From ce86a03726186f4f70457e4b77d4a5e3814207a5 Mon Sep 17 00:00:00 2001 From: Allen Downey Date: Mon, 5 Oct 2020 10:36:45 -0400 Subject: [PATCH] adding notebooks --- 01_query.ipynb | 1644 ++++++++++++++++++++++++++++++++++++++ 02_coords.ipynb | 1968 ++++++++++++++++++++++++++++++++++++++++++++++ 03_motion.ipynb | 1868 +++++++++++++++++++++++++++++++++++++++++++ 04_select.ipynb | 1361 ++++++++++++++++++++++++++++++++ 05_join.ipynb | 1307 ++++++++++++++++++++++++++++++ 06_photo.ipynb | 1380 ++++++++++++++++++++++++++++++++ 07_plot.ipynb | 1186 ++++++++++++++++++++++++++++ test_setup.ipynb | 99 +++ 8 files changed, 10813 insertions(+) create mode 100644 01_query.ipynb create mode 100644 02_coords.ipynb create mode 100644 03_motion.ipynb create mode 100644 04_select.ipynb create mode 100644 05_join.ipynb create mode 100644 06_photo.ipynb create mode 100644 07_plot.ipynb create mode 100644 test_setup.ipynb diff --git a/01_query.ipynb b/01_query.ipynb new file mode 100644 index 0000000..ff38659 --- /dev/null +++ b/01_query.ipynb @@ -0,0 +1,1644 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Astronomical Data in Python\n", + "\n", + "Copyright 2020 [Allen B. Downey](https://allendowney.com)\n", + "\n", + "[MIT License](https://opensource.org/licenses/MIT)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This workshop is an introduction to tools and practices for working with astronomical data. Topics covered include:\n", + "\n", + "* Writing queries that select and download data from a database.\n", + "\n", + "* Using data stored in an Astropy `Table` or Pandas `DataFrame`.\n", + "\n", + "* Working with coordinates and other quantities with units.\n", + "\n", + "* Storing data in various formats.\n", + "\n", + "* Performing database join operations that combine data from multiple tables.\n", + "\n", + "* Visualizing data and preparing publication-quality figures." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a running example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n", + "\n", + "As the abstract explains, \"Using data from the Gaia second data release combined with Pan-STARRS photometry, we present a sample of highly-probable members of the longest cold stream in the Milky Way, GD-1.\"\n", + "\n", + "GD-1 is a [stellar stream](https://en.wikipedia.org/wiki/List_of_stellar_streams), which is \"an association of stars orbiting a galaxy that was once a globular cluster or dwarf galaxy that has now been torn apart and stretched out along its orbit by tidal forces.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "[This article in *Science* magazine](https://www.sciencemag.org/news/2018/10/streams-stars-reveal-galaxy-s-violent-history-and-perhaps-its-unseen-dark-matter) explains some of the background, including the process that led to the paper and an discussion of the scientific implications:\n", + "\n", + "* \"The streams are particularly useful for ... galactic archaeology --- rewinding the cosmic clock to reconstruct the assembly of the Milky Way.\"\n", + "\n", + "* \"They also are being used as exquisitely sensitive scales to measure the galaxy's mass.\"\n", + "\n", + "* \"... the streams are well-positioned to reveal the presence of dark matter ... because the streams are so fragile, theorists say, collisions with marauding clumps of dark matter could leave telltale scars, potential clues to its nature.\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "This workshop is meant for people who are familiar with basic Python, but not necessarily the libraries we will use, like Astropy or Pandas. If you are familiar with Python lists and dictionaries, and you know how to write a function that takes parameters and returns a value, you know enough Python for this workshop.\n", + "\n", + "We assume that you have some familiarity with operating systems, like the ability to use a command-line interface. But we don't assume you have any prior experience with databases.\n", + "\n", + "We assume that you are familiar with astronomy at the undergraduate level, but we will not assume specialized knowledge of the datasets or analysis methods we'll use. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data\n", + "\n", + "The datasets we will work with are:\n", + " \n", + "* [Gaia](https://en.wikipedia.org/wiki/Gaia_(spacecraft)), which is \"a space observatory of the European Space Agency (ESA), launched in 2013 ... designed for astrometry: measuring the positions, distances and motions of stars with unprecedented precision\", and\n", + "\n", + "* [Pan-STARRS](https://en.wikipedia.org/wiki/Pan-STARRS), The Panoramic Survey Telescope and Rapid Response System, which is a survey designed to monitor the sky for transient objects, producing a catalog with accurate astronometry and photometry of detected sources.\n", + "\n", + "Both of these datasets are very large, which can make them challenging to work with. It might not be possible, or practical, to download the entire dataset.\n", + "One of the goals of this workshop is to provide tools for working with large datasets." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lesson 1\n", + "\n", + "The first lesson demonstrates the steps for selecting and downloading data from the Gaia Database:\n", + "\n", + "1. First we'll make a connection to the Gaia server,\n", + "\n", + "2. We will explore information about the database and the tables it contains,\n", + "\n", + "3. We will write a query and send it to the server, and finally\n", + "\n", + "4. We will download the response from the server.\n", + "\n", + "After completing this lesson, you should be able to\n", + "\n", + "* Compose a basic query in ADQL.\n", + "\n", + "* Use queries to explore a database and its tables.\n", + "\n", + "* Use queries to download data.\n", + "\n", + "* Develop, test, and debug a query incrementally." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Query Language\n", + "\n", + "In order to select data from a database, you have to compose a query, which is like a program written in a \"query language\".\n", + "The query language we'll use is ADQL, which stands for \"Astronomical Data Query Language\".\n", + "\n", + "ADQL is a dialect of [SQL](https://en.wikipedia.org/wiki/SQL) (Structured Query Language), which is by far the most commonly used query language. Almost everything you will learn about ADQL also works in SQL.\n", + "\n", + "[The reference manual for ADQL is here](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html).\n", + "But you might find it easier to learn from [this ADQL Cookbook](https://www.gaia.ac.uk/data/gaia-data-release-1/adql-cookbook)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installing libraries\n", + "\n", + "The library we'll use to get Gaia data is [Astroquery](https://astroquery.readthedocs.io/en/latest/).\n", + "\n", + "If you are running this notebook on Colab, you can run the following cell to install Astroquery and the other libraries we'll use.\n", + "\n", + "If you are running this notebook on your own computer, you might have to install these libraries yourself. \n", + "\n", + "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n", + "\n", + "TODO: Add a link to the instructions.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# If we're running on Colab, install libraries\n", + "\n", + "import sys\n", + "IN_COLAB = 'google.colab' in sys.modules\n", + "\n", + "if IN_COLAB:\n", + " !pip install astroquery astro-gala pyia" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Connecting to Gaia\n", + "\n", + "Astroquery provides `Gaia`, which is an [object that represents a connection to the Gaia database](https://astroquery.readthedocs.io/en/latest/gaia/gaia.html).\n", + "\n", + "We can connect to the Gaia database like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created TAP+ (v1.2.1) - Connection:\n", + "\tHost: gea.esac.esa.int\n", + "\tUse HTTPS: True\n", + "\tPort: 443\n", + "\tSSL Port: 443\n", + "Created TAP+ (v1.2.1) - Connection:\n", + "\tHost: geadata.esac.esa.int\n", + "\tUse HTTPS: True\n", + "\tPort: 443\n", + "\tSSL Port: 443\n" + ] + } + ], + "source": [ + "from astroquery.gaia import Gaia" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Optional detail \n", + "\n", + "> Running this import statement has the effect of creating a [TAP+](http://www.ivoa.net/documents/TAP/) connection; TAP stands for \"Table Access Protocol\". It is a network protocol for sending queries to the database and getting back the results. We're not sure why it seems to create two connections." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Databases and Tables\n", + "\n", + "What is a database, anyway? Most generally, it can be any collection of data, but when we are talking about ADQL or SQL:\n", + "\n", + "* A database is a collection of one or more named tables.\n", + "\n", + "* Each table is a 2-D array with one or more named columns of data.\n", + "\n", + "We can use `Gaia.load_tables` to get the names of the tables in the Gaia database. With the option `only_names=True`, it loads information about the tables, called the \"metadata\", not the data itself." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: Retrieving tables... [astroquery.utils.tap.core]\n", + "INFO: Parsing tables... [astroquery.utils.tap.core]\n", + "INFO: Done. [astroquery.utils.tap.core]\n" + ] + } + ], + "source": [ + "tables = Gaia.load_tables(only_names=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "external.external.apassdr9\n", + "external.external.gaiadr2_geometric_distance\n", + "external.external.galex_ais\n", + "external.external.ravedr5_com\n", + "external.external.ravedr5_dr5\n", + "external.external.ravedr5_gra\n", + "external.external.ravedr5_on\n", + "external.external.sdssdr13_photoprimary\n", + "external.external.skymapperdr1_master\n", + "external.external.tmass_xsc\n", + "public.public.hipparcos\n", + "public.public.hipparcos_newreduction\n", + "public.public.hubble_sc\n", + "public.public.igsl_source\n", + "public.public.igsl_source_catalog_ids\n", + "public.public.tycho2\n", + "public.public.dual\n", + "tap_config.tap_config.coord_sys\n", + "tap_config.tap_config.properties\n", + "tap_schema.tap_schema.columns\n", + "tap_schema.tap_schema.key_columns\n", + "tap_schema.tap_schema.keys\n", + "tap_schema.tap_schema.schemas\n", + "tap_schema.tap_schema.tables\n", + "gaiadr1.gaiadr1.aux_qso_icrf2_match\n", + "gaiadr1.gaiadr1.ext_phot_zero_point\n", + "gaiadr1.gaiadr1.allwise_best_neighbour\n", + "gaiadr1.gaiadr1.allwise_neighbourhood\n", + "gaiadr1.gaiadr1.gsc23_best_neighbour\n", + "gaiadr1.gaiadr1.gsc23_neighbourhood\n", + "gaiadr1.gaiadr1.ppmxl_best_neighbour\n", + "gaiadr1.gaiadr1.ppmxl_neighbourhood\n", + "gaiadr1.gaiadr1.sdss_dr9_best_neighbour\n", + "gaiadr1.gaiadr1.sdss_dr9_neighbourhood\n", + "gaiadr1.gaiadr1.tmass_best_neighbour\n", + "gaiadr1.gaiadr1.tmass_neighbourhood\n", + "gaiadr1.gaiadr1.ucac4_best_neighbour\n", + "gaiadr1.gaiadr1.ucac4_neighbourhood\n", + "gaiadr1.gaiadr1.urat1_best_neighbour\n", + "gaiadr1.gaiadr1.urat1_neighbourhood\n", + "gaiadr1.gaiadr1.cepheid\n", + "gaiadr1.gaiadr1.phot_variable_time_series_gfov\n", + "gaiadr1.gaiadr1.phot_variable_time_series_gfov_statistical_parameters\n", + "gaiadr1.gaiadr1.rrlyrae\n", + "gaiadr1.gaiadr1.variable_summary\n", + "gaiadr1.gaiadr1.allwise_original_valid\n", + "gaiadr1.gaiadr1.gsc23_original_valid\n", + "gaiadr1.gaiadr1.ppmxl_original_valid\n", + "gaiadr1.gaiadr1.sdssdr9_original_valid\n", + "gaiadr1.gaiadr1.tmass_original_valid\n", + "gaiadr1.gaiadr1.ucac4_original_valid\n", + "gaiadr1.gaiadr1.urat1_original_valid\n", + "gaiadr1.gaiadr1.gaia_source\n", + "gaiadr1.gaiadr1.tgas_source\n", + "gaiadr2.gaiadr2.aux_allwise_agn_gdr2_cross_id\n", + "gaiadr2.gaiadr2.aux_iers_gdr2_cross_id\n", + "gaiadr2.gaiadr2.aux_sso_orbit_residuals\n", + "gaiadr2.gaiadr2.aux_sso_orbits\n", + "gaiadr2.gaiadr2.dr1_neighbourhood\n", + "gaiadr2.gaiadr2.allwise_best_neighbour\n", + "gaiadr2.gaiadr2.allwise_neighbourhood\n", + "gaiadr2.gaiadr2.apassdr9_best_neighbour\n", + "gaiadr2.gaiadr2.apassdr9_neighbourhood\n", + "gaiadr2.gaiadr2.gsc23_best_neighbour\n", + "gaiadr2.gaiadr2.gsc23_neighbourhood\n", + "gaiadr2.gaiadr2.hipparcos2_best_neighbour\n", + "gaiadr2.gaiadr2.hipparcos2_neighbourhood\n", + "gaiadr2.gaiadr2.panstarrs1_best_neighbour\n", + "gaiadr2.gaiadr2.panstarrs1_neighbourhood\n", + "gaiadr2.gaiadr2.ppmxl_best_neighbour\n", + "gaiadr2.gaiadr2.ppmxl_neighbourhood\n", + "gaiadr2.gaiadr2.ravedr5_best_neighbour\n", + "gaiadr2.gaiadr2.ravedr5_neighbourhood\n", + "gaiadr2.gaiadr2.sdssdr9_best_neighbour\n", + "gaiadr2.gaiadr2.sdssdr9_neighbourhood\n", + "gaiadr2.gaiadr2.tmass_best_neighbour\n", + "gaiadr2.gaiadr2.tmass_neighbourhood\n", + "gaiadr2.gaiadr2.tycho2_best_neighbour\n", + "gaiadr2.gaiadr2.tycho2_neighbourhood\n", + "gaiadr2.gaiadr2.urat1_best_neighbour\n", + "gaiadr2.gaiadr2.urat1_neighbourhood\n", + "gaiadr2.gaiadr2.sso_observation\n", + "gaiadr2.gaiadr2.sso_source\n", + "gaiadr2.gaiadr2.vari_cepheid\n", + "gaiadr2.gaiadr2.vari_classifier_class_definition\n", + "gaiadr2.gaiadr2.vari_classifier_definition\n", + "gaiadr2.gaiadr2.vari_classifier_result\n", + "gaiadr2.gaiadr2.vari_long_period_variable\n", + "gaiadr2.gaiadr2.vari_rotation_modulation\n", + "gaiadr2.gaiadr2.vari_rrlyrae\n", + "gaiadr2.gaiadr2.vari_short_timescale\n", + "gaiadr2.gaiadr2.vari_time_series_statistics\n", + "gaiadr2.gaiadr2.panstarrs1_original_valid\n", + "gaiadr2.gaiadr2.gaia_source\n", + "gaiadr2.gaiadr2.ruwe\n" + ] + } + ], + "source": [ + "for table in (tables):\n", + " print(table.get_qualified_name())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So that's a lot of tables. The ones we'll use are:\n", + "\n", + "* `gaiadr2.gaia_source`, which contains Gaia data from [data release 2](https://www.cosmos.esa.int/web/gaia/data-release-2),\n", + "\n", + "* `gaiadr2.panstarrs1_original_valid`, which contains the photometry data we'll use from PanSTARRS, and\n", + "\n", + "* `gaiadr2.panstarrs1_best_neighbour`, which we'll use to cross-match each star observed by Gaia with the same star observed by PanSTARRS.\n", + "\n", + "We can use `load_table` (not `load_tables`) to get the metadata for a single table. The name of this function is misleading, because it only downloads metadata. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Retrieving table 'gaiadr2.gaia_source'\n", + "Parsing table 'gaiadr2.gaia_source'...\n", + "Done.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "meta = Gaia.load_table('gaiadr2.gaia_source')\n", + "meta" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Jupyter shows that the result is an object of type `TapTableMeta`, but it does not display the contents.\n", + "\n", + "To see the metadata, we have to print the object." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TAP Table name: gaiadr2.gaiadr2.gaia_source\n", + "Description: This table has an entry for every Gaia observed source as listed in the\n", + "Main Database accumulating catalogue version from which the catalogue\n", + "release has been generated. It contains the basic source parameters,\n", + "that is only final data (no epoch data) and no spectra (neither final\n", + "nor epoch).\n", + "Num. columns: 96\n" + ] + } + ], + "source": [ + "print(meta)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice one gotcha: in the list of table names, this table appears as `gaiadr2.gaiadr2.gaia_source`, but when we load the metadata, we refer to it as `gaiadr2.gaia_source`.\n", + "\n", + "**Exercise:** Go back and try\n", + "\n", + "```\n", + "meta = Gaia.load_table('gaiadr2.gaiadr2.gaia_source')\n", + "```\n", + "\n", + "What happens? Is the error message helpful? If you had not made this error deliberately, would you have been able to figure it out?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Columns\n", + "\n", + "The following loop prints the names of the columns in the table." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "solution_id\n", + "designation\n", + "source_id\n", + "random_index\n", + "ref_epoch\n", + "ra\n", + "ra_error\n", + "dec\n", + "dec_error\n", + "parallax\n", + "parallax_error\n", + "parallax_over_error\n", + "pmra\n", + "pmra_error\n", + "pmdec\n", + "pmdec_error\n", + "ra_dec_corr\n", + "ra_parallax_corr\n", + "ra_pmra_corr\n", + "ra_pmdec_corr\n", + "dec_parallax_corr\n", + "dec_pmra_corr\n", + "dec_pmdec_corr\n", + "parallax_pmra_corr\n", + "parallax_pmdec_corr\n", + "pmra_pmdec_corr\n", + "astrometric_n_obs_al\n", + "astrometric_n_obs_ac\n", + "astrometric_n_good_obs_al\n", + "astrometric_n_bad_obs_al\n", + "astrometric_gof_al\n", + "astrometric_chi2_al\n", + "astrometric_excess_noise\n", + "astrometric_excess_noise_sig\n", + "astrometric_params_solved\n", + "astrometric_primary_flag\n", + "astrometric_weight_al\n", + "astrometric_pseudo_colour\n", + "astrometric_pseudo_colour_error\n", + "mean_varpi_factor_al\n", + "astrometric_matched_observations\n", + "visibility_periods_used\n", + "astrometric_sigma5d_max\n", + "frame_rotator_object_type\n", + "matched_observations\n", + "duplicated_source\n", + "phot_g_n_obs\n", + "phot_g_mean_flux\n", + "phot_g_mean_flux_error\n", + "phot_g_mean_flux_over_error\n", + "phot_g_mean_mag\n", + "phot_bp_n_obs\n", + "phot_bp_mean_flux\n", + "phot_bp_mean_flux_error\n", + "phot_bp_mean_flux_over_error\n", + "phot_bp_mean_mag\n", + "phot_rp_n_obs\n", + "phot_rp_mean_flux\n", + "phot_rp_mean_flux_error\n", + "phot_rp_mean_flux_over_error\n", + "phot_rp_mean_mag\n", + "phot_bp_rp_excess_factor\n", + "phot_proc_mode\n", + "bp_rp\n", + "bp_g\n", + "g_rp\n", + "radial_velocity\n", + "radial_velocity_error\n", + "rv_nb_transits\n", + "rv_template_teff\n", + "rv_template_logg\n", + "rv_template_fe_h\n", + "phot_variable_flag\n", + "l\n", + "b\n", + "ecl_lon\n", + "ecl_lat\n", + "priam_flags\n", + "teff_val\n", + "teff_percentile_lower\n", + "teff_percentile_upper\n", + "a_g_val\n", + "a_g_percentile_lower\n", + "a_g_percentile_upper\n", + "e_bp_min_rp_val\n", + "e_bp_min_rp_percentile_lower\n", + "e_bp_min_rp_percentile_upper\n", + "flame_flags\n", + "radius_val\n", + "radius_percentile_lower\n", + "radius_percentile_upper\n", + "lum_val\n", + "lum_percentile_lower\n", + "lum_percentile_upper\n", + "datalink_url\n", + "epoch_photometry_url\n" + ] + } + ], + "source": [ + "for column in meta.columns:\n", + " print(column.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can probably guess what many of these columns are by looking at the names, but you should resist the temptation to guess.\n", + "To find out what the columns mean, [read the documentation](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html).\n", + "\n", + "If you want to know what can go wrong when you don't read the documentation, [you might like this article](https://www.vox.com/future-perfect/2019/6/4/18650969/married-women-miserable-fake-paul-dolan-happiness)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** One of the other tables we'll use is `gaiadr2.gaiadr2.panstarrs1_original_valid`. Use `load_table` to get the metadata for this table. How many columns are there and what are their names?\n", + "\n", + "Hint: Remember the gotcha we mentioned earlier." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Retrieving table 'gaiadr2.panstarrs1_original_valid'\n", + "Parsing table 'gaiadr2.panstarrs1_original_valid'...\n", + "Done.\n", + "TAP Table name: gaiadr2.gaiadr2.panstarrs1_original_valid\n", + "Description: The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) is\n", + "a system for wide-field astronomical imaging developed and operated by\n", + "the Institute for Astronomy at the University of Hawaii. Pan-STARRS1\n", + "(PS1) is the first part of Pan-STARRS to be completed and is the basis\n", + "for Data Release 1 (DR1). The PS1 survey used a 1.8 meter telescope and\n", + "its 1.4 Gigapixel camera to image the sky in five broadband filters (g,\n", + "r, i, z, y).\n", + "\n", + "The current table contains a filtered subsample of the 10 723 304 629\n", + "entries listed in the original ObjectThin table.\n", + "We used only ObjectThin and MeanObject tables to extract\n", + "panstarrs1OriginalValid table, this means that objects detected only in\n", + "stack images are not included here. The main reason for us to avoid the\n", + "use of objects detected in stack images is that their astrometry is not\n", + "as good as the mean objects astrometry: “The stack positions (raStack,\n", + "decStack) have considerably larger systematic astrometric errors than\n", + "the mean epoch positions (raMean, decMean).” The astrometry for the\n", + "MeanObject positions uses Gaia DR1 as a reference catalog, while the\n", + "stack positions use 2MASS as a reference catalog.\n", + "\n", + "In details, we filtered out all objects where:\n", + "\n", + "- nDetections = 1\n", + "\n", + "- no good quality data in Pan-STARRS, objInfoFlag 33554432 not set\n", + "\n", + "- mean astrometry could not be measured, objInfoFlag 524288 set\n", + "\n", + "- stack position used for mean astrometry, objInfoFlag 1048576 set\n", + "\n", + "- error on all magnitudes equal to 0 or to -999;\n", + "\n", + "- all magnitudes set to -999;\n", + "\n", + "- error on RA or DEC greater than 1 arcsec.\n", + "\n", + "The number of objects in panstarrs1OriginalValid is 2 264 263 282.\n", + "\n", + "The panstarrs1OriginalValid table contains only a subset of the columns\n", + "available in the combined ObjectThin and MeanObject tables. A\n", + "description of the original ObjectThin and MeanObjects tables can be\n", + "found at:\n", + "https://outerspace.stsci.edu/display/PANSTARRS/PS1+Database+object+and+detection+tables\n", + "\n", + "Download:\n", + "http://mastweb.stsci.edu/ps1casjobs/home.aspx\n", + "Documentation:\n", + "https://outerspace.stsci.edu/display/PANSTARRS\n", + "http://pswww.ifa.hawaii.edu/pswww/\n", + "References:\n", + "The Pan-STARRS1 Surveys, Chambers, K.C., et al. 2016, arXiv:1612.05560\n", + "Pan-STARRS Data Processing System, Magnier, E. A., et al. 2016,\n", + "arXiv:1612.05240\n", + "Pan-STARRS Pixel Processing: Detrending, Warping, Stacking, Waters, C.\n", + "Z., et al. 2016, arXiv:1612.05245\n", + "Pan-STARRS Pixel Analysis: Source Detection and Characterization,\n", + "Magnier, E. A., et al. 2016, arXiv:1612.05244\n", + "Pan-STARRS Photometric and Astrometric Calibration, Magnier, E. A., et\n", + "al. 2016, arXiv:1612.05242\n", + "The Pan-STARRS1 Database and Data Products, Flewelling, H. A., et al.\n", + "2016, arXiv:1612.05243\n", + "\n", + "Catalogue curator:\n", + "SSDC - ASI Space Science Data Center\n", + "https://www.ssdc.asi.it/\n", + "Num. columns: 26\n" + ] + } + ], + "source": [ + "# Solution\n", + "\n", + "meta2 = Gaia.load_table('gaiadr2.panstarrs1_original_valid')\n", + "print(meta2)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "obj_name\n", + "obj_id\n", + "ra\n", + "dec\n", + "ra_error\n", + "dec_error\n", + "epoch_mean\n", + "g_mean_psf_mag\n", + "g_mean_psf_mag_error\n", + "g_flags\n", + "r_mean_psf_mag\n", + "r_mean_psf_mag_error\n", + "r_flags\n", + "i_mean_psf_mag\n", + "i_mean_psf_mag_error\n", + "i_flags\n", + "z_mean_psf_mag\n", + "z_mean_psf_mag_error\n", + "z_flags\n", + "y_mean_psf_mag\n", + "y_mean_psf_mag_error\n", + "y_flags\n", + "n_detections\n", + "zone_id\n", + "obj_info_flag\n", + "quality_flag\n" + ] + } + ], + "source": [ + "# Solution\n", + "\n", + "for column in meta2.columns:\n", + " print(column.name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Writing queries\n", + "\n", + "By now you might be wondering how we actually download the data. With tables this big, you generally don't. Instead, you use queries to select only the data you want.\n", + "\n", + "A query is a string written in a query language like SQL; for the Gaia database, the query language is a dialect of SQL called ADQL.\n", + "\n", + "Here's an example of an ADQL query." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "query1 = \"\"\"SELECT \n", + "TOP 10\n", + "source_id, ref_epoch, ra, dec, parallax \n", + "FROM gaiadr2.gaia_source\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Python note:** We use a [triple-quoted string](https://docs.python.org/3/tutorial/introduction.html#strings) here so we can include line breaks in the query, which makes it easier to read.\n", + "\n", + "The words in uppercase are ADQL keywords:\n", + "\n", + "* `SELECT` indicates that we are selecting data (as opposed to adding or modifying data).\n", + "\n", + "* `TOP` indicates that we only want the first 10 rows of the table, which is useful for testing a query before asking for all of the data.\n", + "\n", + "* `FROM` specifies which table we want data from.\n", + "\n", + "The third line is a list of column names, indicating which columns we want. \n", + "\n", + "In this example, the keywords are capitalized and the column names are lowercase. This is a common style, but it is not required. ADQL and SQL are not case-sensitive." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To run this query, we use the `Gaia` object, which represents our connection to the Gaia database, and invoke `launch_job`:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "job1 = Gaia.launch_job(query1)\n", + "job1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is an object that represents the job running on a Gaia server.\n", + "\n", + "If you print it, it displays metadata for the forthcoming table." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " name dtype unit description \n", + "--------- ------- ---- ------------------------------------------------------------------\n", + "source_id int64 Unique source identifier (unique within a particular Data Release)\n", + "ref_epoch float64 yr Reference epoch\n", + " ra float64 deg Right ascension\n", + " dec float64 deg Declination\n", + " parallax float64 mas Parallax\n", + "Jobid: None\n", + "Phase: COMPLETED\n", + "Owner: None\n", + "Output file: sync_20201005090721.xml.gz\n", + "Results: None\n" + ] + } + ], + "source": [ + "print(job1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Don't worry about `Results: None`. That does not actually mean there are no results.\n", + "\n", + "However, `Phase: COMPLETED` indicates that the job is complete, so we can get the results like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "astropy.table.table.Table" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results1 = job1.get_results()\n", + "type(results1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Optional detail:** Why is `table` repeated three times? The first is the name of the module, the second is the name of the submodule, and the third is the name of the class. Most of the time we only care about the last one. It's like the Linnean name for gorilla, which is *Gorilla Gorilla Gorilla*." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is an [Astropy Table](https://docs.astropy.org/en/stable/table/), which is similar to a table in an SQL database except:\n", + "\n", + "* SQL databases are stored on disk drives, so they are persistent; that is, they \"survive\" even if you turn off the computer. An Astropy `Table` is stored in memory; it disappears when you turn off the computer (or shut down this Jupyter notebook).\n", + "\n", + "* SQL databases are designed to process queries. An Astropy `Table` can perform some query-like operations, like selecting columns and rows. But these operations use Python syntax, not SQL.\n", + "\n", + "Jupyter knows how to display the contents of a `Table`." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=10\n", + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
source_idref_epochradecparallax
yrdegdegmas
int64float64float64float64float64
45307383617937696002015.5281.5672536244872520.406821174303780.9785380604519425
45307526511350812162015.5281.086156535525720.5233504963518460.2674800612552977
45307433439514055682015.5281.3711441829917720.474147574053124-0.43911323550176806
45307550606271623682015.5281.267623626829920.5585239223461581.1422630184554958
45307468443413159682015.5281.137043174954120.3778523888981841.0092247424630945
45307684566150264322015.5281.872092143634720.31829694530366-0.06900136127674149
45307635131191372802015.5281.921180886411620.209568295785240.1266016679823622
45307363646185392642015.5281.491347561327420.3465790413276930.3894019486060072
45307359523051777282015.5281.408554916570420.3110309037199280.2041189982608354
45307512810560226562015.5281.058532837763820.4603095562147530.10294642821734962
" + ], + "text/plain": [ + "\n", + " source_id ref_epoch ... dec parallax \n", + " yr ... deg mas \n", + " int64 float64 ... float64 float64 \n", + "------------------- --------- ... ------------------ --------------------\n", + "4530738361793769600 2015.5 ... 20.40682117430378 0.9785380604519425\n", + "4530752651135081216 2015.5 ... 20.523350496351846 0.2674800612552977\n", + "4530743343951405568 2015.5 ... 20.474147574053124 -0.43911323550176806\n", + "4530755060627162368 2015.5 ... 20.558523922346158 1.1422630184554958\n", + "4530746844341315968 2015.5 ... 20.377852388898184 1.0092247424630945\n", + "4530768456615026432 2015.5 ... 20.31829694530366 -0.06900136127674149\n", + "4530763513119137280 2015.5 ... 20.20956829578524 0.1266016679823622\n", + "4530736364618539264 2015.5 ... 20.346579041327693 0.3894019486060072\n", + "4530735952305177728 2015.5 ... 20.311030903719928 0.2041189982608354\n", + "4530751281056022656 2015.5 ... 20.460309556214753 0.10294642821734962" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Each column has a name, units, and a data type.\n", + "\n", + "For example, the units of `ra` and `dec` are degrees, and their data type is `float64`, which is a 64-bit floating-point number, used to store measurements with a fraction part.\n", + "\n", + "This information comes from the Gaia database, and has been stored in the Astropy `Table` by Astroquery." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Read [the documentation of this table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html) and choose a column that looks interesting to you. Add the column name to the query and run it again. What are the units of the column you selected? What is its data type?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Asynchronous queries\n", + "\n", + "`launch_job` asks the server to run the job \"synchronously\", which normally means it runs immediately. But synchronous jobs are limited to 2000 rows. For queries that return more rows, you should run \"asynchronously\", which mean they might take longer to get started.\n", + "\n", + "If you are not sure how many rows a query will return, you can use the SQL command `COUNT` to find out how many rows are in the result without actually returning them. We'll see an example of this later.\n", + "\n", + "The results of an asynchronous query are stored in a file on the server, so you can start a query and come back later to get the results.\n", + "\n", + "For anonymous users, files are kept for three days.\n", + "\n", + "As an example, let's try a query that's similar to `query1`, with two changes:\n", + "\n", + "* It selects the first 3000 rows, so it is bigger than we should run synchronously.\n", + "\n", + "* It uses a new keyword, `WHERE`." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "query2 = \"\"\"SELECT TOP 3000\n", + "source_id, ref_epoch, ra, dec, parallax\n", + "FROM gaiadr2.gaia_source\n", + "WHERE parallax < 1\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A `WHERE` clause indicates which rows we want; in this case, the query selects only rows \"where\" `parallax` is less than 1. This has the effect of selecting stars with relatively low parallax, which are farther away. We'll use this clause to exclude nearby stars that are unlikely to be part of GD-1.\n", + "\n", + "`WHERE` is one of the most common clauses in ADQL/SQL, and one of the most useful, because it allows us to select only the rows we need from the database.\n", + "\n", + "We use `launch_job_async` to submit an asynchronous query." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: Query finished. [astroquery.utils.tap.core]\n", + "
\n", + " name dtype unit description \n", + "--------- ------- ---- ------------------------------------------------------------------\n", + "source_id int64 Unique source identifier (unique within a particular Data Release)\n", + "ref_epoch float64 yr Reference epoch\n", + " ra float64 deg Right ascension\n", + " dec float64 deg Declination\n", + " parallax float64 mas Parallax\n", + "Jobid: 1601903242219O\n", + "Phase: COMPLETED\n", + "Owner: None\n", + "Output file: async_20201005090722.vot\n", + "Results: None\n" + ] + } + ], + "source": [ + "job2 = Gaia.launch_job_async(query2)\n", + "print(job2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here are the results." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=3000\n", + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
source_idref_epochradecparallax
yrdegdegmas
int64float64float64float64float64
45307383617937696002015.5281.5672536244872520.406821174303780.9785380604519425
45307526511350812162015.5281.086156535525720.5233504963518460.2674800612552977
45307433439514055682015.5281.3711441829917720.474147574053124-0.43911323550176806
45307684566150264322015.5281.872092143634720.31829694530366-0.06900136127674149
45307635131191372802015.5281.921180886411620.209568295785240.1266016679823622
45307363646185392642015.5281.491347561327420.3465790413276930.3894019486060072
45307359523051777282015.5281.408554916570420.3110309037199280.2041189982608354
45307512810560226562015.5281.058532837763820.4603095562147530.10294642821734962
45307409387744093442015.5281.376256953641620.4361400589412060.9242670062090182
...............
44677109150118026242015.5269.96809693073471.14290850381608820.42361471245557913
44677065513286795522015.5270.0331645898811.05657473236899270.922888231734588
44677122550373000962015.5270.77247179230470.6581664892880896-2.669179465293931
44677350011817617922015.5270.36286062483080.89470793235991240.6117399163086398
44677371014219166722015.5270.51108346614440.9806225910160181-0.39818224846127004
44677075477573274882015.5269.887462805949271.02127599401369620.7741412301054209
44677327720945730562015.5270.559971827601260.9037072088489417-1.7920417800164183
44677323554910877442015.5270.67307907024910.9197224705139885-0.3464446494840354
44677170997669445122015.5270.576671731208250.7262776590095680.05443955111134051
44677190582657812482015.5270.72480529715140.82055519217827850.3733943917490343
" + ], + "text/plain": [ + "\n", + " source_id ref_epoch ... dec parallax \n", + " yr ... deg mas \n", + " int64 float64 ... float64 float64 \n", + "------------------- --------- ... ------------------ --------------------\n", + "4530738361793769600 2015.5 ... 20.40682117430378 0.9785380604519425\n", + "4530752651135081216 2015.5 ... 20.523350496351846 0.2674800612552977\n", + "4530743343951405568 2015.5 ... 20.474147574053124 -0.43911323550176806\n", + "4530768456615026432 2015.5 ... 20.31829694530366 -0.06900136127674149\n", + "4530763513119137280 2015.5 ... 20.20956829578524 0.1266016679823622\n", + "4530736364618539264 2015.5 ... 20.346579041327693 0.3894019486060072\n", + "4530735952305177728 2015.5 ... 20.311030903719928 0.2041189982608354\n", + "4530751281056022656 2015.5 ... 20.460309556214753 0.10294642821734962\n", + "4530740938774409344 2015.5 ... 20.436140058941206 0.9242670062090182\n", + " ... ... ... ... ...\n", + "4467710915011802624 2015.5 ... 1.1429085038160882 0.42361471245557913\n", + "4467706551328679552 2015.5 ... 1.0565747323689927 0.922888231734588\n", + "4467712255037300096 2015.5 ... 0.6581664892880896 -2.669179465293931\n", + "4467735001181761792 2015.5 ... 0.8947079323599124 0.6117399163086398\n", + "4467737101421916672 2015.5 ... 0.9806225910160181 -0.39818224846127004\n", + "4467707547757327488 2015.5 ... 1.0212759940136962 0.7741412301054209\n", + "4467732772094573056 2015.5 ... 0.9037072088489417 -1.7920417800164183\n", + "4467732355491087744 2015.5 ... 0.9197224705139885 -0.3464446494840354\n", + "4467717099766944512 2015.5 ... 0.726277659009568 0.05443955111134051\n", + "4467719058265781248 2015.5 ... 0.8205551921782785 0.3733943917490343" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results2 = job2.get_results()\n", + "results2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You might notice that some values of `parallax` are negative. As [this FAQ explains](https://www.cosmos.esa.int/web/gaia/archive-tips#negative%20parallax), \"Negative parallaxes are caused by errors in the observations.\" Negative parallaxes have \"no physical meaning,\" but they can be a \"useful diagnostic on the quality of the astrometric solution.\"\n", + "\n", + "Later we will see an example where we use `parallax` and `parallax_error` to identify stars where the distance estimate is likely to be inaccurate." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** The clauses in a query have to be in the right order. Go back and change the order of the clauses in `query2` and run it again. \n", + "\n", + "The query should fail, but notice that you don't get much useful debugging information. \n", + "\n", + "For this reason, developing and debugging ADQL queries can be really hard. A few suggestions that might help:\n", + "\n", + "* Whenever possible, start with a working query, either an example you find online or a query you have used in the past.\n", + "\n", + "* Make small changes and test each change before you continue.\n", + "\n", + "* While you are debugging, use `TOP` to limit the number of rows in the result. That will make each attempt run faster, which reduces your testing time. \n", + "\n", + "* Launching test queries synchronously might make them start faster, too." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Operators\n", + "\n", + "In a `WHERE` clause, you can use any of the [SQL comparison operators](https://www.w3schools.com/sql/sql_operators.asp):\n", + "\n", + "* `>`: greater than\n", + "* `<`: less than\n", + "* `>=`: greater than or equal\n", + "* `<=`: less than or equal\n", + "* `=`: equal\n", + "* `!=` or `<>`: not equal\n", + "\n", + "Most of these are the same as Python, but some are not. In particular, notice that the equality operator is `=`, not `==`.\n", + "Be careful to keep your Python out of your ADQL!\n", + "\n", + "You can combine comparisons using the logical operators:\n", + "\n", + "* AND: true if both comparisons are true\n", + "* OR: true if either or both comparisons are true\n", + "\n", + "Finally, you can use `NOT` to invert the result of a comparison. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** [Read about SQL operators here](https://www.w3schools.com/sql/sql_operators.asp) and then modify the previous query to select rows where `bp_rp` is between `-0.75` and `2`.\n", + "\n", + "You can [read about this variable here](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_main_tables/ssec_dm_gaia_source.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "\n", + "# This is what most people will probably do\n", + "\n", + "query = \"\"\"SELECT TOP 10\n", + "source_id, ref_epoch, ra, dec, parallax\n", + "FROM gaiadr2.gaia_source\n", + "WHERE parallax < 1 \n", + " AND bp_rp > -0.75 AND bp_rp < 2\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "\n", + "# But if someone notices the BETWEEN operator, \n", + "# they might do this\n", + "\n", + "query = \"\"\"SELECT TOP 10\n", + "source_id, ref_epoch, ra, dec, parallax\n", + "FROM gaiadr2.gaia_source\n", + "WHERE parallax < 1 \n", + " AND bp_rp BETWEEN -0.75 AND 2\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This [Hertzsprung-Russell diagram](https://sci.esa.int/web/gaia/-/60198-gaia-hertzsprung-russell-diagram) shows the BP-RP color and luminosity of stars in the Gaia catalog.\n", + "\n", + "Selecting stars with `bp-rp` less than 2 excludes many [class M dwarf stars](https://xkcd.com/2360/), which are low temperature, low luminosity. A star like that at GD-1's distance would be hard to detect, so if it is detected, it it more likely to be in the foreground." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cleaning up\n", + "\n", + "Asynchronous jobs have a `jobid`." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(None, '1601903242219O')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "job1.jobid, job2.jobid" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Which you can use to remove the job from the server." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Removed jobs: '['1601903242219O']'.\n" + ] + } + ], + "source": [ + "Gaia.remove_jobs([job2.jobid])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you don't remove it job from the server, it will be removed eventually, so don't feel too bad if you don't clean up after yourself." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Formatting queries\n", + "\n", + "So far the queries have been string \"literals\", meaning that the entire string is part of the program.\n", + "But writing queries yourself can be slow, repetitive, and error-prone.\n", + "\n", + "It is often a good idea to write Python code that assembles a query for you. One useful tool for that is the [string `format` method](https://www.w3schools.com/python/ref_string_format.asp).\n", + "\n", + "As an example, we'll divide the previous query into two parts; a list of column names and a \"base\" for the query that contains everything except the column names.\n", + "\n", + "Here's the list of columns we'll select. " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's the base; it's a string that contains at least one format specifier in curly brackets (braces)." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "query3_base = \"\"\"SELECT TOP 10 \n", + "{columns}\n", + "FROM gaiadr2.gaia_source\n", + "WHERE parallax < 1\n", + " AND bp_rp BETWEEN -0.75 AND 2\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This base query contains one format specifier, `{columns}`, which is a placeholder for the list of column names we will provide.\n", + "\n", + "To assemble the query, we invoke `format` on the base string and provide a keyword argument that assigns a value to `columns`." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "query3 = query3_base.format(columns=columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is a string with line breaks. If you display it, the line breaks appear as `\\n`." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'SELECT TOP 10 \\nsource_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\\nFROM gaiadr2.gaia_source\\nWHERE parallax < 1\\n AND bp_rp BETWEEN -0.75 AND 2\\n'" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But if you print it, the line breaks appear as... line breaks." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT TOP 10 \n", + "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n", + "FROM gaiadr2.gaia_source\n", + "WHERE parallax < 1\n", + " AND bp_rp BETWEEN -0.75 AND 2\n", + "\n" + ] + } + ], + "source": [ + "print(query3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the format specifier has been replaced with the value of `columns`.\n", + "\n", + "Let's run it and see if it works:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "
\n", + " name dtype unit description n_bad\n", + "--------------- ------- -------- ------------------------------------------------------------------ -----\n", + " source_id int64 Unique source identifier (unique within a particular Data Release) 0\n", + " ra float64 deg Right ascension 0\n", + " dec float64 deg Declination 0\n", + " pmra float64 mas / yr Proper motion in right ascension direction 0\n", + " pmdec float64 mas / yr Proper motion in declination direction 0\n", + " parallax float64 mas Parallax 0\n", + " parallax_error float64 mas Standard error of parallax 0\n", + "radial_velocity float64 km / s Radial velocity 10\n", + "Jobid: None\n", + "Phase: COMPLETED\n", + "Owner: None\n", + "Output file: sync_20201005090726.xml.gz\n", + "Results: None\n" + ] + } + ], + "source": [ + "job3 = Gaia.launch_job(query3)\n", + "print(job3)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=10\n", + "
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source_idradecpmrapmdecparallaxparallax_errorradial_velocity
degdegmas / yrmas / yrmasmaskm / s
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4467710915011802624269.96809693073471.14290850381608822.0233280236600626-2.56924278755102660.423614712455579130.470352406647465--
4467706551328679552270.0331645898811.0565747323689927-3.414829591355289-3.84372158574957370.9228882317345880.927008559859825--
4467712255037300096270.77247179230470.6581664892880896-3.5620173752896025-6.595792323153987-2.6691794652939310.9719742773203504--
4467735001181761792270.36286062483080.89470793235991242.13070799264892050.88267277109107120.61173991630863980.509812721702093--
4467737101421916672270.51108346614440.98062259101601810.17532366511560785-5.113270239706202-0.398182248461270040.7549581886719651--
4467707547757327488269.887462805949271.0212759940136962-2.6382230817672987-3.7077765320492870.77414123010542090.3022057897812064--
4467732355491087744270.67307907024910.9197224705139885-2.2735991502653037-11.864952855984358-0.34644464948403540.4937921513912002--
4467717099766944512270.576671731208250.726277659009568-3.4598362614808367-4.6014268933659210.054439551111340510.8867339293525688--
4467719058265781248270.72480529715140.8205551921782785-3.255079498426542-9.2492850691110850.37339439174903430.390952370410666--
4467722326741572352270.874312918885040.85955659758691580.106963983518598261.2035993780158853-0.118509434328643730.1660452431882023--
" + ], + "text/plain": [ + "\n", + " source_id ra ... parallax_error radial_velocity\n", + " deg ... mas km / s \n", + " int64 float64 ... float64 float64 \n", + "------------------- ------------------ ... ------------------ ---------------\n", + "4467710915011802624 269.9680969307347 ... 0.470352406647465 --\n", + "4467706551328679552 270.033164589881 ... 0.927008559859825 --\n", + "4467712255037300096 270.7724717923047 ... 0.9719742773203504 --\n", + "4467735001181761792 270.3628606248308 ... 0.509812721702093 --\n", + "4467737101421916672 270.5110834661444 ... 0.7549581886719651 --\n", + "4467707547757327488 269.88746280594927 ... 0.3022057897812064 --\n", + "4467732355491087744 270.6730790702491 ... 0.4937921513912002 --\n", + "4467717099766944512 270.57667173120825 ... 0.8867339293525688 --\n", + "4467719058265781248 270.7248052971514 ... 0.390952370410666 --\n", + "4467722326741572352 270.87431291888504 ... 0.1660452431882023 --" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results3 = job3.get_results()\n", + "results3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Good so far." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** This query always selects sources with `parallax` less than 1. But suppose you want to take that upper bound as an input.\n", + "\n", + "Modify `query3_base` to replace `1` with a format specifier like `{max_parallax}`. Now, when you call `format`, add a keyword argument that assigns a value to `max_parallax`, and confirm that the format specifier gets replaced with the value you provide." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "\n", + "query4_base = \"\"\"SELECT TOP 10\n", + "{columns}\n", + "FROM gaiadr2.gaia_source\n", + "WHERE parallax < {max_parallax} AND \n", + "bp_rp BETWEEN -0.75 AND 2\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT TOP 10\n", + "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n", + "FROM gaiadr2.gaia_source\n", + "WHERE parallax < 0.5 AND \n", + "bp_rp BETWEEN -0.75 AND 2\n", + "\n" + ] + } + ], + "source": [ + "# Solution\n", + "\n", + "query4 = query4_base.format(columns=columns,\n", + " max_parallax=0.5)\n", + "print(query)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Style note:** You might notice that the variable names in this notebook are numbered, like `query1`, `query2`, etc. \n", + "\n", + "The advantage of this style is that it isolates each section of the notebook from the others, so if you go back and run the cells out of order, it's less likely that you will get unexpected interactions.\n", + "\n", + "A drawback of this style is that it can be a nuisance to update the notebook if you add, remove, or reorder a section.\n", + "\n", + "What do you think of this choice? Are there alternatives you prefer?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "This notebook demonstrates the following steps:\n", + "\n", + "1. Making a connection to the Gaia server,\n", + "\n", + "2. Exploring information about the database and the tables it contains,\n", + "\n", + "3. Writing a query and sending it to the server, and finally\n", + "\n", + "4. Downloading the response from the server as an Astropy `Table`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best practices\n", + "\n", + "* If you can't download an entire dataset (or it's not practical) use queries to select the data you need.\n", + "\n", + "* Read the metadata and the documentation to make sure you understand the tables, their columns, and what they mean.\n", + "\n", + "* Develop queries incrementally: start with something simple, test it, and add a little bit at a time.\n", + "\n", + "* Use ADQL features like `TOP` and `COUNT` to test before you run a query that might return a lot of data.\n", + "\n", + "* If you know your query will return fewer than 3000 rows, you can run it synchronously, which might complete faster (but it doesn't seem to make much difference). If it might return more than 3000 rows, you should run it asynchronously.\n", + "\n", + "* ADQL and SQL are not case-sensitive, so you don't have to capitalize the keywords, but you should.\n", + "\n", + "* ADQL and SQL don't require you to break a query into multiple lines, but you should.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Jupyter notebooks can be good for developing and testing code, but they have some drawbacks. In particular, if you run the cells out of order, you might find that variables don't have the values you expect.\n", + "\n", + "There are a few things you can do to mitigate these problems:\n", + "\n", + "* Make each section of the notebook self-contained. Try not to use the same variable name in more than one section.\n", + "\n", + "* Keep notebooks short. Look for places where you can break your analysis into phases with one notebook per phase." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/02_coords.ipynb b/02_coords.ipynb new file mode 100644 index 0000000..0749945 --- /dev/null +++ b/02_coords.ipynb @@ -0,0 +1,1968 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This is the second in a series of lessons related to astronomy data.\n", + "\n", + "As a running example, we are replicating parts of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n", + "\n", + "In the first notebook, we wrote ADQL queries and used them to select and download data from the Gaia server.\n", + "\n", + "In this notebook, we'll pick up where we left off and write a query to select stars from the region of the sky where we expect GD-1 to be." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lesson 2\n", + "\n", + "We'll start with an example that does a \"cone search\"; that is, it selects stars that appear in a circular region of the sky.\n", + "\n", + "Then, to select stars in the vicinity of GD-1, we'll:\n", + "\n", + "* Use `Quantity` objects to represent measurements with units.\n", + "\n", + "* Use the `Gala` library to convert coordinates from one frame to another.\n", + "\n", + "* Use the ADQL keywords `POLYGON`, `CONTAINS`, and `POINT` to select stars that fall within a polygonal region.\n", + "\n", + "* Submit a query and download the results.\n", + "\n", + "* Store the results in a FITS file.\n", + "\n", + "After completing this lesson, you should be able to\n", + "\n", + "* Use Python string formatting to compose more complex ADQL queries.\n", + "\n", + "* Work with coordinates and other quantities that have units.\n", + "\n", + "* Download the results of a query and store them in a file." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installing libraries\n", + "\n", + "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n", + "\n", + "If you are running this notebook on your own computer, you might have to install these libraries yourself. \n", + "\n", + "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n", + "\n", + "TODO: Add a link to the instructions.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# If we're running on Colab, install libraries\n", + "\n", + "import sys\n", + "IN_COLAB = 'google.colab' in sys.modules\n", + "\n", + "if IN_COLAB:\n", + " !pip install astroquery astro-gala pyia" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Selecting a region" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One of the most common ways to restrict a query is to select stars in a particular region of the sky.\n", + "\n", + "For example, here's a query from the [Gaia archive documentation](https://gea.esac.esa.int/archive-help/adql/examples/index.html) that selects \"all the objects ... in a circular region centered at (266.41683, -29.00781) with a search radius of 5 arcmin (0.08333 deg).\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "query = \"\"\"\n", + "SELECT \n", + "TOP 10 source_id\n", + "FROM gaiadr2.gaia_source\n", + "WHERE 1=CONTAINS(\n", + " POINT(ra, dec),\n", + " CIRCLE(266.41683, -29.00781, 0.08333333))\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This query uses three keywords that are specific to ADQL (not SQL):\n", + "\n", + "* `POINT`: a location in [ICRS coordinates](https://en.wikipedia.org/wiki/International_Celestial_Reference_System), specified in degrees of right ascension and declination.\n", + "\n", + "* `CIRCLE`: a circle where the first two values are the coordinates of the center and the third is the radius in degrees.\n", + "\n", + "* `CONTAINS`: a function that returns `1` if a `POINT` is contained in a shape and `0` otherwise.\n", + "\n", + "Here is the [documentation of `CONTAINS`](http://www.ivoa.net/documents/ADQL/20180112/PR-ADQL-2.1-20180112.html#tth_sEc4.2.12).\n", + "\n", + "A query like this is called a cone search because it selects stars in a cone.\n", + "\n", + "Here's how we run it." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created TAP+ (v1.2.1) - Connection:\n", + "\tHost: gea.esac.esa.int\n", + "\tUse HTTPS: True\n", + "\tPort: 443\n", + "\tSSL Port: 443\n", + "Created TAP+ (v1.2.1) - Connection:\n", + "\tHost: geadata.esac.esa.int\n", + "\tUse HTTPS: True\n", + "\tPort: 443\n", + "\tSSL Port: 443\n" + ] + }, + { + "data": { + "text/html": [ + "Table length=10\n", + "
