diff --git a/CHANGELOG.md b/CHANGELOG.md index 046875a..015b090 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,10 @@ Development * Placeholder +0.3.25 +------- +* Adding international capability to EEWeather (international.py), plus testing & new samples. + 0.3.24 ------ diff --git a/Dockerfile b/Dockerfile index 79747bd..118a587 100644 --- a/Dockerfile +++ b/Dockerfile @@ -7,18 +7,17 @@ RUN groupadd --gid 1000 node \ # gpg keys listed at https://github.com/nodejs/node#release-team RUN set -ex \ && for key in \ - 94AE36675C464D64BAFA68DD7434390BDBE9B9C5 \ - FD3A5288F042B6850C66B31F09FE44734EB7990E \ - 71DCFD284A79C3B38668286BC97EC7A07EDE3FC1 \ - DD8F2338BAE7501E3DD5AC78C273792F7D83545D \ + 4ED778F539E3634C779C87C6D7062848A1AB005C \ + 141F07595B7B3FFE74309A937405533BE57C7D57 \ + 74F12602B6F1C4E913FAA37AD3A89613643B6201 \ + DD792F5973C6DE52C432CBDAC77ABFA00DDBF2B7 \ + 8FCCA13FEF1D0C2E91008E09770F7A9A5AE15600 \ C4F0DFFF4E8C1A8236409D08E73BC641CC11F4C8 \ - B9AE9905FFD7803F25714661B63B535A4C206CA9 \ - 56730D5401028683275BD23C23EFEFE93C4CFFFE \ - 77984A986EBC2AA786BC0F66B01FBB92821C587A \ - ; do \ - gpg --keyserver hkp://p80.pool.sks-keyservers.net:80 --recv-keys "$key" || \ - gpg --keyserver hkp://ipv4.pool.sks-keyservers.net --recv-keys "$key" || \ - gpg --keyserver hkp://pgp.mit.edu:80 --recv-keys "$key" ; \ + 890C08DB8579162FEE0DF9DB8BEAB4DFCF555EF4 \ + C82FA3AE1CBEDC6BE46B9360C43CEC45C17AB93C \ + 108F52B48DB57BB0CC439B2997B01419BD92F80A \ + ; do \ + gpg --batch --keyserver hkps://keys.openpgp.org --recv-keys "$key";\ done ENV NODE_VERSION 10.0.0 @@ -34,7 +33,7 @@ RUN ARCH= && dpkgArch="$(dpkg --print-architecture)" \ *) echo "unsupported architecture"; exit 1 ;; \ esac \ && curl -SLO "https://nodejs.org/dist/v$NODE_VERSION/node-v$NODE_VERSION-linux-$ARCH.tar.xz" \ - && curl -SLO --compressed "https://nodejs.org/dist/v$NODE_VERSION/SHASUMS256.txt.asc" \ + && curl -SLO --compressed "https://nodejs.org/dist/v10.0.0/SHASUMS256.txt.asc" \ && gpg --batch --decrypt --output SHASUMS256.txt SHASUMS256.txt.asc \ && grep " node-v$NODE_VERSION-linux-$ARCH.tar.xz\$" SHASUMS256.txt | sha256sum -c - \ && tar -xJf "node-v$NODE_VERSION-linux-$ARCH.tar.xz" -C /usr/local --strip-components=1 --no-same-owner \ diff --git a/docs/api.rst b/docs/api.rst index 55cc36c..445e6e2 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -1,6 +1,13 @@ API Docs ======== +International +------- + +.. autofunction:: eeweather.get_weather_intl + +.. autofunction:: eeweather.get_weather_intervals_for_similar_sites + Ranking ------- diff --git a/eeweather/__init__.py b/eeweather/__init__.py index 51dc753..ec8971e 100644 --- a/eeweather/__init__.py +++ b/eeweather/__init__.py @@ -2,7 +2,8 @@ # -*- coding: utf-8 -*- """ - Copyright 2018 Open Energy Efficiency, Inc. + Copyright 2023 Open Energy Efficiency, Inc and the Society for + the Reduction of Carbon, Ltd (T/A Carbon Co-op). Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -102,6 +103,7 @@ load_cached_cz2010_hourly_temp_data, ) from .visualization import plot_station_mapping, plot_station_mappings +from .international import * def get_version(): diff --git a/eeweather/database.py b/eeweather/database.py index 8466f9f..6540654 100644 --- a/eeweather/database.py +++ b/eeweather/database.py @@ -158,8 +158,7 @@ def _download_primary_sources(): def _load_isd_station_metadata(download_path): - """ Collect metadata for US isd stations. - """ + """Collect metadata for US isd stations.""" from shapely.geometry import Point # load ISD history which contains metadata @@ -183,7 +182,9 @@ def _load_isd_station_metadata(download_path): isAus = isd_history.CTRY == "AS" metadata = {} - for usaf_station, group in isd_history[hasGEO & hasUSAF & (isUS | isAus)].groupby("USAF"): + for usaf_station, group in isd_history[hasGEO & hasUSAF & (isUS | isAus)].groupby( + "USAF" + ): # find most recent recent = group.loc[group.END.idxmax()] wban_stations = list(group.WBAN) @@ -206,8 +207,7 @@ def _load_isd_station_metadata(download_path): def _load_isd_file_metadata(download_path, isd_station_metadata): - """ Collect data counts for isd files. - """ + """Collect data counts for isd files.""" isd_inventory = pd.read_csv( os.path.join(download_path, "isd-inventory.csv"), dtype=str @@ -1137,7 +1137,7 @@ def build_metadata_db( ba_climate_zone_geometry=True, ca_climate_zone_geometry=True, ): - """ Build database of metadata from primary sources. + """Build database of metadata from primary sources. Downloads primary sources, clears existing DB, and rebuilds from scratch. diff --git a/eeweather/exceptions.py b/eeweather/exceptions.py index b5aea4b..c50dfc1 100644 --- a/eeweather/exceptions.py +++ b/eeweather/exceptions.py @@ -26,7 +26,7 @@ class EEWeatherError(Exception): class UnrecognizedUSAFIDError(EEWeatherError): - """ Raised when an unrecognized USAF station id is encountered. + """Raised when an unrecognized USAF station id is encountered. Attributes ---------- @@ -45,7 +45,7 @@ def __init__(self, value): class UnrecognizedZCTAError(EEWeatherError): - """ Raised when an unrecognized ZCTA is encountered. + """Raised when an unrecognized ZCTA is encountered. Attributes ---------- @@ -57,13 +57,15 @@ class UnrecognizedZCTAError(EEWeatherError): def __init__(self, value): self.value = value - self.message = 'The value "{}" was not recognized as a valid ZCTA identifier.'.format( - value + self.message = ( + 'The value "{}" was not recognized as a valid ZCTA identifier.'.format( + value + ) ) class ISDDataNotAvailableError(EEWeatherError): - """ Raised when ISD data is not available for a particular station and year. + """Raised when ISD data is not available for a particular station and year. Attributes ---------- @@ -84,7 +86,7 @@ def __init__(self, usaf_id, year): class GSODDataNotAvailableError(EEWeatherError): - """ Raised when GSOD data is not available for a particular station and year. + """Raised when GSOD data is not available for a particular station and year. Attributes ---------- @@ -101,7 +103,7 @@ def __init__(self, usaf_id, year): class TMY3DataNotAvailableError(EEWeatherError): - """ Raised when TMY3 data is not available for a particular station. + """Raised when TMY3 data is not available for a particular station. Attributes ---------- @@ -117,7 +119,7 @@ def __init__(self, usaf_id): class CZ2010DataNotAvailableError(EEWeatherError): - """ Raised when CZ2010 data is not available for a particular station. + """Raised when CZ2010 data is not available for a particular station. Attributes ---------- @@ -133,7 +135,7 @@ def __init__(self, usaf_id): class NonUTCTimezoneInfoError(EEWeatherError): - """ Raised when input start and end date aren't explicitly defined + """Raised when input start and end date aren't explicitly defined to have a UTC timezone. Attributes diff --git a/eeweather/geo.py b/eeweather/geo.py index 89f96d7..b5aa491 100644 --- a/eeweather/geo.py +++ b/eeweather/geo.py @@ -102,7 +102,7 @@ def climate_zone_geometry(self): def get_lat_long_climate_zones(latitude, longitude): - """ Get climate zones that contain lat/long coordinates. + """Get climate zones that contain lat/long coordinates. Parameters ---------- @@ -162,7 +162,7 @@ def get_lat_long_climate_zones(latitude, longitude): def get_zcta_metadata(zcta): - """ Get metadata about a ZIP Code Tabulation Area (ZCTA). + """Get metadata about a ZIP Code Tabulation Area (ZCTA). Parameters ---------- diff --git a/eeweather/international.py b/eeweather/international.py new file mode 100644 index 0000000..9d32571 --- /dev/null +++ b/eeweather/international.py @@ -0,0 +1,274 @@ +import warnings + +import numpy as np +import pandas as pd +from dateutil.relativedelta import relativedelta +import xarray as xr +import cdsapi +from datetime import datetime +import tempfile +import logging + +__all__ = ( + "get_weather_intl", + "get_weather_intervals_for_similar_sites", +) + + +# function for api request +def call_ecmwf_api(year, months, days, filename, area): + times = ["{:02d}:00".format(hour) for hour in range(24)] + + c = cdsapi.Client() + c.retrieve( + "reanalysis-era5-single-levels", + { + "product_type": "reanalysis", + "format": "grib", + "variable": "2m_temperature", + "year": year, + "month": months, + "day": days, + "time": times, + "area": area, + }, + filename, + ) + + +def _get_weather_xr(ds, latitude, longitude): + temp = xr.DataArray.to_numpy( + ds.sel(latitude=latitude, method="nearest") + .sel(longitude=longitude, method="nearest") + .t2m + ) + + temp = np.subtract(temp, 273.15) + temp_df = pd.DataFrame( + temp, + columns=["temp"], + index=list(xr.DataArray.to_numpy(ds.time)), + ) + temp_df.index.name = "Datetime" + return temp_df + + +def get_ecmwf_df(year, months, days, filename, area, latitude, longitude): + # Disable logging + logging.disable(logging.INFO) + logging.disable(logging.WARNING) + call_ecmwf_api( + year, + months, + days, + filename, + area, + ) + ds = xr.open_dataset( + filename, engine="cfgrib" + ) + df = _get_weather_xr( + ds, latitude, longitude + ) + # Enable logging + logging.disable(logging.NOTSET) + return df + +def round_quarter(x: float): + quarter = round(x * 4) / 4 + return quarter + +def get_weather_intervals_for_similar_sites(df): + """A function to identify unique co-ordinates across a DataFrame of sites + and weather requirements, and to return a DataFrame with the maximum + intervals required for each site. This function primarily exists to + improve performance of the CDS API, used in EEWeather international. + Instead of duplicating time-consuming weather calls for similar sites, + this function instead identifies similar sites so that fewer calls can be + made to the CDS API to return the same results. + + Parameters + ---------- + df : : pandas 'DataFrame' + Any pandas.DataFrame comprising energy consumption metadata arranged + according to the following categories: + - start_date: any 'datetime' corresponding to the beginning of each + site's weather call interval. For CalTRACK, this is likely to + correspond to the first meter recording. + - end_date: any 'datetime' corresponding to the end of each + site's weather call interval. For CalTRACK, this is likely to + correspond to the last meter recording. + - latitude: any 'float' corresponding to the latitude of the relevant + site. + - longitude: any 'float' corresponding to the latitude of the relevant + site. + Index can be any format, such as unique household identifiers if + desired. + + Returns + ------- + df_collated : : any: 'pandas.DataFrame'. + A maximum-interval dataframe with start_date, end_date, latitude and + longitude for each unique site across a geographically distributed + portfolio of sites. + """ + + df['latitude'] = round_quarter(df['latitude']) + df['longitude'] = round_quarter(df['longitude']) + + df_start_sort = ( + df.sort_values(by="start_date") + .drop_duplicates(["latitude", "longitude"], keep="first") + .sort_values(by=["latitude", "longitude"]) + ) + df_end_sort = ( + df.sort_values(by="end_date") + .drop_duplicates(["latitude", "longitude"], keep="last") + .sort_values(by=["latitude", "longitude"]) + ) + df_collated = pd.DataFrame( + { + "start_date": list(df_start_sort["start_date"]), + "end_date": list(df_end_sort["end_date"]), + "latitude": df_start_sort["latitude"], + "longitude": df_start_sort["longitude"], + } + ) + + # Merge the two dataframes on the index column + merged_df = pd.merge(df, df_collated, left_index=True, right_index=True, how='outer', indicator=True) + # Identify the rows that are only in df + dropped_sites = list(merged_df[merged_df['_merge'] == 'left_only'].index) + + df_shorter_intervals = df.loc[dropped_sites, :] + + def match_index(row): + match = df_collated[ + (df_collated['latitude'] == row['latitude']) & (df_collated['longitude'] == row['longitude'])] + if match.empty: + return None + else: + return match.index[0] + + df_shorter_intervals['matching_index'] = df_shorter_intervals.apply(match_index, axis=1) + + return df_collated, df_shorter_intervals + + +def get_weather_intl( + start_date, + end_date, + latitude=None, + longitude=None, +): + """Download hourly 2m temperature data from the European Centre for Medium-range Weather Forecasts (ECMWF)'s via + its Climate Data Store (CDS) API for anywhere in the world. CDS provides temperature data from 1959 to five days + prior to any given request. + + This API call provides access to a broader range of temperature data than USAF/IECC/ISD data as provided elsewhere + in EEWeather but performs more slowly. USAF/IECC/ISD weather calls should be utilised where possible, principally in + the United States where relevant weather stations can be found. If the distance between the relevant site(s) and a + United States weather station is excessively large to undertake meaningful analysis, the CDS API should be used. + + Note that get_weather_intl can call temperature data to a minimum interval of 1 day. This means that every call will + return hourly temperature data for at least 24h for day(s) concerned, i.e. from 00:00:00 to 23:00:00. If the user + requires temperature data only for a sub-section of a given day, df.loc[] should be used to sub-select temperature + dataframes. + + The CDS API is subject to API rate limits, available at: https://cds.climate.copernicus.eu/live/limits. The relevant + call is the 'online CDS data' as phrased. + + All data generated using Copernicus Climate Change Service information 2022 (or current year) as applicable in the + CDS licence: https://cds.climate.copernicus.eu/cdsapp/#!/terms/licence-to-use-copernicus-products. While this data + is free to use, distribute and adapt, the data is and will remain the property of the European Union. All + reproductions/adaptations of this data should comply with the Copernicus terms of use, including attributing the + Copernicus programme and the European Union as required. + + TECHNICAL NOTES + + 1. Use of the CDS API requires all users to set up a CDS account and agree to the terms of the CDS licence, + linked above. + 2. Users may also be required to set up the CDS API key and install a .cdsapirc file, in their home + directory, guidance on which is available at: https://cds.climate.copernicus.eu/api-how-to. + 3. Users may be required to install the eccodes library, used for the interpretation of GRIB weather files. + 3a. This package has been designed for Linux systems, on which it can be installed using the following link: + https://confluence.ecmwf.int/display/ECC/Releases; alternatively eccodes can be installed on Python 3 using + pip3 install eccodes. + 3b. For Windows users, eccodes and the related Magics package can be installed with conda using + conda install -c conda-forge eccodes Magics. Further details available at: + https://www.ecmwf.int/en/newsletter/159/news/eccodes-and-magics-available-under-windows + 4. The cfgrib package is also required for the functioning of this package. If not already installed, users should + install cfgrib via pip install cfgrib==0.8.4.5. Details available at: https://pypi.org/project/cfgrib/0.8.4.5/ + + To check the progress of your download, visit: https://cds.climate.copernicus.eu/cdsapp#!/yourrequests + + Parameters + ---------- + start_date : :any: 'datetime.datetime' + The start date and time for the requested temperature series. Must be timezone-naive. + end_date : :any: 'datetime.datetime' + The end date and time for the requested temperature series. Must be timezone-naive. + latitude : :any: 'float' between -90 and 90, optional + The actual latitude of the site concerned. + longitude : :any: 'float' between -180 and 180, optional + The actual longitude of the site concerned. + areacode : :any:`str`, optional + The postcode/zipcode relevant to a given site. For example: + 'SW1A 2DX' for Trafalgar Square, London, UK; + '10117 Berlin' for Brandenburger Tor, Berlin, Germany; + 'NY 10036' for Times Square, New York, USA. + + Returns + ------- + weather : :any: 'pandas.DataFrame' + Hourly deg C temperature data for the specified location in ascending order. + + """ + start_date = start_date.replace(tzinfo=None) + end_date = end_date.replace(tzinfo=None) + + if end_date > (datetime.now() - relativedelta(days=5)): + warnings.warn("Data not available for most recent 5 days.") + + latitude = round_quarter(latitude) + longitude = round_quarter(longitude) + + bounding_box = [ + latitude + 0.001, + longitude - 0.001, + latitude - 0.001, + longitude + 0.001, + ] + + end_date = min( + end_date.replace(tzinfo=None), datetime.now() - relativedelta(days=6) + ) + + weather_list = [] + + with tempfile.TemporaryDirectory() as td: + filename = f"{td}/temp.grib" + all_days = [str(n).rjust(2, "0") for n in range(1, 32)] + + for year in range(start_date.year, end_date.year + 1): + if year == start_date.year: + start_month = start_date.month + else: + start_month = 1 + + if year == end_date.year: + end_month = end_date.month + else: + end_month = 12 + + months = [str(n).rjust(2, "0") for n in range(start_month, end_month + 1)] + + for month in months: + weather_list.append(get_ecmwf_df(year, month, all_days, filename, bounding_box, latitude, longitude)) + + weather = pd.concat(weather_list) + weather.sort_index(inplace=True) + # Trim the dataframe to the correct start and end dates + weather = weather[start_date:end_date] + + return weather \ No newline at end of file diff --git a/eeweather/ranking.py b/eeweather/ranking.py index d601368..1caaeea 100644 --- a/eeweather/ranking.py +++ b/eeweather/ranking.py @@ -121,7 +121,7 @@ def rank_stations( is_tmy3=None, is_cz2010=None, ): - """ Get a ranked, filtered set of candidate weather stations and metadata + """Get a ranked, filtered set of candidate weather stations and metadata for a particular site. Parameters @@ -321,7 +321,7 @@ def rank_stations( def combine_ranked_stations(rankings): - """ Combine :any:`pandas.DataFrame` s of candidate weather stations to form + """Combine :any:`pandas.DataFrame` s of candidate weather stations to form a hybrid ranking dataframe. Parameters @@ -367,7 +367,7 @@ def select_station( rank=1, fetch_from_web=True, ): - """ Select a station from a list of candidates that meets given data + """Select a station from a list of candidates that meets given data quality criteria. Parameters diff --git a/eeweather/stations.py b/eeweather/stations.py index 8e4643e..a543458 100644 --- a/eeweather/stations.py +++ b/eeweather/stations.py @@ -1077,7 +1077,7 @@ def load_cached_cz2010_hourly_temp_data(usaf_id): class ISDStation(object): - """ A representation of an Integrated Surface Database weather station. + """A representation of an Integrated Surface Database weather station. Contains data about a particular ISD station, as well as methods to pull data for this station. @@ -1176,7 +1176,7 @@ def _float_or_none(field): } def json(self): - """ Return a JSON-serializeable object containing station metadata.""" + """Return a JSON-serializeable object containing station metadata.""" return { "elevation": self.elevation, "latitude": self.latitude, @@ -1195,230 +1195,230 @@ def json(self): } def get_isd_filenames(self, year=None, with_host=False): - """ Get filenames of raw ISD station data. """ + """Get filenames of raw ISD station data.""" return get_isd_filenames(self.usaf_id, year, with_host=with_host) def get_gsod_filenames(self, year=None, with_host=False): - """ Get filenames of raw GSOD station data. """ + """Get filenames of raw GSOD station data.""" return get_gsod_filenames(self.usaf_id, year, with_host=with_host) def get_isd_file_metadata(self): - """ Get raw file metadata for the station. """ + """Get raw file metadata for the station.""" return get_isd_file_metadata(self.usaf_id) # fetch raw data def fetch_isd_raw_temp_data(self, year): - """ Pull raw ISD data for the given year directly from FTP. """ + """Pull raw ISD data for the given year directly from FTP.""" return fetch_isd_raw_temp_data(self.usaf_id, year) def fetch_gsod_raw_temp_data(self, year): - """ Pull raw GSOD data for the given year directly from FTP. """ + """Pull raw GSOD data for the given year directly from FTP.""" return fetch_gsod_raw_temp_data(self.usaf_id, year) # fetch raw data then frequency-normalize def fetch_isd_hourly_temp_data(self, year): - """ Pull raw ISD temperature data for the given year directly from FTP and resample to hourly time series. """ + """Pull raw ISD temperature data for the given year directly from FTP and resample to hourly time series.""" return fetch_isd_hourly_temp_data(self.usaf_id, year) def fetch_isd_daily_temp_data(self, year): - """ Pull raw ISD temperature data for the given year directly from FTP and resample to daily time series. """ + """Pull raw ISD temperature data for the given year directly from FTP and resample to daily time series.""" return fetch_isd_daily_temp_data(self.usaf_id, year) def fetch_gsod_daily_temp_data(self, year): - """ Pull raw GSOD temperature data for the given year directly from FTP and resample to daily time series. """ + """Pull raw GSOD temperature data for the given year directly from FTP and resample to daily time series.""" return fetch_gsod_daily_temp_data(self.usaf_id, year) def fetch_tmy3_hourly_temp_data(self): - """ Pull hourly TMY3 temperature hourly time series directly from NREL. """ + """Pull hourly TMY3 temperature hourly time series directly from NREL.""" return fetch_tmy3_hourly_temp_data(self.usaf_id) def fetch_cz2010_hourly_temp_data(self): - """ Pull hourly CZ2010 temperature hourly time series from URL. """ + """Pull hourly CZ2010 temperature hourly time series from URL.""" return fetch_cz2010_hourly_temp_data(self.usaf_id) # get key-value store key def get_isd_hourly_temp_data_cache_key(self, year): - """ Get key used to cache resampled hourly ISD temperature data for the given year. """ + """Get key used to cache resampled hourly ISD temperature data for the given year.""" return get_isd_hourly_temp_data_cache_key(self.usaf_id, year) def get_isd_daily_temp_data_cache_key(self, year): - """ Get key used to cache resampled daily ISD temperature data for the given year. """ + """Get key used to cache resampled daily ISD temperature data for the given year.""" return get_isd_daily_temp_data_cache_key(self.usaf_id, year) def get_gsod_daily_temp_data_cache_key(self, year): - """ Get key used to cache resampled daily GSOD temperature data for the given year. """ + """Get key used to cache resampled daily GSOD temperature data for the given year.""" return get_gsod_daily_temp_data_cache_key(self.usaf_id, year) def get_tmy3_hourly_temp_data_cache_key(self): - """ Get key used to cache TMY3 weather-normalized temperature data. """ + """Get key used to cache TMY3 weather-normalized temperature data.""" return get_tmy3_hourly_temp_data_cache_key(self.usaf_id) def get_cz2010_hourly_temp_data_cache_key(self): - """ Get key used to cache CZ2010 weather-normalized temperature data. """ + """Get key used to cache CZ2010 weather-normalized temperature data.""" return get_cz2010_hourly_temp_data_cache_key(self.usaf_id) # is cached data expired? boolean. true if expired or not in cache def cached_isd_hourly_temp_data_is_expired(self, year): - """ Return True if cache of resampled hourly ISD temperature data has expired or does not exist for the given year. """ + """Return True if cache of resampled hourly ISD temperature data has expired or does not exist for the given year.""" return cached_isd_hourly_temp_data_is_expired(self.usaf_id, year) def cached_isd_daily_temp_data_is_expired(self, year): - """ Return True if cache of resampled daily ISD temperature data has expired or does not exist for the given year. """ + """Return True if cache of resampled daily ISD temperature data has expired or does not exist for the given year.""" return cached_isd_daily_temp_data_is_expired(self.usaf_id, year) def cached_gsod_daily_temp_data_is_expired(self, year): - """ Return True if cache of resampled daily GSOD temperature data has expired or does not exist for the given year. """ + """Return True if cache of resampled daily GSOD temperature data has expired or does not exist for the given year.""" return cached_gsod_daily_temp_data_is_expired(self.usaf_id, year) # check if data is available and delete data in the cache if it's expired def validate_isd_hourly_temp_data_cache(self, year): - """ Delete cached resampled hourly ISD temperature data if it has expired for the given year. """ + """Delete cached resampled hourly ISD temperature data if it has expired for the given year.""" return validate_isd_hourly_temp_data_cache(self.usaf_id, year) def validate_isd_daily_temp_data_cache(self, year): - """ Delete cached resampled daily ISD temperature data if it has expired for the given year. """ + """Delete cached resampled daily ISD temperature data if it has expired for the given year.""" return validate_isd_daily_temp_data_cache(self.usaf_id, year) def validate_gsod_daily_temp_data_cache(self, year): - """ Delete cached resampled daily GSOD temperature data if it has expired for the given year. """ + """Delete cached resampled daily GSOD temperature data if it has expired for the given year.""" return validate_gsod_daily_temp_data_cache(self.usaf_id, year) def validate_tmy3_hourly_temp_data_cache(self): - """ Check if TMY3 data exists in cache. """ + """Check if TMY3 data exists in cache.""" return validate_tmy3_hourly_temp_data_cache(self.usaf_id) def validate_cz2010_hourly_temp_data_cache(self): - """ Check if CZ2010 data exists in cache. """ + """Check if CZ2010 data exists in cache.""" return validate_cz2010_hourly_temp_data_cache(self.usaf_id) # pandas time series to json def serialize_isd_hourly_temp_data(self, ts): - """ Serialize resampled hourly ISD pandas time series as JSON for caching. """ + """Serialize resampled hourly ISD pandas time series as JSON for caching.""" return serialize_isd_hourly_temp_data(ts) def serialize_isd_daily_temp_data(self, ts): - """ Serialize resampled daily ISD pandas time series as JSON for caching. """ + """Serialize resampled daily ISD pandas time series as JSON for caching.""" return serialize_isd_daily_temp_data(ts) def serialize_gsod_daily_temp_data(self, ts): - """ Serialize resampled daily GSOD pandas time series as JSON for caching. """ + """Serialize resampled daily GSOD pandas time series as JSON for caching.""" return serialize_gsod_daily_temp_data(ts) def serialize_tmy3_hourly_temp_data(self, ts): - """ Serialize hourly TMY3 pandas time series as JSON for caching. """ + """Serialize hourly TMY3 pandas time series as JSON for caching.""" return serialize_tmy3_hourly_temp_data(ts) def serialize_cz2010_hourly_temp_data(self, ts): - """ Serialize hourly CZ2010 pandas time series as JSON for caching. """ + """Serialize hourly CZ2010 pandas time series as JSON for caching.""" return serialize_cz2010_hourly_temp_data(ts) # json to pandas time series def deserialize_isd_hourly_temp_data(self, data): - """ Deserialize JSON representation of resampled hourly ISD into pandas time series. """ + """Deserialize JSON representation of resampled hourly ISD into pandas time series.""" return deserialize_isd_hourly_temp_data(data) def deserialize_isd_daily_temp_data(self, data): - """ Deserialize JSON representation of resampled daily ISD into pandas time series. """ + """Deserialize JSON representation of resampled daily ISD into pandas time series.""" return deserialize_isd_daily_temp_data(data) def deserialize_gsod_daily_temp_data(self, data): - """ Deserialize JSON representation of resampled daily GSOD into pandas time series. """ + """Deserialize JSON representation of resampled daily GSOD into pandas time series.""" return deserialize_gsod_daily_temp_data(data) def deserialize_tmy3_hourly_temp_data(self, data): - """ Deserialize JSON representation of hourly TMY3 into pandas time series. """ + """Deserialize