Lszt2M/b4Yv/o/6eqs4Xvv4Ph7weY4wxxtymWR0Z50eT3J57ZxkYxlzngRVg9SYfp5KqvllVX33+/LmTXoo5g3KXD7skQgp4XNs20u282Go2uFpt8/hkmwvliIvliPPFGiv5KqUvWMmXceLIXYYTZ4H+5o7s53/P4tsuu8tjNufGGGOOmIj8AvCjwL9VtSRcc6N5iEe2A6OwFhJjFj2pqt960osw5rgFDeSSkzQRNFKnhoQyjzO2mhmbdc08CpOm4cLyGrkrcN1URGAYBGD7+cyd2k8B4rki8rdF5KXcWMDY9/9BEflREXlaRN5xk8s/RUQ2ROTt3cc/3e99H7a+/SJ3GZlkjLIxpRuRu5ykykqWE1UJ3XFElSJ1isxj5Eo169J0IaSGJoVhpOq8G8W6nBdEVapYkTuPamKj3mCj3mAaZkP2BcA81tB9lbmMJgWmYUKdKupUt2E5Ydol/q5ztVoH2n607WYyzGKexbZ9JJNsGKkKMAnXO4C888P2sD4BWBf+ifs+tT50Z95NF2lSN8K1u6w/r58+MotzpguZG3C9b263m52/uIbDcFj3Y8wx+UHgi4D/JiL/QkQ+/NluYO4t8+boChi5dzTBXnIa07G97uaelHcTCRPaTluMNRv1OtfqDa5VFSJwscx4wfIaq/k5RtmIlWy5a6lvb+/FX29h3zXx0Jj92s8OjG8BXg58GfCgiLyTNnH53cB9t/FYPw58P/ATt7jOf1DVz72N+zwSK/kyAMvZEpNmSuELBIgaSDphOS/ZqGekbitVE9vgy6jKtSry4JKQVKlTxIuiCo1GfKwpfIn3GXXaok4VucupaYsSAN5lJBICKMIsNJRFjogjk5zNOKVJidw5yISkEY1K4QqqrkfZSbuti9S0VdIUhvGpmfdDhgciNLGh7Eap5pKxki8zi3OSJgqXM4vXn1wKt3M8X1/86CuxYzcG2rGp7TjZbtRqijfkY9wsL+PZxqweRoAnYPkX5kxR1V8FflVEzgGvAv69iHwA+NfAT6lqc8s7MHc1VWXeHGEGRia2A8OY6z79pBdgzHHpJ47Use4y85TtMEVVmYRtLs83mYT2JchaXnK+GLNWnGeUjdrCherQfhJJZPghkN9ei5uDetYChqq+bvFrEXkQ+AvAnwf2PY1EVX9LRF50m+szxhgDiMgl4IuBLwH+APhp4L8DvhT4lJNbmTlpVWiLCyPLwDDmOPwqXbj9zYjI21T1ltcx5iwIGkC7wH/aaYBVnBNS4Fq1ybWqImqi9J6L5Qq5y7udFo6kadjZHTSQdTvcvSUQmDu0nzGqnwT8jnbJLar6GPAY8JYjWM8nicgfAk8A/2DX+NbFNb0aeDXACx964aEvYruZEDRyvlgbxoS2KbvV9TUg3fapBB5K8XgRCpd4fLpJ6duvlUhIibHP8DRde0li5AoSqR3niQxVyJgCgqPRSExK1NSNZo0IMPIZpYcmRUCJqmQiNKnBOwVVnDjECyml9jFICDIk/PZrbydxuDaIh0SVanz0xBRx4pjFOU1sht61aZixlI2HvyNoW0WUG3dGJNqKbTtC9npbSe7ytorbbRurY33iYT7PNlLVRq6akyYivwh8OO1UqEdV9cnuop8Vkd8/uZWZ02DetM+n46NsIbEChjG9jxCRP7rF5QJYUqY50xanjuQupwmBoIGtepvtZot5bHhiut0e76jnQ1YusJQtkbmcwuWUrmh3lXc7q3OXd8c83iaOmDu2n6PGLwVeKyJ/Avwy8Muq+sEjWMvbgA9R1W0R+Rzg/wZestcVu10hrwN42ctfeuiNuaGbZwxtSwe0P8hNur5L24tQA0ETktrCggMy53hqFnlgrOTiaDQRk+JFEAHvIlVsOF+sUcU5VWrw4nAL/WACNDERNA1ZG7MQyMRTuALvPNpMUNUho6JONQWgKA5HrgVBG1TbPjXXFSratTuCRkRTe5++aIsVqSGmon2CcW0ho9GA02506kJ7R50aMueHNpFxV9jotRkabiiS9Of13+PQXnIK+t9U9ZYdrc92uTHH4IdVdUfRWERKVa1U9eUntShzOsy6AoaNUTXmWOwng+jkX9wYc0DatX30xz3bzYQq1aBK0IZr9YxpaBj5jPvHY0qXs5afH1rSC18Mx0+L7efOdl6YQ7KfFpKvAOhC4z4b+PGuD/vXaQsa/6/qnR+Fqurmwum3iMhrReQ+Vb18p/dtjDFn3Ldz46633+ZZtjGbe8O8OfoWkpCUlBTnrJpr7m2q+mcnvQZjjlI/baRv+djWKU1qmDbbbIUJG3XFLCRW8jaQc5wtkbmsy+vzlK59MzRz2ZB3Ae2brH0OX//mqjEHse99+6raB3d+r4iMgU8FvhD4HtqQzzsiIs8FnlJVFZGPp52QcuVO79cYY86q7nnxBcB41ySoNWDpxBZmTpXjaCEBaFKidNZOZ4wxd7NMMupYDwMA6lizVW8yi3Pev73FyHvGmePB5XMsZ8uUfkTpC7x4vLiheNHe1/XfGYsFCytemDtx0OCBz6ANklPg2/ZzAxF5A23Q3H0i8hjwzUAOoKo/BPwN4H8VkQDMgFf2uRvHZbuZsJIvtz9svmS93iTvRpf2GQhjn6OaKLyn0cRqXhBSok6RKkacCJlTYlJyD2OfESShgJO+6qhMw4QqBaoYUaB0gaiJlXzE2LftGE2XnQHtX/Q8tnkSITbMYyT3yjxE8jyiGnDSTj9x4ihdOwUkaSIjb8eTugyniag1SROqCRUlhSl1aobvMWjEL0wOUdosDkXZbiZkLiNzHpShTaSfSOIQ6L5P113WP0lFErM47yq07XnSfQ4pkLnshie0/vw7tVgB3u3ZevGsV8+coM8C/jbwIG2xuLcFfONJLMicPn0LSXlEBYyiL2BEpTy5uCJjjDHHoG8dqWJNSIFJ2GazmfDUbMpSlnH/aIncedbyNVbyFXzXju7EgepwHGDMUTnoS5FHVfV/BBCRHwR+6dluoKqvepbLv592zOqJ6SuN/RjVp2eXOV+ssV5v4lz7Aq7wJVWsKFxGLZFxkXG1mtPExFajnMsduYsEhYRnKctpuuJGnwWRVNlqapoUaTQRUqJynnkM5M6zlLWP36TExTJviweqVDGSu9DeX4qIQJ0ijUaamLocDSWptNGdmkjajXhNEaduCO8ESLSFkZR0R1hnm1GxUMBQ7bI5hIYACQpXMI/tGNgqVm2BBLqsjfZFdH9ZHwI0DbNhPGvR9cm5ha1kwA3FisTh1LBUE9ykgPFsAZ0W4GlOiqq+Hni9iPx1Vf2Fk16POZ2OfgdG+zzdhATlkTyEMcaYE9SPS4X2jbtJmCEiVHHORr3Fel1RRSV3ysiXZC4bci6GiSOSEQjDa3tjjspBCxhjEXmoO718WIsxxhhznYh8sar+FPAiEfm63Zer6vfscTNzj5kfcYhnNuzAsEkkxuxFRD6Pdsx1At6gqs/6xp4xp0k/LjVpokmBeZwzDRPWqy2emk0ZZZ7CS9c2skLmMlbzFYT2jT7n2ukirvtjzFE6aAHjW4Cv6k5/6+Es5eTlkg1tJNCm6E7ClLoboeolwyGoJmYp4J0wCQ11iuTdDo1pDBSubZGYhGaYEuK7auSkqdgKNUu+3ZWQiaPIPLMQUIV5DFRxjghk0j5W1MRynvPMbAZAlSKZODbqGifCPES8CGlh3Gcda6DdBlbHClSJKRJTwIknaSR3BZGIdm0ebatIBG1bN5y4doqJKF78MFHEiRt2XEzCdGgT6SeLQPsEOItzHMIszsnEo7QTUeputGrQOFRpo8bhPjOXUcUKEXfLKu6t2kJ2X29oVVm4zWGMRw3d1JjD2KVxVgONFqv25tD1BeKVE12FOdWOOsSzbyGprYBhzM187u3uTDbmNBEERalS3U4d6XZePDWbUnjHks95/tI5VrJVVvMVnLSv0J04nPPDa2svnmNOADD3oIMWMJ6jqv8QQEQ+EXjP4S3p5Kzky1yrN4av+/aRfoRq7nIEIaJMQ+BcXjANDSElSt/+4M6CZ3Xcvti7OhdiqvHODVkW26FhGsJQwPAiLOc5W02NotQxUscKQXDSPlZMynIxZh632ieXGFnOczabmrW8oE6Rsc9oUiLPcpImgjY48UNhoC0wBKJGctdmYrQFGUdEybpZzYk26yJopOiKGoqSS0atqa2riqPu+uOaFLpMkIKwMIxGaQsmzmXdZxlaUfpCRkhhGK8UNeEXCiBtcUMp/Oim/17pFm0hi1TT0May2ErSFmTurPBwmEUHPaR2meO2mCptDpeq/qvu8z876bWY0+vIW0iy9uf7sWszHrxg2bHG7MF2JpszqX/zcBpnXXhnwzRMuDLf4nI1YynLWM0K7h9fIJOc5XyZvMus83sEdcLO7Dh7k8schYMWMP4H4D91pz8P+J3DWY4xxpieiLzmVper6lcf11rM6TU74haS1bItAL/ydb/Du771FYwLywUyZpdv4YR2JovIMvBaoAZ+Q1V/+jgf35xdIYU2uL97I26r2WYWpkzClI26QoCYlNViTCYZucuHNx6lC+zMXHbLHc32Jpc5CgfegSEij9BmQD7/ENdz4vJ+5nEzGSqGucuJGoZ3yEeuYFNrHptuE1JiFgWlYRYyCp+Yx0iTElEzCu+JqiRVLs9nzGOgSYlJaLqJIYKIUDhPnSKTEFjOu9BP54hJu/NnlN4Pk0xC6oI4u3DPoAlBCCkxzjKcepI2Q5Bn0EDpR8RUQeoni0QSiUzaJ59hWogGCle2oaYKKUUaUbTboVGnevj7cghRZKjg9rsa+ierpjs/dDsv+pDOPpyzDQxN1wNOUepYD+0ri20lAHVqhidPt9AWAjsrwLdqL+mvf6v9DotPxrd6Yu630O3XrXZs3OkT/J1WuQ96+37dVmU/Em896QWY0+96C8nRFBY+5cPu57M+6jn8yjufYmveWAHDmBsd6s5kEflR4HOBp1X1oxfOfwXwfYAHflhV/wXwBcDPq+qbReRnAStgmFvqd5X3r4enYcY8VmzU15iEOR+cTsmcICq8YHmV5WyZpWwJEYdf2HnRu1XmxVlsjTan30ELGN8EfGV3+psPaS2nQp9/EbosCGizMJrUEDUgCKUfobrFB6eelVyZBU+dlHn0lD5RRWW7yXGilN4zDW3x40rVPlEIjmlourCb9oCvcG2GxHZoixdViqxk7QSSOiVi01A6T+wWVS8UMBpNaGzPDynhnZBJaosGXdEhpMDIj4gpXs+0aCsZQwFjOGBPEe/dUEhA2xaPPudCu1Gt3TeDqAx5Fqk7fu1HqCZNZC5rCxECI1e2bSRdu0jUdp19UUJRIu36Gm1umEKSFtpMsoVWEIEd7SS3mjrSPza36NFTVfp6wuLp3faTwbEooTd9mr/TJ/lEGibAHMRBq+R90cKq7Ievm0JizC1dD/E8mheKmXd8xke0BYwqWA6GMXs47J3JP047me8n+jNExAM/APwV4DHg90TkTbRjtv9Ld7WIMc+ifcOzbQEPGpnFOevVVTbqOU/Pp4y851I5ZuQz1vLzLOVLjHwJqniXkYnf0TZuzHG7kxaSi6r6d0TknwDfdohrMsYYA4jI/6GqXysib2aPTUOq+nknsCxzysybdqx2H7Z5FPrdHVWwF63G7OFQdyar6m+JyIt2nf3xwHtU9b0AIvJG4PNpixkPAm+Hvd8jEZFXA68GeOFDL7zT5ZkzZK83l7zzzMOcJgVmcc48zKhSw9VqTkigDkY+YylbZpSN8Ljhjc/+nhwy3K/tvjXH7aAFjEeAD3SnVw9pLcYYY3b6ye7zd53oKsyp9Xvvu8q//LX3UHh3pC8iy6w9LurbVYwxOxzHzuQXcP21N7SFi08AXgN8v4j8VeDNe91QVV8HvA7gZS9/6dlMDDeHos+9iJqYxhnTZpuNZpvHJ9ssZTm5c7xw5Twr2QqjbEzh8iEIv50o2FLU2kPMiTloAUNpE5c/mrssAwPa/IuYAqUvCRrJXYYXT9RAlSoyyQiqnCsaogpJhZzEUha622fMo+diWTPptvaqghelcFC3nRtk3YvNvg2kSQkRZR4jpfddtkXbcjCPbftKnSJ1gkwSDmg04UXYDpC5xHodOV8olTRsNzWl9zTNtM3aCBNmsUYExr4kaZvtUfoSTUqdalKXVVHFGtWEdONPVdtpJCkmRGRo2whcb31oW0Z0aDHpp42kpN3fgVKnpqvgtveRu5yMvrVChjFO2req0Gd+tH+3iy/Pg8a2pUQVcf6muRfSjWztW0e0u5/FJ94qVmTdv3P/WEfBdd/3UTzp3+nc7d0V+tvNtLD2kcOnqm/tPv+miBTAh9P+F/4TVa1veWNzT/jd914B4Cs+5ZEjfZzSdmAYcyvHsTN5r1+yqqoT4MuO4PHMXWD3a7N5rKhTQx1rtsMmT882uVrNGfmMi+WI0ues5muMszGZeLJu4ogTt+M19p1O8TPmThz0iOe7aZ9IvwT4hsNbzukQNBI0DnkYy9kSzrV/VU03PjSpsla09Z+oghNYytoXdpOQUSXHOIPt4FC6A3RRRj7DSXuQnzuHQwiaaGJq8ytEqGIkE0edErMQkO68KkWmAarouscUYn+b5AjJsVHnNJqoYmCzqaliZL2umIfIpJkzDQ2TphlyKEJqcLR5FTEF6lijQJNqUrftTFESkaiRJjXdmNbQ5lekdjQrQCQRNVKnmpgCKcVhjKt2eRh91TdpInZFkMLlQ3GhL1j02ReZy9oRr2i7noUD/z5jI6FtVVj3fmewv6zPz+ivly2EELUFDh3u96g4cTfkehyWO333dfftb3esq20hPDrdO2t/SvdOG/AeEfnsk12VOQ1mTcQ74e9/xkuO9HFG3Q6MynZgGLOX49iZ/Biw2P/xIPDEfm8sIo+KyOvW1zcOfWHm9Fp8LTcNM4LGrnixxWY9Zbtpj2uqFFnKRpS+JHc5DumC6q/n9RlzWhyogKGq71fVr1fVfwT8hUNekzHGmJ2+G/hUVf0U/f/Ze/NYWda1vO/3ft9XVd1rrT2d4Q5wub4Go8gMtjEXk4goxgmKDQomckzkxLLAcXxl2USOEikmUSI7wXL4J4mIYllCAQMxMUEZuQ6KYyFjgxMcYkhsEZPgAbgH7j3DHtbQ3VX1DW/+eL+q7rX2Pufsfe45Z699dj1bW6u7usZevarre+oZVH8n8LuA/+wp79OCa4A+FlbhvbWPwF6B0S8KjAULHoX3Q5n8s8CXishvrIq8PwD82GPvoOqnVfVTt2/feo92b8F1RC52zk5z3kXPWTzldLzgc7sN3lkT4pfefJGT5oRbzS063xKc1aY6cZdu+C1YcB3wxJ9IEfl3gN8G/BXg9wB/+93eqaeNIB58Nz/fpC1ePK3ruNBzkiZE4Dg0Zv8oCSfKWIyjzCo0rnC3N69YKkLjlF1qSSXR58Dt1qpWUzHbxTZ7VD3bFLjVRrwMFDNTcDoOJFU20eNFKQhjcQzFZDC7rGxig2siwRVe3w2cNC1FlV21niQtpFwYS6YRRy6ZgZ5CYZMuqrLELB+dX9m2ixInBUatR1WmalOliACCU2fKDDKqpjLIWDWqIHjnrW61JKJGggsopoQYSiRIvaNXrSmH1oVUElGTbbuqNubGksmKobV69cBGImL7NCk2RPYGC6kNKflAdXEoi5tUErOyRDOi8lCt6uNWrU6YrCPPCpO9WEKuFV5T1cNavn8EvPa0dmbB9UEf83tWn3qIblFgLFjwVvhPgD/Ou6RMFpG/DHw98JKIvAL8aVX9PhH5DuCvYjWq36+qv/D5bmvBBxdT3kVMkayFIfecjqfcH7acjiNHoeEkNNxoVhyHG6zDkdlGxNP4dlZhwJO37i1Y8F7inVBqv1lV/1UR+WngG1S1f7d36mljso5MiMUG0CF4dFCKZgThZtMhCLu0YSyOMTs6bwaBRpR7Y8uRz4zF0brCJnmiClmFO51lXsQCWR33h5bgyvwza+EomD3kNHpaVzgbG46DkSUJYZs8nStss68VrpnOFz63W/FRGXAi9Dmz8n4mS5IWQnAkVXIZEYQ+9we5D0rrupm0YGocrfYNVTNzgNZBfUBFSWonR4fMGRm+DtYbsY/ZSCSVVAkMreROQty+dnViiie6oaC11tVfIhVsn6BzDWOJ5JJoxepuqSffoaSZtGhcA+KN1AByPb6JwOh8N78HwYVqpbHXshb8I2pVL1WtPsbnaqpQfVZCjxZLyNOHiPy++vAXROTHgR/FPm7fit2NW/CcY/c+ERj7FpKFwFiw4CpU9VeB74TZ8veZt17ibdf3r7zJ9B8HfvydrFNEvhn45i/+kt/4+ezagmcMdt0rDGWkzz19Hnl11yOitOo4bjoa39L6lkaCXbuLw3+euWoLFryXeCcExksi8k3AG8A/KyLTCXXBggULFry7+OaDx68Cv7M+fh248/7vzoLrhiEWVs17f6G5byFZLCQLFhziWVEmq+qngU9/9Se/6o8+7X1Z8N5hqk3d5R5qAP8mbbmIZ1zEHb+2ueBWawryjx3f5lZzG+88a79i5TsQ099ONyPR5YbWguuHtyUwROTLr0jU/lvgZeB/qD8/0Giq76vPA0PuaVxDLInOe351c0YshaG42TZyb2gpKogoQZSkggJDsYu/zhVicZxHQdWsIJ0v3GgiqTheWg14US5ioGjixU4IouS6TrOaCF6UTfIMTu1xrPuZPKuQEWDMSnCY0oP9XbOkpsYYSqZzHi+x2jYcqVorsiZTKLiGIAEnnlwVCkkTjWtMQaGJkjOFghOziqgWytym0rFNO7w4s2IgDHmYLRhOHLEkGhdmBcekfCha6HNv7St5xDtfm1gscMiOsRYxiNDnwZYt+4BPsNvVJQ+zncRsJxYuOgUUjSXafucC9UR92J2tKLHEetw6M9qTdUSwIFCbWS14tFpGkuZH2kam19+rVpLD7czHzZO3i7zXmH7fj7KsPKq//HmCqi7J8gveEu+XAmO2kCwKjAULruIDr0xecP2hKDFHGt/Q54FcLHi/zwMPxnts08i9oedW23EcGo5Cy3E45iisceJofWvVqMjSMLLg2uNxFBj/FfDbAUTkX1fV/3J6QUSOVHX7Xu3cdcBxOAIsB2PIg+VgxJ7OeX5xEyhY+4gqdL5wOjasQsZhtampWkZysUFY6+3i7zwGtL7mJXKrzZxFuNVkLpLj7hDIKnx4XWhcZJvsZLJLnqKCE+UiBbwoN5vExRiIRbi76fjozR0AfXasJSOlkGrriRchFyVpYRMjNOCyNZq0Yu0qWROxWMuIiCNIIIhnVwYcjlQiQQIiQtZEVgURgjQ2rdTcjZJofMNYBoI0c2d0LCMIFIzQSCS81oF8JUbAWk1KKTSVDPDYe3A4IM8lIZWEmOwlBTkgE/adH1JPzME1RMzyggieKXuj1PlcJWLTaoNbAAAgAElEQVT2A/0ps+PwsT/ow/biZ2KFqc2kWkaM0HmYoJhen36+V5jegWkb144U2P+CHv3aNdrVpwURWQF/BPhyYDVNV9V/7ant1IJrgT5m1u8HgbHUqC5Y8GZ4JpTJi4Xkg4nJmi01r2Kbdqgqu9wTy8g2bTkbB17vt5w0LUchcLs9YR2OWYUV3vn5RlxTM+amPDlr+VusJAuuHx7nU3k4fPjjV177qXdxX64tphBPQYgl0jpH6y1zwotyVH9uolWkbmIgqrBJnrOxIVXyYpMCmxhqTaljrMqNVBwX0dEnzy4LQ7Zfy72hZciZs7Hh9b7D1eX67NllT8pWnXoWA0VhM9rP+7uWX992XKRAUeHB6NmkwEUUzqNykSIXMdLnxHkcuYgju2yKizEXxjwSS2bMmVKrU8/jGbmY0kKqmqLPO1KJKJZyvMsbxjzQ555t2s4VqlmL1bJOlapQSZKpkrbQ54GolpBsCpBsRIDqPMCdAj3BSIExj7WWNc+Bnqr7bAurgGXexpSvsUnbOe9iWmfWPNfdTgGfBZ2JEJPTOYY82Pqn9VaGezoWm1kuHZvaDoPqXENrsjydf07bOVSOXMW+hjZfmmdaZsrzsM3p/FxgJmSm9wW4nCdysOy0zqvPr87zVnizea9Wsyo1R+QRJMX82gIwIvkjwO8G/gZWn3f+VPdowbXA+xXiuZotJIsCY8HzDRH58iuTriqTr6U6eWkh+WDCOz9fc03XmHZdOjLmkQfDBafjUF/PnDRrVuEI7zxNvUEZXCDI/p62x66n3yq4882uVRcseD/wOAqMwxHH1eHEc0HLxZII4qsdIOFdIAArX8gqrINjLLDNnsYVToeG1DkuYmAXPbdWamGbo2fbeY5rY4k1djATGbvsCakwVrvJ3V3LUM44Tx13+47jky1JhV3yOMxWogrbsSEXYTvar/N01/C5bsXNNnK7jTwYWzqf691+s7FAZMiepAXVRKfK2gdiyezydNyFtRYSkbPRmk1QqWqHRJ8HGvE4702JUTLqYCwDWZWVbymVXIgUAkbeOISMkRorv0bVQkB9ZXutxcQ+alMTC1iYZi4ZvH0oo6a9kkAcrQQyeVYcSF1eEArlEhFSUNpqB5p0FNZ0st+eF5mfS93SMNlBRGpriiITUXDAUmcthNpmgipad7NUVQvYAH0+PlWoLS9vBkWrCkQu/SVOyyi6V6lUy5DHz6z8o6wk+xDSy6qMRxINvAvNJFcVFW/lrVx8l4f4Tar6rSLyLar6gyLyX2NJ9Auec+xi4YXjJ/gqHjdw+gq88CXgHz8GK3