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" + ], + "text/plain": [ + "\n", + " source_id \n", + " int64 \n", + "-------------------\n", + "4057468321929794432\n", + "4057468287575835392\n", + "4057482027171038976\n", + "4057470349160630656\n", + "4057470039924301696\n", + "4057469868125641984\n", + "4057468351995073024\n", + "4057469661959554560\n", + "4057470520960672640\n", + "4057470555320409600" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from astroquery.gaia import Gaia\n", + "\n", + "job = Gaia.launch_job(query)\n", + "result = job.get_results()\n", + "result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** When you are debugging queries like this, you can use `TOP` to limit the size of the results, but then you still don't know how big the results will be.\n", + "\n", + "An alternative is to use `COUNT`, which asks for the number of rows that would be selected, but it does not return them.\n", + "\n", + "In the previous query, replace `TOP 10 source_id` with `COUNT(source_id)` and run the query again. How many stars has Gaia identified in the cone we searched?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting GD-1 Data\n", + "\n", + "From the Price-Whelan and Bonaca paper, we will try to reproduce Figure 1, which includes this representation of stars likely to belong to GD-1:\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Along the axis of right ascension ($\\phi_1$) the figure extends from -100 to 20 degrees.\n", + "\n", + "Along the axis of declination ($\\phi_2$) the figure extends from about -8 to 4 degrees.\n", + "\n", + "Ideally, we would select all stars from this rectangle, but there are more than 10 million of them, so\n", + "\n", + "* That would be difficult to work with,\n", + "\n", + "* As anonymous users, we are limited to 3 million rows in a single query, and\n", + "\n", + "* While we are developing and testing code, it will be faster to work with a smaller dataset.\n", + "\n", + "So we'll start by selecting stars in a smaller rectangle, from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.\n", + "\n", + "But first we let's see how to represent quantities with units like degrees." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Working with coordinates\n", + "\n", + "Coordinates are physical quantities, which means that they have two parts, a value and a unit.\n", + "\n", + "For example, the coordinate $30^{\\circ}$ has value 30 and its units are degrees.\n", + "\n", + "Until recently, most scientific computation was done with values only; units were left out of the program altogether, [often with disastrous results](https://en.wikipedia.org/wiki/Mars_Climate_Orbiter#Cause_of_failure).\n", + "\n", + "Astropy provides tools for including units explicitly in computations, which makes it possible to detect errors before they cause disasters.\n", + "\n", + "To use Astropy units, we import them like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import astropy.units as u\n", + "\n", + "u" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`u` is an object that contains most common units and all SI units.\n", + "\n", + "You can use `dir` to list them, but you should also [read the documentation](https://docs.astropy.org/en/stable/units/)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['A',\n", + " 'AA',\n", + " 'AB',\n", + " 'ABflux',\n", + " 'ABmag',\n", + " 'AU',\n", + " 'Angstrom',\n", + " 'B',\n", + " 'Ba',\n", + " 'Barye',\n", + " 'Bi',\n", + " 'Biot',\n", + " 'Bol',\n", + " 'Bq',\n", + " 'C',\n", + " 'Celsius',\n", + " 'Ci',\n", + " 'CompositeUnit',\n", + " 'D',\n", + " 'Da',\n", + " 'Dalton',\n", + " 'Debye',\n", + " 'Decibel',\n", + " 'DecibelUnit',\n", + " 'Dex',\n", + " 'DexUnit',\n", + " 'EA',\n", + " 'EAU',\n", + " 'EB',\n", + " 'EBa',\n", + " 'EC',\n", + " 'ED',\n", + " 'EF',\n", + " 'EG',\n", + " 'EGal',\n", + " 'EH',\n", + " 'EHz',\n", + " 'EJ',\n", + " 'EJy',\n", + " 'EK',\n", + " 'EL',\n", + " 'EN',\n", + " 'EOhm',\n", + " 'EP',\n", + " 'EPa',\n", + " 'ER',\n", + " 'ERy',\n", + " 'ES',\n", + " 'ESt',\n", + " 'ET',\n", + " 'EV',\n", + " 'EW',\n", + " 'EWb',\n", + " 'Ea',\n", + " 'Eadu',\n", + " 'Earcmin',\n", + " 'Earcsec',\n", + " 'Eau',\n", + " 'Eb',\n", + " 'Ebarn',\n", + " 'Ebeam',\n", + " 'Ebin',\n", + " 'Ebit',\n", + " 'Ebyte',\n", + " 'Ecd',\n", + " 'Echan',\n", + " 'Ecount',\n", + " 'Ect',\n", + " 'Ed',\n", + " 'Edeg',\n", + " 'Edyn',\n", + " 'EeV',\n", + " 'Eerg',\n", + " 'Eg',\n", + " 'Eh',\n", + " 'EiB',\n", + " 'Eib',\n", + " 'Eibit',\n", + " 'Eibyte',\n", + " 'Ek',\n", + " 'El',\n", + " 'Elm',\n", + " 'Elx',\n", + " 'Elyr',\n", + " 'Em',\n", + " 'Emag',\n", + " 'Emin',\n", + " 'Emol',\n", + " 'Eohm',\n", + " 'Epc',\n", + " 'Eph',\n", + " 'Ephoton',\n", + " 'Epix',\n", + " 'Epixel',\n", + " 'Erad',\n", + " 'Es',\n", + " 'Esr',\n", + " 'Eu',\n", + " 'Evox',\n", + " 'Evoxel',\n", + " 'Eyr',\n", + " 'F',\n", + " 'Farad',\n", + " 'Fr',\n", + " 'Franklin',\n", + " 'FunctionQuantity',\n", + " 'FunctionUnitBase',\n", + " 'G',\n", + " 'GA',\n", + " 'GAU',\n", + " 'GB',\n", + " 'GBa',\n", + " 'GC',\n", + " 'GD',\n", + " 'GF',\n", + " 'GG',\n", + " 'GGal',\n", + " 'GH',\n", + " 'GHz',\n", + " 'GJ',\n", + " 'GJy',\n", + " 'GK',\n", + " 'GL',\n", + " 'GN',\n", + " 'GOhm',\n", + " 'GP',\n", + " 'GPa',\n", + " 'GR',\n", + " 'GRy',\n", + " 'GS',\n", + " 'GSt',\n", + " 'GT',\n", + " 'GV',\n", + " 'GW',\n", + " 'GWb',\n", + " 'Ga',\n", + " 'Gadu',\n", + " 'Gal',\n", + " 'Garcmin',\n", + " 'Garcsec',\n", + " 'Gau',\n", + " 'Gauss',\n", + " 'Gb',\n", + " 'Gbarn',\n", + " 'Gbeam',\n", + " 'Gbin',\n", + " 'Gbit',\n", + " 'Gbyte',\n", + " 'Gcd',\n", + " 'Gchan',\n", + " 'Gcount',\n", + " 'Gct',\n", + " 'Gd',\n", + " 'Gdeg',\n", + " 'Gdyn',\n", + " 'GeV',\n", + " 'Gerg',\n", + " 'Gg',\n", + " 'Gh',\n", + " 'GiB',\n", + " 'Gib',\n", + " 'Gibit',\n", + " 'Gibyte',\n", + " 'Gk',\n", + " 'Gl',\n", + " 'Glm',\n", + " 'Glx',\n", + " 'Glyr',\n", + " 'Gm',\n", + " 'Gmag',\n", + " 'Gmin',\n", + " 'Gmol',\n", + " 'Gohm',\n", + " 'Gpc',\n", + " 'Gph',\n", + " 'Gphoton',\n", + " 'Gpix',\n", + " 'Gpixel',\n", + " 'Grad',\n", + " 'Gs',\n", + " 'Gsr',\n", + " 'Gu',\n", + " 'Gvox',\n", + " 'Gvoxel',\n", + " 'Gyr',\n", + " 'H',\n", + " 'Henry',\n", + " 'Hertz',\n", + " 'Hz',\n", + " 'IrreducibleUnit',\n", + " 'J',\n", + " 'Jansky',\n", + " 'Joule',\n", + " 'Jy',\n", + " 'K',\n", + " 'Kayser',\n", + " 'Kelvin',\n", + " 'KiB',\n", + " 'Kib',\n", + " 'Kibit',\n", + " 'Kibyte',\n", + " 'L',\n", + " 'L_bol',\n", + " 'L_sun',\n", + " 'LogQuantity',\n", + " 'LogUnit',\n", + " 'Lsun',\n", + " 'MA',\n", + " 'MAU',\n", + " 'MB',\n", + " 'MBa',\n", 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'Mlm',\n", + " 'Mlx',\n", + " 'Mlyr',\n", + " 'Mm',\n", + " 'Mmag',\n", + " 'Mmin',\n", + " 'Mmol',\n", + " 'Mohm',\n", + " 'Mpc',\n", + " 'Mph',\n", + " 'Mphoton',\n", + " 'Mpix',\n", + " 'Mpixel',\n", + " 'Mrad',\n", + " 'Ms',\n", + " 'Msr',\n", + " 'Msun',\n", + " 'Mu',\n", + " 'Mvox',\n", + " 'Mvoxel',\n", + " 'Myr',\n", + " 'N',\n", + " 'NamedUnit',\n", + " 'Newton',\n", + " 'Ohm',\n", + " 'P',\n", + " 'PA',\n", + " 'PAU',\n", + " 'PB',\n", + " 'PBa',\n", + " 'PC',\n", + " 'PD',\n", + " 'PF',\n", + " 'PG',\n", + " 'PGal',\n", + " 'PH',\n", + " 'PHz',\n", + " 'PJ',\n", + " 'PJy',\n", + " 'PK',\n", + " 'PL',\n", + " 'PN',\n", + " 'POhm',\n", + " 'PP',\n", + " 'PPa',\n", + " 'PR',\n", + " 'PRy',\n", + " 'PS',\n", + " 'PSt',\n", + " 'PT',\n", + " 'PV',\n", + " 'PW',\n", + " 'PWb',\n", + " 'Pa',\n", + " 'Padu',\n", + " 'Parcmin',\n", + " 'Parcsec',\n", + " 'Pascal',\n", + " 'Pau',\n", + " 'Pb',\n", + " 'Pbarn',\n", + " 'Pbeam',\n", + " 'Pbin',\n", + " 'Pbit',\n", + " 'Pbyte',\n", + " 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'attodyne',\n", + " 'attoelectronvolt',\n", + " 'attofarad',\n", + " 'attogal',\n", + " 'attogauss',\n", + " 'attogram',\n", + " 'attohenry',\n", + " 'attohertz',\n", + " 'attohour',\n", + " 'attohr',\n", + " 'attojansky',\n", + " 'attojoule',\n", + " 'attokayser',\n", + " 'attolightyear',\n", + " 'attoliter',\n", + " 'attolumen',\n", + " 'attolux',\n", + " 'attometer',\n", + " 'attominute',\n", + " 'attomole',\n", + " 'attonewton',\n", + " 'attoparsec',\n", + " 'attopascal',\n", + " 'attophoton',\n", + " 'attopixel',\n", + " 'attopoise',\n", + " 'attoradian',\n", + " 'attorayleigh',\n", + " 'attorydberg',\n", + " 'attosecond',\n", + " 'attosiemens',\n", + " 'attosteradian',\n", + " 'attostokes',\n", + " 'attotesla',\n", + " 'attovolt',\n", + " 'attovoxel',\n", + " 'attowatt',\n", + " 'attoweber',\n", + " 'attoyear',\n", + " 'au',\n", + " 'avox',\n", + " 'avoxel',\n", + " 'ayr',\n", + " 'b',\n", + " 'bar',\n", + " 'barn',\n", + " 'barye',\n", + " 'beam',\n", + " 'beam_angular_area',\n", + " 'becquerel',\n", + " 'bin',\n", + " 'binary_prefixes',\n", + " 'bit',\n", + " 'bol',\n", + " 'brightness_temperature',\n", + " 'byte',\n", + " 'cA',\n", + " 'cAU',\n", + " 'cB',\n", + " 'cBa',\n", + " 'cC',\n", + " 'cD',\n", + " 'cF',\n", + " 'cG',\n", + " 'cGal',\n", + " 'cH',\n", + " 'cHz',\n", + " 'cJ',\n", + " 'cJy',\n", + " 'cK',\n", + " 'cL',\n", + " 'cN',\n", + " 'cOhm',\n", + " 'cP',\n", + " 'cPa',\n", + " 'cR',\n", + " 'cRy',\n", + " 'cS',\n", + " 'cSt',\n", + " 'cT',\n", + " 'cV',\n", + " 'cW',\n", + " 'cWb',\n", + " 'ca',\n", + " 'cadu',\n", + " 'candela',\n", + " 'carcmin',\n", + " 'carcsec',\n", + " 'cau',\n", + " 'cb',\n", + " 'cbarn',\n", + " 'cbeam',\n", + " 'cbin',\n", + " 'cbit',\n", + " 'cbyte',\n", + " 'ccd',\n", + " 'cchan',\n", + " 'ccount',\n", + " 'cct',\n", + " 'cd',\n", + " 'cdeg',\n", + " 'cdyn',\n", + " 'ceV',\n", + " 'centiBarye',\n", + " 'centiDa',\n", + " 'centiDalton',\n", + " 'centiDebye',\n", + " 'centiFarad',\n", + " 'centiGauss',\n", + " 'centiHenry',\n", + " 'centiHertz',\n", + " 'centiJansky',\n", + " 'centiJoule',\n", + " 'centiKayser',\n", + " 'centiKelvin',\n", + " 'centiNewton',\n", + " 'centiOhm',\n", + " 'centiPascal',\n", + " 'centiRayleigh',\n", + " 'centiSiemens',\n", + " 'centiTesla',\n", + " 'centiVolt',\n", + " 'centiWatt',\n", + " 'centiWeber',\n", + " 'centiamp',\n", + " 'centiampere',\n", + " 'centiannum',\n", + " 'centiarcminute',\n", + " 'centiarcsecond',\n", + " 'centiastronomical_unit',\n", + " 'centibarn',\n", + " 'centibarye',\n", + " 'centibit',\n", + " 'centibyte',\n", + " 'centicandela',\n", + " 'centicoulomb',\n", + " 'centicount',\n", + " 'centiday',\n", + " 'centidebye',\n", + " 'centidegree',\n", + " 'centidyne',\n", + " 'centielectronvolt',\n", + " 'centifarad',\n", + " 'centigal',\n", + " 'centigauss',\n", + " 'centigram',\n", + " 'centihenry',\n", + " 'centihertz',\n", + " 'centihour',\n", + " 'centihr',\n", + " 'centijansky',\n", + " 'centijoule',\n", + " 'centikayser',\n", + " 'centilightyear',\n", + " 'centiliter',\n", + " 'centilumen',\n", + " 'centilux',\n", + " 'centimeter',\n", + " 'centiminute',\n", + " 'centimole',\n", + " 'centinewton',\n", + " 'centiparsec',\n", + " 'centipascal',\n", + " 'centiphoton',\n", + " 'centipixel',\n", + " 'centipoise',\n", + " 'centiradian',\n", + " 'centirayleigh',\n", + " 'centirydberg',\n", + " 'centisecond',\n", + " 'centisiemens',\n", + " 'centisteradian',\n", + " 'centistokes',\n", + " 'centitesla',\n", + " 'centivolt',\n", + " 'centivoxel',\n", + " 'centiwatt',\n", + " 'centiweber',\n", + " 'centiyear',\n", + " 'cerg',\n", + " 'cg',\n", + " 'cgs',\n", + " 'ch',\n", + " 'chan',\n", + " 'ck',\n", + " 'cl',\n", + " 'clm',\n", + " 'clx',\n", + " 'clyr',\n", + " 'cm',\n", + " 'cmag',\n", + " 'cmin',\n", + " 'cmol',\n", + " 'cohm',\n", + " 'core',\n", + " 'coulomb',\n", + " 'count',\n", + " 'cpc',\n", + " 'cph',\n", + " 'cphoton',\n", + " 'cpix',\n", + " 'cpixel',\n", + " 'crad',\n", + " 'cs',\n", + " 'csr',\n", + " 'ct',\n", + " 'cu',\n", + " 'curie',\n", + " 'cvox',\n", + " 'cvoxel',\n", + " 'cy',\n", + " 'cycle',\n", + " 'cyr',\n", + " 'd',\n", + " 'dA',\n", + " 'dAU',\n", + " 'dB',\n", + " 'dBa',\n", + " 'dC',\n", + " 'dD',\n", + " 'dF',\n", + " 'dG',\n", + " 'dGal',\n", + " 'dH',\n", + " 'dHz',\n", + " 'dJ',\n", + " 'dJy',\n", + " 'dK',\n", + " 'dL',\n", + " 'dN',\n", + " 'dOhm',\n", + " 'dP',\n", + " 'dPa',\n", + " 'dR',\n", + " 'dRy',\n", + " 'dS',\n", + " 'dSt',\n", + " 'dT',\n", + " ...]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dir(u)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To create a quantity, we multiply a value by a unit." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "astropy.units.quantity.Quantity" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coord = 30 * u.deg\n", + "type(coord)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is a `Quantity` object.\n", + "\n", + "Jupyter knows how to display `Quantities` like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$30 \\; \\mathrm{{}^{\\circ}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coord" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Selecting a rectangle\n", + "\n", + "Now we'll select a rectangle from -55 to -45 degrees right ascension and -8 to 4 degrees of declination.\n", + "\n", + "We'll define variables to contain these limits." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "phi1_min = -55\n", + "phi1_max = -45\n", + "phi2_min = -8\n", + "phi2_max = 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To represent a rectangle, we'll use two lists of coordinates and multiply by their units." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "phi1_rect = [phi1_min, phi1_min, phi1_max, phi1_max] * u.deg\n", + "phi2_rect = [phi2_min, phi2_max, phi2_max, phi2_min] * u.deg" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`phi1_rect` and `phi2_rect` represent the coordinates of the corners of a rectangle. \n", + "\n", + "But they are in \"[a Heliocentric spherical coordinate system defined by the orbit of the GD1 stream](https://gala-astro.readthedocs.io/en/latest/_modules/gala/coordinates/gd1.html)\"\n", + "\n", + "In order to use them in a Gaia query, we have to convert them to [International Celestial Reference System](https://en.wikipedia.org/wiki/International_Celestial_Reference_System) (ICRS) coordinates. We can do that by storing the coordinates in a `GD1Koposov10` object provided by [Gala](https://gala-astro.readthedocs.io/en/latest/coordinates/)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "gala.coordinates.gd1.GD1Koposov10" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import gala.coordinates as gc\n", + "\n", + "corners = gc.GD1Koposov10(phi1=phi1_rect, phi2=phi2_rect)\n", + "type(corners)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can display the result like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "corners" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use `transform_to` to convert to ICRS coordinates." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "astropy.coordinates.builtin_frames.icrs.ICRS" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import astropy.coordinates as coord\n", + "\n", + "corners_icrs = corners.transform_to(coord.ICRS)\n", + "type(corners_icrs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is an `ICRS` object." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "corners_icrs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that a rectangle in one coordinate system is not necessarily a rectangle in another. In this example, the result is a polygon." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Selecting a polygon\n", + "\n", + "In order to use this polygon as part of an ADQL query, we have to convert it to a string with a comma-separated list of coordinates, as in this example:\n", + "\n", + "```\n", + "\"\"\"\n", + "POLYGON(143.65, 20.98, \n", + " 134.46, 26.39, \n", + " 140.58, 34.85, \n", + " 150.16, 29.01)\n", + "\"\"\"\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`corners_icrs` behaves like a list, so we can use a `for` loop to iterate through the points." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "for point in corners_icrs:\n", + " print(point)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From that, we can select the coordinates `ra` and `dec`:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "146d16m31.1993s 19d15m42.8754s\n", + "135d25m17.902s 25d52m38.594s\n", + "141d36m09.5337s 34d18m17.3891s\n", + "152d49m00.1576s 27d08m10.0051s\n" + ] + } + ], + "source": [ + "for point in corners_icrs:\n", + " print(point.ra, point.dec)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results are quantities with units, but if we select the `value` part, we get a dimensionless floating-point number." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "146.27533313607782 19.261909820533692\n", + "135.42163944306296 25.87738722767213\n", + "141.60264825107333 34.304830296257144\n", + "152.81671044675923 27.136112541397996\n" + ] + } + ], + "source": [ + "for point in corners_icrs:\n", + " print(point.ra.value, point.dec.value)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use string `format` to convert these numbers to strings." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['146.27533313607782, 19.261909820533692',\n", + " '135.42163944306296, 25.87738722767213',\n", + " '141.60264825107333, 34.304830296257144',\n", + " '152.81671044675923, 27.136112541397996']" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "point_base = \"{point.ra.value}, {point.dec.value}\"\n", + "\n", + "t = [point_base.format(point=point)\n", + " for point in corners_icrs]\n", + "t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is a list of strings, which we can join into a single string using `join`." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996'" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "point_list = ', '.join(t)\n", + "point_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that we invoke `join` on a string and pass the list as an argument.\n", + "\n", + "Before we can assemble the query, we need `columns` again (as we saw in the previous notebook)." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's the base for the query, with format specifiers for `columns` and `point_list`." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "query_base = \"\"\"SELECT {columns}\n", + "FROM gaiadr2.gaia_source\n", + "WHERE parallax < 1\n", + " AND bp_rp BETWEEN -0.75 AND 2 \n", + " AND 1 = CONTAINS(POINT(ra, dec), \n", + " POLYGON({point_list}))\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's the result:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n", + "FROM gaiadr2.gaia_source\n", + "WHERE parallax < 1\n", + " AND bp_rp BETWEEN -0.75 AND 2 \n", + " AND 1 = CONTAINS(POINT(ra, dec), \n", + " POLYGON(146.27533313607782, 19.261909820533692, 135.42163944306296, 25.87738722767213, 141.60264825107333, 34.304830296257144, 152.81671044675923, 27.136112541397996))\n", + "\n" + ] + } + ], + "source": [ + "query = query_base.format(columns=columns, \n", + " point_list=point_list)\n", + "print(query)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As always, we should take a minute to proof-read the query before we launch it.\n", + "\n", + "The result will be bigger than our previous queries, so it will take a little longer." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: Query finished. [astroquery.utils.tap.core]\n", + "
\n", + " name dtype unit description n_bad \n", + "--------------- ------- -------- ------------------------------------------------------------------ ------\n", + " source_id int64 Unique source identifier (unique within a particular Data Release) 0\n", + " ra float64 deg Right ascension 0\n", + " dec float64 deg Declination 0\n", + " pmra float64 mas / yr Proper motion in right ascension direction 0\n", + " pmdec float64 mas / yr Proper motion in declination direction 0\n", + " parallax float64 mas Parallax 0\n", + " parallax_error float64 mas Standard error of parallax 0\n", + "radial_velocity float64 km / s Radial velocity 139374\n", + "Jobid: 1601903357321O\n", + "Phase: COMPLETED\n", + "Owner: None\n", + "Output file: async_20201005090917.vot\n", + "Results: None\n" + ] + } + ], + "source": [ + "job = Gaia.launch_job_async(query)\n", + "print(job)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are the results." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "140340" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results = job.get_results()\n", + "len(results)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are more than 100,000 stars in this polygon, but that's a manageable size to work with." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Saving results\n", + "\n", + "This is the set of stars we'll work with in the next step. But since we have a substantial dataset now, this is a good time to save it.\n", + "\n", + "Storing the data in a file means we can shut down this notebook and pick up where we left off without running the previous query again.\n", + "\n", + "Astropy `Table` objects provide `write`, which writes the table to disk." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "filename = 'gd1_results.fits'\n", + "results.write(filename, overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because the filename ends with `fits`, the table is written in the [FITS format](https://en.wikipedia.org/wiki/FITS), which preserves the metadata associated with the table.\n", + "\n", + "If the file already exists, the `overwrite` argument causes it to be overwritten.\n", + "\n", + "To see how big the file is, we can use `ls` with the `-lh` option, which prints information about the file including its size in human-readable form." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-rw-rw-r-- 1 downey downey 8.6M Oct 5 09:09 gd1_results.fits\r\n" + ] + } + ], + "source": [ + "!ls -lh gd1_results.fits" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The file is about 8.6 MB." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook, we composed more complex queries to select stars within a polygonal region of the sky. Then we downloaded the results and saved them in a FITS file.\n", + "\n", + "In the next notebook, we'll reload the data from this file and replicate the next step in the analysis, using proper motion to identify stars likely to be in GD-1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best practices\n", + "\n", + "* For measurements with units, use `Quantity` objects that represent units explicitly and check for errors.\n", + "\n", + "* Use the `format` function to compose queries; it is often faster and less error-prone.\n", + "\n", + "* Develop queries incrementally: start with something simple, test it, and add a little bit at a time.\n", + "\n", + "* Once you have a query working, save the data in a local file. If you shut down the notebook and come back to it later, you can reload the file; you don't have to run the query again." + ] + }, + { + "cell_type": "raw", + "metadata": {}, + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/03_motion.ipynb b/03_motion.ipynb new file mode 100644 index 0000000..029157d --- /dev/null +++ b/03_motion.ipynb @@ -0,0 +1,1868 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This is the third in a series of lessons related to astronomy data.\n", + "\n", + "As a running example, we are replicating parts of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n", + "\n", + "In the first notebook, we wrote ADQL queries and used them to select and download data from the Gaia server.\n", + "\n", + "In the second notebook, we wrote a query to select stars from the region of the sky where we expect GD-1 to be, and saved the results in a FITS file.\n", + "\n", + "Now we'll read that data back and implement the next step in the analysis, identifying stars with the proper motion we expect for GD-1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lesson 3\n", + "\n", + "Here are the steps in this notebook:\n", + "\n", + "1. We'll read back the results from the previous notebook, which we saved in a FITS file.\n", + "\n", + "2. Then we'll transform the coordinates and proper motion data from ICRS back to the coordinate frame of GD-1.\n", + "\n", + "3. We'll put those results into a Pandas `DataFrame`, which we'll use to select stars near the centerline of GD-1.\n", + "\n", + "4. Plotting the proper motion of those stars, we'll identify a region of proper motion for stars that are likely to be in GD-1.\n", + "\n", + "5. Finally, we'll select and plot the stars whose proper motion is in that region.\n", + "\n", + "After completing this lesson, you should be able to\n", + "\n", + "* Select rows and columns from an Astropy `Table`.\n", + "\n", + "* Use Matplotlib to make a scatter plot.\n", + "\n", + "* Use Gala to transform coordinates.\n", + "\n", + "* Make a Pandas `DataFrame` and use a Boolean `Series` to select rows.\n", + "\n", + "* Save a `DataFrame` in an HDF5 file.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installing libraries\n", + "\n", + "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n", + "\n", + "If you are running this notebook on your own computer, you might have to install these libraries yourself. \n", + "\n", + "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n", + "\n", + "TODO: Add a link to the instructions.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# If we're running on Colab, install libraries\n", + "\n", + "import sys\n", + "IN_COLAB = 'google.colab' in sys.modules\n", + "\n", + "if IN_COLAB:\n", + " !pip install astroquery astro-gala pyia" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reload the data\n", + "\n", + "In the previous notebook, we ran a query on the Gaia server and downloaded data for roughly 100,000 stars. We saved the data in a FITS file so that now, picking up where we left off, we can read the data from a local file rather than running the query again.\n", + "\n", + "If you ran the previous notebook successfully, you should already have a file called `gd1_results.fits` that contains the data we downloaded.\n", + "\n", + "If not, you can run the following cell, which downloads the data from our repository." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "filename = 'gd1_results.fits'\n", + "\n", + "if not os.path.exists(filename):\n", + " !wget https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_results.fits" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now here's how we can read the data from the file back into an Astropy `Table`:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.table import Table\n", + "\n", + "results = Table.read(filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is an Astropy `Table`.\n", + "\n", + "We can use `info` to refresh our memory of the contents." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
\n", + " name dtype unit description \n", + "--------------- ------- -------- ------------------------------------------------------------------\n", + " source_id int64 Unique source identifier (unique within a particular Data Release)\n", + " ra float64 deg Right ascension\n", + " dec float64 deg Declination\n", + " pmra float64 mas / yr Proper motion in right ascension direction\n", + " pmdec float64 mas / yr Proper motion in declination direction\n", + " parallax float64 mas Parallax\n", + " parallax_error float64 mas Standard error of parallax\n", + "radial_velocity float64 km / s Radial velocity" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.info" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Selecting rows and columns\n", + "\n", + "In this section we'll see operations for selecting columns and rows from an Astropy `Table`. You can find more information about these operations in the [Astropy documentation](https://docs.astropy.org/en/stable/table/access_table.html).\n", + "\n", + "We can get the names of the columns like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['source_id',\n", + " 'ra',\n", + " 'dec',\n", + " 'pmra',\n", + " 'pmdec',\n", + " 'parallax',\n", + " 'parallax_error',\n", + " 'radial_velocity']" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.colnames" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And select an individual column like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<Column name='ra' dtype='float64' unit='deg' description='Right ascension' length=140340>\n", + "
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source_idradecpmrapmdecparallaxparallax_errorradial_velocity
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int64float64float64float64float64float64float64float64
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" + ], + "text/plain": [ + "\n", + " source_id ra dec pmra pmdec parallax parallax_error radial_velocity\n", + " deg deg mas / yr mas / yr mas mas km / s \n", + " int64 float64 float64 float64 float64 float64 float64 float64 \n", + "------------------ ------------------ ----------------- ------------------- ----------------- ------------------- ----------------- ---------------\n", + "637987125186749568 142.48301935991023 21.75771616932985 -2.5168384683875766 2.941813096629439 -0.2573448962333354 0.823720794509811 1e+20" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you might have guessed, the result is a `Row` object." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "astropy.table.row.Row" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(results[0])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the bracket operator selects both columns and rows. You might wonder how it knows which to select.\n", + "\n", + "If the expression in brackets is a string, it selects a column; if the expression is an integer, it selects a row.\n", + "\n", + "If you apply the bracket operator twice, you can select a column and then an element from the column." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "142.48301935991023" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results['ra'][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Or you can select a row and then an element from the row." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "142.48301935991023" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results[0]['ra']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You get the same result either way." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Scatter plot\n", + "\n", + "To see what the results look like, we'll use a scatter plot. The library we'll use is [Matplotlib](https://matplotlib.org/), which is the most widely-used plotting library for Python.\n", + "\n", + "The Matplotlib interface is based on MATLAB (hence the name), so if you know MATLAB, some of it will be familiar.\n", + "\n", + "We'll import like this." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pyplot part of the Matplotlib library. It is conventional to import it using the shortened name `plt`.\n", + "\n", + "Pyplot provides two functions that can make scatterplots, [plt.scatter](https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.pyplot.scatter.html) and [plt.plot](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.plot.html).\n", + "\n", + "* `scatter` is more versatile; for example, you can make every point in a scatter plot a different color.\n", + "\n", + "* `plot` is more limited, but for simple cases, it can be substantially faster. \n", + "\n", + "Jake Vanderplas explains these differences in [The Python Data Science Handbook](https://jakevdp.github.io/PythonDataScienceHandbook/04.02-simple-scatter-plots.html)\n", + "\n", + "Since we are plotting more than 100,000 points and they are all the same size and color, we'll use `plot`.\n", + "\n", + "Here's a scatter plot with right ascension on the x-axis and declination on the y-axis, both ICRS coordinates in degrees." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = results['ra']\n", + "y = results['dec']\n", + "plt.plot(x, y, 'ko')\n", + "\n", + "plt.xlabel('ra (degree ICRS)')\n", + "plt.ylabel('dec (degree ICRS)');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The arguments to `plt.plot` are `x`, `y`, and a string that specifies the style. In this case, the letters `ko` indicate that we want a black, round marker (`k` is for black because `b` is for blue).\n", + "\n", + "The functions `xlabel` and `ylabel` put labels on the axes.\n", + "\n", + "This scatter plot has a problem. It is \"[overplotted](https://python-graph-gallery.com/134-how-to-avoid-overplotting-with-python/)\", which means that there are so many overlapping points, we can't distinguish between high and low density areas.\n", + "\n", + "To fix this, we can provide optional arguments to control the size and transparency of the points.\n", + "\n", + "**Exercise:** In the call to `plt.plot`, add the keyword argument `markersize=0.1` to make the markers smaller.\n", + "\n", + "Then add the argument `alpha=0.1` to make the markers nearly transparent.\n", + "\n", + "Adjust these arguments until you think the figure shows the data most clearly.\n", + "\n", + "Note: Once you have made these changes, you might notice that the figure shows stripes with lower density of stars. These stripes are caused by..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TODO: what are they caused by? Rudy's comments:\n", + "\n", + ">The low density appearance of stars when going from one projection to another is bothersome. It potentially hints at some error in the transform, higher order terms, or something. We'll need to dig around some more. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transform back\n", + "\n", + "Remember that we selected data from a rectangle of coordinates in the `GD1Koposov10` frame, then transformed them to ICRS when we constructed the query.\n", + "The coordinates in `results` are in ICRS.\n", + "\n", + "To plot them, we will transform them back to the `GD1Koposov10` frame; that way, the axes of the figure are aligned with the GD-1, which will make it easy to select stars near the centerline of the stream.\n", + "\n", + "To do that, we'll put the results into a `GaiaData` object, provided by the [pyia library](https://pyia.readthedocs.io/en/latest/api/pyia.GaiaData.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pyia.data.GaiaData" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pyia import GaiaData\n", + "\n", + "gaia_data = GaiaData(results)\n", + "type(gaia_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the `GaiaData` object we can get sky coordinates like this." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "astropy.coordinates.sky_coordinate.SkyCoord" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import astropy.units as u\n", + "\n", + "skycoord = gaia_data.get_skycoord(\n", + " distance=8*u.kpc, \n", + " radial_velocity=0*u.km/u.s)\n", + "type(skycoord)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have to provide `distance` and `radial_velocity` because..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "TODO: why do we have to provide these? They seem kinda arbitrary. Maybe we should be doing this instead:\n", + "\n", + "```\n", + "distance=g.get_distance(min_parallax=1e-3*u.mas,\n", + " parallax_fill_value=1e-5*u.mas)\n", + "radial_velocity=g.get_radial_velocity(fill_value=1e8*u.km/u.s)\n", + "c = g.get_skycoord(distance=distance, radial_velocity=radial_velocity)\n", + "```\n", + "\n", + "Rudy's comments:\n", + "\n", + ">By setting a radial velocity and distance to the get_skycoord function, you override the information in the Gaia table (your alternative solutions seem to do the same thing?). I presume this was done to ignore the Gaia determined values and assume a nominal distance of 8kpc (they use 7.8kpc scaled isochrone in the paper, so probably an estimate from that). At larger distances, Gaia parallaxes are wonky and error dominated, so I don't see a problem from the distance assumption. Assuming zero radial velocity is a safe bet is most of the motion of the stars are tangential to our line of sight, regardless, any non-zero radial velocity measurement by Gaia at these large distances would be error dominated and likely consistent with zero. Your alternative solutions could work. \n", + ">\n", + ">It might be the case that the 0,-8 phi0/phi1 position is related to the distance of GD1 when doing the transform. I don't know how sensitive that will be to the choice of distance in get_skycoord. You can try 9-10 kpc to see if that changes the locust or makes it less coherent. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$[1 \\times 10^{8},~2365.5854,~9648.8101,~\\dots,~8617.6871,~1708.4958,~1045.7865] \\; \\mathrm{pc}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# alternative\n", + "\n", + "distance = gaia_data.get_distance(min_parallax=1e-3*u.mas,\n", + " parallax_fill_value=1e-5*u.mas)\n", + "distance" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# alternative\n", + "\n", + "radial_velocity = gaia_data.get_radial_velocity(fill_value=1e8*u.km/u.s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is an Astropy `SkyCoord` object ([documentation here](https://docs.astropy.org/en/stable/api/astropy.coordinates.SkyCoord.html#astropy.coordinates.SkyCoord)), which is useful because it provides `transform_to`, so we can transform the coordinates to other frames." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "import gala.coordinates as gc\n", + "\n", + "transformed = skycoord.transform_to(gc.GD1Koposov10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we'll use `reflex_correct` from Gala ([documentation here](https://gala-astro.readthedocs.io/en/latest/api/gala.coordinates.reflex_correct.html)) to correct for solar reflex motion.\n", + "\n", + "TODO: Can we explain what that is?" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "astropy.coordinates.sky_coordinate.SkyCoord" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gd1_coord = gc.reflex_correct(transformed)\n", + "\n", + "type(gd1_coord)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is a `SkyCoord` object that contains the transformed coordinates as attributes named `phi1` and `phi2`, which represent right ascension and declination in the `GD1Koposov10` frame.\n", + "\n", + "We can select them like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "phi1 = gd1_coord.phi1\n", + "phi2 = gd1_coord.phi2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And plot them like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(phi1, phi2, 'ko', markersize=0.1, alpha=0.2)\n", + "\n", + "plt.xlabel('ra (degree GD1)')\n", + "plt.ylabel('dec (degree GD1)');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Remember that we started with a rectangle in GD-1 coordinates. When transformed to ICRS, it's a non-rectangular polygon. Now that we have transformed back to GD-1 coordinates, it's a rectangle again." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pandas DataFrame\n", + "\n", + "At this point we have three objects containing different subsets of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "astropy.table.table.Table" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pyia.data.GaiaData" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(gaia_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "astropy.coordinates.sky_coordinate.SkyCoord" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "type(gd1_coord)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On one hand, this makes sense, since each object provides different capabilities. But working with three different object types can be awkward.\n", + "\n", + "It will be more convenient to choose one object and get all of the data into it. We'll use a Pandas DataFrame, for two reasons:\n", + "\n", + "1. It provides capabilities that are pretty much a superset of the other data structures, so it's the all-in-one solution.\n", + "\n", + "2. Pandas is a general-purpose tool that is useful in many domains, especially data science. If you are going to develop expertise in one tool, Pandas is a good choice.\n", + "\n", + "However, compared to an Astropy `Table`, Pandas has one big drawback: it does not keep the metadata associated with the table, including the units for the columns." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It's easy to convert a `Table` to a Pandas `DataFrame`." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(140340, 8)" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "df = results.to_pandas()\n", + "df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`DataFrame` provides `shape`, which shows the number of rows and columns.\n", + "\n", + "It also provides `head`, which displays the first few rows. It is useful for spot-checking large results as you go along." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2638073505568978688142.64528622.16693218.306747-7.9506600.1036400.5445841.000000e+20
3638086386175786752142.57739422.2279200.987786-2.584105-0.8573271.0596071.000000e+20
4638049655615392384142.58913622.1107830.244439-4.9410790.0996250.4862241.000000e+20
\n", + "
" + ], + "text/plain": [ + " source_id ra dec pmra pmdec parallax \\\n", + "0 637987125186749568 142.483019 21.757716 -2.516838 2.941813 -0.257345 \n", + "1 638285195917112960 142.254529 22.476168 2.662702 -12.165984 0.422728 \n", + "2 638073505568978688 142.645286 22.166932 18.306747 -7.950660 0.103640 \n", + "3 638086386175786752 142.577394 22.227920 0.987786 -2.584105 -0.857327 \n", + "4 638049655615392384 142.589136 22.110783 0.244439 -4.941079 0.099625 \n", + "\n", + " parallax_error radial_velocity \n", + "0 0.823721 1.000000e+20 \n", + "1 0.297472 1.000000e+20 \n", + "2 0.544584 1.000000e+20 \n", + "3 1.059607 1.000000e+20 \n", + "4 0.486224 1.000000e+20 " + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Python detail: `shape` is an attribute, so we can display it's value without calling it as a function; `head` is a function, so we need the parentheses." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can extract the columns we want from `gd1_coord` and add them as columns in the `DataFrame`. `phi1` and `phi2` contain the transformed coordinates." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(140340, 10)" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['phi1'] = gd1_coord.phi1\n", + "df['phi2'] = gd1_coord.phi2\n", + "df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`pm_phi1_cosphi2` and `pm_phi2` contain the components of proper motion in the transformed frame." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(140340, 12)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['pm_phi1'] = gd1_coord.pm_phi1_cosphi2\n", + "df['pm_phi2'] = gd1_coord.pm_phi2\n", + "df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Detail:** If you notice that `SkyCoord` has an attribute called `proper_motion`, you might wonder why we are not using it.\n", + "\n", + "We could have: `proper_motion` contains the same data as `pm_phi1_cosphi2` and `pm_phi2`, but in a different format." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot proper motion\n", + "\n", + "Now we are ready to replicate one of the panels in Figure 1 of the Price-Whelan and Bonaca paper, the one that shows the components of proper motion as a scatter plot:\n", + "\n", + "\n", + "\n", + "In this figure, the shaded area is a high-density region of stars with the proper motion we expect for stars in GD-1. After replicating this figure, we will select stars in this region as GD-1 candidates." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Selecting the centerline\n", + "\n", + "Following the analysis in the paper, we'll start by selecting stars near the centerline of GD-1.\n", + "\n", + "As we can see in the following figure, many stars in GD-1 are less than 1 degree of declination from the line `phi2=0`.\n", + "\n", + "\n", + "\n", + "If we select stars near this line, they are more likely to be in GD-1.\n", + "\n", + "We'll start by selecting the `phi2` column from the `DataFrame`:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.series.Series" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "phi2 = df['phi2']\n", + "type(phi2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is a `Series`, which is the structure Pandas uses to represent columns.\n", + "\n", + "We can use a comparison operator, `>`, to compare the values in a `Series` to a constant." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.series.Series" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "phi2_min = -1.0 * u.deg\n", + "phi2_max = 1.0 * u.deg\n", + "\n", + "mask = (df['phi2'] > phi2_min)\n", + "type(mask)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dtype('bool')" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mask.dtype" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is a `Series` of Boolean values, that is, `True` and `False`. " + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 False\n", + "2 False\n", + "3 False\n", + "4 False\n", + "Name: phi2, dtype: bool" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mask.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A Boolean `Series` is sometimes called a \"mask\" because we can use it to mask out some of the rows in a `DataFrame` and select the rest, like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "pandas.core.frame.DataFrame" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selected = df[mask]\n", + "type(selected)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`selected` is a `DataFrame` that contains only the rows from `df` that correspond to `True` values in `mask`.\n", + "\n", + "The previous mask selects all stars where `phi2` exceeds `phi2_min`; now we'll select stars where `phi2` falls between `phi2_min` and `phi2_max`." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "phi_mask = ((df['phi2'] > phi2_min) & \n", + " (df['phi2'] < phi2_max))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `&` operator computes \"logical AND\", which means the result is true where elements from both Boolean `Series` are true.\n", + "\n", + "The sum of a Boolean `Series` is the number of `True` values, so we can use `sum` to see how many stars are in the selected region." + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "25084" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "phi_mask.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can use `phi1_mask` to select stars near the centerline, which are more likely to be in GD-1." + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "25084" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "centerline = df[phi_mask]\n", + "len(centerline)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a scatter plot of proper motion for the selected stars." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pm1 = centerline['pm_phi1']\n", + "pm2 = centerline['pm_phi2']\n", + "\n", + "plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)\n", + " \n", + "plt.xlabel('Proper motion phi1 (GD1 frame)')\n", + "plt.ylabel('Proper motion phi2 (GD1 frame)')\n", + "\n", + "plt.xlim(-12, 8)\n", + "plt.ylim(-10, 10);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`xlim` and `ylim` set the bounds of the x and y axis, so we can zoom in on stars near (-8, 0), which is the proper motion we expect for stars in GD-1.\n", + "\n", + "TODO: Why do we expect that? And it is related to the arbitrary choice of velocity when we transformed coordinates?\n", + "\n", + "You might notice that our figure is less dense than the one in the paper. That's because we're using stars from a relatively small region. Their figure is based on a region about 10 times bigger.\n", + "\n", + "Soon we'll go back and select stars from a larger region. But first we'll use the proper motion data to identify stars likely to be in GD-1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filtering based on proper motion\n", + "\n", + "The next step is to select stars in the \"overdense\" region of proper motion, which are candidates to be in GD-1.\n", + "\n", + "In the original paper, Price-Whelan and Bonaca used a polygon to cover this region, as shown in this figure.\n", + "\n", + "\n", + "\n", + "We'll use a simple rectangle for now, but in a later lesson we'll see how to select a polygonal region as well.\n", + "\n", + "Here are bounds on proper motion we chose by eye," + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "pm1_min = -8.9\n", + "pm1_max = -6.9\n", + "pm2_min = -2.2\n", + "pm2_max = 1.0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To draw these bounds, we'll make two lists containing the coordinates of the corners of the rectangle." + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "pm1_rect = [pm1_min, pm1_min, pm1_max, pm1_max, pm1_min] * u.mas/u.yr\n", + "pm2_rect = [pm2_min, pm2_max, pm2_max, pm2_min, pm2_min] * u.mas/u.yr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what the plot looks like with the bounds we chose." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)\n", + "plt.plot(pm1_rect, pm2_rect, '-')\n", + " \n", + "plt.xlabel('Proper motion phi1 (GD1 frame)')\n", + "plt.ylabel('Proper motion phi2 (GD1 frame)')\n", + "\n", + "plt.xlim(-12, 8)\n", + "plt.ylim(-10, 10);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To select rows that fall within these bounds, we'll use the following function, which uses Pandas operators to make a mask that selects rows where `series` falls between `low` and `high`." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "def between(series, low, high):\n", + " \"\"\"Make a Boolean Series.\n", + " \n", + " series: Pandas Series\n", + " low: lower bound\n", + " high: upper bound\n", + " \n", + " returns: Boolean Series\n", + " \"\"\"\n", + " return (series > low) & (series < high)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following mask select stars with proper motion in the region we chose." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "pm_mask = (between(df['pm_phi1'], pm1_min, pm1_max) & \n", + " between(df['pm_phi2'], pm2_min, pm2_max))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, the sum of a Boolean series is the number of `True` values." + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1049" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pm_mask.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use this mask to select rows from `df`." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1049" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selected = df[pm_mask]\n", + "len(selected)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These are the stars we think are likely to be in GD-1. Let's see what they look like, plotting their coordinates (not their proper motion)." + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "phi1 = selected['phi1']\n", + "phi2 = selected['phi2']\n", + "\n", + "plt.plot(phi1, phi2, 'ko', markersize=0.5, alpha=0.5)\n", + "\n", + "plt.xlabel('ra (degree GD1)')\n", + "plt.ylabel('dec (degree GD1)');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that's starting to look like a tidal stream!" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Saving the DataFrame\n", + "\n", + "At this point we have run a successful query and cleaned up the results; this is a good time to save the data.\n", + "\n", + "To save a Pandas `DataFrame`, one option is to convert it to an Astropy `Table`, like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "astropy.table.table.Table" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "selected_table = Table.from_pandas(selected)\n", + "type(selected_table)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then we could write the `Table` to a FITS file, as we did in the previous notebook. \n", + "\n", + "But Pandas provides functions to write DataFrames in other formats; to see what they are [find the functions here that begin with `to_`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html).\n", + "\n", + "One of the best options is HDF5, which is Version 5 of [Hierarchical Data Format](https://en.wikipedia.org/wiki/Hierarchical_Data_Format).\n", + "\n", + "HDF5 is a binary format, so files are small and fast to read and write (like FITS, but unlike XML).\n", + "\n", + "An HDF5 file is similar to an SQL database in the sense that it can contain more than one table, although in HDF5 vocabulary, a table is called a Dataset. ([Multi-extension FITS files](https://www.stsci.edu/itt/review/dhb_2011/Intro/intro_ch23.html) can also contain more than one table.)\n", + "\n", + "And HDF5 stores the metadata associated with the table, including column names, row labels, and data types (like FITS).\n", + "\n", + "Finally, HDF5 is a cross-language standard, so if you write an HDF5 file with Pandas, you can read it back with many other software tools (more than FITS)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before we write the HDF5, let's delete the old one, if it exists." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "!rm -f gd1_dataframe.hdf5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can write a Pandas `DataFrame` to an HDF5 file like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "filename = 'gd1_dataframe.hdf5'\n", + "\n", + "df.to_hdf(filename, 'df')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because an HDF5 file can contain more than one Dataset, we have to provide a name, or \"key\", that identifies the Dataset in the file.\n", + "\n", + "We could use any string as the key, but in this example I use the variable name `df`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** We're going to need `centerline` later as well. Write a line or two of code to add it as a second Dataset in the HDF5 file." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "\n", + "centerline.to_hdf(filename, 'centerline')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Detail:** Reading and writing HDF5 tables requires a library called `PyTables` that is not always installed with Pandas. You can install it with pip like this:\n", + "\n", + "```\n", + "pip install tables\n", + "```\n", + "\n", + "If you install it using Conda, the name of the package is `pytables`.\n", + "\n", + "```\n", + "conda install pytables\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use `ls` to confirm that the file exists and check the size:" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-rw-rw-r-- 1 downey downey 17M Oct 5 09:17 gd1_dataframe.hdf5\r\n" + ] + } + ], + "source": [ + "!ls -lh gd1_dataframe.hdf5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can read it back like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(140340, 12)" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "read_back_df = pd.read_hdf(filename, 'df')\n", + "read_back_df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Pandas can write a variety of other formats, [which you can read about here](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook, we re-loaded the Gaia data we saved from a previous query.\n", + "\n", + "We transformed the coordinates and proper motion from ICRS to a frame aligned with GD-1, and stored the results in a Pandas `DataFrame`.\n", + "\n", + "The we replicated the selection process from the Price-Whelan and Bonaca paper:\n", + "\n", + "* We selected stars near the centerline of GD-1 and made a scatter plot of their proper motion.\n", + "\n", + "* We identified a rectangular region of proper motion that contains stars likely to be in GD-1.\n", + "\n", + "* We used a Boolean `Series` as a mask to select stars whose proper motion is in that region.\n", + "\n", + "So far, we have used data from a relatively small region of the sky. In the next notebook, we'll write a query that selects stars based on proper motion, which will allow us to explore a larger region." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best practices\n", + "\n", + "* When you make a scatter plot, adjust the size of the markers and their transparency so the figure is not overplotted; otherwise it can misrepresent the data badly.\n", + "\n", + "* For simple scatter plots in Matplotlib, `plot` is faster than `scatter`.\n", + "\n", + "* An Astropy `Table` and a Pandas `DataFrame` are similar in many ways and they provide many of the same functions. They have pros and cons, but for many projects, either one would be a reasonable choice." + ] + }, + { + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/04_select.ipynb b/04_select.ipynb new file mode 100644 index 0000000..6758643 --- /dev/null +++ b/04_select.ipynb @@ -0,0 +1,1361 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This is the fourth in a series of lessons related to astronomy data.\n", + "\n", + "As a running example, we are replicating parts of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n", + "\n", + "In the first notebook, we wrote ADQL queries and used them to select and download data from the Gaia server.\n", + "\n", + "In the second notebook, we write a query to select stars from the region of the sky where we expect GD-1 to be, and save the results in a FITS file.\n", + "\n", + "In the third notebook, we read that data back and identified stars with the proper motion we expect for GD-1.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lesson 4\n", + "\n", + "Here are the steps in this notebook:\n", + "\n", + "1. Using data from the previous notebook, we'll identify the values of proper motion for stars likely to be in GD-1.\n", + "\n", + "2. Then we'll compose an ADQL query that selects stars based on proper motion, so we can download only the data we need.\n", + "\n", + "3. We'll also see how to write the results to a CSV file.\n", + "\n", + "That will make it possible to search a bigger region of the sky in a single query.\n", + "\n", + "After completing this lesson, you should be able to\n", + "\n", + "* Convert proper motion between frames.\n", + "\n", + "* Write an ADQL query that selects based on proper motion." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installing libraries\n", + "\n", + "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n", + "\n", + "If you are running this notebook on your own computer, you might have to install these libraries yourself. \n", + "\n", + "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n", + "\n", + "TODO: Add a link to the instructions.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# If we're running on Colab, install libraries\n", + "\n", + "import sys\n", + "IN_COLAB = 'google.colab' in sys.modules\n", + "\n", + "if IN_COLAB:\n", + " !pip install astroquery astro-gala pyia" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reload the data\n", + "\n", + "The following cells download the data from the previous notebook, if necessary, and load it into a Pandas `DataFrame`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2020-10-05 09:18:27-- https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_dataframe.hdf5\n", + "Resolving github.com (github.com)... 140.82.112.4\n", + "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://raw.githubusercontent.com/AllenDowney/AstronomicalData/main/data/gd1_dataframe.hdf5 [following]\n", + "--2020-10-05 09:18:28-- https://raw.githubusercontent.com/AllenDowney/AstronomicalData/main/data/gd1_dataframe.hdf5\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.116.133\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.116.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 17220088 (16M) [application/octet-stream]\n", + "Saving to: ‘gd1_dataframe.hdf5’\n", + "\n", + "gd1_dataframe.hdf5 100%[===================>] 16.42M 7.19MB/s in 2.3s \n", + "\n", + "2020-10-05 09:18:30 (7.19 MB/s) - ‘gd1_dataframe.hdf5’ saved [17220088/17220088]\n", + "\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "filename = 'gd1_dataframe.hdf5'\n", + "\n", + "if not os.path.exists(filename):\n", + " !wget https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_dataframe.hdf5" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.read_hdf(filename, 'df')\n", + "centerline = pd.read_hdf(filename, 'centerline')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transforming the rectangle\n", + "\n", + "At this point we have downloaded data for a relatively large number of stars (more than 100,000) and selected a relatively small number (around 1000).