JSON representation of hourly TMY3 into pandas time series.""" return deserialize_isd_hourly_temp_data(data) def deserialize_cz2010_hourly_temp_data(self, data): - """ Deserialize JSON representation of hourly CZ2010 into pandas time series. """ + """Deserialize JSON representation of hourly CZ2010 into pandas time series.""" return deserialize_cz2010_hourly_temp_data(data) # return pandas time series of data from cache def read_isd_hourly_temp_data_from_cache(self, year): - """ Get cached version of resampled hourly ISD temperature data for given year. """ + """Get cached version of resampled hourly ISD temperature data for given year.""" return read_isd_hourly_temp_data_from_cache(self.usaf_id, year) def read_isd_daily_temp_data_from_cache(self, year): - """ Get cached version of resampled daily ISD temperature data for given year. """ + """Get cached version of resampled daily ISD temperature data for given year.""" return read_isd_daily_temp_data_from_cache(self.usaf_id, year) def read_gsod_daily_temp_data_from_cache(self, year): - """ Get cached version of resampled daily GSOD temperature data for given year. """ + """Get cached version of resampled daily GSOD temperature data for given year.""" return read_gsod_daily_temp_data_from_cache(self.usaf_id, year) def read_tmy3_hourly_temp_data_from_cache(self): - """ Get cached version of hourly TMY3 temperature data. """ + """Get cached version of hourly TMY3 temperature data.""" return read_tmy3_hourly_temp_data_from_cache(self.usaf_id) def read_cz2010_hourly_temp_data_from_cache(self): - """ Get cached version of hourly TMY3 temperature data. """ + """Get cached version of hourly TMY3 temperature data.""" return read_cz2010_hourly_temp_data_from_cache(self.usaf_id) # write pandas time series of data to cache for a particular year def write_isd_hourly_temp_data_to_cache(self, year, ts): - """ Write resampled hourly ISD temperature data to cache for given year. """ + """Write resampled hourly ISD temperature data to cache for given year.""" return write_isd_hourly_temp_data_to_cache(self.usaf_id, year, ts) def write_isd_daily_temp_data_to_cache(self, year, ts): - """ Write resampled daily ISD temperature data to cache for given year. """ + """Write resampled daily ISD temperature data to cache for given year.""" return write_isd_daily_temp_data_to_cache(self.usaf_id, year, ts) def write_gsod_daily_temp_data_to_cache(self, year, ts): - """ Write resampled daily GSOD temperature data to cache for given year. """ + """Write resampled daily GSOD temperature data to cache for given year.""" return write_gsod_daily_temp_data_to_cache(self.usaf_id, year, ts) def write_tmy3_hourly_temp_data_to_cache(self, ts): - """ Write hourly TMY3 temperature data to cache for given year. """ + """Write hourly TMY3 temperature data to cache for given year.""" return write_tmy3_hourly_temp_data_to_cache(self.usaf_id, ts) def write_cz2010_hourly_temp_data_to_cache(self, ts): - """ Write hourly CZ2010 temperature data to cache for given year. """ + """Write hourly CZ2010 temperature data to cache for given year.""" return write_cz2010_hourly_temp_data_to_cache(self.usaf_id, ts) # delete cached data for a particular year def destroy_cached_isd_hourly_temp_data(self, year): - """ Remove cached resampled hourly ISD temperature data to cache for given year. """ + """Remove cached resampled hourly ISD temperature data to cache for given year.""" return destroy_cached_isd_hourly_temp_data(self.usaf_id, year) def destroy_cached_isd_daily_temp_data(self, year): - """ Remove cached resampled daily ISD temperature data to cache for given year. """ + """Remove cached resampled daily ISD temperature data to cache for given year.""" return destroy_cached_isd_daily_temp_data(self.usaf_id, year) def destroy_cached_gsod_daily_temp_data(self, year): - """ Remove cached resampled daily GSOD temperature data to cache for given year. """ + """Remove cached resampled daily GSOD temperature data to cache for given year.""" return destroy_cached_gsod_daily_temp_data(self.usaf_id, year) def destroy_cached_tmy3_hourly_temp_data(self): - """ Remove cached hourly TMY3 temperature data to cache. """ + """Remove cached hourly TMY3 temperature data to cache.""" return destroy_cached_tmy3_hourly_temp_data(self.usaf_id) def destroy_cached_cz2010_hourly_temp_data(self): - """ Remove cached hourly CZ2010 temperature data to cache. """ + """Remove cached hourly CZ2010 temperature data to cache.""" return destroy_cached_cz2010_hourly_temp_data(self.usaf_id) # load data either from cache if valid or directly from source def load_isd_hourly_temp_data_cached_proxy(self, year, fetch_from_web=True): - """ Load resampled hourly ISD temperature data from cache, or if it is expired or hadn't been cached, fetch from FTP for given year. """ + """Load resampled hourly ISD temperature data from cache, or if it is expired or hadn't been cached, fetch from FTP for given year.""" return load_isd_hourly_temp_data_cached_proxy( self.usaf_id, year, fetch_from_web ) def load_isd_daily_temp_data_cached_proxy(self, year, fetch_from_web=True): - """ Load resampled daily ISD temperature data from cache, or if it is expired or hadn't been cached, fetch from FTP for given year. """ + """Load resampled daily ISD temperature data from cache, or if it is expired or hadn't been cached, fetch from FTP for given year.""" return load_isd_daily_temp_data_cached_proxy(self.usaf_id, year, fetch_from_web) def load_gsod_daily_temp_data_cached_proxy(self, year, fetch_from_web=True): - """ Load resampled daily GSOD temperature data from cache, or if it is expired or hadn't been cached, fetch from FTP for given year. """ + """Load resampled daily GSOD temperature data from cache, or if it is expired or hadn't been cached, fetch from FTP for given year.""" return load_gsod_daily_temp_data_cached_proxy( self.usaf_id, year, fetch_from_web ) def load_tmy3_hourly_temp_data_cached_proxy(self, fetch_from_web=True): - """ Load hourly TMY3 temperature data from cache, or if it is expired or hadn't been cached, fetch from NREL. """ + """Load hourly TMY3 temperature data from cache, or if it is expired or hadn't been cached, fetch from NREL.""" return load_tmy3_hourly_temp_data_cached_proxy(self.usaf_id, fetch_from_web) def load_cz2010_hourly_temp_data_cached_proxy(self, fetch_from_web=True): - """ Load hourly CZ2010 temperature data from cache, or if it is expired or hadn't been cached, fetch from URL. """ + """Load hourly CZ2010 temperature data from cache, or if it is expired or hadn't been cached, fetch from URL.""" return load_cz2010_hourly_temp_data_cached_proxy(self.usaf_id, fetch_from_web) # main interface: load data from start date to end date @@ -1431,7 +1431,7 @@ def load_isd_hourly_temp_data( fetch_from_web=True, error_on_missing_years=True, ): - """ Load resampled hourly ISD temperature data from start date to end date (inclusive). + """Load resampled hourly ISD temperature data from start date to end date (inclusive). This is the primary convenience method for loading resampled hourly ISD temperature data. @@ -1461,7 +1461,7 @@ def load_isd_hourly_temp_data( def load_isd_daily_temp_data( self, start, end, read_from_cache=True, write_to_cache=True, fetch_from_web=True ): - """ Load resampled daily ISD temperature data from start date to end date (inclusive). + """Load resampled daily ISD temperature data from start date to end date (inclusive). This is the primary convenience method for loading resampled daily ISD temperature data. @@ -1490,7 +1490,7 @@ def load_isd_daily_temp_data( def load_gsod_daily_temp_data( self, start, end, read_from_cache=True, write_to_cache=True, fetch_from_web=True ): - """ Load resampled daily GSOD temperature data from start date to end date (inclusive). + """Load resampled daily GSOD temperature data from start date to end date (inclusive). This is the primary convenience method for loading resampled daily GSOD temperature data. @@ -1519,7 +1519,7 @@ def load_gsod_daily_temp_data( def load_tmy3_hourly_temp_data( self, start, end, read_from_cache=True, write_to_cache=True, fetch_from_web=True ): - """ Load hourly TMY3 temperature data from start date to end date (inclusive). + """Load hourly TMY3 temperature data from start date to end date (inclusive). This is the primary convenience method for loading hourly TMY3 temperature data. @@ -1548,7 +1548,7 @@ def load_tmy3_hourly_temp_data( def load_cz2010_hourly_temp_data( self, start, end, read_from_cache=True, write_to_cache=True, fetch_from_web=True ): - """ Load hourly CZ2010 temperature data from start date to end date (inclusive). + """Load hourly CZ2010 temperature data from start date to end date (inclusive). This is the primary convenience method for loading hourly CZ2010 temperature data. @@ -1576,21 +1576,21 @@ def load_cz2010_hourly_temp_data( # load all cached data for this station def load_cached_isd_hourly_temp_data(self): - """ Load all cached resampled hourly ISD temperature data. """ + """Load all cached resampled hourly ISD temperature data.""" return load_cached_isd_hourly_temp_data(self.usaf_id) def load_cached_isd_daily_temp_data(self): - """ Load all cached resampled daily ISD temperature data. """ + """Load all cached resampled daily ISD temperature data.""" return load_cached_isd_daily_temp_data(self.usaf_id) def load_cached_gsod_daily_temp_data(self): - """ Load all cached resampled daily GSOD temperature data. """ + """Load all cached resampled daily GSOD temperature data.""" return load_cached_gsod_daily_temp_data(self.usaf_id) def load_cached_tmy3_hourly_temp_data(self): - """ Load all cached hourly TMY3 temperature data (the year is set to 1900) """ + """Load all cached hourly TMY3 temperature data (the year is set to 1900)""" return load_cached_tmy3_hourly_temp_data(self.usaf_id) def load_cached_cz2010_hourly_temp_data(self): - """ Load all cached hourly TMY3 temperature data (the year is set to 1900) """ + """Load all cached hourly TMY3 temperature data (the year is set to 1900)""" return load_cached_cz2010_hourly_temp_data(self.usaf_id) diff --git a/eeweather/summaries.py b/eeweather/summaries.py index 2a0b3f1..bfdd9f7 100644 --- a/eeweather/summaries.py +++ b/eeweather/summaries.py @@ -23,7 +23,7 @@ def get_zcta_ids(state=None): - """ Get ids of all supported ZCTAs, optionally by state. + """Get ids of all supported ZCTAs, optionally by state. Parameters ---------- @@ -56,7 +56,7 @@ def get_zcta_ids(state=None): def get_isd_station_usaf_ids(state=None): - """ Get USAF IDs of all supported ISD stations, optionally by state. + """Get USAF IDs of all supported ISD stations, optionally by state. Parameters ---------- diff --git a/eeweather/validation.py b/eeweather/validation.py index f84634d..ba859f6 100644 --- a/eeweather/validation.py +++ b/eeweather/validation.py @@ -25,7 +25,7 @@ def valid_zcta_or_raise(zcta): - """ Check if ZCTA is valid and raise eeweather.UnrecognizedZCTAError if not. """ + """Check if ZCTA is valid and raise eeweather.UnrecognizedZCTAError if not.""" conn = metadata_db_connection_proxy.get_connection() cur = conn.cursor() @@ -50,7 +50,7 @@ def valid_zcta_or_raise(zcta): def valid_usaf_id_or_raise(usaf_id): - """ Check if USAF ID is valid and raise eeweather.UnrecognizedUSAFIDError if not. """ + """Check if USAF ID is valid and raise eeweather.UnrecognizedUSAFIDError if not.""" conn = metadata_db_connection_proxy.get_connection() cur = conn.cursor() diff --git a/eeweather/visualization.py b/eeweather/visualization.py index 26a72ec..8d54d59 100644 --- a/eeweather/visualization.py +++ b/eeweather/visualization.py @@ -33,7 +33,7 @@ def plot_station_mapping( distance_meters, target_label="target", ): # pragma: no cover - """ Plots this mapping on a map.""" + """Plots this mapping on a map.""" try: import matplotlib.pyplot as plt except ImportError: @@ -74,21 +74,21 @@ def plot_station_mapping( bottom = y_min - y_diff * 0.3 top = y_max + y_diff * 0.3 - width_ratio = 2. - height_ratio = 1. + width_ratio = 2.0 + height_ratio = 1.0 if (right - left) / (top - bottom) > width_ratio / height_ratio: # too short goal = (right - left) * height_ratio / width_ratio diff = goal - (top - bottom) - bottom = bottom - diff / 2. - top = top + diff / 2. + bottom = bottom - diff / 2.0 + top = top + diff / 2.0 else: # too skinny goal = (top - bottom) * width_ratio / height_ratio diff = goal - (right - left) - left = left - diff / 2. - right = right + diff / 2. + left = left - diff / 2.0 + right = right + diff / 2.0 ax.set_extent([left, right, bottom, top]) @@ -133,7 +133,7 @@ def plot_station_mapping( def plot_station_mappings(mapping_results): # pragma: no cover - """ Plot a list of mapping results on a map. + """Plot a list of mapping results on a map. Requires matplotlib and cartopy. @@ -196,21 +196,21 @@ def plot_station_mappings(mapping_results): # pragma: no cover bottom = y_min - y_pad top = y_max + y_pad - width_ratio = 2. - height_ratio = 1. + width_ratio = 2.0 + height_ratio = 1.0 if (right - left) / (top - bottom) > height_ratio / width_ratio: # too short goal = (right - left) * height_ratio / width_ratio diff = goal - (top - bottom) - bottom = bottom - diff / 2. - top = top + diff / 2. + bottom = bottom - diff / 2.0 + top = top + diff / 2.0 else: # too skinny goal = (top - bottom) * width_ratio / height_ratio diff = goal - (right - left) - left = left - diff / 2. - right = right + diff / 2. + left = left - diff / 2.0 + right = right + diff / 2.0 left = max(left, -179.9) right = min(right, 179.9) diff --git a/eeweather/warnings.py b/eeweather/warnings.py index 80f3a20..133946c 100644 --- a/eeweather/warnings.py +++ b/eeweather/warnings.py @@ -20,7 +20,7 @@ class EEWeatherWarning(object): - """ An object representing a warning and data associated with it. + """An object representing a warning and data associated with it. Attributes ---------- @@ -41,7 +41,7 @@ def __repr__(self): return "EEWeatherWarning(qualified_name={})".format(self.qualified_name) def json(self): - """ Return a JSON-serializable representation of this result. + """Return a JSON-serializable representation of this result. The output of this function can be converted to a serialized string with :any:`json.dumps`. diff --git a/samples/df.csv b/samples/df.csv new file mode 100644 index 0000000..8754459 --- /dev/null +++ b/samples/df.csv @@ -0,0 +1,84 @@ +,start_date,end_date,latitude,longitude +0,2018-04-10 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.25 +1,2018-06-29 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.0 +2,2019-08-19 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-1.75 +3,2019-02-25 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.25 +4,2020-03-29 00:00:00.000000,2022-12-22 12:10:30.014357,51.25,-0.5 +5,2020-04-01 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.25 +6,2018-04-16 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.0 +7,2020-03-31 00:00:00.000000,2022-12-22 12:10:30.014357,51.5,0.0 +8,,2022-12-22 12:10:30.014357,46.25,11.0 +9,2020-01-03 00:00:00.000000,2022-12-22 12:10:30.014357,51.75,0.0 +10,2019-07-29 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.0 +11,,2022-12-22 12:10:30.014357,46.25,11.0 +12,2020-07-16 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.25 +13,2020-09-21 00:00:00.000000,2022-12-22 12:10:30.014357,55.75,-4.25 +14,,2022-12-22 12:10:30.014357,46.25,11.0 +15,,2022-12-22 12:10:30.014357,46.25,11.0 +16,2020-01-21 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-1.5 +17,2020-12-11 00:00:00.000000,2022-12-22 12:10:30.014357,54.0,-1.0 +18,,2022-12-22 12:10:30.014357,52.25,0.25 +19,2020-12-29 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.0 +20,2021-05-20 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.25 +21,2020-01-10 00:00:00.000000,2022-12-22 12:10:30.014357,51.5,-0.5 +22,,2022-12-22 12:10:30.014357,53.5,-2.25 +23,,2022-12-22 12:10:30.014357,51.75,0.25 +24,2020-12-03 00:00:00.000000,2022-12-22 12:10:30.014357,53.25,-2.0 +25,2021-05-07 00:00:00.000000,2022-12-22 12:10:30.014357,54.0,-3.25 +26,2021-05-14 00:00:00.000000,2022-12-22 12:10:30.014357,52.25,0.0 +27,2021-02-14 00:00:00.000000,2022-12-22 12:10:30.014357,51.5,0.0 +28,2020-07-20 00:00:00.000000,2022-12-22 12:10:30.014357,53.0,-1.5 +29,2021-08-16 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.0 +30,2020-07-30 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.0 +31,2020-08-06 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-3.0 +32,2021-08-11 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.25 +33,2020-09-04 00:00:00.000000,2022-12-22 12:10:30.014357,51.5,-0.25 +34,2020-10-02 00:00:00.000000,2022-12-22 12:10:30.014357,51.5,-0.25 +35,2021-03-17 00:00:00.000000,2022-12-22 12:10:30.014357,53.0,-1.25 +36,2021-08-31 00:00:00.000000,2022-12-22 12:10:30.014357,53.25,-2.75 +37,,2022-12-22 12:10:30.014357,51.5,-0.25 +38,2021-06-08 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.25 +39,2021-10-07 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.0 +40,2021-06-18 00:00:00.000000,2022-12-22 12:10:30.014357,54.0,-2.0 +41,2020-12-11 00:00:00.000000,2022-12-22 12:10:30.014357,54.0,-1.0 +42,2021-01-07 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.5 +43,2020-11-08 00:00:00.000000,2022-12-22 12:10:30.014357,53.25,-1.5 +44,,2022-12-22 12:10:30.014357,54.0,-1.0 +45,2021-08-13 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.25 +46,2020-11-15 00:00:00.000000,2022-12-22 12:10:30.014357,50.75,-3.5 +47,2021-09-22 00:00:00.000000,2022-12-22 12:10:30.014357,53.75,-2.0 +48,2020-12-02 00:00:00.000000,2022-12-22 12:10:30.014357,51.0,-1.25 +49,2021-06-16 00:00:00.000000,2022-12-22 12:10:30.014357,56.0,-3.25 +50,2021-01-01 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.0 +51,2022-03-08 00:00:00.000000,2022-12-22 12:10:30.014357,52.5,-1.0 +52,2021-02-01 00:00:00.000000,2022-12-22 12:10:30.014357,53.75,-1.75 +53,2021-02-12 00:00:00.000000,2022-12-22 12:10:30.014357,54.0,-1.25 +54,2022-03-04 00:00:00.000000,2022-12-22 12:10:30.014357,50.75,-1.25 +55,2021-08-17 00:00:00.000000,2022-12-22 12:10:30.014357,51.25,-0.25 +56,2021-03-30 00:00:00.000000,2022-12-22 12:10:30.014357,51.25,0.0 +57,2021-11-23 00:00:00.000000,2022-12-22 12:10:30.014357,53.25,-2.0 +58,2022-04-12 00:00:00.000000,2022-12-22 12:10:30.014357,53.75,-2.0 +59,2021-08-17 00:00:00.000000,2022-12-22 12:10:30.014357,53.0,-2.0 +60,2021-04-20 00:00:00.000000,2022-12-22 12:10:30.014357,51.25,0.5 +61,2021-04-28 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.75 +62,2021-05-05 00:00:00.000000,2022-12-22 12:10:30.014357,54.25,-0.75 +63,,2022-12-22 12:10:30.014357,51.25,0.25 +64,2021-06-27 00:00:00.000000,2022-12-22 12:10:30.014357,53.25,-2.25 +65,2021-06-27 00:00:00.000000,2022-12-22 12:10:30.014357,53.25,-2.25 +66,,2022-12-22 12:10:30.014357,53.5,-2.25 +67,2022-07-31 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.25 +68,2021-09-01 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.25 +69,2021-11-03 00:00:00.000000,2022-12-22 12:10:30.014357,54.75,-1.75 +70,2021-08-27 00:00:00.000000,2022-12-22 12:10:30.014357,53.25,-2.0 +71,2022-03-21 00:00:00.000000,2022-12-22 12:10:30.014357,53.25,-2.5 +72,2022-04-11 00:00:00.000000,2022-12-22 12:10:30.014357,51.5,0.0 +73,2021-09-14 00:00:00.000000,2022-12-22 12:10:30.014357,53.5,-2.25 +74,2022-10-01 00:00:00.000000,2022-12-22 12:10:30.014357,53.25,-1.5 +75,2021-09-25 00:00:00.000000,2022-12-22 12:10:30.014357,51.5,-1.25 +76,,2022-12-22 12:10:30.014357,53.5,-2.25 +77,,2022-12-22 12:10:30.014357,51.5,0.0 +78,,2022-12-22 12:10:30.014357,54.0,-1.75 +79,,2022-12-22 12:10:30.014357,51.5,-2.5 +80,,2022-12-22 12:10:30.014357,53.5,-3.0 +81,,2022-12-22 12:10:30.014357,54.0,-1.0 +82,,2022-12-22 12:10:30.014357,53.5,-2.25 diff --git a/samples/sample_sites.csv b/samples/sample_sites.csv new file mode 100644 index 0000000..65d6975 --- /dev/null +++ b/samples/sample_sites.csv @@ -0,0 +1,66 @@ +,start_date,end_date,latitude,longitude +0,2018-04-10,2022-12-22 12:10:30.014357,51.25,-2.25 +1,2018-06-29,2022-12-22 12:10:30.014357,52.25,-1.0 +2,2019-08-19,2022-12-22 12:10:30.014357,51.25,-1.5 +3,2019-02-25,2022-12-22 12:10:30.014357,53.0,-0.25 +4,2020-03-29,2022-12-22 12:10:30.014357,52.75,-0.75 +5,2020-04-01,2022-12-22 12:10:30.014357,52.0,-1.5 +6,2018-04-16,2022-12-22 12:10:30.014357,52.75,-2.0 +7,2020-03-31,2022-12-22 12:10:30.014357,52.75,-2.0 +9,2020-01-03,2022-12-22 12:10:30.014357,52.0,0.0 +10,2019-07-29,2022-12-22 12:10:30.014357,52.25,0.25 +12,2020-07-16,2022-12-22 12:10:30.014357,53.0,-0.5 +13,2020-09-21,2022-12-22 12:10:30.014357,53.5,-1.5 +16,2020-01-21,2022-12-22 12:10:30.014357,53.5,0.0 +17,2020-12-11,2022-12-22 12:10:30.014357,54.0,0.25 +19,2020-12-29,2022-12-22 12:10:30.014357,52.75,0.0 +20,2021-05-20,2022-12-22 12:10:30.014357,52.75,-0.5 +21,2020-01-10,2022-12-22 12:10:30.014357,51.5,0.25 +24,2020-12-03,2022-12-22 12:10:30.014357,51.5,0.25 +25,2021-05-07,2022-12-22 12:10:30.014357,53.75,-1.0 +26,2021-05-14,2022-12-22 12:10:30.014357,53.75,-1.0 +27,2021-02-14,2022-12-22 12:10:30.014357,53.75,-2.5 +28,2020-07-20,2022-12-22 12:10:30.014357,53.0,-1.0 +29,2021-08-16,2022-12-22 12:10:30.014357,54.0,0.5 +30,2020-07-30,2022-12-22 12:10:30.014357,52.0,-1.0 +31,2020-08-06,2022-12-22 12:10:30.014357,52.5,-2.0 +32,2021-08-11,2022-12-22 12:10:30.014357,52.25,-2.5 +33,2020-09-04,2022-12-22 12:10:30.014357,51.75,-0.5 +34,2020-10-02,2022-12-22 12:10:30.014357,52.0,-1.75 +35,2021-03-17,2022-12-22 12:10:30.014357,51.5,-2.0 +36,2021-08-31,2022-12-22 12:10:30.014357,53.25,-0.25 +38,2021-06-08,2022-12-22 12:10:30.014357,52.25,-1.0 +39,2021-10-07,2022-12-22 12:10:30.014357,51.75,-1.0 +40,2021-06-18,2022-12-22 12:10:30.014357,51.5,-1.25 +41,2020-12-11,2022-12-22 12:10:30.014357,52.5,-0.75 +42,2021-01-07,2022-12-22 12:10:30.014357,53.25,0.25 +43,2020-11-08,2022-12-22 12:10:30.014357,53.0,-0.25 +45,2021-08-13,2022-12-22 12:10:30.014357,53.0,-1.5 +46,2020-11-15,2022-12-22 12:10:30.014357,53.25,0.25 +47,2021-09-22,2022-12-22 12:10:30.014357,51.0,0.5 +48,2020-12-02,2022-12-22 12:10:30.014357,51.0,0.25 +49,2021-06-16,2022-12-22 12:10:30.014357,53.25,-2.25 +50,2021-01-01,2022-12-22 12:10:30.014357,51.5,-2.0 +51,2022-03-08,2022-12-22 12:10:30.014357,54.0,-0.5 +52,2021-02-01,2022-12-22 12:10:30.014357,53.5,-1.5 +53,2021-02-12,2022-12-22 12:10:30.014357,51.0,0.0 +54,2022-03-04,2022-12-22 12:10:30.014357,51.25,-0.25 +55,2021-08-17,2022-12-22 12:10:30.014357,52.0,-2.5 +56,2021-03-30,2022-12-22 12:10:30.014357,54.0,-1.25 +57,2021-11-23,2022-12-22 12:10:30.014357,52.5,-1.5 +58,2022-04-12,2022-12-22 12:10:30.014357,53.0,-2.25 +59,2021-08-17,2022-12-22 12:10:30.014357,53.0,-0.5 +60,2021-04-20,2022-12-22 12:10:30.014357,51.25,-0.5 +61,2021-04-28,2022-12-22 12:10:30.014357,53.25,0.25 +62,2021-05-05,2022-12-22 12:10:30.014357,53.25,-0.25 +64,2021-06-27,2022-12-22 12:10:30.014357,53.5,-2.25 +65,2021-06-27,2022-12-22 12:10:30.014357,51.5,-0.75 +67,2022-07-31,2022-12-22 12:10:30.014357,51.75,-2.0 +68,2021-09-01,2022-12-22 12:10:30.014357,53.75,-2.25 +69,2021-11-03,2022-12-22 12:10:30.014357,52.5,-0.25 +70,2021-08-27,2022-12-22 12:10:30.014357,51.5,-2.0 +71,2022-03-21,2022-12-22 12:10:30.014357,54.0,-0.5 +72,2022-04-11,2022-12-22 12:10:30.014357,51.25,-0.75 +73,2021-09-14,2022-12-22 12:10:30.014357,51.0,-0.25 +74,2022-10-01,2022-12-22 12:10:30.014357,53.25,-0.25 +75,2021-09-25,2022-12-22 12:10:30.014357,51.25,0.0 diff --git a/samples/test_locations.csv b/samples/test_locations.csv new file mode 100644 index 0000000..5ca6444 --- /dev/null +++ b/samples/test_locations.csv @@ -0,0 +1,26 @@ +,postal_code,location,latitude,longitude +0,1432 Stockholm,"Stockholm, Sweden",59.3293,18.0686 +1,04107 Leipzig,"Leipzig, Germany",51.3396,12.3731 +2,10019 New York,"New York, NY, USA",40.764,-73.985 +3,12459 Berlin,"Berlin, Germany",52.5076,13.145 +4,75001 Paris,"Paris, France",48.8588,2.347 +5,20148 Milan,"Milan, Italy",45.4654,9.1859 +6,80805 Munich,"Munich, Germany",48.1351,11.582 +7,SW1A 1AA London,"London, UK",51.5074,-0.1278 +8,8010 Graz,"Graz, Austria",47.06892,15.43934 +9,1040 Vienna,"Vienna, Austria",48.20817,16.37381 +10,1080 Brussels,"Brussels, Belgium",50.84665,4.35247 +11,4000 Liège,"Liège, Belgium",50.63372,5.56774 +12,150-0021 Shibuya,"Shibuya, Tokyo, Japan",35.658034,139.701636 +13,112-0011 Bunkyo,"Bunkyo, Tokyo, Japan",35.7090259,139.7514074 +14,110002 Delhi,"Delhi, India",28.613939,77.20902 +15,104 Taipei,"Taipei, Taiwan",25.032969,121.565418 +16,184001 Dar es Salaam,"Dar es Salaam, Tanzania",-6.792354,39.208328 +17,08003 Barcelona,"Barcelona, Spain",41.385064,2.173403 +18,00184 Rome,"Rome, Italy",41.902783,12.496366 +19,2300 Copenhagen,"Copenhagen, Denmark",55.676097,12.568337 +20,114 28 Stockholm,"Stockholm, Sweden",59.329323,18.06858 +21,0161 Oslo,"Oslo, Norway",59.913869,10.752245 +22,00100 Helsinki,"Helsinki, Finland",60.169856,24.938379 +23,1150-039 Lisbon,"Lisbon, Portugal",38.736946,-9.142685 +24,11528 Athens,"Athens, Greece",37.98381,23.727539 diff --git a/scripts/eeweather-intl tutorial.ipynb b/scripts/eeweather-intl tutorial.ipynb new file mode 100644 index 0000000..e72f208 --- /dev/null +++ b/scripts/eeweather-intl tutorial.ipynb @@ -0,0 +1,630 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "606c7c52", + "metadata": {}, + "source": [ + "# EEWeather international: summary with tutorial " + ] + }, + { + "cell_type": "markdown", + "id": "febcc4c3", + "metadata": {}, + "source": [ + "## Description\n", + "\n", + "Having been developed for the US market, there has until now been no need for an EEWeather module to call air temperature data outside of that region. Open Energy Efficiency Savings (OpenEnEffS) has developed an extension to EEWeather allowing for air temperature calls from anywhere in the world using the European Centre for Medium Range Weather Forecasts (EMCWF)'s Climate Data Store (CDS) API. This temperature data is licensed on a free-to-use and reproduce basis, but remains the property of ECMWF and the European Union.\n", + " \n", + "This extension can source Celsius air temperature at 2m height for anywhere in the world given a area specification code such as a postcode/zipcode. This opens up EEWeather to locations outside of the United States, which it is currently unable to do.\n", + "\n", + "EEWeather international exists as a complement to EEMeter international, a set of amendments to the EEMeter package design to open up the CalTRACK Methods to markets outside of the United States.\n", + "\n", + "EEWeather international was developed by Carbon Co-op and enabled by Innovate UK grant number 10032096 in 2022. Carbon Co-op is a membership-based energy services co-operative in Manchester, UK. Innovate UK is the UK's national innovation agency and an agency of the UK Government." + ] + }, + { + "cell_type": "markdown", + "id": "11bdef6b", + "metadata": {}, + "source": [ + "## Requirements\n", + "\n", + "Use of EEWeather international requires all users to create a CDS account and agree to the terms of the Copernicus licence associated with ERA5 reanalysis data. As of February 2023, this licence allows for the distribution of data on a free of charge, worldwide, non-exclusive, royalty free and perpetual basis, provided that both the Copernicus programme and European Union are clearly attributed.\n", + "\n", + "New users can create a CDS account [here](https://cds.climate.copernicus.eu/user/register?destination=%2F%23!%2Fhome).\n", + "\n", + "Users can check the progress of weather calls [here](https://cds.climate.copernicus.eu/cdsapp#!/yourrequests).\n", + "\n", + "Technical notes (as detailed in lines 251-267 of `eeweather.international.py`) are as follows: \n", + "\n", + " TECHNICAL NOTES\n", + "\n", + " 1. Use of the CDS API requires all users to set up a CDS account and agree to the terms of the CDS licence,\n", + " linked above.