From 5aed91df8d20aee29c1f989867494381cec89620 Mon Sep 17 00:00:00 2001 From: Cees Bassa Date: Wed, 6 Mar 2019 17:06:01 +0100 Subject: [PATCH] Added tutorial on Barnard's Star --- barnards_star.ipynb | 519 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 519 insertions(+) create mode 100644 barnards_star.ipynb diff --git a/barnards_star.ipynb b/barnards_star.ipynb new file mode 100644 index 0000000..4389cb2 --- /dev/null +++ b/barnards_star.ipynb @@ -0,0 +1,519 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Astropy tutorial: putting it all together" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this tutorial we'll learn how to use some of the features of `astropy` as well as the `astroquery` library." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Astroquery\n", + "`astroquery` is a handy python library for accessing information from the online **Simbad** and **Vizier** databases.\n", + "\n", + "Let's first load the **Simbad** interface and send a query for your favorite object." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from astroquery.simbad import Simbad\n", + "import astropy.units as u" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table masked=True length=1\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
MAIN_IDRADECRA_PRECDEC_PRECCOO_ERR_MAJACOO_ERR_MINACOO_ERR_ANGLECOO_QUALCOO_WAVELENGTHCOO_BIBCODE
"h:m:s""d:m:s"masmasdeg
objectstr13str13int16int16float32float32int16str1str1object
NAME Barnard's star17 57 48.4997+04 41 36.11114140.2230.30490AO2018yCat.1345....0G
" + ], + "text/plain": [ + "\n", + " MAIN_ID RA ... COO_WAVELENGTH COO_BIBCODE \n", + " \"h:m:s\" ... \n", + " object str13 ... str1 object \n", + "------------------- ------------- ... -------------- -------------------\n", + "NAME Barnard's star 17 57 48.4997 ... O 2018yCat.1345....0G" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result = Simbad.query_object(\"Barnard's Star\")\n", + "result" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "astropy.table.table.Table" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(result)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You'll see that this query returns an object with several columns but a single row. This object is in fact an `astropy.table` object. To access columns in this table, you can use the following commands." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result[0].columns" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('17 57 48.4997', '+04 41 36.111')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result[0][\"RA\"], result[0][\"DEC\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'17 57 48.4997'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result[0][\"RA\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exercise: Use the RA and Dec columns from the table to create a SkyCoord object" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.coordinates import SkyCoord" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "SkyCoord(ra=result[0][\"RA\"], dec=result[0][\"DEC\"], unit=(u.hourangle, u.deg))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's import the `Vizier` module from `astroquery`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from astroquery.vizier import Vizier" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exercise: Use the `find_catalogs` method from `Vizier` to search for the GAIA DR2 catalog." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "OrderedDict([('I/345', ), ('I/347', ), ('I/348', ), ('IV/35', ), ('IV/36', ), ('J/A+A/616/A12', ), ('J/A+A/616/A37', ), ('J/A+A/616/L2', ), ('J/A+A/616/L15', ), ('J/A+A/617/A135', ), ('J/A+A/618/A44', ), ('J/A+A/618/A56', ), ('J/A+A/618/A93', ), ('J/A+A/619/A8', ), ('J/A+A/619/A106', ), ('J/A+A/619/A155', ), ('J/A+A/619/A180', ), ('J/A+A/619/L8', ), ('J/A+A/620/A91', ), ('J/A+A/620/A128', ), ('J/A+A/620/A141', ), ('J/A+A/620/A155', ), ('J/A+A/620/A172', ), ('J/A+A/621/A38', ), ('J/A+A/621/A48', ), ('J/A+A/621/L2', ), ('J/A+A/621/L3', ), ('J/A+A/622/A60', ), ('J/A+A/622/A165', ), ('J/A+A/622/L13', ), ('J/A+A/623/A22', ), ('J/A+A/623/A25', ), ('J/MNRAS/482/4570', ), ('J/MNRAS/482/5138', ), ('J/MNRAS/484/2832', )])\n" + ] + } + ], + "source": [ + "print(Vizier.find_catalogs(\"GAIA DR2\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This query returns many catalogs that use the GAIA DR2 data. The catalog we are interested is I/345." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exercise: Use the `Vizier.query_region` method to query the GAIA DR2 catalog (I/345) for objects within 0.1 degrees around Barnard's Star." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "TableList with 2 tables:\n", + "\t'0:I/345/gaia2' with 32 column(s) and 50 row(s) \n", + "\t'1:I/345/rvstdcat' with 32 column(s) and 1 row(s) " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result = Vizier.query_region(\"Barnard's Star\", radius=0.1*u.deg, catalog=\"I/345\")\n", + "result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will see that this query returned two results, the gaia2 catalog, as well as the catalog with radial velocity standards. At this point we are only interested in the first catalog. Unfortunately, the first catalog only has 50 rows, because this is the `Vizier` standard. To remove this limit, set `Vizier.ROW_LIMIT = -1`. Be aware that this may mean you will be downloading a large amount of data. Rerun the query with the new limit." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "TableList with 1 tables:\n", + "\t'0:I/345/gaia2' with 32 column(s) and 2336 row(s) " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Vizier.ROW_LIMIT = -1\n", + "result = Vizier.query_region(\"Barnard's Star\", radius=0.1*u.deg, catalog=\"I/345/gaia2\")\n", + "result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This result contains the following columns:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = result[0]\n", + "data.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A cool feature of this table is that it contains information about the unit of each column." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "deg\n", + "mas / yr\n" + ] + } + ], + "source": [ + "print(data[\"RA_ICRS\"].unit)\n", + "print(data[\"pmRA\"].unit)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exercise: use the RA and DEC columns to define `SkyCoord` objects for the entire table, now using the table units." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "columns = data[\"RA_ICRS\"], data[\"DE_ICRS\"]\n", + "p = SkyCoord(ra=columns[0], dec=columns[1], frame=\"icrs\", unit=(columns[0].unit, columns[1].unit))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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faK0Phv8cU0r9dwDfrezUc7GJizqCxEYhQbJ4XlCw5d48V9BjEz6efH3A+E1SoZlLx8CIoYgov82V4cCxk6gtKBRLAb8PT7esWNKIjTv7okgGCLpxyXgR5QKUSqVcSFOkkstIXh/fjpBGCsBtrY0xrzwyggm0Fo/v7Iu4hRRgjXwkDxJ1MacN8aHz5VFL58AIhkbHMKt+kpX+e7zoY8P23gi6KovvX3+kDb8VDgV1QNu25YbVxY+UFJ3kpHtnT1INgtb6ewi8eYQRwndtxkAptQhAA4BXLbu5g/bBtp+jtT6olFIAvgygo+KzzyUmz3QcjP3NDYKkQfC1jpQsFWyXLZyBgqei1A0QKCqlAME8kekFbe8Zxqb2/kg51njK8Eq5F11T8LDq6vlW71SLnIeGQsfASFl5h5QLWSCPScykWRUQj3oIcsu/46erEEfdSLI/2o/NcBDxX7Howwfwq33lLuZ7b7w04m1yRUm87sD7BKjgTddM56MROACbWfc37ZeTBJJQBzSdb1v3UQwwDilO5ieNdVpKKC2llMvpk6r7EJRS6wDs1Fo/FX50B4DHtDZVhlLqYgDzAWwTu3hUKTUTwbvyOoBvVXsuuZTl5pY5UWRAf3ORL9fmXf1RwbYUNpLde+OlWPelFvzgyd3wNVAoKHxt6XxMnVRjUE8oBax1dBZzaesOZhQAwc3+agjDJK+Ue9GlcLstu/qxeVc/WuZOiyiiZQ5c+xoKZWhkIYwQkgbMZGEmzaqAhk+MR53XMurhhtlGqCcjCI4OchWEazyFxY3TsPvASGwmc9YoyZV+oWvesqsfj+3oRSmcF7GpvXwPCPUlOalu+uRFuPPqBbGIp8ZTEXutCn9jq1u4iBLlPckNwZmXigyC1norgK3hv9eI79Y6frMfwDzL55+t5Ni5ZJsmRtGAq4YAxF+uLbv6Y97wotn1QQQRomXIY+8+8psodyyhjS6RHrf0SscnAq+XiPIeD7uPSShyoNGYGoECrasN9rUibKJyecdZEUVJa2Rb/zTWUFqjulovxq4qI7XbWhsNxWsrCJd8jeZ509A1OGoc07imlCgpKfqh7R57rUyUN1FkNN0hamrdl1qCz3yNuloP37r+49aIp+Rr3H7VfMybPjnqYLatFY8QTzUllKORTk3yTuUPiVQyTezOqxcYhiDL0BQbMkVOHmttasA9138cL+09XPFkMPI+tePYVFAdOHYS6wVzp6+BYtHHHVcvwNxQuUjFKVMMSetWTT7aldbg5y/TJJ4qk9dlXXcpUoGvXNIYdRHz37miJDn5zXbOfM2CPory8TUQpQ59rbHm5x3YeM81sQKyjPYoRcZTf7LXhTOsEndSEi257Z7I3oqcNuPUJDcIHwKxhdVJ08RkZ20aZI97wza4pYvZ06ZQaB/ymEBA+MZ5dgC7N8+5kUh8APWTajJz85BkhWZWuh+eauk6NBp1J9cWlDHkJ0nBZUmDuAxHUo6dzpcryBoRhbiUp61+RGkxoMxPxcn2AHvEI+tA8nplDYZmWmTx8m3RRRrfUi7pkhuEc1xsD37BU5hcWwiI5HTwty3nXFfj4fcvmxkjhUt7yZJ4i4CyMiL+mtoaLyJ2cxHRATAUKtUIbB3OxKnTcWAEb/aPRErjkVfew4IZH6sI058VmlnNfmjNeO9EsaSx6uogTSLPsdp0RlbDIe+RDTlE3njBsyvP1qaGWErormsuxiOvvBf9bYuqXIYr6ZptNZisPQc2FBfnW7JFR7mkS24QznHZzBqfPATQ0D2HRvHsW4Pl9IsqTweW8MQX3h4q4+SF4UhqYEqDk9pmEXBP2VYg5QpVA84IhyNibn/o1UjZlnztRKq4pBpFJSUJSy+pKjxPWRFSH0RzlQ05hPC/QU0gUKT8uQCClKNM7xCVdhq8NS1STKtnGM9v0RxXars2Xn/iTkyePqpOcoNwDksMrlkI5g/sPjBi5OJ5jp+/ZIqhQTi6x/WiVoL3ln0Mh0fHDE8ZABbMmILVn/l49CLKtIatmM1Fequew6tNE6moiB6j5OvUbmdbwxYX3vRlo6qwQTDTKD5OV1G0tSkYMbpxR28wmCfsdfAQQH81AL+kDYdCjvzkKcFKz4cr999O+Hho27t4+A+XGudnM9a8ie7lvUewvfsoNqy+xpkmo94KDURT82QvSZ4+yia5QTiHxQbXXLGkMUi1MM9IKtSVSxqtVNSE7uEvKseaZy100jH4LIIL6yfFxmP2HD2Bdb/ojF5SqVSyeO7cW7UhVUiyKtIYPYaDRoPfg6SGraQ144ZH1hZs527L+Sd1C7uujxtdPqv69qsWoDmsIVDxXqb17viHMkIKyMZ35BKqR/ghYuyXbw1i/fZeA/BgS3U9evcyrHu6E2+Eg5fGSzrWD8F/y3sr6N7kzWzVSW4QziGxwRprPBUpXVLoXOHzFIb0/FcuabQqK9ovcQNtau+PFE9WT7C1yZxFAJhpJF/bm6zkPrKkGPh2NlbWrLUPqm8Y9BgiXSLFlnZxpbjkvUyqLQBBpzM3Ah7D6id1C7vEdv85ZHXu9MmRMv7bf95jpJD2Do5am85OxcNubWrAJ+dMjRQ7EG+UpPPm97S1qQEt86YZv1OIS1L0de+Nl+bNbFVIbhDOEXEhc3yEOV8gNsZQeo82JIxEgwDBi8q5gXjKSZ5T0gvl8viTPPmkfWfpEbApX+nFk1euFLD69xfiL754Raw4T4XVNDZS8lip4YoT2yVJUm2B32tuBKCDtJjW2ml8XGJDomnA6SV3Chrxp94YiHWhA+7egKwR2TULZxiKvXmOyZ7vcgJWLGnE4+39sejW9jveACcRcWc6LfdRk9wgnCNiU4YHjp2MaCWKJY2NO3pPmUaYhL9wRMrG5VSKoItm11fMXgnAoGdQSlkJ8Gwvs6ybkFeuNfDgS90RMolQKR6Aay/LjnknxbLCgv93SVJtgd9rMgIKOhpC0zkw4jQ+NiI8IE6kx3sWiJmWi+xo17rcKb24cRqa501Dy9xpUW5e3oOsz0b95NqoC1uFf3NJgvNu+GZG2vWSxuc/OcugRHcp/Sznfj4bjNwgnCPCUROkDAeOnTS2uWjqBUaXalLTUaaHmVxC5hrSy3AgYxGU/y5pyAxXYnLfHILqKUB5gde87hedABBTfLbUEI9Ovh9SbpA803EQ3/785dH6ep6KKD0obZMEZeUKImsfBC/oXjT1AiyaXWaOl4bbluKSxocoxze190cDaogIb0WYGoqM3aUX4uaWOdF6UCpvM0s/8Y725jlT8ZNX95fPJ6QjseXmAeDPN78ZkdulPRvLFs6wzmhwrYXsnE7aL097bnvnML51/ccTQRNAehR6vlNt5wbhHJLPXDYTz701iJKvsfapDqy9tcV4We65/uO45/qPxwqQrnx7krR1H0UxROwQhxEAA1FTE6J6sqZIbC+ajdKgtqCMfXMIqg7x5BoBMuUHP++ADmGmK1hOfHzCj7iWagsKG1ZfEynr3qO/MTiXJtcWAABrljdH6aQf/LwDSgGlUKG4iqeuaCZL49TapzrCEZsj2PrO4ahXAyjXgVxFY34f04jwJJ/T5NoC1j7diWLJT2zWWjS7PjJEElZqS0Ft2dWPn0lakZQaTJqTItFCtkZHWgNZZ3ClPZOUfloUnSVt+VGW3CCcA2KjFB4vaXQMjFjDZgmrGwshfZ/KOEWsvWcYA8dOxhQ+fxlKJR+rrlpgzEpOEteLxvsoSCZKGv/hkxdF5wuUIagFT8FHeWAPFYDHJnwcGR2LvPwAS19eK45Cual5NrqP/Abdh3+N/e+fwHN7BvHS3sNYuaQRvi4bQS60jlsc7J6kIDbv6o887rTGKa44J0JM/c0tc2J1oDSRTVgkRA/RHLKsDo2OYds7h80eFdasVfAUBo6dRHvPMIB4B7scGypTUBowrgkAPjlnaqJRJAhrElqKPnd55kl1hs0W6HJa1PHo3cusabS0354PkhuEc0Bs6A4geOFdHv+yheas4V++NYjn9gymhrl8RGVNwcPtV5lU05KELqt3ZPME12/vxcYdfTFFBgAX1k8y0i/8t1t29RvDcIDAG97aNYQ/uvYSPPLKe0a/A60VYCoP7h2PTfgYGh2LMXVy0QA27ug1rlsqCAV3Qx2XZQtnoLagwggh2PfLe4/gX/cdiYxZVg9UIp3oeq+77ELDwNC18W1qCh5uuHxmlFbZ8FovNu/qN6It19hQnoL69ucvR9eh0di53X5lnDyR0lucpHDTzr5YLwGXJM88qc7govVIS53a0miu355PNYXcIJwDwguQpOdc1MwkrU0NxqxhIF3JSCjkRNE3Zt9WXIOwnBP9Zv323lgu30OgGGsLKuYZS8NHnh+AaFZvyddBg5VQ6HytuPLgTOykEO++7pKIhqGmoPDVpfOxd3AUr+0PvOaiDyPakGvCzy3Jg2xtCmC5m3f147X33se+oV9H11JQwf8qJQekGgLnSXIVqAuewg2LZmFr1xCe2zMYSx3xNJOk4pDRI9UjDhw7GRWIgTLttTxvW3proqRTaw0uzzzN46f7zv9OSp2mpYXot+09w/irJ3ZHdN7nQ00hNwgfkPBCa+fACD5z2UxcWD/JSoHskpVLGgNPJ6FJTVI/c89aozzKsprCadK1BVFI+bMaT+Hu6y5B58HjEYmZS2RemUNYb26Zg+3vvR9FVAUxk8FmXEkmij5Gx4oxps6/emJ3ZBCAOOZdKpesRpN+91dP7I4MAgB87oqLEtN7Sf0YLXOnRdTmtqiOF6jbuo/iuT2DVp4fSRsui7EUPVJzI31WWygbim9d//HYubvSW7WF6msNSd9VUwTOkhayGbbzoaaQ2SAopQoAdgI4oLVeLr77MYAbwz+nAJiltZ4eflcCsDv8rldrfWv4+SUAHgPwOwB2AfhftNbp5PofUpEGgMJp/uLUFRRWrq4uTcMpgMlbAuKonBrGZKlQGaFY0nXJfgJzNjJw93WX4Cev7sd40ceO/e9H3cu2/dA+aJ+yGa1jYMSIjPhMBlqT+597B6/sPRJLOz2+sw8rljQaRq+ZTToDAhqO9p7h1Jy3qwAqRWLq77n+44m/SSrQk3Lma5hkoJLQTFRLsB23VAqiR4LrZq0ryUlxV13cgEsvqk+sIWRxRqRRrpQSRO4rLS0kDZtCMDP7QFiD+agahUoihD8BsAfAVPmF1vpP6d9KqT8G8Hvs65Na609b9vd3AH6stX5MKfUggP8M4O8rOJ8PjXCFK71WLmlhtU35cuUkkUcrRZ54+MR4mRsoRO40TKnLNLEq7bq4IeG0xkDQHFY/udZ5DL6fgqci5A+hh6QyoMiIeijkS9ra1IBvf/5y7Nj/fpR2os5pQlTx/Q2fGDdSIc++FRShbYijtu6jGD05EaSdMpLstTaZmHoAiSycDVPq4KngjLgHm5TqGDh2Ept39UfHo/9W4lm7PGdbXam9Z9h6DXwtPQDXL5qVGHG29wxbWXOTxKiDOZrS0oS/N12HRrH26XIUuvaW5qgnplQKnrEbLp+JF94exIbtvan1kA+zZDIISqlGAH8A4D4A30nZ/A4Af52yPwXgswDuDD/6HwDW4iNiEFzeRpIxAJLDapfyTSui2qifJTcQ7951NYTZZAtDEHEFxZWCAnB8rIibmmc7w3S+Pj5DsSRx2FD38MYdvdaXNCntZMPCT6pNnifsMupjKVxI/JwBGL0ELpjrul90ouQHKZ41y+PpMDl1jPMP2dbBdm4248LpHijS5D0ettSSvIa0vgMpNtbcpLVs7xnG95/YDYJgFH2NVVfNB2Cnt0jaD12DQrlONV4M4MwaQZpz1VULIjQT4T5cz+VHQbJGCPcD+DMA9UkbKaWaAFwC4AX28QVKqZ0AigD+Vmv9JIAZAI5prYvhNv2wjNn8MIrtZSGPjygJPAA1NeWBJdQNmhRWu7xD09gElMYlXS6irr0l3vRESoJPuQqMAaKGMJnSoWvjXu7jO8sIIt7tzBE2GmWuJJe3akPRkCi4IyP5kj647V2jW5UkrXOaF21/trMPxfC8N+7oQ8vcabjz6gVOo+6qw9jWLktOmqcqtNbWdBiHTLZ1H42iICA9ypRrbhuABLghqfwcbSmttm47TbhcC1oneb9TfCY8uO1dGHg8DaNHKP3eAAAgAElEQVTOIVFDLrHBoUnIOBBCqrWpAVvC6IvE9Vx+2CXVICillgMY0lq3K6VuSNl8FYBNWusS+2yB1npAKbUQwAtKqd0Ajlt+a30WlFKrAawGgAUL4hC3c01cuHXy+O6+7hLUT67N/BDx2gMR3XH+evlif+aymREOvVRyzzSQaBLXEHS+vRxOz2sRt7U2Gsra1jRk41Wi7R+9exke2vZuNK8ZCLiGmudOc3qj0iN84e0hPB9Cb20jRpNSF2QkFYBHwxGeNH9h0ex6Z7FaAegYGMFfPrHb6vnLzm+ek660c5eEQybXLG9GbXheQHqUKZFLNoXGn2EZKbnOUUaqLo4onu6h+1RXKBM4rlzSmKhoh47/1vh74ax/Z9Q5bAZK1gkaptQZzgyA8rCpQvBUlZ2C3qgAz+tASc/lh1myRAjXArhVKfVFABcAmKqU+qnW+huWbVcBuJd/oLUeCP/brZTaiqC+sBnAdKVUTRglNAIYsB1ca/0wgIcBYOnSpWkOxAcuLtw65a9Hx4r4iy9ekWlfksArunjGXy9fbACpM4+T0CSu320W6SEJW5QwUlfTUJKcnCgZufxLLvyYwd8kG8f4S+p5Cj6DVfIRo/x3aV7diiWN2LijzxjKQ0NaaJ15DaHGU9gUnoP0/AGz87s84S7A79v6PNKKxNLhGD4xHk2YS4oybZFr16HRSEFKhW9jw6Xj29JIvFGSZi/bgAMG9Tidv2DN5c88Ub7Tfm6/cgHe6N8d7fOPrr0Ei2bXJxoo6SB4Sln7WO64OrgnD257F8+GjkkxhCH/6CuLjTpQGnT1wyqpBkFr/T0A3wOAMEL4rs0YKKUWAWgA8Cr7rAHACa31mFLqQgTG5f/UWmul1IsAbkOANPpPAH5+6pfzwYtNQdOMYO5xZHl4jIeONThJdlKZJ06DRdrQJLYpWSRyUE/BU5HXROkLUi6cWiDrpDIbE6kGDKgmXfvG0LMjxUcvaRI8ldBFzSy1YPPi6Zyo8E71mFf2HsGO/e8bUcaCGR/DMx0HMbm2gOf2DBreJkVwvMZSLPpQXnBlnhdv+rNRM9jE5p0nbW+75xNF34jGiOSO15auYLTVpZJ95CmPuJYtnGE0/dHsZRl5mOgzFTt/Y+xnSWP99l4nB9PNLXOiv5MMlHQQEHZt83PRGpgb9uTMqp9krB25X3Se7T3DRtH5ozSus+o+BKXUOgA7tdZPhR/dAeAxrY2uoSsAPKSU8hGkzv9Wa/1W+N2fA3hMKfU3AP4NwP9T7bmcayJf0OsvnxnzOIB0Phz+8hc8BSiVCU1hUxBc4fD0By8iu353/3PvxAb10ItB6QtOtubiVXLNLeDpFA/BpLX9R09Yr61Y0tYhNUB8VgKHp5Z8bSgFORSGo1wWza7H7VfON2Y6u+CfxPlULOlyblspdB0aNdISitFaS4NeCew3LYKw3W8AMQU2KFIv//ir97D/yG9injOnrZCpUGnA5CxmWzqsNkwPeQpRWomfK9XbyLBoBLxVfJTmnVcviDXEyWdNGs6bW+ZEqLPammBO9HNvD6E7dDr4+SZRb8uofVU4dOijMq6zIoOgtd4KYGv47zXiu7WW7f8VwGLHvroBXFXJ8c8lqaSgJD2OI6NjsbGMtlDfFm1UU8Rav703RgTHid6SisiS04bqB5L8DLDz98vGOE5OR8f3PBWwnCJQPqs/83H89VMdMd4cEttxaL343xyealMK5MVzlMtD297FS3sPWxuxbPBPwub3vn8Cv9p3JPrsmY6DxvkvWTAduw+MpKKssqQf0iICmRKE1ij62kDNdB0aNVIv3UO/hiRPUQhmeBMVNndObPUSOd3O2qcRwmkLBQ+LZtdbzzXGMwXgV/vKUVqWZ99mOCW6brzoR+AOSomRl++i3jbuvR8MHXLVMD6MkncqVyGVNnJJj2Nm/aSyYiyWJ2PZ0Bny5a/0QWvvGcYPntxdhtWFkLl50ydHvDeuh5gjXjinDQBriseDWSi1FhBrynMLKCVDBXfiVQLiBWMV/h/Fn5SWSTLMSUqB/t4s0CODx39rKPvPXXERTk6UjM7gZQsDHinqgyAPcsf+96Ooa8bHzF6Mf+s7hnW3tljRN1kKyWnCieQk3xLC+1P0NXrfDyIvnnq5oLaA5/cMxmAdngL2HBrF7gMjRkQ3cOwkNrzWa1WALpRSa1OAiCuWfCNKguNcpWShZbEh0WwOA08nUcpUnvOa5c3WdZb3qmFKHToGRqrqhTgXJTcIFUjWWQG2fLBsTOJFWgqLufI8XWFnW/dRSEdbIZsSkttQyM7hqgDwu43TcPuVC2JdsLYCIse58++11hGv0gMv7jNSFzTdjAwYpa0Au+KxrT9vpOK5bzkb+vYrF6BrsDNS9lu7hlD0dby7WsySaG1qMKKuX7x50FjLUkk7EV9Z00Aukb0INR6MpiqKEHxt1kMo9dLeM4yX9x6OTZP77CdmRdQXVMC+98ZL0d4znDhcyRXxpDW+yXPl4kJl0fVX4qDZzsOIXovud5Hfq9GTE8bMbIq+PqzRAZAbhMwiQ1vbrAA5xETy0ADljuIVSxpxZHQMW7uGMsE+k86L9un0PhnrJs1mzqKEXNtwFAoQeJEy5ZSlgAjAePFsUNpCiDRpFigo4uGx1QRs8wuSZi5zlAtFEVt29cdqCJQ3P3DsZGyWRGtT0JBHRsv3zcJlFi6fahWJ7EUo+cDXrmw05jdzKg+bV8+VHPFNLZpdHyHWJHW2bbgSic2LJmNse56oyVCjPCc8QnIxEkKXsq0m5WY7D6qrBWvojp7p37c/9KoxM3suI4r8sEpuEDIKR4zYOF0oPcI9W1mIvOPhVyMlSvn4tbe2oGNgJDAO7xyuKOyU3bOyqAsED+/aW1uiyV2cRyeLEnJtI1Eo8qVZtjDoWKUJZTZcugvZ5Hphbdtyw0H0DTYjIWsX0vvj59Z1aBQbd/RFSsEDjLy5yyEwDFnBw/WXz4RCQPWd1HRok7QalQQJyF4EeTxO5WF7vmSqh6IIzrLKqbOJh6toaYTj9882yMkWJUkEU2tTQ2xoj2ttZAovax+GPGeK8Ogdds2TBuwzs/l2H9amtdwgZJD123vx2GtlMjXKG/PCqTQGFOKSd/R637HIGADlnGjHwEhU2FQImDAl+Znr4eKeEeAu6tIL2TU4insYQ2XWh1YiVqh+ACS/NGlTwSp9aWwvsVRYLmXNaxc8Etsi0DLyXioE85cX/M6UKG/uInmj89kSzgJ4PmU+hev601Igtu/TehGyRIQ2T/veGy+Ncv/0+ZHRsTLlOtyd2jJfbxtA5Dou/V5uS45XydeR8wMgMWLJsqZAwMNU4vf9UvfcbY7Uk414p0IW+UFLbhBShBQE6XLZlQvEvYWCQgyOppRZJiWDwQuAQNBtK5V2Em8M756VRV06N1cXZxZSMXl8IsyTheYkhWWbCpZ0XZXCMLnCKpZ8LJ43DS3zphlG28ZrVCh4Ma772L30VFRI53lzV/oiOh8/Of1nu0a6X2k1KpfiriTasxkjV45ffn5h/SR4KnBqPJXMmEveO+8H4bMrbDDo0ZMTMUw/pWMpcgPKPFIArCk8LlnSSpyUUQOJNO1JBrbSFNa5JLlBSBGbgpAKjnLqhK/+4ZcX486rFxjekRfy0fOcKO1n444+Z0NP0sMlQ/NKECxZScXk8TXMDmWbB5XlhUjaptIXShrGN/tH0DU4auDHuTKk1NOBYyfxmEDLuDy/9p7hVC80bc2Trp+P5kybZ32qqCSXM+BScvJzAAacVxZlpZfP+3BKvraOIeXpmgdf6jZmXAMmqo1EI1Ditk5lfq30fqSt2fCJ8Zihc60frYUt/XU6UGMflOQGwSKuJq4kjhaJrwbiD4aL9CupoSft4UqrA7hecqnSXCpOHn/lkgCzLfdnW7NKEEyVXLPrGpMKp7Y1I6im7Pq1rRd5/UleaNqau66/UPDQeWAk1tvgmjuQZf+AOyWV5AzYnifbfuTxuw6NWim723uGse2dw9G+Cp6yjiEFYDhethqQ7RntGBjBnVcvcHbFSzABEUnaJMtzlyV6lY4aXcOHIUrIDYIQ2w3PknuV+Ookj4sfi76TU7xIbN5ZpS3ytpe8RQyFkX+7js8Vx6msWdLaZFV4cn9phdNKrk1e3xt9x6BgT8vx7ZKiNduxqd5AaCaqyaTBF12OAD8HV/esVIhDo2POZ8qlAGX6yUXZTe8GUIYLu3iuKLUExGtTUQ0ojLIpxUo8S7Z7Jud88Hod73SX96TSOgt9bns/kniZzkXJDYKQavKzSZ4FfzH437YXLW1aVBbvJKtSqiY8dq3B6chp276jffO/0/ZXjSFJOk57zzBWPfxq1HVcUDAUHt9OzkuoS6jN0LF45OGqyWQVngqSZH88ouG9FwUvoEl3FcCT0nc2JldJ2S3fDRvkGQiiFt/3wx4Ik/yvvWcYK5Y0QiFo8tyyqz/GpGtzUIy+ioJnjUxs3j0d02YkbZBa1zvJ187Gy3QuSm4QhFST/2ttCiBrctYtkM7fU0nhKS23LqGvPA8r9501PL4jVIb0kjZbZj6fjpypjaqYXmovbJL6VsroSSC9cGo7bpKR3bKr36Cg8LXdeErEF5Bt4Itcu2qNAZ0rx9GHTM5WJ4V6L5K6jm3nR6g5g/4hoeaRFoXZ5kRwojkb7Xrz3GlRPc72vNG94AViaI1mRr+R9JwmPRPyepLeSVo73oB6rheZc4MgpBovk0M7ZUer4SUUyxh4+RJlYUtMUrw26GuaJ5R2nZt39UdQ2aJfnhMgDU01a8bPe8uufmzc2YdSSUdFzrbuo+W+D1/j2bcG8cKewahgn6TskxoEpfD7Y4NFyvSKp5BYE7EVPumcuEfMz72StXOxxbZ1H8XQ6Jix7eeuuAifsgwM4pKkJGm/5MRwI+Ax+G6WmkdadEnrJDuSpcIl1lVfaygFfOaymbF92u5FyddGp3ylES99ziG1JHU1Qb+NnDZI95aexQ8DtUVuECyS9ADbJIuXEOPvYS+RLd9L+01Ce8jQ3hfoF5mHteHEk67TNZLQBmGt1hh8/ZE2/Hai3GU7XvSx7ulO3H7lAhQ8k7e+pIHvP7kbvUd/g3/81/0GSobWQI4FBZK9MgmLfOy1XjSHU9IAM71CCDJXBGIb8tMyd5qTbM7VGJe2Xq4u7MDJCDqVawvK6Gex9ZK4OrddxzKiIB2QEqqwiGzLjWd5LmRxXRI9SidIw4Rp2+ZfS0VcLJaVdZa1pmeCzikpLUTZAXL0JFEkHc8GxDgXJTcIDqlEyaXVEKh4ODQ6hm2sG5leIsndzrnnCdlECsr1QEs0lJzMVk2zDCflo1wsL3wuWxgfROIyZrY1JQUj5Y3+EXQNduLu6y7BP7zynkGB4WvgoZe7I+TneNFkKCXP1eVx2s7jttZGrKcpaczo0PpJagvah209PzV/esT/4ymgc2AEz3QcNIYLAdWlD2S0yRE43FOn/fPr5ef6mctmGudj41iyzcvOippLWh8paRGSq96Qloah96Rl7jSnsk4UBjHuHBgx1p1TcQOIKEuy1CZobc5V45AbBItUqjwzp1+KvkFBnMULck2fcp0Ddave1Dw7c3EwaZ98AA2lDUgJAAFHDlccNox5a1ODlYKbGzFJZjZR9FE/uRY/u+eamNct2wA4Qyn3XAsWZIft3q5c0oifsSlpvkYMCy8VpiutwOcOFAoeNoYzmoHyLG2ieK40fdAwpS5aJ18Db/Qdww2LZhnPDjVDjk34+NmOPqz7UotBzzxe9PE8G+hjo3po7xm2zst2Pee2Amwlzxv/Df+bf88/qyQNk0VZS5EQY+q9GZvwYwSBrU1u0j6bnOtdzLlBsEi1yjNLTpI41KU3w4vSi2bX42cJzWou4Rw8pAwosqDBIzSkZfTkRKa1cF0Xf7AJISM7r7mitFFw/+griw1StVe7j+Ktg8eNYmFrUwNuWDRLzFoOPH9KjXCG0jTP1YWIWvelFnw/NFgkSTnkJLQJGf3Do2PGeS9unIY1twS0ymS4K5HhE+PGiFFKl/Dr5bWXoh84E+u+1BJLW5Jcf/lM6xolzcuWUZ+rQ/lMKUlbGgaww7GrATzI36xc0oiWudPw/Sd3Q2tEDht/JzkKKuncZZQno40PWjIbBKVUAcBOAAe01svFdz8GcGP45xQAs7TW05VSnwbw9wCmAigBuE9rvTH8zU8AXA9gJPzdXVrr10/hWk6bpD1ElYZ8WfbHi9KP3r0M677UEinRmhSmTNoHLyqTMqAmuXW/6GRU04EHvGDGx2KTp7IKf7A9Zc5K4Bjzhil1uP+5d6wU3IAdr/25Ky7ChWyo0DMdJpV0y9xAsfJ7wIefJMFtyTBCayPtNXxiHKt/fyEeCVNUPDXmyiG70CZk9KXCb543LUrfbU7Aw5PYSNwm1cZRKzLlw2svvi4XUzfv6seR0TG88PYgKFO37Z3DaO8ZNo4frRG0k3qEJEnBZe3ByeKA2d45MgyVoIKyvK+237R1HzUiU2LvtaGgkkRGxTLa+KClkgjhTwDsQaDcDdFa/yn9Wyn1xwB+L/zzBIA/1FrvVUrNBdCulPoXrfWx8Pv/orXeVN2pnz6xFVtdD5Ec+pLlRvL92ToXbS/EsoUzUCh48It+2AWdLLaiMkUWAKy5+mc6DjoNQprRS4JL2pguuRAFt8SxB1xEGi+8PQRf60hZ3twyJ5r7CwTwV+mpSsPC741s1vLDtBINQeG/oRQLNywuhSXPwWb0Od5fsXXNogBdRigpXdLaxEZZClpxDkslsY3zdDWZ2YQX5X0dzGfmCs6WN5drzgcnuRympDpV2nomRe+u51z+hoyxZO+V9b+0SJ7uYdau+rMtmQyCUqoRwB8AuA/Ad1I2vwPAXwOA1vod+lBrPaCUGgIwE8Axx2/PuhgIEE/hhkWzIrpimTeWXrgMG137pwdu2cIZmcPrtu6jUTF3PMzNp72YHGqnYM6JtUEim+dMtYbZEhVjG/GZZDTpZTK4nBQM0jlAomOUcy4E3Qc5WF2KK69Px/EUQy2FnrP8jWuIjU1h2VBGcj02rL4mRh+dpgBd18KNUBJqRdKKA2athwtNnpPHDcoxOtZzIa+569AoisLIjE8kz4xeERIk8jVP8uI3iwK3rFPx9eRzG9IUbKWpKs7eCyDqx6imb6nSrvqzJVkjhPsB/BmA+qSNlFJNAC4B8ILlu6sA1AF4l318n1JqDYDnAfyF1npM/u5MC8+5jpd0lPPdtLMPG1ZfEyvM2oa+kLiaq1wvAn/Jbdww/OX92c6+RE59GYXItAnP1XcePI7mOVPxk1f3p3dYFn2s394bKDTRdZvkeQEWLqdbyt6mHGNIEFwajMJTOgCsg9Vdx6P7uXdw1LgWzblyUKZtlimkpLVNQ23ZCqJt3SZ9dBY8fFqaMW3tbekUW83ihkWzMufcJfV0BLcU+5SzAaRxU4gb2KRa1ab2cr3FxoVE6/ngtnfxwttDkeGVDLry/klqC5dzx9dQKYV9g6N4ve9YBB1OqlnZ9kXnUW3vzpmUVIOglFoOYEhr3a6UuiFl81UANmmtS2IfcwD8TwD/SWtNz8/3ABxCYCQeBvDnANZZjr8awGoAWLCgunx3knDKWy4TlsEfrrARMF+WQvidHL5texFI5AshPbOS5XykJCkJ+V1SqCsVK5Ct69Z2TNdDLxUPjxp4SqfS4911zcV48KUAlvrk6wPRd54KFD+vDxC3jUwh2aImuX6VAA9sSpa2JYhxlgisGrgiP09ekCbhtRrXcQFLdDzh45mOg0afCK2zJIC03esVGXH5ZEyBdC6kF98eihw2Hr3b2AJ4r0rSTA+5htAar+0fjr5LiiqluOhqXDQZH4RkiRCuBXCrUuqLAC4AMFUp9VOt9Tcs264CcC//QCk1FcD/C+D7Wus2+lxrTZXCMaXUfwfwXdvBtdYPIzAYWLp0aaXAjFThnD5cCl68I9Xmhbf3BA+HraB793WXGAyQrhfBxj+0bOEM1BZURJugAaMLkv+umgfJ5Qny7tSNO3qjqWh0DpWKy0jZFA83UgrldIXrOm2fdx48HjuWhyBdNWvqBUYfCPc0FQK8uYsQLm39bJ3m0hvkyCLiHaKcvi0ilTn4LOkNWyHa1hhJQo1zsoYm9y1rVJ6ncHPLHPzru2bUvOqqeCRH95pff1qE41pn1/jXB17c54zepfF+puOggY5bPG8amufZyR3pHKi/hUvSnGebpKU0zwUYaqpB0Fp/D4E3jzBC+K7NGCilFgFoAPAq+6wOwBMA/klr/bjYfo7W+qAKJsd8GUDHKVxH1WK8MIxJ0fM86/Z0s+QAmJKwKCVfR4gVWZxz5evJi6NJUF9dOj9qmPIAQ0ESS6bseE2TpJBVKp27rrkYHQdGot4Bjjap1BgloURIqEBJHaI2FAcvFNs+lwVohQD7v+fgcew+MGL0gQAmIorqNVlSCGQ0JaWDrYOYvFKOLFq5pDFqUgPsESmXpIhk/fZePNNxMJYGpPPj57n2qQ5jct+apzrgAanPEUfHULPkotn1saYQF2surXUaskoKKf/Nu/ojZJot/XPg2EnU1ngoFuPRuzQqN7fMifL3hUL52XCdV2tTg4H6A4L3gWaaZI3eXLXCSiHuZ1Kq7kNQSq0DsFNr/VT40R0AHtPaeEK+BuAzAGYope4KPyN46aNKqZkI3tnXAXyr2nOpRmyKkQamAGX0BRDvupU3USNQ4pyCAQCDecaLcyRGOAqz83JlyOzIHyAbGVjag5REicxDXVk7eOSV9+DrIG+79tYWAIgRm7mUSBKqJFEZiCE0rhfG9Tl5p6Qg6yfXGoNwZB+IrQs2jfAvidKBUhVA2biMTfjYuKM39szIOchJnqYrolu/vRd/+cRuAAHCh9JC40X77OjOgZGIkwoIUF30m6TnyOWVy8g6y3OeVfGR43N4dAxbu4ZQ9DUeb+83qD8o/UOgkDuuXhDrBZARyqLZ9bF3Pu28qFBPhsnV7CiZBdLWEEA0XEsW+D8IqcggaK23Atga/nuN+G6tZfufAvipY1+freTYp1OScnlcIbjw5/zlLBSCSOKuay7GI6+8VzYC4bHSwkqJS+Z8+0lplaz7lw9rUtemK72gtUYHS6fwXLxtP3J9VzqK6VLauuNDaFyKMKn4KQvQ7T3DDP5pvnQySqmUD5+w/byDmFJ73Mh3DIwYZIbU7LRxRy9mTb0glcnVpUxkj4ZSwTOkHPeI6EjIENUUVBAhlHSMnM12DvJ5qS2oKOLgENek1FWWNItMqZFI6g9K/7iaPkl40yZFArZ3Pum8XGkuDkzxtcYPEpgFrPsI08pZ4OVnWs7LTmVXLo+nAWzh3GY2kJ08jsd39kVRhW2yYtOMKfivX/t05tSDRCvYXsIkMrCka5WEZGk1Epo9HJ/9HChW7UDm2CKoLMrAVXx10SdXhNIQL10a/txV6ONNWwVPYVPI9UTiIVD+fe+fMA7v+8Cqq+dHkOCuQ6PlKGtwFN9ic7Rd52dTJjJFtvr3F6J+cm3s/vEax4Zvlr3llrnTsLVrCM/vGayY76e1qSGC1XKvOamHIuv9Itg1F3J+fN9HyQ8iV57+SYrqjE75iWSEX6UigSlkdFzv/EPb3sXg8d/i9isXYPjEuHW41gcl56VBsBUEXSkNjm+WdMpzp0+Ohqm75AuCU4ikWk4TmVNNY5mMQT9TxgiS8LCaXjKeb3cxZBK0lRfTXWM3XdeWFVmTtTBJSBV66Vx8S3z9XHULatpSCvj0/OnY2TNsRGs1hbiRAIKUEPEMpUVrlTwbPEVGtCfk4BBuvoUdl/Z331cWV5V+lOIqQGdp5EuSZQtnGCk1msfRMnca1j7diZIfNGzK59S2/827+s1OeUFhXsl52cSWJrO9X+095rClN/p341ufWZgKTjibcl4aBKl4kh5g2o4PEiHe/BVLGg30gQJw3WUXYsbH6vDUGwPwNfCTV/cDAF7tPoqLpl4QURInRR9ZHgRSaDa8NQ21IRI5qdhJOcgimiuVRpJGRSCVS01CMT3p3lSqGCvtqk6bnJVWtwiDLbT3DBtEdre1Bnw2G17rLSNYGgMECxlEHq0pT0FpHSOYqzTfTikyF0CBXy+f+cCvB6gcNeOSStNDNmltasDaW5rxgwjK7UVrKD3qtOl8p5qIyfR8sdRZjQBgkLR1HzWGLQGBXiAepGaL4T7bRuG8NAhA3CtIstLkHVLuVQN4fGcfVixpLNME+Bp1tQGFA3loQPACPvhSd/jXCJ5/exA/u+ffm6kfEX2kNbokKQw+1IaTyNH3Sf0HaYrI5UlRk4/shPUTiulZJIvRtGHMbWm3pGhHKqykugV3ALQOiN/mscEwxFNE95U6s2MRZyFIfUQ7ynD8NOG00EA5z64BY+YDPbtZ04+VIsoqjfRcMnxiPGomJOVfzdqsWNKIx8L6AS2Mre7lgjZn6dqn1NmR0THMFL0dJBJKDgT06LsPjKCuJqhFftCIo/PWIHCx5c5tHai3tTZiA81yDYue9954qUETIF988iZJSj6w7ulOrLml2Rp9SHQIV3BAeQiM66U4IqZmyb+TXqhlC2c4EQ9ZXhgZJstO7izCj8OJ6GwpOxlpjU/YkTV0j7MWj5PqFtIB4LBDciI43xCnq+AQUEK38Gep6voI4pTVQBmgQN4qPbtFVrBPYukk4+aaPJc1nVdpepSj4rLWlORv5X37obhv8tnONBe56GPD9l4rPJX+TfuxzU5ubWrAY6uviWoIs6ZeENZuKqu1nUnJDUIo9AAnedAcBlooeDjAOFPkjedUEeUIIZA3+kfw9UfaDKQDeZUcHcIVXI2nAKVSowjZedp9+NcxXheXEug6NBqhfHgGPOsLoxDUbHUIVZUdqzZxwVNpshh1Ed+waFY0eIbfF4mMSsrLc0WRljd2fS95gsgY0HB7muA2b/rkiK7CZqiAZIhrpXltQmgBwX246ZPm+ExOheLrgP6cp7gf3asAACAASURBVBYlS2dafaESJV9JCixrxOeKVOV9oO1s9812fuPi/Oj5yjIXOct1tjY14OE/XBqd78t7D0fnm7XWdiYlNwhCkjxoUvQcXZTUzMI/41O+AHetgqNDDAVX0qAe0/GJeLs8KbyWudNQ4yGiN953+De44+FXsWH1NQDMfgCuBAiJQVF1MUw32eod/EGXVMmV8rokwVMBRLBXwO49udZOdl+fajeozaCQcBZRqs2sWNJoNVTjE2WK6NPJZcOjqbpazxifCZizFDwEuWuZWrQpSVd9gT8TtlnUXNLSPHxt5bOWlRYCsN+HLClPOXho9OSEkTLm77xtsFFSRJMkSZHoByXnlUHIksdMC0lJQRK6KIvHUz+5Fvd9eTE6BkaC7uJSvJDIH1bO7V82DmUlz4nZ6Bhc4X32ExcZg1moCxZw5yjbuo/G0FJUjHO90MTfVPR1VECuZL5CEjyVcuxFP1BiW985jLW32I2Nbe34NvI4Wyos4KdRnstUmYYjDTnhwwfwq31liuisyi5JCP2UxAG1bGHAw0X38KKpF6A8iiReeJU1Ljl5btnCGda6hG09k94pW0SQRKmSdM+GRHo0DUlHwo2lAsKGTDOamzt9Mtbe2pI4e9o2ujbt3LsOjUbPyAdpCEjOG4Ng8xKBeBdytS3oWY65ZnkzNqEv+NLWtBCKTcENHDuJ9WEOWMGEuklvDUDU7AaYXbB1NQE5n2xCWrZwBgossgCCF4zSTZQXPzI6hoe2BYS1z+8ZjOB8JV/jmY6D2WfWWtZRhsxbdvVH11wqZfMWae14Tt9IK3kq6kwlipC0fHYa5fnKJY1R4xun+JAe6cMvvYueoycMrxqwz5+uRMy0nb2QL5UyEBhZzhGUtL3NMbLV1KSyd0VVtnMfm/DRMTCSmVmWH2fLrn682DUUfVbjwYr0kcJpL0olP9bQx+eb22ooHEzh64CuZuM911gNhvy97DIHUPXAqtMl541BsCFWJBYdQAyymebx2AbeuI5J7JCuF8gm9CKRYgTiRHfSW9vaNYS1t7ZE/QYcFRFRFluakJrnTjPI7J57axAv7z0crc2mnX0GDw4XDdPzzaLcsoTMSWggl9heQjJoG3f0WRuUXNLWnUx5Tue7YfU1iYVOzq4JBOu1cWdfVXxUUiRa6IBjHoBUynxetu0ZTqtjtMydhkLI/0VzCNZv7zX4ncYm/KieZFN2BGQYLwXvxab2/tgskqSUpa3WoVAeosSFanVUPwPMSX2rrloQQT/T+K1sxwWC95pPjUs6d9llnjSw6mzJeWMQsmDRX+87lphX5UKfkwGhxhl60Gw5xSxdlVLIyzpw7KSRA+ZeoM1bGz4xjvu+sji2P9vQcaD8YnDhBTQAMQw1YBaSbZ3fWZAg1abvXGJ7Ce+98VK0dR81ZiIo0aAkpb1nGAPHTqK2oFAs6RhpmjzXtKKqXL1SSaOEdB6hNKmktiV/B8BZiE2Kltt7hrH26WAsq6eCa+ARLDEIE6rpB0/uxtauoWj4FDf+X106P+JXKlrWISkit61twQuaAOX53vHwq9H7/Xh7P25rLdesKCrgSDA6jq34z4/L3wHpGCWdu+wyv7lljvNenS05bwwCf2k0Am9Y9h688PaQ8Zu0hhaO+S/6wKPbe/HYa70RayoxhlJTGhB464dHx2JoIJtIDHRtQVmLWrTfLJwstgdUIoUumjoJR349FqGFBo6dRPPcabGUEjE+tgivytX5nQZjPF2S1EdAkRQAKM99h+Xar7p6vqHIeCGxY2DEitqyFRsLIVqMmtmgtfOeViKV1rZIXIXYtDQN/52vAZ85CxpASZvvT0nDOXyKK29eH0ti5iWR6cDAMYlHv23dZlPYRNGcT+KCNQN2iLJ8xtYsb8YzHQfxq31HYo6I69wXza7HTZ+8CEMhjcUHHR0A55FBIOH5QMlb5LPUQMGLQ/Gk2NRJid4GBC/YP7zcHf45gl++NWgUr9K8OK6o+VQxl7echCkncXndVFvwAQweH0OhoPDZT8zCtncOY/32XniqfMGeAj5/xUUGkmXR7PrI2HYMjFi5oqphaT0Veg9bKopHUjohbSfXfh4jTZMdwSSPs6lyshBt6yeR/yZF7PouTapp2rIVxOX129I0HQdGjN9R6ojv74o59dhzcDR2DEn1zWeSeArRnJGkrnkSfq95P488Z2oKIwdOzidJ+q0t+rM9Y4tm1xsZAN7gKhGBW3b1Y+OO3pCTKWhQs6X5qmnoOxU5rwyCfMg7BkYwb/pkAMnT0FxCzJGEkOEShM0qNlGK55CTFCIVu4gagQ8HkdvJ2Qhphkw+4PRwr3u6E2/0B3WHYklj6Phvo2sr6fLJKwCfmj/d2Adnk6yt8QxmTx6F2GCMroe+Evx60jXy/Z9KJGU7Ly7c+MlCtCyIy/Wn85R9GJXUF6pJsbkK4q7r5+dIUlcI6NE5iq6moPDOYNwYAEChoIw6R1rUmnbvOZCAz18ePTlhbGMj4iPZvKvfeNeyGFPXe2QgyyxR8tcfaTOo8im7YKOhOdvDc84Lg+AK3YmEzPMCb7gSDD0QPmTfLDegEVyNWtxb5k7Dmp/vNtIsJB7K6RjpGRiKgQ10sRmDUyUn49fSMs8sKk+q8VDwTKNm47uRSJxi0ccdV8ejGb72NyyahQvrJ5msn+Khr8bjleIqMGeBH9PUuFlhuo+EzksaBa7M0grRNpFKEKi8vpBWCLZtbyuIu4yLNIafapwWzcpu7wmmB1IUvIHNXaBtL5p6AbZ2DcXqHK6otZJ739rUgC80z8aTrw9Aa+DBl7qxYMbHolSMbW2yvmtS0thyXQ2utH42SaKSGZvwE2uap0s+8gZBKoS7rrkYnQePY3JtAc++NRgVYZ99axDbuoacyCKX8IfspubZsYdEDiTxVEBRfHysaFAbuLp/kzjek7zuamTFkkb8LPQWAeD1vmO4+7pLYoZOcrkEPQxMAbJ0G0ev2LynJNbPaovKXLjHyPO6WV74tU93hi/viPFsyGuhGkLz3GnRZ5VGm0AcLXS66gvyurJg4skxkmka3sQFBM837VdCrOWshDW3NKOt+6i14zzJ26Zrp/QLYE+ltfcM46k3ynO0gXTkTtZ3Ta4hOWISQSWdz/GiCfGW68dFKRhpJhcC60wahcwGQSlVALATwAGt9XLx3Y8B3Bj+OQXALK319PC7/wTg++F3f6O1/h/h560AfgJgMoB/BvAnYtraaRF+w8cnfEO5eQoGLW7aGMO0fJ58qMljKvDjaKB+ci3qJ9dG1AayWSopXOfHlwokbTZCmrQ2BYgPjlaqn1yLjfdcYygKKdxjpolRgH1KmvSekmY0nGr+tL1nGJvay3N8K5lI1dZt8vHLZ8N2r9MoF9KElCCfVVDpPuhcXArT1ovDo8wIyaZgTVPI+1/yy0Zfdhi7UjRZ6Z55KigtlUY9AVLZEnLHtSbVRKFt3eWBODQ/fdHs+mgtufNJY3SpyM2b4KRcNPUCrAnZXeUYXY2zMy+hkgjhTwDsATBVfqG1/lP6t1LqjwH8Xvjv3wHw1wCWIliDdqXUU1rrYQB/D2A1gDYEBuELAJ6p7jLcYqAQmDdaKvlobWrAjp7hqEcsaYxhNQRdtH0hrJhpDYNYiytziUfP2pxTrQftekFsOXb63nX9tvNI4oSyITTkjAbX9UosedI1t3UHVMlAoOi+unR+5jVattDk408bcSnTPZ0DI5gb1qeS1tsmrkaoLJKVd4rXO3iUSf915e658QcCw9EwpQ6LZtdbiehsSj4tz562tnSeNsACiQJwz2cWxmjBXc8uGeEsYhuIYzOKnQePw9eBd88p86ljXHkBnJnk4MhvI4NGPTIrljRW1YtTrWQyCEqpRgB/AOA+AN9J2fwOBEYAAP4jgGe11u+H+3kWwBeUUlsBTNVavxp+/k8AvowzYBBsDyDlsV/vD0J9qiEkjTGstMBpQDl9bUUI0Xnxua7jxTLXTZbmnEpzxkAyCViW3LHt+vl2QDZOKJ4OkDMajMiOvfgSS87PXYo8BxuBm0yR8HXgk8XSIi8ZrXEDv2Z5M9Y+3Wldbylp6B6XUeH9KlkNMXdMZIOVp+zpR1qXh7a9i+ffHoIOIZ5ZazO0jzSnIWltZSpNpk6B4Nxvap4dRQ5pxyEjnKV/Q0ZJPPKUfUfb33s/Ru/B16nr0Cie6TiIybUFPLenTDfjhfs8HWnTSiRrhHA/gD8DUJ+0kVKqCcAlAF4IP5oHEFcDAKA//Gxe+G/5+RkRHnoSNBNABDMrQOPTAjXDxYb4SbPUNmWUFBITrtvXwCt7j2D7e+8bKaDTUWAlqYYELO34lRZv+TFsikESjlE3rcSSp6FPXN6fLMjbUiSVGFv+4srB7Rt39MbWG7DnwV3rTDBWnk7gERNh6GsKcYSX7Rz5cal7vRR2HBMfT1IH86fmT4/VArLUZrhU8kx/5rKZ0dhJF908N2qlUpl2gt/jLCNft4QsBuQIyHu1bOEMXOCoEcn17RgYsVLm82eMopiX9h420q7VPIenKqkGQSm1HMCQ1rpdKXVDyuarAGzSWpfo55ZtdMLntuOvRpBawoIF1TduZCXRSvqdDYWQhDaoxGN69O5luP+5d/DK3iNRu7zkXj9dnoILe54mSX0O8qXavKs/NjjGde4u2CHv2aARkbxQWSjYUVokUlnytZReZTXQVinSwNP1zBJEckOjY5nSb6SQuw6NmjDWCXdvR5Z+FSnUvR6UdHREzuY6R+rgdhmeLELPQ1qthaJZMqh7DnZgw+prYhBePoOiGKaHh0bHonvsAbj20gsjSgkuMgLZGEJnAeBnO3rDRkKTzsb1LkrlnRXmfLYjAZdkiRCuBXCrUuqLAC4AMFUp9VOt9Tcs264CcC/7ux/ADezvRgBbw88bxecmPCAUrfXDAB4GgKVLl1ZddJYKa/jEeKYbkIRCSKsr8IcjS0H625+/HDv2v+/kXj9VT2H99t7IW/XC/deKcX+285TXSakXvq3xUlkmwCXlil0vAyF1OEMo4d2PjI5h6zuHrSgtfs4uSG6UCw/37/IeqxFbSmxb11CE9Z9VPylT+o2zaHIYq1LAwLGTUZpDosxctRVXqpDQLHwwkit1JQu8t181v2IgQyX1uLTiPl+z1qaGEOodNARue+ewYbRsxoB+y6M7DpcNDh0aYkZnk/VdrNQx/KAMAUmqQdBafw/A9wAgjBC+azMGSqlFABoAvMo+/hcAP1JK0VX+BwDf01q/r5QaVUotA7AdwB8C+G+nciFpYvNCs9yApLDWFmpmRXe4jrtiSSP2DY6ivScY3m5r3qJjV+JJcGZFkoIC1t7a4lT80pt28R/JFBFnZh2fCEj90nK48l7wqInTAQyfGMePvrIYD7y4zwpflPfGBcmVnngamidLUTiJo4lj/bsOjUazC1xGiK85TYzztY5qXhte6zXSQ0RRTdBXukYuianCcKZF8F/3c284SKKDO+u6VVKPq7S4z7m6kqIl270ig7epvbxOEhEkJxBmEZeeqQQgcbak6j4EpdQ6ADu11k+FH90B4DEOHQ0V/w8B7Ag/WkcFZgD/K8qw02dwBgrKXKoNyWy/W7+9F890HETznKmJSKFKXgAbHQLNGADik8QqZciUzIpAkCZJotEmheFK6chrojD+oW3vllMxAMYmSlWlF3jUZOMlsuG8SWTEIvn8af9Z1i6LQc8aLRLzadLsAnn+hMQaPjFuUCxIhQe4kWBAMk2FbXC9jdE3Sy2JOy6286mkdtDaZBb3WywGzxWpuqKlpPkW8nhHRseM2SJZOMiyiI1sLwlscLakIoOgtd6KIOUDrfUa8d1ax2/+EcA/Wj7fCaClkuOfqkgFkBUKyH8nOcy//Om5uOyi+lgh0YXucNETGx5hKDocUp8Gu8vyEElmRcBOIeAaekLkXTe3zImOJ19q+ZCT7Ng/jNqa6tILLkNO50TF0LVPdaBzYCQ639OZk81i0LN6vXw7rXXEYWO7Ptv5k1cpkVMUmSWdQ6U0FTJ1lQYWkEZRTsDbzCJo17Ul5eXp+eL09FRf4cX2pPsuu+rHLeskU71bw3RfoaCiz05VcUuAhA3c8UHIR75TGbCHZtXyhEhP++evD+DOqxcEoysLZaVvQ3ck0RNzj9fX5QHpEs5WbQcrdVJu3NGLSTUepk+ps1IIcPI3GsYOlGGhO/a/H7FIyhfvm/+00zovgTxPV3ohyTBTNMYNEQkvho6XNNYzPhjg1AfP0LllKaBm9ZxHT05AhekijeB+PO5g2bRFMLb6BAc9JCHhWpsqo6mgNbRFgmnGjjiwbHUlQtHQPPEHXtyXqSeBswuPlzQe3PYuXnx7KFZsT0I7tXVXRitCa1YptbgU/hzfefWCIBXGABJA8tS5syUfeYPgCs0qyWNyaZ4z1fC0NYKbTROXNADfj3OVUC6e6IklN0laTlsqgWqU3Z1Xlyl2XTn4lrnTDOTNG33HcODYyag4y4fKSE9K0odfOuvfoffobxIVqURx8dRO2kQpUsKyCJ805aoS4YVpz1P47BUXOXtVkpSqix0VCDp9S74fi/iSjCRfdw7ZLfkat18130B32c5TeuSjJyfQefC41ehWkt6R2/IJeLyu5OuguxdANESHzxNwvY8Smjh0/LcxypQscPBKaUXku1spGs31HG9YfQ3WPd2JN0NCyaxDs86kfOQNggzN6GZWg+tv7xlG58Hjsc/pIaajFH3gwW3v4tPzpxsvJqE5XNwkSTlt+d2pPjSu65et9c++NYiagjJqAnIIORCsM2ceKXgKf7fyd6PvXApqC8OJSy/fNlFKYtA53JA8Y4LtniqUtK27TFFQ8jVefHsI37r+487tXffPlg4EAgVX8MJBMrqMHlq/vTdTBy9gV8KV1EU4CstmdCtJvfH7odhnQPA+8Msv+hobd/SWjXn4pYyMuXB24doaD7dfuQBdg2WDsvx35zgL6tVcD5dq9AWJazJaa1MD1tzSjK8/0lbVfs+EfOQNggzNOMKokgdD0v7SVCjPK89g5U/8C28P4fk9g1F4TA/A2eYmcYnr+smD4l53saQTh5DzQqGrsQaIp4bae4bx+M4+Q1FwD1FGY81zplpHnLY2NRizmIHylCsXo2wWWbZwhsH26mu7B5dWi7KlA2tqPFx/+Uxs7RqK5nAU/cCbJERRFoNWrYKTKCwSGxlckqNiE0Izbd7VH0GOOa0ECXnGJArAtZfZewXoPCi659dKNYQnXx+AQvKs7Gr5sarRF7StazJa1l6MsykfeYNAOUAbvEs+6EkPC/fyPFVucuFFLRI+LITCY8q7S24STu5FxzlbD0ZSjpp73bxuIYeQc2WV1LhmY4gcPjFuUGsXvDIUs2FKHf7bC3uj8YTEEOsacSqvhV8D71UAkBnq19rUgHVfajEQKbbaQFotypUObOs+iucYggVAmHIMEEiAtiKoKhXbcy3TbSSnOsZRpmIJcmxrIJKfFTzlNAYk8j7zOhLtMwuSr5pUIh2b6h7csZGOjjzOj76y2Kgh2BplbdHNqRI8ViofeYMAJOOA0yByJDJkvLllTpQbpQeSDEXznKl4+OXuKAz2WXGWewS8kFYtnPRMiMvrJoXGx2USusjWuMaFp19oxu4Pv7zYCq3ksFaNwKOun1yL0bGisU+eU7bhyglOyftEHt/ZVxHUj7xlW2G7vWcY657ujIadJNUAXM8gx9iT1NR4+KN/H2fKtCk4ORxJepsuhlP+HPIawqmOcbS9Jzv2v29NmRnXXAFNuO14LjAGSVZOozQFbFPkMr3nKsTzteXbjE/4VhhsEs32mZLzwiDYRN7Y379sZow3Xz4Q5AHXT6qJooLagonsuLllTogzD36jEDCc2uYM21AZSDh+JdfGvdEsjVc2cdUtZB4/jaSMkDq8NlHSAcImKVy25Wx5Dpl3TGfBu2ugIi4k2rcNYSUpFQBE6LKsniilQP5885vYN/RrAMHzcltrI+on10aOhs3QkGHmHr5NschnbHPI03OqBXeX2FIri2bXG7QsXGoKCl9bWoYjuzrlkwrsaQ2GNt6qrHxcSZkCHgHxZz9LvYFvoxwzQaQTxTMNZ0rOW4NgWOiijxfeHnLy5kskTClEdABBfn3V1WVkB+0XCPlTwpyozWuQ3k0kSlkLt3QuWT0Yvk8Xv301Ig1F0gtg1F5E2+cb/SPoGuxMTbPwa7XlkG1rS5/LWcabWISQpYjnQqPx+0xyw+UzMxlIvjZbdvVj/5FfR59RYRiw93lIOgtaTupglopF3huF9IK7LQWSpHCl2BwJSctC53z70vm47yuLo+O65jWkpeSk8SDZsqsfHQdGMnEa2YYpyW0aptQFXebh/A6KgCQDQlq9QRoyGXUD2WtYp1POW4MgLTQpeBtvvqEUSuYgcaige5KHcvwF5A+ejTpDktoBgbF58KXuWIHM1phD50f7cyFaTgd5m0sxJL0ARu0FgbHl9Zakc3LVOGi/9LdUerZoLDIoCXN1bdf7Rt8xKKXgQRs1n4YpdbFroS7WLB6iDeVD0QGdU9KMCSCgq9BaR8ORWuZOiykWeW8AJPLrJ6VEfI3Uoq1LbLUpSUnuMuxZDZiRgvVULCIkp8hmDIiyIskppPQcUYWsWd7sTCm6UoQ2WTS7PobOon2k1bBOt5y3BiHJQsscOH/BC54ClIpSPL6GkeN1KUf6nFMx04NMXoYs8BF8kl4CW2POyyFlLim+ZQvL3cZceJhcTaFKRh5SMbheAGPtCh4+1TgN7T3DkcGSTXy24/JzddEO8DV3efV0H1zXLGtKPCVUUMBd11xs5Itv+d05+PnrA0EDFuv8lc+WrVhoQ/kUPIWWudOiv9MiMVu6jafzgPLYSc4OmuS9JqVEAHvRttKOf16byhJpZqVd545dzHEDMKt+Ev73z11urAt3MFzDlGzGm1gEXCnFNLEZXo7Oouf6zqvtdN9nSs5bgwCYqIGVSxqdg1BsXhb36l1KR6IRgPIgjk3t/UYRec3yZrzYNYRnBerE14hQJrbGHFsaijfGFTyFb4b89lmK51z4i87DaSCuGJJoB3iX9s79w06PWIpU/kRVYaMdkMqzmh4TWRjnLJu+Bv6/zkNGg97Tbx6EDteYkwTSdSettRGhegpaBykBVwGZr2VaKoLuRxLFtuv5lEq5ec5UvPpu0GNCKRdOv2K7RsA985iMpE1c1yc/48+aEREbfTCAUsqIEA4dH8OapzrgKRXrDJfXzZ3CLeLZ50SJSc6HvO6kNCfvySAeMb6Ocrb1mZLz2iAAdv4Vm0iF4yJds4WvrgIfUFaswyfG8en50/F82D1M4qFMQOdqzJGkczw48H2N0bEi6ifXAjDzpLJb2rUuFH5zj4srhrRGKkplSa9NA4ZHLI8vlf/GHb0GTNVFO5BFcUrh92ZswseR0TEDAaQB9Bw9EfViEDwUCFJ8G3f0xhR5WqTC2WE3CB4s+r3NwKZdTxKiJun5lBEubVP0NTwFfOlTc/GxSTUGhYPkK3J1idsKu0kkgPJ+Sm+de9YSPqsA3H5lMLdk3dOdeKN/JNpX0FMTL9a7nhnZK1NbCLrpueOYNYJxGaCCp9B58LjhbG3c2eckyzyTct4bhKSXNgu6QaaAbEU/2m9UkNIaNTV2TiIJoeMzmFub4o05MpzsOjRqnKdSiB4sqdg1gI07+mI1kNi6MC9LAbjpkxdhZv2kSDF4DpQEFzmHFjCNnVzvtm6Tc0Yphc6B8otdsMAUKy1+yt9wYr+tXUNYe2sLOgdG0HFgJGqior4IWaN5o38EX3+kzfCQG6bUJSoL7qnL3hRbA57rebTBpwkKy71ZPnWN6h9JEe4DL+6LGsp8DfzizYP42pXzDQoHDVMhHh4dsxZmZYosi/GziXxfaa4Jz+9zltM1tzQb1DU1BRW8m6U40aTNGAUOlllf/FFYBKe1SkoPcqSQpH2RDgGXUkmjhMpJLE9VznuD4MpbJoXcXInwWazcW+JFP3rJOe3x2luaY/leQsV0Dozg8OgYLqyfZHgiLlZM/qBI6gkqrMkCG0nJ1/jBzzvQMTBiHMtWN6FC4A2LZgWDdmh/WkMpomKwe+3DJ8bhMUVKcFzXeq9Z3mxwztz4iVl4Ppw5G3iA8w0jZqtxkDfHGUGl98ePecPlM/HsW4MRVUXnwAjmTp+M5rnTokiMIII2scE677rmYidPEIn0TmWtaHOYPrClZmQfwsoljeaweRWMyOw6NIrvP7k7Wn8yCvR8uqCREuWiYBoAzlfUMKUOa5/qKHvq7FmIEHViIJELAOAyfrb3ld6BFaJv5q+e2A0NREOVCEjA1y2NrI4jAT2lrBEt/c5mxLkj5ANGukw6BLxWUygoeOF9OpuUFueNQUjLcWeBMsrcLI8Cxos+Og6MGD0JtkarQFkFBSlbvtdTgEbQ6TyptpzCyoptb5hSFxGFAQA0ymiUULEXi8FLSVLydWxcZ2tTmfK6ec5UHB8rRsNB1jzVEY0YBBC6zQi5LWzTUePFZT4v2rbecqIdALy897ChiPi95SmS8LIjbqTHd/YF180muNFsAX7MC+snoTY8R89T2LizLxidWONh7S1mMyFdxw0h/QS9uBzWOV70I5qPtIIjN+yUP+bLK9eHDI+cCEc1DZ5a6xwYwcYdfUZUw7vFk+oREuWyYkmjoXi5Y/LAi/sMp4PbTelJdw4EEVfnwIg15bQpgQHWlQ7k7xMHBNQxBU1Cnn8a+o7eA4qsXDUemxFvbWowHCFPmRGxXJstu/qj8Z1aA3/Ean9nIzoAzhODkKZMbaGiK3IwYZ3a4J15s38EtYX43GUSV/qA75P2C5ghZtbilWyKm1RrGqauQ6N4puMgZnysDr9482CkOGxFYsoxyzkKUnyWh3LxM9leZF7U5Ok010Q7V46X56blhKtyZFQ23KTgJF10y9xp2LSzL7iOUFkg/M3WriE8/IdLAcQb82TKhuo8NpoPAMYAd9uLxZcc1QAAIABJREFULmtF5NXyMZdkeGShc+WSRmOMZF2YxjFrL8jc9epCubi8aW6MtMDNc4XN4aF0D2jIFK812QqsSXUU7hyQ2MZuLlsYHxvqEk6P4Xr3pBtEf2eBINPatHUfRalUrks9/HI3/ubLi8+aMQAqMAhKqQKAnQAOaK2XW77/GoC1CN7BN7TWdyqlbgTwY7bZJwCs0lo/qZT6CYDrUZ4+fpfW+vWqriJFsihTKS5PhGCdE8WAbmLtLYEXTYgjOXc5bX+0TxuvjGcJuZMeLLpOwGyK48qTXkRPKdx93SUYHSsauVe74csmckylbU1t6ZqaMHJJmyJmE56bpmu+uWUOOgZGylxMLOXFlbSkiyavUcP0bgHg+beHnLlmeV30Y6WCtBWtbcOUOqwK0wpA0CQnPVfan6wVtfcMG2Mum+dOc0ZcvLZEaRySgqfww/+/vXePs6sq87y/65wK4WJuBgIJlQQKIWqF0UkFSASbYKstiKiAIozTzWcGwfk4M/b0qC1e8maw8fWdP2bo6Ze3BW1lHBERuYgzjXINFyWRJE1IhVAhVG6VkAuhEqpJTKrqrPePvZ99nr3OWnvvU6lUKsn+fT71qapz9tln7bXXfp5nPZffU0AZ+Kg3ihx/w0VnpsgPs9ZpzabvgTSZchV60QCrxEgGnEUbbLvptA11r+f7T7/Gjrf+yPwCz55PiTdLXtfoomNEqpM1mtkhfBlYA4x33zDGnE3Ud/lCa22vMWYKgLX2KeD98THvBNYBj6qPftVa+8shjr0wimppF8EHQaSFtcw6bRyzThuXyjjShHV5OxF5/e4b5nHH06/xxCsR+6UbNM1SKKHrdAtw0jsRyw+eW88vbprvdQFo36n45MUCb6kaLpk1hd69B1ixaTfWWlo8bqAsNAatRRDbVPBY4CoQiQ2413zp7Kn07j2Q8m1rJefWnMyeNiHZxrtuLeFBgkZrN+SCTCmVmuUzqsXlAyt60l2yHII+fa0+F6Zuc+m61Nwdl6y1259alwqKurEXH5olgXOPz3NFufdM7sFspeQgojeBcIDV3ZlpZWCAf9E6gSnjj2eKp+2lnk9xVck5H1jRw8//sCn5/pU9e5LOiFlC/TMd9dR1KJ7eLRAXnY71CA/aqFIIxphW4OPArcBfeQ75AnC7tbYXwFq7w3PM1cAj1tq9QxzrkFFEmEKxAhv9wEtDC7f/bFEuexfPvLozpQzcoGne2PKuc17b5KRbF/H4H1jRw62fbtyW+ny+976wicGYgWLBrCmReyoe76JPtDdFvKV3WtVq5AIRV4HbOcqNESSxgeU9XN3R6iULlLnX+dtyPm096x3TLZ+cnZo/YbKtxQovK/feVaRJHr9SOL7NltsTI0RolhVM1Z/zpTeKa0S3zcxCVvzMl8Xli/9k5c27a0vfM0mq2NG3n6fX7mxg2w0RKl41pzUV7K9WDPPbJie7FZft9o2+/ZG7V6259rjS292pAzy8civ3ffED3mfPN5Y8r0ToeZ512jj+9D2nRlQ6NZtKvBgJFN0h3AZ8DRgXeP8cAGPM74AqsMha+xvnmM8B/8157VZjzELgCeDr1tr9BcfTNPL8jm62Rl7Wgbvb0BbZUAtVZCHWarYhHbNoJkbWdXbMnMSH3j0lVfyW5RHS57r9qXXULIki1LTGUrWZBe8DED/AFSIFozN83CC+GyOQeMA9SzclFdNFUohd+mm9Y1r4q07uvWl+IszE9eJmpLi5967ADLHZLjjnFFoqpOpE3JiLXgcDNcs3H1rFU11RY55mKoz1Oa36KQLfGvfVEbQoyoxmd+Ch52X11j1Jxk1LpR6Pk2uUa3c/t7NvPxVTT54Q15XsGCQWodluK6a+ntw17cJagpa6b+6zvBK+jDp3vRjgw+89lZsCHfoOFXIVgjHmcmCHtXa5MWZBxnnOBhYArcCzxpjZ1trd8TmmAucCv1WfuRnYBhwH3An8NXCL5/tvBG4EmDFj+KlffX7HvDhDVlVznnsqJNyz0tN8Cw6a35ICfPHis3g6bhpe1GL0XZeP1CsEn7tHBJ48jCePG8vYMeEgvsQIzm2dwJptfUlGjXYj5KUQu0VRCy9vT2oowL89Fws4K/feZ7G6CurAQI3HXt5OtWo4feJYXt/zR4CGcW7ZvS9pugSRIHrs5e083bWDe26cn+pD7HPxudeu3VT9g7ZQI3ffTlMEcLJGbVoh+2i3s3a0WkHrcYuil/iCjsf54m7i3pNMr1CvDQvs7NufctnVbBSol9Rbvaar1QpnnXwSr2zvAxslZ4TWeHq3m09w564L2YW6hIVPvrKDmzI69B0KFNkhXAhcYYy5DDgeGG+M+am19vPqmB5gibW2H1hvjOkiUhAvxO9/Fngwfh8Aa630ldtvjPkx8BXfl1tr7yRSGMydO7eJEGc+3GpYyA6MFqlq7trWx6xTxzFl/PHe/rshSy4rPc33sA8lUA7pRuu6kEbGFirq8i3wohwrqQcgdveMaamkGte7Pv+QoFv4iXaAhkIk30MI0c5G0ku1MBPXxg0XnZn0rghtz90x+Mbq2xm6yQKWqFJ2y+5IGVTjGgG9E5LU41TqMPVMGfAbAq4bRo51H5idfcU24e5OM4kpxXUEeqfmuonyYhAhC1ncdJpNNCtBQa5XB6NltyquMh1TOHnc2FT3xONUOnFoTRfm/UqMinpWVGi3rteTTnKQeh63j8qo2iFYa28msuaJdwhfcZQBwEPAtcBdxpiTiVxI3er9a+UcAmPMVGvt68YYA3wK6GSEIDd5y+59DZ3OrjlvejAwmieEdTNt2MMls6Y0LKqQJZe1swhZG0MJlMv5gIYsHwmiFmW0LJJ9oq9NC8bBwRqfUwFXnxWYde0dMycF8+Flzq/9wZIkRtFSrST1F7oo6u+efBUgYa/0XU/WGNxrdH38uorWpe6o2bri12vLWnj3aeN4ZXtfIhwkUyalXPtr3PLr1bSfPiG1Zl1h21I1Sd3I4q56tlQz8MWUfNlp7rX4npNQzEGy4DSbaJH1Jy5fd+61speanqvijoUhtlt3TRdZ4zquOBC3Qb0/o9hN5lJiGbqO5fr59cZIlcrBd8trFkOuQzDG3AIss9Y+TOQK+qgx5mVgkCh7aFd83BnAdOBp5xR3G2NOIZI/LwJfHOpYmoEWFC1Vw5j4YakU6EiU5w4KNYV3raUsNlTXwgvFBvKOz4Ob5aNFlZvNIfMWsuryXAK9ew+w8PL2VCqophfIQ+ihzHpYpVoYIqv8XaecyAVtk5MgrytcpViwGbjWY+i+ivK6XykGSKdDikV7IFYaa2IKkqqBP31P2pesLfWVPXtY2bMnSWH1CdvPzp3OPdLHu0mrU6poRYDqYLFPIcu1ZD0nofe1e1DHpfKsdN/cL9/Yy13Pb4jm0FEuw2Fx+4w8bfAU2bXLGm2pVrjm/OlJtpXEP7IK4Q4VmlII1trFwOL474XqdUuUfdSQgWSt3QCc7nn9Q02NdJigBUX/oOWj7z2V902fmL8lxM9fpOFrpu2zlr50ybuCCxuKxwbkdVFwY1oquS0hfb5bUzHpymPStAMQ9nvmuQTcHUeI+ni44d6fdTvfpmf3vgYlVGSXlYqBOL0HfK4bH+S9q+a0egvTOmZO4jNzp/OzpZsaxv6+6RNTxy28vJ07n3mNDbvqCXviUnIbuKRiCU3uJpdv7E3xALktR+W363ac1zY5MwAeEuArN++OGv1AZmymiHFwsMo+b158Rp7b6yFrnvX4BgajILLOeAvxTB1qHBOVyhruw3byuLFNU8smFNbLNif58OJ/lCySagU6t+5JMjAOxP7CvC1gXsqf+4BpBXdgoJYZNAxZ+cJYqjE4aBOivAYF4lB2ZLkEIG0xhZThcOOqOa2p7mjQ+HCFBJMvA0wrw3uWbkpVqGc9tL4Cr9D1yy5CvsvXH1jcKpqvCKLdhmSp+FwuzbK/ynWHWo66mXna7ehL+XWh58FVPHrsRbvPucjbpRwMsoy8ogbPpBOPq9caWNjRt7+BASGLZ+pQ4ZhTCCIoms2yEbgB0rvjfHipLJWbPBj7Eo+rGv7NhcW3gG72RB69tKvgsqLuWb7bX7ywKZUOaYFv/6qTqiF54BfMmsLJ48YyfmwL338mChHVLA1KLglAZgi2Q42OmVHw3Bd8do8LucVcqmLtEqjFPl6TEfwski4M6XRK2YH6yA3lWDfrSmIIPpeLCG5LRDW+pHsXXdv6ClfO6iBsKAUV0sWFRVlMdSwvlf2j0q51MLuIQSUIufCKIM9FlRfvK/JdmoSyQtS8J5U8UbC6ebhxzCkEERRDdVu4wgHqlvmVc1ob3xu0PN+9K5cLRePKOa1JsCmPXrqoglu+MWp0rzN79EKuVCpQS1udtZqlRp0o7rGXtzN2TOT715XLEisJxUOGe2EXoYHW1njI1+0itDtzg8ODtaht5cXnnMKUcWODsZC8dGGfVQ2k+gm499OXdeVzgZmK4cEVPfy3x9amacSp3zdfuqiGPCtuENZNQRVmWc2Im9XGVO6VdsNVVX2GDqaKi0zI5Rb+Kso9KVIEWVQ4a3zvH9ekAtE+F1We6zgL2mWr06xDpIEjjWNOIcDQFor+7N03zGtovGEz3jt1/PF0be9rylddcdLRQtZoSMH5OPJ1AEsEjaRl6vaBQu3SEleISnaMKDQRJhLY/N26N3hhw5verlzDjSyrO69DWB6KWH5XxjGA+5Zt5ok12xMG0KLnc4P5rlW9Zfe+zEbvIctXF8Ut7trBoy9vZ93OtxvGZNXv/f01vv2rTmwgFqSvW87vXpfmUYL6jkBfp27EJOfRTLOScbajb39Uoet0jtPkcgOxUmimVWVRIfuzpZuSnS/x/ISMt65tfdz7wuak2r8IvYfL4upTxodLEQiOSYVwsOiYWW+84VrmyXsq0HvTxWdx08VnpTKCfNv2tO897UfMs+RC1pdYmdpSfaNvf2pxuqyf+rsAb2P0K+e0ctvja/ndujea9u/64Htw8/z5+jvzUh2LoIibQb6rKHVyVrqw22di0onH8bdPvJoI7WpsKft4sTR045uxYyrMOjVEKJBmg5WsI/CnsGpr1nVZhpSSwM2a+uXynlQg3l1zYh1Lx8AD/TVue3wtf/nhc5jXNjm3gNCHZjmZ3CxBY0iegSzepAMZikOPw6Uql34bowmlQiiAEEVEyPXUMbORrVKQ1UawwR0wRD+iKxwtpLqBPfry9pRgcFk/XT/3dz99rjdYFmoj2ux8+h5cmauQPz+rjkPHXpqdv9BuokgtSZHzuUoC0la13qkJX5RvXoTryKVoONBfY8r446mTCMP5Z0ziXaeOS9IaX9y8m8djmhCBm8K66IrZqawX12XpJgf47qHOmhocrCXUEFKFrNccwIubd2OMwVjbsPNM+jI0we/TrJHgZgne+MG21PqUOf/Qu6ekeJMqlWz6bB3bgbiRVEz1PdItMvNQKoQcDNUV4XvPXRghX/VQ/YghOgDZvdyjUhpT/t/4mLzgasiX2sx48ygetK89y5/vU9Dazy/XGurd2+y8umNeeHk7976wiVPHH+89Pm+XEbKq9X07edxY77xoriPxdwsqFcMXLz6LS2ZN4ZHO17l09tQGf/vyjb083bUjlYGlcWAw6hGd7FYzXJYC3axHxipZUyG6E70T0VlGwkotOwVRQL7K+Ky5DqXghnDdBTPYtOttfrN6Gx9rP42vX/YeoJFf6olXdiRV0EKKmLW20oHxqK7EQqKURzq1NAulQsiBK6weWNFz0AFpt42gz1ft46vJQpHCsQdW9KS+W9NI6+9IZVKprXtIuMln9P8h3O8RHCGLeyiEaeLOCSndocAd8/0relRK6x4Wr92Z5OeL+yZUo6ERymfXuwdf/UC6CQ1JCqwWTh0zJ2Wy5YZqHgQvv/5WJPTios0bMrp3Ld+YbkRfqRi27N4HNKa7+oS6m96qFZzm9cpzjbptbpupepbP3PX8Bg4M1Ljr+Q18pP20ZFfqNv75zHmNVfYhdMysB8Zr1rK4a0eKbDCvQc9IolQIOXBdEUPd5vmYMLWfXgv/Zn2fEE4pFWjLOi/zRywr69m6+4RB0bEu39jLL5f3NDwIIavfx0vUt68/SeH1ZYEUUbrNwDdmoeoWaOs95VsOKCJfYDXkinHn4P4VPZw95R28sq0PC0l7z86te3ijbz+dW/c0UFP47lFS89CfbqcqqNVsQsNcs5a7nt8QvLeihCHacdZqNtWr2F2H7jnc9FYNg7/tpHxvyCXkS8HNQ1aWma+VaDMGhgTG68wA9ev7zNzpo2J3AKVCyIVo90c6X+eEMVUej4NeeVZnKMvHtd7d9/JonEMo4tfOcnHpcUdtOFVeuGccIaF2f8YOyvWR6wch5JJyfbhaZPiCec0oviJwx/yeqeNpnzYhJcAkTXJJ965UimfFNFp+LvOrDqxm3TPXrSKoWcumXW8nGS/Q2InNt57cHh6Lu3bQ/cbbrH/jn6nVIh/3KePGNsQO5Hx6TvXa87UNLWJB33PjfO54+jVe6tnN9rf2FwqsZ635ZuI8RT4TaiUKxTKZUnOkmAEs0D5tQu7YRgqlQsjA8o29URezWAmMqWY/wKGsDJ3l49I++Lj1JUMjq9+rL+d+KP78rCweDT2OkFCrVky0g4ob04sbJRTbCKVrunBjLwJjYGvctMZVClnX7+uNkPcgy5ys7NnD6q2d3HDRmXS/8Xa0NuKCw4WXtyfpuMKNFYoj+QKrWWNe0r3La0EPDFrueKY7NTduD+GQoNPzdN0FMxKlU8OCtak2naHaAhmbGDmyg6vZdDOb0LqU9/r29fPkKzsSZWIgCZrrzCTt4sxa86H38oT3n5x9Ctvf+iPXnDcjaKC44w/NiS/GtaQ7Si1OYlyEd0CHA6VCCMBnkQ0MWj53wfSGRu76+JQvlEb+fNeC0u/ph9WqH9/YQkFf3yIPpXQWqcqFRms+JNRWbt7No3HzHSnWg/DuSJSFj33Sl9WjqZcrJrLM7/lDxCzp7rqyBFCIZykkkK+c08rqLXuS2pKBmuWHz63ns+dNb6CAbrbNadGWo/PaJlM19baSGu5Lbg/hPGNB7/Z0jwr3enzxNN3MZsGsKTzZtYOBmk2ovSHMzeXeC4EBLor7gafiWTF1umYSzVL+vpiDTgfX1NcQcYIJDcya1/21Du6zpMe3v7/G959+jWdf3ZlbnT4UbqmRQKkQAnADXRAJIJ1LrQtS7l/R02DBif9a8+e7PX193PrffHBVZlMTnfVQNAfaXaBZ/lIfUZe25pPsDWtTQq1O/R3Bkh3bCJGnQaMQcd1AW3fv454/bGrYdfmqf0MBcxljyNWllXy1alICuWYthkZlnrc7KbKTC6U5f+dT56b67UK6rsAAH/F02dI7oiXd6RoYPdc+F5Z7Pfp6LaSE9aOqE9+gjbi8evceyPXz+3ajOolhKEyiPri8X998cBUmzkK7ck4r/QP1aMqBwcZaB9+z5NZbiDcB0m62IozHowGlQgjADXTpTky+BW4857jwXSenFrb81j19ZVHowJtr8bn/+7qrhbbCIcGf5S8VIeCrPdBFUJK9AfCNB1fxRt9+WioRj5Mu1nPrA8TF4ypdsTo3vbk3N9j6s6WbkpRCXdV9YLC+r9IVsvq+agEjrgldQepT8gODlvPPmMSKTbtTgcUQ3UCWayLkevC5G7VCu+6CGXRu3ZNyN1ScDCNfiqnPCpd0XO2yzHNhucoM0ummLgzp1E/3/rvuOO0m0llrzTKJ+rB8Yy+dW/akXosCzpFyiGoD0qy/Lm9SKPtOZ2zpOZbuab5ncDTtCjRKhRCABLpcd8byjb1eQXrlnFZ+rgJ7EBW6+LS/vBbaSufxE7nd1VZv3RMUIvqh0+RgRSxV35ZbZ9HUapbVW/ew6OHOOgFa1XDtBdMTIrV5bZNT9QE6+8RVui1Vk2okUzGRgHDjBLrtaUssQH70u/UNOzRLYxP7jpn1FMCBWKktmDXFmyzgKvmzTx3HX1/6ntSc6ToCPU/NZImFKEt8lrDQZ8v6u37+Gax+/S1vvQGErXBdtOirWQmlEbtrQgtraUAktS3t0yaw6Ner43z9aFel778vASDVh1rFDELFkUWg59eHijHJDvjumPXXl93kui4l++76+WdQraTbXxrg6o76usuLxYyWXUKpEDLgs+RCgrRj5iSuOW96QiOdFyzKyiQSZaR3EfI6NPqh9dbddy5NDqY5Yrq29SXfUWRBLune1VChaUmnYA4M2hS3uyz4aRNPaKB7+NIl70opXQtJK0SAtpNPYlPvviROIAE7VymNO2FMykpLVWEPNgpVSQGEKCXxZIdpUiv5XyjF3D5tQjJWaN4dV2Qt5FEf6/WnA7hL179J59Y9DTEJnxUuynZMS4XZ0yYkr4ky0Mopq2mU7Gpcl6i4o7SLpmahJm5Qx0WpXXS3Pb627g4NxAyahZ7fioFzT5/AmtffSjXGkudBYGncIcjca8qWAwO1eoczE/URsTWbUq6uzBhKFuFIobBCMMZUgWXAFmvt5Z73PwssIprLldba6+LXBwFxLm+y1l4Rv34m8HPgncAK4F9ba0dPuD0DoYXpWm+hbWEe86h8B/ib34S27qHv1eRgsgC7tvUlPn8p189jkNTCRYTFrNPGpfoOhBRUkUyX5Rt7U+dav2tvVAsRB+weWNHDtIknpJSS5puRqthqTMoXmltfDCRkfZr4x1rLwoc7E5fCfct7uLqjMUMszx2XNa9FKUvktWvueD5V83DP0k0NRGshK9y1xsV9pIVVzYaJ5HzK0O1/cH+cVOBCXCmh82mlfjAxg1B2m/Tldu93Vl9zQcfMSSnKFmPSRYLXzPW73LJiMaPJfdTMDuHLwBpgvPuGMeZsop7JF1pre40xU9Tb+6y17/ec7/8B/ru19ufGmO8D/xb4+ybGM+rgPny+bbdL+yvMo77FntX8xrd1D22nfQLqtsfXpo659wU/74+bHSHU19KKEmhwrUHkqhHhnFV8llVBa2sWU4kEtwXuizl2hJcJIovMnfusTCNdwWoMXD//jIYYj2BJ9y6VdSO8pBGE9TWk5Hz0yFkEfs3yVi3pTtc8QFh4hgwYX/OZeW2NRHK+gHsRK/eqOa3c+4dNDZlR2pWir0fvYs465aTEIMhK8c4KzOdV7hd5Tnxwd2lJbxCinhN5hlURd+3hQiGFYIxpBT4O3IqnTSbwBeB2a20vgLV2R875DPAh4Lr4pf9JtLs44hSCrx4AwvGBVMrmYI3TJ54QXBB5weXQGFz4FqBL5NW5dQ8v9exJpeO5/lzJ3mmpGO4z6SDsdz99bnKuny3dxGBczKV5CHwxiVAFrTyU554+gRc2RH56SYW8uqM1CazaWj0bxD2/by5k/iWg+IPn1vPW/gGvUtYColox1CDZIbgc9toAAFJssg+s6Emap2tKC8hOycxLX5WaBxPzDFnbXFW2KwDlGm646Ex+8Gw3gxYqFbwBd3duQvUg3/nUuXw7dlcCHBfo2aE7iFlg/a63owI5D+1EkRhNVnZbCM0Iallrtz+1LndXkfX50YaiO4TbgK8BIV7dcwCMMb8DqsAia+1v4veON8YsAwaA71lrHwImA7uttQPxMT14+i6PdrjBQPG3ZllPzbgTZk+bkKQ6hh4kXcVrTMTQKKRcGu4CFCvmkc7X2d8/yB9ioXtgoMY3HlyVZHwkAc5Bm1jIPh5/LcgW/qozsQoHamGqYt88uRW0i369OjleF8dJ0L1ZHhjXAh6sWa+rRebMRx3h1kxAut+E7iEBUUbKnc92JwJPKC3kb3edFBF4RXdEWXB3tInyr1YiJTNosZgk+JyVnqzjPO46m3XaOG8faQ3dQSy6L9FvH+2Eb92415/3nIUUrlxbUS6xeW2TaalG39PicYUdachVCMaYy4Ed1trlxpgFGec5G1gAtALPGmNmW2t3AzOstVuNMW3Ak8aYVcBbnnN4DWBjzI3AjQAzZuR3SRpJhPyteSmd7oP8zQdXNTws4tqwRCR0i67wMyrqmgRr4fvPdDNj8kmFOkpdd8EMrrtgBt94cFWiEASWeutMa21Duf2YqqHm+OglKDigXBk++gZBVlwBiM7lobpYvrG3Tokp3XwKomNmnZcm8f3SSOKnBYbLxePCFVByThl3JVasek4mnXgcnVv3eCvfiwYdi+yIisyHWLq+a7A2HOSWAPrAYL0KPzSHt3763OR/31jntaUJ5GTufMI8tTupVnhx827+9olXk92KGGYh922ewm0mS6xrWx8DscKs1fxZTEWoLUYLiuwQLgSuMMZcBhwPjDfG/NRa+3l1TA+wxFrbD6w3xnQRKYgXrLVbAay13caYxcC/BO4HJhpjWuJdQiuw1ffl1to7gTsB5s6dG/KaHBa41qZYw9rKDfnLpZexro7UHDRpn2qYnGte22Ti+GiCRzpfL6QQBL6G9BBtgaX2Qpfbi3B2+yfo1D4RhJe8e0rD9wmy4gq6YK1ion4OVs3hwGAt4dlvNuCorVah2tBphAsvbw+m8frgCigJaBsTxVrmt02OWDRjSguXjuFz589I7TiaDUoXRZZgCl2Dm9oa+pys1edebZxDqWJeHFcxh3z6H3p3Pf23pQLXnDfDSyKnYzT3Lduc6u3g66jWLFdYUYW8fGMv335oVUIOOFCjoe5lKESVhxO5CsFaezNRwJh4h/AVRxkAPARcC9xljDmZyIXUbYyZBOy11u6PX78Q+K/WWmuMeQq4mijT6C+AXw3TNR1yaLqF2dPG86Jql/nq9r7gZ0ILU6A5aHxCwQ3wyt83frAt1frv0tlTm7qejpl1cjFdbTrr1HGs3rqHK+e08tjqbaleCuPHtqQsZ19QcNObe3lizXaefXVn8EHw+VLdqu+2U97Bpl1vJznsCy9vzxWYRXsS+Dq/6UYuRTJcOmbWCRAvnT2VWaeNS1wpq7bsoWt7X0oApuJINcs0J4403EFHWa/GhzMDAAAgAElEQVS+WICeJ58LSruSXtjwZkPGkYz1tsfX8tyrbySuJT2HbhXzgX5VWR73pn567c7Us1CpZDOKitGkXXOCmq0/R3quxVWXp3CLKGTZDbsBc3e/OppTTH0Ych2CMeYWYJm19mHgt8BHjTEvA4PAV621u4wxHwDuMMbUiFLzv2etfTk+xV8DPzfG/A3wT8A/HMyFjBRCrJOCFzfvzhT+emG4ec7VSj2F0udacv3UmoZ7xuSTgg1RiqBj5iTu/PO5/GzpJu59YROdW/awZlsfa7b18Ytlm6k5GS0/fG59whcPUVBQHoYoKLiXWkaBVRbch+qf/9ifqmHI4wxqxirrmBmlES5d/2ZiHbuNXPIsdHHvidC8+4Z5nD7xhMSV4gtqDgczrTsG33zoynK5g1mUCq57zHUl+fqByBzqOZM5dKuYZecoLKoHBmo8ppSFYKDAzk9X7Mu5hYpC5lQHq2uWpOYma/3oHYjvKQ8Vukmtio49HKrd3qFCUwrBWrsYWBz/vVC9bomyj/7KOf73wLl4YK3tBs5varSjAGKVhPCx9tO8wt+3MJZ070oyFAw0MCxqoRDy8cpDOm3