iHd7IoMBYseM6VyQuuF+zmot3sy1pIJbFNW87jGRdx4DObDbe7huAcX3B0g5vNLdZhXa0iblZfZAqhXkM+zrXXsxJIv+CDicf59H1ERL5dRL6KhwmMx78lu2DBggUL3ikmic8DEfkK4Bbwiae3OwuuC4YnVWD8yB+EP/874Cf+zBNvqwtuqVFdsGBRJi+4RsiaySUx5pFd7tnlnrN4yv1xxyvbCz60XvFCt+ILj25yFI5Z+Y7WNazDCu+CWalre9+CBc8KHuf2y58BPgn8YeBjIvILwC/W/y+9d7v2dHARNw/VqIJVazpxe39YiRwHwUmi8w2xFJPqC6waq071TjntGzpf6PHc6JKFPRazi2yj57hN9NnxUjfS+cyQfbWVCDe6xC6ZquPF1UBwhc0YCE5nWwqYZaT1hc3op/xJXj3vyMfCymeGbAqPiW2Krsw5Gmcx8GI3UlA2KbLLab5JHrVwEWMNrSzsUgQiueZpOBG2OVZrgyICOfdVCWA2ilQSYxkJ4lHRas2QaoOY8jbMXiJO5oyMUqVsh0GbWv8d2jNMxmLzDrX6dEpNDi5AtVw4HEVKnceUBGWqfUWsKlVMueBwc4XspEwYauUsVBHBgR1kUjhItYL4ykqXkufcj0nep3W6iCBTgCeTmqPaNETmwFNbRmcL0/R82v+rQaMTpt/htJ5D68gcGir7nI9pngnT+qfH0795/Yf5ILMC5YqCoyooHjc09KFsDrmGeR1PD98rIneA/wD4MeCkPl7wnOOJLSSnr9Sfv/bE21o1fgnxXLDgGVUmLxkYHwxM10XztWD9OCbNxDKyiRs2ceTBMOBFGEtm7U9oXUfjWoKzqlSFen29x5R9sWDBdcfbEhiq+r2Hz0XkY8BvAb4S+Jvv0X49NUwZC1dxHI44HU/pXAcYgXHSNASXWPlAKgkRpXGFdSUNvChnfcOtVWRMjhfX2znYc5c8D/oG55QheSMwXOEsGkGSVbjRRHbZs3KFk1Uiq7AdPXeOImN2OLGT1umu4eYqshmE4B3OKduLDu+Uo8aWi4W5yWQQRyyOk5C4P7R0zjIfOpfo85T5YPaIHYlYMq4OQGMpjCWz8oHbbcc2RWId6K68p8+J1jmCb+hzpPOJrGm2Z0w5FEkjDkcumaSxhnQGvHjLyahn1KSRrM18srZpZuOYsy7UyCM7GRsZgapNq7kP3vnZF+hqm8nlQXgN6Ky2isz++eG8UzLzRETYY9tWmZpWKiGQsf3wzhplpnkL4BQa5xlKAmcexCl3w02xH5O9o+7TZAOZvqzMUmLHZp+Zy9Luw2zMiUix+QoO5sYXQfZ1WRWuHsvh88vrfpg4uWpBeRTx8JZExhV7yUyqLPwFBxLlvwF88dPclwXXC9ZC8gRjpljV7ePFE2+rC26pUV2woCqTgf+bZ0iZvNSofnCwSzsaZ20jANu8Y8wD5/GMISfujz232haAl1Y3OA4nNFV10bpmvpa9SlYs5MWCZwWPb4CtUNVXgFeAa5ew/G6gkYffkqlK1eHBmd/MVc+YIJyOA1mtVeS1XceNJlFUZpJhOwYaX+izo5/UEAo3VwkncNQmLlLgJCTOYsBhDSLg2abAi6uBG75wNjZ0oXA+BFKeBo/Cqsm8ceYopZBioe0C/XbkrG24vYrE4ohZ6EIhuDr4VSNZ+uy5N7asciZrJBVPCpmVd+xyodOCFyEVZRMj3jla51CUB+NAUcWrsksJVWUomSHb/EkLXdoRK9HgxNWBtKeoUshcpAtUCyLCkHurapXLd/pTSTOBMKkZEvtQzmmgbQN6qe0pppqw35WzitWaP5GrAkLFmUqgKiimbIxSK2RtgA9Fs/ViK6imWVVg6cx+Vhv4+nmYQjm98xbomUcjDSZlRk3mGGsVq20313pXvXT8wEy8QM3ZOFBzXEU6IB0mpcVEdMyEgxY4UHRMmLNC6rLT61fJnsPfzazemEiVg2lvhstKEb3MtBxMlytZH88zRORFTA33ddg79lPAd6nq3ae5XwuePvpYnqyFZCIuhifPgO2CWxQYCxY8Z8rkBdcD0w2dVBKNa4iaSJrpc8+YB+4O9xly5u7Qc6tpudm0NL418sI3NBJopqY/kZmsmFpMFix4lrB8Yq/gUfaRqUrVOTerBrx4nAgicD7qbNG4u+m4cTsRa7sIwMUYePFoYMieMRuZ0HjlZhsZiuPIZ/psxMBZ3+CrRSQXW8fN1gav5zHQhcLrFx0F0EqSrJrMZ+6PrI4aSlFC49jtIherwHmtYz3rG+6sR9qwv/icCJYH2rAO1mjiRSkqBJc4j4GsmZNgycX3hpaTJrHuLID0Io2svKeoY5uiBbxls4bYoBnOZbJ7KE6srrVzRjIoSh9HWu/w4ohkWldw4nGVJFGUXKwlxNeaKK0qhFzJhGkAbHNb0Gog1CrYTCstYxlBYRVWppZB5nWJOBoXZkJhUiIUYyzqYN4BSq5BmdZ8onjv6vHmqtCoJIUWvJpNI9V5gVnpoAhSCYZckmk43kSdULCgUAWaQyXEI5o+tLae+JkMqWoRFMdl4uMqIWHEzLymSz3gh4TGfo6H1RuPUl48pLrQw4f7J+5QeVvVGIt9ZMaPYIq3f6k+/4PAfwN8w1stJCJfBPwQ1mBSgO9V1e8RkRfq8p8Afhn4l1X1vtgv63uAbwK2wLer6s/VdX0b8O/XVf9ZVf3Bd+3oFrwjqCp9egILiaqFeMI7IjDMQrIoMBY833jelMkLrgdiibOlus8DSTNjHhnzwC7v6HPmtN5YHEshuEDrWlrf0rqGxjUIzKGdE/yiuljwDGIhMBYsWLDg+uMFVf2ug+d/VkT+xcdYLgH/tqr+nIjcAP6OiPw14NuBn1DV7xaR7wS+E/hTwDcCX1r/fy3wF4CvrYTHn8buOmpdz4+p6v136fgWvAMMqaDK4xMYeYRaIc1w9sTbMwvJosBYsOAQH3Rl8oKnj4m8iLXxL5bIWCJD7rmI55zGgbv9jhe6FV4ct7sjVn7N2q8JzpQXk5o8uHDJIry0vS14FrEQGI+J0/EMX3MKurBiN+zsznu16B83iW3y3FxFzmPgYghsx8Bxm2l9IRXHkEzhEBz00dF6Ty5CDzSuzPeiczGFRlHhuLUMi7OxYcyOzeg57pJVsiZPKZaBsTpShl2cHQglK/ff2HLcrafGT86HhjYVxuzoQiar0PpCF2z/xuK42SSGsaWocJFCtWRkhtxwGhvWIdPnxDZBVlfrVDNDcfiUiKWgKmxSwotyEUdeWq1JRdnlyMp7QuPIWtjEeKAOMKuKWS3k0p15lVo1WkClkGqexGHGRREL31RRUokUyXR+ZVkUJc139Mcy2olbQZ1Syt4yUSjz82naZBfhigVkCsec6quKlhoE6mbVQNSEUzdXsU7ZHeaasGWmHI+MWXEyNdejKjP29gypoZyW9zHbYKi2kQM1hmoxe8x+wpwZMn3Kpv0/rFSdaloFQWuQKOxDPCd7yBQeOi03zXMVV9UYV0NHD4NBZ5vLQXXtIR6l/uBNalavvvYoVcjh+p8RlcdfF5E/APxoff77gf/57RZS1c8Cn62Pz0Xk7wNfCHwL8PV1th8EfhIjML4F+CG1X+zPiMhtEflonfevqeo9gEqC/B7gL78bB7fgnWFqBHlsAmNSXwAM7yQDY1FgLFjwrGIJ8Xz2oOxt1CqVyMAx5JGxWObFg3FglxIvdCtutR1BAkf+iKNwhHeB1jVzhtt0zbeoLhY861gIjMfEkEc635I186HVS9zr30CZwhGVm00mZsed1cirmxUPdi2b0fPRmz2tt9yKMTvWIROc8iB6ulAQgV3ydF2m1OTKrMKRz8RsBEYqwpgDQ/KcDw0fvbljSGZFAdhcRI6OW07v72z5Ogg/e9Bz49YK54Q7N5SLIdCFzHZ0eBdwTml9sW1koY+Otc+cj40pjYtZTPpioZ9nMfDhNfRZuTd0tK6Qap6H7Xsmq0cV7o8tK59RFV7oLLryPI5Ay8oXiirnMdI4R9KCx+wlUmTOFvFij1TM5gFmM9BioZmNa+uAXI0oqLkO1nBS6DxMDSPTIHb+IsBaRkoNztRig+eokSmTuSA49bOVxeNny8qUL5ErCZE1kyXTSEDEEZw3ckP2g3GzWUwhoIqrpInD7DKFiYwBp2UmKeawzRri2bjAqBGPny0jU3uJrXvKubjcYmK2F3svpy/DieaYt1+fyRwkahGgU4Dovg1mv9yjcJinwcG88/oP8zXmqXrp+aVVPyIm7U0bSq6+diUc9G3XfY0gIufsj+DfAv5SfckBF5gq4nHX9Qngq4C/DXy4khuo6mdF5EN1ti8EPnOw2Ct12ptNX/AU0Vcy4bFDPKf8i+MPwe7+IzN33gpd47gYHh10vWDBguuNJcTz2UKqarnpGrYhELMpL8YysIkXnMWRPidSvRYMEvAu4J2vbSN+to3YzcIFCz4YWAiMBQsWLLimUNUb78Z6ROQE+O+Af1NVz95CMvqoFx5FAU3Tr27nU8CnAD7+8Y+/s51d8NjYjUZgPHaI56S6uPER2LwGaYBm9djb64LnjYvxSXdzwYIFCxY8JqYbML7eCBOwTDcyWQtD7rk/ntLnzP2h51bb0TrlQ6ubdL6j9R1rv6L1LR6HiNC45iEV7IIFzzIWAuMx0fnWmiXEc3e4jxPPcWiIpeAk1RtZU3Un3OgiZ31gSMZ33t+1pJI4o+GFoxEn4GsNaiyOB0PLS+uBzRj4yI2exhV24knF0Ve1RuMKXcjsooWBlqp+cN4RRwuKLFnZbkZSKjgnvPbZM269cITzLSkmRicMw4h3dgLzwZGOarBPbShpvHIeG4bk2MZA6wu75Gl94ZfPj+m8XTQPzlGi0LpCVuH1vmMdMiuficX2z4nyud3ASWNVrhdxZMyZ4ByxZPpc8GIBn60zhrhxbg79bJxZbQCCOFyVvTlxpJytIQRoXWtNIVWBESTQpx0gJE04cTh1VXFhNg0ctQHE3qtS7OReqtpgUkZAtT9IgRpaOdlWTDFg01ULqWoPNJdaiVpm5cS8XWw0OIpesjmkkmb7w9QeYl84rm7T7COppL0a4kAVsW8n0fn1pAmBA/UEoIUiDlcVKYg8FAh6uSaVeb5y2BJTVRyKVbJmzXMQ56wsqevNlIe+MguXg0Gv1rFeen0aQr9JQd2lZQVEL1ewPvSl/Qx+f4vI7wX+mfr0J1X1rzzmcg1GXvywqv73dfKrIvLRqr74KPBanf4K8EUHi38M+PU6/euvTP/Jq9uq4XbfC/DJT37y2tYJflCwV2A8oYXkxkfhc3/XFBlPQmA0brGQLFiwYMF7DFW19rnpGlGVXekZsv0vqvQ5cbvtKoHRcRSOWfmOxjd0rjWbiOyvfKbrygULPghYCIzHxK32Jq/1b9C5lk3a4sVzHDo2KRJESGrkhastHje6RM6FIXmcwNlZRG80jFE5bqsdwlntpyqc9YHb3cj5EPiyl0aSCmdOyUU4HwIiieDN8rGLnpQdpQhFwTmh7yNalFwK52cD/ZC4c2fNG29s6dYNWnQeTI5jJgRHSqUSGStKqQPNsuLlk4GizNtZNZlt9Hz4ZODXLtZ0IXNrHWldYTMGTtpEUuH+ruX2KnKnG0lFGNTRuMLndis+TE9Wx5ABlHUYicWxTX4mT458RoHOK2MuBCc0zlN0yskQOm+2myCOrEqsVaoSLg+EvfeMZUQQYolWHSXOrB5TtoM6aydB8RLIJeJdAC1kTUCgYIPzpKkOzq1O1Za3cBHVQnANWQuQcOpt8C2CYv0lRiQIqVIctqPZaqyqpk81z2P06UsmawHBLDKz1YK55jXr3l5zOEi3nhPLuvDiL1eqYhYVai7GRGwI+3H9vg6VuTJ2akJxHL7X+1pWVUXc/stW3FQ3W2bryiH227yceTKTOnqwP6iREkxdM1eEkAdVrBOJUbTMy9jvaj/7s3YHQkS+G/ga4IfrpD8pIv+0qn7n2ywnwPcBf19V/9ODl34M+Dbgu+vP/+lg+neIyI9gIZ6nleT4q8CfE5E7db5/Hvh334VDW/B5oJ8zMJ7QQnLjw/ZzOIPjx299XAU/524sWLBgwYJ3F1OmmWL2vqyZXOy61ePYpg19GTmPcV6mdaa6CC7gxNFUG8kEd3Dt9axd+yxY8GZYCIy3wSZt58de/NyVHFzDLm3xtRrUA0ch8w/PWtZNpgmFJjhanxiSY7X2nJ9H7twKeKcMCTajKTRaX8hFOBsabnSJN4YWB3Po55A8Zz18+GSg1OrUCaXYXf3gHeOYTXnhhbbx9H3i4nzktc9d8NKHjhl2cR4cikBoPATH+WmP845hSOS8og0NwVlA6cUYaEZPLA5Vy8lwzvI4bq4iD3YNQ3aMs9KkYRstnBSMmOhCwYnSucJFChyFxC57HPDG0NI4pXGFcwl4UVpXOAqZlS/EUhhz5rgxgiCWXOtrhUYcQ5nuBvYAlfDQ/eC41on2ed9zHUukqNEEk4JjyD1OhFJMHm2KCxsVW9aFhakWzQTXzIoFqaGdYx5sN8STxdQGola96iVQmKpU9zKCOdej6JwZ4WvZqRd3QA6oVbpWuCoDjHUgH4t9kU2hnKWGiWb2AaXzMVWSo4gRJtPg/lDcUDUkUEmOySe/D9estakiODWyIpFndQhiy6eDlGtlT7x4cXX9lvdBJVumfTwkNObtsCcvbIl9iKjIlBGyDxqdjuPw57TctP5n7Iv8m4Dfpmq/TBH5QeDnsfaQt8LXAX8I+Hsi8n/Vaf8eRlz8qIj8EeBXgW+tr/143dY/wGpU/zCAqt4Tke8CfrbO9x9NgZ4Lnh4mC8k7UmDAE1epLgqMBQsWLHj3MV3TeBwIDMVy93bjjiGP7PKOWAa2eeDBOHDkGxrnuNkecxxOaFzDyneWfSGTavnR3wvTja0FC55lLATG2yCWfeNFEM9xOOJ0PKNxDX3OeHG1NQPWAc6HwJ2jkUaULmS6kNlsW9YtvPFaz8c+tMKLklXYjp6LIXDnKJLVlBYnXeL+tqX1hT56Gl+MIMiOj9/aogrxkMDIOttIZgLDCU3r2WxGduc7VJUbN1oePOhxTmhbu1t+cqMDoB8zoXGcnw1oUVbrGwTvaHxhu811UKj0vbO76kXpuxaAe9uWmK1h5eYqcT7sT4reKb6+DyLKzSZxd9fCGlIxNcXdXce6ybTOIh+DGJmRNVI01/dKWflCRonZWkoA1iHQ5xpyVGz5tbdB7tQIYjYUzy5FVr7gxdPnSC6HwZR2Qm9mFYUaCVLf5sl6Yp+HiODQagdx4mef4sxyzw0YpjzAUZtKzGIxhVu6KdCzSg2KZopMIZ/+YBCvldag2jbc3vYhVaWhinfeAkar9WKydBwO7qeB++X9kL3+YVZdmM1kUl14hMwUZspekXFARkwKD1GdSZNDtQZq81mTyaSC2W/Xjrsec13nbI2Z35eJAjokfXz9G5VLtpO5QeYQhxzSM8VfAHAbmEiDW4+zgKr+NG9+pP/cI+ZX4E+8ybq+H/j+x9nugvcHn5eFBJ64iaQLjgfbyJ/44Z/jd3/FR/i9v/ULnmj5BQsWPD0sLSTXG7Gkqvi18/ombhnKyFAGLuIZQ86MJXMSGo5CS3Bhto04cTW00+HEvSVBsZAXCz4IWAJpFyxYsOD64z8Gfl5EfqCqL/4O8Oee8j4teIpQVX7+Vx8AZu14LMwWknemwPi6L3mJL375mL/+/77GX/xb//iJll2wYMHThap+WlU/dfv2Y/HfC95H5JJpXVMr7mGXe3a5p087zsdTkhYu4kjjHMeh5Tgcc6O5OSsvOt/RVAvJggXPAxYFxtugOfCRTZVGne8oWjhuulqhWXh11zMUx4tHI29sWrwzlcXZ0ACWjbFaNQzJ86CH011DW/McttWisQqFu5uWLkySf1NbTLdPX9uuyCpz+KeI5VqMYyKnYhkYuRDHQi6F0QIn6C963nh9a9aS1rPbRULwjGMmp0jOhabx5FTo+8hrnz1nfdRwfNIRYybFTNOGalehbifwaukY+shuJ/ggxNIyRmVt4gyCVy56h0jLvW3LJ17YcjEGsgpdKGyz0kfHWR9YN5ku1EBPp2xT4LhJHPnJCBFxogRRNslTVHih6+mzEFwhiLDygYFE1EIjjqiF1nla52uwpdL4TCpmTXFpRFHGYiqMo6CksrdZxGKaAydCzIXGO3JRoK/ZGCCScFWqUYrO6pCpb7uokjTNNaZ7WwMUmYJBp9wGy0NRCkXyrNCY8hwme4RDcWqqicnmoiglR5zzVaUx1aJOVou9GuJqbanZSaoipVa3AojugzdLnX/KtLDlbb+KlqqqKPOX56OCoq5OO8z6cJeUGGYpKQfzz0dypfZxUppYoOnediLIbC25lJlRj/Xw+N8M18VmUnMsfhr4J7EcDAH+lKp+7qnu2IKnir/7yin/+U/8EgB3jpvHW2hWYEwZGE9GYHzDl32Yb/iyD/OpH/o/+dV727dfYMGCBQsWvCXGEnFiduA+D3jxpJIYck8qkaiZ89HszbEUjoOjcQ3BBVNduGAh8KoEF5iC3BcyY8EHGQuB8TY4Dkfz49PxDIAXuzucjees/dqk8K3yKxcjFynw8mrgl944QQRSzYZYNxnn4Oi4ZUhmtzg/j6zWgRtrZRc9WW2gdPfC89INIy9UhbFYkweYXSO4gqujPV9zKsY+GRlRlBQLw5BIqTAORrhwcc7dEHjhhTXeC32fWK+FcUjsdjZPSkophX5nuRk3b3bzQLbvkzWdDImUjcQoRdltYyVQMscnLUPviGPC3TlCVWlUOHuwI6eCOOHW2gici2qzidmxS54H25bjLnGjSzhRXM39OG4Td9bTSdvyQG42kc/tVvX9MtKocwXvFCeZospQMl6EPiudz3TO03nPqJlWLSMjlcIOO+FvU5pbWWKxwbmvx55rG8qQM0caKrFh1ECoXw4illkRy56gaL2ncYEaWUBwDTplUNTcDDSDY57HuwZQs36II7jJJbIPwwwEs4I4N0+bcjOyZnzN15stGHUfixa88/t8CaWSM85sJ1NOBqVmYsj8+vTaRFjkAwJhHyhqeR/U7I7J3jKTFqroI6o75xaV+pIoB3aSy6GiVwmFOc/iIAdjmg77cNBD2uSSzeQZsZGoqorI/6iqX42FbC5YwP2tnRu/61u+nI/eWj/eQhOBcXIQ4vkOcNR6NmN6R8suWLBgwQIjLuy60OzJqpYdtks9fd5xFs+JJXMWR4I4kiovNtY20viWIIGV76ASF4f5X29Rlb5gwQcCC4GxYMGCBdcfPyMiX6OqP/v2sy54HjAmIxa/6uN33mbOw4UuoDmGVZWQ/3//C+QIX/1t0DwmCQIcdWEOEF2wYMGCBU+Gq61yuSp++9QTy8gmbRhy4jxGjkPDSdPixbHya1rfsfYrvHi8+H2b22F125tgUWYs+KBgITCeAFOLxTbtSJoRcaQykFW53ULnR85j4CM3eh70DbvRc9RmUhGCFMTtTxo3bzbEBLvRsW4zKQpvbMx7IaI0XokZPPBgaHBisvqP3IykLGzGQOOV0Dicd1BbLlZrkxL3fSIlR9lcWJvE3TfwH/oNnJ+NxLEwDj1NDfNcdYHTBz3HJw3jmBmHzNFRw9mDHc47UsyUXOj7RNt6hj4Rq7rDeVMCnJ/2tG1gqNN9cJSsDD0zDM0AACAASURBVEMijmZL+OzpEd4JUkNMp/NsLsq9M9iuOryz44zZse28WXGKsPGFVZO5O7TkIjROOY0NY3YEZ80lQ7YL6tpSSyqOm21kyIkmJwRosyepBYG6XNsyiqkOcrHA0Mk2EqoFZCKyL2IkOAuqzEXnxx6ZrSNyoNyILldLiHIUlFjM6uLEVcuF1pYRC+Qs1aIhYr+yqXp1qkEFEFfVFOoBQQQKfm8zmVQKk+xAqyVEQcpBhqXs7RZubjzJl567qlaw9ez/DqbA0sk6Yi/v20lUS1WAWO0XHIR+YkqRq40nonW/a8DmFN6p9qZe+k7WS48v20EO9RaztQTdW0fkyrJXVBhX13Go5njK+F3AHxORXwY21HdKVX/LU92rBU8NY7a/zTY8wcXouIH2GJojuPMJ+KX/1f7f+Q3wT3zjY6/muPVshoXAWLBgwYJ3glyy2XrFkTUTNZFKIpaRXd4y5MRYFcGxWMg8QOMaGtfM14WH1uAgbz+kW5QZCz4oWAiMJ8BkJ4klzjL/VBKKcqfrWOfMkBMfu7GjT5ZfcNRk7m1bVsHaQSa8eDTy6nlH3ydurZXd6HlwGulWAQcEV4jZ1/YPs2CIwHGb6KPnbBAaVwi+wdcLWFVYr81iUIoSY6bsthAC3HsD7z/B2Wlvr11s6ZuGpmvo2sDmdMt6fZNciYpSlPOzgdDYuscxs91Gbtzs2G4jY7371rYecUKKmfW6sNtFcip060C/jTgnDPVC98G9Let1UytbLY9j1WTb1mnPbuvnY8mpMB63HLdW2epFubWO3Nu23F6PaCicx8CYHU6g8xliYCyOO22cx6Wdz2xTILhC5wtBCrEI65BR3VsVQNlVKV9RqbWvkYLZSYI4hpJYeU9WJZdSCYx9tkTr3TzQzVIYsn1Z5Jq5EEuh857grLo0lUJymSB+HvSXWm+VteDV4cSjTkklWX2sCrkkilSiQa25RWbyYN8GQj0up/ssDa1EgbBv8Qj1NDBlXADsqZDJJnNlUK9X7CZzu0i5bEepX5YzMaIzJbInTmb6Y2pEuWIFObCVXMKBreSwXMTVIyxovTNxQEzonox4u4yLq2TGU8bjjy4XPBeIlcBo/GMSGLGHX/3foT2yv6d/4+fg7j+AP/87YHf/iba9bgO7aOfuw++1BQsWLFjwaKSSKCiNhDnja8wjQx4YS2QsI33esYkju5w4aRq8CC90xzXzomHlV3SuxYmbM/qmm6vT9ddbtYxM14qLCmPBs46FwHhCbNI+uMyLp/UdmgeEPFd69tnhRLl9NPKgb1g1mZiFYRcJJxbe+ep5h6rig2MzgnPKah0I3s2qg9YXhuS5eWwDzzG7ubL0uE2M2apOS72QdU7mfIoYCzEW6FYQI3QrXnt1w9VWyThEHpyCaxvuP+jJY8S3Da9+7gLnhVUXCI2b19tvI/0uERqH90LOivdCSoUUC7kYedL3EeccOdugWhXuvbHlxZePEaGSGIkLMfLjjdc3HB01lKKsj1u8Ey7OBz6jLauVnYzPh4AT5VQahlAQAsHbe9V4T+OVlIU4vT/F0WdHVmHlC60riCgOGLKtM1YSo3WF4JRNNLLDizKIs31FCU4ZS5gzMlLxVu2qgpNCUWEokVAH2sG5OUdDEMZSKFPehsiswPDFiCgAX385SSxnBFcjKIspNYrWSlVxFE3IFBzhqpIAwdda1UvhlhP5oAEUMhmnbj/49/vaU2BWghSxIE9b8z5U0+NQ2ZMd+/VzsI6CU0fGyCs3ESyVspgJFzhQWjCTEtN+1FLXeduKzoGfE0ExqTUKFoRa6jYsd+NyWOghITGTF5X9mEiYA3nIU4eIrIA/Bvwm4O8B36eqS/jAgtlC8tgKjJ/4D+H1X4SPfY09dx6OX7bH/ekTbfu4tfPnLmaOu+UyYsGCZwFLjer7D1Ps5lpx6imaTIWqhbFEkmb63KMo23RBLpldTtxoWpwIt9uW1nWswhqHo/VtzTOza5jgguWfiYXVP05F6kJeLPggYLnyeEJMTSRgUq7Or+rJI9LnDHh22awPd9aRz94VbrwAZ31gtzObRuOVz7665ebtFc4Ju0E56pTjzgaSwdnAeN1kdslzZz0yZsfFEEhF6rRI2jnaUChlCvUUSrHnORcjLlar+Wd59XNw50VwNtCjKAw9+eKC7uUXGF6/B/2O/PKH2d67DycnpKOO9boh5ULbePo+0feJkB1t41HNM3ESU6YJnnEwO8uNmx27XcRXm8l2Ezk+MZuM8zaIT9FsFnfvbslpxTBmXhShXQXSLrK5GHjxpWPAVCC376yJxdH7Qi7CzVWkrwqNozaTi3BWBOeUzRg4w+w3x22mrURB5zNbsWV2VSlzo0l0PvNgbFiHTKiD8uBKbRsx28sWTyNKVEEkkYpQ1P6HSoIArHyeyQwngi/2Ho0lEyoxompER9aqFJBC6xwpK74SHMEpXso84BcpNK6paoligaBqCggvHhVH0YzDXbZQAKKuridj8Z+KF4eqfQE63EwiHAZzmgBCKKWSEU6Y3BeTCsNwYCmpdo1pH9yBDWbSXOiBdaZONmJkmmMO56TaTybCpH751kYSPViro76xV6H7eSZMzSRvpcS4BtaRHwQi8FOYCuPLgD/5VPdowbXAmO2z3D6uAuOVn4X2Bvz+v7if1t20n/2ThXkeVQJjOy4ExoIFzwpU9dPAp7/6k1/1R5/2vjwvyGVvtYslgghDGRGEpJkhDWTNbNOGsaS5beQijry4OqJxLd4FGteY/Rgxa+58I2yvpliIiQXPE5Yrj3eAVNnOWCJgA6F1CNxoGiDSusJRk/FO6VYNXkaO2szJjY6YlAsNiIxsNyNdF6zRQzwijq5RHvQtJ21iOwZUxdYjhW2E875hFQpDcgRvcvxu1ZBSITSWrbBaBTYXI37dkU/PICcYBhvYvSiID2jf42+uyTGC82bziBHiaP9XK5jVE5bLsNtFnBd2u0hXPCmaLcbICGiCx3nh/Gzg+Lit+ReFUTNN68m5cHE+0Lae0Hia1hOrFaVrAzGVajlJ9L29t01rpElOVjl7etrTdYEhOJwTsrbEBF2js/UD4KxviNlO5sEVnMAOT+sLfTaVC1jTS3A2rc+OosImBrOkAFLcbE1IxZQcA1aLex4Dqbi52rZVIRWdLRKtM2UGQFE7nlig9UojjrFYK8o0qPYYaWHzKd45sppVZSIipCowmEkJnb8gFavSLbX+9GrJRqlkQp6+8FTJVMuHQqKqOmp7SNEyr0Cm2tZ5erWNsFdCFHH7LAsukwVZp1LXycIi87KTkkNm9UTdJg9nW1hWh15qKrlMWFwmL2ZLjOybVPZ0S9nv60G16qNsI0+RyPgyVf1KABH5PuD/eFo7suB6YVZgPA6BUQq8+v/Ab/9DcPuL9tN9sFDPJ1RgHLU1D2pMQPdEyy5YsGDB84Kp/S2VZArRavVVlF3a0ueeXdoyZFNxr7ydW9eh4SicAHAUjmikVqaK29fVq+KdnxUYj7pOuS518AsWvNt43wgMEfl+4F8AXlPVr3jE6wJ8D/BNwBb4dlX9ufdr/54EWTMr1zHoAFiFZuc7bjXJwh1FWYeMq6qA4K3mczxqON8UxlpLut2MuFqHqVVF0YSGsy0cNczKguAU7wpnvbIZPTdXkc1oVofWF7pVyzgkIwmSWo6GF7rWs40RUoJxMIICaBrHmJK97p1lZOx2Nk9KNt/xiT2vcCLsdokQBHY7BtagibYLEMF7x2plH6d+O3By0hp5MWZizJw4IcbCZjMCpsJoWj9naTStI46FbhUYhszQJ5wXbjhHv4ukmGm6wG634+RGhw+OpvEMOyXlQl41lJWncQXn4GIIM3nQBRvvDslx0iVidrTV/uFFWVk2EkPyBF+Iyc21tpPlZCyOXATaSFGzfQyV8IjFzYQHTDkMQIAxW85DLEaiJBWUjPNKnwWRAtkG8kEcrpJFRRWvShahaBXNICiFpn5mZGbg86yoUbGBOao48XWcX5UOOlWq5vnrTKvaQqn1rextLyr7zIhSUzGYsi+wPAuVff6Fq8+n3IkyV6QekANTboVcJg4mHIZSHeZeHKosCvvckcs5GA9XrZaqyphIDwtN3ZNSs51lVm9cq+BOMPUFAKqalgCuBROeyEJy/x9D3MCHH/rqtUaS4UkJjL0CY8GCBQueZ8QS55DNh6AwlGGutk+aGUsk5kgskSFbEcBFHDlpWlrnCC6wDsd0rsM7T+MCK9cxVdvL4Q0ceNvMiwULPoh4PxUYPwD8F8APvcnr3wh8af3/tcBfqD8XLFiw4HnFbxWRSd8vwLo+n1pIbj69XVvwNBEfp4WkZPhHPwm/8rfs+UceRWDcfHIFRneowFiwYMGC5xdTiOYhkubZjivicFqImslaqhoDdmlLLoldspD2okrjrS41SMA7TzsRI9XG612Y7b7TDajFOrLgecT7RmCo6t8UkU+8xSzfAvyQ2i3ZnxGR2yLyUVX97Puyg4+J6USVNNP5jpIK3gVi2pG04J3wBUc7huz55YsjulA46xvLrhjsprIPjvPzgbbKcFPMZBGOjs0O0TZCzI7OF7bRM2ZHiykGYnHc37Xc6CK7aKxrExw3b6/wwVQdu+1IitXIcHwCu61ZQpoW+h5WLaxWxJhNcZGLXejKgRqjKjcGcbNKBGoFU2MnVOcdKVnmxskNz+Yi0nUe8YGcD60BMNbE+hRNPtf3yewnNcG+bX2td01ItaVoUcYhcXTcWIhotoyFYUh0U3OGgtbMj2EUonM0AYqDWKrSIgbALrQvhvqeFyE4JSPVHuJI2XIqslrOiEMnrQGuWk522TIwphDQybISi9W5Tlx3VIFkLTKqB0qCuv4+myJjzOCDVbk2oVpGartIFQRU+4VpBsw3aSGfta8FJ362UQj7ME5TTOxDMOcqVh6uCp2CMC1TolbJHmREHKocTK2gtd5ULi9bsy+mNhSrSGW+czCta5+x8SY4sKiYYmM/72Ft2N6M8vCdhqsVq4dWk6smEbmyzFxFK/vpT+NOhqq+fSLXgucSY7JziH+rFpBf+d/gL/0+e9yewMu/+eF5Vrc+rwyMBQsWLHiecXhtkErCOz+TF4jZR4Y8oECfe4bc0+eesUT6lAlOaH3DUVix8mtrHJFA51sEoXNGajjX4GuFvRadxyOLTWTB84jrlIHxhcBnDp6/UqddKwJjqlJ9vb/Ly6sX+Wwe8HiSZmNPxfGFR46hZH7xvmfdZO5eWNbDOGaOjs0+YQNze5yiNVTcvrNmGBJHrTBmx1EbORsCQ7JWk1IgZeF01/DS8cD5UHuhfeGFo8R21XAvOIad1Zw2jaM9XjG+AXRr0ALjaAPDbmX5E+ngDpp3oB6GHZxZkBBNw9C1RhTUAaBrrS3Ee7F1xJHgVzw47xHX0jSOdJCf4ZwjjhbWOdbQzonAaFtPUaV1gc1FZIwZ7xzdyjOmwlCXg/34c+wTTeMhFXIq+ODIWck54b1Qiid5y9xwnVhLibPGlm0MeNHZ3pNLJYtCni0nYATH1AYjYnW4IkofPa7JjDVHY2o2KSpkta8QVwM/U7WWWAiorXcK/NwWx5HPjMXRqRILHNV8Cl/bRuyYzTIhdUDvqr2kqH1lmZ1lv99TMvWEiXBQ1RqQacTEZO8QmciMw+yH/SB+so3Mq5SrTR4cbGv/RTpTLtVWQiU0pn0zm0tdRz22qw0hlvAil7YjSCVOYEq1kJrncdlycpmskMk+cvhBkocJj0NCZLkoWHCdEXN5e/vIVI/6rT8AH/+noFk9PE93EzavP9G2JwJjMywExoIFCxZM1wvBhVp77+Za06yZooVYErkkxjyQS6JPmca52TYSJNDWitTOtxbMfnhtU28aefFP/ebKggVPG9eJwHjUX+Aj6gRARD4FfArgiz7+RY+a5T3HJOvqfMeuepGdCK33XMRIKspLRwOvnB2xagHsgs97qxZtWk8pStd51sctIdgg2vIXAIVcLKOhT1YR2oUy14aeD4EbXWTMjs1oKo1UhFysmrXrAjkXUiqmwnDOwjnPz4ghsH75BXbbEdZHsN0YkRGCqTG8g5UzwsPbfq9WgTFmi1ooSlsvYBGBtmOzGXFOSNHCNqdwTucEH4Qg+4/aOGa8d4TGgjhFhZRtOe+soSN4Bx00jccHt99uF8jJBt6h8TjvLLiyFCM16jYB1h1zK8hEMLTeKlK9UwRlyr8TqfxOrcCdluuCNYuoQvDWuyGilmtS1RdeFETxlcgINTeD+rPArKhwdT5X2zY6nxE8XozEmtQFwV3WJ1hLieBFcLIfZLuDgbl3FgR7abBPrTCV/XMb8JdL86geEh+PzqeYnxszYX+clUyYErEn0mq/42IZFHIYxHlAVEyqDXjoS1je7GtZ92u4qqy4Gvh5cHCPxES6XJ5yuO5JAbJcICy4XhhSoXm7AM/U288PfyXc+Mij51ndgnv/8Im2PYV47uJiIVmwYMGCIY+sfEcqiagJr46kmTxlXmgilpGLdEEuiYsUaZxjHRqCNHjnWfsjVr5DsZZDXxXQjWtqo9vDWMiLBc8rrpNx6hXgkI34GPDrj5pRVb9XVT+pqp98+eUX35edu4pbrVnPX+huz/6zII6VDwzFfG4fWdvF4yoU2kZoWk8bbKDedYGSrXXj6Ljl9g1h1djzlB0pW2ikcxY+GbOwajLHbaILhfOh4eYqsW6MKIjZljNlhKPrjLktReHkBNoWnIfNBdy/Z4GbWnCdqUAYB7OYgFlEuhW0naXUYwTGLFVWJVRSQURoGkfc7PBeGMeMiBBTIVZiJ9SAz661UMlxyIQgMznhfB1YO8HXelUf6jKrQBMcofFoJU6axtYTgpuJFFUjLkQE72xQfNTkWkNaFRE1vLMNhS4YGePEiIzZClLfd+8sTLPxhePGLtK9KI3bkxatM41AcDpvR7HWExFFpvDKA2WHrwRGUxtKOufxInjnKDXAU1FCJQT2yxl5YRVaRmJM0zNKPlAPGGXg5pBPqanVUpe3x/vaLalGDWP5Bce+9aRQLjH9cEB6MFWsunmrh/MdKhrcocpC90TDHBp65XinIND6Ibs0vXBZrXGJgJhqVx/xpX5VXfGQtGde3369jwoGXbDgOmDMhe7tFBgTgfEo5cWE1a0nzsA4XhQYCxYsWDCjdQ1jiVZPT7XP1uuePu0Y88B5PGMskYsUyaqMpcxNIk0lMRAhODu/2nWSFQc88m7uggXPMa6TAuPHgO8QkR/BwjtPr1v+xaOwTTugDgTrGGfIdif+7tCxCpk+OTq1AagCx50ydgFxQqk5DxDIxZ4Hb4PeoQ6405y38P+z926xlmzredD3/+NSNedcqy/7co59bsQ2DoIjkJzjJJCnoAQ7RLYsRUGyJcBKAlYkI5EHBEGIWDJC4QFeCOFigTF5MeLBgB1ZBCsKcaQIyQEBchw5MY5Ijk/sc9t7d/dac1bVGOPn4R9j1JhzzdW9unfv7t77jG+rNdeqWZcxq3vPqvHVd8mZCzZmxbxOnEer5ZP7BdhsHCQJxo2F8wb7/YJ5ihBxWK6vlZyw+tfOVseA7U4zL1IqBwGS6HpMSPOCw8GqmsIwkhcMg8V+v8C5/Lkz+eEcY7tVi0mICd4ZzEuETUpIUM69MIZVhZLlJrsLjxgSljmCDWGzcQgx6fIk2F0McM7AeVt5FusMUtTmEusMvCOIULWDMAscJVhDcNkOsvVBsy8SwZqEEKkSGdbHXHEFjC5qhW0mG5QM0XrSos5wnGCTEhlFWWEzsVFgSODyKTVZueFZCYXIAscMwwRKyPJDhiWC46ZelQgm//tS2wfDAhBSW4bPZESVFkJTL9pucMn1poUkUFXGOvkhYpgy9Sda7R2VEmEkHIdFFTqgWDRSJSxQj6GWDxyRFoVIWY/d5FhUouPYudKSCNwsLePQitl12Uq2ZJWFrH8n5abgnKqiNKrcWH5C3nR0vG4sIT27QnXJBIbd3L7OeE8zME5sWE9DCfHc9wyMjo6Ob0G09yQ1TBOEKc1wpDaSQ5ywj3sN6ozXSJKwDwGeDeYUcc8N2NodHDs442HJwtFaleqNR5CY7+3epOfNHR2vH6+yRvVnAfxBAO8Q0ZcB/AQABwAi8l8C+EVohepvQGtU/8SrGtuHQUj6dJ6J61PxKWoN53uTw9ZHXM0WsBHeUK5BDbiaHJgZh+u55kUkrxNyM2ig5H4xeHs74xqmkhfOJDBpRgZDJ8WbnMkgIthtCGwGbDYOw2Dx/vt7XNGito4YlWjIDIC1jJhEczKumn8KbABEVV+wAaYDDs5hs7FwjiFi4AeDw0FrVVMSGLdaZHYXOecjW0X2+wDxwDCux7CO4b3FNAUgAruLAQBw9WSGNYztzuNwWLC7GHD1RGtZw8YihgTn1R7jLCMQ4Jix9ZpRUewfxX4z2JTPm2C/GOy8EhHXi8XWBUxkYFiQBHAssJywDwZbE2uVqmOB41BrVze5MrWQGwDgOUGE4Gysig/Ok97BqNrCcsLOlouTJk7bYglhnYQPxsARw7IGenJjGTG05kEYMrX6cw24FPVMQhBFsy6ICEwGgnQUhEmkSg7dSmqqte4/2zCa31v1RlEvUFFHZOtLudAWG0tLTCSkSoycs4rUf3pQRUlVVZRJ1YkPtG7XvC84VmaUPBBZ+Zh6npDVLjq2XLWK9abkmDzBMZvS0fEGYM7fgU9FJthhh9vXGe8DaQGWPeC3dzr2Jn/fX/UWko6Ojw2I6AcB/OB3ftd3vO6hfOxB0HDOIBE+WzwMGTAxDnHCkhbs4x4QwXW4gkBwFQIcMwZjsbEuB3Z6bDLB7NnBss0PyiyiRFgyiNKJ4o6OU7zKFpIfecb7AuDHX9FwOjo6Ojo6PrZY4nMoMNxTFBhDbuKdHt2ZwDBMGB13BUZHx8cIIvILAH7hS9/7Pf/66x7LJwGG9aFjyPWoSwoa2AnBIhrYOcUDBMD786QbJYZzFgDBsoNjpw9AQTBstFkkq1BNfmDUa1I7Om7iTbKQfCxh2cKxw9ZsQQAezfrEy7Hg7XHG+7PTp/qLw8ZFfBAd5qitHEQJISZcXI5Y5oCwaKMGoPYQbxKWRPA2wXLCYBMOC6uqABoAeeECpsiwgTENjMNi4Kzg3U9fICXBk8cTdjuHGAV46+1sEVFribUMigkhCnBxqTkY250qL2LQV0mAtWBDagEhwnbLmKdY9zvPEcyEIcuKazaGZSQBdjuHcbRwzqg9Y2PV5jJaDeWMCZutg2GCNQxiwjA6OG+w2TpYy7gYAkIiTMFg64PmR9gZUzBgCti6WFtDSr6FnieBNZpHsXERo4lqw3EBAycYJxhNwhQZjrWWkAB4o3YRzioLU4I58zLDahexScNBd1YVG44THAOxNLZAgzYNERw7eC62Dg22dMywxAi5FcSQ2kgcG1ihrKDgWktaLmo5GvNIHaHnvgnxJLV+MBiJ8sVQinVijcGsVVxNFgQzV9WBwZpxQfkYa2YGjtQZ3NhUdO/H67QqhqLgoJNQzjZKtHweoePlR4qKM/WoRUXSWkmO8i/qpllxcRIkej489LY3OjpePeZwhxaSsAfYZVXdLRjv6+v/+KeBf/nnNPD5Dth62xUYHR0d3zIIKVSVpiFTrbMM0sYRKIkRkgZ2BlmwpAWHGHHhNPjfs4XLTSOePZxxGIzXLAwyEEnZ0suIKWZ1N/csro6OE3QC40NiazcIKcD7+6CF8I3pSos5OOHdccLjxcLbhLAnOKOExRwZ86y2jhgSHmwWfDMZXF/N2PkBWg2pE/AQWUMijWCwEU8miy3rUy/Hgm/bHPDB7OCMIKSAJwfCxSj4zLcN+OAJ8PWvPsG4dZgOAfcejHgU7gHLjMN+wb0Ho9ajLgHjxYjDY9EGEmIlM4wFUgTY6BO3jUOMCcNg8PWvXePdT+1wOIQ6+bu85zHlUDdjGMYwDoeAi0sP5zWwcyDg3oMNrh5PGDYOgxOEyBhdRPSaZQEg16sCWx8hA+NymBGFcDVb3BsWPfc+4nrOLSkuwbESEcUmMrBmgzgWhETY5GwMtYLEau24dAseL0o0qS1Em0g8J20UyeGdnhOClOrSBM/APurvO0sIkmCI4Jv8ijKRHtjAGaUCoqQ6yR+MgSGGA8OR+iJdrtSSWnXKmncBhiBpo8uJraLYRYpPstAYTKy5GCIwZJGQYLIksZAfnj1iTssu7IBaVFJ+KqBjQd63gY6HKH+WJpuiZHAAOM7MkNWqciNjonyW+ivn41ElHnRYxxfwmm+RzzZnS0lrbblxHBwTNSk/3SgZGSWQFHW/p5ka/Qai483AnVpIlsPT1RcA8Nkv6etv/jXg8T8C7n/2TsffeoNf+rXfwW+9t8e/9f3/BL74mft32q6jo6Pj44AlLXDs6r2HZYslLbUqdUmhWkkEgiUFzGnCHGdM8YAoEYskbKwFkwEgGMyYq1INLBmM7PP9GeeHZvqdnpC0Wa4+VOn3Hh0dLTqB8RLBxLi0Dm8PM5akk9ytjZCBABxgWbDxwIUPeOctj/c+CHAbg62P+OaVtmuMTp/uz1HzMiwL3tlN2A0BUzCwnDAFhsmT8m9OHlsbQGQwOsbbF9qisUQCG8LuYkAMCdFq7oLfjVgWDyJgt/OYbYSIqiFERsxz0lYQt8XgDZZF1RV+sCresJxVGA4xKJlR5orMutw6rXFlw3COYSxjs3EwVhUAJbDTGiVpdl5Jj42L2HqD69lgN8wIkbFxEc6o+sSbhMsh1BBNw9o0Amjzh+eEObESD6zKCRGqy7d2zbEQUUIpJsKFC7UdJAphY7S9pA3j9KzVpqmENWWiwlBCgmAwFk7yOc7rlfwKQNUPRV1RKnUFAkdcCQJDBp4Bw1Yn+qzny2TlhQZcMpi0LrUIGgoxoBfZouBBfi25FWZtHCHUphHKJAGDgNxOIpm4QDOZL0qJ0mYClNBQzmIMunGBbYtg22BQRktC6JOLViFBQCVLynmqDSK00hntQU62YgAAIABJREFUfm+QIkeKipvEx9pWgmY9hYaRYiVFThUbvVK14w3AEu+owLBPaSABgLe+A/hj/zXwc//a2lpyB/zxL30Of/3vfg1/7de/ht/zhYedwOjo6PhEIqZYlaoEwpwWzb2Iev85pRkQ0crUOGv2BQT7uOR7RsGl8yAQHDk49nBsq6rWsUXKD1EEgM1ZZinfF3Z0dNxEJzBeIhgGo93g7SHhvfkAAmFrIiwJLlzAo8ViNwTsfMC9ccHjqxF+sNi4GYC2apRa1DkyrveC7Ybw1mbGYBK+MllYIyuBIYT3ZoeHfkESVRhsLyN+58mAQ7BgZlxcaghmTDrx3O08lhAxHQJ2Fx7OBcxLhB8MmAkiM5i18nWblRtEBOe51pwCSkJMU8DFdoAxanEwhrHZuvz+oM0gXmV2F5caIseGcH+bMB8sLAsGk3BvXHA1W7y9mxAi4+tXHg+3Cw4LwxnBg3HGFA3u+wWeEw6RkYQQhPDOMCMBlaiIQkgAHAmGHMRpWeAi48IFLImxtaW6SokGDVXSSfQhqurCGwPHFnPMtbfGwGYrR8gBkI4YG2sRU8rEhFpDHDNCEnjDcMQoNEiUpKRHvVjpFNqxRZSkDLzw0YVSSFRxgawCoFySSozSGCJZ0VGUHetkP/+7LMqJHOYZszKiEhiUe0aItP6rhHCikAc6GiEc7x96oQ1QNUcN+ASq5aUlAlLlQo6XR8ix9aX5XEVhwWW57qB6RDkrXAzl0FNQJSCOCJUTWwtlAuMImcwREUhD0rRVql3G2fGmYA4Ju+EZl/Dl8PQK1YKi0piv7nz8P/OHfzf+zB/+3fjin/tf8N71cuftOjo6Oj4OcKz3s9VmC7V5JAnYx0NurdNQ8CnOmJLmXIgkTClgaz2uw4xLt1G7OTkYtro/omxB132bet+H/HrzoVBHR8eKTmC8BBQ52WA8ngRgYz0GY/Db+yu8M054slhcBQsGMNqEkNQKcbljWBPxtScDmHVyvF8M3t7NmALDbXOdJ4BD5NpEshDgjDZpDDZiYyKCEHxWFIQtYbQJTyaLsPOIUafQ9+6rGmO/1wlcDAnbrc8ZFgxrV6a3VJ0yE3YXHsaQqgEMI8aE7VZzLYxl0GC1itRoVWoMCd4bWGdgrdaljk5tMJo5ADy8b7DxAfc3Cwar+RVbE2FcAJHmUhgyGGzE1ioJtLWaW+Ebj/bWBiQQhkYtEUS/9rc2IYrAEGFnE1zdTnMnPDMiBJ4NphhzTWmEN6y2DmYY0rwRZ0ydFG9IW2ZNVlhEVvIhicAw5ZaRBJ8ZdgPJjSMMm1OmU06VFgGYjKoqQDBUJtjrJN+QzXN2yX5LXokEqkkYqqyoFhJelRaZmKj5Fc3MnbHmP5T8Cq7vr8qHNhmiHFs9n1LJC2AlGVLLHehGul+iqhgpJg1ulR553fV4x+qLetJa5OrVNiPj6G002xUbyfppTo6JI7KjRScvOt4kLFGebSEJ+6dXqBYUAmPZP/c4Hmw93t/Pz71dR0dHx5uEoqwoWNKCYh0pjYMh220HM+Dx/BgJgkPYI0jEoVamLlmxm3BhR1iy1TaytVvYrKpw7EDEtWlEoPdwUWIP7uzoeAY6gfESsLUbPJof4+HwAN+YvoHRjNjYLf7R9RXeHQc4nhByJedoIw7B4PFk8XCrN31f/obDOApSIhwWtVI8MRYbFzHYCBHCIarqgiGYiOFNwvVisLERo41YhLDN+zYs2PkAyx5TGBGj1poSEeYpIIkqIUJIePiWx7JEhJDgvMk1qYIQBNYSmAkXFx7EhOurGc4ZxJiwuxx0f3OodhFmgnMG8xxgncE4apbEmlkRMEcNGn33YgYR8PZmQhS1cexcwIUNGE1ESAybLThDzrPY2oid1QBPAEdhmfe9xxQjDBGWlJBEcOF8zWiwRDCsKoPrsGBrHUaT8zbY4NEyg0GwxBiNQSzKjBx+ZzgfkzjXW6mCgskg5TyJOUV4VsWJFa5WELVkqMrAs2+2R81ecOyyNYXra/k741zNJSKIEsAwmbg4TqeuVaiCavXgPPHnJmyqqBp0/1wn84UoEMoT9cZj0eZCFOIEsloqbuRISDqa8LcKjZSVJCRrhanuu5AjGXmd1rJSxtBSGEWFccMrei4Ho1nOZJAoHX3+1j5SFChHm3YCo+MNwRzu2EJyJwVGbh9Zrp97HPc3Dh90BUZHR8fHHC15AawKjDnO9d5MIIgp4iAHzGlBSAsSBFPcI0nEVZiRBFhSwAO/yfdp+oDK5brVcpyiMi1RyASs93+CTmJ0dDwFncB4SbBscR32NV04poiHw4CvHfZ54qmZGCERYlK1RIiMKATvDRxHTEmVCkskMKlVJAnw3sHB5syLe0PEktTW8HAzY2MjpsgwJHi8WLic5WBJcG8MuJ4DQtIvS+cNrq908hniqli4/2CDadKvUCJow8iFRYqCe/dVSTGM+vu41XaQ3YVaQsZxJS+GgUEEWMfwjnJeRcLWRx2TEQw25dyLAG+UlCAAG5OzLjghSYSYCMsGGxMxGg3PvLQBjg1MMxkvGRM+hx15ZhxiBBNhY6xOlqHEAxPVh+v19zx13dos44sEn4mNcumorRtQdYVlC5ZiK0D1KJqipMjzZlMyJSRV+4UhAwLD5s9A9QJFDelwM6+Cc3AmYOtyEsoKizXM0pDRwM/cCFIn51kBUdQYVTnRTMiLnYQl52k0uRZEbVOH4kYydiUaVkKg7hsrGcAny1UVIijSEGn2IW22Rtm/AKfUwlmFRqO4aLcvOCVC1rGvoz2lK7oKo+NNwRwT3DMzMA7PzsAAPpQC4+HO4f19JzA6Ojo+3jhVYOzjAQN7bRuRhEX0PjkiYR8OWNKMQzxgjhMEgjlFWGbEJNi5AaPR71VvBjjWtpGiii2h5wKBJVOJC4FUC25HR8ft6ATGS8LWbvBoeYyBdWI/pwkP/CX+wRNtJQlJazrnqCGbziQskTFHxmAjnBHMUSf4Iemk9Ho2SEJYIuPeqDkXo4lYLCElwsOcCTEnxsAJ31ws3hpm2Fz9OZiEq9FocCUbzCHXOHn9az/stQnl8v6I/fWCJdehhiXh8nLE4bBgs3EQATYbhxC07pSZcbErlgFGSJoz8WCj4aWTVULmQc7uYBZscs1oCcocOOHSBQxG20AundosCABT0BaQkDCahMEQQoq4cB5MhMEYxKyyGIzNFwC1hGyMBdOC0VjYPGFNAFxWUkgO19TJOyqBMJoBIgmWEmwOySxNGIYMYgr5Z4YlB2K9oEUJcDwcqQ9qwGRRHZQJNZWWECUpCBEm51tECSCYqoAwWXUBoJIJDD7aX6JU1Q/I9hLL5oi5L20jhgwiYg4HpVrPVfefJ+Wcxx/kONeCiZGQjuwnN0mKQnboPkpmRCEUCiGwHrfJw2iJipxBoZ8r1XNXlBaFsan0Q/te3hcaskWa10JqtA0jR2Nv1SVn3u/oeFNwJwXGnQmMF1dgPNh4/J0PHj33dh0dHR1vEnxuHAGyXdiM2Ic9LFsc0oQkgjnNCEnrUUtdqgDYh6BtfcaBrYElWx9EGTLYGP0eNnlZeWBVMs6ObLg9KLyj45noBMbLhKgSYx/3OER9kjUaRpAEykGeD8YFc1SC4r29w8gRh0XDOwXarEEQXA4Lti7g/YOHNdqqIShKDgFYv2YNCa6DzfYKJQM2JuIr1xskAd7azFgig0jwSBy2O20AiSFhnoNWmw5WGeaN4LBfYKw2h4yjgx9zsBAT7j/Y6HuGsfMLiARLZFghXPigYyfBg3GBILeK2FgzKXy2gRQ1ys4FWBJcOjmqHt1YzdvwHOHYgIkwGluDNC0ThBhRBKM1OUdCv/ANG2xtaf1oLwiNJUOK2mR9n3PYZqnRtGSQ8kTckEEJhqBMIgCAJVVD6KTc1Im+ALlVJF+YuI6iXpyICCTUXKyyAiSrOagZX1FWMCnxkHcII+v4hY6VDeWzVtuFSCVAynsAjhh/U5QQeWwFpZ60hnSW/dJq3ShrF5LAYFVnlEDM0/G1uRMt0YCWzGjeT1hJCRytuzaHtPsu5wbNdpLJjnTydKPsWz9rs3+0BEhXX3S8OZjv0kKy7IHNw2fv7EMoMO5vu4Wko6Pj44lT1UVIoYZ2HqKGcl6Ffb1PEtG61CUtmOIEAvB4mXBhNc/CkIVjBybGmPOHRqMPNg2b3DbnsKTlSAEaJfbGkY6O50AnMF4yvHF4vDzCPk7wbDFag+ugLQr3XMDDccac9Kbzg73D6CKWyBhz+0hManO4v1nAAL557eG4BGACKU/+i5KBSDBFVsuIXzBwwsYCv3Wtqo93xwlXs4U3CVeTxcUoiElzKOzVrASGjRisBnd+wB6brcM0BThvMOSU+5QEb98HpgAMNuByWGBY8P7ew1DCw82M60UVI99+ccAUdSJ/zy94slgQAMuCe26pSoydDTBEuHSDZjRkBcaF0zyLMimNIjWvolhINNAS8GzrRaA83TdGa0ltviiViW6p9TQwax1mzkIAgIC1htTmTIqEVP2L5am9IQ3htOzAohkYWoW6VmxG6MVKiQFzFHzJlJUUJPWiVZQV5UIac5VpIUMsGRg2iJLgMjEC0oue/pgn/Se5FaVetZAg5TO31hAGI0I/Q8y95qXVoxApSRJstrGU9g8iQpDYZFisJEIbRCXFbkPF0rJSA+W8UlZbFDuJEkrHBg/JeSZErJYcOXozEyr5+KCG8FiDQ1NuYJF63hplBkomSFObWhQk/YlIxxuGJSZ484x/l69AgfFwqxaSXjHc0dHxcYNnhzkt9b7KFXJBpAZ3CgRLWsBg7MM1okRMcUKQqN/DbHCIETtn1odppPd/lvVBl2FTG+SWtFTlRUEnLzo6ng+dwHgJuA57bO2msraWHSwZMBiX1iMmJRe2VuXwc2KEyHhnN2FJaikBlLxIUBXG9awNIxdDyARDxBQMmLQ+9cKFqmoYTKrVoFFUvXDP6RfvYCI+tZ3waLE4BFOtK7/9aMTuUitQoxDe3U3YB4NxdFgCcLFdZf4hMpgFhIiHG1VeOCMwLHhnN4EIuPBBG1OEsCQlWZDHpzaQCEclcHOBIYJjA8+mVpDqMsZgHBwbzOXiIU2IZiYFrHH6pD9fHHTSuYYliaRaS3VqAKAcrGkz+VGrN4WPJuDFaMIggG2uK2W01gtdd7V75AOAwVq3hVQDN2vjRsmayPsuk2bt9DjOpmCo2qQQAfbkIlcuegmpEjTAGoZZiYh6TK0tbeWK5TiQNZODKO8t74dOFQ0tGXGmHaSQEpL3sZImKxlAsr53BDoO0yyKjPZYq+1k3aaer1ZxcUKC6DBXYqP+PVJLwtDxdvn45/bV0fG6MIe7KDAOq7riafAfzkISk+DJFHA5umdv0NHR0fGGoeRbOJSae11myWCOM6JELGlGkAVz1AD+JSZsrcN1WHDpRjh2cDkzYzAjBuOrMrvcq+k9mN6zAfpArUBENN+sKz07Op6JTmC8BBSWdpvlYo+WTGCQwc4N2MeAey7C5wn1EhhTZHz+co/3Zs2YEAFCKsyt4LDkBo9RrRkbExFyWOchGmxNxJwYjgUDJ4xGmd+rZUES4GHOwohC+Ox2D7MfITvCYCL20eDrVx67nY4nRMI7uxlfv/IIjnENg8/cVynxBweHw2IqOfHWdkbIGR5MSkYASpSUW+mvT74SKJYEF1YbRhwnPFks7uUsCwDY2ZWAMMRqFWGvxALNSFkFsOYnGAiS1k+BjtQLZRK/kglU58caQqkUgcmkwGg2VRGxpEWrbPMkdd2fZksYMkDOvyhqgiRavcow+f01QdpwsYJQVZEIBIa5Tog15NOs0sR8dCK9qKVMPJTQpySp1nmtdal5Up9U8VF6yWv7CAixjKkhOAytBNVKNkiug01oa1xLXkhZryof8nGo7qecdQUTI6WoY1//RhpTR7GGHFtmOCtV2t+BlUQ5JRnasM1qB5JUFRft+zdDO6melar4oOMV9Vwicy/9xqLjzcCdCIywv5sCo6zzghYSAHj/eukERkdHx8cCU5zg2WNKc7V4THHCLDOmOOv9XIq4lhlRQiUxYlK7d5CI0Whe2qUbYTN5MeTv0tEMEAi88TWnjLNV2pw+9EK3kHR0PC86gfESUBpICoHBUO9blIjrZQ/HjI21eG9asLMGexfVOpLUSpGE4Izg/mZBTITB6lRq50OdXA0mIViVvW+ttnsAwJQYWxvwZIm4cKpmCCnlIE8gRsCyKiHu+QX7oNPYd3cTHk8OsVaSJowuQaCKD4IqQbxJcCxwRhtUti5gHwy2NuagUCU2HAnGPD4iJVyI9LghESwJtsZiNAJb1ABENYTT5ImnYVsn3sVq0bLRmsPAVW2gE1lFzY8gyrkYeQJMpXLUokyldWIfq2pG5X1cQy/VKpEgQpmM4np8oGn8KIqJ3PoR889lHJU0KCqCTAycToRbRUa1urTBoyAcp000CoJGUVAJDay2EtOQF8dWDhxt11oudN0mZ6IhLRpW6EhZUY5dR9hYL45tJutnOB7HMcHQEhVtyOYpqF1O58I32/2cV2W0Y4ScqDPafZ2rJunoeMVISRCSwN2pRvUOCgwitZG8kIVEa7rfv17w+beee/OOjo6OjwyHOFWCooVnn19Lpale62N+0BVTVFWGQH9OC6Z4QIQgJL2PjCBcGFOD30vGRcp2V5vvEcu9UFGIlodW7X1gq6Dt6Oh4NjqB8RKwtRs8mh/X3w0xLuwFrsIV5hjh80T078/ApVtwFQwGk/B4sbA5U8CbhMtBbSIbGxES5VDOiCW3jKS8fGsjLAuAhCkyHnjd58YKXCEwGLDM4JRgiGA54T4nXAcDIsG3XR4gIMxBAz69Sdh6bf8wJFgSw2YCY7CauXG9KHEhomPYmAibW1Acp5ppcc/F3PShyoMlkyk75zAagykqg63NIbYqC5jMap0gA0Ocl518sdNxP3axdlhyORNClReVYMjhl4YNWNYchJiihnWiEAQ4qrJSckcqybHaSgAQwZGOveRVaDCnfoIgsdo9lOygqghoVQLrv5mVebdsESXWzvCyTbGFtPkVEKn70XHp8YtyBUQwMGDWJpVSn3pyOqtCpe43j7v8naQqYZCsmpCbORN0TFRIPa/SED8nkJWWqQQS8WpLyWRL2U8Zu56TMv617UXtQ8ekQ7vNOrabPERpH2m3vTHcM+evo+NVY8412C9NgQEo0fECCowHWYHxf335ffzTn7v/3Nt3dHR8OBDRdwL49wDcF5E//rrH8ybhHHkxxQmuCe4seRaGDEIKiBL1NUUECQiZvEgALBkM1iGkAG8GWLZwrPWogxnBxLV6Ve9L871dQ1ZQUXU26OrOjo7nQ6f8XhKKCgMAnHFgVkb2nttovae1eGeYQaT5FAOrSmJrI97dTaBs97j0i9Z+5tyIJTFSDuu8dAsuXdDf83E3JmJj1EqiqgWCNwYbY+HZYGMYlhg7G+FNws5qM8g252s83M54sFlgSLA1EW+NOsb7uQVl5yPe2Uy4Nyy4N2ruRlFeDCbi0gVcWCU+tCAUamfJY7lwDhvD2FpX60sdcz0nRLyGRJKSGHoxIDBpFVWR2xERDGs1lSGT05599Rcy51oqtsquZxLDZAtG+Xsqyxy7SlpUkF5IbD52JS+whnBSVjS0k/S15lR/Npl4KSqSQsJw3dNqf2nJmBoWWnxFaCwUmWQpao+C0rhypJzIvxfbTPmMRdlSiJD1dzqymNTjylo32l5e6WhMN98/Xac5vY1a4lgtUq05SCf7aStTceN9YH16UgNDcZxZsbalPOUm4Yww47a61Y6O14mlEBhPU2DEAKTwHATG9oUIjE9f6v7//f/pV/G3v/LBc2/f0fGtDCL6aSL6KhH96snyP0JEv05Ev0FEf/Zp+xCR3xSRP/XRjvTjg308nF1+FVRhNpgBTIwpzWBiBIk4hAOmOOGQpkxgLBAkLPl3te+uD06c8fDsMfAASxbeeIxmgMv3jY6skhdZAVyUvCbntPV7iY6OD4euwHhJ2NrNSmCQhSPgGgyffXAGwLdvd/jGdMC7Y8LVsuBJ0BDJb788wJuEQzC45wLemx0cC0JWNogQNjnzYsiqh42JMMTYWg22fGsoGRIGW0uZaGA8XhY4ZliOuAoLtrtr7KNByo0mgM7bRhNxzy0o1pGHfkYUwrWNeOC1OeWeC9UecuEC7nsAYPhsZ9lanytPNZHZEGFrLe45YGCz1nDmf3YbMyIh5TwGgjcDShBnkgQWVWAYVlY8SYQlB2au5MSas1BsFwxmA0dWL05QFhxJJ/WObd5vY2OQvA4EFkqQHLHmsmZEkKBeiAop0KonXG4vYeRa0qKSyKREaSbR82wrSVHsDVGShpPmbUmoXgCLxaKQFQKptaBlrCxFOcF1Ul+pjSZTQpqwThEND+WsFDpq5BBNqCjEg9D69KCoQsokvzSIlG1PLSFH5EVGq+I4toy0ORnHRIgAlWw5JTNuWlSyZUaOl5+7dTi3bTle+f3Ut9rR8Srxn/yvv46//ne/hh/+vV8A8AwFRr4ewT2HAmO+eu4xfeHtLf7cD/xT+Mm//Gv4yvsHfPEzXYXR0fEc+BkA/xmAv1QWEJEB8BcB/AsAvgzgV4jo5wEYAH/+ZPs/KSJffTVDfbOxjwdszIiRh2odaa0aG6PfhUUhKyI4xCnfm5V7P4s5ad7FnGY8yTZwgGCYsEjErt6r6j1qybmg/OBOJGnwO6Q+jGvR1RYdHR8encB4iSgZGIsE3HOXwPQ+5qRpxSIJ3hi8PYxwbOpE6fFi4Tnh0oa6H0uCGcB1MPjsbsYSGYNJ+MY0YDARD/yC0VgQRWyswz4EPPADogiGHOYZk2A0Dh/MMzamdFoHbJ3HGCOuY8BgIkKudHUkGE3Ckghv+RnbnGdRbo8ZwM4FhEQYTNJMDTfi0TKDibC1Djtrax3qaEz+0ge2doAhhmN/dD4sO4S0gIjh2deLx5IWtX/kC8JoBlyntUnDGw8RDfIMucLUktHEaHa1daOQDAYAs1pSWia8hGSWD5lySGO9tGTFiGWLhISYQ5gc2xwGypWA0VCngJFHLHFGzBP6YrdAPY909Hu5kBUSZB8O9fdao5r/rIGZxyRBPqEolhHJNaRBtBKVWT2aQeIRwVDQhnZWu0hWgJTnBNXCkm8GiqKkqkVOtm3bQACgWESA1f4hTetJu245F2W8hTRZbSKopER7Hov64tSec2ueBW4hPI5UJW2GRkfH68XP/Z+/hd96f4+///W/A+AZCoww6au9QwYG8MIWEgD4Q//kp/CTf/nX8Gi/vND2HR3fqhCRXyai33Wy+PcB+A0R+U0AIKL/HsAPicifB/ADL3IcIvoxAD8GAJ//wudfeLxvMgpBQaT3jW0wZgntLPcGmncG7MO+PmiaUsAUJyxpxpQmiCQMRmtWPVt4smoTyS1zjr0qrkE1S6Nttyv3oWftqL12uqPjQ6ETGC8R12Gvygey+qUIYDQbRFEvnZGIOe4xxYiQEi7dolkXLuBxsLhaLIYctumjWkw8a+3pnBgbE7G1AZ4TnHGYU8QUlWjQphMPEcAbA7CywPe9x2AMomjd08ZYWCIESbh0AUtk7FzAlBtOjFEry2B0YrqxEYYEkQWOE2bSPI5LZ7GPAdtMjnijqg4mzd7Y5EDOOS3QCaA+zSdkCwQpO+3MUHMcWo66SO90MsyquoB+6ccUwGwa9UaGiF6wspKgKBUKcVEzKE4uGjVjg9LRhcaAq6WEhavaIDbNKAyuto61OYQBSUeT4zXgcn0tZEYhKpa0rGOjNl/iGFXpIGsgZ903dNJdQqTK/qLEI/LkXGgnnx6pDQMtP58oEKRZtto8VqXEOdTldLOa9NSSIvlzaXbHsSWk3eZo+ybUtCUi5OTzPo3EON1vUYV0dLxOXM8B3jIeH3Ll39MIjOV5FRgvFuIJAPc3evP+QScwOjpeBj4L4B82v38ZwO+/bWUiehvAfwjge4jo381ExxFE5KcA/BQAfOl7v+cTzceLCKb8sMwYg+uwx2D0AdohTpVgOKQpt4oIlrQgSqgZGFNU9fJ1WDAYW23OIgKT73uLVQQ5/8vmTDcCAfme9ZxSF2iyxjqR0dHxQuh66JeIkAIWCdjYTe2UvrA7jGaENx7OaOrxkmIlEAaTcN8teLxYPJmVuX3gFzz0Mx74Ga60jUTGaDS7wrNaMogIS9Jp6CFGJS6gX5SD8YgQXLpt9ucZ7KzFhXPYOYfBGNxzagV5d3RKXpBgNILBRDhmGGbsbMLWAlubMBrCaCIGQ7jvPQ4xYuccLpzHxto68XPEGO2IjdlUmd6aj0gwZDGYjRIkZgOTFQ5lTirQ4Myh5Fuw5lE4yrWveTIuInDscgI01ffKhcKRzfkQJudrMNC0k5TxmpyvUVj12tedsys4W0/KxSrJqqGwRSZIXN+rNoPGtsDER3kaJv8ukuqEP6Z4XI9aFRDHCoKitNCfSwAmHWVKlIti/azS2B+a5frrOsaCIn0s77cd5u1YynhO7SKqzDjJsmgv0icqjeNgUhw1gZT1Tw5QiYfj0NG1Crc93inJUbc75WxaUqM5x/rZ+9dlx+vF1RTxw7/38/jCW1s82Dr845+6uH3l/+Nn9PUVKDBKfeqjQycwOjpeAs67HG+BiHxDRP60iHzXOfLiWw1FgeFKyxwx9uGgD8pIA82DRM1Hy+rOJc24Xq4QJeD9Wb8HH2eF8T4EzWPL990MwsZs1nun/CAryhrSXlByL24f7Ed2Gjo6PtHoCoyXiDJBBfLkmQ0iEpYUwGRwCPs6GbrvByQ54J1xgiHBp8cJk1+QhDBFRhSCZcGTxeK+X2rdacrNGFHW0M5L53CIpcJUJ1/KFq85Do4MEguCJIgAo7GYY4JwRMpPrC3rF/+FsbCZeTbMGIyBCLI1xMIxIwlwmYM59fMawGqzSPH7RYnw7EEEWMpVVRB49noRIc6tIdo6Uiafnn1+6q5EQUwxWzvlVN4gAAAgAElEQVTUPlJ+ZzBCClWFoRYUaibeDIP1+lCUEsVGcXpRKccvf0dFyQGUwEu1g5iGCABWsiEi1WyGsm3CSQMH1ekzQgpVTSHQfIyi+tB9n0zgcUICZEvNqjgoiohV6dEuT4UsqUGXq42D8j5qu8gJCSMNmXJca5qPiVXV0Z7/c6hWl0axUa0bTQhnXfdkHS7nSE732+wHq7XlNkVF+/NtEs/2/XP76eh4VZhDwhwTPnU54Jf/7X/+6StffR34G/+x/vzWd97tAG4LPP6dFxqbYcLlYPFoH569ckdHx7PwZQCtz+NzAL7yYXdKRD8I4Ae/87u+48Pu6o1Byb0AtDK1NIAAwBRnDWRnzURLkhCl3JNzVlocECVCRJAATGGGZ0aUhPt+g32Y8XDY1gB4fWjm4NnVhztFWVwsu4S1Ba88TGotri36/URHx4uhP1J8idjaTc3B2NjNWrMpOsme0lIn/A/9mJtJGJYEn9lO+Md2Kt+dkxIYBODx4nDPW1w4wsaiEhlBtB7VG8bDYYMxkwyaQZEgolkPZdKpX75asSrQjArLBJu/qAHU6lNtDtE8C8eMC+twmVUbD/yAe95DILg/DGBSEsWwzYy3yzI7IIrWTA1ZZVFCkkYz1r7sQmBYstWiMeRApPKFnzIxwCDs7LYqI0rdaIEhA8+uNnsQoAx8o0xw2Z+oxzRHl442YwEoYayZlCKqE3LHTlUM+dJTAj4ld31TXkcn5Gn1QOZ9cw7oLORG6bnw7GBYA6Fuu6St9EdRcqwkS7mQUlZ3ULNNPV6rMGgIh1Y1UlpO1kpbrhfeU6lj+Vxlvfb1aZ+hPLFoL96teqL9rGVfrbLiXK5FXtD8KEfL2v3V5acpo82x2waSlsDo6HgduJqUHNgNd3jusH9fX3/oLwKf+9LdDvAhLCQAcG/juoWko+Pl4FcAfDcRfQcReQA/DODnP+xOReQXROTHHjz4eAbtFmt2i0JeiAjmNGNOM6Y4YcohnnNaNFtMgCUuiEmr6ZkYV+ExgiyY04TruEeUhNHovdvODjBkcOFGePYYzQYDj7Dsamhnuc8qgfLe+CNbc7lvKHlrPQS8o+PloSswPgKUL1lmA8SgE3bSCf5COcSyTsYAbwjXARgNasbFnEoAZsB10BtXEcE9ZyAgWGJEkkogANpGUmT7JnvxvPHZ8iBwopkRUwy5LcQCCNhZBxlyfStzDggFdsnBMsNncmRJCYbLsXR8LQnAua5UCROHmJbccAEYshpyacZm0rzWo0JWAiFm9YjWnWaQhj8uaamWkSIHNGSQJFTSAlj3pePKOQokVYVBAGK+iCVou0jZjohhmrwLPffZDpFVD0a4khUts16zJTJxcXOOTEgpqqJAEnAy2S/5FcWjuaoUSqtH0zBCpEFURcVR/uha9ZhFbXHK9BepY83CyE8KqiomUxinFaXliUJVPJScCklHR7iRQdEoGtox0YnC4VwuRguB3LSYtCcR0PaWsu4tao2yPsmpquTUstOfkHS8fjx5HgJjeqSvm7fufgC3+VAExuVou4Wko+M5QUQ/C+APAniHiL4M4CdE5L8hon8DwF+BNo/8tIj87dc4zDcCm2fY4QZWm7bPgfH7eKgPDw/hAGcc9ssVLDvso96nz1HvyZeo97flQcqcAi6sh2ELxw4MxpBt4JzvA2vLHfShlapqjx+UdNKio+OjQScwPkIUJnZjtwgp4MJd4MnyBIY0NGhggyQCtoSYZmyMw6c3ATElhDwBu3BqWUgiiAA+tdng0TLDMsMyZ/ZXLR+WDCLUKlDSlssEX595c05lniEALBEeLYQHfoRngyUrN1xu7LjvKbeH2EwqrNYAby0A0lpSMkgSM4GhFhJLBsxUJ+pa9sHwPCDl8kvP7qi60xuf61ITkOtTkT8PE4OlXCj0n22CaPIzEQrVUdhuw7aILsDQi4uGNBW1iYaKGjIgSQ2DwNmzqPaUNgtCz1nOyOD8AL80mYDy35PWoIIIloxaMXKTRj55qFPiSniU86ST7aM8iPxbCYJa8xvytvncJForRW9M+EWqUqMQDsVOYvK/CVVopCNVTDlPZbv2NTUUybF1Yx2rQJtaTi0aRz8TgSRf8E+UEit5g6P36yutREU7jrLfU9vLEegmwVKWZ2nIuu8TZUZHx+vA1Zxzle5EYDzW1/He3Q/gtsD1N4EUAX6KZ/sW3N+43kLS0fGcEJEfuWX5LwL4xZd5rE+ihQTQAP2ifi7X9SXbi+e0YIpTzhwTOPY4xD2CBExRMy6uw4zRGLWQxAUb47C1u3w/q/e0oxlBUKu4JVMVpOUeVh8mHati20y08vCnExodHS8H/f+kjwAbuzliipOkTCaoh46IYcnVylNLjAd+wGAMLq2DY4N7zoJJsLUWD/yAjbVVHTEala3trINjlbE51ifp7UTXkFFGGMoWD2bMFo5NJiZ0+/KFu7Pq6Qv5qb4p9pAcgOnNAGCdxDu2sOTg2GvGRWaeDTGWpDeymoHBNXCzLC9tHcWiQcTY2S288dXuUdYrICJs7UYzG7Ito3zmMY8NIkjQAEmbiZQWhQxIsjaYFOlfa50w+QJlcrBnqRl1uSrLkKntKUXVYfOy8nnKhcqyrWPWc+Lg2FW7SyE72gscNeOt5ENtHVmtKK2aoZyj8qdsl7BuV8ZezlFZVppQ0B4Pkj/7CfnQ5GiU/bf5IivZgSqbbMdVlB9ncyewWlXK32+RsUjzXx3LzR0cqS3W9pRj0uSUz1j5C1r3o/xRJy063gg8l4WkEBjD5d0PMFwCaQH+2z/6AqPrFpKOjjcdn0QLiYhgazd4tDxGkIggEXNaqn1YoPdth7jHFA/YR1WZJYm5NUQwGgfDFhszYjAOO3cBxw6DGbC1W+zsFjbnXwz5ntbmh2zl7sCQuRH0fRSWjm4h6eh4megKjI8QbYZCrbWE5j1oPoQSGFfLknMkGB/ME6akLSCeGfugBERMAkv6u2cGEyGkYm8oVhIG2IJBWNIMZwYYmDrBFaRqJ+H85XsBDXEcc4PJaEydsHEmWiTXlTIYwg7cTGrLxN5mlYbO+ZQcKRP2Yi1hMMBlgiw1q4JFJ7bXYZ/rVMv6ZQaZCQVJ2kDS2C7qJBeqeIkAOBMUACoZE2pYKNdxFxTbRMmkWGuvVpKEQZC8TUghT6yzsiC3rLT7F0kI+kNVRpxDklSVFDWrQo6VFCXTpKgKyjHKNmWuXc51OV48+cztPmtriZ6AqlMo/56q3qKp+FI1Sja2NDLJ1p5yKp88DeUs+7kBQlVMtNsUG82pvaPdrsW5Y1WlReEmstLlSPGST+IRQSJUl7VKjk5odLwOPJn0/+eL4Q7qiBchML73TwJ/8y8AX/21FxgdcG90td61o6Oj42WjPBg8xKkuixIrucAgREl4ND/GkBW9S7V1MILo/fScZhAYIhEmPwhScmKAQGAzeWHzwx4C5Ta80vTXWpT56EFJr0Xt6Hg16HTgR4iN3cCZ0r6RkBBzwKLLNg+L0TgkqKduYINDjDhE/ZIcjMF1AOaotauWGfsY4NhgNA5LJjBUplZqQBnO+PxFrW0YVXkvOo6YE5g3Zoud3eWaUCUcBrb6j0J0surYqc2FDGxWDpRaqDLBJBC8GbRmKqs1HPvqR6yhmkRwZGsqdAkNLaGcxT5iuGRjcCUvfB6fnjdVLwxmOFJrOHZKtDQkRFFilAyLwqq3lxeTq14hWt9aCBHHrlad2uyDBFRxUAiP0o5RVAs2161qHoWSRWvp6k20QZ5UMjWKKiJ/llVlQVWhcBR4KU3wJbWTd0WrQinv20x0ATkj5DQYs8nTAI4JCn3ScKzKKCRJCbNqSYrTz18yLNrtW4LiaSqLlkw593773tFJkJvHqe+fFXLc/Dv7OBIXRPTTRPRVIvrVZtlbRPRLRPT38uvDvJyI6D8lot8gov+HiH5Ps82P5vX/HhH96Ov4LN/quM4KjK1/HgXGc1hI7n078M/9ODA/OfpOuSu6haSj480GEf0gEf3U++9/8LqH8tKws1sA2jhSHqLZxmYMZNUtEmJasI97JIkIsiBKRJS0hq5DLSalcQTQXA1DfPSdWB4knQs2rw9HOjo6PlJ0AuMjhiObJ9sa5KkNHQkuT+6nGLCzFp5XBcQ7A2PnHDwbbKwuG4zBJleYMrRaNOdpIkqocjYlRyIMadsGw8BmFYggZevKmN8XmPyeyb4+DXUsFoicbVEJBr05LZ5Ay67aE3Q8K7FRJoDlQmDJrmqBrBbRvIVV7cDESriIZOZ8uVF1KpIwp+VIUcGNwqJYN8p2RWlRLByn6+n5WzMfQgqZxY/1T6vWUEvOOlHnZj8tKLeWlKyK9s850Jn/jnIvmgtl/QyZeCgkTpEvMtY2lfLzev5utnwckQdNXeupmqJ9v93+dL/re+Xnsx/4iCSoyobWstGscy5Ho1huzp3H032X11Mbym2cxFmy4uPHXwDAzwD4IyfL/iyAvyoi3w3gr+bfAeBfBPDd+c+PAfgvACU8APwEgN8P4PcB+IlCenS8OpQQz7tlYOQQz+dRYJT1Jb1QmOe9jcXjKSCmfvPe0fEm4mNvIYkH7OMBoxnqn2JPHs2gOWRQa2/IyoyEhCABMQXEJpNCm+wcLt0lCGqR9mZQtQXUIuLzQ6u1YaTcV968Pyoo92WdxOjo+GjRLSQfMdosjG9M7ylzKxGeCBSAZBIsDRowCcE7wwZzirBEkKzK8JnAEAEsMxZJ8Gy0IYQtLDvYLO9nYiwi9bhtW0T5orfsECXU9wcedBLPgiSCgQljrqZKua5VZ5NRrSe0KiRU4eByOOiqQgh5/wN7RNL3gsSqhBAIHFlMMkOgbR8lE2KRULM7fE52BlQpUKiGEvRpyaCIlsvEviglAFUftMcFoGGlQL3YqdpkJQLMSYCdoZu+xiNihW5Kugk5xDNfMIPESl60uR5tVenpe6fHLK/SLDtt/mgvmkVZ0k7+tZJ2vQifyh0T1gaUSpDg5kW5fa8qNQTH6+OYWFk/M9d1irXjKNyKABau77fqj+YE67HPWTrolp/rIjp+rwkPLUqYs+t/DCEiv0xEv+tk8Q9BU+8B4L8D8L8B+Hfy8r8k+pf8vxPRAyL69rzuL4nINwGAiH4JSor87Ec8/I4Gz5eB8QgwA2CH5ztIITwOjwC/e65N72/0e/f//vL7+K53L+rvHR0dHR8WUWKtTJ3ihCGTF1Oa9T5TYr1XJABLWqD264ApTfVBniULYtJGO1bVMDHBkdP3CPVhWqmm1+MnONYHcacP1s6h20g6Oj5adAXGKwYTY2M3sGSxsTvs7EVuzNAwz53z2MeAfQiIeWKoIUMeS0qwxJhjqkoHzx4X7jJnVSjzbNnC86DLRO0OVd2Q12GY+kR9qD3aOlkbzAjPg/4xPk845chCol/OKrczrP7D8sR8NAMM1gm3hoWuCo9SlVogOQzTG5XubcwIy7amShc5YAnD9Hx8Y5wam4icIQAkZ2IUciBJQpRYbSNFGsigqhg5/VPQSgufhjbgso4h20naP6fqjVYhcNQAki0d5iSYtLWUpOa/cvE8zd84fSZwqlQQHSyAM8GXZ16ftq+yXjuWOrZCdpyrQ8UxEXLueC1BcjqGqiup2R63/9zs8Gi/RxaTk+N+AvBpEflHAJBfP5WXfxbAP2zW+3JedtvyGyCiHyOiv0VEf+trX/vaSx/4tzKuZiVod3fNwHhe9QUAjPfX7Z8T71woWfLH/vO/iR/4C3/j+Y/d0dHRcQva+64hh7bPacaSFixpgYhgTgv24QBkle+SZoAIjl1W1Aa98xKpORZEhNFucqOeKi1KnobPdu9il5Zsre7o6Hj96P8nvmKUiXRCwpwmBFmAZrK6D0u2jmg16mhsrec0OW/B8jqJihKw5B7rNXxSn/wvaVYLRP6vZFM4dmBmMHMlHlaiQYmQJc1IiGCoNUXldqqsCBJBWX6XkKoFozDTtWGkIRoK6VHUG8Uy0uYnFPa8fIaCdvk5u0YbzEln3l+rsBinFhM0FoznYcxjTrs+XVaPWdUHZd83rSTlvSDxRk1qi2K7qUTAGZKmhoDiWPHAWNs+ztk9CDnAtB4fNdSzrHsqhSzLztk3yvun268BoMeBmnUdyNHnOsqguOWv5SigsxnDDZvI6f5OcSYI9FkWk08ozn3aW1JCzp9QEfkpEfleEfned99996UO7lsdT6YAZwiD/QgJjLLNCxAY3/fFT+O/+le+hO//4qfx2x8cnv/YHR0dHyk+rhkY+6jfJyW8U0RwHfYYzHD0QEsgMGwQU0TI9akhLYgp5HtnCwbDs2+qUddw9KKw8OxgyFQbM5E+3LrNHvLU+4uOjo6PBJ3AeIUwOaeiTMjXCRtVa0cSwc46bK2Hy+0gF07DKXdugGOHS7cBE2MwqkyIuQ6KADj2Tfe0qiRKo4clm8M6CRszYuQBGzvWitchKx88+1WpkfMuNJRTgzpL4rPLX/JlPlrk9+W1WBxaPyLlylCfg5JKp3ZRWZRzUy5KLXkBrIGUbe5FWdaGdZ5uU8YLoFaYKqGiZEohV0ruRYvT3yvyRay8Xy5hbfd3OQdFfdISMG3YaIvTilE18JyXLZasjFaxcWT1aIiPsq/Ti600yxhc1TNPwylBUI51G1rlQ/m9Dgg3wzvbAd9KpsjxMddQ07MDrvs4HdfReNr1pbG5fLLwO9kagvz61bz8ywA+36z3OQBfecryjleIqynczT4CvAQC4/knOIM1+P4vfhv+mc89wBIFU7jle7Ojo+O14OOYgbGPB2zMWMmLKU6Y04yNGXEd9iAQ5rTULLZy75okYTSbfA1fw+JHu8FgRmzMFoMZMeTsjEJQOF7vq0vNPZAfEtEnz2ba0fFxRScwXiEMMUYzICHlxhADS6XVQ4mD0Rh4XqMeKRMPnj02ZoPBbLCpzSEehnJIZq433dgNgoTa0nFqAxBoQOalu1BbB1vEFGuZR5KErd3Ctqx2budgNti6HcZMdCBnapQgydMJZjs5jyliMMNa7dkoFAwZbO0md3IfT+hTZs1PUfIlThHPLGvREhznbCK3KQ5OYRrS5fTdU2vIaQtHsY+Unwu5oftaG0jaY7WT6FMS5NwE+1x167m8CFVGNMRCJjzOWUAKnmUfaS/y7Z+6vyNyhirZRaCjPIr2tRzjrE3ljN3jVMnyPCQEKWvyScbPAyhNIj8K4H9ulv+ruY3knwXwQbaY/BUA30dED3N45/flZR2vEE+mgN1dGkgAJTDGF5iklNaSF1BgFOy8fpdeT53A6OjoeH5ch339eWS1i4zZNuLZI0EwpxlJEq7iHouEeo8pubr+EA+QbFvVezy1IEfRUM+13U2twUW9IYCqUMsDIeDGw6eOjo7Xjx7i+QphyQCkhESxMZRwyzL78sbXcKGdYzyaD9jHOVsQBJJy7aY2eMKxw5SmqiBQyZvFIU2ZJLFZ+cBY0rKqJgAsKdQWkbWa02LJXdmU+S1vtDGlKA4SBI4IIKMd29kOURQmAtGn+KQT6RKeVOR42qt9PCGf46x5G/kiMcWp2i5uxVliYb3IlM9bfr4Lzk3OT5cVEqYmUp+uf/I7Z5XEmn5NR+8VtDaQG2N4ynmgo/0VIuRu+7hNYVBklUcKh+b3UxKivA80wbFnsjSehlPi4FzzyLn3bthNznz4535Ccptx4mMGIvpZaAjnO0T0ZWibyH8E4H8goj8F4B8A+Jfy6r8I4I8C+A0A1wD+BACIyDeJ6D8A8Ct5vZ8sgZ4drw6qwLiDfQTQEM/7n3/2eqf4EBaSgm1WiTyZAh7u/Avvp6Oj45ONNvy9vUfa2k19QHUd9pqzRqoyLpbXQ9R8i33Yw7PHlKZsyw61ie/J8riqLwYestpYLdFlP8QOAug9a76xlpwlF1PU/IwU12rWvI+Ojo7Xi05gvEKUZpBgA5YUYLJdowQQJSQcwr6yxYOxWv1U9OwAggQYqAUDUHJhWqajL//RjHUCZnMwEYMQRGtdnbHYhz2mNMOSwcAe3DRvMBiJEjz7+iTbVDY6Z2mQVbVDVmcQ1mpRgcAaezSBLQFIjpQgKUqIBIFnW+WBp2qLc+qLijMTY8fnk+/LfPRcRkaLcxem2y5WxRZzSmScrn9qFTlVUACrWuU2lr/so30t27XbrATDTdSAyxOLSTvhL8vPtns0OFX2FMLirMojEx8tAdLmV9wW1Hk6jqoMWf93OBnUTUKmVZLcdozTcbT7+rhDRH7klrf+0Jl1BcCP37Kfnwbw0y9xaB3Piasp3t1CcngEfOrVZmAUlJrX67krMDo6Ou6Gch/zwfwIl+6iLrtwOzxZrkA5++2D5XENp2/vO+Y0QaBqX28GTPFQ71c19JNy/5oqLSAaBsr54R+B4I1HTLHeZ3njK8myWqrX+y3R5PtPxL1CR8fHDV0T9YqxjwdYtjXxWEo+AjM2ZsxqCIMlzRAReONxz13CsYclW8MvLVsYXmuhLDtYcjlLwwKkZAdEsKQFc1ow5GwLk2V0lgy88di6bU1bhgCOLUYzYjBefYX5QlEIisEM2QaR4NhhyKTBYHwNQSoT4dIkkiCISFgkwNHa5MEgzHFWgiUFzDmQVFnyp18UTttITnEaIurYHTV4vCiq5SRfRE8VGedwZClpGlHO4WnvPc8YC1pS4hxObR7t8qPAz+b3syGed7CaPA136U0vBIyyUc2yE8vIjX2f+ey3nY+y/IZFpaPjNeJqDpUceCq+8f8C7/9/L5iBkS0kh0fPv23GNltIrubwjDU7OjpeJd60EM9z90w7pxbpKU7YxwOeLFfY2S00XF6VtG0QOxHhanmSsy/URnKIewCC0WpeXEhhtYpIJjly9aole1RHb9hUVTKwPuBof684IS9exr1bR0fH3dAJjFeMkELNoACAOS2ISEhJyYCdvczLA1Kue/KZvAAAAiNl6ZucPNUvVg8RQUoJSSJiniwvOeQo5PaMksfBpMQJU5bU5eyEB/6eKjGylK4gISGksEr/aJ2cD2ZQD2LO2aiqAqz5GFEivPF1rIXhBnK9KVZlxml2xIfByyAubuyT7a1igKfh3Gdrsyxum1i3waCny55+vKbhpM2iuMUu0168bwZ+rmN8Wq7EueMch4mez7g4OdhTLTW3vndynDZb43T/p+Pp6HhTcXXXDIyf/WF9vfeZ5z+IsYDbqgXlBVFIlqupExgdHW8S3tQQz0JMJEn1IRYAbMwIQFUXU5zXFj9JqgomIKQF3niICAYzYIpTfkhmEVKAIQOf8zMAJSgGq/epNueLDexziLvaeItNpK2pL+NrcXrf0HMyOjpeHbqF5BWjEBcA8KnxHezjATFFPElX2McDEhIe+Id10jmRxZIWJIng2mIilVXW7Aj9cp7iBALVdg+NCuX65exzF/acFpjMPhswHi9PcOkuEFOsFVTvz4/g2WFnt0dfyiGFGrhYSIwgEWO+cHAmRpYU4FjHPpgBS1oQJMLTTU90tdaksFpjGvVESOHpVpLXgPLZbyNGTq0dLW7bpjxReJa/svWNnsNpdkXJxaikhNxM0W63BVYS4PQznCNR2n3e9pmfdsyC51Fw3GZxuU01cevnPbP+00iTZ1lrOjo+KtzZQvL4d4Av/AHgD/ybL3ag4d6Hy8DwhcDoFpKOjo6n49HyuBIV+3jAzm5zuwjwZLkCcKxaNbQ2+YWc+bYPe4xmgyUt+WGQXvP1PkzVvIkYltYMuKJI5uZeVhvzcg7GuWywYoG9w/1MR0fHR4tOF75mxKQ3eS7XiTpSkiGmULupvfEwbGt+Q60NZVsDiRzZSlAkpFqHWsI9CyEw5y/4OQdq1oBOKDNdXv//9u49TrK6vPP45zmnrt09PT13hpkBBhgJ91FGlBijEaKgAvGSgGvcSFBWoyauWVdZE4MkGpNdczGiBIWFsK66SwQBQbysF1BERkTkzgw6ODDMfaZn+lZV5/z2j3NOTXVNV1d1d3V1VfX3zatfXZdTp36nmqn61XOe5/lVBhCSqHhSE9iTyo9buaO6SWWyMkl1qYRX0Z8gyURJNBqkSJZOrXW9GWoum1qhXpBhskh8vRKSeup9gW4kUDBRCch0JTWoMxkTTL5k6WFLtk72GpRXU61zfBUlKNV9M+plmIi02sGxEn31mng6B4WDcPRvRtkU05FdMKMMjKTRqDIwRKSeqEQ6XZ4T7S8MUnIl0n6GnJ9lOBihN27qWQjHKAQFAhcwVBqKV52LVhKJykbifhZxX668n8fFy5+mvTS+VzFvrcisSObCaS8VrUBC1Sp68Zxwsn5dItJa7XVaex7LxlkUaS/N3sI+osUUwnKdnoeHZ370Zux55ayL5MtwEtwIXAkv9HAWlpcU7Uv3AjBSGomaZsZZE0nwIskKyfu58prbI8EoAMUgSu0LCMmYVw5s5PwsQ6VhfPPL2ReVb/h5P8dY3JgzGWe2Io3vsOVSJ/lCP66ExYVQETyovt4MSWOm2ZKUj0w3ejjVNMXJlj1tNFNhMk39ol9r9Y8GVwUZF4yo85i6mRRdshKJdD7nXLwKSZ2P7OIwuGB6/S8S2QUwsg+KI5DOT/nhveUmngpgiEh9SaaFmdEbz1eHikPRPNXLMByMUnJF8n6esaBA4KKeFqX4RFwyAymGY/gWNZBP+3HPNy8FRKv/+V7UrDMpHfHiJp8pL1XOwkjmBUkjz8kyS0Vk7uhf5RzzvagspBgWGQlG8b0o3S3jZcl42fKypxk/i+NQoKIY1wkmUeQkQhw9Lo3FwQYjClyMlEYI4rrBpPRhqDTMUGl4/HjMpxCvigJR4CIVZ4cAjAZj5RVDorP4YTltb7K1spPbk8yL6m2TkphEdYZG9X5qXW+G6XyJbyRrI+FhkzYobUUjqHpNL+upzOCYzr5qBj1q7WYqu7eq3zWee6qBF2VkyFwZK4WUQlc/gJGUfswkgJFfBE9/Fz5+BPzoX6b88KRPx5BWIRGRCsmcbm/hUBPR7SM76Uv3Hq+cjaMAACAASURBVCpbTRq7JyflwhLFsEjK0oyFhbhxejTvTPqxBa4Uz8HipvNmcQ+4AC+enyTBi2jVkWhlPuJMjWS+mTw2agga92ObZHl7EZk7CmDMsbyfI+/nqpbZjLIafM8n5fk458oBhMCVyqUYaUvFXS68ctpcX9zB2cPIxRkPRVei6Er48fKnQLner9aX5aT0xDePnlQ+Xm5qfJDBrwgepL10+SfJvEg+gKIeHuOfJ+nTUXm9EdX9I2ajOedM1/iuF4CYLNDTLPXKQ2o18ZyOae2rZvyidv+J2VTdA8OwCQMgmsjIXEjKMXozdd6bygGM/uk/