\n", + "\n", + "It would be more efficient to use ADQL to select only the stars we need. That would also make it possible to download data covering a larger region of the sky.\n", + "\n", + "However, the selection we just did was based on proper motion in the `GD1Koposov10` frame. In order to do the same selection in ADQL, we have to translate these motions into ICRS.\n", + "\n", + "We'll start by creating a `GD1Koposov10` object that contains the corners of the rectangle we just selected in proper motion.\n", + "\n", + "But we also need to specify right ascension and declination. For those, we'll use a point in the center of the selected region." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import astropy.units as u\n", + "\n", + "phi1 = [-50, -50, -50, -50] * u.deg\n", + "phi2 = [2, 2, 2, 2] * u.deg" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "pm1_min = -8.9\n", + "pm1_max = -6.9\n", + "pm2_min = -2.2\n", + "pm2_max = 1.0" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "pm1_rect = [pm1_min, pm1_min, pm1_max, pm1_max] * u.mas/u.yr\n", + "pm2_rect = [pm2_min, pm2_max, pm2_max, pm2_min] * u.mas/u.yr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can put the coordinates and proper motion into the `GD1Koposov10` object." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import gala.coordinates as gc\n", + "\n", + "corners = gc.GD1Koposov10(phi1=phi1, phi2=phi2,\n", + " pm_phi1_cosphi2=pm1_rect, \n", + " pm_phi2=pm2_rect)\n", + "corners" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And translate the the ICRS frame." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$[-3.0392085,~-5.7154808,~-4.6190435,~-1.9427713] \\; \\mathrm{\\frac{mas}{yr}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import astropy.coordinates as coord\n", + "\n", + "corners_icrs = corners.transform_to(coord.ICRS)\n", + "corners_icrs.pm_ra_cosdec" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/latex": [ + "$[-8.6494631,~-6.8951635,~-5.2224934,~-6.9767929] \\; \\mathrm{\\frac{mas}{yr}}$" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "corners_icrs.pm_dec" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To see whether the transform worked as expected, we'll plot the polygon over the proper motions in ICRS, which we can select from `centerline`:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "pmra = centerline['pmra']\n", + "pmdec = centerline['pmdec']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's the transformed polygon superimposed on a scatter plot of proper motion in the ICRS frame." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(pmra, pmdec, 'ko', markersize=0.3, alpha=0.3)\n", + "plt.plot(corners_icrs.pm_ra_cosdec,\n", + " corners_icrs.pm_dec, '-')\n", + "\n", + "plt.xlabel('Proper motion ra (ICRS)')\n", + "plt.ylabel('Proper motion dec (ICRS)')\n", + "\n", + "plt.xlim([-10, 5])\n", + "plt.ylim([-20, 5]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The transformed polygon does not contain the region of high density, so it looks like the transformation did not work as expected.\n", + "\n", + "An alternative is to choose a bounding box that is as big as needed to cover the region of high density. Here are the bounds we chose." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "pmra_min = -7 \n", + "pmra_max = -2\n", + "pmdec_min = -15\n", + "pmdec_max = -11" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To see what they look like on the plot, we'll make a list of coordinates for the four corners." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "pmra_rect = [pmra_min, pmra_min, pmra_max, pmra_max] * u.mas/u.yr\n", + "pmdec_rect = [pmdec_min, pmdec_max, pmdec_max, pmdec_min] * u.mas/u.yr" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what the rectangle we chose looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(pmra, pmdec, 'ko', markersize=0.3, alpha=0.3)\n", + "plt.plot(pmra_rect, pmdec_rect, '-')\n", + "\n", + "plt.xlabel('Proper motion ra (ICRS)')\n", + "plt.ylabel('Proper motion dec (ICRS)')\n", + "\n", + "plt.xlim([-10, 5])\n", + "plt.ylim([-20, 5]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The advantage of the ICRS frame is that we can use ADQL to do this selection on the Gaia server, rather than downloading a lot of data we don't need." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Selecting the region\n", + "\n", + "Let's review how we got to this point.\n", + "\n", + "1. We made an ADQL query to the Gaia server to get data for stars in the vicinity of GD-1.\n", + "\n", + "2. We transformed to `GD1` coordinates so we could select stars along the centerline of GD-1.\n", + "\n", + "3. We plotted the proper motion of the centerline stars to identify the bounds of the overdense region.\n", + "\n", + "4. We made a mask that selects stars whose proper motion is in the overdense region.\n", + "\n", + "The problem is that we downloaded data for more than 100,000 stars and selected only about 1000 of them.\n", + "\n", + "It will be more efficient if we select on proper motion as part of the query. That will allow us to work with a larger region of the sky in a single query, and download less unneeded data.\n", + "\n", + "This query will select on the following conditions:\n", + "\n", + "* `parallax < 1`\n", + "\n", + "* `bp_rp BETWEEN -0.75 AND 2`\n", + "\n", + "* Coordinates within the transformed polygon\n", + "\n", + "* Proper motion with the rectangle we just defined.\n", + "\n", + "The first three conditions are the same as in `query4`. Only the last one is new.\n", + "\n", + "Now, we have code in the previous notebook than transforms the polygon and constructs an ADQL string. We could copy it from the previous notebook, but when you find yourself using the same code more than once, you might consider putting it in a function.\n", + "\n", + "For example, here's the code from the previous notebook that takes the boundaries of a rectangle in the GD1 frame and returns the coordinates of the corners tranformed to the ICRS frame." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def transform_rectangle(phi1_min, phi1_max, phi2_min, phi2_max):\n", + " \"\"\"Transform a rectange in GD1 to a polygon in ICRS.\n", + " \n", + " phi1_min, phi1_max: lower and upper bound of ra\n", + " phi2_min, phi2_max: lower and upper bound of dec\n", + " \n", + " returns: astropy.coordinates.builtin_frames.icrs.ICRS\n", + " \"\"\"\n", + " phi1_rect = [phi1_min, phi1_min, phi1_max, phi1_max] * u.deg\n", + " phi2_rect = [phi2_min, phi2_max, phi2_max, phi2_min] * u.deg\n", + " corners = gc.GD1Koposov10(phi1=phi1_rect, phi2=phi2_rect)\n", + " corners_icrs = corners.transform_to(coord.ICRS)\n", + " return corners_icrs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can call it like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "corners_icrs = transform_rectangle(-70, -20, -5, 5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Here's the code we used in a previous notebook to take a list of coordinates and write a string that represents a polygon in ADQL." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'135.30559858565638, 8.398623940157561, 126.50951508623503, 13.44494195652069, 163.0173655836748, 54.24242734020255, 172.9328536286811, 46.47260492416258'" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "point_base = \"{point.ra.value}, {point.dec.value}\"\n", + "\n", + "t = [point_base.format(point=point)\n", + " for point in corners_icrs]\n", + "\n", + "point_list = ', '.join(t)\n", + "point_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Wrap this code in a function that takes a list of coordinates, like `corner_icrs`, as a parameter and returns a string like `point_list`." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "\n", + "def make_adql_point_list(coords):\n", + " \"\"\"Make an ADQL string of coordinates.\n", + " \n", + " coords: object that behaves like a list of coordinates\n", + " \n", + " returns: string\n", + " \"\"\"\n", + " point_base = \"{point.ra.value}, {point.dec.value}\"\n", + "\n", + " t = [point_base.format(point=point)\n", + " for point in coords]\n", + "\n", + " return ', '.join(t)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'135.30559858565638, 8.398623940157561, 126.50951508623503, 13.44494195652069, 163.0173655836748, 54.24242734020255, 172.9328536286811, 46.47260492416258'" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Solution\n", + "\n", + "point_list = make_adql_point_list(corners_icrs)\n", + "point_list" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add WHERE clause for proper motion\n", + "\n", + "Now let's assemble the query. Here's the base string we used for the query in the previous notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "query_base = \"\"\"SELECT \n", + "{columns}\n", + "FROM gaiadr2.gaia_source\n", + "WHERE parallax < 1\n", + " AND bp_rp BETWEEN -0.75 AND 2 \n", + " AND 1 = CONTAINS(POINT(ra, dec), \n", + " POLYGON({point_list}))\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Modify `query_base` by adding new clauses to select `pmra` between `pmra_min` and `pmra_max` and `pmdec` between `pmdec_min` and pmdec_max." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "\n", + "query_base = \"\"\"SELECT \n", + "{columns}\n", + "FROM gaiadr2.gaia_source\n", + "WHERE parallax < 1\n", + " AND bp_rp BETWEEN -0.75 AND 2 \n", + " AND 1 = CONTAINS(POINT(ra, dec), \n", + " POLYGON({point_list}))\n", + " AND pmra BETWEEN {pmra_min} AND {pmra_max} \n", + " AND pmdec BETWEEN {pmdec_min} AND {pmdec_max}\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here again are the variables we want to select." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "columns = 'source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Use `format` to format `query_base` and define `query`, filling in the values of `columns`, `point_list`, and the boundaries of proper motion." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT \n", + "source_id, ra, dec, pmra, pmdec, parallax, parallax_error, radial_velocity\n", + "FROM gaiadr2.gaia_source\n", + "WHERE parallax < 1\n", + " AND bp_rp BETWEEN -0.75 AND 2 \n", + " AND 1 = CONTAINS(POINT(ra, dec), \n", + " POLYGON(135.30559858565638, 8.398623940157561, 126.50951508623503, 13.44494195652069, 163.0173655836748, 54.24242734020255, 172.9328536286811, 46.47260492416258))\n", + " AND pmra BETWEEN -7 AND -2 \n", + " AND pmdec BETWEEN -15 AND -11\n", + "\n" + ] + } + ], + "source": [ + "# Solution\n", + "\n", + "query = query_base.format(columns=columns, \n", + " point_list=point_list,\n", + " pmra_min=pmra_min,\n", + " pmra_max=pmra_max,\n", + " pmdec_min=pmdec_min,\n", + " pmdec_max=pmdec_max)\n", + "print(query)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we run it." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created TAP+ (v1.2.1) - Connection:\n", + "\tHost: gea.esac.esa.int\n", + "\tUse HTTPS: True\n", + "\tPort: 443\n", + "\tSSL Port: 443\n", + "Created TAP+ (v1.2.1) - Connection:\n", + "\tHost: geadata.esac.esa.int\n", + "\tUse HTTPS: True\n", + "\tPort: 443\n", + "\tSSL Port: 443\n", + "INFO: Query finished. [astroquery.utils.tap.core]\n", + "\n", + " name dtype unit description n_bad\n", + "--------------- ------- -------- ------------------------------------------------------------------ -----\n", + " source_id int64 Unique source identifier (unique within a particular Data Release) 0\n", + " ra float64 deg Right ascension 0\n", + " dec float64 deg Declination 0\n", + " pmra float64 mas / yr Proper motion in right ascension direction 0\n", + " pmdec float64 mas / yr Proper motion in declination direction 0\n", + " parallax float64 mas Parallax 0\n", + " parallax_error float64 mas Standard error of parallax 0\n", + "radial_velocity float64 km / s Radial velocity 13885\n", + "Jobid: 1601903914183O\n", + "Phase: COMPLETED\n", + "Owner: None\n", + "Output file: async_20201005091834.vot\n", + "Results: None\n" + ] + } + ], + "source": [ + "from astroquery.gaia import Gaia\n", + "\n", + "job = Gaia.launch_job_async(query)\n", + "print(job)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And get the results." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "13976" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "candidate_table = job.get_results()\n", + "len(candidate_table)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting one more time\n", + "\n", + "Let's see what the results look like." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = candidate_table['ra']\n", + "y = candidate_table['dec']\n", + "plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n", + "\n", + "plt.xlabel('ra (degree ICRS)')\n", + "plt.ylabel('dec (degree ICRS)');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we can see more clearly the effect of transforming the coordinates. In ICRS, it would be more difficulty to identity the stars near the centerline of GD-1.\n", + "\n", + "So, before we move on to the next step, let's collect the code we used to transform the coordinates and make a Pandas `DataFrame`:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "from pyia import GaiaData\n", + "\n", + "def make_dataframe(table):\n", + " \"\"\"Transform coordinates from ICRS to GD-1 frame.\n", + " \n", + " table: Astropy Table\n", + " \n", + " returns: Pandas DataFrame\n", + " \"\"\"\n", + " gaia_data = GaiaData(table)\n", + "\n", + " c_sky = gaia_data.get_skycoord(distance=8*u.kpc, \n", + " radial_velocity=0*u.km/u.s)\n", + " c_gd1 = gc.reflex_correct(\n", + " c_sky.transform_to(gc.GD1Koposov10))\n", + "\n", + " df = table.to_pandas()\n", + " df['phi1'] = c_gd1.phi1\n", + " df['phi2'] = c_gd1.phi2\n", + " df['pm_phi1'] = c_gd1.pm_phi1_cosphi2\n", + " df['pm_phi2'] = c_gd1.pm_phi2\n", + " return df" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's how we can use this function:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "candidate_df = make_dataframe(candidate_table)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And let's see the results." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = candidate_df['phi1']\n", + "y = candidate_df['phi2']\n", + "\n", + "plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n", + "\n", + "plt.xlabel('ra (degree GD1)')\n", + "plt.ylabel('dec (degree GD1)');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're starting to see GD-1 more clearly.\n", + "\n", + "We can compare this figure with one of these panels in Figure 1 from the original paper:\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "The top panel shows stars selected based on proper motion only, so it is comparable to our figure (although notice that it covers a wider region).\n", + "\n", + "In the next lesson, we will use photometry data from Pan-STARRS to do a second round of filtering, and see if we can replicate the bottom panel.\n", + "\n", + "We'll also learn how to add annotations like the ones in the figure from the paper, and customize the style of the figure to present the results clearly and compellingly." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Saving the DataFrame\n", + "\n", + "Let's save this `DataFrame` so we can pick up where we left off without running this query again." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "!rm -f gd1_candidates.hdf5" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "filename = 'gd1_candidates.hdf5'\n", + "\n", + "candidate_df.to_hdf(filename, 'candidate_df')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can use `ls` to confirm that the file exists and check the size:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-rw-rw-r-- 1 downey downey 1.4M Oct 5 09:19 gd1_candidates.hdf5\r\n" + ] + } + ], + "source": [ + "!ls -lh gd1_candidates.hdf5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## CSV\n", + "\n", + "Pandas can write a variety of other formats, [which you can read about here](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html).\n", + "\n", + "We won't cover all of them, but one other important one is [CSV](https://en.wikipedia.org/wiki/Comma-separated_values), which stands for \"comma-separated values\".\n", + "\n", + "CSV is a plain-text format with minimal formatting requirements, so it can be read and written by pretty much any tool that works with data. In that sense, it is the \"least common denominator\" of data formats.\n", + "\n", + "However, it has an important limitation: some information about the data gets lost in translation, notably the data types. If you read a CSV file from someone else, you might need some additional information to make sure you are getting it right.\n", + "\n", + "Also, CSV files tend to be big, and slow to read and write.\n", + "\n", + "With those caveats, here's how to write one:" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "candidate_df.to_csv('gd1_candidates.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can check the file size like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-rw-rw-r-- 1 downey downey 2.9M Oct 5 09:19 gd1_candidates.csv\r\n" + ] + } + ], + "source": [ + "!ls -lh gd1_candidates.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The CSV file about 2 times bigger than the HDF5 file (so that's not that bad, really).\n", + "\n", + "We can see the first few lines like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + ",source_id,ra,dec,pmra,pmdec,parallax,parallax_error,radial_velocity,phi1,phi2,pm_phi1,pm_phi2\r\n", + "0,635559124339440000,137.58671691646745,19.1965441084838,-3.770521900009566,-12.490481778113859,0.7913934419894347,0.2717538145759051,,-59.63048941944396,-1.21648525150429,-7.361362712556612,-0.5926328820420083\r\n", + "1,635860218726658176,138.5187065217173,19.09233926905897,-5.941679495793577,-11.346409129876392,0.30745551377348623,0.19946557779138105,,-59.247329893833296,-2.0160784008206476,-7.527126084599517,1.7487794924398758\r\n" + ] + } + ], + "source": [ + "!head -3 gd1_candidates.csv" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The CSV file contains the names of the columns, but not the data types.\n", + "\n", + "We can read the CSV file back like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "read_back_csv = pd.read_csv('gd1_candidates.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's compare the first few rows of `candidate_df` and `read_back_csv`" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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The Pandas functions for writing and reading CSV files provide options to avoid that problem, but this is an example of the kind of thing that can go wrong with CSV files." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In the previous notebook we downloaded a large dataset and then selected a small fraction of them based on proper motion.\n", + "\n", + "In this notebook, we improved this process by writing a more complex query that uses the database to select stars based on proper motion. This process requires more computation on the Gaia server, but then we're able to either:\n", + "\n", + "1. Search the same region and download less data, or\n", + "\n", + "2. Search a larger region while still downloading a manageable amount of data.\n", + "\n", + "In the next lesson, we'll learn about the databased `JOIN` operation and use it to download photometry data from Pan-STARRS." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best practice\n", + "\n", + "* When possible, it is often good practice to \"move the computation to the data\"; that is, to do as much of the work as possible on the database server before downloading the data.\n", + "\n", + "* For most applications, saving data in FITS or HDF5 is better than CSV. FITS and HDF5 are binary formats, so the file are usually smaller, and they store metadata, so you don't lose anything when you read the file back. The only advantage of CSV is that it is a \"least common denominator\" format; that is, it can be read by practically any application that works with data." + ] + }, + { + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/05_join.ipynb b/05_join.ipynb new file mode 100644 index 0000000..d42de8c --- /dev/null +++ b/05_join.ipynb @@ -0,0 +1,1307 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This is the fifth in a series of lessons related to astronomy data.\n", + "\n", + "As a continuing example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n", + "\n", + "Picking up where we left off, the next step in the analysis is to select candidate stars based on photometry. The following figure from the paper is a color-magnitude diagram for the stars selected based on proper motion:\n", + "\n", + "\n", + "\n", + "In red is a theoretical isochrone, showing where we expect the stars in GD-1 to fall based on the metallicity and age of their original globular cluster. \n", + "\n", + "By selecting stars in the shaded area, we can further distinguish the main sequence of GD-1 from younger background stars." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lesson 5\n", + "\n", + "Here are the steps in this notebook:\n", + "\n", + "1. We'll reload the candidate stars we identified in the previous notebook.\n", + "\n", + "2. Then we'll run a query on the Gaia server that uploads the table of candidates and uses a `JOIN` operation to select photometry data for the candidate stars.\n", + "\n", + "3. We'll write the results to a file for use in the next notebook.\n", + "\n", + "After completing this lesson, you should be able to\n", + "\n", + "* Upload a table to the Gaia server.\n", + "\n", + "* Write ADQL queries involving `JOIN` operations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installing libraries\n", + "\n", + "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n", + "\n", + "If you are running this notebook on your own computer, you might have to install these libraries yourself. \n", + "\n", + "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n", + "\n", + "TODO: Add a link to the instructions." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# If we're running on Colab, install libraries\n", + "\n", + "import sys\n", + "IN_COLAB = 'google.colab' in sys.modules\n", + "\n", + "if IN_COLAB:\n", + " !pip install astroquery astro-gala pyia\n", + " !mkdir data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reloading the data\n", + "\n", + "The following cell downloads the data from the previous notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2020-10-05 09:20:34-- https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_candidates.hdf5\n", + "Resolving github.com (github.com)... 140.82.112.4\n", + "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://raw.githubusercontent.com/AllenDowney/AstronomicalData/main/data/gd1_candidates.hdf5 [following]\n", + "--2020-10-05 09:20:35-- https://raw.githubusercontent.com/AllenDowney/AstronomicalData/main/data/gd1_candidates.hdf5\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.116.133\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.116.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 1462792 (1.4M) [application/octet-stream]\n", + "Saving to: ‘gd1_candidates.hdf5’\n", + "\n", + "gd1_candidates.hdf5 100%[===================>] 1.39M 2.37MB/s in 0.6s \n", + "\n", + "2020-10-05 09:20:36 (2.37 MB/s) - ‘gd1_candidates.hdf5’ saved [1462792/1462792]\n", + "\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "filename = 'gd1_candidates.hdf5'\n", + "\n", + "if not os.path.exists(filename):\n", + " !wget https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_candidates.hdf5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we can read it back." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "candidate_df = pd.read_hdf(filename, 'candidate_df')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`candidate_df` is the Pandas DataFrame that contains results from the query in the previous notebook, which selects stars likely to be in GD-1 based on proper motion. It also includes position and proper motion transformed to the ICRS frame." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "x = candidate_df['phi1']\n", + "y = candidate_df['phi2']\n", + "\n", + "plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n", + "\n", + "plt.xlabel('ra (degree GD1)')\n", + "plt.ylabel('dec (degree GD1)');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is the same figure we saw at the end of the previous notebook. GD-1 is visible against the background stars, but we will be able to see it more clearly after selecting based on photometry data." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting photometry data\n", + "\n", + "The Gaia dataset contains some photometry data, including the variable `bp_rp`, which we used in the original query to select stars with BP - RP color between -0.75 and 2.\n", + "\n", + "Selecting stars with `bp-rp` less than 2 excludes many class M dwarf stars, which are low temperature, low luminosity. A star like that at GD-1's distance would be hard to detect, so if it is detected, it it more likely to be in the foreground.\n", + "\n", + "Now, to select stars with the age and metal richness we expect in GD-1, we will use `g − i` color and apparent `g`-band magnitude, which are available from the Pan-STARRS survey." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Conveniently, the Gaia server provides data from Pan-STARRS as a table in the same database we have been using, so we can access it by making ADQL queries.\n", + "\n", + "In general, looking up a star from the Gaia catalog and finding the corresponding star in the Pan-STARRS catalog is not easy. This kind of cross matching is not always possible, because a star might appear in one catalog and not the other. And even when both stars are present, there might not be a clear one-to-one relationship between stars in the two catalogs.\n", + "\n", + "Fortunately, smart people have worked on this problem, and the Gaia database includes cross-matching tables that suggest a best neighbor in the Pan-STARRS catalog for many stars in the Gaia catalog.\n", + "\n", + "[This document describes the cross matching process](https://gea.esac.esa.int/archive/documentation/GDR2/Catalogue_consolidation/chap_cu9val_cu9val/ssec_cu9xma/sssec_cu9xma_extcat.html). Briefly, it uses a cone search to find possible matches in approximately the right position, then uses attributes like color and magnitude to choose pairs of stars most likely to be identical." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So the hard part of cross-matching has been done for us. However, using the results is a little tricky.\n", + "\n", + "But, it is also an opportunity to learn about one of the most important tools for working with databases: \"joining\" tables.\n", + "\n", + "In general, a \"join\" is an operation where you match up records from one table with records from another table using as a \"key\" a piece of information that is common to both tables, usually some kind of ID code.\n", + "\n", + "In this example:\n", + "\n", + "* Stars in the Gaia dataset are identified by `source_id`.\n", + "\n", + "* Stars in the Pan-STARRS dataset are identified by `obj_id`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For each candidate star we have selected so far, we have the `source_id`; the goal is to find the `obj_id` for the same star (we hope) in the Pan-STARRS catalog.\n", + "\n", + "To do that we will:\n", + "\n", + "1. Make a table that contains the `source_id` for each candidate star and upload the table to the Gaia server;\n", + "\n", + "2. Use the `JOIN` operator to look up each `source_id` in the `gaiadr2.panstarrs1_best_neighbour` table, which contains the `obj_id` of the best match for each star in the Gaia catalog; then\n", + "\n", + "3. Use the `JOIN` operator again to look up each `obj_id` in the `panstarrs1_original_valid` table, which contains the Pan-STARRS photometry data we want.\n", + "\n", + "Let's start with the first step, uploading a table." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preparing a table for uploading\n", + "\n", + "For each candidate star, we want to find the corresponding row in the `gaiadr2.panstarrs1_best_neighbour` table.\n", + "\n", + "In order to do that, we have to:\n", + "\n", + "1. Write the table in a local file as an XML VOTable, which is a format suitable for transmitting a table over a network.\n", + "\n", + "2. Write an ADQL query that refers to the uploaded table.\n", + "\n", + "3. Change the way we submit the job so it uploads the table before running the query." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first step is not too difficult because Astropy provides a function called `writeto` that can write a `Table` in `XML`.\n", + "\n", + "[The documentation of this process is here](https://docs.astropy.org/en/stable/io/votable/).\n", + "\n", + "First we have to convert our Pandas `DataFrame` to an Astropy `Table`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "astropy.table.table.Table" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from astropy.table import Table\n", + "\n", + "candidate_table = Table.from_pandas(candidate_df)\n", + "type(candidate_table)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To write the file, we can use `Table.write` with `format='votable'`, [as described here](https://docs.astropy.org/en/stable/io/unified.html#vo-tables)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "table = candidate_table[['source_id']]\n", + "table.write('candidate_df.xml', format='votable', overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that we select a single column from the table, `source_id`.\n", + "We could write the entire table to a file, but that would take longer to transmit over the network, and we really only need one column.\n", + "\n", + "This process, taking a structure like a `Table` and translating it into a form that can be transmitted over a network, is called [serialization](https://en.wikipedia.org/wiki/Serialization).\n", + "\n", + "XML is one of the most common serialization formats. One nice feature is that XML data is plain text, as opposed to binary digits, so you can read the file we just wrote:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "\r\n", + "\r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n", + " \r\n" + ] + } + ], + "source": [ + "!head candidate_df.xml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "XML is a general format, so different XML files contain different kinds of data. In order to read an XML file, it's not enough to know that it's XML; you also have to know the data format, which is called a [schema](https://en.wikipedia.org/wiki/XML_schema).\n", + "\n", + "In this example, the schema is VOTable; notice that one of the first tags in the file specifies the schema, and even includes the URL where you can get its definition.\n", + "\n", + "So this is an example of a self-documenting format.\n", + "\n", + "A drawback of XML is that it tends to be big, which is why we wrote just the `source_id` column rather than the whole table.\n", + "The size of the file is about 750 KB, so that's not too bad." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-rw-rw-r-- 1 downey downey 752K Oct 5 09:20 candidate_df.xml\r\n" + ] + } + ], + "source": [ + "!ls -lh candidate_df.xml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** There's a gotcha here we want to warn you about. Why do you think we used double brackets to specify the column we wanted? What happens if you use single brackets?\n", + "\n", + "Run these cells to find out." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "astropy.table.table.Table" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "table = candidate_table[['source_id']]\n", + "type(table)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "astropy.table.column.Column" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "column = candidate_table['source_id']\n", + "type(column)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# writeto(column, 'candidate_df.xml')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Uploading a table\n", + "\n", + "The next step is to upload this table to the Gaia server and use it as part of a query.\n", + "\n", + "[Here's the documentation that explains how to run a query with an uploaded table](https://astroquery.readthedocs.io/en/latest/gaia/gaia.html#synchronous-query-on-an-on-the-fly-uploaded-table).\n", + "\n", + "In the spirit of incremental development and testing, let's start with the simplest possible query." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "query = \"\"\"SELECT *\n", + "FROM tap_upload.candidate_df\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This query downloads all rows and all columns from the uploaded table. The name of the table has two parts: `tap_upload` specifies a table that was uploaded using TAP+ (remember that's the name of the protocol we're using to talk to the Gaia server).\n", + "\n", + "And `candidate_df` is the name of the table, which we get to choose (unlike `tap_upload`, which we didn't get to choose).\n", + "\n", + "Here's how we run the query:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Created TAP+ (v1.2.1) - Connection:\n", + "\tHost: gea.esac.esa.int\n", + "\tUse HTTPS: True\n", + "\tPort: 443\n", + "\tSSL Port: 443\n", + "Created TAP+ (v1.2.1) - Connection:\n", + "\tHost: geadata.esac.esa.int\n", + "\tUse HTTPS: True\n", + "\tPort: 443\n", + "\tSSL Port: 443\n", + "INFO: Query finished. [astroquery.utils.tap.core]\n" + ] + } + ], + "source": [ + "from astroquery.gaia import Gaia\n", + "\n", + "job = Gaia.launch_job_async(query=query, \n", + " upload_resource='candidate_df.xml', \n", + " upload_table_name='candidate_df')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`upload_resource` specifies the name of the file we want to upload, which is the file we just wrote.\n", + "\n", + "`upload_table_name` is the name we assign to this table, which is the name we used in the query.\n", + "\n", + "And here are the results:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=13976\n", + "
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" + ], + "text/plain": [ + "\n", + " source_id \n", + " int64 \n", + "------------------\n", + "635559124339440000\n", + "635860218726658176\n", + "635674126383965568\n", + "635535454774983040\n", + "635875994141946752\n", + "635614168640132864\n", + "635685125795548160\n", + "635865853723704576\n", + "635523531945742592\n", + "635821843194387840\n", + " ...\n", + "612394926899159168\n", + "612372352551038464\n", + "612288854091187712\n", + "612426705361398528\n", + "612428870024913152\n", + "612256418500423168\n", + "612340153181820544\n", + "612429144902815104\n", + "612288755307666176\n", + "612415538446105216" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results = job.get_results()\n", + "results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If things go according to plan, the result should contain the same rows and columns as the uploaded table." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(13976, 13976)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(candidate_table), len(results)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set(candidate_table['source_id']) == set(results['source_id'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example, we uploaded a table and then downloaded it again, so that's not too useful.\n", + "\n", + "But now that we can upload a table, we can join it with other tables on the Gaia server." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Joining with an uploaded table\n", + "\n", + "Here's the first example of a query that contains a `JOIN` clause." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "query1 = \"\"\"SELECT *\n", + "FROM gaiadr2.panstarrs1_best_neighbour as best\n", + "JOIN tap_upload.candidate_df as candidate_df\n", + "ON best.source_id = candidate_df.source_id\n", + "\"\"\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's break that down one clause at a time:\n", + "\n", + "* `SELECT *` means we will download all columns from both tables.\n", + "\n", + "* `FROM gaiadr2.panstarrs1_best_neighbour as best` means that we'll get the columns from the Pan-STARRS best neighbor table, which we'll refer to using the short name `best`.\n", + "\n", + "* `JOIN tap_upload.candidate_df as candidate_df` means that we'll also get columns from the uploaded table, which we'll refer to using the short name `candidate_df`.\n", + "\n", + "* `ON best.source_id = candidate_df.source_id` specifies that we will use `source_id ` to match up the rows from the two tables.\n", + "\n", + "Here's the [documentation of the best neighbor table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_crossmatches/ssec_dm_panstarrs1_best_neighbour.html).\n", + "\n", + "Let's run the query:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: Query finished. [astroquery.utils.tap.core]\n" + ] + } + ], + "source": [ + "job1 = Gaia.launch_job_async(query=query1, \n", + " upload_resource='candidate_df.xml', \n", + " upload_table_name='candidate_df')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And get the results." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=7189\n", + "
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6355354547749830401306313783776573690.0343230288289910761015635535454774983040
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........................
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" + ], + "text/plain": [ + "\n", + " source_id original_ext_source_id ... source_id_2 \n", + " ... \n", + " int64 int64 ... int64 \n", + "------------------ ---------------------- ... ------------------\n", + "635860218726658176 130911385187671349 ... 635860218726658176\n", + "635674126383965568 130831388428488720 ... 635674126383965568\n", + "635535454774983040 130631378377657369 ... 635535454774983040\n", + "635614168640132864 130571395922140135 ... 635614168640132864\n", + "635685125795548160 130831393661234780 ... 635685125795548160\n", + "635694467349085056 130881390618041541 ... 635694467349085056\n", + "635877570395672448 131071382899919816 ... 635877570395672448\n", + "635745358416794496 131241398972307680 ... 635745358416794496\n", + "635598607974369792 130341392091279513 ... 635598607974369792\n", + " ... ... ... ...\n", + "612296172717818624 129691338006168780 ... 612296172717818624\n", + "612250375480101760 129741346475897464 ... 612250375480101760\n", + "612240411155898240 129691341820952769 ... 612240411155898240\n", + "612394926899159168 130581355199751795 ... 612394926899159168\n", + "612372352551038464 130241347078637742 ... 612372352551038464\n", + "612426705361398528 130521346747100502 ... 612426705361398528\n", + "612256418500423168 129931349075297310 ... 612256418500423168\n", + "612340153181820544 130261343209165040 ... 612340153181820544\n", + "612288755307666176 129761340827717697 ... 612288755307666176\n", + "612415538446105216 130791353111275866 ... 612415538446105216" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results1 = job1.get_results()\n", + "results1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This table contains all of the columns from the best neighbor table, plus the single column from the uploaded table." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['source_id',\n", + " 'original_ext_source_id',\n", + " 'angular_distance',\n", + " 'number_of_neighbours',\n", + " 'number_of_mates',\n", + " 'best_neighbour_multiplicity',\n", + " 'gaia_astrometric_params',\n", + " 'source_id_2']" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results1.colnames" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because one of the column names appears in both tables, the second instance of `source_id` has been appended with the suffix `_2`.\n", + "\n", + "The length of the results table is about 2000, which means we were not able to find matches for all stars in the list of candidate_df." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7189" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(results1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get more information about the matching process, we can inspect `best_neighbour_multiplicity`, which indicates for each star in Gaia how many stars in Pan-STARRS are equally likely matches.\n", + "\n", + "For this kind of data exploration, we'll convert a column from the table to a Pandas `Series` so we can use `value_counts`, which counts the number of times each value appears in a `Series`, like a histogram." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1 7189\n", + "dtype: int64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "nn = pd.Series(results1['best_neighbour_multiplicity'])\n", + "nn.value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result shows that `1` is the only value in the `Series`, appearing xxx times.\n", + "\n", + "That means that in every case where a match was found, the matching algorithm identified a single neighbor as the most likely match.\n", + "\n", + "Similarly, `number_of_mates` indicates the number of other stars in Gaia that match with the same star in Pan-STARRS." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 7186\n", + "1 3\n", + "dtype: int64" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nm = pd.Series(results1['number_of_mates'])\n", + "nm.value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this set of candidate_df, almost all of the stars we've selected from Pan-STARRS are only matched with a single star in the Gaia catalog.\n", + "\n", + "**Detail** The table also contains `number_of_neighbors` which is the number of stars in Pan-STARRS that match in terms of position, before using other critieria to choose the most likely match." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Getting the photometry data\n", + "\n", + "The most important column in `results1` is `original_ext_source_id` which is the `obj_id` we will use to look up the likely matches in Pan-STARRS to get photometry data.\n", + "\n", + "The process is similar to what we just did to look up the matches. We will:\n", + "\n", + "1. Make a table that contains `source_id` and `original_ext_source_id`.\n", + "\n", + "2. Write the table to an XML VOTable file.\n", + "\n", + "3. Write a query that joins the uploaded table with `gaiadr2.panstarrs1_original_valid` and selects the photometry data we want.\n", + "\n", + "4. Run the query using the uploaded table.\n", + "\n", + "Since we've done everything here before, we'll do these steps as an exercise.\n", + "\n", + "**Exercise:** Select `source_id` and `original_ext_source_id` from `results1` and write the resulting table as a file named `external.xml`." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "\n", + "table = results1[['source_id', 'original_ext_source_id']]\n", + "table.write('external.xml', format='votable', overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Use `!head` to confirm that the file exists and contains an XML VOTable." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "\r\n", + "\r\n", + " \r\n", + "
\r\n", + " \r\n", + " \r\n", + " Unique Gaia source identifier\r\n", + " \r\n" + ] + } + ], + "source": [ + "!head external.xml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Read [the documentation of the Pan-STARRS table](https://gea.esac.esa.int/archive/documentation/GDR2/Gaia_archive/chap_datamodel/sec_dm_external_catalogues/ssec_dm_panstarrs1_original_valid.html) and make note of `obj_id`, which contains the object IDs we'll use to find the rows we want.\n", + "\n", + "Write a query that uses each value of `original_ext_source_id` from the uploaded table to find a row in `gaiadr2.panstarrs1_original_valid` with the same value in `obj_id`, and select all columns from both tables.\n", + "\n", + "Suggestion: Develop and test your query incrementally. For example:\n", + "\n", + "1. Write a query that downloads all columns from the uploaded table. Test to make sure we can read the uploaded table.\n", + "\n", + "2. Write a query that downloads the first 10 rows from `gaiadr2.panstarrs1_original_valid`. Test to make sure we can access Pan-STARRS data.\n", + "\n", + "3. Write a query that joins the two tables and selects all columns. Test that the join works as expected.\n", + "\n", + "\n", + "As a bonus exercise, write a query that joins the two tables and selects just the columns we need:\n", + "\n", + "* `source_id` from the uploaded table\n", + "\n", + "* `g_mean_psf_mag` from `gaiadr2.panstarrs1_original_valid`\n", + "\n", + "* `i_mean_psf_mag` from `gaiadr2.panstarrs1_original_valid`\n", + "\n", + "Hint: When you select a column from a join, you have to specify which table the column is in." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "\n", + "query2 = \"\"\"SELECT *\n", + "FROM tap_upload.external as external\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "\n", + "query2 = \"\"\"SELECT TOP 10 *\n", + "FROM gaiadr2.panstarrs1_original_valid\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "\n", + "query2 = \"\"\"SELECT *\n", + "FROM gaiadr2.panstarrs1_original_valid as ps\n", + "JOIN tap_upload.external as external\n", + "ON ps.obj_id = external.original_ext_source_id\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "\n", + "query2 = \"\"\"SELECT\n", + "external.source_id, ps.g_mean_psf_mag, ps.i_mean_psf_mag\n", + "FROM gaiadr2.panstarrs1_original_valid as ps\n", + "JOIN tap_upload.external as external\n", + "ON ps.obj_id = external.original_ext_source_id\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SELECT\n", + "external.source_id, ps.g_mean_psf_mag, ps.i_mean_psf_mag\n", + "FROM gaiadr2.panstarrs1_original_valid as ps\n", + "JOIN tap_upload.external as external\n", + "ON ps.obj_id = external.original_ext_source_id\n", + "\n" + ] + } + ], + "source": [ + "print(query2)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: Query finished. [astroquery.utils.tap.core]\n" + ] + } + ], + "source": [ + "job2 = Gaia.launch_job_async(query=query2, \n", + " upload_resource='external.xml', \n", + " upload_table_name='external')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Table length=7189\n", + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
source_idg_mean_psf_magi_mean_psf_mag
mag
int64float64float64
63586021872665817617.897800445556617.5174007415771
63567412638396556819.287300109863317.6781005859375
63553545477498304016.923799514770516.478099822998
63561416864013286416.151599884033214.6662998199463
63568512579554816020.01479911804218.3934993743896
63569446734908505618.670900344848617.9841995239258
63587757039567244821.83959960937519.7248001098633
63574535841679449615.362500190734914.8073997497559
63559860797436979216.522399902343816.1375007629395
.........
61229617271781862417.494400024414116.926700592041
61225037548010176015.333000183105514.6280002593994
61224041115589824021.344699859619119.6156997680664
61239492689915916816.441400527954115.8212003707886
61237235255103846418.775199890136717.2695007324219
61242670536139852820.647300720214819.7865009307861
61225641850042316820.871599197387719.9612007141113
61234015318182054422.253799438476619.6744995117188
61228875530766617617.367700576782216.392599105835
61241553844610521619.422800064086918.8339996337891
" + ], + "text/plain": [ + "\n", + " source_id g_mean_psf_mag i_mean_psf_mag \n", + " mag \n", + " int64 float64 float64 \n", + "------------------ ---------------- ----------------\n", + "635860218726658176 17.8978004455566 17.5174007415771\n", + "635674126383965568 19.2873001098633 17.6781005859375\n", + "635535454774983040 16.9237995147705 16.478099822998\n", + "635614168640132864 16.1515998840332 14.6662998199463\n", + "635685125795548160 20.014799118042 18.3934993743896\n", + "635694467349085056 18.6709003448486 17.9841995239258\n", + "635877570395672448 21.839599609375 19.7248001098633\n", + "635745358416794496 15.3625001907349 14.8073997497559\n", + "635598607974369792 16.5223999023438 16.1375007629395\n", + " ... ... ...\n", + "612296172717818624 17.4944000244141 16.926700592041\n", + "612250375480101760 15.3330001831055 14.6280002593994\n", + "612240411155898240 21.3446998596191 19.6156997680664\n", + "612394926899159168 16.4414005279541 15.8212003707886\n", + "612372352551038464 18.7751998901367 17.2695007324219\n", + "612426705361398528 20.6473007202148 19.7865009307861\n", + "612256418500423168 20.8715991973877 19.9612007141113\n", + "612340153181820544 22.2537994384766 19.6744995117188\n", + "612288755307666176 17.3677005767822 16.392599105835\n", + "612415538446105216 19.4228000640869 18.8339996337891" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results2 = job2.get_results()\n", + "results2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Challenge exercise**\n", + "\n", + "Do both joins in one query.\n", + "\n", + "There's an [example here](https://github.com/smoh/Getting-started-with-Gaia/blob/master/gaia-adql-snippets.md) you could start with." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Write the data\n", + "\n", + "Since we have the data in an Astropy `Table`, let's store it in a FITS file." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "filename = 'gd1_photo.fits'\n", + "results2.write(filename, overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can check that the file exists, and see how big it is." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-rw-rw-r-- 1 downey downey 175K Oct 5 09:21 gd1_photo.fits\r\n" + ] + } + ], + "source": [ + "!ls -lh gd1_photo.fits" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "At around 175 KB, it is smaller than some of the other files we've been working with." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook, we used database `JOIN` operations to select photometry data for the stars we've identified as candidates to be in GD-1.\n", + "\n", + "In the next notebook, we'll use this data for a second round of selection, identifying stars that have photometry data consistent with GD-1." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best practice\n", + "\n", + "* Use `JOIN` operations to combine data from multiple tables in a databased, using some kind of identifier to match up records from one table with records from another.\n", + "\n", + "* This is another example of a practice we saw in the previous notebook, moving the computation to the data." + ] + }, + { + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/06_photo.ipynb b/06_photo.ipynb new file mode 100644 index 0000000..1be02c7 --- /dev/null +++ b/06_photo.ipynb @@ -0,0 +1,1380 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This is the sixth in a series of lessons related to astronomy data.\n", + "\n", + "As a continuing example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n", + "\n", + "In the previous lesson we downloaded photometry data from Pan-STARRS, which is available from the same server we've been using to get Gaia data. \n", + "\n", + "The next step in the analysis is to select candidate stars based on the photometry data. The following figure from the paper is a color-magnitude diagram for the stars selected based on proper motion:\n", + "\n", + "\n", + "\n", + "In red is a theoretical isochrone, showing where we expect the stars in GD-1 to fall based on the metallicity and age of their original globular cluster. \n", + "\n", + "By selecting stars in the shaded area, we can further distinguish the main sequence of GD-1 from younger background stars." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lesson 6\n", + "\n", + "Here are the steps in this notebook:\n", + "\n", + "1. We'll reload the data from the previous notebook and make a color-magnitude diagram.\n", + "\n", + "2. Then we'll specify a polygon in the diagram that contains stars with the photometry we expect.\n", + "\n", + "3. Then we'll merge the photometry data with the list of candidate stars, storing the result in a Pandas `DataFrame`.\n", + "\n", + "After completing this lesson, you should be able to\n", + "\n", + "* Use Matplotlib to specify a `Polygon` and determine which points fall inside it.\n", + "\n", + "* Use Pandas to merge data from multiple `DataFrames`, much like a database `JOIN` operation." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installing libraries\n", + "\n", + "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n", + "\n", + "If you are running this notebook on your own computer, you might have to install these libraries yourself. \n", + "\n", + "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n", + "\n", + "TODO: Add a link to the instructions." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# If we're running on Colab, install libraries\n", + "\n", + "import sys\n", + "IN_COLAB = 'google.colab' in sys.modules\n", + "\n", + "if IN_COLAB:\n", + " !pip install astroquery astro-gala pyia\n", + " !mkdir data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reload the data\n", + "\n", + "The following cell downloads the photometry data we created in the previous notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2020-10-05 09:33:14-- https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_photo.fits\n", + "Resolving github.com (github.com)... 140.82.113.4\n", + "Connecting to github.com (github.com)|140.82.113.4|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://raw.githubusercontent.com/AllenDowney/AstronomicalData/main/data/gd1_photo.fits [following]\n", + "--2020-10-05 09:33:14-- https://raw.githubusercontent.com/AllenDowney/AstronomicalData/main/data/gd1_photo.fits\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.116.133\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.116.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 178560 (174K) [application/octet-stream]\n", + "Saving to: ‘gd1_photo.fits’\n", + "\n", + "gd1_photo.fits 100%[===================>] 174.38K --.-KB/s in 0.04s \n", + "\n", + "2020-10-05 09:33:15 (3.98 MB/s) - ‘gd1_photo.fits’ saved [178560/178560]\n", + "\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "filename = 'gd1_photo.fits'\n", + "\n", + "if not os.path.exists(filename):\n", + " !wget https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_photo.fits" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can read the data back into an Astropy `Table`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from astropy.table import Table\n", + "\n", + "photo_table = Table.read(filename)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting photometry data\n", + "\n", + "Now that we have photometry data from Pan-STARRS, we can replicate the [color-magnitude diagram](https://en.wikipedia.org/wiki/Galaxy_color%E2%80%93magnitude_diagram) from the original paper:\n", + "\n", + "\n", + "\n", + "The y-axis shows the apparent magnitude of each source with the [g filter](https://en.wikipedia.org/wiki/Photometric_system).\n", + "\n", + "The x-axis shows the difference in apparent magnitude between the g and i filters, which indicates color.\n", + "\n", + "Stars with lower values of (g-i) are brighter in g-band than in i-band, compared to other stars, which means they are bluer.\n", + "\n", + "Stars in the lower-left quadrant of this diagram are less bright and less metallic than the others, which means they are [likely to be older](http://spiff.rit.edu/classes/ladder/lectures/ordinary_stars/ordinary.html).\n", + "\n", + "Since we expect the stars in GD-1 to be older than the background stars, the stars in the lower-left are more likely to be in GD-1." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_cmd(table):\n", + " \"\"\"Plot a color magnitude diagram.\n", + " \n", + " table: Table or DataFrame with photometry data\n", + " \"\"\"\n", + " y = table['g_mean_psf_mag']\n", + " x = table['g_mean_psf_mag'] - table['i_mean_psf_mag']\n", + "\n", + " plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n", + "\n", + " plt.xlim([0, 1.5])\n", + " plt.ylim([14, 22])\n", + " plt.gca().invert_yaxis()\n", + "\n", + " plt.ylabel('$g_0$')\n", + " plt.xlabel('$(g-i)_0$')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`plot_cmd` uses a new function, `invert_yaxis`, to invert the `y` axis, which is conventional when plotting magnitudes, since lower magnitude indicates higher brightness.\n", + "\n", + "`invert_yaxis` is a little different from the other functions we've used. You can't call it like this:\n", + "\n", + "```\n", + "plt.invert_yaxis() # doesn't work\n", + "```\n", + "\n", + "You have to call it like this:\n", + "\n", + "```\n", + "plt.gca().invert_yaxis() # works\n", + "```\n", + "\n", + "`gca` stands for \"get current axis\". It returns an object that represents the axes of the current figure, and that object provides `invert_yaxis`.\n", + "\n", + "**In case anyone asks:** The most likely reason for this inconsistency in the interface is that `invert_yaxis` is a lesser-used function, so it's not made available at the top level of the interface." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's what the results look like." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_cmd(photo_table)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our figure does not look exactly like the one in the paper because we are working with a smaller region of the sky, so we don't have as many stars. But we can see an overdense region in the lower left that contains stars with the photometry we expect for GD-1.