iCcEmNs3gugtm0Lv3AC+pXY/venVf2ySVUx2W5XvOw5VzWrkvTlu1wLa39gPp3gZZArMZq2z5xl4eWNFT9/9ay6zTxjVloRe934JDsQPwKUC3shzSfvki8+S6krL6gUhKslj2eqc0OBi5y947dXziQgtRXkBj7Mmn8NxAdKViuOa86Unyxe1PrWPL7n3JMYbInSq7nLx5l8ZIv3hhc6pAT8+bFLrNPn0C7YrnTM/PaE0x9aGsVG4S89rSdAvHtVSYd+Y7eWnLHhaccwpfv+w9XnqL0MJw6aBDlp721RpjorhBLeKHuXfZZgYV5TQ0n3GSdY1jqvWmMPJg6b62rutL8vOH2hO6Y2bUk1q7ISo0ckNljb+IVebrszCo4kBFx+z7viJW6HAJh5BglzoKDROzkPrWX2inommbfx4TCmZlRV2pKnQ7Zk5KCjbvW7Y55UJbvXUP9y3bnMRbLFGiggSGdUwpxMzrdjI7PW5ar3fTY2KOorxmT+6c6raZEpcA2LJ7XyoZQArdbnt8bXIfNI/WcN7rQ41SITQJ8bdLDEFbBb9ZvS3JxQ5lOGT5iiGcly5+6oSDJ+7NCqQop+94+jWe8dDvHsw1ygMu1+MKeTcn3deWU6No1sWMd57ImGq9mrvoDqioVaZrEsCf1fKzpZtyXXG+7yt6jc30ZgghpABdlyREu1Hpqraku7EgLsS0Ku/5KuJ9fnp3nYty0i60K+e0JgaGXmPuHIQUXsfMxk5mDTGawRqfO38Gm97cm4oT5bmj5rVNTmWH1axtoPuWZACgwbCwNPJoHQkoFcIQkOXK0YsVwgK+6LmgLogf6Xw9sYYGLTz5yo6GLJ7tb/0x9XD60gCLCB+fVaMttvtX9PDAip5E8BfdFhepIG2mmjuEIlaZ6w65uqM19T0/W7qpML2H/r6sawS8BoAY8i2VbP6g0HfroLZ2qYhLUuONvv2p+b26o9U77ry6DB0/cv30WXPtEry1xNXmV85p9RaPZe34Qp3M3J030FRcCKLsPoFkufmSAW5/ap3X/TWUDLjDjVIhHCSyFmuzGQZZD43EGTSEk19jfttkurb3edMANSspRA+N8CMVRaiHgSsQQ8rBtSZ9DeldCy+rmvtgkKfIHul8veF/H720+/nUNToZNToZQFNPC3xpk77vc3ty+zKB3JRQiO75KePGpu6BFOb5uvcVcXVpX36oB7I716k58pDYFblPbhMlvRP3Hd9sXEjmTJhLQ1xlbnBbPnMkBJFdlArhIJElVJrNMMh6aKD+wEktljzcunR+3AljGtIADwzU+B9PrG3IjHL5kYrADar7KpWzrEw9J273OOHMccnI3JTb4VQOWTsJl97DTefN6zon16j7EsvMHeivU0+7SkGnTfq+T6xR6WlwZUCQu65LrUAkYGqJ1tX+/ho7+/YH12vW7rJvX3/KVeLbIfjmWvel0Nlo4G9BqT8r2VNZTZEOJm7jPrtXZeyE3eB21dBQW3KkoFQIw4DQQivqSgnxIUFaYFSIgrm6UEgyOdyApqQBymclU8eFDYwhhIaAc5O7IldISfc4t0mN62Y5HMU9mt7DF0PwXau8Lveofep47np+Q6QcVLW3EKEJw6hQWUc0yiYlVOXebNm9LxXzkO8NkexBeG0uvLw9Si/euicm7oPFXTtYdMXsTBee7FSl7SjAD57tTp27CJ+PrAO38U2RXgFu9lQWlcpQEXp2ffM5ry3mlRrIpwwf7SgVwjBBBx9dn2aWKyXlL89oIu6jLJZsidDCvfuGedzy69VJL2ANQ72rmW4VOaZqkmppH3wB52Z2Re4cyFzplphu3r6OrWTRIxRVuL771j51PG/tH2i4JqH38KGIi0/cdW7bTyFC0+tjxuSTGvpWQDpjpqVSz5iRNFxf57Ysi17XTST03tTJA10/vm+nKoJ3y+59DcVZoR2CC7l2TT2eF6CW8aSpQCJK7aF2U8vC1t37EhrvrGfiSEotzUKpEIYBbvBRMmPcnPC8ZioHBi13L93Evcs2c42jGAQhyuIQ1qimH0IHINTVsni/8eCqxOI/MGgbyu8FWrBqdlP9Xt++fmadOo4p44/ni57evr45kPlxdzqCeW1R39r+QRukRwhZk9/7xzXc+Ww31tat2qygMaTjIlko4uI7MBBV7f7lh88BSF3jpBOPSxUx6b4VkrZ4+sQTUvf7I+89lfdNn0jfvv4GOgnXyHBdS741V4FUvYg7Jpl/d6cq9+gBT8+DrB1CVhYTRNxARQLUwvQqDW58geWDQRatjO86mnFHjWaUCmEY4AYfxcfu5oT73AuS06z9ywOxYvAF2bKsUh/VtiaHu+a8Gdz66cY6QTcwbagXbFkaO2lp+mbt+klnWuzhkllTUmPPciflWlnGJGyrYqGm6BEUOZ18l+aqB/hjLGTl/UdXb2uYC/e++VDUxWcgpcD0bs7XO1kUnzRs/+XyHhZ9oj3p/yBunQWzpvB3T74apJNw02nd+XbXkOxgQv2cQ7GIjplRlzGdyVQ14R2CVlTVQDaV9seHug5muXOGC0u6dyUFoJCmldHV366RcaSjVAjDADf46GMC1VZutWIa0u7OOuUk1u18O3XerMYjIqizhKz74Ev6HaSFmq4Mll642jr6xQub+Ox5M+qtDWPBKg18dB9gDTcrJ8+dFLKytGKDegaH0CNIj9vnXn2D5197g0qlkpDfubhv2WZ+GVfbVgJEqdVqmKE1K2juCs5HOl9P8fss6a4XvPl2el+65F2pBkGDg7WG/g+DNZvLtZRY9PG86Apvd5xaoIZSqEP3RlxPxN+BjQS5btPq3kdfsZc79jHV6DlpybgP2ujR/w8X5rVNZkxLvRHTmHgsbvziQP+Rl1qahVIhDAPc4KNsX6UorWtbH51b99QFlDF0bt2Tymk+v20ym97cm2L49GV66P4Ls6dNyBSyoQffJ9Tu+UKj60MwUIN12/vqFidRLnvdPWIbmowDTD4p7X7I2wVkVWnrLmmVuNpWctAlTmLjsRKgIQZSFN4G+Mh7T+WP/YNMPuk4fv3S60lQNwTXx63jGTLn8ves08YFc99D981tECSv63RHlyeob19/w9xpxbRar70YoeBoM1lxei4SHquMlNV5benK4lA2VRFq84NlEdUElSFG1Xu+MK+hj8PtT63D7St+pKWWZqFUCMMEX/BR+3Hdxu9uZkioUXnI5SL9F+69aX7TFAm+XYWmauja1pcaL8D+gVoqvdVtTC9uh1e39/Hwyq3ULDz04tYG/3VoF5BneWsrGaJqW1E2s0+fkAqc+4qx5PUWh9pZ4hy3P7WOmKk9s6DI9afreIavRWPo3sh7LoFax0x/kZl7nk273ub7z3QnPTB++FzUgMeXoSVuIF8TIJzvbiY4qufCmKhrW60GxsmS0ufXlcUtnk5rshuUHVLoPuRls2Uhq5bGHW9IaeqMoqNldwClQjhkcP248lvcHb7MEMje+s5ra+x36wrzIkiazNNIPLd8Yy+LHu5MCakxVcM1582ga/vqXAXmwpctklfM5XvAdVGQL0VVK6eLzzkl4civmIgD6dLZUxvy8LX9mWUd+6zvVJ1Hv7/ADvJz34UKQYQ1+IvMXBfJ6tfTPaZkQ+cW+4VqFEJoJjjaMXMS188/I+oGV4tcPMZYatYG3Uayq3M7rek4RpFdSrO7GY28WhpBKAB+tGQU+VAqhEMEnx/XTSuFYr5PvTATC6tmUwRzRaGbzPv61S7p3tVQwLZg1pQgRQDQQNOgC44gnS1SpJgrFF8Qi7pzyx5WbdmTSlHVLi+AZ1/dmZzLR90h8Q8duC/qXoM0z5JbfFbUWg0lGvgEuJuefNYp70idq6USN0kydZdMqEZhuIr8lm/sjftER/9rd1zWPEiShe60ppVYEVLEgxHMebU0Wf0j5LuPNkUgKBXCIUJWZkYz8Amke2+aP+QHWu9cfP1q57VNTvo4C6aMG5tcU5b7SVIsxeWhmUrle0I7gaIPuK6wzaLD9pHN6fiLT4AXca/pYLrwLM2OCQ6btVZDStD3mpueLOnEBrjpT9r4SPtpPLCih3tf2JScv1pt3InC8BX5NdQDVEy0dmr5lOf62t2KdV8thA9DFcwdM8O1ND4G3OEqdjsSUCqEQ4iiCzariMgnQIsInDwa7SxL/AsfbOOOOHdf0xn7kOVT9wVU84LgWfOllZmPDjsrHfQBpUiIe0QbG9GHFxVcY1oqGBTBWcyzlLV7ykJICfpek3H4SNTGnTAmsbpd/h13J5qVSdTszsGtB7jhojO9xX15164r1ofaAW8o0C1gBa6r90jlJBoqCisEY0wVWAZssdZe7nn/s8Aioh3jSmvtdcaY9wN/D4wn6qR2q7X23vj4u4CLAYkGXm+tfXHol3JkwqUFAGipGj47N2L41AKpWq2wcvNu/jZuZO+z8KR+INTIJM8SX76xl7ue3wA2cnEt+kR7oQdb+9T399dYvXVPZgW1a70Xedhd4ewqA70D0Dnuyzf2ct+yzfVGKtV69GBwsEbXtr7CrgmgIQvoYISVTwmGXhOqh/uWbU584GNUaqZkY+k4jzu2kEIeStaOT6hLjUH7tAmFPi/HZCnU4e5LnHW+1PNWgMr9aEMzO4QvA2uIhHsKxpizifouX2it7TXGCCfzXuDPrbWvGmOmAcuNMb+11u6O3/+qtfaXBzH+Ix56ByAYiNkfJdAovvP7lm3mMdVQPEQsl7fdzbLE81xKPnTMjLiTJFhnifL9Q3TG+vubbXcZUma+DCwJai7prjeKMUD71PG8FKepDloa8uHzqlCL9rBoFkX7QF81p5U7nn6N7W/9saHLHpISaS1d2/oKd/DKCupnjUvGpCmg8xhbfSiyJoeSUZR3Pjfh4WgPGuehkEIwxrQCHwduxWmTGeMLwO3W2l4Aa+2O+PdaOcBau9UYswM4BdjtOccxiSxXgHYRbXpzb6qhuG8rOxzb3YPJ3tB9l6Wys2tbX2aDmbyHPU84a3ebLwPLZxXPb5ucSlMdrKXbgR5sD4us8YeQxWnlgzRB6tq+ukHx+QrY9vdHzZPeN30i89om86VL3sXyjb1J6m7ovmtW0SyFV7jGoMl5kXMPZ2A8r3/D0Rw0zkPRHcJtwNeAcYH3zwEwxvwOqAKLrLW/0QcYY84HjgNeUy/faoxZCDwBfN1a66fkPIoRcgWIMNcVzeI7r1aj3rRiHWqhmNXwpeh4fHnwedD+a4j8hku7d/FMXMEdajDj5rK7LJ+hXsEPrOhhZ99+FnftSFxjN1x0ZpT14mRguVbfku5dabriuHJcevA2Y41mCasQFYS+Pj0mHTTO6g8QUqJJOnHMS3Tp7KksXf9msnYefXk7j6/ZnmTyuGPzufJSVblxemyorsLXvcyHIko3VGg3HLuyIvQYxypyFYIx5nJgh7V2uTFmQcZ5zgYWAK3As8aY2eIaMsZMBf4X8BfWWikjvRnYRqQk7gT+GrjF8/03AjcCzJhxZFLK5kEsktnTJiQcKZIS2rv3QJ1ULG7oveb1t+LetBFtgH6wh9rHWKCZMH08OXKMfjhF+GkLEdKEcVBvcO5a/NIaVLN8isXrS8vUtBqC/oEa404YE8zAcq0+TVd8w0VnJtcsjKJFMmXkvFpYdW3rS93DUDqqG/O44aIzg/0B3Pmf15amQRElJOnExsD188/gugtm8FTXDh6LW6xCndfIpb8QIj2tDG6LY1UCY0wwNgXh7mUuiuwKfam+QLLjTGhUhuBCkmD4UHbBRzuK7BAuBK4wxlwGHA+MN8b81Fr7eXVMD7DEWtsPrDfGdBEpiBeMMeOB/wN8y1q7RD5grRVGuP3GmB8DX/F9ubX2TiKFwdy5cz31p0cPNNul+O9dC7T99AmpHHz3wQ6l7LkWV2jL3czDWqkYrI3GKhb6nc92e6uEIfLd+2i29XVnkbCJJd3vKAPtGiuy3fftGHTm0OfOn8E0TwZK1vl8FrUoBeMpAHRjHj94bj3f+eRsOrfuSfUHCAorh94hHfuBHz63no+0n5akDLtzJfQXbqFfSzUq7Ht67c4UMWK1YvjQu6fw+JrtTXdUc5HnlnR9/LqXsbu2QoyoWTjW4wRZyFUI1tqbiax54h3CVxxlAPAQcC1wlzHmZCIXUrcx5jjgQeAn1tr79AeMMVOtta8bYwzwKaDzYC/mSIfvQfFtl328NiFKZfD3+A25MtwsCx+1QCLIVLHCgdhC/5tPnZtyG+hmPp1b93hptkMCIvTgatIxnZHVrGtMH++SAA5FSERus3Re/i2fbGw4A41tFwdrlsVdO3jf9Iks+kT2Lk+KujS9w7w2fxW7Ji6sVKKmPNecNyNlzes+DQcGaqkdhVR6C4vsM6rgL8sl5O4g8zLNNFwf/44Ub1YaIZdPkTGUiqARQ65DMMbcAiyz1j4M/Bb4qDHmZaL00q9aa3cZYz4P/Akw2RhzffxRSS+92xhzCpEB8iLwxYO4jqMCoQclK8ulY+akFJmeT9C7Vn8eW+ZVc1rZ0befp9fuTKgF3IpiNwhujGHL7n3Ma5vMvTfNTzh6PtJ+Gl+/7D0AfDPuPSB4o29/orzyeubqufCRjh2KeW8WkvYpyspggy63W/73aqwj4J5YU/fxZ/nFQ4aDr4pdz9d9yzYnrkY9rvtX9ETuLadCXaqcdXpv3jylguNOD+mi1b6uj39KzJulifSkMNGNVbhVxlljKNGIphSCtXYxsDj+e6F63RJlH/2Vc/xPgZ8GzvWh5oZ6bKCoy8PnHw9lvLgCxN1V+HLRs6p5dRpsnRPG8vM/RKmyCy9vb+Do6Zg5KWWttlQNi7t2pASguG8EIe78Q2HddW3rS5TqUM/dMTNNwmctXreKKGiNakzIp91mcqwvHuITzCEfvs5AcuMxbpqyoKVqUk2a9HdnZQ7d9vja5Hzi2suKh/gwry2inu4fqNHSEtGxy3lmx82dXP6s259a5+3LMdQxHKsoK5WPIjTjevEJjlRNhLVUlP/bdUV1zJxEuwqCS7uCrB2ItlY7t+xJagH6HT+xVEcPNa+92ZREX+e0oXbg0iR8IbeKW2x4dUdrA/1FXm9hVzC7DY18gWhfPEanKWt8du50bzOlELQxIZZ9S0uaWdZNFwW/wgOo1eousUW/Xp1wCrnX5hoxmoJd4iXuGEqEUSqEowhZrg8d+BTB7gafXaGR10lLB4PBH7B0H8KubX3c+8Lm5MGVdoySMSJKRAKZWXnteT2q3VTVkPDR/D8AP3qum544/bRITUDRe5B3jFZCzRRj5bV7zPpOTcAoOC7utQ3pXuFZjeP1eN24gy9dNMuVc/+KnoS9ddDCYPyPbx5SRkzcl8Nam6oy1mModwfZKBXCUYa8LX1WC0MtNPSWPCSc3AC0Fpw+C9vNwjHAhWfXBYe2rK+c05rsQGrW0lKtsEUFuEOFXL6x6lRQX8vDKeOPp86gAvv6BwvXBPjmuIjw8d0nX6D7QH8kDFdu3p0K7mu4mVe63WMW5H7rGphqxbDoiojj37dzCikFiZ/0x8LebRoEjUV8Yki4qaNuW5xqpV5b4Vr4ISPGZxCVyEepEI5ShHoO5Llh5G9fjnzRLCB5z+c71+yY1YrJDFiKYpF4hcQo5Dif0HZ7I0w68biUEnL7Ks9rm8wXLz6LJ9dsTxhet/ftT/W5Lup/Hk7OnY6ZUX3Gtx9axaCNisoWr93pbeQiPnfZIVQrNAhOX6aZCM7TJ56Q3JdazXLnM1HtqNsr3G2J2