2TlXwDkfg+xC2Pn4lB+eS3t4phISkXYz16uQ7CnsY/fY3vLn6P7CIL2pnkMZGBj7C4OMBKNRg/u4xNo5x0gwwmhphJFgGOeipvchh5puRnNBRyqeFwNx1kUqWpHORSvw4eJV8ZIm896hkpNkDMl8OpmrjSsxbVLprYjMTEsDGGZ2rpk9YWabzOzDE9z/djPbaWYPxj/vaOX45lLWz9KX7iUIg/KX+SAMKARFQkKKYQnfUtGbs/mkvQyYRUul2qEv8qW4SWfKS1FyQRzMiP5LmmWWn9PLHBYACFxAxkuXVwzpT0dn8pJlVlNeqhwYyafy8ZKwUVlJMSyWu0knvyEuc6n4AKgVrEh5qXIkvDrA0S5qZVqMW7Z0imfqqwMe0wlutHr5rhkHQOYwDjDRyieN/M0UvJC5kjTErJ+BEfeuyM0ggLHyNPit90PfMigM19++ipnRm0mpiadIm5nrVUgWZwZYmOlnQVwmYli5xDlpqhn1b0uXMyKSeWDKUvF8Myp9DuLV9MbCMVJeOprvcmj1tLSXIe1l8IhKRJKVSpKgRMpLlTNhk9v88onC4LCTGuU5gqYBIm2hZd8QzcwHrgJ+F9gK3G9mtzrnHq3a9CvOufe2alztotynglFSLhWt9GE2bsWJjJchn8pTCAo4HMPFYUJC+tJ9UcpbGC2RmjRE8szD93xCQtJxp+bKL9oTfRmfLPsgCWAUwmK5F0boQsaCsXH9LZJVRxKeeTCP1seeaQZHM7R7k6lGgwG1enU0su+ZBhwUsJB2cTDOZuhrRQlJIt0T9dSYhp6srwwMESkrhsXyya5KSfbFWFgg62XYVxgkcAHORWUcB4sH8cyn5IpxKUmK0AVY3KMi7aUphUVSXjo+wRetNuJwh07I4SiExXIGcpJv4ZmHcyFg5UCJc648h0t6YFSWyWpeINIeWpmBcSawyTn3tHOuAHwZuLCFz9+2koaZEJWU+IwvzfDNJ+2l6Unl8cxIe6mK/gNRXWGS4WB2qHQkKfHwqv7MSeAiiNe6hmh1Eji0fGlPKk/ez5Vvh/GZAUmAwnF4NUDyQZX20njmlX9D/VKRds28SMxGcKIZ5SRqMjV1yaSk3pKsInMtaYhZNwNjNM7AaEYAI9MLhaFpPbQ3m2JITTxF5rXKRu6VimGR/YXB8ry1EBbJehm2j+yMVtJzIUUXZRIXwyKFMGoS72GMBaN45lMMC/iWilbH87LxZ3k0H/U9n6yfLc89U+aT8zJR7ws71IGsnHURnywEyv0vkpOAE80NWp3xKiKHa+W3nlXAryuub41vq/YmM3vIzG4yszUT7cjMLjOzjWa2cefO3bMx1pZK6vwSyZdR56I+F0lKXc7PRsumJsuV+mlSXrrczMjMIxVHmPvSveWsiCS7I5/KH2r26Udv5slKJLVKI6rfqDNeOl6uNbo952fLAZPkTb/yMUnEfaLIu0g9M202KtINkgyMljTxTKR7ph/AyKQYVhNPkXktmYM+N/w8aS/N7rG9AOwrDMYl0yVyqRwjpRH2FwZxhASuROgCnAsZC0YPzW8xxsICZl45oFEKi+WecF48N854UamIQTnLwrDyCbskO9X3UjjiUpS4zJpk+wlKZCtPaOiEkcjca+W/wlqLI1a6DTjGOXca8G3ghol25Jy7xjm3wTm3YdmyJU0eZuv53uFn9ZOgRcihYECy+kfKS+F5UUPOcoQ5XikkFTfnLO+nomlnISjEjTkPfz5/gv8VCvHqIoWwWL6cmOgNvN5KJNI9OrmRlTIrpNMk/SQaLyGZQQ+MRGb6JSS9Wb8cdBGR+eX+nQ8C8KsDW/j10LOAi7J243lDxksThAGjwRg7RnbEmRYlnHMUggLFsEjgAsaCsXgOWmA0GInLkwtxP4w0mThAkvWzVOZKJNnHvpcqrypiFq06Ul4qNV5Fz+KAhWPyldV0IkWkvbQyV38rUJlRsRp4rnID51xlOsXngb9rwbjmXN7Pjbvuez6lsER/ZgGDhWhCmgQyimGJvJ8tr1ySShoSWbJElMdYMMZwaQSISkFG4svVgYvK6xk/c1gZSXK5EK8ikvOzE26TSLIsxoKx8gdPszIvktVPKstLSi6YlVVIZH6Zq5VPRBqV9JPoaXgVkmaUkPRNq4knRBkYDzyzlw/+358DYAb5tM/7z3kBi3ozMx+biLSNO359L69dcxa3bvkh6xYu5sXL1gOwsueIcf3RADYNbqI3tYADxQNxnwvHcDiMbz5jwSg5P49vPkVXpOSKpEhjQNbPMVwajlcfceT9fBQUMY8gDOJV+9JRLkXFkqkZL1PuZeFhh5ZJxZXnzpD0w9DJDZFO0cpT5fcD68xsrZllgIuBWys3MLOVFVcvAB5r4fjaRmVAI8lmKJWXkwrLq4skmRZevIxUVM/nYfFjQhcyUhqh6EoELiTjZ8rraBeCQrl8pJYkeJGp6GGRqFxtpLJhZ6VOqhOsVUIjE2t1k9DZyprQhEU6QeNNPPdDKg9+EwLH6R4oTq+E5LfWLSWf9rln0y7u2bSL7z6xkxvu3cJ3n9gx83GJyLTM1jKqr11zFgCvXHnauJ5rlcGLkWCUsbBAIYiyKQIXBR2cCwnCUtw3LUMhHGMsHGMsGCXr5xgqjeCZRzEs4lwYlYpgjAajcaZyhrSfwTO/nEnsxcGLtJeOsiwY39w7CV5Um8qqZCIyt1qWgeGcK5nZe4G7AB+4zjn3iJldCWx0zt0K/KmZXQCUgD3A21s1vnaT9K1IeSkIoxckcEHcu8JnqDQcv0nHqXLm05PKMxaMkfezFMNSlLXhKHdehjioYH45iAGAWbm3RS3JfcnvQo2gBRz60KoV2JiOiRp7KvtCROaDoan0wGhG9gVEJSTT7IFxycvWcsnL1pavjxQCTvzoN9i2f3SSR4nIbHLO3QbcdsaGF75zNvbfn1nAtoqkreHSCD2pPNuGtzOQXQg4etN9gEVBjLDEcGmY3nRfFKAgJGUpxsIxfEsxFozRn+4liDMocqlcdNLBO9SI08ziDOFclIlshnNxOUjFCYqkV0boolX5kiVaQxdGPTQYX/5cq7m3iLSHljYrcM7d4Zx7gXPuOOfcx+PbPhoHL3DOXe6cO9k5d7pz7necc4+3cnztZLBwoFw+AuAljYfMJ+dnx2UMRH0tKlcIiSa7Hh6ZeBUTIMq6qLhe5lzNgEStoEamojlnrTKRTmrc2Q5Ln0ptzZxIjFvfvc2XmxUBOFgokfE9Mqk6H9nNDGCke6E0CuHMs9PyGZ+BnjTPK4Ah0jU+cv+hJOlrn9gYXYg/Ux/b9zg9qTw/2/1QdH3vk+wY2cVIaZiDxUFCF0RLp/o5nAsZLg2XszdSliZwJYxktbwouJD1cqS9NFkvF89vs+RTPeWs5YAwKkvBlTOMfS9VzriwuITELFo21bno9uqV+oDDOvQpO0OkvajbYptJGnUGhBTdoSZoHtFyT4GLGh+ly+td+zgcQ3Gfi6yfjdfGTpWzOODQF/TK3xk/U/6ZismyL0REpHmcc/xy5xD5ev0vghJsf7S5GRgw7Uae1Y7ozykDQ6SL/OdTV5Qvv3HtOgCO6jsqyhA2j+HSCMtzS1mSW8yy3GL6M31k/RwpL01fup+sn8XMymXOI8EohbBAISzgmU/WzxG4EiVXIm1RU3s/zsbNpfLl5zbzSHk+WS+DZ160Mp5zUb+MuH9biCsHVxKVQYnKwIRhhy874CruE5E518omntKA5E007+cohaXojbiijjAJcCRBh6yfhWCMghnDpRF888jG9xWCAqPhGP3pQxPapGxkKkGLpA9GM5TC0oTlINPenxp5SpV6qZ9KDZVO8oW7f8k3H93O2qW9k2945wdh52Nw3Kua88SZ+PkKw00JiqxcmOP5wZEZ70dE2sPS3OLy5UWZhQCMBQW2HnyGdDzH3Dz4HAeKg4yFRVLmxyuGeOwe3UPWT5HxslGTTi8dBTPCElk/arxZCAukvUyciRGVfBge4CpW2Dt0HtaAdDy/TMVz1qQ5Z7JdZf5E6EJCHD5eufSkvGpJ9SokytYUaSvKwGgzSSpczs+Oy6BI5PxsFNQg6gExFgc0euJVSYKKxpkTpbpVN6scCeIzYpOUkVTLeOmm9rcQEZGJbdpxEIB/vnj95Bvueir6/dr/0ZwnTj5/ptnIs9oRC/Ns26cMDJFu8Vc/fZTPPvbAuNse27eZm35l5YBB1k8xFhYphSF5P0cxLFByRbJ+GuccgStxsDhK4AICF+KZUQyLZP1cvPpIFj8ObqQsKoFekF5A2kvFAY0osODhlee/WT9bzqrIeOlyj4vAhXhYeR5cuQoJHApSJPusXFpVRNqLAhhtKMmymOz25E235AKy8fKmnnmUwtK4N/Gcl6UQFMo/h0nW5Z4kI2Oi7IuJ3tIbCWo0M/sC1MhTDlcvu0LZF9JJdg8V+I0jFnDa6oHJNxzaCSdeAEuOa84TJyUk02zkWW3lwhy7hwqMFrXik0g3+NgZJ9Gbikqdv/z0j7n7+Z8CcNGxHsvzK7hr688ACEJHTypaKWT7yBAeURNOP+5D0ZPKYGZk/Sx5v4e0FzWr700twPBIWYoF6X5SXoqeVJ6iK2EWBUlSXoqsFz0+Ey+xWgpLWNzzAqKSaTMr98FIel4kTTsrsyuqAxbKvBBpTwpgtLkkaFH5ezQYI+tnxy1RVXJBudFRZUpddcZFPpUfF6zIV9QRTqVMpFklJdIaOosg0pn2DI2xuLeBkr+hndC7rHlPnK4oIWmCIxZG2YWf+95mvvXo9qbsU0Raa9Frrx53/YKjXsCPtj/Axce+lJcfcQanLFrHiQMn8sjeTazr72d17wrGwoBCWGIsHGNxNodnPikvzXBplJSXjsul83h4FMJCeb7ie1HJSTrOwDCLelOkvVS5L5wfZ1akvTQkS6cSrUCS9Hwb198i2U8DdLJDpH0pgNGGchWBiXq3V2YgjMUZFpWBDQfTbtY5VZ206ki3CCtKhqZDgQ2R9rZnqFA/gBGUYHhPcwMY5SaezcnAOGllP75n/PN3nuI/3bhRmRgiHWjvHe8qX949ugeA31zxIh7Y9RCDxQMsSPfxyN5H2TM2ipkxEgyzNLugXB59qNdFkYFMPwB96QV45pNL5cn5ORak+8nE89hCWChn7vbE+6hsYu/FWRi4ZKURysGMwAVx/4tonjxRxm71HEoZFyKdQQGMNpcELSp/VwYykmBF1ssQEL0RD5dGyrf3pPKHlY9Ul5JMWFoiXUUfyiKdac9QgSX1AhjDuwEHvUub98SZ5mZgnLJqIQ9f8RquOP8kQheVxohI51oSN/H80H2PA/DArie4f+eDrO5dTcbzOLJnFYVgjIWZAfrSC8h6UTaFZx4ZL3pPS3lpsn6OnJ8DB/lUT1QeQrRNzs9F/S7Mx8yjx4+yhtNeisAFUWkIh7IqfPNxzpWDFRPNfSpP3Himr0EinUj/cjtIrd4YEAUqkiWkKtUKTtTsiSEdZaYfvgpsiLSvYhAyOFpice/EWXllQzuj333Lm/fk5SaezQlgAOQzPqsWRZkduw/W/jwTkc6wKDvASYsHecHC43hqEIZKJbaPbOdFS9bx7NBWelK9PDv8HKWwSOiCcm+KIO7fBlGJRyGe34YujEqhvRTp+CdlfjmDo+SCqKQEwywuI4kzMZKebmF8Mq/W/EjzHpHOp2VUO8xoMDYuA2Ms7ocB0XJSgQvH9cBwuHElJRCVlFQGLyYqLWnm0qkiItKYsVLAn33pQYaLAR989QkALO6rk4ExtCP6PRslJE1q4plYGh/L7oMKoIu0kpmdD5x/7HFrm7rfP1p3JgeLQ1y09gT2FvYxGoywt7CXxbklHCweZGX+CAaL+1iYWYRzjlwqRzEsEoQBeT9N2k8TupC0pQgIMSzOuDDSlir3sPDNK3ez8Mwj56UIXYgjKhMpuVK8nV/eJlkaVUS6izIwOkit3hjJUqpZP0tPKk/gwvJthk2YadGKnhgiIjI1m3cM8Y1HnucHT+7kf//kGYD6JSRDu6LfTW3iGQcwnrgDfvw5KDUnY2JpX/Q5tksZGCIt5Zy7zTl32cDAwoYfs+gN19XaFwCX/yQqH3n6wC/pzyzg6L41rO5dzfH9x9Gf7o8yLwjpTw/gmUdvuqe8PGnGz5D1M/jmkfUylFxAxktjRFkSWS9TDl4UXQnf/OjEmhmhCym5IG5cH5eNeClKYWncOBW8EOlOCmB0mOogRnV2BXBYBka1RkpHlH0xddUrvoiITNX2A6Ply994eBsAi3rqBTDiEpJm9sDILoCBo+Cpb8I3Pgy//EFTdrskycBQDwyRtnf/Da+Y8PYkMPCJM6MssTV9axgsHgCgNxUFPw+WhsinelicXcQRPcvJeGmyfhbD4hVGMuVlTc2MfHxfzs+Stii7IuVFDTt9vEMlIXFjzsqmnBY37aQqYOGc09xMpAspgNEFkiBGZSZGublnjawNERFpPzsHo/fxDUcvYu9wETj0pX9Cv/oh3PXfAIPcQPMG4vnwpz+H9z0QXT+wrSm77cmkyKd9dh1QBoZIuzu+/7ia913/5P3lpUbTlgIHI8EomwefBmBRZiGreo4sb59kBPele8t9KzJ+Bt/zSVsqDm5E2yW3O6JVRSozhlNeasLMiiSoUR2w8CdYfUREOpsCGF2uVvnIZPfL9OhDUkRmavtglIFx/umHJv5J2cWEnrwz+v2qjxx29nHGPA/6V0WXD+5o2m6XLsgoA0Okw739BS8uX+5L9/LYvs0AHNd/LFuHngVgsHiQnJ+lGBbpzyygL24OPJDpJ+OlSXupcoNOzzyyfrY8R63OsmiUX5WZISLdR008u0RlM0+gbpNOERFpP9sPjLKoJ81FL15DJuWxqCfD4sl6YOzdAkvWwW9/cHYGlM5BbmFTAxhLerPqgSHSYRZd8AX23vqOmvdnfK8cjFjdu4odI7tYno/K2kZKo4zaGAvSfeWgRGWgIZmnallTEWmEAhhdaKxiudXqoEZ1MEPBDRGR9rF9cIzlC3Lk0j5vOfOo+g/Y90zUq6KO25++nS889AX6Mn0szS/lVUe9itetfR1mVv9LQ98KOLi9wSOob2lfhl/vGSkHMZb0ZnSmVKTNTRS8+M5zP2Flvo+TFp3EC5ecNu6+JHgBkE/lSFf1VhsJRsl4aWWvisiUKdTZJap7XVT2wZiJQlic8T5ERKQxOwZHWd4/hffufVtg0dF1N/vSY19i8/7NpLwUj+95nI/c8xHW37ieDf9rAw/venjyB/etaGoGxvL+HE9sP8CGv/k2G/7m2/z3u55o2r5FpHXOPvJMTlp0Ut3tqoMXAHk/p+CFiEyLMjC60ESBi8qSkokyMUREZO5tHxxj3YoFjW08Oggje2Fg8gDG3tG9/GLXL/iT9X/Cu09/N0EYcOvmW3l++Hmu+fk1fHvLtzll6Sm1d9C7DLb9fApHMbn3vep4TlzZD85x44+38MPNu5u2bxEREeluCmDMY40EMrScqojI7Lnpp1v57Pc28bsnruDPX30Cuw6OsXxBgxkY+56JftcoIdk3uo9/euCf2HpwKw7Hy458GQC+5/OGdW8A4MfP/Zh7t93L+3l/7edpcgbGyoV53vbSKOjyzJ5hbrh3C8UgJO0rKVREREQmp9nCPJHxM+VghbIvRETaw+0PPcfTO4f4wj2/5MYfb6EUOl68dnH9Bx54Hq59dXS5RgnJ13/5df79qX/n2QPPctbKszh5ycmHbXPWkWfx6O5H+cg9HyEIgwn2AvQth8IBKAw1elgNO2XVQgqlkCe3H2j6vkVERKT7KIAxjymQISIyt57eOcTpawYIQsdf3/4oqwby/Pa6ZfUfuOk7UByCtb8NKyYu//j+r7/P2oVrufNNd3LNq6/B9w6vNz9v7XlkvAy3br6VJ/bW6EXRtyL6/YVzIGhuX6TTVg8AcOVtj/KRm3/BvmEtryoiIiK1KYAxjxWCwrjeGCIi0jpjpYCte4d5xQuW8fbfPIZjl/by/nPW4XsNrMjx/EOQ7oG33QKp8SUnQRjwqY2f4t5t9/KK1a+YdDdH9x/NLb93C0DtZp7HnwO9y2HHo4fKVprk6MU9vHzdUp7ZM8wX73uG7z+5s6n7FxERke6iHhgiIiJzYMvuYUIHxy3r5cL1q6b24G0PRZkXE2RVfH/r97n+kes5svdIXn/s6+vuanXfagayAzy862H+4IQ/OHyDBSvg96+H618Le38FS46b2lgn4XnGjZe+hJFCwIkf/QbP7B5u2r5FRESk+ygDYx6r7IshIiKtsevgGP/l//6c/3jtTwA4dmnf1HYwOgjP/AhWnnbYXV996qv8w0//gSN7j+Trb/w6Jyw+oe7uzIyTl57MzZtu5pFdj0y8UdJnY9+WqY21QfmMz/IFWZ7ZowCGyFSY2e+Z2efN7Gtm9uq5Ho+IyGxTAEPqKoRFCmFz655FROarux55npt+upXnB0cBOGZpT+MPPrgT/n5tdHnl+nF37Rndw5X3XsmWwS2854XvIeU1nmR55hFnAnDpNy9luDhBEGHBSvDSTS8hqXT0kh62KIAh84iZXWdmO8zs4arbzzWzJ8xsk5l9eLJ9OOducc69E3g7cNEsDldEpC2ohERERKSFfrF1PwM9af7tj8/k6Z1DLMhNYbnqB66HsASv+DCc+ubyzcPFYT618VMELuDmC27m+EXHT2lMl5x8Cct7lnP53Zfztc1f49xjzmUgO4BZ3I/D82Hhatg7OxkYAGsW93Dv5t2ztn+RNnQ98Bng35IbzMwHrgJ+F9gK3G9mtwI+8LdVj/9j51yyxvFfxI8TEelqCmBIXRlvCpNrERGZ1ENb93PqqoWctnqgvApHXU99C+78r7DnaTj2lfA7l5fveu7gc7z3/72Xp/Y+xWlLT5ty8AKiMpLXrX0d1zx0DZ+47xN84r5PcMnJl/CBDR84tNGio2c3A2NxLzf/7FlGiwG59OG9PQC27B7iytsepRCEAJx5zGLed/a6WRuTyGxyzv3AzI6puvlMYJNz7mkAM/sycKFz7m+Bw5raWBRl/CRwp3PugYmex8wuAy4DWHPUmqaNX0RkLiiAISIi0gDnHFv3jrBmce2Sj6d3HmS0GHLiygWMFAMefW6QdSsWYAYX/euPeeL5QUIHf/LKBhthFobgkVvg6x+ARccclnlx1YNXcfXPr8YzjyvOuoJXrJl81ZHJmBmfesWn2Lh9I9/a8i1uevIm3r3+3eRT+WiDgaPhwf8Nn/utQw/yfHjNx+GY35p4p1Nw9JIenIP1V36T3ziiny++4yX0ZsdPUz5/99Pc/dQuTl7Vz56hAj/ctIs3nrGaVQP5GT+/SJtYBfy64vpW4CWTbP8+4BxgoZkd75y7unoD59w1wDUAZ2x4oWviWEVEWk4BDBERaYiZnQv8M1Eq8xecc5+c4yHNqoe27iN0sH7NAEHo+K83PcS/P7CVf7zodN7wwtWHbf/dx3dwyfX3A/CWM49irBTw1QeeZdVAnpevW8rjzw/y4qMX85Nf7eHMtYsbG8RNl8KTd0L/Kvij26FvGcWwyC+2P8BVD17Fxu0bOfuos7nklEs4fdnpMz7mdYvWRT8D67jkrkt4/Vdfz+L8Yq78zSs5cf1bYXg3uIrvP8/8CO75x6YEMF514nLe96rj2T9S5N/u3cKffflBTlu9cNw2t/zsOV5/2kr+4aL1bN07zMv//rv85S0Ps37NAG980SpWL5pCPxGR9jTROso1gw7OuU8Dn5694YiItBcFMEREpK5addnOuUebsf8wCAjCoHzdVczXXfXc3dHQds5Nct/4Davuc+wZKvCH196Nc/Dqk45gqFDirkefZ2lfmo9/fSPP7tvNPU/t5AUrFlAMQ57afoDNOw9y3PIsLztuCTfeF70sRy/J89y+7ez62V184OR1vPWNL+HXe0c4apHP3uHd48fvAoaffQDb9jNyowfwdjzK8Ja72XnmH7HvN85j37Z72D+2n2seuoYDxQOs6FnBBcddwIde/CH6MlNcyaSOM1acwTtPfSfbhrZx37b7eO933stLj3wp61/0e6Qsxaq+VRw3cBx2zz/BvZ+Bmy+LghthAAe3w+BzANiqF8GqF4GfgRNeCwuOPOy5yn02gEtfvgwAzz/IDfdu5jtPMu6PlUl5vPnFJ7B/ZC8LcvDaU/u44+Gn+e6Txh0Pb+J/veMlpGyC738TfSUk+tP/zx/9ins37QIMz4w3bVjNa05aAcA3HnmeW372LGFUscLLX7CUt730mFq7G3csE95f85HjdlJnHw3sos5W9cbZyD4a2qaRw6031jr3+56P509cctShtgKVdR6rgedmulMzOx84/9jj1s50VyIic8qc6+xMsjM2vND98L7vzfUwREQOk08N/NQ5t2Gux9EMZnYWcIVz7jXx9csB4rrsw2zYsMFt3Lix4f3/8tnHueDbv9+MoXa905edznlrz+N1a1/HQK7BHhoz8Iudv+CTP/kkzxx4hn1j+2b9+USm4tMnXcHvvPhNU3qMmbXNe3PcA+N259wp8fUU8CRwNvAscD/wH5xzNdY4nhrNm0WkXTU6b1YGhoiINKJuXXZlo7ijjjpqSjsf6FvCBeH4ZoyTnXetPCs7+XaNXTt8H8bCfNTA2DcYKQYs6smQTfmMFgP2MTe/JQAAD2FJREFUDRdY0Z9j/0gJAwZ60uPGNFoKGBwpsmJBLrqh/0gYG4Sxg4ePv+LJexYciVv6Aka9FM4zsn6WFT0r6M/0szi/GA+PFb0r8Kx1q6CfuuxUvvi6L1IKS+wY3oHD8fjux9kxEi9+4MIoS8IbPybnXJzdEsLoIGx7EIKJs2xmwsX72jE4xkgxmOD+yZ+nJ+OzbEEWMELn2LZ/hGIQPSbjexyxMFf+G9V6jmQc9Uc6sy0ae8Um36pZp63qj3Xmz9TIHlYt79wmrmb2JeCVwFIz2wr8lXPuWjN7L3AXUbnedc0KXoiIdAMFMEREpBF167IrG8Vt2LBhSt9eFi1cxscv+er0RyezLuWlOLIvKgFZ1bdqjkcj0vmcc2+pcfsdwB3NfC6VkIhIt2jdKRwREelks1KXLSIis885d5tz7rKBgYX1NxYRaWMKYIiISCPuB9aZ2VozywAXA7fO8ZhEREREZB5RCYmIiNTlnCupLltERERE5pICGCIi0pDZqMsWEZHZpx4YItItVEIiIiIiItLF1ANDRLqFAhgiIiIiIiIi0vYUwBARERERERGRtqcAhoiIiIhIFzOz883smn379s/1UEREZqSlAQwzO9fMnjCzTWb24Qnuz5rZV+L77zOzY1o5PhERERGRbqMeGCLSLVoWwDAzH7gKOA84CXiLmZ1UtdmlwF7n3PHAPwJ/16rxiYiIiIiIiEj7amUGxpnAJufc0865AvBl4MKqbS4Ebogv3wScbWbWwjGKiIiIiIiISBtKtfC5VgG/rri+FXhJrW2ccyUz2w8sAXZVbmRmlwGXxVdH86mBR6r2sxDYX+N6rctLq59nmqqfezrb1bpvott1rJNfbqdjrXW/jrX2sVZfTy53wrECrJvOoLrBT3/6011mtmUaD23W37aVOm3MnTZe0JhbZb6M+ejZGEg7M7PzgfOB4Xxq4LGquxudc1Re75TP4anMMaqvz9Wx1ttWx9qcY4XmHG+rj3Wi2zrte8LM5s3OuZb8AL8PfKHi+tuAf6na5hFgdcX1zcCSOvu9pt5tldcnubyxScd52Himul2t+3SsnX2sjR6XjrX+sXfCsU5l//oZ95o15W+rMXfPeDVmjbnbxjzHr9e05xyV1zv1c7gT5hz1ttWxNudYm3W8rT7WKR5fW35PmOm8uZUlJFuBNRXXVwPP1drGzFJE0Zk9dfZ7WwO33dbA5WZpdJ+TbVfrPh1r7eudcKy17texNn692cc7m8c6lf2LiIi0wkzmHLUePxOt/hzuhDlHvW11rPP7WCe6rdO+J8xo3mxxtGPWxQGJJ4GzgWeB+4H/4Jx7pGKb9wCnOufeZWYXA290zv1Bi8a30Tm3oRXPNdd0rN1JxyrdoBP/tp025k4bL2jMraIxS6Pm0+uuY+1e8+l4u+lYW9YDw0U9Ld4L3AX4wHXOuUfM7EqilJZbgWuBG81sE1HmxcWtGh9wTQufa67pWLuTjlW6QSf+bTttzJ02XtCYW0VjlkbNp9ddx9q95tPxds2xtiwDQ0RERERERERkulrZA0NEREREREREZFoUwBARERERERGRtqcAhoiItJSZnWtmT5jZJjP78AT3Z83sK/H995nZMa0f5WFjqjfmt5vZTjN7MP55x1yMs2pM15nZDjN7uMb9Zmafjo/pITN7UavHWDWeeuN9pZntr3iNP9rqMU4wpjVm9l0ze8zMHjGzP5tgm3Z7nRsZc1u91maWM7OfmNnP4zF/bIJt2u59Q0REmk8BDBERaRkz84GrgPOAk4C3mNlJVZtdCux1zh0P/CPwd60d5XgNjhngK8659fHPF1o6yIldD5w7yf3nAevin8uAz7VgTJO5nsnHC3B3xWt8ZQvGVE8J+HPn3InAS4H3TPD/Rru9zo2MGdrrtR4DXuWcOx1YD5xrZi+t2qat3jdERGR2KIDRADP7PTP7vJl9zcxePdfjmU1mdqyZXWtmN831WGaDmfWa2Q3x3/Otcz2e2dTtf8tK8+nfaBc4E9jknHvaOVcAvgxcWLXNhcAN8eWbgLPNzFo4xmqNjLntOOd+QLSiVy0XAv/mIj8GBsxsZWtGd7gGxtt2nHPbnHMPxJcPAI8Bq6o2a7fXuZExt5X4tTsYX03HP9Vd6NvtfWPemk+fyd0+19K8uTt1+r/Rrg9g1EpJrZcOXMk5d4tz7p3A24GLZnG4M9KkY33aOXfp7I60uaZ43G8Ebor/nhe0fLAzNJVj7cS/ZaUpHmtH/BsVIPqi9OuK61s5/MtTeRvnXAnYDyxpyegm1siYAd4UlwjcZGZrWjO0GWn0uNrJWXEZwZ1mdvJcD6ZSXLLwQuC+qrva9nWeZMzQZq+1mflm9iCwA/iWc67m69wm7xsdSfNmzZs1b+5M82ne3PUBDCZISbUa6cBmdqqZ3V71s7zioX8RP65dXU/zjrWTXE+Dxw2s5tBEMmjhGJvleho/1k53PVM/1nb/Nyow0RnR6jOpjWzTSo2M5zbgGOfcacC3OXQmuJ212+tczwPA0XEZwb8At8zxeMrMrA/4d+D9zrnB6rsneMicv851xtx2r7VzLnDOrSf6HD/TzE6p2qQtX+cOdD2aN2verHlzJ7qeeTJvTs31AGabc+4Hdngjp3I6MICZfRm40Dn3t8Drq/dhZgZ8ErgzSbtsR8041k40leMmOvO1GniQDgzgTfFYH23t6JprKsdqZo/RAf9GBYj+DVZmJ6wGnquxzVYzSwELmdvSgrpjds7trrj6eTqj/r6Rv0XbqPyS7Zy7w8w+a2ZLnXO75nJcZpYmCgR80Tn31Qk2abvXud6Y2/W1jsezz8y+RzRRrzzT2G7vGx1J82bNm0Hz5k40n+bNHfc/YpNMNZ3zfcA5wJvN7F2zObBZMKVjNbMlZnY18EIzu3y2BzeLah33V4nSvD9HdMa0G0x4rF30t6xU6+/ayf9G55v7gXVmttbMMsDFwK1V29wK/FF8+c3A/3POzeWZ1LpjtvE9DS4g6ivQ7m4F/qNFXgrsd85tm+tB1WJmR8RfjDCzM4nmMLsnf9Ssj8mAa4HHnHP/UGOztnqdGxlzu73WZrbMzAbiy3mi9/vHqzZrt/eNbqJ5cw1dNNfSvLl7/paVunLe3PUZGDVMKc3QOfdp4NOzN5xZNdVj3Q103P/IE5jwuJ1zQ8AlrR7MLKt1rN3yt6xU61g7+d/ovOKcK5nZe4G7AB+4zjn3iJldCWx0zt1K9OXqRjPbRHQG9eK5G3HDY/5TM7uAaIWHPUR1pXPKzL4EvBJYamZbgb8ian6Ic+5q4A7gtcAmYJg5fm9sYLxvBt5tZiVgBLi4Db6gvgx4G/ALi/ozAPw34Choz9eZxsbcbq/1SuCGOB3aA/6Pc+72dn7f6DKaN9fQRXMtzZu7529ZqSvnzfM1gNF26ZyzaD4da6X5dNw6Vukozrk7iL7UVd720YrLo8Dvt3pck2lgzJcDbXXGxjn3ljr3O+A9LRpOXQ2M9zPAZ1o0nIY45+5h4gli5Tbt9jo3Mua2eq2dcw8RNRutvr2t3ze6yHz67J1Px1ppPh23jrXDzdcSkkZSmLvFfDrWSvPpuHWsIiIiMlvm02fvfDrWSvPpuHWsHa7rAxhxSuq9wAlmttXMLo2X10rSgR8jSkV8ZC7H2Qzz6Vgrzafj1rF257GKiIi0g/n02TufjrXSfDpuHWuXHuvcl4+KiIiIiIiIiEyu6zMwRERERERERKTzKYAhIiIiIiIiIm1PAQwRERERERERaXsKYIiIiIiIiIhI21MAQ0RERGbEzP6TmW0zswcrfk5t4v7/1cxe1ornEhERkfalVUhERERkRszsKuAB59y1s7T/B4EznHPBbD+XiIiItC9lYIiIiMhMnQo8OBs7NrMTgSedc8FsP5eIiIi0N2VgiIiIyIyY2W7gWSCMb/qsc+6aJu37A8A+59x1s/1cIiIi0t6UgSEdowU11seY2Ui83yUVz/G8mT1bcT1T4/HfM7PXVN32fjP7rJnl48cWzGxps8YsIjLXzGwNsMM5d5pzbn38c42Zvd3MbjezG8zsQzN4itcA36j1XMCNZna1md1qZnfHl4+Lt9c8R0TmJc2bpVul5noAIlNwGvAXs1z3vDmeEAOsBzCzK4CDzrn/UeexXwIuBu6quO1i4IPOuRFgvZn9qrnDFRGZc6cBj9e472rn3O1m9mUAMzsa+HPAgM3AzcDfADviy88DVwCjwG3At4AB59xztZ4rfn99l5m9EjjFOfeZOHjyMWCjme0DdiXjcM5dXD0O59w/NeOFEBFpI5o3S1fSmQnpJG1T92xmf2hmP4mjw/9qZj5wE/B6M8vG2xwDHAncM3cjFRGZdadSO4DxTjP7EXB7fP1PgBFgd/y49wBXOuf+3Dl3D/Au4C+dc+8A/hD4HeC7DT5XtTsnCUxUj0NEpNto3ixdSQEM6SQnA/+zIiXtsrkYRNxQ7iLgZXHUOQDe6pzbDfwEODfe9GLgK06NZkSku50KvK3ivflnZtYX3/d54ByiMhCI5h1fdM5d4Zy7lCgDIqzYlwHJe6YDziMuH2nguartj3+PcSjjtLfGOEREuo3mzdKVVEIiHaGy7rnitryZXU0UrV0EPAL8d+fcZjPznHNhjd3N1NnAGcD9ZgaQJ0p/hkPpcF+Lf//xLI1BRKQtOOfeOtHt8fsjzrnh+Mzb+cBngE+Y2TbgAPBZ4Ir4+q3AvwJ/bWbDRO+nfwn853rPVcf3gb83s7XAQHzbuHE45z42jf2KiLQlzZulm2kVEukIZvY64B3OuTdMcN8rqah7JjrbtxEYV/cMfIhJap7j1LXbnXOnVN1+BRW1fGb2PuBI59zlE4ylD3iaKJr8JefcCVX3/wrY4JzbNcWXQERERESkLs2bpZuphEQ6RTPqnptV8/wd4M1mthzAzBbHDeFwzh0EvgdcRxRVFhERERFpJc2bpWuphEQ6xanAK8zsvPi6A14ev/FVq1X37AE3OucemslAnHOPmtlfAN+Ml+grEjWi2xJv8iXgq0SpcCIiIiIiraR5s3QtBTCkIzSp7nlaNc/OuSsmuO0rwFdqbH8zUbqdiIiIiEhLad4s3Uw9MERiccOjHwG7K9a0bta+88C9wDLgVOfcnmbuX0RERESkVTRvlrmiAIaIiIiIiIiItD018RQRERERERGRtqcAhoiIiIiIiIi0PQUwRERERERERKTtKYAhIiIiIiIiIm1PAQwRERERERERaXsKYIiIiIiIiIhI21MAQ0RERERERETa3v8HzLXzDZKV5TIAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"edisp.peek()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This is how for analysis you could slice out an `EnergyDispersion`\n",
"# object at a given offset:\n",
"edisp.to_energy_dispersion(offset=\"0 deg\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Point spread function\n",
"\n",
"The point spread function (PSF) in this case is given as an analytical Gaussian model."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Summary PSF info\n",
"----------------\n",
"Theta : size = 6, min = 0.000 deg, max = 5.000 deg\n",
"Energy hi : size = 25, min = 0.020 TeV, max = 1258.925 TeV\n",
"Energy lo : size = 25, min = 0.013 TeV, max = 794.328 TeV\n",
"Safe energy threshold lo: 0.100 TeV\n",
"Safe energy threshold hi: 100.000 TeV\n",
"68% containment radius at theta = 0.0 deg and E = 1.0 TeV: 0.05099999 deg\n",
"68% containment radius at theta = 0.0 deg and E = 10.0 TeV: 0.03700000 deg\n",
"95% containment radius at theta = 0.0 deg and E = 1.0 TeV: 0.08269457 deg\n",
"95% containment radius at theta = 0.0 deg and E = 10.0 TeV: 0.05999410 deg\n",
"\n"
]
}
],
"source": [
"from gammapy.irf import EnergyDependentMultiGaussPSF\n",
"\n",
"psf = EnergyDependentMultiGaussPSF.read(\n",
" irf_filename, hdu=\"POINT SPREAD FUNCTION\"\n",
")\n",
"print(psf.info())"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"psf.peek()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# This is how for analysis you could slice out the PSF\n",
"# at a given field of view offset\n",
"psf.to_energy_dependent_table_psf(\"1 deg\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Background\n",
"\n",
"The hadronic background for CTA DC-1 is given as a template model with an absolute rate that depends on `energy`, `detx` and `dety`. The coordinates `detx` and `dety` are angles in the \"field of view\" coordinate frame.\n",
"\n",
"Note that really the background model for DC-1 and most CTA IRFs produced so far are radially symmetric, i.e. only depend on the FOV offset. The background model here was rotated to fill the FOV in a rotationally symmetric way, for no good reason."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Background3D\n",
"NDDataArray summary info\n",
"MapAxis\n",
"\n",
"\tname : energy \n",
"\tunit : 'TeV' \n",
"\tnbins : 21 \n",
"\tnode type : edges \n",
"\tedges min : 1.3e-02 TeV\n",
"\tedges max : 2.0e+02 TeV\n",
"\tinterp : log \n",
"MapAxis\n",
"\n",
"\tname : fov_lon \n",
"\tunit : 'deg' \n",
"\tnbins : 36 \n",
"\tnode type : edges \n",
"\tedges min : -6.0e+00 deg\n",
"\tedges max : 6.0e+00 deg\n",
"\tinterp : lin \n",
"MapAxis\n",
"\n",
"\tname : fov_lat \n",
"\tunit : 'deg' \n",
"\tnbins : 36 \n",
"\tnode type : edges \n",
"\tedges min : -6.0e+00 deg\n",
"\tedges max : 6.0e+00 deg\n",
"\tinterp : lin \n",
"Data : size = 27216, min = 0.000 1 / (MeV s sr), max = 0.421 1 / (MeV s sr)\n",
"\n"
]
}
],
"source": [
"from gammapy.irf import Background3D\n",
"\n",
"bkg = Background3D.read(irf_filename, hdu=\"BACKGROUND\")\n",
"print(bkg)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"# TODO: implement a peek method for Background3D\n",
"# bkg.peek()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$$[1.2053316 \\times 10^{-5}] \\; \\mathrm{\\frac{1}{MeV\\,s\\,sr}}$$"
],
"text/plain": [
""
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bkg.data.evaluate(energy=\"3 TeV\", fov_lon=\"1 deg\", fov_lat=\"0 deg\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Index files and DataStore\n",
"\n",
"As we saw, you can access all of the CTA data using Astropy and Gammapy.\n",
"\n",
"But wouldn't it be nice if there were a better, simpler way?\n",
"\n",
"Imagine what life could be like if you had a butler that knows where all the files and HDUs are, and hands you the 1DC data on a silver platter, you just have to ask for it.\n",
"\n",
"![gammapy.data.DataStore - your butler for CTA data](images/gammapy_datastore_butler.png)\n",
"\n",
"Well, the butler exists. He's called `gammapy.data.DataStore` and he keeps track of the data using index files.\n",
"\n",
"### Index files\n",
"\n",
"The files with in the `index` folder with names `obs-index.fits.gz` and `hdu-index.fits.gz` are so called \"observation index files\" and \"HDU index files\".\n",
"\n",
"* The purpose of observation index files is to get a table of available observations, with the most relevant parameters commonly used for observation selection (e.g. pointing position or observation time). Their format is described in detail [here](http://gamma-astro-data-formats.readthedocs.io/en/latest/data_storage/obs_index/index.html).\n",
"* The purpose of HDU index files is to locate all FITS header data units (HDUs) for a given observation. At the moment, for each observation, there are six relevant HDUs: EVENTS, GTI, AEFF, EDISP, PSF and BKG. The format is described in detail [here](http://gamma-astro-data-formats.readthedocs.io/en/latest/data_storage/hdu_index/index.html).\n",
"\n",
"For 1DC there is one set of index files per simulated dataset, as well as a set of index files listing all available data in the all directory."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[01;34mindex\u001b[00m\r\n",
"└── \u001b[01;34mgps\u001b[00m\r\n",
" ├── \u001b[01;31mhdu-index.fits.gz\u001b[00m\r\n",
" └── \u001b[01;31mobs-index.fits.gz\u001b[00m\r\n",
"\r\n",
"1 directory, 2 files\r\n"
]
}
],
"source": [
"!(cd $GAMMAPY_DATA/cta-1dc && tree index)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Gammapy DataStore\n",