\n", + "\n", + "The authors of the original paper derive a detailed polygon that defines a boundary between stars that are likely to be in GD-1 or not.\n", + "\n", + "As a simplification, we'll choose a boundary by eye that seems to contain the overdense region." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Drawing a polygon\n", + "\n", + "Matplotlib provides a function called `ginput` that lets us click on the figure and make a list of coordinates.\n", + "\n", + "It's a little tricky to use `ginput` in a Jupyter notebook. \n", + "Before calling `plt.ginput` we have to tell Matplotlib to use `TkAgg` to draw the figure in a new window.\n", + "\n", + "When you run the following cell, a figure should appear in a new window. Click on it 10 times to draw a polygon around the overdense area. A red cross should appear where you click." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib as mpl\n", + "\n", + "if IN_COLAB:\n", + " coords = [(0.2, 17.5), \n", + " (0.2, 19.5), \n", + " (0.65, 22),\n", + " (0.75, 21),\n", + " (0.4, 19),\n", + " (0.4, 17.5)] \n", + "else:\n", + " mpl.use('TkAgg')\n", + " plot_cmd(photo_table)\n", + " coords = plt.ginput(10)\n", + " mpl.use('agg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The argument to `ginput` is the number of times the user has to click on the figure.\n", + "\n", + "The result from `ginput` is a list of coordinate pairs." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(0.2643369175627239, 18.210448859455482),\n", + " (0.3853046594982078, 19.093451066961002),\n", + " (0.528673835125448, 19.82928623988227),\n", + " (0.6317204301075269, 20.344370860927153),\n", + " (0.7258064516129031, 20.896247240618102),\n", + " (0.6675627240143369, 21.374540103016926),\n", + " (0.4928315412186379, 21.337748344370862),\n", + " (0.3539426523297491, 20.41795437821928),\n", + " (0.2419354838709677, 19.571743929359826),\n", + " (0.18369175627240142, 18.357615894039736)]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coords" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If `ginput` doesn't work for you, you could use the following coordinates.\n", + "\n", + "```\n", + "coords = [(0.2, 17.5), \n", + " (0.2, 19.5), \n", + " (0.65, 22),\n", + " (0.75, 21),\n", + " (0.4, 19),\n", + " (0.4, 17.5)]\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The next step is to convert the coordinates to a format we can use to plot them, which is a sequence of `x` coordinates and a sequence of `y` coordinates. The NumPy function `transpose` does what we want. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([0.26433692, 0.38530466, 0.52867384, 0.63172043, 0.72580645,\n", + " 0.66756272, 0.49283154, 0.35394265, 0.24193548, 0.18369176]),\n", + " array([18.21044886, 19.09345107, 19.82928624, 20.34437086, 20.89624724,\n", + " 21.3745401 , 21.33774834, 20.41795438, 19.57174393, 18.35761589]))" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "xs, ys = np.transpose(coords)\n", + "xs, ys" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To display the polygon, we'll draw the figure again and use `plt.plot` to draw the polygon." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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XPqvBV+UxcUy6CUgpdTGAPIAfaa0vGD/2dgBfAPAHWusRpdTvaK2fimurEiYgr85PDKboHR6hA6DkvK2ezdwCIDQ1SWZMILONbMMWbVRJxLVZjhnJdn46nlfXPv27NHMwbSYgrfU2ADlx+K8BfF1rPTJeJpb5VwJearEjakxszlo6xh22xORJ4ieYJg3eFjcXcam7vb0dHR0dJfRQaChvI5/P46abbiqKNjLRO5H7T+sceB/yvMl8Fccobeeng8G69umZ/8zHdPkAXgfgLUqpXUqpe5RSLbaCSqlPKKWySqns008/PaFOfXSHGVETI2fmpmgaOsalbvIF9PT0hAyX9yEnjVwuV+QzoDaA0hW8nJ64SCF5v/P5fMkiNRd7vfw/NjaG7u7ukgnIZury8KhaaK0n/QPgHAAPsv8PAvg2AAXgjQB+g3FzVNSnqalJe1QeIyMjemRkxHp+aGhI79q1Sw8NDRXVMbVDGB4e1tu3b9fDw8N6165demRkRA8PD1vr87apPqdtZGQkbIfqmmjgfUja6Bp4fWpTlosrI2mwfXt4VAMAZLWBp06XBnAIwPpx2u4DcALAK6eJlqrEVEmP0sFqOj8wMIC6urowLQNQmtWTTCNcwl+yZEm4gIuO2SRuGaq5du3aohXFpFVwB65J8ucRPdIE1dDQgL6+vqJjcU5fWxl+nms3UbR5eFQbpmsCuA3AOwBAKfU6ACkAz0wTLZOCidqZp8qEEGcWo/MLFiwo2QpS0srNNXxfYABF5hppDjIlY1u0aFGRszhqAqEy/Hd7e3voD+BRR5xGOma6Ztt/U/98DF3umTcNeUw2XJ+xqQgD/SmADgDnK6UOKaU+DuAGAOeOh4auBfCRcTXllMBEGfhU+ypcHZSmvD/cB0C5dbLZbEnIJfcTmM7JNltbW4smCwBWe78c71SqsLnM4sWLART7KygFRDnO4DjbPteCbKj05D5Rp7bHqYckz9hURAF9UGt9ptZ6jtb6LK31D7TWgdb6Cq31BVrrZVrruyebjqlEJRh4tZgQTA+RzJ0jzR58LrfF/Etp2ZaSmU8QNg1AjjeZrShZnTTpBEFhoVhHR4cz85SOa+74tWlBJlRycqcJR2Ze9ZjdSPKM+ZXAk4RqYeBJwRmJSeI15c7hSKVSaGkpBHVFMScppbgkeoszVfHf9fX1oQnKNPlkMhnU1NQY6Yrqn9Ye8Ggfop+0IBeTUSVAYy37jLsWj1Mfrs+YnwA8QkSFahLS6bTRF8BBpp0o5iRDSiUdJppsNnZTnP/AwEBsiGhLS0uJnyFKdZZaCdcupBbk0l4lwPvmmEo/ksfMhZ8AZglcGYHNbs8RxfylGUcyZ/5NjEvG6kt7Pk/aZkuzQM5eLpHbmD+ZgGT8vq1elBQfxXyj6JhsxuzXvHi4wE8AswAu0iBnWiS9JmUe0gnKzUWmRVKSLpvWQROJ7RwtOgOKGW7UNY+Ojob7GUintGlcTCavOHPRdEvnnvl7xMGng54lcGHoxJCy2Sy01kUmEpe28/k8enp6isw+plTLnBa501cUnbZzNPGQ74GbYaLa4mWjypt2IyMmXq6UXc4EWw6mqh+P6oYtF5CfAGYwJuPlllKpyaEpj1HiNpLQo0xEtj5NTDvJZEC08SRyLu3w+h0dHWhqajImmTO1YYpacsFUMv+JTFIepw78fgCnGCbDjMDNQABKzDPZbLYkzl06RF2Yv4xdl1J1EASx+f5NYaPcHMRXJXOTUxRNfX19yGazRl+DLbKHRzrJ9l1pt9Fj+p/kfsf5AVza8k7kUxym/BDV+vG5gIpRTr4Zlxw+trJxOYNkfVv/27dv1zt27LDSMjw8rG+88caS/ECyTFQfptw9MqePifaoXD6mdky5iui4rT+XMTK1xfMqlQsX+mx0eMxcwJILyJuAqhgTMRXEmUjkLlg8JLNckwE5fS+99FKrA5TTEtUHzwMk6yb1UfA2TRqKq4mIj5PNF8H9GKb+XM0yNhPURJ8J3reLic37EE4NeBPQDEOcqcAloscU5ki2elOYpUvoYBAE1kVg6XQaK1euRCqVcoqnj7oGWxupVCGdQ1LmT1FEJtNKXPoG6jcqUogzTIoaMvXH1z/E9Wf6PxFmLO9vFPOXye08Tk14DaCKERX1EidF2jQALtUlfcGDoJBkbWBgAKtWrSrK4Cnpor6i2iFmbis7USnU1ZGbzWatC9ai6vL6XCOx7VbG67iMUxK4Os9dzruW8Zg58BrADITtBXSR1E3n5IraJE5kqrdixQqsWrWqKNNnErp4OulMJlNEl+kaiMEmdUbaHMWma3Jh/qYFaNQm10i4pmGLGuJaQCUc+dLRHac5uvTpmf/swKzWAKpFypksOuIkYFdJ0aRxRNU1xc3TcZOPIEp6JRONNPnYGKxrmTgtymQqMvlNojQXOSbd3d1O11EO4jSAJBqCx6kHrwEIJJWAZxod3BZNsNmVTeChkFxijfNJRG3VyPMI8XOcMZns8SamKUM7Xa5X2rZlZk9ejrKF0p7EtGqY2pX5/+M0je7uboyNjRm1pkrANJ68f5leY7qfe4/qwKydAGzminJfjHLruZhNym1XOnw54pzIxMQ5KOZdxuhLx2fUYjAuRZuY59jYWBEdnZ2dxmuTJhS6XtqH2ATJuLu7u43rDcis09raihUrVqC1tbVkEpIOXzkest/GxkY0NTVFbmpjw0SZtXzGooQOPzHMLszaCQCIl5RcMVEpfrLU8ahJLi5Kh+rxci0tLchkMiWZNqm8XAwWpS1ks1m0t7eHDNgWWUT59U1M1RTVEmfS5GMxNjaG/v5+o7ZC7fOPqS2a7PjEYvIXdHd3x072Nobs+mxFlZGTVznPxUTgJ5bqxKyeACTKlcYnS4qfCHiueqDUROFCr5S0gYIEb5LwOROm/vhqXFk2k8lAKYW+vj7U19dbQyYpSijKCcvR2NjodB9SqcKuY83NzUZtxTYhcZgcvraxpYlMmrL4b9OKZddJI+lEYZvQJuM5rhZzq0cp/AQgUO7DXw3MnzMDbsIxvYCuUS9c0uZtuzBIpVRJ9k9COp1Ga2srGhsbUVtbGzIe2qSFM3ibCYNooLaz2WxJWoYoLUQ6ogm2TW9kmzaGafpv2ihG2uVtkUFRzF/6aeKeQ9PYRdFeCVSjgORRwFTsCXyDUuqp8f1/6dgtSqnd45/9Sqndk03HTIWr1EQMkMpzEw7/79qXySxCzKahoQFdXV0lTJ0zSJLyycRC9SVT5RI02cp5XwQZwUL2fq7pkFZB2gedMzFykw+DM2O56Q21JX0GrkzNNCnIeyInW5c2eVlXzYd8Q/l8PnGIbbWZOT0mhqnQAH4I4BJ+QGv9J1rrpVrrpQB+BmD9FNAxI2AzC7hAax3WAaJXr5r6JabZ2dlZ4kyVTuV9+/YZc/vL6CGtddi/ySktJfve3t6S46bxSKVSGB0dLSrLjymlSiYKfi3Sd0ATHtEvpfXe3t5wm8moncaSIKqNJPc9yocClDJt7rNIEgbuTTmnIEwJgir9AXAOgAcNxxWAxwAscmnnVE8GZ0q+lSQRV1wis7i6lKAtKukbHaeEZqZEcqZEbCYaTYnGXOik7y1bthTRPDw8rNesWRMmdaMPL2fqRyZbMyVrixrbJGVsdVyPm8CTxMl6w8PDRWM0kX7KKe9RHYAlGdx0+wDeAuBJrfU+WwGl1CeUUlmlVPbpp5+eQtKmHjazQJL6pjqukhvZqaM0BjouUx1wh6iEyWTiaj+PusYVK1YUOYkBYMmSJWEZOkblpA9B2t+jUlrzfqOidUyppG0wmaJcx4GD6KZr5u11d3djdHTU+gyUE/DgcepgSlYCK6XOAbBRa32BOL4GwIDW+lsu7cy2XEAStugNl3rASeZlmyCS+glkHTpnOu5CN5WT36YyMtMmZQ/l1xkEgXU9QpwN33beNlZyjKPatl2PadexuPGwtWejy1R/Is/VVE4IU93fqYSqWwmslKoBsArALdNFw0xCufZXsu1zO71rNIutLZqEo5yYNi3GJj1LCdrkoKQyuVwON910U1EkC20CQ/+z2WzR6t04zcR0nR0dHcbzJubPQ26j2pYgxixXT8vxiNogJ659m2bI+6F7kCToYCr9Ad7/MEkw2YUq/YHBB4CCY/ieJO2YfACzySZpsmHbNnDh9vi2tja9ffv2Ijsw2fypXNwmJbwP17KmuiYfh7Rf02+imfc3PDysh4aGQls/b8fmexgaGkrsb+C2c94epzXKp1AJ2zq/xzbfhCxfzgYufLz5M1EOzUnOJ0W1v+vVTB+myweglPopgA4A5yulDimlPj5+6nIAP51I27NFKiDJTEqHuVwujFfnYyHj2OfNmxcukKI28vk8RkdHw3QI69atQy6Xs/bPEZXOIO5emLSDpUuXFl0f0dnS0oIgCIquMZvNor+/H5dddlnoh+BhpLxdMq1s2LAB9fX1JaYq+Z8jnU6HfgMu4ZOtXW6PKe9NUpikc+5viVthzcNjy7Hr03hnMhnn9CE2fwiVjYtOSopqNv/MWF5kmhWq9TMbNQC5haKMNDFpACQ126JSaMvFwcHBsD6XkmU/rpFJPJrGds62ReJ1111nrUfXMjw8rLdu3Vqk3URtL8nb5+1Jyd5GV9Q183FKGsk0EURpUXFbRrrSZRsL2XZcf5XYwnImoZqvE35LyJkJKVHEOWvJfr1kyRIMDAxYy+ZyOezZswda6zAdAvXV2dkJpVSRBOzq0CR7dpQzEyje8jEIAnR0dKC1tdVoW29oaAizaQJAa2srAHeHq3SEyvTSpuuzpbSWbcqxmgyYHLs253iUo9fV0W/rmzve+b2xOdpttLr2mcSZ7hENmxPYTwBVDNNLFhchAwAdHR2YM2cOMplMZBRMR0cHRkdHMX/+/JIwSaDUeWuLLuG0konEdJwzkrVr12LRokUh07cxOckMCK7MjPLwJ5nQ5LibaOPXKscqCqZxc6GlEqkUoiaOuL7lxO468ZYTtURj39PTA62T7/08lZjuyCTX/m0TwLSbdZJ8TvWFYFqbzTVRqjf/cEevizrKF0zF0cRpIDMK9WejPeo4N1HZ+jMtLEvSF2/HdZGWLGcza5nMcXE00bht2bKlxKnsQtNEYTJV2RaQ8TKm+xFH90TKSMf7RDDV5ripRJL+YTEBTTtTT/KxTQDVbHtLgihmYyojIzfoe2hoyLmvpHZh7nuYzHHnkS+udMb5K+Ku2daX6bi0u8syNh+DXJkb5z+ImlySjL+MauJtmCb4qL5cGXOlypSLqWDQ0817XPs/ZSeAuJd+OjCR/m1MyPYCckY8PDys29ra9FVXXWWcBKRklZROk7Ox3Gs1MRubRO0qacedizofp3XYtBhJl01ilm2ZnlvTJGKbBJMwNxIU2trarMzdNmGZyk6X1FtOn9PNC6oFp+wEoHUyKW+icGEwE+0/SlXn/00MY2RkRA8ODpa0xeuYzDdR12Zj0uVeq20iiZL4o665UuBtmsaHl7MJHnTMRXOxaRQ2mqLojQNFUMU9W1HtTcaYu2I6J55KYTppP6UnAInJZP5TabONatMkFW/fvr3IFGRT7YlBuUyc/LhJc3C9VtJIoqR5rsmYrt9UdzK0v5GREWtoKWfUUUzZREuS58b1OpI8k1HXFFVPPktJJoKk/cW1NVMngOmewAB0aQNPne5kcJOCyfLKu6ZMmIz+bW3yaBLadYoWeAVBYNz4PAgKScJ4qgPbtdFxoLD46Z577ilK0eByrblcDtdccw0OHz5clM5Z1uc5cUwpIExjYdooJm4xTtwip1SqsJeBbXEWhaWaEJUCw0Sb7NdW1pbSwuWZpPGmKCi5OCsqCqijowNr165FEARFCedckt6ZFoNNdMFUpRdbTdXCLVfeMRkYv8Z5xpOmWaFaP7MhCkgiTgqV//miKTL1DA0Nhekg6PjQ0FBZUUNJUgVwcA0gyg4dl45ZjkFSM1ac6Ubr+AVMNmk6qWYYJRXazEMuKSFMffCUHzfeeKO1DRONcWapKPpt/otyUWmTn+szNFM1DwIsGsC0M/Ukn9k2AUSZOIaHh4vs1PSi8pebJoStW7fqNWvW6MHBwSJTETHduNW0sv+4lyEufFMyMZuZqVKmNhODl3VNk2lUe0mYeRxtLmVsDDXqntnqRDH/pP6VuPvp2k65mGjbLvcxqbBRjcBs8gGcSrA9iDYplEvavCyleqBJgZ+LCum0RabYGPTQ0JC+8cYbrQnYeJu2NBYUK1+OzdoG3r6c8JIwLBepfSI2bvk/SgiIYkyma7T1Y6IhSfhtpaV81/rlTrrl9F2pCX+64CeAUwRRLxmXxkwMmmsNfBKxRQTZpOEoxrRr165Ic48Lg4malCrx4g0NDcVKc1HtRy2gm4gmIDW6KGdzHJ1x4xen4ZRjakpyPK4t07Nl+u/a/mQw6sli/pPRrm0COCWdwKcaTI5CE1KpFOrr60MHr6yfSqXCfDrUTnNzc5h1k8rK7Jc8nQTPvCkzT5KjsLa2Njwm9x+g/3xfWtO12jJS0jXa6sWBsoNSeXJQcgesdFTaHOim/QpcnX0mZzDdG6KH9lCOgqkfcqTbypv2ZpZlkjosTWlCynXKy/6DICjKvkr5nFzTwrg44MvBZDh0pzyrqGlWqNbPbNQAotR80xoAksB5Ln0uSW7evLkorI9MQtJsY9I0THH7/JzJuWjSQlyuNWpVruwnqdRtc4iazFs2cxU3n7nQYNKUTGavJJqIrR8X6T2OTqmRJO3f1RzmWt6mASShK6rfqLLl9lEuplIDmHamnuRzKk8A5aj5JlswN0/QS0yMnTZSoZTKnLkNDQ0ZzQZyUuEMLwlzp/Muq5zjrpsz8LiycXBJu8EnVFmXJs4oGmwmDVkmiVkqimFOhIHF+Zhc+iyXOU8kyqwcTMSEl2RCqQbYJgBvAqoCxKl9cWsA+KYq3d3dRaqxUio8X1tbiyuuuAJNTU2hiYCyLt5///1Yu3ZtuPEKN400NDSgr68PY2NjJRukcJrj0gLTtoeu12677nQ6XbKJjUs6aFMfcpMVGcdPJjClVEn9dDqNlStXYmBgwJq1lP5HbZNJ/6PWDfAtMnl8fdKN3qPWQHDTC183IMua1ihIc1hSMwY9u0eOHEFPT0/FTSCmZ8C2zsNlXYW8vsmK8086DonHzTQrVOtntmoALmVJuiZJXh7nawToOE9KNjQ0pAcHB/WOHTv0pk2bwg1apImJ02AzE3ANxFTHJaolSoqkcoODg07pFmjdgy16yUV6i3NaS3MbHXfZsCaKdt6O3AyHf0fVjbpOF+3E1jb/Lx3YrtdruueVlqK5WaxSUvpUSPq25zKJpkmA3xBm5oM2UZH54Ukiqa+vR39/P7Q+mUOdJPmdO3diYGAA9fX1WL58OVKpFDo7O9HY2AgAWLt2Lerr65HJZNDT0xPuJRCVi54kP7mXQGdnJ8bGxgoPmFIlOf/5SmBTPnigsKdBTU0NGhsbjZqFqS0TgiBAe3t70epe0zXF5cWP22wlm81Ca12yEU+SFdNRIMlf5uS30UM59W2b85iuj9dNIs1KqbOc+pO9QtZ2D6eibxdE3UvbXhQu5QkT3g8AwJ8CWAvgJwBuBvBBx3o3AHgKbFN4AEsB7ASwG0AWwBtd2jqVNYA4cIdllA1WSvk33nijPnDggN6+fbseHBzUW7ZsKfEBSN8Bl1hNIZOSLhMt1CYPO02ywIbodZXY4rQAV/u56fpcbcJJ2rX1JWk09UPfUdKhjRYX+3oSidMkXZfrl3Dpt5KSt02DnWok9R+UQy8m6gQG8D3x/78d610MYJmYADYDePf47/cA2OrS1mybACTTclmdKh+kwcHBcBLQusAANm/eHGaGtDFwmiDIaezqMIt6iV1eaD55uUazJFWVTbRHXV+SiBqX+2Tqjy+gc2UIcaYsE/OPSgNh68MlSaCpPzpmoytKGEgiMCS5FklXJRcbTgST3Z9tAkjiBJ6rlPoDpVRGKfUe2JILlWoY2wDk5GEALxn//VIATySg45SDTM4FFDua6HeUiaK9vR1AqbNxwYIFuOiii7B58+aifXi11qFJgfrg7ZFDePHixWhqaioyERCk48vklIwzg0TF35MD1jQ+vD9+3bYY9KhYc5sjmq91MDl3pUM2CE4mT4u6p3KcgiBAX18fLrnkEgwMDBRdj4l2U7tyDYLpmtLpNC699NJIZ70JfOtLPjZxjm0eLCDpkmNgas/UfrmOVtP7BMDZ0T3ZSHpNFaPNNCuYPgDmA7gCwJXj3/MT1D0HxRrAEgAHATwG4HEACyPqfgIFM1G2rq5uUmfJ6UCUVGaTnqWzbHh4OHTayvokxW/atMm4Qtcmof3iF78IQ0i5ecgmYZqk5SjTj6RTpqagMYlaX+Cag8ZFcrRJgbSRinRycueuKbVGXD9yPLhDnp+PCp2l67c5muPMSFFlTH24aoFxNERpDLLNSkjGtjGPKjMVGkDc/XIZH1egXBMQgH0A1gP4MoA/AnBOXB1DG3IC+DaA94//vgzAnS7tnKomoKjkaaayO3bs0Fu3bg0ZNG+DMDIyEtr6t27dqg8cOFAySZheiJGRkXC9wODgoG5ra9Nr1qzRbW1tJVFBUYzd9hLHmWs445PjI+GquruYKWwg5mdLh+HSRtLznPFGmaDkJO7SL7Xtamqitm1mQK3L8yu4lDdN8knbdvHhyPGeKuYfZfIqZ2KNgm0CcDEBfQ/AYQBDAN4N4EGl1B6l1FeVUnPKVDw+Mj6pAMD/AHhjme3MCMSpa+l02hpfLdvp7e1FJpNBU1MTlixZgg0bNiCfz5eo9UEQYN++fQCApqYmHDp0COecc06ohsv+KGIom82ip6cHCxcuRDqdRk1NDd73vvdhzpw56OrqilTZJXhMOSGqHqeJX4+M1XcxKRFyuVzRmgXTGEep++l0Gi0tLSXjS9clabGZiuJMCrb1B6ZUHLKOHOOoa6NUGpQOI+4e0rmamhrjeW5GtEGaX1zA041EtR/13hBtMmWJCXy8KxEVFEWvNNdxGuTaHlOEHJ2vCEyzgi6W3neL/0sB/BeAvwdwbVx9bdYA+gC8bfz3O2HJVS0/M1EDSKKuRUnK9E0mGRm9Q2X4b7lqlz48Mkf2R2YX0h64mYNn6IwySdgktyip33Y9pvZdx3NwcFBfd911RfsjJ9Ea4uAixUVtAJ+kL1P7cWWj2pb3ZiJStovpgkv05WgDUXS6vDeu5SuBKC3XlHzRVRObCJ2YgAnoHgAZcWzX+Pf9DvV/CmAQwCiAQwA+DuAiAF0AugHsAtAU146eoROA1snVteePBnp07HgJYxkaGtLXXXed3rJlSxFzp+XzZAuWi57kCxEV1UPftkmCM3jZhpyY5KIovszfRBf1S5NP1IKxuHElv8gvfvGLxKkFJsJooybbOP9EUiHB9D8p/Vq7RQeZ7oOtnGs4aNL3ohx/WRR9/Fg5Yx/Vl+0ZdRkXl/6Tjt1EJoDF48z6BwA+A+A7ADrGzz0YV7+Sn5k6ASTB6Nhx/YE17fpjN96nXxgZ1VoXPzRyHYBkMpLxRpWn/xLUDv9P31ErdeUx6RyVOf5NfcsVylE0xoV+Dg0Nhb6SiUidcceJFhprLuW5TKB8gjA50aNodElo5nLtUX4oed9d/QZxNuxyNGMb859IHH0SP4GkJW4jpcnwKdAzlqTdsieAQl2cDuADAL4G4LMAXgHgDABfdKlfqc9smAC01vrHHfv1a6/cqFd+Z7sezNk3T4n7L6NpCHxyMElDPKpEPsQyIkhK+DYpnTM/SXPU9bgyOVtZExMu94WMooWn4rBNjrbFfHxseRum6J64e5+EZlmukhOKK20ubZSzJsIVcvJ27YPuT5RzPI6ucsdRCnEumNAEUC2f2TIBjIyM6O/8fJt+3Rc26bf++936oQNPJZbA6JuHUhJjIVOMLcJCSvj8mz/0vA25xzAxMl7PFDbpKn0lGbuoc1E7ZSVp31SPL+bSunSbSbpe2R6/fjLz0X1znQxdaI46X8mwy0piKmhyfa9s9ZKU578nokklHRM/AUwBTpw4EZptOMp5SEZGRnR2f04v/codetlXN+vOR58Ky0Q58LhEw6WEoaGhIl8Bl+Rl/1EPnlR7yTksna2SGcp2uSmE14sak6SwSbJ8jMtlqFIrIvDU23KvARtd8pzNB2K7pokiipFNxYQQ1fdUhWVKOiqt5ZiuJe45iDK3JqFtZGTETwCTjRMnTuhV392hP7v2gaLjSaQrE0MceGpYv/nrd+nFX9yk7+570igxmjZIIUmfPwRbtmzRQ0NDRfsDS1u0NP+YaDZFF5kkW9u+wPx/nAbAJ4wkcGUeE51cTI5J08SQhC6bljFRJm2b8OMW91VyAjaVcVnrMNF+4upGMd+ouq5+jnLakpOHy3ohqXWSwGaLtJx2pp7kU80TgNZaf+HnPfp1X9ikn30hKDqeRMWUkrPWWu89MKjf9I8/1+deuVHfct/BkgdDStC2h1ou6pJlOK28fek/4A+WKaSNX2uU74KODQ4OWs9Tn0k2CrExUFMZ17aikJR5lStp2iZT14kuav9nW1vlaiD82YmrEyUExN3DciYp0/Nt8x1N1DyZhMY4xh4XBi2FEep7+/btfgKYCuw59KxeuHqj/uGO3xjPuz6kpjDOg088pa+4fqdeuHqjvqbtYX3ixIlYic50zra7VRJaOMOW0rw0L0X5LoaHh3VbW5u+6qqr9IEDByKZQFwEEZe64xhPHKOTbSWVvGnik/chKfOeqAYQNbnbEMWEkzJbm5Bgo83Ub9QzUY7fQrYZJywkaX+iAofrvXHRAGR5bwKaIrz32/fq37/6Hn3ixAmn8pzZmI7z38HYcf23P+3SC1dv1P9w6wO6vWOnMbKGGH0SqUpqBHJBF70IlF2UjvFzW7Zs0W1tbeHetvLl4fTxtintc5SEL69TvsRcOqJJLi5Ej/+WE1WUk9xEAz9OfcsxtPlUTHSVI9m60DfRdpK2FyekuNR3bdv1WZ/IhOFSrpwxr9T9srXnJ4Apwk079+uFqzfqBw7+NrYsl6hdFuIQc/n6rx7SC1dv1B++vl3fceeWUOrmZpcbb7wxlNTjwMtLByb1SzuF8QRxW7du1ddee224MI0WocnFaLIv04siJx553XGmCU6rafKJg22icq0jj7u0Z5vIougv95omgqT+F1v/ccy6nPajNMxKTjSu9PAJP0ndpGWTtucngCnC80cDvfiLv9ar13U7ledRI7YXnkujpE7/cPuAfu2VG/U7/u3X+sDh0iyfURpAFB304MqNzik8kScRGxoaKlmVrHVxPDsHHbdNTLR6l3wUMrSU9yHrmVZBy3GMQtSE4Xq8XMlP/o+aJKVWUq7E6UIL77Mcyd82uVUS5WgApjJJtZyoMi72+krB9dm0TQB+U/gK48UvmoM/bDwTG7qfQH5kLLJsEAQYGBhAQ0NDuP2iTGrV2dmJnp6esExLSwsAYMmcIXzn8kYcymt8+EfdODD0QlFirNra2sikazxffT6fLypPdPGc7el0GpdffnmYmKyhoQG1tbVobm4Oj1Ff6XQaDQ0N6O/vD/uhxFx1dXW4/fbbw/z5nJ50Oo1Vq1ZBa42dO3fi1ltvxdGjR8O2TTn+g6CwmXhjYyMymUy47WXUBuum8Whvb0dHR0fJ+NvqmmixlYuCaa8BSmAGFG+uLpOjBUFhK8rOzk6nfl0TtgEn96hImoOfJ3yLS26WFCb6o/YjsB0zlTHto2Dbh8IlgWBccrlK5PN3fTYjYZoVqvXjqgFM5ozrguz+nF64eqO+edcBaxkubcRJt1GSzc59T+qlX7lDv+Grd+if/Lo0vt9kKtm6dWvRYiO+ATxfrMVNESTRS1NQVHK4zZs3Gx230jZO9Bw4cCCkgTQAU7tS1SbJn9pOogFQW1u3bg19F3ScO7RtdeP+u0SRuDikbf3Yxl46wl1o4SvETftL2Ojnv3mq8kpqJkmkdFkmiWRP5Uzh1aY+ykEltSGvAQhMx24+EsvqXobX/W4aa+87aDzPd0UCYKWXpGbTcZJWVG4/fvoXLZifqsHXdjyP9v3PobOzE9lsFrlcDp2dnSVSeFNTU5E0v2jRolBKGR0dRRAE6OrqQjabDftqaGjAwMAAzj33XDz++OM4++yz0dXVhSNHjoSaipTMx8bGwo3YgWINATiZgrepqQnvete78N3vfhf5fD7cpYl2x+LXTeNF9VOpFM4991xs2LABuVzOKHXx8TLdBwBobW3FihUris6NjY2hu7vbeA/59cj/USl/TYjaWStOqjWlgqb71dPTE96XOFp4WudUKhWmDo+CKaV4c3MzWltbQ001qi7/lscJpl3fTDvPSZqojPx25QsDAwNYuXKlMc161PW4oBLaEG/LRIvhuDLVP+UmgEoObrlQSuHyljp0H3oOvU88X3Keq/LAyS33TLnbTcyMytH5c195Bm75ixYsmH8a/vrmbhw4/dVYvHgx+vv7cfToUfT09BSVT6fT4R4Evb29uPDCC0vyvtfU1GDx4sVFTKGhoQELFizAqlWr8PDDD2Pv3r1F10xmnfb2dtx777245557jCYVzniDIEBPTw/S6TTOOusso8lA1uFjks1m8cgjj4TbKcqHXzIEznh4H1SHytJE2dLSYpw8bJBmmajnkF+TBDc/lCPMpNNpNDc3h/QDpXsWRD1r8+YV7/jqUoeO8bF0YdLyubDl86f7wI/J9k17KSTN9U9t1NbWxtINmLdCdenDBUnvfYQArI0VTGpBtX5mghOY8NsXRvSiL2zSX7ptj7WMyTxjOi7Lm9TukZERPfT8C/rPfrCrECb6w7v1888/H+skNK0Glt/UB3fIktmFyh04cCBcwLZjxw49ODhYdF62wU02/Bi/HvrIbKeSPpdxk2axqNWvvI6pfBySOGZtZj8ehZV0JXQlHJRyTKIWTJVDhxxnWwilbTU1pyupiccFru8gN49WGuVen6kcZosJqFrwsvkpvPuCBfj5A4/j2Ojx2PLkzLRJE1Ly5yjcXyA9twY/+Egz3r/sLNzadwT/2vYbnHZ6TaS0ISUj+U2/SZokWjKZDGpra0OV/O6778Yll1yC2tpaLF68GBs3bgRQLAVmMhm0tLQgCILQZNPb24vGxkYACCV47uAMggBKqfDYunXrcPjw4fAcgHCnsqjrJEmbJGuuXWSz2SKnJ6+TxJTDxytJWRpT+k6n06H5wWVXLAKNkckpTOdJeo6TiKN2KOM0R0m+LjuN0ViRFiy1N37d3NTE6YozCyWFNNHa6KZvMo/azFrlgo9LEtO2ZdxnhwmomnB5Sx2Gj41h057BknOmG6qUKmE2/Dzf2o6/DMSYd+/eDX18DP/xgQw+8456/PS+x/DJH3fhSBAdjWTbdo7bTwnt7e246aab0NXVFR5Pp9O49NJLsWDBgvD/a17zmjAaCEAYzcTLc/XcxAS01kilUshkMujt7UUqlcLKlSvxyCOP4MiRIyG9+/btK3n55Mu7dOlSBEGAtWvXor29vcQ0RWYyPia2+2AaK3k8CRMw2av5ZGgzBco+6XlSyviuFzFy+u8KmixNE4NLOy7jIbeNtEXnmOiKMgu5gvfb0NCA7u7uoufK1i6fGJMwalPfJphMn2W0Pd940qQWVOtnJpmAtC4kiHvbN7foD6xpN563RbhwkwZFxOzYsSOMuTepzCb1+aadJ/cVeGb4WCy9vF0eyy+3gqToIZtpgqJIaGEZ1ZGmA27akZE9cnz4b1NGUk6/XK3M1WhOu8mMZIIt4ZvLSmB5zjbmJtOWyfRm6ttU1wVx5WxmmiQw3VeXsraxstHNx6JS6xb4Mx+3stxEy0T6TtKei0kOPhfQ9GDN1gG9cPVGve/J+O32RkZGikI06T8xra1bt5bYx2XaAvkAbOj6jT7/i4V9BfYN/jY8HhXexpk/n4QkvXzRlWnhGNFksx1Tu1F7DfM2+X9ZVjIAWmwWF0ZLv6OW8ZsmMBNd/LjMueTC2GxtUXsmJmGanFzbjfMB0DMQl9Moqg8agySMOYqmqMnEdaJx7Vc+I0kZczl9m+6L632MuqeYLh+AUuoGpdRTSqkH2bFGpVSHUmqPUuqXSqmXTDYd04X3LzsLNacp3NIZHRJK6nVra2tRiKbWGj09PeG55uZmAMW2UzK1dHd3F9mzc7kchnq24voPZfDs0VGsWtOOjoefQD6fx7p164rCQzl6e3tRX18fhmFmMpkwaojbR2kRWz6fxzXXXIPDhw+HYae1tbWhSi7Vc7rWTCYTXsvixYvD35wm7guQvgFuMuAL1hoaGnDw4EHkcjmsW7euxNzD+6HftkgsHqljitKyoa+vD0ePHi0yHXAzAW8/zlZOPhO+wI2+uTmNw8U+H+cDoGdARuEkMXGQOSppFI4Evw82n4g0cSWF7NcW3cTpsS0WK6dvk7kvyX0sKwLSNCtU8gPgYgDLwPYPBtAJ4K3jvz8G4GsubVWbBuAqAXzyR1n9hq9u1sdGx4z1otqRi6GiFjrR4imZGE1rrR95ali/+d/u1K+7aqO+Y8+hErOJSeKQElfUXr1EZ1tbW9GOY7I8N2Xx9A2mBGpUl2+QwqOGuBQstQL6LRdDxanxUeanOElMtrN169ZwgRntosavzRaBFUVXEkxEQjVJ05JOV23Nlca4uq5SsamPSkrrhEqmenDVEOOelQgT0PRoAFrrbQBy4vD5ALaN/24D8P7JpqPSSCIJXf7Gs5F7IUBb75PGWGebxJPP57F+/XpjXDRHPp9HZ2cnurq60N/fXxTlQbHM574qjVv+8o1YcEZhrcCmvt8CsMdy0zeXjgvPUamUDiDsZ86cOaHGQBIrl25oYRg5ryma6LzzzgudvUEQhE5boLAmgUt3jY2NYRv5fB5r165FPp8vSYtgktzpGkyg8c3lclaHo62eLJ9Op9Ha2hpqbXPmzCkab3kv456ncqTachyGvC6XpqWmaqI3yTshNTfTOg2TxlWOc5U/F+U4iMvVopK0GRV0ILUBl0hBZ5hmhUp/AJyDYg2gHcAfjf/+OwDDEXU/ASALIFtXV2ebQKcFrjP/2PETesW/3aU/dN3Oonq2WZ9LX5s3bzbaIk3pi+PslMPDw/rOe7brK67rKNpXQLZtuk4pDcqMoVSO23z5eduG8Tt27NAHDhzQV111lR4cHCyy4a9Zs8aY2kJmzCQNQdLPNQTTNZquVTq34zQk0zibysiy/J7FZQC1YTKk2qj2XaR703Xanm+upZli6aPeCxdbP7+HJhu5y/i5XINLG0nqumgDldIApmsCWAxgM4AuAF8GMOTSTrWZgJLgmraH9cLVG/XBoReKjkfdyCiThUk1N9U3TRTB2HH9d7fs1gtXb9RX/qxbj44dD/vi+XBMdPK2eGZQE7PluWX4HgH8/G233aa3bt2qb7vtNt3W1hYyhC1btuirr7467IM7VKVjOW7vAx49JZmIaUKIMz2YnKM2Bp9UrXd1ACZhQDZE1Y169lzqm8rIe0/HaMJ3uR7+DETlfLI5x6PGOuk1uNAaNYG49mlqUyJuMrRNANOyDkBr3a+1fpfWugnATwE8Mh10TCakGnZZy1k4TQG3dD5WdDzKCUcLsKTziVIVmByaVEbGDnOVfs7ppxWtFfjQ9btw6LmgKG+PiR7ZLi0E44txent7ixZVUYw9Lebijtt7770X27dvx/nnn4+3vOUt2L9/f+jIbW5uxutf/3r09/cjCAp5eejastlsmCEVQJGjUo5/EATo7+/Hzp070dnZGdJOJjKgYCLiZh+bSY6uiZyj9fX1AAprHHgmUdvCKdkOH1tej6v8fMGbvB8yVYaNZtN/F3OBFuaycsw9Jmcmmb9orMlpHxXHL8eN3z+T2cjmHI9ytrpeg2nRoIneqPj9KHOi7bzNnCWDOqiskynINCtU+oNSDeB3xr9PA/AjAB9zaWemaAC2Wf+jN96n3/gvbXp07HjZ7W7fvl1v3bo1dum5lAhsEsKtnQf1BV++XS+6apO+pu1hPfzC0ZI++e+o8FEuGXM1XZqJ6LNlyxa9adOmIk2BS3DU38jISOhEJWe0qY5NOjSZkeTY2EwKlNqCO56lBsAd07ax4WNkC02ViGuX2rTtzexqyorqX7ZnkuJdEdV3nBYUt+bC9AzYaIhq32VMTGXjtDwbLXG0mfo3aRbyXslnAtNlAkJBwh8EMArgEICPA/hbAA+Pf74OQLm0NVMmAK3NN/+OBwf1wtUb9eaHDieuTzedoksoVTFB2jvlwxGV2vaxZ57Xn/pJYavJt//HFt3xyDNhWzIaR/ZropH3K1MDy2giuYZh165dRXmFeOSPnFSkn4EzZtsEKRmjbbz5uMlILJPJyNVUw6Of4hi7a0x7lFmg3Fw5NkbpYkKZiFnKVj+JSYTftyTtx0X1yPttMq+6XlM5pqEopm8qy9uatgmgkp+ZNAGYEIwd183/3KY/duN9keVsTIOHEkpmKBmm6aWI6+vu/if1m79+l164eqP++1t361y+lJlH2WpNkjefMEhi405brXUYBjo8PByu4uUbz9M3v3Zi/Lxt7kyMWixk8hnYxidugRVJW3EvrvzvwpRtzDcJJsKM45iWjZHGSbHlTEhRdJnguiMef66imLhtQiREaZFRzD7pGLhMwCb4CaBK8I1f9+nXXrlRP/Hskchy8kbzzVcInOHSGgATE5Ztmvqic0dGxvS/berT533+V/oNX92s12Uf08eOHdPbt28vivE3tWEyOfAVwpyB87j/7du361/84hfhimDbRjD8urh0J7UKOi+ZFTFrudFLnMMwio6RkRGr4zwOSZzEppQQ04GkE5fpnFwnkrR/V8nZhfnbUm3YykfB5X7K+zoRR75LPSrjJ4Aqwf5n8nrh6o36v+582LmOSQrmH60LDO+qq64KJwGti3d2Mj2cUdJE3+Bz+o//e7teuHqj/uD3O3T/4zkj87FJRNQ/N+FIWy2nne83bHuRTLt+jYwUTFM8XFZORtLMxCU+00RpGxPbNZNmlgQmidNGW9y5qcZEaBgZGXFmuLb6ccJB0rYmypSj3oE4OsudbFxpof7gcwFVD/70ug694t/u0sePn4gvPA56UEkS5/lz6PzmzZuLHIdktjGppyZJVj50R48e0z/u2F/kJKbVzKY2oug2vWgcUnOQZiyS0skEdeDAgbA8rbiVTIUzTJtGEAVX+7ttYjOVtf13lfJd6HaVDCcyiVSqbhKGa5o8XOhxFXrKnZBc/AYudLq2maQejRWAXu0ngOrAht2P64WrN+p79j7lVJ7fWG5Xp/9UhkvEZDKS52W7pj74/6GhIX371nb91z/uLHESax3PJCVDj5JiJROVkS2c+X/pS18q8hPIdqWPhGjl5+IYrcuLLaOQbA5eYvBR0VtxTNmFJj7eUWVs0TNJ4Sr92urY6tmOJTUf8TGT42cSiKLGzfV64tp0pbsc2OrZNAC/H8A04F2v/128fP4crB1PEOcSS02xxOl0Gi0tLUin02H8L8WwE0ZHR7Fnzx48++yzYXw+YN+iT/ZB/2mjizc3XYjvXtGMGz/agmD0OC7//k78w/9048ln85EblfA47+7ubnR0dKC9vR1AaWy0TDUAIFw7QOcpVryurg5/8zd/g4MHD4bl+VoHijOvr69HV1cXuru7cfjwYfT09KC9vb1oDYFM6EXx01HL/IOgsOnK4cOHw2RzVKelpaVo7QYfmyNHjmD9+vUlCcR47HdcWgETTTytAmBPlsbLcDrLSiGA0th+lzZsKR5M1yLHidbFJE0sR/darp2Qa2lo3Khfniwx6cY3/NmXm9qYUmi4tCnLJqkHvyVkdeFrv3xI11/1K/3EkPvWfPK4zcm6fft2feDAAX3ttdcWRdVw6dMlSsJkttm2Y6f+6i+6Qyfx2l2/KUonwcvKnP3cke2iektJj/s3qA0pXVEffAUxjQEd4/1L/4A0rdnGhVJzU5smP4BJq0ria3ApJyVcOaamdlyOlyPNl6sB8ONSg4uj12XcTI5/Omd65ui+8lDSiZiFkmjeru1K7SKuPqppJbBHIUHc6HGNDXuetK4WNUlCcrVpbW1tSfKzxsZGPPzwwzh27BhSqRQuueQS3Hbbbbj11ltx8OBB5PP5kvTCUdIiIZVK4Q2Z1+P3FxzDz//6TTjnFfOxev1D+ND1u/Do06VpcQvPXaGtrq4u9PX1IZPJFF1XlHTFV3vmcjncfPPNuOmmm0KNR66ApOR5zz33XLj7WEtLCxYsWIBLL720aJNvmT6ar0yNWhFNZSjJG43/vn37jBKrTAFNabVNZaJgakseN60uNtFua5/KJNleMa7PuDryOK0GjpLy45KiyTabm5vR2NiInp6eku0yTdqI1jrclpN2ZkuS8M2kTduuP0lCOVmWayzlaHAAvAYwnXj/d3fot39zS0lCNmmnlAulpARhStPc1tamr7766nCl7fDwsN63b5/+0pe+VCRJm/rkx6Pss8ePn4h1EhPd27dvL8odRPZ828pG3j+lU968eXOJRMbt+/TNw0JlW1JaNn27SKhyLKKczFwKdY1PtyGJxJi0Ht0nnpbbVm6isD1XSdcQJNF6TNcU9XxHtTmZSKINSo3WBvgw0OrD/2Qf0wtXbyxyqmodbQoxPcCmyIjh4WF94MABvWbNGt3W1hYy3H379hlpsbVtYnhS7XzyuaNFK4nv7T+5GxdfsCXVe9suV1LF5QxJMlZqz7RlI58spENYHufH5K5rtntDY29yxHMnK6fTdXVvFJIwKPlMxCFqQqb2kpgsbMflbmn8XJIJOO4cHY9LGDiZSDJW8jriJjXX+2GbALwJaBrxBxeeiRe/qAbX3PkwjgbHw+NSHeTqsEmdpE3heZKo7u5uHDp0CO973/swOjqK2267DQsWLMD27dutuxiZ9imQCbPa29uxdu3aouRpv/OSF+E7f7oMP/xoC4KxE7jixi587pYHMKpSYUIufg1kdqmtrS1RlWUSLQBYvHhxaDqhZFiU/7+9vR2p1Ml+eIKsa665BrlcDqlUCnV1dejp6Sna6JsSu3HHH1AQiqgv2kCeO3o5xsbGitrkZjjavY2O0RiUu2MVwZRgzQRpapOOYZPZr7e3N+zDlCzN1WQRRxvtFGZKeGerx01EJtiSrlEwg6muyxhOBC7OcX6fpKnPlvdfBmu4JLUzwjQrVOvnVNMAtNb6lvsO6nOu3KgvXbNDP3skKLsdk+TKTShkCjKtsiXIWHyTNGXSADieHT6i/2Xjg0UricnExaUWl+Rpw8PD+tprr9Vr1qzRg4ODxhW80tHME+XxhWXSCczHTKaO4OGipH2QCcrkxDUt5nKR4kzXHHfMtQwfE5PZK+oey2+TpuOCOO2JvqU2EPV82kJsXVJUuNAV1W65cKkvnd62a+E0Sa0yil54DaA6cVnL2bj2g2/A7seexZ98rwNPDR8rqx1ydgEnJU7SGDKZDObPn4+Ojg7s2bMnTF3MU8bKFL0mxzRpGCRpm6Spvb178Pe/tyh0En/uf7rxp9ftwt4nflukoZAEaLsWwoUXXoj3ve992Lt3L8bGxorOpdPp0Kmcz+fR1dWFBx54ADfffDPy+TzS6XQ4FtwJzPcY1lqHu5f19PQgl8uFeyvTmKZSKYyOjmLv3r3o6uoq0hby+Ty6u7uLUgTz0EN+PSZJjr5Nzn7XdMu8Hfrm94rv6UsOQ8C8RzGXPLlzkTSdch3Esh6XYFtaWkq0P9NY9fT0lFwrtSGlZpND3jauUU7pKEe0K6Kkcx4cYJL85f0JgiDUjDs7O9HT0xNqsYBZC4qEaVao1s+pqAEQ7tn7lF7ypV/rt3zjbn3gmRfiK4wjTtrh9vYdO3bowcHBom+exybKRjwyUpwNNMonQZLI0aPH9E07C07i+qt+pT93w5166LfPxdrBpQ+ApEQZcjkyUlj9vGbNmnDF89DQUFFaCKJ1cHCwRJqlcSFNYPPmzUXt8LGkYzwZHqXa2LRpU9E4ktYlF1pJ577MkmryR0TdZzpuuidcG7SFDprokP2ZFhIm1QBkv3GSepwGKq9JwjWENClM/bu2barj0g4vI31dcfeEH4N3Alc/7j+Q041fuUM3/3Ob7n3iudjycaovf/Eo8ofi4q+77rpwErAxB1N/LmYETsvIyIh+8vmj+tM3368Xrt6o3/bNu0Mncdw12SYynjeItpTkL4VkvIODg3r16tXhjmMmJkwvGJmQaO0ET1jX1tYWjhtvg5smOAO2RSwR0960aZPxGuNMHPKcyWltmzzk7x07dlhzGdFEFvdM2I7bTBr0nE0khULcRFAuk7eBxioqVUfUfbLR5MLA+bm4fUBsAgd8LqCZgYcPP6/f9C936gu+fLu+7zfRN1trs61XnqdEcZyh2bZolPZF2Y9EHHPgNLU9+Lhu/sqmonTTpjpxLxC32dOEQMydt8EnDfKBmBggt/2TBiA1JGqPUldTOd4WHz/TxjhyURvteWy6RtOYxI2LnPziENfu8PBwqF1ViuFJel3LR02AcXREIUmdqLEytZfkPpi0vyQag+m8iwbgfQBVhkW/+2Ks++tWvCo9F1dcvwt39z8ZWZ4va5fRAGRDTKfTeNvb3oba2tpwkZNc7MRt/wDQ0dGBtWvXFi3WMqUwIL+BLWqD2zUvXvRKfOOtL8Yn3nIOfv7A43jnf96Dn7QPFCQRmBefyfYAoLa2NlwEk06nsWrVKsyfPz9sAwDa29vR1dWFuro67NmzB/PmzQOAEjv0ueeei56eHhw+fBjd3d0YHR1FNpvF3r17sXjxYqxYsSLst7e3F6lUCo2NjQAQRv9Qe3QPgMI2lXV1dUW2XW6fTafTWLJkSfjfts0gRTtFpRAwLSKU9ylqoZ/NZpxOp3HFFVegtbXVWCYuIshlcZukJ+45Mp1LspiKw9XPIvuKK0PjncRXIlNGmK6Jv5/yOmQd18Vril6+mYDm5madzWanm4wpwVB+BH9+Yyd6B5/HNy/NYNWysyLL8xseBAG2bduGefPmhSGidL6jowOjo6N48MEHcfnll4f5dbq7uzE2NobW1taih5ZCFsnZyR12HLt3745duUkPbyqVwp7HhvDFX/Si+9DzaD77Jfjq+16PhtfUOj24VIbC5iQdDQ0N6OrqwpEjRzBv3jwcPXoUb3nLW0rCTYMgwE033YRXv/rVeOKJJ3DZZZchnU4jl8th7969AFDE/GjPY/kSymvL5/PI5/O4/fbbcemllyKVShWND3fuUfm1a9di1apVRauVyZl79OhRXHzxxWH7ckz5sWw2GwYDEChUlo7z8kmZZrng12x7VpLQI8ewXJqAyo6H6VlPUtc1vFY6jHl/pjFWSt2vC3uwF8FrAFWKV6Tn4qefWI43vbYWf3drN36w/TeR5aU0tX//fixZsgTAyWiBIAhQU1OD173udTh48CAeeOCBUGJtbGzEnDlzkMvlsHbtWnR1dZU8jJScjRgNbbIuJQ8TbZJRPX+wH7f8xRtx1bvORe/hYfzhdzrwr796CGPskZRRSvS9e3wTd0owJ+kACtFD8+bNw+LFi/H444+XRJjQ7yVLluCiiy4K02IEQYDbb78dr33ta1FTU1MyGfKIGil9cmbe39+PlStXlpSRER5E9znnnIM9e/aURKfQfaH/XLMzSYjE5PkahlQqhcWLF4fPgonx2daGSCSOMwdKrjlKmk/S3kRSIEgJPak2wNvhkM+DrZwJpgnR1r7pv22Mx9uYb+zUZBeq5AfA2QC2AOgD8BCAvx0/XgugDcC+8e+Xx7U1G3wAEkeDMf3JH2X1wtUb9Tdv7zcmXjPBFJtOv8kmvmXLltDhyVfrcicn1ZGrKU32UJMfwtQOT7iltdZPDA3r/7O24CRe8W936dsfHNTPP/98URIvGUEjVwcTuE+AbyRvGhM+NpRugpzG5Cw3peWISxDG1xGYVrzKMaGytoRy3PEo/RJRNJA/gPshTCnCt27dGjrWZd+Szsm2tUeVMz1XE3H2cj9NVOoLG6L8bvJ/OVFDptXkUe1FtQWgT0+HExjAmQCWjf9+MQobwTcA+HcAV44fvxLAN+Lamo0TgNZajx0/oVev69YLV2/Un1/fo8eOn0j0MPHzpkVRlFdf65P789qYlm0xEJ88eH+2/Dh84qHjP/zVvfr3vrVVL1y9Uf/Rt27XvQefKumX2uFMkX94aGeUU81EP4Wa7tixI9z+kjN7Cp+l8TNlNpU08pBV04IsChfljvkoxzDf5yFuc3kTw5ROaRp/U1oOU/joRBhuHOIYZSUWZcn2hoaG9JYtW8LoLjrHv3kd+jY5bqPoNNEdlxeKP9dx+zfEjQuA+/V0TAAlHQK/APB7APYCOFOfnCT2xtWdrROA1lqfOHFCf+PXfXrh6o36kz+6T29v32mUruOkb85gRkZGwtBGHm9PES7EZDiTpv9SkiTmz5k6p8HEALnWQceDseP6u3fv1edftVG/7gub9LfvfFgPv3DUmiiO6CV6olYzm85J5soZHb9OYtB8NTXXTkzRUHIcuMRNTJfGWk4wthXHtk1+kkjEcjIwTbKyLTlhuLTvCtszW245V1po/CjqiyZ2U9QWZ/hxidhcmDMJYlxgiruOqIkibiKBZUewKXUCK6XOAbANwAUADmqtX8bO/VZr/XJDnU8A+AQA1NXVNR04cGBqiK1SXLftUfzLpj60nluL6z7SgvTcmvCcdASbnIRydWZ3dzeWLFlSlFY6n8+jv78fR44cAVDYYOalL30pMpkMurq60NTUhK6uLtTU1JQ4hXlEjCmXDFCwQ5Nzmpy20mZ68Onn8Y3NA/jVnkG89pVn4B/fcz7e3nBmyXiQIzifz6Ovry90ZEsHocl5JvvPZrNF0VFBEKCjoyP8Pzo6CgCYP39+WI767u7uRmNjY3hMOud2796N+vp6AMD69evx3ve+F7fddhvq6+tx8cUXF7Vz3nnnYcGCBUXjtm3bNmitkUqlSqJyqC+KJImLiOE+gCjnrBy/IAiKggVkG6ZxjkOS8i6O5CRtk5NdKYXm5mYEQWBM123qn76z2Sy01mhsbCzK8WSjlY9VLpfDwMCAcdW9yX8Qda208t02DnPnzp1eJ7BSKg3gZwA+q7V+3rWe1vr7WutmrXXzq171qskjcIbgLy8+F//xgUbct/+3+NB1O5F7wfyw2pyEhCAohHA2NjaGzJ8ca319fVi8eDFqampwwQUX4MCBA1i8eDFSqRTmzJkTMiFi/gQKV+ThkNQXD++kNBAmBxah7lUvwX9/aBn+38feiBMnTuCjP7ofn7r5fhx+7ljRREWhcwMDA1iyZAlqak5OiFHjQYyah98Rg89ms+jo6EA2m4VSCk1NTWhtbcWyZctQU1ODc889tyQEdGxsDDt37gydrNIZWFdXh/Xr1+P+++/HqlWrwjDQ5cuXF9E0NjaGBx98MHTM0j0ZGBgIaZGMgPri+yeYnOf0TTuzSUc0Z1LcKUq/U6lUOMY2R6zFAWlFlFOY13V1JLu2Tef5zmicgZocwnys6Jv2GuAhnPy+yXGlRIYASpIh8ro07jzgwjSWVMa2M5989ySmRANQSs0BsBHAHVrr/xw/thfA27TWg0qpMwFs1VqfH9XObAoDjUNb75P49M3346yXz8OPP/4mvPpl8xK3ISWNXC6HPXv2AEAo5V944YXYs2cPmpqarDmAqC0KNyTp2CRxcymIXhCTFsAlqPu6HsB9z78E//fe/ZhzmsL7z38RPv37F2L/o4+goaGhiK5yQvmIDq6Z0PG+vr6ibKvPP/98GJVTU1MTRkZdeOGFePTRR5HJZEokMXoJ6+rq8Mgjj4QMg2jn/QVBgFtvvRWXXXYZ+vv7sXjxYtTW1haFvNrGn0ulo6OjaGpqCvshzSAICpvzjI6OYvny5VZaTfcDOKkxuYx5EkndFAETp8FOJnjIr6smwyX++vr6EumeJl8AxrUVcuy5BgmYo4RsIadcIxjXALq01sXxwcCUOIEVgB8BuEYc/yaKncD/HtfWbPYBmLDzkWf0Bf94u2791zv1vieT5Zg32SkpwoTsvJs2bSrZxMRkA+a2UJP9n35LezPZXKmOdDrKdvY/k9cfuWGXXrh6o774X3+t7+k9VNY12+iS22vyiB+imWcFHRkpONB5LiLXvrlfhcaNbMJtbW1hXzLHkC1vD/dDkCOf3wuesoJ8DzYHclSaBhf7fxJbfZT9Omo8k9DgSstEyxOiosRsuZdMfSbx58g+pB8JlpXAk64BKKUuAnAvgD0ATowfvgrALgC3AqgDcBDAB7TWuai2vAZQioeeeA4fueE+HD+h8cOPvhGNZ78stg6potKEw6X0XC6H9evXh4uTgiAI1wi84Q1vwIUXXhgeJztqJpMJ1xvwFbTSpkrSUi6XQ19fX4kNluqYJC+tNe546El85ZcPYfC5Y/hA01m48t2L8Yr03EgJ0WQn5/0EQYB169aF2wByaVkuBuvp6Qnp7e3tRV1dHWpra0s0Hd63vLbOzk6MjY1hdHQ09CkEQYD+/v5w/IIgCPuievw/H1OSWOn6uAQKIJQ8tdZh29LvYtLSkoB8GEqpUHMxtSPt1ZXUAKRdv1zfRFJwrXbdunXh/hRRZePamgjkGCulpkcDqOTHawBm/ObpvL7oG3fpJV/6tb734adjy5O0HxdVwLeOHB4e1ldffbX+3//7f+t9+/aVbJjNJU0ef06RRnJLSLlBu5RiJT0S+WOj+p9/Wdh3IPNPd+gb7tmrO3budJbCKRqHx7/zayWNwJSvh2sscm0E16Kory1btug1a9boAwcOhMdIIqfkc6b1FXwM5cbmFP3E1w5EfctoLlNUFU92Zht303F5/3gyPVP2UYo6i2vbRF/Uef7b9TkqF3Habbltuq4tiDsuAZ8L6NTFOa88Az/7qxWoq52Pj/2wE5v2DEaWJ+dV1HmSlGjT9SAIcMEFF+Diiy9GXV0dLrnkEuOG2el0Gs3NzaEkdPjwYezfvx9nn302NmzYgHw+j7q6Otx6662hVGpyyBKkM5LOzVEn8Hu/8wLW/WUTzv/dNL6yaR/+eecxbNxxMjc/b4P64f21trZi1apV4XWk0+lQwm9vb8eePXtQX19fQg9pOUuXLi1y5JH/QymFrq6usF5zczNe/epX43vf+x4OHjyIzs5OdHV1ASj4EeSOb9yW393dHbbR0tKCdDodOi+bmpqKVivz65P3hWt7NNbSSU59cFu2dCzK49zJSI7o2tparFy5EgcPHkR9fX3JLmyLFi2K9BnYol40s1aYnNScVlOOqqRStc2BbaKTaxbl7PjG77e8Ny73Is7ZboVpVqjWj9cAovHsC4Fe9d0d+pwrN+qf7DwQWTYudphAkup1112nDxw4EC6YIcmVJGUu6fFFYVzaJ9tzW1tbaOc20SV/k4QpF8NQP8eOHdO33rdfN31ts37tlRv1J793p3762XxYN24BDT8nF0fRymIuPZNGY5P0+PoJLtnz8ZBpqCUd1I7L/ZH+EtkvXRONH/cRmNqTmkjUPZL/5e84yT2q7Tgp2DRm8txEVi67ZDp1vRbXPm2+C9tYyOu00QWvAZz6eOn8Objp42/CW1/3Klz18z347y0nM21KuIbSkTSzaNEi1NbWYtWqVdi7dy/27NmD+++/H9u2bcOtt96Kurq6cG9dskWTdJxOp9HZ2Rn2pbU2RqAQXVKqI6mYh+zxa5g7dy7eveQVuOtzb8MVyxfijt8cwyXXduC2Bx7nQQeR0isd41FLGzZsCK8dOBkaOGfOHCxZsqRIsuVSWDqdDuPCqb18Po8FCxaE40FrBoIgwNq1a7Ft2za0t7eHey1TrqMokNbC+yApknIbAQglypaWFmQyGfT39+P5558PNRkevki0kHbGx0ZqQlH3jI6TZiG1DVM7/JxJ6jU9qzKvEi8rNZ0kUrJLCKmJRvlsyV3eTDTI8ZLnZBgnaQv83eDh1IlyJJlmhWr9eA3ADcHYcf23Py3k1vnqLx/Sx4+75Q8yQdq6+SpZ+ubpDqiOtAnzvDwu/gdbtJEsR23yFcg9jz2rV157r164eqO+4vqd+rFnni9pQ9r7STrm0rMsw8vy66Zr4lK9zP3/pS99KcyxxO351D61R74SnkbCNEZ8nOgY0c/vFe1yxlcckzZA9Mg9IHikCu+HngOTr8C2MYu8b6Z7nbQeR9RzIp8RV403CeJ8AdI/JCOAXLQUOT70DLpoBgT4HcFmF44fP6G//IsH9cLVG/X/WfuADsaOO9WLehGjHFQm1ZOY2tVXX12yYYutL94PD8GMU3N5mOPIyIgeO35C/2DbgD7/i5v00q/coTc/dLioLF/2z00etpeNh03yF5rTGmW+oARzxIxMDlIyOUmTkGlsOCOnsjI1BZnuKLSUb1NJjlpbGK7pWmTbtuvl1xTH4Kgd22RnGkvTvTExffovJ0sb+HNRLuKEFtOYxU1KpjG0mX5M57TWfkewUwUuUhHhxIkT+tt3PqwXrt6o/+wHu/SzLwSxbbtII7Z6MsZ5ZKRg97722muLIopc+uLJ4ngd20RADJlnyty1a5d+6NCQfvc12/TC1Rv1Vet79JGRMa21NjLZuAlOvmQ87j6OwRCTI22IfCrEkGmcrr76an3ttdca11PQNfGoJekTIbroGE1MlPmVXwPRIrOLxmloNmmWaLDtW2tqi3xF0s/Cy0TFzcvkhXy8ZF+2c7K9qJxa5SBuIk/aRtxEYHo/YMkF5H0AMwi2aAAblFL4zDsX4eurLkTHI8/gj/57O/Y9OVzSJsHFL2A6x+uNjY2F7XZ2duLBBx/E+eefH8bZ852vTH0RPbW1tcZYamnvJJt5EARobm4O1x9QuYbX1OLnn1qBj795IX6y6yD+8Dvb8dATz4UrLXm/fFc1OS5yVSzZ2ilNQ319feTuaHSsr68PTU1NWLVqFQ4dOoQgKKR82LhxI84991ysWrUK5513HoBCSgq6Phq3hoYG9Pf3hz4CWnUdBEG4P8K9996Lzs5O5HK5cF2BUipc5U1Ip9PQWhel5giCoCAZwmzz53Z9uY6E2qT7ZrJfy7YaGhrw6KOPYtWqVUilUiVpD4h2E4IgQF9fX+hP4fdLIiqyiUOmZ0j6zkmY7PHyWYsD93HY/Cx8NTCPKGN1jxobN80K1frxGkD50sh9vxnSTV9r0w1f+rW+48GTaW8rnWJXSqSU4ZIkVxlzL6V4m7RnopWvbI2L2Ni1a5e+u/cJ3fLPbXrRVZv0ddse0UePHjNKU66akElCt6nrJr8Gb4PMKrRmQO7ZQCYcMqvxNQC8DYpaokgtnmKaryq2XQc38djs5zap3uUYNyOZ7rmNHts9SLIHsqTH1d5fblw/v1bZd5RmxMvanifXcnQM3gfg8cSzR0Ln6NVte/Vxy74CSWF7sIl5EJMmZ+i+fftCJiXt1lHpCUz9yH0BJD2SxqH8iP6L/9epF67eqP/0++369q3txpcojpm59BV3zAQas+HhwobsmzZtChekUegst8FzsxBPaUHMkZeNcirTNzfxSKe31troBOZjJ012sh+TIznK3BI3blGTfhSkqSqq/XKEJDkesp2odk0TrumaTG2a6IiaALwJaBbhzJfOwy2fbMX7l52Fa+7ch7+6qQuBLv8RsC3CIZMFbdUYBIUUB6lUCn/xF3+BJ598Ei9+8YuxZ88eHD16tEhtlcv3yUSybt06Y4gdN0NQpsWoTI61Z6TwnT+5EH+5NI2ug89i9T3DuGfgt2EZrj5zyCyNpnA708bupkVNVM5mVkin00in07jsssswf/78cHP25cuXo7u7G11dXaH5icwoAwMDaGhoQBAUMqMChcVjuVwuNL3JLS75dZmyUJIJQSkVhpmuX78e5557rtHsIs1gpjBOCoPlJiRp3uChk0lSJsjnI8psQ6aqqJBUTrNLCgpTPTJh8naIbpsJVJqHbOYr2aYN42Y4ox3NTwAzAGWv8jPgRXNOx398IIN/fG8D7up/Cn/83zvwm2deKIsmvgJUxuanUqmQcaxYsQKZTAbd3d04dOgQzj77bDzwwAM4evRomL45CAob2dNvsrNThlFuVwaA+vp6rF+/voiZUn9R6YWDIMDcuXPxD6tasfEzF+HVL52Hv/xRFl/4+R48lz9qZAh8bQO3udIewVSG+zjomGRofIW0fKEpjp/Kp9PpcHz4CuDW1lYsWbIkXFnN9yru7u5GXV1dyLCvv/56vOMd70BtbS2WLFkSrtWg6+vp6QmzezY0NIQTDGU57e3tLdoj4ZxzzsHevXuxc+fOIn8HTTxyjJLG7svV4LZn37amQzLXqHeH/EB0HeR7sAkPNtgmHNP18Yk2yp9mWiNjGk+6RlvKaOYfMC8IMqkF1fqZjSagStvpOXbse1ov/cod+oIv36639D9ZFm1R56R6zyOBZC6gtrY2vXr1an3gwIEiUwbPsklmDDJPbN68Ofxt2ymL02MyXRwbHdP/8qvewn7E//xr/cD+Z8LyPLLJtmqZt0lx9TbTDB8Hk7lky5Yt4Spjk/ov6eC5mKi9TZs2hVFEWutwxykersrNXDKLqbxv0oTBV0fTvbL5c/gx/ltGi5nGVV6XbMv1f1R92ZcM641q29ZOXBmbLyiuns00KsfcZtKCNwHNTLiqoOVgRf0rseHTF+Gsl8/HR3/YiTVbHyk4hhLQRpBRDiTlUKQOmQ+6urqQz+dRW1sbtpHP57F8+XJ89rOfxeHDh0NTRn9/P0ZHR8MVq0Ahd04mkwFQyKlOJiOimyQ5WinJYYpcmVtzOq56zxLc+JFlGMXpuOz79+H6ex9FTc2cEk3GdP2ZTAYtLS0IggC33XYbtm3bFvbNtQRen6+Q5lBKYfny5SV5ewhjY2Nh2zQ+XOKl8RkbGwvLPPLII8bV2dzswsdXmh24xkCmpnQ6jfe+9724++67wxXg3GRE428ym1E5ihaLeq64RiDb4uVMZrao+qY6ZH5Mp9Oor6/Hhg0bkMvlwrK2Fb+S5jiNg8Y+Ko+RCab3Uo5pEAQlWmgsTLNCtX5mowYwFXhhZFR/6iddeuHqjfpTP+nSL4yMJqofFanA4/lJA+ArSwcHB/Xq1avDhWLSCSmlJb7AiTs15apLk4PSJjHS+cHccOggvuL6nfrJ544WlZOQfVKcv3Rqy7w/kgY6HpdfiDvJSSOQEixFDPG9m6Oc2nzjez4WctWwXARHNEVdk83BK2k3jbFJi5AL1ji9cdoo1ef9m1YRE6Tm5vo8uWrrSTQAKhOlAdDvpBrAtDP1JB8/AUweTpw4ob+7ZUCfc+VGfck12/TBoRciy8ep3xy2lab0wG7atKnEfCOTqdE5boaQTMLEiCQN/Jwphe+xY8f0/9vxiHEFsawvGbZpEuKfzZs3F6VCJuZKKSD4wjA5fjSJSnMNNwOZJpo4piFNQnTMxvTlfZRpLVwYs22C5m3s2LHDusJXMuU4ZsrDWnlEFF8ZbYKp/ah+XGhxgen64iYX08TIYZsAvAnIA0DB/PDXbzsPN/55Cw799ghWfmc72geesS5qsiXqMpUnByY3PZBpqKenB/Pnzy+KdABOpi/mic5SqVSYSrqnpyeMNMrn8+jo6AiPcRpzuRyCoLBIipuFpKOQzARdXV04Tw/i53+1HK9+WcFBfNXPuvFc/mjJNZtMQ/Ia+dicccYZ4X7A1GdjY2Po1N28eTOOHDlS4lzlESs8yiafz+Oaa67B4cOHQwcx9UXXIyOUuGM6CAoLqciRTccHBgbQ2NgYmtdojKSDlcwmtMWldHDaolz44rBcLheaaii5HpnWeIAAH1sZFRO1aIsS3O3cuTOMUgJOmg9lojoO2Y/peef08eOJErIZxkdGZ9H1yz5tz7NtD+ESmGaFav14DWBq8OjTef3Ob23V537+V/rLP9mijx07VlLGJhW55FzRWoeORFOKCK2LF9Hwstu3b9dtbW1hLDk5MeXWldQHl5zjTA70HdLwwlH9N99v0wtXb9Tv+I8tunPfYFF5Kf1THhlTW1QmagzpWkmqNjmQpTbFcwpJbYg0jgMHDoS0UbvceW4ytUi6pMlN0m+rK8E1CloXwtctmK6VNCQXM5GpjEy/TcejNBRTPzazj8uaiCSwXRNvl3+baJJjCW8C8kiC4WOjoT38727ZrY8GY271HHOc0IttMkFwUw+p6rQISpoqol4WosdkSrLtfGViyoUVxJv1eZ/fqL/T1quPHj1WlISM6gwODpbs3GXK0SLHQY6d9COYInH4WHA7PW9DrsLm0VImhkht8YgqgpyobW3w1cOma5WMi5uwpCBAZek5MT1D5EcymXI4Pbb6/NplHfkc2CYL03jJyd+EOIHAdMxmHoqqNzIyjQvBlFJnK6W2KKX6lFIPKaX+dvz4B8b/n1BK2ben8pgWpOfW4HtXNOGz/2sRfnb/IfzJ9zow+Jw5nUhRPRb1EhXLnUql0NTUFMaOAygxH6TTacyZMweZTAbLli0L1XcyCW3YsMEavcLb4rssUXnKLyNVfB5lQxEybz7vFbj9s2/Fm8+rxTfvfBT/dfcjaGxsLKqfy+Xw6KOPIpPJhCYRoFhtl7npZd89PT3hXr48sociO2ix1fr167Fz5050dHSgo6MDqVSqyGRD0TqZTKZoDwKK6U+n00WLsIIgQEdHB9rb29HV1YVnn322KDKIIo5obGjXNIo2kveNzC4dHR1FZih+X8lcQfsG89xKvCyNF6eHP0N1dXXYuHEjXnjBvJaF6OFRPby+jKE3ma5o7QaZEyUymUzJTnb8eTZBrhuxRRrZTK1RZi9+bczkZk6oZJoVKvkBcCaAZeO/XwzgYQANAJYAOB/AVgDNLm15DWB6cMeDg7rhS7/WTV9r052/MafKdVVToyRgkyTDHZI2Byj9N5lbRkZGirQHU6QQd8pS7h2Z3XT42Kj+4//ers+9cqO+s/dwWJZL4HLNgjxn2i/ApJWYpE8+PjJCR0YdmdrgErYcc+qbRxpJkxI3G5lMTpxWrnHwfETyPsn7aWqbysn1Cqa+bNdEWoItUs1FWqeMpSbHtNRMZfsmSI1H0iOvxdaGKXLM9IxhujQArfWg1vr+8d/DAPoAvEZr3ae13jvZ/XtMHO96/QLc9qk3Iz33dHzwup24edfBovMmKZCkPC7tRUk08je1yx2SFDdPTi6KgycnMElxFPdOEtDAwACy2SwAhNoGcNLpSQ7M7u5ujI2NhY5R2rkr0Kfhwz/Yhd2PPYtPNs7Fm84+Az09PUVZNBsbG3H++ecXSYK09y85ernULVNOUBv8GIFn1iSnMB2XO49prUsco1LCljHoAML9hilNA8Wq53I5ZLNZ9PT0hCuC6b7SPeV98fUGRA9fl0DS9uHDh7F27doiiZ+vRpYOe6110Q5mvC/SbkjboLp0f5YuXRpqQlJyJs3K5Ajmz+mCBQtw+eWXo7W1taisbZ2B6Xnm7e7evbvEkWx7R+Rqcg6ZKZXfU1takyKYZoXJ+gA4B8BBAC9hx7bCawAzAs8eCfSHf7ArzK0/Mnpyk5k4CYqXcy0bVZ5L8bQPAJfyuVREK3RlXL50DkvNYGRkRG/d3lGQ/D//K712x8OhhM2lvh07dui2trbQUU2Ii8OX18NXoJrKS1+ADDvVujSs06Tx2KRgOd7c/m5zGtsyedJ/aoP7RUhz4ZqZSYKX4xOnxdH48fsb5XeJ8lfF+bL4tXPNxRVynKL2rZbjbqLTFEjBgekOA1VKpQH8DMBntdbPJ6j3CaVUVimVffrppyePQI9YvHTeHNzw5y34q7eeh5/sOogPXb8TTw+PAIjPl8Jhs43aVlrabJzUJ4Xz8XBTkpLz+Txuv/32UNLnK3ez2SzWrl1bJGkWhT3q0/Bf3cfRfeg5/Pv7luC1Nb9FJpMJ7fUktTU3N+Piiy/G5ZdfHq5wBhBK0/IaZEghgVagUp4gbj+nUFbqkyTf+vr6sP1cLmfMx8PHjH9LqZOkfQqtDYIA+/btC6+RJHEOvv8DaVn0n6TcpqYmNDY2oqenJxyXlpYWLFu2DN3d3bjrrrtw0003hTZ6m9RKfhWpxdEYke/jvPPOCzUKkz2fa1I0PvyeyNxPNpD0zbVdE6QvjGtNBD6uphXNo6Ojxra51hQT9jl9yeCUUnNQYP4/0VqvT1JXa/19rXWz1rr5Va961eQQ6OGM009TuPLdi3HtB9+APY8/hz+8dju6H3vWuX5cfLh0jtrKA8UvMze98LIUQy9TINBkcPnll5e8uEEQoKPzfnz4B7vQ/diz+NQb5uHMscGQIWSzWRw5cqRoErAxLTrOndCUKoGcinS96XQaK1euxMGDB1FfX4+enp6ieG7uuO7t7UUulwsd4TwjqHTAd3R0YNu2bUYHI3cuDwwMYPHixeGG9/39/eG6BboWvpk9UEg9QeYJycTIAUuMW04efX19WLJkCebMmYO6ujrs2bOnZBN12R6A0EQEFMwcQVBIgQAUJlGa8CVD5GYjfg2dnZ2hCZHora+vtzJ0oocmRWkG4/3JeH6+gRFvy7S5Docpkyuvzydii4A1PcngUJh5fgTgGsv5rfAmoBmJhx5/Tq/4t7v0oi9s0uuyj024vTiHsCxrS0QWV8dkHihyOpLD9/O/0tf8bFuJmUg6SWXbUf3Sh9Y28O0YeXmTY9H02+QIJ6c5mW5kgrjwOoUTma9BoDUJ1K7J2Tw8XNjbmNZaSLMMX79huhYekkohvyZHOadX9iHP8d+2fQts94TXt4WVmo5JmmX/PMig3PUBUSYpkylOXi+max0AgIvGZ58eALvHP+8B8McADgEYAfAkgDvi2vITQPVhKD+iL/9eh164eqP+yoaH9Kjj5vMThYmhRDFhXk7WKbLnHhvV7//uDn3u53+lb+s6GPlSRbVto1emn7DZdWXKCFubJoYi7dLDw8OhvV2Wk2sZ6Pfw8HARY5e+jy1btoQMm2dB5ZMPX7QXtcGJzAxrYvI0JpxeeV72YWLYcoxs90nunhY1ofAIJemXkXTY6LHZ8E3Pq6mdOMEDwP16OnwAWuvtWmultc5orZeOfzZprX+utT5Laz1Xa/27Wuvfn2xaPCqP2jNS+PHH34iPvvkc3LDjN/jwDfch90Ll9i8wgUc6yGiWpJlTSV1WSiE/MoYP/2AXHnjsWfznpRfizLHBov5MJiY6bzrOIaN/yAZtMxstWrQoXO8QleHRlD+f7N9kxoiKpad0C9w/QPVpDwYyd5AJBSj4XSiFxd69e8PNf1paWpDJZMLoreXLl5eYprh5L5/P49Zbb0V7e3tIP5nI5JhQFA6Aoth98o/IPmygqDFpN4/L1W/KJkv3hdrifgcqJ9cZSPBNe+j6pSmUwM1JLhFHzAf0ImMB06xQrR+vAVQ3/if7mF70hU36zV+/Sz/0+HOT2peUqKOkb17HZhIYev4F/b5rt+nXXrlR39Z1sOic1vFSuEv/tjo20wKBr3g2tWEya0gJPmpPA9exIzMR1zzk+gfepun6TJob1eftmlYF29oxmWxkxBDVsdEsV0DH9a+1Ds1f8v6Y6IyS/GX0l4wIsmmtNqlf0qy11gC69HSYgCr58RNA9WP3wd/qN/3LnXrxF3+tf9n9+KT3l5TxGrNtjtv8X3vlRr1u16PGenEhgXE02coQTVEbtptMD7INUz9xoZV03JTjhzNX3p6NEdOHMzQZjhoXlsnbk9crTVGS0Uk/jckMw3MsmSYQ7o8xCQqcfppgacMdTicPe41bKGYz4fHfMiurbYGlacIj2CYAnw3Uo6JoPPtl2PCZN+P1r34JPn3zA/jG7f04fsJ9k5mkSGLu4ZEbhPzIGP78hvvQfeg5fHrZfPzh0tcY60WZlmxhhoSoBXCpVAqLFy8uWRTEzRgm0wPVlf3JCCgOGVpKfe3bt6/IhNTe3o6Ojg4cPny4yPwkF/Xx/ijSqKurK4woMm2hKRdj8Ta4iYS2pOSLvoIggNbamnWUR0zxfsicRJlL+/v7i6JzKCJsxYoVYZ2jR0vTnlB0EI0FRZfxe9bT0wOtdbhAr76+Ht3d3UVRRvy6bds8clCGWHoeKASYh7CaMoZSO+P3b37JBQGoMR308JgIfufFL8LNf7kc//TLh7Bm6yP4cccBvGjOaZhz+mlI1Yx/n34a5tSchrnhMXXyXE3hvOl/6nQqe3pYJzwXUX/O6QpzTz8dqZrTMXb8BGpOPy1k/g889iy+ffkb8HuLX+HE5G2QYYa8rm0CCYIgzNvD7ddLly6N9WfY+rPRT/HiY2Nj4YrWdDpdEgpLzPfRRx/FypUrSyYSyvXD6xWEzAJM8femcEm6TjqXzWbDXEW0h7RSCs3NzSHTW7JkCQYGBlBfX19yzbTiWI4J0Ub/R0dH0dfXFzJpOX5BEGD//v1Yvnx50URdX1+P/v7+kMGnUoXV0uSHaWhoCNd90DWRL4T+82uitvk4mO7xwMAAli1bFjL33t7e8Ld8VmhFO11bEATo6uoCLOsAFL9x1Y7m5mYtnUMe1Y1fdj+BrgO/xejxEwjGThS+j59AMKYRHD+B0TH6fyIsY/o/eryyz+lpCjhNKWgA3778DfiDzJkVadfm6EtSJ0kbSfujRG5RefBtzkVi2g0NDejq6godslwjkHV4ex0dHUWpFEhq5lpETU1NqO1I5ycxWtIwOBNsaGgIr4smgQhpuIjGzs5OjI2NYc6cOeG45HK5okV9/NqBk0yYL6aTfVJ/sh3ef1RgAbW7bds2zJ8/37rugP/u7OzE0aNHMW/ePLS0tITXd9FFFz2gtV4GAT8BeMwIaK3DiYBPDiPsd+kEohEcPz5+rLgefV/8ulfhzfWvnO7Lm1JMZILhjAmIll6p/O7du8MsplxryOVyWL9+Perr67FixYqwjmRysl+SdPlqb5J2JWPl9JHmsmjRIjQ1NRkZNrUhtRPbgq/Ozs5QoqfzxIS11njkkUdwxRVXFEUF2eiTY0ZaD58k4u6djAYjzJ07t0trXZp12eQYqNaPdwJ7eFQWUU50W/SLS32TY1ru38CjijhMzuYoukxbUsq+ucOXnLgmJy+PnJJ9mcAdvHzvAXI2U44q09amJjpM6wmoThwt/Foo0okcyPAbwnh4eHC4MJQkjCeqXWJKUUnPeF1bGCcvR9+mqBtZhk8YfKc4rXVJdI+JJtu4mJLhmSYkE01yTPgERHVk9JEp+sp2D/jkAstCMG8C8vCYxXA1ByX1NZjKRy2Yi7Jrc+c4mUVkXVqExRO5dXd3F/kTuBN28eLF4WY0sl2byUc6sMnvQA587szm9nqg2IcRFQlFuZmAk76Jmpqa0KlLdfg5U4SY9PUopYwmID8BeHh4VBRRk4VtYrD5Ejgz5o5WGXrLj0unM7XDo4OiQlElswZO2uqpLPdFUOgnMWLOxGkykPVs48HHQtLHfR/c1m/yhXR0dEBrHYa22iYAvw7Aw8OjbPBIHfofle7bltrbxPxlJk1TOVPYJ31TBk3aDEdqDzx+XtIiUzrwiYAzbUr5zZk/39CGBGxOG2fe1B5lhpXX2N3dja6uLnR2dhaljKDzlN5DppOoqamRkV4+DNTDw6NysEnuSTWAqLZ5JEzSNmXEksmUlNQsFdUXcNLUw01CvH3T+od8Po9vfetbePvb346LL764RDuh3+l0uig8lUxBtAc1rY2g37W1teFkMXfu3D6tdQMEvAbg4eFRFmySexSDd/UjcEm83Da51M1/ywR6Jq2ES+ekRZi0Gl4WQBHNVJe3I8cslUqhtrYWn/nMZ4qYP2k9BNp2cmBgoKgt2sqTJh2+ToK0gnG6S5c2w2sAHh4esxBxTmf+m6+u5fsQm9ojiVs6cAEY/RdUV2oF0jHN7fs0EfCFaNlstigFBR0njcH7ADw8PDzGwZmwNM9Ik1Fvb29R2giuPXBQ6u4gCNDS0oLW1tbQNxCVssOkFUhNgvoMggAbNmxAfX09giAI+6M8R1xjCoIg1Bhs8BqAh4eHxzhsUUpRPg4ecsmjcuLaLZcu2ypoGXFE9QD7SmCvAXh4eHiMwyah2/4HQVC00Y5tH+GJMH9Zn/fBf2utjZFW474EHwXk4eHhUWlERRJNNR22tQY2DcCng/bw8PCYAKab8RPkOgh53IRJNwEppc5WSm1RSvUppR5SSv3t+PFvKqX6lVI9SqmfK6VeNtm0eHh4eHicxFT4AMYAfE5rvQTAcgCfUko1AGgDcIHWOgPgYQCfnwJaPDw8PDzGMekTgNZ6UGt9//jvYQB9AF6jtd6stR4bL7YTwFmTTYuHh4eHx0lMqQ9AKXUOgDcA2CVOfQzALZY6nwDwifG/I0qpByeNwMnBKwE8M91EJMBMoxfwNE8FZhq9wMyjeTLpXWg6OGVRQEqpNIB7APyL1no9O/4FAM0AVukYYpRSWZMnu5ox02ieafQCnuapwEyjF5h5NE8HvVOiASil5gD4GYCfCOb/EQDvBfDOOObv4eHh4VFZTPoEoJRSAH4AoE9r/Z/s+CUAVgN4q9b6yGTT4eHh4eFRjKnQAN4M4M8A7FFK7R4/dhWAbwOYC6CtMEdgp9b6r2La+v5kETmJmGk0zzR6AU/zVGCm0QvMPJqnnN4ZtRLYw8PDw6Ny8LmAPDw8PGYp/ATg4eHhMUtRlROAUuoSpdRepdSAUupKw3mllPr2+PkepdSy6aCT0RNH74fG6exRSrUrpRqng05BUyTNrFyLUuq4UurSqaTPQEcsvUqptymldo+nHLlnqmk00BP3XLxUKfVLpVT3OM0fnQ46GT03KKWesq21qbb3bpymOJqr8d2LpJmVm/x3T2tdVR8ApwN4BMC5AFIAugE0iDLvAfBrFFKcLgewq8rpXQHg5eO/3z2d9LrSzMrdDWATgEurmV4ALwPQC6Bu/P/vVPsYoxAM8Y3x368CkAOQmkaaLwawDMCDlvNV894loLmq3j0XmtnzM+nvXjVqAG8EMKC1flRrHQBYC+CPRJk/AvAjXcBOAC9TSp051YSOI5ZerXW71vq343+rIe2FyxgDwGdQWL/x1FQSZ4ALvX8KYL3W+iAAaK1nAs0awIvHQ6XTKEwAY5gmaK23jdNgQzW9dwDiaa7Cd89lnIEpeveqcQJ4DYDH2P9D48eSlpkqJKXl4yhIUdOJWJqVUq8B8McA/u8U0mWDyxi/DsDLlVJblVJdSqkPTxl1ZrjQ/B0ASwA8AWAPgL/VWp+YGvLKQjW9d+WgGt69WEzlu1eN+wGYdq6RsaouZaYKzrQopd6OwkN40aRSFA8Xmq8BsFprfXx8ncZ0woXeGgBNAN4JYB6ADqXUTq31w5NNnAUuNP8+gN0A3gHgPBTWxNyrtX5+kmkrF9X03iVCFb17LrgGU/TuVeMEcAjA2ez/WShISEnLTBWcaFFKZQBcD+DdWuuhKaLNBheamwGsHX8AXwngPUqpMa31bVNCYTFcn4lntNYvAHhBKbUNQCMKqcanAy40fxTA13XB6DuglPoNgMUA7psaEhOjmt47Z1TZu+eCqXv3ptshYnB+1AB4FMBrcdJ59npR5g9Q7Iy6r8rprQMwAGDFdI+vK82i/A8xvU5glzFeAuCu8bLzATyIwn4T1UzzGgD/NP77dwE8DuCV0/xsnAO7Q7Vq3rsENFfVu+dCsyg3qe9e1WkAWusxpdSnAdyBgif8Bq31Q0qpvxo//39R8Iy/B4UbewQFSaqa6f1HAK8A8N3xWX1MT2OWQkeaqwYu9Gqt+5RStwPoAXACwPVa62lLHe44xl8D8EOl1B4UmOpqrfW0pS9WSv0UwNsAvFIpdQjAlwHMAarvvSM40FxV7x7gRPPU0TI+y3h4eHh4zDJUYxSQh4eHh8cUwE8AHh4eHrMUfgLw8PDwmKXwE4CHh4fHLIWfADw8PDxmKfwE4OHh4TFL4ScADw8Pj1kKPwF4eIxDKTVPKXWPUur0Cre7Qin1FaVUSim1TSlVdQswPWYn/EIwD49xKKU+BaBGa/1fk9jHl1FIE/2TyerDw8MVXgPw8DiJDwH4BQAopZaMS+s9Sql/UEoNlNuoUup/lFKUhfK28X48PKYdfgLw8ACglEoBOFdrvX/cRPMTFPLzZ1DY1WsieYUuQCHfP8bbaZkQsR4eFYK3RXp4FPBKAM+O/14FoFtr/cD4/14YdmZSSt0JYIGhrS9orUmTeBGAOVrr5wBAF3K8B0qpF6OQtO67AAIAW71ZyGOq4ScAD48CjgJ40fjvDAobtRAuAHC7rKC1/l8O7b4ehQmEYy6AYwAuB7BOa/1LpdQtKGgdHh5TBm8C8vAAoAv7xp4+LrEPobDFJJRSSwFcgUI+/3JwIQopqjHe3isAPK21HkVhQxXaYvF4me17eJQNPwF4eJzEZhS2DPwxgGalVCeAjwHYr7V+tMw2iyYAAG9HIa8+UNhhizYp9++ix5TDh4F6eIxDKfUGAH8H4K+11vnxY/8A4KVa6y9WqI/1AD6vtd6rlDoDhY3hjwHY7n0AHlMNPwF4eDAopT6Gwr63lwEYBbADwN9prUcq0HYKwOVa6x9NtC0Pj0rATwAeHh4esxTe7ujh4eExS+EnAA8PD49ZCj8BeHh4eMxS+AnAw8PDY5bCTwAeHh4esxR+AvDw8PCYpfATgIeHh8csxf8PkMYpdbli/WMAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_cmd(photo_table)\n", + "plt.plot(xs, ys);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If it looks like your polygon does a good job surrounding the overdense area, go on to the next section. Otherwise you can try again.\n", + "\n", + "If you want a polygon with more points (or fewer), you can change the argument to `ginput`.\n", + "\n", + "The polygon does not have to be \"closed\". When we use this polygon in the next section, the last and first points will be connected by a straight line.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Which points are in the polygon?\n", + "\n", + "Matplotlib provides a `Path` object that we can use to check which points fall in the polygon we selected.\n", + "\n", + "Here's how we make a `Path` using a list of coordinates." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Path(array([[ 0.26433692, 18.21044886],\n", + " [ 0.38530466, 19.09345107],\n", + " [ 0.52867384, 19.82928624],\n", + " [ 0.63172043, 20.34437086],\n", + " [ 0.72580645, 20.89624724],\n", + " [ 0.66756272, 21.3745401 ],\n", + " [ 0.49283154, 21.33774834],\n", + " [ 0.35394265, 20.41795438],\n", + " [ 0.24193548, 19.57174393],\n", + " [ 0.18369176, 18.35761589]]), None)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from matplotlib.path import Path\n", + "\n", + "path = Path(coords)\n", + "path" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`Path` provides `contains_points`, which figures out which points are inside the polygon.\n", + "\n", + "To test it, we'll create a list with two points, one inside the polygon and one outside." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "points = [(0.4, 20), \n", + " (0.4, 30)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can make sure `contains_points` does what we expect." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, False])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inside = path.contains_points(points)\n", + "inside" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is an array of Boolean values.\n", + "\n", + "We are almost ready to select stars whose photometry data falls in this polygon. But first we need to do some data cleaning." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Reloading the data\n", + "\n", + "Now we need to combine the photometry data with the list of candidate stars we identified in a previous notebook. The following cell downloads it:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "filename = 'gd1_candidates.hdf5'\n", + "\n", + "if not os.path.exists(filename):\n", + " !wget https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_candidates.hdf5" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "candidate_df = pd.read_hdf(filename, 'candidate_df')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`candidate_df` is the Pandas DataFrame that contains the results from Notebook XX, which selects stars likely to be in GD-1 based on proper motion. It also includes position and proper motion transformed to the ICRS frame." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Merging photometry data\n", + "\n", + "Before we select stars based on photometry data, we have to solve two problems:\n", + "\n", + "1. We only have Pan-STARRS data for some stars in `candidate_df`.\n", + "\n", + "2. Even for the stars where we have Pan-STARRS data in `photo_table`, some photometry data is missing.\n", + "\n", + "We will solve these problems in two step:\n", + "\n", + "1. We'll merge the data from `candidate_df` and `photo_table` into a single Pandas `DataFrame`.\n", + "\n", + "2. We'll use Pandas functions to deal with missing data.\n", + "\n", + "`candidate_df` is already a `DataFrame`, but `results` is an Astropy `Table`. Let's convert it to Pandas:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "source_id\n", + "g_mean_psf_mag\n", + "i_mean_psf_mag\n" + ] + } + ], + "source": [ + "photo_df = photo_table.to_pandas()\n", + "\n", + "for colname in photo_df.columns:\n", + " print(colname)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we want to combine `candidate_df` and `photo_df` into a single table, using `source_id` to match up the rows.\n", + "\n", + "You might recognize this task; it's the same as the JOIN operation in ADQL/SQL.\n", + "\n", + "Pandas provides a function called `merge` that does what we want. Here's how we use it." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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source_idradecpmrapmdecparallaxparallax_errorradial_velocityphi1phi2pm_phi1pm_phi2g_mean_psf_magi_mean_psf_mag
0635559124339440000137.58671719.196544-3.770522-12.4904820.7913930.271754NaN-59.630489-1.216485-7.361363-0.592633NaNNaN
1635860218726658176138.51870719.092339-5.941679-11.3464090.3074560.199466NaN-59.247330-2.016078-7.5271261.74877917.897817.517401
2635674126383965568138.84287419.031798-3.897001-12.7027800.7794630.223692NaN-59.133391-2.306901-7.560608-0.74180019.287317.678101
3635535454774983040137.83775218.864007-4.335041-14.4923090.3145140.102775NaN-59.785300-1.594569-9.357536-1.21849216.923816.478100
4635875994141946752138.23605919.205240-2.593401-14.4792420.8556620.070623NaN-59.295378-1.730187-8.381356-2.723334NaNNaN
\n", + "" + ], + "text/plain": [ + " source_id ra dec pmra pmdec parallax \\\n", + "0 635559124339440000 137.586717 19.196544 -3.770522 -12.490482 0.791393 \n", + "1 635860218726658176 138.518707 19.092339 -5.941679 -11.346409 0.307456 \n", + "2 635674126383965568 138.842874 19.031798 -3.897001 -12.702780 0.779463 \n", + "3 635535454774983040 137.837752 18.864007 -4.335041 -14.492309 0.314514 \n", + "4 635875994141946752 138.236059 19.205240 -2.593401 -14.479242 0.855662 \n", + "\n", + " parallax_error radial_velocity phi1 phi2 pm_phi1 pm_phi2 \\\n", + "0 0.271754 NaN -59.630489 -1.216485 -7.361363 -0.592633 \n", + "1 0.199466 NaN -59.247330 -2.016078 -7.527126 1.748779 \n", + "2 0.223692 NaN -59.133391 -2.306901 -7.560608 -0.741800 \n", + "3 0.102775 NaN -59.785300 -1.594569 -9.357536 -1.218492 \n", + "4 0.070623 NaN -59.295378 -1.730187 -8.381356 -2.723334 \n", + "\n", + " g_mean_psf_mag i_mean_psf_mag \n", + "0 NaN NaN \n", + "1 17.8978 17.517401 \n", + "2 19.2873 17.678101 \n", + "3 16.9238 16.478100 \n", + "4 NaN NaN " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged = pd.merge(candidate_df, \n", + " photo_df, \n", + " on='source_id', \n", + " how='left')\n", + "merged.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first argument is the \"left\" table, the second argument is the \"right\" table, and the keyword argument `on='source_id'` specifies a column to use to match up the rows.\n", + "\n", + "The argument `how='left'` means that the result should have all rows from the left table, even if some of them don't match up with a row in the right table.\n", + "\n", + "If you are interested in the other options for `how`, you can [read the documentation of `merge`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html).\n", + "\n", + "You can also do different types of join in ADQL/SQL; [you can read about that here](https://www.w3schools.com/sql/sql_join.asp).\n", + "\n", + "The result is a `DataFrame` that contains the same number of rows as `candidate_df`. " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(13976, 7189, 13976)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(candidate_df), len(photo_df), len(merged)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And all columns from both tables." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "source_id\n", + "ra\n", + "dec\n", + "pmra\n", + "pmdec\n", + "parallax\n", + "parallax_error\n", + "radial_velocity\n", + "phi1\n", + "phi2\n", + "pm_phi1\n", + "pm_phi2\n", + "g_mean_psf_mag\n", + "i_mean_psf_mag\n" + ] + } + ], + "source": [ + "for colname in merged.columns:\n", + " print(colname)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Detail** You might notice that Pandas also provides a function called `join`; it does almost the same thing, but the interface is slightly different. We think `merge` is a little easier to use, so that's what we chose. It's also more consistent with JOIN in SQL, so if you learn how to use `pd.merge`, you are also learning how to use SQL JOIN.\n", + "\n", + "Also, someone might ask why we have to use Pandas to do this join; why didn't we do it in ADQL. The answer is that we could have done that, but since we already have the data we need, we should probably do the computation locally rather than make another round trip to the Gaia server." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Missing data\n", + "\n", + "Let's add columns to the merged table for magnitude and color." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "merged['mag'] = merged['g_mean_psf_mag']\n", + "merged['color'] = merged['g_mean_psf_mag'] - merged['i_mean_psf_mag']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These columns contain the special value `NaN` where we are missing data.\n", + "\n", + "We can use `notnull` to see which rows contain value data, that is, not null values." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 False\n", + "1 True\n", + "2 True\n", + "3 True\n", + "4 False\n", + " ... \n", + "13971 True\n", + "13972 True\n", + "13973 False\n", + "13974 True\n", + "13975 True\n", + "Name: color, Length: 13976, dtype: bool" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged['color'].notnull()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And `sum` to count the number of valid values." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7189" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "merged['color'].notnull().sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For scientific purposes, it's not obvious what we should do with candidate stars if we don't have photometry data. Should we give them the benefit of the doubt or leave them out?\n", + "\n", + "In part the answer depends on the goal: are we trying to identify more stars that might be in GD-1, or a smaller set of stars that have higher probability?\n", + "\n", + "In the next section, we'll leave them out, but you can experiment with the alternative." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Selecting based on photometry\n", + "\n", + "Now let's see how many of these points are inside the polygon we chose.\n", + "\n", + "We can use a list of column names to select `color` and `mag`." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " color mag\n", + "0 NaN NaN\n", + "1 0.3804 17.8978\n", + "2 1.6092 19.2873\n", + "3 0.4457 16.9238\n", + "4 NaN NaN" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "points = merged[['color', 'mag']]\n", + "points.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is a `DataFrame` that can be treated as a sequence of coordinates, so we can pass it to `contains_points`:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([False, False, False, ..., False, False, False])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inside = path.contains_points(points)\n", + "inside" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The result is a Boolean array. We can use `sum` to see how many stars fall in the polygon." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "659" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "inside.sum()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use `inside` as a mask to select stars that fall inside the polygon." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "selected = merged[inside]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make a color-magnitude plot one more time, highlighting the selected stars with green `x` marks." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_cmd(photo_table)\n", + "plt.plot(xs, ys)\n", + "\n", + "plt.plot(selected['color'], selected['mag'], 'gx');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks like the selected stars are, in fact, inside the polygon, which means they have photometry data consistent with GD-1.\n", + "\n", + "Finally, we can plot the coordinates of the selected stars:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,2.5))\n", + "\n", + "x = selected['phi1']\n", + "y = selected['phi2']\n", + "\n", + "plt.plot(x, y, 'ko', markersize=0.7, alpha=0.9)\n", + "\n", + "plt.xlabel('ra (degree GD1)')\n", + "plt.ylabel('dec (degree GD1)')\n", + "\n", + "plt.axis('equal');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This example includes two new Matplotlib commands:\n", + "\n", + "* `figure` creates the figure. In previous examples, we didn't have to use this function; the figure was created automatically. But when we call it explicitly, we can provide arguments like `figsize`, which sets the size of the figure.\n", + "\n", + "* `axis` with the parameter `equal` sets up the axes so a unit is the same size along the `x` and `y` axes.\n", + "\n", + "In an example like this, where `x` and `y` represent coordinates in space, equal axes ensures that the distance between points is represented accurately. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Write the data\n", + "\n", + "Let's write the merged DataFrame to a file." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "filename = 'gd1_merged.hdf5'\n", + "\n", + "merged.to_hdf(filename, 'merged')\n", + "selected.to_hdf(filename, 'selected')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-rw-rw-r-- 1 downey downey 2.0M Oct 5 09:33 gd1_merged.hdf5\r\n" + ] + } + ], + "source": [ + "!ls -lh gd1_merged.hdf5" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save the polygon\n", + "\n", + "[Reproducibile research](https://en.wikipedia.org/wiki/Reproducibility#Reproducible_research) is \"the idea that ... the full computational environment used to produce the results in the paper such as the code, data, etc. can be used to reproduce the results and create new work based on the research.\"\n", + "\n", + "This Jupyter notebook is an example of reproducible research because it contains all of the code needed to reproduce the results, including the database queries that download the data and and analysis.\n", + "\n", + "However, when we used `ginput` to define a polygon by hand, we introduced a non-reproducible element to the analysis. If someone running this notebook chooses a different polygon, they will get different results. So it is important to record the polygon we chose as part of the data analysis pipeline.\n", + "\n", + "Since `coords` is a NumPy array, we can't use `to_hdf` to save it in a file. But we can convert it to a Pandas `DataFrame` and save that.\n", + "\n", + "As an alternative, we could use [PyTables](http://www.pytables.org/index.html), which is the library Pandas uses to read and write files. It is a powerful library, but not easy to use directly. So let's take advantage of Pandas." + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [], + "source": [ + "coords_df = pd.DataFrame(coords)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "filename = 'gd1_polygon.hdf5'\n", + "coords_df.to_hdf(filename, 'coords_df')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can read it back like this." + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [], + "source": [ + "coords2_df = pd.read_hdf(filename, 'coords_df')\n", + "coords2 = coords2_df.to_numpy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And verify that the data we read back is the same." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.all(coords2 == coords)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook, we worked with two datasets: the list of candidate stars from Gaia and the photometry data from Pan-STARRS.