gBFm+GDLkLUGUNu6qjb0jUr62q4EgJKRCgVwlGIrJ4D2g0zWLMNjendHUHNWn743Ppg+mQzAV4pCBJ2TLfblFZc+ty+gKib7SRCu3fvgVSltbi1NHRfZZmfP33PqTwap1vamuXq86cDFKsJiBHaSeX5931YvrGXRzpfTwnOrNz/RZ9o59vxLqhSqfDY6m3c9vjaxNWjx7a/v8a3HlqFtVGBntvDYsOuvXzjwVV86v3TUt9z6eypmdeuaTNC4xQiRLdoUaeOSqypqJA/FEkGxypKhXAUIiSYOmbWUxPl4X3u1TdYuv7NlD934eXtDTntRTnqs5BnzTXbPEenx4rQ1vEOoeF202TdRi4PrOhhcdeO+kDiftXXXTAj6QpXRDBpC1hoOIr49134SAohm5eqd+8BbHy/+gdqfP+ZbqDu6knGFscLREce6I+U6N03zOM//+JFNuzam5xz19sH+O6nzy0UQ8grNtOY8c4TaanUu7z5aC5KIX94UCqEoxBZD6c81N96aFUqzRHSVvbBdmYTFM0qkfeKNM+BNJeNdim45xBhJ26nwVrUA0EyT6oVQ+eWPamWioM1y6Jfr6Zz6x6uCjC2hmokpKWk0HBcNae1af++zv6pAOe2TmD26RMyA9v6ntcg1RBbXD0uTTnUm8R3zJzEjX9yVhIzABIlkOkmilFE2Wta9pZqhY+8+xSmjBt7TNFLj3aUCuEoRN7DGVmT9f+NiYt9bJoCAmia5E5Dmpn3D1qqVUMFMguEshSZthjd4KTevXRt62voFy2fdTuHSQHTqi1R+qsmvDswUOOepfW6Crcfsy58EhJBINVSUoS69u/r/gUhNARKc3pSiHK6fv4ZPN+9i1VxOq+gfer4RCAfP6bKmJYKA4P1+JC46K67YAabdr3Nb1Zv4/3TJ9K79wDLN/YCxbJ0Qla9b8czOFjj/dMnHvSus8TwolQIRymyHs6tu/cxpmrqzKoGajUSUjTxe+eR3OXh/hU9CVXFgLLAs3zhRQKEIcWR1y/anROhgEgRB27rSwWTDwzUUplOWJuiIdeK46o5remWkiZK35TahKIxhGYCpb4GSy5+9Lv1/MPv1if3W651fttk7np+QyrQLP9v2LU3SiOumqhP8+DQyRPdeoc8WvZD0R2tRDGUCuEYghYe1YqhElfHSlGZJkXzuW+guXzuhvTB+AWfMNBCIM9qDAlMnUUlcY/QPEhqrWuJAyn3ktvvF1LemOR/USKhoHmzgq2oDz2dh++HVmAQ3fOXevaweutbqWp0XVCYXNdgpBotUbyhSG8EF75CvJBiHO7uaCWaQ6kQjiFo4WFVYFVDSNHch/jFzbv52ydeTSzFIhz2V85p5RdxfvuYquG/XOHPVBpq+0b3GE0YVwP69vU3fC4r/VILb3Ev6X6/buxhwawpLO7akVTBym6gqNIcDkvYTeU0RDu9qROOZ0ff/kipxfOh3YSiNHU1uhQUJgVmRBQWGMPgYC2lHJuhnW5mx9NMQV6J4UepEI4huEJehJupGGzNYi0pUjQdjH08o3Un+DtrQSyg4p+Q2+lgdiNaqGpSNKjvdlw3URG6cK1wdJGdOy4frbhvbO6xgLewTr6zmQI3EbZ9+/r5wXPrGaxZtuz+IxXAVAy1WhTDqcWpu3JfjhvTqBA1UaKOm8j5JXtJ1w6ExurrdpeHZrKVSgw/SoVwDMGXrZOVAaRrALT/16XF9nXWEqGel5sOjUIgj8dH4CqhhZe3p+kuPN85r62xuUyRefPVS7jvZTF9upTjV85pDVZDQ3MdwWQMtz+1LhXDqAGiAWzN8uH3nMqTr+xIxVjcDKKQ4Jbzu53GsqhChuL6aWY3UWL4USqEYwx5ws2FS0+xYNaUqGJW0WK7FcgVUxe0Raw9VwgUcRv4lFDhdFmnuczBINgGU6eaDjQS/gm1tK9DmlxPUR4j+X4pPNT3Aup8USePG9vQgKkZSGGhjyDPHevBuH7KGoTDh1IhlMiET1i7XbOyKpCLWnuuEMhTJK4SgsiFkdfGUTKLLPXmMkMVPq4VfP38M7zB3Wq1kfDvyjmtXDmnNUlh1dXQvtRZ33fr9Fexwr9w0ZmJWwey+aKadceErHd3dydU1KXr58hDqRBK5CJPWGdt87OsvZDvuYjbQJRQklVkSfVjzktZ1dXEQ4VrBa9+/a1UDAMigXx1R2tmr4KrnPqIrNRZmTc3r1+U87gTxqSOzeOLOljoe+Wy4h5sf+8SI4/CCsEYUwWWAVustZd73v8ssIjoeVhprb0ufv0vgG/Fh/2NtfZ/xq93AHcBJwD/CHzZ2gArVolRg5CwbnabX4R5tUgwVVfeFnWvXD//DH743HoGapZvP7SKxV07uOnis3KzplxMOvG4yJK3TpZOTA8hHb2kaC3LPy+vS9FdllsnlNc/6cTjWL11D8dVDQODFmPgQ++e0nAtX7rkXSkadF9w3Hf9obiAjmEUCdiXGL1oZofwZWANMN59wxhzNlHf5Quttb3GmCnx6+8E/i9gLpGiWG6Medha2wv8PXAjsIRIIXwMeOQgrqXECGE4fLxFmFeLjOMvP3xOsPeChttIJSH4sxGT6JOvbOea82YkmT7CQSTndVlGpQiuFjcSkgCtL0unmWsqkmXjxnU+M+SXf2oAAAviSURBVHc67XGDHQlo/+l7p/D02p08viZiSc0KcPt6bENjYDsvLlBmCB35KKQQjDGtwMeBW3HaZMb4AnB7LOix1gpT2J8Bj1lr34zP8xjwMWPMYmC8tfb5+PWfAJ+iVAjHDHzMq0Px5xfNSkkXcNmkKE8wUIO7l27ivuU9SVc3CQwfGKhxx9Ov8b7pE5Pv0Ocz1C35g1WWedcjVrvrjtHW+eBgjT/2DyZV2C5XlRvg1v/v74/I/qZNPKFB+OcJ/DJD6MhH0R3CbcDXgHGB988BMMb8DqgCi6y1vwFOBzar43ri106P/3ZfL3GMoGPmJG5QAVBLVEgmAUkt7PJcN0WEsFuD8f7WCSzb2NsQBJY+0K7v8ok12xv6P4foM/JcL0XmxndcViqnOx4dxNY1J76OdpfOnsrS9W8mbqj7lm1m0RWzm4oVNXMvSoxe5CoEY8zlwA5r7XJjzIKM85wNLABagWeNMbNpZC8AEh4x3+u+77+RyLXEjBn5rIsljhyMO2FMEoQ1RIVkSWvN2P++8PJ2Fj3cmVQ7h6ij81hVRZjd8fRrPL5mO3/Y0EtL1fDhWVPo3vnPrNv5dnIu4Rz6ZVxlLbsJHaf40iXvahCOPoENzdUUZCHLZeMT1lkFdW6Au3PrHu5ZuimpGRGW2IONFZU4slBkh3AhcIUx5jLgeGC8Mean1trPq2N6gCXW2n5gvTGmi0hB9BApCUErsDh+vdV5favvy621dwJ3AsydO7cMOh9F0HntxmmQLkLv3hc2JQR5BwYt96/oabC8dXZLVq9egCde2ZHsCgYGLaeMG8tNF5+VsLKOifsJd8ycxD03zm+gr3A7xulzhyquh4OKYfnGXrbs3kdLtRJs2JMlrH31J/p/IeBzdwSl8D+2kKsQrLU3EwWMiXcIX3GUAcBDwLXAXcaYk4lcSN3Aa8B3jTGyqj4K3GytfdMY02eMmQcsBf4c+LthuJ4SRxC8KYsqQ2dMS4VTnX7HsrV0g8Q+Ajq3a9ltj69tqF2w8ThE+Ies4azaBkHIjSSNaYaa5ur2j/7c+TNyewg0Wylc+v9LwEHUIRhjbgGWWWsfBn4LfNQY8zIwCHzVWrsrPu47wAvxx26RADPw76innT5CGVA+JuETui6PzuK1O1MFXdAYJK5WDNbaBn/5vLbJKeGoUQFmT5vQ9DizjvEJ1YWXtycUG7pWoij0tQ7WLNMmnpD7+aFUCpc7ghJNKQRr7WIilw/W2oXqdUuUfdSQgWSt/RHwI8/ry4DZTY22xFGNkEDy9ddtaCLjNLHRx+sMnIqBtpNPYv2uvdhYQAPetMuhWMu+a5C+zkN1Gw0lnbNMAS0xFJSVyiVGPXxCNs/FkZUff0HbZLrfeNubdtk/UOP+FT1J2ulwcPIfrHAeijundAGVGApKhVDiiEVRF4crHCHq5uZL0RzTUsEwPIHg0PcP5VxDceeULqASzaJUCCWOCbjCMS9F8/6DIIEr8v0lSoxGmCOJPmju3Ll22bJlh3sYJY4BlH19SxxNMMYst9bOzTuu3CGUKOFBadGXOBZROdwDKFGiRIkSowOlQihRokSJEkCpEEqUKFGiRIxSIZQoUaJECaBUCCVKlChRIkapEEqUKFGiBHCE1SEYY3YCGwscejLwxiEezlAwWscFo3ds5biax2gd22gdF4zesQ3XuGZaa0/JO+iIUghFYYxZVqQIY6QxWscFo3ds5biax2gd22gdF4zesY30uEqXUYkSJUqUAEqFUKJEiRIlYhytCuHOwz2AAEbruGD0jq0cV/MYrWMbreOC0Tu2ER3XURlDKFGiRIkSzeNo3SGUKFGiRIlmYa097D/AdOApYA2wGviyeu8/AF3x6/81fu044MfAKmAlsCBw3u8ALwEvAo8C0+LXFxB1bn8x/lmoPvOx+PvWAf/3CI/rq2pMnUT9qd8Zv7chPu+LwLJDNWfqHF8h6kF/cvy/Af5HPC8vAXPUsX8BvBr//KcRHte/isfzEvB74H3q2MM9Z4d1nWWMq+g6W3koxgUsAraoMVym3rs5npMu4M8C8/X1Q3UvQ2MDPgIsjz+/HPiQ+szi+PtejL/zuREc1xnAPvX699VnOuLzriN6dk2uLB6qEB/OH2AqsYABxgFrgfcClwCPA2Pj96bEv78E/Fhei29QxXPe8erv/yiTRfSg/m/P8VXgNaAtvlGrgatGalzOMZ8AnlT/byB+oA/lnMXvTwd+S1TzIULkMuARIsUwD1gav/5OoDv+PSn+zMUjOK4PAJPivy+VcY2SOVvAYVxnoXEVXWeHar6IhNtXPK+/l0gojgXOjOeo6pmvlcCfjPDY/iV1w202sEW9txiYe5jm7AygM3Dv/wDMJ3pmHwEu9R2nf0aFy8ha+7q1dkX8dx+Rdj0d+HfA96y1++P3dsQfeS/whHptN9CQq2utfUv9exKRlZSF84F11tpua+0B4KfAOYdpXNcC94QGeqjmLMZ/B77mjOuTwE9shCXARGPMVODPgMestW9aa3uB3wDTRmpc1trfx98LsARoDXz2cMxZCCOyzgqOK7jODvF8+fBJ4OfW2v3W2vVElu35NM7Xz4ELR3Js1tp/stZujf9dDRxvjBnrOW6k58yL+Nkcb6193kba4SfAp/I+NyoUgoYx5gwibbyU6CH5oDFmqTHmaWPMefFhK4FPGmNajDFnEm2NpgfOd6sxZjORa2Ghemu+MWalMeYRY0x7/NrpwGZ1TE/82kiOC2PMiURb5PvVyxZ41Biz3Bhz46GaM2PMFUTWz0rnrdDcjMicZYxL498SWUKCwz1ncBjXWd6cNbPOhnv9A//eGPOSMeZHxhjpRNT0GhvBsWlcBfyTCPcYPzbGvGiM+bYxxozwuM40xvxTfN4Pxq+dHs+TIDVnQeRtIUbyB3gH0Zbpyvj/TmLfF5GVsD7+u4XI8nkR+BXwj8Anc859M/Bf4r/HA++I/74MeDX++zPAD9Vn/jXwdyM1LvXaNcCvnddkuzqFeMs83HMGnEi0eCfYRvfB/wEuUsc+QbR4vwp8S73+beA/j9S41DGXEFljk0fRnB22dVZwzgqts+EcV/z5U4ncQBXgVuBH8eu3A59Xx/0DkfD1ztehkBmhsan324ncV2ep106Pf48jign++QjO2VjiNU/0PG4mWnfnAY+rz3/QvddeeXSwQny4foAxRL7Ov1Kv/QYVZIlvxCmez/4eeG/O+WcS9rVtIOIMmQ/8Vr1+M/DNkR4X8CBwXcZnFhEFCod1zoBzgR3xfGwABoBNwGnAHcC16tguIn/ptcAd6vU7gM+P1Lji9/9FfL5zRtOcHc51VmRcBdfZ14ZzXJ5jziBe//E83Kze+208V775unm472XW2OL/W4liAhdmfOZ64P8byXE57y0mcjlNBV5Rr6ee0+C58w4YiR8iTfkT4Dbn9S8Ct8R/n0Ok/QyR9XNS/PpHgGcC5z1b/f0fgF/Gf59GvQbj/PhBEW3dTRTQkuDVr0ZqXPH/E4A35TzxaycB49Tfvyfa6g/7nDnn2kDd2v046aDyH+LX30lk7UyKf9YT+XhHalwziHzNH3COGQ1zdljXWWhcTa6zx4d7XMBU9fd/IoobQGR966ByN5FV7Juv9kNxLzPGNjH+3quc41vU/R4D/JIoljVS4zoFqMZ/txFlIknG2AtEz6oElS/znTv1PXkHjMQPcBGR71JSMV8k2mIfRxRw6wRWEKd6EWnILiIXweNETH5yrh9Sj/jfH3/2JeDX1Ld2/54oMLQyvnkfUJ+/jMgKeI2oSnDExhW/d73cbPVaWzzWlfG4v3mo5iwkROJFdXs8L6v08cC/IRLK64DvjvC4fgj0qu9aNorm7LCus9C4mlhnh2r9/y+iNfQS8DBpYffNeE66UFkxznwdsnsZGhvwLeBt9V0vErnVTiJyDb0Uz9l9Izyuq6ivsRXAJ9Tn58bf9xrw/1Ig7bSsVC5RokSJEsAozDIqUaJEiRKHB6VCKFGiRIkSQKkQSpQoUaJEjFIhlChRokQJoFQIJUqUKFEiRqkQSpQoUaIEUCqEEiVKlCgRo1QIJUqUKFECgP8fsba5yJP01V8AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(p.ra, p.dec, '.')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "c = data[\"e_RPmag\"]<0.01" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots()\n", + "ax.plot(data[\"BP-RP\"][c], data[\"RPmag\"][c], '.', alpha=0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.plot(data[\"pmRA\"], data[\"pmDE\"], '.')\n", + "#plt.xlim(-1000, 1000)\n", + "#plt.ylim(-1000, 1000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "c = data[\"RV\"]>0\n", + "data[\"RV\"][c]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "d = Distance(parallax=data[\"Plx\"], allow_negative=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "SkyCoord(ra=1*u.deg, dec=2*u.deg, pm_ra_cosdec=2*u.mas/u.yr, pm_dec=1*u.mas/u.yr)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p = SkyCoord(ra=data[\"RA_ICRS\"], dec=data[\"DE_ICRS\"], distance=d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "p" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}