"\n",
"If you want to access data and IRFs from the CTA 1DC GPS dataset, just create a `DataStore` by pointing at a folder where the index files are located."
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"from gammapy.data import DataStore\n",
"\n",
"data_store = DataStore.from_dir(\"$GAMMAPY_DATA/cta-1dc/index/gps\")\n",
"\n",
"# If you want to access all CTA DC-1 data,\n",
"# set the CTADATA env var and use this:\n",
"# data_store = DataStore.from_dir(\"$CTADATA/index/gps\")\n",
"# Or point at the directly with the index files directly"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data store:\n",
"HDU index table:\n",
"BASE_DIR: /Users/adonath/data/gammapy-datasets/cta-1dc/index/gps\n",
"Rows: 24\n",
"OBS_ID: 110380 -- 111630\n",
"HDU_TYPE: ['aeff', 'bkg', 'edisp', 'events', 'gti', 'psf']\n",
"HDU_CLASS: ['aeff_2d', 'bkg_3d', 'edisp_2d', 'events', 'gti', 'psf_3gauss']\n",
"\n",
"Observation table:\n",
"Observatory name: 'N/A'\n",
"Number of observations: 4\n"
]
}
],
"source": [
"# Print out some basic information about the available data:\n",
"data_store.info()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of observations: 4\n",
"['OBS_ID', 'RA_PNT', 'DEC_PNT', 'GLON_PNT', 'GLAT_PNT', 'ZEN_PNT', 'ALT_PNT', 'AZ_PNT', 'ONTIME', 'LIVETIME', 'DEADC', 'TSTART', 'TSTOP', 'DATE-OBS', 'TIME-OBS', 'DATE-END', 'TIME-END', 'N_TELS', 'OBJECT', 'CALDB', 'IRF', 'EVENTS_FILENAME', 'EVENT_COUNT']\n",
"2.0\n"
]
}
],
"source": [
"# The observation index is loaded as a table\n",
"print(\"Number of observations: \", len(data_store.obs_table))\n",
"print(data_store.obs_table.colnames)\n",
"# Compute and print total obs time in hours\n",
"print(data_store.obs_table[\"ONTIME\"].sum() / 3600)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"24\n",
"['OBS_ID', 'HDU_TYPE', 'HDU_CLASS', 'FILE_DIR', 'FILE_NAME', 'HDU_NAME']\n"
]
}
],
"source": [
"# The HDU index is loaded as a table\n",
"print(len(data_store.hdu_table))\n",
"print(data_store.hdu_table.colnames)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"# Of course, you can look at the tables if you like\n",
"# data_store.obs_table[:10].show_in_browser(jsviewer=True)\n",
"# data_store.hdu_table[:10].show_in_browser(jsviewer=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Select observations\n",
"\n",
"With ``data_store.obs_table`` you have a table with the most common per-observation parameters that are used for observation selection. Using Python / Table methods it's easy to apply any selection you like, always with the goal of making a list or array of `OBS_ID`, which is then the input to analysis.\n",
"\n",
"For the current 1DC dataset it's pretty simple, because the only quantities useful for selection are:\n",
"* pointing position\n",
"* which irf (i.e. array / zenith angle)\n",
"\n",
"With real data, there will be more parameters of interest, such as data quality, observation duration, zenith angle, time of observation, ...\n",
"\n",
"Let's look at one example: select observations that are at offset 1 to 2 deg from the Galactic center."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of selected observations: 3\n"
]
}
],
"source": [
"from astropy.coordinates import SkyCoord\n",
"\n",
"table = data_store.obs_table\n",
"pos_obs = SkyCoord(\n",
" table[\"GLON_PNT\"], table[\"GLAT_PNT\"], frame=\"galactic\", unit=\"deg\"\n",
")\n",
"pos_target = SkyCoord(0, 0, frame=\"galactic\", unit=\"deg\")\n",
"offset = pos_target.separation(pos_obs).deg\n",
"mask = (1 < offset) & (offset < 2)\n",
"table = table[mask]\n",
"print(\"Number of selected observations: \", len(table))"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"ObservationTable length=3 \n",
"\n",
"OBS_ID GLON_PNT GLAT_PNT IRF \n",
"deg deg \n",
"int64 float64 float64 bytes13 \n",
"110380 359.9999912037958 -1.299995937905366 South_z20_50h \n",
"111140 358.4999833830074 1.3000020211954284 South_z20_50h \n",
"111159 1.5000056568267741 1.299940468335294 South_z20_50h \n",
"
"
],
"text/plain": [
"\n",
"OBS_ID GLON_PNT GLAT_PNT IRF \n",
" deg deg \n",
"int64 float64 float64 bytes13 \n",
"------ ------------------ ------------------ -------------\n",
"110380 359.9999912037958 -1.299995937905366 South_z20_50h\n",
"111140 358.4999833830074 1.3000020211954284 South_z20_50h\n",
"111159 1.5000056568267741 1.299940468335294 South_z20_50h"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Look at the first few\n",
"table[[\"OBS_ID\", \"GLON_PNT\", \"GLAT_PNT\", \"IRF\"]][:5]"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'South_z20_50h'}"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check which IRFs were used ... it's all south and 20 deg zenith angle\n",
"set(table[\"IRF\"])"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Galactic latitude (deg)')"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Check the pointing positions\n",
"# The grid pointing positions at GLAT = +- 1.2 deg are visible\n",
"from astropy.coordinates import Angle\n",
"\n",
"plt.scatter(\n",
" Angle(table[\"GLON_PNT\"], unit=\"deg\").wrap_at(\"180 deg\"), table[\"GLAT_PNT\"]\n",
")\n",
"plt.xlabel(\"Galactic longitude (deg)\")\n",
"plt.ylabel(\"Galactic latitude (deg)\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load data\n",
"\n",
"Once you have selected the observations of interest, use the `DataStore` to load the data and IRF for those observations. Let's say we're interested in `OBS_ID=110380`."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [],
"source": [
"obs = data_store.obs(obs_id=110380)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Info for OBS_ID = 110380\n",
"- Start time: 59235.50\n",
"- Pointing pos: RA 267.68 deg / Dec -29.61 deg\n",
"- Observation duration: 1800.0 s\n",
"- Dead-time fraction: 2.000 %\n",
"\n"
]
}
],
"source": [
"print(obs)"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.events"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"Table length=3 \n",
"\n",
"EVENT_ID TIME RA DEC ENERGY DETX DETY MC_ID \n",
"s deg deg TeV deg deg \n",
"uint32 float64 float32 float32 float32 float32 float32 int32 \n",
"1 664502403.0454683 -92.63541 -30.514854 0.03902182 -0.9077294 -0.2727693 2 \n",
"2 664502405.2579999 -92.64103 -28.262728 0.030796371 1.3443842 -0.2838398 2 \n",
"3 664502408.8205513 -93.20372 -28.599625 0.04009629 1.0049409 -0.7769775 2 \n",
"
"
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"text/plain": [
"\n",
"EVENT_ID TIME RA DEC ... DETX DETY MC_ID\n",
" s deg deg ... deg deg \n",
" uint32 float64 float32 float32 ... float32 float32 int32\n",
"-------- ----------------- --------- ---------- ... ---------- ---------- -----\n",
" 1 664502403.0454683 -92.63541 -30.514854 ... -0.9077294 -0.2727693 2\n",
" 2 664502405.2579999 -92.64103 -28.262728 ... 1.3443842 -0.2838398 2\n",
" 3 664502408.8205513 -93.20372 -28.599625 ... 1.0049409 -0.7769775 2"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.events.table[:3]"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.gti"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.aeff"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.edisp"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.psf"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"obs.bkg"
]
},
{
"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": 65,
"metadata": {},
"outputs": [],
"source": [
"# TODO: copy an example XML file for tutorials\n",
"# This one is no good: $CTADATA/models/models_gps.xml\n",
"# Too large, private in CTA, loads large diffuse model\n",
"# We need to prepare something custom.\n",
"# !ls $CTADATA/models/*.xml | xargs -n 1 basename"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"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](..\/api/gammapy.spectrum.models.PowerLaw.rst) object."
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"# Read XML file and access spectrum parameters\n",
"# from gammapy.extern import xmltodict\n",
"\n",
"# filename = os.path.join(os.environ[\"CTADATA\"], \"models/models_gps.xml\")\n",
"# data = xmltodict.parse(open(filename).read())\n",
"# data = data[\"source_library\"][\"source\"][-1]\n",
"# data = data[\"spectrum\"][\"parameter\"]\n",
"# data"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"# Create a spectral model the the right units\n",
"# from astropy import units as u\n",
"# from gammapy.spectrum.models import PowerLaw\n",
"\n",
"# par_to_val = lambda par: float(par[\"@value\"]) * float(par[\"@scale\"])\n",
"# spec = PowerLaw(\n",
"# amplitude=par_to_val(data[0]) * u.Unit(\"cm-2 s-1 MeV-1\"),\n",
"# index=par_to_val(data[1]),\n",
"# reference=par_to_val(data[2]) * u.Unit(\"MeV\"),\n",
"# )\n",
"# print(spec)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercises\n",
"\n",
"* Easy: Go over this notebook again, and change the data / code a little bit:\n",
" * Pick another run (any run you like)\n",
" * Plot the energy-offset histogram of the events separately for gammas and background\n",
"\n",
"* Medium difficulty: Find all runs within 1 deg of the Crab nebula.\n",
" * Load the \"all\" index file via the `DataStore`, then access the ``.obs_table``, then get an array-valued ``SkyCoord`` with all the observation pointing positions.\n",
" * You can get the Crab nebula position with `astropy.coordinates.SkyCoord` via ``SkyCoord.from_name('crab')`` \n",
" * Note that to compute the angular separation between two ``SkyCoord`` objects can be computed via ``separation = coord1.separation(coord2)``.\n",
"\n",
"* Hard: Find the PeVatrons in the 1DC data, i.e. the ~ 10 sources that are brightest at high energies (e.g. above 10 TeV).\n",
" * This is difficult, because it's note clear how to best do this, and also because it's time-consuming to crunch through all relevant data for any given method.\n",
" * One idea could be to go brute-force through **all** events, select the ones above 10 TeV and stack them all into one table. Then make an all-sky image and run a peak finder, or use an event cluster-finding method e.g. from scikit-learn.\n",
" * Another idea could be to first make a list of targets of interest, either from the CTA 1DC sky model or from gamma-cat, compute the model integral flux above 10 TeV and pick candidates that way, then run analyses."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"# start typing here ..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## What next?\n",
"\n",
"* This notebook gave you an overview of the 1DC files and showed you have to access and work with them using Gammapy.\n",
"* To see how to do analysis, i.e. make a sky image and spectrum, see [cta_data_analysis.ipynb](cta_data_analysis.ipynb) next."
]
}
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