\n", + "\n", + "We drew a color-magnitude diagram and used it to identify stars we think are likely to be in GD-1.\n", + "\n", + "Then we used a Pandas `merge` operation to combine the data into a single `DataFrame`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best practices\n", + "\n", + "* If you want to perform something like a database `JOIN` operation with data that is in a Pandas `DataFrame`, you can use the `join` or `merge` function. In many cases, `merge` is easier to use because the arguments are more like SQL.\n", + "\n", + "* Use Matplotlib options to control the size and aspect ratio of figures to make them easier to interpret. In this example, we scaled the axes so the size of a degree is equal along both axes.\n", + "\n", + "* Matplotlib also provides operations for working with points, polygons, and other geometric entities, so it's not just for making figures.\n", + "\n", + "* Be sure to record every element of the data analysis pipeline that would be needed to replicate the results." + ] + }, + { + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/07_plot.ipynb b/07_plot.ipynb new file mode 100644 index 0000000..f93331b --- /dev/null +++ b/07_plot.ipynb @@ -0,0 +1,1186 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This is the seventh in a series of lessons related to astronomy data.\n", + "\n", + "As a continuing example, we will replicate part of the analysis in a recent paper, \"[Off the beaten path: Gaia reveals GD-1 stars outside of the main stream](https://arxiv.org/abs/1805.00425)\" by Adrian M. Price-Whelan and Ana Bonaca.\n", + "\n", + "In the previous notebook we selected photometry data from Pan-STARRS and used it to identify stars we think are likely to be in GD-1\n", + "\n", + "In this notebook, we'll take the results from previous lessons and use them to make a figure that tells a compelling scientific story." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lesson 7\n", + "\n", + "Here are the steps in this notebook:\n", + "\n", + "1. Starting with the figure from the previous notebook, we'll add annotations to present the results more clearly.\n", + "\n", + "2. The we'll see several ways to customize figures to make them more appealing and effective.\n", + "\n", + "3. Finally, we'll see how to make a figure with multiple panels or subplots.\n", + "\n", + "After completing this lesson, you should be able to\n", + "\n", + "* Design a figure that tells a compelling story.\n", + "\n", + "* Use Matplotlib features to customize the appearance of figures.\n", + "\n", + "* Generate a figure with multiple subplots." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installing libraries\n", + "\n", + "If you are running this notebook on Colab, you can run the following cell to install Astroquery and a the other libraries we'll use.\n", + "\n", + "If you are running this notebook on your own computer, you might have to install these libraries yourself. \n", + "\n", + "If you are using this notebook as part of a Carpentries workshop, you should have received setup instructions.\n", + "\n", + "TODO: Add a link to the instructions." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# If we're running on Colab, install libraries\n", + "\n", + "import sys\n", + "IN_COLAB = 'google.colab' in sys.modules\n", + "\n", + "if IN_COLAB:\n", + " !pip install astroquery astro-gala pyia\n", + " !mkdir data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Making Figures That Tell a Story\n", + "\n", + "So far the figure we've made have been \"quick and dirty\". Mostly we have used Matplotlib's default style, although we have adjusted a few parameters, like `markersize` and `alpha`, to improve legibility.\n", + "\n", + "Now that the analysis is done, it's time to think more about:\n", + "\n", + "1. Making professional-looking figures that are ready for publication, and\n", + "\n", + "2. Making figures that communicate a scientific result clearly and compellingly.\n", + "\n", + "Not necessarily in that order." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's start by reviewing Figure 1 from the original paper. We've seen the individual panels, but now let's look at the whole thing, along with the caption:\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Think about the following questions:\n", + "\n", + "1. What is the primary scientific result of this work?\n", + "\n", + "2. What story is this figure telling?\n", + "\n", + "3. In the design of this figure, can you identify 1-2 choices the authors made that you think are effective? Think about big-picture elements, like the number of panels and how they are arranged, as well as details like the choice of typeface.\n", + "\n", + "4. Can you identify 1-2 elements that could be improved, or that you might have done differently?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some topics that might come up in this discussion:\n", + "\n", + "1. The primary result is that the multiple stages of selection make it possible to separate likely candidates from the background more effectively than in previous work, which makes it possible to see the structure of GD-1 in \"unprecedented detail\".\n", + "\n", + "2. The figure documents the selection process as a sequence of steps. Reading right-to-left, top-to-bottom, we see selection based on proper motion, the results of the first selection, selection based on color and magnitude, and the results of the second selection. So this figure documents the methodology and presents the primary result.\n", + "\n", + "3. It's mostly black and white, with minimal use of color, so it will work well in print. The annotations in the bottom left panel guide the reader to the most important results. It contains enough technical detail for a professional audience, but most of it is also comprehensible to a more general audience. The two left panels have the same dimensions and their axes are aligned.\n", + "\n", + "4. Since the panels represent a sequence, it might be better to arrange them left-to-right. The placement and size of the axis labels could be tweaked. The entire figure could be a little bigger to match the width and proportion of the caption. The top left panel has unnused white space (but that leaves space for the annotations in the bottom left)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting GD-1\n", + "\n", + "Let's focus on the figure in the lower left..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2020-10-05 10:24:55-- https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_merged.hdf5\n", + "Resolving github.com (github.com)... 140.82.112.4\n", + "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://raw.githubusercontent.com/AllenDowney/AstronomicalData/main/data/gd1_merged.hdf5 [following]\n", + "--2020-10-05 10:24:56-- https://raw.githubusercontent.com/AllenDowney/AstronomicalData/main/data/gd1_merged.hdf5\n", + "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.116.133\n", + "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.116.133|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 2006352 (1.9M) [application/octet-stream]\n", + "Saving to: ‘gd1_merged.hdf5’\n", + "\n", + "gd1_merged.hdf5 100%[===================>] 1.91M 6.14MB/s in 0.3s \n", + "\n", + "2020-10-05 10:24:56 (6.14 MB/s) - ‘gd1_merged.hdf5’ saved [2006352/2006352]\n", + "\n" + ] + } + ], + "source": [ + "import os\n", + "\n", + "filename = 'gd1_merged.hdf5'\n", + "\n", + "if not os.path.exists(filename):\n", + " !wget https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_merged.hdf5" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "selected = pd.read_hdf(filename, 'selected')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_second_selection(df):\n", + " x = df['phi1']\n", + " y = df['phi2']\n", + "\n", + " plt.plot(x, y, 'ko', markersize=0.7, alpha=0.9)\n", + "\n", + " plt.xlabel('$\\phi_1$ [deg]')\n", + " plt.ylabel('$\\phi_2$ [deg]')\n", + " plt.title('Proper motion + photometry selection', fontsize='medium')\n", + "\n", + " plt.axis('equal')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's what it looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10,2.5))\n", + "plot_second_selection(selected)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Annotations\n", + "\n", + "The figure in the paper uses three other features to present the results more clearly and compellingly:\n", + "\n", + "* A vertical dashed line to distinguish the previously undetected region of GD-1,\n", + "\n", + "* A label that identifies the new region, and\n", + "\n", + "* Several annotations that combine text and arrows to identify features of GD-1.\n", + "\n", + "As an exercise, choose any or all of these features and add them to the figure:\n", + "\n", + "* To draw vertical lines, see [`plt.vlines`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.vlines.html) and [`plt.axvline`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.axvline.html#matplotlib.pyplot.axvline).\n", + "\n", + "* To add text, see [`plt.text`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.text.html).\n", + "\n", + "* To add an annotation with text and an arrow, see [plt.annotate]().\n", + "\n", + "And here is some [additional information about text and arrows](https://matplotlib.org/3.3.1/tutorials/text/annotations.html#plotting-guide-annotation)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "\n", + "# plt.axvline(-55, ls='--', color='gray', \n", + "# alpha=0.4, dashes=(6,4), lw=2)\n", + "# plt.text(-60, 5.5, 'Previously\\nundetected', \n", + "# fontsize='small', ha='right', va='top');\n", + "\n", + "# arrowprops=dict(color='gray', shrink=0.05, width=1.5, \n", + "# headwidth=6, headlength=8, alpha=0.4)\n", + "\n", + "# plt.annotate('Spur', xy=(-33, 2), xytext=(-35, 5.5),\n", + "# arrowprops=arrowprops,\n", + "# fontsize='small')\n", + "\n", + "# plt.annotate('Gap', xy=(-22, -1), xytext=(-25, -5.5),\n", + "# arrowprops=arrowprops,\n", + "# fontsize='small')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Customization\n", + "\n", + "Matplotlib provides a default style that determines things like the colors of lines, the placement of labels and ticks on the axes, and many other properties.\n", + "\n", + "There are several ways to override these defaults and customize your figures:\n", + "\n", + "* To customize only the current figure, you can call functions like `tick_params`, which we'll demonstrate below.\n", + "\n", + "* To customize all figures in a notebook, you use `rcParams`.\n", + "\n", + "* To override more than a few defaults at the same time, you can use a style sheet." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a simple example, notice that Matplotlib puts ticks on the outside of the figures by default, and only on the left and bottom sides of the axes.\n", + "\n", + "To change this behavior, you can use `gca()` to get the current axes and `tick_params` to change the settings.\n", + "\n", + "Here's how you can put the ticks on the inside of the figure:\n", + "\n", + "```\n", + "plt.gca().tick_params(direction='in')\n", + "```\n", + "\n", + "**Exercise:** Read the documentation of [`tick_params`](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.axes.Axes.tick_params.html) and use it to put ticks on the top and right sides of the axes." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "\n", + "# plt.gca().tick_params(top=True, right=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## rcParams\n", + "\n", + "If you want to make a customization that applies to all figures in a notebook, you can use `rcParams`.\n", + "\n", + "Here's an example that reads the current font size from `rcParams`:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "10.0" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import matplotlib as mpl\n", + "\n", + "mpl.rcParams['font.size']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And sets it to a new value:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "mpl.rcParams['font.size'] = 14" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Plot the previous figure again, and see what font sizes have changed. Look up any other element of `rcParams`, change its value, and check the effect on the figure." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you find yourself making the same customizations in several notebooks, you can put changes to `rcParams` in a `matplotlibrc` file, [which you can read about here](https://matplotlib.org/3.3.1/tutorials/introductory/customizing.html#customizing-with-matplotlibrc-files)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Style sheets\n", + "\n", + "The `matplotlibrc` file is read when you import Matplotlib, so it is not easy to switch from one set of options to another.\n", + "\n", + "The solution to this problem is style sheets, [which you can read about here](https://matplotlib.org/3.1.1/tutorials/introductory/customizing.html).\n", + "\n", + "Matplotlib provides a set of predefined style sheets, or you can make your own.\n", + "\n", + "The following cell displays a list of style sheets installed on your system." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['Solarize_Light2',\n", + " '_classic_test_patch',\n", + " 'bmh',\n", + " 'classic',\n", + " 'dark_background',\n", + " 'fast',\n", + " 'fivethirtyeight',\n", + " 'ggplot',\n", + " 'grayscale',\n", + " 'seaborn',\n", + " 'seaborn-bright',\n", + " 'seaborn-colorblind',\n", + " 'seaborn-dark',\n", + " 'seaborn-dark-palette',\n", + " 'seaborn-darkgrid',\n", + " 'seaborn-deep',\n", + " 'seaborn-muted',\n", + " 'seaborn-notebook',\n", + " 'seaborn-paper',\n", + " 'seaborn-pastel',\n", + " 'seaborn-poster',\n", + " 'seaborn-talk',\n", + " 'seaborn-ticks',\n", + " 'seaborn-white',\n", + " 'seaborn-whitegrid',\n", + " 'tableau-colorblind10']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plt.style.available" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that `seaborn-paper`, `seaborn-talk` and `seaborn-poster` are particularly intended to prepare versions of a figure with text sizes and other features that work well in papers, talks, and posters.\n", + "\n", + "To use any of these style sheets, run `plt.style.use` like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use('fivethirtyeight')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The style sheet you choose will affect the appearance of all figures you plot after calling `use`, unless you override any of the options or call `use` again.\n", + "\n", + "**Exercise:** Choose one of the styles on the list and select it by calling `use`. Then go back and plot one of the figures above and see what effect it has." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you can't find a style sheet that's exactly what you want, you can make your own. This repository includes a style sheet called `az-paper-twocol.mplstyle`, with customizations chosen by Azalee Bostroem for publication in astronomy journals.\n", + "\n", + "You can use it like this:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "filename = 'az-paper-twocol.mplstyle'\n", + "\n", + "if not os.path.exists(filename):\n", + " !wget https://github.com/AllenDowney/AstronomicalData/raw/main/az-paper-twocol.mplstyle" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "plt.style.use('./az-paper-twocol.mplstyle')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The prefix `./` tells Matplotlib to look for the file in the current directory.\n", + "\n", + "As an alternative, you can install a style sheet for your own use by putting it in your configuration directory. To find out where that is, you can run the following command:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The prefix `./` tells Matplotlib to look for the file in the current directory.\n", + "\n", + "As an alternative, you can install a style sheet for your own use by putting it in your configuration directory. To find out where that is, you can run the following command:\n", + "\n", + "```\n", + "mpl.get_configdir()\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## LaTeX fonts\n", + "\n", + "When you include mathematical expressions in titles, labels, and annotations, Matplotlib uses [`mathtext`](https://matplotlib.org/3.1.0/tutorials/text/mathtext.html) to typeset them. `mathtext` uses the same syntax as LaTeX, but it provides only a subset of its features.\n", + "\n", + "If you need features that are not provided by `mathtext`, or you prefer the way LaTeX typesets mathematical expressions, you can customize Matplotlib to use LaTeX.\n", + "\n", + "In `matplotlibrc` or in a style sheet, you can add the following line:\n", + "\n", + "```\n", + "text.usetex : true\n", + "```\n", + "\n", + "Or in a notebook you can run the following cell." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "mpl.rcParams['text.usetex'] = True" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you go back and draw the figure again, you should see the difference.\n", + "\n", + "If you get an error message like\n", + "\n", + "```\n", + "LaTeX Error: File `type1cm.sty' not found.\n", + "```\n", + "\n", + "You might have to install a package that contains the fonts LaTeX needs. On some systems, the packages `texlive-latex-extra` or `cm-super` might be what you need. [See here for more help with this](https://stackoverflow.com/questions/11354149/python-unable-to-render-tex-in-matplotlib).\n", + "\n", + "In case you are curious, `cm` stands for [Computer Modern](https://en.wikipedia.org/wiki/Computer_Modern), the font LaTeX uses to typeset math." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multiple panels\n", + "\n", + "So far we've been working with one figure at a time, but the figure we are replicating contains multiple panels, also known as \"subplots\".\n", + "\n", + "Confusingly, Matplotlib provides *three* functions for making figures like this: `subplot`, `subplots`, and `subplot2grid`.\n", + "\n", + "* [`subplot`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplot.html) is simple and similar to MATLAB, so if you are familiar with that interface, you might like `subplot`\n", + "\n", + "* [`subplots`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplots.html) is more object-oriented, which some people prefer.\n", + "\n", + "* [`subplot2grid`](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplot2grid.html) is most convenient if you want to control the relative sizes of the subplots. \n", + "\n", + "So we'll use `subplot2grid`.\n", + "\n", + "All of these functions are easier to use if we put the code that generates each panel in a function." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Upper right\n", + "\n", + "To make the panel in the upper right, we have to reload `centerline`." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "filename = 'gd1_dataframe.hdf5'\n", + "\n", + "if not os.path.exists(filename):\n", + " !wget https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_dataframe.hdf5" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "centerline = pd.read_hdf(filename, 'centerline')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And define the coordinates of the rectangle we selected." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "pm1_min = -8.9\n", + "pm1_max = -6.9\n", + "pm2_min = -2.2\n", + "pm2_max = 1.0\n", + "\n", + "pm1_rect = [pm1_min, pm1_min, pm1_max, pm1_max]\n", + "pm2_rect = [pm2_min, pm2_max, pm2_max, pm2_min]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To plot this rectangle, we'll use a feature we have not seen before: `Polygon`, which is provided by Matplotlib.\n", + "\n", + "To create a `Polygon`, we have to put the coordinates in an array with `x` values in the first column and `y` values in the second column. " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[-8.9, -2.2],\n", + " [-8.9, 1. ],\n", + " [-6.9, 1. ],\n", + " [-6.9, -2.2]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "vertices = np.transpose([pm1_rect, pm2_rect])\n", + "vertices" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following function takes a `DataFrame` as a parameter, plots the proper motion for each star, and adds a shaded `Polygon` to show the region we selected." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "from matplotlib.patches import Polygon\n", + "\n", + "def plot_proper_motion(df):\n", + " pm1 = df['pm_phi1']\n", + " pm2 = df['pm_phi2']\n", + "\n", + " plt.plot(pm1, pm2, 'ko', markersize=0.3, alpha=0.3)\n", + " \n", + " poly = Polygon(vertices, closed=True, \n", + " facecolor='C1', alpha=0.4)\n", + " plt.gca().add_patch(poly)\n", + " \n", + " plt.xlabel('$\\mu_{\\phi_1} [\\mathrm{mas~yr}^{-1}]$')\n", + " plt.ylabel('$\\mu_{\\phi_2} [\\mathrm{mas~yr}^{-1}]$')\n", + "\n", + " plt.xlim(-12, 8)\n", + " plt.ylim(-10, 10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that `add_patch` is like `invert_yaxis`; in order to call it, we have to use `gca` to get the current axes.\n", + "\n", + "Here's what the new version of the figure looks like. We've changed the labels on the axes to be consistent with the paper." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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OVFqwR6kEFbOA8ikPH/aiPA4pBqrXEj19pIaeFCNHdfMrZu6JlwT+siAiEErvqV0qwuEw804rpF6HIYWLilGx/UqK5OLiIkO79Jpq+f4TEQ8RaVRDo6LRaE7b6bC3Wq2y+F2iwqvklKIWGV5UdIEDpaK/vx9msxk9PT1IpVJobGyUlcmTz6kd2Wy2YPOfnnmhZmZTQmOJdtHa2sp4ZHqcDvSgeGp10SpXTxv5C67Ss0+SEvOkEdlLCtUvuNDmxrt+qz1TukUUL4WiaPkW+nGaw45ik9E7ZkommHw3WL1mBLok0P/b7XZIkgSLxaKIQGi1W+QTFoIuHuX8LbYsUiQtFotutEvpspUP8RD7UEv5EdEgUqaqq6uZwkvz22AwsP9W48EpoWGimTYQCLB8hrxiSKgXIVekSNpsNqytrcnWWT6aRL5LidhHSmZCvjxeoVYaf62/5YvSrzXn1bipepBlJcRTzzdPWj7ss02UkpffCcnHObDnL5pHhyhHXf+T9BIqVvS2+caNGwgGg/B4PLh06RKAg2jgi4uLMJvNGBwczHm/WC+k4/Re4ttLhxl9h7wje3p6VKPCF/MdvV5bet47zPfzReJWCnyqJDTuFosFfX19soCbStHoRVHypuMVcwDMdOZwOJDJZLC2tsa4Y6RAWSwW5lygFPSTvqNVJz39za9jKs/lcuXlL/Hl8sga/16x482/V4yHY0mUpeTl94TKk0KeO2rRQh1O4hZxnOTEo0bYiuVp6BG9/XBUZliHw4Hy8nI4HA5Z2bOzs9je3tZ1w6d3gsEgFhYWVOOmqb3H86sOY04j05D4HUJ1AoHAoedBMR6Veuqvt91Go5EdtiIXS+Te8SgJKZr5TFWkyPj9fqRSKQD6kxqL/U4KQSwWw+7uLgAwRMztdiORSCCTybBnCPUiThmh9mK8Jz00CeonLWcefh0nEgk0NDQwVEdJlJSkSEQ5/16xeyY/fwpB1T8scveTRio/rJQUqhOWkwzseZJCC5lcnU96gRynWfGoIW4lMvFRid5+OKr+8ng8ePrpp2U3YKPRiM7OTlRUVKgeVvkivSvVS+09nl+lN+gnv5HzvCwirPOHHvGOenp6jnQeKB0manMtHz9MiU9ESqYYtoH6emlpCaFQiLWXN3OpkfiVzFK8cka5I0Wemx4FhcYXkPOMeII6P/4OhyOH8K+kmKs5DGjRJKgOWuFcyGwPgClnWiZ8pfIK3Vt4c2WhSbTV5gPw4dEyPgp0EF5KCtUJS3Nz86HeP2qN/qjJx4C+dAridw9bj3wbyWHkqBE2sbyjHFO9G/RR9ZdS39DN2OVyFVRvPtK7XrI4z7nR4vuIQocbHSzEy4pEIjmHHiFU8Xj80PNAVORIAaQDMhQKKZp58vHD+CjZYnRyMWwDzycDHq9XvhyexE9KkBhril/zfFodt9sti2yvFiNLrX+o/kbjQfokCv0iKtK7u7swm82MnxQKhTA5OYmbN29qKuZ6xkavUNT8WCxWlEdroXsL72iQLxwHzfGpqamcFEj0nl7y+2FEq18/TD7WcaBjJYXqhOWwh/JxmZ8OWx7vJq5ngYi3NbV66J30+dCL45bDLM5C66ll5tKrrGn112FRRr3hEvjbNpko2tra8uax4+vLm0t4snNDQwNu3LiBhYUFzXaQOUnLVCMqLHq8tLTqzCswpACqHZBUPnGZlMxu/G+hUEiGDolkfEBOlObXK48QSZIEl8uFlpYWSJKkGGuKX/Mul4uZ+/i5xfednsMzFAohnU4zwrlSWAZxTHhpaGhAc3MzHA4HXC4X7Ha77vRb4jp0Op0wm82aHnZiX2sFYHU6nYcOD8ObK/WG47Db7cwBxm63y96jfZjWn551V+hep7W/fZik8uM4H0oK1S+YHJf5qZDy1BbUYYizWqaOYnO3neTt5zDcl0Jvh6KZ66jCM2ihjLzyo2czVQpIKM4b/ratB7nQMo+J5N5AIIBgMIhbt24plksoWl9fX05gT0JulJTUUCiE+fl5TExM5LSjkD6mNUIKoNYBqdVPVJ8f//jHmJ+fhyRJOdHJtZItqynWIvqjJfQdWv/8WCl5jFG9tUx/LpcLgUAAyWQSfr9fs968wuL1euHxeNDc3Ayz2YxYLKaqqIrCz1lJkjA1NYV0Oq24Doh7phYJXkQitTz2CuXAkbmSxlWtP2mO0xhSyBAxjAcR9/XWo9C97knyCuTlOOpVUqh+weRJMD+pLahCFhotdrol0d+UTB16zTj52nacUszizLfR6vlWvu8WUi81lJE3wxDZOJ+CG4lEmPKlFlRSixyspnzxPCFeeRd/9/l88Hg8uHjxomKgUzUejRa6JkkSwuEwS9A8MTGBYDBYkEKsxedRU3yIq9PY2Cgzu/H12d7eRjweV5zzhRzYIncK0A4ZolS2yK3i0SHRJCkK/y2KOdXT06NZXyqP1pHb7c5BZFwulya/CZAH0aQ2xONx2TP8PNPaX0QksthYUnpE7X01pFB8hlBFvfUQy8o3v9QUzQ9bjuN8KIVNOCH5RQmboMc1XQ2JKgahOsz3jkuO6nv5ytH7nWLqc5R9Fgo9TnJL0aopOTHxbfjvSJKEiYkJNq5NTU0wGAyIxWKw2+26UA8lF3r6jiRJGBkZgdfrRU1Njex3JRf/fGEB8s1jHnmhQ9ZgMCCTyTCej1a5xY4F/x4ddnwbaFzi8TjzslQ6JApZY2TekyQJZrM5bxgAKptPBg1AVhbwODk7P5fyRTPXE87B7/ejuroayWSS5VckUQqVoDSf+HlLf6PvA8WFMyjkOa0LpdK8pv6lv5EHqs/ng9lsLqgeYpuLmaeFhDM5ztAnwPGdFXrP75JCdULyJCtUR7GolMrSa48/SWVJjwSDwSOJ33JUm0cx5WgdXBRXSWkDBvIrBHoPelLAYrEY+1t1dTUGBwdV6y1ygoDcg2Z8fJy5zA8PD8sUKKW+UptjehUtNaVBPAh5pUTrMNeqkziG9J6a4qpUT6X2aB3a/LfIay0cDrNgrVarVVX50VJKlcx+eg94tfEnRIr2Uvp/3qFAkiTWZ/SuGEtMHKPjED0Ku9L64UUpZhYA2d+04mkVepk4ynYe9tli5LgUtpJC9YTJSSlUx4US6ZXjvoGchOQLTKlXTgrpUnuHDh6KzUMyPj6OZDKpqtwUgmgoIUJK9VVS4pQUNb7OABQVkWAwiFgsxoJHqiEPgPblQK+ipbf/lRQvNSXisH1c6PP5vifWMZvNYmpqCpIkoampiQXL1NuXhewDIuJE+6SSkqCkrPP9HAqFEA6HZcoHCR9gVBwjknyXDa36K80jpUuNkqKsNsb5ECpS4NXMV4VeJvS27UmVo0QQ+We2trZKgT0/bkIH0ubmZkEk7qMk5z2pBEQSPTZ8t9sNq9WKhoaGHJJtIVIMn0XpdyoH0Cbzit9WSu8BIC83Rc8YavG/6KCivgPAlFP+kBI5G5FIfq+pSCSC/f19xjsSielavCqlPlLqV/G7WjxD/r/pPT3xxZTaJo49fTcSieQQ4Un41D/0vFLaFOJh8Twi+h55WfIHdCwWQ1NTEwuBAGiHQ+F5U4U6WtC7vDJFSFJDQ0PO2BARnPhvNH7AQVy0wcFBVmcipAPyAKNqY0TJl5WI8Fr1V+IVqvE/+bHXGjNAmevH/81oNGJ/f5/9XRQ1RYvqwPMLtfhwevhdx8GPKrRMvbwoPe0qhttWQqhOSE4CoaIb0dzcHDo6OnKQiZJo38zU0A01WF6PKUWUfOZENZRDrLuSCUiPaCFXhUi+m7weM5cSKqJUpl7USQ1ZyofwaPGAtL5jMpkQjUY1+zJff/NtIxd2i8WCgYEB9t1gMIjx8XHs7++zIKo0927duoXKykrYbDYMDg5qzkmxnWT2UjIdifNLz9jxiJbYVi1EQGmcaM3pSQ+jZr4T+0IcU6U6pdNpjIyMoLu7G21tbbppC2pjfBRcKj19VyyCxK8Z4rzxe1Mh3z4O68RxWTxKCFVJ8grd3oaHhxWRieOW47ihHLVooS/8jYS/6ai9E4nkBk08qvoBuYgAX49ibk9UZzGKdTFSaJJd+jZfZ/E2GYvFWEwmtVuy1g1UDVnSSnTLvwc87nP+myIiJUkHQS4pGOfKygr7JiFG6XSaoTV8FHYtj7hIJIJYLIZ0Oi1DOwhNcTgcrC6kfG1vbyOVSsHn87HyxLha9A6hRtROSZJkEeD5JNS8Aqt00ItjSfG8dnd3FeeW1nxVQmFoTLTSw1CZgHIKJx6BJKWU9y5UqhOFEAiHwzIPVS0hNFiMlK62H6r9nYKEUn34sc7Xd4B+9JqXfGi01noT++84rBPHZfHQMpEqcf/0SgmhOiE5aoTqSbRtP+n8qXwkURHJyNfHxSBUR3FjLaScQr5/GHJpoTc+Jb4VAMWbfqFt1VM3LW6K+N+h0OMks0ajkc1xUmpaW1sxNDQESZJw7do1WCwWZDIZdHV1yVAvJaSTR1kymQxWVlawsrICr9eLdDrNuDciGmMymRCJRJBOp2G1WhmipYTm8QiO0+lEMBhkh2B9fT2SyaQqxwfI5bGp9bHe+cr37WHmbr71TH2+uLiIaDSKxsZGGcFeCWW7du0aqqqqUFVVBZfLpYp86eEl0fwAIPMEVdonlfhWWmi1KEeFXh9mnX3YchT1UTvDSqT0J0zUBqTYSVAscfaoJN/h+CQsMFHUFkuhfz8uEQ8lNVOj2rtHsZnodWk/CrhfNJWQ8gLoV1DV2pGvLkreU2rP8mZap9PJzJIAZCbKUCiEzc1N3Lt3D11dXTkxrpRMUPRvp9PJ0JFMJiMz2/MHJCBX+rTMiUoHMvGxZmZm2Htnz56VKVW86ZW4aPkUZb3jls1mWciLysrKvIoCXzYhRxSVXel7anNKD+LAe49evXpV1tdaihP/bSUlU+TEKZWrpHDqdUYQ3z/pfUOvHOe3jorKoFbHkkL1hInagBR7aJ+E/VpLnlQ0qhgEptC/5/utWBFvtLQ56tlYj2I8CtmURC5YMbfbyclJFkOI3N4LvWXrRZ8OA+2Lh5WodNLvFA+osbER+/v7TJERuUEUO0qMZcUrlKRgKR2QamgPgByFTalvxsbGMDU1hbKyMrz00ktYX1+HzWZjiFQkEkF9fb3ugykUOojWHo/H0d/fL+MGiuPDKy0XLlyQIWdKbSKESZIkJBIJVFdXI5FIQJIOgnZ6vV5FL8B4PJ43hpYoap6oauEx9CJlxE0C5EiVmiJ62LVcyFo8zCWqUCkEcSumbL3tKEb0KlQlDtWHLMXaiMVD4DhszVqcqOOybR9W9HKL9ByohfAHjoI/5nTK867l4wDx36TxUOLp6BXigtDNXosDIuZY06OU8DwkUZkixSMej8Nms+HatWvY3NzMy2PhOUjieNJhTIeWWBdK/ssfZErfEfl0vOcWtSWVSiEQCMBmsyEWi7F4TuS9trm5iWvXrqGhoQGJRALZbJYdmnxeNTp4yYORFDU+Jcr4+Dju3buH8fFxAI8PY5HTx9dbPLybm5tht9thNpvZmAMHXnHpdBpzc3OytEFaQnOvoaFBdXxo7vp8PthsNgwPD8PtdsuUKSX+Gq0Jk8mEmpoabG5uoq6uDoAyb8rpPOCROhwOLC0tsYTTNO/Iq5GP4E5/NxqNOZ6ooVAIN2/eZPnuxPmg1Eb+3cXFRZab0OFwsPrSvFlcXMzhYCqlbRJFa20WovQ4nU6sra0xzp+W6N1btb6lxA89qr2TTwOkV47i27yUEKoTkic5sKcoIl/jJE1eh0V9tMrQww0p9jtHjdjp4ZyocTGOO6ZYMbdBse+14vPMzMwwjtDzzz+P/f39vPGTlObqwsICJicn4XA4WCJgo9HICMo7OzuoqqrCyy+/nBPPSiyf73seyaA0NHwUeTFytyRJuHPnDvPG6+/vV0QmRNSP+oNMgcThmp+fx+zsLDo7O2UIjZbpTeTY8EgYTyomhaYQhEqtn7T+rvW+JEk5SBH1jcvlAgAWh0wtVtTCwgJu3bqFpqYmlJWVyfhvKysrePToERobG+H1egGo7wVqMen0rE+q88rKCjKZDDweDy5dusTGQw1J02uyzhfjSs+84OuZL5DxUaHyH7Z1hRe9qFkJoSpJwUILjyYZoHwLPC45zA1IDylWkiSWC+2wCJsWQqj31qP1HN8XauiPUhv03G7zCdVLREZ4NKzQ2yBfV7X36ZkrV64gm82itbUVgPYcpL7h0Q7+N6PRiGg0quiJGY/HEQqFMDExoRo3iVAG/vZOHo6kLFksFhYXyefzYX5+HlarlcUy2t/fx4ULF2Cz2dDT06N6KLnd8px51B/k3UdmzNbWVnzqU59Ca2srGhoaZHGoSHGkuol9Sx58brcbHo8HkUiEISWUOHdgYEDRS1iSpJzcfOI4qPHFRCVLnPf8+7wHKT3rdDpZol+z2YympiZVD1Mqr7GxkeXlE1HcxsZGhgJqrRm32w2v15sT4FdrfYp1Pn/+PIxGI/b29libaQ0MDg7mpGJSWtciwitJEtbW1jRjXPH7+dLSEubn53Ht2jUWAR8Ai0FGSp2W5EOh9Ype68pRo0dKooaaFSslheoJlKOcSIWURZsEAN0BCo9SDqPk5FPGIhG5O/lRbQ4kfHl6FUOt5/T0hVIblA4jfuz1zAeqVyAQkCWLJbOVJEkF9x1fVy0Tq9PpZK785eXlis+qHciEsNDf3W43+vv70d/fD5fLxUw9TqcTg4ODOHXqFPb29hAMBjEyMsIO1Hz9w2/CYoiCWCyGjo4OLC4uwmazAThYSxRwMhaLYXFxEZOTkzljL7aV/u3xeGC1WlFfX8+Unra2NrjdboyOjiKRSMgCUSopgUrmY1LSbDabLOin2viIJkUtUTLFir+plcHPfXqWAsgCkF2MeBGV/s3NTVRVVWF9fV3WB263W+btpxUCRGleiXVUazvVua2tDR6PB/X19Swchtb+o/Qb32ekINElgFeyeNSMTNEA0NLSgs3NTVgsFtlcmZqaYsq03vV81IqO1nw7CiVHz7eVLmTFSEmhegLlsBOJn/CFlEWbxGEUKZ6nIHIX8i3AYpQcKjtfZOaT5Hzp/ZbWc3oVCfE3vh+UeF60yWrNB6oXHwPI6XRifn4+Z0NWksNsuFRno1FO1M53ICu1jRCbtrY2mM1mhMNhGb/o5ZdfxlNPPQWr1craJZYtokZUrtomTMgDxYJzu905B7IkSdjZ2cHKyoouJJG4bSaTCUtLSwxhCIVCqKyslMWhyieiskKmPTKH5ZsXPMdPjLmlpHAAyIkino/vJ3LW+GfJBKdUT37sYrEYvF4vwuEw2tvbGfcsm83mKFBO5+Mo8vy+BaivGa29SlzXNH7JZDIHUdIrSnsFKfK7u7s53LNIJILq6mrMzc3B6XTC4/Hg6tWrsNlssrlCPLpCEO2TUHSAk92ztcZTkiQ8evRIVzklheoJlMNOJJHUqbesQg9xrW/7/X5Fkmmx5eb7nlIKFP4bR41I8SK2Re+3lEjDSiRTPcqxUj+IY09oRL6gnlQvMd3F8PBwzoasVFcyGR/m4CCFRS2NilLQUJvNhrW1NabE8/WSJAkulwstLS0AHieXvXTpEj7zmc+wdokHOKAeCkBpnPm+I6WF7w9CR/b397G7u6tpthK/tbq6inQ6LVNojUYjmpubZXUQlUBxHVC9SPne399HbW2trnnh8XiYiWpqagoLCwt48803VdE2p9OJubk5VFdXIxgMYmxsjKGEkchjpwGqYzqdZooPleN0HoSqSKVSiEajiMfjOReFYDCIdDrNlFSn04lkMolLly6hqqoK0WiUceqUxpgUE37fAtTXjGiCy7f+SakqNuAyX6bb7WbzmL9AiWb1jY0N1NTUsLaYzWZ0dXXh29/+NpLJJIDHqXoKSQJfzPlUzH5/nHt2IcJbbvJJSaF6AkXNJq93MvIT/rCTstDbiBq6IS5AJbNEMaIHej/um1Qh31EbS7Uy9CrHajdYkatQjCcMiVI+PqW6AnLeU7GmR73t4tvmdDrZjZ2v1+7uLsxmM+MY8WXy7VIyjRUr1B+SJLF4V5FIBD6fD62trXC5XDnKn5JQX/X09KC6upoptNQOMq2JfUqKxsTEhGKEcDLzNDQ05AT3FL+tVEdCNyiAKZlU+WfJBJpMJhGJRHDz5k1MTk6qzvPR0dGcXHq8UkMmXEIv6felpSX4/X4W4Zz6mTz+HA4HysvL0djYyNYSP8bivkXKlt1uz1kzImolrl0+vyIvWvu6ngsVz0sjBZC/QPHKMgAZT4/e/c53voOVlRW8+uqrOXXSa10o9EyRJAkTExOYn5+X7fd62qxXDrPH5BMebc0nJS+/E5LDePl9mF4QvF3+KG8Kej1LDlO346r7Yb6jNpZqZWiVfVLt0ytq9RHbTAeSUryrw851pTrk+xugHWhRT9+Kz/ObeSwWQ319PRKJhCyIqN62Knl00fey2SzC4bDMi43PzZfNZpHNZpFKpTA8PAyj0Zjj4aeF4IZCB8FK5+fnceXKFcTjcVkbqc2ENgFgdRH7dmFhAaOjo+js7ERbWxtTavjnKJYXJUfWMz5Uz1AoxDw9m5qaZGRjCmMRDofR0NCQEzCVb4/dbmeJmtUCpvLjIUlyr8Tx8XEkk0lYLBaUl5fryndJ9dQKLsw/Q6ZwIDeGFe91KsYy29nZwY0bN/DKK6+gurpasT58sFu1eoljoLVGQiF5nDLeaYL3zKXyxL/rkaP0eFZqVymw5xMmh1GonrSDU4+chLJTyIJROvAOe4gW8w653PMHRrGK0oepaOsV/lZNGz9t+mtra3A6nTm39pOY63oOMz2idghQG+fm5uD1elkYBaXURnoCkPKKAK+MKSV0pvLIo4/nSvFtBZA3mXo2m8W3v/1thvx1dXUpBl7lxxmA4oFIlyij0chMVVphAbQuHpJ0EFFeSckU0wnx9RLfUfomxUIjpYTn04ltpbHgL4e0xnd2dpDJZFBdXY3BwUFZ/UVFkZQycV/QUvzVQiQoXVYoebXdbmcIrFqqLXH+8G0X+0tP+BS1/nK5XMw0zrenmMCfR7WfU7vEfi0pVE+YFKpQ/SIqUbycxGGvp4/UDjwit1osFpYHrZg6F/qO0i1Q6QasVabawXFU86WQgx6QR/VWO6T4ttCBU1dXh2g0eiiUUqneevqjkGe1RO0QoIOturoaGxsbTHFU+pZ4MPHPELJACIte0ws/hvyhyJcNgNUxkUjI4iHx62ZzcxMTExP4lV/5FczMzKCxsRHxeFw1VlU+tJU/sNVyZ6qNCR+PanV1VVdUf70HtdK855E3pXx8JpOJKbz9/f1oa2tj5SlFXufXA6+UkpJBorYm1MaVb4/ad6kdZrOZIZhaaZf07EPFpHu5d+8e3n77bbzwwgs4e/ZsTtuUlPST5FIdBqEqcaieUDlK/s9R2ZILkWKIiyR666vHlq/G6wGQQ24tps6FvqPEMdNTJt8nPAemkFANenkGVJZI0OWFeCvEXVF6TpIOvKaWl5eRzWbZN3gvKwrvQM+rxTnSI3wfRCKRHO8yXvi5ozSP9MxB4piYTKYcz1giIdfU1Mh4XXSwUTuDwSDsdrss+roab47iafn9fiwtLSEajSrWl+f4TE1NsYNabKvRaITP50MymYTD4ZBxz/h1k06n8cwzz2Bubg7ZbBZvv/02UwD5OFhKfavU54RKTE1NMV4N1XlzcxPj4+OMv0RKHd9OIvL39/fDYrEocrd4oThTvFKr5S3LH6ZO52PPRrvdjoWFBYyNjTFFhPqdj3fF15Pik5Hw46m0Zg0Gg2KdxLXv9/tzovwToT8UCuWEgeDbQXuP1h4k1lVJiiXax+Nx1NbWIh6P55THzxtSZicnJw/NtS1EDsM7/tgpVGfOnIHBYMj53+///u8rPv/WW28pPj8zM3Os9TyMQiLKUSpneuUwk/Io60v9KB54brc7h9yqVmetw1VPO/n3+UNF7VtKN3O1Q1Zsp9Z8UepXpb/pVfrEDVp8LhKJIBwOIxqNIhwOs2/wgRTtdrtsA9Ub50jsV7EPnE55ihi9QmXm81QkBYBCPCiNP421SIKXJAmBQACLi4tYXFxkaEJlZSULokpxluhQpwjsNC4mk0mWLkRUJsllnlLhqCkbpNwajUZZLCp+3dChSYhHe3s7I2oHAoEcEnm+caK+o5QswGOF4N69e8hms6qKOpkLqc0AZOEwlL4nhkmgvqJxpueU5p/R+DhYqt/vZ0FQA4GAbP1ubGzIyhTnjxLawq8D8swcGBhgSBhdMMbHxxkBXmltUnuI0B+LxRQdOchDk/YetT1InL9qFw29FgJx7Ck4bV9fn+qcAbRTGqmVf5RSTNkfO5OfuLncuXMHn/70pzE6OooXX3wx5/m33noLV65cwQcffCCD+viDQI/ohQyPw9T3YZoPT4KXdNx1Oqz5spD3tUxIh+2Tw/IMCjGx8vwVXpHU4kpks1lZqph89VHjkOQzG2mZFoAD/k8+85Be/ohS//BmNoPBIDOdqZFrxW9pJacGwFAjk8mE/f191ef4Q54OZq15Kvadknkp3zgpceh4Xg31pZIpWTThkymU2iCa7smcxv9O82xvbw9NTU2s78WLD32fSOrV1dWIx+MwmUzo6+tj36LvVVZWykj1VquV1V3JCUfJWUPkSFFiaD4QqdqY0Le1xuKwUij3UByLQk2E+fYcvfSIw5jzLRYLampqSiY/JaEYJfS/H/7wh2hvb8cLL7yg+R4ltqT/HaWyI96SjhpNOgxadFgppj35uCGHvZEUWqfDooVOp1N2++dFDWUB5KkQjmIMlcoopFw9/cY/Q7diihTNH5RKqCEhCaurq6rlU3+RCVGMmC1+n27wSiZN3rxFyASQmyVA6ZZN33Y4HHn7TTyoyRTo8/kgSZIswKca+si77lM9+KTKwGOFwWg0spx39BzPn+KRLD7Sez5Ts1I/kFmLFACldcmXS+VYLBamlIimUxFJUeLTEOrHPy/OL5GbxM8z8o4khIjKpu9mMhmMjIxgc3MTIyMjjAvncrnY/k/953a70dHRgYGBAeZZSeVOTk7mhE9Q6xcxgCihwK2trZpKO40dedGJyhS/Zvj/z2fOVjK/iygwX3+lMkUTp1ocPLXv0f4EIGfukVlYa2/OZ/rXkmL2/Y+dQsVLNpvF3/zN3+C3f/u3FW3XvFD6ipdeegmjo6N5y85kMtjY2JD9jxc108JRmvqeBDnq9hyFwllonUSlQ+uQUXvfaDTKOCpq469mJtIrxwmB68kTqKdv1ZQ4JTOd2oWD55Dw5YjKq5ZJk+rImy9FJY//Jm/O2d3dxerqKjKZjOJmrcR7Ecc4EAhgaWkJy8vLzBylpAQqcUuoPteuXcPm5mbOenC7DwKINjU1yfpI7UBUGxdRAeNNYvxhRU4eItdFRAh4JY4SS1+7do2ZAP1+v+LcJbRGjUvDH8jUflLEotGoTImgOQIcXJTFmGM0D71eL+bm5lBVVcWUADIvUlk8fw6AzJzl9/sRj8fx7W9/Gw0NDSwiuRJqS8oGH5iWN9Plu/ComTH538iEqsWN5N9RMr/zdRZRZzFIq9hG8WKg53tiG0SOX76AzsWa/sW26pWPtUL1ve99D+vr6/j617+u+ozL5cL//J//E3//93+PV199FefOncNLL72Ed955R7PsP/mTP4HNZmP/O336tOx3Hs7Nt6n9IstRt+coFLTD1kk8ZPQoeGK9edKvUnv01vEkEE4SPimwmkJ5GHSR0Bue5CreMOkwbGxshMlkgt1ul91saaMnRUfpBivWUe3govqSR5QkSbIx8/l8qgogryirIT+NjY1wu924ePGi7Jafj79Fh0Q4HMbm5ibGxsZylFylvtQ7VuKBxCtgLS0tLCCpzWbDu+++i42NDYTDYUiSxP6fRFSE+PLsdjvm5uZYOAeiRZAyqYSGkWJEfRQKhVjw0sXFRabs8O8aDAZEo1EZcul2u2G32xGNRnPmCI8iXrhwAeXl5cwDkhRvADmk8FAohP39fcZPopALTqcT77zzDjPJKY0tOSbY7XZ28VJDbdTmhBKyzf+ml4xO7/BjXcgFTdyDRHRRaW/gLzVK9VK6BKmhqfz6UVoHxykfOw4VL1evXoXZbMYPfvCDgt77/Oc/D4PBgO9///uqz2QyGbb5AgebxenTp5kN9igCWx6HHDd/6aMgeng6Wu/o4UTpHYd8PIyjFJ6/QvF8yDtMi3Mk8l4K4aKRKYTnXfCcIofDwUw6PJmXf4cQH73cMbG+xLsU4+YolaPED9LL/VDjlin1CZluAoEAbDYbvF4v40jxhxcgD8HAhwBQ6heqhxY/jOo5MzODzc1NJJNJfOpTn1IMY8CHORD7LhQ6CBp679499PT0sPhIVC++f6gck8mEpqYm1kfZbBa3bt1CXV0drFYrM3XS/GxsbEQ4HM75vtK8EseT6kh9ya9bMbRCJpOBwWDA6uqqjMOUTqcxOjqKy5cvM4VJKWQE9SlfRiQSweTkJBoaGuD1eo+Ec1To+3r5n1p7YrEcVD1t4Z+hcSgmjlU+KYVNyCOLi4sYGRnB7/zO7xT87ic+8QnMzc1pPmOxWFBbWyv7Hy/k1VEMyfk45TgRjo+KqMHeWgiMiLTkQ6CUxkGpfJHHoFe5owOZvwHrQZDIvLG2toZ0Os0QViD3Zkzf4rPeGwwG9m09YjQaGRLE84wIGQKQc7MVb6VKfcn/TewPStUCHKxj4knpQQ2pblVVVbhz5w7rSz4diSQdhJMQkyMrccuUxoTMZmazWUbiBg4UAPJEExFUEXXgUR6eWyOGceCFnjEYDLhy5QrOnDmDT33qU/B4PIpIAO1zhBry/W2325FMJnHu3DmYzWYZZ0pEIKicvr4+Zq6jed7Y2Air1cpy0rndblZ/o9Eoi9zOo5JKCB6fIkXkmPHzhlemCOEyGo05CG48HkdXVxcSiUSO2ZbfD8icDkDmkUjhGbR4SrwUir7nQ7j1WgSU9kQAOTwnPW3Qi9SK9VVaPyctH1uF6lvf+hYcDgd+6Zd+qeB3JycnZaTPYuRJNe0dNefpODk9x1l2MaJFgCzUlq80DkqKAc+50bMB8eVQPCOeE6Pnff5wysf54omobrcbq6urSKVSmm72gHxsRbd3/jBUIiXTM2qHM9+/RBymOi0tLTF0g9rl8Xhk6Jd4ANGYZ7NZRCIHOeQWFxdhMpnwxhtvIBgMYmpqioUXIL7Io0ePVPkp1AciUZkUX1Iq6uvrYbVaZRezhoYGxiPjDzMybZrNZuzu7gKALGL6yMgIUqkUI5orxXiiJMWrq6uwWq24dOkS3G43m/NqTg92ux0zMzOw2WysTfQdJXOM2v5Ic47MYm63G16vF4ODg4rKEvXL0tISgsGgpsmT5j8pNvwcIYUzm83KoojTvCE0UrxciLxD3tTP7wd8+ArexGq1WmUehUeR/5SXfArUYc4pJZ6Tnj0mHx2CF76+hdSVv0Qd5RnysVSo9vb28K1vfQu/9Vu/JXO7BYBvfOMb+NrXvsb+/Wd/9mf43ve+h7m5OQQCAXzjG9/A3//93+Nf/at/VfT3nzRFgJejVvSOE/Eqtmyt/j/M2GgpTYXa8pXGQU3ZLWQD4svx+XwydEdEu7T6QURY1eaNSETt6elBKpVCV1eX5hhQkMeJiQnm/q/Fg1IqQyTGArlhHGKxGIvZREl+W1paWMRy0eQjzjniAVVXV8Pv9zOvsMuXL2N3dxc1NTVYWlqC3W5niY2dTifMZjPq6uoQiURUN/VI5CCe1AcffMAOcz72ldPpxNraGhobG9k7hC6RWVaJtGu327G8vMzQA7fbjZWVFWxtbWFlZYX1MwUS5YnCPp+PtYP6mFcW1SQQCKCyshLvvPMO628KPaNnv1FCItQOUTWkNl/Ca6fTyTzqeAWVv4AQR4yQtrW1NbbelS4X4mWAR1FoP+Bjj/HoivjucchRKlD5ylb7m9p7epCmYuvLj+lRnk8fS4VqZGQEwWAQv/3bv53zWzgcZp4iwAHE/Ud/9Efw+Xx4/vnn8Y//+I/40Y9+hFdeeaWobyvdOk9aTlKhO2rE6yjK1lLEDqMAqilNWqTMQsZCS2kpxmvRbDbL0B2+fOoHJa8hrbrkqzOZQGZmZnLQPB7qt9lsmJ+fRyaTQTgclpldSJSIy2TCVELrlMbW6XQimUyio6MDMzMzaGpqgtlszkEEeDMV39ehUAjb29tYW1tDT08PxsbGYDKZMDMzg6tXr7K+pQOUDsfBwUFUV1ejvr5edVOnutlsNoTDYRYLiZSRWCyG3d1dWdDUQCDAAn5ev35dMShiLBbDo0ePWKBKo9GIsrKDo6CsrIyNv81mg8VikZlTzWYzBgcHYTabWX/yyiIvvJmTFLErV66w/tZSFERCNo8WaXHfiKTOIzm8uTBf5G9+PYgXFf4CAhygUPv7+zkhUbTQUSXTGCl6fOR7Gn8ls6cSTaTQPV0PUfywooS46qEknIT1hr9UHuX59LEmpZ+kEKltenoaRqOx4ICARynFkAQ/SmR1rbboCVRY6DdEgi0vR+mcoDSuxZLflQjohc5XpbJ5Uu/U1BQymQxaW1vh8XhySNl6krOKASOJdK3Em1EbWyWisdEoD8ZIHCA+po7T6WTeZUTyXlhYwMbGBnN4IeL1/Pw8uru7sb+/n+NAoDdnot1ux8jICGpqahhJmfeeI0Tj9u3bePToEQtGSIl5+TLHxsZk+efEpN1qddLrkCFJEq5duwaLxZJTh3xzTolALxL3ldYS/UZOBHrXlNo45HNcIFI7peAplgytRpAvRNT6Ra0NxZDND7vvF0tOfxKkREp/QoVIvMUoU0eFLBWD7OhFbo6qjkeJoollad2Ajgpm5/tLT/ymo5B8vCs1grPSuPLmCzUzZr4x0uJ8EQ9FNEXxOddE1Cxfe4l0rRZPSmls+QNDTMXBIwL8t/h29fb2oqqqCr29vQAOEJ7m5mZWhtPpxPz8PCwWC6LRqKKrN39gKc1VQk3i8Tjq6uqwvr6e8xuhHPF4HENDQ/jc5z6H6upq1NbW4saNG8wJgL7X19fHCNzAY+QpFosxQrtSv/OcMeojpbGJRCLwer3IZDLo6enRtf5Esx7FiEomk1hYWGB5D/k+5FEwmj8+n08RyRFRL3o3GAwqmn/UzInUFofDAYPBgO3tbSwvL7PLB89307uH1dXVYWZmRjPNilIdSPLRAZTQTz1ngF7elp626vmmOEbHIcdpoSkpVCcsSsEI9Uo+pUbvRNEDqYpl6V2AR8WZUiunmMVQSJ2OykTJl6OlpB2lt2c+3pWaySufGYQIyrxZA8gfhTjfJm80GmVtV8u5pjZuIjcKgGY8KaWYVFoHhpJ5RjTjkAkzHo/D7T4IpllbWyvz5hweHkZNTQ3LtkCKE3nj8RwlrTYTx+fixYsAkBOskm+b2WxGS0sL7t69i2AwyPhNVH4gEFBULsPhMHZ2dhiJm8acJ2XfvXsX6+vr+MEPfoCFhQVFDpjT6URNTQ2uXr2KeDyuOE+09hij0Yj19XVsb2/j+9//PkZGRrC4uJjDCfP7/Yy/Jc4fvnyl/p6amsK9e/cwOTmJlZUV9PT05F371H+Tk5NYWlrC9PQ0wuEwlpeXYTQaZe/r3XcikQgCgQC2t7cRCAQUnyFFY2FhgXmhajk00DvEzRJ5kTSP6aKlJTxardWGfBHJ9Zw75LChN59nvnqrcROLVXzzSUmhOmE5zGGd7/A7SgK4WJZeu3axColeBa6YNhZSp6Oy32vxKI7je8XWQ4nnIG4upFDs7u7KOFVOp7bnolh2NpvFjRs3kE6ncwi4gL7gfST8Ia/kCce3gZQmNdRFLK8Q13SnUx6Z3eFw5ASoJDPS/v6+THGy2WzMTNTQ0JDXxZwQKaPRiKWlJWYyJUK0eEA6nU5cvHgRHo+H8ZsIxenp6cnh/kQiB/ngtra2IElSTkT0kZERLC0tQZIk3Lx5E9PT05icnGSef/xhyvcREfetVqvsmXx7jM/nQ1VVFTweD+rq6hTnQldXF4LBILq6uth8AeRei9QG6m8KCGu325FKpRiyFAgEVGkAhILReO/t7UGSJHR3d8Pj8eDixYt557OaOJ1O9PT0oKKiAj09PYrPkKJBKJpS+hZeSIGkiwvPCaM5osbl5dcCcHBm1dfXa+65vHOGnr1ZTYnhUWalNVCI6EXnjvLcLHGoTkj02mAPI0dp7z5pztRhbPpPAr/rw65Dsdwvsd75uBiSJA/OWUi7x8fHsbCwgPLycjz99NO6UDm18eb5K/T/fE45paCQfMwmEdkiBWJ9fR1dXV15uSwi34fMbSKXhn5TS3ZN/an1rjgWCwsLjO/EJ+DlOWgkhJAqjS8AFsCTAknSOxSHicyvRIinYKoPHz7E9PQ0zp8/D5fLhZmZGbS1tWFra0tmsqU+TqVSmJubQ0dHB3PaEPlfWhwyQlVEhXh8fByJRAKpVAoXLlxgfUf1TSaT6O/vRzabxZtvvom6ujo0NzfL6kBKL5+kmh9jqj+fRDmVSmF+fh7Dw8N515ueNRIMBjE/P4/NzU3GvxPLCAaDrBwehVUqn+rMrwtxzhK378qVK8zUzs8RpTmpxi2j7ykFtFUya+vljB2Gd6V3b9LzXIlD9TGUo0Q79CAXRyl6b3NKbVSCcI/CDq/UZr0w8nGKUh1400ch5SglZNVKhcO7hPN/V+OZ8OLz+dhtnt7XGiu1WzShDaQI8ME3qQ08+uJ2H6QMiUQiGB8flwW95NOHzM7OoqqqSpZPTU1Evg+fDodHKqg/RfRC7E9qBwBZuXwcJB79sdvtLPI3KWY8QkNmExE94vl8PMJIyEc0GoXRaITZbMb+/j4WFxfxxhtvoK6uDpWVlRgcHERbWxvOnDmD8+fPY319HeFwGDabDYuLi8hkMjnmGqfzIHTG8PAwCxPAoyeU00/JXESoHCUsFr1CGxsbkUwmsb29jYmJCUxOTiIUCqGxsVEWUoK8H8PhsCxkAyXv7uvrkwWPBQ4Oc/I0pXqT15+Sp+Jh94a5uTk8fPgQU1NTir+vrq4yL1SqHykDYvkUPsPn8ykqqZIkYWNjAx0dHTnpcGge0Zzk567amqQxdjqdspylSu3n168e9I7fbwo5h/TuTUd5bpYUqo+RHAd8elTlH2ZSK0G4vB2+2HoptfmwJM+jEKU68PGBCilH3NjyjYPa73wfq/WR2WzGpUuX0NbWxt7X4kyobbziYcHnaaM6komSzGH0b+Cx2V0cs9raWmxububkU1MzTfB8H6PRiP39/YL5kdSfpBwBYJwX+k2Mn8Tz7vi+pudJwXO5XDn5BiORCAtASvy4ysrKnOdI4djY2EAymUQgEJChEU7nQdDJjo4OWCwWtLa2Ynh4GK2trar52NTGVcl0LJpg+ejtZHYOhQ5y57lcrhz0JBwOszhak5OT6OnpQUtLC8xmM7xebw6fke8X/tuzs7PY2dlBWVkZU375GFL8Qc8rGvy84ecKbz4kobnV3d0Np9MpU+rowjExMcEQQj4YLYUIMRgMsrr4/X7Wn+K6pMTiVK+6ujrmJCLOSfoW1ZfGTu3S4XQ6c7IhKJnyxSTJWnt0vr2lkP39uC++JYXqYySHmUx6FIaTRGl4EQ950Q5fbL2U2szf8POZI45DaBMTA13y8YHE59U2G6WNrVgRERu9yqU4VuJvIvoEyMnrkUiExWYiHpJ4iAFg3KSenh7s7+/njJnRaER5eTmLVk0Il1I8K0B5zh2G86fEeRH7gRJBk8cb7/XGR/P2+/2or69n6I/P51MNKMu3w+FwyBwYJEnC+fPn4fV6WUBSno9GCYQHBgYUY5mJfUAoGACmxFG/KCXF5t/ho6oDkMWH6uvrg9frRV9fH1MKSJmksA2xWAzl5eU4d+4c1tbWsLS0lKPUULiFYDCIGzduwO/3o729HVtbW6wMMjuJJiJRSSTeGe8NCQDXrl3D7Owsrl27xtYyKUe0hikcBikQFL2fzJeRSATJZBLT09MsmOzq6qpiXaiv+HXJI090IaH2izIxMYHx8XFMTEywuaiERNF4xWKxnGwI4pzIZ2XgFVr6Ps05pXVWyP6u9H4hloh8UlKoPkZyGBRFj8JwlCiNOKELhXp5by8100m+MpXaLB7mWiEJjqrtvPBmKj1KkNZmowdt0isiYqNXueTHCoDsuyLSRCKarZLJJLxeL0ZHRxlCwPOQ6LBoamrC/fv3kU6nmWmIvuV2u/HUU08x5EcJ0SLhUYZsNouxsTGm3ND3lPpFq295xVBNWY9EDpLljo6Owu/3M882HsUiZII/THnPONGrlL4TCoUU55XZbMbQ0JAMVeTHjh/nfOitaELSilwuviNeakj54ZUap9OJzc1NpkyazWb09fUxJcxmsyGZTMJoNCKdTueYx4lOHIvFEIvFsLOzg1QqxUyVVAclz1DxcqJE0g4Gg3j06BGCwSBqamoYYmiz2XDv3j2mJPL9QhcOiuBObU0kEtjd3UVdXR0ymYwscr3dbmd1IVRNLSCq3W5naKbSvr2/vw+DwcD6Rsn0L17ylNDyfHsKb6anSwyvgPOJrbU8mfOJHsqI2t/0SImUfkJyEqT0YkQvcU/tPa2ghIcRkYx4FIHoii1TrXzg8U2VFqDesrTqqRXss9Dxyve80jjybaF/6/meFjFeqx70myTlBhFVek8cN7rlk0nE5XLJzGF8OxoaGhAIBNDY2CgLsqmn33jEIJ1Ow2q1YnV1FZubmygvL0dFRQUjXavNAb7ufN8CkBF1AeS0cXx8nHlIksJIlwalcQTUA2+K9dFDPNY7jkplUF2ofnS5iUajDHWhOU9jJ5aTzWYRDodhMpmQyWSwurqKp556CkajkZGobTYb4vE49vb2sLu7y4LG8nXJZrMYHR3FlStXWMR3KptytIbDYRgMBtTX1yOZTKKnpweBQAA9PT24ffu2LCCt2hwVSeFjY2OYn59HZWUl+vr6ZI4R5D3Jm3KVxlBNMaHv8uuHf5cQM3IqIMcScZ6JosfZRW1O8/NDz14rzkWtwL7FnltKorXW6W96z++SQnVC8qQqVEoTXc9kFT1Bjjr6rdbmrLWRaykDhZR52PrmE60N5rDR0wupC32LNjCx3wpRFMfHx5FMJlFdXZ0TnVurvfxGGo1G83r/aG2AvFJGh2ChCp7a9yTpwCPPYDBgdXUVjY2NyGQyuHfvHhobG1FfX4+NjY0cLoraQUCkZ/LsMhqNqgcppVOxWCwYGBjQPV/zHRb8d2gsgMcmqsMo7/yYS5KExcVFJBIJnDt3DolEgpncWltbIUmSYlR6/uJDqJTL5UI0GkU2m2XPS9JBoE7KyRgMBnHz5k184QtfwNmzZ2X1VPJ0JO80AEzZcDqdTMmdm5tDZWUlUqkUOjo6NL1K1fqYlHlxPiopX1SG6A2ndCmkiPIulwvT09NobW1FMpmUKZVut5uVZbFYmPIsjn+hSrT4u9p+oWe9qc1LpecP4/1XjJS8/EoCQB/UqtcmrUS0LCYXkh6Tkh67u1J9+faI7SikTL6earGJDsOh0oKpebOMln1fK6luIXC1JEnY29vLIUPz5lKC9tW+K0kHHlfEjRH7R6u99BtP9hXNs3x51EdKCoLb/Tiyuxi8Mt94aZnjeM6Ox+PB4OAgPB4Pa+/AwABqampgt9tZjjwlor343fv372NrawtTU1O61kQ+5UZsQz5zBtUHQE7gS615pNRX4vM0rg0NDbh16xYza5JDAc+d4+e8OOckSYLBYIDD4WDR0+n9QCCAVCqFWCyGd955B8FgELFYDFtbWzh9+jRWV1dz6s6X73Q62SEZjUbhdB6QqomjRvtbZ2cnrFYrrly5ksM75M1VvLmaxpn6JR6Ps3yOYlBWnlPGjx0RwLPZrGKkeKfTKQsH4vV6MTExge3tbYTDYcTjcVaffMnZ1SgM4riKY6+2X/CiZ39U6jN+7kmSlMMdzGdK5989Ti91kpJC9RGXfIdrITZpcSN2OtXjxxymToVOfjX+TiG2daXvUj3VktcWa2cHtDcYnv+jRIrOV69C2u12H0T3bmpqYvwZJcVDkg5ys21ubip+NxI54HZRct1C+ofvC/pvkV9Dt3WlQIRkziACMO+5xnPnlPpSz8FBm7dS6APiWvE8JQogqcRL4ZVSAHjhhRfg9Xpht9uxuLjIuF1KygnvCagm4qHIc1vo+6SgpNNplpYmFAohnU7DYDCwwJda80jp8BUPORrLQCCAyspKVFRUwOv1or+/PyfNDz/nRSVtd3cXq6ur2N/fx8zMDBYWFvDmm28iGo3CarVifn4eDQ0N2N3dxfr6OiRJwvDwMNrb2+Hz+WREflGZJkWDklATqZpCTkQiEUSjUezv76OlpYXF/SIzIc+jWlpakv2b728ag2AwyEjeolLLh49Ip9NYWlpi74XDYSwtLeVEiieuGHllJhIJtLa2oqysjBHzSWFW+g6/JtQuo2LqLDVlh9quNI6FitpFnzyCxX7It9ccZq+WJAmPHj3S9WxJofqIS6FKBaB+2BP/IZvNKh5AR1WnQstVq2+hqJHaDVsNhSumb/UqiyIqwn+DvssnsuXLBvRnkKcDhcwyauPq9/thsVgwPz+f0x/0HnFY+P7L5y1H74uxqMS+pdu6UuwaQhno2zTudGiHQiH8/Oc/x4MHD7C8vMzI5KKSRfUlRYNyvImbt/htvp5utxvV1dXo7u7OiRfEjyu5rlutVgwNDcHj8TA0B0DOIaY3xyTVh4/1RHUgF3tSUO7cuYNgMIjJyUkZiksR8UVToFqqm3wIhs/ng81mw9WrV2UBKcX3yAT6xhtvIJVKsTJMJhObc11dXdja2mKpfGw2Gwuu2dzczAj9U1NT2N7exo9//GNcv36dpa5Rmn9GoxE+nw/xeBzZbBYdHR3MtMejavxczGQymJqawvz8PEOEpqenczwHyUONvkP7J6C8rmkej46OIp1OY3V1FW63WzN6OI/wulwulJeXw2KxoLm5GWazOeeSxH+HrwePVvJKoDj3lPY9nqh/FBdmtYt+S0sLXC6XDA1UqxMvevZqsV70b9oj9EiJQ3VC8qRyqAoR3l7f2tqaw1EqRPRyog7LbTqqOh2VHIZcr8VRKZZkr1QvpXElciqvxIkkdpHYzJer9Bv/bZpXxImh56jNWmRrEVETkdMbN25gamoKZWVlcDgcLCgmIRImkwl9fX0wm80IhUJ4//33sbOzA4/HIwvnoHduKHFKeKItkbJFdJdXiIFcgrGWsizyT8bHx7G9vY2Kigo4HA7cunUL9fX17IA9f/48pqamEI/Hcf78eezv7yORSKCxsRHhcJiVS+PBRzrnOTj8GEmSPOq7FuFfNBk5nU5MTEzgzp07aG9vR21tLRwOh6KTgprZl8oMhUJ47733kM1mUVZWBpPJBK/XC4/Hw/hnItmaFMuGhgZsbm6q8qX4tVBXVwe/3w9JkrC5ucnS5Hg8HoZGjYyMoK6uDpcuXYLb7cbCwgJmZmbw0ksvwWq1Ko4dz7ei9cb/ls1msbS0pMinE9cpjRGfLYAQLTXrgpLTR76LGs/7FJ0KRCmWt6v1/lGI2n5qMpmQSqXQ3d1dIqU/KaKlUH1YCkShUgwCwr+rpQzoeeejIHrbpPSc0kYnKhbF9Fch40oEWjGNiNJ39ZYrKhK7u7sM2QoEAjh79iwqKytlZiGljY8OcjFlBt34z507h/LyclaO3+9HOp0GIA/2yXuf8SiT2nxV8oQSFUFSNvIdAtS/8XiceZmRAiNJuV6Q/Hs8sX9rawtra2t46qmnAACLi4tYX19HbW0t1tbWcOrUKRYWoqWlhZVDyih5vdEByXtRUr1Ex4F8HDW+rqRAm81mNmbpdBqxWAwOhwNmsxnnz5/HO++8A6/XK+On5UuvNDY2htnZWYRCIXziE59giAwpzcBjBwpqw9bWFkZHR+FwOPCpT30K7777Li5fvsyQKaW+NhgMLAJ7Y2Mjtre30d3djd3dXSQSCezs7GBxcZF59QEHZkEAstAVhXp+UsgWm82G6upq2VxQm6ek8FAIBC3FR+kSw685pfVeyF6k9NvCwgJu3bqFixcvoq2tTXVs1d4/CtFqA8UiKylUT4hoKVQn7bHwYUg+ZUDPO8XKk6SY6a1LMbe4YtupB0Xiv0GHazKZhM/nU0RaeMSimLAU9J10Oo3a2lpcuHCBKVokdPiLqI8YGoFHTEhh4s0FxNcyGo2yg05v3yp5N6rNdxFpExVOajfvjUX9aDAYZF5cDodDZqahfiOF98KFC8xLlL5NUa8JeaJvEw+NlDVJkmTKC4/KkHn2K1/5CkNZqI94ZGxwcFDxcBX5NjR2ZCKiuhBCtLa2BrvdjkAgAKvVCpvNxvpZqV+z2Sz+7//9v2hpaUFHRweMRmPOWuLbE41GEY1Gsby8DLvdjpaWFqTTaWxsbKjm1aO+zmQysvx1wGMlyGQyMeWUlBi+z/lxUVJcAOVci3a7HVNTU4jFYmhvb0c6ndaF4BG6RYoy3y+issrPQ/HSJF5olKTQvZtM7B6PB5cuXVJE7T7M/bsUNuEJk48CQnUYKaaNR9UvJ62watW7GJMfkLvx6oXS9dRVRIfyKXE8IkPKCP9OIQqaVr3IY6uvrw9GozFHSQPkLu90gPAIwurqqsz8EYlEsLi4yA5xMl3rRdKUxpXvD/o+b87iy+TrNj09jaqqKpSXl+cE21S6KdMBPjc3h5qaGmZ65c2kAHJc7dXaADw+qCRJwsjICEODAMgQCSpzbW0Nt2/fRlVVFbxeL/PKI7PS+Pg4VldX0dnZycyN0WiUKbRGo1ExfABfL1LmaNwk6SCkws7ODra2tmQJiUV0kubF/Pw886pzuw8SZIuoI/AYuaF6GgwG2O12vP3226iurkZ7e3tOCBCxL8npgZ+r/JpaXFxkdaG4VcTN4WNlaY2PqGgtLi4yU6MY+0zrMqZkOrfb7RgZGYHFYmHKKiGlSjGt+P9Wu9gVaskQUV6+DfxcPAprRjHnSkmhesLko8ChelKk0AVxGFNlMfXRUmyKWcxKh4ZoGtCjcGmVrQblKylHRwn/FyIishGJHORsy2azMnRJCSGj5/lo5kp8K/5b/PMkaiY3vj/JnGUymbC7u8viifEmsdu3b2NjYwOpVAoOhwPDw8OK5iU1xZrQhlgshv39fRYd2+125xxOSoepOE8BqB6g1Caz2Yz+/n6mPNhsNkxPTzMzDcXKMplMKCsrw/b2Nu7du4ezZ8+ioqICBoMBtbW1GBsbg9vtliFN+UyqY2NjWFlZwdWrV3NQMTXkjzc9Tk5OKsZIEy8HpKA3NDQgGo3C5XLloHzifAmFQizoam1tLYaHh1nEeupbvi6k1Ecij/Mp8vHm8l2mJEnCxMQE9vf30d/fL+NK8e83NDTA7/ez1E2ETImx7UKhEDY3NxVRTZGDpSc2Hs0DSqWjxBHMtw8XilAVcpksJs5fKQ5VSQ4tInn0SflWPi8SUYxG5RQmRyVifZzOo02UTOXxhHD+m3TgRSIRXRuVkocQbVZqITSAA2WC97zi3wFyDzH+wOFTdYh1UBp7pb9Rm6PRKCKRA3d6l8uVk5CXvu92u2XKFPFPPB4P2tra4PF4ZOEZ6JvZbJaFaPD7/cxVGzhI+VFdXY2JiQkEg0EWH4g8FJ3Ox7kJ+SS31A9LS0uIRqOora1FJBLB3t4ednZ28M4776iG5tjc3GS538h70ePxwGq1sryEvBcaKTtTU1PMsy+VSmFkZEQWdoI8CRsaGiBJEiwWC1MUxTE0Go0s3pHZbMbAwADW19exs7ODaDQK4OCQ9Hq9GBoawuDgICoqKnD27FmUl5czZWp+fh4DAwPIZB6nS6F+pDkljn0sFsPKygoymQxGR0dl3oZqSjG1IZPJYHJyEl1dXdjc3ERdXZ3sfd6DzW6344MPPmB59VpbW7G7u4vJyUmmJExNTeWMk91uZwqt1+vF1NQU0uk01tbW4Ha74fF4YDab2f5D6V58Ph9D0MQxp28o7XWxWIyF6iAPVDK30uWCwlUEg0HcunVL0TuUUig1NDSgpqYGFy9exP7+PlP0RJOhGD+LF3HMVldXWbJpvl2Li4sYHx/H+Pi45l7Pf1+pLvQt+m8xZEchopSwulgpKVQfQdFzYOmRQhWXw0gh3xJdyvXIUSs5WmVrEXKL6VMqjw4/OjyKaQ99n3ejF2+3SiYrUk4AKOZ8E9ulNef4OvBuyeLmK/6NyMW8uzcdWLw7vthvdOjyManoYOCTC/NKFN1G+QCUbrcbPp8P8/PzSCaTmJycxMTEBG7evClDHKg+pPzQGEmShGg0isbGRni9XnzhC1/AhQsX4Ha7ceXKFdX6zc3NYXNzk8XZ4vvD4XBgfX0dPT09AMDeSSQSyGQyMkWwtbVVlkSaUIxAIMAI6jQv+HFzOh/HwOLHpb+/H21tbYxzxR9y/O8mkwm1tbVIJpN47rnnMDExgcuXL7O0L6SwkmImjr3dbkdTUxO2trbQ2trK4qAtLCzgtddew8LCgux5UkJv3bqFlZUV2Gw2zMzMoLGxEdFoFFNTU0zB59dRLBZDdXU15ufnmXK8srLC1gkARqDnQ5X4/X7Y7Xb09vZiY2ODkespNpQkSQxNJKSpvr4e6+vrOZcR+h8pl+KFhhSyRCKBuro6VoeRkREkEglZjkqfzwePx4OLFy+ir6+POR/Q2Pr9fiSTSQQCgZwLiChGo5Htu2qKP8W2cjqdLC8mXxbVm/qj2HNFSeHkOWi8oqV0BlIYCn78kslkTm7HYqSkUH0ERdyQ+MleiFJ1nEqI1rfyKYB0qyR05iQQNC3RUqBEOUyfqqFJbrdblUytVgYAdpDlU2QAZdRHLNdkMjHEhleSxPqJhwQgj8kjHiq8OBwOxSCbvBDqwSsGVH8+iTAfY4oO07m5OfT09LBo2IQGkcIWi8XQ0dGBzc1NdjDs7OzI4rORiPF7JElikbsJYeJjBfH9z9fvwoULsFgszHTDy+zsLCorKxEIBGRIZX9/P1pbW1luOp/Ph3Q6LUsiTf3e09ODWCzG3leaE+l0GlNTUzKFgketeMXI7/czxRQAamtrMTs7i52dHbz99tswGAx49dVXmdLicDhw//59WK1WZtrh50MsFkNZWRk6OjqQTqdZTKqZmRmsra1hZmYmJ3r4vXv3sLW1BQAsyCspxjQm4r5B3yVFhS4NNB8paCj1BZkiKb4Z9QGNN80JippPimY2m8UHH3wAq9WaE1yTkFf6ttK6i8ViOHfuHKqqqpgJ0ev1IpPJsFATkUgEZrMZly5dQltbG5tjPEovJjHW2sdoDff19ckUfx7ppssOocDiJYfq3dramqNsFSL8/OD/WwvZ4/8tWiyUkjkXKyWF6iMo4obET/ZikJGTIMrz38qH4ijd2vLJSaJtgDpCc5g+VXu3kDL5DZoPFkjCzx0lKFzrW9FoVGYeUzMliocErxxJksQCUiqhYPxho3YLpZhCSoEclcyndGBubGygo6MD8XhcNdYTmcZ6enpYVPjy8nIABxwVEVHjU5Ksrq6iuroa09PTkCSJmZiSySQzJVG/8cFTCWWIRCJYWFiQBSWlg7Snp4ehTmQKI1MTKWg+nw+JRAIVFRWYmZlhRH4ifhPfzOVyMcWYN7stLS0xc9bY2Bhef/11bG5uysxYFJ2bRxI3NjYQjUYRDAbR0NCA/f19DAwMsMNtfX0dp0+fxuLioqIZmUJgtLS0yKKsv/jii5AkCc8//3zOHO/p6YHVakVDQwOLq0WHPHBgkiIFh79sEhJD86uhoQEVFRXY29vDgwcP8M477yAWiyGdTuPatWvw+/2IRqNYW1tDJpPBysoK7HY7ysvLUVtbK+NSESqytLSEbDaL+fn5nOCa4pxRWndOp1OW/sbpdKKmpgZXrlxBNBrF3t5ezvv83CdlyGg0YnBwUOYYkO+CKl5MaL6RYsKntlGiF1RWVmJgYEARUdYrauZAJUWL2qqUsof+TVwvrVAceqWkUH0ERekAy5fH6UmSfChOPrSkmDJF0bO5aD0j8oaeNNGjnE1NTWFhYQFTU1N5y4tEDuLimM1mZh7Lp+Qp1YHKIU4Q38fiGKrdQgkZEsebJ7ry5lNS1GjDV8ojSOWTQmc2m9HY2IiysjJcuHABLpcLDoeDoVY07vxNuL+/HzU1NSwCN3GdEomELL2NyAkyGo1YXV3FgwcP8Oqrr2J8fJwpXzU1Nbh69SqL8r2+vi6rs4gcOBwOLCwsMA4X8ZeIi0aBIm/duiXj5bndbtjtdhaGYnp6GpubmxgbG2NmLEpfRLn2iCTvdDrR0dGBqqoqDA0N4Z/9s3+Guro6NjY9PT3IZDK4cuWKbC7QGrp9+zbq6+sRi8UY8pjNZjEzM4NnnnmGmUSvXbvGlH+qczwezzHnEHJEfUz9Q0otheYIhUIIh8PY29vD2toaNjY2UFtbi5aWFpjNZqRSKezt7aGlpQXDw8OwWq3o7u6G1WpFV1cXCytCSgQpiQaDAQDQ3d3NLob8nJYkiaWxUbo08ARt/t8UE2t2dlaTLyqa3KlsJUsGfV80yyshQ6LZTVyfvKIsIsj8Gj2M1YHepzlAip9Syh61S9Nhvm/K/0hJPgrCT+YnScQNAtBXV6X3juJZEn4zUKuLnmeeZMlXf7vdjnA4rIurRhup1+st6uZJY0SHM5Ujmg55N3DRLEgeaYQwKClrVBbVV/QiAsDyrEmSJPMCUnqHvC+bm5uxsrLC0rYQWZxMMjT3rl69Kisjm80yfhIlsuUPPLp5d3V14Qc/+AHq6upYImuqk9F4ELz05s2b6Onpyel/MhVS/5w9exZra2uMKG00GmWZDyTpINE11YuQDBofMlvNzMzg7NmzjBvk9/vR2NiIVCqFmzdv4sKFCwAee3SR1xnll+OdAOrq6pipiJ8P29vbzLy6u7uLW7duweFwMO9OSZLQ39/P3P4nJyfhcrlYSIKmpia8//77eOWVV2TjSImQ+f6h+pWVlaG+vh5ra2tYW1uDzWZjEca7urrQ1tbGlK3m5mb09fXBarXK+oifw3y/7u7usn4lRIQQXSLRr6ys5MxZmgtUBs0hfq76fD74/X5cunRJFl2d+pOebWlpgcVykGqKX1tO5+PwGMFgEKurq2hsbMTu7i4LTSLyRMV1qLXG+Dbxbeb3nkL3VH5vVyqXr4OobOXjuBazp5cQqpIcuRSi5RdriisEAVL6Rr46ipCx0nNaqFchvCa9cpTeKEB+1I4UGKU2aPWf3vHnn+NvufxmJ9aRvzGL5Hj6b4oEQwc/fYP3auM32cnJSaaEEV9odnY2LzpJXA1Cw0jx5CPRKKHFtMkDYGlAbt++jfn5eVy/fh0LCwtYXl6WHXgzMzPo6urC3t4eK49HId555x00NTWxJK4i6jA1NcXQJKvVir29PWSzWZmJb3x8HKlUCkajEV6vFz6fjwUCFdthNpvx8ssvo6amhrUnnU4jHA4z/tLk5KQM7RDJv1S3O3fu5PQ1KdL3799nnK6WlhacP38eq6urqK+vh9FohMPhQCQSQWdnJ6qrq+FwOLC4uIg7d+7g0aNH+P73v4/NzU1MTEywtePxeDA4OAiPx8PmXTQahdFoZMrU/Pw8dnd3YbPZkEwmEYvFUFNTw7hmFP+rv78fTqdT5uAhJn6mb0iShOXlZWxvb+eY5XjFpK+vj+0dNGclSWIemgaDga0DCuJK5krizJHCKa4vUtqV1hZZMiwWCwKBABKJBCKRCObm5lioAFIMSQi1pTHjvTVF0y1v+lOiG9BvSiZPpfXHI2dUvsvlUvT4FXl++egkxVpySgrVEyCHgRkLffewkKYeKURJOuwE1iNK38hXR1qIkUhEVXHT4hMdB//sKL1RAOU68vNDKyGv2H+8gqumwIowv3ibVZoH4g2f3gEe806UzIIAZORuMrGRVxv9TexTu92Ozc1NtLe3s35R2rxHRkawsbGBxcVFmQnabDbD5/PJDgVxzfHt5pW8eDyO27dv40c/+hFDJKicnp4etLe345VXXoHb7ZZ5jQHAlStX0NjYiKGhIebmDxzM/fn5eYRCIfj9fgQCATidTthsNjx48ADnzp3D9PQ0M6MlEgnWFjpUR0ZGZHVfXFzEzZs3ZWEwqH+prl6vFy6XCzabjY2Bw+GQkX/Jg9Hr9TLEhA9FMTMzg9raWoZQmc1mrK+vY3t7G6urq2htbYXRaGQegkSQN5lM6OzsZBHbg8EgJiYmkEgk4Pf7c3g3ZPIzGo146qmn0NbWhuHhYbS2tjIOWih0EGtqaWkJi4uLGB0dRW1tLVZXV2UHtWhG4xUlAJiensadO3cwOzuLcDjM0EFSkMmcTIoIcbCMRiMSiQTq6+uxurrKlJjKykq0t7czRY9CSySTSYyOjsrWV2trK+NdKV1eaL1JkoTt7W1sbW3B6XTC6/Uyz0clz1veyYXapIYM0ze0PHPFy4KSiPsAAIb+qfGztBQ5/vuH2bdLCtUTIMWiNMW8yz9/XMpVIUpSsRO4EARI6RsnocgBR6PAUhk9PT3sQDosx0tNtJQcLT4TL2oKrHg75MNfaM2DfIoXbxahZ4hfx5O76V3+b6KHD3kiVVdXMxMZHVTA4827tbUVsVhMFuCTV9jC4TBrq8hZ4Qmy5F04MzODnp4eOJ1OuFwubG5uslt+JpNBIBBguegikYOApqT0SpKEeDyOq1evIplMMmWLpLu7m5Xd09MDt9vNTD//8A//AIvFgs3NTXbgAmDzLZPJwOv1sro3NDRgfX2dcZpIaa2vr2dmMer/vr4+JBIJ9PT0sMCwfKRy8phMp9NwOp2sz4joTSbTT37ykyyZdTabZYgNfaelpQUul4v1s9vtxtmzZ/HlL38ZQ0NDuHTpEnw+H1KpFCPv82vC4XAwhYeUSZ4r5HA4UFFRgYGBAWYuIyWDSOV0UAOQ8ZB4BCcajTLPy46ODrhcLhkKLCrhfP5Et9vN0CMyjzqdTgwMDKCqqgr19fVsfl65cgXV1dW4cuUKKxOALJyH0tolhfb27duQJAnNzc1wu92Yn5/H6dOncfv2bSwvL7OwG4SKaTm5qO0HWnuTnr2ZnuH7L987WoqcKMXu26VI6Sckx5V6ptB3RZuz3uiyhxG9dTxMPxy1iJyeYutEh/Fh+lipDD3lFvNttWz1+crTGjt+c+IVJ6XylPqdvs2/z89dUnwikYhi0l49In6XOEF8Tj2eCDwxMYFUKgWr1Qqfz4dAIACv14sf/vCHeOqppzA7OwuXy4WBgQGEQiFW5v7+fk57X3/9dSSTSXi9XvT19WFkZESWr4/iFiUSCebxRkJ9QVHOe3p6ZGldqI/j8Tjbf/hcfV1dXZiZmZGlgaF36DDm606EboqaTsT60dFR1NfXo6mpCfPz8+jo6GD1JfSJolNTndXyGtJ4UioXQtwo6vvKygq6u7tZ6ht6l4/0TnMwm83i2rVrqK2tRVtbG1NQScEjTpKYuoai3btcLhY7jI/FBYCZau12OzukSRESc+Bls1ksLS2x+aqU4ok4ToRYKbUnFAphfn6eoUt8pHk6/GmuFpKUm8pYXFxkKX4uX76Md955B62trXj//feRTqfR3NwMj8fD+uoo9zQScS/h/w2cTF4/sX6lSOm/QHIYmLHQd0W4+yRQGr0oGh0MWvGyTsJkSXVRg8ULkaPoY6UyCrnFafHARFEyi+n5ppb5kPhOIhyvhTbxpjp6nvc8498lM0FtbS3m5uYKCvhKEgqFMDc3hx//+Mcs6CBtoDyPTDTnxGIxltbkr//6r2E0GjE+Pi7jZIXDYcbTEWNrGY2PXfztdjvMZjOuXr2KdDrNEtk6nU4WS4lHUqgvebNVPB5n6T4IUVpZWUFdXR1TqqhPXS4XrFYrenp6MDIygmQyiWAwyII90vdpHZhMJkbgNhgMbMxnZmawvb2NSCSCsbExtLa2sqCoSsjE5OQkNjc3WUBMmmOEHpjNZrS1tWFgYACxWAyNjY0oLy9HY2MjGhsb0dzczJAQQlUoMjnlUyShYK0bGxs5nCTiNcXjcfT09LC1QhxF4iPRGBqNRhal3Gg0Ih6PY3d3VxYug8aTAqiKa4RQNSU0KpVKMY4TjTmvTPHmw/39fTZP6Xe/34/x8XFMTk5iamqKca74CPRa3nXZbBYGgwEejwdXr15l8b0WFxfxyU9+Es3NzXC5XLKQI2qitE/nQ7hF0zr1KX9+HMaaU4gUu2+XEKoTkuPK5fckoDr56lAIQqWW4VwPSnKSbfpF+ma+PlO6AR4WmeO/qydJshJCxH9fLT8Y/76YFDgfysi3OxQK4dq1a4jFYujq6mLfUJp7Kysr2NzcxM9+9jNcunQJNTU12NjYwCc+8Qn8/Oc/x+XLlzEzMwObzQaTycQOYT7fHl83Mr/zSikdLgBkf6M2KqFm/BjSc4lEAltbW7h//z4uX77MlALKVdfS0gK/389CKXR0dGB6ehq1tbXweDzMlEaRzsmTjO9nCl65t7eH2tpaLC4u4sqVKzKEk+onSQfk6p///Oc4c+YMixUlIjFKbW1oaMCbb74Jq9WKmpoaFstqfn4es7OzaG9vR3V1taychYUF3Lp1CxcuXIDVamWIjcFgUBwXQmkkSWLIWjqdRiqVwosvvojbt28DAAsvoZR0+caNGwgGg/B4PGhpaWH5AUkBoboRobypqQn19fXY2NhgpmlaA6Q48EgklcUjUYSAra2tob6+Hi0tLVhdXZWNE7UNQE4y7snJSaTTacbponEltJpMyCI6rIYu8cgYz4Hc3d1V3Q/U9gt+bhOyyiOqWnJUe2opOfITJselUB2HgqG0mR9lHfKZh7RMksUukKN4DzheuLnYscw3XkptV+pnLcWnkP4rRJHRqyjrmZPiM9QupaTSdrudxR2iA2RsbAyhUAhutxtDQ0Oq5oZsNsvy4tlsNrzwwguKpjytQ4QStJpMJjQ1NcnqyLefbxNwcAjPz8+js7OTKURKyW7JXOTz+fDGG29ga2sLbW1tKCsrQzKZxOrqKhKJBC5fvgxJkvDDH/4Qn/3sZ1FdXY1sNovV1VWmJChddJRMuLwpdGNjA/X19WhtbWWJoanf/X4/tra2sL6+Dp/Pl3PwUxs2NzcxPz+P4eFhmM1mLCwsYGRkBLu7u2hubsbg4CAbNzKndXd3yxS9iYkJAAeIw/7+vszUt7i4iFgsBpfLlYNERKNRpNNpxONxljpncXERZWVlePToEZ577jnW75mMPGE2KXEXL16Ex+ORKbcU6sFsNiMajSKVSsFkMsn4a/R8PB5HJHLgRer1euF0OnMuDaISIppR+XVGiq/D4ZAhxTRf4vE443Xx62thYYGFoxgaGsopV5x7vLk4Go3C4XBgf38fBoOBhWLg14uamV/pG4XukeLzxexjTqcTW1tbJZPfR0nUTF3HYbZTIhBrSaF10IJt85kk85k41fqpWKj4MHCzlnlS6TetftQqK994KfWZEtEbUI88X0jb85lLRZI2HTJKqWay2Sy78QNQ9f6hzY+eob6iJLRiEEIyKZKJxWg0YmhoCM888wxTpsR2Uz96PB5cuHABp0+fxgsvvACPx8PMRWRSAR4TnMWYUbwQQV00j5F5Znx8nKWDAcC4VjMzMzLTo+iWbrEcpKoxGo14+eWX0d7ejr6+PhZosqysDJWVlYjH4zCbzWhvb4fVaoXH48HZs2dx6dIlpkxFIo+jYPOIGiFblCiavk9BRqlveZMWEfkpB6DH48kJOkzmJ/IAJHSEkkpTX1MfEZrT29srK4cSW4fDYYTDYRbFnPqI56KFw2H4/X6Ew2GGglJ7yUxYUVGBaDSKqqoqTE9Ps4jn169fZ2lzgAPT5VNPPcUUN0qXROlvjEYjtra2WDDZU6dOoaenB5HIQSotOrgdDocsJAeNAyFnvOmM9zLleV48OkT9RMgOzVVC6EhBpUsD7+27vLyMO3fuMO9TtT2Yr8/q6iqSySTu3LnDFL6GhgYYjUbFAL38fiHuNzTHC02CzO+ppPCT16uS8GupmDOjhFCdkBwWoTopUxcA2UZFnkVHiQwdpzlNjeisZD4plCgPQPG/1d7XGrNCzHBqBG7+2UIQRb58PbfCQsvXi1AReqOGzgDA+Pg47t+/j2QyiU996lOsHLEe4k2dR4ZI3G430uk0Xn31VfzKr/wKEolE3jYpzZNsNsvy2vExh9RMKgsLC8xMQbGP9PQNIShOp5Nxhkixr6qqwuLiIoaHh5m3HX8LF5EMsWxCuoaHh5lCzqNdSqYbHhFZWVlhyAy59ZMnXiwWg8FgYHwhv9/PvNVIydPqc97sZrVaGfKTTqcxOjqK5557Dh988AGy2SwCgQB2dnbg8XjQ19cnM5eRowIFWQWA9fV11NbWoqKiggUIpXAL9AzVj+oiSRLL0dfT08PI9uRxub6+jqWlJXR3d2NgYCDHGYAcCQhBmpqawq1bt1BbW4vKyko0NjbCZDKxYKJk9uPNfdQnZrMZLpdLNibiHCX00+VyydA/nkIRiUQU56pYb1LmeGK/iIiK64X6TJIkFvy1srJStg7zIdaF7H9aIiK8i4uLiMfjTJlXmnu0j/D1LSFUHzERNW0tknGhyIj4O93Gm5qa2O3wKBAeknwbqh4hpU8kWNLNkxY01UEMAqm3PXxd+Y2Wbtxa7yuRwrVuWuLtiCfoF0sIVxtneofPyaU2Lkr9Jwr/PSqHNm3xNki/k6uzSHLlx5Zc7vl0LjxKRd+lOE50iFPZAGRxk9555x0YDAZ873vfA6DffMg/MzU1hcXFRUxPT8vqwicMttvtWFhYwPXr1zE1NcWI3rRha6F3PJmYAkh6PB7EYjGkUiksLy9jbW0NNTU17NDh53wkIk/fw5dN4R8qKytx4cIFNhZiCBL+WX4O07zt6+uD1+vFyy+/jNbWVrS0tDD0hMyYZrOZeQEmk0m4XC5ZOAm1PicUgwJnSpKE8fFxRCIRdHV1YW5ujoWSiMfjMBqN2NvbQyqVYqZYv9+P/f19eDweXLp0CS0tLUgkEqiqqsK9e/cY8ZyPBk+EeEI4CamZnJxEJBLBzs4Opqen0dfXh7Nnz8Lj8aC3t5cpuJTrkGJpmUwm5kWZzWYZMlpbW8u8Bq1WKwKBABsvCljKk99pjqyurrJ682fB+Pg4Q114RTgSiTCyPrWRR/9dLhebYyS0z/CIqdlsxuc+9zmcPXsWTqdTk9hOOTRv3bqFaDQKn8+Hmpoatm7FPURUwESEF0BRyBQ/j/n8omRaVVPKaC3R/Nfa85SkpFA9IaL3AFSCQ0XR+l3vuwBkE7hY0+JxmCSpnouLiyxbOwktAP6gU6rDYeqldmCJQhuX3+9nhyQPb/NwPJXLm+H4hNZKt2ZA2zMmn8ek3n7Q84wSRB8Oh7Gzs4OlpSXFDZjaxOfVo02ZeBuxWAynT59GWVkZotEoAPm8pHaOjo6yvuKjVpMZpLGxEcBBAMy9vT1UVFQoJk+mupMnGpmzxIjrRqMR9fX1Mg8qv9/PkItYLAa/34/JyUlEo1Hs7u6ylCUrKytoaGiQHUx0mGSzWYb+OJ0HwRipTk7nQZDOyspKrK2tyZBW4MBcOzk5yUjeFHOJyiUTKO0jvOIgImWiKYlXvGnsCA0iM6jZbGb9QmvDbrcjkUigq6sLkUgEDocDLS0tOZ6nFN5gc3MTgUBAZpoaGRlBPB5HIBBgJlyTyYS9vT3U1dXh1KlT6OjowNjYGBwOB370ox+ho6MDBoOBfcNsNuPcuXOoqKjA+fPnGdk+nU4zBZnmDG/+npmZQTQaRTwex8OHD5lSRGMSCAQwNDSE06dPo6WlhSVNpoCvMzMzzLRKikoymYTdbkdZWRkWFhawt7fHOFhkZpQkCXt7ewiFQkin0wCAixcvslhXNF6k9K2urrL1QPOSYnnx6YL4Po9Go0gmk7IciLQmxWjv/F5GJmiaWzSHaV80m80s7RDNW7rgiHsIKTyLi4u4du0aM3Hz+5jWBSTfuUnoLoXryBeHis4Pfg4XIh87k983v/lN/PEf/7Hsb83NzZpIw9tvv40//MM/RCAQwKlTp/Bv/+2/xe/93u8V9N18Jr9CIE01M5Qek1o+U9dRmuPy1fOw5fFxW3j4ViuW0mG/W4i5EMiNjwM87gfaLNSge6XviPNEzTOGiNNerxc1NTW6YHLqt3weNEr9y7eLIHryYqqqqsLa2houXryoaLLj20kK0gcffACbzcZyzE1NTcni/dB7pBCQF5rD4WC3UeKrqHkMWiwWDAwMyOoOgAVUnJ+fR1VVFVZXV1lKkoqKCvT39+d4uimNMwVJrK2tRWtrK1ZXV5knldlsZkqO1WqV1dtgMLDbPUXqpvEW+543bUajUbbHEGLHIzG8WZXMZ52dnSgrK8vrickjH2TqczgcOYRsEakkxToej6O6uhrV1dUYHBzMGYPJyUnE43EsLS2hs7OTHX6RSASbm5v4+c9/jra2NmZyIn4ZABYPi3IIGgwG1NfXo7u7G8lkEplMhpk2ab1sbGwgk8lgZ2cHjx49Qn9/Pzo6Otg+Qv3c1dWFu3fvshAIRLKm9UHmMZfLBUmSMDIywoKmPvXUU7JYVYTa8oc/cZScTieb21RHUsZSqRS6urpy4nkR2Z9HUiVJwtTUFFPEKEAtH8eMyiFzusfjQXt7O/r7+5lS9M477+DKlSuwWq2srjTHKdegJB2Qzu12O7xeLzNn0oWDR4XUnIr4NZxOp7GysoLTp08zL8p8ey2ZN5WcM8S5q/ccUHq+5OWnIt/85jfxne98ByMjI+xvtOkqycLCAi5cuIB/8S/+BX73d38X7777Lv7lv/yX+Lu/+zt88Ytf1P3dfANSrCLzJIcR4OsG4MgDXKrVSYkHcJi+ycdfKlZBVftNS7ER31F7Vjzg9YzZ+Pg4kskkO/TUFOKJiQksLCxge3sbzz77rGIgTZ5HRfngiIsHQDXKPc9P4m/HSrdT2kwbGxsxNzeHrq4udgiTKAU1FNtFChQpZKQk+Hw+TExM4M6dO0xh6+zsZKgRkMvJEzlpPBeRlLlYLIbe3l7EYjEZosCnkgkGg9jY2MDw8DAzh+bzmKQx4kNP8Cgt//74+DgSiQTS6TRzi6+trUUikVD09KJvhUIHSYFpXdG/iZNCCgMdpiaTCbu7u3A4HFhfX2ff4suxWq2ora3Fa6+9hoqKCjQ2NrKgndSGYDCIcDiMlpYW9PX1YWpqCpIkMcUrFAphaWkJOzs7eP/991kKmenpaXapAID5+XnMzMwwdOvu3bs4c+YMDAYDXnjhBdZWCqYqzu2FhQX89Kc/RVtbG8xmM9bW1tDZ2YnV1VUYDAZks1msra2hubkZ9fX1bE6lUikWo6usrIwFhCXUur6+noWHoPGjeHB82ALqW0IaqW407n6/HxsbGxgbG0N9fT18Ph+ampoYYkmINZHrHz58iO7ubgwNDTG0aGZmBltbWzhz5gw+//nP58w1qkM4HIbBYIDb7WaJoYmXRUgaP/f4YLliv2qZ2NVEkiTVSzX/jBKfsFApKVQq8s1vfhPf+973MDU1pev5f/fv/h2+//3vY3p6mv3t937v93Dr1i1cv35d93ePMmyC1iZ6HERvoDjFja8nmUT0xg9RKgPQh3LxhxsdKIfhbGkpRUep0NJ3lpaW2M366tWrmvVW+34h/UbP0gYuRtnmFWKTyYSlpSUEAgG0t7ejtrY2r8LGj0co9DgCttI84IndhOIAygoYoRz3799HRUUFrFYr81QSv6u2Nsg9f2xsDENDQ6isrGSHGiEapAzy7/NKF69AidGuxQOGDk8xHhUhTWQWee2117C5uYmamhp87nOfy4tmFiJkXgwEAjh79ixTovkDn+rBm5nD4TBsNhuLl+R2uzE+Pi6LuSQSeu12O5tTAPD6668jEomgo6MDVVVVjJdEyYLJhNrU1MS8zwhN4b3U0uk0ysvLcfHiRayursp4eKFQiMWGIrMYjQ+veG1sbKClpQWjo6Po7+/HysoK1tfX0djYiIaGBiSTSXR0dDAU1uPxYHx8HA8ePEA6nUZtbS1DGBsaGljqnWQyibfffht2ux0XLlyAyWTCz372M9jtdhbyoba2lplvCeEiUxM/N0jJIC4XcZ4ePXoEk8mE/v5+rK6uorq6GnNzc6ipqcH8/DwsFgtsNhtefvllNr9isRiWlpbwwQcfoK6uDplMBr29vbDZbOjv72dzFwBmZ2fx/PPPo6qqSlGJ581sdOEhdIyQZJrvNLfFaP1aFhW9a5cPD6LkXEOIdyEXS3GtRCIRVFZWMlN6iZQuyNzcHE6dOoW2tjb8+q//Oubn51WfvX79Ol5++WXZ365evYrx8XHs7Owcd1UVhTZXnv8gchxI8tmY9UoxnCMeWdBKtKslPDdHDamgA2JhYUFGaCRORyqVkvEEChE1iJr6s9B+0RoPaiuhJHwONS2OgNL3i+HcUZRtUnT4snluid1uh8/nw9mzZ1U3KaV2kqmLd3ZQag/lZmtsbMzJHs+XS2Ps8XjYjV/pObX2E3dpbW0Nra2tWFlZYYolcUV2d3dRVVWFoaEhZm5yu92M38ZHdOf7kfqJSOpkgk0mk0gkEuzgpzYDYMqE0WjEhQsXsLW1hVQqhYmJCdjtdhnxnExeCwsLTOnTIgvz64PQnp6eHlRWVqKurg737t3D+fPnMT4+zuIjEU+GiMbZbBbz8/Oor69nnKeuri60traisbGREe/tdjucTiei0SjrT+KUEYcslUqx4KmZTAYGgwE+nw+f/vSn0dvbi2w2iw8++IAhdnV1dcxMV1NTA5fLxaga9F3aB8n7LxgMytYBEc/7+/uxtbWFK1euQJIkPPXUU7h37x5WV1dRV1cHh8OBlZUVBAIB/PSnP4Xf78etW7cQiUTQ39+P9vZ2fOUrX2Fj9PzzzzOExO12Y3Z2likm5Ek6NDSE/f19XL16FQMDAyz8xebmJuNTibydYDCIa9euYWxsDHfv3sX9+/cRDocRi8Xw4MED7OzsMFPZvXv3GML4iU98ApIk4cUXX2R7IEW8d7lceP7553Hx4kX8zu/8Dmw2G0OuKCSFy+VCb28vysvLFdcN9SOhdMTFo5yO9E1SZsLhMOO+idHf+f2H/w79jV9f4t5CCjeZfPl1SPxReobQwELPQarH8vKyruc/dgjV66+/jlQqhc7OTiwvL+M//+f/jJmZGQQCAUZc5aWzsxNf//rX8e///b9nf3vvvffw3HPP4dGjRyw9gSiZTIYtKuAAoTp9+vSRIlTirVbL/KXGkTgpTlWx5eh5j1ANusGKNvtr167BYrEwLpFWWeL31Nzxi0Gk8sHPSoieGASvGDnKMdb7rJq5V22eKr2vlsuMR2rUOBQ80kVcKrW1QWR4IoNbrVacO3dOMdChyBESEZiJiQlGxI1EIsxMJUkSbt++DbfbjV/6pV9i3CAxSCcRf3t6ejA1NYVAIICOjg6cPXuW9aPJZGLBMauqqnD+/HlmRpMkCR6Ph5lsaQ4tLS1hdnYWnZ2dDPkjF3xCbEZHR1FTUwNJkvDss8+ydC9LS0vY2NhAb28vI6gTMnXmzBk89dRTmJ+fx40bN+ByuVBfX4/6+nosLi6ioqKCceeIYwMAfX19iMfjSCaT+MEPfoCnnnoK586dYyZl3qWf8huura3h4cOHaGxsRHd3N8rKypgpkQ7M+fl5jIyMwGAwwOv14mtf+xoAOUJLpm0igN++fRvV1dVMEQAOYlg9evQI1dXVGBoagtlsxvnz5/Huu+/iypUrMJvNuHbtGkwmEzKZDC5evMhQmrGxMcTjcZw/fz6Hn0ZmKpojpOjRPOLNe6FQCFNTUzCZTLhy5YrM7BcKhVjoh93dXTbHqa2pVEpmuhfNaXyQT1Jq4/E4DAYD825T4yDy+wD9Rtw68ijkvaHVwndocUXFdcavQbXQOKIZnw8XUazZr4RQ5ZHPfvaz+OIXv4je3l4MDw/jRz/6EQDgr/7qr1TfMRgMsn+TDir+nZc/+ZM/gc1mY/87ffp03rrRTVLplskLTTDec0INvRG9VsTbhl70Qu13vaJWv2Lf4xccoRoUOI9HKSKRCK5cuQKbzQa73Z63LWJ7qf8kSZKlpCjU+4PKVvIOVNrwCNEzGo1FfU9rQxKfK0TR1TuO1G/kscSjLzzvSA19s9vtmJubQ3V1tWy8RESOd/lXmhPk3aO1NsjEee7cORiNRtTV1cFisWBwcFCW9JbQQuKPGI1GZsbw+/2IRA5iHq2urmJychI//vGPMTs7i3A4DADsG3QQzc/PM3NQOBxmClAikWAcz7NnzzLuGbmOA0BbWxsymQyef/55xONxdhlcX19nOd78fj+SySQikYNYSW1tbQCAvb09GI1GRrRuaWmB1WrFr/zKr2BjYwM+nw9ra2uM2B6Px5FOpxEIBFifd3d3I5VKIZPJMD7VU089hd3dXXi9Xsazqaqqwp07d5jnlsfjwdDQEOLxOFKpFGZnZ/H000+juroaDQ0NuHbtGqqqqmAymbCwsIBkMolYLIbh4WF0dHTg3LlzLFwE0TAIoaDI6A0NDSx3H3kB05qWJAmNjY2wWCxMqbDZbKiurkZ5eTl2d3eRyWRYuIqnnnoKFRUVaGpqwre+9S2srq7izTffxMTEBCoqKvDw4UOkUincuHEDY2NjeP3115m3oMfjkYXyMBqNmJqawttvvw2/34+ZmRmGAk5NTSGdTrNgpKRQ9vb2oru7G0ajPPBse3s740eRImi32xGNRnHu3DlGZFfjJfn9fjx48AA//elP2Z5mMBhQW1sLk8nE1isp+HyIGLoY8n9zOp1MeSEPa6PRiP7+flkgUt6bk95V2lNI0dzf32f/rbYHiGXQd/nYW1pe2Vr7UKHn1sdOoRKlqqoKvb29mJubU/ydBoQXSnKqhGiRfOMb30AikWD/e/jwYd66EH9Gb4RyPYOtpHzxks9kxf+uNfEOY1pUejdfeaIpkCBo3gtMNGVRRGst5UTsj3zKiJ42K0HUIlJCbtv8s7zbeqGKqJaZi68z/+1C2lRIPdRiWPHQPG249O1YLIaOjg4kk0nZ3APkh4OWaZNQAXLvFucXKZLkgfbBBx/AaDyIv8bXlz9AAMji9zidj8NbAJDFY+rq6oLT6WRxbzo7OzE7O8sSA1++fBnhcJgdZkajET6fj5m1dnd3UV1dDbfbzcwfdMgtLCzA4/Hggw8+YKEaBgcHMTw8zDwjfT4f8/Lq6upih+XS0hIMBgO7rRNqu7Ozgy9/+cuIRqPwer1IJBLo6+uD2+3GxsYGtra2MDU1hYWFBdy9exe1tbXMlDcwMICzZ8/i8uXLMJlMWF1dxcWLFxEMBtnBGwgE2H9TvxGXamBgAIFAABaLBfPz8zAYDDh9+jS2trYYCjIwMIDPfOYzqK2thcPhQG1tLUvPkkqlMDMzg76+PrS1teFLX/oSampq2LjQmg4GgyxGFV2MzWYz9vf3UV1djbW1NSQSCZw/fx4XL16E1WplTg+f/OQnsbq6io6ODiQSCYyOjqKtrQ3l5eWIx+O4c+cOtre3WZ1p3o2NjbH5Z7fb0dbWBqvVio6ODkQiEfzsZz/D/fv3cePGDZaHz2KxoKmpCRUVFSgrK5MlYKax9ng8qKysZIE+R0dHkUwm8e6777L4V7QeQqEQ5ufnMT4+DkmS4PP5YLVaWf8YjUbG6yKuF71HsayoTaHQ42juAGRhDdxuN4sbR/QLo/FxGBu/38/GON8lUQ+dQU34Z0jZU6MnHBVoAJQUKmQyGUxPT6ua7p555hn85Cc/kf3tjTfewODgIMrLy1XLtVgsqK2tlf0vn/C36mIQEC1Rm4RKf1dDN7QmXqGTMh/Hhf+bJElYWFjA2NgY40Hp4S4Vohyp9QfV0+l0MqKxyJmhTUaM8yK2RS2WSrHPaola/5wUIknlAFAdJ14Z4fuTEBwimwKFBVKlSxDFZRofH8f4+LgsXhn/LafzIL5TKpWCJElMieOfJYUrm80yD7BYLCa7hVMdjEYjzp8/j87OTnz1q19lZZhMJnR0dGB+fh42mw0zMzNobW3F1tYWQzHMZjOGh4dhtVpZYEvibFVXV2NychIPHjzA9PQ0lpeXGbcKOFA0+UuFePD29/fD6XSirKxMVldC3RoaGrC4uIiBgQFsbW0xc1B/fz8uXLgAj8cDu92OW7duIZPJwGw2o7m5mT0nSRJu3bqF27dvI51O4+HDh6iursb6+josFgvOnj2LtbU1pjw3NjYyRMxoNKKnpweZTAadnZ2oqqpi6DLPj4nH4+jq6sLp06fR1taG3t5exoM7e/YsbDYbBgYGcOrUKTa/CCWldRqJHATpJHTSaDSiuroaExMTsNlsjJDe19eH1tZWmM1mdHR0MG9GUiIbGxuxvr6OlpYW9PT0oLOzEx6PB1euXGH9+sEHH2BqagojIyNYWFhglz9CHq1WK06dOoW5uTk0Nzfjgw8+gNvtRl9fH/PMUzsTSFkADvh3ly9fRiKRgNVqxfLyMguKS/1NgUGpD+rr61FWVsaQI0r1Q+E4aO+l70QiEYbS03wSL+q8yZS/JBLi5fP5YLPZMDw8rIlS5zMDFiL59n0eTT/sZfJjp1D90R/9Ed5++20sLCzgxo0b+LVf+zVsbGzgt37rtwAcIEtkdwcOPPoWFxfxh3/4h5iensb//t//G//rf/0v/NEf/dGx1C/f4B8WQdCDBqkdqlpKjNOZG6Fc69vigaYVeJNuNouLiyyo3mGUo2KQMN6DxGAw5Jhg1AiU+fqNficvsnzP6h3/fCZg0WRGSE6+CO56hb7DI2yiSVuE5vm6kckzEomwsAY8mVtJeaU2U51NJhMcDodm/WjDHh4ehtfrRX9/P+Pj0C07m80yL91AIMA8/MRDhCJlLy0tIRqNwul0IhAIYHJyUpaL7/Lly1hbW2OmRf4QI7MfoU7AAYpYV1fHTKDvvfceysvLmaIfjUZlij2P9pH5j9rp8XjQ2tqKpqamHKLvzMwMvF4v0uk0i/T+wx/+EBMTE6ivr0d5eTncbjcuXryI06dP4/nnn2foFilli4uL+OlPf4q9vT20t7djY2MDDQ0NLM8e8HitkOmU6kHKUiwWw1tvvQWDwcCCPe7t7cFutzOFlnfVHx8fRzabhcVykL+QxtZoNMJms2F0dFSWD5E4TU1NTSwVzcTEBAYGBmC1WtHa2gqfz4dgMMgCpVZWVqKzsxPvv/8+QqEQ+vv70dDQgO7ubmZ9aGtrY3kgl5aWsL+/j3PnzsFqtaKqqgozMzPY3d0FAMZ56+jowO7uLr7whS9gf3+foVYUYZ7I9gBy6CCEnNLFhfI7zs7OYmlpCaOjo8hms1haWoIkSWhubmZ7NKXSKSsrQzweRzgcZghVb28vrFYri9quFuKE5j6FRkmn02z+kRmYxpuC8pKST3NbvDjTnKXAunxgYhFNPyqhfUPNsasQ+dgpVEtLS/in//Sf4ty5c3jllVdgNpvx85//nMWWCYfDDKoEDhbJa6+9hrfeegt9fX34T//pP+G///f/XlAMKr2iBx04DIKgZPtWKrMYqJVMJGrJa8Xv5FPARMjW5/OxjU6tbeJhooYUabVdLI/3rgqFQtjd3cXq6iprJ31PTMKrt98AFOQBeVgESayLuJkoIWMiEqdHseJvtbyirmTSJkWLFAT6m8FgYOjQxsYGHA4Hqwspr0p14s2MpND5fD72HR55BSAzJVIspfn5ebzxxhtYXFxEOBxmCCXNQ9Htm279DQ0NDFGQJAnpdBoOh4MprRQ0kQ4ZMudZrVbGw+L5HqFQCOPj4/jJT36CR48eYWVlBc8++yzzdPvJT37ClE0AzIRKISWmpqawubmJ1157DQsLC5Ckx8l6KZkzcWja2tpw584dhjDdv38fjx49wqNHj3Dv3j2k02lEIhG0tbUxr0hJOiBZ//CHP0QgEMAHH3wASZKYh97+/j5DxKg/pqenUVdXB5PJhIaGhpy1Fo1GYbFYmLL04MEDzM7OIhaLsXAPNF9CoRBmZ2cRiURk6xI42MfW1tZQWVkJk8nEUJ76+npIkoTl5WXmPTg0NIR0Oo3e3l5Eo1GEQiG8+eabeOutt/DTn/6UoYQulwvr6+vY2NjAl770JWxsbDDv0Gg0yuLCpdNpGAwGtLS04Ctf+Qra29vx0ksvyUJKRKNRzMzM4MyZMwiHw3jllVeQTCZZn5Apmjz9RkZGcqKI86a36upqhvSZzWZYLBbMzMwAOFBWycxpNBqZafrUqVPo7e1FS0sLKioqWJgIkftE64uI6rypnrh6o6OjjNDu9XrR19fHFPXq6mrFtaqEKlPux7GxMcah5BEzXvE6SnqCHqtHPvnYefl9WKInDpUSzFnMM2qi5jl1mDILqZv4O5FFjyIAJ5+AVowuTaIUBFOtznxfkScNn+CUPFuUogEXKnwdaFPR8spUI5oeRrTGjt/M1Dwcld4XvXFIoaEYQRTrSvTQBB5HoKY1ozUGYp2U+pOPGi4mg6WI7h0dHaisrEQ2m8Xo6ChOnz6NdDqNpqYm2a2a/wZ5rlGqETKxAY/jZFEQRqfzIOJ7KpWSoXf0PfIe4/uRwiPMzs6ySOOUHJhPW/M7v/M7sFqtDM27d+8ebDYbWlpaMD09jc3NTVgsFrS0tMjQg/n5eayuruLChQuYnZ3F1NQUotEoPB4PfvmXfxmhUAiJRALnzp3D6uoqI/nTgRaNRnH79m1MTEzg9OnTcDgcePjwIV544QWkUilMT0+jvr4e586dw4MHD1BVVcXmtcvlYmTy/f19ZlKfm5tjfLbTp08jHA7jK1/5CsxmM15//XWsr68jFovhs5/9LFZWVjA9Pc2CU/IeZ/F4HHt7ezIvOFLmJUnCxsYGM70ZjUYWMJTiOEWjUSwvL6OrqwtPP/00Uw62trawvr6Ovr4+Fr8pFouhvb0d7733HuPFXbp0CWazmSnT9F3yhqOQEBRUdW5uDnV1dTCbzbDb7djb28N7770Hl8uFBw8esKTOXV1dSCQS6O3tZaR3SZJkCYztdjtGRkbQ2dkJq9Uq4wLTWhwZGUFrayvS6XROFgf6vbKyEmazWRZp3+l0yoLh9vT0MA9FPggt7Y/ZbBapVArd3d05Hsv8fsYj06Ojo7K6EU+O3wsKiWt1mP0P0B9H8mOHUD1JImraQP5DslCvA17UyHmHKbOQuom/042AR3a0bOpatxHamMk0p3TTUEKCtExjPNmYPwApz5Xb7T4SzpuIxml5ZfJIYCGIEaDeh/k2E5GroTROSiiW3W6XJT0mXg9tuADYwepwOOByuWSmPz5uDXE1lMZAy+OHL4viW/E3bhpn3uwGAB0dHchkMqivrweAHJd1UhbJxEcRo/mDAgCL00MmFjLvkkcWIREdHR0IBAKyG3gymWQhFH75l38ZOzs7uHDhAubm5vDiiy/i9OnT2NjYwMOHD/Hmm28yUvv8/DwjPpNH3unTp3HhwgU0NjYyQj0hdY2NjYjFYqipqUFrays2NjaYxyOFPojFYszbjwJArq+vAziIEeX1etHd3Y2XXnoJX//612Gz2dDd3Y329nY4HA4kEgmcOnUKVqsV/f39aGlpwc7ODt566y3cunULy8vLCIfDLI1Jf38/nnnmGUiShK9+9assBUpPTw92dnZgt9tx7949OJ1O1NXVsXQoNAaJRALZbBa7u7vs3fHxcTx69AgOhwPl5eVoaWnBxMQE0uk0Q0ZsNhuCwSAikYPgo2fOnEFzczNu3bqFcDgMh8MBt9uNpqYmxh0rLy9He3s73n//fSwvL2NpaQk2mw0Wi4WhcBMTE7h37x5CoRAsFgsLbEuK9traGra3txGLxRiCuLq6Crfbje3tbVy+fBnpdBpWqxU//OEPsbCwgEAgwOY5IaQ0T8nzk3L88TkXjUYjI4dPTEwwbh71H3CQP9FkMmFzc5PFhKN5E4lEWE7C6upqBAIB9Pf3s/lGXKvt7W3E43HGHTQajTlcpVDoIM7Z/Pw8JiYmmMLb0dHBlCmRWE5r3ufzyTiYJMXwefVwNPVICaE6IVHScI8SoTkKOQqkqtgy8qEgSrFHeMmXh46/CfGxg9Sith8HElRI3yg9y6MiIjpDN1A1hIknooqxWPLFdeHRRHqGR/CIc0RCdQJy0w2J/Qroi1VTTD/qQc3456g/DQYDi75NBxR/cKVSKRZvh27hxA2h71B8LIqAzbc3FDoIgmg0HoTH+NKXvoRAIMD2B0LS7t27B6PRyOI60eFPQUadTif+8i//EtFoFC+++CKeeeYZGXKgdrPnw4AQekbrh0xkNTU1zDEgEonA6/Xizp07MBqN6OrqgtlszuGnEVmfFFOXy4XV1VXYbDbm+cfnf3zttddw8+ZNWCwW9PT0MK9OMSULvVtXV4doNIq9vT1Eo1GmgP/0pz9FdXU12tvbZXGXstksiwc1OjqKVCoFk8mEU6dOYXd3F/fv30dzczOi0Sg6OzvR1NSElZUV3LlzB1VVVaitrcWzzz6L73//+xgcHMTCwgJDMemQD4VCmJubg9/vR29vL1ZXV5FMJnHu3DnY7XbMzMywfqc4YF6vl60NUiQNBgNsNhs2NzfR09PDOIerq6ssd2QikcDS0hJOnTqFR48e4ctf/jJTjra3t/Hw4UOk02lcvnw5x2SfzWZhtVpZjjw+JyTNPT5mk9VqxeLiIoaHhxkdgOYNxX1zOp0YGRnB6dOn8eDBAxbeIRQ6iFRfVlaGuro65mChtJfQPKR5L0kSQ5CV9j4lxF5tn9R7BqlZbngpIVS/AKKE0HyYclh+zmHKoPcAZa8w4lyl02mZtx+J2WxmHjhKiA3PqwkEAuz2r1Uf3t0/H0KmJvx7hfSNEnKmxnUjUqjaDYu4SyKZmkQJ0RMJoPwzhKKsra2xGEw8Z0mMri6iRzzXTuQMkSwsLODatWvsVi9ypPT0IykTPLFVrBO/+ZLLdywWYzdvnhC8sLDAcrURYZdI3mpewvQMIQM0npWVlfj5z38uU+5Fr65XXnkF9fX1eO6553D37l2sr6+juroaq6urjMz93HPPscOK3NlJmSLTKJHZCQ2jMQuHw8xsR3XzeDzY2dnBiy++iMbGRmQyGbzyyisMyeru7mbt3d/fZ5HqY7EYc6/nFa3GxkasrKwgm81iZ2cHExMTmJ+fRygUwvDwMC5evIhnnnkG3d3dWFtbQzqdxtTUFDKZDDMPRaNRJJNJ3Lp1CzMzM0zxoBhbL7zwAjweD+rq6hhxmxDB3d1dvPXWWzAajbh79y56e3tht9uxurqKZ599Fvv7+3jqqaewvLyMlZUVNDQ0oL6+HqlUCpWVlfirv/orGI1G3Lx5E11dXVhZWcHNmzextraG1157Del0GvPz8ygvL0cqlcIzzzyDT3/60zh16hRmZ2cZ78xgMODcuXNwuVxIp9MIBoPY29vD8vIylpeXsbu7i+3tbdTV1WF0dBRvvPEG/H4/S2fj8/lQU1OD5557DjabDc8++yzi8TjjL+3v72NnZwdnzpzBnTt3EA6Hmakuk8ngxo0bWFhYYGETjEYjHA4HYrEYQ4LpUmaz2ZBOp5kypeao4vf74fV6GYH8e9/7HiYmJjA9Pc34mJQex263sxygfHlutxterxcvv/wyfD4fKioqWABZce8T17Ka4qRmdVDjXqlZboqREkJ1QnKUufyOS4pBZcRJnQ8p0luOkhBBN5vNoq2tDYODg7L3C41Cng+h0kJo9N6C+Pe0UCQl4W+RIjeBR/Hy3bCKGVetLO7UJpHPpLddfN8ByrkoyaxRU1ODy5cvy/pPzDmo9Z18EZLVkDfiVFEC4dnZWdTW1rL8caRkU3uVeFxqfU7eY9XV1chkMjkogIicLS0tIZFIIBQKwev1Ym1tDV1dXaisrEQ0GkU6nQZwYI4tKytjCA+ZuEOhEDY2NtDd3Y1oNIp4PA6r1cq8+Ww2GwtnEI/H0dnZiWQyiXQ6zdAkihJN84tvXzqdxj/8wz+gtbUVzzzzDDP7ZLNZjI+P4/79+9jb2wNwoGA5nU709PSwvHkU+JSijhuNRphMJoZOEYKzu7uLQCDA4hwlk0kMDQ3BarXCYDAgmUzi/v37aGpqQnV1Nfx+P0va/MMf/hCSJKG+vh5dXV0MCXE6nfjbv/1bFiqjoqKCKX8PHz5ET08P7t69i4GBAbS0tOAf//EfEY/HcePGDQwNDaGhoQF1dXXY3d1lvECaF3a7HW+++SbS6TRDtEwmE27duoWdnR0WPuHevXvo6emB2+3GxMQE/H4/9vb20NzczMrkEwuTB6PT6WQpjerr61lKJwohkUwm0dvby4J4PnjwAGfOnGHrORwOo7GxEVVVVSwXHpn0JOkggbKYu5HWK3kPWiwW9Pb2YmRkBFVVVdjY2GBoqt1uR2NjIz73uc+xebC1tYXh4eGcPSXfWqV2iryqQvbWw1iESsmRnzD5sBSqQg/TfKY1ped5EwiQe7gUU2c1eDcYDCom1z0qwr0ahCz2oVY/KSkMxUDS4+Pj7CB95plnchLq0vtHYarV0w/8b2J/FAO987+T2z0pcMlkEt/5zncwNDTEglvyBHO9GyJvJg2FchMzK/Ulby7q7e1lXm/r6+toaGhgxHm67fPR2AF5YlYlJbyhoQFTU1PY399Hf38/U5bpoKKDLR6Py0xelAaGeDZmsxm9vb2IRA6CZlL6FCL7p9NpbG9vo7a2lsV8qqmpYZ5mZWVl6O/vRyAQYMpTQ0MDrFYrenp68Oabb6K6uhotLS1YX1+XmUB5gj2lcnG5XCyfosfjwcLCAr71rW/BaDRie3sb9fX1ePbZZ9HW1sYQ0MrKSvT29jIFgZQGg8GA2dlZVFZWYnl5GZWVleju7mZoKB1blECanANI+aDAo1VVVWhpaUFrayveeOMNtLS0yLhckUgE9+7dw+zsLJqbm9He3o6FhQVUVlZibm4ODocD+/v72N7eRjQaxfPPP4+/+Zu/Ycjf8PAwizS/vb2N69ev48yZM3jmmWfg8XgQDAYxPz/PIs/HYjEsLy/DZDLhxRdfxObmJouFRWtgZ2eHcbwkSZIplBaLBefPn8dbb73FTK8UFsFqtWJnZwdLS0tIpVJobW1FdXU1+vv7MTY2xvpua2uLJcHe2trCJz7xCeZdSF6WiUQC9+7dQ1lZGbtIUOJxmqfhcJghaLwiZjQexPX667/+a3zta19DOp1GQ0MD3nzzTdTU1KCtrY2VQ5ejxsZG7O7uql4KlZxHeCVKXGv59oJCLoBASaF64uTDUqho4wL0eaIVo4BQDBKj8XH+p2L4LXydC1HqlMrLx8lSE6VvK/1NC4nLV3+97VO6lRXbp8U8p/UOtYH4RsRvoQ2W3+R4V3FeqRe5FLxCxc9bMaeYuHnruShQotmdnR0MDg5iYGBAVenl4w95vV7WFvo+HQ5bW1vY2NhgCYKpHOKpibdmOhRmZmYYsdpqtcoOMiJEp1IpPP3006iqqpKhQsSjevDgAVpaWlBTU4OysjJUV1djZWUFa2truHLlCiKRCN566y1UVVXh9OnTKC8vZ8Erh4aGmCcbIWTEsSNlKBJ5nGuQvLOIlB6JRLC1tQWPx4P5+XlkMhlsbW2xmEq0D4RCIRbHy+PxsEOZlK3R0VF4vV54vV6Z40MkEmF9tbGxgVgshrNnz6KtrQ3hcBjpdBrJZJLF2CovL4fL5YLH48H4+Djq6+tZGAqDwQCn8yBUgs/nY4pyX18fG2sa22w2i9deew0XL15EKpVCNptlqNLdu3cZ+uTxeDA6OsrG7+LFi3A6nbh27RpmZmbgdDrxa7/2a2zMyBz6/vvvw2QyYXl5GUNDQyxEA3k7Li4usgjxhL5PTEywfqivr0drayv8fj8mJiZgtVrxpS99SYaWptNpvPvuu3j66afx8OFDXLlyBVarlZn7Hjx4AKfTyaLNZ7NZVFRUMOWSLhNzc3OIRqNMoW9sbMxJZr26uora2lrcvn0bzc3NMJvNbO6Hw2Ekk0lsbm6yJOc8l5D2hpmZGVgsFqTTafh8vpy1LCpB4v/ruVQexYWzxKEqCYDCoq9rIUNqMZ6MRiPz2qFNUc8Bp8WDUeLe5BPxYM3HyVITpW8r/Y33GBT7JF/9tX7nyzIajbhw4QJqampyTC08j6BQjlYxPDc1ThXxWyjeDR8cj56RJAk3b96UxdChuURBUp1Op0zh5+ct8Di9hdPpZChJOBzG4uKijFehJXV1dSgvL2cKmRjxnu8PUnKIb8Fv5KOjozCbzaiqqmKpXviLBQUgpYOAlD+n88CjsKamBgAYL4ninCWTSaYAPPXUUwxF4Q+M3t5ebG1tob+/n+XRI+7S6uoqtra2cPfuXVitVtTV1cnCOWxtbcHtdmNubg6XL19GJpOB1+tlEd/pbk31TiaT7HeDwYC6ujqW289sNrPDsqmpCc8//zw+85nPwOPxwOVyIZPJYHd3F5WVlRgaGoLb7WYkcuBxPkCaH3waKbf7IFJ4dXU1Pv3pT+PTn/40I+FT/tS6ujo4HA5cuHABFy5cwIsvvoilpSW0t7ejoaEBu7u7eP7553H16lXs7+/jpZdegtvtxvr6Ourr6xGJRFi8ruXlZcTjcbz77rsIh8OYmJhAY2MjWlpaGDLU0dGBra0tWK1WmM1m/MZv/AaefvppXLlyBXa7Hel0GvX19bh06RJ+9Vd/FUajkQUiptyCFy9eRDabZXw28sALh8NYWlpCPB7H7u6ubE9JpVJ48OAB2tvbmRddV1cXrFYr4+W53W6srq4yjmRHRwczl5OSJEkS1tfXcerUKezt7eHll1+G2+1GWVkZW4O3bt1CMBjE6uoqvF4vTCYTzp49iwsXLjDvOkJOJekguvrdu3cRjUbx3nvvIZlMsmwCFKU9Eolge3ubtZHmPe0NV65cQSaTwdmzZ2UcQ3HfiUQiMu/eWCzGOFVi8FpeaF0ehQefHikpVCcsauTmYknP+UQkxWqJ2kGb7/AxGnPd4fOJllKhF3XQUz6ZYtTKUlIQxeeV/sbXX+wT2jDoIC2kfbziQuVSlGEgd4z4f4vEcbX5xJPZteacVhnUBp/Ph83NTXR2dqKlpQV9fX2yCOTUzsbGRoZcUb35IKliQFH+XaW+Bg6QK7PZnOM2rSRutxuXLl3C1atXmfmNDx9A4SDoWa/XC7fbzZS2kZERpFIpRsTd3d3Fyy+/zMwXxDfhTUmk7FE4iUjkwN3c6/VicHAQLS0tsuSviUQCTU1NjKBOMXyov1KpFEN1stksrl69ymIoGY0HUcFTqRTLMeh2u9He3s5M5XSo1tTU4O7du+ju7mZIGglFXY9EInA4HAwZWV1dRTQaRV1dHUODSDFZX19nBy0dvC0tLRgcHMSFCxfYu/v7+0xZIPMTjT3f/zT+hGrQXIhEIgydkyQJZWVlqKysRHNzM4vyvr6+jkQiwRJBr6+vo7KyEu+//z4LFJlIJCBJB0FGV1ZWsLOzg2w2i/b2dpZrjwKSStJB5PH9/X1sbGzg7t27uH79OsxmM4aGhrC+vo6lpSW8++67yGQy2N7ehsfjgdN5kCNye3sbt27dQkVFBUKhEP7JP/kn6OzsZGvC4XBgZWUFdXV1LPcioWsAWFsIwYlEDhJMf+lLX5KhgDabjZmOLRYLqqurMTMzw+JqxWIxNDY2IpVKMSU5m81ienqaOYAQZaK8vJw5G4RCIVkCZt4sTfHDnM6DwMu3bt2C2WzGrVu3EIvFsLGxgYqKCubtyu8pZDI0Gg+yFCSTSRZeRY/Q5YQcLETCOgmZvokLeNxSUqhOWJaXl/MqLR+WqCk5/AEsHj753gWUD+ajUJq0RE/5h7m9UPlUjtgnhYynmuLCbxpUjtjP/L/5Nmt9n57Ll2qBL8PtdssQJCIcRyIR5slFijXf79QmigHFt433cFVS7uj7hEzRRkyu/h6PB4ODgzlKgZLwFwvy/jIajbL/5pU5Ks/lcsFiseD06dO4fv06c+0fHh5mJiAyw1VWVsrc0m/fvs1MSoTCkEs5KTy8txP1RSwWQyAQwLVr1/Dtb39bhm55vd4cL7719XV4PB7cvHmTKXhG40GgynQ6zczTu7u7OH36NCorK5HJZNiBRkK5A00mE1PsKaE3IQ5msxnd3d2oqKjA8vIyFhYWsLa2xuYSfcdsNrO62O12uFwuFgOMvBFNJhPW19dRVVXFLg30O5mDxXRO6+vrOHPmDKxWKxwOB3Z2dmAwGODz+bCxsQFJklBbW4uFhQWEw2G88847+Nu//Vu8+uqr+NnPfobl5WVsb29jcnISjx49gsFgwPr6OuMgdXd3szm+t7eH8vJyeDwejI2NwWw2IxaLwWw2s2jq6XQae3t7qKqqwvT0NBoaGljEe0k6CB66v7/PlKpkMom2tjY0NTUhGo1idnYWVVVV2N7extDQEF5++WXYbDb4fD4Wad5oPAhV8cYbb2Bubg6hUIgpqRS3icJi7O/vM3Rxb2+PmTjtdjtaWlpw5coVhqi+9dZbmJubw9zcHPb39+F2u9HQ0IDt7W3Y7XbU1tbi6aeflqV8SqfTuHPnDra2tjA5OYmKigoWeqKrqwuPHj1CY2MjAODy5cuQJImZLx0OBwKBADY3NzE6OopEIoGRkRHGpRIv4/zeoUSNoHhdSvGoRKSfNzmKvx+llBSqE5bm5mZNpeUktGg1UVNC+ANYPHzyvVuI0nJck1xNIhFll/1Cy1DqE71oEZWhpLiQOVVUGPT0kx4UKt+c4wNziuNL7tqxWEzVpExjn8lkctKCkNLCm/HEnF5i2h/K/aY0/5SEkBni8VA/8O0mjgyZIPkxoXF1Op148OAB3G433n33XZmplzfz8SaHeDyOtbU13L59Gz/+8Y+ZInjnzh3E43G88cYbCIVC7B1SRpuamtihVllZicrKStZndXV1CAQC2N7eZnUlr7f/+B//I6LRKL773e+ydhLP5+HDh+jq6mKIXG9vL0wmEwwGAxoaGmT54OhQI34LKbG3b99GfX09Cy2xs7MDh8OB9vZ2hgaRWXBlZYVxkoi47HQ6GdpHoSeAgwCqi4uL7DAMBoO4ceMGgsEgdnZ2EAqFsLe3x5TO/v5+ltZkenqaje/IyAhsNhtDaBobG9HY2Aij8SCOVzabZZHW/8//+T949913sbCwwFA34kE2NDTA7XbDarWiqakJLS0tCIfDKC8vRywWw/PPP4/a2lrU1NTg5s2b2Nvbw/r6Oubm5pBOp1kYAlo/Fy5cwMDAAL7whS+gtbUVdrudBZk1GAw4c+YMix1FJk+Kyn/79m2Ew2FMT0/jrbfewvr6On76058iGAwy8zApqel0GpIksYwAoVCIoTLkKdjU1CQzFdbU1LB8kpR3EQBWV1eRSCRw9epVVFZWYm9vj5kfr1+/jt3dXdy8eRPNzc2IxWJwuVwYGhrC/v4+fumXfgnvv/8+tra2EIvFUFdXh5mZGYTDYczMzCCZTOLevXvM1EcxuZT2SrW9FQDbD4DHIXP4VDm0pxqNyrkICfHVSxfQKyVS+gnJSZPSj4KIdxRlUvoNi8XCbu9qopesfVRtKzbEQ6F10QpBoLcMEtHJAMgNnsmXmy9sgFZdeM8ZkWCdTqdZuhSKRK30Ph/8U/SsEYOD0u/8e+RWTzdMkYiuNmd4ZQc48AYj7yCxH0QPURoz4kzt7+9jb28Ps7OzuHz5MlOeqJ7k4cornITgvfvuuygrK0NTUxOuXLmCVCqFsbExeDwelpaEAmtOTU0xs/n6+joePXqET37yk2xuTk5OIhAIYG9vDy0tLfjqV78KAPj2t7+N6upqvPbaa3j++efR1dWFlpYWrK6u4tVXX8Urr7yCbDbLzIdms1n239XV1Zifn0dnZ6eMJM1zXsj7z+fz4cc//jFCoRB6e3uZWYgcPyj5b2trK0KhEBYWFrC1tYXa2lqsrq4ypGR3d5ehQvzYj42N4e2330Z7eztSqRTW19dRV1eH3/iN35CtjVAoxLzzNjY20NTUBAAsZldFRQW2t7dRXV3NUroQFyccDjOS+tmzZ5FOp9HZ2YlAIMAiodMcisfjOHfuHEZGRmAwGBgKNDs7C6vVykK4vPfee5AkCS+++CLa2tpYzkZ+ffDhRiKRCB4+fAiDwYDGxkYsLi5iYGBAlgZmbGwMt2/fRkNDA/r6+vCzn/0MW1tbaG5uxuLiIi5evIhMJsPmOOXiu379OgKBAIxGIz7/+c/D4/Egm82yIKGErI6PjyMUCqGhoYGZAimyeV9fH5tbTudBNPqysjJYrVY2V0hho8u23W7HX/zFX7DLx4svvsjG8OLFi5AkiYU7oXkjUi14RxYi9Ise3bQ+yZQoOsHoCaNQ6N5Y8vJ7woQGhFyhj1uK8ZQ7DsmnTPCiV7E4qrYVUk6+umn9XoxCRTc1QO4ZJ/4dyA3LwLcvXwRg8dti3Cz6FiAPh6HWd2pxt5Tqqbap8X9fW1tjudhoE9aKB0b/JhPbysoKVlZW0NraiqWlJWaq44W8Kb1eLyOMk9LqcrlYdO6mpiYWwoBc3fONWzabxf/7f/8PAwMDqKmpYWY6kV93584dVFZWMnPPxMQEgAMUcXd3lyUijkQiuH//Pk6dOoXGxkZcvXqVcZK6urowMzPD6vbaa68hkUhgd3cXX/nKV2QhDyiERG9vL4uUzfc1jbF44IVCIbz22mtYWVlhSk91dTWam5tlno5O50GMpH/4h39AVVUVS8HS2tqKvr4+3L9/H6+//jr++T//5yy9E3AQHX50dBTd3d2w2+347ne/C5vNhs985jMMCaM+TqfTeOedd2AwGPDee+/h93//91lye+J+kSJ49uxZht6MjY2hqqoKnZ2dCIfDGBgYwI0bN7C1tYXKykp2YG5ubqKiogI1NTVoampCMBhkHoUNDQ2YnZ1lY2owGFj/dnd3Y35+HpWVlaiurobP55OND4W/ePToEeLxOB49eoTBwUEkEgkMDQ0hmUyyZ8i87Xa7mfdpPB5HVVUVMpkMTp8+jfn5eTQ1NaG+vh5ra2t49OgRgsEg2tracPHiRezv7yMajbIk3bzZfnJykiF5PI0hEnkc/+zRo0d44YUXmMJDc5tXYCh34N7eHn784x/j137t1/Dss8/KYmeR8O+urKwwxX1wcBBO50HoDD4+mRLCxK9zPdkilERt71B6v+Tl94TK8vLyiXznqEyIhzXDidybo/g2b6Y5TMbxQvooHydK9ILjJV8fKMHPtKERR4JEdDLQ4opR+8QUIVrtEjlZAFicGzXultI36X06vCcmJrC5uSlro5pJk/97X18f8/QTzXX0LLU/m83i2rVr2NzcBACmpHV1dWFpaQkdHR2KPI2JiQlsbW0xpIn3MKSNlmLkkFcjpY4R+z4UOshNRnMhHo/j13/911FXV8dMGrRpr66uYmlpCbdu3UJVVRVCoRBLO7K2tobNzU3cuXMHKysrCIfD2N/fx9DQEL74xS9Ckg4CU0YiEWbyoPxtNJYXLlxAJpNBW1sb4vE4mzcUq6q+vp4ltaVku7y52ePxoK2tTRY3KJvNor6+HhaLBTdv3sR3v/tdfP/738f8/Dx7LxwOs4O/ra0NZWVlcDgcGBwcRF9fHyKRCF5//XVIkoRvfetbsjVsNpvR1dWF8vJytLW1oaenBzU1NXj77beRTqdl/Krr169jeXkZb7/9Nmw2G+7cuYOuri44HA40NDSwYKJdXV2Md2a1WuH1etHU1IRQKMSSG58+fRqZTAbV1dWIx+O4f/8+0uk0FhcXGV2BOFr7+/uorq7G1atXYbFYsLe3xzz0MpkM5ufnceXKFZkylUgk8Oqrr8pyg5pMJkxOTiIUCuH+/ft45ZVXMDc3h42NDdy6dYutJ5pfNH5DQ0PY2dnBr/7qryKdTqO9vZ09s7a2hv39fZw5cwanTp1iwTUJXaN1Q0p4Q0MD1tfXsbu7y3JLTk5OMoQ0lUrhk5/8JHMoAA4Sar/++uvME1aSJExNTcFkMmFubg5f+tKX4PV6WYT3eDzOxoz2HJvNxqLe8xKLxWCz2ZgpsaWlRZG2QGuP5yEWyssVn6e9kOZYMWdKSaE6YWlubj4RrtBRkb4PS5bXSw7nN0r6lpqSQorE7u5uDmk1n4hkRTXelzg++Uj3ah59evpAjXyuJ9yF1lzi+0ltk1BKYKzFo+OJ20rImVg+KYZ00FAiVhH5UPseeRdlMpmctBPi9yjh671791gfut1uVFZWYnh4mCluPLcqFAohHA4jHo+z/uKVVvJYa21thc/ng8PhQGVlJePJKNWDTGjZbJaR6sksQkoy3dzNZjMuXryI6upqPPPMM4jH44hEImhtbcXdu3eZ0kShC4jf84lPfAILCwssjQmtlTfeeAPXrl3DwsICAOD8+fPM64yfM5L0OHVQIBDIIQVTu0jZIdN9LBZDRUUFM2uVl5czUrb4PinhTU1NzFOM+oDCP3zqU5+SrWG32w2DwQC/349gMIjh4WHm8PDmm28ypfzOnTssMvylS5fgdDrxiU98Am1tbThz5gzOnj3L0B2z2czI4rSXUNiL+fl5DA0Noba2Fl/84hcxMDAAl8uFtrY2OBwO9PT0oLy8HDabjSGUDoeDpVLx+/24fv06NjY2kEwm0dLSgs7OTuasEQwGsb29jY2NDdjtdrz33ntIp9MIh8PY29tDTU0N8xgk9OPmzZuora2F1WplprlgMMguGtlsFkNDQ3j33Xdx9uxZZlLd3NzEpUuXcOrUKVgsFlRUVCAQCGBmZgbb29uYnp5m4xMKhbC1tYWZmRmmUNtsNkxNTWFraws/+clPsL6+jps3b7JxtNvtCIfDmJubQzKZZMm8Kep/JBLBSy+9hIqKCrbGyLs1m80ikUgAAFKpFL7zne9gY2MD8Xgcg4ODDJ2ieVlfXy9La6Sm6BzFOcdzNvkcpMWceSWT3wkJDxlubm4+EeY4PXIcXCxReG4BoRpGo1HTVEb10puGRPyWVt/ne4a+zaeCoSi+Fy5cyAlRoacPi+1nrbry5idAOYK93raKZi3xb1QO5Sb7/9n70+DGrjM9HH8AkCABYgcBAgRJkOBONtlkk81u9b6pW4tlSR7LW5KZLFPZPE6qknxJVarycb6kUvmQxPObxOUp2TPW2GPZlq2lpVYv7J3i1tyaK8AFIEEABIiNIPb/B+Z9dYkGSHZrsf8zOlVdUjeAe889995z3vO8z/s8hw8fRl1d3a4+EJx/EN6C8BzApwbKwWAQOp0ur1o6ceL0ej37wOVLCwp5aKRGXYirkW+shc+pMO1B57p69SpKSkqgVCphsVj4GmiBkUh2KvCIgyJMN1A60O12Q6VSwW63IxqNsiRBX1/fLn4YaUhRSX1/fz+LedLCI7QvoeeVFOeJME7nJGFGALtU6X0+H1KpFFdq0rs3NTWFRCLBVZd0LBLgXFtbg8vlwiuvvIK6ujreeAiLEugdJoulgYEBjI6OQqFQ4NSpU9ja2sLS0hLOnDmD2dlZhMNhhEIhZDIZiMVi1NfXY3FxESdPnuQghAjNhLiR5MLIyAjfm4cPH+LYsWOQy+Us60HX7fP5IBaL2Tw4k8nA6XRifX2d7ZAGBwcZ3ZLL5airq4NcLmcpCOLFmUw7YqkbGxtcYUnPYXV1NSveS6VSDj5KS0vR29uLoaEhLC0twWq1oq+vj5/za9euoaqqCk6nEw0NDZiamsKhQ4cgk8nYd9LtduPIkSMoKirCzMwM6urqoFKp0NnZidHRUTidTmg0GkSjUTQ3NyMQCECj0eDu3buoqqrCvXv3oFKp0NDQgJ6eHn5uVldXsbi4iNdeew23b99GJBKBQqHgY9AYptNpVp0nZA0AC4wGg0HWGBOm+YXWOvTeCPmMe81V+WgS+7X9aATAVym/P+j2h1DRd9D2eSFdezUaj1zNKJPJtEu7KF+/aMLfT/8qdxdCxyTycCwWg8PhwMDAAC/Uhe4PLaK0swaASCQCjUbDprW539+vomS/cd4r/Vmor8IqGYLGCT4XClbuda35+pUPtaR+kEkrlcELf18oxZevCc8h/B1JC+QzFJdKpazELfwsX1qT0D+LxYK6ujr09fU9EUzljjldI/DpDtZgMLD5LwUKly5dglqtRkdHBx9reXkZ169fZ9mEfLpbFHDG43GYTCY2ww2Hw2hpaWGiMI2H1WqF0WjE9vY2k5dtNhuqq6vR3t7O5xZKGtDzSnwiahMTE5BKpbh//z50Oh0HS2RV09XVxd53k5OTiMfjkMlk+PrXv45Tp06hpqZml54YyU2srKyguLgYc3NzrBdE7ypd89DQEIaGhvDBBx8gHA4jnU7D5/NhdXWVK8bOnz+Puro6XLp0CRaLBVqtFnq9HjabDXa7Hdvb25ienmYEMLe6i4JmiUTCulpUnbe9vY1bt25xBeLc3BwMBgMbV6fTaRgMBhQXFyMSiTDSRkjW4cOHce7cOQ5aq6qqeINnNBoRi8XQ3t7OkgXHjx+Hz+dDcXEx1tbWUFRUxJVwJpNplzo+jbtOp2MtMbfbjdLSUnz88ceoqqrC1NQUMpkMv2smkwnb29vo6OhAOByGyWTC2bNnEYlEEIlEMDo6Cq1Wi6qqKoRCITidTty6dYtJ+BqNBnK5HN/97nfR1NSE5uZmLC0tIZ1OQy6XQyaTwWq14tatW2wiffHiRcjlchgMBmxtbcHv9yMUCqGkpATRaJRT/y6XC0ajERUVFaisrNzF2SIeH71rxFFcW1tjpDefZI9wzshHk9iv7UUjeNpWtP9XvmqfdxOS/3Lbl4EIHaR9mf0oNB5CNfJC45VLfC7Ub1pUc481MjKCxcVFTExMQKVSIZ3eEQwUGi9TEy6wRUVFu6qTiLBLfcrtI+2+KK2Re9xCY51Lvsz9/V7PUi5Jk/qhUCjw6NEj2Gw2rK2tFTRULtQn8k0jXTIaX5Npx0SXfOcowBAGNDTh7/ds0QRMqSPhdeb+N7fljonwfAfdxVKqhQIl4f2lhZbufyqVgtPpZLK6270jikmfud1ueDweKBQKRCIRVFZWcj+EY0BpSGGlWX9/P4qLi7G4uLhLY4qaWCzmcnfynguHw5zqEgbgpA8E7Gj4EOmXODMPHjxAS0sLxsfHuT9ra2ssIGqxWNi3z+VysaEw7erpfhGyLBKJcPToUdjtdjQ2NkIsFmNubg5KpZKDHgB4/Pgxp2kpRVhVVQWHwwGn08kpN6/Xi7a2NtjtdhQVFaG4uBgrKysoKytDNptFa2srAoEARCIRtFot8+p+8YtfMB9OKpVCo9Hgww8/hEwmg1qtxubmJhQKBTweD2ZmZiCXy+Fy7RhKazQafr4zmQxOnDiB0tJS5oJR8KfX63dV0bndbly+fBnj4+MwGAxcaXfr1i3U1NTgxRdfxMzMDFQqFSorK7GxscFIXUNDA89TNO6EqEokEjaj7urqwuDgIJLJJMrKynDz5k1+Vtvb2xnB6+/vR2trK7RaLfx+P6xWK6RSKWw2G8+vVBlK80wgEEBFRQU6OzuxvLyMx48f8/MTi8UQCoWgUCjgcrlQWVnJG9+BgQHMzc2hvr6enz9CS4mzRR6DQpK7kKMpnOeEvEO6p3vNGcLNT753Ot+8s9cc+rTtq5Tfl9QOAhkKYc+DlrkLf/t5BkB/CFWCz3JN+fq91yLqcDhw/fp11NXVQSaT8a5QiFYIgxqqSNmr8qRQYFSoD3v1mSYSIQz+rPeXqv7m5uZ4Z5/PaJTSEYXufyHpBvKjI19HGqN8KdKDVOYUSgXv1fZ7ZnL7vlfa1263Y2NjA4cOHWKjXolEskuCgQK/tbU16HQ6BINBDsKEop2JRAI3btxg2YVYLMZpLpIxuH37Niu/63Q6ADtB0Pvvv4/29naUlpayErvL5cLw8DBXDDscDrz++uu4e/cuSkpKoFar0dvby8GNRCJhvpyQTEx+bbdu3UIikUBJSQkuXrwIAPjwww+RyWTQ1dWFnp4e3uCQPpJCoeDzCO+X1+tlQj7xxAixUavVuPn/jH3JMzAWi2FmZob5Xm73jsr81NQUfD4fgsEgxGIxV1zq9Xokk0k0NTVBLpdjZGQENpsNxcXFjLwQCuZyuTA+Po6ysjI0NDRAr9djcHAQPp8PW1tbeOWVV7C0tASFQoFAIMDCy4cPH4bD4UBtbS2nKkkFvaamBmq1GtevX8fa2hqSySS2traYwG80GtmXTyqV8nzy29/+FoODg0ilUvjTP/1Ttu8ZGBhAX18f5ufnEQqFkEwm8d3vfneXgbdws/azn/0Mq6urmJubw5EjRzioI5ucV155BRUVFRCJRByQUzCem25NJBJwOp2M7gLgTVckEoHRaGQBX6qAXF9fR0lJCWpra3H9+nW0t7fj+PHjbH3k9XqZkyUWi2EwGBAMBqFQKFj8lgKs3Go+mg+exqvvoK2QTMtBjveVbMIfWDvIDTlImbuwCR+yXC7HQVuhB/XLRKg+r3PlC1pyg9TcBfwgiIVwYaeW77v7BaF7aSYJJxGDwYCxsTHWlxF6uj3LmAgRPOF5hPwzSgvtxSOgJtRMEu4ygU95N2azuWDguBcHKV/fibshnAiFEy5dV6Hj5S5I1N+DpFiBnSrHlZUVlJSUoKKiAolEAjKZDD09PXnHdHx8HOl0GuXl5fzMCRepzs5OrkYk4+AbN26goaEBIpEIb7zxBlwuF6anp9HS0oJsNsvyDxTIra2tYXt7G8XFxfD7/QB2JAcuX77MOlk1NTVYXl7G2NgY2tvbIZPJdo0bkZ0tlh2xzkgkgq2tLVy+fBkSiYT5O7SDp4Cxs7OTZRfIZFjI/6L7RkRkCnKkUimjcKWlpTAajczLIlQzdw4aHx/H+vo6kskkZmZm2F+xo6OD05I6nQ4ul4tV1M+fPw+Px8PnX1hYQFVVFZPY6+rqcOPGDX4WyPsPAIqLi5FIJGC1WjEzM4N0Oo3vfe978Hg8CIVCOHToEFKpFN59912IRCJsbW1BJBJxqrWrq4v1wlKpFLLZLCNSDocDP//5zyESifCNb3wDMpkMQ0NDzBMtLy/H9PQ0mpqadklECFNQLteOSvr//J//E+Xl5SzD09HRgZs3byKTyeD06dNIJpPQaDRoamqC0+nE+fPn4fV6sba2BrVaDbvdDpvNhkAgwM+VUqlksn06nd71bm1sbMBiseD69etIJBJQq9UwmUyYm5uDWq2GQqFAb28vQqEQfD4fysrKsLm5CaPRiEAgAJvNtss+hzYguXIKuZzMveaFp10vhGsBcTEPul5+xaH6/8NmMpkgl8sPvHgKUynPyssqVMX3RXCnCvGADlJJWOi3ucfJVdal3DzZJ+SqtgsruvaCkunF2+u7he5BIf4WNRprCmpGR0cRi8VQVFSEqqqqPWUP9hubXC6S8J4KU6rUd51Oh8HBQU4d5LtOr9eL8vJySCQS5uXQseVyOfOYhAEr8VAIoRKmIXPHJDfItVgsu6qQlpaWeLEnFG+v94DQHIfDgdHR0SfGqdD40TURH+jw4cOwWCwoLi5+guBOKQq/34/y8nIeC3rmqPKPvMesVitmZ2fR1tbGQpbZbBbf+c53IJPJ4Pf7mXROaHVjYyPsdjvS6TR0Oh3KyspgNpshl8vx8ccfI5lM4v3334dYLEY2m+V7TiR+ISeRkM9QKASj0Yiuri5sb2+jrq6OfeR6enpY3Vuv17Pv29DQEFuVUHqLfA7T6TQvlqFQCI2NjbvI3sAO33B7exsTExNYXV3Fb3/7W0xPT+Ov/uqvMDg4iEePHmFtbQ2bm5tobW1lTlJrayt0Oh06OjoYbaPUskqlQjab5esmz8HTp0/j61//OlpbW1mtv7a2Fq+++ipMJhMOHTqEbDbL6uBTU1OoqqpCSUkJ6z397Gc/QzqdhlarZcSroaGBg2EAKC8vR01NDRsH6/V6toCRyWT8fMhkMpjNZrz77ruw2+3IZrOQSqUQiUTswUjHXFpawo0bN+BwONgEOJFIYHl5Gf/m3/wbaLVaVFdXo6qqCuFwGDqdDmKxGJ988glmZmYwPT2N4eFhtLa2wuVy4aOPPkI0GoXdbofVasXAwADi8Tg0Gg176V27dg3z8/P44IMPODAUi8Xo7u7GxMQE1tfXsby8jNXVVaRSKYhEIiwvL8Pn8+Hhw4fQ6XRIJpMs5VFcXAybzYalpSXeDJFHJfGlaF4VzheF5koKivZz3hC+08L5JJeLKaQB7LW2HLR9hVB9Se2zKKXni8iftaLhIMf+otp+CM1efThICrLQOAnRi3A4DLvdnlfg8fNuufyngyrACy07NjY29k0B51MNPkhFXr5UGnFkaMe533VtbW3tOZ7C+waA02iHDx/mcwqf31yRzdwKPo1GA4/HA5FIhGw2y7ISIyMjrLMjrBijgIZK1zUaDf+7EBkhnSnazBBaHAgEdlnvALsFSnOdAOjzfBV1RqORx/vDDz9EMBiE1+tFc3MzvF4vvvOd7/AY0iJKth7pdJrHhVKPlG4cHBzksvLq6mp0d3cjFAqhvb2dUQmdTrcLLVOr1axvRWgi2c1oNBpYrVbm/dC437hxA4lEAhUVFSgqKuKKVrd7xzJmfX0d5eXl2NzchEgkQiQSwePHj3Hx4kXYbDYOtoxGI9xuN1wuF+uD0bVVV1fjyJEjfH2jo6OIx+PIZrOsRl5eXo6Wlhb4/X6srKxga2sLL730EhYWFuD3+yGVSpFMJtHa2opsNguz2YyJiQnY7XbEYjGcOnWK5wPiWd28eRN2ux1tbW2IRCI4f/480uk0bt++ja6uLoyNje3iaJFEAt17hUKBtrY2Fko1Go1Ip3c0zoiPRITte/fuob6+Htvb22htbWWdLuJR0X0aGhrC1tYWIpEImpubWVJDqVQiHA4jmUwimUwiHA6z7U4wGIRSqcTGxgbC4TBTGTY2NphAfuXKFYyNjUEmk+Hx48fw+XwoLy9HR0cH1tbWMDg4yB6GwkIIckcgyyKRSISBgQEsLS2huLgYJ0+ehMViQSQSQTKZxJkzZ5BKpTA3N8fIqpB/KZFInpgX90vzH9R5I3fe2WvtOIj49FcI1d+jlg/ByYfG/KG3QgjOQdCwgyBw+Y4jXAjT6R2NE6VS+VRVIM/a6L4BOBB6SH2tqamB1WqFRCLhyhfaaQlb7s6KuCdCqYR8YyJE3HI/I8d70lnKPY8wQKPJM59gZu65TCYTDAYDowRra2t5K3KEWlLCayY0jexaCD0hZI8W6xs3biASieDGjRtc2UZozpUrV1hPing25EeYTwcsEAhgaWkJIyMjPJbCVCWheDKZjNELGhuv18u76JKSEpjNZtDe1ev1orGxEfF4HBcvXkQ2m8Ubb7zBJF6Xy8XPQXl5OdxuN/+GtHyEz5LZbEZlZSUaGxuZ4Ez+e5RSE6a7FQoFpqenkUgkGDFIp3cquA4dOsTPEgVTarUaGxsbqK+v53L49fV1jI2N4cMPP8TS0hJ8Ph8ymQwHoG1tbUgkEmhubkY4HIbBYEB/fz/sdjump6dhMpkQjUbR3t4Ou92Ol19+GUqlEh0dHZyuo2cuk8lgbm4O8Xicg2KpVIqKigqsra1hbW0NDx48QElJCbq6ujA/P8/yCTTeNpsN2WyW+VC3bt2Cx+NBKpXC5uYmWlpacPz4cUYGCYk6ffo0gsEg89GMRiMaGxtZILW7uxvpdJqRIwo6PR4PJBIJIpEIHA4HJicneXP0zW9+E6lUCk1NTRyAZrNZGI1Gfg6HhoaY10WVliRYGolEsL6+DpfLhYmJCSSTSYRCIZatoGDKZrOhqKgIPp8PtbW1HMAkEgno9XosLCxgbm4Ot2/fxgcffIDf/e53EIlEqKmpgVwuR0dHB+rq6iCVStnwure3F3/8x3+MEydOoLOzE2KxGAqFApWVlSgvL0dbWxtSqRQbIRcVFaG1tZU5e263m9FtAFy1R9XWwjRgrrdnIpHY9X4edL141uzNsyBXXyFUX1L7IhCqgyBLByHp/r7J50/bPgtZnRbfvXYjz4L+fZZ7tN8xCyFcubs5QrY+D8/AQvw8YPdu72mOKSR65yJU9Dl5jpFn2l4WM/l4UVSBRwiVkCcGYJd3o9DLkVAp6g+hT4ODgwA+1XLKx9PKp38lRIKE5HWXa8f3rqioCNlsFkVFRSgvL+ffUGUhBYvCQJaQNTpHOr2j80MISGlpKW7duoWLFy/CbrejpaUF4XB4l00OIXZ03M3NTej1ek7TUkqFUpRWqxUOhwONjY2Ym5tDTU0NFhcXeS5TKBQIh8Nob2/H5ubmLmRweXkZ09PTuHjxIiYnJzEzM4OZmRlcvHgRUqmUy+EXFhZQXl6Os2fP4v79+9ja2kJRUREHzy6XC2azGcPDw1AoFKiqqkJ7ezvbzDx8+BAnT56EVqvFnTt3oNfrUVpais7OTk7pTE1NoaysjHWinE4notEoWlpaoNVqMTQ0BIvFgk8++YQVz8PhMHvcaTQa5s61traylUtxcTG2t7fh9XpZQJQMvCnYGh0dhVgsxosvvgi/34+HDx+yiXJxcfEuAngqlcL09DQCgQDu37+PZDIJs9mM2tpafPvb32a5i6WlJdjtdpSWlrLNjFwux9bWFm7cuIFsNouenh5UVlYiFovB5/NhaGgILS0tEIvFqK2tZZkLCpovXLgAj8fDcgpHjhzZhVLq9XpsbW1haGgIb7zxBtxuNz788ENks1lGK+ldJcQxEolArVZzIUwwGGRDZ0JHpVIpnE4nYrEYI+PCd9PtdnMxUDqd5sAuX+HPs3Kr8v1OuDYqlcqvSOl/SO3LNEd+GrL6l5ny+7zas6T/conf+6UXD1IJ9rR9KtS3/dpBCwf2M3t+GqKn8LtCIj+AA49j7nGpD0D+SkfhmAtTA/k4a+RrRmXYhBxR//Jdx9ra2hPpTGH/qA+EPAgRJ9rd5xYmACgoSJh7rS7XjqHv/Pw8zp49y0EO8WZcLhcGBga4ZP6FF16A1+uF3W7ntInVakU6neZqt1u3bnFwlUwmWTW7ubkZRUVF0Gq1CAaDMBqNTOwnBW8KfIQcFvJ3SyaTSKfTnE6iir3l5WX2myP0lNBKCh6Il7e2tgaDwQCbzcZkZ/IKpHv461//mvmN7e3tyGaz+OSTT3D27FkYDAaEQiGoVCoUFRUxeiQWi1FUVIR4PI7FxUVGVCORCIqLizE/P89SCDKZDHV1dSzLQEHW9evXIZVKEYvFOMVHFWoGgwFqtRp/8Rd/AafTCavVCrPZjFAohM7OTtTW1sLj8UCn06GqqoorGhsaGjA5OYlQKIT6+npYrVZG24QBst1ux8DAAF555RUUFxdz0El8NLVajb/8y79k25vS0lIcPXoU6XQazc3N6OzsxNTUFNRqNWZmZlBWVoZoNIqysjLMz89jbm4OmUwGx48fR0VFBfx+P3w+HzY3N1kGY2FhARKJBGVlZVhaWkJDQwPfQ5JqoPee5DPMZjNmZ2e5mu973/se1tbWEA6Hcfny5Scqo5eXlzmTEovFcOfOHbz++utIp9PQaDSYnJyEXq+HWCyGXq/H7OwsWltbGUWk9zadTrMMB6X58wk9P2uFfKEmnBui0ehXKb8/xPZ5EuAKtachqwtTYvv168vo+0HafteUj7iYm/ra61oMBgOKiopYRfrz6JOwHYSEL2yF7pHwmtLpNO80iZ+S63NoMn3qgbhXYQB9nk9EL5dAn+8aaDIlZXAhKb4Qqd9kMsFsNqOoqIiDNUoNFhqnfOmB3LEV3hdhOpPuv7B/ADhdNjY2hrW1NfY4o3QDfZ/Sf7QAUqUdjdfIyAh78dF4UtUZafQQr0VoCSIWixEOhxEOh3Ht2jUm51qtVq7e29jYYH7S2bNnGaXUaDQoKyvj9E5bWxump6cRjUaRTqcZUaJFnaxxHj9+zM/D2NgYIpEI5ufnIRaL0djYyGmnUCiErq4u2O12tLe3s1As3fOrV69iYGAAv/rVr1jdmkRP6d4bDAY8fvyYF6lz586hsrISPT09ePXVV5HNZnHixAlUVVWhsbERbW1tnOYkHSKHw4GtrS2Mj49je3sbW1tbqKysRGtrK8rKyniMbty4gZWVFeaAZbNZjI6OYmBgAEqlEuvr61hdXYXT6cS1a9cwPT2Nzc1NfPLJJygqKkJ7eztzfxQKBcrKypi83d7eDpFIhEePHrFsxu3bt1kkuKSkBF6vF7FYDB6PhwOTpaUlzM3NQS6Xo7+/H2+//TZCoRAmJyf5GfB6vaisrERFRQWef/55fPOb30QsFsP4+Dh+/etf49q1a0ilUlxJKJfLodPpYLfboVKpUF5ejsbGRkQiEaytrUEsFuPkyZMoLi7Gf/kv/wVlZWVYXV1FNBrF9evXsbGxgVAohGw2i9nZWaRSKZhMOyKzRFCfnp7G48ePUV1djYGBAbjdbrz55puQSCTMARTOqRKJhNPJgUCAUbqbN2/y+0I8KgAc6NNYCd9bi8WyK81Pmx/hHCYsPnra1F6hlju/HqR9FVB9yY10Tj4rh2evgIAeRipjzkdm32tBLdSeNhA4aH+ftu3Hudrr5aJr2MsD8Gn4afnSg/muVfhvn3dFJn2mUCgwMzPDhGpSpiaFdroWp9O5i2cF7A48cq8/33lzr4E4EBR4OJ1Orn7bS91Y2DY2NqDVahn5IjXzfOaoFosFNpuNSbN0DuGzn3tfKFUgrEwjpWqauI8cOQKr1Yr29nYUFRUhk8lgbGwMIyMjrNszMzPDHnoGgwFyuRxGo5HHle7FRx99hKKiIoyNjfGY0AJosVjQ2dmJeDwOq9WKkZERmEwmdHd348SJE5DL5ZDL5VheXgawY/Tc3NzMFVbBYBCNjY2QyWS4cOECFAoFOjo6YDKZ4PF4mAO0vb2N2dlZpNNpGI1GhEIheDweRKNRLC0tYXFxcZdptUajgdPpxOHDh7G5ucnXuri4CK1WyyRyt9uNiYkJLC0tsYfg1tYWwuEw5HI5QqEQdDodYrEY3n77bTgcDhgMBgwMDKCoqAiTk5M4f/48xGIxLl++DLPZjGAwyPyijo4OriKkhVcikTBZOhaLoaqqChUVFTh9+jQkEgk++eQTTE5OQq1WQ6vVoq6ujj0E6RnZ2NjA8PAw5ufnUVZWxgt/JBLB6uoqbt26BZ/Ph7feegtarRYNDQ148cUXYTabGdEZGxvD1NQUHj9+jK2tLUSjURgMBuh0Omg0GvYiHBoagkgkQkVFBZaXlzE6OorV1VX4fD6Ew2H4/X6UlJQgmUyipaWFpTQ8Hg+kUimam5tRW1uL6upqLgTIZrMsfEpioul0mhXQ7XY7ent7OU0ok8nQ3t6O+fl5GI1G3Lt3D2KxGG1tbQgEAmhtbUVxcTELmCaTSUxPT2N0dBQymQy3bt3i56ypqQmbm5vQarVwOByIRqPY2NhgFCl3c0KIZkNDA06cOAGLxYKXXnqJTc+FQRKZoFORiXCOp/8n5CwfV9RkMnH1Hm0K87WDrEP5vrO+vr7v/AV8FVB96a2iouJzKdfca3EthCLkQ26oHWSRf9ZAYL/+ft6NXq588hN0DfmsS56lFSoYKPRvtOAKNXue5rooWCAESIg+URqGlI71ej0b4D7NuOfe53z3PTeoHRsbQzAYZFSFSs/3UzcWIkXCIFh4/NzxFELxhGTROfZD0Oh8VBGl0+k41XX16lVOMZL8QTgcZlIwyQ/EYjGMjY3B6XTuspBJJpN49OgRE4eff/55pFIpTjPRvaPrk0qluHLlCqLRKGKxGKN4QjHEqakpbG1tIZ1OY3p6Grdu3UIgEEAwGGQ+jcViQW9vL2QyGRQKBRYXFxkdWVxchEqlgtfrZfuQ9vZ2lrdobm5mg2NCWpRKJVZWVpBMJhmZOXPmDKxWK86cOYO5uTm4XC4UFRUxh0sul6O4uBgtLS0oKSlBfX09ampqEI/HIRaLcfv2bYyMjKCnpwepVIo98qhqc2pqCqOjo7yQE5KUSCRQVVWFnp4eGI1GZDIZVFRUQK/Xo76+nj8DgMXFRa46NRgM6Ovrw/b2NhOvq6qqYDabcfnyZQ6ASAyT3skrV66w0vzjx4/ZpLeiogKNjY3Y3t5GMpnE2toaVCoVlEol/uiP/gilpaW4fPkyTp8+je3tbaytrcFut8Pv92NhYQErKyuYmZlh8rher4dGo4HFYsHx48fxi1/8Al6vFw8ePEB7ezuqqqpw5MgRdHV1cUXlCy+8gLa2NnR1dcFisXA6VCKRYHJyku+x0+lEW1sbV3rSM0ufabVaaDQafPvb34ZIJILZbMa9e/eYlF5WVobt7W08fPgQDQ0NSKfTeO2111i+pKmpiefPxsZGSCQSntt8Ph+jln6/HzKZjIsQDh069ATaIwyWctHrp1kfD4KcA88OHFRUVOzbB+ArDtWX1vJxqD4LIfwgPJx8vJjPSzbg8+IBHeR7XwTPa69jPg0pPR83C0DBayCCeT5z34M2Id+ILDUoQB8aGgKwEwQR6VlYflyIW1aI+H7Qlk/u4CD3jd4BkUjE1i30XBYqoabUCXGd8nG5hOemdCjp4OSTQ7h69Sq73L/00kv8e7Vajf7+fjQ1NXE6AgBLDQhRyffffx/RaBR1dXXo6enZFTjTPaH/p/ERSkEQt4gIuMCORYzf72chz2QyieHhYSYQNzY2wmaz7SK9E98kHA6jpKQEKpUKLS0t+Pjjj5HJZGAy7VgEEXH7xo0bLMewtraGRCKBoqIifh6oPJ3SeiUlJZDL5RxISSSSXZtEp9O5q3rrwYMHqKysRDgc5gCIEEK/349kMonbt29zdRil3OPxOKOKFosF7733HgKBALxeL4xGIzweD1544QUolUqk02nY7XY4HA6Ulpayvhb59KVSKRw9ehQXLlzgSjqfz4exsTEm29tsNjQ2NmJ4eBgDAwM4fPgwkskkDh06xPeOiiYymQwsFgv6+vpYINVsNnOqNBqNori4GEqlEtFoFPfu3cPp06dRW1uLaDSK0tJSTE9Po6SkBKlUCuvr69je3sbly5dRUlKC9fV1HDp0CFKplAsZAoEA4vE4qqqqkM1m2Sexu7sbsVgMP/nJTyCXy9HU1ITS0lJWxCc195WVFchkMk4nikQi3kgMDQ1BrVajsbERtbW1AACFQoGlpSWUl5fj1KlTmJ+fh9/vx/DwMOrr63n+oYpBq9WK0tJS5ovNz8/j17/+Naqrq1FSUgKRSARgh1JBBH5h0Uk+zmG+9XEvruh+c/ezrJvAV0rpf3At3w35sgnhnydxL9/D/qwPa77jHlRNe69r3e88+VTpnzXAOChRPpcDRSjL01TeCRdpANxPAHkDrb2Ou5/uS77zF/rOszzP+Srk8imcCyfH5eVlDA8PsznufuP96NEjyOVyqNVqdHd3szq1wWDYJU/R39+P8+fPc8m98DkQln0TiTz3Gh0OBwYHB7nCy2KxsOK9RLJjxZNIJPDo0SMcPnyYuVD0LgqDcapOJCSnpKQEQ0NDkMvlnBIiWxdKe1IgRsT5srIyjIyMsBwApYSoyqujo4PROVJ+pyCPNKwoVUypFuKFhUIh/NEf/dEuGQeh+j5ZiqTTaYhEIjx+/Jgr9IgHs7y8jMnJSeh0Ojx69AhTU1M4ceIENBoNGhsbWYyUxjoYDOLHP/4xTp8+jZs3b7Jaend3NxKJBD7++GPI5XLMzMxALBYzyf3+/ftYWVlBPB7nYLe6uhq3b99GSUkJB6A1NTUoLS1lw2Sz2YyKigqo1WrIZDIAYGI/STdQlefExASy2SzPK6dPn+a/P3r0CI2NjRCJRDh9+jRSqRQeP36M9fV1DlrITNtiseDRo0fY2trC9vY2NBoNMpkMiouLOcWn0Wjw8OFDlJaWwmazobm5GT6fD9lsFplMhq1cCAkjPl91dTWnbkmfCwCGh4exuLgIvV6Pvr4+RmWdTiebWZeVlaGjowPvvfceP8dFRUUoLS3F5OQkKioqUF1djZaWFphMJpYAIdsjOpewCVHofJtE4TtAz8JegRa1g+hKPW37KqD6A2tfZpUf8MVbyuQ7zkGCiv2+ky+gEVaZHbTP+4mIkr2LQqFAKBRiDg0Fb/vZzOT2+SCIFvVJiE4Be4vOCc8xNDTEnBuaKHKDLOEC+LTI2n5BkjCwPcg9KYSGCQMHYcCSL6ArFLjvNd7CII1259FolFFZIcJF3KfcUuxCgX1uEJ57TcSDKSkpQVVV1ROVcIRm1NTUoLu7m4OXXIkHSl34/X4oFAr2e5uZmeH7Ruc3GAzsQ6dSqSAWizm9SOmszc1NqFQqDA8PI5VKoby8HOXl5cydKS0txaVLlzA5OQmZTIalpSW0tray6bNQ5oKIzBKJBD/4wQ94kdva2sLMzAzUajUvimtra3C5XFhdXeW0bl9fH1QqFdxuNxwOB6RSKSQSCaLRKDweDw4dOsSBl0Qi4WeUSusptU0oWDqdxs2bN5mAXVRUxFpK7e3tcDqdePPNN7G+vo5wOIznnnuOET1SZK+oqIBMJoPFYkEgEEA2m0VjYyN0Oh0jlwCwtbWF7u5ujI6OwmQyYXFxEQ0NDdjc3GT1746ODvT19e0q8PB4PCgvL2fPPZVKhYWFhV3m2yaTCVNTU5BIJBgYGMBzzz2Hra0tlJeXIxgMcrqVpAs2NzfxZ3/2Z/jd736H7e1t9kKcnJxkIr3BYGDvwoqKCmSzWdbe0mg0cLlckEgkKC0thcVigVKphNfrhd/vZyV0u92OVCoFn88HrVaLTCaDkydPQiwWY3h4GH6/HxqNhnWv9Ho9cyCpCQ3FiU8oFCKmd47eb5KeEPpPHlSqJTeg2q/6+SDtK2HPf+CNJn8im1Lba7F9lpy18DifBw+LFi8AXPH1LAFgofNQUOD1etHd3Y1IJIJ4PM4Ck/Q7ujZakPZqByWx5+NvHZSXRv2majJqQu4Aie9RYLjXfRSOw34Ef+JHCCv/Cj1fwmdI+Nvl5WVWfna7P63GAz5FVPLd40L8LdK8oWML+0B8romJCZjNZtTV1eHKlSucLkyn07Bareju7obb7cb09DS2t7efIN3nVkSaTKYnBEBzx6GhoQHRaBRtbW18n4WVcHq9HjKZjCf33t5e+P1+bG1t4Re/+AWCwSDb4xQVFaGzs5N5N4FAAO3t7ejs7Nz1hyQCVCoVVlZW4PP5GBUjW5KysjLIZDL09vaioaGB5Qyqqqogk8kgl8tx7do1xGIxrnIjYUaqvorH4/B4PHj99dchkUhw4cKFXenMYDCIkpISDAwMIJ1Oo6amBr29veju7mbCcSqVYsXyxsZGVFdXw2g0wmazwefzQSwWY3JyEplMBqOjo5ibm8Po6Ci2trZYFfzYsWNwOBzQ6XRwOp34zW9+g9XVVUZmyErF6XRiamoKFosF/+gf/SN0dHTgzJkz0Gq1uHDhAhobG1FVVcXk9a6uLtTV1cFms8FoNO5ChzQaDRYWFjgg7+vrg9PphM/nw8DAAGZnZzE/P49oNLqLQA2AddVu3bqFoaEhDiRJuLO4uBjFxcUYGxvD2NgYrl69isbGRubHicVibG9vo7+/n+1l/H4/amtr8b/+1/9CKBSC1+vF8PAwP4MrKysIh8Nwu90Qi8XweDyssQUA4XAY6+vru9T66+rq4HA4AOzwnzY2NlBRUYETJ07wuzoxMQG1Ws3PGSFXNpuNn0Oz2bwLkUqlUiy4S/ZEXq93F1eJ3mnaKJIsCD1/wsIW4QanUFEWBWrp9I4mVjAYZEulL7J9FVD9PW35Jv/cJlz8cgnrz0KYPwgycpDvCAOUfBUdz9KEZGRaoCk1Y7Vaubqk0Hf3Go+DBkW51SpCEvV+Aa7JZOIgIBepoc+FyNF+xMuD9pl2iIQkUZ8LPV8E3btcLj4HPVuxWIxJ5wcNXGl86NjC8aedbq7aent7O1wuF2w2G5NdKZi6du0alpeXIZFIOAglIUjh/afjra2tsVo6BURU8k3jMzc3B4VCwdVfJDFAyAupwo+NjbF+EQXG9O75/X709PQgHo9DoVDg6tWrcDgcu9Aon8/HXJpYLIbbt28jnd6pzKOgqLOzE2azmSUKJBIJp9Gef/55HDt2DD09PXjuuecQi8VgMplw8eJFKJVKRmGee+45uN1u1NXV8X2iAgeZTIa7d+/iT//0T5FIJHgc6F2KxWKQy+WsnC+RSFBXV4dXXnkF3d3daG5uhkajwfr6Ovx+Pw4fPgydTofNzU1cuHABNTU1uHDhAjY3N7G0tIQHDx4gkUjA5/MxAvfXf/3XEIlEmJ6eht/vR2VlJdra2vC9730PZ8+eRVtbGy/CWq0WcrkcjY2N+Bf/4l+gqakJR48eRTwex4svvogrV66gqqoKfX19OH36NGw2G55//nmuSqPnbmhoCGVlZQiFQlAoFJidnUV1dTX0ej2OHj2KxsZGtLS04OjRo7tQzmvXrmF4eBgTExPQaDSIxWLsyUcokdlsRnd3N4xGI0tVkE2LTqcDsIOQNDQ0cOrr/PnzcDgcLAFC6UnSMqusrMTS0hISiQSy2SycTicWFxeRyWRQVlaG1tZWOBwORCIRvsd2ux0Wy45R9quvvoquri5cvnwZFRUVOHnyJJRKJQ4dOoRAIIBkMgmHwwGXywWPxwONRgO1Wo3S0lKIxWJO96XTaZSUlODMmTPY2tpCe3s7I0+0aSGVeeG7nclk4PF4AADZbHbXZjV3rcptlHp2u90YGRmB1WpFPB7f5f7wRbWvAqq/R024wNIERyrN+Zpw4SUCLi14hRblZwm0nrYVWuxzz71XX3L7XwiREe6MhAHcyMgI3G53wUozYZ8KpVbz9e0g40ff2U/DKXeHR301mUxsWupwOPKeS/j9vfpEk5OQeEy/3+/5or54PB7moVCqTLjLJBJyOp3epZ2VTufXs6JGaYVcaQW/378rYBBeS1lZGcLhMAd1FKSSejMhXlQRR9WKCoUC165d40WCAgayNSFtpkOHDjGiQYEu9ZEsb4TBO20ezGYzL1jz8/NYX1/H/fv3OZALh8Oora3FwMAAzGYzfvzjHyOdTuPOnTvY2NiAyWTCpUuXkEqlcOnSJUgkEu6rUqnEpUuXuM9dXV2IxWJQq9VwOp2YnJyEyWRCRUUFpFIp/H4/ampqEAwGd70PiUQCDx8+xMbGBv7u7/4OarWaeVZ0n3Q6HSoqKthkO5FI8DNN2m7ZbJaJ9i6XC7Ozs1hbW8PCwgJOnjyJ7e1tJlYT3yedTsPn87EoKqXkVldXsbS0hLa2NpSWlqKrqwsymQwXL15EZWUl9Ho9pxpnZmbQ29vLwUo6vaMUT5pU/f39SCaTkEh2Ki3FYjG2trYQCoVQVlYGlUqFtrY27vvs7CwT8Lu7u9HX17dLIJMqAalK79ixY3jjjTc42CWldUJUampq0NLSAotlx5Cb7IO8Xi/q6+vh8/n4Xdja2sIbb7yBUCiEnp4etLa2QiqVYmBgANlsFktLS2hqamJfQ6fTiUgkgmg0isuXLzNC39jYiNLSUsTjcZSVleHRo0coKyuDw+HAlStX4Pf7eS46ffo0mpqaUF1dDbVaDbVaDWDHGJqqqkUiEYLBIO7cubOLfkBpQZp/ibZAzymp99O8IRaLuZqSMhXCuWAvzSlaP4Ad6YZYLIYrV6584d6twFccqi+tfRkcqly+ydOQiIHdlWmFfvtZKhP3On+hcxbi8FC6pxCnJZcbdFCeUyGuUqE8fKHxKPTvexkZ5/52P6L4Xvd3cHAQCwsLXL6/lxWNkK+2n4J6oTHLJcsLyab5yP+5YwTs8FOERqrAbpI9nSsXtaJG10D8uNx7lUgknlBfp90ucZ2INyJUZKZnIhKJ8CTe09PD9jVC02KXy8X8rb6+Pk6/7ncPqc9qtZrRF6VSyXworVbL9h0DAwMoLy/H7OwsXnzxRWxsbOwy0g4Gg4hGo5ifn8eFCxdQV1e3i/RLSvRDQ0NYX19HY2MjotEozwNLS0ts5uxy7ai7a7VaGAwGJJNJTE5O4sSJE8hkMjh//jwmJycRi8U4YKN7RWa8Op0O77zzDlpbWxGPx5HJZCASiaDVahEKhRCPx2G321FSUoLl5WWcPXuWvQPpXq+treHRo0cQi8Xo7Ozk9//69evIZrOQyWQchJw8eRLBYBCJRALT09OQSCS4e/cuenp6MDExgbNnz+Lw4cNYX1/H9PQ0UqkUHA4HkskkSkpK8PLLL/P7otPpcOPGDSgUCg5cDQYDpqamMDc3x+Tpc+fOcaBNc08ikUBxcTGqqqrybl4oXRyNRllDbGRkBCKRCGq1Gu+//z7kcjm0Wi1UKhW0Wi3GxsYgFovR0tICqVSKTCbD6W2SO/D5fCytYDQaWWg1k8ng3/27f4eGhgYMDAxwsEL6YWQcnslkOGXb1taGjz76CJubmzh69Chu376Nzc1NlJSUsFhqeXk5zp07xym/oaEh9PT08DNlsViwvLzMfEGZTMZjpNPpcO3aNahUKtTV1fF9Jd4ZmUUXmguAwlmP/daTp6GQfEVK/wNrX0ZAlfuwfNbg5/N8IPO13P4dlLAuDJJyqxb3OsZBxyNf8LUfyf2g5H8KqIqKiqBSqVjCglCwfMHtXsfL/Yx+mztR5QuoDhLc7TX+ueOaTwpivxJnYb9HRkaYEE1jIrS9oN0ssBMUAGAkSaiqnBt4C89DE3tnZycHGhTwEYndaDRic3Nzl8cfEZjJxJbQNiFR3Wg0YnBwEI8fP+ZFr6KigsU/C5WH0xjm2xy4XC422y0vL2ce1fT0NPdveXmZkYXt7W2cP38eN2/ehEKhgM1m46BQeF5Cnih1U1NTg7KyMnR2dmJ0dJSvkzgvhKrRWIRCITQ2NnLZv0gkgt/vh0qlYhmH0dFRJBIJ3Llzh4MrEqUkv776+nqEQiGk02k8ePAA2WwWq6urqK2txdbWFnQ6Hdrb26HRaPDzn/8cJ06cgFqtRjabhUajwb1795BMJiGTyTA4OIienh74/X5cunSJBU3v378PvV6Pu3fvMm/q0qVL2N7exsDAAEZHR3HhwgWsrq4ik8ngzJkzqK+v5w1CIpFAf38/tra2YDAYEIvFUFpaio2NDcTjcVy4cAFjY2OQy+XY2NjYJZRJbgu0MRoZGcH29jan++bm5rC+vo7y8nJOmc7Pz0Oj0WB1dRWlpaVobW1l0jrx6+jZicfjePPNN7G6ugqNRoOKigocOXIEkUgEbW1tmJmZQSAQwNTUFGpraxnJ0mq1iEQiaGlpwS9/+UtOEYbDYSwsLECv16OyshLt7e24d+8eSktLEYvFsL29jWg0ymbTYrEYKpUKjx8/xqlTp/h+jY6OwuPxsAXS3bt3UVlZiXg8jubmZqYL0CaBCoMA5C1Gonck9/kldL67u3vPir6DbB73+h1tDL4KqP5A2pdd5Qd89uDnaRGvp+1b7sLyLMc/CMpV6Lt7ffZ5XHu+QId2ZgaDAdeuXUNJSQnUajXMZvOewV6h/giRDdrJFarEy72Gpy0vFvZBeGwKbPV6/S5+mDDgFfrc7YVGEnFdLpezXlHuzhTY4S6Nj49Dp9NhY2ODd7L57pWw34TUkEpzbqqT+uZ0Ovk6nU4nEokEwuEwWltbd52PEIzz588zYXl0dBQzMzOQy+VMQKcKQpfLxYKPwuq+Qnw66q/X6+UgR2i/Qd8ZHR3lCjaZTMZzTXt7O8bHx9mzT6iVRkEiqanr9XpUVVVxcEwBWyKRgN/vR1dXF9xuNwYGBjA/P4+6ujpIpVJks1lGbgjZEt5/CsKy2SyKi4sRiUTQ29uLWCyGw4cPw2QyIR6PY2JiAj6fD1arFY8ePWL+DwXi9K40Nzczl8zpdCIUCgHYCd4dDgcsFgtWV1dRXl7OyI7b7YbNZoPH48FLL73EKc8///M/RyAQQE1NDb7xjW9genoaxcXFOHXqFEKhEJxOJxYWFvg512q12NrawsTEBKqqqlhRPpPJsATE8ePHEQwGsbGxwSKl1dXVSCQSqK6uxtzcHAKBAOrq6pgcbjAYcOHCBdy+fZuRnVAohOrqaqytraGjowOPHj1CQ0MDysvLEYlEuBp0bm4Ov/vd7yAWi2G1WneZSsvlcoTDYSSTSSwtLbEmWU1NDQfPQoX7d955B9XV1exbaDQasb6+Dp/Ph9HRUZSWluL06dOwWCwIh8P8bMZiMdhsNvzxH/8xXC4X3nvvPTgcDhw/fhwSyY5dUDabxQsvvMCB3Ntvv81BJLBjLZQ7TxSaUynYJY0uYTYhd+4VzgH5UP+95nb6XSKRQGtr61dVfrntz//8z3H06FEolUoYjUa89tprmJmZ2fM3N2/ehEgkeuLP9PT0l9TrZ2sHIYDv1XK5TELOzkG5VIW+R9wR+n9hKkf4oO93jtzf7HXNe32Wy5HKvfZnGctcjpqQwyWVSnHp0iWo1Wq0t7czx6QQH4kWKyIB0/EomMole9PkIOyzsOqOSOP7mT8L70Eh4jv9Id0hIqAPDg4iGo0yqV1YXEDIDskI0P0nsjsdW+iraLFYmEc2Pj7OqSnixRS6V8J+UyUXBQfkZed0OrlUWyKRwO/3c2VTVVUV5HI5WlpauFKOyPbj4+OQy+VsL0Nin6QIXl9fz+gaccRIvX56ehpLS0vo7+/nCrXcyiXiYLW3t6O8vBwikYgXE7L3IfTqxIkTsFqt6OzsZE7L2NgYBgcH8cEHH+AnP/kJq5zTOHV2djIKotPp+DmkIM9isWB6ehp2u52lAqiSjQLNTCaDVCqFqakp5uuQijwFclqtFtvb25iamkJ1dTU8Hg9qa2uRzWah0+lw//591NbWMr+mvr4ep06dQmdnJ3p6enDs2DHYbDYcO3YMq6urKCoqQnNzM/ObTpw4gUOHDuHixYsQiURobm5GOBxGNpuFSCRCR0cHtre30dDQgMHBQRapfOGFFyCVSlFfX49AIICtrS3IZDLMzc0hkUiw0KlCocD3vvc9vPjii6isrER1dTUMBgM6OzvR1NSE+fl5lJSUYGNjA4uLixCLxdDpdFhdXeXnIxwOIxKJwGw2o7q6GrFYDMeOHYPZbEZbWxvzJGOxGIxGI86cOYPV1VWoVCr8t//23+D1evH48WPMzs7C4XBgaGgIBoMBlZWVkMvlHNArFApMTU2hpKQEm5ubrK5++PBhlJeXQ6fTMbfM6XTC7/ejtbUVSqUS/+pf/Su0tLTgO9/5DsRiMVcoulwuFBcXsygx8ZmSySTzqU6ePMnP7+bmJpLJJAKBAJqbm6FUKvHiiy9CqVSis7MT/f39MBgMGBkZgcFggEqlwtzcHM9hbreb5xAhD4/I6F6vFzU1Nejr68urRVdoPqf3UziP71XEQ787qFL6P7iA6tatW/j+97+PBw8e4KOPPkIqlcLly5cRjUb3/e3MzAzW1tb4T2Nj45fQ499f22thWl5e3rUYHpQcTk1IHNzLn+6LsKvJ19+DVr09TRNWsSQSCYhEIiZbA9hVNk8VdMCT1Wzp9KfGx8RTof4KF89CKEfuNQLYUzIhl+eRew8oMKAFE/jUqNjj8SASieDGjRu7VKUpiKOJmKxVEokEV+nR2BgMhl0k8VxJikQigdXVVT6vcMeaO265KKhE8qnIYDqdRiAQgM1mY90oShtSqT/9tqenZ1fQRjICOp0OW1tb0Ov1HOBqtVokk0m89NJLUCgUTNomREir1UKhUODixYuwWq04efIkFxHY7XYeexorelbEYjGy2SyjTOR/V1dXh7KyMlY0Fz4DRHwOBoNsxHz9+nU4HA7E43FMTk5y0LO1tcW7b3oXXC4XYrEYwuEwj3FtbS3S6TS6u7shFovZhHdychLj4+MIBAIcJITDYbS3t3MasKenB8lkklXIdTod+vv7WQRVqVRyJZvFYkF5eTlKSkpQU1ODK1euYH5+HqlUCmKxmP3lzGYzxGIx9/tb3/oWotEoysvLEY1GIRKJIBaLEQwGMTAwAIfDwZ5/VIFmNpthNBpx+fJlxGIxRKNRvhZK4lB6KRqNcrBw48YNjI6Owmw2c6VlIpGA0WhEVVUVVwt2dXXBZDKho6MDL7zwAmw2G+rq6jAzM4NkMsnvk5Cb5Pf72efx7NmzjJAqFAoMDg7i5s2bGBoawsTEBKf/amtrUVpayrxJk8kEo9GInp4e2Gw2lJeXw+v1YmRkBKOjo8hkMqzGr9VqoVar8bWvfQ0KhQISiQRarRbRaBQvv/wytre3ceLECcRiMfT397NJ+KFDh/Ctb30LxcXFsNvtcLvd+PrXv47q6mp0d3cjGAyyYC4h6tXV1ZiZmeFnNhKJwGazYWxsjFEhkloQopy5ZPR88x3NI0Rm3487u9fc/7Qb6X/wKT+yMbh16xbOnDmT9zs3b97E+fPnEQgEODf+tO33kfL7ItvAwACnTiwWS8F01X6psoNybD5LivFZuGV7fedp+rW8vIyHDx9y+oW8HAuJVObyf6gvQn4NkL+AYC+RTuE4E1+r0Pepyq2kpISRHGq0Q6VdIxGc6dgAdiltUxCRTqcRCoWY3EypMCGJXZjeo+vKJYgDO4R7r9eLmZkZfO1rX2NdHnq/KBVCfAmhSGs6nYbD4cCjR4/Y5mN2dpYn/ELPgDDF6Xa72cKptbV1l8WPkGxLXnWUCiLeF/Cp0CEpoUciEVb4JgFSu93OJH3hb3MJ//Qd8iYUBkO0uLhcLni9XiwuLrKW0YULF2AymXiswuEwPvroI7S3tyMajeLw4cOMhJEUw/b2Nq5fvw6DwYDDhw+zAGkgEMDq6ipWVlZw6NAhKBQKiMVilJWVobi4GPPz87DZbBCJRHC73ZBKpejo6GDz48nJSSiVSh6zYDAIn88HhUKBra0ttLa2oqioCJFIBMvLy/jmN78Jr9eL5eVlzM3NIZlMIp1OQyaToaKiAn6/H9euXUN9fT06OjrQ2NiI/+//+//4Gejs7ERlZSW2t7extLQEpVKJTCYDt9uN4uJiRKNRKJVKSKVSKJVKiEQiDgzVajXeeustJJNJKJVKtmdpa2vD3Nwcp1wPHTqEpaUlTE1NIZvNorKyEi+88ALS6TT+9//+32xPo1arUVZWhmPHjmFubg5KpZI3PiTC+sknn+DYsWMQi8Xo7+/H9PQ0G2KLRCKsr69DJBJBr9dDLBbz5oBQXOLdUQqQNkVbW1toaWmBTqfD/Pw8WlpaOM07Pj4OiWRHb+zHP/4xk+PtdjujyLW1tejq6kJPTw8GBgZw584d1NXVoa6ujgsphM8mvafEnYpEIjynjYyMQKFQIBgMwmw287ySyWQ4sM6lDRTiIwqLXT4Pl42vhD0P2Mg6gfQ+9mpUXn3x4kXcuHFjz+/G43GEQqFdf36f7aApuoM2Yerks0T4Xq8XarWaya/CvgIoqEH0NC031SXUlxK2Qukt4WeJRGJPDZR8bXNzE9vb2/B6vU+k9Sj1SchCvrE0mXabPeeiRsL0XyFEb2xsjIX16J5QAEPXTNdHKTxCjiQSCadnCRUTlv8L76FEImGjXhLfJLPkCxcuoLOzE8FgEAqFgmF7qrij9FZVVRWMRiOAT32/hONBAVNzczMeP34MkUgEjUaDmZkZKBQKjI6O7iKrA9iVhhoeHgYAJpCXlJRgcnLyiTGjNCtVXFG6zGTaMaImY9h8qWGq8vJ6vdBoNAgEAkwmppSlw+HAtWvXoFAoYLfbkclkoNVq+V6IRCLU19fzYiAUM3U4HEgkEkgmk1hdXcXq6ira29t3BX+Uxtzc3ERxcTHS6TTi8ThmZ2c5ZUmcN5PJhKGhITQ3N8PlckGr1WJiYgKJRAISiQSbm5ucHl1cXMTjx48ZSQoGg+jo6GA9t4mJCTx+/BiRSITfuQcPHuD+/fuQSqUoLi6Gz+fblWKlqi63242PPvoIk5OTiMfjiMfjOHv2LHMMFxYWUFtbi+npaZZaSCQSEIvFqK6uxtmzZ9HY2Aiv1wur1cro4ccffwyFQoH19XUmSjc1NXEgIxaLMT4+jtXVVWxtbeHo0aPo6OjAiy++CJlMhrq6OiwsLDAxuby8HOl0mp8bqlw8efIkEokEB8BFRUXMP4rFYnC5XPiLv/gLLCws4NGjR6itrUV3dzeOHj2K+/fvo7i4mEn6sVgMHo8HDx48YCTn/v37OHLkCI4fPw6DwYCHDx9ia2sLhw4dQmdnJ8rLy/nZJU0pSsfKZDL2C6T3rL6+HiKRiAOGsbExXLt2Db/5zW8wOzsLl8uF//t//y8T6GUyGaqqqlBWVgaLZccs2eVy4erVq3C73chkMgiFQlyIcenSJU6XT09Po7GxEYFAgIsRaIMHAHq9HnNzc1Cr1ZBIJHwuUorPTde5XC4MDw/zHJZv7i40j++1lnyWzMg/6IAqm83iP/yH/4BTp07h0KFDBb9nNpvxl3/5l/jlL3+Jt99+G83Nzbh48SL6+/sL/ubP//zPObesVqtRXV39RVzCgdvnnT6jdBWJF+6XOirUTCYTCxUK+TW5AUM+Ve6Dtr1SXYlEgvP0wvMW4h/lcpb2axaLBZcuXYLVakVTU9Ou1JWwb3tB2PRvAPIGhML0X6HAtrOzEwqFgkUvc1NjFCSOjo7yjo7Sebn8A9LMoaCM0nf5RGQtFgtsNhv6+vpQV1cHmUyG5uZmFhQUNuKQ0B8i2Op0Ok51AjvP3htvvMEaUBsbG6x1FYlEWGOI0DfaaZrNZnR1deG1115DQ0MDTp48icbGRhb9y31eCWnq7+9HJBKB3W7nflJgSKkpr9eLcDiM999/f5f2l06ng8PhQCwW2yVASqKbJB1gs9nYD83v9yMcDmNzc5O954Ad5HBgYACDg4MYGRnB2toarl69iqmpKTx8+BB+v38X8mk2m1n/ymw2Y3l5GT6fD+FwmE2GqXm9XvT09MDr9eL1119HNBqF1WpFKBSC2WxGQ0MDfD4fMpkMC1rSuyKXy3H37l2cPn0aR48exUsvvQSr1Qq3242urq5dZs80Xk1NTUgmk/joo49w//59LC4u4u7du1heXkZJSQmnqb797W9DIpFgfHwcTqcTmUxmV1B/8uRJmM1mtLS0oKWlBVNTUxgbG2MCsdlsZq7r1tYWvva1rzExe3Nzkxfv1tZWHD58GN3d3XjppZfQ19eHF198EZFIBFKpFHfu3EF5eTlu3LgBnU7HiElVVRUrs8diMdy/f59J3XS95eXlnFJdW1vjSsGOjg6UlZXBYDDg/fffRyAQwHvvvceorsPhwMzMDCYmJnD//n1Guq5fv84SCgMDA/jggw8A7JC65+fn4fF4+PmTSCTQ6/XQ6XRQKpUsc6FSqThQJoFWq9UKo9GIVCoFmUyG8vJylJaWsr4VGXPPz88z14uMlJVKJUwmE1paWtDU1MTP/tjYGAwGAxtB/+Y3v+FU8+rq6q45fm1tjccyd26jDVZuo0IYAHnnbuH/0zwXDof3XEs+C/3jmVJ+77zzzlOf6Pnnn38CUv99t+9///t49913cefOnV0VRAdpr7zyCkQiUcGxoN0VNarY+H2l/AqlqT7Pyr3cdlCZgtxqs3yVe7nyCJ9HRSCwg1JEIhEoFAoug9+rCjCf1tJBxhZ4UgrhafpKFXC5+lhPexzhWBPa4Xa7oVKpEIlEoNfrWdYhH4FT2AqlxPa61r3QR0KpcivRcgsW6HlQKBSIRCJob29n7SdCs4SVj1S1l1vNmC8VLEytptNpjI6OQq1Wc0GAMCAWehAaDAZ8+OGHWFxcRHFx8S6tpJGREUgkO6XdhDQkEglcvXoVKpVqlyaWRLKja/T222+jp6cHGo2Gj0Nl4sDOQhIOh1FRUYE333wTX//613HhwoVdKW1h38bGxhAIBHDr1i20trZCrVbj8uXLvBgJ3zEKjqenp6FSqZDNZhGJRDgV9/DhQ3R2dsJgMEAsFmNoaIjRtK997WtIp9O4evUqRCIRZmZmcPbsWTx69AgnTpxgRXqv14urV6/CbrdDqVRyGnhzcxNFRUW7TKqJQxMOh9HY2Ije3l4O5imF+8knn0Amk2FxcZEDe5VKxQEhIReNjY3Y3NxkY+iBgQEMDw9DrVbj+PHjUCgUSKVSSKfTXCn3zjvvsJTG2bNnIZPJsL6+zlmI8vJyDmZI26m9vR319fVYWFjg50er1aKqqooNjmdnZ2Gz2TA4OAixWIy5uTnodDrE43EcP36cSeYLCwtobW2FVqvFRx99hNLSUk6jTk5Ooq+vD+fOnUMwGEQwGEQsFsP58+dhNpsxPz+PsbExPP/88ygqKsL777+P5uZmpNNpKBQKPH78GOFwGN/4xjfQ0NDA3Fiv18uefEKl9bq6OoRCISwsLCCVSqGhoQEVFRWQSCSMQhEPcmFhATKZjFG4a9euwWq1IhqN7hpjoaxHvnkgHw0i9/3da84RzldCv8mnmUcPmvIrKvjJHu211157qu+LRCLOmf6htB/84Ad455130N/f/9TBFAAcP34cP/3pTwt+XlJSwqjI59WeJojI/a4Q5RA2ISrzLFpVe7XcRbZQo4UyF6WhRotR7svztP3ONwadnZ27uEV78aXoMyKA0rkL9YWqaNLpNBNED3r/hGlKWmy3trawsLCwS9hur+MJF8l840TPhVar3cXPIoHIXDQtt+VeTy7na2Rk5AkxTzqmEIFcWlrC7OwsmpqamG9FEytxtoh8TuNNkxs9F8L0HN1PIdE939jkjp3JZOJAbWRkhHfrHo8HMpkMUqmU0RZSUCdSfTqdRktLCyYmJpDNZjldQxYbZrOZkSZaODQaDTY2NlgnjMbtxo0bqKmpQTQaRUtLC4AdRMvpdKK1tRXz8/MAgFgshvHxcZw6dYo3b0JCLgBGVRUKBdxuN/7JP/knnKqjvhDviyQWqMKvvLwca2trnL6UyWQIBoPQarUIBAKorKxEUVERLl++jAcPHnBfAaCpqQm3b99Ge3s7kskkvve977HaOgCu5rt58yZqa2tRW1vLCuJutxtTU1Noampig2ORSIS6ujosLi4y0XhpaQlFRUVwu92cdlar1YhGo9Dr9XA4HLDZbFAoFPD7/TCbzfjtb3+Lw4cPY3Z2FleuXMGtW7dYFoJERckQ2mq1QqfTMcfp8OHDCIfDkEql2NrawtTUFM6ePYu6ujpsbGxAq9WyRhcJXBYXF3NqM5FIsHGzxWJBXV0drl27hu7ubjidTrz++uv46KOP4PV6mTvY1dUFuVyO+vp6lq4gUr5SqYTVakV5eTkSiQRKSkrY4FmpVOL+/fu4ffs2enp6WN7A4/HA4XDgBz/4AbxeL4LBIEpLS3Hnzh2eo0wmEzKZDG8iGhoaUFJSgqKiIlRUVLAR8+rqKkwmE1KpFPPwgJ1gPxgMwmAw4MGDB+jq6gLwqaJ6Y2Mjb+SIy0VzAb3ztCEaHByE0WjMWwGdO18XWgdyOabt7e1cVbtXyw3YDtKeOeVH+dKD/CF9nD+Els1m8Wd/9md4++23cf36ddTV1T3TcUZGRnYZQH4Z7WnSdgf97hdR3UbtoBUSB/le7neogq7QgnnQJkxdUtuvyi13zA46hk9z/3LTlAC4qik3DVnoeMLAg/pGCxmRlonrIKwS3MtSJncnme++Ea+mkI9kLsdBKpWioaEBUql0F7RfKJWZyykTfo/O6/V6eXKWSCRPqCuTHtTQ0NCu9Eh3dzdCoRBisRjS6R1BS6lUyjYbJIFAAbbw2qVSKZqamtggWaFQwOPxsLgjoYx2u50XwNbWVjidTjgcDk6dKpVKbG5u7gp6aEFwOBxQKpXw+XxYWlpCX18fysrKoFQq+Xu0+3e5XFhfX0d7ezsCgQDC4TDeffddTj2NjIxgfn4ef/M3f4NwOMwcIgqiyBKnra0N0WgU29vb6OnpgVar5crEeDwOqVSKzs5O3u1T+iaVSmFzc5MD++3tbeZJuVwuBINBVFRUwO12s4K9VCrF5OQkRkdHcf/+fX4+ysvLsbi4iJKSEvzt3/4tZmdn8cEHHyASiTDJWqfT4eLFi3jxxRdhMpnYekUqlaKtrQ3z8/N47rnncPv2bUZFnnvuObS0tKC+vp6Ddb1eD7d7R29rfX0dNTU1KC4uRk1NDXtprq2tQa1W486dO/jkk09w/fp1xGIxTitLJBJYrVZEIhFkMhnE43HmnjkcDra2IhPkK1euQCaTQSwWIx6P4/79+3C5XCyW+t5772FlZQWbm5soLS2FRCJhJKyrqwuZTAb379+HXC5HMBjE3bt3MTQ0hO3tbczNzUGlUuGVV16BwWDAyy+/zM9tXV0dvyPvvfcec70IOSTUa3l5GUVFRRCJRKiuroZOp8N3vvMddHV18TubSqXYd08ul+Phw4dobm6G3+9nNNLl2vE3JLmPtbU1jI2NMccxnU7v0mijTamQa7cfjUT4HUKCw+EwUz2ITrDfRvxZqCbPhFD9yZ/8yVOl7/7xP/7HfzCVbd///vfxN3/zN/jNb34DpVLJEz55jQHAf/7P/xkulwtvvvkmAOB//I//gdraWrS3tyORSOCnP/0pfvnLX+KXv/zll9r3gyI+T/PdQsjV77MdBInLRTr2u4anQfeE6BCAJ3ZHNGb04grRK2HLRd7ontAOe6/UIh0z9+/Ce7rfPabPbTbbE6lJukaaZHLHNvf6clOve415PkQx3/Gp9fb27vouVX7lQ78KnY8+7+zsxMjICKcshWmDcDiMq1ev4vz584xq0HmEY01oEPGKhOMuTPtSOs3j8TxxLyntSAtENptlZW2/3892Mvfv32e5CalUiu7u7l3VeQMDA0ilUqiqqmIitd/vR1FRETKZDKLRKKc8Ka01NDTEnBVKDwE7hQmURiID4Q8//JCJ3S0tLYjH49jY2MChQ4fg8Xg47QsAU1NT2NjYYGsXQiYohTUyMoLe3l4YDAZ88sknUCgUCIfDTBovLi5mFIUqQbPZLGZnZyGXy/H48WPmslJAKuR51dfX4+c//zlsNhs++ugjBW/p+QABAABJREFUBAIB2O12aLVaDkTW19dx5coVNvulijSNRoOenh4MDg6ira0N7733HqcHDQYD60e1tLQwT21ra4u1lbRaLcbHxxltuXz5Mv7P//k/EIlE+PWvf41AIICuri58/etfRzabhd1u53Gn9CIhMXNzc2hoaOAUKvEa19bWoFQqmbu3vLyM7u5uPH78GJWVlVhdXeWK6rt376KhoYEDuAcPHnCKUqFQoKioCEeOHMHi4iKUSiXGxsawsbGBV155hYPaSCQCp9OJI0eOsPVOPB7HysoKKisr4Xa7cfjwYbz11lsQi8UoLS0FAFRUVODEiRMsPSGVSnlzG4lEUF5ejuHhYTQ0NCCdTiObzaK0tBQzMzO8OSFhUpofCa3yer3QarWMKNLGQ+jVmUqlGLnPN4cKN5pra2v8LFy5ciXv/FOoEWKtVquxvr6+7/eBZ0SofvzjH0OpVB74+z/84Q+5+uD33X74wx8iGAzi3LlzMJvN/Odv//Zv+Ttra2us/wLslJz/p//0n9DZ2YnTp0/jzp07ePfdd/GNb3zjS+37QRGfp/3us7an3S0ctH0R6NqzokO5Gkj5jimsMBG23HtAf6dSc2FfaJyI/yKEwPMRLPMdP3escz8XjsFBxk54fcvLyxgaGuKy5oMG6k/7rNIkOTY2hq2trbxjS+keCniFjVIPTqeTbTqAnXtqt9tRVFSEX/ziF1CpVKiqqmK0Tni9EslOhZFKpcLIyAiAT9MJiUQCPp+PK+ZIEoHOS2k+l8uFZDIJs9nMpHCNRoOioiK0tLQgkUjg8ePHnNIhxXCJZIeYT+nFyclJDio8Hg+ne8gEWK/XA9ghANNCScFZIpGA2WyGWq2GyWTCSy+9hJ6eHpw4cYIRRplMBpFIhEAgAI/Hg5WVFWi1WgA71anEG7NaraioqEB5eTn8fj+rqldVVUGhUODjjz9GNBqF2/2piO3ExASqq6u5cIXSdh999BEWFxfh9/vx2muv4Rvf+AbS6TRKS0uRTqfR0dGBl156CVVVVdDpdKxn5vf70dfXx2nvqqoqDuyKi4u52nFychK9vb1QKBTIZrPweDy4f/8+4vE4jh49ysR6CjwymQyLd05NTaG4uBgSiQRlZWXY3NxEKBTCxx9/zCa+AwMD+OUvf4mamhoObnU6HRO9KTB2u92sQyaR7GiCLS0tsdQFpSk//vhjDA4OYnt7GzKZDJcvX0Z1dTVz665cuYL6+npcuXIF29vbCAQCEIvFWF1dhc/ng9vtxsWLFyGR7FTYOp1ObG9vo6ysDN/85jdhNBoRDAbhdDpx7do19Pf3Y3FxEUNDQ4jFYix82t3dDavVim9/+9tQKpWorKxEMBjE6dOnoVKpoFarGZEUypQQGiiTybjy1Wq1QqPR4MyZMzAYDNje3saxY8fQ2dnJ/D1KdVORC1niCEWKZTIZurq6eM4EwBWqheYA4dxG5yMrq/0azaEU/JLO30GFPf/B61B9We2L0KH6IgnlB2kHISUXIhTu1b6I6/q8SOz5Pqdd00FtanJz+sJxyrVGyHes3N/Tf3P7kXsNwG4/rELjQJpVBLtTgJJO75TZk2r5F/XMEZfI5/PB5/OhsbERSqWSr2kvyxyaEIXBEcH7ZGRstVoRi8We4HflanUNDAxgZWUF1dXV6Ovrg9v9pA2OkERLVkJkVZFMJjlgE/ZnY2MDq6ur0Ol0qKurQ0tLC/r7+9m+hu4tpV9kMhlMph17FtL18Xq9mJ6ehtVqhVwu53Eiex2v14u2tra8VbhCnbHm5mb86le/Qnd3Nx4+fMiSBqQjpFar2Sbm2rVr7AUn9Ft79913sby8DLPZjKNHjyKdTuPDDz+Ex+NBa2srJBIJp58JcVhaWsLJkydhs9lYBoLQNp1Ox8ThkpISrK6u8hwSDAYZEYlGo6wl1dDQgJmZGRgMBvT19XFA+c477+Djjz9m6Y5MJgO1Wo3S0lKuYp2cnMT6+joH3FRUYDKZEIvF8N5770EikeDkyZNobm7GL3/5SxiNRoyPj+Oll16CxWLB4uIiNBoNWltbWSQ6FothYWGBhTt9Ph+0Wi08Hg+6u7vR29uLjY0NRCIReL1elJaWoqmpCdPT09Dr9Wy709vbC5fLhY8++ggKhYKfDblczrpYlH5NpVJ48OABMpkMamtrEYvFOIVM8glSqRSlpaWoqqpCOBxGU1MTTCbTLg9Lkh6hd4w2CV6vl1E6elfS6TRu3LiBM2fOsFiv0H8TwBPFJvnmx9yCFeHaQRQF+izXk/PzarSm5fqSfuk6VENDQ5/Xob5qB2xPg7rs154FSRLuBIR92QsJOch5vgh07VmOud9v6HOLxVLQomev3wmRKiJVC7k5lGLL1byi45PuFP0XwBOoE313eXmZuQCpVCpv3+jejIyMIBKJYHJyko9lNpthtVphNpsL/j73WMvLy3A4HKymf9BGHClgZyPy8OFDJqgmEgnmge2Vcuzq6mJdJGEq8cqVK9BoNE8EU8BuTTQAbL0xMTHBqU+h/pbH42FfN5fLBZvNhng8jjNnzqC8vBzFxcXQ6XRwOp27guX6+nqIxWJGr95++22sr6/jhz/8IQYGBthao6enBw0NDZx2JM5VSUkJRCIR5HI5nE4nUqkUl5sDYATtzp07u/gn1CwWC2QyGQsvnjlzBsvLy0ilUhgaGoLL5cLU1BQePXqEkpIS3L9/H1evXoVcLmduFyEUhFqWlpaioqKCEd329nZeMEkCYGJiAhaLBWKxGEVFRSgvL2e1eblcjpMnT0KpVOLdd99FUVERk6Kbm5sxMTGB0tJSnDx5ErW1tSzlQAgGCWkWFxdzgOZ2u9lOZ3t7m1M3d+/eRSgUwocffoiVlRVoNBpsb29zWvXQoUPo6upCR0cHlpaWUFxcDIVCgVOnTiEcDjPJu6urCwsLCygtLUVzczMqKiqwtLTE4rXEqWtubkY0GmVun8ViYaFUQoWkUinKyspw9+5dbG1tYWNjg70LBwYGMDIywsFzXV0djh8/jpaWFqRSKXR3d/Omym63Q61WQywW4/79+1hdXcXm5iaMRiNKSkqYq0jSEVQ9SIUApFVHOoP07AmlUgBwsO92uzE2NoZoNMrvDb2fABhFEqbKhSh8LieTeI+5a0fuZ5Ruf5r5/CBrD3Fz9Xr9nlZghdrnFlC9/vrrn9ehvmoHbAdJ2xw0Lfe0YpXA7oAjl2i8n5/c0waB+a7jWYLApz0WBQZ7BQX5CPO59yXf8YXf83q9UCgUuHbtGldCuVw7th/Cqizh74isTf/NJ3NA3/V6vYhEIvB4PAX7RhOd0WiEQqHgSrlUKrVrQj0oAd/pdDLh9FmeK0JlrFYra0EJhUn3SsMKRUOFY0neYfl+m0umJ/sZ0tWhdFxNTQ0HX0NDQzy+SqUSV65cQTAYRDabRSaTgcfj4Sol8q0jqQc6TmdnJ+7evYvNzU0sLy+zCCiwW1qCrs3j8aCjowMqlQp/9Ed/xIGj0WhEOp1GJpOBWCxGTU0Ni1HmpjIIfaQ0ZGtrK0wmE06ePAm5XM6pq5/97Ge4ceMGy8CQqCqZQo+MjHC5PxGl6d6JxWI0Nzejs7MTNpsNZrMZS0tLLDR67949rK+vw+PxoKWlBX6/Hz/96U+xuLgIp9PJBPCBgQGIxWKMjo7yu7CxscHVeS6Xi9W3o9Eorl27hmvXrrGcQiaTYfkJqjIj7lUoFML29jZOnToFhUKBM2fOQCaToa2tDVNTU/D5fCgqKkJ1dTWbAKtUKnz3u99FOByGVqvF1NQUXC4XBgYGcPz4cd4MeDweZLNZWK1WtLa2oqysjIM2KoKh9yKZTGJwcBBVVVXMpTIYDAiFQhgZGcH4+Dg2NjYAgNE2m82GK1eu8LE2Nzc5VXjixAmcPn0aHR0dzFWz2Wyora1FeXk5qqqqsLW1BYPBgEAgAAAc2JJrBRl6OxwO3L9/HwMDAwiHw7h27RrPS8BOVV8oFGLyvVDnz+v1sp/k0NDQLsHd3I04XZtQoqTQvLrfpipf24sqQI0CNyLXP+2m/qlSft/61rfy/ns2m8X777/PO6Sv2pPt92U9k6tJky8Nlau/AzydZtLTtL3SYXt9lk/T6qA6V7ktX6oyX7qMvktpnoMYCT9N/3N/e/XqVd6V9vb27pnaetqWz+InN2VJpfbCCj7g2Z6FRCKB0dFR6HQ6VuN+1t0k7UYnJyefsCjKva5894DGnoja8XgcV65c2TMdu1/KlSZl0ngyGo3MeUkkEvjwww8Ri8WYB5JIJJBO71ijkN0MWfW89dZbbN1y7NgxWCwWXtyIZO50OqFWq+FyudDX14dIJLIrJUHX6PP5uDpvYmICW1tbqK2t5QWUUoaBQICFFMvKyrhPGo0Ga2tryGQyHKBEIhG0trbia1/7GgBwZSSRiGlsyAPvypUrGBoawuDgIBQKBc6dO4eRkRHMzs6yWfTs7Czb0mg0GjidTlRXV+O3v/0t1tfX8fWvfx3PPfccAGB+fh4DAwPQ6/U4ceIE7HY75HI5VlZWIJPJEAgEUFFRgUQigaWlJdTU1EAsFkMul6OoqAj9/f04fPgwy1ZsbGygpaUFd+/ehUql4gq8dDrNx7ZaraiursbDhw8hkewIuU5OTsJsNkOpVKKlpQXLy8vo7+9HTU0NPvzwQ9TV1aGiogKvvfYalpaWcOfOHRiNRpw9exYejwcikQjAzqLd09PDKeSNjQ0kk0kAO6iox+OBRCLB888/DwD4m7/5G7a3aW1thV6vx5UrV3al8gcGBuByuWCx7PhOEu9xc3MTTU1NePz4MXQ6HYaHh/n+v/HGG7zxIfXyWCy2yxIKAFckGgwGlJaWwmazIRQK7eIOZrNZmM1mbG5ush4VyXA4HA6EQiG0trayLQ7NZ7n0BZIhEQZ0z9py311hqpv04A7yO+Dg6/dTBVQ6nQ4/+clPoFAodv17NpvFt7/97QMz4f8hts8SUH0WTtFBeD7A7kXzWQOVz9KEFWQk3pnvOvbiEuUugoW86vJVuxUKOAmhIu5Art+g8HiFgrK9+ipsucGBkMskNEUu9PtC/SrEVaDqJaFHlpAnlBtA7hds5BvXfGOxl38jtUL8vFzO01tvvQWTyQSdTofe3t49x4FQFZvNxijMXoFToX7mBrq5f6cNSiAQYMTW7XYzokIEcuJhzc/Pw263M6E39z0dGhqC1+tFcXExbDYbSktLYTQamT9HYqZCD8H29nYMDQ1hamoKbW1t6OnpwY0bN5g/RgKugUCArW/UajW6u7uZxxWLxTA9PY1QKIS+vj40NDRw+m5zcxMajQYymQznzp3DBx98gJs3b+Lo0aNob2+HRqPBj3/8YxgMBr4vXq8XHR0dqKurYwFJkUgEv9+PWCyG0tJSpFIprKyswGw2M6coHA5Do9HwZ729vewj+uDBA9ZMm5mZgVwuh0qlwqlTpwDs8KPI5y8UCnE6b3R0FKlUChUVFQgEAtDr9RgaGsLQ0BC0Wi1effVV2O125kOtrKzAaDQik8ng9ddfx927dxGPxxGJRDA8PAyTyYS5uTl885vfZK7R/Pw8pFIpnn/++V2kZpLbGRkZwfr6OsrKyuDz+fDyyy/D7/djdHQURUVFXJX27rvv4tGjRyzI+u1vfxt+v5/nLZfLhdHRUYjFYpw7d47TkDdv3oRer4dKpWIfPbFYjEAggH/7b/8tpFIpbxjT6R0vR5vNxkEl+WICn64PwsrZkZERLCwsIBQKcVEFST6YTDv6VJcuXcLIyAhisdguFJ3ecdowATsFQD6fD6urq9BqtSgrK8ubmj9oKyRwS9y83Pl+r3X2C+FQnTt3DgqFAmfPnt3159y5c4xsfNU+/3bQNJlwV59L8NuL53OQlNUX3Yi0WqiCLF+aJx8XiY61n7cdNbrWfOkyOgfp4AhLuIVjTecDnuQw7ddXYcvVxKJFmGw3KIWW7/f5UoqFnhvalcbj8Sc8snJ5QsKWC5kLjy9MG1M/8nEQ0uk0rl27hmAwyJyLfI3ui8FgeCIdTdc6OjrKwUo+65h843vlyhWuUM73zAirCsn/cHR09KlSy8QB6+np4eoli8WC2dlZhMNhPH78eNd4OBwONDQ0oKysDNlsllOG5MUH7EzoyWSSlcQ9Hg/W19cxOTm5K6VKz8zo6CijItlsFuPj41AqlYhGo0y6t1qtaGtrw/DwMJxOJ4aHh1m/S6VSIRwOo6WlBXV1dRx0tre3c/UdBTg//OEPce/ePdaZ0mg0nMosKysDsJP+ymQyTJKna0qnd2QlVlZW4Pf7UVFRAYvFgsHBQTx8+BAffPABo+ebm5uQy+WIRCK4dOkSYrEYjh8/jpKSErS3t+O5556DXC7n6ru1tTUkEgk+p8vlwm9/+1t88MEHePToEe7evYuJiQmIRCJO7ygUCpSXl0MqlUIul7NWWXFxMfsKzs3NQS6XQ61Ww+fzsT3Ov//3/x5qtRoNDQ04evQoewBubGzAaDRCJBIhkUhgcnISQ0NDmJmZQSgUwszMDEQiER4+fIiuri4cOXIEZ8+eZa2rkpISdHR0wGQyoa2tje+FSCTiOYGCpZmZGWxubmJwcBCNjY1Ip9M4duwYm3Nns1n86Z/+KY8HpQ+tVivOnz+P+fl5lJaWMgJF7w1ZRwHgcdXr9YwwkndgMBhEV1cXpqamYLVa4fV60d3djUgkwil72qR+9NFHGBwcZLHcoqIiNt0uLS3Nq2P3NDQPmkOEFYHEJct9/4VzwGfhJD9VQPX222/j7NmzeT8jP6Gv2uffDhrgFCKGH/SYuUHYF1XFVag/QrHGp/3t03jb7RVM5mvCvH6+YzxNAPoswSpNeuTLtt81AbsVs3O/S0GT1WrNq2QuNCzObUI0TjguLpcL0WgUMzMzjNTl4yC43W7Wy2lvby94zcIAVKFQME+HkA3if2g0Gpw+fZoryfIJ8eW730SszvUrBD6daNvb21lHSSjhYDKZdlUOut1urnwUIpE0ThRsWq1WxONx9nY0GAwYHx/n8n2SIiATdgC8ANTW1kKn06G4uBgdHR2oqqrC4cOHWUV8eXkZdrt91/0HgNbWVj6XEIW02+149OgRAODIkSOQyWSoqKjA1NQU1Go1q32T+KZEIsHS0hKGhobg8/lgs9kwPT3N3Dyfzwer1YqGhgYOBAmd0+v12NjYQCqVwvLyMotW1tfXo7S0FEePHoXRaERZWRlzoih9ub29jYmJCZw8eZJ5YhQAdXd3IxaLQaPRYH19HdFoFA0NDQiHw2xiDICvg8RJU6kUDh8+jOPHj0MkEmFhYQEff/wx0uk0V9zRgj86Oorl5WVEIhEWsGxvb+d7Fo/HcePGDSQSCdy9exeJRAKhUIh1oIaGhhAKhTA+Po5UKoVIJML2LbW1tWhqamKdRrPZjLGxMXR3d2N+fh6RSAS/+c1vUFVVxUr9W1tbnAL2er0so6DX69Hc3Ay1Wo1wOAylUont7W0cPXoUCoUCMpkMxcXFaGpqYkNjsjKi+Y+C8Y2NDeh0OlahF25maJNx48YNZLPZXQbpwI4O3PLyMjo7OxGNRhkVKikpwbvvvguPx4O33noLLpeLU75CgWxhZWM+ceGnARdy0SaSHaEmEon4/RTOAftxX/dqn4mU/lkiua/awdtBK9QKEcOBJx/EfMf8PCL0Qm2/B/Og13iQ39LfC3F3niaoIaSCKnTyHUO4+AsX9HzXvN91Cn9jsVhQVVXFvxGiHXtdEy3gVKaf71zPMt7C6jC3280pJhqXzc1NtjihdFRuM5l2StGfe+45JrXu1QwGA+x2O2w2G6MxpP5MyFo2m+XJM59CO42NTqfjSsHcvtN4CJFcIm17PB7uay7plsrLCdlxu3cERN966y2Ew2GumlQoFIjFYqyIbbFYMDk5iVAohLGxMUYuqT9EupVKpWhpaYHFYkEsFoPNZuNz0fOwtrbGpf8AGEEi9XUqJiCj6aGhIVy9ehXBYBAejwfb29vo7e2F3+9HJBJBf38/Xz8hPPR38n771a9+Ba1WywUMFy9eRHV1NZqamrC4uMheg3K5HA8ePIBKpeKFeWRkBJOTk9jc3ER7ezusVisaGxtRXFyMZDIJuVyO8vJyNDQ08Lj/1V/9FfPxgsEg3nzzTTgcDjY5zmQyKC0tRTAYZA5PVVUVIpEI5ufnOWBtbm5mT8VIJIJEIoHFxUXmLZE9yo0bNyAWi+H3+5mLpVAosLa2Br/fj97eXojFYpZhEIvFyGazmJ6exurqKvx+P+x2O8rLyzE2NsYLuUajwdzcHGpra1miori4GGfPnsXg4CAGBwfx/vvvo6qqCi6XC6+++iri8ThUKhXbMBHXKJ1OIxgMIpPJsJYYAJSWlqKhoQHl5eW8oaJqwnzIc+68T6gOVf3dvXsXkUgEy8vLHEyeP3+e9bc2NzehUqn4vaegjgIttVqN1dVV9PT0YHl5mefMhoYGXLlyBV6v9wmyupDDKZxDTaa9HTJo/iS+FPHKaPMo1BekIP8ga+JB6UyfSYeKfNC+avu33xcpndpBeFifhau1X/uyeVmf17XkEvb3OlYuDwzAU19z7jjlG7f9rm2/Phe6F0/7jNCxhBNbrgAf8S4Kcdj2uzfCa+ns7ITL5cKjR4+g1WpRV1fHejVCLlQhlPWgRtjUR0KkUqkU60kJdaqIZ0QE87q6Oi4uKCoqQiqVwqFDhzi1ShVydM5EIoGf/OQncLvdLLmgUChQUVEBm83GiJfL5WKVczomFRGsra1hcXERGxsbkMlkeO6556BUKjE0NIRHjx7h8OHDePnll/m5nJubw+LiInw+H+RyOY4fP47y8nKen2ZmZtDc3AyJRILx8XGWqRCib+vr65ienkYikYBarcaZM2fQ39+PF198kY9DlYrUr/HxcXR2diIWiyGVSiGZTCIUCqGlpQXhcBglJSVwOp0oKyvbtXHZ3t7G2NgYnnvuOYRCIRw/fhz379/H+vo6mpqaUFVVBZFIhAcPHqCtrQ2VlZUIBAJwOBxYWFhAfX09pFIpwuEwm+GaTCZMTEwgEAhgZmYGnZ2dEIlEqKyshF6vx89+9jN0dHTg6tWrkMlkSCaTqKqqQktLC29YRCIRPB4Pc4Cy2SxMJhP/O/Eeiejf09MDv98Pp9OJTCYDYEd6QCQSwWazoaWlBdevX4fL5cLZs2ehVCqZ/J1Op3elLsvKyjA0NIS+vj4EAgE4nU6IRCIYjUbuk1QqRTabhcFggM1mY2QyV7tO+B7Ts15VVcWBtsPhQGlpKaLRKKOdwkCHAqGioiKIxWImtBPnlN4T4vZRsEu8RJdrxwLK7/eju7v7ieKbpy1GEqKb9B2hhybwKQ91Lz5r7lwll8sZ9fzCdKi+0gT9+9U+C0pErRAStR8q9LTQ6n7np4W4kA/TQc+XLxW5128pRZOLEubjt+X7faH0pXDcDpLOFfY593y5/SJZCKE+TKEmfEboz9raGtbW1iCVStHT08PISjwe38XveZaUsvBayJeNUkh03Fx0EgCcTucT5dGdnZ1QKBRob2/fMyikMU6lUrt4GLQweL1eToUIFwK6vqamJg40XK4dPz2yZhGmT6RSKS5cuIDGxkZsbW1BJpNxyoPuDX23oaEBiUQCTU1NvKC73TtK3KFQCMvLy5BKpQiFQsy1sVgsaG5uxsjICEpKSnD37l3U1dWhs7MTXV1dLMJpt9tZd4jSPBMTE0ilUlhaWmJkcHNzE5WVlZBKpairq0M2m0VzczN+97vfwWQyYWZmBkajEaFQCGtra8xZId2vpaUlJBIJHDp0CDKZDHK5HIFAACdPnsTW1haOHz+Orq4u9jRUKBRwOBw4dOgQlpeXUVFRAa/Xy2bKBoMBJ0+exMOHD1FdXY1YLAar1coIqk6nQzweR1tbGxobGwHspH0ohffJJ5+guLgYer0esVgMsVgMDx48QFNTE5xOJ77//e+ju7sbnZ2dSCQSGB4exvDwMH70ox/h7bffxr1793Dv3j2UlZUhFApx2pdQZbFYjIsXL6K2tpZJ0QaDgcewrKwM9+/fh1qtRjKZhFQqxYkTJ1BTU4NLly4hFAphZWUF8XgcpaWlMJlMLKVQU1ODUCjEhTIbGxvweDwIBALY3NyESCTaVbBAz+u1a9fyyuN4PB5otVrIZDLU1NSgt7cXdXV1OHXqFKxWK06dOsXXQM1gMKCoqIivNxgM4he/+AUAMOdUmC3Q6/WYmpraVeRB9AOaL/O9/7nz315rCX1GTgL0LBBvlFKle2UwhO1p18TPFFBRGehX7Q+/fZHpvIOcZ78H87P0rxBBfC+D3oNokhTq915kbyF3SAhbP3z4EPfu3ePy5kLXWmichNeYj9OVewyaRIVjkg/aFk40QGFSfaFmMBggEol4AqdUVG9v7xMphme5x/kKJui4NFkKx4gCw3zBqkQigdlshtvt3iU4mC/wpolZItnxixwZGYHL5eIUCqUXtVotB61kAEuGyDdu3MDQ0BDu3LmDGzduYHt7+4miC0qjKhQKLiknTonwWVYqlejs7GRD2Y2NDZSVleGjjz6Cx+NBIpHAysoKAHA14fnz5/nYH3/8MUpKSjAzM4O6ujq88MILkMlkkEgk7Gk6NjaGoqIiLCwsoLy8HGazGXV1ddBoNJyeKykpwaVLl9DU1IT/+B//I7xeL/r6+rC9vc0+fKRsvrKyAoPBgJ6eHrYu2djYwOTkJCNrq6urmJqa4iB5c3OTJQ8++eQTlJWVYWJiAm+88QZUKhWT3hUKBVQqFW7duoW+vj4sLi6iuroa6XQanZ2daGpqwokTJ2Cz2ZiLtbS0xHyqlZUV6PV6xONxtoMZGhpCJBLBxMQEKisrodFocP78eRgMBmxubkIikWB4eJgJ+dFoFHK5HFevXoVCoYDL9anxL6FsXq8XDocDf/EXf4GHDx+iqKgI3d3dUCgUuHv3Lmpra/Hee+9hfHwccrkcCoViF5pkNBohlUqRTCZZW2t9fZ3ff3Iy2N7exuLiIurr69HZ2YkrV67AZrOx9AA9r1arFXNzc4jFYhgcHOT3JRaLIRAIME+M5i/SM5PJZLv4k0LdOlLln5qaYm/LfJvAwcFBXL9+nedAet+Is0USDMLPKIgbGRlhfb691hJhAEdm6QaD4Ylim89rA5/bPjdhz6/aH27bi6D8ebf9ctx7/e5ZKwvzEcQtFgu6u7vzEhs/S9uP7J1r5En9c7vdmJ+f56qtQtea+6LTtQmr+3L5P3v9bj/SvHCiKVTpuFfzer3IZrPMYyK0CwBzd4QkdiH/g/qcSCQKiqrm46DV1NSgp6eHkSP6nPhKiUSCeUrCJiTiCsnj+QJvIXk9GAwinU4jFothYmKCzydEI4l4Gw6HWQ6BeENqtRparRalpaVPoIZutxsqlQqhUIgneyJ/GwyGJ9BKYXHC0tISDAYDdDodDh06hOeeew6xWAw///nPEQ6HsbGxAYvFgkgkgosXLzJaE4lE8Nd//dfY3NxkjlI6nYZKpcLk5CQqKyv5t8eOHUNdXR0aGxtZOZ+0tH71q1+huLgYjx49Yh87QjqISE/PKCE9hKL19/dz8EmkfvI7NBgMSCaT6O7uxvr6OktTkC+hy+VCXV0d7HY7XC4Xbt++jXg8jg8//BCDg4P49a9/DZlMxoHnzMwMWwgtLy9DoVDAarWivb0dL7/8Murq6iAWizk9mEql8Mknn+Djjz/GwsIC7HY7XnzxRdTW1uLb3/42Dh8+jP/6X/8rLl68yHY50WgUGxsbnA4dGxtDJpOBXq/H7du3MTU1hWvXruG9997jCka9Xo/19XWcOHEC4XAYUqkUFRUVWFtbY4V7q9XK0gg+n48tboRoOSGv29vb2NjY4ABIuKkSkvirqqrws5/9DKFQCG63m+ckALDb7XjnnXdw//599jaMxWL8nrnd7l2BlNfrhdPpxNTUFF544QVks1kWZKXnfHl5GYlEgqtHSfKBjkXFLFTIkbvBISI8VQgeNBASzpO5xTb7be6eNeD6ikP1JTXiFvj9fjYffZr2WThB9AKIRCJsbGzsqf/zebSDaAh9nu1Zxibfbw5ynL3y98JFUqvVsp4WTSp0bKF6N7A7ZZXLfwKwi/sgRHuE/59O59cZy8cT+Lz4ZcJJR5hqAz7VsBKOF4BdXngej4d5hSRSKXxeaNIrxEHLHSsKmEitOzc4nJ+fR39/P06ePIlwOAyFQoFIJILGxkb85je/wauvvopIJLKLj0WEcuLbVFZWQqfTwWw279K4IY6IUGCUgkUaf2HlJPXd7/ezwjTxNG7cuAGNRsMSGnRvc7XSEokEW36RUCT1Nx6P4/z584xkDA0Nsb/a5OQkB4avvfYa1Go1jEYjhoeHEY/HsbW1BZVKhSNHjjAvjI4biUTQ3d2Nq1evYm5uDm73jtxJfX09rFYrVCoV7HY76uvrcfPmTWQyGXR0dKCmpgZutxtzc3N4++232cKmo6MDR48ehVwuZ7NfjUYDAAiHw6itrcXW1hbOnj2LyclJiEQidHV1we12Y3x8HFtbW3A4HMwxamhoQCaTQSgUwpkzZ5hYL5HseCjGYjGEw2Ho9Xr2motEInA4HDhx4gSmp6cxMTGB6elpNDY2IpVKQSQSIRKJ4IUXXsDg4CDzpdRqNVKpFBKJBM6cOYNkMolf//rXWF9fZ4uarq4u2O12jI6OIpvNYnt7G3K5HGVlZVhdXWWj6ueee46DIJfLBY/Hw556xJEiTtTm5iYOHTrEwV9RURFXI9bU1ODll1+GRPKpnqBQsBcAbt26hUwmg83NTTQ0NKC+vh6hUIiD242NDWg0GiQSCQ6gu7u74fF4+F0AdnQpp6amkE7vGFqHw+EnBHbT6TTPCUajEbOzs+z5R9c6MjICnU7HXK9cPcJYLIYbN27w87wXPzSX41lonjsID1X4fn8pHKovclH++9qeVfw0N6J+mgiaEAoqcd4vCP6scKgQEfmidgLC3wLYtSM6yPGEsHEuhJ3bV+ExC6FQtOgsLS0xCVmICAm1XCSSHdXeq1ev4uHDh3wNtAsMBAKMmOSDt4UpPeozgF28KJoshLpXe13ns9wHSnEKbWlyNayEz4IQYQHAwZRQCuKgyBodW4guWSwWJnPn8xv0+/1QKpU8GQeDQcRiMfzqV7+CSCTCj3/8Y4TDYd4dU7AQiURgsVhw9OhRuN1u9qkTatx4vd5dARD1h+QIiFCcTqfZ39Dn80Gn08FkMmFrawtlZWWYmpriai4h6Tefdg7xY9bX11nOwWQysSUJIQG0Q6d729TUxNwhKrk3mUw4fPgwLBYLVCoVysvLdz1zRqMRPp+PEcjz58/DZrPhxRdfxPHjx2G1WtHR0QG73Q6r1YrZ2Vke88ePH/N1xONxtLS0MFFbr9ez1tPAwABfC7Bjx+JwONDR0cFpqo2NDUa9aNPS19fHhHDiKQFgP8Samhp4PB7I5XLEYjEcPXqUBTTD4TA2NzeRSCTwu9/9DpFIBFtbW2hubobJZEJ7eztCoRDa2tpw584drKysYGJiArOzsxCLxVAoFPjWt76Furo6FBcXo66uDnq9HpWVlZBIdhTWT548iX/9r/81+vr6ePMRiUS4YIGkDahSmN7Fq1evMudHIpGgtLQUq6urLFdBFal0T202G1544QUAYI0p+pz0qtbW1nDq1CmYTCb09fWhrKwMS0tLEIvFkMlkKCoqwqlTp2CxWHD27Flsb2+jvb2dEf9IJIJUKgWPx4Pp6WlkMhkUFxdjc3NzF2ouTFebzWaIRCK4XC5sb2/vMoQ3mT6tQKTgpru7G0VFRXA6nZy+Jmui3Hc+d+46KK0gd14txDOld+5LqfL7qh285UOongYpyP3us1TNHUShGjhYRd5B+y7cXchksid+ux8KsVd72gqQ3EbjQc7uhdTS81XN5avGI8SByKlCNCK3TwMDAxgcHERxcTGuXLnCCtu5yr7CySdf9Z/wu8JJhNCqfIhPvus86Ljl2wEWUqrf6++FyOn7/bbQPcxnqSNEjSiIFSreU2l1JpOBw+HAkSNH4HQ60djYyKli4bXmU/J3OBwYHh5GZWUlo0RChJJ24bQwGAwGPHr0CMlkchdZN5VKYW5uDiUlJRgaGkJtbS2qq6tZ66empgaJRAIjIyPQ6/VMqB0cHMTi4iLkcjkOHz7MiIVw3IgwL7SMuXfvHvx+P2w2GyorK9He3o7NzU0kk0mEw2G43W688cYbzNGJx+O4e/cuSktLd1XL0bND4+5wODAyMsL3IxgMorm5eRdSlE7vWNb4fD54vV4WkVQqlUgkErh06RJze1KpFAwGA4qLi5HJZKDVapnPpNPpUFVVBbfbjV/96lfw+/14/vnnEY/HEQ6HIZFI0Nvbi0wmg8HBQaysrMBkMrFafTQaxaFDh5BOp/HgwQOYzWZkMhkYjUbE4zumzcQtisd3fAxXV1fx4MEDNDY2orS0FDqdDtXV1ejt7WU0SalUYmJiAm1tbUgmk7h8+TK8Xi/C4TCmpqawsLCA4uJieL1etLS0IJvN4jvf+Q7Lrty5cwdra2uoq6uDXC6HSCSCSqWCSCTC0NAQF3+QNQ8JuZJERu57LtREEwYSkUgEb7/9Nl599VX4/X6Mj49DpVLxpiTf8y4MPOg+mkwmnpNyq/voHbLb7bh//z6USiUbLu813wgrcikTRu9x7txRKCNC6cODoFmF1iH6zpeCUCWTSZw/f553I1+1/dtByM2FfpdLzn1azlGuErew5UNh9jr2Qfs+OTkJuVyOycnJvAjJZ+FO5SNn5zte7iRA/0+5ea/Xu6daer5dUS5XzGTaqUbr7e19QhRT2Cc6f0dHB3p7e3Hp0iV+gYX8r1xdq3x8mtzvOp1O5gjtxfEqxKXaT9SOUDiqEqJnkkjetAjmImC5z0oueT83dSjkftBv6bjCvlB6NZdPRv2anJxkRJbOKRaLd71D4XAYFRUVuHDhAvR6PS5dusTIE32H+k06NkLelkSyYwicSCRw7do12O12RhWE35mcnMTS0hI8Hg8OHz6MmpoamM1mNhUuKSlBa2sr5HI5jh07htraWvT09KCqqmpXypieWzKbJTVylUq1C8kaGBjAO++8g5WVFf7d9PQ0bt26hampKeh0OjQ0NMBkMnHg6XA4OL3Y19eHsbExrsybmZlBRUUF4vE4DAYDk7hpcSNEcHh4GEVFRVhZWWGHAeKzkZRCKBSCWCyGVqtlO53Tp0+jp6cHR44cQSwWw7lz52A2m1FRUYH5+XmMjo4iFApBIpGwpcvCwgKPsVwuZ9JyVVUVSkpKcOjQIXR2dqKtrQ2JRAJtbW38vJSWlqKtrQ11dXWYmZlBVVUVqqurceTIEayurrL/IiFyZFGzvr4Ok8kEj8cDj8eDmzdvor+/H/fu3cPw8DDW19cRDAZRXl6On/70pxgcHMTAwADS6TRXq/b09CCdTuP48eOYn59HV1cXBybBYBA1NTWsh9Xc3MzVdH6/H21tbSgrK4PBYGDuocvlwtraGux2O65evcrpcwDY2trC2NgY69gJ37epqSnU1NTg7t27T7wbwKccQYPBwOMQi8Xw6NEjHnePxwMAXGlI5xOuVzS39Pb2orq6Gl1dXbvmm0Qiwfpw1IgXJjTjpuraeDzO5wWeNFSmd1an02Fubo618XKbcF7S6XSYnp5mdfjcueSg1IjPjFAZDAbcu3ePy1K/avlbPh2qz4vL8nm0p0W8Dtp3IYJAAUI+FOfz7PNeaB7wqTYUqfjmQzdyrysfMiPkLRESAewtCSDsS77zCxsFMIX8DXO/S5MrackI+TfPguIVQuFyNZUoNUVoBFXHHRShyt1REyIiPE/udeQidPk4YrkI1dDQEOLxOCMsLpcL4XAY8/PznMYj3SCh157T6YTZbH5iPOna6XrW1tZY7NDv9yOZTHK6j3a3hI7l459Req6oqIh3+YODg3C5XFCr1YjFYmhpaYHL5cLdu3eh1Wrx/PPPs7J3SUkJXnrpJQDA//yf/5MX59bWVvj9fszNzWFkZAQnTpzApUuXdnkAEuGe0n5jY2NQqVSYmppCRUUFI2iNjY2oqqrC48eP2Q+RNIg0Gg2n0U6cOIHHjx/D5/Ph/PnzKC0tRSKRwNTUFKLR6C4OFaFtDocDt27dwtmzZ+H3+7G0tASJRIL19XUsLy/jwoULWFlZQTgchtfr5bFtaWnB3NwclEolKioqMDAwAIvFwguy2+2Gw+HA1tYWFAoFFhcXcezYMZSUlGBiYoLTs88//zwGBgYwOzsLvV4PkUiE2dlZhEIh1NfXw2azYW5ujlEziUQCr9cLlUqF9fV13sgcOXIEDocDDocDGo0GL774Ijo6OjgYHx4ehlarhd1uZ/HTc+fOQSqVckVbJpOBwWBgmxuHw8EK9hqNBiaTCVKpFGq1GhMTE/B4PBCLxVCpVKitrUVvb+8T88HGxsYuziJxaltaWtDf388+j6TRRn6U6XQaa2trMBgMcDgcCAaDkMvlqKioYKmKvr6+PTMge6HShfThaJ7N5Y/mzon55ql4PA6/38/Xux9CNTIywn3I5wn6hXj55Wt//Md/jB/96Eef9TD/INvTRr9fZHtapOigfReiYrQDo135Xr89CKcnH/JD/xUiJrTAUfks/SYfYlcIeRNer5AfIISZl5aWWO04X78J2SDPqlxPu9xrJj5BLiqSr1EVC/Uxn8Bmbh9yx22v8aW/0y5byM0irlBDQ8MT3oC5Y0eTGLCDatC5iThLfQ4EAojFYnx84osUQujyIb9er5eJx5T6oNQTcUwikQjUajXW1tZw48YNRCIRTE9PPzEW+cZTyCErLi4GsKO/QymzhYUFPHz4EE6nk8eF+km/3djY4HGk9BrpBVF1FLAT1MlkMmxubnIqzW63M+dqaWkJa2trGBkZgdfrxZUrV1BfX4/nnnsOXq8XjY2N0Gg0aGxsZOuWdDqN0dFRpiIIgykS56R0eFVVFc6fPw+r1coSDevr6wgEAkgkEkgkEpidnWXE6cMPP8Ty8jIWFxcxNjaGkZERuN071iZkrkxjR4vgxMQEFAoFPB4PYrEYFhYWkEwmodFouAJPJpPB7XZDr9djZmaGz2u1WhGNRvH48WNsbW3h7t27GBkZwbVr15BOpxGNRpFKpbC4uIiysjLY7XYkEgn2N1QoFKzwbbVaIZFIoFarWQWdtKRqa2tRVVWF5uZmlJeX4+zZs6irq8Pp06dhNBpx6NAhtLS0oKenB6dOncKVK1dw8eJFTjMCYEI5nTuZTOLGjRsYHBzE+Pg4AMDn82FycpKtbBoaGmCxWHDmzBm232lvb0cwGEQqlUI6vaNNVlxcDKPRyAHRzMwMkskkJiYmIJPJMDc3x/IXFosFZrOZU792ux0ajYbfjWAwiHg8vqsy+Rvf+AZqa2tx9uxZXLx4EXV1dejq6uL0H+nF5ZufJBJJXm6jEI0Szr/C91iofUfV28RjLYTed3Z25rUzyw3uAECv10Mul7MZ9LO2z4xQ/eAHP8Cbb76JhoYG9Pb2shkmtf/+3//7Z+rg35f2+1ZKP2j7olGz/Y5Pn+dDVvZqwl0KEYU3NzfR0tKCQCDAu7RcJd6n7V/ud4BPd1FUaSMWizn1V6iPwE4Vk91uZ7Qgd6f1tGNBqSGqECISZy4fbD8l9tzjCXlHhcbpIPw8IeJGKFYikcDa2hrLNtAxXS4XnE5n3nuWb/zzIVS547a8vLyrYorI88Q/IYSqvb2dxy436CCxRBorOjel4srKyvDb3/4WZrMZW1tb0Gq1qKys5DQRXSdN6LQ73t7eZoTi9u3bkEqlzGUBdiqq+vv7cf78ebjdbly9ehWbm5v4+te/zt5smUwGR48eZR4NPZP0+euvv45bt24hFotBKpXC5/Ox51tpaSlzvlQqFSKRCHNXhPfLZDLhvffew/DwMBKJBJ5//nnI5XJks1nIZDIMDw9DrVajtLQUjx8/Zm2o0dFRdHd3o7i4mNNidXV1qKurg8lkgtPpRDKZRCAQgFgsRiwWw8zMDDweDywWC+rq6qBQKBCNRjE6Oso6VFKpFJlMBpFIhAPOWCwGs9nMvnnEP7t//z5MJhMWFxfx6quvMoJjt9thNptht9uh0+kgl8vR1dWFWCyG7e1tjIyMoLq6GkajERKJhAMWr9eLjo4O9PT0YHx8nDeKdFypVIqOjg74/X6ufmxsbITP5+PgsaioCMFgEGVlZSgp2TF5np2dRVNTE6e4DAYDZDIZOjo6MDk5iVgsBolEwkRuh8OBO3fu4MiRI0gkEuju7kY6ncZbb70FvV6Pubk5PP/88xwwUQWu8F2kIJYI8PQOCDe/BoNhVyUr8CQPVTh3FSoYEv5bbhPOI0IeVr7vPguPON/v8h0nd3770hCqiYkJHDlyBCqVCrOzsxgZGeE/o6Ojn/XwX7UC7SAIzrO0p+F1fREtH/pzkCZEUwgxaWxsZGG3g7ZcDs9+38nlg1kslrznS6d37CN8Ph8v3kqlEleuXNmlGiy85r3GIt/9d7t3NGXS6TT8fn9BPth+SuzC4w0ODuLjjz/m8nxho7L9SCSCa9euQaFQ7OIyUT+Jf0EcjEAgkBdZEiJZFouFFwz6rZAzQtwy4f8Lkb18qJbwmNS8Xi/Ky8sBgBcL4vwQ0pdKpTA9PQ2Px4OxsTEMDw/zwkCN0M75+XmuqCXR09LS0l0pZaHPolQqhdls5qB/Y2OD5QmEPC4q46dAj0yFqYqOys6JJ0O8Oq1Wi/HxcSwtLeH69euoqKhAcXExEokEMpkMZDIZent7MT4+jpWVFcRiMYRCIej1erjdbt7lA2AuW2trKyorK3Ho0CFWpTabzVhaWkJ1dTVMph1F73/5L/8lLBYLFhcXGVXr7u5GR0cH6uvrIZPJoFKp4HK5IBKJYLFYIBaLEQ6H2XhYpVJBLBbDarXi3Llz2N7eRmtrK/R6PSthp9NprpI7c+YMp8+OHTvG5Of5+XnI5XLY7XYcO3YMa2traG9vR3NzM49jJpPB3NwcNBoNSkpKsLGxgWQyyTprwpT0xsYGFhcXMT4+jo8//hgqlYrJ8h6PBw6HgwOnra0tPHjwACUlJSw5QOT/aDQKi8WC7e1tRovkcjkWFhYY0V9dXcWjR4/wwQcfIBgMIhAIwGw2M9lcKpXi+PHjcDqdHIiQ4KrX62Wj4tbWVmxsbHCwQAECVfBZrVYYjUak02lMTk5ia2sLm5ub/EyNj48/USVOPFQS8yTUiDZEQqeEvVAq4fuoVqv53d5rY/us/NuDzIHPug5+VeX3JbXPA6E6SFXCZ21Pi1A97ff34z0V8lc6aIUiHSsX0n3WPgqrRoQpK0I/hJMsadbkan3RIircWR6kenKvfhfaVR2Ex7Xf2NF9cLvd+OSTT7C+vo7u7m6uKqLzE5E0FovBYrFga2uL00Z0bkq/AmCfM/K9y71eIcJEwSKl2oQEdrPZzP9uNpsxOzsLm80GpVK5J5durzGmwEzIoRBW1Qmvx+PxcGoj9x5QVevJkycxPj6OjY0NXL58GVKplJ+beDyOQCDAATgARgLVajXGx8c59UQms8TB6u7uxsjICPx+P1ZXV6FWq7G1tYVUKoXvfve7jJBQapeMk0m9/fTp0wgGg0gmk5xOBXZ4KSKRCFVVVWz1Mj8/jwsXLjDfjMZubW0NIpFoF5eMSsupvPzKlSuYmprCo0ePUFJSgoWFBZw6dQpqtRoGgwFra2swGo2Ynp5GNBrl4DIajWJxcRFGoxFVVVW4ffs22tvbUV9fD5NpR15iYGAAMpkMXq8XIpEI0WgUlZWVvPD39/fD4/GgpaUFhw4dwvj4OJLJJKLRKA4fPgy3243jx4/z++n1elFaWoqBgQFks1nU19djZWUFYrEYHo8HNTU1KCoqYqFOIsCPjo7C5XLh1KlTjAKVlJSwByLdX7/fj1AohNHRUbz22muoqalhnpRKpcLQ0BAbXZvNZkxMTECpVKKoqAgejwc+nw8+nw9qtZqNrBsaGhiJp3Q5eU4KbZCEHCpS1Kf5J9+7QSiuXq/HwsICaz8V0jEUVnFLpVJGvcjCCAC6urp4I0Dv214Zinxz5ZfNN35WhOozB1T/9J/+U/zzf/7PcebMmc9ymL/37fMIqPZa6H9f7aDEcOG/51v094NvhcTF3t7evETnvYLNp3khc4n0wjHPTVnlmo3mBlrUHyJ40iR7ENL6QeULCgULB30u8o0dwf2FxGDT6Z0S+enpaZw+fRpzc3NPwP70PVrgqfqtu7ubA6p8faEJlfogrAYSksxJTVoYbOyV3sx3LnoWc9MZ6XQav/3tb+Fy7bjVv/LKKwCwi9hOaURK/VBKkJ4Dp9OJdDq9S4eLgipCj4TE2nA4jAcPHkChUMBgMCAcDkOtVjM5nlKvAHDt2jUuqV9aWkJfXx/kcjncbjcLKRJasr29jVQqhcuXLyMUCu2652RoS/yt8vJylJSU4NGjRyyj0Nrayv9OyEN5eTkWFxdRU1MDm83Gopl3796F1+uF0WiEXC7H8vIybDYbmpubMTMzAwDo6OjgBd3pdMLn87EHHSFSZWVlUCqVaGhoYASO+I5qtRoff/wxtre3sbm5CY1Gg2g0CqVSyZpSoVAITU1N0Gq1GBgYQHV1NSOHgUAANpsNUqmUA0Oz2cypwcnJSRiNRvzoRz/CCy+8gM3NTVitVgSDQVRWVqK3txdut5urHLe3t1FZWQmVSgW9Xo+xsTFks1kYjUau5Pzd736H8vJy9jUUFohQ8Ot0OpkvRe+TWCxGJpOBy+XCysoKOjs7WcNrfn4eb775Jg4dOgSr1coFDsL0ORWRkBTEfibv+dLle601wuIQj8cDmUyGpaUlNDU1YWJigpFEeo8PWvAkTIkTnypXLPmg1IyDmCALv59vTYlGowdav4v2vbp9WjgcxuXLl1FdXY1/9s/+Gf7kT/7kc0VM/iG33Buc+2Dn47w86yL7rDsAYZ+ETQiZCvtJSEPuZ4WOg8EfAwA6EymMLi5Dr1EinX4E90YQ8UQSbmkxLEYt3J4A/92kV8O9EYRJrwbWdrLaws8txr2V6t1rG0h4/HCv3YTFqIXLE0AagAuAOpFEMLqN7uYaSCRiuB7tHNf7/46bTmfg3gginc4gnk7v9OfKv4fb7YbNZtt3bIWVf0Dh+5Lv/gvH3eVyPVH9lq9Rub/NZtt1H4RVfPmCH4lEAplMhra2NgSDQU5BCKtx6HuEcGQyGU55FGput5vLnNvb23cZmC4vL+8qjfZ4PGhvb+cFPLfRtVAaIneHTCgbAPT29u6q7qFxnJqaQnl5OSNTxC8SpnzJm66jowOjo6O8wJBvIBncejwe6HQ6+Hw+RqmE4/7o0SNUVlYikUgwt4jGk+QqxsbG0N3djUOHDrG2FelFUUm7SqVinajl5WXMzs6yATGwg0KEQiFUVFRAq9VCKpWiq6sLAwMDmJiYQEdHBy5duoRf/OIXKC0thdPpRCAQQGtrKxKJBJaWljgISqfTsNlsnCqqq6vj1O7k5CSKiopYPiEcDiOVSmFtbQ2BQACRSARLS0uor69HMBjE9vY2YrEYTpw4gYmJCTQ3NyMUCrFuH80X4+PjiEajcLvdOHz4MGsE3bt3DzU1NYjFYjh8+DA8Hg8eP36MjY0NVs+/f/8+X3NtbS3m5+eZi0XBSzqdxs9+9jMEg0H86Ec/Qm1tLXw+H1paWlBVVcVpYJNpR0xUp9MhEAigvb0diUSCPfOAnWKC/v5+FiWtrq6GWq3e9QzLZDIMDQ0hGo3C5/NhY2MDzc3NMBqN2NjYgF6vx/z8PFpaWuB2u3Hu3DlIJBLMzs6iqKiIaTcLCwss1ElIsNfr5WfM7XbDarUWnOf3QscpiCFbKaEkAgVgarUac3NzsNlsmJycZMRer9fz8SnoK3ReWh+EbhK0MQkGg7uuJd/6kjuXUIFHY2Pjru/mu1YKIul7wnMolcqCc5awfWYO1S9/+Uu4XC782Z/9GX7xi1+gtrYWL774Iv7u7/4OyWTysx7+H3TLzePuV1lXKO97kHywcCF+Gm5WoT7tld/O99l+1yaVFsFi1CKLLAdLJdJiGDRKuDwB6JRl8Ie2YNAoIZGIOahKpzM75/x/3zfp1XmPn05ndgKndAbpTAaeQBjpTAYSiRgSiRip/zceclkJB1O5x6VgyqRXw2LU8r/vd23CRmkf8q162ly+0DC10O+EqBF50Nntdk7zmUymXS71wmdByIkSKjHnVuPkNuL9NDU17TkOxBtRq9WswA6AAyJChkibxu/3c4Cea3As5BLRWLhcLszPz+O9995jT0Fh5R9pDyUSCVgsFpw7dw4WiwVra2uQyWSw2+1cjUQK6dlsFnq9HiUlJdDpdJidnUU8HudgkIx/NRoNV3gKg126z+fPn4dOp8Ply5e53/QdYSUULTJutxurq6t8z8bHx+F0OrGysoK2tjZsbGzsKu9PJBLQ6/Wsdt3c3MwL/+joKCYnJ/H48WP09/fD6/XiO9/5Durr62E0GhGNRuFwODA3N8fvrVarRVNTE9zuHeFGt9vNIqElJSW4cOECLBYL+vr6sLy8jJdeegnnz5+H2WzG9vY25ubmWFj19ddfR1VVFRoaGhCNRtHR0cEL2+rqKnPBHj58iFgshoGBAahUKjZgnp6ehkQiYQmf4uJiLiAg8c1oNIq6ujr8/9h79+i2rutM/ANAAgQBECRAgIAA8U2KFCWKNGnqadmy5UeUt1MnmU6b6cNZyaw100ymndXVTjud6a8znTUzXX1Om6adSSdJ26R5NE7iOJJl6+GHLJk0KVB8U6RAAQQIECBBggAB4pK/P5C9dXB5AYJ6OG6TvZaXbRC499xzz2Ofb3/72yaTCb/0S7/ETq/f7+fSNwsLC4zIAsCTTz6JQ4cOoa+vDzU1NZAkicfdwYMHUVNTwyhfbW0twuEwNjc34XA40NvbC7fbzU44hWkHBgaYJ2Q0GjEwMACHwwG1Wo2ysjJWnidS9vXr16HX6zExMQGn04mhoSFIksRRoRMnTnDYu6ysDCUlJYjH4xgaGspxUmhMEedQ1NmSrzUU2pVzNL1eL/Ob5PO2vLwcp0+f5sxZynwdHBzMqUUqX4vosCDuPbRG2Ww2uN1u1NXV5SBrxfCnHA4HlpaWYDAYODoghkLl2YRALlf1bjha96U4stVqxec+9zkMDg7i2rVraG5uxs///M9jz549+PznP4+pqan7cZufONvtC833faXP5aRm+g6AXW3g+UzJidgtx0eSJHZygFznRaNRw2WvQnh5Fan0BkZm52GpKEd4eRUA7iBYkdiP2pP9vkajznWefvTffkKwIjFo1GrYq0zQqHOdJpe9iq9x5znvXFe8p/j5bkx0KGij3GkMyDlHolikvEQOkOs8W61WGI1GnD59OsfxoO/JCd8UpqHwnRL5W8k0mqxqtZi5ls9UKhU2Nzd5YSceFm2w5IyIoTSlAsckGChm6AHZmn6kh1ZXV4fGxka8/vrrXHamv78fg4ODcLmyxYG1Wi1vfI2NjUy8l6Rswefu7m709fXB5coqn7e2tnJdNtJDslqtCIVCfPqmMJ64kUWjUQ5rzc3N4e233+bNTiklPRQKIRAI4Pr168yViUQiKCsrwwsvvIB4PI6rV69ibW0Nb731FrxeL15//XUYjUZkMhm8+eabvLFYLBbU1NRg3759qK+vRzCYFW7t7u7GrVu3sLCwwJw5n8+H5eVlqNVqLilCbSVOVF1dHVQqFZ544gncvHkTarUa/f396OvrQ21tLRdAJoHOaDSKY8eOYe/evTh27BgqKio4K7GqqgoejwfpdBq3bt1CNBpFS0sLlpeXYTAYWJJhYmICLS0tuHnzJhfcPXToEFQqFQDgyJEjKC8vh9PpxCuvvIJAIIC3334bJSUlGBwcRCQSwdTUFKuSf+xjH8PRo0fxwQ9+EM3Nzdja2mJOmtlsxsWLF+HxeODxeDg7TqPRMAJGc4NEY6n9Wq0WL774Il599VWEQiE8++yzyGQyeOyxx3DkyBGsrKywI0QipFTmxuv18pycmJjAyZMnWRZjZWUFTqeT54PSOi7OFY/Hw3NZlC+xWCz42te+hmg0mkNAJ0SOnHMAinOTDhciQT6dTjOiKTpMpLEWi2XXafHgQ9cVixrvxmidKC0t5f5Ucp4cDgfz0O6VQnNfHCqyQCCAc+fO4dy5c9BoNDhz5gxGRkawf/9+/OEf/uH9vNU/WZOf9u9npl4+JEQpcy0f+pVvc7ybtsp/s1u0JRgM5nWKyMjZ6Wx25yBQIoI1F4hgLhBhx0x0fOi/AfDvXfYq1Dmr4bCa4Q9lQ1S2ShMGJ+aQTmfyttdhNaNEo2FHbTd9Q0abmJJaN/2/+FvxhCc6YJRteP78edZF4nYKzjPJBxAiJT+hKSnEy+v17YTAkWMjSdKOjnQwGERFRQW8Xi9ngAUCAWxsbOScmMWQJ4UoKe2f+oh0vi5evIh0Os2L/uOPP469e/fCZrOhq6sLa2tr0Gq1XJuM/iEuVCqVwuzsLPr6+jjc53K5mHi7ubnJz+RyZesJkkAhAM6gcjqd/M5CodC2agGiIjXNlRs3bmzLZKRsxc7OTpSUlLAOUW9vLz7xiU+gp6cHzz77LIxGI44fP848sitXrqC0tBRlZWUwGAxYW1tjjSytVouOjg7s2bMHq6uriMVi+MEPfoCBgQE4nU4YjUZ84AMfYBTJZDJhY2MDb7/9NtLpNOs0bW5uorOzExsbG3j44Ye5TIokSSzn4HBkFepPnDiB2tpa7N27F6+99hrS6TRqa2thNBrR1taGiYkJdHR0YHFxEU899RQTz4lg3tvby5thZWUlDh48yKHAN998E+Pj4/jCF76Amzdv4sqVK/irv/or6HQ6eL1evP3225iYmEAqlYJer8cHP/hB1NTUoLa2FmVlZThx4gSMRiO6urp4XaRx39nZiaWlJZY92dra4nfQ09PD/8zNzWFwcBALCwvMXTMajSgtLWW0b2JigmtMhsNhLC8v59SmdLvdzGU0mUzsrBiNRqTTacTjcc5SpHAsAK6PabPZctYLWl9Ic0msrUlhaY/HA5vNhtHRUXR0dOQchInDFo1GFeev3++Hx+NhjbGenh6+j81m45AaOUySdEdBvpiDmbhO5EO85EABrW009+TF0/NlH+52vwLug0O1sbGBb33rW/jABz6Auro6fOMb38DnP/95BAIB/L//9/9w7tw5fOUrX8Hv/u7v3uut/lmYWGRxpxd2Ny9UbkoQJ5Afzcq3Od5NW+S/uSvETSFMJyJM5GRRSJCcLRHB8oWi8IWi7JiJSFch9El0vDzTPsQTSXimfXnbS7/1haLsiBXTN/JFQD7B5d8lB4oKLOv1ei7GLL4/KtcxOTmZczKkTU2+gMnfvbj4it8pdGJUchQptZqyogodKhwOB+LxODsvALjumsVi4bEkH9eElgHgz+vq6ri22sbGBn9eW1uLuro6bG1twe/3Y2trC6WlpaipqUF1dTVv8lVVVYwqtbW1sTYTvR+1Wo1wOMxOj9iHFFohpXPiy3i9XgwMDLB8hMViweDgICwWC5d68fl8cDgcTMyOx+N48cUXWauMQpMajQbt7e1Qq9XbCMn0bvbs2YPjx4/D7XbjyJEjMJlMeN/73odTp05hz549cDqdsFqtkCSJRTpDoRAGBwfZEU+lUvjEJz4Bo9GIqqoqOBwO9PT0wGAwoKqqCsFgEG63G1arFdFoFLOzs4wQud1u9PX14ZFHHsnJPiOn6Nlnn4XJZIJOp8OtW7fQ2dnJ3LF0Os1EdZIKWFlZwezsLIxGI3Q6HdxuN4dNr1+/junpaayvr3O9Q+qfubk5bGxsYGpqCiqVCkajERsbG9Dr9VhZWcHy8jKefvpptLa2oqOjA+Pj4zmOA417erckfGkymVBWVsbhco1Gw2KyFCL75je/iYaGBhgMBuzfvx/t7e1oamrikCmQ3RfefPNNlJaW4q233kIymQQARCIRBAIB1teqrq6Gy+VCPB7H1tYWJClbl3F+fh5jY2MIBoOMllVXVyMcDrPWFh1qaIxSSJ2ei8R0q6qqEA6H8eSTTyIajeasP11dXSzmCdyRIhGJ4pS0Q85MbW0tampqGDUvZv0nZ5LmutzEvURcQ+R7DiH2fn+22HQikeD5m+96dE9yvt7VkJ/T6cSnP/1p1NXVccHXz372szkkrqeffhqVlZX3eqt/FkZxeIoPFxpgdxPDlZsSxHk3BPRiwoY7/WYnJEPJsVAKm8nDeQXbbTXDbbfAbbewYyYiXUp8KyXUqrPZDWO5Hp3NblmbN3MQMEnaRHhpNQehUuonsW+UnFWxPpX8uwSRk/aL1+tVzNwhQcDq6uptzpnf72fCtpwnJbYZyA3PinW3lJ5L6VmI/2Oz2bb9Tc7dA7KIjslk4lMrcTNExy4f74E+l6RsSjmV3CBCNHGPCNkYGRlBaWkpEokEzGYzxsfHsW/fPqhUKpSUlDC6RCfzwcFBDsN1d3djz549rIUk55fFYjFGDc6fP4+1tTVMTk5iY2OD3y3VG7xw4QKMRiNnZAFguYarV69CpVKhv78fwWC2jMqFCxcwOzvLzjLVcnzrrbfwV3/1V3jxxRcRj8dx8eJFbG1t8Ymc6g4GAgEu/xQIBODz+bC1tQWNJqsQ/tBDD2FhYYHDleFwGLdu3cIPf/hDJBIJuFwuPPLIIwCyaxqVP6HwGj17KBRiB2R5eRmrq6t44YUXUF5ezmGxQ4cOwWg0or6+nusuSpKEra0t5t1R3Ux6txQOGxwcZD0oEvlMp9PYt28f9u7di83NTRw6dIg5WWq1GsvLy6iqqsJjjz3Gm73P54PH42HxTrFEizjGyTnx+XyoqqpCSUkJjh49Cp1Oh2QyiRdffBETExMYGBhg6QHK4ty/fz9UKhVmZ2dZ0oFCxTqdDnv37sXo6CgnHxDy6na70dbWhkcffZSdFxH1bGpqgkqlQlVVFdRqNfeROC8ikQijtOIcp/FKa5JGo8HS0hKOHj2KZDLJITFCu+ShZxEpHhwcREVFBYxGI3p6erbxnURUiBymUCjE/Cly4gcHB/na+TSrxL1EXHOUUHbK7tTpdJienua5mu96tI6I9UaLtXuWTfjKV76C5557DmVlZfdymX/2RrIJ0WgUN2/eVKzPdjeOzk6mdE15Orn8Ozu1g/5OCxyl4gLbay/t5jkUZSF8P9zmUKXTGXimfehsdkOrvedEVeZP6WSZesTV2um33sAiAKDOWQ1J2oQvFIXbbkGtM5vd4nc+tev+LkaOgkjlO9XOohO/UkZLvnpX+e4vylfY7fZtauaFxg79TZ4NSGE9UqkuNrVavA9JXZDCM43N8fFxVFZWMpIB5FapX11dxbVr11BbW4ubN2+ioqICyWSSQ0+U+SdJEgYGBlijx2AwoLu7m8M65JyQZIEoSnrjxo2c0h70zrVaLTo7OzEyMsL11BobGxGPx1m12u12o7KyEpcuXUJ1dTXa29sxPT3NfBqtVotEIoHHH38cGo0GX//61+Hz+bB//3709PSgsrISgUCAuSyUSWcwGFBaWsrq2q+//joefvhhFhSNRqPQ6/VYW1uDw5HVwfr2t7/NxO7HH38cmUwGi4uLGBoaQjqdRllZGSwWC1paWvDOO+/w5k/hZUmScOnSJahUKiwtLaGnp4e/Q2OA6tUR342cxXQ6jfHxcUQiEQBZZ7OkpARra2sYGBhAfX09NjY2UF5ezn28srKCvr4+SJKEr371qzAajVhaWoLRaMTDDz+Mo0ePYnx8nB3JqqoqRlG2traYN9fT08M17mZnZ/Gd73wHe/fuRSwWw9bWFjo7O3PCeKWlpXj44YcRi8UQj8cRDAbx1FNPYXR0FH6/H6dOncL169dRX1/PYqw0fsvLy/Haa6+huroaH/zgB6HVajE9PY2LFy+ira0NR48ezVkz6EBEqGUsFkNVVVUOckcmOj/U3+vr61haWsKhQ4dyHCCl9VuUSCCHdnh4mBMfipFlEOeuJGXLLM3MzKC9vT2nXiQdnorZT4rZN/Mpuhe7vrxrOlQ/teKMXsjY2Bg0Gs1d6ebcL5MPFvl9d2qHXHuEnEMAORpGu9XLEhcJKnlRPvW9bTIHcgdI+VpZp8hWaWIkKx9JfCcHqtDfKfxI1wew7btS96dyFgZyZgq953yLRLFOdzEaTNQmyvghUcBC9xE1uiikodPpck6k8uvLF0O540zZTvF4nOfEbp5fkiScPXsWOp0OZrM5R6dM7rwpObK0KaXTaS4W+8Ybb+DUqVPQ6/V877m5OXi9XiwtLaGiooLriXk8HlgsFs6YA3KdNnlBabPZjG9961uw2+1YX19HTU0NnE4nUqkUZmZmcPLkSVy4cAEGg4HJ7XQKF0/f6XQaExMT2LdvHztMsVgMr732Gv7lv/yX2L9/f44sxtLSEhYWFrB//34sLS3BbrfD5XLh/PnzUKlUWFhYwIkTJzgz7uLFizCbzXA4HFhcXMTc3BxisRgef/xxOBwOXL58GcePH8fo6CjC4TCampqQTCaxubmJ5eVlBAIBnDx5Ei6XC0NDQ0gkElCpVFhdXUVjYyOT6T/72c9Cr9fnCFHW1NTgzTffhF6vR2lpKauWW61WrKysoKKiAjabDTdv3sT8/DwSiQQeeughLqmTyWSwtbWFvXv3YnFxERMTE7h16xaOHj0KrVaL/fv34+bNmywgWV1dzc+9tbWFmpoa/OAHP0BLSwsikQiOHj2K8vJy+Hw+3Lp1i5ELQpADgQAWFxchSRKPLyLvt7W1QafTYX5+HpWVlUgkEjAYDAiHw9wWsabh22+/DZVKxeWrvvvd7+LSpUuw2+34F//iX7AMiVjGKpFIsGRBPB6H1Wplon1bW1tOMojIXyIdL51Ox1pj+Q5wNH9o/RofH8fKygoSiQQLexazzivtH3Jdv2IOl/nWtEIH1d0IRIv2U4fqPWYiQrW6ms1EU8qCu98IVTFWzEBUmlj5/lucFOJGUKyTKG5C3dJ1RSdmJwTJH1pCIpnC1O0QTOU6aDRq1Dmrd9SgkqNfkrSJwYk5mI1lKC/T7fh7Rev9RX4u8YR3N++5kLhrseiVfFEhdWSn07ltUSw0FnIcX+FEKb532tzyoVjB4B3xwWIQMiV0dXBwkAUFT58+zWEA+fiUj8V8Tp94D1EXjP4mR9REGQT5ZqDUf/39/Ziensbo6Ci0Wi1KSkpw/PhxVkf3er2MaBkMBiwvL8NqtSIQCLAWz+rqKiRJ4rCuz+fDwsIC1Go17HZ7jkBpLBbDK6+8gk996lOYnp5mJ2B4eBipVApra2u4cuUKjh8/DqPRyKGYlZUV3Lx5E/v27UM0GuXsta2tLUQiEZSXl8Pr9aKysjJnnNlsNnzjG9+Aw+FAZWUlh9kuXbqEkydPoqqqClarFV/84hcRDodx/PhxPPHEE4zuAcD4+Dh8Ph+0Wi1OnDjBnweDQYyPj8NqtaKrqwsWiwWvv/46amtrYTab0dXVhWAwiGQyiStXruChhx7C4uIivv/97+PEiRNYXV1Fa2srF+YeHh5GV1cXIy6kIh+JRPi9nTp1CktLS4zCDwwMYGRkBAaDgbMZaf6Q/tfQ0BDXTKRMWxobPT09CIfDuH79OlZXVxEIBNDX18cOOTmVhHJ+//vfx/j4OBe7Ji6afK53dHQwh8psNuPy5cuoqKhAQ0NDzoGHVN5tNluOBAnVIxS135TmId3bYrHglVdeQXl5ORP4dyugKc7PndClnZygYoCBuwUtfupQvcdMfCGrq6u7Uhd/L1ixiMfdIip5rzX45btqLzlCRr0OsbUknD/K3isUwpOkTZy9cgM6rQamcj2H/1LpDURXE0VdQ9F+5FAV0xfFhluVHAT5qTXf+xLDdt3d3RwalCSJCxWT80POFn1GDgwhSqIAKDlHS0tLHLIhLoKIfonPYbFYtimUF4L5ldBVuUOm1A/EwRELPdP3RFV42oyI2EscjtraWvT29vKzU9ZWVVUVn67FQs+i8yjnotGGZjabMTo6inQ6Db1ej9bWVszMzLBTRUiVyWTiLMTV1VUm4FutVtTX16OrqytHw4eQlpGREVgsFrzzzjtQqVSorKxkZ4bqyC0vL7MCuMFgYL5URUUFZmdnsbGxwW3WarWwWq1MjJ6cnMTq6iqWl5eZ70b9brFYcO7cOWxsbMBisWBmZgaJRALpdJoLOPf392NgYABPPfUUjEYjOyAulwvJZBKXL19mCYqKigpMTU3B5/NhdnYWDQ0N2Lt3L0wmEyKRCOx2O0pLS9HW1obXXnuNS9zMzMzAZDIxp66+vp7fzSuvvIJUKoXOzk7U1dWxw3r58mWkUilsbW1xvT8AHEabnZ3l0CWFqpaXl9Ha2soIWWNjI4dLg8Gsqvrc3BwefvjhnNJGHo8HGxsbTAynKgtkgUAA8XicyzopFSgW58DGxgZ8Ph90Oh2PzZ6eHp6LSmgnoU4khULaT+Rkyu8nn78XLlxgZGw3iuiFrNA6J/87PZsSlUCcd/l4ocXYu1Yc+ae2e8tHNlci9L5XbCeCfL6270REV7K7+c32a6jRva8WJkMZultri3KCgpEYGvdUI5HaQDqTwYwvBEnaRLleB6fVjIwk5SXCi5mH2z4XUpaLkQ2Qaz/dudZ25fx8QnQiqV1uokhkMeRLkfNApPiZmRkYjUZOsaYFjfRvAoEAQqEQp087HA5FqQfa9CkUSsRU8bokuqnUhw6Hg1PAqY1EOE+n00in0+xMkbgg3YuyeIh3RSHMN954A5OTk6wQXlpaCrvdzs8ej8fR1dXF/Q3cSTmn/iJOy8DAAK5evcrZfsAdGRNSKK+trUVFRQVisRhOnz6NZDKJxsZGXL58GXV1dZibm0NnZydOnjyJiooKdHR0MDpGziGl+6vVamxtbWFiYgKjo6M4e/Ys7HY7Njc3UVdXh83NTZSUlODkyZOsFk5OBmW33bhxg8nu9Gyjo6MIBAIYHR3FzMwMXC4XDhw4gJ6eHi54nE6nGe0YGRmBJGXlIW7duoW2tjYYDAbs3bsXy8vLkCQJbrcbH/rQh9DQ0ICKigrcunWLEw0aGhrQ3d3NfXP16lUkEglYLBacOHECXV1daG5uxurqKiwWC6anpxGPx/GlL30Js7OzGB0dxdTUFFpbW+F2u9HY2Ijjx4/zvSORCJefoUwuu93OyUINDQ3o6OjAsWPHmKhOCQYLCwswm81QqVQYHx/HxsYGDhw4wCjpjRs3WAA1lUrxGOzs7GTpjMHBQXa+qaA0AE7pJ2febrfDbDbj6aefRm1tLScxiPpQNpsNU1NTMBqNALLSBtQ3Bw8eBIAc8Uwa96KzIkkSE9tVKhU/K4Wz5UXP6Xcejwcmk4lD2jslUe2U1CK/vhLRXP53+m/xYEcon7iu0ffk81Tp/uLatxu5oJ86VD8Gy7ex7uS0/DhtJ2fgQbc9n8NS6LsAcsQ/RWdI6XoOqxkmox6Hmvdmwxora6CMQFI+J2V2URSU/u0NLG6TSwhGlAX28k1Uh2O79hN9Vy59IX8n4v/TxiZfCIHsgtvb28unNpVKxY6IvHCzy+WCXq/n9Hh6z+3t7XxqEx0Vyj5yu928EYnFjCmbhzLaxEVYdNbE6+ZTWRaf2e/PlpGZm5vjRTQQCDAhm/pR3EhowSWEip7/5MmTOHnyJNxuN/bv38+EXQoJkfNGISrS7CHyOwDMzMzg3LlzSCaTLFrIY+JHv71+/ToCgQAAcFgvHA6jo6MDMzMzcLvdePXVV2Gz2XD58mUOfzU1NaGhoQHNzc0YGhrC3NwcCySSlADxchyOrPzC4cOHEQqFsLCwgFQqxQV8iT8n1vgjNfVUKoWVlRXE43F2KGOxGEpKSvCNb3wDiUQCer0efX19CAaDePXVV7G+vg6Px4NkMgm73Y7u7m6cOnUKXV1dqK6uhtPpREdHB/r7+/GVr3yF5QEmJiag0Wjw1ltvsQ6XxWKBJEm4cuUKlpeXEY1GsXfvXrz//e9HX18fTCYTnnjiCeZPJRIJVpnXaDQwGAwIBAJMxr98+TLKy8sxNjYGo9EIg8HANeeuXbsGr9eLdDoNu90OvV6PY8eOMX9LkiTMz89jZGSEZTjKy8sRCoUwPz8Ph8OBrq4uDn+p1Wqey1tbW2hpaYHZbGa0iOadiAZlMlmNu9nZWQwMDDA6KCKvHR0dfBgio/IyMzMz6OrqQm9vL+LxOMrLyzEyMsLzisah6GjQfKb7T0xMIBAIIBgMIhQKMflfnhlHvxPnOIU3+/v7MTs7y98X1zqSUKEsxnzVOXbaT8S/i/9N1wSw7ffytbUYAIO+I8odFbJ7dqiSySTXiAIAr9eLP/qjP8K5c+fu9dI/cXa3yMxuveh7/Z2S3Q9UqVC7ipFKUFI9B5TLzsivJy8b47ZZsKe6cpvUQnh5FYlkCoMTc9vuo2QOq3lXaCQtoIUkApTUz7fd13En1Xl2dhZXr15ldWOxDaSunclkFEvHyNtDThidXAkdonsFg1lBQtK3IQdNRHMI5aH0a1H9vLy8nDkrVKpFdLryjVnaNIeHh1m2wOl0svIz9bMSukf/XVtbi56eHuj1ejQ0NKC2thZbW1sYGxtDKpViNI0WY2rDyMgI166j0280GoXRaIRer8cTTzyB3t5e5m1JkgSn04lDhw6xQKrVasXY2Bijdi0tLfD5fHjyyScRDoc5JKXT6RgdW1lZQSqVgtfrhd/v53IoGk1WM+m5557D0aNH0dXVhYWFBQwODmJ4eBgTExPw+/2YnJxEMBjEyMgI3G43gsGsrIDT6UR3dzfW19fR3NyM3t5ebG1toa+vD1VVVXjnnXdQVVXF5YqIU6bX6zE7O8u120jYEQC+8Y1v4NatWyxkSfITL7zwAtdqm5+fx9bWFkKhEGf1BYNBrK+vw+fzYXV1NQfhIRSWHM/W1laUlZWhtLQUer0et27dgkqlwtTUFH7rt34LwWAQ3/ve9xCLxTA+Po7Ozk7MzMxw+Z7p6Wn4fD688MILnHGm0+nY0V1cXMTm5iZndZLqusvlwvXr1xEMBvHMM8+gqakJ9fX1OH/+PDo6Olh2w2QyIRgM5iCUVNuQnH0AuHz5MrxeL65evbpN7kRU0xfnejweR0tLC6LRKFwuFw4ePIhEIoG2tjau+djZ2ZlXe450qUjeiLh4Wq0WlMFHYUsRKRbnuCRlxXRv3brF/SFf60R0vFCZLHE/EX+vFLaj7wJ3Dk2iREO+tawYEIC+U1NTk/c7ot2zQ/XhD38YX/5ylueyvLyMw4cP4w/+4A/w4Q9/GH/xF39xr5f/qRVhxYYKxc1IDLvsNsRYyBHbjZOW77tKz7NTLT5AWT8KuOMMSdImrt2YwbR3Ael0BiqocpAmQphInyq1kcHQ5Nw2FCu2tg6jXotAJIYSjSZHXV1OWtdo1AXRSCXnSCmsJeq4yEvEKPUnXYPIr3NzczlhAjHktRNUL28PpbHT4i2H1JUQNHHhppNtvvtEo1FkMhkW1RTrChJKNzc3h6tXr/JJuKurCyaTCWazGbOzszh//jyALMfpwoULSKVSOfC/uAiLXAy56nw0GoXBYOANiZzCqqoq6PV6uFwudHR0QK/Xw2azwWw2c/+73W709PTkiKD6/XeKvVLokfrEZDJhaWmJVd9Pnz4Nk8mERx55hIVNATBiR4gabcySJGFkZITfATk0LpcLpaWlcDqdqKioQEtLC1wuF9RqNQKBADtlGo0GlZWVjGo1NDSgpqYGZrMZH//4x2G1WlFWVoZ9+/bh1VdfhcFgYP7O5uYm1tfX+Vl1Oh1SqRSGhoZw/fp1xGIxXL9+HQaDARaLhRGrp556iuUjjh07hmPHjsFkMuFv/uZvGF3t6urC4cOHuUj30NAQZmdnmVj96quvoqqqClNTUygpKWGl/bW1NYyPj2N4eBhWqxVXr15FfX09hoeHEQgE8PWvf51V4B2ObK3JCxcuoKKigqUsyPGxWCyw2WyoqanBBz/4QTgcDmQyGezdu5fvSWjo008/DZ/Ph5KSEly4cIE1l2gMkBjm/v378cYbb+DWrVuMdkqShKamJtTV1eHZZ5/dVh5FjhTRWJYfwqLRKNra2jA+Ps4OG/ECldYjjUbDBcJFnSs6DFEon2r5SZLECCfRCgYHB7F3714kk0kcOHBAkYpAaJaIotN4yXdgEtfKgYEBzMzMMCqbc+gugr6gtCYVAgF2CxTcs0P1zjvvsMDbN7/5TdTU1MDr9eLLX/4y/uRP/uReL//A7M///M/R0NCAsrIy9PT04LXXXiv4/UuXLqGnpwdlZWVobGzEF77whXeppTtbseE2edyZYODdhukKOXC7ce7yOXTy5ylWF6qQ6jkADE3O4Z0JLy68M4ZAZBmRlTVkpGy9wGAkBmkzN5wYjcWR2tiOQNmrTFhZW4elopydr93W7hMdnp36K5+DlQ/1EiF0h8OBQ4cOoba2NidMIC4+IkpUjMnVkoPBIFZXV3H9+nWoVKpt7ZIvkmLZFZFvJLZZXt6GuGXBYBAlJSUIh8OsATUwMIBgMIjTp0+joaEBKysrLOZHWXFieYt8fSaGIgltI7Kz0+kEkCUJ22w2vh6FDPft2wedToeSkhKMjY1Bq9VicnIy5x70vKFQCKOjo8xRIZ7Y1NQUZ1mJDqTP5+N7AuB5S5sdkBVYJtVz2ni9Xi870k1NTQCAAwcOYHV1FTabDU1NTThw4AAaGhpQVVUFm82Guro69PT0wOFwYHl5md+JyWRCV1cXnnnmGQSDQdTX12NkZAQ3btzA4uIilwdaXl7O6V+bzYb9+/cjGo3i5MmTMBqNiEaj2LNnD973vvdxWEmr1TIx3uPxIBaLYXBwEA6HA7W1tcwVS6fTmJ+fh0qlgtlsxvnz53HixAksLS3hkUce4VCVWq1mvlN3dzeam5vx/PPPcyIGIS0jIyM4fPgwenp6kE6nceLECWxubuIzn/kMtra2MDAwAEnKioxeuXIFWq2WszTHx8dx9uxZbG5uYm5uDslkkjf61tZWpFIpNDY2MmpL4WAg61SNjo4imUxieXkZOl221NT169ehVqsZKaX5Mzc3x1mBIlJElm+NEMvLkMMyNzeH2dlZzM7OciFwQn0obEfOvii263a7EYvFYDQaEQwGc8J3fr8fyWQSXq8Xx48fh16vV6QikGQDtV+OJintH/K1MhqNKq6b95t2cjcRnHt2qBKJBKuinzt3Ds8++yzUajWOHDnCdYXea/b1r38d/+7f/Tv8x//4HzE4OIhHHnkE73vf+1gxVm6zs7M4c+YMHnnkEQwODuI3f/M38Su/8iv41re+9S63XNmK9aLlYQ5xU9iNFSI878a5y+fQyZ+nULiP0KV0OsNOFwBFvpWt0gR7pQltDXvgtlvQ2exGiUaDQCQGQ5kOq2sp/r3LXoXufXVw2yyQpE2k0xn4Q0uYC0TgDS5CkjahK82PmOUnqSsXpS4mjJevf8jkJGkKLzQ0NODw4cO8oJHTovSelBYR+WfEwaKwg8PhwMzMDMrLyxGJRHLalU6ncfbsWSwvL3OVeqPRyM4DOdUej4fJ+AA4ZAiAicKUYRMKhXDw4EHU1tayfpNYrPnAgQPsAHR2djKJXKlcjsORLfg6ODiIlZUVDusQ8hIIBFBSUsJ1EcPhML7+9a/nhE8dDgeX7RgZGUFjYyMCgQDKy8u57eJ8UalU2LdvH9c3I7L/4uIiozLEswoEAohGoxwmArJhX5q3FDojwUvqY7JwOAyj0Yi3334bbW1tCAaDmJ+f57DV6dOncejQIYyNjWFubg5qtZpP76T5RNmL6XQaa2tr+LVf+zU+XNbV1WFhYYH7jRxPyjqz2Wx466230Nvbi6WlJZjNZiwvL2NiYgJzc3PQarUYGRnh5+rs7MQHPvABHDp0CB/60IcQDAYxNTWF4eFhdoCSySTi8TjC4TBWVlZw48YNvP/978fU1BRSqRQ2NzdhtVrR19eH5uZmuFzZItfz8/Mcbq6pqYHH44HD4eB+P3nyJPbt24dPfvKTWFpawuLiIhcPnp6eRjKZxNmzZ9mZq6urw+OPP47V1VXodDpMTU1hbm4O58+fh8/nw4EDBxCPx1lhX5KkHOVvQrw6OztZ1oCcS4fDwQWyBwYGcP36dSQSCQwMDPB4Fw8kcr4SkJvdJiaVkCr89evX+b/lBHA68FKBYjp47du3j+tZiuE7Wpc6OjpYSkR+UBKNElbkaFKh/YMQ2e7ubnR1dW373v2knQB3lyR2z7IJnZ2deP755/HRj34UBw4cwA9/+EMcPXoUAwMDeP/73/+ezFg7fPgwHnrooZyQZHt7Oz7ykY/g93//97d9/9d//dfx3e9+F2NjY/zZZz/7WVy/fh1Xrlwp6p7Fpl3+U7DdaHnIM9N2+nyb9X+pIELlDy0hsZ7ChDcIs1GPOkd1NlyX3kCJRgMqLaPRqLddR5I2MTB2C4lUCvFECvtqHSjX52pNEU8qupKApaIci8tx+ELZ+l697Q2shi6/do746Jl/n7OoKYl63q143W6+U8w9dqPdIurDSJKEV155BS0tLRwK02g06O/vRzQaxejoKJ555hkm8hqNRszMzKCxsZHLrYRCIeZlEUIj9pfDkRUBFasMyMOckpQr/VBIt4uMSmapVCrE43GUlZWhu7sbZWVlTJomjaiXX34ZNpsN6+vrePLJJ+FyuRAMZnWPLl26hPr6epjNZlRWVsLj8cDpdMLhcHCpFzpZu93unP8OBAKYn5/Hnj17cPjwYQBgcjYdgF555RWYTCY0NDTk3PfGjRs4dOgQAoEALl++jIaGBtTX1zP3h/okGo1iaWmJ9ZOIb+Xz+XD79m0kk0kcP348R9lckiSMj4+jvLwcs7OzaG1tRWNjIyNgVGtvcnISVquVJQH6+/tZkVqr1WJ0dBQulwubm5sYGRmBTqdDRUUFMpkMzpw5wyFTStEPBoPo7+9HOByGWq1m9fPNzU1MT0/D7XZjfX0dlZWVOehVMBjE5OQknE4nNjY2sLW1xf9eXFyE3+9HW1sb5ufnsXfvXhgMBuzbtw+Tk5NoaWnBysoK5ufnuQgxJRXQIYDES0+dOsWZoaS2r1Kp0NzcjFQqhUwmg+PHjyMUCiESibCw56OPPsqOvXyeyv//ypUrrC/lcrkQCoVYpgTIOhiiDpyS9hI5eGVlZaisrMTp06dz+H/kqMslSkjEVhTtLaQHJW/77Owsrl+/jpqaGtTU1OTVfrvbqhr3YsXuPeL31tbW3h3ZhP/0n/4Tfu3Xfg319fXo6+vD0aNHAWTRKhJCey8ZpTI/9dRTOZ8/9dRTePPNNxV/c+XKlW3ff/rpp9Hf34+NjQ3F31CGjPgP8N4gkBd77Xyf7wZVKUS+luuDKN5fyNZTCqc5rGbE4uswlpchGlvjz3TaUgBgZCudzmBwYg62ShNfJxiJIbWRQWw1iY4GF3TaUuZUkdkqTYiuJNDRsAc6bSm6WmvhsltgqzL9qI3ZWn79Y7eQSKbyEuELZZ+IfbqbsG0+02g0OQroO91D7H+l7+T7nQj5U6YcCTJS2LGjowOZTAZPPvkkYrEYJElCZWUlpqamUFdXh3g8DqfTiUwmA5VKVTBbR4krQuMoEolwpqG8XykEJs+SpM/MZjNKS0uh0WiQTCbh8/n4XhTmqKurQzKZxGc+8xmUl5cjk8nwBp5KpRCJRFgzyWq1wu/3M6oRDGbFFgOBACoqKljYl8IXGo0Ghw8fRn19Pc8pv9+PUCjECt7Dw8O4ffv2tjCiVqvFww8/zM6KyWTC3NwcjEYjJicnkUqlsLy8DIfDAafTib6+PjQ0NOCZZ55h7hXVg/vEJz6B8vJySJKEqakpXLp0CQDQ3NyMW7duMQJJSJtGo+GQ4alTpzgESf9WqVRoaWlh4c9QKITZ2VkcPnwYdrsdbrcb73vf+7C6uopEIoEvfOELmJycZJRSkiRsbm7CYrFwuJY2teHhYdbRIqeRnJempibWE5MkCWq1mhHOkpISlJaWorq6GjU1NTh48CAmJyexvLyMN954A+l0GgcOHEBdXV2OXhmFcd1uNw4dOoTx8XEkEglIkoS1tTVUVlbCYDBAo9GgoqIChw8fRjQaRTqdRiaTwe3bt1FTU8OHctqsCaWlcSI6VwBYfJNQZr1ez+gtPaOI8ivN1cbGRmxsbKCxsRHhcBi1tbVoaGjgxAulkL/L5UJjY2OOIGihzGGRk0hJCuTIKpHg6Z7y0J2c5/sg9rx8a6j8fneDeN2zQ/UzP/MzmJubQ39/P86ePcufP/HEE/jDP/zDe738fTcqDyBn7dfU1OTdpILBoOL3qZaVkv3+7/8+zGYz/7N3714A4LTl3SJ3dwM/3uu1lbIr5ITnndq0k6NQiEsVDAbZIcoXQiO9qcY9NnTvq2PHSy51cPbqDUzfDjLJ3B9agsVkgK60BJ3Ne1HrtEKjUW/TmgpGYkhvbCC8vMrXtleaONwXjMTgC0WR3thAbG1dsQCz2A/y7BMliL6QFepP8R2Jzs6dvlJeIIifRBuZUnaM0u9EyJ94T8TZAO44DKStZLfbEQgEMDIygsrKSq6P5nK5uAYZbSY+nw8qlWpbf9GJWjxli+Hjrq6ubdIPxNkCkEPspfG9vLyMjo4O7N+/H+3t7Thy5Aj3L2n6UPaaRqNBSUkJTCYTxsfHYbPZUFJSArvdznIRgUAA4+PjrOVjs9k46zAej8PtdnMpke7ubn4eIr0Hg0Em3k5MTPDva2pq0N7eDkmSuNafKKC6tbWF9fV17NmzBzMzM2hubkYkEoHBYEBJSQmefPJJ7Nu3D+973/s4rKS02Wk0Gib/vvDCC1haWmINpIqKCg6tEtGa+Fa02RIhn5wY0kkaHh5GKBTC+Pg4jh07xqHSyspKVnAndAgAS29QtpnT6UQymeSwImUAEg+NEh2cTiceeeQR5g06nU4sLS2xKvzt27fhdDoZATQYDFhYWOCMO3JyaczdunULXq+XxWwBsLxHJpOB2WzGnj174Ha7UVVVxZIiFosFq6urMBgMXKC8rq6Ow7KpVAoXLlzg4tjLy8v427/9W3auSECV5oScbyhy+QAoOh8ulwstLS34+Mc/jng8vo2mkc9pUZrzxRz6aE45nU40NDSgt7c3Z77mozyIXEm5vpS4L+QLcRbrdInJOPk4qPeyx94XHSrqlHPnzuG73/0uvvvd7zL0+l41qopOtrW1te2znb6v9DnZb/zGbyAWi/E/t2/fBgCGP3dLnNsN4W63gyzftcXPFTPvimjTTl5+IS6Vw+FglEeJRyXqTdU6rewUpdMZ9I/egiRtstSBqVyHlcQ6pM1NDscNTc4hsLj8o3aqYTEZMH4rCHO5Psd5kzY3EfiRUxeMxBCILCMjSQgvr8JhNcNtt6DOUY3ufbWQpE30j95COp0pqh9EkuZOC8VOULVIsLZarSxpsJNRFo/ZbFbUhBHvT4RW0nsiLpVGo+GTL6k+09gQs3nI6SIuBG1cIvJEOkahUCjHcZI/J41FkQ8oErqpv6jkE1kqleLsJOIj1dXVoa+vD8eOHUNPTw+2tra4r4kHEwgE4PF40NLSgkwmg+bmZnYgqMAuORodHR2oqalBY2NjDvG/u7sbQ0ND2Nrawne/+1288847mJubyxEcJUFRjUbDqu2k1E5h1FdeeYXDOYSU6HQ6fOADH0BjYyOqqqrg8Xhw8+ZNeDwevPnmm/w8RIKn0MuFCxcQi8UwNDQEv98Ph8OBY8eO8cFTrVYjmUxi//79rP5N7y2TyXCZlaWlJZ7Don5ZOByG1WqF0Wjk8A5pPt24cQNDQ0MwmUwwGAw4cuQIX9flcnF9Pgr97t+/H0ajkfu5tLSUU/0jkQjMZjOmp6dZDoRK5SSTSSSTSVgsFhw4cADBYBA6nQ5DQ0OIRqM4cuQIHnvsMdTV1cFisbAUhcPhQG9vL/r6+pj0b7FYMDU1xVplNHeoX0T5i/b2dqyvryOdTqO9vR3JZBJ6vR6Dg4OYn59HfX094vE4Tp48iYGBAahUKly4cIHHNKG3ooMxNDTE3CZ6n3Nzc5iensbf/d3fYXV1lecGjUkqgu7xeHJ4TTs5LUoHaflhUJyfhAjTnNZqtQWdpEIJN0r7i/z3u3WCgsH8mYD3g9R+zw7VzMwMDh06hAMHDuD9738/PvKRj+AjH/kIPvrRj+KjH/3ovV7+vlt1dTVnE4gWCoXyak3QoJV/v6SkhE9TciOOgPgPsB0aLdbp2Q38mA9Z2u21xc/vdwYFWSFyvEajYZTHYTWjRKNBeiODuUCEnRslsrpn2ofY6hrOXxuFJG3CYTWjcY8dXS21qK7MqgnrtKWQtjYRXFxGMJr9/fBNH1bXEvjWhX4sr6zh7JUbsFWaoNfpYDGVM0fKbbfAbbcoolGeaR/iiSQ80z7sxuRon3yhkCSJUQORdCzvy5KSEl40CFrfyUSHBkDO2BEdKMo483g8TIotxkR0jpwu4gApweuEfNntdsXFUk7y3pbEIPQd3VulUjFKRYV+/X5/TviB/u1yuVhp3e/3swNIjlIsFsOxY8cU9WzIsTx69CiefvppNDQ0QKPR5KA3zz33HGKxGJLJJIaHhzE2NsYh9OvXr2NhYQFVVVXsNIgOtM/nw82bNyFJEmZmZlBWVoapqSkA2bXNaDSyflYkEmERSofDgZGRESbbnz17FlNTU3jppZdQWlqK27dvw2w280Z99OhRPP/882hsbITVasW+fftQVlYGs9nMGVo2mw2Li4vcr2K4anZ2Frdv30YwGMTBgwfR19eH48eP4/DhwzAajTh9+jT0ej0qKysRiUQwOTmJra0tdtCuX7/OTrWYWq/RaLiYL62/FJZbXV3F7Ows6uvrWQeMuGtUvqayshKpVAptbW2Ix+NYWlrC0tIStra2GE0jxxbIJkeQkx0KhTA4OIgLFy6gvLwc4+Pj0GiyGk0TExPs1La0tHAZFq1Wi+rqaiwuLuLGjRvo7OyE1+tFMpnE5OQkpqamYDabMT4+jmeffRZWqxWnTp3iMS0eTESqBZnf78fMzAwGBgZw9uxZRKNRXL16lb9Dc5hkToxGI86ePcsIqIjW0F4hhsaV1iG5MyYewkRHWzzwFHKS5GuRkr5UPkqC/P932u8K3f9+kNrv2aH63Oc+h4aGBiwsLLAy6+XLl9Hb24uLFy/e6+Xvu5E2y8svv5zz+csvv4xjx44p/ubo0aPbvn/u3Dn09vaitLT0rtrxIEN4OyFLd2NKg00MFe0Gcs0Xpy40GUiawBeKYnDCC39oiflNtkpTznc7m91IbUhodFUjGIlBo1Gj1mlFT3s9Fzi2VZoQXV5Dpdnwo3Ztwlyux8jMPGosZgyMz6FEo8Yr/aOwVhgYKaNrERo2F4igf3QWc4EI39tYrkdn850U6ULPRZo08qyVfFA4IS1K16RFqKqqatdyGEoLeDCYzQgaHBzkdyyG9QotYuK4yzd2igkXK2nvFOJyyPuO+oRqwZETsLGxgWAwuK3dlMlFmW2UPUi8FRIipNI66XSa9bDypbKLfQpk16APf/jDeOyxx3Dw4EGcOnWK+6SiogJra2usXk/tpz4OhUL8eX19PZepCYfDvDGKYcjm5mY0NTXBYDCgubkZAJhYe/PmTWxsbOD27dtoampijhttkLW1tXC73RgZGUF/fz8uXryIixcv4tq1azh79izm5uZY44gSEeg5Lly4gDfffBM3btzA+fPnIUkSnnzySTQ2NuKJJ55gdKy7uxtOpxONjY1wOp1YWVnB2NgYxsbGcPnyZQ57EjdLo8lmwtntdj7MSpKE6elpLmkyNTUFu93Oivt0IGxqaoLFYsHm5iYqKyvR3t6OEydOoL29HSUlJazKn8lkmCQ/MDDAvLqFhQUsLCygoqKCBT/JUVlaWkImk4HD4YDJZMJjjz3G8hSkIk5o3enTp9HY2IiTJ09ymNdoNGJ8fBynT5/mMSaOIREVArLhUCJ5T01NIRwOo7y8HOFwGGVlZSytQIcgCqGvrKxw/cNwOJyD1hBaTo6hkgOkdFgBsA0xzhdK3glQ2O1aUugwpWTF7jd3a/ec5VddXY1XX30VnZ2dMJvNuHbtGgu//eqv/irzMt5L9vWvfx0///M/jy984Qs4evQovvjFL+Kv/uqvMDIygrq6OvzGb/wG/H4/C5bOzs7iwIED+MxnPoNPf/rTuHLlCj772c/i7//+7/Gxj32sqHvKs/x2Ct/cL3uQ9yH+k5hlspMVyjRT/Fv/l4T7ZbPyUhsZ1DmyDk0imUJsbR3d+3Lr9YnhQIfVzOE5IMuL8oeWsLy6htHZIJ452gGTIRvmm/YtYGNDwsdO9eLy0CTK9VpoSzSKop0AcO3GDLyBRbjtFtQ6rbBVmvhe1B6/8ynFZ97NuxErxNfW1vLCQfC6iA7cr/ctbq5KBY7J6L2JbQEKV5GXt1P8/8HBQRYSbG9vVyw4LL92oc9sNhtnQ8ViMej1egwMDODYsWPIZDKYmZnB6dOnodVq8YMf/ABLS0ss9kgIiHwRpj6JxWLw+7Mq5fv372dhRHlRabE9JK/Q2dmJhoaGnP4jQq/dbodarWbH9fz58ygtLcXFixeh1+u5jEtnZycikQjXkaMsrv7+fhBhmooIi7pVtJGk02mMjY3hwIEDcLlcjL5Q6LG2thaLi4u4desWO1wtLS1wu92Yn59HQ0MD9Ho9K6ITx2dwcBChUIhLoDgcDuj1ei4snclkYLPZoNfr+b10d3djdnYWL7/8MtRqNVpaWnDz5k00NjbCbDYzV4hI3KFQCCqVip1Pm83GOkokyOr1etHc3IwbN25ApVIxcd1ms6GtrQ2rq6toaWlhZJeuEw6HMT4+jmQyibKyMlRVVWF6ehpbW1tobm5GbW0tbDYbFhYW4PF4sLm5if3790Ov16Orq4u1tFKpFE6dOoXh4WGeu0ohM0KVdTodqqurFdeKgYEB1iRrbGzkceb1erG5ucmhR5LQoJA2PQ8VSyajcDzNGaUxu9O83Wku5/sOIaViUXNxLRGfP9+aVuw6kM92k63+rhVHliSJizJWV1djfn4eAFBXV4eJiYl7vfwDsU984hP4oz/6I/zu7/4uurq6cPnyZfzgBz9AXV0dgKx4n6hJ1dDQgB/84Ae4ePEiurq68P/9f/8f/uRP/qRoZ0rJioEX74cHfT9gzELXFrkvctKkkgZJIch1ZzhYjZ72ejS6bHDZq1i13Gws2xb2I0QrI0nwTPuQWM8tF2OrNCEjbeHJw+2IJ9OwVZpgqTAgvSGht70e4eVVVFca4bZVwW235NTwE62rtRYNLjscVjMS6ymcvzaak+1X6Ll2gx7SgkiLpfx0SJs8sJ1UXuw4knMngsGsIGFfXx8v4EqmdFLdadwVOll2dnYilUqhrq6Oi+wW+q34e+oH2tQTiQTXGCwvL0dHRwe8Xi/6+vqg1WoxMzPDXBq/34+TJ0+iqqoKHR0dsNlsWF5ezglfyK2trQ0HDx7EI488wuR86ksxdESHj0QigbGxMeYwEblYRNQos4vI9UNDQ1hZWcHt27fx+OOPo6OjAwcOHGBdqcrKSszOzkKn0+UkIahUKjQ0NCCZTOLgwYMwm81MenY4HIhEIohGo6isrORsufLyciwvL2Nubg7Ly8tcW++ZZ55BX18fnnnmGdYY1Gq1nAggSRK+//3v46WXXmJ+msFg4CwyUoKfmpqC3+/HwsICotEoOjo6UFJSgo2NDV5zKWyWSCTQ0tLCvKMXX3wRly5dgtfrhdVqxZ49e+B0OtHe3g6Hw4HV1VV0dXVxeG9gYAButxsvvPACxsbGsLGxAYvFAqvVyqiUwWBgHapMJgO1Wo2bN29yDbz19XW0traipKQEdXV1zJm1Wq1YWFiAz+fD5uYmqqursbKywoi91WrF2toaSktL8bWvfQ2JRGIbb4cQ4EAggK2tLYTDYSSTSSwuLsJiseTMWRrfQG5iBY0TvV6P9vZ2JoJ3d3dDp9OhqqqKi3ATak1oqzy7j65F9fjkparEuQfcccrpECXyFuWhQCXeo7xmqbiWiGtlvrVEaf0stO7I18EHQWO5Z4TqkUcewa/+6q/iIx/5CH72Z38WS0tL+K3f+i188YtfxMDAAG7cuHG/2vpP2sjDHRsbQ0tLS1EOzm486PeCUXsphZ7+fU/tFxAqJZMXQgaQI/AZjMRgqzTBM+2D2VAGnbaUOVmStAnPtA+dzW4EIzEMTnhhNpXDqC/D5uYmEuspGMv16N5Xi8GJOZiNZSgv0zFJXkShJGkTgxNzMOq1iCfT6Gx230GqDv+yrM13kBNaDAo5H7QQBAIB1mmSn96ItFs08gds05URT6jEDaETLunhEHdJSTum0OmQEDbilYjhISVEixwQyqySc+zynYqpH4iEq3QCFk/GkpTNiKysrORaeYS2BIPZtHZCk4irQ2ERvV7PukAkdxAOh9HV1cWbJYVmxGdpa2vD5cuX0dTUhEgkovgdsc2zs7Pc/0TypVIra2trXLqF2kH8tLm5OYyNjaGpqQkVFRW4ceMGotEoqqur0dbWhunpaayursLhcOCJJ57A6Ogobt++zZl1Fy9exOzsLNrb29lpISkOErE8dOgQ3v/+9yMUCuHatWtYXl5Ge3s7SktL2Tlzu905fTI/P49IJAKLxYK6ujp+16WlpThw4AAjfw0NDZiZmUF7eztGR0cxPDyM0dFRJq8/9thj7Fy88MILrPb+9NNPc19SmwKBAM6cOQOdTsdjMJlM4o033kBrayuqqqpw9epVtLa2oqurC0NDQwiHwzh48CC0Wi1SqRRu3LiBUCjEulDnzp3DzZs30dDQgJ6eHhw8eBAjIyOwWq3sII2MjGB1dRUbGxv42Mc+xugyjXnKNJQkietA2mw2Rs6U9NbocCVqTymhPUoIrTySoDRnqVA0JWoomRyVFtcf0bmSf7YbNKnQ38X+KBY0uJf99F1DqH7rt34Lmz8q2fF7v/d78Hq9eOSRR/CDH/zgPV165sdlu+Ez5eOQ3K09iJixaOTxEy9gpxpxd2uk/US8JVHuQCSqi2Rxa4UBS6u5fKvw8iosFeUIL69C2tyEtLkJXWkJuvfVoqu1FsZyPToa9mBwYg56XQmm5kKwVZoUyfAk32AyZB2w8PJqXnV3OlkRSbkQAgLcqf9mt9tzNJhEK0TOzHcSU5JWULre4OAg18ejDDnKghJNie8hyjh4vV5cunSJCe07cSO6u7thMpl4k5CHSFZXV5nvJIYaqT6hUrKDPAlCq9Wiu7ubQzyrq6s58hF0KBDFFKmUh91uZwdrYGAAFy5cYOeHTvvUf1TSxm63c501sUxLMBjMKRottpky/IjgThy5U6dOoa6uDh0dHfz8tJHX1taitLQUFRUVmJubg9lsxrFjx1BdXY1nn30WNpuN+WHpdBqXL19GRUUF9u7dy/UBV1dXUV1dzQrnlI1HyEdFRQXU6uwW0tnZie7ubpw6dYpLE3m9XtjtdtZzqq2tZeSktbUV0WgUXq8XmUwGpaWlnClXV1eHtrY2TExMYH5+Hj6fDxaLBWq1GgaDAQaDgXXLHA4HxsbGWCLCaDRibm4OHR0diMfjePbZZ/Hwww/jQx/6EMsaTE5OIpFI4K233kJ5eTk0Gg28Xi/KysowOTmJcDjMZHJy/ktKSljElLJaAaCiogIul4tLxfT29qK2tpazR0+dOsWoEZBbK5LG39bWFra2tuB2u7l/3G43rly5AovFwuOe5oXIkaS9QVTPl89HJb0qcdzJ9ySbzcbzTURYRfRaku4Uc06lUpAkiQ9bStxBObJFCG2h0FwhBJ9CpFTYvZg9dSdE6n7sj/eMUClZNBpFVVVVQRmCnzS7G4QKuL8o1Y8T8bprbo8CQkWFjAGgzlmdgxgB2xEqSdqELxSFtLmJ1bUUGl3VjCKRwyNJmwhEluG0VkKrLWHHKRCJwVimw8C4F30d9TAZ9IoI1fbnFZTTf4RQiSdGQniAwlwjAAW5Dfne6U7vmgRuKWxA11VqC6keHzhwAHq9ngnb1J5871Zsg81mK4hQKZ1A5X8Tr+dwOHD27FnodDrm1+Tj8+3EtQgGs7pP5OASckLPphRKpbZQWE+lUiGZTOL27dv42Mc+hnA4zIhRJpPB0tISn9jdbjdzfWgjIJVwQgELncqV2pJIJLC0tMQbGvU3kK29KEcWgSwS4ff7odVq0dTUBK/Xi1OnTiEcDsPn82FtbQ1+vx8nTpzAyMgIEokEUqkUTpw4we9Oo9Fsuz6V6VlZWWEng97HlStXMDg4yG2vrKzMeVeExly4cIEJ+Hv37sUjjzzCJVJGRkZQXV3Nkh2xWAw+nw+VlZVIp9MoLS1FWVlZDsJD/Uck89XVVbjdbly4cAFnzpxBbW0tXnnlFRiNRuh0OgQCAQBgHaWvfe1rqKqqws2bN/GZz3wGWq0Ws7OzGB0dxb59+3KqA8jHFx0oTCYTt110gMR3Su/T4/Ewv8zpdHIZN6fTiUgkgs7OTmg0Gi5Fo8RvlK83Sshyvrkhn0vi3AOwrfqAfE3IN5fF5JpC0Yt8Y13+990gVDtZoTWzWITqgThUP7XtJr4Qg8FQtHPxIEjGQHEikvfT7tqZ+5FDJTooAHZUUKfvUAka+v9kKo3ZwCKaXXY0uGw/CvvdRlutE8uJZJYPUWlELL6OZCqFcHQVao0aVcZylJVp0dteDwA7OlSiSd2f2jEsV/D3O0DfxRI2Revv70c8HkcikUBbW9uuFjb5Qq20mO5mrIltBaC4QCtdTwxZhsNhxbAHsHM5HYcjWzeNsgdDoRBnZjkcDkYInU4nPz+wXWSUQjGxWAxzc3OYn5+HwWDAI488gkgkgmQyiWg0CpfLxZpS5AyNj4+jsrKSHS2l96FUDgTIOsfnz59HY2Mjp+tTuRij0Yje3t6cEOrKygpncInhJovFglgshnQ6DUmSWEyTnLNwOAy9Xo+ZmRneDFtbW/meNBYoBDkwMIBoNIrHH3+c0ZGrV6/i0qVL2NraQmtrK0pLS7GxsYHFxUVsbW1BrVZDrVZjfn4e8XgcVqsVR44c4XI3N27cQDweh9frRXt7O2tCGQwGxGIxVFZWYm1tjYsd01igsVJZWcnvd2xsjOvwPfTQQwCyWZ5vvvkm3G436135/X7cvHkTU1NT+PjHP861aykMD9xBLskhP3/+PCoqKhhVTKVSGB0dRTweh8/ng8FgwP79+2EwGNDR0cF9RwdunU6X4ziR80DvSafTMemfxvxOh63drMGFiOZ0bXEuFkrCUAr97VRyppi23m+AoNCa+a6F/ABgfX0d165dw/e//30W9qR/fmrbbTdk5Hze993Ak0SKzAeR3i3kWczv7iV8SfwkInuTdIHLXsVlZZQI46RdRZ87rGasJtZh1Osw7QtB2txEeHkV66k03hq5iXgyieGbPiwux7lwMlRAtdmI0lINnEKdPm9gkZ06aqNSG4Cdy84oP3NxJ7B8f9vp1EYhsdbWVs6SUnovBK3T2BEXHdLOIu5Hvt8BhTXXxPkghkPFBVneDiC3SLM8lCf2I4UnxH4XxyP1F12/rq6ON0HR4SAO0PXr13kj9fv9XLiWODktLS1Mkvb5fBgdHeVNUKvVMhGXkjYWFxdRUVGBSCQCu91ecIxEo1GkUinMzc3h2rVrmJ6extDQEDtTFGY/depUTuFaCinOzMwglUrB6/XC4/FAkiQmVut0OnR0dEClUrHsQiaTwcjICKqqsvxEcjz7+/vh8/kwMzPDDsHGxgYWFhaYS6bRaDA/P4+XX36ZQ8S9vb04cOAAjhw5gr1796Kjo4N1oJaXlzE7O4t0Os3I1okTJ6DT6XgetLW1ob6+HqdOncITTzwBnU6HkydPskZWIpFAbW0tE9fn5uYwNzeHoaEhxGIxjI+PM2ettbUVZWVlOHDgAIAs6vLiiy/i1q1bLFxL83Z9fR379+9npXSfz4dbt25hYGAAlZWVcLvdkKSsNtg//MM/YHV1FdPT0znj74knnuAQYSwWw/T0NKuk+3w+XLp0CWtra5iZmeEwL4UXKXRHTjmQVWvX6XRcL5LWDFFJvZAUi5JJksRyC6JjIa4nSvuImEQhn9cAGKUuFhygthYqb3a/SeX3A+m6Z4Tqhz/8IT71qU8plmChhfqndvcIVT67W++8kBcuP7XvJv00HzpwN23OaePglzEXiMAbjEBXWoKe9npGheSFi3XaUiamp9OZbJmZzU1kJAkatRpuuwXpdAYXBsdhLCvF3ppqOCxmjM8F0OC04e2xWWhLNNCXaeGyVcFSYUBoaQVOayXf02Wvgj+0BF8oylIJYltKhGcn9IwQqt0gTO9GeJZOlRQiUrqXPHRx/vx5rmsnEqELhRJoYS0UfiSkSaPRKBJp5eOxWORW3o9K7SopKUEgEEA6nc4J9dH3iCclCiBSyn4gEEAikcD09DSXo2poaEBnZyeuXr2K6elpHD16FC0tLZAkCalUCrFYDJ2dnTh//jyn8h8/fnxb1pVcKoOeB7iTiUzkZZqzchSR+of6mJwfSnBYWFjAW2+9he7ubrS1tTGSVVlZCb1eD4cjq2v1jW98A2q1Gmtra6iursbm5ibXBgwG78g0kKCxXq/HxsYGhoeHuZYelRkSn4n6VqPJFj6mNtM4Iaddo9FwSSNyUgjNMZlMPF5aWlpw+fJlVFVVYWtrC16vF2+//TY+9alPYWRkBD09PaysPjMzg2g0yuV/SOx0aGgI73vf+7hkjSiSSbIIwWAQ58+fx+rqKhobG/HBD34Qc3NzjAImEgkcOHCAHQ9x/FGosLm5GfF4HAcPHmSEKhqNMudMPo4pLE0kfzp00PWBrFO4uLjIoeVCIfl884XCi3IHKd+6kO+a8u/kCxsWWt/uZh28l6hOvt++awjVv/k3/wbPPfccAoEANjc3c/75qTOlbPfDExa9890gS4XuLV5zNyhavhTYQtcH8iNbiumwatW2EBsVIO5sducUIgayaunewCICi8vQlpay9MGbwzehLy3B7dAyxm4FMLcQRWojg5v+EN5/pBOhpTgSyXUMjt9CaHkVdc5qdpp8oSirtJdoNDn3I5FR4mv5QlHmZxUiays9az5UJV9/0SJ99epVxVRn+Xfl1yBUROk0KLbd4/FAp9NhYGCAS6IUg5wpnSTFdohCneLvrFYrFhcXkUwm+cQMgNOx5anZSkZIVDqdZrSClKXJiQQAi8WSI04phvMcDgdrP2k0GkSjUX5vTqcTqVQKzc3NsNlsqK+vR0tLC4cI29vbeWOieUKOaGNjIyYmJuB0OrG8vJyjUQSAifxUq06jyaqwOxwOdmhOnjwJp9OJQCDA6tezs7N46aWXckqQyPuYZCQ0Gg327NmD27dvs+NgsVi49h2QRcV6enqwuLiI2tpaxONx7Nu3DysrKzyGNzY2sLy8zKVhLBYLXK6s0js5MED2AH716lUMDw8DuJPR1t3djb6+Puzdu5c5fYQQlZSUcP0+IIsOfec730E8HsfMzAw79N3d3ayVRc7dxMQEVlZW8MILL+Do0aMsGUFj0mKxALgjUjw5OYny8nKo1WrEYjGEQiHOVI/FYpidncW1a9cgSRKOHz+OxsZGnD59GkAWpenu7kZLSwvOnDmDhoYGOBwO5gvROF1eXkZVVRWWl5fhcrkYhWpoaGANK/k8JrK+2+1GRUUFPB4PO55yEU55vT55Akcho9Cl0+ksGE0oJnIiJ6KLiFmx6FKh78kROWorvcvdCE6T7WbfU7J7RqgqKiowODiIpqame7nMP3sr1sO9G3sQaMZuvfy7ORXka7ccocoheBfBWQLuIFS2ShOrmveP3kJ4KYax2QDcDguqzUbc8i/idmgJiWQKLbU10OtLcTu4hEpTOU491AajoQwOqxlzwQiuT91GjaUCGWkT0VicizET8T2V3uBMwpziyL2/mPe5xU0bQEGuVSExvLfffhsbGxs5GUU79Tmd6kVJAKX3kUwmceHCBRw5cgRvvfUWTp48yWrOhIbkI6SKlg+1UkKfSLwxGo1ia2uLiw8D2ZOtSqVCJBLhFPWduBZ06iY0ymKxsLaWyM+itlB76blIikCSJHi9XoTDYTidTrhcLkZ/CLGRc67ouoS4UQgrGAwyb0mOSlDohfpFdLaI/0aEYZ/Ph9nZWcTjcTgcDqTTaaTTaRgMBhw6dChnU5NLb4j8Kyo9JD47PUMwmFVxv3btGhwOB0ZHR7F//34EAgHU19cz8p5IJHDq1CmMj4+js7MTfr8fAwMDKC0tRUdHB1555RXMzMygt7cXhw8fxo0bNzixwGq14tVXX0VDQwOMRiPPAzEESwKc4XAY165dw+c+9zlMTU0hnU4zL4oyAXt7ezE8PIw/+7M/w7/+1/+aHcqDBw9uI9EPDg7inXfewTvvvAO1Wo0nn3wSPT098Pv9CAaD7JRPT09DrVazRANl9Slxjubm5nDjxg00NjbC6/WipaWFD55U95DGFc3rzc1NJBIJ5r4pzSElIrr8oCR3Zq5fv86cOnk5I/nclKNJu9lT5HIKxSDUuzGlNUROcKf1YzeC00rXvxuE6p4dql/6pV/C8ePH8cu//Ms7f/kn2B6kQ3UvEOf9+P3dWlH33UGH6s618jtd6XQGnmkfWlx2vPDaEJzWCqwm1+GyW1BlKMf/+e5lSJsSeg80IJFIw6DXokyrhbO6ElUV5YjF12GtMHAWYHh5FamNDbhtFkRW1ljfKrS0ylpVOarqModqpzCfXP1c/J3SQkGLdygU4oKk+e5RKBSX7zekHi5JEnNaALCTQhuI3KEqtEgXCinLs9boHmJmIG22lLoN5Ce/iw6TzWbD8PBwTrah2LZoNMqkbNr8CGGiUiskJWC1WpngHAqFsL6+jrKyMpZhsFgsuHDhAvObaP7b7XaWLLDZbExmFvui0GGDeEw2m42LulP4p6qqCouLi6zDtbW1hfLycnYOZmdnsbq6iqeeeoodOAotSpKE6urqnBCr2M9EuB4eHoZer8fLL78Mu92OsrIyOBwOTExMQK/Xw2QysWAn8c4sFgtWVlawtraGtbU1PPbYY0xmpqxGSZJw+/ZtzM/PY8+ePVhZWUFnZydrIYkHj69+9auIRqNQqVScZbiysoI9e/Ygk8ng6aefhkajwbVr1zAzM8O1IROJBJPoabzSeBsZGUEgEMDy8jKOHTuG5uZmOBwOXLt2jTlVGo2Gszlra2u5ALkY2nM4HOjv78fQ0BA2NjZgNBpx5MgR5riJvCPxAGE2m1FSUpKjJ6YUSifRTdGhokMD/VYcsyS5sry8zAcQep/Ea5I72ne7JxQaSzsR0YsxpTWk2OzFe7F3zaFKJBJ47rnnYLPZcPDgwW217X7lV37lXi7/z8YepEOVz2hQ7STEmA8ReU9YkQ4VcZhEHhVfYvQW4okkEusbaNlrx8RcEGZjOeoc2VDetZEZLK2s4dHuNoSWVxBeXsXBRjdc9ipcGBhHndOCtWQaDqs5RzxUkjaR2thALJ4tfUOfy526Qhwq0e6Gl5DvO6Ljlc95UbqW6HzQgk8I1cmTJ3O0bJROxLRBiGE5IhgDO0tEyO8v/418TItcK51Op+iIiiY6TuKpVs4xou+RMxIMBhkF1Ov1XBOOHCqLxYLvfe97OHToEKqqqtDd3c3FrI1GI5566imeg5IkYWhoiNcDi8WSIyoqPqeSsKhcikIU86TfUY09Ci/V1tbCYrHg8uXLaGxsZOK8iLotLCxAp9Ph9OnTObpbYrs9Hg+sVisGBwcxPj6Oqqoq6PV6NDQ0IBQKYWVlBY8++ij0ej1zxpaWsskbyWQSN2/eREtLC2fteb1e3Lx5E9XV1RgbG0NlZSX27t2L1157DQ6HA93d3SgrK+Pnp/dUXl6Ol156CR0dHdjY2EBFRQX8fj8XOCYtrHQ6jW984xvo6elBeXk5IpEIZ/rJOUbkXKyvr2NsbAwdHR1oaGjAwMAA+vv7odFomORPxH6dTge9Xs9lcci8Xi9GR0ehVqtx8uRJrK6ubnuH+Uo15UNeaPyurq7mlEvKh05pNBrMzs5icHCQ0VO6D6354gGCnqMYjmKhQ6G8HqB4aCmGa1vIflyH/3fNofrrv/5rfPazn4Ver4fVas3RnlKpVJiZmbmXy/+zsXfDocqHCOTT/NgJEbmfbblrU3ColNAoQqE6m93Qaktyvk/hP4vJAK22ZFttP39oCdLmJkJLq/CHlpDObMCoL4PVbEQilYYvGMXhA40wGfQ5zppcyiEfQjZnf2LHGln30+QLF4Wg8t1fjlqJZN1CmjL5QgO0qAaDQSwtLaGpqQlGo3FHtXP6rbhhEClZnvotKjQT0dtqtRYMPeYL7ZEzIt5HLlEAgFFAQqaIUE6cI6/Xi42NDZw5cwYmkymHbN/Q0MDtINSgpKQE1dXVWFxczBuG9Hg80Gq1jLpIkoRr165x7UDKsNRqtbBYLFhcXMTc3BwikQja29sRjUaRyWRQU1MDnU6HVCqFra0tVkynz4PBICOOpH+k0WgY6Uin0/i7v/s7aDQaGI1GbG5uMnJVXV0NAFhYWEBpaSmefPLJnJCwOC7ETd9iseDb3/42Ojo68JWvfIUz4BwOB9bX19lxDQQCrNothjsdDgejhiT34Pf7odfrsWfPHjz99NMAkDMXAGB6ehqvv/46Tpw4gebm5m1JABSCo4OAxWLB3//933NtycbGRm6/qPU1MjLCKBM5LeRMyInpSuhfoUODqB8mR6gKheeIKC+OLXHukRMpvqtiQn6FEFSaY/KQOYVPKyoqFAVs74eJawqw8wGuWHtXldJ/93d/F7FYDLdu3cLs7Cz/81Nn6v7aTuRzOaGOCH35FMvp7y6X677rUolt2andu762glK5qHouN622BC57FdQaFTQaNf8/XcthNSMSW4O5vAzOajMM+jLoy0oxfNOHGV8Ie+0WzPgXc1TWATBPCkCOrMODsGL7UCTkFrtgie9KTtYt9F1AmTRKxNbu7m48/vjjzN2Qk0TJ8aIFmH47MzODkpISnD9/Pm/qNwAmTSvJJSj1y8DAADsNFO7y+XxMeBfvY7FYmPtDyukNDQ04fPgw10Hr6upCd3c36urqWFOoq6uLuUyU+i7qaInPT89G16HUe6pD6PV6YTQaGXl58cUX4ff7MTk5Ca/XyxIAbreb5zg5d5WVlSywTBsKhbmmp6cxODiI119/HUtLS3C5XOjq6oJOp0M0GoVOp+PQJJH5SYzz7bffRjqd5nCO1WrF2NgY3nzzTfT398Pv9+Py5ctco47es9/vh8vlYl0mn8+HixcvwuFw4PLly+jt7UVZWRnsdjs6OjqwZ88eOBzZDMOVlRUejx0dHUgkEjh48CBcLheWlpaYFO9yufDJT34SLpcLx48fZ/KzOD7m5ubw1a9+Fbdv38brr7+ew5c7f/48JicnMTo6yusmkcqbmppQUlKSM0ZI/b26uhoXLlxgNXxyoCKRCDsp4u/oM/qcxrKIMDocDtYko2xCSkwQuXk0jogTJjciypNqPz2rSByn8UwyCDQGFxYWkEwmMTc3t23dyUcWp+vJEyxo3dja2mLn5G6J34VMXJ92QzC/X3vUPTtU6XQan/jEJ7gEwU/twdlOA0Q+yGniytOxyQpl/OWzYgee2JZ7zZzIvf8m0ukMFpfj7OBI0iaUMu9y2vOjjEDx76QnNTSZrdMXX0+jr6MRZ451wqTXY1+tAwea3DAaylDntODc1RuY9YUhak2JNfxia+uK91fSaNmt5csGlL+LYHC7XhPd3+Fw5Hyffi8vztvT07MNKaHfyBdvkXgqtkMce+Rk0CK60xhqbW3F/Pw86urqtmUSipuRSqXiUzZ9LvYz3SedTqO/v585JkomnwuUev/CCy/AaDTmbLxDQ0NIJpM5bautrcXp06fR3NzMZGxRy0futJJ2lriBRiIRpFIpeDweGI1GLC4uYmVlhUnNfr+fJRZMJhMee+yxnEK3tPm3tLSgrKwMqVQKk5OT/EyBQAA3btyAXq/H+vo6o2ik02Q0Glnxfd++fVwDb2ZmBgsLC9Dr9Th69Ci0Wi20Wi0qKysxPj6OGzduYHFxkTfikydPcqYYIZ6Dg4M5ma2SJKGtrQ2ZTAaHDh1CeXk52tvbQQETvV7PmXBUpkaSJC7bQ5mWJJvQ3t6O7u5uWCwWPProozAajTwfxBA1/WZzcxOHDx+G1+vF9PQ0rl27hpKSErz99tus60ROt8PhQFlZGdra2pjXJL7LWCzGPDmShtDpssWgSeuL5ohYOJ5+73K5WBNNnr06ODgInU6HmZkZfg75OCe+XjAYVMzSlTs4xGujElD0jOJ6HQgEOMNR/F6++aJkYjupnywWCzu5cmfsfpj4HPmcvnzr5v3Yo+455Pf5z38eNpsNv/mbv3lPDfnnboUgw524TvLv3W84czf2buiC5Hx/8Ms/+iwrnBmIxJDe2IBGrUadsxouexXmApFtulB3rqVMVpekTVy7MYNAZBmdTXuh12v5O0oFl89euYGVRAJ6rRa9+xv4c39oCYn1FPOoFLMQf0RKv5cwqBK3SeldFLqH/PvFvEv6jkh8JnRCDOUoXVcMNYsEUuI8lZSUsBo0bV4iB6OkpARqtZqJ9nKj8I+YEaVEuo9Go1hbW8Pk5CQOHDiAvr4+RZ6I+P/Evenq6spR3CY9IAqJAtimXi7X8hG5TlQcVyTAA9nsQ+rDrq4uDA4OwuPxcBiM+pJ0n5RCuH6/H8vLy7h69SoqKiogSRJKS0u5CPQbb7wBl8uFlZUVDldubm5CrVajsrIS8XgcTU1NnFVHG87w8DBWV1extrYGlUrFHCh69pGRERgMBoTDYTz55JO8afb392N9fR2RSASbm5u8sfp8Pu4vclAp9OTz+VBXV8ckehJLra+vZ3SL5gCQXQONRiO+853v4OGHH4ZerwcA/h6F1CjsXVNTww7QwMAAdDod4vE4qqqqMDMzg5qaGkiShMbGRiwuLnLYW0R0qEyQqGIObOebfe1rX4PD4UBlZSXUanUOZ47eJyVVBIPZgteEWNH/q1QqTE5O4tSpU1wYuxAXlsLfhRBbSmygJBbxGfKFZ+WyL7tdx5TWL3n7d3u9u11Ld7tuAsDS0hJn5D7QkJ8kSfgf/+N/4NFHH8W//bf/Fv/+3//7nH9+ajsbLbBUqyqflywO7PuJ+uzGCsHL+UzkUZAWUCGES+nZgpEYfKEo0ukN1pWSo0FKauWiqrn492AkhszmJjRqNfOqiGhOIcXQ0p3wYXu9E3U11ehs3ptzX4fVjPIyHZPS6fpKbSFuhpI+ivzUpEQ0pVAC9YvSu8h3cqRQoKhtJT/B5QtLERowOzuLGzdusGOVD7VSCjVTu6hQ69LSEv+3iBpRuLCurg5qtRrJZHJb8WZqZ0dHR44aOPWxPHzZ2dmZgzAAUNxAaKENBoPQ6/X45Cc/iXQ6jY6ODt6wHI6sHhCJQQYCgRzEgp6BwjLkQK6urmJsbGxb/1DIjlA26iuVSoWmpiYYDAa4XC4cOHAAtbW1OX3v8/l4LtE/s7OzWF9fRygUgl6vZz2h8fFxOJ1OrK2twe12Y//+/djc3MTs7CyCwSCGhoZQV1cHo9HIThohExaLBfF4HJIkMf+F2kvhRpPJBKfTyfpehMYsLS3BbrdjaWkJPp8PXq8XU1NTsFgs0Gg0XK4nFAqxw3nw4EHuy/HxcaRSKdy8eRM3btxgkU+R9/SlL30J0WgUL730EiMrPp+Px3sgEIDP58PU1BTGxsZQXV2NWCwGt9sNnU6HRx55BA6HAx/60IfQ1NSEffv2sTL+yMgI/H5/TiHekZERlmAgRw0Alz8iNK62thapVAo2m42dqc7OTpSUlKCyshILCwvwer3o7+9nnhfN3aqqKkSjUUYcg8Egzp49m6MtJp/zLldWgV0eXldaS8RakhRepvdNqFZDQwMaGhpy0C1JknIKPBdrSsg5tUucd7u53t3uf0rI1U6I28LCQlHXvmeE6tSpU/kvrlLh1VdfvZfL/7OxnRAquRjaTl73jwutupdMDfnJPR8qUgihArbX7yMnSJKyquhipp+osu6wmvnvDqsZc4FIDnqVkbJFbsNLq5B+VNOvvOxHHAchg1AJwZLfn35TotFA89C/4PeUTx9Fnr1F2kAmkykH3RERzGJkFkR+0k6ZnPkI5oRQjYyMwGQyschlMUhXPhRIkiSsrq5iYGAAzz33HCML4m/kGW6F2pnvfmSi6ricJCzvR+pnIg0r6dxQ+MhgMGB2dhZtbW2KMgxK71MekgRy0Q1yvEUiNTkoRDhXqVRcCJjqAqZSKWxubuLGjRuorq6Gy+Vih4/KslBYLRwOY3h4GMFgVvTRZDLhwIEDOHPmDMLhMOLxOF544QUOp8XjcZhMJqyurmLfvn2MtGxubmJychINDQ14/fXXYTQaYTQacfr0aQSDQQ5HUeiLyOPd3d3o6enB0NAQMpkMNjY2EIlEWMCT3geF+Ww2GyoqKjA0NMTjxe/3M0oZDofxvve9j7MLiQyt0Wh485+cnEQ6nUZ7ezt0Oh0WFhZw6NAhaLXaHAVvcn6j0SgefvhhJJNJ2O12ANtD3OQoOJ1OhMNhRpZKSkqwuLiIQ4cOMSpJY5I4afTMAFgeguaI3+9nHTKXywW1Wo14PM4VCgpJhBQqbCxHZAjVEnWxCkVHikXB8v1enoByt0lRhRCvB2HFIlQ/LY78LtlOWQJ3K6QG3MkaejeyyIjcKxeWK/R9OY8B2MUkkGX5FdKbUvqbJG1iYOwW60YRGiXW5AMAt90CjUaNWX8YIzN+dDS40OC2bcvgA7IE9GQqDY1ahTpnNsOJnSeNGrZKUw45PdPx7LbNOh9s73A4MDg4iFgshlQqhaeffppPY/kEUGnjV8oimp2dxeTkJNrb21FbW1uw35WcdHmGkZJjVyh0Rm1XymQ6e/YsizqKIoaFMojE+8vbVmhsidlOFNJSug5teCTMSdlzcukCKgNSWlqKtrY2djBIcLSjo4P1leR9Qv0oSVnZgPn5eSQSCTzyyCOcDah0WJqdncX/+T//B+l0Gs3NzcxbPXjwIDo6OvDCCy/g8OHDKC0t5RM48XXo+QglpAw1SZKwsbGB6elpDiuRRtbt27exurqK/fv346mnntomhhkOhzEzM8MinzqdDtevX0dFRQWamppYRZ7CWoFAAGtra5idncXjjz+OSCTCGXtbW1vw+/3Y2tpCJpOBx+OB2+1GTU0NI36vvfYah8s++clP8rsKh8MsjKoUzqK+npmZwdjYGMrKynDs2DFEIpEc0VYK1Z0/fx4GgwFANqTZ19eXoyPW3d2NYDDIoWly4ui+6+vr6OnpgVarVVyTKYN0cXGRs0b7+vq2hW+vXr3Kz9bd3c2SFaQ9Jg+XK81jpfmpNJd2cmZ24/wUG44r1K5inKt72TN3aw80y8/j8WBzc3sR2Hw2MjKCTCZzN7f6ibF8BLoftynBxbs5SYhhLgCKGSA7t2EzJ2QnL0xMlqNOLnyWdYRULJVAPKl0OgMVVHBaK+GyV9357dYWNCXqbZmAGo0awUgsK+RZWsJhRyK8u+xVcFjN8Ez7kNrY4PaImTz5yrWIyGRnZyeMRiPa29sBFM6oEbOFqHQMLTRAdiHIZDJYWFjIgduVTCmkLLY3X5KDHFUV37lY3kWE6DUaDU6fPg2z2ZwTsgOyGXbj4+PbMg3lbRJDbcS/8Xq9GBwc3FaOgq4rZrXS75XeiSRl6/UR0ZtCPGLo1Gaz4cCBAyzuqNPpEAgEMDs7C4/Hk7eP6DmAbNFYyo7+9re/jWvXruUls0ciES6L4nA40NHRAbVajY2NDbzwwgvQaDSYmprKCbMSYT0UCnEZG+IAVVVVQaPRIBaLobW1FRqNhvWrOjo6UFtbC6vVisrKSiYqy3k1ooL4wYMHcfr0adTX16Ojo4P7ifrbbrdDq9Vi3759AMAh24MHD8LpdKK7uxuHDh1CIpHIQYQItaqrq8Pk5CQcjqzqvEaTLdfS09PD72Zubg6vvvoqJw3QegVkeWqrq6uczUhEfpGS4PF40NjYiLKyMuj1evT19WFlZQVWqxVLS0vQ6/U4e/YsZ0y63W7Y7XZ0dnaitrYWe/bs4VCiHIWn9qXTaR4/+/bt44OOaA5HVoOrs7MTdrt9W3g9GAwiFovh3LlzfE0xyiGiaEocKArbFSofJW9PsRnhxYbjlKIxO/1W3I/u956pRHnYrd0VQkUTvVgeDUG1RGT8SbR/qkrpd0Pgk19bDkHvqr39X8oR7czqTd1GZ/NeNLiLG3+UiWc2lKFcr+MCx97AIiRpE/ofcaA0GjWSyTQuDIzjVE8b9HrtNsHQnRAyyviLJ9N8Tan7U7siZCr1WaH+LYQG0bXsdnuOIytqJCmdcOUn/0L3VQoziiTbYpE5+lwkmxMaQHwPsQ/lRZX9/julVUjlXB42k/ezUqhd3v9iyHVoaAipVAputztnQ6Y+femll7CxsYGenp4c7al8fQcAs7OzeO2111BWVoZ4PI62trZtKBoR5c+dO8eIxuzsLP7xH/8RTU1NeOqpp/DGG29wWSCxnwgRicViaG5uxszMDBobGzEzM4NkMolMJsMhPzGc4/dnldVXVlZQXV2NVCqFRCKBtrY23sjm5uZw6dIl7NmzBxaLhZFG0hObnp5m5+rSpUuoqKhg/g4VO6axUl5ezqhqLBZDdXU1v0vSvaK6hzRu6V0ZjUbE43FsbGzA6/XCYDDgzJkzOYijSqVCIpHA7du3ceLECajV6m3hbVLopywxctQoLLawsIDy8nKYzWZ0d3dvGyfivKF9UgzRi5mmVD9SDJ3JQ1k0p8fHx1FeXs79r1KpMDY2hvLycs66JJI7jeF8IX7xOcUMWXGOKM2XYu1e9qOdfvsgUalC1y52/y7J+5cCtrW1hd/+7d/Oqf1UyHYqyPiTZPPz8ygrK7tnCX4gd/Ddr5R8pZOSOMmAXJL5TtCxRqPhTVGcqPnup2QOqzmH4G2rMm1zZgqZRqNG977aHMK4w2qGJG0iEInBbCxDMBKDy16F6Ooa2uodiK6uwaXXwlZpYsFQupZciZ378UfoVWwtN+OPeBbkXIj9lk6n+eQvlpFIJpPY3Nzc8RAiJ2HLof5wOMwcG1qsbTYbzp07h7W1NWxubrKCshgqJA0hOsEWuq/4d3pWm83GtfYymcyurtPR0YELFy7g8OHD/HePx8MIE/WhWPDX5XLxST8YDKKuro6zCena8vvTdeja9BmhP+fPn0d7ezufWKkt0WhUkQNIcgeJRIL7m36XL/Tn9/uh1WrxyU9+Eq+88goXqk6n0xxmJMcsGo3i4MGD0Ol00Gq1mJqaQiaTwfLyMmKxGA4cOACPx4P5+XlsbW1xtpw4V6koczwex+nTpzE3N4exsTGcOnUKGo2G1eLF8aRWq2Gz2bC8vIzDhw9zKJMc2BMnTrCTQ+ZwOHD9+nVEIhF86UtfQnt7O4/zxx9/PEcHjHhSVOOwtLQUXV1dcLlcjDYCYEV26kdKcCGV+fb2ds5Graqqyul/ScqWRgqHw2hqamLElRBHEdXR6XQ5Y5acNsqcJBQ5GMxmGE5NTeH06dPcH8TdImeLxh8dcAhxo3WUrk9Fs6lPQqEQf3by5Elcvnw5p1oBZR9Sf4vOn1wzSzTxOYlQT31B9yZOXrFrtHxeFfMbJedJfA9Khy9CPcXnuldAgUxpr9ut3VXI7+TJk5iYmMDg4GBR/xw9ejSHdPqTbMVk8wHFwY/3kukg2r1Ap+KGl68tcmh3t/fTaNQg0rhGc0cuoZDJs+zEawQjMWg0atQ6rehtr0d5mY75UaJelSRtwjPtg9lYtk0wVCmLz2E1o1y/PeNPzIKj90qnoUK6SMUsEGJfKoXeRE0buqfH44HJZMLa2hqjzGIYAACHM+gdiRuYJEl5sz2pPRqNhjO55O96J9ieMpuGh4eRTqehUqlgtVpzFtJ8i6sYluzu7i5K74YcNeBOWHJkZATr6+u4ceMGj23qF4fDweFVMdTS1tYGv9+P1tZWpFIpDA4OMjpEG584Z4mHQxIJzc3NSKVSXJuOuE9K71qSJLS0tGDPnj04efIkgCzfZ3h4GNPT04hGo3wfch62trbgcDhgMplYikKv1zPfi8jrFy5cwPLyMs6fP8+OJslEyAs4U/vISaC+kCSJs/eqqqrg8/mg0WhgNpuxvLwMm82GQCAAu90Ot9sNlUrFGXzkrJLjUlJSApVKBZPJxCrhREi/fPkyZw0SWrO1tQWfz4fz58/D7/dzeZza2lqeJ/SPmDUrDw/Sf9OhMB6PM9mf0Ml4PI6WlpZt8zgYDDKiIV5HifLg8XgQj8eZ0+Z0OplbSE5dLBZjrh5t/ABydMjE8H+h0JxIFZDP83xjTXyv+fal3YbM8u1fhT6XZwoW+v5uTSkEuVv7KSn9XTKCDMfGxtDY2LiN6CvfPIuBNu+XZ17IdmoHtSFfRtY9t/NHpPRCoTbFdguhOkK40hsZhJeyPKpap5UdJqVyNUA2Q3DGH8JqMoWnDx/I+U6h2oHyv+PQJ3KyiJQyynYiZhbqw0LhN5G0Li+ICuRqWlFmGZ2KxevJMzTpOUSNqWLbu1MYGchmQyaTSWg0Gg5nKGUf7pTpBxQOY4ghFjrli6FSq9XKC7gYgiFelZgBSCVQ1tbWAGSpDqLWVkNDw7a2UYIHoSeU7EE8oXzzSewf+v61a9e4zl0oFMIHPvABLq2i1CfycUDtoZp7ZWVlKC8vx549e3JCcpQAAyCnhI68L+LxOEpKSrCysoLjx49jYmKC0UvieqVSKQ41iqE2GrfE3QKymVYVFRVoaGiA3W5Hf38/bt++jenpaTz77LPsWM7OzuKNN95Ab28v9wWFvHcaD/KwnxgGloft5Z+J15OkrL4WFeEWx1YwmFveid41Ec7lcwoAI3Ekx6GUhKKEguZbb/M9z057EYCceSf+hiQkik2O2mndutfP76e9a6Vnfmq7sz179uSQevN518WgOPfDo97JdmoHtUEk7srtfpwglAjnBdstIE3BSAze4CIuD04gEFlGILKMYCQGz7QP8UQSnmmfIuIEALF4EpXG7SVt5EiWElpFfxf7kP6bToj5VOzlFgzm17DK17/iSVR+T/lJ2eG4U6JDrqtEfxdPs+IzKbVNzkkST65KY0p8BkIE6urqcsqqiN9XuoYc/fP7/VygmHhdSv0UCoWQSCTg8Xh4UdZqtTh8+DD0ej0ymQzXzKP7EqpDqt0WiwVWqxXJZBIVFRXIZDJchoRQPPkGptFo0NXVBb1ezyEhMVRLiJAcHST0o6SkhMnNAKBWq7Fv3z4sLi7Cbrfj5s2bivel93X+/HlG3qhPKFRrMBjg8/lQUVHBz5xOp+Hz+bCxsYFAIACbzQatVsthSOoTm83GJWJ6enpw5swZmM1m9PX1obe3F7FYDMePH4ff78fevXvh9/v5d6lUCufPn2fk7saNG9jczKK8ra2tKC0tRUdHB4/HsrIylJSU4K233sK1a9cwNzeHaDSKw4cPs8NJIWwaA/n4eP39/czZSiaTrCdF/SOiI+TUirpYYriKwn8ejwc+ny8Hwaf+JLK/RqPhEkUkFSF/XyR/QWHTxcVFLmosjmsRaS603gaDd9TSac6I96S5RPcgxE4+7+5lbc+3fxX6XHS2d/o+2f0gmxdrDxShunDhAr71rW/h+eefR1dXF/76r/8azz///IO63Xva8nm474Z3LbcHcc+7QVCKaotCceTdty0rnZBIpbN6VJYsymWrNGFkdh6dzW6El1eRSm9ABRUiK2vobHaDpBWA7dpXou2EVpFSerGWD8FRIqornTTv1XaD8BRqW6Hnkd+r2PTtQqdUagOFHCUpK0tASICYHi8iEqurq7h27Rr6+vpQXl7OThBtiiqVClVVVTkFXemZ5EgVcXgo3En8IJvNxuiD2Adi34ibhYjYANj2HKLjSGFNIpS3tbXh8uXLOHXqFMLhcI6SO/XT5uYmP5PdbmfEg/6+sbEBtVoNl8vFKftra2sIBoO4ffs2HnroIRgMBthstm1q38StVXrnhABNTU0xMd5kMkGSshpVKpUK9fX1mJubg9lshiRJ/HzE9xER0vn5ebz55pswmUyoqakBkOX47t27F11dXdyfIkdoeXkZLS0tiMfjjMZSu8rLy2Gz2TA8PIyKigo0NjaitrYWyWQS58+fR1NTEwubZjKZbcTuubm5HLRJHjoUHRZSki8rK2OeY765LyZPAMjpC0KHiHdlNpuxsrLCDlC+ZBBRo06uLSW+p5aWlrzJMXezVtzL3nM3pPT7QWR/TyBUf/qnf4r/+l//K772ta/h/PnzeOeddx7k7f5J2ruBMsntfsWcRSv0HIX+9iDasv3+avS016Nlbw36Ohqh0ajhC0URXl5F7/561qYq0WgwMutHLL4Gz7SPeVa1Tis7UzuhUTtZvtPSTrwiQm3knKB8vIKd7q8kKyDeSwlBLYSEFeIrFUI56ZpiOORueIOE2iwtLTHXg1AM2liUxiHxYEhriJywoaEhXL9+HX6/HxqNJqcWIXAnfZ0Qqo6OjpyQCGXaXb9+nTc/uSq9mEIvto84M6lUiknClLVI/U2oyrVr11gPiRBGo9GIM2fOMG9V5FN5PB5GbohL5XJl6yJSezo7O9HT04Pu7m44nU5OkohGo/B6vSgpKcHVq1cxPz8PlUoFILvBJ5NJ5jMR/0vkDabTaSSTSbz22msoKSnB0tISWltbOSw6Pz+PdDoNr9cLs9kMt9uNlpYW9PT0wGazYXFxEevr65ibm+PQYk9PDx577DE8/vjjcDqdsFqt2Nzc5I07k8kwIgsAU1NTKCsrw7Vr16DX63H+/HkkEgnYbDbWk/J6vUin04jFYlhfX8fVq1cxNDSE9fV1fPe738Urr7wCv9/P4z0QCDCiRk44ADQ2NrJYJ31GY0+jycpBLC0tcV0/cVzJqx+QcjklXpBcg+hoBQIBpNNpLs5Nel4ij1JEkAmlpvEmjm+H406NQpEHJjdxTuVbN3aav7tBkIqJ3IimxLV8kIjVA3WorFYrzGYz/vt//+944403cOXKlQd5u59akbbbQfnjaMudQV+83lkh2ylkSKT1ZpcdqbTEWX1yo7I0onAnaV2RWvqdZyDna+cinCQWOTAwAEAZbZI7BJKULUa7uLhYlISJGA7Il0SgFJ6jMIRYRHl7/92dQy0PHe6kQaNERKfriBsciVNSij5lQMkXUtEZJO0jILuxkco1CSuKDmMwmM3QHB0dZYSKTr/j4+Osck0OhzxcQSEX2mhFR1IMeTqdToRCITQ3N8NoNPI9HA4H9Ho96uvrt21m8v4i4rfD4UBnZyfKy8vZ6SGUIhQK4Z133sG3v/1tANnxNzY2xlpF1P7Dhw+joqIClZWVHIaiDV6v16OlpQUjIyNcxiQYDGJubg79/f0YGBhgsj/JB3g8HqTTaajVas4mrK6uzhk3QDbakUwm8c4778Dn82FkZAQOh4P1tFZXV1FTUwO9Xs/OBY2VZDKJ119/HUNDQ6ivr8f6+jr6+vowPT2NRCKB+fl5FtgkTa1IJMIO1K1btwBk0aCmpiZUVFTkzDmn08kOKZHK5aFoGn+iuVwuPPTQQ4wyAeDEAFKWl49/el+1tbVYXl7OCVVTySaxQDOFywHkdWQA5IxvGp9UUmi3FTFofCqtFTuF+3ey3QIQSgfOB3mIf6AO1c/+7M/yf//O7/wO/sN/+A8P8nY/tSLt3UTFdkJkAGXngQe94Ljs/t7KvCiXvUoxU9BhNcNk1OPpo1kC+m7QKNHRot9R4WSRW1TImRWRhGKMNuX5+XnFhVKOQhGKQ8RzpXbIFxtaiMQ0ctGhK3TSK/TuiRME3Hn/hXhRxHEhYrycmyWiO263m2vYiU5gIYRNo7mTJk4Om1arRW9vL7Ra7bZwTSAQwMbGBpP9KUQoSRI2NzcRiURgs9nQ29uLzs5O+P1+XLlyhflcIicNuLPZyTdNIuSL4UZqc09PD44cOYLe3l4mCIvZWIODg/D5fNja2mLUgJ4jFAoxv8fv92NzcxOrq6tM4r569SoGBwfx+uuvY//+/dBqtejq6kJzczN+7ud+DocOHYLD4eBwV21tLXp6enJETilDMBwOs/RBR0cH3G432traYDQaEQ6HMTU1hXQ6DavVitXVVc7Ko1CW3+9HY2MjysvLGXkjaQeSLSCEkOQWyNkJBoN4+eWX8Xd/93fwer24desW66NVVFRgYGAAo6OjmJubw40bN7C8vAyz2czj5qGHHkJ5eTl6enrQ09OD3t5euN1uVFVVcdkcrVaL0tJSSJIErVaLzs5OjI2Ncd09CiHT2JmdncW1a9cgSZIiYipHi8S5KXIVSQCYdNhEXiS9ZyLEA9lScDQ25HNdji7JOYW7td3wnXY+VBeHJCl9Xwnxe5CAwgNxqL73ve+hu7sbzz//PJ599lmcO3cOQK6D9VN7d+zdJOQp3VuJIFkMcZIHfRFhtHymhCYBymiVUhZhMBJDYj2F/rFbmAtEIEmbeZEuW6UJ0ZUELCYDBifmkFjPkn1j8XUuVkrQvtIiRVwV8bS6k9Gmb7Vac587qCxlIRLP85HhlRabfAsQ3Yc2PSUHLh/sT6rm/f39fLJXWmxFAnUymeTaZ/muTxsL1WcjZXDiaYmK8uKcoI0sk8lwAVylUAnd12KxsOyERqPJUV1Xq9Wc3eVyuTA5OYmbN2/ipZdeykGoaByIKCDxPcR3ptPpYLVaFedxofAs8cncbjeHskiHrKSkBG1tbXxfg8HABPPu7m4sLS1heXkZkiRheHiYCd3Eyent7cXhw4eZNzQ7O8vXJmHLxsZGuFwudHV18bjWarVobW1FLBZj9LC0tBQ//OEPkclk0NzczM4j9SkAmEwmJrfv37+fEZ+VlRW0tLQwQkhyGSUlJRgeHsbq6ipisRhvrHq9HiMjIwiHw1haWuIQokqlwoEDB9Dd3Y3e3l709vbizJkzaG5uxqFDhwBkM08pS3F5eZnDjg5HVtWcVNtJ0X5mZoY3crHEjcfjYdV6pTFMWmTycepwZFX7k8kk65eRw1/ICKWh4s7ysSXeh/5ZWlpSdOpE22lvobkrzvHd2G6RJKXvi1p1ZA8SULivpPQ/+qM/wkMPPYRPf/rT+OY3v4nm5mZcv34d/+2//TecOXMGn/3sZ+/Xrf7J2YNUSi9kd0vIux/EdTEVXzxd5/tc0e6ClE7OkVhTb6cMQSViOSmfryXWsRRP4KF9dah1Wgv+PrqSgLFMi5n5RZzu2w+NRo1BzSF+9/L0/3s1pfe0W6L3vdybOCOUHSUvIpyPmEq/S6fT0Gg0eYtlk/NtNBq3EW3NZjOTr4n/IpJiiaQuV02nhVepVuDs7CwLXVIhX9JLikQisNvtHGoS6/UBd8KApLlE6FMikcBLL70Em80Gg8GAkpIStLS0YGVlJYeQTmgXGS36FDLb3NzkEKRYvFne37RB0n9TpqDP58u5FxHpS0pKcPHiRahUKhgMBpSVleHIkSN47bXX2FkPBAL8jsS+o1D15OQkWltbWRSTHAg58XlpaQmhUAharRbpdBodHR34m7/5GywuLqKhoQGPPfZYjgq6HIFMp9O4cOECWltbsbW1xcR9KqdDJPP+/n4kk0kOb1ImYWVlJZaXl1FTU4P29nb87d/+LVpaWmCz2fD0009vmye0Vk1MTMBkMrHzIvaBfHPOV4ibELd0Oo1oNJqjpi6GpYkgbrVaWSiUOGCka0b8MUK9Cq3VO60HSrIIYrUFGtuFfkdolHwdkst7KM3xfLVoC1VzULJCa+G9roE/FlJ6SUkJvvKVr+D27dv4xCc+gU9+8pP43ve+h+eeew5//Md/jJ9KXr37drfw5m5OB/lOKiIiIod45Z+LYaDdnGSUwnKETIWXVyGKeRYypVAeKayXlWlhNRsK/p4Qqo6GPYivp9FSa+f7Ez8hX5gt/7MVF1IjZCidTmNubo5DSsVKMtyLUVZRoTCivM20wDmdzpzwXD6CfHd3N0wmE3p6elBbW8vIxeXLl1FeXo6RkREAytIL1O9iGCXfnNBoNIhGo1hfX8fw8HDO38bGxjA3N4d33nkHFy5cYNFF8bcuV1atvbGxEW63m+uXlpeX44Mf/CBaWlpQU1OTg16Iyti0QdMmTGgW8YGWl5dzkEcgl+hObaD0+lAoxHwmScoWB47H48y7q6ys5Fp7zz33HCorK1ng87vf/S6cTidqampgs9mQTCaxb98+5iXZbLYcUn1TUxOjHfSOqSA1hWljsRiqqqrQ0tKCGzducDbm5z73OVitVnR1dcFoNObwh0KhEHw+H2ZnZ3H27FkMDQ2hvLycQ4UVFRXQaDQYGRnJEcdsa2uDwWDAvn37oFarkUgk2DnWaDSYn5/H+Pg4nnnmGbS0tOD06dMAkBe51Ov1uHnzJra2tlBdXY1EIoFMJsNcMXGu0ryj/U50ODKZDPR6PdcfBMDvjN4poVBKa6/L5YJer0dVVRX/Pd9aLadVFINK03+LjmK+6+/EfxS5gErioTuZErpUyJSQp3yhxwdlD0Q24fHHH8cXv/hFbGxsYHh4GB6PB3/2Z38Gp9MJo9GIt99++37f8j1vPy6E6m5tN579/UhLlQtH8nV2QKjyIUsUvgOwK0FQ0USkSyysvFM7iKDO3/+RbEK+k2K+k9VO9fyozyibik792/qQn+dOjb6DBw8yulLo/crbJv4/Qfr5kEb5uBCfSUSzAOUU+0JtEk/69CxycVmxv2mhl3PA5P2RTCYxMjICu92O6upqLC4uQqPRYP/+/fB4PFhcXER9fT18Ph/a29uRSqUwMzOD06dP59yT2tLR0YGRkRF+jzabLaf+4LVr1xAIBDhkJO9z6mMxPZ8QOtqg5NlbVHOQ0D+Hw4GxsTHEYjEsLCzg6NGjfB+xlhyhON/61rdw/PhxLhAsSRKSyWROHT8APF+p1IocGVxaWuL32tPTw231eDxYWVlBMpnEo48+isnJSRgMBr4myVeEQiFGJhcWFqDT6bi4cWtrKwKBAFZXV/HUU08BAD9zJpNBPB7Hvn37sLS0hEwmA6vViitXrsDhyNbb29ragtVqZSJ7PuSSxhqNN3JKxE2axpOIMNLnSu+KTEQXRTSGQuIk4ikft3LkJh8fleafiDbdzeGqGKToXpCgQkj2/UCX3k3ZhAfiUI2MjODnfu7ncOzYMRw8eBATExN4++238frrr+ek//4k2T81h2on22mzupvrKSr3nv3jgo7MbhXUd2M76ksptEPR+er9xbzOBIVF5BM+X1hUHtYSQyK0yAPK78Lv9+Pq1asIh8PYs2cPE3kLLTLytu0E8+f2Se6CKIYrKGxVKKSg3M+5NRBFxW5yJqitFEogJW8lTSQqZWIwGJhTRNcRJQbcbjdv8FNTU6isrITb7cbY2BiXCaFiuUajETMzM2hpadkWbpRvGEpaROLfaU6IG7P4HUKBJEmCWq2G3W5nx1p0tI1GI65evYq6ujouvBwMBlFRUcGcJQDc/lgsBqfTCZfLhWQyiW9+85vo7e1FIpHAwYMHOTwlSRKHr2izlR8cxAMEhQij0SicTidSqRTGxsZgtVpRXl6OhoYGJJNJWK1WfndEMr9w4QIMBgNKS0u5/RaLBW63G5FIBJWVlfB4PIyaJpNJHD16FIuLiygpKYHFYsHrr7+OhoYGGAwG2O12hEIhLlsjH8/inC0pKeEqFyaTiccRjTGn05kTWgW2VxQQ54/cyck3r0Qnj/5fHPti/U/54SWf87Zb282cfy/a/XDYfqw6VB0dHbhy5QqefPJJLCwsoLa2Fi+88AIA/EQ6U/8cTSQ+F6ODpBS+knMk5OrdwWAwmyU3MYd88gm7VVAv1qiIcolGUxQxntpBAqHyEGOhDLt8mShK4VJ5WEvUp1FSQBeNwmw2mw3t7e1MSFaCwundyKUSRJh/Jzg9H/mTss6U9Gt2MkLllMIhdrsdKpWK0QRJknjDzJfC3dHRAb1ej5MnTzK6QOHS7u5urjPn9/thNBqxtLSEzc1N1jqijDRCFsxmM2ZmZli/h2QKlBA8kdxOPB8lUn8gEMDQ0JBiEge9P7vdjgMHDkCj0XAmXUdHB9fAW1lZwcc//nG0tLRwX3d1dW3T17Lb7SgvL2flbo1Gg/HxcRgMBly7do0J+5TdJqbuy987oTnBYLZm4fT0NF599VVsbm7C7XajpqYGS0tLiMViUKvVjGpR39LYIL7X008/zWriHR0dHDKem5vDyy+/jFdffRUWiwV79uzBysoKgsEgLl++jGvXrmFmZgZvvvkmKioqoNfr+fmobh6NZ3EcEgWBUEoSA1XKRiOHkbJMlSoK0NgSQ2o0pi0WC6ufA7mZr/KEBaUQmPg9chRoXSYJBbHd4tq7E7Vgp9Dee93yrS+7pbUUY/e8C92+fVvx87KyMnzkIx/B7/zO7+Dzn//8tkykn9p2K5Yz827EgncymmTF8oKUBu9OA9rhcCAWX4fZUHZP8gl3Y8FIjIsxy7MBibO1G1mFQhl2xWSi5HNwdmMajYZ5SLRZilwdpc1cLpUgb9dO75DkDtLpNBwOB1ZXV7dlD8m5YDuNb0mSsLW1xcRnl8uFuro61NbWIhKJIJFIYGhoCOFwGFardVt/i6d3vV6P/fv3IxqNIhQK5WT2AeBiuxRGAoD19XXEYjHWadJqtZyertPp0N7eztytQtwNGhOnT5+GTqeD3+/HxMQEXnzxRczOzrKjLaKVSrwvh8PBDnUymcQrr7zCmWiZTAbj4+N8+KHNlt6Z3W7njZjQlng8ji9/+cu4dOkSC33W1dXh5MmT7KjRAUBM3Rf7VumZr1y5Ao1GA5/Ph66uLuh0Ohw8eBCtra38bshBi0ajqKur41AshTu1Wi2qq6sxPj6Oqqoq5lJFIhFIkgSj0YhnnnkG+/fvh9VqZW5WJpNBXV3dtrJfxHNTek+EwtGYl79T+r5Wq0VVVRUnAtCYFL+Xr0/oXZCTGggEtvGQaJ5KkoSFhQWsr69DpVLB4XDkjGVxPssPPkrZszsJ9optEEOgSjpw98Pe7f1tN/zihYWFoq55zyE/tVqNqqoqHDp0CIcOHUJXVxcOHTqEVCqF//2//ze+/OUv38vl/9lYMZDhTrHe3cSCi4Uz71eceie72wyM9Jt/VbCAcfH3311oMN/3c4oeA8WFBHcoPVNMPyhB+rt5b/R9SZJyso7oGn5/bmHT3Y6ffBlE/f39iMfjMBqNcDqdebM+xTIuO5Wo8Xg8Odwysa2SJGFgYIDFMJPJ5LaQKYW14vE4c5ysVivX3xOzq8RMNQojejwe2O12VFVV5fCn6FnoGe12e074R86VEfkoFAq7du0aysvLUV1dDYPBwFyhfFldkiTlcG/OnTuHdDqNvXv3oq2tDQsLC1hcXER1dTUjg06nE5FIhMujUL8vLCwgFArh5s2b8Hq92NjYwJkzZ/DUU0/lvNtCWbpKa1QymcSFCxdw5MgRvPXWWzh16hS0Wi1vnmJYi5xICiHmG/diSJbU1Zuamlj5e2hoCKlUtug0yTOInCdqG31GmXViYXDqW9GBpLGn0+k4tCiWOspkMlCpVBgbG0NdXR28Xi/Ky8tz+iqVSmFzcxOTk5M4efIkyzqQQyMP74pzd3BwkN99b28vgMJFyneat7sJud8PLlIhe5AZ6TutUzvZ0tISZzI/UA7VrVu3MDQ0hKGhIQwODuKdd97B/Pw8gGy19aWlpXu5/D8bK8ah2mlg7CYWXOzgfNCT5F5tp/YVO1HyXWc3jomc5wUUz/25Gyu06O32ve1EUFXiN+1m8dkp9bmjoyMnq0ncKJS4NnKnO58jqNQX/f39XLrl9OnTis7A+Pg4KisrodPpUF1dndMvonNJSIN8c8uXWGCxWHDhwgXU1dVhZWUFABhB0mg07GASqiUSiylDE7gjhkloYr65DQCrq6uYmZnByZMn8corr2BjY4OLLtPzyssCKRGoif+TSCTw4osvwmq14sSJEzCbzdvkMMgpjUajjJbQe5f319mzZ5lnRk4AhW4l6U5ZHXoX4juWczTF65PzTH1MqvNarTYnvC6OLXEc0fXS6TTOnj3L4UA5307k4lVVVbGzLK9/R2PA6/Xi9u3bSCaTOHLkCKanp2E2m9mpDgaDeOedd7CysoKKigrWuRJlMpTmEo0Ren6l/lay+3W4zkd+v9vr3ev3yYrZI/LxV4sxSZIwNTWF9vb2B8+hqq+vx0c+8hH85//8n/HCCy/g9u3beP3119HU1IT/+3//771e/r7arVu38Mu//MtoaGiAXq9HU1MTfud3fofj1vnsF37hF6BSqXL+OXLkyH1v3264JCI8qgTZFgtnit8TwzP3YsVAt/m+I/98p+cQuVw7hQ+LUQYvZHL+zm7el2jFQttyvpR4L3oeJZFKJZNzN+RtpnTsiooKrm+2U58qPRdpS4kp5L29vYhGo3lLQFBYUSmtm74D3Ek/l3O35PyRzs5OmM1mdqZEdWkKsx04cIDbTOVhxPCIuPmJv6d7yx0/MXTT0tICr9eLTCaDra0tVloHwCEylUqFeDzO3CMKZ+3ZswcNDQ04ePAgC4fK+0Qcy2azGV/72tewsLCAb33rW9i3bx/q6+vR0NDAoppGoxEHDx7kZyNODV2XkKva2lro9Xqsra2hpaUFer1eUe5Do8mmw8fjcfj9fly6dInr2Mk3xGAwiLq6Ovh8PphMJpZFIb7g0tISr8dieE8cG2IoS5wT4XCYw7EiB8xqtUKn0+WIg3o8HiQSCQQCgW113TweD0wmE5aXlwuKv9psNg7bOxwOdtjk4XC73Y54PI5jx47BZDLhqaeeYpFTslOnTqGqqgrt7e3w+Xwsv0BtCoVC2+a0yDMl5JQkUgqtQcWuccWE/sR3sZPtlm+Vbz3dDcdLyfz+O7UodytdIz5HMfZASOlHjx7FH//xH+P3fu/3HsTl79rGx8exubmJv/zLv8TIyAj+8A//EF/4whfwm7/5mzv+9plnnkEgEOB/fvCDH7wLLb5j5GUTMVUcrEoDqtjNXvyex+PJWeTv1oqZSOSciJko4m9FHkuh5yiWyyXfhOW/L3aC5SsPU6yTJH+P4udyHa5CbaP3RhuGnNC827aJmyQRqjs6OpgoW4gLIv6/3+/HwMBAUQ5+Mc68kiMo70P5Qi8qSDscuerS1G/kPFRXV7OCNAloStKdkiC0cYq8L6XxTaEqypxsb29X1NiitvX09ORwj+gahExpNBq0tbXlpMWLtfeofZcvX4bZbMb169exd+9exOPxHJ6P6NAmEgkMDAwwF48QIHJGgWxNt+7ubuzduxe9vb1Qq9U5ITIxiYRI+83NzXA6nQBy68XRc9OYGhkZyXG8wuEwjEYjvF4vOjs7OfRIzqqoa0VOkDhmHI5s+R4SEwWyHCwKutDz6XQ6dHR0YGpqCmazeZtTT1mNdrudxxnNK1pfydmnfqDQn1gjkt6PRqNBe3s7ysrKth0UaOzEYjGcOXMGDQ0NOSR2mot2u13RsRBrccodMSWjd1AM76mYtbDQd3Z7GC7WiuV4FdrrKNSdj09ZaG2k5yjG7jnkt7GxgdLS0m2fT09P48CBA1hfX7+Xyz9w+5//83/iL/7iLzAzM5P3O7/wC7+A5eVlfOc737nr+9yrbIKctwDc/3DTbpVp81kx0G0hWFse4rmXUKTYFpGrsRsOGnBncRbDD+Kzzc7Oct+RnpCS5eOf5NXhKvL5CvVXsdeWQ/oUvqDfAcp6UQS5j46OIplM8jvdzdgUuVYUFsoXYlCaC0rfo76Rp5nnCzX6/X6srq5uCxUCyPm+xWLB8PAwbDYbh/Lk3ClSBd/a2uK0/J3eqd/vx/T0NKanp/Hoo49yPTsRrSEtJJJYIBJ6S0tLDhJGIT1yCgHkqFZTjUCdTgdJyop+rq6u4umnn2bZhfPnz+fIBIhhYzHEuby8nKNlJY6fVCoFlUqFcDiM9fV1hEIhlkHweDxQqVQs3dDR0bFtw9uJOyhuiBqNhhXTiR8myhUsLy9jYGAAzz33HPR6PY+RgYEB+Hw+2Gw2RpLk64Y43pT+X+QAitIb8jGUTyutGG6pOMbIaZaPeaUx9W5ROu7HvXbqD2D3+16h/ajYNr9rsgkGgwFdXV34xV/8RfzxH/8xLl++jOnpafzpn/4pC669l41iqjvZxYsXYbfb0draik9/+tMIhUIFv59KpbCyspLzD1mxaIZoFKqghetuw02FrFBtqN20uZi2iWEVpd+Kqb53019kciSvkFSA0u/8fj+fCsPhcF5ZAtrsdlL1lb9H8XP5SbUYU+ovIPd9FXvtYDC3Mrv8d/lOnNSvbW1tqKurQ09Pz67HpjxbjNqjdAqX9yFxnqg/yPI5kvITL/VhV1cXUqkUGhsbc8KdcjRQKSNLRLIAMIcrnU4z6biYk/DKygqMRiMikQj3IaFflZWVmJyczFHR1uv1+MAHPoDm5mZEIpGc8CgAHsPBYJCz9dxu9zZE9+bNm4hGo/jzP/9zRvPW1tZw48YNVkQXT+oejwcejwevv/46JEni0BwALrhM7QbAvKTS0lKWQaDMvampKYyMjMDj8eSgR3J0RY5MSpLEBbMpHEbzUJIkdnCoLV6vFy6XCx6PJ8cJczgc27JnRVSOriUq7dP4o7+XlJRwn8pRLnENGRkZycnoVUJfxPVTPo/FTOFCEinimNoNNeBebLeIlNK6LvaHeKAix5YcoEKIvNwK7Uc7tXm3e889I1Svv/46rl+/juvXr2NoaAgjIyNIJpMAgKeeego9PT3o7OxEZ2cn2tvb7+VW991u3ryJhx56CH/wB3+A559/Pu/3vv71r8NoNKKurg6zs7P47d/+bWQyGQwMDOSFAv/zf/7P+C//5b9s+zwWi/Ep+L1KBFeyB5Fh+CDuvVNbir2WiPzIyadKdr/QvXs1OWJVSCVZjgABKOokqHRivF+IYqH27QbtpM02nU7n8KFElBHYLngoR6+UTsr5MrLk/aeE1iipcIvPR+Oora0N4+PjTKpOpVJYXFxEMpnEysoKI0lkSshnIfRS/ixra2v4y7/8S+zfvx9lZWVoaWmBx+NBLBZjnTMK6zkcDnbSyCmiTUsk9Gs0Gka06J7pdBqTk5M4deoUxsfHYTQaMTo6inA4jI6ODvT19XGbRQ0xeh4xG8/j8WBtbQ1LS0s4dOgQy0bcuHEDNTU1qKmp4ZAcHQqoNE11dTWjhoXmrjyZQz4ulBA0Ut/fv38/xsfHOYN0cXGR+48coULjTbz/brN75d/LFxH4cZrSs+WbM0BujUFxTJOzdT+jNfI2ptPpokjp910pfXNzExMTE5z5R86WEsnuflk+50W0t99+m0MJADA/P49HH30Ujz76KP76r/96V/ejQqFf+9rX8Oyzzyp+J5VK5RDZVlZWsHfvXsRiMRgMhgc2AB6U7TTxxe/czeZaCNYtBvK924Xmfjz3vVy/2GsU67DJN4BCCw+pbFsslm2k2UKOpzzLjMI6kUjkgTuUYtagvDCx2F9yB0MelrmXNPNCG95ufi9muoklYIA7IVAqV0PlccQyNvJs1bm5OQQCATidzpwNO1/YVOyPkZERLC8vY2pqCp/4xCdQXl7ONQSJf+Lz+RCNRtHd3Z3juA4MDCAWi8Hv9+NnfuZnGJUSnU3q//HxcZSXl8NoNPJ7EUOyGo0GXq8XoVCIyeUAUFVVhZKSEkQiEZa6EBXdAfD1ScWeQpei4jqQLVYtyl0UyoCVhxSBXCdc6e+Dg4OK6vs+nw/JZJLD2uK7kV8337i52+ztYhyq+30Q3sl2erZi94TdUDnuto1UZPvHUnpGyRYWFlBTU/NArr24uMjefz6rr69HWVkZgKwzderUKRw+fBh/8zd/A7V695HPlpYWPP/88/j1X//1or5/txyqd3uQ72TFbLS7qR2lxHOhk+1Om3mxDsD9MPH6hTbi+9EOpWsocYxEky/scmkC+cLjcDhw9epV+P1+uN1u9PX1Fe2gFkKodnr3xaJO+e5FKfipVCpns6L7KTlcxSABu3knhNAA+UvCFOpHsS1+vx/Dw8MwGo0wGo2sNUXaTU1NTRgfH4fFYmEem1LfSVJWnoBq3T388MNwOBw59SDFcUvvjfqDkJ0DBw7kcLdEzg5pO5GIKvXLzMwMfvjDH3Jm3UMPPbRtHaD7iTUOlRxiADnSALQWEDeNwoVU3oeconQ6jXPnzkGv18NkMuVIfyiVEFLSMcuHRouhY7FuYb6/UwiUECo6ZMgPReLvSBuMsgaVOH674RDl45opjR2lMX6/eKu7mWPv9u92Yz/WWn7vZfP7/Th16hR6enrw1a9+9a5eAPEbvvjFL+JTn/pUUb+5W4fq3RrkxZIji91oi90oaSMWoXmCe3c6mdzrRllMe5WuX+hEtJtr7uZdFEKoiFtCm0Y+4rncuZqZmUEkEsFDDz10V2EAJceKHGPKqJG/w2AwmJcgnw/FUdJcOnXqFKLR6DY0dCfHs9jnURrbFMqx2WyseSQ+g9KmpTR/5c80NTWFt99+m8sI9fT08BhbXFzMceTyhe7i8TguXrwIs9nMc0h0RvIV/y30LskZIRFUUfRSXA/m5uZw7do13Lx5E8ePH8fRo0cVNZ+KQRUI8UqlUuy8UH9S35HDIqJWVPsvHA5zeE9EZvOFtuVOqVJB8nQ6jaGhIZawUOoDJQd7J+QkH0IlSdkkgVu3buHIkSOIx+MFtZOKPaTstJfcL8ekmD3rxwkU3M29f6y1/N6rNj8/j8ceewx79+7F//pf/wvhcBjBYDCHUwEAbW1t+Md//EcAQDwex6/92q/hypUruHXrFi5evIgPfvCDqK6uxkc/+tEH3ubdEv3ymRL5kYwWEnkq/06ESfk1inEkaEGkawPgky1tTuI95G2Q35/+Li+Rcq99Iv+OeH2HQ5ncXuxEFe+rRHoUCbH0t0IJA8FgkIURCxHPxb6jormHDh3alaOu9A6Dwe06WVTGheaXmBjgdDqh0WiYMEzPLn8f1L9iyrzJZMLTTz8NvV6vSMZXKoUil6NQ6nOl+4u/9/v9rGlFm7a8r4PB7SR6pfkrfuZwZInG9fX1WFlZ2ZZAYbPZcoj+8mtRm9966y2k02mkUimo1Wp2+JTI0w5HrkwFvUuLxYLBwUHMzc3xmCcpDZIIkJOlaVyVlpbCZrNBrVbz9xwOR06NSkK8BgcHt5HAqT10byoFREkStEZJUrbskM1mQywWQyAQQDweRygU4vWjoqICwWCQ+y8YzJaDUSJw03Mkk0n09/ez2KvYx+FwGNXV1dBqtXwgoPFDfUAEcbncQr61O51OY3BwkPlULpcrZ025efMmSktLMTMzw2T3fMRypXEnfz5JknbcS/Kt7bu1YvasYtbeB2UP8t4/UQ7VuXPnuEAnacTQP6JNTEwgFsvWjtNoNBgeHsaHP/xhtLa24l/9q3+F1tZWXLlyBSaT6YG3+W4HuXzTKDTIaUOW1wrbjTO3k8NGWRnyxYY2RSVRx2LacC8OZzG/VfoOkXDlAnfFTlTxmoV+s5vrlZeXc50+AIoLr2iEtJDTWui7olOi9A7lY4YUwEWngTYEcr4ovV18Pvn1gsHcrENyZgkJk2cAiXwf0eEWN5t8hwe58ya+A6/Xy1wzo9GIrq6ubQ4vtUN0tPI52OKc1miy9RW7u7tht9tRU1OT82yZTGZbRjHNJ3KGSkpK8LGPfQxNTU342Mc+xm2guSXy5aiPRM05GmcXLlxANBrF5cuXWf0cAKxW67bi3aJzJEkSzGYzSkpK4HQ6+XckMREMBjnTLhaLwWg0wuPx5JSDofaEw2HU1dWhu7s7R5vq/PnziMViCIVCTFK3WCyw2+1IJBLo7OxEbW0tent7EY/HUVVVBY1Gk+PgiG2XO/sXLlzArVu3cOnSJf6ekiOSTqcxNjaGdDq9rQ+AO3pk9Nz5DldyzT9xTdFoNHj88cfR0tKCU6dO5YQulQpki+POZrNhbm4Os7Oz2+br/dpLdrJi7lPsur3bexdju9kzdnv/n7iQ34/L7ibkdy+w6G5ChfcjQ61QW++GW3U/7vsgbbdhud1cp5i/FbJi+F7ykGuhcbITh0T+3Z3qvOXLmCqmb+Rk6pKSkhxODJCrlSXnltHflXgySu0WQ1Aid2hubg7vvPMOrFbrNkK/0nugDVHkyIjPRaHKeDwOSZJgsVjgdrsxNjbGWlBAllA9NTWFlpYWlmlQyuyT9+m1a9fg9XpRV1eHvr4+xTCWxWLBP/zDP6C0tBTNzc3o6+tTlJ6gPg0Gg6iqqoJKpcL3v/997Nu3DzabLUdHjMaYPDtPXgaEnKb29nY0NDTkhEtJw2plZQWnT5/OIdLL9aaAO7UDT506BQD836L2lBjeJMeDHJWenh5otVrF8SD2Y09PzzaOGpXkmZqaQmVlZc6YEU1cK+hZaYyKzohS2ZtC2cb0vsgJ22m+FmO7pZ3czzX5fmZ438v9i83y+4lCqP6p2b1Ak7vxwmkxFTWUxFNwMR56oVOJHI3KB7sXcwoQv5sPbdjN7+/29KMUllPqx2Kvo9Rvd3uilCNg8lM0XZvGhkql2ha+lF9PCfXI911RWVwp5FAIlRTHnXwxFFEkIjVLksQbmM1m2zbuNZqsPpFWq2U17Xw6YHJFdPp9T0+PotNUVVXF5UrEtiuhwkqIkNg/HR0dMBqNaGtry3EUGxsbEY/HGelbWlpCWVkZJiYm0NHRsa3NtKGK3BwAOHjwIPR6PSwWC4eQXS4Xtra2OESn1+tx8uRJGAwGfiYl1G1wcBCpVIqR0Wg0in379mFiYgKVlZXbFN1dLhcWFxcxOzuLgYEBdHZ2bitpE41GuSg1hf5IxX1zcxNqtRqHDh1COByG1+vF8PAwLBYLNBoNO2Rvvvkmrly5gqGhIeh0Oly4cAHDw8MoLy/H8PCwotOi0WhYKV+n02HPnj08NwhNE+fOwYMHYTAYsH//fgSDQX4Wmmtmsxnj4+Pw+XwYHR1VnFO0VtAYHBgYQH9/P3w+H7dJbmLZm52QH9IZK2a+FmPyObXTGno/Q2r3EoG4H+2g+xebUPdTh+o9bPcymHazGSvdhwbjbuu5Ads3lp3ashOnKN9384UqC5kS3J/v2YpxuOT8oHvlu8kd2WIdWtHE/s7nKFDbM5kMbyj5+oGcEnk4MR//q7u7O2eT2U3Igcq/KJWvoWuFQiF2XgFgZmYGjY2NLC6p5KSRI5Zvg5G3W/yt0inX4XBwzbt8Io2i0221WqHX69HR0ZETWiQHKBgMstxBY2Mjh5dERILe5+rqKsxmM9cmI+FQeZvFMURyC6FQiNtH4SnxtxQ2o3ctvntCUpLJJJaWlrifDh48iK2tLXR1dSEUCm1bM6jt0Wg0RwyVnofa6HQ6odPpWOqA+jWdTrPzLIbWSN/KZrPh/PnzXFMQyKKQjY2NsNlsMBqNsNls28ZTMpnE97//fZSXl6O8vBynT///7X1rdFzFle4nt9xCsmShd8uSLWwwmNjIFjYvJxMgGDsJj0zIJSGZJJAErxACCY+sGXKHGUhmCIE7edzFTSaQy/UkK9w4sxKYYSbh5WCTIXAByVJkyzEYJCzUqJEsybJlCTVqzv2htQ/V1VV1qs45/ZBc31pa4D7nVO1679r7q12b0g4Z1NfXZyhVo6OjbowwEbeyrKwMDQ0NrhLIWq9ffPFF9PX1uVaPeDzuHiYBIJw7mpqaXCsX69qWgdqLYoexfDe/bjN+TLH9XKTAhzEPyvI2QRhymOZvXX45QtCrZ0wQhqmT0vBzes7Uxceav4nzoHMSBdA/Qiw6heP1fdDTKqbtQLt/1h3Cxk3yezJH9g7vDtOZOPgTamGd5qH3+vr63AuGjx07luYCYutHFuxRFRzRj8tZFaZjcnISo6OjaGxsVJ4eY68likajrizArAI5NDSExsZGNDQ0uO7BJ554AosXL3YXRgKRmevr67Fs2TJ3cWNdnGwfqqysxIEDB7BixQocOXIEsVgs7QSizD0rah/iQc7MzLghHCi2UVNTk3QTRRw8tm7IIgQgzaUYi8Xc05Tr1q1DIpHA7t278e6777rWF3IXU7ynaDSKY8eO4cUXX8Rpp52GSy65JMOtyrvYEokEOjo60N3djYaGBnz0ox8VhmHhXcqk1IjmRXKjkrIbi70XwLO9vR19fX2IRqNp7tnp6WlXORVtBNgyyPqi15yomsf8rBXsN/F4PNSAofmicHjBnvKbx9C14tAEyL+ns2OhCVFGFleBdgYAtKxbrKvMa1fBm+t1ZGOJyeyO0ut7nR2OKg2Zu00lJw1acod4Xfqsk4dMRqoDx3GkrgYebJ3I6sfLbcefumPLcejQIdTV1WFiYgJvv/02uru70dHRgf7+fgDIsCKRVaCtrU1IQGZlBsT9UXYxMyC24rBpFhUVufUPiI+ts9cSUXqpVAoHDx7Evn37MDIyknbqrbu7G2VlZe51VVRPRN6urq527+0TuTipjsh61dLSgt7eXpdMT1aOuro6HDhwAOXl5Z5jlMbQzMyMe8KSh6w/JRIJOI7jWk7Y04q8SzESmb1nsLa21pWzoaEhw4VFdToyMoL6+nqUl5fj/e9/v8t/4vs8O8eQVaWyshINDQ04/fTT007Q8RZNts+pxnt3d7frjhwbG0t7j679aW1tzei7dJiEtybxFk+Z640lnous7qp5zI9bjK0DsqCZ8ptkCNNdmA9YhWoOwqvTeS0gJp3Wj7mYBpromLdKXtWEFcRszU/apuXwu1NSudtE5aFTUGTJIIWW3Y3r5GFSV1T3oiPZfuucdxezij2r3PKT/tjYGKqqqtDc3IyLL77Yve5kenrafZ/aRKQ8yRYOL74Yf+KKBX9SLZlMuiRmsjCQUkKKH19f69atw7Jly9xTbSz3h+7+IxkTiQRWr16NyspKrFmzJs0VSwr32NiYWwbWbUYnu6guhoeH0draimPHjqGsrAxdXV1pi+/w8DBWrlzpcrRUbU5jiE7gEdi4ccRlZO/eo9N9MsWbrD2Dg4Nu3bFK5+DgIBzHcZUkSrOyshIjIyOora11lV2ShS8HuT7Zk4vEI9uyZYsbN4sUY7LCkVIomgf4TWsymURNTQ2am5uxcOFClJSUoLu725UjEom4QVvZu0H5sa2iEMhcbwCUmxzVPBaWey6oW5HSYN3hcxHW5ZcjhOnyM3WjiFw9umZVk1MWQeVSvS8KuJdLmMis45YU1ausrnWipOu4BlTQlcfL5Sdyr7JR8GOxWJrLiq0T2Z18rAuITk+yPCFKW+Vy4E8Hyk6FUawhqkvKJ5VKuVG6afErLi7GzMyMG+Wa5cJQSAq2DtgrUChv9gRhJBJJcyNSaAn2JBiBPZnJn+widxy1DY0firbOKpgq9xApeKI+z7sLAWSMUZW7lG2Lnp4evPnmm6irq8M555zjys7eAUjKw5EjR7Bo0SLs2bMH9fX1KCsrQzQada+zofHB5k2xquh5MpnEjh07sGLFCpSVlbkuRDp9SH0bSLc4suOMrIV0ypDtV+yJTpbCACDjqqHOzk5Xtra2tjQyv8k8w/bVoO4yP+uDFzVBB0FO9GUb1uU3j6FrOVG5eoIQ1gn8rkRk+RLtXGQnkWTv+yGfhw0Tqx7rhgPEk6OoXmOxzIChqVTKJTRTsEoefHvq7Dr5ema/UVkW2Dg7ot0kcSoACN3FqvhXIvcB6wIikrKfhYPKJ3KhEtF4dHRUSixvapoNvFhTU4PGxkY0NzfjjDPOcMtIgWlLSmavxens7ERnZyc6OjrQ29uLrq4u7N+/H8lkMs3ywZ4grKurw/79+/H666/jueeew7/+67+ivLzcdVNRvbKWX7LAFBUVYfXq1a7SyrYnydfc3OzWBZB5kXMqlR5zia0LUX9hLaOsq5HkEvUP+r2oqAg1NTUoLi7G6aef7p5sZE9qUr2PjIxgenoavb29mJ6eRldXF2pra5FMJrF27VpceOGFKC8vx+rVq11rEZs3H+yVtcwBSAtiyipT1I6i+Y0sl2RpbG1tRXFxsevmJmWKHUMiKx87tumQiMj9LpoXeZ6aLL4a+52OJclkrlONK1OEZS0jhGE1M4W1UOUI8+UuPxb8jkIkq2jXobqoU/R+GJa2oBDlpZLLr0WNL38YuzbddpHJIHom25HKrEy8xY4sPjpEcf57lphOIEuQyiqoqh+vQwtsvioLDMtrofINDQ25ilRTUxM2bNiQ0YfIqnHs2DG88sorGBkZQXl5OZYuXYq2tjb3pF5rayuWL1/ufsueEqOFWXaIRGZdZP9fRnbmye/8c7481L68pSuRSODo0aN48cUXcfbZZ6OiosK1WorGC1nCxsbGUFdX59bv+Pg4Lr74YvdOPAJZEfn4XKr+xLY9ud+am5sxMjIivPNPZd0jgn00GvW02MjmTxHhXfQua82iTZwovprpfOJnXi3EdSpMi5e9y6/AkOu7/Px2cFPFwctEbZKertyiSTsMd6TXO/xzXnnQcfV5IRtmfBMllX0mmtjZZ7SDlblFvNy+8fh7wSF1FU/W1USX5ZJbi9oiEpFfri2Tpb29HVNTUy5JGEhXoACkXRTMX1qrsiikUin3Pd7NySqodDqsvr4e8Xg87fQfe9clG1z04MGDrlJKgR9pIedddiKX1erVq13Ln2oMi8a5qK1pTLB3ObLKWCwWw2OPPYajR4+ioqICH/nIR9K4UV55sEoLuTZ7e3sxPDzsnraMxWKu+46/FJqfv/r6+vDss8/irLPOwtTUFKamptz7LUlRCaKY831ENEfw73htSvh6IMsj1R2bLj9G/VIXwqSG+IGfTXWYSp51+c0T+DWDmphtZd+xu21RWmSa9ophxE/UKpejjjtSRMY0KRcPr3LqpME/N3GrqsCSlWUxqbxM26I+pNMGIuI3PSOSMN/2qnRZOanvkBLAyqY6dReLvXeqLxaLKU/g0TUcIqI4KxNZeV577bW0+DpsvCU2X6oXOjEKZMbmorpwHAeHDx9GQ0MDotEoUqkUXnjhBfT19bkuNnIl0tUtTU1Nbjyo9evXo6Qk/a5Ltn6ovtetW4fx8XGsXr3adRuT8klWF3ZxIZdVT09P2n15FCuJd7mLXFG86z6VmiWRU/tSv6XwH2Rhevfdd93grJQeP5fI+jS5i2trazEwMOCeeASQJmNNTY1r1ZKN20QigWeeeQbj4+NuwNGWlhZXmeJdaqoxRos3tR25gvkxJJojZKdTWYjc+o2NjW5bs2WnPLq7uzPazaRMlI7otDh9S3OS6uBBGG43mSyqOTmsOdgEVqEqcPjtFCaKGNvpRRwKQK64hO331gHPsdDhG6jk1FXQ+DR4zo8f7pJMFnaCYCdIP6c2s9WHTNueFmA6JcdGf6bnZDmRnbpjF2ri1nV3dyMWi7lxjEhxGB4eFp4oZNuA0igrK8OHPvQhN41UKuUqAbQgsdY9lhvE1j878dN7LL+ku7vbvVevo6PDVUYcx3EDrLILIVl5WAsGRdZOJBJu/yMFaXR01FVMAGBsbAxTU1Po6uoSHsHnZTt48KAbzZ1tZ9Fl4CxYNzeFVaAgrRSbqaurC0888QROOOEELFq0yHVBUZpsiAq2z7NhQcjq8+qrr2JiYgI7d+5EW1uby3+ibwcHBzEzM6MMxRKLxXD++edjxYoVuOKKK1wFWXSJMpWFlHMRz1MVsoPyFsnCh+bQCUVAfaO2ttblcfF56IRc8XtanJ+TZJfT+93Y68hC/bGQTgVahWqewmQRZRcBdhfrpbiY5hMWvPIUDWLVNzrlFKXh9W9d2USy0KLO7v5kE2S2lFpWeRHtTgG900g8hoaG3Ek4Fps98t/R0eESaolETCRjnlBLlpd4PI79+/e7xG12t0/fEYGcrRvWskKWp/Xr16dFl45EIq4yxefN7/rZ+ucn/qGhobR+QZf4suEgAKRZqfh25vuM4zgoKirCggUL3HRZGdgFmhgdvAWBtTSysvGxkuhdkTWSXfhJMT1y5Ajq6+sBvBd/CwDGx8cxPT2NxYsXY2ZmBps2bUIikUizcIni0bW2trpKIbVXSUkJPvjBDyIej6OlpQXDw8OIRqOoqqpy43RFIhH3ombRuCRlevny5bjsssswPj6eZs0jCyVZAtlYYqIxLBuDOnMEa302seZQO8disTRLKbWN6monfvPMP2NlZQ9B8HmvXr061E2XDCJZ2IMauVx/VLAKlYV0J5IPZSkMmA5i2YSbjRMirDtKlD4rC03cpCzIgqzKFB8dqMpJVgf+5JDKFaDKR2SxoUV1YGDAXRwomnRPT09a3qwlcXx8HDMzMzjxxBPT4iixfXlmZgbRaFRocWDrTlSnvJVAZcVkrVe0IWlqahLGIItGozjnnHNw9tlno6WlxY2dRO5BsjzITm3Sib7W1ta0EBpsOVh3XnV1NUpLS7Fs2bIM9yTbNvH4bKyks88+241Mz0Kk6PMWYOIrkeK1bt06LF++HOvXr3ddlsuXL8eWLVsywl/wCzyr8LEcGPp9fHwc5513HqamplxFdnh42LX+1dXVuW5pyod1//JKUSwWw6FDh9Df3++Gsti5c6drKaU7/M444wxhsFdSwGXx4nTmE9oM8JZo4nq98MILrmJJ1jr27lDVZo3cxP39/VJagux70Rjxc28pL4uonrxQ6GuSVagslDuRfCCoMhPGoBNNLrpy8e/x3CFd3oTXIiaSl1dyZN/x1h5RpHWeR8TLBaSb373kIzfM6OhohmWlrq4OjY2NaZM7HyqDtSTSAk1H0NkFl96XuQJIWaJdrkhmEWfFy4rJW3rZi3NFu3/2jjwvawH9tmPHDkxOTmLhwoVpSgkf0oCsO6xLldxWrJJKSvPRo0fdPsCnR9fdsOEu+IVfNIdEo1Fs2LDBVWzY8lI+rPWJBckwNTWFRCKRdr0PWXOKi4vdsiUSCbz77rsYGhpyOVqsizKRmA0oe/DgQXcxZ4O8RiIRLFiwAO+88w7eeustFBUV4dRTT3VPTrKhNWSXye/YsQPj4+Ouq5p3LcvGO/XHdevWZViik8kknnjiCTdo7M6dO6WBP2VR/YH3gtcODw9LN3QmG1GVO5CFqNwqlz5fbyrouEZzDatQzQMEUUBEiz2QuYsNS04vkjDlzZNdsyGPCqLJRVfJ4t/TcRGI0uGtVaorZtgJjn1PRchlrT2iaO4kA2shYOViF08vVybxe+rr6zOsO2wsJrY8pAyI3CX8As2XTUSi5l2VOkotX2ZAbuEhtxqANMsiq4To8tx4Xtbk5CR27NiBlpYWTE9Pp8UkY9+lNudPjpFCQRcus+7EyspK9Pb2un2Al5VfAFUcnVTqPWI6Xz/8GKGrd/bt25dxKIRk2LlzJyYnJzEyMuLWDfGq6Df2xFt9fb373vT0tKuI1NXVobm5GdFo1A0Gy18K3tbWhoqKCqxatQrDw8MYGhpCfX29MJ4UP3Y7OjpQUlKCqakpt23YelQpK7w1jm237u5ulJSUuJbWCy+8MMOKR+Xlo/qz9U1u9DPOOCNDNlkfVEF3Ey4qNx8XjIfXWAlKOcgmrEKVZ8h2oiYKRBDin4oEzafn5R7yKkc8Hsfu3bvR19enfb9d0PL5hWhy0VWydIiouuZ2Nk0vxYcmOPY9FSGXtfaw9+MRvHaAbBlE+bDt39PTg/Lychw+fFjosjQ5CeoFNvgofyKIVTZV7ldZX1eNF1KgRIuMSoEW5cVaG2Kx2dOMixYtwsTERJrLjE2bXE6Tk5MZbqNYLOaGMli1apXrmiLFddOmTW4f4C2jNTU17t2AXu1GyldHR4d7YrC/vx+9vb148cUX3c1ULBZzI4QfPXrUJdHzSgsF7WTzXrVqFfr7+7Fq1ao0lyERyquqqpBIJDA2Nuby1MiKRpbN5uZmNxgntQFdRVNRUeFaefgTnTynjt20TExMuNdG8W0u6898P+Bd7K2traisrMSHP/xhnHPOOS7hXzQuZFYjcg+2tbVhdHTU+IS0TF5ZH2Ahes5aLkXwspTlYz3QhY1DlSPI4liI4neYxvSgQc1bEnQg+laWnkounXJQ3JzDhw9j1apV0thDfP4m5QvrXd10TGSVPZPtuPymDfiLgWUCnfph25+9jkM2karyAPTLRPnywUdpwSovL8fExIRr/dIdg7rtpFMuNjjp0NBQRlBL0dgRBcNlZUqlZmNxlZSUYN26ddLgkPv27cPU1BRaWlpw9tlna9WlzvyUTCbR0dHh/ntgYCAtFhUpUmz4h66uLpSXl6O3txcXXXQRSktLPeuvvb3djWr/F3/xF5iZmRFegVNcXJwWmX96eloY+JIChxL3jX4H0gO9st+wsbDIdQyYxePj6xKAMDinKfi82DyoLfzODaZrUzYQZL3zCxuHao5ApI2b+LKBYLt5E+uAakevU46mpiasWLECW7ZsEVpEZPl7lU+Xq8CDdy2y0LXSmViaZC7Dzs5OTE9PZ7io+PdVbif2XVF9he029XJBAulWFtGulGSSxdfi3V6iuhO5kGUuqUhk9iRbb28vysvL06w3vPUolUo/jq1qJx6yuqbf+/v7MTU1hdHRUQAQXqskGjsiayHLDerp6VFaDijNVatWuZG8vfoEz8sR1Q2BrGILFy7E+vXr0dLSgtWrV6O+vh6tra3u9Tc0f0QisyT4yclJlJeXo6enRyoHC7qmhe46pDhdVB/EHYtEZuNVEcl/fHwc5eXlaX2WLL+sJYt1GfMHBKj89fX17lU5In6Yn7AKIje3LkTUDZ7/p7KU6UK1BphCd04ytYrlE1ahyjHefPNNKU9G9VsuIevoJJcq4KOqHCxXQKd8ugNOl6tgAlk6Oq45Wf4yl6HsnkLZ5EUysAT0XJvJY7GYlFBM8DoJRDLJ4mvxi42o7vg4U6qFhWSie9xYRYtXimlBBWaVV3JdHTp0SNoeMs4cyUS7++HhYUQiEZdozS+iXnw6vo6am5tRUlKCU045BZOTk67LiwW7q1++fLmr3FI/YhVTNn/RSTLZfXMsNyYajeLss89GaWkpHMdBNBp1w1IQf4r6DfudTEmmMtCzFStW4J133sEpp5ziysG7y3hFoq2tDRMTE2luc94NKOtrBDpFODQ05N4vqXLbkczJZFKohLLtarJx5H8XncYV5aGCznwrWgN0vjNRMnkUsouPh1Wocoy50DGCKA0mlhCvd3UHkt8dGLvr190FeSkuKiuSympAsXtEafEKbCw2G2hxcHDQnUS9yh2WosnK5mVy98qTtSSJAkd6LTakTLBxpmix7uzsFCp6IisA3/as3NQH9+/f7yppovbgOXNseSgNAK47jg9CylsmRQFJVW2xbt06VFRUpHGh2LKxhG+qS+J6AXCtXFRv/AaFlMi6ujppkE+yQpKbKZVKSflsBJbfQ/fytbe3o729PaP9qF7+8Ic/IB6Po6ioCCUlJW4ZaWMCZB4KoHqiAKCs/GRhampqcutK1rcpHhXlzfYRsm5VVlZibGzMDQ9w8OBBDA4Oelo2vSCzCqs2Zabp665PbJ9ng56apK07J4U9d2UTVqHKMUQdI2x3TFAEURpE1hMZTBQ3VR3JlAmvemW/051MdBU2E5ch8Tu8TvCxFpVIJILq6mp3EjUpqwh++iAppHV1dcK4Ml55shbLSER9hZFumaiuqqurtb+RuVZJaSwpKcFFF12E5cuXY926dcL24NOk4KWsUkJKjOxaEuA9l5IoIClroWEDQRIJPRaLZXCWaLFLJpPSgxFNTU2ulYtcoWRNoXcSiQR6e3vdCO0q6wTrSmdjYtE3lB+9q5ov2DLTSb0PfvCDWLRoEVasWIE9e/bgpZdeQjKZdBVlUhRJCWQDdNLY4a+4odAQdGBGZnVlY2vRIQD27kRS2GOxGAYHB/H2229jfHzcDTIaBOSe5A+mmLgKVePcRHFh+zwb9FQGkWKtO5fm22NjAktKzxFUpLZCIPr5gYy4SxOV18XFPLlQRTY0qSMTGWSyBIUsPVmdsRfx+jmIoGoLVZn81BWP9vZ298QWG2zSC0RKrqurcxUM0/r3W24Wuu/TYgR4X+CrQy5OJpMZRH2+PGzaZH0DZq1BZAlxHAfV1dVu32HTIOWGJaDLyptKpdygli0tLWnk976+Pjz99NMoLy9HcXExli5divXr17vKFV3QTErG22+/jRNOOAFtbW1u/2IXRpKRrF1dXV1IJpOugkfKbHd3N6amphCJRDKsyVQfhw4dwpIlS9Da2pq22aMx9fLLL6eR8Pk6pQMM9G5lZWVa+WX11d7e7vKvtmzZklGX8Xgcg4ODrsIlsuqK+pSqPwadp7Kx1oj6sSzfQ4cOYWZmJuNwBSHsedgLuvlZUvocgJd/PZv5hmERM+ULeUFFEte1VgH+Lk8Oexdk4jIkV4SKqO+Vvoxj5GV181NXPLziysjA3h0numhYB6J+AZjFp6GFjhZ+GWSuOL6edSwGqdTsnYWVlZXuzp6sU2zASRoTNE8AQGNjI1pbWzE+Po6qqio0NjamBRBNJpMu14ssKuvWrXPzFo2zZDKJ559/Hnv27MHbb7+dEUsqEong1FNPxQknnODGEmMteKlUKi1o5pEjR1zuVSTyXhR4UhhIxlgshpGREQwODqKrqwtDQ0NuzKXh4WH3JCTv1p2ZmUEsFsOGDRvQ3NyME0880e1PZGEihfPEE090lT0qC2+BJCL75s2bM2KiycYRXY3U0tKS8YzciBs2bEBZWRkAMd2DlMLdu3e77aEat6p5Smdez4b7zCsMApuvKOgoi1zzpcLOz1qocgSRhssfv82VdSqbFjGTtHWPhpvmketdjhfyJY+JhYoulQVyFyyPtVDRCakgfdP0W558XVVVJbUksO8DZmETRM8phEhJSQnWr1+fVnbaxTc2NmJoaAjJZBLRaNRdKFVH9Yk0XV1dnaEYEETjrL29HTt27MDU1BSKiopw1VVXoaKiwv2erBCrV692rwhiy0+WreLiYjiO48a8YpUg1oJ38OBBpFIplJaWYvXq1ejq6kIq9V4sKdZSQ0FK+f+ylm1y2+3fvx+nnHKKe8ovlUphcnISBw4cwJo1a5TXEMnajOTn85VZIkXtrbIKkhJKVjG/80UuPR1+ZfQzVrKJsC1UVqHKEUQN4ndHLYJJR8xmpw0ih4nrRTTB5RKqCZL/XTbRFZLil23lXretg9SJyvUga5eDBw+6PJ/S0lLU19f7dn2yedG4JrcVWVrIUtLV1ZXhWiMZKZI3y5OjUAEAXLeeaPNBilpxcbHrPgPgWfdTU1N4/PHHMTo6irPOOgvJZNJVEkSLPgtWsVi9ejV6enqU7h/WZVdVVeW6Kr3c5MTBIfei6D1ewSF3Ou829IJozKrcsV5ufRVM571sKyM66YSpvAWVW8flGBRWoSowUIOMjo5icnIylEVUxAXwGuz5hF8FSoZ8cs9keXtxegAUZJvpKvdh7Z51rYw6SrOKB+b1jCfIAt4BRL3qgLXAHD16FCtWrMDExISrrBGJVxaAU6fsqkCffHkBZPCc+HQpPbpPka0TIqQPDQ253CmZBYmURxUXkL4jsjothF4bD8pHxvdTKemqvq1rUZKN4yA8J5N3szXf8TKI+pbMWhjGXKUql45VuLOzM43DmY151HKoChRvvfVWaD5b1v/L+8Zz7YvWAS9TIuEdHFIFvsxBuGE637LviLgIxHshPlgBSEQAAFbqSURBVJxoMmDLzN4PBpi1GS9vUF4ccT68XCK6bcbLw9dXLBaTHr+nfFQxqliwCoyMRwYgo68kErMnzig+EnF8vKzFonbi+wadmiNlij19RvfNxWKzUbgnJyfR0dHhxroC4BmvjQ35wYLSBeBysfg7BukuPLYNye1JaQwODrpcsVhsNrr50qVL02JcUT3Qwtrd3Y3y8nLP4/v0XU9PT1qMKxlPktqE6oPq0YRvyPdtNn3dccf2jbB4TiZjPsz5joUJ15LGIx+SIghUvC4d3iLP4czn2mcVqhyjoaEhNFIg2xF1SMoihDUoTeWlf4uOAevKxJc5yEAymVTIXUG8I3ZyZIMeitKUlZme6fYNVhY+zlDY4JUF1Z2C9D4faJBvK6ojPlQC5UVKgOgCXhlEC5lskQ7SV7wOANACvn79elRUVLjuJ5Jv2bJlrjJE9UmLFZGqZdHjVWWl8sXjcczMzLiX//J3DBKhnQ9wScoNhScgIjhbHpb8zm4eEonZWEhHjhwRxlTj66+4uBg1NTVpBHxdhUUGL0WXf5eUSip3MpnUVnRkYzWVygxMqup3qjHPpyWb73RC1KjAyyBS1lnyvsn6pTOXqxRQ2pzwIURYmXlSvMk8Gjasyy9H0DUZ5hphmJH9mFhVbg2/MgUx9eqY/UUcF1r4RS47L56QzHVhIi+lIeOVhAGemwKo3R2i+/JUZQja9jouMp4jRkpAED4Ka4EkaxNN4ibuU4qXRItsbW0txsfH0+4h1EE8HsfRo0fx6quvYtWqVUqLm6ivErG8sbERhw8f1joGz/d9VZ8WURTY+xbZNgHkfUyWt8lcwvbT3t5eVFRUpHGs/LrtyD06OjqKtrY2LFu2LM2FRi5UXRefitcYxjwiKkOY88dc4I/yEMlmXX4WWghDmw+ymxSZjv3KpOOuoUWQtwCIvuXLxb9TX1+vDPAok4d+510XJtZCPg02fo8udPPjrVKqeiZLhZcyxZbBtO15uSkdUaBMNl3e6sLG/eHrQddiSRYlch+SdUUV6Zy1JNEYiEajaGhoQFPT7FU0MkuAqs1isRh6e3tRWlqKV155JS1UAQ9RXyWF5L/+678wPj7uhh+YmJjAf/7nf+LVV191rUmsdYq+512NIqsQT1Fgy8mGc+Dbp6+vDy+++CKSyaSU3uA1l/BuxLa2Nhw5cgRlZWUoLi5Os4KwnDrZVTgEVm7Kr7q6WtlWomf8bxSVXhYUVDaP+EG23GSy8ew3P7aO/HhXdL4JUhdWoTrOoaOEeMGPAqT6xq9MJoOF5+aIOEn8okEgkzhdVyHiZeggDJelSV354W/Qbo0WPhnniVBXV+eeShPJRNdx8FHVZeUxUXa8+pSMIyZzzYo4cjwvjOeB0f+LIp3z+QFwv2ctnSr+lIrDFolEsGnTJlRUVODkk092Y1HJIFJMI5EImpubEY/HUVNTg+npaTz88MM4cOAAfvazn6G/vz/DtS1zNarah+dFiSxorJuMjVnGQ3f+4eNvkRJIMvB9g6xYBw8edBVnkRtPJE9LSwsikdkbEFjLl2z+ATL74fDwsHu5M8kvyi9fc7gOZLL5zY+to7DcwjxkY18HVqGy0IZsEvEzoINOAiYLLc8BEnEB+G/p30NDQ9qys2n42T2JFugwYcLf4L+hAI1e18N4XYjc3d2NiYkJ4cKo26Yyuf32KVF6IgsWy1Nj3QLs6TNSNig4JYC0HTUbyJfSZ3lOIk4UWx91dXU4cOCAe0UMD1IOWKVGZgkhVy4pCk1NTWhra0NlZSXOO+88RKNRlJSU4IorrgAAnHrqqa5LjR87k5OTGBwcRHFxsdS6xlp9RGOD5e5Qu/f39yMej2PVqlVoaWnB6tWrPfl5LHS4UGy8LP5bCndBkemp7VkZeLlJ2eStx1T3sjoScUxZVyjb97ysZqYIQynLRX5snYS9kVfJ9tZbb2mlbzlUOUKYHCpT/3NY/mqv46259Il7hSdgZdDh5Yg4JUGug6EJUIfX4Pc7U/hpIzbGC7mzVN97xYSRPefrm+pCxovKZX+j/lNUVISRkRG0trZieHgYR48exYEDB1BbW4uqqipMTEy4z0gutu8ByOBxqbhWIu6aDj+N5XYBsyf7xsbGEIvF0pSV4uJiDA0NZfRxUd16xfiitvMKjMpzfmRcNnqXeGF0vUs8HkdfXx+OHDmCLVu2uLKwMgPw/H8dzp6s75MCKbpaSNUvg4QK4eeIMGLGFTKPyRTZLsvY2JgbE85yqOYZTE2dvKnbL1TavR/zqx8rjkqWIOZlEadEdB2MyIdPfCzgvYWRLgP1umoBUIe/MIWqTmmho0VN5xvW4qSzqxRZqNh6Gh4eRltbW8aiTNYAOnIv4sXwbiAdd6UotITsBJ3sfXLHRSLvnYQjvlJZWRkWLFiAiYkJ9xoZ2WlbnsfFWjOATKsNz12Lx2cv+hVF5uZdd+TejMViOHDgACYnJzEwMOC+RxYyUR8XtbPqehF2rADpV6wkk0m8+OKL6OvrcxUKAELLC59mU1MT1q1bh+npaaxYscJ95/DhwzjxxBPT+hilE4/H0d7ejt7e3gwuloqzRwcD2PoX9X2q75KSEuPLiL0s0Cp3ICs734/8Ilu8qXwg22XRVdKsQjUHkS1/txdUC6ofmYIMAhOTsV/zsug7kQ9fNAF6ub5YiLglosUyDEIlr1zz7guVbDoQkYBpIVXFlIrF0u+/E+VrqnjyfCNVe7Hp86Eo+LAD1E6bNm1CZWUl2tra0hQTtp34BZ3lcbGLuagNREp9JBJxg2/K2pTF8PAwVq5cibKyMjQ3NwNAGv8pLL4ipdPU1JSmNPDcJ5mbS6TQULqnn346jhw54l6i3dbWhubmZkxNTWUQ1YFZJW5kZEQpL5u+7ECDaFPE88d49Pf3o7293Y0nxvZByk/mNqf8Vq9enVYnogM0OjHjRBDRH3K9jmQDYZYlyEb/uFOoTjrpJBQVFaX93XbbbcpvHMfBnXfeiSVLlqC0tBQXXHABenp6ciRxJkwnQVFckaCQTagmA3wuDmiRD1+HD6GCLgdEl1BpwsPiLUMmsoksPuz7tNDTjl4Vw0ZkIVSd2JJZ2/i6YC089D3dR8dbD9lFWRQMlOdVDQ8Puy4+ID14q0mgSLoEWNYGVM+xWMx4HNMhgXXr1mHZsmWoq6vD/v37pafQdPk5KssSMOva7OjowOrVq13uE/BeeAk2ZpfqhGY8Hk8LJknKRDQaRU9Pj6ussQpdS0sL1q5d65aHl1dUbtF4FW2KVOM6lUqhp6cHU1NTacFKRX1Q9D3lNzo6mlYnOsFtdSE7STnXEWZZgmz0jzuFCgC+/e1vY3Bw0P27/fbble/fe++9+P73v4//9b/+F1566SXEYjFcfPHFOHr0aI4kfg9+tOdsDJwgnY7leMy1AS1yIYhOKoVV56YkTJJLFjCTFmXi79TV1bmWIUBMFpb1OS+LDyuT6kSXLvg69eqDvIWHvh8dHUVlZSW6u7vTysQuynwoCr4OaGGiO/nYhZpchDKrCw/eOscikXgvBIMsOrVsw5RKpdDV1YWpqSm3jvbs2YNjx45hz549wvpj8/OjuJMlkJSB0dFRnH322SgtLXX7JN+PKQ3RiVr2WXV1Ndrb2zE1NYVUKuUqaxQhm9qQFC52DIgI3mSF5DcCbBl1aQVUd6eccgoWL16MdevWuXXR2tqa0QdF/DK+DlQbtlwjiNVmriHIRv+4VKgqKircBSoWi6G8vFz6ruM4+OEPf4i//du/xRVXXIE1a9bgZz/7GSYnJ/F//+//zaHUs8in31vHXKwaeLwLaD747rMBlctIRyERLVjkUqJFmRY8dpGW9S3Z7yKLDyu7iWVUZ8Lm3/HDjaPvVNHevVy9LChWEMk2OTmJ7u7uDEuMCiprmw5fRtYvSObR0dE0eaPRKOrq6oT1p8vPkSnuicTshceNjY2uUknpytzaMi5ZKpXC0NCQK+vOnTsxPj6OnTt3YmZmBqWlpTj77LNdayhvcWIVPraO2at4WJdwUKt7LBZDRUWFS5YX8QBlkLkS/ZKtZePJr7eiUNadXCDIZvi4VKjuuece1NTUYN26dbjrrrukMXEAoK+vD4lEAps3b3Z/Kykpwfnnn4/nnntO+t309DSOHDmS9hcG8ukmYweV1yQuGnj0LJVKaRO25ztERGgVp0kHogWLXEpkNeHvEQTkfUv0OzvRU7Tr4eFhrT4iqgOdMvN9S8ftJwMflJWVRTR589wwAO7lsZFIxJWLrF+sJUbHUqVyoenyZXi+XSqVQnNzs3uPIAAsW7YszRImsqx65cdb4vg+RCcGa2trpRxC2eaMtR7RiUbyBKxYsQLT09O48MILlSFPqBzULiyHjlWgeMU6qNLA93fZuPHqXwRda6EIXmR/U2WB78teVyOFiblEnj/uFKqvf/3r2L59O3bu3IkbbrgBP/zhD3H99ddL36dGbGhoSPu9oaFB2cB33303Kisr3b+lS5eGIn8+/d46ypzqHXoWiUS0CduFjDB2Tvxk4cVp8pM361ISWaYIor4l2yHLCOJ+FH4q89jYmJL7xSs1fq2dKmKxzkJE71CMMlqYyTLDu3d0LFW6Fl8Vx4lXRqanp914WCw3TUWMZtOfmprKCMIqsnYCyLCosgR+en7w4EE3rhTbbmzdsjy2yspKHDx4ECtXrkQkEnGtP6WlpZ6KC9surII7Pj6OZDLpulBZl7BJ3+WVV934fKL+JRtjutZCkVwiZTcI+L4cJq/LC/k0IphiXihUd955ZwbRnP9rb28HANx88804//zz0draimuvvRY/+clP8OCDD2acCuFRVFSU9m/HcTJ+Y/HNb34T4+Pj7t8bb7whfG8u+aZpUAGQTiYqhU822fpFmHXnJy1+x+snDX6yUPFp+Lx1JzTRguVV914Ki8iFQ3Kxi6iJwheLxZQLPZselR+A8c5ZR+lXWZX4hZq4WqQIiBZGXhEU8dQGBwelShKrcMusFuTmSiQSKC8vd5VyPk9VSI9EYvYuuh07duCJJ57ICMLKK/wi66LKTUULMpB54g9InyPKysqwadMm1+LV1DR7xx6v5PFzDlteskTSReatra1oaWkRXkHk16KqOxZlXDFe2WTnUtPTfJRWIpHIysY7H7yuMI0I2V5v54VCdcMNN+DPf/6z8m/NmjXCb88991wAwKuvvip8zpqVWQwNDWVYrViUlJRg8eLFaX8ihGnOVE3WYXYiVmY/8nsNEF15wyDG8wu0SVqmLgNRufi68MuT0oVu3YtiBqnS8bvIsGmpFG1ekSDlgeqC3zmzhGOTOhBZlUSbBlE4A5nlR2Td4t+RRZAXKdyNjY0oLi7OUIhIhqqqKhw5cgT19fVu3bGKvyqkRywWw8TEBMrKylBbW4vy8nK0tramWT5YhZ+UtwMHDrjyiMpI3J1169ZlEP5FoDrjDzKoIu0TKP/u7m7XEslaZk2UFJGFsKOjAxMTExgdHTWybKkso6nU7AlBXVe/H86hbpqqTYmsXeYKsu0+nBcKVW1tLVatWqX8O+GEE4TfUnyQxsZG4fPly5cjFovhqaeecn9LJpN45plnsHHjRl/yyvgDQWFKKlbJpUJQF48XaAHo6OhQHt8OkjdfJyZ8FwK/sHrJE+ZgDrprk7U1b/nxWvjY79iF1U/bqMrEuz94BYbfOQPpASZFC6Oqndn+IIsR5XUqjNxnfX19bj+W1Utra6urvIjqBIB7MjYajUr5Sby1j8oo4naJ2iYSiWDz5s045ZRTcPbZZ2PDhg2IRCIZbj7W3TYxMYEVK1a4pyZF6QddiKm9Vq1ahcnJSTcMg6gd+XhOQeYpkYVwenoa4+PjbjwwnbEos04Bs8pmaWkpTjnllAxXv0zJkc0lLOncdL5hFdF8cJZy4a3JtvtwXihUunj++efxgx/8AF1dXejr68O//uu/4stf/jIuv/xy994tAFi1ahUeeeQRALOuvptuugnf+c538Mgjj2Dv3r245pprUFZWhs985jO+5PBD3NWBikugu4OSXbzKwo+Z3ARk+Zmens64lJSgcivoDEy+Tkz4LmweADLqAhC7u0QLbjYnEFX6ssmWFqNYLGZ8wmliYgIrV67UjqpuWn4V6ZdfsHlrl2xhlLUzpZdIJDA1NeVe3WICsqp1d3e7ljWZeyoSiUgjkVNaR48exRNPPIHq6mop3yqRSLiWIrLgzczMCLldPFeO2oKPiq7i9dGmgiLF82U0saSoLCPUXvv378eqVavck4uiduTjOenMUzI5RRbClpaWNKK/DlTWKarDiooKtLa2ulZRtny8kiOb08mtT/3AT1Be3p2XjXlKlGa2rUdA9jnIx9Vdfrt378b111+P/fv3Y3p6Gi0tLbjqqqvw13/91+61CcCsErVt2zZcc801AGb5Ut/61rdw//33Y2xsDOeccw5+9KMfSd2IIrB3+S1atEiqDOQT5LaR3V+nUmL85uelFCUSs8eweXnIJUVWFBb0rLi42J1gdOVlZQIy7/4yyV/0zM97fkHH+Nm74LzKFkQmkZLpJZ9uXkHriu9run25v78fAwMD7ok+Vfr82GEXDdlErpt+MpnE9u3bEYvFUF1djQ0bNmS8Q3VEpy7Zk3Ne5fTTV/jym/Yn+iaVmr3bj5Vb9i5/z53ojkE/85TOnOGVrmru0JWJl4PKy5abT1uUhuyuQRG8ZMvGPCVKM+z1JUzo3sV7XClU+USQy5HD6GheyktQJcIPdNKTya1THpqoVRO0qk51FgM/E6/pe37BL/IAfNe3Ltg6q6urU16UbJKX16XLftLUeVf3HZ1Li0XpJpNJDA4OugqVSjGhy5jXrFkj5ADJlA6d8nj1Fb8KqUzJZvOjk79EHOffVcnupQiZjkV+zmC/j8fjSuWX7ft8/enKojN3ec2bpv1RJz0T5VoHYcyfuYTu+n1cufzmKsIwhVIaIrKurgvSxP/sx+0mgkwelZz0zIvk7Ocou8oKo/OMrY9sm591OV46lhRdsHmICMR+89K9G5F3WxOPSRVmQNUHZDKy5SCXGL94qcYAncYCkBZoUSZTLDYbNHLt2rVwHEc4jnXqU5a+V1/h5w/dcBXk4uLJ+vF4HBMTE9i/f7/rXo5Go8J3AWB8fBz/8A//gKGhobRnsdh7JxtFhG7duVM2Z7DlZl1xLKid2Qj5Ir6USBa+j+jMXV7zpsgNyyOZTLqnJXXS4w9V8P0vqPueRS5cf9mCVajmAMIg0lEaADI6q276JgtgkMUqCEQLNpDJafKrzKl4Zl7Pcj1J8Ds9WX37lc1LSRQRrUmRIMVTlRYLnfYiBYolYMfjszGZ6Ci5aZqyMrMKRSwmDnXB16uOUq3ixrALLZA5jmX5slCFTGBlEcnFKi5AergK1UIqy/Pw4cM48cQTte7K27ZtG6anp/Hv//7vaXmSrFVVVcIwESQ39Q0eMoWGb49UKuVawug7vr7ZWwhEfCm2bKJ+JGsLVTuJykDv8CdRWbCbHZN5WLaO6CiLuqA8TA4IFQqsQjUHEIbiodr5ZEOxCUMJ9APRwBb9pltmflKIxeTXlng9y3V96CpKfhULr+jmPLk5iKwq5ZhNgydgA5CSuKkcJpM2yQmIw0mwkFl5EolE2mksUTnJrcaTtUXjWNRHZbLpWvp4iBQXvkyAuD5FeTY1NeHMM89ES0tLhoJEJyvZWFNf+MIX0NDQgJtvvjnj4Aiv0PIyDQ0NGSufImtzJBLBggULMixofH3zCqQoLZN+5AWV1VE2x8lOlXpBto6IFFe/GzXKQ/eAUNjwqwgClkOVMwThUFnoQ+R/D+KTNyVPFpL/P5s8LxHx1S+HR+Uq5WHKa1PJJHO7qepBlo4OCdi0b4hI5iIZTUjIfrlQ/LfEJ2psbEQ0GnW/F5HsvfLg27S9vd2NhdXU1GTMi2Kfk0VNVjeyvsfL5NW3+TzZb/2OFV2EOecEmTNkdeZXrnzNpaI5xpLSCwwmCpXfSS7fC7gX5pKshGxMCqZphvm+DumVhUyBURGEvRQA3TxMy6aTB0tept8BpHGYRGmrFgsAytOxunLLlB2vRZclIY+NjWHBggVoa2vLsAyq2sz0oInsdKLuqUWVXHQAoaamBjMzM0plyLRORQjjxBmbBilVbDvyBHyVAmpSV2EjyOnluTi/iyAqhyWlz2GYmEplnJRChG65gphcw0ZQd6iuC9I0DZ33VQcQAD1XgxenR8QRMb2WwsTlKGsPXQ5WKpVyx0skMnu1B8WbUhGtVe47L74K/74IIhcqldUrGCblPzExgTfffBP9/f3o6urSivNTV1eHQ4cOIZlMCgnGfL3Sv2OxmNCq19TUhObmZvddHfBtSq7iZcuWSV3oVB6vuHk641fU/0z7GZuG6Fu6AgeQc9904MeNZjKfqjhnJuT1XCLs9SJIOaxCVYDIF/9IBd1Oq3pPt1w6E6Uf2XIJkkkUXE+XYM0Sa00C7bHEUb4e6RnxIGQWDz8n8XQUABmBNuhErMvBEuUhUjJ1ycrUJl5EYa82TyQy78gzPTXV1taGM888E83NzUilUjh69Kiw7VkZiEA9ODgoJBjzGzYam93d3UJLBHGNpqenXR6U37EpUlTZeqmrq8OBAwdQWlqaMVfIFEGRHCb9zw9fibh99A7PPSKXqQ505wL2dxMljNrP6wolWV75gF+uVjZgFaoChNcAZzuwjNwaNnTIp6L3WOhOXCpyt45shQCSib+qAzA/3s6/ryLS0gWrsdjsUXS+HmULP7vwqerT7+RJ3/FWoCCTsUrplEE0XkRKJqtI+JGRr0OvNpcRq+PxuDTsA49IJILly5dj2bJlqK2tRW9vr7LtKV/2Sh+2PkR1qTM22XeCXmOi6vvDw8NYuXIlDh48mCEPX/9hzREqC46J9QqAG4rB5JCArD54azRbXtMNuun7+Z5/s2GA8DsvWQ5VjhAmKd0v7yEIeL+yilujSzA2yU/1e1hcijCRLUKmqn5FJGtdIikfXVuVhx8ehYjDpOpHOsjmOGC5LWRxYfMJwifh+Veqfp5KpTAwMAAAriyq79hvRVG2TfuiznhTvROGDDJ5qA5EaXvl79UGquc8yZye6/ISgWC3OYjqg887l/PdfOFOseDHtyWlFxjCVKhMlI1sQZVXNhc6v2mHIVO2lCQTyEisvJsuFotpLWD8whNUYZYRfGXf6EZAN5Hd9Hs/So+oTLrysHUEqKPXs/mkUikMDg6isbERIyMjShK8LD+vgwV+6kxnbIV1WtZUZhlZnKwqxcXFGBoayqhLVRulUuKo8hSjClBvJv261FWYj0pNPsHXpyWlzyN48TkIQUyvJpwDlQyAuQnWxLxKadfV1Wm7QvzIJJJPRVrWgQ4x2aseUqkUhoeHha7WmZkZRKNRLFu2LCOGiyxtlvdEC43KhabiWADiemY5JHx/8RsXiZVdt6w8ZO4SNm2Sma0bth6IK8S6Sr3cXGwd6RB9ly1blnbVzPDwsPSyYoKOO1RnvuDrUsRvZMckX4/0Pc8lJKWEJeHzvB8Rj5KssOTa9mpjtuwiNxgAYV2q2ojndrEuY9XYYOt9ZmYGIyMjnu/qIizFbL7Br+vOb31ahWoOQFdRUvn3TfPQzVPUYU07oylpkhbRgYEBHDx4UIu8riuTqDwkHxAsCJ8OMVknunxdXV1GOdi0qf3Zqy900mYVDNVuV1UOGU/H7zUagD4J36Ss7HeA98krUZo8n4jSo1OOskjPvLKmO1aI/7Vu3TphZHaZvKI8RH1Ep9wiDpVMsWW/57mEiUQiQ5HhFR7KR6Tw6gZ9ZMtO8yPxBYlnKKpLrzaSPdfpz3w/CZP7o4MgvMW5lneu+V3W5ZcjmLr8dHgWIvh1benwIsLMT5W31+/0jBb+qqoqLdeHDnh+hEldBIVXPrr8NFHMJJ3veD5GUH6HDnTqNix3URAZ/PRRHbl128YUXvLq8n10OFQm74rcooCYCyXqj2Shfd/73of9+/cbu4rj8Th6e3sxMjKCtWvXpgUlPV6QSw4u395sv2NdsPmcX3TetxyqAoOpQhWWYpRtZDM/nToIO3+eHxHmhBNkcAPewSNl+Zj2JdNFNwhM2xjQIwDr1nUY/YdXikhGEZeK58CJDhKY5GuqnFF9FxUVYWRkxBd3jc+byqujkIraTUTQZtOk/0+l3iPoR6NRo+CxrBzt7e14++23ceTIEaxatSrUca6zKQp7vszGBiKs7/lDL2wbk1t3fHwcra2toRxaCArZfGQVqgJDEAtVIe+ewlqQwiCghoVs5WuqPJC5mo0ppXO9iCpNLyuD13dBIVM+dBdmfvGVnfASKZ+i8ugqm16WqIMHDyKVSqG0tBT19fVSZY9XqFT1IRoLbHmpf1BdsAoHr5zR99XV1ejp6UFNTQ0cxzFWSEVWIxGpW/U+b/XUTZOtq1hM79CFqDwA3MjyExMTxuNJBa/+FEZ/85tmWDDJT9RvReN0fHzcl4IcNqyFao5gvt7lJ3IvmS7ChRAGwusdQN/tGiRP2akkNv8wZMlHnVO+MouMicIpciGwFg+R8mnqOuTlVl09w7qf+etFePlNT0myv/PhLXhFhCAKdzE5OYkDBw5g5cqVShl5OWKx98JoAEhTjFKpFKanp6WKvkl4AFaxYtMEwu/vfi1sXpSIsCxUfpSWueCV0HEBF6IBwSpUBYYgClUhW6uCupdEaeQCpke9AfXx9rDgl1NkmjYQfJHyA5VCYdIPyNLDX86ryiOIG1DVz0mx0Y11RK6O0dFRNDY2asnotdP3WpTIGqBrleEtpaQEs7GwdPiFfvqc6ZwS1NpqOhfEYjF0dHRgenoaLS0t2ncWmshcyHO+X4RFp8hH3diwCfMIZNpPJAonEjghElFfyeEnDT+gRTSV0js9oiMn+05dXR1GR0dRV1enzJuXw1QunbrwU8eA98kvXZiWiUUkkhkGIAj4ctBkCyDjSLpumUXjTdXP6X3RSTZKh/3/WGz2BFsymcTAwICWjPS76lofNg++jSKR2aP+FRUVWi4uVo5Y7L1o6k1N6VcWieRl82af+53HvPq7Kl0dXpkqffqeDftA+Y2OjnrKnkwm0d7ejmQyqS0z4F2v2UI280gkMk92+k2nUNdDq1DNAfhdQPOBMJQjL4gGvekg05GTfUcVL0m2cIrkkk2wJvA72Yr6kZ8JlC8TpWFyb5voG682ZGWlEAL8LpfSAPyHuNAZb7zCIXqf/T0Wey+kCQC0traiuLgYRUVFQiVdB1QfZD0BkLHo+1EovcrrBXLrsm5CQD+si0oxFEHVXjrzgqpsImU5Fpu9GLqtrc3TytLd3Y2JiQl0d3dryyyDbNyFqfyEqazw8sViMc9wHzoo5PXQKlRzALlQUlRQWWDyAdGgD2uQycqnSp9fONn3+H/LJtig0F04WN6N7nc8+DJRGib3tom+MbFEyMYEpcFbUoJC1e+9rEqRSMT9o0CgiUQCjuNgZmYG3d3dwnS9xhqvPLJl5utSlpbod/43clF6XUDM/i4KPkt1wFoORWnw1r+BgYEMS56snnnZRJeT69YvLwubn66VtbW1FeXl5WhtbfWU2QuycSeK0+UXYSorfL8Jax3L93qoguVQ5QhzmZSeDy6RCtn0oWebrK1z1Yqf8oVBrvZblyyHhxY9rwnPDxk1V9wJUR8IK94acUhoLA0ODqK6ulrIKZGR69n0dOvDi+wuKysR0olwL7uehU1Tdj2SSGYdfpSfOF2mvKigYz3MvmnK9aNDCX7Kkc0xFRZnitLKJ6fMktILDHNZoSoEMrNMnmxMAvkuXzaVumyXLyzZTYnGQHj9UoeUrpLP5JnXu14Lkg5ZXVU/onfYtOLxuJL8b0qg16lrE3jJ4CdvPzKFOWb9xo3zU1ZVXmFutoKOy3ydSiZYharAMJcVqnxCNCBzNbj8TgbZWCRyqehlY5EyLYNOG7OWkEgk4jtOlx+oQjAEsRjw8FKKROEUVAEzTayXgPoybq+2zIY1lM+b3F58OwSJPu8noHA2LFRBQwmI6onvCyq5863EsMj3Rtee8jvOUQhcpzCQTb6UDDzZ15SgGZTYKeII+EnTbx+Q5SXj0vCy8zwtr3S9eDS6XBf+jjkTpFKpwJdtmxLidcrldUqO5BDdC0fPgPS7CnV5gvScTvex0OkLMs5PUMIzm46sHbx4VyrozC98WUx4PV7tTmmx9xX6GcuiegKgLbfJPJvt9aaQeVMsrEI1TxHmaY18QkYK9TO4dAc9G/naj+KWDYXPK01R2Uz6APu9LC9dpYXNVyddkZxeigS/2Ecis2EBysrKfNW76SIs6oMyQrys3/Hl4t/j/y0bC3V1deju7s64NJtkbGpqylBsRMoQX6ZEIoGZmRmXTM5Cpy/w6YU1Lth0ZO1AYR50FXKV3EBmW6jCqPAQkfx1xiVPzg9yeITtCzonLQGzedZLPq82ED2fi0YBq1DNU9Bgkt14HwSqju5n4KgQ1s6E+CiTk5Pak5LfvHUmZJF8qtADbJq6ypPJAsZ+rzq1pqO0yBYCWbpecsqsEPxi76e9qC7r6urSFmE/kOUvW2xUFhxRf+WtfyR7V1eX8uQo33dSqRRGR0elyhBbJ7J28aPAqqyXuvBy/dDzpqYm9xSeLIyDCfg2VIVR8fpW1J9FyjRbTj/WIuC964zYuGCRSCQjRltQeMnnpXCJns9Fo4BVqOYpRGbjsKDq6F6TV7YGiZfCkkjoB5WjGEdEyg1DGdWdUHRCD+gqTyYKhu6ErZMm+47O4uGVpsoaFNTaQXU5PDxsHHBUd2Mhs2aIFJ3i4mJX6RD1V7bt6f+rq6sxOTmJ1atXe8pHimhjY6NUGWLrRKXE+1Fg/VpNCbrzS5BQAjIXNGvZMel//Lsqlz7JzdMNgliLdBS6oPCSz8+mKdvUjmzAKlTzHNnwg+u4n2S70LAHiS7fKRbTDypHikB3d7eRRUsF3QlFxIWRvctaH70sWCzCWhxNOUCEMJRqSheAr4VTxwrj9X1HRwd6e3uFiztbRh1rBm9xk/VXth/R/0ejUaxatSojcrdK8Va5JVX8sKBjwa/V1HRuAt7jCsmCwOrkS+AtOybWNp2xRQobuWOB8ALT6ih02YafTdNc4U2xsKf8coS5cMov7Fg7QWOP6MD0JJNJurk8NeYHJrGFdL4LK38veLltciFD0DqIx+Po7e3F6Ogo2trasGzZsrRy0TvA7II2PDzsedeejitL9q3oVJtJPevEgxKlFWZbqvIkJUf3lJpXffmpa/73sEOEpFLqi6Yt3kM2+50I9pSfhTHCsB4FJQebQrbjDiPdMK5JMIEpv4x1JemQvwnZIAebIMydJyuDSf3pWFlZTlsymUw7BRiLZV4/wrqjWIsGuc5U7ne+TkyIzDJeTJguX5l8rGXYL4lY9h1rhZyamkJPTw/q6uoyiPOielGV3cvapuIQ+iXZq+qG5EmlUhgfH0dra6sR//J4RKHyq6xCZeEirIXOK50wJ4igMntN5rncJZpOEqwrSYf8TZA9T6XMQgfkyyTPthkrg0n9ecnOc9q6u7vTTgFGIt7Xj8hcLzoHRbx4L3y/5Tk+phDVh64SQEq916lF3bKKnu/duxdvv/02enp60p75Uep1vmEVatmYCMJrEskTiURQXV2NRCKRlqfpvDDfFTCeb1hIsAqVRdYgG9iFtLsoJFlMFwcRnybIBJNIvBc6IAipVwdBJn3dU3NB8uU5ba2trZ6nAHmlhue1kYtC56CIF++Fr4NsnN4yUQLIlekn7pSORXXt2rVYtmwZVq9ebXSgQQSdb1iF2jSmlYzUrjoxyYa3AJCWp+nYLqQ5LRvg+YaFBMuhyhEKmUOVLX6EjGOQa/+3CoUkS77BcnEAhBbtm88jkUggmUxicHDQjcBt0g5B2iyb0Z/J5QcggwTN5ktxo9j7HE3Lzx+LTySCR9bm4ffeSZkFMQyw9UjcKlaB81N2WRmoTkkx0S1H0D4m48SZfD+f57R8lM9ePVNgKDSFSkT4NCU38+mYkj+PVwSdMHMB3bbjidhe31CfOnTokHt8PxqNIpUK77oWlZxeSkeQPqtqV6/xZrII8+9mS0n0m25/fz92796NmpoarFixIisKOV+PgP9L21WXOQeVsRDHth/kqkzZyidoupaUbqEEaxaWmZR1TM1eZNlCVRjyCda1Vqhmed22Y9tf1Rf4MAXr1q1DS0sLAASKSs+nL3PlkWxsbCWv8pjmT0f0WV6VSMkSjSsTt47XsXgTmVV1pkrX69uamhr3MuUwwfZLXZd3rjlF83Hey5UbMVv55Er+40qh2rVrF4qKioR/L730kvS7a665JuP9c889N5As+SYOshOQ7qkWr3TmAvJd70Dm1RjZgklZdd8VkaF1FjVeoYlGo2l9y4QULfrda8LU7ad++7OMkC2KjyYqq8kizL9LygWdtGPzNyG+89/5PSnX1NSEFStWYMOGDVlVKlj5gpzqE8WpMj2gwSOZTKK9vR3JZNL420JErub5bOWTK/mPK4Vq48aNGBwcTPu79tprcdJJJ2HDhg3Kbz/84Q+nffe73/0ukCz5JA6auHP8BG6UfWuqzGRD+QnjGoqg0DkhFkbZTfqY7ruJRPo9cLqLmiy8gSrookwm0e9ep9x0FJYgbgF+wqZ+FtTyxsvndeqO6kSnPUWLTBhEclVdh9GvTdMwDQkBBLcid3d3K68DKjR41WmurG7ZyidX8h9XChWZoOmvpqYGjz76KL74xS+iqKhI+S27C4/FYqiurg4kSz4tOyaLp2oHa/qtqRIZhtJZCEqcH1DZg5y207nAlXfF6VhwdC5I5iELbyCyrrB56bqiVafcgvRZ3W91rLx+rYAq+Qh+3IAkG/BetHndeclkI8X+TgFz+YuZTWA6L/hZTHWtyLLytra2ory8HK2trdp5miLMuWq+nwzMFY4rhYrHo48+ikOHDuGaa67xfHfXrl2or6/Hqaeeiq1bt2JoaChQ3mFrzCaDy6/7I5XSv2A4KE/Ez/skoyogotc1FIUysVDZAfiWR/fKEx1uESESyQzcGtRiIKtzU1e0rL/oKqd+LTai8ov6WZCNjKp8gNgN6CdGUpB5STU/UB4AfCnkLKgesnHxO4G1IgPi641U5Y1Go9iwYYP0dGQYCHOuyscGP6hCWCibXxbH9Sm/j370owDg6b771a9+hfLycrS0tKCvrw9/93d/h5mZGXR0dLiLHo/p6Wl3AgFmTwksXbo0a6f8snkcnM1jrl3HYurGyecJHdXx7bCOg/t5xwumdc4/133fb1gA+t7PSUKd+tE9JaZb1zrhCsKSXXUy0RSq+YGVBYBR+8vK5ac9/ZZLdO2NrLxz/URcrvIJ4/qnbK95hOPqlN+dd94pJZvTX3t7e9o3AwMDeOKJJ/ClL33JM/1PfepTuOSSS7BmzRpcdtlleOyxx/DKK6/gt7/9rfSbu+++G5WVle7f0qVLA5dThVzsMGKx3F/HYgq+Hkx33KYummya3YNOZDplD8NSqmtxIvDPvWSg9ylquemOnNJvamoyjiYepiVZt2/JLIumfU3HgpFIiIMk+nGVE19MND+w7kW+T5vyGllrVy6J0kC6tVg0H5pY8YMiV7ygbFntg65ZhXggal5YqA4dOoRDhw4p3znppJNwwgknuP/+h3/4B9x3332Ix+NYuHChcZ4rV67Etddei7/5m78RPs+1harQEOauJl9WI50dUBhB/GQ791zuwMKEqYVKNz3eQuWnX2SjTsOWQ9YnaGHTld1vYE4v+UTQtdKJ0jWNAxWWBc8UOu1cqFb8IJbIfFrtCwU2sKcCjuPg5JNPxhVXXIF/+qd/Mv5+ZGQETU1NeOCBB/D5z39e65tCC+yZbYS5cIW9CKomCJWCY5qWzju6C+t8mcjCLJNuv5C1Kfv/fhU0r/z89huRW4uUKt2+GWTcmI4Dv25PPwt9oSktuv0rn5BF8Z+Pc0w2cFy5/Ezx9NNPo6+vT+ruW7VqFR555BEAwMTEBL7xjW/g+eefx+uvv45du3bhsssuQ21tLT7+8Y9nXVY/LqVCIOuFaY4N27SrMmHrnkAj6JjdZfmRm0R2yadO/nMN+SDSykjXvKspLNnYdGRjUdfNCbzn1pKdlJQhyLgxzUt12IOtA77cMpejCn5PmgY9ZSmDrH/l+oCLSm7ZqcVCOYQzX3BcKlQPPvggNm7ciNNPP134/OWXX8b4+DiA2Yllz549+NjHPoZTTz0VV199NU499VQ8//zzqKioyLqsfjp8IQwS0ZHsoGmFtYMi11EymfQ8qq+zOHpBdfrMazEphLYME7GYOl6UCXT7RZBTrfF4HMlk0mghZkNQqNpP1J/4NIj7pXuikYVO/ej0aZO8gMzxruJI6aTNyyg6aaoCG7IhyClLWV3JypBrjo+qr7GnFtn+UIg8pLmM49Lllw/4dfn5MckWkhm3EHlAMvO3CGxdmnJYvKDjVgmjLbPdH0zTL5Q+4SU3yTk6Oorq6mpPeUXlUp1oFPWnXNdN2PmFwZHyI6OOW50/pSeD7LRtZ2cnKisrUVZWFsg9L3s36DgtpHk/H8hm+a3Lb55AtMvkd0qiHVyh3CVViDsgmflbBLYuwy6LzD2gcpH4QRhWLlWf80qf/7ZQ+oRX3ZKcra2tWvKKAqmKXFwU2FIUTNWPxSYIdIK/mkAkv647UPa7Tp2w9SqzIMmsfXx+on6RSCRQXl6OAwcOCOvKZIzx75pa0GSyA4V72XouEKY13+8YswrVHATfcUw6Uq75VToKYa7Bm7915cmmosouGmG7+cJQYFR9TuYqo/rkvy0khV8FkpO/d5AHlTeRSLjhDlTuIeL/iIKpBuHk+QEboiEMjpFIflWZZGVhFSTAW1FQ8arCqNNYLIaJiQmsXLkyLZyFyM3rBRmtAHiPL2cyR843WoBfhLlR81unVqGag+A7jklHytXgU00IhTYBFIJCmitLmF+o+pzICsPH68mXRcrLmus3HRb8glhXVyeMRUTWvNbWVm3+j0gGk8XbC34Ued33dOpexT9iFSSvduN5Vez7YfDEZLwtqgvdmwYoLfZdkQXNhMdVKBbffCPMjZrfOrUcqhyhUMIm5MrP7hUOIIiJOmyugVd6YfGojheOQyGVk++HutG8g4S3kOWRi8jQQTiXupHodfPguVOm5Q8y7ti8ALj/T2mFxUkEshMaQVbHhcI/PN5gOVQFiny5uQi5creoNHw61Sa6yNYLRA4NEonY1AWlcm8FyZcQttWrEFyquehjppYHslaMjY0JXUMqy4vqdKLI4iC6USCoJUH2vQmfTfWNyX2OftrXtPxBrLYsNyxsdzqbRrb6uixda40qbFiFKsd46623lM9potM9qq2LQuBOsfA7MSQSCXenYMo18Ju3yr1lAlm+fid5WdlVBF2/aXo9ywd06o0n/s/MzLgKD8/5Yl1pooMeupsAWQiBoIuv7HtThZ/flIi+CaOtY7EYotGom26Q8ptyMVluWNjudF5J93M9ld/6nSv8w7BRaHOPDFahyjEaGhqUz2ly9HtvGSDufIXAW9I5veZ16qeuri5t9++nXKaTUtBJTOTiND3FJIIsvo+KoOsFVX0G6UPZmBD9Ksb8aS+RhUZUVtP8ZKe5wl4UTBV+flOiI7sfyO4kDAteFkVRW4WhkLAKs461XCRnIczHwNxRVAqlvrxgFaocw2sgmx7VFiGMxSAb0BkUqlM/IrdEIZRLBC83TDbdBiICre7EqarPMNyddDw8jAncVCk3caOIfpNZnmSQneYKe1Ew7T+8S1I2XwQNwJrtselFK8i2JUdHMZXJqZI9iJJj+q2fcZkPJaxQ53kelpSeI+SSlF5IpGAWOnLJCOuqbwulvDISrYgIG4bMJmnkm8xKsqZS791N5yWHqnyqC3L5sobdP0wI6uxvuqTvXMMSoP0hW/NOkHr3S/w3GZdzqV+E1UaWlD7HEKbWr7M7y8cuQ0cuGVfFTyybXIPd7aVS793RJ5I9yA7azynJXO7wRH2LytvU1BRKiI/u7m5MTEygu7s741k2LEO6LlqVNdLkaH0uMZcI0GHykYIiW1awIPXul/hvMi4LsV/IkOu1wSpUBYJcN3yhKCEiBCGN5xMkBwDjC19NIGo7r0Ull2RWVd8ykUPVrq2trSgvL0dra6tnHmH0D10Xral7xwTZVhxERHzeSpxvvk0h85F0oFOHpq5snW+9YPLdXCLG53ptsApVgSDXDV8oSogIuSaNhwU/uz0/ELVdoSwqxLkh61wQqNo1Go1iw4YNiEajoSmTqnR0x0vY1kgWYbWx6nSoKn0V3yYMZUsnjWwqrCo5wlImg7RhoYzxbCEbCnuu1warUBUIct3whaKEzEdku25F6ReKgkyhCcg65zVJhjGJhrXQsOEm+LAlYbSpV1mTySTa29uRTCaFz8NqY1l9eaXPWmCzYSUyDYOh+i0IsmkF021DUV8plDGeLcwHhdEqVAUKdkDly9ReCCb+4xWmdZ8PBVln0hdZNbxOQJoirIWGDTehE7bEdIx6nahS8cKA8NrYb0gBlQU27PhO+UQ2rWC6bSgaF/N9E1wo7R8E9pRfjkCnBEZHR1FVVeX5vuzqhFyeqphLpznmG6jui4uLEYlEcnYyLOyTg5ReMpnE4OAgmpubEYlEAl0Fks1TnSYn8kzHqNeJKtXJxTCR71OxQfLPRdvn6uRtLtKxCAf2lF+BwitSOoHV1vOluc+HHUMYyFfcleLiYtcNlS0zOF82E4uRTv/wck/62XWHzSVi3XskTzQa9ZRLZ4yy9evFsWN5YUGh6rOygLC5gp/2o/KQEst/Wyiu47D65lyxRlkvRjqsQpVjeEVKJ7ADKl+DK+x8C3nwqWTLh2+f2r2qqgrj4+Ooq6vLSt3xZTNRok36R1NTE1paWtKuACFrjSnC5BJNTk5ix44dvpRWfoyKyhSm68Zk/BQyH8VP+1F5APi6vsmE8B5krPFlK+Q5LwwUcj/LB6xClWMU+o4jm9AdfKlUCv39/ejv78/ZRKSSLZ8WQopoPTw8nJWJiy9bGPGxRG3GpxtkIubTIjL31NSU0eJFnKkVK1Z4RrvWgahMYfadsKyHrHKbD/jpY1Qe/tog/rmsnk0I70HGWpj9fC4g3/HtCg1WoToOka+OqTv4EokEBgYGMDAwkLOJSCVbri2EosCdIvnCaEcTa5FXfmG7C3VBZO6dO3cKSd8yuSOR2St6Kioq3GtY/IDSr6ury7iuJcy+E5b1kH0WpA+Zfsu/b/K9LmFeViaTuguzb2Zr3BYKCiW+XRCE2R5WoZrnEHWWfO2adAcfLUyNjY05swoVEmdB100UZrgAnUChXvlly13oBQryeeGFFwqP9YcVaFQGSn94eBiRSGaUf7/g2yAbfVTGpzI5tahbVv590+91Fz6v8eOl2PH1rMrXSybVuA3zXstCQrYUxmxZw3TnPx1YhWoeQXcRDLNjZmPwDA8Po7a2FtFotCAUnFxDt33CDBegEyiUiPKsBYZFvpRSInOXlpYKSd/ZdkuEdYAkyAEBVTp+oJO3aVn5902/160PU/efV7qq536UI1U8r/mAbG3YszW/6M5/OrBhE3KEXFyOLDrGnu3jt9kIrTBfjwzPpXLJZO3v78fAwACam5uxbNmyPEo4/xDWpc5sOl5hKWR55CqEgwmyFZLAK13Vc69QGLkoT6FhPpSLL4MNmzAHEXRnKdK0s201yMbuv5Dcb2FiLhFUw2yD+cQZ8QM+PAMfhZ2QDYujV5+TtfPw8DCqq6sxPDwcSBZTmBxs8As+HRN+luyZn+umsjHPmYy1bI3L+TB/+y2DVagKCEEX3Hx05PkweLKBoMTYbMviNx0AaG5u9tyFs3nOJUWSh18itaj8FH1dFIVdtKv3W2/smPTb57LZV3MZoiRXyrzJPJjNU8wUCqSzs9MX10wkq+xeQ9nGIGzMpQ2ZVagKCPP9CGo288xG2kHS1CGWByG7BpXFT16JRPo9fbp5mvZr1SSe60mVJ23rLvii8re2tqb9V4ezFnQ+8LvhCWOjJGuzoCFKTPpCvpR5L6UxW6eYKRRIZWWlMddMl4PLbxCyXbdzaUNmFaoCwnw4gpqvPLORdpDTODoLgw7ZNYzy1NXVYXR0FHV1dcZysPB79Ny0X6sm8Vz1V9mCyJZLtWiKyk/R10VR2PPhrg8buqT6ICFKUqkUOjs7tQOx+lVKgyrwXkpjc3MzmpubEYvFPC/FNgGFAikrK/Mss07MLFH98RuEoAYAr7rOl2XfDywpPUfIBSndBPkgDmYzz2wQVgH4Jpya5iUju4ZRV14HB0RlDquN/JRD9E2Q+tD9ln2PFhfVXYqmBzLmA1lXBb+kepN6icfjmJycxPj4eKDYYV4IetjGpEzt7e2YmJhAeXk5NmzY4FfkwMhX/5wLd8bqrt9WocoRCk2hshAjHyclsw3aAQLwtHiEPbnlcrKUtZOuDCan41T56aRfqAtHEIRxKlGHm5eLsUinHFevXo3R0dGs5sefqJzr840p5kJ57Sk/i+MGYfJr/Lpe8sXx0cmXOE86ASep/GHdHZhLc70fF5PsPZ02N3XJqeQoJOKtX1l03HVBTzfmyg1Kpxx7enqy7mrmL8WeS5yhMDDXXNsqWIXKYk7DlFPhBb+DO1+TYNj8Jyp/WHcH5nKylJVRV4Zsy6pKv5AWUZOTYiy8FDFZGQtxQQ2bJ6SLVGo2aG5xcfGc4AxlE4W0ydCFVags5gxkp1DIFJtr4imLfBEnTRWlMKwphYpCtiR6QVTf+ZK1rq4OBw4cQHl5uZGC56UUmvSpXB/N58EfJMiVsmdykna+o5A2GbqwCpXFnIHsFEpZWZkvgmqYAzZfu2zTfHXjKhWi1SAMFOokLapv1SnTbClbqVQK3d3dWLFiBSYmJowUai+FyaRP5fpofqFgLm5ksoW5WBdWobKYMwj7aPlcHLBBwSsUhapgZAt+2jyflqLR0VGkUillGImwY5ZVVlZiYmLCeJMSphKeL5cbIV9x+rJJzi5U66wMc3FTZxUqizmDsAfYXBywQcErFMebUumnzWVKZ7YXKCJGRyIRaSwgNsRDGEoxWXxbW1tdZY1FtsvMn0j163LLZhypbCHbeYaZPlu/c01RyyasQmVRsDgeB2q2y8wrFMejUmkKmdKZ7QWQ8qW74tg2CuN6GRG8DiUUwqLvNUbCOKgS9kZDZ1xne3MTZvpsOwXpE/NtjrcKlUXB4nhzRwHHZ5nnKrK9AObzdKKsbIWw6HuNkSAHVQhh16nOuM7nKVNTsO0UpE/ouq7niuI1rxSqu+66Cxs3bkRZWRlOPPFE4Tv9/f247LLLsGjRItTW1uJrX/uaZ8j/6elp3HjjjaitrcWiRYtw+eWXY2BgIAslOD4RRnyabOQfxnemaWejzKYyFNrklW955tJx/7AgK1shLPpeYyTIQZVsIez4brmCzqGVsHisubqKK5uYVwpVMpnElVdeia985SvC56lUCpdccgmOHTuGZ599Ftu3b8dvfvMb3Hrrrcp0b7rpJjzyyCPYvn07nn32WUxMTODSSy+dM4Oi0JHvBcvvYNX5zjTtbJTZVIZCm7z8yhOWImaq5GZTQc82csWRCpK+1xgpREU3zPhuuewn2Z4LdF3Xc4br6cxDbNu2zamsrMz4/Xe/+52zYMECJx6Pu7/98pe/dEpKSpzx8XFhWocPH3YWLlzobN++3f0tHo87CxYscB5//HFtmcbHxx0A0nyOZ8zMzDgDAwPOzMzMnMpf57tslk03bVMZ8t0ePHh5dOUbGBhwXnnlFeell15Kezfb5RsYGHBee+01Z2BgICffhYlsy3Dw4EHnj3/8o3Pw4MFQ0iu0vuqFMOTNZT/J1hyTrTSyBd31uzjfCl0u8fzzz2PNmjVYsmSJ+9uWLVswPT2Njo4OXHjhhRnfdHR04J133sHmzZvd35YsWYI1a9bgueeew5YtW4R5TU9PY3p62v33+Pg4gNk7gQoRqVQKb731FhoaGvKys6uoqMCxY8dynm/Q/HW+y1bZ3nzzTUxPT+Po0aNpfToMGfLdHjxYedhyNzQ04K233kJtbS0OHTqU1n/Lyspw4MABVFZW4sCBA24dmdSbH5SVleHo0aOoqqoyGu9+vwsT2Zbh6NGjOHbsGI4ePRpK+n7acq7PdbptFFY5deQNY0xle1wGAdWz43H18XGlUCUSCTQ0NKT9VlVVhWg0qiQ4RqNRVFVVpf3e0NCgNIPefffd+Na3vpXx+9KlS31IbmFhYWFhYZFPHD16FJWVldLnBa9Q3XnnnULFhMVLL72EDRs2aKVXVFSU8ZvjOMLfVfD65pvf/CZuueUW99/vvvsuRkdHUVNTY5xXoeDIkSNYunQp3njjDeWN2/MNtty23McDbLltuY8H+Cm34zhalrOCV6huuOEGXHXVVcp3TjrpJK20YrEYXnjhhbTfxsbG8M4772RYrthvkskkxsbG0qxUQ0ND2LhxozSvkpISlJSUpP0mO3k417B48eLjagASbLmPL9hyH1+w5T6+YFpulWWKUPAKVW1tLWpra0NJ67zzzsNdd92FwcFBNDY2AgCefPJJlJSUYP369cJv1q9fj4ULF+Kpp57CJz/5SQDA4OAg9u7di3vvvTcUuSwsLCwsLCzmNuZV2IT+/n50dXWhv78fqVQKXV1d6OrqwsTEBABg8+bNeN/73ofPfe5z6OzsxO9//3t84xvfwNatW11NNR6PY9WqVXjxxRcBzGqlX/rSl3Drrbfi97//PTo7O/HZz34WZ5xxBjZt2pS3slpYWFhYWFgUDgreQmWCv//7v8fPfvYz999tbW0AgJ07d+KCCy5AJBLBb3/7W1x//fV4//vfj9LSUnzmM5/BP/3TP7nfvPPOO3j55ZcxOTnp/vaDH/wAxcXF+OQnP4mpqSlcdNFF+Jd/+ZeCinOSC5SUlOCOO+7IcGXOd9hy23IfD7DltuU+HpDNchc5XucALSwsLCwsLCwslJhXLj8LCwsLCwsLi3zAKlQWFhYWFhYWFgFhFSoLCwsLCwsLi4CwCpWFhYWFhYWFRUBYhcoiDXfddRc2btyIsrIyaSDS/v5+XHbZZVi0aBFqa2vxta99DclkUpnu9PQ0brzxRtTW1mLRokW4/PLLMTAwkIUSBMeuXbtQVFQk/HvppZek311zzTUZ75977rk5lDw4TjrppIwy3HbbbcpvHMfBnXfeiSVLlqC0tBQXXHABenp6ciRxcLz++uv40pe+hOXLl6O0tBQnn3wy7rjjDs8+PRfb+8c//jGWL1+OE044AevXr8d//dd/Kd9/5plnsH79epxwwglYsWIFfvKTn+RI0nBw991346yzzkJFRQXq6+vxl3/5l3j55ZeV38jG//79+3MkdXDceeedGfLHYjHlN3O9rQHx/FVUVISvfvWrwvfDbut5FTbBIjiSySSuvPJKnHfeeXjwwQcznqdSKVxyySWoq6vDs88+i5GREVx99dVwHAf33XefNN2bbroJ//Ef/4Ht27ejpqYGt956Ky699FJ0dHQUXPiJjRs3YnBwMO23v/u7v8OOHTs8rzj68Ic/jG3btrn/jkajWZExm/j2t7+NrVu3uv8uLy9Xvn/vvffi+9//Pv7lX/4Fp556Kv7xH/8RF198MV5++WVUVFRkW9zA2L9/P959913cf//9OOWUU7B3715s3boVx44dSwupIsJcau9f/epXuOmmm/DjH/8Y73//+3H//ffjIx/5CPbt24dly5ZlvN/X14ePfvSj2Lp1K37xi1/gj3/8I66//nrU1dXhE5/4RB5KYI5nnnkGX/3qV3HWWWdhZmYGf/u3f4vNmzdj3759WLRokfLbl19+OS2Sdl1dXbbFDRWrV6/Gjh073H+r5tn50NbA7DV0qVTK/ffevXtx8cUX48orr1R+F1pbOxYWAmzbts2prKzM+P13v/uds2DBAicej7u//fKXv3RKSkqc8fFxYVqHDx92Fi5c6Gzfvt39LR6POwsWLHAef/zx0GUPG8lk0qmvr3e+/e1vK9+7+uqrnY997GO5ESpLaGlpcX7wgx9ov//uu+86sVjM+e53v+v+9vbbbzuVlZXOT37ykyxImBvce++9zvLly5XvzLX2Pvvss53rrrsu7bdVq1Y5t912m/D9v/7rv3ZWrVqV9tuXv/xl59xzz82ajNnG0NCQA8B55plnpO/s3LnTAeCMjY3lTrCQcccddzhr167Vfn8+trXjOM7Xv/515+STT3beffdd4fOw29q6/CyM8Pzzz2PNmjVpl0Ru2bIF09PT6OjoEH7T0dGBd955B5s3b3Z/W7JkCdasWYPnnnsu6zIHxaOPPopDhw7hmmuu8Xx3165dqK+vx6mnnoqtW7diaGgo+wKGjHvuuQc1NTVYt24d7rrrLqXrq6+vD4lEIq1tS0pKcP7558+JtpVhfHwc1dXVnu/NlfZOJpPo6OhIaydg9vYIWTs9//zzGe9v2bIF7e3teOedd7ImazYxPj4OAFpt29bWhsbGRlx00UXYuXNntkULHQcOHMCSJUuwfPlyXHXVVejt7ZW+Ox/bOplM4he/+AW++MUvoqioSPluWG1tFSoLIyQSiYyLpKuqqhCNRpFIJKTfRKPRtMulAaChoUH6TSHhwQcfxJYtW7B06VLlex/5yEfw0EMP4emnn8b3vvc9vPTSS/jQhz6E6enpHEkaHF//+texfft27Ny5EzfccAN++MMf4vrrr5e+T+3H94m50rYivPbaa7jvvvtw3XXXKd+bS+196NAhpFIpo3YSjfWGhgbMzMzg0KFDWZM1W3AcB7fccgs+8IEPYM2aNdL3Ghsb8cADD+A3v/kNHn74YZx22mm46KKL8Ic//CGH0gbDOeecg5///Od44okn8NOf/hSJRAIbN27EyMiI8P351tYA8G//9m84fPiwciMceluHYueyKGjccccdDgDl30svvZT2jczlt3XrVmfz5s0Zvy9cuND55S9/Kcz/oYcecqLRaMbvmzZtcr785S/7K5QP+KmHN954w1mwYIHz61//2ji/N99801m4cKHzm9/8Jqwi+IKfchN+/etfOwCcQ4cOCZ//8Y9/dAA4b775Ztrv1157rbNly5bQy2ICP+WOx+POKaec4nzpS18yzq9Q2luEeDzuAHCee+65tN//8R//0TnttNOE36xcudL5zne+k/bbs88+6wBwBgcHsyZrtnD99dc7LS0tzhtvvGH87aWXXupcdtllWZAqN5iYmHAaGhqc733ve8Ln862tHcdxNm/e7Fx66aXG3wVpa0tKPw5www034KqrrlK+c9JJJ2mlFYvF8MILL6T9NjY2hnfeeSdjh8N+k0wmMTY2lmalGhoawsaNG7XyDQN+6mHbtm2oqanB5ZdfbpxfY2MjWlpacODAAeNvw0SQ9qdTa6+++ipqamoyntPJoUQigcbGRvf3oaEhaX/IFUzL/eabb+LCCy/EeeedhwceeMA4v0JpbxFqa2sRiUQyrFGqdorFYsL3i4uLhX2hkHHjjTfi0UcfxR/+8Ac0Nzcbf3/uuefiF7/4RRYkyw0WLVqEM844Q9o351NbA8DBgwexY8cOPPzww8bfBmlrq1AdB6itrUVtbW0oaZ133nm46667MDg46C6gTz75JEpKSrB+/XrhN+vXr8fChQvx1FNP4ZOf/CQAYHBwEHv37sW9994bilw6MK0Hx3Gwbds2fP7zn8fChQuN8xsZGcEbb7yRpmjkA0Hav7OzEwCkZVi+fDlisRieeuop9zLyZDKJZ555Bvfcc48/gUOCSbnj8TguvPBCrF+/Htu2bcOCBeZsiEJpbxGi0SjWr1+Pp556Ch//+Mfd35966il87GMfE35z3nnn4T/+4z/SfnvyySexYcMGX+MhH3AcBzfeeCMeeeQR7Nq1C8uXL/eVTmdnZ0G2qy6mp6fx5z//GX/xF38hfD4f2prFtm3bUF9fj0suucT420Bt7cuuZTFvcfDgQaezs9P51re+5ZSXlzudnZ1OZ2enc/ToUcdxHGdmZsZZs2aNc9FFFzm7d+92duzY4TQ3Nzs33HCDm8bAwIBz2mmnOS+88IL723XXXec0Nzc7O3bscHbv3u186EMfctauXevMzMzkvIy62LFjhwPA2bdvn/D5aaed5jz88MOO4zjO0aNHnVtvvdV57rnnnL6+Pmfnzp3Oeeed5zQ1NTlHjhzJpdi+8dxzzznf//73nc7OTqe3t9f51a9+5SxZssS5/PLL095jy+04jvPd737XqaysdB5++GFnz549zqc//WmnsbFxzpSb3Hwf+tCHnIGBAWdwcND9YzHX23v79u3OwoULnQcffNDZt2+fc9NNNzmLFi1yXn/9dcdxHOe2225zPve5z7nv9/b2OmVlZc7NN9/s7Nu3z3nwwQedhQsX+nJ/5wtf+cpXnMrKSmfXrl1p7To5Oem+w5f7Bz/4gfPII484r7zyirN3717ntttucwAUpCtXhltvvdXZtWuX09vb6/y///f/nEsvvdSpqKiY121NSKVSzrJly5y/+Zu/yXiW7ba2CpVFGq6++moh12Tnzp3uOwcPHnQuueQSp7S01KmurnZuuOEG5+2333af9/X1ZXwzNTXl3HDDDU51dbVTWlrqXHrppU5/f38OS2aOT3/6087GjRulzwE427ZtcxzHcSYnJ53Nmzc7dXV1zsKFC51ly5Y5V199dcGXkUVHR4dzzjnnOJWVlc4JJ5zgnHbaac4dd9zhHDt2LO09ttyOMxs64Y477nBisZhTUlLifPCDH3T27NmTY+n9Y9u2bVKOFYv50N4/+tGPnJaWFicajTpnnnlmWviAq6++2jn//PPT3t+1a5fT1tbmRKNR56STTnL++Z//OccSB4OsXdn+y5f7nnvucU4++WTnhBNOcKqqqpwPfOADzm9/+9vcCx8An/rUp5zGxkZn4cKFzpIlS5wrrrjC6enpcZ/Px7YmPPHEEw4A5+WXX854lu22LnIcx/Fn27KwsLCwsLCwsABs2AQLCwsLCwsLi8CwCpWFhYWFhYWFRUBYhcrCwsLCwsLCIiCsQmVhYWFhYWFhERBWobKwsLCwsLCwCAirUFlYWFhYWFhYBIRVqCwsLCwsLCwsAsIqVBYWFhYWFhYWAWEVKgsLCwsLCwuLgLAKlYWFhcU8w8c//nFUVVXhv/23/5ZvUSwsjhtYhcrCwsJinuFrX/safv7zn+dbDAuL4wpWobKwsJjXuOCCC1BUVISioiJ0dXXlW5yc4MILL0RFRYXw2TXXXOPWx7/927/lVjALi3kMq1BZWFjMe2zduhWDg4NYs2ZNvkXJO/7n//yfGBwczLcYFhbzDsX5FsDCwsIi2ygrK0MsFsu3GIGRTCYRjUaxfv16TE9PZzx/8sknsWTJEmUalZWVqKyszJaIFhbHLayFysLCoqDw7LPPYuHChWkKQ19fH4qKinDw4MFQ8rjgggtw44034qabbkJVVRUaGhrwwAMP4NixY/jCF76AiooKnHzyyXjsscfSvnv88cfxgQ98ACeeeCJqampw6aWX4rXXXnOf//rXv8YZZ5yB0tJS1NTUYNOmTTh27JhQhp///OeoqanJUIw+8YlP4POf/7wr5w033IBbbrkFtbW1uPjiiwEAHR0d2Lt3b8aflzJlYWGRPViFysLCoqDQ1dWF008/HSUlJWm/nXjiiWhpaQktn5/97Geora3Fiy++iBtvvBFf+cpXcOWVV2Ljxo3YvXs3tmzZgs997nOYnJx0vzl27BhuueUWvPTSS/j973+PBQsW4OMf/zjeffddDA4O4tOf/jS++MUv4s9//jN27dqFK664Ao7jCPO/8sorkUql8Oijj7q/HTp0CP/5n/+JL3zhC2lyFhcX449//CPuv//+0MpvYWERLqzLz8LCoqDwpz/9CW1tbWm/dXV1Ye3ataHms3btWtx+++0AgG9+85v47ne/i9raWmzduhUA8Pd///f453/+Z3R3d+Pcc88FMGs9YvHggw+ivr4e+/btQzKZxMzMDK644gpX8TvjjDOk+ZeWluIzn/kMtm3bhiuvvBIA8NBDD6G5uRkXXHCB+94pp5yCe++916hsW7Zswe7du3Hs2DE0NzfjkUcewVlnnWWUhoWFhRmshcrCwqKg0NXVhXXr1qX91tnZKVSo7rvvPnz/+9/3lU9ra6v7/5FIBDU1NWkKUENDAwBgaGjI/e21117DZz7zGaxYsQKLFy/G8uXLAQD9/f1Yu3YtLrroIpxxxhm48sor8dOf/hRjY2NKGbZu3Yonn3wS8XgcALBt2zb3FB5hw4YNxmV74oknMDw8jMnJSQwMDFhlysIiB7AKlYWFRcEglUqhp6cnw0K1e/fuDCULmLVmnXnmmb7yWrhwYdq/i4qK0n4jpebdd991f7vsssswMjKCn/70p3jhhRfwwgsvAJgli0ciETz11FN47LHH8L73vQ/33XcfTjvtNPT19UllaGtrw9q1a/Hzn/8cu3fvxp49e3DNNdekvbNo0SJf5bOwsMgtrEJlYWFRMHj55ZcxNTWVRq5+/vnnEY/H0yxUe/fuxYUXXoiHHnoIW7duxb//+79nXbaRkRH8+c9/xu23346LLroIp59+eoYFqqioCO9///vxrW99C52dnYhGo3jkkUeU6V577bXYtm0b/s//+T/YtGkTli5dms1iWFhYZAlWobKwsCgYUODN++67DwcOHMBjjz3mnnij03BTU1P47Gc/i/vvvx8tLS349a9/jTvuuCPrslVVVaGmpgYPPPAAXn31VTz99NO45ZZb3OcvvPACvvOd76C9vR39/f14+OGHMTw8jNNPP12Z7l/91V8hHo/jpz/9Kb74xS9muxgWFhZZglWoLCwsCgZdXV24+OKL0dfXhzVr1uC///f/ju9+97tYvHgxfvSjHwGYDV1w3nnnYWZmBitXrsTKlStx5MgRdHZ24lOf+pRLNA8bCxYswPbt29HR0YE1a9bg5ptvxv/4H//Dfb548WL84Q9/wEc/+lGceuqpuP322/G9730PH/nIR5TpLl68GJ/4xCdQXl6Ov/zLv8yK7BYWFtmHPeVnYWFRMPjTn/6E9evX4+677077nT1d96c//QlnnHEG9uzZgzVr1uBPf/oTWltb0dbWhnvuuQf/+3//b898du3alfHb66+/nvEbH/Jg06ZN2Ldvn/Sdxx9/3DNvEQYHB/FXf/VXaaEiZHJaWFgUJqyFysLComBAypEKixcvxiuvvILu7m6cfvrp+M53voPrrrtO+c2Pf/xjlJeXY8+ePWGKGxijo6PYvn07nn76aXz1q1/NSZ7XXXcdysvLc5KXhcXxBGuhsrCwKAgkEgm89dZbngrV5z//eXzsYx/D3r17EYvF8LWvfQ0f/vCHpe8/9NBDmJqaAgAsW7YsVJmD4swzz8TY2BjuuecenHbaaTnJ89vf/ja+8Y1vAAAaGxtzkqeFxfGAIkcWxtfCwsKigHHaaaehp6cHxcWz+8K+vj7cfvvt2L9/P26++WZ89rOfzbOEFhYWxxOsQmVhYTHncPjwYVx00UXo6OjItygWFhYWAKxCZWFhYWFhYWERGJaUbmFhYWFhYWEREFahsrCwsLCwsLAICKtQWVhYWFhYWFgEhFWoLCwsLCwsLCwCwipUFhYWFhYWFhYBYRUqCwsLCwsLC4uAsAqVhYWFhYWFhUVAWIXKwsLCwsLCwiIgrEJlYWFhYWFhYREQVqGysLCwsLCwsAgIq1BZWFhYWFhYWASEVagsLCwsLCwsLALi/wN7p5YVj97xgAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mpl.rcParams['text.usetex'] = False\n", + "plt.style.use('default')\n", + "\n", + "plot_proper_motion(centerline)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Upper left\n", + "\n", + "Now let's work on the panel in the upper left. We have to reload `candidates`." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "filename = 'gd1_candidates.hdf5'\n", + "\n", + "if not os.path.exists(filename):\n", + " !wget https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_candidates.hdf5" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "filename = 'gd1_candidates.hdf5'\n", + "\n", + "candidate_df = pd.read_hdf(filename, 'candidate_df')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a function that takes a `DataFrame` of candidate stars and plots their positions in GD-1 coordindates. " + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_first_selection(df):\n", + " x = df['phi1']\n", + " y = df['phi2']\n", + "\n", + " plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n", + "\n", + " plt.xlabel('$\\phi_1$ [deg]')\n", + " plt.ylabel('$\\phi_2$ [deg]')\n", + " plt.title('Proper motion selection', fontsize='medium')\n", + "\n", + " plt.axis('equal')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's what it looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_first_selection(candidate_df)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Lower right\n", + "\n", + "For the figure in the lower right, we need to reload the merged `DataFrame`, which contains data from Gaia and photometry data from Pan-STARRS." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "filename = 'gd1_merged.hdf5'\n", + "\n", + "merged = pd.read_hdf(filename, 'merged')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From the previous notebook, here's the function that plots the color-magnitude diagram." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "def plot_cmd(table):\n", + " \"\"\"Plot a color magnitude diagram.\n", + " \n", + " table: Table or DataFrame with photometry data\n", + " \"\"\"\n", + " y = table['g_mean_psf_mag']\n", + " x = table['g_mean_psf_mag'] - table['i_mean_psf_mag']\n", + "\n", + " plt.plot(x, y, 'ko', markersize=0.3, alpha=0.3)\n", + "\n", + " plt.xlim([0, 1.5])\n", + " plt.ylim([14, 22])\n", + " plt.gca().invert_yaxis()\n", + "\n", + " plt.ylabel('$g_0$')\n", + " plt.xlabel('$(g-i)_0$')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And here's what it looks like." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_cmd(merged)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Exercise:** Add a few lines to `plot_cmd` to show the Polygon we selected as a shaded area. \n", + "\n", + "Run these cells to get the polygon coordinates we saved in the previous notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "filename = 'gd1_polygon.hdf5'\n", + "\n", + "if not os.path.exists(filename):\n", + " !wget https://github.com/AllenDowney/AstronomicalData/raw/main/data/gd1_polygon.hdf5" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0.26433692, 18.21044886],\n", + " [ 0.38530466, 19.09345107],\n", + " [ 0.52867384, 19.82928624],\n", + " [ 0.63172043, 20.34437086],\n", + " [ 0.72580645, 20.89624724],\n", + " [ 0.66756272, 21.3745401 ],\n", + " [ 0.49283154, 21.33774834],\n", + " [ 0.35394265, 20.41795438],\n", + " [ 0.24193548, 19.57174393],\n", + " [ 0.18369176, 18.35761589]])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "coords_df = pd.read_hdf(filename, 'coords_df')\n", + "coords = coords_df.to_numpy()\n", + "coords" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "# Solution\n", + "\n", + "#poly = Polygon(coords, closed=True, \n", + "# facecolor='C1', alpha=0.4)\n", + "#plt.gca().add_patch(poly)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Subplots\n", + "\n", + "Now we're ready to put it all together. To make a figure with four subplots, we'll use `subplot2grid`, [which requires two arguments](https://matplotlib.org/3.3.1/api/_as_gen/matplotlib.pyplot.subplot2grid.html):\n", + "\n", + "* `shape`, which is a tuple with the number of rows and columns in the grid, and\n", + "\n", + "* `loc`, which is a tuple identifying the location in the grid we're about to fill.\n", + "\n", + "In this example, `shape` is `(2, 2)` to create two rows and two columns.\n", + "\n", + "For the first panel, `loc` is `(0, 0)`, which indicates row 0 and column 0, which is the upper-left panel.\n", + "\n", + "Here's how we use it to draw the four panels." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "shape = (2, 2)\n", + "plt.subplot2grid(shape, (0, 0))\n", + "plot_first_selection(candidate_df)\n", + "\n", + "plt.subplot2grid(shape, (0, 1))\n", + "plot_proper_motion(centerline)\n", + "\n", + "plt.subplot2grid(shape, (1, 0))\n", + "plot_second_selection(selected)\n", + "\n", + "plt.subplot2grid(shape, (1, 1))\n", + "plot_cmd(merged)\n", + "poly = Polygon(coords, closed=True, \n", + " facecolor='C1', alpha=0.4)\n", + "plt.gca().add_patch(poly)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We use [`plt.tight_layout`](https://matplotlib.org/3.3.1/tutorials/intermediate/tight_layout_guide.html) at the end, which adjusts the sizes of the panels to make sure the titles and axis labels don't overlap.\n", + "\n", + "**Exercise:** See what happens if you leave out `tight_layout`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Adjusting proportions\n", + "\n", + "In the previous figure, the panels are all the same size. To get a better view of GD-1, we'd like to stretch the panels on the left and compress the ones on the right.\n", + "\n", + "To do that, we'll use the `colspan` argument to make a panel that spans multiple columns in the grid.\n", + "\n", + "In the following example, `shape` is `(2, 4)`, which means 2 rows and 4 columns.\n", + "\n", + "The panels on the left span three columns, so they are three times wider than the panels on the right.\n", + "\n", + "At the same time, we use `figsize` to adjust the aspect ratio of the whole figure." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(9, 4.5))\n", + "\n", + "shape = (2, 4)\n", + "plt.subplot2grid(shape, (0, 0), colspan=3)\n", + "plot_first_selection(candidate_df)\n", + "\n", + "plt.subplot2grid(shape, (0, 3))\n", + "plot_proper_motion(centerline)\n", + "\n", + "plt.subplot2grid(shape, (1, 0), colspan=3)\n", + "plot_second_selection(selected)\n", + "\n", + "plt.subplot2grid(shape, (1, 3))\n", + "plot_cmd(merged)\n", + "poly = Polygon(coords, closed=True, \n", + " facecolor='C1', alpha=0.4)\n", + "plt.gca().add_patch(poly)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is looking more and more like the figure in the paper.\n", + "\n", + "**Exercise:** In this example, the ratio of the widths of the panels is 3:1. How would you adjust it if you wanted the ratio to be 3:2?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook, we reverse-engineered the figure we've been replicating, identifying elements that seem effective and others that could be improved.\n", + "\n", + "We explored features Matplotlib provides for adding annotations to figures -- including text, lines, arrows, and polygons -- and several ways to customize the appearance of figures. And we learned how to create figures that contain multiple panels." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Best practices\n", + "\n", + "* The most effective figures focus on telling a single story clearly and compellingly.\n", + "\n", + "* Consider using annotations to guide the readers attention to the most important elements of a figure.\n", + "\n", + "* The default Matplotlib style generates good quality figures, but there are several ways you can override the defaults.\n", + "\n", + "* If you find yourself making the same customizations on several projects, you might want to create your own style sheet." + ] + }, + { + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/test_setup.ipynb b/test_setup.ipynb new file mode 100644 index 0000000..99b3932 --- /dev/null +++ b/test_setup.ipynb @@ -0,0 +1,99 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook imports the libraries we need for the workshop.\n", + "\n", + "If any of them are missing, you'll get an error message.\n", + "\n", + "If you don't get any error messages, you are all set." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.path import Path\n", + "from matplotlib.patches import Polygon" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import astropy.coordinates as coord\n", + "import astropy.units as u\n", + "from astropy.table import Table" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import gala.coordinates as gc\n", + "from pyia import GaiaData" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Note: running this import statement opens a connection\n", + "# to a Gaia server, so it will fail if you are not connected\n", + "# to the internet.\n", + "\n", + "from astroquery.gaia import Gaia" + ] + }, + { + "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": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}