\n", + " 2. Users may also be required to set up the CDS API key and install a .cdsapirc file, in their home\n", + " directory, guidance on which is available at: https://cds.climate.copernicus.eu/api-how-to.\n", + " 3. Users may be required to install the eccodes library, used for the interpretation of GRIB weather files.\n", + " 3a. This package has been designed for Linux systems, on which it can be installed using the following link:\n", + " https://confluence.ecmwf.int/display/ECC/Releases; alternatively eccodes can be installed on Python 3 using\n", + " pip3 install eccodes.\n", + " 3b. For Windows users, eccodes and the related Magics package can be installed with conda using\n", + " conda install -c conda-forge eccodes Magics. Further details available at:\n", + " https://www.ecmwf.int/en/newsletter/159/news/eccodes-and-magics-available-under-windows\n", + " 4. The cfgrib package is also required for the functioning of this package. If not already installed, users should\n", + " install cfgrib via pip install cfgrib==0.8.4.5. Details available at: https://pypi.org/project/cfgrib/0.8.4.5/\n", + "\n", + " To check the progress of your download, visit: https://cds.climate.copernicus.eu/cdsapp#!/yourrequests" + ] + }, + { + "cell_type": "markdown", + "id": "ae198231", + "metadata": {}, + "source": [ + "## Performance" + ] + }, + { + "cell_type": "markdown", + "id": "4a081f26", + "metadata": {}, + "source": [ + "### 1. Accuracy: CDS vs. existing EEWeather data\n", + " \n", + "The advantage of using the CDS API in the United States over 'original' EEWeather is that the while the original package relies on a network of weather stations, there can be significant variance between sites concerned and the nearest weather station. **Figure 1** illustrates the variance in distance of weather stations from sites for the existing EEWeather package, with the distance from site distribution for the CDS overlaid. The CDS extension allows for external temperature to be called to a bounding box of precision 0.25 decimal degrees (approximately 27.75x27.75km) for each given areacode. This means that each site is consistently matched to weather data of no more than 14km distance from the given areacode and mean 7km." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7c208fcf", + "metadata": {}, + "outputs": [], + "source": [ + "import eemeter\n", + "import eeweather\n", + "import pandas as pd\n", + "import numpy as np\n", + "from faker import Faker\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "import seaborn as sns\n", + "import statsmodels.formula.api as smf\n", + "import scipy\n", + "from pathlib import Path\n", + "matplotlib.rcParams['figure.dpi'] = 250" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ec6b7cd2", + "metadata": {}, + "outputs": [], + "source": [ + "def eeweather_run(latitude, longitude):\n", + " ranked_stations_closest_within_climate_zone = eeweather.rank_stations(\n", + " latitude,\n", + " longitude,\n", + " match_iecc_climate_zone=True,\n", + " match_iecc_moisture_regime=True,\n", + " match_ba_climate_zone=True,\n", + " match_ca_climate_zone=True,\n", + " max_distance_meters=100000,\n", + " )\n", + "\n", + " ranked_stations_closest_anywhere = eeweather.rank_stations(\n", + " latitude,\n", + " longitude,\n", + " )\n", + "\n", + " ranked_stations = eeweather.combine_ranked_stations([\n", + " ranked_stations_closest_within_climate_zone,\n", + " ranked_stations_closest_anywhere,\n", + " ])\n", + " return ranked_stations\n", + "\n", + "fake = Faker()\n", + "Faker.seed(0)\n", + "\n", + "lat_log = []\n", + "boundingbox = []\n", + "\n", + "counter = 0 ;\n", + "countries = ['US'] \n", + "total = 1000\n", + "size = total / len(countries) -1\n", + "country = -1\n", + "for _ in range(total):\n", + " if counter >= size:\n", + " counter =0\n", + " country = country + 1\n", + " unit = list(fake.local_latlng(country_code= countries[country]))\n", + " boundingbox = [\n", + " float(unit[0]) - 0.16,\n", + " float(unit[0]) + 0.16,\n", + " float(unit[1]) - 0.16,\n", + " float(unit[1]) + 0.16,\n", + " ]\n", + " for i in boundingbox:\n", + " unit.append(i)\n", + " lat_log.append(unit)\n", + " counter = counter + 1\n", + "\n", + "df = pd.DataFrame(lat_log)\n", + "\n", + "distance_list = []\n", + "progress = list(range(0,1000))\n", + "for latitude, longitude, progress in zip(df[0], df[1], progress):\n", + " latitude = float(latitude)\n", + " longitude = float(longitude)\n", + " min_distance = eeweather_run(latitude, longitude)['distance_meters'].min()\n", + " distance_list.append(min_distance)\n", + "\n", + "df['Minimum distance'] = distance_list" + ] + }, + { + "cell_type": "markdown", + "id": "a18e495e", + "metadata": {}, + "source": [ + "### 2. Temperature values variance\n", + "\n", + "Temperature data sourced via the CDS API for United States locations returns values of minimal deviation from 'original' EEWeather values for given sites. Across 100 randomly sourced United States locations, the average difference between CDS and 'original' EEWeather corresponds to **0.136** degrees Celsius, with a very small variance corresponding to a standard error of **0.002**, as demonstrated in **Figure 2**. Use of the CDS therefore ought to return similar savings figures to the use of EEWeather for any given consumption dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "9b2cbb28", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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HP/xg7r/0EtxwgwnSe/Y056Snm5D89tth0yYTknvD9VKlTGVL1aqXbLkiIiIiIlcKBeQiIiIiImfidpvbZcvgmWfgo4/MY7vdTHpnJzwcypQx93fsgI0bzfnecPzzz+Hll839Bx/0T5jXrg39+vlrWiwLVqyALl1g8WI4fNj/PRyOi7dGEREREZErmAJyEREREZEz8U5ub9liHj/8sJkmBwgONsdeegmOHDHPeTzgckH58uaxywULFviv9+uv8Oqr5n6bNiYQDwz0T4ffcIPpI/ey22HPHmjVCp591kyki4iIiIjIRePM7TcgIiIiIpKn2WxQpIj/8dChEBJiOsK7dTOh94YNMGaMmR63202YvXGjCbyffNK87tAh+P572LcPIiKgd29o0cL/Pbyc//wVPTTUXCsxEQICTK1KWFjOrPkisCwLW+Z1iYiIiIjkQZogFxERERE5E2/X+IIF0Lq1//n77oOaNU04Hhpq6k/27PG/5uqrYcIE/7Q4wLZt8OOPkJFhgvUHHjDPe6fHXS44dgxOnDCPBw+GdevMeevWme+Xh3msrL3ssfGxHEs5xv7E/bn0jkREREREzk0T5CIiIiIiZ2K3m+Da6YTffjPB9pw55ti2bWay+/rrTZhdo4b/NQC1apnbjAzz3OjRcPIkFCsGQ4aYY95rW5a53bgR1q83xywLypY1G3o68/Zf290eNw676UWftmUac3fOZfyG8QQ5gghwBHBnvTvpWrkrV5e8OpffqYiIiIhIVnn7b9oiIiIiIrnN6TQbcgYHw6xZJuA+ccJs4Ol2Q9Gi0KyZOdcbeGcWEGBuvZPhx46Z88B0nHs8JkDftQuee84E49Wr+zfvzOMbcro8Lpx2s+b7pt/HL9t/YU/8niznPDf3OaoUrkK9EvVUuyIiIiIieYoqVkREREREziU42IThbdvC0aP+KXGADz+EDz4w951Ofy2Ll3fjzvh4/7UWLYKEBNM9brdDbKypY1m71pzTuLF/o888HCi7PW5fOH7DuBv4bPVnvnC8WlQ1ioUVI8gZRP/a/WlcurHCcRERERHJczRBLiIiIiJyPmJjzfQ3QNeupnP8r7/M40ceMZPfQ4aYwNs7FQ4m4HY64dprYfFiSEqCr7+G5GSzSWdqKrz1FmzaZKbMa9SAF16AyMjcWOV5syzLV6vSf1J/ft7yM8HOYGoVq8WQxkPoUrkLbo+bOTvnUCOqBmULlPW9LnNQrs08RURERCQ32SzLuyuQiIiIiIickWWZfvCJE+GGG6BBAzNRvmCB/5x33/X3i2cOyQHmzYO77oLdu821HA4zlZ5Z0aLmetWrX/r1XCRvLX2L5+Y+h4VFx4odebzZ47Qp38Z3PN2dTqAjMMtjj+XBY3kIDQjNhXcsIiIiIuKngFxERERE5HxZFqSkQOg/wa7bDR06nH9I/tlnpmc8Pt5s3hkQYG5DQqBCBZg0KV+F4+nudHpP6M30rdMpFFKIH3r+QOfKnQFTv2K32X3T4btO7GJL3BY+WPkBcSfjCA8Mp06xOrzc9mUig/L2tLyIiIiIXL4UkIuIiIiIZGZZZ+/99obe3oDbsqBdu7OH5OAPyidNMpt9TpliXl+xIrRvD/fcA2XLXpo1XSKLYhfR+qvWAAxuNJgPupou9swbdwJ8+deXjN8wniV7lpCcngyAzWbDsiw6V+7MR10/olLhSqpbEREREZEcpw5yEREREREvt9tUnwBs324209ywAU6ehE6doF49/+aZAQFm802nE+bOzRqSDx1qbr2d5G63/9zevc3XiBEmPC9WzJybD4PhE6knAHDYHRQOKQzAyYyThAaEkuHOYNaOWUzeNJmv1nzlC8S9vPd/2/4bT85+ksl9JyscFxEREZEcpwlyERERERHwB9gAL70Ev/8OS5f6jwcGQnQ0LF/uD7Uzv+5ck+QABw+aILxwYROwe51raj2PWhi7kE7fdiLdnU6d4nWYO2Au4YHhHEs5xtDfhvLH/j/YeXyn7/zqUdVpUbYFLcq2YOW+lYz8YyQ2mw2HzcG0m6bRpXKXXFyNiIiIiFyJNEEuIiIiIuJ2+8Pxvn1NDcqp0tOhe3cIDs76vNN59klyj8fc7t4Nzz4LR4/C7bfD9df7u8zzcDju9rhx2B2+x5lrUCoVqkRkUCRxJ+NYd2gdV396NUXDirIvYR+Hkw/7XlMwuCDtKrTjjQ5vUCqyFMHOYAbUHQDAyD9G4rJc7E/cn7MLExERERFBAbmIiIiIXOk8Hn+tyvXXw/TpZlq8aFG46y4oUsRszLl6NfTvD5HZbCiZOSSfN8+E5PPnm2OPPQaLF0NMDBw4AIcPm2tce22OLfHfytwl/uaSN+lerTvVo6pjWRYey0OpyFJ8fO3H3PnTnZzMOMm+xH3sT9yPx/L4rtG3Vl86V+rMnVff6Xsuw51BgCOAusXrYrfZ8VgeXze5iIiIiEhOUkAuIiIiIlc27+aZjzxiwvHgYGjb1lSjdOrkPy8pCcLDz3wdp9NMmQcGmknyTp1g9mxz7Mcf/eeVLAmvvJJ90J7HeMPxpl80ZcXeFWyK28SwlsOoVLgSdsyfW/dq3fn8+s8ZPHMwyRnJpLnSKBxSmIqFKvLANQ/Qp2YfwgLDAP80unci3Wl3YmEaHyOCInJhhSIiIiJypVNALiIiIiKXr9hYKFv2zBUm3u7vpUthzhzzXO3a8MQT0KaNeezxmC9vOG5ZsGMHLFkC+/ZB/fpQsSJUrWrC8bQ0CAqCWbPMxPmvv0JCgqlTqVgRJkww5+YTY9eNZcXeFQBM3zodj+XhuVbPUblwZQCCncH0r92fRqUase3oNvYk7KFJ6SYUCCpAmQJlfNexLAuH3YFlWdhtdmKOx/D2srexLIt6JerRrUq3XFmfiIiIiFzZFJCLiIiIyOWpe3fYuxe+/BLq1s0+JPc+t2gRbNhg7j/4oD8cd7tN/Yp3ynzWLBOkf/qpCb3BhOKtWsHAgdCnjwnHMzLMJpzjxsHMmbBnj9ngs2FDM0GejzQv25xX27/Km0vfJO5kHNO3TgfwheTeupWKhSpSsVDF016fucPcY3mw2+zsjt/Nm0veZE/CHgIcAbQq18o3ZS4iIiIikpMUkIuIiIjI5WfIEJgxw9x/9ll46SUTTp8aknsnyL2bahYtCp07m/upqaZuxe2GXbtM0P7RR+b59HQTmluWuT9/Phw5AgULQseOJhz3huv5oGv8bMoWKMttV92Gx/Lw5tI3OZZy7LSQ3Fu34uXdyPNw8mE2HdlEjaI1KBZWDBs2Nh7ZyIvzX2Rh7EIS0xKpVawWQ5sMJTQgNDeWJyIiIiJXOAXkIiIiInL5qVDB1JkcOGAqTqKiTMDt3YzzVCkp5jYtDf7+G0qUMOF4ejqMGGEC8EWL/OdHRZnNO0ND4eBB8302bTLndOxogvMzfa98qFRkKW6vezvAGUPyzGz//CDi1UWv8s3f31CmQBmalm5KUnoSM7bNICk9CbfHTfHw4kzqM4lyBcvl7IJERERERP5hsyzLyu03ISIiIiJyUXintgHGjDGT5CVKwMKFZ682ef11M2kOcN110LixCci//RbWrs16bosW8Pjj0LKlOeeHH+DFF00fedmysHIlFCt2SZaX2/Yl7OPrv7/mzaVvEp8aT+GQwlxX9bpsQ/JpW6Zx47gbAXzd4zabDbfHTbAzmIqFKjKp7ySqR1XPhZWIiIiIiBgKyEVERETk8uLt/wYYPx7atzcT3147dpjpcpvNX7Eydy4MHQrr1vnPs9vN5pxeTZpAhw6mriVzVcuxY9C2rXltyZImIC9V6tKu8SJyeVw47f5fLPUG2WdyrpDc+/rZMbOZtmUaX635iqT0JN/r6xSvQ+tyrXms6WOaHBcRERGRXKeAXERERETyv9deg8qVzSaZkDUkz6xJE1Ojkt3GnR98YDbf3LTJ/1xQkAnRhw2Dbt2gfn3zvHdS3ft9unc3m3GWKWP6zMvlneA3MS2RiKCIbI9lDsf/2PcH15S6Bu8/D/5LSJ7ZjmM72BS3iaMnjxIaEEqHih0IdgYTEhBykVYoIiIiIvLvqYNcRERERPK3nj1h6lTo1AlCQkxFSkCAfzrca8AAM90N8OST8MYbUK+e/5yHH4Zq1WD5cliyBJxOE4rXrWtqVby8/eKWZb7Pli2me9yy4Kqr8lQ4/sCMBygdWZqB9QdSNKzoace94Xjrr1qzKHYRM2+ZSZfKXbAs66yT5N5OcsuyfJ3kP2/9GcjaSe72uKlUuBKVCle6RCsUEREREflvFJCLiIiISP61eLG/I3zWLBNSW5aZ6M5coQJm+nvZMti1C2bPNiH5//1f1pC8c2fzlZ5uQvBTN9r0To57r7t7N7zwAiQkmN7xvn3NeaeG87ng7p/u5ss1X1IyoiQFggrQu2ZviocXP+28W6fcyqJYswHptd9fy4ybZ9C1StfzCslvq3sb8WnxjPxjJMdTjjNtyzTAH5LbbfZLt0ARERERkYtAf2MVERERkfyrSRPTCd6smXn8++/w0Ufws5lmxmYDl8vcf+QRE4qXKWOenzPHPF6zxgTa4L8NDPQH4QAnTphbb2Bus8H27TB8uNkAFMz0eYcO/uO5aHf8bmLjYykQXIADiQcY8usQft3+K9m1K77a/lUqFKrge9zth278su0XXzB+tkbGsgXKcnOdm6lboi4AJ1JP8NOWnxi+cDjbj20/a02LiIiIiEheoA5yEREREcmfvFPabjeMHQsffwwrVphjHTvC4MFmkhyydpKPHm06y2NjzTXatz99kjzz9dPT4YEHzOOePSE0FI4cMeF4bCwkJppNOefNMz3oecTaQ2t5Y8kbjF03lsIhhdn04KbTalYy3BkEOALYl7CP5mOaszt+t+9Y5klyOHsn+ejVo7l3+r2+x4VCCtGjeg+eaPYE1aKqXeSViYiIiIhcPArIRURERCT/8obYLheMG3fxQ3IwXeXPPGPuR0RASgp4POYLzET6r79CjRqXfLnnI/PGm6v3r+arNV/xfOvnKRZWzHdOfGo8BYILAP8tJPdYHuw2O5uObKL5mOYEOYNIc6VxIvUENpuNexvcy/td3ifAkc2GqSIiIiIieYAqVkREREQk//L2jDudcNNN8OCD0LixOXZq3UpAgAnJAQYONKF3uXJnrlsBSEszG3B6v1diognjPR4zNd6li3ltHgjHx64by9GTR3Hanbg9bgAaRDfgvS7vZQnH+03qx8d/fMyR5CMABDgCyHBnUCqyFEvuWkLZAmV9556rbsXbMZ6UnkR8WjxVCldh2k3TCAkIwbIshjQeonBcRERERPI0bdIpIiIiIvmbt2bF4YD+/c3joCDTDf777/5J7+7d/SF5QIAJycE/Se4NyTNPkgcFwfTp8O23ZjPQmTOhcGGoWBF694amTSEqKteW7tVjfA9mbJ3B0CZDebrF0xQKKYTb48Zhd+Cw+zcavXHcjUzbMo0lu5cQHhjOTbVvomhY0dNC8syT5N1+6HbaJLmXdxPP/Yn7sWHjWMoxmpVpxvK7lxPkDKJqkao5+ucgIiIiInKhFJCLiIiISP7mDce9k+SNGsGePbB7N+zaZYJvrwsJyevWBfs/v3B5223mdvhwE5rnIV+v+ZqfNv8EwISNEwB8Ibk3wAaIOR5DUnoSBYMLsj9xP68vfh3gvEPyn2/6mW5Vu/mu6a1y2Xl8Jy8teAmP5aFaVDXsNjt1itfJhT8JEREREZELp4oVEREREcm/XC4TjgO88w7062f6xN9+2wTk3mNz5sAHH1xY3crff/vrVry3zkzzJXlkK58G0Q14qNFDOOwOYk/EMnb9WF5f/DrHU45js9nwWGaCvmKhirzT+R06VepERFAEB5MO8vri1xm7fux51a10H9udsevGku5OB8Bpd7I7fjdvLHmDmOMxRARF0KVSF+D0KhYRERERkbxKm3SKiIiISP7knRwHU3cyZYr/WIECJjwHSE72P3+xNu7MY7bEbWHkHyP5cOWHAJSOLM1NtW86rW4FYO2htQxfOJxZO2aRkJZAifASPN3iad8kOZx9485br7qVUhGlKB5enC/++oK9CXuJT43n6pJXM63/NEpFlsr5PwARERERkX9JAbmIiIiI5G99+8KkSRAcDJUrw733mo067XZISIAPP4R162D7dnN+p05mM8/zDcnffNPUreTxkHxz3GZG/THqkoTk7b9pz9ajWwGw2WynTYhHR0QzZ8AcqkVVy8EVi4iIiIj8dwrIRURERCT/GjUKHn8c0tKgTRt47jlzm1l6OkydCt99ZzbchAsLyRs2hM8/h6uuyqFF/XuXKiQ/mHSQgT8P5K8Df7E/cb/v+0VHRFMtqhqjuo3ShpwiIiIiki9pk04RERERyb+WLoWUFHP/vvv84bjHYybIPR4IDDQVLNWrQ8GCJiifNcvfIX6mjTvfegu2bYNVq0xlSz5QPao6919zPxmeDD5Z9Ql7E/by/brvAU4Lya8qfhXPtXoOgFk7Zvk6ySHrxp0uj4sS4SX4rsd3LNu7jF+2/UJieiJhAWHcWP1Grip+lS9QFxERERHJbzRBLiIiIiL5j2XBjh1mqjs1FVq2hAUL/MfOVIeyeTO88AJMnGged+gADz2U/ST5hx/CF1/ADz9AzZqXdj3/UebJcIC/D/7Nt2u/5Z1l7wBQMqIkt9a59V9Pkp96fRERERGRy4UmyEVEREQk/7HZsobgJ0+ampXAwLN3hVevDvffD3v2wPLlMHv26ZPkLhc4nSY4v+02M3Weh7k8Lpx289f6yRsns/bQWpbtXcbh5MOEBISQkpHCgcQDfLfuO+DfTZJ7w3HLsrBl+vM99bGIiIiISH6jgFxERERE8qfMwWxa2umh+Zk0bw7NmpmAHGDOHP+x7t1NOO4NyfN4OO72uH3h+IAfBzBj2wyOpxw/7TybzcaBxAOMXT8W+Hchufc6p15XRERERCQ/U0AuIiIiIvmPxwNBQVC0qJkG37ULJk+Gm246++ssy0yJP/44jB0LBw6Y5+fMMdPn6enQq5cJx/M4y7J8k93Xj72e6VunE+AIoFBIIe6++m7KRJbBwmLNwTXMjpnN3oS97E3Yy7gN44B/H5KLiIiIiFxO8v7f/EVERERETmW3Q6lSJsx+7z1ISjIbb153HUREnLmH3PtcSIiZDj94ECpXNptx/vILBAdD584QHp6Tq/lXvNPbz8993heOd6rUiSGNh9ChYocs5y6MXcjPW37mneXvsCd+D+M2jMPC4pkWz2QbkttsNn7b/hsHkw7y1tK3OJlxknvq30NUaFRuLFVERERE5JKx5/YbEBERERG5YN7e8B49oEYNc//rr2HkSHPfZvOfcyqPxxx3Os3tkCHQv7859tJL+SIc9zqSfITFexYDEOwMZvA1g33huNvjxuVxAdCqXCuGtRrGqG6jsNls7Infw/gN43lt8WscTzmOw+7A7XEDcFXxqxjWchidK3emSGgR9ibs5as1X2G36Z8OIiIiInL50d9yRURERCT/8U6CN2sGTZv6n3/mGRg1Kus5p7LbYedO2LHDhOXR0fDCCxAbC7VrX9r3fZH9sf8PFuxaAEC/Wv3oXLkz4K9f8faTAxQMLsigBoP44vovfCH5uPXjzhiSP9fyORpGN6RMgTJM6DOBwiGFc36BIiIiIiKXmCpWRERERCR/8njA4TBT4xs2wIoV5vkHHzSVKw88AGFh5jnL8p8fGwuvvAInT0KVKtCokQnJ86EjyUew2+x4LA+RQZEAeCzPWae9b65zM4eSDvHs3GfZm7CX79d9D3Ba3Uqd4nV4q+NbFAguQOnI0jmyHhERERGRnKYJchERERHJn+x2cLnM5prTpkHduv5jTz1lqlPGjzfngAnHN2+GYcNg6VLz3FVXQWRkzr/3i+Rw8mE8lgebzUaQIwgwAfnZBDoCuaH6DbQq1wqbzcaBxAN8t/a7LJPk3mqWWsVqKRwXERERkcuazbLOVM4oIiIiIpIPeDwmLD94EG68EVau9B8LDzebcBYpYjrHly2DhARzrFQpmD8fKlXKjXd9UcyJmcON428kOT2ZmkVrsujORRQKKXRer31lwSu8MP8FbDYblmURHRHNbVfdxuPNHqdIaJFL/M5FRERERPIGTZCLiIiISP5mt5uQvEQJWLAAbr3VhOJgqlbWrIE5c2DWLBOOh4RAtWrw++/5OhwHKFugrK9O5WDSQaZsmkKGO+Osr/H2jF9b5VrCA8MJDQgFYH/ifj764yM+WPHBOafQRUREREQuFwrIRURERHKB95f4MgeRCiX/A2/dSlAQjBkDn30GQ4dCrVoQFWVC8cKFoXVrs5Hnb79B9eq5/a7Pyfs58YbamZ+zLIsqRaowrOUwHHYHx1KO8c3ab9h2bNtZr+mwOwBIcaWQlJ5E6cjSvNHhDQCS05O55apbztphLiIiIiJyOdEmnSIiIiI5yLuBos1mA0yHNECJ8BK+cyzL8h2/rFkWXMx1Op1mktzphDZtzNeRI5CaCsnJpoO8cmXzfe15OwA+9XOS6kolLNBsOOp9znvbsmxLaherzdpDa1kUu4inZz/N6O6jKR5ePNtrez9fx1KOAXD05FH61OpDkdAiNCndhKpFql7q5YmIiIiI5BnqIBcRERHJIW6P2ze9O2XTFObEzOG7dd/h9ripFlWNRtGNeKjxQ9QsWjOX32kOcLtNYA0mvP7rL7PZpt0ODRv6z/P2i18ob/ieOYTP7rk8KPPnZO7OuczbOY8Z22ZQPLw4FQtW5IU2L1A4pDBOu3/W5b3l7/HU7Kd89Srdq3XnjQ5vULlwZZx2Jy6PK8vt7vjd3DT5JpbtWUbbCm2ZM2BOrqxVRERERCS3aYJcREREJAd4g0mAe3++l1kxs4g9EQuA0+7krwN/8ffBv6lXoh41ompc3hPkLpeZ8gYYPhwWLoTZs01Anp4Od9wB114LvXv7+8UvNCT3/vll/nPM7rk8JvPnZPDMwfwe8zvbjmatTFmxbwXPtnyWzpU6+6bKH2nyCIeTD/P64tcB+HnLz8SnxnPX1XdxQ7UbKBBcADCftdgTsfzfkv9j69Gt2Gw2GpZsiMvjwmFzXN6fOxERERGRbGiCXEREROQSyzwRfOO4G5m2ZZrvWI2iNTiUdIik9CSuq3od73V5j9KRpXPrrV56mSfHe/WCn34yAfipatSA22+HJ580j//tJHk+kvlz0nN8T6Zunuo7FuAIyLL5Zq1itXi6+dPcUP0GwgPDfc8/9ftTvL3sbSwsLMuiSGgRiocVp0f1HoAJ4L9f9z3HU4+TnJ5MlSJV+O3W3yhfsHyOrFFEREREJK9RQC4iIiJyCWXuE+83qR8TN0wk2BlM7WK1eajRQ/Sq2Ys98Xs4lHwIy7JoXb414O+g9lgebNguj8nezNUmN90E48f7J8Tr1YP4eDh6FBISzDmlSsHAgfC//5nHl3FI7v3vDdBrQi9+3PQjAY4AwgLCuPvquykZUZKDSQf5+I+PSXen4/a4qR5VnWdaPMON1W8kIijCd613lr3DmL/GsDlus2/jV2+9ivczBVAqshS/3/Y71aPy/malIiIiIiKXigJyERERkRzw7rJ3eWbOM1hYdKjYgSeaPUGb8m3OeH6aK40gZxBJ6UlZJoQvC++9B48/DgEB0KQJPPAAdO8OGRkwebL5mjHDnFuiBNx7L7zwgnl8GYfkYGpVRv4xkmBnMK3Lt+ahRg9xbZVrfcenbJrC07OfJuZ4DB7LQ/Wo6jzd4ml6VO+RJSRfFLuIJXuW8Nri17Asi6T0JN+xGkVr0DC6If9r9T8qFa6Uo+sTEREREclr1EEuIiIicomlu9OZHzufdHc6hUIKMfiawb5w3O1xY7fZfRPisSdi2XJ0C+8tf4/E9ESOnjzKgLoD6FCxAw2jG57lu+RhmUNttxuWLTPP1akDL74Irc3UPMHBcOutcPXVEBkJY8fCwYPwySfm+Asv/PtO8jzM+1sGkzZOYua2mdhtdpqXbc6zLZ6lZbmWgP8HJj1r9OT9Fe+z/dh27DY7m+M288aSNwCyhOQty7WkZbmW3FT7Jg4nH2blvpUUCC5ggvdyrQkLDCM0IDTX1iwiIiIiklcoIBcRERG5xFbsXcHPW34G4M56d9K1Slcg64aMAF/+9SU/rP+BJbuXkOpK9T3/7JxnWRC7gKFNhtKpUqecffP/VeYNOcEE3pMnm/sPPugPx731K04n1K0LL79susq/+w4OHbqsQ3LvD0fmxMxh14ldADzU6CFfOO6xPAQ5g3B5XPyw7gc2HtlIkDOIgsEFiTsZx6Yjm3ybc3pDcu8viZYrWI5yBctxTalrcn5hIiIiIiL5gAJyERERkUvM5XEB4LA7SHenAyb0dNqdZLgz+HX7r0zZPIWv13yNzWbzhZuZ+6Jnx8ymWFgxWpRtQYgzJH90kluWPxzv1s3UpQwbZrrFAwKgh9k4Mtuwu1Ilf63KFRCSbz26la/+/gqA3jV7c3216wHI3Ia4P3E/UzdP5ejJo7Qs15JuVbrx2erP2HliJ5vjNvPa4tdweVz0qtGLAsEFsrw2cxe+iIiIiIj4KSAXERERucS8U+Juj5uY4zHEnYwDINWVytDfhvLXgb+IOR4DmCCzRtEaNCrViE4VO/Hbjt9YGLuQXSd28e3f3/LgNQ/SqFSjXFvLBfEGstddB7/8AiEhsHMnHDsGZctC+D/d6mcKuS/TkDy7sDohLYE0Vxo2m81XfeLdVNO7eefHKz9m6uapAAxpPISeNXoSFRrFoJ8HYWGxJW4Lry9+neT0ZG6qcxNRoVG+76VwXEREREQkewrIRURERC6CU+tSMj+uWKgiTUo3Yfne5czcNpOO33Yk1ZVKUnoS+xL2+V5TMLggHSp24I0Ob1A0rCjhgeF0qdyFIb8OYW/CXlweF6v2r6JRqUb5ayK4Y0dYsACSk2HVKnO7Zw9s3w5Vq579tecTkrvdpo4lH3B73Djs5r2eugFrSEAIaa40Vh9YTdzJOKJCo3zHRv4xkjeXvgnAU82fomeNngDcdfVd7Dqxi+ELh2O32dl+bDvvLH+Hg0kHua/hfZQpUCYHVyciIiIikv/kr3EbERERkTwocxg+fOFwwEyNe6tVSkWWomeNnoQGhGK32fn74N/EHI/JEo73rdWXdzq/w4Q+E6hQqALhgeG4PC4KhRSiXYV2uC2373sB+SMc91Z8DBkC775rNuFMSjL1Km43TJ0KqalnvQTgD8lvvdU89obkw82fdX4Jx10ely8cf3nByzw+63H2JuwFoFyBcoQHhuOxPOxN2Mv2Y9t9r5u6eSovzn8RgG5Vu9Gvdj/f9QCuq3odIQEhOOwO7DY7sSdiGf3naOp/Vp+JGybm4ApFRERERPIfTZCLiIiI/EfecLzTt52YHTObDUc2MLbXWJx2J2muNIKcQTze7HFcHhcTN07krwN/keHOIDQglNrFanN/w/vpU6uPr1rDO2XsrdZIyUjBbrPjttxEBkXm2jovmM3mr0C55x4TmD/yCKSkQEaGmQjv0AHq1z/3tSpVghdfNPe/+w6OHIFXXjFh+1NPXcpVXBTeznmA3hN68+PmH4kIjKBp6aYMqDuAomFFGXzNYMasGUPXyl1pUroJYLrJR/85muOpxykVWYreNXpTr0Q9ABw2E7bXKVaHAkEFOJh0kIigCADiTsYR5AyiZtGaOb9YEREREZF8RAG5iIiIyEUw5q8xzI6Zjd1mZ/z68XgsD+N7jyfIGUSqK5VgZzBPt3iavrX6EnM8hqMnj1KrWC2iQqMoEV4iy7UcdgeWZWG32dl5fCcfrvwQt8dNvRL16F61ey6t8DxYlj8Ut9nMV+ae8IEDzXlDh8LJk7B+PTz8MHz/PZQrd+7rV6xoQnKHA77+GkJD/Rt95nHeH3bcPvV2pmyaApje8eV7l3N7vdsB6FGjB3VL1PVt0Anw54E/mb9rPm6Pm+uqXOc711ux47E8bDiygSMnjwBwf8P7qV+yPsPmDuPHfj9Sq1itnFymiIiIiEi+o4BcRERE5CK46+q72HFsB68tfg3AV20xvvd4gp3BvknyioUqUrFQxdNenznw9E4b747fzZtL32Rf4j4cdgfNyjTzTZnnKd4A3Fv7cuqmmZl7wjOH5CkpsHQp3HabCcnLnEdfdsWK8OyzEBQEDz547g7zXJa5fifuZBx/HvjTd+zpFk/Ttnxb328M1C5Wm9rFavtel+HO4M2lb5KSkUKVIlV4tf2rgP83DDyWB7vNzu743Vj/1NnUL1mfvrX6cn216wl2BufwakVERERE8h8F5CIiIiL/kTewHNF+BBYWry9+Hcgakgc5g07byNMbcB5KOsSfB/6ka5Wu2G127DY7m+M2M2zuMJbuWUpiWiK1i9XmiWZPEBIQkitrPKPMG2T++SfMnw+LFkGxYtCoEdx9tznmcIDLBU7n6SH54sVwyy3nH5JXqQIffmjqVfKwzP+9/z74N9WiqrHh8AbAhOPewBv8nwUvp91Jujud4ynHsdlsZLgzSHenY1lWlt8w2HFsB8/NfQ6P5eHqkldzbZVrAQhyBOXgSkVERERE8i8F5CIiIiL/kcPu8IXk3tAzu5Dcu3GnNzT1BqIfrPiA1xa/RsdKHYkKjSLUGcpPW34i7mQcAEXDijKxz0TKFTyPGpKc5A28AR5/HGbPhrVr/cdHj4YFC2DwYLjmGnPuxQrJ83g4Dv5u+hZjWrB0z1Km3TSN1uVbs2T3EtqWbwv4g/HM4bhXYloiR04e8YXhNpsNm83m+22D3fG7eW3xa8TGxxISEEK78u0IsJs/l3yxiauIiIiISB5gs7y/jykiIiIi/4k3JAd4ds6zvpAcoE+tPozvPR7IOlk8c9tMrvvhuizX8YagQc4gKhWqxOS+k6kWVS2HVnGeMk+O9+0Lkyb5j3mDcK/rr4dHH4WWLU0NS+ZgffRof0huWdCixfmH5PnAiIUjeH7e8wBEBkUS4AggJSOFVYNWUT2qepbPTGaWZZGckUzbr9uyev9qANpXbM+obqMICwjjUPIhXlrwEsv2LONw8mEaRDfgx34/UjqydI6uT0REREQkv9MEuYiIiMg/vJO52TlTkJnZqZPkNmxZOskty2JCnwlZJsmrFqnKrVfdyrxd89iXsM/3PuqVqEercq0Y2mRo3psc93j84fiNN8K0af7A++abISICNm6EefPMc9OmQUaGeV3r1qdPktts/o07Fy821/jhh8siJL+n/j1sjNvIz1t+JiEtwff8D+t+4OW2L5/xM2Wz2QgPDKdPzT6+gHxOzByaj2mOw+Yg7mQcLo/5IUSJ8BJ83/N7heMiIiIiIv+CJshFRERE8Ifj2QXhmSe+5++aT8PohoQHhp/xWpmvMWzOMF9IDtC7Zm8m9Jlw2nlrDq5hf+J+jqUcw4aNLpW7EBoQmvc6xzMbNAg+/xyCg6F5c1OlcsMN/uMvvACffAJHjpjHXbrAE09AmzanT5J//rk/JLcsc718HpJ7//seST7Cw78+zM9bfuZkxklsNht1itXh3c7v0rZC22xf6/08prvTeWjmQ4z+czSAr47F5XFhs9koW6AsM2+eSY2iNXJyaSIiIiIilw0F5CIiInLFu/fne/FYHj7t/il2m/2M0+JNPm/CrhO7eL/L+1xX9TrCAsPOeM2z1a1kDslTXakEO4Mv8opywNdfw9NPw9Gj0L49PPusqVABSE01oXlGBtSrB5s2+V/XtavpKz+fkLx2bfjlFyhVKqdXd9F4O8ZPDckB7m94P0+3eJoyBc7+Q4AjyUd4b/l7fPTHR6S700lzpVGjaA0al2rMc62eo2KhijmxFBERERGRy5IqVkREROSK1nN8T6ZunkrRsKIUmVOEEe1GZBuOP/X7U6zctxKA1xa/hoVF96rdzxiSn1q3kpKRwvsr3gdg0sZJ9J3Ylwl9JhDsDM4yoZ5v/P47HDoEISHw0EP+cNzj8YfjX34Ju3dDUBCEhsLx4ybw9mrTJmvdyj33mOeffBJOnID168118jG7zY7H8lA0rCgfdPkAgBlbZ5CUnsSoVaMoHl6cwY0GUzik8BmvUTSsKCPaj6Bf7X6kulI5nHyYhtENiQyKJDQgNKeWIiIiIiJyWcpn/xITERERuXiOpRzzBYxxJ+P4vyX/R2RQJM+2fPa0c1uUbcFfB/9i0e5FrD20llcXvQpwzpDcO0H8UtuXiI2PZermqdht9iwheeZO8nxh5UpTfwJwxx1w7bXmvmWZqXCAnTtN93hyMnTqBOXLm408jx0zIbnHY4Lx9u1PD8lTU+H112HWLPO6fC67kHzmtpkkpiXy4vwXCXQEMqjBoDOG5N7P0FXFr8rJty0iIiIickWw5/YbEBEREckthUMK83qH13m48cNYlkXpyNIMrD8wyzlujxuA7tW680SzJ2hepjkBjgDWH17Pq4te5eetP5OcnnzG72G3mb9uhQeG06lSJ8AEng67g0kbJ3HLlFsA8k84DmZyHCAwEIoVM/fT07MG5J9+CjNnmvuPP266yG+6yX+N336D//s/mD4d3G4Tjqenm2ODB5vp8Zo1c2Y9OeDUkPzaKtcSERQBmAqeT1d9yrGUY2d8rYiIiIiIXBr56F9iIiIiIhdPhjuDAEcApSNLM7TJUAoGF+S+hvdRNKyo7xzLsnDYHb5zO1bqiIXZvmXR7kW+kBzOPkluWRZ2m52eNXry1OynAKhVtBZrD61l7LqxBDmCGHPDmEu84ovI4zG36emwdq25HxjoP/7BB/Duu+b+//4HHTqY+x9+CNu3m3AcYM4cSEmBw4fN5HhgoLm23Q4FC+bIUnJSdpPkv2z7hYS0BIbNHQbAvQ3vPWvdioiIiIiIXFwaRxEREZErysQNEwEIcATg8rgAKFewHC+0foES4SV85/We0JuBPw/EY3kIcASQ4TZd2J0qdeLJ5k/SsmzL854kt9lseCwP+xL2kZiWSPGw4gyoO8D0nocW4eHGD1/iVV9k0dH+++vWwebN/seTJsHzz5v7PXpAv37mvnc6/J57wOHwB+pLl8Jrr0GrVuZa9sv7r6enhuRdq3QlMigSgGFzh511klxERERERC4+TZCLiIjIFePmyTczbv041h1ex8ttX87S/W3zVoMALb9syZLdSygWVownf3+S/+v4f76QPMAR4KtKgfObJPdOkB9MOghAoCOQ+xreR/Gw4tQtUZeKhSrmwOovomuugVtvNR3jbdtC9erm+bVrYeRISEqCSpWgTx+oUcMcCwgwt1WrmvupqRAebs7dudN8BQfnznpy2Kkh+cM8rElyEREREZFcooBcRERErgjvLX+PcevHATB84XAsy+KVdq/gtDtxe9w47A7AbNZZrkA5lrCEw8mH+ebvb7Asizc7vXleIbnb46Z7te5EBkXisUwVid1mJ/ZELC8teAmAKkWqANCjRo+c/CO4cJZlvrKb6r7lFujSBW6+2f/csmVmA0/Lgu7doX9//3W8P4DYu9eE4wBDhpjNOT/8EFasgCpVLu168pBzheQOu4O7r76bIqFFcvutioiIiIhc1i7v32EVERER+ccjTR6hV81evscjFo3g+bmmCsRhd/g244wKjeL1Dq/zwDUPACYw/3bttzwx64nzq1tZ/Crf/v0th5MPY7fZsdvs7Dy+k9cWv8aWo1sIDQilfYX2AL4APc+wrKyPbbbTw3Fv/3jnzv5w3OOBEyfgzTfh5EmoVw9ef91/zGbzv27fPvPY6YR27Uy9yp49UKvWpVrVRXEp/ludrW7l6dlP883f3+S9z4iIiIiIyGXGZlmn/ktIRERE5PLirVEB0y0+ZdMU37FhLYfxSrtXTjtvb8JeXlv0GqNWjQJMcH7bVbfxZqc3sdvsvklygFk7ZvHm0jdZGLuQDHcGJSNKUjqyNN2rdsflcTF2/VgOJx8mPjWeBtENmNpvKqUiS+XkH8G5ud2mGxzgwAFYsAC2bIEdO0x4XayY6RMPCDBfmc8HE3LXrw9Hj0Lz5vD776Zn3G73T5Bv32427Ny9G5o2Nd/D6cw6YZ4HZf5crNq/ii1xWzhy8ggNoxtSsVBFoiOi8Vge7LZ/N3vife2R5CM8OutRvl/7PU67k7/u/YtaxfL2Dw5ERERERPI7VayIiIjIZS9z1/ikvpOyhOQjFo0A8NWteM8rHVmaZ1o+A8CoVaN8k+RAtnUrdpudIEcQc3bO4UDiAQ4kHmD1/tWAf/q4RHgJvuvxXd4Lx10uE1SD2WBz8WITXp9q9GizmeZjj0HRollD8kOHTDgO5lrePnFv+L1nD7zyCuzfDwUKmHqWzOF5HuX2uH3h+MBpA5m3ax4xx2MAKBRSiPIFy/NBlw9oXrb5v/4emSfJ3+r4FkGOIB5v9jjVo6pflDWIiIiIiMiZaYJcRERErhiXepJ82Z5ljF0/lk9Xf+qrYQEoGVGSmkVr8vG1H1O1SNUcWet5yxxy9+kDkyf7jzmdEBICiYn+50JDzaacU6dC6dKmOsVuN9UpzZubINyy4Lnn4MknISwMNm2C4cNh7lw4fBiaNYMJEyA6OkeXeqEyT4VfP/Z6pm+d7juWuWrHaXcyvvf4/9wp7/1+/2UaXURERERELowCchEREbmiZA60e4zvwU+bf/IduxgheXJ6MusOr+Obv78hzZVGZFAk11W9jrol6hIVGpWTSz23zNPbvXrBjz+aWpRixeCBB6BRIyhfHqZPhzVr4Kuv/K8tXBjmzYM6dfzP9e4NU/w/dKBWLQgKgs2bzcacHo8JxefNy1cbcg79dSjvr3ifQEcgNpuNDhU7kJiWyJqDa0hISwBMj/13Pb6jX+1+ufxuRURERETkQiggFxERkSuG2+PGYXdkCb9vGHcDP2/52XfOvw3JvedaloUtD1eGZOull8xXYCB07AhDhpiu8Mz27DHPz5gBGf9Mx48bB337+qfI9+wxPeXLl/un0m02U+ECUKoUzJoFNWrk3Nr+o5jjMfQY34PNcZvpVKkTD17zIF0qdyHDncHWo1u5cfyN7Di2A1BILiIiIiKSH+l3N0VEROSK4PK4cNhNaDtl0xSe/P1JrvvhOuJT47Hb7AQ6AgHTSf783OcBf3c54Oskv7/h/QC+TvInZj2Bx/LgtDtxe9y+cNzbOw6Qp+cR9u6F+fPN/ZIls4bj3iDc7TbVKzt2+J/78ksTjoMJx72v/+QTaN/evMbtNuF4+fLQo4eZHM9H4TjAXwf+Yt2hdYQFhPFwo4fpUrmL71itYrWYdess6pesD5gfwNz6462MXz8+t96uiIiIiIhcIG3SKSIiIpe9zBst3jrlVqZvne6rxvBKd6f77o9YNAKbzcbLbV++oI07HXaHrz86c4d0np4oX7rUvyHn/ff7w3GXCwICzO1HH8GYMbB+vTk2Zgzcfru5n5hoalW6djXVLFddBb//bibN4+MhORlatDDT45GROb++f8n733xf4j4A7r76bjpW6giYH3gEOAJwe9xUKFTBt/Hrnwf+9IXkgCbJRURERETyAQXkIiIiclmzLMs3Oe6tUwl0BFIivAR31ruT6IhoioQWYfrW6aw/vJ61h9YCMHzhcIALDsnz3SaLO0w9CCEhcM015n5GxpnD8a++ggEDzP3ly2H0aDNN/tFHprc8Lc30jnfrluNL+S+89Tte8anxFAktQmSQCfULBhcEsm7c6bA7cHvclC9YXiG5iIiIiEg+pYBcRERELmve6e3n5z7vC8fbV2zP0CZD6VDR37N9Q7UbiE+L59HfHmX8hvFYlnVBIfm4DeNIzkhmZLeR+SccB9i509w6nWbKG84vHM/IgFWrTDgOJix/4AETjuczmbvmP1zxIasOrGJ2zGxqFa1F3Mk4gCzVOZn/+yokFxERERHJ3/LRv95ERERE/p3EtEQW71kMmEBz8DWDfeG4x/JgWRYhASGUCC/BD71+4OnmT1MsrBhgJsn/N+9/wNk7yQ8kHmB2zGyOJB/J6eX9N1FR5jY93QTeACkpZw/HPR4TohcsaB4HBsKKFXDyJOTlvvVsZK7f6T+pP0/8/gTf/v0tBxIPMGfnHP4+9DcA3639jqMnj/o2Ys3s1JBcneQiIiIiIvmHAnIRERG57C3bu4wFu0zPdu+avelapStg6lfsNrtvOtjtcQMwov0I7mt4HxFBEYAJyc+2cedtdW/DaXcytf9UiocXz9G1/WcNG5rbtDTTHQ4mGP/iizOH495O9euvh+hoE64fPGgC8rzct54Nb61K34l9mbBhAhkeswlp4ZDCviDcZrOx88RO3ljyBglpCdhstgsOySdumJiDqxIRERERkfOlgFxEREQueweTDgImxCwcXBgwtRqnbp7p3WQT4MU2L3JrnVt9x0YsGnHGkPy19q+xe+huaherfcnX8q+dabK7UiUoW9YE219/bTbp/Owz2LDBHD81HLfb/SH4/v1mk06nE0qXhvDwS76MS+H95e8zaeMkgp3BNCndhE+u+4RpN03juVbPUT2qOgBprjR+2vITX635iqT0pAsOyftN6sePm37M8bWJiIiIiMjZKSAXERGRy5Y3wEx3p/seH0s9BoCN7Ced7Ta7b5L8424f07JcS9+xU0PyDLeZNo6OiKZEeIlLs4h/y+0+v+N165pJcMsywffChbBunTn23Xenh+PgD9v37oWkJNNXXrMmBAdf/HVcAqcG2yv2rQCgTvE6jGg3gkENBtGsTDNebvsyb3V8ixuq3YDNZmPb0W2M/nM0X6/5+rxD8kalGgHm81ItqlrOLFBERERERM6bNukUERGRy5Z3QrxMZBnA9I3vid8D+KfFs9tQ0xtyOuwObqh2A4tiFxHgCCDDncGIRSNw2p280OYFAhwBObeYC+FymalugF9+gTVrYM8eE2I3aABNm4LDYWpVgoLghRfMxPi8ef7g/J574Oabzf2MDP/1vEF5bCy88ooJy4sXh549zXFv0J5HZd6Qc1/CPhx2Bz9t+QmABxo+QJvybQDIcGcQ4Aiga5WuhAWG4bA7mLJpChsOb+CT1Z8AcHu92wkPDMeyrCy/jZA5JP++5/fc8/M9fNT1I2oWrZmzixURERERkXNSQC4iIiKXvZpFa1K7WG02HNnA/F3z+WDFBzzc+GHsNvtp4aaXt5u6Q8UOviA8OiKaA0kHeGnBSwQ4Ani25bM5uo7z4nb7w+xbb4VZsyAuzn+8UiUzMf722yYcd7nMZpsPPQTHj5sw3WaDRYtgxAgYNsxsyOllt8OuXfDGG7Bxo3muWjVo187cz8PhOOALx68fez3REdF0q9KNAHsAUQWi6FipI2AmzAMcAb7PRqtyrXyvv9CQvFLhSvx+2+++7ysiIiIiInmL/qYuIiIi+d6ZQm6vMgXK0Lpca9YfXo/NZmPMX2OoWqQqXSp38dVkZPd6j+UhJSOFDHcGRcOK0rVyV7746wsigiK4rup1l3JJ/45lmclwgBtvhGnT/MdsNnN8xw54911TjzJ+vD9M79wZjhyBjz+GtWthyxZ4/nlYvBhatICOHeHwYROiv/467NsHCQlQsqTpLC+efzYnffiXh5m+dTqRQZEkpCWQ5k7Dho3wQNOh7v0sZP5stCrXylfL4w3JR60ahYXFHfXuOGNIDuCwOXJ4hSIiIiIicr4UkIuIiEi+5a1I8YaS6e50PJaHYKe/C9tblfFKu1f48+CfLNuzjA1HNvDJqk8IDwynRdkW2Gy20+pWLMvCbrNzIvUEAGEBYbzT+R3KFSxH75q9fZs35inecPa550w4HhQERYtC796mK3z/fpg505wzcaKpTpkyxTwOCYFbboGICPjoI1i61Fzvt9/M14svnt5rXqECzJgBVavm2BL/K4/loUf1Hqzav4rle5czZdMU0t3pnEg9wer9q2lboW2Wz0LmkLxluZZYmM7xKZumsPHIRj5ZZSbJzxSSe68hIiIiIiJ5kwJyERERyZe8HeEAv27/lYWxC/l1+6+4PC761epH87LNaVO+DQGOAFweF2EBYQysP5C4k3FsO7qNmdtmYrPZSE5PpnPlzr7NOTMHo7vjd/P6ktcBKBFegoigCJ5r9Vyurfm8nDgBS5aYSfLWreHJJ039iWVBaqoJut9805w7dSr06gWTJ5vHoaGmS/yqq0yFyvffm+cty4TjdrvpIK9UyXSZjxhh7ucjdpudVuVa8Vant3jk10dYtX8VdpudhLQEZm6bSdsKbbHb7GcMyU+dJD/fkFxERERERPImm2VZVm6/CREREZELkXmjxYdmPsSsmFlsO7otyzn1StTjnvr38MA1D/ieO5F6gveWv8fnf37O/sT9OOwO6havy+11b+ehxg9lef32Y9t5a+lbTNk0hbiTcTzW9DFe7/B6lon1PGnJEmjZ0oTZU6fCdf9UwWTePPN//4Phw/2vufFG/yS5dxNOgJ9/NlUrv/5qJtBLlzZf/ftD9eqmuzwPy/w5AXw/ALHZbLg9bpbvXc4jvz3C6v2rfed8et2nDGwwEDi9uifz44WxC/lw5YdM2TQFy7KoW6Iut9S5hfsb3k9YYFgOrVBERERERP4rBeQiIiKSr2SeHO85vidTN0/1HfNO+nqVLVCWIY2HMLTpUN9zh5MP88GKD5iwYQLbj233vaZjpY6UiihF+YLlSUxLZNyGccSnxpOUnkTFQhWZPWA25QuWz6llnp/MobfXrFnQpYupVZkwIet5mcPvs4XkqakQ7K+pwe0213Dmn18+zPw5ufb7a3my+ZO0Kd8Gj+XBhi3bkNxms1EmsgxvdHiDfrX7AecXkv+85WfS3ek0Kd2EGTfPoFBIoZxfsIiIiIiI/CsKyEVERCTfyFx74Q3HAxwBBDuDGVh/IJULV2bNwTUs2bOEDYc3AKYa5bGmj/FYs8d81zmecpwpm6Yw+s/RrNy3Msv3sNls2DCd5AClI0sz67ZZea9z3O32b8iZlgZbt5oNM3/+GR54AK6/3mzCmfm8U193akjeo4e/biUjAwICsv/e2QXzeVTzMc1ZtmcZwc5g5gyYQ9MyTbMNyYf+NpRV+1fhsDuoXLgyL7V5ib61+gJnD8kX717M8IXD+WP/H8y/fT51itfJlXWKiIiIiMi/o4BcRERE8p3BMwcz8o+RBDuDaVO+DYMbDebaKtcCZqPORbGLeGH+CyzdsxSAYmHFeKLZE1lC8nR3OkdPHuXVRa+yIHYB6w+vz/I9aherTYPoBjzX8jkqFc5jPdsul3+a+4UXYMUKmDsXKlc2E+IbN0L37vDTT+acUwPt8w3JM3+ffGhP/B7KvVfO9zjAEcC82+fRrEyzixqSL92zlLIFylI6snTOLlBERERERP4zBeQiIiKSr/y0+Sce+e0R9sTvoXX51rzY+kValmsJQJorjSBnEEnpSXT7oRuLYhf5XnfqJHnmCo6DSQeJOR7DgcQDAAQ5g2hRtgUB9oC81yedOdzu08cfZns5nSbYDguDr74ym3Ce6zpnq1vJpyG597cNNsdtptWXrYg7GQdcupBcRERERETyJ3tuvwERERGRCzFrxyxiT8QC8FCjh3zhuGVZBDmDcHvc/LT5J7bEbSEkIMTXG34w6SBvL3ubt5a+BYDD7iDdnQ6Y8LxZmWb0qtmLXjV7cV3V6ygYXDDvhePgD7VvucWE43a7mQ4vV870hrtc5nFKCkycCOvWnfk6bre5//LL8Nxz/mNTp0JfEwrnx3AcwG6z4/a4qR5VnYV3LqRwSGEAMtwZtP26LUv3LMVus2NhYVkWDruDJqWb8G7nd2kY3RC3x832Y9t5Yf4LTNhgutxP7bhXOC4iIiIikv8pIBcREZF8Y3PcZr76+ysAbqpzEzdWvxEw4biFCS73Je5j8qbJHE4+TPWo6jzV/ClalG0BmJD8raVv8cbiNwAIdATi9rhzfB3/2Q8/wPTpEBQE7duboHzxYli0CNq1g0KFzIackyfDl1/C9u3ZX+dMIbnTCZMmwW235cx6LhGH3eELyRfftfiiheTefnoREREREcn/FJCLiIhInpRdC5xlWb5wMiUjBcuycHlcWFi+zTtH/jGSqZunAvBcy+e4t+G93NfwPmoWrQnA4eTDvL7kdV5e8DJgQtQ8H3h6Tnl/y5dDYiKULg1PPWUqUUqVggYN4Jtv4OabzYadbjd8+CF88gls25b9tU8NyV94wUyhFygATzxxSZeVEy5mSD5p4yQA32dNRERERETyP/3tXkRERPIct8ftq684nnLc93yaO400VxoAR1OOYrPZcNqdWcLx/1vyfwA82fxJetToAcB1Va+jdbnWgAk341PjeXnBywz5ZQhA3g7IXS5TowKwfj0cPgxLlpjHDz5oJsbBhOiWBdHR8PzzpiIlOtqE3++/D59+en4h+QsvwIgRsGABXHXVpV3bf+TyuM7rvIsRku+O383gmYN9P3wREREREZHLgwJyERERyVNcHpdv88xXFrzCiEUjfMcKhxSmSpEqACzYtYAZW2f4jv20+SdenP8iANdWuZZ+tfoBJvyODIrkocYPUSC4gG/a3MLiw5UfctWoqxj5x8i8G5J7O8CvuQbuugtWr4akJAgMhIYNzTG3299FbllQtCg8+6zZxPPfhOTPPJPnw3G3x43Tbv5sRv0xig2HN5z1/P8Skjct05SUjBTclpvaxWpf8rWJiIiIiEjOUUAuIiIieYbH8vhCzx7je/Dighd5Z9k7vLvsXQDKFijLDdVuIMgZxOPNHqdb1W4AbInbwud/fc7x1OOUjixNn5p9uLrk1QDYMJPoEYERRAZFYlkWAY4AX4XL5rjNdK3cNW/XZowcaYLxVavgpZcgOdkE4elmk1Hfxp3w30PyfML7Q5TmY5rz4MwH+eKvL9h6dOs5X+MNyRfduei8Q/IR7UbQtkJb5gyYQ+XClS/52kREREREJOfk4X8JioiIyJXGG1Lf9dNd/LT5J1+IHXM8hr0JewF4tuWzTOk7hf/r+H++1609tJYFuxbg9ri5rup13F7vdsB0lnurWg4lHyIxLRGbzcaobqN4o8MbtC7fmjX3rfFNpedZLVvCkCFmSnzlSjh40Dy/1/yZ+Ca/vf5LSJ6PfLb6M1buWwnAhys/ZNQfo9gSt+Wsr/GG5DWK1mD6zdMpEFwAOHtI3qpcK6bfNJ2riuftqXoREREREblwCshFREQk17k9/oA37mQcf+z/w/d4aJOh3F3/bkpFlAKgYHBBulbp6ntdYloiby17i6T0JCoXrszLbc3mmx7Lg81m84XsO4/vJCEtAcuyCHGG8ETzJ5jWf5pv8848rU4duPdeuP9+89jjgYwMGD4cDhwwk9+nbuR5PiH59u05v5aLqGeNngxtMpQyBcrg9rj5YOUHfLLqk/MKyS3LoknpJgxvO5wgZxBBziBfSL5szzLsNjsey4NlmUqekICQHFqViIiIiIjkJAXkIiIikqsyd46v3LeSfQn7OJR0CIAnmj3B253fpl6JelnCbi/v6xLTEn2Pg53BvuPeCfIdx3bw4oIX8VgeGkQ3oF0Fs7FleGD4JV/fRVO9utmU88EH/c/t3AlPPQXHjpnp8gsJyUeOhLffhpiYnF3HReKxPESFRvF0i6fpW7MvpSJLYVnWeYfk3t8saFy6MQ6bgzRXGuGB4WS4M+j0XScWxi7EYXf4zhMRERERkcuTAnIRERHJVd7O8QafNWDAjwPYcnQL4YHhBDmDfEG2d8L81LDSY3k4nHyYw8mHATiZcZJV+1cBpq7FZrOx68QuXl30KjuP7yQkIIR25dsRGRSZ7fXyvFNDcrcbZs+G116DEyfOPyQvVw5SU2HSJAgLy/FlXAzeCe/CIYV5puUz9K/V/4JDcoCG0Q2pWbQmQc4gmpRuQtkCZUlOT6bPxD6kZKSc9kMZERERERG5vDhz+w2IiIiIfPnXl/x14C8AXln4CmnuNFwel++4d1L8VHabnUqFK9EwuiGzdsxiT/wevlzzJSczTtKkdBO2H9vOKwtfYeW+lZzMOEmD6AYMbjSYIGdQjqzrkvCG5DYbfPSR6SOfMMGE4888AwULmpDcnmkOInNIPmyY2eRz8WIYPx6KF8+1pZwPj+XJdgNVb/WJ2+P2heQA4zaMY1/CPj5Y+QEA9zW8j2pR1bK9ttvjJjE9kSMnjxAeGM7DjR/m27XfEuAIYHLfyapVERERERG5AtgsjcWIiIhILtt2dBvf/P0N7yx/h5SMFF8g/m7ndxncaPAZQ1IwAeqri17ljSVvkJyeDEBEUARFQ4uyN2Ev6e50AIqFFWPhnQupWqRqzizqUtu82dSkfPSReVymDPTvf+aQHExIbrNBXJyZPs/j4bjL4/L9hsHu+N1sjttMhjuDq0teTcHggoQGhAIm6HbYHRxLOcZri17zheQ2m42HGz2cbUjurd9ZuW8lzcc0x+1xEzMkhiBHEBYW0RHROb5eERERERHJeQrIRUREJE/YeXwno/8czTvL3vGF2gWDC7L4rsXULFrTF2hm53jKcW6ecjOzY2bj9rix2+zYbXZcHhd2m52yBcoy4+YZ1ChaIyeXdG7ewPrf+i8heR7nDb0BHpzxIAt3L2TD4Q0AFA8vzvVVr+fu+nfTqFQjwB+mZxeSP3jNgwysP5A6xesAkO5OJ9ARyO743dw3/T5+3f4rLcq2YPaA2QQ6AnNnwSIiIiIikivUQS4iIiJ5QoVCFRhYfyCPNn3UF1KeSD3Bo789yu743dlu0gkmSC0UUojve37PXfXuok7xOngsDy6Pi+pR1bmt7m3MHjA774Xjbrc/qE5I8D93IapXhwcegMGDzeM9e2DcuHN3kudxHsvjC8e7j+3OqFWjfOG40+7kUNIhvl37LXf9dBezY2b7nnd5XNl2kn/8x8e8s/wdft3+KwCBjkB2Ht/Ji/Nf5I/9fwDQuFRjbGT/GRMRERERkcuXJshFREQkT/FOkr+7/F3SXGkUCilEj+o9eLnty0RHRGc7Se6dNk5zpXEy4yTrDq/Dho06xesQ5AjKe13SLhc4/9kK5sUX4cABMwnuyL5r/Zz+zSR5PnD/9Pv5dPWnBDgCcHlctCjbgt3xu0lIS+B4ynFsNhvFw4rz1Y1f0alSJyDrJPnri19n/Ibx7Infg81mIzIokmuiryE0IJRle5cRnxpPujudGkVrMPPmmZQrWC6XVywiIiIiIjlNAbmIiIjkOaeG5MXDi9OrRi+ebfnsGUPys1Ww5CmZw+qePWHaNPPc66/Dk0/+++tmF5Lfcgs8/jgULvzf33cOW7V/Ff0m9WNfwj7aVmjLAw0foHu17uw8vpPVB1bzxO9PEHsiFjCVK1/f+HW2Ifm7y95lwsYJbDu6DbvN7uuz91hmsr50ZGlm3TaL6lHVc22tIiIiIiKSe/LfKJGIiIhc9rx1K0ObDCXIGcShpENM3jSZEQtHsD9xf7Z1K/kiHAd/OH733TB1qr8C5fBhOHr03183u7qVDz4wX6fWrOQDaw6uYefxnRQLK8bjTR+ne7XugPls9K7Zm/G9x1OhUAUADiUd4vaptzNrxywga93KE82fYFjLYbSr0M4XinssD5ULV6Zf7X7Mu32ewnERERERkSuYJshFREQkz9p5fCef//k57yx/xzdJ3rN6T4a1GnbGSfI8y+32V6gcOwbt28Pff5vHjz0GffpAo0b//fts3gyffgrvv2/C+I0boWrV/37dHJLhziDAEcCQX4bw4coPeaXtKwxrNQzAN/3t9eeBP+kzsQ87j+8ETp8kz7zRp8fyMG79ONLd6SSnJ9O5cmeKhxUnIigih1coIiIiIiJ5iTO334CIiIjImVQoVIF76t8DwDvL3+FQ0iGmbJ4CkL9C8szh+OrVEBYGW7eax088AW+8cfG+V/XqcM89EBQEAwbk+XA8c4gNkOpKJcARgNsyG5ZGR0QDp4fjAPVL1mdin4n0ndiXmOMxHEo6xIAfB/D1jV/TuXJnHHYHHsuDDRt2m52b69yccwsTEREREZF8QRUrIiIikqd5Q/JHmzzqq1uZsnnKWetW8hxvOH711dCxo6k/qVbNbNTZvr055nZfvO9Xqxa8/DLUrHnxrnkJuDwuXzj+5V9fMuSXIbT7ph1PzHqCvw7+BUCwMxgAG9n/EKR+yfpM6DOBioUqAnA4+TC3T72d37b/BnBaqC4iIiIiIpKZKlZEREQkX8iubqVPzT480ewJyhQok9tv79w++wzuu8/cL1kSbDY4dAhmzIBOnbJu3nkFyDw5ftuPtzF181SS05NPO69z5c58fePXFAsrdtbr/XngT98kOUCxsGK+SXLIR5u4ioiIiIhIjrpy/hUmIiIil9zZfu7u3SDx38pukvyTVZ/w/or3cXsu4vT1pdKmDTz+OERFwYEDcPCgmRpft84cv4LCccuyfOF4z/E9+X7t975wPCQgxHeezWZj45GNzImZQ7o7/azXPNMkuXfjToXjIiIiIiKSHU2Qi4iIyEWReSLY5XGx/vB6HDYHYYFhvtDyYth5fCdfrvmS4QuHA7DpwU1Ui6p20a5/ScXEwMiR8OWXcPy4eS4sDKZPh9atc/e95YJXF73Kc3OfI9gZTPWo6txe93bCA8NZc3ANH//xsa8+p37J+rzX5T2alWl2zsqUzJPkAY4ALMvil1t+oX3F9jm0KhERERERyU+0SaeIiIj8Zy6PC6fd/LXi7aVvs2TPEmZsm4HD5qBEeAnalm/Li21eJDoiOsuGjP9GhUIVuL3u7QQ6AulVo1f+CccBKlaEBx4w970heXo6fPABFCsGNWrk7vu7xDLXnKS50li6Zylgpr+HtxtOm/JtfOdeXfJqXl30KrHxsfx54E+e+P0J3un0Do1LNz5rSO6dJL958s1sPbqVgsEFKR1Z+pKuS0RERERE8i9NkIuIiMh/knlyvNeEXszYOsNXh2Gz2bDb7Lg9blqWa8mzLZ6lTfk2BDmD/vP3zRzK50kej+kZt9nAssytl3eS/Kuv4NgxKFgQbrkFhg41IXo+l13f96n/vf488CcNP2sIwPc9v+emOjcBkOHOIMARAMC49eN4cf6L7Di+A7fHTePSjc8rJAdYuW8l902/j69u/Iqril91MZcnIiIiIiKXkSun7FJEREQuusxd0v0m9ePHTT+S4ckAIDoimuJhxXF73NhsNhbFLuJ/8//HL9t/IdWV+p+/d54Lx0+dObDb/aG499blMrfeSfI77oAiReDECRg3DkaNMuF5PvTAjAeYvnU6gK8axcuyLN9/ry7fdWH06tEkpSfhtDupX7I+N1a/0XdegCPA11ffv3Z/XmzzIpUKVcJhd7Bi7woenfUoK/auOGenfaNSjVh29zKF4yIiIiIiclZ57F+WIiIikp94p4RfW/QaEzdMJMgZRKNSjRhUfxDdqnbD7XEz+s/RjP5zNLEnYvlj3x+8uuhVLMuia5WuBDuDc3kFF4nbDY5/qmP274eFC2HjRjh8GAoVMtUpnTpBiRL+12SuW/n6azh61EyUW5Z5Ph9Nkl8/9nqmb53OT1t+4tse39KuQrssE+Te+wOnDWTWjlnM3zWf/rX7Y7fZSXGlkOHJIIQQ33l2mx2P5cFus9O/dn8A3yS5NyQ/n0nyi/GbCiIiIiIicnlTQC4iIiIXLHOFRoY7g+X7lgNwTfQ1vNL2FVqVa+U79+kWT1OhYAU+WPkBK/auYNX+Vby2+DWAyyMkd7nA+c9fqZ591oTjS5dmPcdmM+H4ww9Dly5Qt655PruQ/OuvzeN8EpLHHI9hdsxsAI6lHKPTt51YcMcCmpdtnuW8lIwUKhaqSJ3idVh3aB3frv0Wy7KwLAu3xw3gC8Xh4oXkIiIiIiIiZ6N/TYiIiMgFcXlcvnA8JSOF7ce28/OWnwG4v+H9vnDcsixfDUa/2v14stmTNCvTDLvN7gvJf9l2cepWco3b7Q/He/eG11/3h+MFC0JEhLlvWXDgAAwfDo8+Cr/84r+GNyS//XZTt+INyUeOzBd1KxULVWThnQupWqQqaa40yhcsT9UiVbOc47E8hASEMLjRYO65+h6qRVXz/ZBlc9xmRv4xEjCheOZqFm9IDv+tbkVERERERORMFJCLiIjIecvcJX3PtHv4dPWn7Dqxi0BHIA2jG9K3Vl/fed4NOr2B5w3Vb+DxZo9fPiG5ZflrVfr0gSlTICgIypSB116DadNgwQKYPBm6dTMT5MnJsGwZ3Hsv/PCD/1r5PCRvGN2QL2/4kpblWrLozkUUDSua5bgNG26Pm4igCO6odwf3N7yfqkWq+j4bP6z/gWlbpplzT+kvP1dI/uTsJ1m8e7FCchERERER+VdUsSIiIiLnzTs5ft0P1zFz20zqlajHNdHXkO5OJ9gZ7Nuw89T+aW9gfn216wF4a+lbLN2zNH/XrXjX+MorJgQPDIR27cyEePv2/vPq1TOVKo89BnPmQFIS7N3rnzz3OlPdisMB99wDVarkyLL+raZlmjJnwJwsm6f2m9SP2666jeuqXofD5vCF5HfWuxOAUatGsSVuC5vjNvP5n59TMLggrcq1yvKZgTPXreyO382S3Ut4ZeErTOs/jZCAkJxfuIiIiIiI5GsKyEVEROSCpGSk0LpcaxbELmDNwTXsTdgLwNGUoxxIPECxsGK+oNzrsg3J4+Jg3jxzv2hReOQRfzienm5C8/R0mDkTtm0z4TjAF19A376nXy+7kPzNN811/vc/CAi45Ev6LzKH400+b8LKfStZumcpwc5gOlTsgMN+ekj+yapP2By3mZnbZhISEILdZqdF2RbnDMntNjuDZw4mLDCMtzu9rXBcRERERET+FQXkIiIickFCAkK4r+F9hAWG8disx4g7GYfdZifmeAzrD6+nY6WOWTZb9MouJH972dss2b3EF5LbbDY6V+qcf8LOJUtg/nxz/777oGNHc9/lMqG2y2VqUsaMgY0bzbExY+COO8z9EydMH3mNGmbSHPwhucNhwnGAW2/N8+F4ZluPbmXtobUAHEg8wIAfB/BNj2/OGJJ/uvpTNh3ZxOSNk33XOFdI3rdWXxw2B3VL1KVy4co5v0gREREREbksqINcRERELlhEUAS3XXUbb3V8iyBnEB7LQ5orjbum3cW2o9uy9EZnlrlf+vpq1/NY08eydJI/+fuTzNk5J6eX8+/t2mVuCxSABg3M/fR0U5/icsFHH5lAfP16c+yrr/zh+Jo1MHQo3HILbNhgnvP882dWsaKpVRk2DDZtgmrVcmY9/5Lb487yuGqRqiy6cxEVClXAY3k4mHSQAT8OYHbMbIDTQvJ7G9xLjaI18FgeJm+czIcrP2Tx7sXA2TvJe9XspXBcRERERET+EwXkIiIi8q9EBEUwoO4AX0gOsD9xPw/OfJCdx3eed0j+eLPHaV2+NQDbj22nZtGaObeI/+rQIXObng6hoea+d3I8u3B8wABzPyXF1K58/bV5/OWXkJYG9kx/NatSxdSq5PFw3OVx+Sp1pm+dDoDH8tAgugHje4+nXMFyABc9JBcREREREbkY9K8LEREROc2pwXbmcBJMKAqnh+SWZbF0z1Ie/vVhdp3Ydd4h+X0N7qNrla6sf2A9FQtVvESrugSC/+lLtyzTFw6Qmnr2cNzjgZAQaNLEPLbZ4MgRE6yfKo/Xqrg9bl/vePtv2jNq1SjAH2A3jG7IxD4TL0lILiIiIiIicjHYLP1LQ0RERDJxe9y+ieBtR7exMHYhm+I2USSkCPVL1qdz5c4AZLgzCHCYADcxLZFv/v6GJ35/glRXKmGBYbQp34YPu35I+YLls+0kB7L0SyelJxEeGJ5Dq7xIZs+GXr0gMRFatICxY2HaNBg16szhuHdKfM8eaNkSdu820+dbt0KJElmnyPOJFmNasHTPUuqVqMfye5Zjt9mzbNi5av8q+kzsQ+yJWABKhJfwdZKD/zOXmJbIl2u+9HWS2212etXsxcONHqZ52ea5sjYREREREbm8KSAXERERH5fH5Qs2h80ZxqyYWazev9p3vGhYUdpVaMfYXmOBix+S5zvbtkHTpnDsGBQqBLVrQ1yc6Q2HrOG4ZZlpca+dO6FVK9i/33SOr14NERFZz8kHTqSeoNx75UhMS6RYWDHWP7CeqNCo0877tyF5kDOItuXb8r/W/6NJ6SY5ujYREREREbn85b8RJREREbkkMtdl9J3Yl9eXvJ4lHAc4knyE8evH0+nbTgAEOALIcGcA/rqVNzu+SbAzmOT0ZObvms9Dvzx0zrqVPOds8wPejTQty98TbrPB8eOwcqU/HP/+++zDce+1Y2Ph8GHzuG5diIzMd+G4x/IQYA+gZHhJAFJdqcQcjwFOr+X5N3UrdYrXIc2Vxsp9KykdWToHVyYiIiIiIlcKBeQiIiKSpVblhnE3MGnjJN+0d7/a/bih+g20rdDWd/7smNm0/6Y9bo+bAEfAaZ3kFxqS57izBeButz+oPngQfvsNpk41m2pmZJxegdKuHXTubF6TlmaeGzQIbrrJ3E9PN8csyx+U79xpgvWMDChaFHr2PPf7yoPsNjthgWG0q9AOgIS0BP468BeQ/Q8+LjQkv6XOLTQq1Yh5t89TQC4iIiIiIpeEAnIREZErnGVZvnB84LSB/LzlZ4KdwbQq14pJfScxttdYJvedzJc3fMmwlsMAE4zO2zmPTt918k2eX0hInqsNb9u2mZDak01Q73KBw/xZMGwY9O4NXbuaAPu666B1axg3zmzI6Q2Aa9eGO+6A6tX9z61aBS+8YAJ27+abNpv5iomBN97wT5pXr25Cdu85+VB0RDRgPheHkw+f9dwLCckfuOYBZt4ykzrF61zaBYiIiIiIyBVLHeQiIiICwCerPuHZOc+SlJ5Ex0odebr507Qs1xLw94QnpydT/7P67Dy+EwsLt8dN2wptmXXrLBx2R5YO81M7yQsGF6R2sdp81/M7yhYomzuL7NnTTIMvWWK6wzNvmul2+8Nx73mZ2e3m/IoVoU8feOghiI72H//0U3jnHdi+3UyCh4RAuXJw331Q9p/1HjsGb78N+/ZBQoLZlHP+fKha9RIv/L/J/BsGmbvkvfcXxi6k83edSXOl0b5ie6bfNJ1AR+BZ63PO1kluWRYey+P7niIiIiIiIpeKJshFREQEl8fFvF3zOJF6goLBBXm40cO+cNxjebDZbLg9bj7/83O2H9uO0+7E7XED+CbJveF4dpPkEUERnEg94eunzhWDBvlD7/btYelSf+jt8fjD8VtuMec5TdBPixbQrBkEBZnnYmLg669hxAgTdHvdey8895wJ3u12SEmBzZth6FATuPfqBffcYybHExKgQgWYOzfPh+Muj8sXVMenxpPmSvMd8wblpSNLE+wMBuB4ynEcdsc5u+WzmyS/e9rdzNw2E5vNpnBcRERERERyhAJyERERYXbMbCZumAjAvQ3upXPlzsA/k+OYoHNT3CZmbp+JZVl0rdKVQQ0G+V4/b+c8On7bkVRXapbw3BuS/6/V/ygeXpxfbvkl96bHH34YoqLM/dRUaNPGTJJ7Q3KA0aPhxx9NLUqHDjBtGixcCIsXm9D8zjshONhUp0yeDMOHw969/u9x223w+uvw1FMQEGCu7f1lPe9t5crQvz/8/rupV8nDMm/c2vSLppR/vzzNxjTjxfkvMnr1aPbE7+FI8hFKRZSiVtFa2G12Yo7HsDluMx7L45sEP5OG0Q2Z1GcS5QuWB2BP/B4e/e1RktOTc7eGR0RERERErhjO3H4DIiIikvv2JuzFZrNhWRZlCpQB8AWb3inh0atH8/uO3wG45+p76FqlK4GOQD5a+RE2m40FuxbQ+bvOTOk7hSKhRXzXjgiK4N6G93LX1XdRKKRQDq/sHy6X6QpftMhMhB89ap5r2xbmzYPmzc15y5eb8LxqVXj8cX83OEDHjmbqu2hRePddOHzYhOlg+spL/7OJZIsW5uvGG019ysqVkJwMRYqYc/r3N9UrBQvm4B/Av+Od4u78XWdW7F2Bw+7g74N/8/fBvwEzOR4WGEafmn3YcXwHHstDiiuFmOMx1C5WG8D3AxZvTYu3rgfMD2AaRDdgUt9JtPu6HQlpCUzuO5mwwLBcWK2IiIiIiFyJ1EEuIiJyBfOGlS/Of5GXF7yM0+7k2ZbP8mKbF8lwZxDgCADg3WXv8tisxwB4pe0rDGtlNuvcdnQbD858kNkxs331Kk1KN+Gz7p9RtUhVAh2Buba203g7xrds8YfkYGpTZs+GVq0gLMxUo3zwAQwebI5bVtbNM2Nj4fPPTd94SgoUKwY9evhDcssyX/ZMv6jncvkrW/KZ0atH89EfH7E/cT+prlSS05MBfD9QAQhwBPjuuzwuQgNCubbKtRQPK07r8q2pHlWdoqFFKR5ePEtAntmag2sICwijSpEqObc4ERERERG54ikgFxERERbFLqLL911IyUihSpEqrL9/vS8cn7BhAndMvYNUVyp9avVhRLsRVC5c2ffa5+c+z4hFI3Danb6NO+uVqEfD6IY81Ogh6hSvk1vLMrLbiPPUkDwgAH74AZ5/HnbvhhUroFatrK/NbNcu+OKLM4fk3tedGq5D9s/lA7vjd3Ms5RhLdi9hc9xmdhzfweoDq8lwZ3Ai9YTvvCBnULY95WULlOXqkldTMrwknSp1onBIYa4ucTU2m43QgNCcXo6IiIiIiAiggFxEREQwk+C9JvRiT8Iebqh2A1/d+BUAq/evZuhvQ1m8ezHVo6rzctuX6V2zN4BvU84lu5f4+se9QefJjJMUDSvKkruWZAnTc8zJk7B+PTRqZB6fT0ju7SKPioIdOyAi4uxh9rlC8nwahGfm/Wuid1o88+S32+Nmf+J+EtISmLtzLrvjd/P2srd9x8MCw0hzpfk2bc08cR7oCMRms1EwuCCNSzVmUt9Jvq5zERERERGRnKR/iYiIiAhVilThhdYvsOvELh5r9pjv+bk75/LngT8BuKHaDb5w3LIsX6AZczyGVFcqAIMaDGLDkQ1sOrKJmbfMzJ1wHGDdOujcGf7v/2DQoKwbcTocJiSvVs1svukNyZ1OSE+HuDhTudKjx9kD7vLl4e67zf133sm+kzyfheTeENx7mznU9objHsuD3WbHbrP7+uprFatFujudpXuXsnLfSoqHFeexpo8RFRrF1C1T2Z+4n81xm4lPjQcg3Z0OQIY7g5favKRwXEREREREco3+NSIiInIZO1Pfc2bervFeNXtleT7uZBxvL3ubkxknqV+yPq91eA3wB6Te2wNJB7Db7AQ7g3m06aO4PC7CA8OJCo26ZOs6pyNHzGabTz5p6lPuvNM8750id5jNJ08LyYODIS3NVKx06waB5+hQP1NI7nCY71227CVZ3sXm/W/p/awcSzkGQJHQItkG48Bpn6tARyBRoVG4PW6OnDxC/ZL1aV2+NbdcdQt2m53V+1cTGx/Lsj3LWLZ3GcdSjjGxz0RqFauVgysVERERERHJSgG5iIjIZcrtceOwmyA4KT2JpXuWsjdhL0dPHqVAcAHqFq9LneJ1svQ/Z54W3nl8J/Fp8QQ6AqlQqAIAaa40gpxBWJaF3WZnS9wW3lr6Fh7LQ6NSjSgdWTrnF5qdhQvNNLjLBUOGmNuBA82xhg2hUCH4/Xfz+NSQHODtt6FxYzNFfqYecq/sQvJPPzXh+v/9X57fnDPz52TChgks2LWA8RvGY7fZaVehHd2qdKN3zd6EBIT4QvIzXaNhyYb8vOVnMtwZ/Lr9V1qXb02GO4MgZxANohvQILoBPWv0BEwIXzikcI6uVURERERE5FR5+19sIiIi8q94+8EBhi8czsLYhcyOmZ3lnCKhRSgTWYZX2r5C/ZL1KRlRMkulRmJ6om+zxbiTcYB/A8YgZxC743czYtEIjqUco0hoEa6vdv1pdRy5ZsgQWLrUfCUnw2OPmaD6p5/gT1MZwzvvwKOPmvD81JDc7YZ+/WDOHGjZ8vxDcocDXn7ZvP7ee/N8OJ75czLo50H8tuM39sTv8R2fsGECy/YuY+W+lbze4XXCAsOyBOpe3sdNyzT1PbcpbhNgPjOZeUN2heMiIiIiIpIXnOVfeiIiIpIfuT1uX+jZb1I/Xpz/oi8cD3YGE+gwtSFHTx5lzcE13D3tbl5Z+Ap/H/wb8Ifb4YHhvmuuObiG1xaZipUgZxA7ju3gmTnPMGvHLDyWh1pFa9GvVj9fb3WusiwoVQq+/x6aNDGPk5NNYD1tGoSHw403wlVXmfOdzqyd5EWKmOddLmjfHpYsydphfibly8OAAfDKK7Bhg7leHpb5c9JjfA8+//NzXzgeFhjmO29P/B5+WP8DQ34dQnJ6Mg67A7fHfdr1PJaH4mHFCXIGYbfZ2Xp0K4eSDp12XnYT6CIiIiIiIrnFZnlHvURERCTfy1yB0WtCL37c9COBjkAigiK4t8G91Cpai5CAEHYc28Enqz/hUNIhktKTKBBcgGuir+F/rf9Hi7ItfNfrO7EvkzZOAiAkIISaRWtSIrwEK/au4ETqCVweF6UjSzP39rm5tyHn2Rw7Bt27w7Jl/s0y69aFb7+FWqd0X7vdZgJ8y5asdStOJ8ybB82bn3uSHEywnscnxzN/TvpM7MPkjZMJdARSOKQwD1zzANdEX8OGIxuYtWMWs3bMAswPTHrX7M2HXT884yQ5QLMvmrF873IcdgeL7lxEk9JNcnRtIiIiIiIiFyJv/+tNRERELog39Hxm9jP8uOlHgpxBtK/QnocbP0ynSp2ynNuyXEse+fUR1h5aS3xqPLNjZvNo00eznHN9tevZenQraw+tJSUjhdX7V2epYSkZUZLfbv0t74XjlmUC8cKFzSadmZ/bsgX+/tsfkHtDb4cj6yS5NyR3uaBt2/MPyfN4OA7+z8mjvz3K5I2TCXYG065Cuyyfk86VO9O1cldeW/wa3639jqT0JCZsmACQbUhuWRYujytLdcq2o9toUrrJeW0WKyIiIiIikhv0O64iIiKXmR3HdjBv1zwAyhYoy6NNH/WFnunudAAy3Bms3r+a46nHOZlxEoDR3UfTpXKXLNe6pc4tPHjNg7Sv2N73nGVZlC1Qlm5VuzH/9vnUKFojJ5Z1Ybxh7COPmA07g4NNdYplQWoq3HcffPGFOSdzfcqpIXnmupW2bc+/biUP8/5wY+7Oufy24zcAGkY35MnmT/o+J97u+apFqhKfFu977cmMk0zeNJmHfnko27qVAEcAHSt2xGaz4fa4mRVjps8VjouIiIiISF6V90ecRERE5IIs2bOElftWAnB/w/tpV6EdYDZkDHQE4vK4+GjlR3y55ku2xG0BYMwNY7ij3h0AJKUnsXzvcmpE1aBUZCkGNhjItVWuZe7OucSdjCPFlUKnSp2oWKhi3t5o0eWCpk1hzRoICTGbaL7/vgm5k5Nh6FATdt95pz/0vpiT5HmUN6z+bftvbDpiNtJ8qNFDtCrXCjABepAzCI/l4actP7Hu0DqCncFUi6rG3wf/JjEtkcmbJmNh8WHXDwkPDM8ySV46srQvhD+WcoyUjBRCAkJyYaUiIiIiIiLnpoBcRETkMuGtsfButhkdEU3b8m0BMzEe4AjIEo6vP7wegK9u/IoBdQcAZjPOkX+M5Lu137HgjgWUiiwFQKnIUtxW97ZcWNV/4HRCjx5QsSIEBpru8UaNoH9/WL7chORDhphzLzQknz8fmjXLlyG5x/JwJPkI3679FoD2FdvTp1YfwHyGLCxs2Dh68igTNkxg14ldVClShaebP80v23/hm7+/ITEtkYkbJuL2uBnZbSThgeG+z1jdEnWx2+wUCC7AGx3eUDguIiIiIiJ5Wv76F52IiIickXcy+GDyQQBSXamEBoQCZAnHx/w1JttwPCEtgc///JzP//ycVFcqY/4ag8fykG/28/a+T7e/8oPAQGjY0ITjAGXLwtixZrLcsvwh+ZdfmuPnW7fSogWsWJHvwnEw/eM2m81XrZOYlki6Ox23x42F5esn/2DFB77O8Rdav0C/2v246+q76FCxA2DqVsZvGM+dP91JYloiAQ7T9R4RGEGT0k2YO2AutYvVzoUVioiIiIiInL/89686EREROatARyBgJoWT0pMA0yl9tnDcY3mIDIqkfsn6vuvsTdzrC1PzNG+g7X2fSUmQkeE/fur7L1cOfvjh/ENyl8uE5IsWQYkS5vmAAIiIuHRrusQCHYFEBJn3vydhD8dSjuGwO3zh+KerPmXEohEAPNLkEW6qfRMArcq1onvV7oD5gYzL42Lyxslc+8O17E3YS3xqPMXDizPrtlnULVE3F1YmIiIiIiJyYRSQi4iIXCa8k94NSjbAYXdwIvUEn6z6hPjUeD7/8/OzhuM2TIhcrUg1IoMiAdh4ZCNJ6UlZNmHMc9xu/xT3jBnw+ONmWrxxY7j1VtM/npxsjmeehD+fkNzlMved/zTSVa8Os2ZBhQqwahXUrJkjS7zYLMvCYXNQoWAFAA4kHmDalmm+4zO2zmD4ouEAdKjYgX61+mGz2fBY5ocGAxsMpGqRqr7Pm91mZ8nuJVT5sArDFw4n3Z3u+80FERERERGRvE4d5CIiIpcJ76T31SWu9oXaC2IXMPDngWyK28SGwxuA08Nx79Qw4KvJACgVUYqwgLCcevsXzuXyh9f332/C6507zePdu004/vffMHCgCcsLFzZhuHei3BuS33wzLFt2eie502muN20a9O5tKlZq14ZNm0x1Sz5ls9mICIrg4cYPs/XoVu6odweDGgwCYHf8br7++2sOJR0iKjSK3jV707h0YwDf58RusxPsDAYgxBniq2pxeVzcXf9u328wiIiIiIiI5AcKyEVERPKJU8PsU3k36Wxetjn3N7yfUatGsfXoVvYm7PWFmN/0+IZbr7r1tOt5X7s/cT/JGWbiunLhynm3XsXt9ofjPXvC1Kn+YyEhkJJi7m/YAB9+aB7fc48Juc8Vkj/4oDm/VSsYORI++cT0lr/9ttmYMyCAvOxcnxPv8R7Ve1AmsowvAAfYHLeZX7b/gsvj4toq1/qCc+/nw+1xczDpIAlpCQC83PZlGpRswMhVI3mpzUtUj6p+aRcnIiIiIiJykSkgFxERyQdcHhdOu/nf9vxd81l3aB0prhQigyK5pc4thASE4LQ7fUHmtVWuZe6uuWyJ20KKy4TF/2v9P184nuHO8E2Le18TczyG4QuH4/a4KRVZiv61+2c5nmd4PKYbHMxk99SpZqK7QAEYNAgqVzYT5F9+CXv3wo4d8Omn5nWDBmUfko8dCzfdZELy1FQYPBgiIyE93Zxz5IipVslLfw7ZuJDPicPu8IXjGe4MPJaHD1d+SHJ6MqUjS/NMi2cAf6Dufc3Wo1uJjY/1va51+dY0Lt3YN1UuIiIiIiKSnyggFxERyePcHrcv9LzzpzuZHTObfQn7fMfHrh/LbVfdRu+avSkYXBCAblW78fehv3l10au+gHzNwTWMXTeW66pe59ugEUzlxq4Tu/i/Jf/HzhOmoqRW0VpcXeJq3/E8xds5/vDDMGUKBAdDmzbmcZcu/vOaN4eXXoIlS2DXLvjsMxOM33vv6SF52bIwcSL07w+LF5vvkWCmpClVCmbOhOjonFzlBbvQz0nmH3x4f1jinQxPSk/CYXf4Xus9d+fxnTw39zksy6J2sdr0rtkbgCBHUI6sUURERERE5GJTQC4iIpKHead2AW4cd2OWzRTBhNeLYhexP3E/McdjeKzpYxQJLQLAsy2f5VjKMT7+42PSXGlM2zKNNQfX8N267xjSeAhRoVFkuDM4lHyI4QuHs+P4Do6nHCc6IpqPrv2IomFFc3y95+QNtadPh99+M/ebNoVnnoGWLc05aWkQFATt2sHrr5vJcbsdYmPh88/NOaeG5JZlAvDZs+H222H1avOaOnXg1VehSpXcW/N5+LefE2/w7bE8JKYlciT5CGB6xhfvXky5AuV84XnsiVheXfQqm+M247A7aFG2BSXCS/iuLyIiIiIikh8pIBcREcnDvMHjE7OeYNqWaQQ6AikYXJCuVbqSkpHC2kNr2Xp0KzuO7eDbtd+S5krj2ZbP+kLytzq9RVhAGGPWjGFfwj52x+9md/xu5sTMwWF3YLfZOZlxEsuyAChXsBwzb55J5cKVc23NZ+UNYn//HbZtM/cHD/aH45ZlwnG320yEb9hgHpcqZWpXdu3yh+SDBkFUlD8kd7tNVcvYsebc0FDTZx6Whzcq/cd/+ZxYloXdZqdAcAE6VOzA5rjNHEs5xpi/xpCcnkzLci05knyE1xa/xtpDa0lIS6BO8To83eJpwgLz/p+NiIiIiIjI2SggFxERyeP2J+5n0e5FOOwO2ldsz6NNHqV9xfYALIxdyA/rfuDzPz9nX8I+JmycAJAlJH+p7UtULlyZaVunMXnjZADS3eng9n+PcgXL/T979x3eVn22cfw+krwdx9vOtLNJQhJCWGFTNpRdZpiFDiiFUnYXlNGyCn0ZpYUChUCBlr3KaggZrLBCSMiOnTjO8I73kPT+cWrZzjlOHFvS0fh+rosr0k9H0iNxhMmtx89PUwum6t6j7tWY7DHhfYG7av166cknzcvHHiudcop52e/vCru3bJFefFHatEmaMUO68ELpX/+SPvqoa9xKR4f0059K+fnm/dzurm7zkSOdenX9NpDzpHPO+MzhM/Xct8+psqlSC9Yv0KcbP1WiO1HN7c3y+X2SpLy0PP3rB//SyMHR9x4BAAAAwPYIyAEAiHCLNy/WZxs/U4I7QZfvfXkg9JSkg4sOVn5avlITUnX/p/f3GpKfN+08HTP2GJ2z+zl6dcWrWlG1QvWt9SpIL9DYrLE6Z8o5mpw/WbmpuY68xl3S3Cw1NZlBtud//yvT0WFe75xP/uCDZkAuSdddJ51+ulRQYIbjGzaYIfujj0qNjdKVV5rjVTrD8SgVjPPk7Cln6+vNX+uhRQ+ppaNF7d52SeZGnYnuRBVlFunVs17VhNwJ4X+BAAAAABACBOQAAESQ7hsndqpvq5cknb372Tp23LGW43bL3U2X7nWpDBn6v0//Txu3bdTzS5+XJN140I2B0DsvLU+nTDxFJ0w4QZLU2NaoZE+ykjxRtsFiUpI0eLBUXS0tWiSVlUnDh3fd/vDD5uxxSbr6ajMcl6Qf/EBat066/nqzy3zjRukf/zC7zG+9VSoqCvtL6a9Qnid3HnmnCtMLNbd0rl5f8bpchkvTh0zXwSMP1pX7XanizOIwvUoAAAAACD0CcgAAIoTX5w1stOj1ebW5YbNSE1LNcSiSBicNliS1e9sDGyd2GpczTj/d66eSpP/79P9UXl9uCT87H99tuGUYhgYnDw7c3y5wjVjZ2VJGhlRVJdXWSosXdwXkr79uht2SdMwx0llnmZe9XnOEyqxZ0p/+JNXUmF3nFRXSCy9IzzwjPfCAuXmn2+3Iy+qrUJ8nknTVzKt05X5Xal3NOvnlV9HgIhmGIY+L/3UEAAAAEFv4Ww4AABGgw9cRCB/vWHCHPin7RJ9t/ExJniRNzJ0oSdqwbYMkWULPTn0JP3sLwqMmHPf7zXD8xhvNLvHvf186/njztrVrzQ04KyvNueI/+IE5f1zqCr0LCszNN7duldLTzcdrbDS70g89NOLD8XCdJ50zybvPo+/cyBUAAAAAYgkBOQAADvP6vIHQ8/R/n67XVrwWmP0sSWXbyiRJJbUlWlm1UuNzxvcadPcWfvrl168O+lWP8DNidW606TM3hZTLZa5J5rokHXWUNGqUdHjXnG0tWSK9/77ZGf7970s//GHPx/N6pZUrzdEsknTFFdKRR0pXXSXNni1NmhSe19dPTp8nUfMlCgAAAADsggj+2zEAAPGhc1zGrJdm6cVlL6rD1yFJGpQ0SAnuBHl9XhmGoa83f60HP3tQkhlWen1e28frDD+v3PdKuQyXyuvL9a+l/9KdC+7U1satkRuOdwbinUFs9003DaNrXZJGjuwKx9vbpdZW6d57zQ08R42SbrnFvM3rNe/n95vd4evWSfXmrG6NGCEdcoj0ySfS5Mmhf30DxHkCAAAAAMFHBzkAABHgkS8e0YvLXlSSJ0kHjDhAl+x5iWYMmaFF5Yv06JeP6sOSDyVJD372oLKSs/T7w34vt8vdYx51d3Ydwg8tekiJ7kTd+r1bIy/87JwRLklffCHNnWv+s3GjOV88O9vsCB81ygy2O7vCJSnhf6NEamvNP+vrpZYW87Lb3XXs2rXSb35jXp86VTr1VPMYT/T871DcnycAAAAAEGTR8zdCAABiyPaB5aKNi9TmbdOUgin6zcG/0aHFh0oyA8xTJ56qM184U6+veF2SdOs8M7i86dCb+hR+ul1u3fPRPWrztumCPS6IvNCzo6MrpP7FL8xg/Jtvum7/+mvzz7feksaONbvDjzii63afzwzFt2wxrycnmxt4jh5tXjcMaf166Y9/lFatkhITzc7x9HTzdleEvR/dcJ4AAAAAQGgZfnZcAgAgrLpvtFjbUqv0xHQN+dMQVTVV6a/f/6t+POPHkiSf3xw50hlUnvzcyXptxWuBx7npkJt006E3SbIGqd19V/GdnlnyjM6Zco4m5UXYnO3uneOnnCK9+mrXbcOGmWF2ebl5TFNT121PPCGdf37X+BS/3+wIf+1/789BB0k332x2n1dXS7ffLn32mbk557Rp5nEjRoTtZfYH5wkAAAAAhB4BOQAADpn52ExVNlXq7Vlv65yXztGyimX6+idfa0z2mB4bJHYPSvsbfrZ725XgTgjxK9pF3ceknH229PzzZvf3uHHSz34mnXCCVFAgff65tHy59KtfmWF5pz/8Qbruuq4O8L/+Vbrssq7bBw82u8sbGro2+czPlz78UJowITyvMQji/jwBAAAAgBDid2cBAHDAFf+5Qp+Wfao11Wt0zDPHqKS2RB6XJxBOGurakNLj8gQ2ZHzlrFd04oQTA7f9/sPf6/dzfy9JgTEadhwJPf/5T7Njuzed4fgDD5id4x6PdOih0v/9n/TjH0tDhpjh9z77SOeeK513npSa2nX//Hzz9s7w+6c/la6+uuux6+rMWeR+v/nYo0dLH3wQVeF4XJwnAAAAAOAgAnIAABxw2sTTNHPETCW6E7Wmeo0qGitU11KnRRsXSZIMw+hxfDDCz7A67TTp4oul1avN695eamprk+bNM4Ps1FTp8svNkFyS2tvNPzs6pD//WXrnna4xK48/bm7aKXUF7ZJ0993mOJUJE6SkJPO+u+8uXXKJ9N570sSJwX6lIRXz5wkAAAAAOIwRKwAAOOTjDR/rqneu0pebvpTP75PP79NJu52k+46+T8WZxbb36esYje6jN8Lummuke+81L3/ve9L775uXu49U6TR/vrlhpiRddZX0pz+Zlzs37uzokB580AzEv/3WvO2JJ6QLLjAvV1RIf/mLtO++0jHHdD3u+vXmxp21tdLkyVJKihmYR6GYPU8AAAAAIALwNyIAABwyc8RM3Xf0fdpzyJ6BkHJ+6Xy9teotNbQ12N5nZx3C1793vSQ5G3p2jjAxDGnOHOnOO7uub6+xUUr431iPzk0z29p6D8f/8Y+ucPyzz8yO89//3gzNOzq6us5HjjSD8QMOkDIzozYcl2L4PAEAAACACMDfigAAcFD38DPRnajq5mr9Yf4f9Pbqt9Xa0Wp7nx2Fn3d/dLcqGivk6C+I/ehHZnDdWcO770orV5qXt6+rrMwMtQ3DnCkuSYmJ5ppdOH7++V33bWqSXn7ZvLx4sRmsJ8TmDO2YPE8AAAAAIAIQkAMA4LDO8HOPwj2U5ElSeX25fvnOL/XWqrf6HH4eWnyoJGnJpUuUl5ZnmU0dNp2B69lnm5trSubGmP/6l3l5+7pyc7vut2CBebm1VXrood7DcZ/P/LOgQEpLMwP1khLznxgWU+cJAAAAAEQIZpADABAhOmdNL96yWK0drRqeMVz/d8z/6bhxxynJYz8ipPus6Q11GzRi8Ihwltw7n0+66SZzw0zJHJnyxhvSUUf1PG79emnmTGnTJmm33aTXXzdnlj/4oLR0qXnM9uG4q9v3+wcfLC1caAbsixZJM2aE/KU5LabOEwAAAABwGB3kAADsIp/fZ7kcjO+bOzuEpxVMU5InSWXbynTl21futEPY6/NKUuSEnn6/GWLffLMZfktm5/gzz0ibN/c8LitL2n9/8/jly6XDDus9HO98XMkMypuapG3bJLfbnDGelhauV9gnnCcAAAAAEPkIyAEA2EV+v18+v09t3ja1edskKTCqonso2h/9CT/dLveAnjPoDEPyes3g+t57peJic6b4W291jUzxes3jBg2Szjmna2xKWVlXOP788z07xzvHgXQG5cuXS999Z27Oud9+Zgd6BOE8AQAAAIDIx4gVAAD6qLKpUnNL5urNVW9qZdVKNbQ1KDslWweOOFCnTTpNk/MmK8GdIL/fP+DZzv0ZoxGR6uqk226T/vpXqbFRmjTJnDWemdkVokvmOJZbbzUvu91m5/m8eeb1jo6u43w+8/L69dIVV0ivvSYNHiz96lfSNdeYxzg8V5vzBAAAAACiBwE5AAB98MgXj+idNe/o5e9etr19Yt5EnTHpDP3m4N8ErVPXLvy8/9j7dczYY5TsSQ7Kc/TL5ZebQfdll3WtbT8bvLvFi6WTTjJD7YQE6dprpd/+1hyL4vebgfaGDdItt0iPPWbexzCkgw4yx62MHCllZHQ93tq10h//aM4r37pV2nNP6dVXpWHDQvea+4jzBAAAAACiCwE5AAA7cc271+jRLx9VfWu9JCkvLU+tHa1KdCeqsqkycNy1+1+ra/a/RnlpeUF77u3Dz5SEFD3/g+f1/fHfD9pz7JJzz5X++U/z8nnnSd//vnT66eb1zrDbzgsvSGecYV7ebz/p8cfNkSjdu8i//lr6v/+Tnnyy637Dh0tTpkiHHy61tkotLdKjj0rV1VJbm5SfL334oTRhQkhe7q7gPAEAAACA6ENADgDADlz4yoV6avFTchkupSem68czfqwTxp+g3NRcdfg69I+v/6EFGxYoJyVHvz34t5o5YmbQa/h4w8e67v3rtHD9QknS8suXa3zO+KA/z06dfbY5Fzw52QyqJWnoUHOTzT//WcrLMzvEu+sMzdvapKuvlh56yFy/9NKuy927z1etkh5+2Hw8O4ZhPqbHI40ZI73ySkSE45wnAAAAABCdCMgBAOjFL9/5pf78yZ/lcXk0c8RM/XTGT3X2lLMtx31R/oU8Lo+mFU4LWS3zSufp1nm36v5j7tfEvIkhe55elZdLF14o/fe/ZpidlCQ1NXXdPnGi2V1+8snm5U7du8rfflv65S/NzTUTEqQnnjA36Nz+OEl66inpvvukkhJzjnl3U6aY41euucbcANRhnCcAAAAAEL0IyAEAsPG3z/+ma967Rs3tzTqo6CBdt/91OnbcsZIkr88rt8stn98nl9Fz7nb3tWBswthdc3uzUhJSgvZ4u+zvf5d+/GMzyN57b2naNOm558zRJ21t5maZeXnSHXdIRxzRNTe8o8Ps+JakG26Q7rrLDNl/8APpnnvMMSp2SkqkTZukN96Q6uulnBzzOU47zbycmhqWl70jnCcAAAAAEN0IyAEA2E5lU6V+8sZP9PJ3Lys/LV8PHPuATp9sztneUZjZGYj2xi4ojQrdu7tnzZKefdYMvF95RSoslP7yF/NydXXXfS68UDr6aOnMM83r3ceoHHus9M47UmKiNHu2OcO8+3PsaJZ5BOE8AQAAAIDoR0AOAMB2Xln+ik59/lRJ0u8O+Z1uPvRmSb2Hntuv/2fVf7Sudp3W1qxVojtRwzOG6/RJpysnNSd6g8/OzTS/+kq66CLpm2/MDvEvvpCKiqQFC6Sf/9zs+m5sNO+TnS2dcor0u9+Zm2kmJZnrb75pziNfudJcnzdPGr+DWdkRGphzngAAAABA9PM4XQAAAJGi8zvj/679ryRpTPYYnTf1PEm9d/V2hp4bt23Uf9f9V08uflJzS+Zq+++f//Txn/TL/X6pEyecqBGDR4T4lYSA+38dz+PHS4ccIi1dao49ue02c0zKoYdKH34o/fWv5qzxBQvMjvLHHpOWLDFnk59/vrmp5377mTPEV66Utm0zN+W85RZp0CD7546wcJzzBAAAAABiB+1JAAD8j2EYMgxDa2vXSpKa2puUmmDOue4eevr8vsBlv/z6aMNHOu1fp+nqd6/WB+s+kCFDHpdHSZ4kuQyXPC6P1tWs0y3zbtFfP/+ryuvLw/vCgsXvl9LSzO7v/Hzz+sKF0rJl5u3Z2dK115pjU664QkpPN8eqfPaZdPfd0gknSJ98Ys4Pv/lmMyxvaZHef19as8Z8DK/XsZfXV5wnAAAAABA7CMgBAOim3duuDl+HJCktIU0el/nLVl5fV3DbGYIuWL9Av53zW33vye/ps42fqa6lTpIZjHb4OuRxeZSSkBJ4vIrGCj3x9RNasH5B4LioYhjmLPGRI6X77jMD8NWrpVtv7TomIcEcufLnP0tPPGF2jbvdUk2NOZ7lyCOlX//a3Lhz9mxzTMvSpdLvf2/e3+02g/cIx3kCAAAAALGBgBwAgG4S3AmakDNBkrS6erXuWniXJAU2VezwdajD16E7F9ypn7zxE9390d1q87bJ7XIHAs7jxh2nmw65SUsuXaLPf/S5Xj7zZc0cMVMpCSna3LBZt3x4i+pb6yNvzvSCBVJTU9d1n00w27nR5t57S4cdZgba778v3XVX1zGdXeCnnWaOX3npJam42Byh0tgo3XuvOcd8yRLpyiullBTp1Velhx4y7xdhI1XsxPV5AgAAAAAxhBnkAABsZ0r+lMDlv3/1d7V0tOiH038ot8utF5e9qM/KP9M7q9+R1BWIugyXxueN14XTLtRVM6+SIUNul1s+v08TcicoPy1fP3zth1pRuULratfp042f6ojRRzjy+mz94AdmkH3ZZdKJJ0pHHWWG4T5fVyje3ahR0llnSa+/bl5//31zzvj48ebxnRtrZmebo1UmTTI7xl99VVq8WJo/X/r8c2nPPaXCQmndOuk//5G+/32zAz1CN+bsLi7PEwAAAACIMYZ/+92hAACAzvj3GXph2QuB64OTB6vD16HGtsbAmtvlltfnVZInSdfuf62OGXuM9h+xf4/H6dycUZIeXvSwfvbWz8zLxz+sn+z1kzC8kj740Y/MzTQlsyN87Fgz/L7ppq5jugfW3S9feaX0wAPm5TvvNGeQb6/z+OZmqaJCuv566a23zE0+JSkxUWprMy/fc4/0y18G/zWGSFydJwAAAAAQg/idXQAAbFy575U6euzRgesNbQ1q97bLZbgC3cD5afk6tPhQzbtwnm457JZA6Nl9ZrRhGIG51MWZxRqcPNhyjKNaW82O8UmTzJniXq+0cqU5E/zUU6U5c8yxKIbRNTrFMLrmhJ91ljRjhnn517+WPvjA+hydYXpKijm//Nlnpfvvl84801xvazM3/5TMzTwbGqJiDrkUR+cJAAAAAMQoRqwAAOKKz+8LzHTuvNy9e7fTfsP30z1H3qOh6UM1+5vZ6vB1yCszwBycNFgHFx2sn8z4ifYaupfy0/J7PO72M6M7g9Lq5urABo1ZKVkhfZ19lpRkjkCZOVP6y1+kN9+UFi0yQ+1XXpFWrTI31rz+eqmgwHr/vfYy7/vFF+ZolaeflnbfXcrLs38+r9fsUr/gAumkk8zHvvlmqazMDNDff98M6h3GeQIAAAAA8YERKwCAuNTa0apEd2KPwLO3IPSjDR9pU/0m1bXWqTC9UEWDizQ5f3LgdrvgtLvOx7157s26bd5tchkuvXveuzq0+NCQvLY+Oeccs0v72We71lpbpfXrzQ03n3zSDLP9fnMESlGR9Oij5szwzgC7o0PyeMwO85kzpW+/NeeJP/usdMghXWH49rafL75okRnMn322NGFCaF/3Lor78wQAAAAAYhwBOQAgLlQ2VWp+6Xy9u+Zdrapepcb2RhWmF2po+lAdMPIAHT7qcBWkd3VId4622L7Lt7vu3cC96QxFv936rQ564iDVtdTpqDFH6e1z3w7OC+uP886TnnnGDKnvv1/62c+sofWTT5obcL70UtdaZqb04x+bnd8zZ5prbW1mgP788+Y88q1bpenTpXnzzLEpO9pss/ttnWG7wzhPAAAAACC+OP83UQAAQuzBzx7Ue2vf0+srXre9/Ymvn1B+Wr4u3+dyHT7qcE0fMj3QIdzd9h3AfQ09S2tL9es5v1ZdS50K0gt05uQzbR8vLDrD8YQE6bDDpDFjzPXOOrqPQPne96Tvf1+66iqpqUmqrTUD9TfflH7+czMsT0w077fXXtIee0jvvistXSrde6904407Dr27v/YICMc5TwAAAAAg/tBBDgCIab94+xd6/KvH1dDWIElK8iTJbbhlGIYa2xp7HDsoaZBGZY7SNftfo3OnntvrY/YlsOzsGi6tLdUfF/xRL333kiqbKnX46MM1+5TZKkwvHPiL21Wd4bjHY4bfV10lHX209bjtu76/+cacT/7++9LatV3rl15qjkWZOdMM1V9/3ewul8wRK088IRUXSz6fOZ88gnGeAAAAAEB8IiAHAMSsi169SE9+/aQMw5AhQz/d66c6cOSBmpAzQe2+dn1R/oXW1qzVI18+opaOFrV72wP3vevIu3TVflcFNk60s2D9AqV4UjRj6AzbMHTp1qX67Qe/1YL1C1TZVKnizGK9d957GpM9JmSvuVc7Csf7MgalutrsDL/pJmnu3K7b991XOuII6Xe/M7vSf/tb6fbbzduuucacZ76z53AY5wkAAAAAxC8CcgBATDrv5fP0zDfPyO1ya1rBNF2292X64fQf2h77efnneuKrJ/Tayte0cdvGwPp1B1ynq2derby0PNv73DbvNr224jXd/r3btfewvXXgyAPl8/v00YaPtLxyuX4z5zfa1rpNklSQXqA558/RxLyJoXnBO9LfcLw3t9wi/fe/0vz55nW32+wif/xxacsW8/b33pOSk6V//lM6+eSgvpxg4jwBAAAAgPhGQA4AiDmdoafH5dEhxYfoin2u0AkTTpAkeX1e227fisYKfVj6oX4z5zdaWbUysH7t/tfq94f9Xsme5MBaQ1uD/jD/D7pjwR2SJMMwlOJJUX5avgzDUEltiQwZ8vl9SvYka0LuBD132nOakDshxK/cRjDD8c755JK0eLH04ovSbbeZ41N8PmnSJHPDz6++MoPxlhbp/PPNLvLc3OC/tgHiPAEAAAAAEJADAGJK99Dze6O+p6v2u0pHjzUD4Z3NhPb6vPpi0xe65LVL9O3WbwPrt33vNv3qoF8FHqPd165Xlr+in731M1U1VUkyw8/OH6mdlyfkTtAhRYfoVwf9SiMHjwzVS+5df8Lx7dd3dv2NN6RHHpE+/liqqpKKiswNOz/8UKqslFJSpJdeMp83gsascJ4AAAAAACQCcgBADBlI6Nnd0q1LdeYLZ2pZxbLA2lOnPGXZkHF93Xr99oPfannlci3ZskRul1sdvg5NLZiqyXmT9eMZP9bE3IkanDw4eC+yrwYSjm/ebIbdkyf3fnz3tXXrpE8/NeePr1kjJSZKbW1dfxYWmrePGBG617sLOE8AAAAAAJ0IyAEAMWHWS7P07JJnA6HnlfteqWPHHStp10LPTvNL5+uS1y/RqqpVkqTRWaP19KlPa7/h+0mSfH6fXIZL7d52tXnbtLJqpZI8SfL5fRo5eKQykjKC+wJ3xUDD8T/8QXrzTXOzzbPO6v15tn+sigrphhukjz6SVqwwn7+jwwzIFy2Shg0L7uvsB84TAAAAAEB3BOQAgKh30nMn6fUVr8tluLR7/u76+4l/115D95LUv9BTktq97Xrpu5f0y3d/qU31m5SSkKKr9rtKt33vtkDoubPH7u9zD8isWdKzz/Y/HL/7bnMkSmmplJ0trV0rDRq089EonfPJm5ulBQukp54yQ/rMTHMzz85udAdxngAAAAAAtudyugAAAAbiy01f6r0170mSXIZL32z5Rutq1kkyu3f7K8GdoEOKD9Hx445XgjtBze3Nevyrx1VeXy6XYf743FmoGfbQ89xzzXDc7ZYOPVT65S93LRy/6y7p3/82w/HMTOn996WMjL7NDXe7zcdKSZGOPFKaPVt66CGzmzwCwnHOEwAAAACAHQJyAEBUm5w3Wf86/V8anTVaHb4OSdKZL5yp2Ytny2W4emyKuKsK0wt1yZ6XKMmdJEna3LBZTy1+SpL6/Zghc/vt0j//aXaOe71mUH3UUeZtPl/fwvEXXpDKysyO8blzpT32MB+rrzqfw/e/wPnSS6XddhvIqwoazhMAAAAAgB0CcgBAVEvyJOmoMUfpvqPvU1FmUWD9glcu0DPfPCNJAwo/9xm2j67d/9rA9cVbFgceM6JceKF0+OHmzG9Jev116corzZEnLpsf9zsKx+fPl6ZO7Rqbsqvsns9hnCcAAAAAADuR9zdYAAB2UaI7UUePPVr3H3N/j/DzvJfPC0r4OSlvUuAxlm5dqprmmoEXHWzDhpljTb73va61Bx6Qrr9eamvreez2M8eDGY5HMM4TAAAAAMD2CMgBADEhlOHnEaOP0IjBI+T3+1XXWhcY0RERur+ewkJrSP7gg9I113SF5J3jVrrPHN/VcLzzOZuapNbW4L+mEIrb8wQAAAAAYIuAHAAQNexCy+5roQo/PS6PPC5P4Pm8/l2Yyx0qf/yj9PXX1tniQ4b0HpK3tJjjTzZt6v9Ylc7u87Iy6b77zIC9tjYUr7DfOE8AAAAAAH1FQA4AiBqd85y9Pm+va6EIP5dWLFVVU5UMw9BuubspOyV7wK9lQGbNkn79a+m666T166239xaS//rXUmmpdM89AwvHN240H+Oee6TLL5deeaVnJ7vDOE8AAAAAAH3lcboAAAB2pq6lTh+XfawF6xdoydYlau1oVWZypoakD9GRY47UHoV7aOigoYHjO8PPB459QFf85wqV1JZIMsNPv/w6d+q5gfBzR5so+vw+uQyXllcuV0Nbg/x+vybmTlSiOzHUL7l3550nPfusGVQnJkpbt0ojR1qPGzJEevpp8/j//tdcu+8+ac4cqbLSDLkHDZLmzdv1cPyuu6SXXpLq6qSEBOmgg6yd7A7gPAEAAAAA7CrD39+dqAAACINHvnhE7619Ty8ue9H29gR3goYNGqb/nv9fjcoa1eO2Nm+b3l3zbo/wU5KeOuUpnTv1XEnqNfzsXF9ZtVLfe/J7Kq8v15SCKXr+B89rt9zddhqahsR550nPPCN5PGZ3+C9+IR1zzI7vs3lzV0huGF2d3oMGSQsWSFOm7Ho4/uKLUnm5lJNjBuwTJwbtJfYX5wkAAAAAoD8YsQIAiFhX/udKXfPuNT1Cz0R3otwuM8w1DEPt3nZNzJsov6zf9ya6E3XUmKN0/7H3qzizOLB+/svn6+lvng48xvbfFXeGmuvr1ut3H/xOmxs2KzUhVYcUHaLhGcMD9wur7cPxq67qCsd39F1358adhx9uHud2m3PIjzvODMf9fvN6b3oLx7OypA8/jIhwnPMEAAAAANBfjFgBAESkC1+5UE8tfioQMP5w+g+1R+Ee2nPInqpurtanZZ/q6y1f653V7+j4ccdrdNZo28fpHn527xA+/+XzJckyRqPzz43bNuruhXdrbslc+fw+TcybqGv3v1bpielhef092IXjRx9t3tYZYO9IZ0jefdzK889L2dnSvfdKSUn299tROD5/vjRpUvBeYz9xngAAAAAABoIRKwCAiPOLt3+h+z+9Xx6XR/sN30+X7HmJzp92vuW4hrYGratZpykFUyT1PgZD6tsYjQ5fhzwujzZu26i7Ft6lF797UeX15Ro6aKjmXDBH43PGB//F7sxAw/Huuo9b6fSzn0l/+pM5z7y7HYXjCxZEROc45wkAAAAAYKAIyAEAEeWRLx7Rte9dq8a2Rh0w8gDdcMANOnbcsZIkr88rt8ttG3B23rYjvYWfT578pM6bdp4kaUPdBt3z0T2B0DMrJUsLLlqgiXkOBMIDDcftjulLSB4F4TjnCQAAAAAgGJhBDgCIGFVNVXpnzTuqb61XWmKafjLjJ4HQ0+/395gpvb2dhZ5S77OmL3jlAr2w7AV5fd7ICT37E453fuddX2/+aRjmBpzddZ9J3umhh6Srr5ZaW7vuV1YWseE45wkAAAAAIFjoIAcARIyXv3tZp/3rNEnSNftfo7uOvEvSjkdi7Ehv9+utQ/jIMUfqu4rvVLatTFkpWZp/0XxNynN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zz8eXlZVp4cKFgevHHHNMKMoCAFu1Te3a2tBmWR+fTwf5QE0ssI5Y8fnNLySmDRvsQEUAAAAAAKCvCMj7afr06Zo+fXqfjm1padHpp58euH7yySdr1qxZoSoNACzsNug0JI3NpYN8oHLSElUwKElb6lt7rC/bQkAOAAAAAECkY8RKGNxxxx1auXKlJGn48OH67W9/63BFAOKN3fzxEVkpSk10O1BN7JlYYO3EX8ZGnQAAAAAARDwC8hD75JNP9NxzzwWu//GPf1R6OiMNAISX3fxxNugMnkmF1jEr3xGQAwAAAAAQ8QjIQ6i9vV0333yz/H6/JHO0yj777ONwVQDikd2IlfH5jFcJlkk2c8hLa5rV0NrhQDUAAAAAAKCvCMhD6KmnntK6deskSampqbr22msdrghAPGrt8KmkutmyTgd58OxmM2JFkpZvsX4xAQAAAAAAIgcBeYjU19frb3/7W+D6JZdcotzcXAcrAhCv1lU1yuvzW9bpIA+ewSkJGp6ZbFlnDjkAAAAAAJGNgDxEHnvsMdXV1UmSsrOzddFFFzlcEYB4tXKrdYPOrJQE5aYlOlBN7JpoM2Zluc3sdwAAAAAAEDkIyEOgpaVFzz77bOD6RRddpNTUVAcrAhDPeps/bhiGA9XErt3yrWNWVhCQAwAAAAAQ0TxOFxCLXnvtNdXW1kqSMjIydM455zhaj8tlKDubUQoID5fLCPzJeRcZ1tVY549PGZHlyL+fZo/9l4WZmanKzoju82XvsbnS/HU91jbUNispLcn+c9Hc+3uhOP7sxPI5gp74eQFY8bkArPhcAFZ8LsKr8/1G7CIgD4Gnn346cPnUU09VerqzG+EZhiG3mw8zwovzLjL4/X59t8k6B3v3YYPldof/l4h6e0632+VIPcE0ZXimZc3vl1ZubdBexdmStvtc7OC96O22eBDL5wjs8fMCsOJzAVjxuQCs+FwAwUFAHmSfffaZVqxYIcn8D9XZZ5/tcEVmQOaz2aAPCAWXy5BhGJx3EWJDdZPqWzss6xMK0uX1+sJeT2/P6fX6HKknmDKSPRoyOFmb6lp6rH9TVqsZRVnWz0V2jvT5F9YHys6Rovy9GIhYPkfQEz8vACs+F4AVnwvAis9FeHW+34hdBORB9tprrwUu77XXXiouLnaumP/x+fyqrrZu0geEQnZ2mtxug/MuQny+utKylug2lOk2HPn3U9vQZL9e26SUjug/X8blplkC8q/WVeuCmcX2n4uR46wP0tAuqT20hUawWD9H0IWfF4AVnwvAis8FYMXnIrw632/ELn5XOYj8fr/mzp0buH7iiSc6VwwASFpTaQ0bR+WkycMMtZCYkG+d/8dGnQAAAAAARC4C8iD65ptvVFFRIUlyu9066qijHK4IQLxbU2ntJhiTa78JIgZuQr51z4m1VU1q62A0CAAAAAAAkYiAPIjmzJkTuDx9+nRlZmY6VwwASFpTZQ3Ix+ayy3mo2AXkHT6/Vm21bpQKAAAAAACcR0AeRPPmzQtcPuSQQxysBACkdq9PJdXNlvXRBOQhUzAoSYOTrdt7LCvf5kA1AAAAAABgZwjIg6SxsVErVqwIXN9zzz0drAYApNKaZnltdjQfk8OIlVAxDEPjbbrIl26igxwAAAAAgEhEQB4k3377rbxeryRz/vjkyZMdrghAvFtrM388LdGtgkFJDlQTP+zGrCzbRAc5AAAAAACRyPp74OiX7t3j48aNU0pKioPVAEBvG3SmyTAMB6qJH7vZBOTfbdpm6eY3GuqV8uhfLcc2/+in8qcPCll9AAAAAACgCwF5kKxevTpwefz48Q5WAgCmNZVNlrUxuYxXCTW7DvKmNq9KqhpVnN31/hv19Ur7462WY1vOmkVADgAAAABAmDBiJUjKy8sDl4cMGeJgJQBgWlNl00GewwadoTYiK0XJHuuP16Vs1AkAAAAAQMQhIA+SysrKwOWCggIHKwEAqbndq421LZb1sXkE5KHmdhkal2ezUWd5nQPVAAAAAACAHSEgD5Kmpq5RBoWFhQ5WAgDSuqom+W3WR+cwYiUcJuRbv4hYRgc5AAAAAAARhxnkQfLuu+86XQIABKy22aAzOzVBWamJDlQTf+zmkC8t3ya/3+5rCwAAAAAA4BQ6yAEgBq2xCcjH5DJeJVwmFFgD8urGNm3eZh17AwAAAAAAnENADgAxaG1lk2WNgDx8xuSkye0yLOuMWQEAAAAAILIQkANADFpTZdNBzvzxsEn0uGznvS/bREAOAAAAAEAkISAHgBhT19yuioY2yzod5OFlN4f8WzrIAQAAAACIKATkABBj1lZZx6tI0uhcOsjDyS4gp4McAAAAAIDIQkAOADFmtc0GnUMzkpSW6HGgmvhlF5CX17aotrndgWoAAAAAAIAdAnIAiDFrbALy0YxXCbtxefbv+cqtDWGuBAAAAAAA9IaAHABizFqbgJz54+GXnuTRiMxky/oKAnIAAAAAACIGATkAxBC/3681NjPIxzB/3BF2Y1YIyAEAAAAAiBwE5AAQQyob27StpcOyPiaHDnInjLcJyFdutXb4AwAAAAAAZxCQA0AMsZs/7jak4mw6yJ1g10FeWtOklnavA9UAAAAAAIDtEZADQAxZXWkdrzIyK1WJHv5z7wS7gNznl1ZV0EUOAAAAAEAkIDEBgBhi10HO/HHn5KQlKjct0bK+qoI55AAAAAAARAKP0wUAAILHLiAfncv8cSeNy0tTZWNbj7WVFY3yD81U+x7TLcf7PQnhKg0AAAAAgLhHQA4AMcLn92ttlXXEyhgCckeNy0vXxyU1PdZWVTTKnzdOte9+6FBVAAAAAABAYsQKAMSM8roWtXb4LOtjchix4qTxedYvKFZXNMrn9ztQDQAAAAAA6I6AHABihN14lUS3oeGZKQ5Ug07j8q0BeVO7V+V1LQ5UAwAAAAAAuiMgB4AYsabSOl5lVE6a3C7DgWrQaWRWqhI91h+3KyusX2gAAAAAAIDwIiAHgBix2qaDfGwu41Wc5nEZGp+fbllftbXBgWoAAAAAAEB3BOQAECPsRqywQWdkmDgkw7K2ig5yAAAAAAAcR0AOADGgw+vT+ppmy/roHALySLBb4SDL2qoKOsgBAAAAAHAaATkAxICy2hZ1+PyW9dGMWIkIE4dYA/Lyba1qaO1woBoAAAAAANDJ43QBAICBW1tt3aAzJcGlgkFJDlSD7e1WaB2xktdQo8J9plo28Kx567/yFxSEqzQAAAAAAOIaATkAxICSKmtAXpydKpdhOFANtjc4JUHDMlO0sbZrDI7L71XKpjLLsYbPK+vvAgAAAAAAgFBgxAoAxIC1VdYNH0flMF4lktiNWQEAAAAAAM4iIAeAGLDOpoN8VDYBeSSZOMQ6ZgUAAAAAADiLgBwAopzX51dpTbNlnQ7yyEJADgAAAABA5CEgB4Aot2lbi1o7fJb1UTlpDlSD3hCQAwAAAAAQeQjIASDK2Y1XSXQbGjo42YFq0Jui7FSlJrqdLgMAAAAAAHRDQA4AUc4uIB+ZlSqPy3CgGvTG5TI0oYCNOgEAAAAAiCQE5AAQ5dZV22zQyfzxiDShkIAcAAAAAIBIQkAOAFHOroOcgDwyTSQgBwAAAAAgohCQA0AU8/v9KrHrIM8mII9Eu7FRJwAAAAAAEYWAHACi2NaGNjW2eS3rdJBHpt3oIAcAAAAAIKIQkANAFFtX1WhZcxvSyKwUB6rBzqQneTRscLLTZQAAAAAAgP8hIAeAKLbWZv748MwUJbj5z3ukGpeX5nQJAAAAAADgf0hQACCK2c4fZ7xKRBufl+50CQAAAAAA4H8IyAEgiq2z6SAfTUAe0eggBwAAAAAgchCQA0CU8vv9tiNWignII9q4fAJyAAAAAAAiBQE5AESp6qZ2bWvpsKyPziaAjWRDM5KVluh2ugwAAAAAACACcgCIWnbzxw1JRdkp4S8GfWYYBmNWAAAAAACIEB6nCwAA9I/deJUhg5OVnEB3cqQbl5euFevSdcPRlwfWRuem6ew9h8mXMdjBygAAAAAAiC8E5AAQpdigM3qNy0tTc2KyntvjmMBaTlqiTjlvPwerAgAAAAAg/jBiBQCi1DqbESujsgnIo8F4mxErVY1tqm1qd6AaAAAAAADiFwE5AEQpuw7yYjrIo8Lo3DQZNuurKxvDXgsAAAAAAPGMgBwAotC2lnZVNbZZ1hmxEh1SEtwalplsWScgBwAAAAAgvAjIASAK2XWPS1IxI1aixthc65iV1RUE5AAAAAAAhBMBOQBEIbuAPD89UelJ7L0cLcbYBeR0kAMAAAAAEFYE5AAQhWw36GS8SlSx6yBfW9Uon9/vQDUAAAAAAMQnWg0BIArZdZCPyrEGrohc4zLcOnDdV5b1TVsna1hBlgMVAQAAAAAQfwjIASAK2QfkdJBHk5G+Zj39r99a1l8/53ACcgAAAAAAwoQRKwAQZRrbOrS5vtWyPooNOqOK22XYrtuNzwEAAAAAAKFBQA4AUaakutl2nQ7y2FBKQA4AAAAAQNgQkANAlCmxGa+SnZqgzJQEB6pBsNn9+wUAAAAAAKFBQA4AUWatTYBazHiVmFG+rUUt7V6nywAAAAAAIC4QkANAlLEbwcF4ldjh80sljFkBAAAAACAsCMgBIMrYhad0kMeWVRWNTpcAAAAAAEBcICAHgCjS4fWprK7Fsl6cneJANQiV1ZUE5AAAAAAAhAMBOQBEkQ21LfL6/JZ1OshjyxoCcgAAAAAAwoKAHACiiN14lZQEl/IHJTlQDUJldSUzyAEAAAAACAcCcgCIInYBeVFWqlyG4UA1CJWqxjbVNLU5XQYAAAAAADGPgBwAokipXUDO/PGYxBxyAAAAAABCj4AcAKJISXWzZY3547GJMSsAAAAAAIQeATkARAm/3287YoWAPDatqaCDHAAAAACAUCMgB4AoUdXYpsY2r2WdgDw2MWIFAAAAAIDQIyAHgCixzqZ73GVII7KYQR6L1lQ2yuf3O10GAAAAAAAxjYAcAKKE3fzxoYOTleThP+WxqKXDp421LU6XAQAAAABATCNVAYAoUcr88bjDmBUAAAAAAEKLgBwAooTdBp1FWQTksYyAHAAAAACA0PI4XQAAoG/sRqwUZzN/PFr5hgxVxdZtges3v71Cby7d0uOYNQTkAAAAAACEFB3kABAFmtq82lLfallnxErsGJubZllbXUFADgAAAABAKBGQA0AUWF9jHa8iScU5BOSxYpxNQL6htlkt7V4HqgEAAAAAID4QkANAFFhnM388MyVBmSkJDlSDUBiTZw3IfX77f/cAAAAAACA4CMgBIAowfzz25aTaf+HBmBUAAAAAAEKHgBwAokCpTRdxEfPHY4phGBqba/13upqNOgEAAAAACBkCcgCIAiU2ATkbdMaeMTZzyNcQkAMAAAAAEDIE5AAQ4bw+v9bXMGIlHoyzmUO+ihErAAAAAACEjMfpAgAAO7ZpW4vavX7LOh3k0c2ortKga6/qsXZiS7senDhLtSkZgbXqpnbVNLUpKzUx3CUCAAAAABDzCMgBIMLZjVdJdBsakpHsQDUIFqO1VUmvv9JjLV9S8rjTLceurmzU3iMJyAEAAAAACDYC8iBavny5Xn75ZS1cuFBbtmxRS0uL8vLyNH36dJ1xxhnad999nS4RQBQqqbaOVxmZlSq3y3CgGoRaYUayNm/3CwOrK5u098gsZwoCAAAAACCGEZAHQUdHh+6++27Nnj1bXq+3x20bN27Uxo0b9cYbb+jkk0/WrbfeqsREugAB9F1Jld0Gncwfj1XF2Sn6uqrn2hrmkAMAAAAAEBIE5APk8/l03XXX6c033wysFRcXa9KkSTIMQ59//rm2bNkiSXrllVfU1tam++67z6lyAUQhuxErRcwfj1nF2alSVWuPtVWVBOQAAAAAAIQCAfkAzZ49OxCOJyQk6LbbbtNJJ50kwzBHH3R0dOivf/2rHnjgAUnSW2+9peOPP15HHHGEYzUDiC52ATkbdMauUTmp0qqeAfnaykb5/H65DMbqAAAAAAAQTC6nC4hmlZWVuv/++yVJLpdLDz74oE4++eRAOC5JHo9Hl19+ub7//e8H1p588smw1wogOtU2tauupcOyPoqAPGbZ/XZAS4dPG2tbHKgGAAAAAIDYRkA+AC+88IIaGhokSSeffLIOPfTQXo+9+OKLA5e/+OILtbQQdADYObvucUkayQzymDU0I1lJHuuP59WMWQEAAAAAIOgYsTIAb731liTJ7Xbryiuv3OGxkyZN0uWXX660tDRlZ2dbNvMEADt2AXnhoCSlJLgdqAbh4HYZGpWdquVbG3qsr65s1GHjch2qCgAAAACA2ERA3k+bNm3SihUrJEnTp09XYWHhTu/z85//PNRlAYgxJdXNljXmj8e+MXlp1oC8gg5yAAAAAACCjREr/dQZjkvStGnTHKwEQCyz6yAvYrxKzBuXm2ZZY8QKAAAAAADBRwd5P61cuTJwuaioSJLU3NysV199VW+++abWrl2rbdu2KS8vT/vuu6/OOeccTZkyxalyAUQpu4CcDvLYN9YmIC+rbVZLu1fJjNcBAAAAACBoCMj7qaysLHA5NzdX33zzja699lqVlJT0OG7jxo166aWX9PLLL+uiiy7StddeK5eLxn0AO9fa4VN5nXVD31E5BOSxbkyeNSD3+aV11U2aWDDIgYoAAAAAAIhNBOT91NjY9avuGzZs0HXXXaeGhga53W7tueeeGjFihOrq6rRo0SJt27ZNfr9fjz/+uOrr63Xbbbc5WDmAaLGhpll+m/UiOshjXk5qgjJTElTb3N5jfXVFIwE5AAAAAABBREDeT90D8nvuuUft7e2aNm2a7rrrLhUXFwdua2pq0p133qnnnntOkvTvf/9b++yzj0488cSw1epyGcrOtnYjAqHgchmBPznvBqZy4zbLWnqSR+OGZ8owDAcqGrhmj324n5mZquyM2D1fbD8Xzb2/F8pJ125DBumTtdU9bitraIv5z1W8niPxiJ8XgBWfC8CKzwVgxecivDrfb8QuAvJ+amnpGnvQ3t6ucePG6R//+IdSU3v+xT41NVW///3v1dbWppdeekmSdP/99+v444+X2x2eObKGYcjt5sOM8OK8G7iSKuv88TH56fJ4oncGtdttP2LK7Xb1elss6fG52MF7IbdLuxVmWALylVsaYv59ivdzJB7x8wKw4nMBWPG5AKz4XADBQUDeTwkJCT2u//rXv7aE491de+21evPNN9Xa2qoNGzboq6++0l577RXqMiVJfr9fPp/doAYg+FwuQ4ZhcN4FwZqKBsvaqJxUeb0+B6oJjt5q93p9Uf26dsb2c+H1ye6rDq/XJ3l9mlCQbrlt+eZtMf0+SfF7jsQjfl4AVnwuACs+F4AVn4vw6ny/EbsIyPspLa3rV1iys7M1c+bMHR6fnZ2tvffeWwsWLJAkffHFF2ELyH0+v6qrG3d+IBAE2dlpcrsNzrsgWLm53rI2JC0xqt/X2gZrV7wk1dY2KaUjel/Xzth9Lly1TcqxOba2tkm+lEYNSbH+iK5saNOashplpSaGuGLnxOs5Eo/4eQFY8bkArPhcAFZ8LsKr8/1G7OJ3lfspPb2rs2/s2LF9us+YMWMCl7ds2RL0mgDEDr/fr9Jqa1BYlJ3iQDVwwujcNNn9L9jqSv4HGAAAAACAYKGDvJ9GjhwZuNzXWeIpKV3BVnNzc9BrAhA7qhrb1NjmtawXZfc+ygnRxZ+SouZzL7Bdl6SUBLeGZSarrLalx+2rKhq198issNQIAAAAAECsIyDvp/Hjxwcu97UbvK6uLnA5IyMj6DUBiB2lNdYv0QxJIzLpII8V/swsNdz7wA6PGZubZgnI19BBDgAAAABA0DBipZ+mTJkil8t8+9atW9enkHz58uWBy30dywIgPtmNVxkyOFlJHv6zHU/G5qZZ1lZX2s/oBgAAAAAAu46kpZ9ycnK07777SjJnBb/44os7PL6srEyLFy+WJBmGoQMOOCDkNQKIXiXV1g7yoiy6x+PN2DxrQL62slE+PzvVAwAAAAAQDATkA3DGGWcELj/yyCNat26d7XF+v1+33XabfD6fJOmAAw7Q0KFDw1IjgOhUWmPtEi5m/njcGWPTQd7S4dPG7cauAAAAAACA/iEgH4DjjjtO++23nyRz081zzz1XH330UY9jGhoadO211+qDDz6QJCUmJupXv/pV2GsFEF1K7TrIs+kgjzcjMlNsx+qsYg45AAAAAABBwSadA3TXXXfp/PPPV0lJiSorK3XRRRdp/PjxmjBhglpaWvTJJ5+ovr4+cPy1116rMWPGOFgxgEjX1uHTpm3WDuGiLDrI443bZWhUdqqWb23osb6molHfG5frUFUAAAAAAMQOOsgHqKCgQE8//bQOPPDAwNrKlSv1+uuv67333guE44mJibrzzjt1/vnnO1UqgCixobZZPpsR03SQx6cxNnPIV9NBDgAAAABAUNBBHgR5eXl67LHH9OGHH+r111/X119/rYqKCknSyJEjdeCBB+rcc8/VsGHDHK4UQDQorbbOH09NcCs3LdGBahAyPp+M+m2WZf+gDMnV9f31OJs55ATkAAAAAAAEBwF5EB1yyCE65JBDnC4DQJQrrbGfP24YhgPVIFRcWzYrZ9pulvWqxcvlG9K1kfNYm4C8rLZZLe1eJSe4Q1ojAAAAAACxjhErABBh7DrIi7KZPx6v7Eas+PzS2irreQIAAAAAAHYNATkARBjbDvIs5o/Hq5zUBGWmJFjW1zBmBQAAAACAASMgB4AI4vf7VUIHOboxDENjc63//plDDgAAAADAwBGQA0AEqW5qV0Or17JOB3l8G2Mzh5wOcgAAAAAABo6AHAAiSGmNtXvckDSSgDyu2QfkzCAHAAAAAGCgCMgBIIKUVlvnjxdmJCk5we1ANYgUY20C8srGNtU2tztQDQAAAAAAsYOAHAAiiF1AXpTF/PF4N9pmBrnEmBUAAAAAAAaKgBwAIojdiJWibMarxLu0RI+GZiRZ1gnIAQAAAAAYGAJyAIggpdXWgHwkHeSQ/Rzy1QTkAAAAAAAMCAE5AESIdq9P5XUtlvViOsghaWyeTUBewUadAAAAAAAMBAE5AESIstoWef3W9aJsOsghjcmxBuRrqxrl99ucNAAAAAAAoE8IyAEgQpTYjFdJSXApPz3RgWoQacbYdJA3tnm1ub7VgWoAAAAAAIgNBOQAECF6mz9uGIYD1SDSFGelyOOyngurK5hDDgAAAABAfxGQA0CEKK1ptqwxfxydPG6Xim3G7axho04AAAAAAPqNgBwAIkRptTUgL8pi/ji6jMm1ng+rCcgBAAAAAOg3AnIAiAB+v1+lNdYRK0V0kKObMbnWOeRrKq3nDQAAAAAA6BsCcgCIALXN7drW0mFZp4Mc3Y21CchLqpvU4fU5UA0AAAAAANGPgBwAIoDdeBVJGkkHObqx6yDv8Plt59cDAAAAAICd8zhdAABAtuNVCgYlKSXB7UA1CAdfTq5q3vnAdr03QzKSlJboVmObt8f6mspG2/AcAAAAAADsGAE5AEQA+w066R6PaYmJ6pg+Y5fuYhiGRuekacmmbT3WV1c26qhg1gYAAAAAQJxgxAoARICSarsNOpk/Dqsxudbzgo06AQAAAADoHwJyAIgAdjOk6SCHHbuNOldXNjpQCQAAAAAA0Y+AHAAc1uH1aWNdi2W9iA06YWNsnjUgL69rUWNbhwPVAAAAAAAQ3QjIAcBhZXUt8vr8lvViRqzAxpgc+80411UxZgUAAAAAgF1FQA4ADrPboDPJ41L+oCQHqkGky0xNUE5aomV9dQVjVgAAAAAA2FUE5ADgsFKbDTpHZqXIZRgOVINoMNZmo07mkAMAAAAAsOs8ThcAAPGutMYakBdlMV4l1hkN9Ur+x+OW9ZYLfyh/+qAd3ndMbpo+La3tsbaGESsAAAAAAOwyAnIAcJjdiJViNuiMeUZ9vdJv+a1lvfW00/sUkG9vDSNWAAAAAADYZYxYAQCHldZYA/IiNujEDoy1CchrmttV1djmQDUAAAAAAEQvAnIAcFBtc7tqm9st60V0kGMHRuekym5C/RrmkAMAAAAAsEsIyAHAQXYbdErmJp1Ab5IT3BqemWxZZ6NOAAAAAAB2DQE5ADjIbrxKXnqi0hLZIgI7ZjuHnIAcAAAAAIBdQkAOAA6y26CT+ePoC7s55Gsq7X8jAQAAAAAA2CMgBwAHra+xBppFjFdBH9h1kK+tapTP73egGgAAAAAAohMBOQA4qMRmBjkd5OgLuw7y5nafyutaHKgGAAAAAIDoREAOAA7p8PlVVmsNM+kgR18Mz0pRotuwrK+uYA45AAAAAAB9RUAOAA4pr2tRh886DqMom4AcO+dxGSq2+W2DNVUE5AAAAAAA9BUBOQA4pNRmvEqSx6XCQckOVINoNDbPOmZldQUbdQIAAAAA0FcE5ADgELv54yMyU+R2WcdmAHbs5pCvqaSDHAAAAACAviIgBwCHlNY0W9YYr4JdMcYmIF9f06S2Dp8D1QAAAAAAEH0IyAHAIettOsjZoBO7wi4g9/rtfzsBAAAAAABYEZADgEPsO8itmy4CvclPT9SgJI9lfTVjVgAAAAAA6BMCcgBwwLaWdlU3tVvWCcixKwzD0Nhc6zmzppIOcgAAAAAA+oKAHAAcUFpt7R6XGLGCXTeajToBAAAAAOg3AnIAcEBpjbXDNyctUek24zKAHRlrE5AzYgUAAAAAgL4hiQEAB9h1kNM9Hl/8ngR1TJ5iu74r7ALyLfWtamjt4AsXAAAAAAB2gr85A4AD7DboLGb+eFzx5+Wp5oOFA36c0TYzyCVzzMq0YYMH/PgAAAAAAMQyRqwAgANKqq0jVoqy6SDHrstITlB+eqJlnTErAAAAAADsHAE5AISZ1+dXWa3diBU6yNE/Y/PsNuq0fgkDAAAAAAB6IiAHgDDbtK1F7V6/ZZ0OcvTXmBw26gQAAAAAoD8IyAEgzOw26ExwGxqSkexANYgF9h3kjfL7rV/EAAAAAACALgTkABBmdvPHR2SmyO0yHKgGsWBMrjUg39bSocrGNgeqAQAAAAAgehCQA0CYldbYbdDJ/HH0X3F2qtw2368wZgUAAAAAgB0jIAeAMLMbsVKUxfxx9F+Sx6URNufQ6goCcgAAAAAAdsTjdAEAEG9Ka2wCcjbojDvGli3KPPFoy3rta+/IX1Cwy483NjdNJdt9+bKGDnIAAAAAAHaIgBwAwqihtUNVNnOhixmxEncMn1eedWtt1/uzteaY3DS9v7Kyx9qaSus4HwAAAAAA0IURKwAQRqU2G3RKUlEWATkGxm6jznXVTfL6+hO3AwAAAAAQHwjIASCM7MarZKcmaFAyv9CDgRlrE5C3dvi0odZ6zgEAAAAAABMBOQCEkV0HORt0IhiGZSYryWP9sb6WOeQAAAAAAPSKgBwAwsiug3wk88cRBC7D0Ogc67m0moAcAAAAAIBeEZADQBiV2HSQs0EngsVuzMpqNuoEAAAAAKBXBOQAECZen18bbDrIi7MZsYLgGJtnDcjX0EEOAAAAAECvCMgBIEw2bWtRm9dvWaeDHMEyxqaDvKy2WS3tXgeqAQAAAAAg8hGQA0CYlFZbu8cT3IaGZCQ7UA1ikV1A7vNL62xG+wAAAAAAAAJyAAgbu/njIzJT5HYZDlSDWJSTmqDMlATLOmNWAAAAAACwR0AOAGFSWmMNyIsYr4IgMgxDY3Ot59TqCjrIAQAAAACwQ0AOAGFSYjNihQ06EWx2Y1boIAcAAAAAwB4BOQCESanNiBU26ESw2QbkVQTkAAAAAADYISAHgDDY1tKu6qZ2y3pRFh3kCK6xNgF5RUOb6pqt5x8AAAAAAPGOgBwAwqDUZryKxAxyBN9omxnkkrSaMSsAAAAAAFgQkANAGNht0Jmblqj0JI8D1SCWpSV6NDQjybK+ppKNOgEAAAAA2B4BOQCEgd0GnUVs0IkQYaNOAAAAAAD6hoAcAMKADToRTnYBOSNWAAAAAACw4nf7ASAM7GaQM388vvkGZaj+j/fYrg+U3Uadayob5ff7ZRjGgB8fAAAAAIBYQUAOACHW4fVpQ61NQJ7FiJW4lp6ulot/HJKHHpNnDcgb27zaUt+qwozkkDwnAAAAAADRiBErABBiG+ta1OHzW9YZsYJQKcpKkcdl7RRnzAoAAAAAAD0RkANAiJXWWLvHkzwuFWYkOVAN4kGC22W7CezqCgJyAAAAAAC6IyAHgBCz26BzZFaKXMyCRgjZziGvsp6LAAAAAADEMwJyAAixEpuAvCiL8SoIrTG9bNQJAAAAAAC6EJADQIiVVltHrBTbjL8AgskuIF9X1aQOr8+BagAAAAAAiEwepwuIBWeffba+/PLLPh9/6KGH6m9/+1sIKwIQSWw7yNmgEyFmN2Klw+fX+tpmjc6x3gYAAAAAQDwiIB8gv9+vlStXOl0GgAhV29SuupYOyzod5FBrqxI+/8yy3L7XPlLSwDdwHZKRpNQEt5ravT3WV1c0EpADAAAAAPA/BOQDVFZWpoaGBklSfn6+DjvssJ3eZ8KECaEuC0CEKK2x3xRxJDPI456rukqZpxxvWa9avFy+IUMH/PiGYWhMbqqWbKrvsc4ccgAAAAAAuhCQD9CKFSsClw8++GDdcsstDlYDINLYjVfJT09UaqLbgWoQb8bkptkE5PZf2gAAAAAAEI/YpHOAli9fHri82267OVgJgEhkv0En3eMID7s55KvpIAcAAAAAIICAfIAIyAHsCBt0wkljbALyjXUtamrz2hwNAAAAAED8ISAfoO4jVpgtDmB7pTV2HeRs0InwsOsgl6R1VXSRAwAAAAAgEZAPSGNjozZs2CBJGjZsmDIyMhyuCEAkaff6tLHWGpDTQY5wyUxNUE5aomWdMSsAAAAAAJjYpHMAVqxYIb/fL6mre7ysrEwLFy5USUmJfD6f8vPzte+++2r33Xd3slQADiirbZHXb10vyqKDHOEzNjdVVY1tPdZWs1EnAAAAAACSCMgHpPv88ZSUFF166aX64IMPAqF5d7vvvrt+97vfadq0aeEsEYCD7OaPpyS4lD8oyYFqEK/G5Kbp09LaHmt0kAMAAAAAYGLEygB0D8jffPNNzZkzxzYcl6Rvv/1Ws2bN0htvvBGu8gA4rNRug86sVLkMw4FqEK/sNupcS0AOAAAAAIAkOsgHpPsGnZI0fvx4XXLJJdpvv/2UlZWlzZs367333tMjjzyi2tpatbe364YbbtCQIUM0Y8YMh6oGEC4lNht0FrFBJ8LMbqPO6qZ2VTe1KTvVOp8cAAAAAIB4QkDeT36/XytXrgxcP+6443THHXcoKalrdMLIkSN18cUX6+ijj9b555+vjRs3qr29Xb/5zW/05ptvyuUKTwO/y2UoO9sakACh4HIZgT/j/bzbuK3FsjZx2OC4fl+aPfYblGZmpio7I3bfF9vPRXPv74WCeI7MSE+WYUjb/4LTllavxg6PvPc8Xs+ReMTPC8CKzwVgxecCsOJzEV6d7zdiFwF5P/n9fj366KPasGGDtmzZoosuuqhHON7d8OHDdc899+jss8+WJK1du1Zz5szREUccEZZaDcOQ282HGeEV7+ed3+/X2grrGIuxBYPkdsfvdKveXrvb7YqL96XH52IH70Vvt/VHeopLRdmpKqnqOfJn9dZGHTw+P2jPEyzxfo7Eo3j/eQHY4XMBWPG5AKz4XADBQUDeTy6XS3vttZf22muvPh2/5557as8999SXX34pSZo3b17YAnK/3y+fz342OhBsLpchwzDi/ryrbGjVtpYOy3pxdqq8Xp8DFUWG3l671+uL6ffF9nPh9cltc6zX65OC/F6MLxhkCciXb9oWke95vJ4j8YifF4AVnwvAis8FYMXnIrw632/ELgLyMNpnn30CAXn38Syh5vP5VV3NhmwIj+zsNLndRtyfd1+X1VrWDEmDXYrr96W2wbpxqSTV1jYppSN23xe7z4Wrtkk5NsfW1jbJlxLc92JEhvU3nJZurIvIczFez5F4xM8LwIrPBWDF5wKw4nMRXp3vN2IXv6scRoWFhYHLtbW1zhUCIORKqq0bdBZmJCk5wa5nGAgtu40611Y1yrf9YHIAAAAAAOIMAXkY+XxdvxqekJDgYCUAQq202toFW5Rtv/kgEGp2AXlzu0/lddaNZAEAAAAAiCeMWOmn7777Tt9++60qKys1dOhQnXTSSTu9z9atWwOX8/LyQlkeAIeV2nSQFxOQwyHDs1KU6DbU5u3ZMb6mslHDM1McqgoAAAAAAOcRkPfThx9+qPvuu0+SNH78+D4F5J3zxyVp6tSpIasNgPNK7DrIswgi4QyPy9ConDSt2NrQY31VRaMOGZvrUFUAAAAAADiPESv9NG3atMDllStXatWqVTs8fvXq1Vq0aFHg+pFHHhmy2gA4q7XDfnQFHeRw0tg865iV1ZVs6AMAAAAAiG8E5P20zz779Nh088477+wxY7y71tZW/epXv5L/f5uh7bPPPpo8eXJY6gQQfhtqm2W39WFxNh3k6OIrHKKKjVWWf3yFQ0LyfONs5pCvqiAgBwAAAADENwLyfnK73frFL34RuD5//nxde+212rZtW4/jysrKdPHFF2vx4sWSpMTERN18881hrBRAuNlt0JmW6FZOWqID1SBiGYaUkGD9xzBC8nR2HeRltc1qafeG5PkAAAAAAIgGzCAfgFNOOUWLFi3Siy++KEl644039MEHH2jfffdVZmamNm7cqC+++EIdHR2SJI/Ho/vuu09jxoxxsmwAIWY7fzw7VUaIgk+gL8bZBOQ+v7S2qkmTCgc5UBEAAAAAAM4jIB+g2267TQUFBfrb3/4mr9erxsZGzZkzx3JcYWGhbr31Vh188MEOVAkgnEqrmy1rjFeB07JTE5WdmqDqpvYe66srGgnIAQAAAABxi4B8gFwul6688kqdcsopevbZZ/XJJ5+orKxMzc3Nys7O1ujRo3XUUUfpxBNPVHp6utPlAggD2w7yLDbohPPG5qbps/W1PdZWsVEnAAAAACCOEZAHyciRI3X99dc7XQYAh/n9fq2voYMckWlsnjUgX13R4EwxAAAAAABEADbpBIAgqmxsU2ObddPDomw6yOE8uznkqyoa5ff7HagGAAAAAADnEZADQBDZjVdxGdKITDrI4bxxudZRX3UtHapsbHOgGgAAAAAAnBexI1YWLVqkvffe2+kyAGCX2G3QOXRwshI9fB+JnoyaaqXfeK1lveGPd8uflR2S5yzOSZXbkLzbNYyvrmxUXnpSSJ4TAAAAAIBIFrEB+XnnnaehQ4fqxBNP1EknnaRRo0Y5XRIA7BQbdKKvjJYWJb/0b8t64023KlQDT5I8Lo3MTtW6qp7n6eqKRs0sDk0oDwAAAABAJIvolsZNmzbpb3/7m4477jidfvrpeuaZZ1RTU+N0WQDQK7sO8iI26EQEGZtrP4ccAAAAAIB4FLEB+aRJk+T3+wP/fPvtt7rtttt00EEH6dJLL9U777yjtjZmpgKILKU11g7yYjboRASx26hzdSUBOQAAAAAgPkXsiJWXXnpJJSUlev311/Xmm2+qpKREktTR0aG5c+dq7ty5GjRokI499lideOKJmjFjhrMFA4h7Le1ebdrWalmngxyRxK6DfF1Vk9q9PiW4I/Z7cwAAAAAAQiKi/yZcXFysn//853r77bf14osv6qKLLlJhYWGgq3zbtm3617/+pXPPPVdHHnmkHnzwQa1fv97psgHEqdIa63gViQ5yRBa7DvIOn992PBAAAAAAALEuogPy7iZPnqzrr79ec+fO1ezZs3XGGWcoMzMzEJZv2LBBDz30kI4++midddZZeu6551RXV+d02QDiSKnNBp0ZyR5lpSQ4UA1gr2BQktKT3Jb1VZUNDlQDAAAA/D979x0e112m//8+U6QZjXq33GRb7i1OcRppDm0hPRBIKPmxu7C7kJBlNyEkEDoLgS89kAAhG2IglAAJaSwJISENOy6JuyV32bJ6HUmjaef3h2wR5RzZsjyaM+X9ui5dkp4zGj2W56jc8znPBwCclTYB+eudccYZ+uIXv6gXXnhBP/rRj3TVVVepoqJiJCx/9dVX9YUvfEHnnXeebrzxRj399NOKRqNOtw0gw9lu0Fnil2EYDnQD2DMMQ3NtxqzsarM+wQMAAAAAQKZL2Rnk4+HxeHTBBRfoggsukCRt27ZNjz76qH7+858rEokoHA7r6aef1tNPP62SkhJdddVVuu6661RTU+Nw5wAykd0GnTMZr4IUVFeRr42HekfVdrGCHAAAAACQhdI6ID+qsbFRf/3rX/XXv/5V69atUzQalWEYIyvKJamzs1M//elPdf/99+v666/XTTfdpJycHIc7B5BJ9tmsIGf+OFJRXbn1cbmrrd+BTgAAAAAAcFbaBuSNjY168skn9fjjj6u+vn6kfjQQd7lcOvPMM3XZZZepr69Pjz32mDZv3qxoNKr77rtPr776qu677z7l5uY69U8AkEHipmk7g3xmid+BboBjq6vIt9Rag2F1D0ZUzMx8AAAAAEAWSauAvKWlZSQU37Jly0j9aCguSfPmzdNll12mSy+9VFVVVSP166+/Xjt37tTnP/95bdy4URs2bNA999yjm266Kan/BgCZqbVvSKFo3FJnBTlS0RybFeSStLu9X6dNL05uMwAAAAAAOCjlA/LOzk796U9/0hNPPKENGzaMhOGvD8UrKyv1zne+U5dffrkWLFgw5n3Nnz9fP/nJT/S2t71NHR0devzxxwnIASSE3QadbpehacU+B7oBji2Q49HUIp8O9YRG1RvaCMgBAAAAANklZQPy3/3ud3riiSe0Zs0axWIxSaND8by8PL31rW/VZZddprPPPluGYYzrfvPz83XWWWfp8ccfV3Nz86T0DiD77LMZrzK1yCeP2+VAN8Dxza0IWAJy5pADAAAAALJNygbkn/70p0c22jzK7XbrnHPO0eWXX643v/nN8vkmtjLT4xn+Z5eVlSWkVwDY38UGnUgvdeUBPburY1RtVzsBOQAAAAAgu6RsQC79Y8X44sWLddlll+mSSy5JSKgdCAR0xRVX6Kyzzjrp+wIAyX4FeW0pG3Qidc2tCFhqu9v7FYubcrvGd1UWAAAAAADpLmUD8pqaGl166aW6/PLLNXv27ITe92c/+9mE3h8A7LcJyGeWsIIcqWtOuTUgD0XjOtQT0owSntwBAAAAAGSHlA3In3nmGadbAIBx6Q9H1RoMW+ozWUGOFDat2K9cj0tD0fio+q62IAE5AAAAACBrpOzucbfddptuu+02PfzwwxP6+B//+Me65JJL9O53vzuxjQHAGxywmT8uSTOZQY4U5nYZtqvIG9ioEwAAAACQRVJ2Bfkf/vAHGYah3NxcXXHFFSf88QcOHNCuXbtUUlKS+OYA4HXs5o8X+70q9nsd6AbpwvT7FXrPdbb1ZJlbHtC25r5RNTbqBAAAAABkk5QNyE/WwYMHJUkDA9bgCgASaX+ndQU5G3TieMziEvV9/x5He6iz2aiTFeQAAAAAgGziaEC+a9cu/elPfzrmbbZs2aK7SBY33AABAABJREFU7rpr3PcZiUS0fft2rVmzRpJUUVFxUj0CwPGwQSfS1VybgPxQT0gD4ZjyctwOdAQAAAAAQHI5GpDPnDlTTzzxhPbu3Wt73DRNbd26VVu3bj3h+zZNU4ZhaNWqVSfbJgAc036bGeRs0Il0YDeDXJJ2t/draU1hkrsBAAAAACD5HN2k0+v16gtf+IKk4UD79S9HvbE+3hdJWrRokT7+8Y878m8DkB1icdN+BTkbdCINFPu9qsjPsdQbmEMOAAAAAMgSjs8gP+OMM/TlL39Zhw8fHlW/6667ZBiGFi9erAsvvHBc92UYhrxerwoKClRbW6uVK1fK7eYScQCT53BvSOGYaanPIiBHmqgrD6gtGB5V28UccgAAAABAlnA8IJekq6++2lI7Ond8yZIluuGGG5LdEgCMy94O6+rxHLehmiKfA90AJ25uRUAv7+saVdvVFnSoGwAAAAAAkislAnI7Z5xxhiSptrbW2UYA4Bj22YxXmVGSJ7fLcKAb4MTV2WzU2dDeP7KXBwAAAAAAmSxlA/LVq1c73QIAHJfdCvJaxqtgPOJxadC6wav8fsmVvC1C5pbnW2rBoZha+oZUXciVEAAAAACAzJayATkApAO7FeSzyvwOdIJ042ppVtnyBZZ6x2s7FJ9Sk7Q+Zpb65XEZisZHz9Lf1d5PQA4AAAAAyHiOBuSvvPLKqPePjlWxO3YyXn+/AJAopmlqX6d1BTAryJFOvG6XakvztKt99MacDW39etPsMoe6AgAAAAAgORwNyD/wgQ+MzDc1DEPbtm2zPXYy3ni/AJAoHQMR9Q1FLXUCcqSbuoqAJSDf1dY/xq0BAAAAAMgcyRtyOgbTNEdejnXsZF4AYDLss5k/bkiaUcKIFaSXueX2G3UCAAAAAJDpHF1BfqzRJ4xFAZDq9trMH68p8snndTvQDTBxdRXWgPxA54CGonHlehx/Lh0AAAAAgEnjaEC+evXqCR0DgFRgt4J8VhnjVZB+5toE5DFz+DE+vyrfgY4AAAAAAEgOloUBwATZrSBn/jjSUXkgR0U+63Pmb5xLDgAAAABApiEgB4AJ2mcTkM8iIEcaMgzDdhV5Axt1AgAAAAAynKMjVhLlxRdf1Nq1a9Xf36/a2lq9853vVElJidNtAchgwaGo2oJhS31mKRt0Ij3NKQ9oXWPPqNqu9qBD3QAAAAAAkBwpH5AHg0GtXr1azz77rL7whS9owYIFI8cGBwd100036fnnnx/1Md/85jf1yU9+Utdee22y2wWQJexWj0vMIEf6YgU5AAAAACAbpXRAvnv3bl1//fXq6OiQJO3du3dUQH7nnXfqb3/7m+XjBgcH9cUvflGSCMkBTAq7gLw0z6tCn9eBboCTV1dh3YyzcyCijv6wygI5DnQEAAAAAMDkS9kZ5PF4XB/72MfU3t4u0zRlmqYaGxtHjjc3N+s3v/mNDMOQYRiaM2eOPvjBD+qMM86QJJmmqf/3//6f2tranPonAMhgezsGLTVWjyOdzSnLk2FTZ6NOAAAAAEAmS9mA/Mknn9S+fftkGIYCgYC++MUvjloN/thjjykej0uS6urq9NBDD+n222/X6tWr9ZGPfESSNDAwoEceecSR/gFkNrsV5LVs0Ik05vO6Nb3EOkN/F2NWAAAAAAAZLGUD8r/+9a8jb//gBz/QNddco4KCgpHa008/PfL2e9/7Xvn9//ij/qabblJZWZkk6dlnn538ZgFkHbuAfBYBOdKc3RxyVpADAAAAADJZygbkmzZtkmEYWrhwoc4666xRx7q6urRp06aR9y+66KJRx91ut1asWCHTNHXgwIGk9Asge4SjcR3sto5YqWXECtJcXblNQM4KcgAAAABABkvZgLyzs1OSNGvWLMuxl156SfF4XIZhaMaMGaqpqbHcpri4WNJwmA4AiXSge1Bx01pnxArSnV1AvqejX1G7BzwAAAAAABkgZQPyUCgkSfL5fJZjzz///Mjbb1xdflR7e7skyePxTEJ3ALLZvg7reJVAjluV+TkOdAMkTp3NiJVwzFRjl/WKCQAAAAAAMkHKBuSlpaWSpObmZsux1wfk5513nuW4aZpqaGiQJFVUVExShwCy1V6b+eMzS/NkGIYD3QCJU1PkU57Xbak3tAUd6AYAAAAAgMmXsgH5okWLZJqmNmzYoJ6enpH6iy++qI6ODklSTk6OzjnnHMvHPv744zp06JAMw9CiRYuS1jOA7GC3gnxWqd/mlkB6cRmG5tjNIWejTgAAAABAhkrZ+SMXX3yxnn32WYVCIX34wx/WbbfdplAopDvuuEOSZBiGLrzwQuXljZ75+8c//lGf+9znRt5/61vfmtS+AWS+fTYryJk/jhMVLy1T16N/tq07aW5FQJsP946qNbBRJwAAAAAgQ6VsQH755Zfr7rvv1uHDh7V582Zdd911o467XC59+MMfHnl/06ZN+o//+A91dnbKNE0ZhqH58+fr7W9/e7JbB5DB4qap/TbzmGeVEZDjBOXmKnqm/T4aTrKbQ76bFeQAAAAAgAyVsiNWcnJy9MMf/lAlJSUyTXPUi2EYuvnmm7VkyZKR2wcCgZHRK5I0ZcoU3XXXXXK5UvafCCANHe4NaSgat9RZQY5MUWczYuVw75CCQ1EHugEAAAAAYHKl7ApySVqwYIEee+wx3XfffVqzZo0GBwdVV1en6667TitXrhx125kzZ8rtdsvn8+nqq6/WjTfeqIKCAoc6B5Cp9nVYV497XIamFjODHJnBLiCXpF1t/TplWlGSuwEAAAAAYHKldEAuSaWlpbr55puPezuPx6MHH3xQCxYsUE5OThI6A5CN9trMH59e4pfHZTjQDZB4BT6Pqgty1dw3NKre0E5ADgAAAADIPBk1f2TZsmWE4wAm1b4Oa0A+i/EqyDB2c8gb2oIOdAIAAAAAwOTKqIAcACab3QryWjboRIaZaxuQs1EnAAAAACDzpPyIldcLBoMaGBhQLBaTaZrj/riamppJ7ApAtjBNU/tsAnJWkCPTzK3It9R2tfUrFjflZpwQAAAAACCDpHxA3tDQoB//+Md6/vnn1dPTc8IfbxiGtm3bNgmdAcg2XYMR9YailjoBOSYkGJT/Fz+zlAffd72Ubw2ok2mezQryUDSug92DmsnjHQAAAACQQVI6IH/iiSf0yU9+8oRXjAPAZNhrM39ckmaW+pPcCTKBq69X+XfcZqkPXXal4g4H5NOK/fJ5XApF46PqDW39BOQAAAAAgIySsgF5c3OzPv3pTysajcowhi/nNgxDBQUFCgQCIzUASBa78SpTCnPl87od6AaYPG6XobqKgLYc7htVb2gL6s3zKxzqCgAAAACAxEvZgPwXv/iFBgcHZRiGiouLdeutt+riiy9WQUGB060ByFJ2K8hrWU2LDDXXJiCvZ6NOAAAAAECGSdmA/Lnnnht5+wc/+IFOPfVUB7sBAPuAfFYZATkyk91GnfWtQQc6AQAAAABg8ricbmAsTU1NMgxDy5cvJxwHkBL22oxYmU1Ajgxlt1FnazCsnsGIA90AAAAAADA5UjYgj8eHNwabNWuWw50AgNQXiqotGLbUZ5VZQ0QgE9TZBOTS8EadAAAAAABkipQNyKuqqiRJ/f38IQ7AeXarxyVpFjPIkaECOR5NK/ZZ6vVtjFkBAAAAAGSOlA3Izz33XJmmqfXr1ysajTrdDoAst7fD+mRdRX6OCnwpu5UDcNJs55CzghwAAAAAkEFSNiC/5ppr5PF41NnZqZ/97GdOtwMgy+2x26CT1ePIcHNtxqw0sFEnAAAAACCDpGxAPm/ePH384x+XaZr69re/rfvvv1/hsHX+LwAkw167gJwNOpHh5tmsIN/bOaBoLO5ANwAAAAAAJF7KzgbYunWrzj33XO3evVuPPPKI7rzzTt19991asWKFZs6cqUAgIJdrfPn+DTfcMMndAsh0dgH5bAJyZLh5ldYV5JGYqX2dg2Nu4gkAAAAAQDpJ2YD86quvlmEYkiTDMGSapnp6evTcc8+d8H0RkAM4Gf3hqJr7hiz1WWUEhMhs1QW5Ksj1qG9o9F4g9W1BAnIAAAAAQEZI2RErkmSa5siLXW08LwBwsvZ1DtrWGbGCTGcYhm0Q3sBGnQAAAACADJGyK8hZ9Q0gVeztsIaBpXleFfu9DnQDJNe8ioA2HuwZVatno04AAAAAQIYgIAeA42CDTmQzu406G9r6ZZrmyCg0AAAAAADSVUqPWAGAVLDHLiAvJSBHdphrs1Fn12BEHf1hB7oBAAAAACCxCMgB4DjsV5CzQSGyw+yygNw2C8XrmUMOAAAAAMgAKTtixc7LL7+stWvXqqGhQd3d3RoYGNDvf/97SVJvb6/uueceXX311ZozZ47DnQLIFKFITE09IUt9NiNWkCVyPS7NKM2zPFHU0Navc2aVOtQVAAAAAACJkRYB+Z///Gd985vf1IEDB0Zqb5x9euDAAd133326//77dfnll+szn/mMAgFWeAI4Ofs7B2Xa1JlBjpNluj2Kzl9gW0818yoCloCcjToBAAAAAJkg9f4Kf4M777xT999/v6ThUHwsBw8eHLnNww8/rG3btumBBx5QUVFRMtoEkKH2dFrHSBT5PCrN8zrQDTKJWVmprufXOt3GuMyryNf/7WgbVWtgxAoAAAAAIAOk9Azy//3f/9X//u//ShoOvufOnat/+Zd/0WmnnWa57ZQpUzR37tyREL2+vl633nprUvsFkHns5o/PLssbdQULkOnsNurc3zWgUCTmQDcAAAAAACROygbkLS0t+u53vytJcrvd+trXvqZHH31Ut9xyi+rq6iy3X758uR599FHdcccdcrvdMk1Tzz33nP7+978nu/VRvv71r2v+/PmaP3++1qxZ42gvAE4cG3QC0tyKfEstbkp7bM4PAAAAAADSScoG5L/61a8UCoVkGIZuueUWXXHFFeP6uPe973265ZZbRt5/5JFHJqnD49u0adPIeBgA6ckuAGT+OLJNeSDHdqxQQxtzyAEAAAAA6S1lA/Lnn39eklRaWqoPfOADJ/Sx73vf+1RVVSXTNLVx48bJaO+4wuGwbrvtNsViXH4OpKtwNK6D3YOWOgE5stHcCuuVE8whBwAAAACku5QNyA8ePCjDMHTaaafJ5TqxNj0ejxYvXixpeFSLE773ve9p165djnxuAIlxoGtQcZu9gWcTkCMLzbMZs1LfygpyAAAAAEB6S9mAvL9/eFVaQUHBhD6+qKhIkhxZwb1p0ybdd999Sf+8ABJrT4d1dWx+rlvlgRwHugGcZbdRZ0N7/8jm2AAAAAAApKOUDchLSkokSa2trRP6+L179466n2QJh8O6/fbbFYvFVFhYqNmzZyf18wNIHNsNOksDMgzDgW4AZ9lt1Bkciulw75AD3QAAAAAAkBgpG5DPnTtXpmlq/fr1CgZP7BLuXbt2adOmTTIMQ3Pnzp2kDu3dddddamhokCR96lOfUnl5eVI/P4DE2dtpDcgZr4JEMVpbVXLeSsuLMcEnhidbbYlfOW7rk0OMWQEAAAAApLOUDcgvvPBCSdLg4KC+853vjPvjBgcHdeuttyoej0uSzj///Enozt6WLVv005/+VJL0pje9SVdffXXSPjeAxNtjt4KcgBwJYsSi8uzcYXkxYlGnW7Plcbs0u8w6ZqW+jYAcAAAAAJC+UjYgf9e73qWKigpJ0i9+8QvdeeedCoVCx/yY9evX65prrtG2bdskScXFxUkLqcPhsG677TZFo1EFAgF96UtfSsrnBTA5orG4DnQNWuoE5Mhm8yutY1Z2tlpn9QMAAAAAkC48TjcwFr/fry9/+cv66Ec/qng8rvvvv18PPfSQVqxYoQMHDozc7v7771djY6PWrl2rXbt2SZJM05RhGLrjjjsUCFhXu02GH/zgB6qvr5ck3XzzzaqpqUnK5wUwORq7Q4rFrZsPMmIF2WyezUadjFgBAAAAAKSzlA3IJemCCy7QV7/6VX32s59VKBRSX1+fnn/+eUka2STvzjvvHLm9aQ6HWW63W5/61Kf0jne8Iyl9bt26Vffee68kaeXKlbr22muT8nkBTJ69HdZVsXlet6oKch3oBkgNdivIm/uG1D0YUbHf60BHAAAAAACcnJQdsXLUZZddpt/97ne68MIL5XK5ZJrmmC+SdNppp2n16tX6wAc+kJT+wuGwPvWpTykajcrn8+nLX/7ySHgPIH3ZzR+vLcvj/EZWq6sIyO4MaGAOOQAAAAAgTaX0CvKj5syZo3vuuUdNTU16+eWXtWXLFnV0dCgYDMrn86m4uFgLFy7UWWedpblz5ya1tx/+8Icjo1X+8z//UzNnzkzq5x8Pl8tQaWlyRs0ALpcx8jqdH3eHgmFLbcGUwrT+N6WCQY/9iJri4jyVFmbu19b2vBgc+2uhFH2clUqaWZanfW94AqkxGNHbEtRztj5GslGm/LwAEonzArDivACsOC+S6+jXG5krLQLyo2pqanT11VcnbePN49m2bZt+8pOfSJKWL1+u66+/3uGO7BmGIbebkxnJle6Pu102c5XnVRfI7U75C29S2lhfP7fblRVf21HnxTG+FmMdSwWLa4osAfmO5r6E/f9l+2MkG6X7zwtgMnBeAFacF4AV5wWQGGkTkEejUfX09MjlcqmgoEAej7OtRyIR3XbbbYpGo8rJydH//M//yOVKzT/cTdNU3GazQWAyuFyGDMNI68ddNBbXnnbrDPI55QHFYnEHOsocY339YrF4Rn9tbc+LWFxum9vGYnEphb8WC6oL9Pjmw6NqWw71JOz/L1sfI9koE35eAInGeQFYcV4AVpwXyXX0643MlZIBeU9Pj1588UWtWbNG69atU0tLi/r7R4dVPp9P8+bN0+LFi3X22WfrwgsvlNebvA3C7r77bu3YsUOS9NGPflR1dXVJ+9wnKh431dlpDfuAyVBaGpDbbaT14+5A16DCUWsQV57jStt/U6roDlpnu0tSd/eA/NHM/dranReu7gGV2dy2u3tAcX/qfi2mF+RYanvagmpq6ZXPaxf5n5hsfYxko0z4eQEkGucFYMV5AVhxXiTX0a83MldKBeQdHR2699579atf/UqhUGikfnQDztcbHBzUpk2btGnTJj344IMqLCzUNddco3/7t39Tfn7+pPa5fft2/fjHP5YkLVy4UB/+8Icn9fMBSC671eO5HpemFPoc6AZILfMrrDMOY+bwxraLqgsc6AgAAAAAgIlLmYD8L3/5i26++WaFQiHbQNzO62/X09Oje++9V7/73e/0jW98Q+eee+5ktaqnn35akUhE0nBYvnjx4nF93Ac/+MGRt1euXKnVq1dPSn8ATs7uDmtAPqs0T2425gBUnp+r0jyvOgcio+o7W4ME5AAAAACAtJMSAfkDDzygO++8U7FYbKTm9/t1xhlnaOnSpZo+fboKCwvl9/sVDocVDAbV09OjhoYGbd68WTt27FA0GpUkdXZ26iMf+Yi+/OUv68orr3TqnwQgje1pt454mF2e50AnQGqaV5mvv+/rGlWrt9nYFgAAAACAVOd4QP73v/9dX/3qV0feLykp0Q033KCrrrpKfr9/XPfR1tamhx56SPfff796enoUi8V0xx13aObMmTr11FMT3vOSJUv0nve8Z1y3feaZZ9TW1iZJuuiii1RZWSlJmjVrVsL7ApAYezqsAfmcMutYCSBbzauwBuQ7W5l9CAAAAABIP44G5PF4XLfffrtM05RhGDrttNP0/e9/X6WlpSd0PxUVFfqP//gPXX311brlllu0Zs0aRaNRffnLX9bvf//7hPd90UUX6aKLLhrXbffu3TsSkH/oQx/SmWeemfB+ACRONBbXvk5WkAPHMr/S+oTRrvagYnGTUUQAAAAAgLTicvKTv/DCC2pqapJhGFqwYIF++tOfnnA4/nqVlZW65557NHfuXEnD88HXr1+fqHYBZIHG7pCices+CLNZQQ6MmFdp3Qx7MBJXY/egA90AAAAAADBxjgbkTz755Mjbd9xxh3w+30nfp9/v10033TTy/lNPPXXS9wkge+yx2aDT73WpujDXgW6A1DS92C+/1/orBHPIAQAAAADpxtGAfMeOHZKGV34nclb4qlWrVFRUJEnatm1bwu4XQObb3W4NyGeXBeQyGBsBHOV2Gaort64iZw45AAAAACDdOBqQt7S0yDAM1dXVJfR+XS6XFixYINM0tX///oTeN4DMZrdB5+wy5o8Db2Q3h7y+jRXkAAAAAID04ugmncHg8B/S5eXlCb/vyspKSVJfX1/C7xtA5trTbrdBJ/PHkXjxgkIFv/RV23o6sJtDXt8aHNl4GwAAAACAdOBoQB6JRCRJubmJn+3r9/slSaFQKOH3fSJWr17t6OcHMH7haFwHuqwB+ZxyVpBjEuTna/DfPuZ0FxNmF5B3DkTU0R9WeT4z+wEAAAAA6cHRESumaQ434Up8G0dXrx39HABwPAe6BhWz+ZYxu4wV5MAbzSnLk9tmofjONuaQAwAAAADSh6MBOQCkkj0d1mAvkONWZX6OA90Aqc3ndWtmqfXqivpW5pADAAAAANIHATkAHLHbZoPOOeUB5ikDY5g/xhxyAAAAAADSBQE5AByxp926gnx2GfPHgbHYzSHfSUAOAAAAAEgjBOQAcMQemxXks8uZPw6MZV6F9fxo7A6pPxx1oBsAAAAAAE6cx+kGJOnRRx/V888/n9D77O7uTuj9AchsoUhMB7sHLfU5rCAHxmS3glySGlr7dcq0oiR3AwAAAADAiUuJgHxgYEADA9aVmwCQLPs7BxU3rXVWkGPSDA3J8+pGSzl6ygopN9eBhk5csd+rqoJctfQNjarXtwUJyAEAAAAAacHxgNw0bRIpAEiy3R3W+eNFPo/K8rwOdINs4OrsUMmlb7XUO17bofiUGgc6mpj5lfmWgJw55AAAAACAdOFoQP7Vr37VyU8PACPGmj9uGIYD3QDpY15FQH/b3TGqtrPV+oQTAAAAAACpyNGA/Morr3Ty0wPAiN3t1kBvNvPHgeOym0O+p6NfkVhcXjd7gQMAAAAAUht/uQKAxlhBXsb8ceB4FlRZA/JIzNSedvYWAQAAAACkPgJyAFlvMBJTU0/IUp9Tzgpy4HiqC3JV5LNekLajtc+BbgAAAAAAODEE5ACynt3qcUmawwpy4LgMw9B8mzErO1rYqBMAAAAAkPoIyAFkvT0288dL87wqzvM60A2QfhZUFVhqO1sJyAEAAAAAqY+AHEDWs50/Xs7qcWC87OaQ17f1Kxo3HegGAAAAAIDxIyAHkPV226wgn1PG/HFgvBbYjFgZisa1v5ONOgEAAAAAqY2AHEDWs11BTkAOjNvUYp8COW5LnTErAAAAAIBUR0AOIKsFh6Jq6Ruy1GezQScwbi426gQAAAAApCkCcgBZzW71uCTNLmcFOXAi7OaQ72AFOQAAAAAgxRGQA8hqe2zmj1fk56jQ53WgGyB92a0gr28NKm6yUScAAAAAIHURkAPIaswfBxLDbgV5fzimg90hB7oBAAAAAGB8CMgBZLXdNivI55Qzfxw4UTNL8pTrsf5asaOlz4FuAAAAAAAYHwJyAFmNFeRAYrhdhuZVWFeR72QOOQAAAAAghRGQA8ha3YMRtfeHLfXZZawgBybCdqPOFgJyAAAAAEDqIiAHkLXsxqtI0uxyVpADE2EXkO9sDcpko04AAAAAQIoiIAeQtewC8poinwI5Hge6AdLfgkprQN4Tiqq5b8iBbgAAAAAAOD5SIABZa5dNQF7HBp1IknhVtdr2HrYe8PuT30yCzC7Lk9dtKBIbvWJ8R0tQUwp9DnUFAAAAAMDYWEEOIGvtarNu0DmH8SpIFpdLCgSsL670/dHscbtsn2TawUadAAAAAIAUlb5/hQPASTBNU3s6WEEOJJrtHHI26gQAAAAApCgCcgBZqblvSP3hmKU+h4AcOCl2c8hZQQ4AAAAASFUE5ACy0q426+pxj8vQzJL0nf8MpIL5VQWWWkd/WO1BNuoEAAAAAKQeAnIAWWm3zQadtaV58rj5tgicjLrygNyGtb6dMSsAAAAAgBREEgQgK+2yCcjZoBM4ebkel2azUScAAAAAIE0QkAPISrvbByw1NugEEmO+zRxyNuoEAAAAAKQij9MNAECyRWNx7eu0BuRs0IlkMrq7lH/HbZZ68EtflVlc4kBHibOgMl+PbW0ZVWMFOQAAAAAgFRGQA8g6+7sGFY2blnpdBQE5kscYHJTv17+01Ptv/2z6B+RV1hXkLX1D6hoIqyQvx4GOAAAAAACwx4gVAFnHboPOQI5b1QW5DnQDZJ55lfmy2adTO1lFDgAAAABIMQTkALKOXUA+uywgw7CL9ACcKL/XrdpS66a325lDDgAAAABIMQTkALLOLrsNOiusYR6AiZtvM2aFFeQAAAAAgFRDQA4g6+yyWUFexwadQEIttAnItzX3OdAJAAAAAABjIyAHkFUGwjE19YQs9TkE5EBCLawqsNQO9w5v1AkAAAAAQKogIAeQVfZ0WFePSwTkQKLNr8yXy2asP3PIAQAAAACphIAcQFbZ1WYNyMsDOSr2ex3oBshceTljbdTJmBUAAAAAQOogIAeQVXZ3WDfonFPOBp3AZFhYbR2zsr2ZFeQAAAAAgNRBQA4gq9ht0Ml4FWByLLLZqJMV5AAAAACAVEJADiCr7LYZsVJHQA5MCruNOluDYbUHhxzoBgAAAAAAKwJyAFmjcyCsrsGIpc4KcmByzK0IyG2zUec2NuoEAAAAAKQIAnIAWcNug05D0uwyZpADk8HndWu2zRNQ25sZswIAAAAASA0E5ACyht388eklfvm8bge6AbLDIruNOllBDgAAAABIEQTkALLGbpuAnNXjwOQaa6NO0zQd6AYAAAAAgNEIyAFkjd3tA5YaG3QCk2uhzQryzoGIWvrYqBMAAAAA4DwCcgBZIW6a2tNhXUFeV0FADkymOWUBeW126mTMCgAAAAAgFRCQA8gKTT0hDUbilvqcMgJyYDLleFy2V2psY6NOAAAAAEAK8DjdAAAkg9388Ry3oWklfge6ASTT51Poqnfb1jPNouoCy4rx7S0E5AAAAAAA5xGQA8gKu2wC8lllAXlc1tEPQDKYJaXqu+enTreRFAttN+oMyjRNGQbnIAAAAADAOYxYAZAVdrVZN+icU57nQCdA9llYZd2oszcU1aGekAPdAAAAAADwDwTkALJCQ5t1Q0C7ucgAEm92eUC5HuuvHGzUCQAAAABwGgE5gIwXisTU2D1oqc+rsI59AJB4Hpdhe75tZ6NOAAAAAIDDCMgBZLw9HQOKm9Z6XQUryIFkWVRtN4ecgBwAAAAA4CwCcgAZb1ebdYPO0jyvygI5DnQDZCe7OeTbW4KKmzbPXgEAAAAAkCQE5AAyXkO7NSBn/jiQXAttVpD3h2M60GUdfwQAAAAAQLIQkAPIeLvsNuhkvAqcZppSJGJ9ydAV1TNL8uT32m3UyZgVAAAAAIBzCMgBZDTTNNVgM2JlLgE5HOZqPqyKqWWWF1fzYadbmxRul6EFlXYbdVqfwAIAAAAAIFkIyAFktLZgWD2hqKU+t9wa1AGYXAur7eaQs4IcAAAAAOAcAnIAGc1u/rjbkGrL8hzoBshui2w26tzRElQsnpljZQAAAAAAqY+AHEBG22UzXmVmaZ5yPXz7A5LNbgV5KBrX3s4BB7oBAAAAAICAHECGa7DZoJP544AzphX7lJ/rttS3Hu51oBsAAAAAAAjIAWS4XTYjVurKCcgBJ7gMw3bMypbDzCEHAAAAADiDgBxAxgpH49rXOWipz61gg07AKUumWAPyrc0E5AAAAAAAZxCQA8hY+zoHbDf/q2PECuCYxVMKLbXd7f0ajMQc6AYAAAAAkO0IyAFkLLvxKoU+jyrzcxzoBoBkv4I8bkrbW1hFDgAAAABIPgJyABmrvtV+/rhhGA50A0CSSvNyVFOYa6lvZQ45AAAAAMABBOQAMtau9qClNpfxKoDj7MassFEnAAAAAMAJBOQAMlZDm3UFOQE54LzF1dYxK1sO9zrQCQAAAAAg2xGQA8hIHf1hdQ5ELPW6inwHugHwenZzyFuDYbUFhxzoBgAAAACQzQjIAWQkuw06DUlzyvKS3wyAUeZX5svtsu4FwJgVAAAAAECyEZADyEi7bMarTC/xy+d1O9ANgNfzed2aW24dd0RADgAAAABINgJyABmpwWYFOfPHgdSx2GbMytZm5pADAAAAAJKLgBxARmpoDVpqdTYrVgE4w24O+fbmoGJx04FuAAAAAADZioAcQMaJxuLa2zlgqbOCHEgdS6oLLbWBSEyN3YMOdAMAAAAAyFYepxvIFAcPHtQvf/lLvfTSS2psbFQ0GlVVVZUWLVqkyy67TBdeeKFcLp6PAJJhf9egIjHrKtQ6AnIgZcwo9Ss/163gUGxUfafN1R8AAAAAAEwWAvIEWL16tb7+9a8rHA6Pqu/fv1/79+/Xk08+qdNOO0133nmnpk+f7lCXQPaw26AzkONWTaHPgW4Ae/HSMnX/4XHbejZwGYYWVxdozf7uUXUCcgAAAABAMhGQn6R7771X3/jGN0beLy4u1hlnnKGCggIdPHhQ69atUzwe1/r16/X+979fDz30kCoqKhzsGMh8dht01pUHZBiGA90AY8jNVeTc85zuwlEE5AAAAAAApxGQn4QdO3bo29/+9sj71157rW699Vb5/f6RWkNDg2644Qbt27dPzc3N+sxnPqMf/ehHTrQLZA27FeSMVwFSz+Ip1jnk+zsHJC72AAAAAAAkCUOxT8I999yjaDQqSVq1apU+//nPjwrHJWnu3Lm6++675fV6JUnPPfecdu/enfRegWzS0GZdgcoGnUDqWTKlwFKLW7cPAAAAAABg0hCQT9DQ0JCeeeaZkfdvvPHGMW87e/ZsnXrqqZIk0zS1cePGSe8PyFY9gxG1BsOWel05ATmQakrzclRTmOt0GwAAAACALMaIlQnq7+/X+973PjU0NKizs1OLFi065u3Ly8tH3m5vb5/s9oCstctm/rgkzSEgB1LS4imFauptc7oNAAAAAECWIiCfoNLSUt16663jvv3+/ftH3q6urp6MlgDIfoO/miKf8nP5dgekoiVTCvTUTgJyAAAAAIAzGLGSBC+//LK2bNkiScrNzdV5553ncEdA5mqw2aBzHvPHgZS1uNo6hxwAAAAAgGRhSeUkCgaDeuihh/Td7353pPaRj3xEZWVlDnYFZLZ6mxXk8yrzHegEOI5gUL5f/9JSDr3nOik/ex6z8yvz5XYZirE7JwAAAADAAQTkCXbgwAH98Ic/1KFDh7Rt2zYFg8NhnWEYuv7663XDDTc43CGQuSKxuPZ0DFjq8yqyJ2xE+nD19argtpst9fA7LlE8iwJyn9eteRUBbW+xPrkFAAAAAMBkIyBPsJ07d+oPf/iDpX7llVfqox/9qAMdAdljX+eAojarUOdVMmIFSGWLqwsIyAEAAAAAjiAgT7Cmpibb+u9//3s99dRT+sxnPqMrrrgiqT25XIZKSwkIkRwulzHyOtmPu0P7uy21Qp9Hi2aWyjCMpPYCe4OePNt6cXGeSgsz9/uU7XkxOPbXQln2PXtlXbkeeu3wMW+T6Y+RbOTkzwsgVXFeAFacF4AV50VyHf16I3MRkCfYeeedp6efflpVVVXq7e3Viy++qB/84Afav3+/+vr6dOutt8rtduvSSy9NWk+GYcjt5mRGcjnxuNvZ3GepLZxSKI/HndQ+MDa3235vaLfbNeaxTDLqvDjG12KsY5nq9NrS494mWx4j2YjfUwArzgvAivMCsOK8ABKDgDzBZs+ePfJ2eXm5Lr/8cq1atUof+tCHtHnzZknSl770JV1wwQUqLCxMSk+maSrO5mdIEpfLkGEYjjzutjb1WmoLpxQoFosntQ+Mbaz/i1gsntH/T7bnRSwuu6duYrG4lMFfCzszSvwqzfOqcyAy5m0y/TGSjZz8eQGkKs4LwIrzArDivEiuo19vZC4C8iQoKCjQt771Lf3TP/2TotGoenp69OSTT+o973lPUj5/PG6qs7M/KZ8LKC0NyO02kv64M01T25p6LPUZBbk8/lNId9C6iaokdXcPyB/N3P8nu/PC1T2gMpvbdncPKO7P3K/FWJZMKdTfdneMeTzTHyPZyKmfF0Aq47wArDgvACvOi+Q6+vVG5uJa5SSZMWOGzj777JH3N2zY4GA3QOZpDYbVE4pa6vMq8h3oBsCJWlaTnKuqAAAAAAB4PQLyJJo7d+7I2+3t7Q52AmSe+tagpeZ2GZpVZr8RIoDUspyAHAAAAADgAEasTNDevXu1bt06HThwQLW1tbr66quP+zGvn1eUm5s7me0BWaehzXpZ2azSPOV4eB4QSAcLqvLlcRmKMmYcAAAAAJBEBOQT9NJLL+mLX/yiJGnhwoXjCsj37Nkz8vaUKVMmrTcgG9W3WVeQz6sMONAJgInwed1aWJWvjYfbnG4FAAAAAJBFWFo5QStWrBh5e/v27aqvrz/m7dva2vTyyy+PvH/OOedMWm9ANrJbQc78cSC9LGXMCgAAAAAgyQjIJ2jRokWaN2/eyPtf//rXZZqm7W3j8bg++9nPKhQKSZKmTZum8847Lyl9AtlgIBxTY9egpT63ghXkQDpZPrXI6RYAAAAAAFmGgPwk3HLLLSNvP//88/rUpz6lYHD0mIfW1lZ97GMf0zPPPCNpeA757bffrpycnKT2CmSyXe39snt6ihXkQHpZNqXA6RYAAAAAAFmGGeQn4fzzz9dHP/pR/fCHP5QkPfzww3r66ae1cuVKlZSU6PDhw1q3bp3C4fDIx/zXf/2XLr74YqdaBjJSfat1/nhlfo6K87wOdANgosrzc1VV6NOhiNOdAAAAAACyBQH5SbrppptUUlKib3zjGwqHwwoGgyOrxV+vuLhYn/nMZ3TppZc60CWQ2ew36GT1OJCOFlXla8NBp7sAAAAAAGQLAvIE+OAHP6i3ve1t+uUvf6kXXnhBBw4c0ODgoIqKijRnzhxdeOGFeve7362CAi4dByaD/QadzB8H0tGiqgLJJiCPj7HPBwAAAAAAJ4OAPEGqqqr0iU98Qp/4xCecbgXIKrG4aRuQz2X+OJCWFk2xP3cbuwc1leeZAQAAAAAJxiadANJaY/eghqJxS50RK0B6mlGcZ1vf3mwdpQQAAAAAwMliBTmAtGa3Qaff69K0Yp8D3QDjZ7rcis6abVvPZm6XYVvf3tKX5E4AAAAAANmAgBxAWrMbr1JXni+XYR+yAanCrKpS15pXnW4jbWwjIAcAAAAATAJGrABIa/Vt1hXk8yrZoBPINIe6Q+oeiDjdBgAAAAAgwxCQA0hr9a3WFeTzKgjIgUz0WlOv0y0AAAAAADIMATmAtNU5EFZ7f9hSZ4NOIDNtIiAHAAAAACQYATmAtNVgs3rckDSnnBXkQCba1NTjdAsAAAAAgAxDQA4gbdnNH59R4pff63agGwCTbVtznyKxuNNtAAAAAAAyCAE5gLRV32Yzf5zxKkDGCsdM7Wy1PjEGAAAAAMBEEZADSFv1NkHZXDboBDLaa4eYQw4AAAAASByP0w0AwESEIjHt7xyw1OdVsIIc6cFoa1PxNVdY6t2/eVhmRUXyG0oTGw/26H2nT3O6DQAAAABAhiAgB5CWdrf3K2Za6/OrCMiRHoxoRJ6tm23rNg9tHPHqoR7FTVMuw3C6FQAAAABABmDECoC0ZDeHuCyQo/JAjgPdAEiWnlBUu9ut+w8AAAAAADARBOQA0tLOVmtANr+S+eNANljf2ON0CwAAAACADEFADiAt2a0gn1/JeBUgG2w4SEAOAAAAAEgMAnIAaScaN7XLZsQCATmQHTYe7JFpMqkdAAAAAHDyCMgBpJ19nQMaisYtdQJyIDt0D0a0p2PA6TYAAAAAABmAgBxA2qm3Ga+Sn+vW1CKfA90AcAJjVgAAAAAAiUBADiDt2M0fn1eRL8MwHOgGgBM2sFEnAAAAACABCMgBpB026ASw4WA3c8gBAAAAACeNgBxAWjFNk4AcgDoHItrfNeh0GwAAAACANEdADiCtHOoJKTgUs9QJyIHM5ve6LbUNjd3JbwQAAAAAkFEIyAGkFbsNOnPchmpL/Q50AyBZFlYXWGps1AkAAAAAOFkE5ADSit14lTnlAXncfDsDMtnSKfYBOXPIAQAAAAAng0QJQFrZ2dpvqS2oYrwKkOnsAvK2YFgHu0MOdAMAAAAAyBQE5ADSCht0AtlpbmW+cj3WX1s2HOxOfjMAAAAAgIxBQA4gbbT3h9XeH7bUCciBzOd1ubSsptBSZw45AAAAAOBkEJADSBt2q8ddhlRXHnCgGwDJduq0IkttQyMBOQAAAABg4gjIAaSNepuAfGZpnnxetwPdAEi2U6dbA/LmviE19TCHHAAAAAAwMR6nGwCA8WL+ODKJWVCg4Ge/ZFuHvcXVhcpxGwrHzFH1DQe7VVNU7VBXAAAAAIB0RkAOIG3YBeQLCMiRpsz8Ag3ecJPTbaSVXI9LS2sKtf4NY1XWN/boksUE5AAAAACAE8eIFQBpITgU1cFu6xgFVpAD2cV2DjkbdQIAAAAAJoiAHEBaqG+zrh6XpHmVbNAJZJNTpxVbak09ITX3MoccAAAAAHDiCMgBpIUdLdaAvKYwV4U+rwPdAHDKkikF8roNS33tge7kNwMAAAAASHsE5ADSQr3N/PF5jFcBso7P69aymkJLfe3+Lge6AQAAAACkOwJyAGlhZ2u/pcb8cSA7nTmzxFJbs79bcdN0oBsAAAAAQDojIAeQ8oaice3tICAHMGylTUDePRhRg80TaQAAAAAAHIvH6QYA4Hh2t/crZrMwdEEVATnSWDgsz9bNlnJ08VIpJ8eBhtLHgsp8Ffo86g1FR9XX7O/SfL4vAAAAAABOAAE5gJS3w2b+eGmeV+UBQkSkL1dHu0redpGl3vHaDsWn1DjQUfpwuwydMaNYf6lvH1Vfs79LH1w53aGuAAAAAADpiBErAFLeWBt0GobhQDcAUoHdHPJXD/UoFIk50A0AAAAAIF0RkANIedtbrAH5AuaPA1nNLiAPx0y9eqjHgW4AAAAAAOmKgBxASovG4trVZg3IFzJnGMhqNUU+TS/2Wepr9ncnvxkAAAAAQNoiIAeQ0nZ3DChss0MnG/EBWGmzinzN/i4HOgEAAAAApCsCcgApbUdLn6VW6POoptC6chRAdrEbs9LQ1q+O/rAD3QAAAAAA0hEBOYCUNtb8cTboBHD69GK5bb4VvHKgO+m9AAAAAADSEwE5gJS2wy4grypwoBMAqabA59Gi6kJL/e+MWQEAAAAAjBMBOYCUFY3F1cAGnQCO4cyZxZba2v1dMk3r3gUAAAAAALwRATmAlLW3036DzgUE5ACOsJtD3hYMa2/ngAPdAAAAAADSDQE5gJRlN3+80OfR1CI26AQwbMmUAgVy3Jb6mv3dyW8GAAAAAJB2CMgBpCy7+ePz2aATwOt43C6dNr3YUl/LHHIAAAAAwDgQkANIWTta+iw15o8DeCO7OeTrG7sVicWT3wwAAAAAIK0QkANISdG4qfq2fkt9fiUBOYDRVtrMIR+MxLWpqdeBbgAAAAAA6YSAHEBK2tcxoKGodfXnwqoCB7oBkMpmlvhVVZBrqTNmBQAAAABwPATkAFLSdpvxKvm5bk0rZoNOAKMZhqGzbFaR/52NOgEAAAAAx0FADiAl2W3QuYANOgGMYaXNHPJtzX1q7w8nvxkAAAAAQNogIAeQkna02gTkjFcBMIYzZ5bIbfP82Yt7OpLfDAAAAAAgbRCQA0g5sbipepuAfGEVG3QCsFfk92rZ1CJL/fndnQ50AwAAAABIFx6nGwCAN9rXOaCQzQadrCBHJolXVau94YClbhYUOtBNZjhvdqk2HuwZVfv7/i6FIjH5vG6HugIAAAAApDJWkANIOXbzxwM5bNCJDONyySwqtrzIxY/miTpvTpmlNhSNa11jd/KbAQAAAACkBf4KB5Bytrf0WWrzK/PlYoNOAMdQW5qnGSV+S50xKwAAAACAsRCQA0g5divIFzB/HMA4nDfbuor8hT0dMk3TgW4AAAAAAKmOgBxASonFTe203aCT+eMAju+8OaWWWmswbPt9BQAAAAAAAnIAKWV/11gbdLKCHMDxLZ9apEKfdQ9yxqwAAAAAAOwQkANIKWNt0Gk3VxgA3sjjMnR2bYml/vyeDge6AQAAAACkOgJyACnFLiCfxwadAE7A+XOsc8i3twTVFhxyoBsAAAAAQCqzXoMMAA7a0dJnqS1kvAoykNHdpcAXP2up93/2izKLrSugMX5n15bK7TIUi4/emPP5PZ26atkUh7oCAAAAAKQiAnIAKSNumtrZ2m+pM38cmcgYHJT/5z+z1AduuY2A/CQV+DxaMbVQ6xp7RtWf391BQA4AAAAAGIURKwBSxoHOQQ1EYpb6gsoCB7oBkM7Osxmz8sqBboVsvscAAAAAALIXATmAlLHNZryK3+tig04AJ+y82daAfCga15r93clvBgAAAACQsgjIAaSMbc3WgHxBVYHcLjboBHBippf4VVtqfXLt+T0dDnQDAAAAAEhVBOQAUoZdQL6oivEqACbGbhX5C3s6FTdNm1sDAAAAALIRATmAlBCNxVXfZt2gc1E1G3QCmBi7OeQd/WFtbwk60A0AAAAAIBURkANICbs7BjQUjVvqi6pZQQ5gYpbWFKrI57HUn9vV7kA3AAAAAIBUREAOICXYjVcp8nk0tcjnQDcAMoHHZejc2aWW+lM722QyZgUAAAAAIAJyACnCLiBfWFUgw2CDTgATt2puhaV2sDukHa2MWQEAAAAAEJADSBG2G3QyfxzASTq7tkT5uW5L/akdbQ50AwAAAABINQTkABwXisS0u91ug07mjwM4OTkely6oK7fUGbMCAAAAAJAIyAGkgPq2fsVscioCcgCJ8Nb51jErzX1D2nzYeuUKAAAAACC7EJADcJzdeJWK/BxV5Oc60A2ATLNyRrGKfB5L/c87Wh3oBgAAAACQSqx/LWJC2tra9Otf/1ovvfSS9u7dq76+PuXl5WnKlCk688wz9d73vlezZ892uk0gJdnOH69i9TiAxPC4Xbpobrke3tw8qv6X+nZ94sI5crvYDBgAAAAAshUBeQL8/Oc/19e//nUNDQ2Nqvf09Kinp0c7duzQ6tWr9S//8i/6r//6L7lcLNwHXs9+g04CcgCJ89YFFZaAvL0/rFcP9ei06cXONAUAAAAAcBwB+Un60Y9+pG9961sj7+fl5em0005TZWWluru7tX79enV3dysej+snP/mJurq69JWvfMXBjoHUEhyKan/XoKW+qDrfgW4AZKpTpxWrNM+rzoHIqPpTO9sIyAEAAAAgixGQn4Rt27bp29/+9sj7V1xxhW6//XYVFRWN1AYHB/W9731P9913nyTpoYce0jnnnKN3vvOdSe8XSEU7WoK29YWMWAGQQG6XoYvnVei3rzaNqj9T366bV9XJw5gVAAAAAMhKzPo4CT/4wQ9kmqYk6c1vfrPuvPPOUeG4JPn9ft1666267rrrRmrf+973ktonkMrsxqtMK/apyO91oBsAmeyt8yssta7BiNYf6E5+MwAAAACAlMAK8gnq7+/X3/72t5H3b7755mPe/j//8z/129/+VpFIRPv27dPu3bs1Z86cyW4TSHnbWqwBOavHkQ3M3FwNXXqFbR2TY9nUQlXm56g1GB5V//POVp1ZW+JQVwAAAAAAJxGQT9D27dsVDg//gT19+nTNmjXrmLcvKipSXV2dtm/fLknau3cvATkgNuhE9jJLy9T70wecbiOruAxDb55foV+uPzSq/teGDn3qzXF53VxYBwAAAADZhr8EJ8jr9eotb3mLli5dqiVLlozrYzyefzwf0d/fP1mtAWmjayCsw71DljobdAKYLHZjVvqGovr7vi4HugEAAAAAOI0V5BO0fPly3XXXXeO+fTgc1u7du0feLysrm4y2gLSyrdm6QafLkBZUsoIcwORYVF2gmiKfmnpCo+pP7WzTeXP42QwAAAAA2YYV5Enypz/9SQMDA5Ikt9s97lXnQCazG69SW5qnvBy3A90AyAaGYegtNqvI/7a7Q6FIzIGOAAAAAABOIiBPgsHBQX33u98def/MM89UcXGxcw0BKcJug07mjwOYbHYBeX84pmca2h3oBgAAAADgJALyJPj85z+vgwcPjrz/sY99zMFugNRgmiYbdAJwxLyKgGpL/Zb6w5ubHegGAAAAAOAkZpBPsrvuuksPP/zwyPtXXXWVTj/99KT24HIZKi0NJPVzInu5XMbI62M97pq6B9U5ELHUz55XweM1gw168mzrxcV5Ki3M3P/38Z4XSN5j5L0rZ+hrf9o5qrbxYI+6Y6ZmV7BRcDJwXgBWnBeAFecFYMV5kVxHv97IXATkk+jee+/V97///ZH358yZozvuuCPpfRiGIbebkxnJdbzH3ZamXkvN6za0aGqR3G4ubslUY/3fut2urPh/5/vx8SXrMfKu06frm0/VKxIzR9V/u+GQbn/HwoR9Hhwf5wVgxXkBWHFeAFacF0BiEJBPkrvuumtUOF5ZWakf//jHysuzXxk3mUzTVDxuHv+GQAK4XIYMwzju4+61xm5LbUF1oTyGoVgsPokdwklj/d/GYvGM/n+3PS8OHZJ75gzLbWP7D0hTpya5w9SRrMdIid+rNy+s0pNbRo9VeWj9QX3i4rnK8WT+EzZOG+/PCyCbcF4AVpwXgBXnRXId/XojcxGQJ1gsFtOXvvQlPfjggyO1iooK/exnP9O0adMc6SkeN9XZ2e/I50b2KS0NyO02jvu4W7+v01KbV57HYzXDdQcH7OvdA/JHM/f/3u68cHUPqMzmtt3dA4r7M/drcTzJfIy8Y365JSDv7A/rkXUHdPE860aeSKzx/rwAsgnnBWDFeQFYcV4k19GvNzIXy6MSaHBwUDfccMOocLy6ulo///nPNXv2bAc7A1JLfKwNOqvYoBNA8qycWaKawlxL/eFNbNYJAAAAANmCgDxBuru7df311+uZZ54Zqc2aNUsPPvigamtrnWsMSEH7OgfUH45Z6oumEJADSB6XYeiypdWW+pr9XWrqCTnQEQAAAAAg2QjIE6Czs1Mf+MAH9Nprr43Uli5dql/+8peqqalxsDMgNW05bF09Hshxa1Zp8mf0A8hulyyu1hs3pTclPbKFVeQAAAAAkA0IyE9SMBjUhz70IdXX14/U3vSmN+mBBx5QaWmpg50BqWvL4V5LbWF1gdxvTKkAYJJVFeTqnFnWn9ePbmlWlA2PAAAAACDjEZCfpP/+7//Wjh07Rt5/+9vfrnvuuUd5eayEBcZit4J8KeNVADjkiqVTLLW2YFgv7bVuJgwAAAAAyCwE5CfhF7/4hZ599tmR99/ylrfoW9/6lrxer3NNASluIBzT7nbrLtuLqwsd6AYApHNnl6oiP8dSf2QzY1YAAAAAINMRkE9Qb2+vvvnNb468P2PGDH3ta1+T2+12sCsg9W1v6ZPd1IIlrCAH4BCPy9Cli6ss9Rf3dKgtOORARwAAAACAZPE43UC6evDBB9Xf/49VsFVVVfr6178+7o+/6KKLdNFFF01Ga0BK22ozXqWmMFdlAevqTQBIlsuWVuu+NY2jajFTenRLi/75rBkOdQUAAAAAmGwE5BP0f//3f6Pef+WVV/TKK6+M++MrKioIyJGVNtts0Ll4CuNVADhrapFfK2cUa+2B7lH1373WpA+cMU1eNxfdAQAAAEAm4q+9CdqzZ4/TLQBpxzRN2w06Ga8CIBVcscy6WWdrMKz/29HqQDcAAAAAgGRgBfkEvfrqq063AKSdlr4htfeHLfUlrCAHkAIurCtTZX6OWoOjv0+tfuWg3rGoSi7DcKgzAAAAAMBkYQU5gKTZ2mxdPe5xGZpfme9ANwAwmtft0rWnTbPU93QM6KW9nQ50BAAAAACYbATkAJLGbrzKvMp85Xr4VgQgNVy5rFr5uW5L/YG1jTa3BgAAAACkO1IpAEmzxWaDziXVzB8HkDoCOR69a3mNpb7xUK82NVm/hwEAAAAA0hsBOYCkiMbi2t4StNSX1BCQA0gt7zl1qnLc1nnjq19hFTkAAAAAZBoCcgBJsau9X0PRuKW+pJoNOgGklvJAjt65uMpSf25Xh/Z1DjjQEQAAAABgshCQA0gKu/njRT6PphX7HOgGAI7tfadN0xvXkJuSfr7uoBPtAAAAAAAmicfpBgBkhy3N1oB8yZRCGYZ1jAGQDeIlper+zcO2dThvZmmeLppbrmca2kfVn9jWon8/Z6bK83Md6gwAAAAAkEgE5ACSYovN5nZLpjB/HFnM51PkwlVOd4Fj+OAZ0ywBeSRm6sENTbrx/FkOdQUAAAAASCRGrACYdL2hiPZ3DVrqBOQAUtniKYU6bXqRpf6715oUHIo60BEAAAAAINEIyAFMuq0241UkaTEbdAJIcR84Y7ql1h+O6cENhxzoBgAAAACQaATkACad3QadtaV+FfiY8gQgtZ1TW6K68oClvvqVRrX3hx3oCAAAAACQSATkACbdlsPW+eOLp7B6HEDqMwxD/99K6yrywUhcP3lpvwMdAQAAAAASiYAcwKQyTVNbbVaQL2X+OIA08ZYFFVpQmW+pP7L5sPZ09DvQEQAAAAAgUQjIAUyqg90h9YSsm9ktYf44gDThMgzddMFsSz1mSt//214HOgIAAAAAJAoDgAFMqs0241VyPS7NqbDO9AWySn+/fL//raUcuurdUoDzI9WcPqNYb5pdqhf2dI6qv7CnU+sbu3Xa9GJnGgMAAAAAnBQCcgCTym6DzkVV+fK4DAe6AVKHq7dHBf/9cUs9/Oa3Kk5AnpJuPH+WXtrbqbg5uv7d5/bo/vetkMvg+xoAAAAApBtGrACYVK8d6rHUlrBBJ4A0NLssoMuXVlvq21uC+r8drQ50BAAAAAA4WQTkACZNfziqXe3WDeyWTyUgB5CePnJOrfxe669PP3x+n4aicQc6AgAAAACcDAJyAJNmy+E+yygCSVpaQ0AOID2VB3L0gTOmW+rNfUP6zcZDDnQEAAAAADgZBOQAJs2mQ9YNOmeU+FWal+NANwCQGO8/fZrKA9bvYz/9+wG19A050BEAAAAAYKIIyAFMmk1N1oCc1eMA0p3f69a/nTPTUu8Px/SVP9fLNG0unQEAAAAApCQCcgCTIhY3tfmwNSBfTkAOIANcuqRac8rzLPWX93Xp0S0tDnQEAAAAAJgIAnIAk6KhtU/94ZilvoyAHEAGcLsM3fbmuTJsjn3r2d1q7g0lvScAAAAAwIkjIAcwKdbv77LUCnI9mlVmXXEJAOlo+dQiXXfaNEt9eNRKA6NWAAAAACANEJADmBQbDnRbaktrCuQy7NZbAkB6+vdzZ2pmid9S//v+Lj28udmBjgAAAAAAJ4KAHMCksFtBvrymyIFOAGDy+Lxufe7t8+Wyee7vu8/tYdQKAAAAAKQ4AnIACdfaG1Jj16Clvnwq88cBZJ6lNYV63xijVr70f/WMWgEAAACAFEZADiDh7FaPuw1pUXWBA90AwOT7t3NrVVtqHbWy9kC3frOxyYGOAAAAAADjQUAOIOHsAvJ5lfnye90OdAMAky/X4xpz1Mq3n92tdTb7MgAAAAAAnEdADiDh1tkE5MtqGK8CILMtmVKo958+3VKPmdKnHt2mg93W0VMAAAAAAGcRkANIqFAkpq1NPZb68qls0Akg833knJmaWxGw1HtCUf3Xw1sVHIo60BUAAAAAYCwE5AASavOhHkVi1g3pWEEOIBvkelz6xuWLVOz3Wo7t7RjQHU/sUCzOpp0AAAAAkCoIyAEklN388eqCXFUV5DrQDQAk39Qiv7526UK5bQaSv7CnUz98YV/ymwIAAAAA2CIgB5BQG2w2omP1OGBlutyKTZ9heTFdbGabCU6bXqxPXlxne+yBVxr1xLaWJHcEAAAAALDjcboBAJnDNE1tOGBdQb58KgE58EZmVZU6129xug1MoquWTdHutn795tUmy7Ev/7leRT6vzp1d6kBnAAAAAICjWEEOIGH2dw2qayBiqbOCHEC2+sRFc3TGjGJLPRIzdfMjW/VsQ3vymwIAAAAAjCAgB5Awmw71Wmp+r0t1FfkOdAMAzvO4DH31koWaXuyzHIvGTX3qse16emebA50BAAAAACQCcgAJtKnJGpAvmVIoj81GdQCQLYr8Xn3ziiUq9Fkn28Xipj79+HY9uZ2Z5AAAAADgBAJyAAnzWlOPpcZ4FQCQZpXl6YfvXqZiv9dyLG5Kn3tip/64pdmBzgAAAAAguxGQA0iI7sGI9nUOWups0AkAw+ZX5uvua5apNM8akpuSvvR/9fr5uoMyTTP5zQEAAABAliIgB5AQrx2yrh43JC2dQkAOAEfVlQf0o2uWqzyQY3v8u8/t0e2PbVd/OJrkzgAAAAAgOxGQA0iIDQetAXldRUD5udaZuwCQzWrL8vTj9yxXVUGu7fGn69t1/c83and7f5I7AwAAAIDsQ0AOICE22gTkp04rcqATID0YbW0qfusFlhejrc3p1pAE00v8+vF7lqum0D4k3981qP/vFxvZvBMAAAAAJhlLOwGctOBQVDtbg5Y6ATkwNiMakffVjbZ1JlBnh5oin370nuW65ZFt2mHzPTQUjeuzT+zUa4d6deP5sxTI4dc2AAAAAEg0VpADOGmvNfUqbpPonUJADgDHVF3o073XnqIrllaPeZvfvXZY1/zvOj21s40NPAEAAAAgwQjIAZy0DY3288dL8+w3oQMA/EOux6VPv3WePvu2ecr12P9q1hoM6/bHtuuGhzZrX+dAkjsEAAAAgMxFQA7gpNnNH185q9SBTgAgfV26pFr3XXuKphf7xrzN2gPduvZn6/WD5/dqIBxLYncAAAAAkJkIyAGclMFITNta+iz1M2oJyAHgRM2rzNcD7z9VF9aVjXmbaNzU/WsbddlP1uieF/epayCcxA4BAAAAILMQkAM4KZubehWzGUB+JivIAWBC8nM9+vpli/Q/lyxURf7Yo6p6QlH99O8HdOlP1urrf9mlg92DSewSAAAAADIDATmAk7LBZrxKbVmeqgrHHhEAADg2wzD0lvkV+u2HTtf7T58mt8sY87ZD0bh++2qTrr7vFd326DY9v7tDkVg8id0CAAAAQPryON0AgPRmF5AzfxwAEiOQ49FNF8zWJYur9PW/7LL9nntU3JSerm/X0/XtKvJ5tGpeud62oFIrphXJZYwdsAMAAABANiMgBzBhQ9G4th7utdTPnDX27FwAwImbUx7QPdcs0593tOnev+/Xvs5jj1PpCUX1h03N+sOmZlXk5+hNs0t12rRinTq9SBX5uUnqGgAAAABSHwE5gAnb2tyrcMw6f5wV5ACQeIZh6G0LK/WWBRV6fneHfrb2oDbbPEn5Rm3B8EhYLkkzSvw6bXqRTplapDnlAdWW5inXw9Q9AAAAANmJgBzAhG20udS/ptin6aV5ijH/FgAmhcswdEFduc6fU6ZXD/XqgVca9cKeznF//IGuQR3oGhwJzF2GNK3Yr1mleZpdnqcphT6VBXJUHshRWSBHpXleed0TD9BjcVODkZj6wzENhGMaCEfVH47J1RzUYCSmvsGI2roHR+oD4ZgGIzFFYqYi8biiMVPRuKlIzJRkKsfjUo7bpdwjr3M8LhX6PKrMz1VlQa4q83NUVZCr8kCOPCfRNwAAAIDsQEAOYMI2NNrMH69l9TgAJINhGFoxrUgrphVpT0e/ntjWqj/vaNXh3qETup+4+Y/Q/LndHba3KfR55PMMh9K5HveR1y65DClmSvG4qbg5HGS/MRAPRZ15wvRo8L+oukCLqwu0qLpA8yoC8nndjvQDAAAAIDURkAOYkGgsrk1N1kv7CcgBIPlmlwV0w3mz9LE31WpTU6/+vKNNT9e3qXMgkpD77w1FdfxhLqnl9cH/n7a3SpLcLkN15QGdObNEq+aVa1FVvgw2MAUAAACyGgE5gAnZ3hK0XRXI/HEAcI5hGFo+tUjLpxbpExfN0caD3XrlQLfWN/Zoa3OfYnHrvhHZJBY3tbM1qJ2tQT3wSqOmFObqornlWjW3XEtrCuUiLAcAAACyDgE5gAnZYDN/vCyQo9qyPAe6AQC8kcdl6IwZJTpjRokkaTAS06ZDvVp/sFuvHuzR7o4B9YaiDnfprMO9Q/rl+kP65fpDqsjP0ZXLpujdy2tUnOd1ujUAAAAASUJADmBC7DboPHVaEZeqA0CK8nvdOrO2RGfWDgfmpmmqoz+sPR0DR176daBrUB39YXX0R9Q3NHnhuc/jUr7Po/xcjwI5HuW4DQVy3MrzupWXM/yS43bJ4zLkcRvyulzyuId/vkRipsLRuIZicQ1F4xqKxtQ1EFFL35Bag2F19Icn1FNbMKwfv7RfP1vbqMuWVOu606ZqWrE/kf9sAAAAACmIgBzACYvFTb16yD4gBwCkB8MwVJ6fq/L8XK2cWWI5HorE1DkQUUd/WD2hiMLRuELRo6H08ItpmnK7DLkMQy6XIbcxPOfb7/1H2B3IcSsvxzPytt/rlttlqLQ0ILfbpVgsrs7O/oT9uyKxuNqCYTV2DWpbS5+2Nfdpa3Of2oLjC86HonH99tUm/e61Jq2aW673nzFdi6sLEtYfAAAAgNRCQA7ghNW3BdUfjlnqKwjIASBj+Lxu1RS5VVPkc7qVE+J1u1RT5FNNkW9ktbwktQWHtPlwn/62u0N/29Vx3BXycVN6ur5dT9e3663zK3TTBbNVWZA72e0DAAAASDICcgAnzG68SrHfq9nMHwfGzSwoUP9td9jWASReRX6uVs3N1aq55YrE4lrX2K1n6tv17K4OdQ9Gjvmxf97Zphf2dOpfz56h9546VV63K0ldAwAAAJhsBOQATtiGRmtAvoL548AJMfMLNPCJW5xuA8hKXrdLZ9eW6uzaUt36ZlN/29Wu1esOasvhvjE/ZiAS0/f+tld/3NKsm1fV6UybsTQAAAAA0g/LXwCckFjc1AabFeSMVwEApCOPy9CqeRW679pT9OP3LNd5s0uPeft9nYO64aHNuu3R7cddeQ4AAAAg9bGCHMAJ2dEatJ3behoBOQAgjRmGoRXTirRiWpH2dgzof9cc0JPbW8e8/dP1bdrU1KOvvHOhTuFnIAAAAJC2WEEO4ISs3d9lqZX4vaqrCDjQDQAAiTerLE9ffMcC3fve5Zp3jJ9vrcGw/v03r+m+vx9QLG4msUMAAAAAiUJADuCErD3QbamdMaNYLuaPAwAyzPKpRXrg/afqkxfXqSDX/sLLmCnd/eI+3fi7zWrvDye5QwAAAAAni4AcwLiFIjFtOmSdP75yZnHymwEAIAncLkPvPqVGv/vn03XZkqoxb/fKgW6974H1WmNzpRUAAACA1EVADmDcXmvqVThmvYR85cwSB7oBACB5SvJydMfb5uv/Xb5IhT771eSdAxHd9LvN+sOmw0nuDgAAAMBEsUkngHFbu7/bUpte7NOUQl/ymwHSXTgs984dlnJs/gIpJ8eBhgCMxwV15fpFZb4+/fgObWrqtRyPmdL/PNWg5t6Q/v3cWhmMIAMAAABSGgE5gHF75YD1snFWjwMT4+poV+nFb7LUO17bofiUGgc6AjBe1YU+/eiaZfrxy/t1/5pG2W3Ped+aRh3uHdIdb5snr5uLNgEAAIBUxW/rAMalezCiHS1BS33ljOLkNwMAgMM8bpc++qZZ+v7VS1Wa57W9zZPbW/Xx321WXyia5O4AAAAAjBcBOYBxWd/YbVkhZ0g6bXqxA90AAJAazqwt0X3XnaLaUr/t8XWNPfrXX72q5t5QkjsDAAAAMB4E5ADGxW7++IKqfBX57VfNAQCQLaYW+XXve0/RiqmFtsf3dAzo3379mg4TkgMAAAAph4AcwListZk/fsYM5o8DACBJRX6vvv+uZXrr/Arb4029Q/p3QnIAAAAg5RCQAziuQz2DOtht/YN+5czi5DcDAECKyvW49KV3LtAHz5hue7ypd0j//ptNjFsBAAAAUggBOYDjesVmvEqO29DyGvtLyQEAyFYuw9CN58/SJy+usz3e1BPSvxGSAwAAACmDgBzAca090G2pLZ9aJJ/XnfxmAABIA+8+pUafeetc22NNPSFWkgMAAAApgoAcwDHFTVOv2ATkK2cUJ70XAADSyeVLp+jTb7EPyQ8dCclb+4aS3BUAAACA1yMgB3BMDW396h6MWOorZ7JBJwAAx3PFsim6/Rgh+cd/v1l9oWiSuwIAAABwFAH5JHriiSc0f/58rVq1yulWgAlbu7/LUiv0eTS/Mt+BbgAASD9XHiMk390+oP9+ZKuGovEkdwUAAABAIiCfNO3t7frKV77idBvASbMbr3La9GK5XUbymwEAIE1duWyKbhsjJN94sEeffWKHYnEzyV0BAAAAICCfBP39/froRz+q9vZ2p1sBTko4GtfGgz2WOvPHAQA4cVctm6L/vmiO7bFnGtr1zb/ulmkSkgMAAADJRECeYJ2dnfrXf/1Xvfbaa063Apy0zYd7FbK55Jv54wAATMx7T52q61dOtz3221ebdP/axiR3BAAAAGQ3AvIE2rJli66++mpt2LDB6VaAhFhjM3+8uiBX04t9DnQDAEBm+NibavXORZW2x374wj49uqU5yR0BAAAA2YuAPAEGBgb0ne98R9dee62ampokSX6/3+GugJP3wp5OS23lzGIZBvPHAQCYKMMw9Jm3ztPZtfZXZH3lz/V65YD1SWoAAAAAiUdAfpL279+vt7/97br77rsVDoclSbNmzdLdd9/tcGfAyWnpG1JDW7+lfs6sUge6AQAgs3jcLn3t0kVaVF1gORYzpU89ul0HugYd6AwAAADILgTkJ6m5uVktLS0j77/3ve/V73//e02fbj9bEkgXL+21rh53uwydyfxxAAASIi/Hre9cudh2dFlvKKr/+sMW9YWiDnQGAAAAZA8C8gRZsWKFfv3rX+sLX/iC8vLynG4HOGkv2oxXWTG1UPm5Hge6AQAgM5Xk5eh7Vy9Vsd9rOba/a1C3P75d0bjpQGcAAABAdiDpOkk1NTVavXq1Vq5c6XQrQMKEo3GttZl9eu7sMge6ATJTvLJK7dv2WOpmCVdpANlmWrFfX79skT76202WMPzv+7r03ef26L8vmuNQdwAAAEBmYwX5SZo+fTrhODLOxoM9GozELfU3MX8cSBy3W2Z5ueVFbrfTnQFwwIppRbrtzXNtj/1qwyH9YdPhJHcEAAAAZAcCcgAWL9jMH68p8mlmqd+BbgAAyA6XLa3WdadNtT125192aX1jd3IbAgAAALIAI1aygMtlqLQ04HQbSCMv77eOV7l4YZXKyvKP+7EulzHymscd3mjQY79HQ3FxnkoLM/fxwnkxftn6GMlGnBf2Pn/FUh3qC+u5+rZR9Vjc1O2PbdcjHztXNcU8YZ2pOC8AK84LwIrzIrmOfr2RuQjIs4BhGHK7OZkxPnvagtrfMWCpX7ywUm73+C864XEHO2M9htxu1wk9vtIV58XxZftjJBtxXozmdkvfv26FrvrhS9rVGhx1rHMgohse3Khf/9vZ8nkZx5TJOC8AK84LwIrzAkgMAvIsYJqm4m/Y8AkYyzPbWyw1v9etlTNLFItZ55K/kctlyDAMHnewNdZjKBaLj+vxla44L8YvWx8j2YjzYmwBr1s/fv+puurul9U9GBl17LWDPfrcI1v1P1cucag7TCbOC8CK8wKw4rxIrqNfb2QuAvIsEI+b6uzsd7oNpIk/b2m21E6fXqT+vpDG8ygqLQ3I7TZ43MFWd9B6dYIkdXcPyB/N3McL58X4ZetjJBtxXhxbgSF99ZKF+thDm/TGv3t/va5Rc0p8unLZFGeaw6ThvACsOC8AK86L5Dr69Ubm4lplACP6w1FtONhjqZ87u9SBbgAAyG6nzyjWDefNsj32jWd2aevh3iR3BAAAAGQeVpADGLF2f7eiNpdnnTuLgBxINKOnW4H/+aKl3n/7Z2UWFSe/IQAp6f2nT9O25j49Xd8+qh6JmfrkH7dp9QdOVWlejkPdAQAAAOmPgBzAiBf3dlpqdeUBVRf6HOgGyGzGwID8/3uvpT7wnzcTkAMYYRiG7njbfO3uGNDeN2yi3RoM69OPbdf337VMHheX/QIAAAATwYgVAJKGN3N9ySYgZ7wKAADOystx6+uXLVIgx205tq6xR3e/sNeBrgAAAIDMQEAOQJJU39qvtmDYUn8T41UAAHBcbWmevvBP822PPfDKQT23qyPJHQEAAACZgYAcgCT78SqFPo+W1BQ60A0AAHijC+rK9c9nTrc99oU/7dTB7sEkdwQAAACkPwJyAJKkF/ZYA/KzZpYw0xQAgBTykXNqddbMEku9byiq2x7drqFo3IGuAAAAgPRFQA5AXQNhbTnca6kzfxwAgNTidhn64jvmqzI/x3JsR2tQ3/rrbge6AgAAANIXATkAPberQ+Ybaoakc2oJyAEASDUleTn66qWL5La5yuv3mw7riW0tDnQFAAAApCcCcgB6pqHdUls+tVDFeV4HugEAAMezrKZQHz9/lu2xrz7VoN3t/UnuCAAAAEhPBOSTZNq0adq5c6d27typZ555xul2gDH1hiJae6DbUl81ryL5zQAAgHG79tSpWjW33FIPReO69Y/b1B+OOtAVAAAAkF4IyIEs97fdHYrF3zhgRbqorsyBbgAAwHgZhqE73jZPM0r8lmP7uwb1P39ukGlaf8YDAAAA+AcCciDL/aXeOl5lyZQCVRf6HOgGAACciPxcj7526ULleqy/1v95Z5t+++phB7oCAAAA0gcBOZDFgkNRrdnfZanbXa4NAABS09yKfH3y4jrbY99+dre2Hu5NckcAAABA+iAgB7LYC3s6FYlZL71eNY+AHACAdHLZkmpdtqTKUo/GTX3q0e3qHow40BUAAACQ+gjIgSz2l/o2S21BZb6mFllnmQIAgNR2y6o6za0IWOrNfUP6/JM7FWceOQAAAGBBQA5kqYFwTC/vsxmvwupxAADSks/r1tcuXaRAjtty7MW9nfrZ2kYHugIAAABSGwE5kKVe3NupoWjcUmf+OAAA6WtGiV+ffft822P3vLhP6w50J7chAAAAIMURkANZ6hmb8SpzKwKaWZrnQDcAACBRVs0t13WnTbXU46b06ce3qy045EBXAAAAQGoiIAeyUCgS04t7Oy31i1g9DgBARrjxvFlaVlNoqXcORPTpx7YrGmceOQAAACBJHqcbAJB8L+/r0mDEOl7lYuaPA0lj5uZq6O3vtK0DwMnyuF36n0sW6v2rN6h7MDLq2MZDvbr7hb268fzZDnUHAAAApA4CciAL/cVmvMqs0jzNLgs40A2QnczSMvU+8KDTbQDIYFUFufryOxboxt9t1hvXiz/wykEtqynSBXVljvQGAAAApApGrABZZiga1wt7rONVVrF6HACAjHNmbYk+fPZM22Nf+NNOHeweTHJHAAAAQGohIAeyzJr9XeoPxyz1VcwfBwAgI/3zWTN01swSS71vKKrbHt2uoah17BoAAACQLQjIgSzzjM14lenFPs2tYLwKAACZyO0y9MV3zFdlfo7l2I7WoL71190OdAUAAACkBgJyIIsMRmJ6dleHpb5qXoUMw3CgIwAAkAwleTn66qWL5HZZf97/ftNhPb61xYGuAAAAAOcRkANZ5K8N7bbjVS5m/jgAABlvWU2hPn7+LNtjX326Qdtb+pLcEQAAAOA8AnIgizxmszpsdlmeFlTmO9ANAABItmtPnWq778hQNK5PPrJNXQNhB7oCAAAAnENADmSJ5t6Q1h3ottQvWVzFeBUAALKEYRi6423zNKPEbznW3Dek2x/brmjcdKAzAAAAwBkE5ECWeGJbq974567LkP5pYaUj/QDZznW4SeU1pZYX1+Emp1sDkOHycz36xuWLlOd1W46ta+zR9/+2x4GuAAAAAGcQkANZwDRNPb7NOl7lrNoSlefnOtARAEkyolHLCwAkw+yygD73T/Ntj/1y/SE9uZ1NOwEAAJAdCMiBLLCpqVcHugYt9UsWVzvQDQAASAWr5pbrn8+cbnvsK39u0M6WYJI7AgAAAJKPgBzIAnabcxbkenT+nDIHugEAAKniI+fU6pxZJZb6UDSuW/64VZ1s2gkAAIAMR0AOZLhQJKandrZZ6m9dUKFcD98CAADIZm6XoS+9Y4GmFfssxw73DumWR7ZpKBp3oDMAAAAgOUjHgAz33K4O9Ydjlvoli6sc6AYAAKSaQp9X37h8sfxe658Gm5p69ZU/18s037jVNwAAAJAZCMiBDPeYzeacM0v8Wlxd4EA3AAAgFdWVB/S5t9tv2vnk9lbdv7YxyR0BAAAAyUFADmSw1r4hrd3fZalfsrhKhmE40BEAAEhVF8+r0EfOmWl77Icv7NNf6q0j2wAAAIB0R0AOZLAnt7cq/oYrol2G9I5FjFcBAABW/3rWDL1tQYXtsc89uVNbm/uS3BEAAAAwuQjIgQxlmqYe29psqa+cUaLKglwHOgIAAKnOMAzd8bb5WjrFOoptKBrXzQ9vVUvfkAOdAQAAAJODgBzIUFub+7Svc9BSZ3NOAABwLLkel75x+WJV2zyh3t4f1if+sEV9oagDnQEAAACJR0AOZKiHXjtsqQVy3LqgrsyBbgAAQDopC+To21cuUZ7XbTnW0Nav/35kq0KRmAOdAQAAAIlFQA5koI7+sP68o9VSf8v8Cvls/tAFAAB4o7qKgL5yyQK5bPb13niwR3c8sUOxN252AgAAAKQZAnIgA/3+tcOKxKx/sL7rlBoHugEAAOnqTbPL9IkL59gee3ZXh772dINMk5AcAAAA6YuAHMgw4WhcD73WZKmvmFak+ZX5DnQEAADS2XtPnaoPnjHd9tjDm5t1z0v7k9wRAAAAkDgE5ECGeWpnmzoHIpb6tadOdaAbAACQCW44r3bMjb7v+/sB/WbjoSR3BAAAACQGATmQQUzT1K82WP9ArSnM1flz2JwTAABMjGEY+vRb5+lNs0ttj/+/Z3briW0tSe4KAAAAOHkE5EAGee1Qr3a0Bi31d6+YKrfdDlsAAADj5HEZ+uolC7W8ptByzJT0+Sd3EpIDAAAg7RCQAxnkQZvV436vS5cvqXagGwAAkGl8Xre+deVizS7LsxwjJAcAAEA68jjdAIDEONwb0rO72i31SxZXq8DHqQ6kmnhJqXp+8RvbOgCkskKfV9+/eqn+5cFX1dw3NOrY0ZBckt6xyH5mOQAAAJBKSM2ADPHbjU2Km9b6NStqkt8MgOPz+RR+y9ud7gIAJqSyIFd3X7NM//6bTWohJAcAAEAaY8QKkAEGIzE9vLnZUj9nVolqS62XQAMAAJysacV+3XPNMlUV5FqOHQ3JH9tq/f0EAAAASCUE5EAGeHxri/qGopb6tadOdaAbAACQLY4Xkn/hT/X6+bqDyW8MAAAAGCcCciDNxU1Tv95o3ZxzVmmezpxZ4kBHAAAgmxwrJJek7z63R99+drfips0sOAAAAMBhBORAmvtLfbv2dQ5a6u85tUaGYTjQEQAAyDbHC8l/uf6QPv3YDoWj8SR3BgAAABwbATmQxqJxUz96cZ+lXpDrYVMsAACQVEdD8imF9iH50/VtuvF3m9UXso6FAwAAAJxCQA6ksT9tb9H+Luvq8WtPmyq/1+1ARwAAIJtNK/brvmtP0byKgO3xDQd79OFfv6rm3lCSOwMAAADseZxuAMDERGJx/eTlA5Z6kc/D5pxAOujvV+6jD1vKQ5deIQXsgyUASAfl+bn60XuW69Y/btPaA92W47vbB/SBn2/UV965QCvZLwUAAAAOIyAH0tSjW5rV1GNdffXBM6YrP5dTG0h1rt4eFX78Pyz1jgsuUpyAHECay8/16DtXLdGX/q9eT25vtRzvHozoxt9t1r+fW6vrV06Xi31TAAAA4BBGrABpaCga10//bl09Xprn1btX1DjQEQAAwGhet0tf+Kf5un7ldNvjcVP64Qv7dPPDW5lLDgAAAMcQkANp6PebDqs1GLbUP3TmDGaPAwCAlGEYhm44b5ZuWVUn1xiLxJ/f06kP/mKD6luDyW0OAAAAEAE5kHYGIzHdv8a6erwyP0dXLpviQEcAAADHds2KGv3gXctUmue1PX6wO6QP/XKjVr/SqFjcTHJ3AAAAyGYE5ECa+c3GJnUORCz1fzl7pnI9nNIAACA1nT6jWKvff6qWTim0PR6Omfre3/bqw796Tfs6B5LcHQAAALIVaRqQRoJDUT3wSqOlPrXIp8sWVznQEQAAwPhVFuTqR+9ZpvccY8+UzYd79f7VG/TzdQdZTQ4AAIBJR0AOpJGfrzuoXptNrD589kx53JzOAAAg9XndLt28qk5ffscC+ca4+m0oGtd3n9ujD//qNe3p6E9yhwAAAMgmJGpAmtjfOaDVNqvHa0v9evvCSgc6AgAAmLi3LazUz96/Qgur8se8zebDvbruZ+t159MN6hqwblAOAAAAnCwCciANmKapr/1ll8Ix62XGHzmnVm6X4UBXAAAAJ2d2WUD3XbdCH31TrTxj/D4TM6WHXjusK3/6ila/0qhwNJ7kLgEAAJDJCMiBNPDk9latO9BtqS+dUqCL55UnvyEAAIAE8bgMfejMGVr9gVOPuZq8PxzT9/62V+++f52e2tmmuMl8cgAAAJw8AnIgxXUPRvTtZ/dY6m6XodvfMk8ug9XjAAAg/dWVH381uSQ19YR0+2Pb9d771+uxrc2KxlhRDgAAgIkjIAdS3Pee26PuwYil/v7Tp6muIuBARwAAAJPj6GryX37wNJ07q/SYt93bOaAv/KleV/z0Ff1qwyENRmJJ6hIAAACZhIAcSGHrG7v16NYWS72myKd/PWuGAx0BAABMvlllefrOVUv0/auXaE553jFv29I3pG/+dbcu/fEa3f3CXh3qGUxSlwAAAMgEHqcbAGAvHI3rq0812B679eI6+bzuJHcEAACQXGfVluoXM0r0xy3NuufFfeocsF5Vd1RPKKr71jTqvjWNWjmjWJcvrdaFdeXK8bAmCAAAAGMjIAdS1M9eadT+LusKqLfOr9A5x7nkGAAAIFO4XYauXDZFb11QoV+sO6hfb2xSbyh6zI9Ze6Bbaw90q8jn0TsWVeltCyq0qLpABnu3AAAA4A0IyIEUtKu9X/+75oClnp/r1icumuNARwAAAM4K5Hj0kXNq9b7Tp+nhTc36xfqDaguGj/kxPaGoHtxwSA9uOKTqglytmleuVXPLtbSmkI3OAQAAIImAHEg5waGobv3jNkVipuXYjefNUnkgx4GuAAAAUkMgx6P3nT5N7z6lRk9ub9EDrxzUAZur7t6ouW9Iv1x/SL9cf0gV+Tm6YE6ZzplVqtNnFMvP6DoAAICsRUAOpBDTNPXF/6u3/SNvWU2hrlg2xYGuAAAAUk+Ox6XLl07RJYur9eLeTj2yuVkv7umQzRoDi7ZgWA+9dlgPvXZYXrehFVOLdPasUp1dW6LZZXmMYgEAAMgiBORACvn5uoP6a0O7pZ7rcen2t8zlUmAgk7hcilXbPOnlYjM5ADgRbpeh8+eU6fw5ZWoLDumxrS16ZHOzDvWExvXxkZg5MrP8u89JpXlenTK1SCumDb/UlQfkdvE7GAAAQKYiIAdSxPrGbv3g+b22x25/y1zNKQ8kuSMAkyleVa3OTTudbgMAMkpFfq4+dOYMXb9yutY3dutP21v13K4O9RxnU8/X6xyI6JmGdj1zZNFCIMc9EpifMrVQi6oL5HXzZCYA55mmqWjcVCgS11A0plA0rqFo/MjrmIaicQ1F3vB+NK7QkVo4FpdpmoqbUtwcvvzm6GuPyyWv25DHdeTF7ZLf61Z+jlv5uR7l5x597VF5IEeBHDdX3wBIWwTkQApoDw7p9se2214SfPXyKXrHoqrkNwUAAJCmXIahM2aU6IwZJbrtzXGta+zWX+rb9eyuDnUPRk7ovvrDMb24t1Mv7u2UNHxl39IpBVoxrUjLa4q0eEqB8nP5swrAaNFYXAORmAYjcQ2GY0fePvoSVyjyusD6deH1cIAde13QHR8JwIdsAvD4OMZKJYPf61JFfq7KAzmqyM9RVUGuZpT4NaMkTzNL/SrxewnQAaQsfpMDHBaNxXXbY9vVOWD9Y21RdYH+68I5DnQFAACQGTxul86qLdVZtaW69c2mNh7s1gt7OvXy3i7t7Rw44fsbisa1rrFH6xp7JEmGpDnlAS2rKdTSmgItqynS9GIfQRCQAcLRuHpCEfUMRtU9GDnydkQ9oSPvH3m7ZzCi4NDoEDwyng0RMshgJK4DXYNjbppckOvRjBK/ZpflaUFVgRZW5WtuRUA+NkkGkAIIyAEHmaapbz+7R68e6rUcK/J5dOelC5Xj4RJeAACARPC4/rGy/BMXSod7Q3p5X5de3tupVw50qz8cO+H7NCXtau/XrvZ+/X7TYUlSsd+rJVMKtKymUMtqhsey+AmBYCMUial7MDK8QjgWV/jI2ItwLK5IzBwZb+F1D4+78LpcyvW4VOj3qDDXIw/jfibMNE11D0bU1Duk5t6QmnpCOtw7pMO9IR3uDam5d2hC3xNgr28oqq3Nfdra3KdHt7ZIktyGNLs8oAWV+VpaU6hTpxVpRomfJxgBJB0BOeAQ0zT1gxf26TevNlmOGZK+9M4Fqi70Jb8xAACALDGl0Kerlk3RVcumKBo3Vd8a1MaDPdp4sEevHuo5odnlr9c9GNELezr1wp7hsSxuQ5pbMRwAHV1pXlPIKvNMF4rEdKgnpIPdgzrYPfy6LRhW50BEXYNhdQ1ETjqADeS4VejzqNDnVUmeV5X5OarIz1XFkdeV+TmqLvSpyOfJusebaZrqGozocE9ITb1DOtwTOhJ+D6mpN6TDPSGFonGn28xqMVNqaOtXQ1v/SGheFsjRiqlFOnV6kU6dVqTZZXlZ99gFkHwE5IBDfvTSfv1sbaPtsQ+fM1Nn15YmuSMAAIDs5XEZWlRdoEXVBXrf6dMUN03t7RgYCcw3HupRWzA8ofuOmdKO1qB2tAb12yOLI8oCOVr6ulXmC6oKEvnPQRJFYnHt6xxQQ1u/6lv71dAW1L7OAbVO8PFyIvrDMfWHYzrcO3TM2/m9Lk0p9B15yR1+XfSPt0vz0m8+tGma6hiIjAq+D4+sBB9+fyiLA3Cv21CuxyWfx61cj2vkxed1H6m7lON2ye0yZBiSYRhyaXgPh/iRzT+PvkRicUVjpvrDMQXDUfUPRUdGyiRaR39YT9e36en6NklSRX6Ozq4t0dm1pVo5s1iFPm/CPycAEJADDrj35f366d8P2B47Z1aJ/uWsGUnuCAAAAK/nMgzNKQ9oTnlA7zqlRqZp6lBPSBsP9mjDwR5tauodc9bueHT0h/Xsrg49u6tD0nBAv2RqoU6dUaoV04s0qzBXVQW5ifrnIEGOPg42NfXqtUO92nK4V3s6BhRNlZ0SxzAYiWtPx4D2dNjP3c/1uFRdcDQ4zx0VplcX+lQWyJHHldwAfSAcU2twSDu6QmrvH9KhrkHtbu4dDsJ7QmruS78A/Ggw7fe65fO6lOtxy/e68DrXc7TuGrnt0UD7H7WxA++j7x8NvidbLG6qLxRVW/+Q2oJhtQWPvg7rYPeg9ncNqqXv2E/eHE9bMKw/bmnRH7e0yGVIS6YU6txZpbqgrozV5QAShoAcSLKfrW3Uj17ab3ts6ZQCfeWdC+XihzwAAEBKMQxD04r9mlbs16VLqiVJ3QMRbT7cq01Nvdp8uFdbD/dNeGRDNG7q1cYevXpk809JqirI1dIpwyNZltcUal5lvrzMnE6qSCyuHS3B4UC8afj/uqN/8leGJ9tQNK79XcOBph1DUpHfq9I8r0oDOSr1D490CeS4lZfjOfLarUCOW163Sy5j+Ekml2HIdeQhG4maCkXjGorGNBSNaygaV384dmSjy+GNMHtCEXUPRtQWDKfc/G+v21CRz6siv0fFfu/I20U+r4r9XhXkepSX45Y/xy2/16U8r1v+Iy95OW75vO6kP8kw2dwuQ8V5XhXneTW3wv42g5HYyOade9r7h6+maQmqfQLnUdyUNh05D+9+cZ9mlPh1YV25Vs0t06LqAsJyABNGQA4k0S/WHdRdz++1PbawKl/fu3qp8nM5LYFsYLS3q+j6ay31np89KLO83IGOAAAnqjjPq/PmlOm8OWWShkPuXW1BbWrq06amHm0+3KemntCE77+lb0gtff8YNZDrcWlhVf6R0Hz4pTyQk5B/C4Z1D0ZGVodvburRtpZgUlcpG5JyjqwE9rgMxeKmIjFTkfjwpp1OMTX8tekejIy5Cj2dFfu9/xg9U+hTzZFV9OX5OSMBuN/rIoCdAL/XrfmV+ZpfmS/N/0eK3h4c0vaWoLa39I2ccyf6BOOBrkE98EqjHnilUZX5ObqwrlwXzS3XKdOKMu7JCACTiyQOSIJo3NQPn9+r1esO2h6fX5mvu95FOA5kEyMSlveVNbb11L5IGwAwFo/L0IKqAi2oKtA1K2okSe39YW1u6tXmI6set7f0KTzBoHMoGterh3r16qHekVpNkW94488phVpeU6g5FQGCoXEyTVP7uwa16VCvXmsaHpuzr3PiY3PG4ve6Rq4+qCn0qSzgVWlejkryhldkF/u9KvB5lOMeDsXHCmFN01QsPjwHum8oqp5QVL2hiPpCUXUPRtTeH1ZrMKz24JBaj4y7CA6l1ipsp5T4vZpS5FPNkZExrw/BpxT6lJfjdrrFrFOen6vz8nP/8QRjLK4drUFtaBze82HjwZ4TuoqgNRjWb15t0m9ebVKRz6Pz55TpwrnlOnNmiXI9XHkD4NhI44BJ1jUQ1u2P79C6A922x+vKA7rrXUvZbAQAACADlQdydNHc4VWN0vDIjp2twyM7Nh9ZaX4ymzk29QxvSvin7a2ShsPYRdUFWjqlUIurC7SgKl9VBbmsfNXwEwzbm/v0WlOvXjs0HIj3hKIJu/+CXI/qKgKaVxFQXXlAs8ryNK3Yn7ANMA3DkMdtqMjvUpHfq2nj+Jj+cFSHe4fU3BtSU8/w68O9ITUdqXUORE66r1RQmue1rP5+/Sakfi8BeKrzuF1aMqVQS6YU6oOarmjc1JamXr28r1Mv7+vS9pbguO+rJxTVo1tb9OjWFuV53Tp3dqkunleuc2aV8lgAYIuAHJhE25r7dOsft6l5jI1JZpXl6QfvXqpiP+E4AABANvC+LgTSacO1lr4hbW7qVX3noDY2dmtrU8+Ex2kMRuJa39ij9a+bZV6a59XCquGwfGFVvuZW5GtKYWaH5kc309zW3KetzX3a3NSr7S3BhG2mmesZfiJiec3w/+X8ykBKPhERyPGortyjuvKA7fFQJKbm3iE19YaGQ/QjG2Ae7h3S4d7QhOZEJ5rHZaiyIFdVhT5V5HlV/boQvKbQp+rCXPkIPTOOx2XolGlFOmVakf7jTbPU0R/Wmv1den53h17c26nByPjGsQxEYnpqZ5ue2tmmXI9L584aDsvPnV2qQA6RGIBhfDcAJskfNzfrzr80jHkJbW2pXz989zKV5jE3EgAAIJtVFeSqan6FrikNyO12qT8U0cs7WkY2o9vU1HtSK307ByJ6cW+nXtzbOVIL5LhVVx7Q3Irhl9llAdWW5qk4L/0WbkTjpg52DWp3R792tga1rblP21uC6k3g6vDyQI6WTy3UsprCjNow1ed1q7YsT7VlebbHh6JxtQWH1DkQUWd/WJ0DYXUcebtvKKr+cEz94ZgGwjH1h6MaCMcUjZuKm6ZMU4rFTZmS4qapHPfwbPV/vAxvZlnk86rQ51GR36uiI69L83JUWZCjivxc1U0tltfrViwWV2dnf3K/QEgZZYEcvWNRld6xqEqhSExrD3Trrw3ten53x7ivBBmKxvVMQ7ueaWhXjtvQ2bWlWjWvXOfPKWPcKZDl+A4AJFjXQFjf/dtePb61ZczbnDWzRF965wJWjgMAAMDC53Vr+dQiLZ9aJGl4NXRTb+jISJbheeYNbUGdzJ6N/eHY8KiRpt5R9SKfRzNL8zSzxK+ZpXmqed2YikSNCpmo3lBEh3pCOtQdUmP3oPZ0DGh3e7/2dQ4kdANLQ1JdRUDLao4E4lMLVVPoS7nV4cmQ6/nH/HSnuJipjzfwed06f06Zzp9Tpmjc1MaD3Xq2oUPP7mof98iqcMzUc7s79NzuDnndhs6cWaJVc4fD8iL+TgeyDgF5gsTjcT355JN65JFHtHXrVvX09Ki4uFgzZ87UO9/5Tl1++eUKBOwva0NmiMVN/X7TYd39wj71DY39DPaHzpyufzunVm5+0QMAAMA4GIahqUV+TS3y6+0LKyVJg5GYtjX3jaww35ygedo9oejIfb5Rrsel6oJcVRbkqjTvHxtNlh15Hch1K8/rVl6OR3lel/w5buW6XZJhyJDkMob/LaZpKhIzNRiJKRSND7+ODK9E7hqIqHMgPLxieSCsroGImnuHdKgndMzfsU+G3zs89mZ5TaGWTR3e8JTVpEB68LgMnTGjRGfMKNF/r5qjbc19+uuRsPxA1/g23Y3ETL2wp1Mv7OmU25BOmVak8+eU6YK6Mk0tcu7JIQDJw0/9BOjs7NSNN96odevWjaq3tbWpra1N69at0/3336/vfOc7WrRokUNdYjJtaurV1/+ySztbx944JJDj1ufePn9kgyYAAABgovxet06bXqzTphdLGl5l3tgd0qamHm1u6tP2lj41tPUnbOa2NDyeYH/XoPaPM3RKVVUFuVp+ZGX48poizakIyMPiFSDtuQxjZI+HG86r1e72AT3T0Ka/1LdrT8fAuO4jZmpkH4dvP7tHdeUBnV9XpnNnlWpxdQEL3YAMRUB+kkKhkP75n/9Z27dvlzS8ImLFihWaOXOmWlpatHbtWkWjUe3fv18f+tCH9NBDD2n69OkOd41Eaewa1H1rDuixY4xTkYbnjX/jssVjzvYDAAAAToZhGJpR4teMEr8uWVwtSQpH49rV3q8dLX3a1hLUjpag9nT0J3QcSarL9bg0v3J4c9KjI1OqC31OtwVgkhmGobqKgOoqAvrIObXa1zGgZxra9Zf6NtW3jX+W/a72fu1q79d9fz+gQp9HZ84s0TmzSnRWbanKA+wnhv+/vbsOi2L7/wD+phEQQcFuUSz02ondrde+tl7jqle99r12d3e3oojdrdiiIqKImIQCUtK5vz/4Md+dbWApeb+ex8ed3ZnZs8uc2TOfOedz6FfBAHk6rV27VgiOW1tbY8uWLahWrZrw+pcvXzB+/Hi8e/cOoaGhmDp1Ko4dO5ZVxSUtkEgkeO4ThiMuvrj3IQjqLi/a2FpjZuvyHKZJRERERJnKUF8XlQvnReXCedHj/59LSEzuBe4VGAnPwEh4/YjAhx9R8A+PzdKyakMeA12ULWAK24JmqFzYDJUK5UVZK/YOJyKgdAETDCtQEsPql4R3SLQQLH/rr3wUuKyfMQm49i4Q194FAgDKW5uiTsnkkTw1i+fjNT9RDsbamw7fv3/H4cOHASTfndy8ebMoOA4ApUqVwp49e9C9e3cEBATgxYsXuH37Npo1a5YFJab0iEtIwnXPQBx+5qPRHedSlnkwtYUN6pW2zITSERERERGpp6+ni3JWpihnZYq2lf73fHR8Ir4GR+NLSBQ+B0fhc3A0/MJi8O1nDIKj4rOuwArkM9ZH0XzGKGGRBzbWpv//eUxQxNwYurlwIk0iSp0SlnkwuG4JDK5bAn5hMbj5/gduegbC7Vt4qvbzPjAS7wMjccTFF7o6gG1BM9QqYYHfiuWDXdG8yG/CHuZEOQUD5Ong6OiI+PjkxmLz5s1RvXp1hetZWVlh/PjxmD17NgDg2LFjDJDnENHxiXj4OQS33v+A88cgRMQmqt0mj4EuRtQvhX61isFATzcTSklERERElD55DPRgW8gMtoXM5F6Ljk/E95+x8PsZg+8/YxAUmTyJpvSEmqHR8YiMS0RiOnKe6+nqwDKPgTABaH7T5P8LmBqiaD5jFPv/f+ylSUTaUjSfMQbULo4BtYvjR0Qs7n4Mxr0PQXjyJQRxqUhHlSQB3vpH4K1/BA498wEAFMtnDLuiyRP/Vi2SFzZWpjDUZ4yAKDtiyyIdbt68KTzu0KGDynXbt2+P+fPnIyEhAffv30dERATMzOQbn5S1JBIJvv2MxQufMNz2+oGHn0MQm5Ck8fatba0xoWlZFMprlIGlJCIiIiLKPHkM9FCmgAnKaDCfTnxiEqLiEhEdn4io+ETEJSRBAkAiQXJqQokEEgAGerrIY6AHY/3k//MY6EKfnUuIKAtZmRmhR7Ui6FGtCKLiEvHoSwjufgjCw0/BaRpJ4xsWA9+wGFx+GwAg+SZgmfwmKG9tigoFzVDh/3OkW+YxgA5HvxBlKQbI0ygqKkrIPQ4A9erVU7l+3rx5Ub58ebx9+xZxcXFwcXFB06ZNM7qYpEZsQhI8AyLwyu+n8O9HZFyq9qEDoKlNAQyoXRzVi+XLmIISEREREeUABnq6yJdHF/nyGGR1UYiI0szEUA8tyluhRXkrJEkkeB8QiQefg/HgUzDc/H4iLXMdJyZJhEk/L/1/0BwAzI31UcoyD0rlNxH+L25hjCLmHDFDlFlY09Lo48ePSEpK7lmcN29eFCxYUO025cqVE4Lq7969Y4A8k8QnJiEwIg7+4bH4EhyFT8FR+BIcjc/BUfALi1E7yaYyJgZ66GJXGH1qFEVxizxaLTMRERERERERZT1dHR0hBdXQeiURHpMAF+9QPPv/fx9+RKVr/z9jEuD2LVxhDvS8RvooYm6EovmMUSivEaxMDWFlZogyhc1R2CIPCpgYIkki4fwLROnEAHka+fr6Co+LFSum0TaFChVSuD2lTUx8Ity/hyMsOh5hMQn4GZOAnzHJj4Mi4xAYEYeA8FiERGt3UqFi+YzR87ei6GZXmHdziYiIiIiIiHKRvMb6aFbeCs3KWwEAQqLi8NwnDM++hsLV7yc+/IhEOqZjEAmPTUB4YAI8AyOVrqMDwMxIH3mN9JDX2AB5jfVhbqSPvMb6yGukD3Op/00N9WGkryv3z1hfD0YGyY8ZbKfciNG9NAoKChIeW1lZabSNpaWl8Dg0NFTbRcpV7n4IwpyLHoiMUz9ppjaULWCC5uWt0Ly8FSpYmzI/GBERERERERHB0sQQLStYo2UFawBAZFwC3n6PgNu3n3Dz+4nX38K13nFPmgT/H0iPTQB+xqZ7fwZ6OjDS14Whni70dXWS/+npQk9HB3r/v6ynK35soKeDqoXN0btGUabYohyJAfI0ioz83907IyPNJmQ0MfnfpDbS21PqJCZJMjw4bqing8qF86JRmfxoVt4KpfOrn5CIiIiIiIiIiHI3U0N91C5pgdolLQAAEokEgRFx8AyMgGdAJDwDI/A+MBJfQ6KztqBKxCdKEJ+YCCB1MZcHn0Jw72MQ9vavAT1ddiqknIUB8jSKi/vfRI6aBsj19f/3dcfHZ9zdQ1m6ujqwsjLLtPfLaIkSCa5P1m7+dl0dHRjq6cLg/++SGujxZJ5ev9pxR9phWaAcvCd5yz1f2Kww9HT0sqBEmUtULyzLAd7y30X+woUBvV//u1Amtx8juRF/L4jksV4QyWO9oJzM2hqoXKaA6DkJgMRECRKSkpCQJEFCkuT/lyWQpHm2tKyX39SIMRXKcRggTyNdXV2Fj1WRSP53gtN0G2341dKB6OvooEg+ToqZ3f1qxx1ph76OPoqbF8/qYmQZUb3Q1weKy38Xub3m5PZjJDfi7wWRPNYLInmsF/Sr0QGgq68DA2RefIiIFGMtTCPpdCmJiZoNO5Fez9DQUOtlIiIiIiIiIiIiIiLNMUCeRtIB8qioKI22kV5PensiIiIiIiIiIiIiynwMkKeRhYWF8Dg0NFSjbUJCQoTHBQoUULEmEREREREREREREWU0BsjTqGzZssLjb9++abSNv7+/8LhYsWJaLxMRERERERERERERaY4B8jQqVaoUDAwMAACBgYEIDw9Xu42Xl5fw2MbGJsPKRkRERERERERERETqMUCeRvr6+qhatSoAQCKR4Pnz5yrX//nzJ96/fy9sW6NGjQwvIxEREREREREREREpxwB5OrRu3Vp4fP78eZXrXrp0CYmJiQCA2rVrw8zMLEPLRkRERERERERERESqMUCeDh06dBDSrFy4cAHPnj1TuN6PHz+wadMmYblPnz6ZUj4iIiIiIiIiIiIiUo4B8nQoUqQIBg0aBABITEzE2LFj8eDBA9E63t7eGD58OAICAgAAlStXRrt27TK9rEREREREREREREQkpiORSCRZXYicLDIyEn379oWnp6fwnJ2dHWxsbBAYGIhHjx4hISEBAGBmZgYHBwdO0ElERERERERERESUDTBArgVBQUEYP348XFxclK5TuHBhbNy4EdWqVcvEkhERERERERERERGRMgyQa4lEIsHFixdx7tw5uLu7IyQkBMbGxrCxsUHLli3Rr18/TsxJRERERERERERElI0wQE5EREREREREREREuRIn6SQiIiIiIiIiIiKiXIkBciIiIiIiIiIiIiLKlRggJyIiIiIiIiIiIqJciQFyIiIiIiIiIiIiIsqVGCAnIiIiIiIiIiIiolyJAXIiIiIiIiIiIiIiypUYICciIiIiIiIiIiKiXIkBciIiIiIiIiIiIiLKlRggJyIiIiIiIiIiIqJciQFyIiIiIiIiIiIiIsqVGCAnIiIiIiIiIiIiolxJP6sLQEQ5X1JSEi5duoQzZ87A3d0dYWFhsLCwQKlSpdCxY0d07doVpqamWV1MykUuXryISZMmoVixYrh586bG2z1//hzHjh3Ds2fPEBgYiDx58qBo0aJo3rw5evfujSJFimi8Lx8fHxw6dAj379+Hj48PAKBgwYKoW7cuevXqhWrVqmm8r4iICDg4OOD69evw8vJCdHQ0ChYsiIoVK6J79+5o1aoVdHR0NNoX6+uvLzAwEA4ODnjw4AE+ffqE8PBwmJiYoEiRIqhXrx769u2LsmXLarw/1gvWi1+Fj48Pjhw5ggcPHsDb2xsJCQkoVKgQKleujC5duqBZs2bQ1dWs/xDrBevFr27FihXYvXs3AODAgQOoV6+e2m1YL1gvfgX9+vXD8+fPNV6/WbNm2L59u9LXWS9YLyhn0JFIJJKsLgQR5VzBwcEYP348nj17pnSdUqVKYd26dahcuXImloxyqx8/fqBr16748eOHxgHypKQkLF68GIcOHVK6jqmpKebPn4/OnTur3Z+TkxPmz5+PmJgYha/r6Ohg2LBhmDx5MvT09FTuy83NDX///Tf8/PyUrtO4cWOsXLkS+fPnV7kv1tdf36FDh7BixQrExsYqXUdXVxfDhw/HP//8ozIYyHrxP6wXOd/BgwexYsUKxMXFKV2nVq1aWL58OUqUKKF0HdaL/2G9+HW9evUKffv2RWJiIgD1AXLWi/9hvcjZJBIJateujYiICI23URYgZ734H9YLygkYICeiNIuJiUHfvn3x9u1bAMk/yjVq1ECpUqXg7++PJ0+eICEhAQBgYWEBR0dHlRedROkVGRmJoUOHwtXVFQA0DpDPmTMHDg4OwnLZsmVRtWpVREdH4+HDh0IjWUdHB5s2bUKrVq2U7uvcuXOYOnUqUn5e8+fPj7p160JfXx8vXryAr6+vsO6QIUMwc+ZMpfv68OED+vTpg/DwcACAkZER6tevD0tLS7x//x7u7u7Cur/99hsOHjwIQ0NDhftiff31bd++HWvWrBGWTUxMUKtWLRQsWBChoaFwcXFBaGio8HrPnj2xePFipftjvWC9+FXs2rULK1euFJYtLCxQp04d5M2bFz4+Pnj27BmSkpIAAIULF4ajoyOsra0V7ov1gvXiVxcXF4fu3bvDy8tLeE5dgJz1gvXiV+Ht7S0cnwULFkTz5s3VbmNra4s//vhD7nnWC9YLymEkRERptGTJEkmFChUkFSpUkDRq1Eji6uoqev3z58+Szp07C+v06dMni0pKuUFQUJCkb9++wvFWoUIFSfPmzdVud/36dWH9ypUrSxwdHUWvh4eHSyZOnCisU7NmTUlQUJDCfX3//l3y22+/CevOnTtXEhsbK7yemJgo2bVrl8TW1lZY5+7duwr3lZSUJOnevbuwXr9+/ST+/v6ide7evSupU6eOsM7q1auVfk7W11+bu7u76LiaNm2aJDQ0VLROVFSUZNmyZaI6cv78eYX7Y71IxnqR8719+1ZSuXJl0fEXFRUlWsfT01PSpk0bYZ2RI0cq3BfrRTLWi1/bypUrRb8TFSpUkDx69Ejp+qwXyVgvfg3Xrl0T/ob//vtvmvfDepGM9YJyEk7SSURp8v37dxw+fBhA8p3izZs3y+U8K1WqFPbs2YOCBQsCAF68eIHbt29ndlEpF3j9+jV+//33VOULTCHd43bixIn4/fffRa+bmZlh1apVaNy4MYDkfH07d+5UuK+tW7ciKioKANC0aVPMmzdP1OMiJbXF+PHjhefWr1+vcF+XLl0SenBYW1tj27ZtQl1KYW9vj02bNglpMg4ePIigoCC5fbG+/vo2b94s9Cpq1aoVli9fjnz58onWyZMnD6ZPn47+/fsLz23YsEHh/lgvkrFe5Hzbtm0Teq+1aNEC8+bNQ548eUTrlC9fHlu3boWBgQEA4M6dO/jw4YPcvlgvkrFe/LpevXqFPXv2pGob1otkrBe/Bg8PD+FxxYoV07wf1otkrBeUkzBATkRp4ujoiPj4eABA8+bNUb16dYXrWVlZiX6sjx07linlo9whKioK69atQ79+/YQcerKBD1WePn0qDCG2srLCoEGDFK6np6eHWbNmCRPVnDx5Ujj+pcty9uxZYXnixIlK33fUqFEoWrQogOQcgK9fv5ZbR7qujBgxAubm5gr3VbduXXTo0EEow5kzZ+TWYX39tUVGRuLu3bvC8pQpU1SuP3HiRCEQ+PnzZ7lAIOuFGOtFzhUbGytKsyX9d5RVtmxZ1KxZE0ByDtoXL16IXme9EGO9+PXExcXh33//RWJiIszNzTWazJn1Qoz1IufTRoCc9UKM9YJyCgbIiShNpC84U348lWnfvj309fUBAPfv30/VpCdEynz58gXt2rXD1q1bhUnXypQpg61bt2q8D+njuEWLFjAyMlK6bpkyZVClShUAQFhYGB4/fix6/fHjx4iMjAQAlC5dWuUkNPr6+mjXrp2wfPXqVdHr4eHhoolu2rdvr/JzSE/sI7svgPX1V/f27VuhDpQoUQJlypRRuX6+fPlgY2MjLH/69En0OuuFPNaLnCkyMhJ//PEH7O3tUaVKFbWTg1lZWQmPf/z4IXqN9UIe68WvZdOmTXj//j0AYMaMGaL6oAzrhTzWi5zt3bt3wmNbW9s07YP1Qh7rBeUEDJATUapFRUUJE3EAUDlpDwDkzZsX5cuXB5DcO8XFxSVDy0e5w/fv3+Hv7y8s9+3bF05OTqma+EX6WFR3HANArVq1hMcPHz7MsH29ePECiYmJAJIbw4UKFVK5r5RejwDg6uoqNKQB1tfcwMDAAK1bt4adnR2qVq2q0TYpFykARMcLwHqhCOtFzpQ/f35Mnz4du3btgpOTk9r1v3z5IjwuXLiw6DXWC3msF7+O169fY/fu3QCAxo0by6WDUIb1Qh7rRc4VGRkJb29vAECxYsWU9q5Wh/VCHusF5QQMkBNRqn38+BFJSUkAkn/sZPOWKVKuXDnhsfSdeaL0qlGjBhwcHDB//nyYmJikatuU4Y+A+BhVRtVxLL0vTYYlS+/L09NTyB+dlnKZm5vD2toaAJCUlCTanvX111e9enVs2rQJjo6OWLdundr14+LiRGlVChQoIHqd9UJ92Vgvfj0PHz4UhqMbGRnB3t5e9DrrhfqysV7kTHFxcZg5cyYSEhJgamqKhQsXarwt64X6srFe5Bzv3r0TjqOU3uM+Pj5wcHDA8uXLsXTpUuzevVth6hJprBfqy8Z6QdmRvvpViIjEfH19hcfFihXTaBvpO9TS2xOlVdGiRXHw4EHUrVs3TduHhISIekJocixL9yiUPY6ll4sXL56qfcXExCAoKEgYzpyWOla4cGEEBgYK26fkAWR9JVmXL18WJnvS09MT9TpnvVCO9eLXFBERAUdHR9GEZiNHjhTdOGK9UI71IufbvHkzPD09ASTPYZGSw1gd1gvlWC9yJun843ny5MGYMWNw69YtUZA5RdWqVTFnzhy5vNusF8qxXlB2xwA5EaWa9CzWmuQnBABLS0vhcWhoqLaLRLlQiRIlUpVORVZwcLDw2MDAQKNhlKqOY+l6IdsjV5E8efLA2NgYMTExwv5S6lN661hISIjCcrG+UnR0tCgQWK9ePVhYWAjLrBea7Yv1Imf7+vUrtmzZAl9fX7x580bIhaqjo4PBgwdj3LhxovVZLzTbF+tFzuPu7o5du3YBSJ6Ur1+/fhpvy3qh2b5YL3IO6QD5hQsXVK77+vVr/PHHH1i2bBk6deokPM96odm+WC8oO2KAnIhSTfquuKpJR6RJp76QzXdLlBWkj0NjY2ONtsmTJ4/C7dOzv5QGrPT2aaljysrG+krS5s2bBx8fH2F57NixotdZL5Rjvfh1vHv3DqdOnZJ7vnv37vjrr7/knme9UI71IueKi4vDjBkzkJCQAGNjYyxatAg6Ojoab896oRzrRc4km/ajQoUKGDFiBOrXrw9LS0t8//4d165dw44dOxAaGor4+HjMmDEDRYoUEXJ/s14ox3pB2R1zkBNRqsXFxQmPNf1BlJ4QLj4+XutlIkotbR/H0vszNDRM1/7SUjYDAwOt7Yv19de0adMmnD59Wlju0aMHateuLVqH9SL15aKcx8/PT+HzTk5OaNmypaieAKwXaSkXZX9btmwRUqtMnDgRpUqVStX2rBepLxdlXxKJRKgPANChQwc4Ojqia9euKFSoEAwNDVGyZEkMHz4cJ0+eFNKKxMfHY9asWUI+btaL1JeLKLtgD3IiSjVdXV2Fj1WRzt2m6TZEGUm6l5Q2jmM9PT2hcaynp6e18mi6L2VlY30lANi1axc2btwoLJcrVw6zZ8+WW4/1IvX7opzH3t4e169fR6FChfDz50/cv38fmzdvxpcvXxAeHo7p06dDT08PnTt3BsB6kZZ9Ufb25s0b7Ny5E0DyRM+DBw9O9T5YL1K/L8q+JBIJdu7cCW9vb/j7+2Po0KFKA7/FixfHqlWrhJREHz9+xM2bN9GqVSvWizTsiyi74FFJRKkmPTwqMTFRo22k19P07jdRRjI1NRUea+M4lh6CqOn+EhISFO5Puo5Jr5PefbG+5k6bNm3CypUrheWCBQtix44domMjBeuFcqwXv46yZcuiRIkSMDQ0hJWVFbp27YqTJ0/Czs5OWGfhwoX4+fMnANYLVVgvcp74+HjMnDkTCQkJMDQ0xJIlS9IUrGK9UI71IufR1dVF7dq10b17d4wePVptr+iaNWuiZs2awvLdu3cBsF6ownpB2R0D5ESUatI/iFFRURptI72eoqAMUWZLy3EcHR2tcHvZZU3z6inbnzbLxvqaeyUmJmLevHminuPW1tbYv38/ihcvrnAb1gvlWC9+bXnz5sWaNWuEIeBhYWG4dOkSANYLVVgvcp6tW7cKkxH+9ddfsLGxSdN+WC+UY73IHerWrSs8TknPwnqhHOsFZXcMkBNRqllYWAiPNZ2BWnrWa01m4CbKaNLHcXR0tDCZjSrSx3H+/PlFr+XLl094rEm9kH1P6XqR2n3Jlk16X6yvuVN0dDTGjRuHo0ePCs8VLlwYhw4dQtmyZZVux3qR+n3Rr6NkyZJo0KCBsPz8+XMArBdp2RdlT2/fvsWOHTsAAJUqVcKff/6Z5n2xXqR+X/RrKVy4sPA45dhgvUj9voiyCwbIiSjVpIMr375902gbf39/4XHKpCZEWcnc3FzUOPv+/bvabaTXkT2OpetFavdlZmYmamimpY4pKxvra+4TGhqKwYMH4+bNm8JzZcqUwdGjR1G6dGmV27JeKMd6kTuUL19eePzjxw8ArBeqsF7kLNevXxcmx3v79i2qVKkCW1tbhf+ePHkibDdo0CDh+YEDBwJgvVCF9SJ3SMkNDvxvMkvWC+VYLyi7Y4CciFKtVKlSQiMgMDAQ4eHharfx8vISHqd1KCeRtpUrV054/OHDB7XrSx/H0kEUQHxca7Iv6XVk64T08sePH9XuKywsDIGBgQCScyhKN1pZX3OX4OBgDBw4EK6ursJzdnZ2OHLkCIoWLarRPlgvFGO9yHk+ffqEEydOYPXq1Th58qRG20hPaCadg5b1QjHWi9yN9UIx1ouc5+3btzhx4gS2bt2KM2fOaLRNQECA8Nja2lp4zHqhGOsFZXcMkBNRqunr66Nq1aoAkmejThmCrMzPnz/x/v17YdsaNWpkeBmJNCF9LKo7jgHAxcVFeFy7dm3Ra7/99lua91WrVi3Ra5UrVxYanZ6enoiIiFC5L+n3q1KlimhCH9bX3CMiIgJDhw4V8mACQOPGjXHgwAG5IbuqsF7IY73ImR48eIBZs2Zhx44dOHjwoEbbSAcNihQpIjxmvZDHepHzVK1aFX369NHon3TAr3nz5sLzLVq0EJ5nvZDHepEz3blzB7NmzcK6deuwa9cujbaRPhaqVasmPGa9kMd6QTkBA+RElCatW7cWHp8/f17lupcuXRJmra5duzbMzMwytGxEmpI+jqWPU0U+fvyIN2/eAEieWEZ6Yh4AqFevnpDbz83NDV++fFG6r4SEBFy+fFlYlr7YBJKHRDZq1EhYN2WiOGWk62Dz5s3lXmd9zR0mT54sTLwGAO3atcO2bdtSPRES64U81oucSfoC/O3bt6KbR4oEBgbi4cOHwnLDhg2Fx6wX8lgvcp7mzZtjwYIFGv0rU6aMsN3QoUOF54cOHSo8z3ohj/UiZ6pevbrw2NPTUwjmKuPl5YWnT58Ky9LHCOuFPNYLygkYICeiNOnQoYNwB/rChQt49uyZwvV+/PiBTZs2Cct9+vTJlPIRacLOzk4YLujr66u0x0hSUhIWL14MiUQCAOjSpYuoFwWQnHuwffv2AJJ7UixatEiUm1Dajh074OfnByB5GKZsbxEA6Nq1q/B406ZNCA4OVrivJ0+e4OLFi0IZevToIbcO6+uv7/Dhw7h9+7aw3Lp1a6xZs0b4u6cG64UY60XOVblyZVSoUEFYXrFihXC8ykpKSsKcOXOEyc2KFy8Oe3t74XXWCzHWCwJYL2SxXuRcdevWFU26uXz5cqXHX2xsLP7991/heK5bty6qVKkivM56IcZ6QTkFA+RElCZFihTBoEGDAACJiYkYO3YsHjx4IFrH29sbw4cPF/KzVa5cGe3atcv0shKpMmPGDOHxunXrsG/fPlEAJTIyElOnToWzszOA5N4dY8aMUbivcePGCT0i7t69i2nTpiEyMlJ4PSkpCXv37sWGDRuE5yZNmqRwX+3btxd6s3z//h3Dhw+Ht7e3aJ379+9j/PjxQkO5X79+opQAKVhff20/f/7E6tWrheWSJUti2bJl0NPTS/M+WS+SsV7kfFOnThUe37t3DzNmzJAbVh4QEICxY8cKE9vq6Ojg33//haGhoWg91otkrBckjfUiGetFzqanp4eJEycKy/fu3cPUqVPx8+dP0Xo+Pj4YPny4MNeLoaEh5s2bJ7c/1otkrBeUk+hIlHWjICJSIzIyEn379hUNWbazs4ONjQ0CAwPx6NEjJCQkAEge0uXg4MAJOSjD+fj4oGXLlgCSZ0hPCXioMnPmTDg5OQnLxYsXR40aNRAXF4eHDx+KGscrVqwQ9b6Q5eTkJOpVYm5ujgYNGsDQ0BAvXryAj4+PsG6vXr2waNEipfvy8PDAgAEDhIlvDAwMUK9ePVhbW8PLywtubm7Cura2tnBwcJDreZKC9fXXtX37dqxZs0ZYrlOnjmgiJXWaN2+ucOgs6wXrxa9i/fr12LJli7BsZmaGunXrwtLSEt++fcOzZ88QFxcnvD558mSMHDlS4b5YL1gvcouBAwfiyZMnAIADBw6gXr16StdlvWC9+FX8+++/okmdTU1NUa9ePVhYWMDX1xcuLi7C31lfXx/r169Hq1atFO6L9YL1gnIWBsiJKF2CgoIwfvx40YQgsgoXLoyNGzeKJi8hyihpCZAnJiZiwYIFcHBwUDr83tjYGLNnz0bPnj3V7u/EiRNYvHgxoqOjla7Tt29fzJkzR20v35cvX2LSpEnCkElFatasic2bN6udiJH19dfUo0cPuLu7p3n7cePGYfz48XLPs178D+tFznfgwAGsXLlSFAiXZWFhgVmzZqFz585K12G9+B/Wi19bagLkrBf/w3qRsyUlJWHjxo3Yvn27ytzhhQsXxsKFC9GkSROl67Be/A/rBeUEDJATUbpJJBJcvHgR586dg7u7O0JCQmBsbAwbGxu0bNkS/fr140QclGnSEiBP8fz5c5w4cQJPnz5FYGAgdHR0ULx4cTRq1AgDBgxAiRIlUlWOo0eP4u7du/Dz80NcXBysra1Rs2ZN9O3bV2FeQGWioqJw7NgxXLt2DZ8+fUJERATMzc1RpUoVdOnSBR07doSurmZZ01hffz2//fabyosldZQFyFOwXrBe/Cr8/f1x5MgRODs74+vXr4iOjka+fPlQrlw5NGvWDL169ULevHk12hfrBevFry41AfIUrBesF7+Kr1+/4ujRo3j06BF8fHwQHR2N/Pnzo2zZsmjTpg26dOmi8d+Z9YL1gnIGBsiJiIiIiIiIiIiIKFfiJJ1ERERERERERERElCsxQE5EREREREREREREuRID5ERERERERERERESUKzFATkRERERERERERES5EgPkRERERERERERERJQrMUBORERERERERERERLkSA+RERERERERERERElCsxQE5EREREREREREREuRID5ERERERERERERESUKzFATkRERERERERERES5EgPkRERERERERERERJQrMUBORERERERERERERLkSA+RERERERERERERElCsxQE5EREREREREREREuRID5ERERERERERERESUKzFATkRERERERERERES5EgPkRERERERERERERJQrMUBORERERERERERERLkSA+RERERERERERERElCvpZ3UBiIiIiChrOTk5YebMmSrX0dHRgYGBAczMzFCoUCFUqlQJ7dq1Q+PGjaGnp6f2PQYOHIgnT54AAA4cOIB69epppezKuLu7o0qVKhn6HqTarl27sHLlStjZ2eHEiRPQ0dHJsrIcOXIE8+fPh62tLRwdHWFoaJhlZSEiIiKi7IU9yImIiIhILYlEgri4OAQHB+Pt27dwcnLCyJEj0bp1azx8+DCriyeIiorCsmXL0KtXr6wuSq7m6emJ9evXAwBmzJiRpcFxAOjTpw9sbGzw7t07bNiwIUvLQkRERETZC3uQExEREZHAxMQEnTt3lnteIpEgJiYGQUFBeP/+PQICAgAAvr6+GDp0KGbOnInBgwdndnHldOzYEX5+flldjFwtKSkJ//77L+Li4tCqVSvUrl07q4sEPT09TJ48GWPGjMHu3bvRvn17jjAgIiIiIgAMkBMRERGRFEtLSyxYsEDtes+ePcPChQvh4eEBiUSCZcuWoVixYmjVqlUmlFI5BseznqOjI9zc3KCrq4tJkyZldXEELVq0QI0aNfDixQssXLgQR48ezfKe7URERESU9ZhihYiIiIhSrXbt2jh69Chq1aoFILnX8JQpUxAYGKhw/YMHD+Ldu3d49+5dhucfp6wTGRmJtWvXAgDatWsHGxubLC6R2Lhx4wAAL168wPnz57O4NERERESUHTBATkRERERpYmJigk2bNsHS0hIAEB0djV27dmVxqSgrHT58GMHBwQCAQYMGZXFp5DVu3BjlypUDAGzZsgVJSUlZXCIiIiIiymoMkBMRERFRmuXPnx/jx48Xlh0cHBASEpKFJaKsEhcXh7179wIAypUrhxo1amRxiRTr0aMHAODjx4+4cuVKFpeGiIiIiLIac5ATERERUbp0794dq1atQlRUFKKjo/HgwQN07NhRtM7AgQPx5MkTAMCBAwcUpllJTEzElStXcPnyZbi5ueHHjx/Q19eHpaUlKleujCZNmqBr164wMjISbff48WOFvZVtbW2Fxzdu3EDx4sXl1nn8+DGuX78OFxcXBAQEIDQ0FAYGBsiXLx8qVqwIe3t79OjRA3ny5FH42Tdu3IhNmzYBSM69bWdnh3fv3uH48eO4f/8+/P39oauri6JFi6Jp06b4448/UKRIETXfaDIXFxecOnUKz549w7dv35CUlAQrKyvY2dmhe/fuaN68udp9hISE4MSJE7hz5w4+f/6MsLAwmJubo3Tp0mjatCn69OkDCwsLjcqjzuXLl4Xe4926dVO6npOTE2bOnAkA2Lx5M1q1aoWPHz/iyJEjuHfvHvz9/WFsbIwSJUqga9eu6NWrl+hv/vjxYxw+fBivXr3Cjx8/YG5ujurVq2PQoEFo0KCB2nJ269YNq1evRlJSEo4ePYr27dun74MTERERUY7GADkRERERpYuJiQlq166Nu3fvAkgOYMoGyNX59u0bxowZg7dv34qej4uLQ1RUFHx9fXHt2jVs3LgRa9asQZ06ddJV5m/fvmHy5MlwcXGRey0+Ph5RUVH49u0bbt26ha1bt2LLli2oVq2a2v3u2LEDGzZsQHx8vOh5T09PeHp64tChQ1i8eLHK7yc4OBj//vsvbt26Jfean58f/Pz8cOXKFTRu3Bhr166Fubm5wv04OTlh6dKl+Pnzp+j5oKAgBAUFwcXFBbt27cLs2bPRpUsXtZ9NHUdHR+Fxs2bNNN7uyJEjWLp0KeLi4oTnoqOjERISglevXuH8+fPYuXMnTExMsHTpUhw8eFDu89y8eRM3b97EP//8g1GjRql8PysrK1SuXBmvX7/GkydP8PXrV5QsWVLj8hIRERHRr4UBciIiIiJKNzs7OyFA/vTp01RtGxMTg+HDh+PDhw8AAAMDA1SvXh0lSpRAYmIivn79CldXV0gkEgQEBODPP//E2bNnhaBmwYIF0adPHwDJKV5SpDwHAKampsLjHz9+oG/fvvj+/TsAQFdXF1WrVkXp0qVhaGiIoKAguLq6Cr2hAwMDMXr0aFy+fFlpMBpInoj0zJkzAJJTz9SqVQtmZmb4+PEjXF1dASQHfqdNmwYbGxtRD/cUYWFh6N+/Pz59+iQ8V6RIEfz222/Q09ODh4cHvLy8AADOzs4YPnw4jhw5AgMDA9F+duzYgdWrVwvL5ubmqFWrFvLnz4/g4GA8ffoUERER+PnzJ6ZOnYrQ0NB05QxP2SeQ/PeoUKGCRtudPXtWSHNiaWmJmjVrwtzcHK6urvj48SOA5Ak1N2zYAIlEIgTH7ezsYGNjg7CwMNy/fx+xsbEAgLVr16JBgwZqb2Y0atQIr1+/hkQiwdWrVzFixIg0fW4iIiIiyvkYICciIiKidCtRooTw2N/fP1Xbnjp1SgiOFytWDPv37xftDwDevHmDUaNGISAgANHR0di2bRuWLFkCAChTpgwWLFgAQBwgT3lO1rp164TgeMmSJbFt2zZh4sYU8fHx2L9/P1auXAkguZfyuXPn8Mcffyj9HGfOnIG+vj4mTJiAIUOGwNDQUHjt2bNnGDduHEJCQpCQkCAXwE4xf/58IThubGyMuXPnonv37tDR0RHWOXv2LP7991/Ex8fj1atX2Lt3L0aOHCm8/vDhQ6xduxZAcvB/7NixGDFiBIyNjYV1YmJisGnTJuzcuRMAsHz5clSrVg2//fab0s+nyr1794QJL1OTezwlOD58+HD8/fffQhklEgkWL14sBMQPHTqEpKQkWFtbY926dahdu7awDz8/PwwZMgRfvnyBRCLB0aNH1QbIa9WqJTy+c+cOA+REREREuRgn6SQiIiKidJPuWR0ZGSlKl6HOo0ePhMcTJkyQC44DQOXKlbF06VJh+c6dO2kqZ1RUFM6ePSssr169Wi44DiT3Yh8xYgTatGkjPPfs2TO1+//nn38wcuRIUXAcAGrXro0ZM2YIy87OznLbenl54eLFi8Ly2rVr0aNHD1FwHAC6dOmCqVOnCssHDhyARCIRlpcuXSoEq2fMmIFx48aJguNAcvB9ypQpwgSrCQkJWLNmjdrPp8zz58+Fx+XLl0/Vtj179sS0adNEZdTR0cGUKVOEnv9JSUnQ0dHB5s2bRcFxAChatKjo+5AuizLSZXz16hUSEhJSVWYiIiIi+nUwQE5ERERE6WZiYiJaDgsL03jb6Oho4bGqwHrDhg0xadIkrFy5Eps3bxaCwKnx7ds31KtXD6VLl0bt2rXV9jSWDsaGhoaqXDdfvnwYMGCA0tdbtGghBLtDQ0MRHh4uev3q1atCoNve3h4tWrRQuq++ffsib968wrKPjw+A5PQ27969AwCUKlUKAwcOVFnmkSNHCpN0Pn78GJ8/f1a5vjLu7u7C49QEyHV0dIQgvSxjY2NUr15dWK5Tp45oWZp0r/WAgAC171u0aFGYmZkBSO5NnzKCgYiIiIhyH6ZYISIiIqJ0kw1s6+npabytjY2N0CN85cqVMDU1RZs2baCvL26q6urqYvTo0ekqZ7ly5YS0IpqQDkLLTrwpq0aNGjAyMlL6urm5OfLmzStMmhkVFSXav/SknJ06dVL5XkZGRnByckL+/PmFQC8APHjwQHjcoEED6Oqq7g9jaGiI+vXr4/LlywCSA+ylS5dWuY0i3t7ewuNSpUppvF2ZMmVQuHBhpa8XKFBAeCydFkVWSpAfSP5eNVGqVCkhsO/t7a0wJzwRERER/foYICciIiKidJPtDS0dtFWnV69eOHDgAOLj4xEWFoZJkyYhb968aNSoEezt7WFvb49ChQppu8gKhYaG4sOHD/Dw8MDz589FqVDU9VhPmTRUFVNTUyFALpvWQ7oXc9WqVdXuS9H7vX79Wnjs5uaGOXPmqN2PdK9xT09PtevLiomJEfWulw76q6PuO5O+SaLqGJC9maIJ6XKm5KQnIiIiotyHAXIiIiIiSjfplCp58uSRy8GtSpkyZbBs2TL8+++/iI2NBZAccL98+bLQs7lixYpo2bIlOnXqhLJly2qlzD9+/MCVK1fw4sULfPr0Cd7e3ipTw0jn+VYkJV+2KtI9uqX3Fxsbi8jISGHZ2tpa7b4UCQoKEh67u7uLUp9oQl0aGUWkyw2k7uZIaoLpqnrnp4V0OWU/AxERERHlHgyQExEREVG6vX//Xnic2kkageSUIlWqVMH27dtx9epVuYClh4cHPDw8sHnzZrRr1w5z585F/vz501TWxMREbNiwAXv27FGa81xPTw92dnawtLQUpT5RJTVpZWTJBqbz5MmTpv1ERESkuQyA5ulJpMl+h7L56FVJS89vbZEuZ2omlSUiIiKiXwsD5ERERESUbm5ubsLjKlWqpGkfKT3J58+fj0ePHsHZ2RmPHj2SS/tx+fJlfPnyBQ4ODmnqVTxt2jScP39eWDYxMYGdnR0qVKiAMmXKwNbWFpUqVYKpqSkcHR01DpCnh2xAPCYmJlW98FMYGxsLjzdu3Ig2bdqku2zqyP4N4uLi0lT2zJYyWgEQf29ERERElLswQE5ERERE6RIQEAAPDw9huUGDBunan5GREZo2bYqmTZsCAAIDA3H79m2cOHECrq6uAIC3b9/i1KlT6Nu3b6r2fe3aNVFwfOLEiRg2bJjSQLtsbvWMkjdvXujp6SExMRFA8mc2NzdXuU1KEF06bYv0ZJVfv37NkLLKkk2pEh0dnao0K1klOjpaeKxJehwiIiIi+jWpntaeiIiIiEiNkydPChNOmpubo3nz5qnaPjExEV++fMGzZ88Uvm5tbY1evXrh6NGjqFevnvD806dPU13WU6dOCY+7dOmCMWPGqOyF7uvrKzxWl4M8PXR0dFChQgVh+c2bN2q3WbNmDapVq4Z27drh7NmzAIBKlSoJrz958kSj975//z6uXbuGt2/fpilFi6GhoSjdjb+/f6r3kRUCAgKEx0WKFMnCkhARERFRVmKAnIiIiIjSLCAgAPv27ROWe/Xqlar0GgEBAahevTratGmDwYMHq5wkU09PDy1atBCWFQVzdXR0VL7f58+fhce//fabynXj4+Nx+/ZtYTkpKUnl+ulVu3Zt4XHK5KSq3Lt3D/Hx8fj06ZMQoK5fv77wurOzM7y9vVXuIzo6GlOnTsW4cePQrVs3nD59Ok1lL126tPDYz88vTfvIbN++fRMeS5efiIiIiHIXBsiJiIiIKE2ioqIwdepUYYJJS0tLjBkzJlX7KFiwIIoVKwYASEhIwKFDh1SuL53KRVFQU3rSR0UTL0oH79+9e6fyvdauXSsKMMfHx6tcP7169+4tPL5x4wYeP36sdN2LFy/i48ePAJJ72Kf0rG/SpInwfSYmJmL27NlC735FNm7ciKCgIADJedA7dOiQprJXrlxZeCybMz47+vbtm3AzxtTUFCVLlsziEhERERFRVmGAnIiIiIhSJTExEdevX0fv3r3x6NEjAICuri5WrFiBvHnzpnp//fr1Ex5v2rQJ+/fvlwtuSyQSHD58WOjhrKOjgx49esjtSzpvt6I0JTVq1BAenzx5UmFPbV9fX0yePBm7d+8WPS+dszojVKhQAZ07dwaQ/HnHjh2L69evy61348YNzJo1S1gePXo0DAwMACT3sp8yZYrw2sOHDzFq1ChRb2kg+ebG2rVrRZ9x6NCholQpqVGnTh3hsbu7e5r2kZmky1irVi3o6ellYWmIiIiIKCtxkk4iIiIiEoSEhGDOnDlyz0skEkRHRwsTckqnQtHX18f8+fPRpEmTNL3nH3/8gXPnzuH169dISkrCkiVLsHPnTtja2qJQoUKIjIyEq6urKMg7bNgw2Nrayu2rdOnSQo/oMWPGoGnTpoiPj8fEiRNRokQJDBkyBCdPnkRsbCwSEhIwYcIEVKhQATY2NtDV1YW3tzfc3NyEdCrm5ub4+fOn8N1ktHnz5uHdu3fw9PREeHg4xo4dizJlyqBq1arQ09ODu7s73r9/L6zfsmVL9O/fX7SPDh06wNXVVUh94+zsjNatW6NGjRooXrw4fvz4ATc3N9HnqV+/PsaOHZvmcterVw/6+vpISEiAi4sLkpKSRJOHZjfS+dkbN26chSUhIiIioqzGADkRERERCaKiouDg4KDx+hUrVsTs2bNF+bNTy8DAALt27cLff/8tBC4DAwMRGBiocN0RI0ZgwoQJCvc1dOhQPH/+HBKJBMHBwcKknC1btkSJEiVQqlQprF27FlOmTEFUVBSA5JQgitKCNGjQAEuWLEHHjh0RFRWF0NBQfPnyBaVKlUrzZ1XHzMwMhw8fxrRp03Dr1i0AwKdPn/Dp0ye5dXv27Im5c+cqDETPnDkTRYoUwfr16xEVFYX4+Hg8efJE4cSd3bt3x7x580TpaVLL0tISDRs2xN27dxEWFgY3NzdUr149zfvLaA8fPgSQ3OM+rWlliIiIiOjXwAA5EREREamlo6ODPHnywMzMDKVLl0alSpXQpk0b1KpVS+3EmJqwtLTEgQMHcPv2bVy8eBFubm7w9/dHXFwcLCwsUKRIEdjb26Nr164qJ1Rs3bo1tm7dit27d+Pdu3eIjY2FtbU1IiMjhXVatmyJc+fO4eDBg3jw4AF8fHwQFxcHU1NTFCtWDFWqVEH79u3RqFEjAECjRo1w7do1AMDp06eVBue1xdzcHNu2bcOjR49w9uxZPHv2DIGBgUhMTETBggVRp04d9O3bV20AesiQIejcuTNOnDgBZ2dnfP78GaGhoTAwMECxYsVQq1Yt/P7776hWrZpWyt2tWzfcvXsXAHDlypVsGyD/9OmTcEOkQYMGsLa2zuISEREREVFW0pFIJJKsLgQREREREeVs8fHxaNmyJfz9/VG0aFHcvHlTKzdPtG3Tpk3YuHEjAGDHjh1o2rRpFpeIiIiIiLJS9k0MSEREREREOYaBgQEGDhwIAPDz88O9e/eyuETyEhMTcfLkSQCAjY1NmvPmExEREdGvgwFyIiIiIiLSij/++AMFChQAABw+fDiLSyPv1q1b8PPzAwCMHz8+W/ZwJyIiIqLMxQA5ERERERFphYmJCcaOHQsAuHPnDt69e5fFJRLbsWMHAKBq1apo27ZtFpeGiIiIiLIDBsiJiIiIiEhr+vbti8qVK0MikWDz5s1ZXRzBnTt34OrqCl1dXcydO5e9x4mIiIgIAAPkRERERESkRXp6eliwYAEMDAxw9epVuLq6ZnWRkJSUhDVr1gAABgwYgGrVqmVxiYiIiIgou2CAnIiIiIiItMrOzg5///03JBIJFi9eDIlEkqXlOX78ODw8PFChQgVMmTIlS8tCRERERNmLjiSrW6tERERERERERERERFmAPciJiIiIiIiIiIiIKFdigJyIiIiIiIiIiIiIciUGyImIiIiIiIiIiIgoV2KAnIiIiIiIiIiIiIhyJQbIiYiIiIiIiIiIiChXYoCciIiIiIiIiIiIiHIlBsiJiIiIiIiIiIiIKFdigJyIiIiIiIiIiIiIciUGyImIiIiIiIiIiIgoV2KAnIiIiIiIiIiIiIhyJQbIiYiIiIiIiIiIiChXYoCciIiIiIiIiIiIiHIlBsiJiIiIiIiIiIiIKFdigJyIiIiIiIiIiIiIciUGyImIiIiIiIiIiIgoV2KAnIiIiIiIiIiIiIhyJQbIiYiIiIiIiIiIiChXYoCciIiIiIiIiIiIiHIlBsiJiIiIiIiIiIiIKFdigJyIiIiIiIiIiIiIciUGyImIiIiIiIiIiIgoV2KAnIiIiIiIiIiIiIhyJQbIiYiIiIiIiIiIiChXYoCciIiIiIiIiIiIiHIlBsiJiIiIiIiIiIiIKFdigJyIiIiIiIiIiIiIciUGyImIiIiIiIiIiIgoV2KAnIiIiIiIiIiIiIhyJf2sLgARERERERER5V4hISHw8PDAly9fEB4ejqSkJJibm6NQoUKws7ODtbV1VhdRK75+/QoPDw/4+fkhKioK+vr6MDMzQ7FixVCpUiUULFgwq4tIRJQrZXqAfMaMGTh16pTW9jdu3DiMHz9e9FyLFi3g6+sLAKhbty4OHjyotfejzDNz5kw4OTkBAN69e5fFpUlma2ur9X0OGjQI//33n9zzjx8/xqBBg7T+fps3b0arVq0AAP7+/mjSpInwmqWlJR49epSq/a1duxbbtm0TPTdy5EhMnjxZ433Ex8ejTp06iI6OBgCYmJjgyZMnMDAwSFVZsrOwsDAEBwejTJkyCl93cnLCzJkzheUDBw6gXr16mVU8SoWs/o3ZuHEjNm3aJCzfuHEDxYsXz9QyUNbQ5G/v4+ODli1bCsuK2kmZQd05j34tsu17Re022XbN0qVL0aNHj0wpn7Tv378DAAoXLqzV/WryHfwqv/Vs05C2REREwNHRERcvXsSrV68gkUiUrmtjY4Pff/8dvXv3hpmZmUb7T03sQU9PD4aGhjAzM4O1tTXKlCmDxo0bo02bNhq/nzKBgYE4dOgQzp07J7QhlSlbtiw6deqEfv36IX/+/Ol6X9KurL4GIJI2cOBAPHnyBABQrFgx3Lx5U24d/h6nDlOsULZ07949rd5IIcUKFSqEsmXLCsshISH4+PFjqvZx7949jZ5T5fXr10JwHADq1av3ywTHJRIJHB0d0a5dO7x48SKri0NElKF4zqPsKj4+Hrt27UL79u3x5cuXrC5OjsT6TdqSmJiIPXv2oHnz5li6dClcXV1VBscBwMvLC8uXL0e7du1w4cKFDClTdHQ0AgMD8ebNG1y4cAEzZ85Ew4YNMWfOHISHh6dpv/v370erVq2wbds2tcFxAPj48SM2bNiAli1b4sCBA2l6TyIiSj2mWKFs58OHD5g8ebLaRhJpR4MGDURB8efPn4uC5qoEBwfjzZs3cs97eHggMDBQ46GQT58+FS03atRIo+2yu4CAAPz999+8iCSiXIHnPMquvLy8MGHCBHh5eWV1UXIs1m/SlsDAQIwdOxaurq6i5/X09FCxYkVUrFgRlpaW0NHRQVBQENzc3PD+/XvR9v/88w88PDzwzz//QEdHJ0PLGxsbCwcHB9y+fRsrVqxA/fr1Nd524cKFOHTokOg5CwsL2NnZoVSpUjA1NUViYiJCQ0Px7t07vHnzBomJiQCAqKgoLF68GJ6enli0aJFWPxMREcnL8gB5Vg2vpOzpw4cPGDJkCMLCwrK6KBrJzNQvGTWMq2HDhjh8+LCw/Pz5c/Ts2VOjbZ2dnYUbGUZGRoiNjQWQ3MPI2dkZ3bt312g/z549Ey3/KgHyT58+8UKSiHINnvMou3r16hWD4+nE+k3a4OfnhwEDBoh6UhsbG2PYsGHo16+f0vzbHh4eWLVqlWiU6o4dO2BgYIC///5b4/dXFXuIi4tDdHQ0fvz4AS8vL9y7dw9nzpxBXFwcgOTUlCNHjsSWLVvQuHFjte918uRJUXDc2toa06dPR7t27ZSOlP3+/Tu2bt0KBwcH4RrrxIkTKF26NEaMGKHx5yQiotTL8gB5RlCUe4eyv5s3b2LGjBk5Jjj+q6hbty709PSE3grPnz/XeFvpRmrDhg3h6ekpNHjv3bunUYA8KSlJ9J5FixbVuAc7UVbI6t+Y8ePHZ0lOacoZihcvnm3m7SCSVq9evV/62Fy2bBmWLVuW1cXIFnr06MEOUCQnKioKY8aMEQXHy5Yti23btqFUqVIqt61YsSJ27dqF9evXY8uWLcLzW7ZsQY0aNWBvb5/u8hkaGsLQ0BD58uVDuXLl0LZtW4waNQrjx4/H27dvAST3Jp84cSIcHR1RunRppfuKjo7G6tWrhWUrKyscPXoUJUqUUFmGwoULY/78+ahYsSLmzZsnPL9p0yZ07twZhQoVStdnpPTJ6msAotTi73HqMAc5ZbmEhASsWrUKf/31F4PjWcDc3BxVqlQRlj99+oTg4GC120kkEjx48EBYrlOnDho2bCgs379/H0lJSWr34+HhIcrpJ70PIiIiIiLK+dauXQsPDw9huWTJkjhy5Ija4Li0CRMmoFOnTsKyRCLBkiVLkJCQoNWypihRogQOHjyIChUqCM+Fh4dj9uzZKre7fv06goKChOVJkyapDY5L69evH1q1aiUsR0dH4+TJk6koORERpRYD5JSlbt26hc6dO2Pnzp2inOOWlpZZWKrcp0GDBqJlTYbQvnnzBj9+/BCW69evL0qNEhoaCjc3N7X7+VXzjxMRERERUfI8AEeOHBGW9fT0sH79+jRd882ePRt58+YVlj9+/IjLly9rpZyK5M2bF+vXr4eRkZHw3JMnT3D79m2l2zx+/Fi03KJFi1S/77Bhw0TLDx8+TPU+iIhIcwyQU5b5888/MXr0aNEEkUZGRli1ahWaNWuWdQXLhWQD5JqkWZFOr2JhYYFKlSqhYcOG0NXVVbiOMtL5x3V1deXKQkREREREOdfevXtFvbx79OiBypUrp2lfFhYW+OOPP0TPOTk5pat86pQtWxZDhgwRPbd//36l63/79k20bGhomOr3rFmzJszMzITlr1+/pnofRESkuV8yB3mLFi2E3GaaTmz45s0bODk54enTp/Dx8UFsbCysra1ha2uLjh07okOHDtDT08Ply5cxYcIEYTtFuRRnzJiBU6dOqVxHkYEDB+LJkycAgGLFiinMceXk5ISZM2cCAAoVKoS7d+8CAE6fPo29e/fi8+fPyJcvH0qXLo1WrVqhW7duMDc3l9tPYmIibty4gdu3b+Ply5cICgpCZGQk8ufPj2LFiqFx48bo1KlTqoa8pZZsL+VatWph8eLFKFOmDO7fv5/m/cp+/+PGjWO+XjVq1aoFY2NjxMTEANAsQO7s7Cw8btSoEXR1dZEvXz5UrVoVr169ApAcIB83bpzK/UgHyCtXrqxRTxJnZ2fcvHkTz549Q2BgIMLDw2FhYYHChQujYcOGaN++PSpVqqR2P7JCQ0Nx8+ZNPH36FK9fv0ZISAjCwsIgkUhgamqKokWLomrVqmjbtq3KyXlsbW0VPj9z5kyh/gKanRtiY2Nx9uxZXL9+HZ6envjx4wdMTU1RqFAh1KxZE127dsVvv/2W6s/67t07XL58GQ8fPoSvry9CQkJgamoKKysr1KpVCy1btkTTpk012pf05927dy8aNmyI9+/fY926dXj69CkkEgmKFCmCBg0aoFu3bmn622zcuBGbNm0CID6v+/v74/Tp07h+/Tr8/PwQHh4Oa2trlC5dGt26dUPbtm3lLkq+fv0KR0dH3L17F35+foiJiUGBAgXw22+/oWfPnmpHMWjyGyN9HurcuTNWrVoFAPD19cXZs2dx+/Zt+Pn5ISQkBBYWFihRogSaNWuGbt26qc0vKf1dAMCNGzdQvHhx0TqPHz/GoEGDhOWUYy0qKgqXLl3ChQsX8PnzZwQGBsLCwgJFixZFmzZt0KNHD7k6GBYWhjNnzuDy5cv4+vUrQkNDYW5ujkqVKqFdu3bo1q2b0smmAM1+2xTR5HvOTsdFegQEBMDJyQn37t2Dl5eX8Htcrlw5dO7cGZ06ddL44trHxwctW7YUltX9BkZFReHq1au4c+cOXr9+jR8/fiA+Ph4WFhbC52/UqBFatGgBfX3FzUZtnPP8/f2F8++7d+8QGhoqpF0zNzdHsWLFUL16dXTs2BE1atRI1Xdw9epVlCpVCklJSbh58yYuXboEd3d3BAQEQCKRwNraGjVr1kTbtm3RvHlzlftW5OPHjzh//jwePXqET58+ITw8HIaGhrCyskLVqlXRpk0btGzZUmU9kaXNc3R6fP36FSdPnsSDBw/w6dMnxMTECO3jbt26oU2bNqIb46rInpdUTZYHJP8mX7p0Cc7Oznj79i2Cg4ORlJQECwsLWFtbo1atWrC3t0fjxo2ho6Mjt73scSBNuhyy5yXp80rNmjVx9OhRJCYm4uDBgzh27Bj8/PxgZWWFcuXKoU2bNujSpQuMjIzS3P4HgKCgIBw/fhzXr1+Hj48PYmJiYGVlhYoVK6J169Zo3769qPeqIqn9flNIX1sAwIEDB1CvXj1hOS31W90+lXF1dcWlS5fw7Nkz+Pn54efPnzA1NRXORU2bNkWrVq2gp6endl+K2iZAci/cCxcu4MWLFwgICEBcXJyorrZr106j/atz584djBw5UlgeNGgQ/vvvP423nz17No4fPy4sX7x4EeXKlZNbz8PDAxcuXICLi4tw/jE2NoaFhQVKlSqF+vXro23btihZsmT6PlA6REZG4vz586LnZAPcqdW9e3dcvnwZtWvXRp06dVC3bt107U8TAwcOxO7du4VA/6NHjxAUFIQCBQrIrSt7Tnr69Gmqf190dHQwc+ZMJCYmwtLSEtbW1mkvvBTpc1X37t2FuRO+fPkCJycn3L17F9++fUN0dDQKFSoEGxsb9OzZE82aNZM733t4eODEiRN4/Pgxvn37hsTERFhbW6N27dro168fqlWrpnG54uPjcefOHTx+/BgvX77Ejx8/EBoaitjYWOTJk0f47WnWrBk6duyotF10//59DB8+XDQ6fceOHWp/M52dnTFixAhhOxMTE5w+fVoUD0ntNUBWfr+pbQ+m0PS3RPq7kF7n2bNncHJywsuXL/H9+3fo6uqicOHCqFGjBvr27StK7Qokp0m6e/cuTp06hTdv3uD79+8wNDREsWLF0KhRIwwaNAiFCxdWW25NKbpGi4mJwY4dO3DmzBkEBgaiYMGCqFSpEtq2bYsOHToobOe4uLjgwYMHePr0Kfz8/BAWFobIyEgYGhoif/78KFu2LOrXr4/u3bsrPEeo4uHhgZMnT+LJkyfw9vZGQkICChYsiGrVqqFHjx4aTRKcQpPf44xqP8hKb7suM/ySAfLUiIiIwLx583Du3Dm51/z8/ODn54dbt25h7969WL58eRaUUL21a9di27ZtwnJMTAz8/f3x+PFjtGrVSi5Afu/ePSxduhQfPnyQ25e/vz/8/f3x/PlzbNmyBb1798bUqVNhYmKSYeUvXLgwJkyYgO7du2dZRcjtDA0NUbNmTSGn+OvXrxEXF6e04REREYGXL18Ky9In6caNGwsBcjc3N4SGhsLCwkLhfj58+CDKd67uZP/q1SssWrQIrq6ucq8FBgYiMDAQbm5u2LFjBzp06ID//vtPox+kmJgYbNiwAUePHkVUVJTCdUJDQxEaGoo3b97g+PHjqFKlClauXKnwYkVb7ty5g/nz54smMwKAuLg4hISEwMPDA0eOHEHz5s2xfPly5MuXT+0+AwMDsWzZMly4cEHUcAT+9xm9vLzg4OCAatWqYe7cuahatWqqyv3mzRv88ccfou/y58+fePfunRBY1YajR49i+fLliI6OFj3v4+MDHx8fODs7Y//+/di2bRusrKwgkUiECZ7i4+NF26Sc7y9evIguXbpgyZIlqQpmqZOUlIR9+/Zh3bp1iI2NFb2Wcuw+f/4cW7duxYQJEzB06FCtvXeKp0+fYvr06XLHU0BAAAICAvDy5Uvs3r0bW7duRfXq1QEkH4MzZ84U5dEEkoM5zs7OcHZ2xtGjR7F169ZsM3FUTjsudu7cia1bt8qVN+X3+MGDB9izZ49wk0WbLl26hIULF8r9fYH/HZceHh44duwYSpYsif/++0/rI7yCg4Oxdu1anDp1Su77TxEUFISgoCC8evUKBw8eRIMGDbBixQoULFhQ4/f59OkTZsyYIfrtSvHlyxd8+fIFp06dwm+//YbVq1fL3XRSJDAwEEuWLMGlS5fkzqfx8fGIjIzEly9fcOHCBZQpUwZLlixBzZo11e4zo8/RmoiLi8Pq1atx6NAhudy+0u3jWrVqYeXKlVp//0OHDmHNmjWIjIyUey2lbrx+/Rr79+9HpUqVMHfuXLU3TtJj+vTpomsFX19f+Pr64tmzZ+jcuXO69n3p0iXMmzcPoaGhoudTzlnXr1/Hhg0b8O+//4pyEv9KPn78iLlz5wo3U6WlHPcfPnzAyZMnUbp0acyYMSPVwcaAgADMnj1bYVqMlO/68uXL2LJlC1auXJnm3s0pGjdujIIFCyIgIABA8t95xowZGgXf4+LicOXKFWG5evXqcu3NsLAw/Pfff7h27Zrc9vHx8QgPD4e3tzecnZ2xdu1adO3aFf/995+oR3JmefjwodAJB4AQfEqP0qVLi76jzGBtbY3q1avDxcUFQPJv+N27d9G9e3e5dUuVKiUaSbts2TJUq1Yt1YGynj17pq/QGkhMTMTWrVuxdetWufN9yu/jjRs30KxZM6xZswampqaIi4vDmjVrsG/fPrnfqq9fv+Lr169wcnLCn3/+iSlTpqgtg4ODA7Zu3SrX8z5FREQEIiIi8OnTJ1y+fBlr1qzB3LlzFZ4TGzVqhAEDBoiC13PnzsX58+eVHv8hISGYMWOG6LPMmTNHK50Fs8P3mxlCQ0MxZ84chfUyPDwc79+/x/HjxzF27Fj8/fffAJLbPDNmzBB1vAOSO4h5eHgI17rLly9H27ZtM6Tc8fHx+PPPP0W/P97e3vD29sbbt29Fcx4AyemVVq1apTAeASTPF5DSRrh37x42btyIP//8E2PHjlUb6woPD8eCBQtw7tw5ub97SpkuXLiAli1bYvHixWn8xFkju7XrlMnVKVaCg4PRp08fhcFxWe7u7hgwYIBoYpHs4OHDh6LguLSqVauiaNGiouf27t2LkSNHKgyOy0pISMCRI0cwYMAAoXGnTaVLl8bs2bNx9epV9OjRg8HxLCad2iQuLg6vX79Wuu7Dhw9FgQzpnpXSjxMTE0UTecpKTf7xCxcuYMCAAUp/jKRJJBJcuHABvXv3Vnush4SEYPDgwdi9e7fS4Lgi7u7u6NevH/z8/DTeJjWcnJwwZswYuWCmIrdu3cLw4cMRFxencj0PDw/06tUL58+fl/vRVeTVq1cYMGAArl+/rnG5Y2NjMWnSJKXfZZs2bTTelypr167FvHnz5IKKstzc3DB69GhIJBLMnTsXq1atUhqES3H27FmsWbNGK+VMsWTJEixfvlwuOC4rOjoay5Ytw549e7T6/nfv3sWQIUPUHk9BQUEYMmQIAgICcP78eYwaNUph8FSau7s7xo8fj8TERG0WOU1y0nGRmJiIf/75B2vWrFFb3vfv32Pw4MHw8vLS2vufOHECkyZNUvv3TfH161f89ddfcr0A08PHxwf9+vXD8ePH1X7/0h4+fIi+ffsiIiJCo/U/f/6Mfv36KQyOy3r58iUGDRqEkJAQlet5eHigW7duuHjxokbn00+fPmHw4MG4c+eOyn1m9DlaE9HR0Rg2bBj27dunduI7FxcXDBo0CIGBgVp7//Xr12PhwoUKL6IUefv2LYYOHSqX81dbTp48qfRawd7eHsbGxmne97lz5/DPP//IBcdl+fr6Yty4cTh8+HCa3yu7un79Onr06KEwOK7I58+fMWbMGNFIKnWCg4MxYMAAlTmjU3h5eWHIkCH4/PmzxvtXRE9PD126dBGWAwMD8ejRI422vXPnjjCCBgC6desmej0iIgL9+/dXGBxXJDExEU5OThgyZEiq2rraIls369Spk+ll0BbZ3pHKfleaNGkiWv78+TM6deqEXbt2qf19yWwzZ87Exo0b1Z7vb9++LfRoHz9+PPbu3av2t2rnzp04dOiQ0teTkpIwZ84czJkzR2lwXJGAgACMHz9e6e/flClTUKZMGWH527dvKttwc+bMEf2OdezYUeGNj7TIyu83s4SHh2PgwIEa3bTavHkzDh06hMDAQPTp00cuOC4rOjoakydPVhmbSI+tW7cq/f1p3bq1aPnUqVMYOnSoRvGIFDExMdi4caPazrZBQUHo06cPzp49q/bvfuPGDYwYMULjdlJWy27tOlVybYA8KSkJ48ePF11s5s2bF5MnT8aVK1fg5uaGe/fuYcWKFcId+9DQUGzdujWriiwnKSkJixYtUvq6bCDK0dERy5YtQ1JSkvBcy5YtsWPHDjx8+BBubm64desWFi1ahNKlSwvruLu7Y8yYMWqDb6nl6OiIAQMGqB0ySpkjNXnIpXtEVKhQQdRz9LfffhPdnVeVh1w6QG5iYqL0LuHDhw8xZcoUUXCxTp062LhxI5ydneHm5oa7d+9i9erVsLOzE9bx8fHBn3/+KbrIkLVgwQJR47ZkyZKYP38+Ll++DFdXV7i7u+Phw4fYt28fevbsKRpiFRYWprCx9e7dO7x79w4HDhwQPb906VLhNXVDr0+fPo3ExEQYGhqid+/eOHr0KB4/fgxXV1ecOnUK/fv3F91UcnNzw9GjR5XuLzAwEMOHDxc1PkuUKIG5c+fi+vXrcHNzw6NHj7B371506NBBWCc6OhoTJ07UuCGwb98+pReVpUuXRvny5TXajyqvX78WbgwWKlQIM2fOxNWrV+Hm5oY7d+5gxowZotEPbm5uGDZsGBwcHAAkpxTaunUrHj16BFdXV5w8eRLt2rUTvcfBgwfh7++f7rICycHplF4sRYsWxaxZs3Dt2jW4ubnB2dkZ69atkxvCvn79eq29PwBMnDgRCQkJMDY2xpAhQ3D69Gm8ePECT548wbp161CkSBFh3aioKEyaNEnoSVOsWDEsXLgQt2/fhpubG65fv44xY8aIesG5urri6tWrWitvWuS042LDhg24dOmSsKynp4f+/fvj5MmTePnyJZ48eYLdu3cLF9mhoaFam4TM19cXCxcuFBrgefPmxT///IOzZ8/i+fPneP36NW7cuIHNmzeLzsuJiYlYuHAhfv78KdpfWs55EokEU6ZMEZ0vKleujJUrV+L69et49eoVXr9+DWdnZ2zfvl2u55Cvry927typ0eedOnUqQkJCoKOjg06dOmHfvn14+vQpXF1dcfbsWQwdOlR0PPv6+mLz5s1K9/fjxw/8+eefoomqraysMH36dFy8eBGurq5wcXHBnj175G4+T5kyReGN1cw6R2ti1qxZot9nIyMjjB49GufPn8erV6/w8OFDbNq0SUjvlTIyQxtcXV1FbW1ra2vMnj0bly5dgqurK9zc3HDt2jWsXLkSFSpUENaLjo7GnDlzRAGI4sWLC8fe0qVLRe9z4MAB4TVVaZ8iIiJUjt5I703fEydOICkpCbq6uujTpw9OnDgh1P8dO3aI0kZIJBIsWLAg0yfq00abRpknT57gn3/+Ed0kLFu2LBYvXoxbt27Bzc0NDx8+xJYtW0SjDCUSCTZu3Ih9+/Zp9D4LFizAly9fACQHLrdt24ZHjx7Bzc0NV65cwfjx45EnTx5h/bCwMK2MHJYNsGl6g1H6hoyhoaGozgPAqlWrRNewjRo1wvbt2+Hs7IzXr1/j6dOnOHPmDEaNGgVTU1NhPTc3N63fgNfE+/fvRcu1atXK9DJoi3TQFYDSG9f29vZyveSDg4OxcuVKNG7cGIMHD8aePXvg7u4uujbPbFevXsWZM2cAJH+2RYsW4caNG0J7b/To0aLrjStXrmDEiBHCzabmzZtj3759ePbsGZ4/f47Dhw+jfv36ovfYuHGjaASBNAcHB6EdBgBmZmYYN24cnJyc4OLigjdv3sDFxQWOjo4YN26caHLWpKQkLF68WOH3Z2xsjJUrV4pSwx05ckTo/S/t5MmTojZssWLFMH/+fFVfm8ay+vvNLOvXr4enpyeA5JjC9u3bcf/+fbx69QqnTp2S6+m/ceNGjBo1Cr6+vjAwMMCgQYNw6tQpuLq64uHDh1i3bh1KlCghrB8fH48NGzZovdzq2pLSv/Genp6YNWuW0M7Q1dVFz549sX//fjx8+BDu7u54+fIlLl26hLlz54riaUDynAXSc+9JS0pKwt9//y3q2Gdubo4pU6YI14wpscmyZcsCSL7ucXd3T+tHzzTabNdlhlwbIHdwcBDlPi5VqhTOnDmDkSNHonTp0jA0NETBggXRtWtXnD59Gu3bt8/C0ioWGBgo/Cj3798fly9fxsuXL3H27FkMHz5cdDH5+fNnLFy4UFjW19fH6tWrsWXLFjRt2hT58+eHoaEhihYtil69euHcuXPo2LGjsP7r16+xbt26TPts6bFs2TJRg535xzVTpUoVUYoOVQFy6Qth2bQo+vr6oh9uVRfN0o2UunXrKkxdEBoaismTJ4saP1OnTsWhQ4fQpk0bWFtbw9DQEIUKFUKnTp1w/Phx0azvvr6+mDNnjsL3f/nyJS5evCgsV6xYEadOnULfvn1RpkwZGBsbQ19fH/nz50eDBg2wePFiuREbN27cUNsrOK2sra1x+PBhLFy4EDVr1oSFhQWMjY1RuXJlzJ07V+5znT59Wum+pk2bJgrmtGvXDufOnUP//v1RokQJGBoawtLSEg0bNsTatWuxYcMG4e8RHx+PiRMnatT4SukdVaVKFezZswfPnz/HnTt3sHTpUtHfJT1Sej9VrVoVp06dwpAhQ1CqVCkYGhqicOHCGDp0qNx3kzKS4Y8//sChQ4fQokULWFpawtjYGFWrVsX69evRtWtXYf34+Hit9cpMuUFjb2+Pc+fOYeDAgShZsiQMDQ1hbW2N9u3bw8HBQZR6ISYmRnRspldkZCQsLS1x8OBBzJw5E5UqVYKJiQny5cuH9u3bY/fu3aL69+zZM8THx6NGjRo4ffo0evfujSJFisDQ0BAlSpTAxIkT5S4epIO9WSEnHRcfP34UNciNjY2xc+dOIV1Gnjx5kC9fPjRu3Bg7d+7E1KlT0/2e0k6dOiWct/T09LB//36MGjUKtra2MDU1hYGBAYoXL45WrVrhyJEjovZEaGgozp49m+4yXLx4UTQXSePGjeHg4IAuXbqgRIkSMDIygoGBAaytrdGsWTNs2LBB7u934cIFjd4rLCwMBgYG2LZtG1avXo0GDRrA3NwcxsbGsLW1xYwZM+QuvM6ePau0Ub5ixQrRyLpatWrh4sWLGDZsGMqVKwdjY2OYmZmhUaNG2Lt3L/r06SOs+/PnT2zZskVun5l1jlbnwYMHoiCepaUlHBwcMGnSJJQvXx5GRkbInz8/WrdujaNHj2LAgAHpfk9px44dE924OX78OAYMGICyZcvC2NgYhoaGKFmyJLp06QJHR0fRDZzPnz9rNDl4anh6eiI4OBj6+voYO3Ysbt68iRcvXsDR0RH9+/fXSsohY2NjbN++HQsWLEC1atWE+t+0aVMcPHgQf/75p2j9WbNmpWrERXYVFxeHadOmidpQv//+O86ePYuePXuiaNGiQi7Xli1bYvfu3Zg7d66oo8LKlSvx5s0bte8VFhYGHR0dLFiwADt37kTz5s1haWkJQ0NDlC5dGuPGjcPBgwdFv4O3b98WpQFMCxsbG1HHjatXr6ptM4aHh+PWrVvCcvPmzUWpCqOjo4WAG5AcwNm9ezeaNWsGa2trGBgYwNzcHBUrVsQ///yDPXv2iEY5HDp0SKMRKtokO7mk7AjnnKRYsWKiZWUjSXV1dbFixQqFKT0SEhLw6NEjLF++HD169EC9evUwatQo7N69Gx4eHpn690np0dmkSRM4OTmhV69eKF68uNDemzRpEkaPHi3aJqXtNGXKFGzbtg0NGjRA3rx5YWpqitq1a2P37t2im3uhoaEKR09ER0dj7dq1wrKJiQmOHj2K8ePHo0qVKjAzM4Oenh7MzMxgZ2eH8ePH4/Tp08ifP7+wjZ+fn9JrVjs7O4wZM0ZYlkgkmDVrlqjTn7e3tyhVhZ6eHlatWiUKxKdHVn6/mSnlc44dOxb79u1Ds2bNYGVlBSMjI1SuXBkbN24UjRwJDQ2Fu7s7jIyMsHfvXvz333+oXLkyjI2NkT9/fuHaSPrcd+/ePY1HDmrq+fPniIuLg4mJCWbOnIm7d+/CxcUFhw8fRv/+/UXzfC1fvlzULlyyZAkWL16M+vXrI3/+/NDX10eePHlQtmxZ9O/fH2fPnhV95qSkJKXXSidPnhTFJkuWLInTp0/jzz//FK4ZU2KTZ86ckevZnp1lt3adOlkeIJ85cyZsbW1T/S89F6iJiYmiIJeRkRG2bdsm94OXwtDQEKtWrcqQHI/a8Ndff2Hu3LkoU6YM8uTJA1tbW0ybNk1012rnzp2iC6d///1XLp+SNENDQ6xcuVJ0gB45ckTtENDcJi3HrvQ/TXu+AMm9bNL7fm/fvlW6f11dXdGwQWWNjQ8fPojSNNjb28utI50qJSAgQGFqIm9vb1FPOWXpVY4cOSJKATBs2DCMGDFC5eeYPn266KbWlStXFKZake3JM3/+fLW5GZs2bSpqlERFRWXYrPIrVqxQOflK//79YWNjIyx7eHgoTNXw7NkzUaqbGjVqYNWqVaLeUrLatm2Lf//9V1j28/MTTT6mSvny5XHgwAE0atQIpqamKFy4MHr06CEKEqWXgYEB1qxZozSXY/fu3eVy31esWBH//fef0gnl/vrrL9GyNofyWVtbY926dUqPrzx58shN3qXtxu6MGTOUHk/lypWTu9llaGiIdevWKZzoGUjOiyk9eiQ79GLIKcfFnj17RClppk+frjLF1IgRI9C7d+90v28K6d+CypUry02YJE1XVxezZ88WfT/aODalz78GBgZYuHCh2olI+/fvL8oN7u3trXG6gH/++UdlMLNVq1ainMZhYWEKfzN9fX1FvTsLFiyIrVu3Kp0DQkdHB7NmzRK1yc6ePSs6V2f2OVoV2Z5Uy5cvV5orOOXY0OYEttLfeaNGjVQG0oyMjETfAaD982aK+fPn4++//0axYsVgYmICOzs7zJ07Vyv5nOfPny+XjkHalClTRMemj4+Pxqk1srPDhw+L2oGNGzfG4sWLVc7z0L9/f9Hk7wkJCRqnWvnjjz9UtkPs7OxEryclJWmc9kUV6V7kERERatO8XL58WRTAk+2F/uXLF9F5r2fPnirTVP7222+ifYSEhKS5x39ayY7klJ0MPCeR7pEPyH82aRUqVMDevXvVztHy8+dP3L59GytWrEDXrl3RtGlTLFq0KNNSu1pYWGDVqlVK5xwbNGiQXBupWbNmcjfvUujr68sFfRW1nZydnUXf35gxY0Q9SBUpXry43ASvqo7n0aNHi9q+Hz9+FOJASUlJmDZtmijtw19//aV2rpDUyqrvN7PVq1dPyC0uS1dXVzQJZIoJEyYoTblUoEAB9O3bV1hOSkpSGctIjy1btmDIkCEoVKgQzMzMULt2bcydO1c4twYHB4tGbzVu3FhtCh4jIyO570PZsbpr1y7hsYGBATZu3KgyNrlmzRqtjMrODNm1XadMlgfIs8LDhw/x/ft3Yblv377CUAVl9PX15f5Y2YGFhQVGjRqlcp2fP3+KenuVK1cO/fr1U7tvPT09USM0OjpaKxdflH1J9/wOCQnBp0+f5NaR7hFubGyM2rVry60jGzRXdOdP+i4poDhALpFIcOTIEWHZwsJC4xEBEyZMEO1HUfqRUqVKoWPHjqhevTrq1KkjukusimwwSVXjOK3q16+Phg0bql1P+qI6KSlJYU8W2dx0M2bM0GiiwT59+ohSb0j/LVQZO3Zshk8C1b59e5UT5+jr68tNsNWvXz+Vk2OVLl1a1MtKmzkie/furfY7qVq1quiiUZspVooWLSrqCa1IysScKVq3bq1y1ngdHR3RhUx6e9ppQ044LuLi4kSpUooUKSK6AFDmn3/+URtA1pR07zRNgszW1tbYsGED9u/fjxs3bmD9+vXpLkPlypXRtm1bVKlSBW3bttWoR6GOjo7c30823YsiJiYmGt2gk71JpKgOnjt3TjSiafTo0WonSDY0NET//v0BJDf+S5YsKaR7ADL/HK1MQECA6EKkVq1aaNq0qdrtZsyYka73lSZ9bHp5ealNPVCtWjWsXLkShw8fFlIpaZuNjQ1+//13re8XSA7KyuaXVkR2EraTJ09mSHkyk5OTk/BYR0dHFIxQZfTo0aLAwc2bNzX6vRwyZIjadTQ5B6RWp06dROdudWlWpK/ZChQoINemlu1drEmwe+jQodi4cSNOnTqFZ8+eoWLFipoUXWtkO2/I3qjOSWSDnOpG7lSrVg3nz5/HoEGDNJ6vwN/fHwcPHkTXrl0xduxYjeYjSo9+/fqp/B3Lnz+/KN0FALWjh2RTBypqOxkZGaFHjx6oW7cuihUrpvF5VrYdoOo6TF9fHytWrBDdcN65cyc+ffokjHRNUatWLVGPc23Jqu83s6n77mSvMwwMDNS2zWQ/Z0ZcazRt2lQu1ayssLAw9O7dG40bN0bp0qU1iqUB8seqojarq6urKN1g+/bt1Z6jDQ0NMXnyZI3KkNWyY7tOlVwZIL97965oWV3QIEWtWrXk8o5ltXr16qn9sXVxcRH1ROjQoYPSnnKy6tevL2oIZEWifMo8muQhlw5216lTR2HApkSJEihZsqSwrGiiTun8pkWKFBFy/Ut7//69aMKUFi1aKL37LqtMmTKi+qro2B04cCDWrFmD48ePp2qCE9neIxmRG0uT4DggP9QzPDxcbh3pgEfx4sU1vhGgp6cnCsC/f/9ebcNEV1dX4agCbZOdJEkR6SGYABTezJEl3VtamxOfSI86UEU6SKhu4sbUqFOnjtrAg2yva01yhEo3+LNi4i9ZOeG4cHNzE9XTdu3aafSbbGlpqVGwUhPSvU5CQ0MxcuRItUGW1q1bo379+ihevLjKGwqaGj9+PDZs2AAnJyesXr1a4+1kz7+apJqoUqWK3HaKyAbpFdVB6R5EOjo6onR0qvTs2RO3bt2Cq6srzp8/L7r4ycxztCqPHj0SXbhoml6wQoUKcheBaSU9KsrLywt///03fHx8VG7TpUsX1K5dG4ULF86QCd+bNGmSYRPJaxoQsrGxER0zLi4umZ6XU5t+/Pgh5KsFks/d0u1GVfT09ETfm0QiUdvDrGjRonIBKGXrSdPG73C+fPlEIwBu376tsK0GAN+/fxd1IOncubMohzIAYbh9io0bN+LgwYMqU7eUKlUKbdq0QeXKlbWWOiI1ZH8zMjvFizbJTkiuyY1rc3Nz/Pfff7hz5w7mzp2L+vXra3QTFEiexLZLly5y8QttSm3bSVdXV20va9mAsKI2YpMmTbB06VIcPHgQN2/eVDr6T1Zqr8PKlCkjSlWXkt5JOrWaubk5Vq1apZX2jays+n4zk76+vtrrBtn2d8WKFdV2HsqMz6lqBFeKMmXKYN68edi9ezeuXLkil1NdGdnPp+hYlY2TaNr2atKkSY4YjZMd23Wq6KtfJWMtXboUPXr0yNT3fPXqlfDY2Ng4VXfRa9SoobBXbVbR5CJKNsipbKisIvr6+ihXrhzc3NwAKJ+pO7fKzCGKdevWFSb6yyhly5ZF4cKFhREWz58/F12ExMbGigLbsj1tpDVq1EhIPZKS30u6ESl9AaAsGJyeYxdIvuucUl/fv3+PiIiINPVsTkhIwJcvX4SJYmRTPGVEQ1/TC0XZGwayk+l+/PhR1KsgLd9hColEglevXqlMU2BjY5PhvccByE18oojsxYeq3tDKttEWRTeAFJHu4aLN4IeqXtUpZC/CpXumKpNR31da5YTjQnZ4aGrSt1WrVk0rqRV69eqFXbt2CcfY06dP0aVLF9jY2MDe3h4NGzZEnTp1VKb4yCxxcXH49OkTXr9+jWfPnslNqKjJ+VfdKMEUsp9XNhACQDTkvVSpUhr3hDQ1NVUYpM/sc7Qq6T02NckFrU6/fv1EPWyvXbuG69evo3LlyrC3t0ejRo3w22+/aW00hSY0vWGRFqkZym9nZyccf9HR0fD09NTajYnMJjupbGonbZRd383NTWWHJ03PAbJtKm39Dvfo0QNXrlwBkHxOu3r1qsKbI7IjVBQN4Tc1NRVytabsb9GiRVizZg3q16+Pxo0bo2HDhtmqU5epqamofZrVEwqmh+yNiNTccLCwsED//v3Rv39/RERE4MmTJ3j06BGePHmCd+/eKe1ZGRERgb/++gsHDhzQevoPIPVtJ3Nzc7U3nWXbTem9Vvr27Rs8PDzw8uVLuXaAJpOc9u/fHzdv3hRGQkvHg4DkVFcZlRs/J3y/6ZUyT5EqsmXW5DpD9tokIz6ntn/jQ0JC8P79e2HCUWmKjtW0tr309PRQtWrVTM/RnVrZsV2nSpYHyLOCdL7gokWLpupOYXZqbABQm9MMgCidDCCfTzU1QkJCkJCQIHeyol9HgwYNhFQ6sgHqp0+fihq1qnoKN27cWEhrEhMTg1evXgk9NQMCAkTDy5UF2mWP3cWLF4smUkkNiUSCoKAglcHbiIgIuLq6wtPTE58/f4aPjw98fX3h4+OjspdiRvxYqxuyr4xsWWSHB1+7dk1uuFpqSPfoV6RgwYJp3ndqpOWOuSY9SDOKphdQ0j2JtXlcyfba0ISmozWyk5xwXMgOlZbOqa2OpkEedYoXL4558+Zh1qxZoue9vLzg5eWFvXv3wsDAADVq1IC9vT2aNWumNi9oeoWEhODly5d4//49vn79Ch8fH/j4+MDPz09hoDqFJvUkLfUPkL+QiYyMFM3Fkpq/nTKZfY5WJTscm7Vr18aYMWOwdetW4TmJRAJ3d3e4u7tj27ZtyJMnD2rXrg17e3u0aNFCo57B6aFJWzutNAmcpJD9nNKTuuY0siMdUvM9APLXY9Jz1Sii6TlAtqeatn6HGzduDGtra6F+nj9/XmmAPEXFihWVduKaMWMGXr16JeqFHxUVhZs3bwrBwyJFiqBRo0Zo0qQJ7O3ts/Q3vVChQqIbgQEBAam+GZhdyB5ryuZpUcfMzAwtWrRAixYtACSP5nr8+DHu3r2LGzduyKXMiI+Px/Tp03HhwgWtB5JS20bMqGMpISEBr1+/xtu3b/H582d4e3vD19cXX79+VdlzWJN6qqOjgyVLlqBz585yKVl69OiBDh06pLv8ymSX7zcj5eTrjLT+xnt5eeH169f49OkTvn79Cl9fX3h7e6scyafoWJXuTZ0yGaemypYtm+0D5NmxXadKroxySuf+SW1Px7T+CGYUTYJomuTnTI2wsDCNh0BRziMdIE/p2ZYSdJI+AStLi5Kifv360NfXF3rfPHr0SAiQS/ce19XVVZr3KyOOXUW8vLywadMm3Lx5U+UQ1RTSnyujaGuIn7bzo6ubqDez8kqm5eIgs4doSdM072RGyWnfV1rlhM8ZEREhWk5NO0SbQ+N79eqFwoULY8GCBQonGo6Pj8eTJ0/w5MkTrF69GmXLlkX//v3Rt29frfaof/HiBbZs2YIHDx5odF5Ny/lXW/UvPX87ZTL7HK1Kdjk2J06ciDJlymDFihUKg8DR0dG4d+8e7t27hyVLlqBKlSoYPHgwunTpkiH1Oa03rNUxMDCAkZGRxuvL/j0yYv6TzCJ7nKb2+kp2fXXfRVb/Buvr66Nz587Ys2cPgOS0f4GBgbC2thbW8fT0FI1MVTUBXN68eXHkyBGsXLkSjo6OCm8ifvv2DY6OjnB0dISRkRFatmyJsWPHioa7Z5ZSpUqJRt94e3tnehm0RfZGorKJ9FLLwsICbdu2Rdu2bTF37lycPn0aa9euFQXbvn79isuXL6NLly5aec8UqW07afs8GxISgh07duDUqVMa5dJO63VYoUKFMHz4cKxZs0b0vKLJI7Upq7/fzJAT2t/KpOY3Pj4+Hg4ODjh48KAob7gymhyr0qkbU9uuzIqUWWmR3dp1quTKHOTSVPVKUiSrh6/I0uQiVduBvJw8LI7Ukw1Wv3jxQngsPUGnqvQqQPIJXnoyjidPngiPpQPklSpVUtrrMzOO3cOHD6NLly64dOmS0uC4lZUV7O3t8ffff+PYsWNqJ8bNTrT9Haq7gZBZKTeyS6Mqp8gt39ev/jlTE0zThL29Pa5cuYKdO3eiV69eKnutfPz4EYsWLUKPHj200nNVIpFgzZo16Nu3L+7evav0XFWkSBG0bNkSU6ZMwZkzZ9C5c+d0v3daZcTxldnn6Iyi7WOza9euuHXrFjZs2IBOnTqpvPnq7u6OadOmYfDgwRmSnzS7pJKSvQbJ6qBveqT3ekr2+k3TuZWyknTAOzExERcvXhS9Lj05Z0pAXZW8efNiwYIFuHXrFqZPn47atWsrHeEbGxuLixcvokuXLjhw4EA6PkXayKYMcHd318p+P3z4kOkThEsH+oHk9FLaZmhoiN69e8PJyUku7cft27e1/n5Z2XZycXFB+/btsWfPHqXBcVNTU9SqVQtDhw7F9u3bsW3btjS9V3h4uDC6Wdr8+fNTHRNKjV+9bQrk7M+oaXDf398fffr0wcKFC5UGxw0MDFCxYkX06tULy5cvF8VOMoK2214ZKTu161TJlT3I8+XLJ1zcpXayLdneNdqkyURTaSHby+L+/fuwsrLKkPeinK9gwYKwsbGBl5cXgOQcbS1atIC/v7/wHKA+QA4k5yF3cXEBkJy/PiUPuaZ5zGWP3ePHj8vNgJ0ely5dwoIFC0TPlShRAvb29qhSpQrKlSuHsmXLyt1ZvnPnjtbKkNFkv8PJkydj5MiRWVQaIs3J5tP/Vcj2DknNSJmMaIPo6uqiSZMmwiRF7969w+PHj4Xe47I9Mz09PTFmzBgcP348XRdEu3btwvbt20XPVahQAY0aNUKlSpVQtmxZlClTRu77ysiLWHVkG/PamMg3O52jFR2bmuahz4hj09DQUOhRmZSUhNevX+Px48d4/PgxXFxc5C6aHj9+jJkzZ2L9+vVaL0tGiI+PR3x8vMYBeNmJHbXVsz2jrj9UkS17akcMyn4X2WWovioVKlRAlSpVhODwuXPnMHjwYADJNwwuXLggrGtvb6/xaN1ChQph2LBhGDZsGMLDw/H06VOhnnh4eIhuRiQmJmLx4sUoWbJkmucqSAvZSQqlO82kx6xZs/DixQvY2NigXr16GDhwYKrT9aSWbNnt7Ozk1jl79iy+fv2KoKAgxMTEYOnSpWl6ryJFimDatGmYOHGi8NyHDx/StK/s6OvXr/jzzz9Fv6X58uVDkyZNUL16ddjY2KBs2bJyKTBk8zprauHChfj27Zvc8y9evMCuXbtyVAeojJQVvwnZXVxcHEaNGiXKF25gYIAGDRqgdu3aKF++PMqWLYvixYuLblRq0maVTvWobAJnZTIyNqmJ1B4rOaFdlysD5EWKFBEC5L6+vqlqnGoyJEz2glHTnN2aDClKC9kG1tevXxkgJ5UaNGggBMNTJmh9/Pix8Lqenp7StCjS7O3thRnCY2Nj4eXlhWLFiuH9+/fCOo0aNVK6vaJjV1sB8vj4eCxatEj03NSpUzFs2DC1PZGy+scoNWS/w5w8rJVyvtT0ls3J6QNUkc3r/OXLF40n3ZKdlyEj2NrawtbWFoMGDUJiYiJcXFzg4OAgmmDn1atXuHv3Lpo2bZqm9wgODhZ+G4Dk3pJLlixROcleiqw8/xoaGsLU1FS4mJfOG6kJb29vFCpUSNRbKTudoxUdm5rm5szoY1NXVxfVqlVDtWrV8OeffyIuLg4PHz7EwYMHRenfrly5gvfv36N8+fIZWh5t+fbtm8aTcqdMOp5CdoIz2esPTW8mpSctT1rJXofIfjZ1ZIOE2kpzkdG6d+8uBMjd3Nzg7e2NEiVK4NmzZ/Dz8xOtlxZ58+YV5bUODAzE5cuXsWvXLlEd3bRpU6YGyO3s7FCoUCFhzgUfHx+4u7ujSpUqad5nSEgIXr16BYlEgvfv3+P9+/cYOHCgtoqs0KdPn0TXMGZmZnLBfyD5BrB0upyJEyemOc9xy5YtoaenJ9TnX2kk94oVK0TB8fbt22PRokVq00ykpR1w5coVnDlzRliuWrUq/P39hXkBNm7ciCZNmuTY3PiKKIpJaSIrfhOyu2PHjomC4+XLl8eWLVvU/n5rcqyWKFFC+F2Ij4+Hr6+vxr9p2mp7ZUX7Ibu267L/eLQMID1TbXx8vOgHTB3ZGY8Vkc0drMmwgISEBFHDSJtk72yn9q7rx48fM334GmUt6eD369evAYjTotjZ2WnUc6lq1aqiHnceHh5wcXERerOYmJigRo0aSrdP77Hr7e2NgIAAhUN579+/L0oT0LZtW4wYMUKjYbqyw6qyW+olaeXLlxcNw3706FGqtv/x4wd8fHwyPOc6/bqkbxBrOkzu+/fvv2wPctnzmnQaK3VSblhqQ3BwMFxcXFRObqenp4e6deti9erVGD16tOi1ly9fpvm9L126JPr7Dho0SKPgOJD151/pVAFfvnzRuLdPeHg4WrdujWrVqqFx48bYvHkzgOx1jpZNg5Cav7E2j82AgAA8fvxYZQ99Q0NDNG3aFLt27RLl45VIJHB1ddVaWTLamzdvNF5X+u9hZWUlN4mV7PWHpiMcFM1BkNFkOzukjDbUlOwk8qrmxMlOOnXqJOqUdePGDQAQ3YC0sLBA8+bNVe4nPj4eHz58UPv7YW1tjYEDB+LkyZOi9vibN28ytZeorq4uOnXqJHru0KFD6drn8ePHRec9W1vbDO89fvjwYdFy+/btFaY6kg2aSXcySq2UG7MpMmuun4wWHh6OW7duCcuFChXCihUrNMrBnNobaj9+/MDcuXOFZQMDAyxbtgyzZ88WnouPj8e0adN+qbanbAdNTdvgWfGbkN1J31wBgLVr12p0c1uTNmt2aHtldPshJ7XrcmWA3N7eXrQsPaRNFS8vL9GdI2Wkf8QACHcmVXnx4kWGnZDr1q0ruit06tQpjS+k/P390aVLFzRo0ADVq1fHgAEDMqSMlL3Uq1dPOFGGhobC29tb47Qo0nR1dVG/fn1h2cPDQ3TSr1Onjsq8X9WqVRMN77569arGw3BjYmLQu3dv2Nvbo1q1aujYsaMoN6ts40pRDxBFQkJCRN8FoPwua3bIx2ZoaCjqnfr169dUBWD+++8/tGzZEnZ2dmjatGmOCjxQ9iD9mxgREaFR76fUBglzkipVqiB//vzC8uXLlzX6TuLi4nD16tV0v//Tp09Rt25dNGjQAP3795fLg6tMhw4dRMuKAuuanvNkz7/SvxOqeHl54ePHj6LnMjvlSspk0wCQlJSEy5cva7TdvXv3IJFIIJFIEBgYKPSgzU7n6AYNGoiCd6dOndLoBkRQUJBW6uz58+dRs2ZN2NvbY9CgQRoHldq3by9XHlnZ4fdYkWvXrmm03rNnz/DlyxdhWVGdkb3+0GSuAIlEovH3rM3v0MrKCmXLlhWWnzx5ovGFdmJiIk6fPi16TtM2XFaztLQU9dy+fv06EhMTRef2Dh06qGwb//XXX/jtt9/QoUMHjBkzBklJSWrf18rKCnXr1hWWExMTM2zksjIDBw4UnV/Onj2b5lzkwcHB2Lt3r9z+M9LXr19x/PhxYVlXVxf9+/dXuG6dOnVEy/v370/z+wYGBoqufWSDaTnV169fRfGIGjVqaJwL+vr166Jlde2AWbNmiY73MWPGoHz58mjbti1at24tPO/p6Ym1a9dqVIacIC2/CcCv3QZPK+l2a/78+TXuzSzbbld0vk5JcZjCyclJo32/efNGrk2cVhnVfsjIdl1GybUBculhC8eOHdNomOyKFSs02r/skMeU3gGqZGRenSJFioiGQXt7e2P37t0abbty5Uqhh0FMTAwqV66cIWWk7MXMzEzUy/Hu3buiE7CmAXLZdT08PESjMFSlVwGSAwc9evQQlsPDw7Fq1SqN3nfr1q3CyIe4uDiULl1aNJGF7EW/phcKc+bMkQtmKeuFI3s3VpOLmIzQp08f0fLSpUs1Csg9ePBAmAwoKSkJcXFxPAdQqkn/JkokErW/iXFxcdi6dWtGFyvL6Onp4ffffxeWw8LCROlGlNm7d69WRnOVL19eNOTzxIkTGgWZZdtJhQsXlltH03NeWs6/CQkJmDlzptzzmZ0rs0ePHqLPuX37drW9siQSiSiYY2BggJYtWwrL2eUcbWlpKQoWfPz4EUeOHFG73fr167Xyd7CxsRH1Ljp27JhG22nz2Mxsly5dUttbLCEhQS6HsaIOK4quP9Td4Dh58qTGaX20/R1KH/cSiQTz58/X6IbMzp074evrKyzXrl1brjd9diadPsXFxQXXr18XndvVpVcpXry4EFgMCQnBlStXNHpf6XpiZGQES0vL1BQ73YoUKYK+ffsKywkJCZgyZUqqA/WJiYlyAc8SJUqkOS2NJqKiojBp0iRRR5vu3bsrPd926tRJFOx9/fq1xkEvWQcPHhQtywaOcqq0XocdOHBA7kawqt+f48ePi3qqV6xYUTTPx5w5c0Rzgezbt0+uI1ROZWZmJvps9+/fR3R0tMptHj58mK4RD78q6eM1KipKo46tr1+/lrs5puhYtbW1FY2qcnZ21mi+s9WrV6tdR1MZ1X7IyHZdRsmVAXJdXV389ddfwnJUVBTGjBmjtKe3RCLB0qVLNZ6YTzY38549e5Te3UlISMCCBQsy/EQ8evRoUeqIdevWqb3o2bFjB86dOycsGxsbY/jw4RlWRspepHsnSc94b25unqoZ26UD5O/evRMNBdIk0D5s2DDR8EUHBwesXbtW5Un7/Pnz2LFjh7Cso6MjqvOA/HDc48ePqww+RUZGYtq0aQp7cEo3mKXJ3o3Nqpxubdq0Ed3p9vDwwLhx41TmRXv37h0mT54sem7UqFEaz9dAlEL2N3H9+vVKewJERUVh8uTJSmeH/1UMGzYMefPmFZb37NmDo0ePKl3/8uXL2Lhxo1be28LCQhScfffuHZYtW6bynBoWFibXqyolx600Tc95suffvXv3qrxoCwoKwqhRoxSmuVN2/s0oxYsXFwUovL29MWHCBKXll0gkWLFihajsXbp0EeVgzk7n6HHjxomGZS9btkzlTa19+/bBwcEhXe+ZomLFiqKcxHfu3MG+fftUbuPn5yf6vTcwMFDYtpCdxDG75FhNTEzE33//rXTivfj4eEydOlVIdwckdy5QlJ4uX758ou/v48ePKr+/u3fvpmryQG23aXr37g1ra2th2dnZGbNmzVIb7JK9oTh+/Ph0lSOzNW3aVJh7ICkpSfQ3KFu2rNo2tvQNVgBYtGiR2okbjx8/LkrnY29vnyXtuUmTJoluZnz8+BGDBw/WePRATEwMpk+fLjon6erqYvHixRrN95UWXl5e6NOnj6gOFi1aFFOmTFG6TYECBYQJWFPMnj0bJ06cSNV7X758WdSprU6dOqJRTDlZqVKlRMfg06dPRek8ZUkkEuzZs0dhh0VlN5S9vb1F9StlvhPp9y1YsCCmTZsmLCclJWH69Ok5ar4pVaSv58PCwlQGVd3c3DB9+vTMKFaOY2NjIzyOiYnBnj17VK7v7OyMUaNGyf2eKTtWpSfiBZLnRVN281wikWDZsmVwdnZWX3ANZVT7ISPbdRklVwbIAaBnz56iE4anpyc6deqEPXv24MuXL4iLi0NwcDCuXbuGvn37KvxDKhtqaGtrKxr+FBISggEDBmD//v3w9vZGbGwsvL294ejoiB49egj5zIoWLardDymlRo0aGDt2rLCclJSE+fPnY/Dgwbhy5Qp+/PiBuLg4+Pj44Nq1a+jXr5/cCXTatGlpnmAkM82YMUOYZMzW1lZrQYXcpmHDhsJj6WBVgwYN5HoRqVKkSBFhGG1oaKjQ4ChcuLBGOSOLFy+O+fPni57btm0bfv/9d5w+fVrIVfz9+3fcu3cPI0eOxOTJk0U9m4YNGyY3EVCDBg1QsGBBYdnf3x+9evXC6dOn4e/vj4SEBAQHB8PNzQ3r1q1D+/bt5fKPpVCWg1b6wg9Izrfo6ekp7Duz6OrqYt26daIAwb1799CuXTvs3r0bHz58QGxsLEJCQuDq6op58+ahZ8+eojLWrFkTf/zxR6aVmX4djRo1Et35//LlC3r37g0nJyd8+/YNsbGx+PTpE/bv348uXboIN6Ey8jcxq+XPn190XpNIJJg3bx7++usvPH78GBEREYiMjMSLFy8wffp0TJw4EfHx8VoLAIwbN050gXjgwAH069cPFy9exPfv3xEfH4/Y2Fh8+fIFR48eRffu3UUTk3Xo0AEVK1aU26+m57w2bdqI0md5eHigX79+uHr1KoKDg5GQkICAgAC4uLhg8eLF6NChg9ILAU1zgGvT7NmzRb1t7t69i06dOsHBwUGY/D00NBS3b9/GoEGDRBdS1tbWmDp1qmh/2ekcXa5cOUyYMEFYjouLw9ixYzFz5ky4uroiOjoa4eHhePjwIUaPHi1cIGnr2Jw0aZJoeenSpRg1ahRu3ryJwMBAJCQkIDo6Gl5eXti9ezd69Ogh6uAyZMgQuYlPAYh+74Hknv8+Pj5ITEzM0nl2dHV14e/vj549e2LTpk34+PEjYmNjERQUhPPnz6N79+6iNEj58+fHsmXLlO5PNni6bNkyzJw5E25uboiMjERwcDAePXqEyZMnY9SoUYiIiEDhwoU1mn9F220aExMTrF27VnTsODo6okuXLnB0dISfn59wPXbr1i2MGDECs2fPFo14GTZsmMYpmrILfX19UT7ub9++CY816QVta2sr2v7Hjx/o2bMn1qxZAzc3N0RERCAxMRGhoaF4/Pgxpk+fjjlz5gjrGxgYKL2psHHjRtE11IwZM9LyEZUyNTXFpk2bRL1a3717h65du2LdunUqO6tdv34dv//+u6jzFgBMmDBBayl2Us7dHh4eOH36NMaMGYNOnTrB09NTWMfMzAxbt24VpUpTZPz48aJrj4SEBMyaNQtDhgzB7du3VfZA/fTpE6ZPn45JkyYJowVMTEywcOHCdH7C7CNv3ryiXPtJSUn4888/sX37dnz69AlxcXGIiIjA+/fvcfjwYfTo0QPLly9XeANNUTA7JdAtPcJr+PDhCieG7dWrl6gzh6+vL5YsWZLej5gtyP4mHDx4EGPGjMGzZ8/w8+dPhIWF4cWLF5g7dy7++OMP+Pv7w8zMTNSJgyDKiQ0kdzadPXs2Xr16hejoaMTGxuLr16+4dOkSRo0ahREjRihMU6KszdqwYUPRCJuwsDAMGDAAy5Ytg4eHB2JjYxEaGoqbN2+if//+wqhEbd4YzKj2Q0a16zJKxtxqzSE2bNiAgQMHCpN0hoaGYvny5Vi+fLnC9YsWLYr8+fMLd5BV5cmaM2cOBg8eLPQoCgoKwpIlS5SebJs0aYL27dsrHD6sLWPHjkV4eLgo2P/o0SON8kyNHDmSwTEFbG1ttbKfAwcOqG3cPXnyRGvvp8nEtDVq1ECePHnkesXJ5vDXROPGjeVGUUgH4NXp1q0bgoODsXLlSiHw7e7urtFd7i5dusj1sgOS6+/cuXMxfvx4YZ8+Pj4a7bNVq1ai/HfKhhcVKFAAJUqUEF7/9u0bOnfuLLx+/fr1TBsWbGNjgy1btmDixIlCr6/AwECsWLFCbfqoSpUqYePGjew9TmliZGSE2bNn4++//xYCGz4+Pip/73r37o0iRYpkaPqxrNaxY0d8//5dVP9u3LihtLeukZERJkyYoHG6N1VsbW0xZ84czJkzR+g5/uLFC40mDK1Zs6bStoym5zwrKytMnjwZixYtEl57+/at2p6gurq6aNasGW7evCk8p2l6CG2ysLDA9u3bMWrUKCG45ePjIwpCKdtu165dClMbZKdz9MiRIxEQECAM7ZdIJHByclKaIsDS0hJ//PEHNm3alO73tre3x+jRo7Ft2zbhudu3bwupZFRp27at3IVYivLly8PExEQIlrx9+1YYSWFgYIDnz59rnP9WW/T09DBt2jQsXboUUVFR2Lhxo8pOHVZWVtixY4dcsF9a7969cfbsWVHPM1V/O3Nzc+zatUujSXIzok1Tp04drFq1CjNmzBB61n38+BH//fef2m0HDhwo6vmZk/To0UNu6L2urq5cEEaZOXPmwNPTUwjcRkVFYfv27di+fbvK7fT09LB69WqFNzgzS8WKFbFr1y789ddfQgApKioKW7duxY4dO2BnZwdbW1tYWloiJiYG3759w7NnzxSOPPvnn38watSoVL3/zJkz03y9Xbx4cWzbtk2j/MNGRkbYuXMnhg8fLprD7OHDh3j48CFMTU1Rs2ZNFCpUCAUKFEB0dLTQMUd6vgEgOTi+ZcsWlClTJk3lzq6mTZuGp0+fCulVoqKisGbNGqxZs0bldjVq1MDXr1+FY0JRO2DXrl2iyX/Lli2LcePGKd3nwoUL0blzZ+G69+TJk2jZsqVoxF1O1KxZM7Rt21aUiunmzZuidpQ0AwMDbN68GbNnz86SDgjZVd++fXH+/Hnht1UikeD48eOieQkUKViwIAoXLiyMIgwKCkJUVJTcqDYgOVd+UFCQMDdJfHw89u7dKzffQorSpUvD3t5eLg1TWmVU+yGj2nUZJdf2IAeShxLs379fblZtRWrVqoXDhw+LelCrakhXr14du3btUju7ra6uLoYNG4bNmzenqlduWujo6GDmzJlYs2aNKAe7KtbW1lizZo3CACP92gwNDVGrVi2559XlDVdE0bCY1A6VGTZsGHbv3o0KFSpotH7evHkxa9YsrFixQmndatWqFVavXq3RjOlAcm/4DRs2YNOmTaKhxqpuMs2ePVtp0EKTGxXa1KBBA5w4cUI0QZQqKRMQHTp0SJQOgCi1WrVqhfXr16s9jgwNDTF16lS5USO/quHDh2Pr1q1qR2dZW1tj+/btWp2cq3fv3ti4caPKYOZFj1IAAAokSURBVJs0AwMDjBgxArt27RL1/pal6Tlv4MCB+PfffzUOStrY2GDfvn1YvHix6PmsmkzK1tYWJ06cQNu2bTWavLBOnTo4efKkysBUdjpHz5o1C4sWLUK+fPlUrle6dGns3btX43alJiZNmoT58+eLepmqYmpqismTJ2P16tVKf+/z5MmDmTNnKvxbxcfHa22iq9QaMmSIRvWgUaNGcHBwUNj7UZqBgQF2796Ntm3bqn3vatWqwcHBQePJxoCMadO0b98eR48eFU1Wq0rx4sWxadMmzJo1K9tOvqpOxYoV5c4FDRo00DjPar58+XDw4EGNrmGl33P//v0aHRsZrXr16jh16pRcqq7ExES8fPkSDg4O2LZtG/bt24crV67IBceLFSuGXbt2pTo4nlbm5uYYP348zp49m6r6UqBAARw7dgyDBg2S6+kZGRmJe/fuwdHREdu3b8eBAwdw/vx5ueB41apVceLECbl0db+CEiVKYN++fShdurRG65uYmGDChAk4ePCgaK4sd3d3UTDXw8NDlIpJV1cXS5YsUXmeLVGihFyai9mzZ2fpCCNtWb16Nfr27au2p2/p0qVx+PDhHDcqJzMYGBhg+/btchNqKqOrq4uuXbvizJkzaNeunfB8UlKS0tTKBgYG2LBhAyZNmiRKL6tItWrVsHv3brVttNTIyPZDRrTrMkqu7kEOJPd6Wb16NQYPHozz58/j0aNH8Pf3R2RkJAoUKAA7Ozt07twZbdq0gY6OjihvkLqhVbVr18aFCxdw4cIFXLt2DW/fvsWPHz9gYmKCwoULo1GjRujevXuqfmi1oWPHjmjTpg2uXbsGZ2dnuLq64sePH4iIiICxsTGsrKxQtWpVNG3aFG3atFFbQenXVb9+fdGw9rJly6Yp7UHdunVhaGgoDCfU0dFJU0OvYcOGOHv2LO7cuYM7d+7g+fPnwuzuBgYGyJ8/PypVqgR7e3t06NBBo+FhHTp0QP369eHo6AhnZ2d4eXnh58+f0NXVRd68eVG0aFFUrFgRDRo0QKtWrYTGVevWrXH69GkAyTnb3rx5o3CynqZNm+LIkSPYu3cvnj9/jqCgIOjp6cHKykrtRCkZoWTJkti+fTvc3d1x9epVPH78GH5+fkKPxXz58sHGxgZ16tRB165dtRr0oNytdevWaNCgAc6cOYObN2/i/fv3CA4Ohrm5OYoWLYpmzZqhW7duKF68eFYXNVO1aNFCOLddvXoVb9++RVhYGPLmzYsSJUqgdevW6NmzJywtLbU+cVLr1q1hb2+PGzdu4Pbt2/Dw8BDaQEZGRrCyskKpUqXQpEkTtG7dWqPgTWrOeYMHD0br1q1x/PhxPHr0CJ8/f0Z4eDj09fWFz1+pUiU0bdoU9vb2wsVdnTp1hAuMlCGasukfMoO1tTU2bNgADw8PXLp0CQ8fPhTOp0ZGRihUqBBq1aqFTp06aZwCIDudo3v16oW2bdvCyckJN2/exLt37xAREQFLS0uULl0a7du3R/fu3WFiYiLqIakNffv2RceOHXH58mU4Ozvj3bt3op5XBQoUQLly5dCsWTO0bNlSbZscSL4pVKxYMRw8eBCvX79GaGgoDAwMULBgwSzNST548GA0adIEx48fx927d/Ht2zdIJBIULFhQOH5SM+rOzMwMGzZswMuXL3Hu3Dk8fvwY379/R0JCAgoWLIiKFSuiS5cuaN68eaovPDOqTVO5cmUcPXoUjx8/xo0bN/D06VMEBAQgLCwMBgYGKFKkCKpVq4ZWrVqlqdzZUffu3UU5XLt165aq7S0sLLB69WqMGTMG58+fh6urKz58+IDw8HDEx8fDwsIC1tbWqFmzJpo3b46GDRtqlEonsxQsWBBbt27F8+fPceLECdy4cQNhYWFK19fR0UHlypXRp08fdOnSReWN2rTS19eHqakpzMzMUKpUKVSoUAENGzZEgwYN0jzCxNjYGP/99x8GDRoEBwcHXL16VS4IrqgcdevWRZ8+fTS+CZtTVaxYEefOnRPFS4KDg5GYmAhTU1NYW1ujQoUKqFWrFjp06CCMwGrTpo3QAzUhIQEnT57EkCFDEBcXh2nTpolSsQwcOFDhvA2yBg0ahIsXLwqTgAYFBWH27NnYvHmz9j94JjIwMMD8+fPxxx9/4MyZM7h//z6+ffuGqKgoWFtbo3z58ujQoQPatWsHIyOjrC5utmVhYYGdO3fiwYMHQk/rgIAAxMTEwMTEBBYWFrCxsYGdnR06dOggjPho06YNVq5cKYzYPHbsGJo2barwPXR1dTF69Gj06NEDjo6OuHv3Lj58+IDo6GgUKFAANjY26Ny5Mzp16pQh8y5kZPshI9p1GUFHosl04STo1q2bcBFQp04dHDp0KItLRERERERERJQzJSYm4vPnz/D09ERwcDAiIiKgo6MDc3NzFClSBNWrV4eFhUVWF1Mrfvz4AU9PT/j5+SEsLAyxsbHQ19dHvnz5UKpUKVStWlXj0a1ERKQ9ubIHua+vL9zc3FCiRAkUL15c46EJERER+PTpk7CsaaoHIiIiIiIiIpKnp6eHcuXKoVy5clldlAxnZWXF1IVERNlQrgyQBwQEYMKECcLynDlzNJqA8ujRo6IUK3Xq1MmQ8hERERERERERERFRxss+icgyUfny5UX5lQ4cOKB2AoZ79+5h06ZNwnKBAgXkJhYhIiIiIiIiIiIiopwjVwbIzczMRLN+f/78Gd27d8e2bdvg4eGBkJAQJCYmIiQkBI8fP8a8efMwZswYUe/xf//9l5MYEBEREREREREREeVguXaSzrCwMPTr1w8fPnxI9baTJk3C6NGjM6BURERERERERERERJRZcmUPcgDIly8fjh07hm7dukFHR0ejbYoVK4atW7cyOE5ERERERERERET0C8i1Pcilff78GRcuXICLiwu8vLwQFhaG+Ph45MmTB0WLFkWVKlXQvHlztGzZEvr6uXJeUyIiIiIiIiIiIqJfDgPkRERERERERERERJQr5doUK0RERERERERERESUuzFATkRERERERERERES5EgPkRERERERERERERJQrMUBORERERERERERERLkSA+RERERERERERERElCsxQE5EREREREREREREuRID5ERERERERERERESUKzFATkRERERERERERES5EgPkRERERERERERERJQrMUBORERERERERERERLkSA+RERERERERERERElCsxQE5EREREREREREREuRID5ERERERERERERESUKzFATkRERERERERERES5EgPkRERERERERERERJQrMUBORERERERERERERLkSA+RERERERERERERElCsxQE5EREREREREREREuRID5ERERERERERERESUKzFATkRERERERERERES5EgPkRERERERERERERJQrMUBORERERERERERERLkSA+RERERERERERERElCsxQE5EREREREREREREudL/AWay8kF6+oPGAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EEWeather mean distance: 10319m\n", + "EEWeather median distance: 8412m\n", + "EEWeather standard deviation: 7725m\n", + "CDS maximum radius: 16000m\n" + ] + } + ], + "source": [ + "sns.set_style('darkgrid')\n", + "sns.kdeplot(df['Minimum distance'])\n", + "plt.axvline(x=df['Minimum distance'].mean(), color='r', linestyle='dashed')\n", + "plt.text(\n", + " df['Minimum distance'].mean(),\n", + " 0.00009,\n", + " 'EEWeather mean distance',\n", + " rotation=45,\n", + " color='r',\n", + ")\n", + "\n", + "\n", + "plt.axvline(x=13875, color='g')\n", + "plt.text(\n", + " 13875,\n", + " 0.00009,\n", + " 'CDS maximum radius',\n", + " rotation=45,\n", + " color='g',\n", + ")\n", + "\n", + "plt.axvline(x=6938, color='g')\n", + "plt.text(\n", + " 6938,\n", + " 0.00009,\n", + " 'CDS mean radius',\n", + " rotation=45,\n", + " color='g',\n", + ")\n", + "\n", + "plt.title('Figure 1: EEWeather minimum distance distribution vs. CDS maximum radius', y=-.2)\n", + "plt.xlabel('Distance (m)')\n", + "plt.show()\n", + "\n", + "print('EEWeather mean distance: ' + str(int(df['Minimum distance'].mean()))+ 'm')\n", + "print('EEWeather median distance: ' + str(int(df['Minimum distance'].median()))+ 'm')\n", + "print('EEWeather standard deviation: ' + str(int(df['Minimum distance'].std()))+ 'm')\n", + "print('CDS maximum radius: 16000m')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8b8f61fd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1000" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(distance_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3b97710c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Group mean: 0.136\n", + "Standard error: 0.002\n" + ] + } + ], + "source": [ + "p = Path().resolve().parents[0]\n", + "\n", + "diff_list = []\n", + "\n", + "for i in list(range(100)):\n", + " control = pd.read_csv(str(p) + '/samples/intl_samples/' + str(i) + '_control.csv', index_col=0)\n", + " control = control.rename(columns={'0': 'Control'})\n", + " control.index = pd.to_datetime(control.index)\n", + " test = pd.read_csv(str(p) + '/samples/intl_samples/' + str(i) + '.csv', index_col=0)\n", + " test = test.rename(columns={'temp':'Test'})\n", + " test.index = pd.to_datetime(test.index)\n", + " diff = list(control['Control']-test['Test'])\n", + " for j in diff:\n", + " diff_list.append(j)\n", + "\n", + "diff = pd.DataFrame(index=range(len(diff_list)), data=diff_list).dropna()\n", + "\n", + "sns.set_style('darkgrid')\n", + "sns.kdeplot(data=diff, x=diff[0])\n", + "plt.axvline(np.mean(diff[0]), color='r')\n", + "plt.text(\n", + " np.mean(diff[0])+0.2,\n", + " 0.02,\n", + " 'Mean difference = ' + str(round(np.mean(diff[0]), 3)),\n", + " rotation=90,\n", + " color='r',\n", + " horizontalalignment='right'\n", + ")\n", + "plt.xlabel('Difference (degC)')\n", + "plt.title(\"Figure 2: difference distribution between CDS and 'original' EEWeather across 100 US sites\", y=-.2)\n", + "plt.show()\n", + "\n", + "print('Group mean: ' + str(round(np.mean(diff[0]), 3)))\n", + "print('Standard error: ' + str(round(scipy.stats.sem(diff[0]), 3)))" + ] + }, + { + "cell_type": "markdown", + "id": "6b1e30c0", + "metadata": {}, + "source": [ + "### 3. Speed\n", + "\n", + "While outputs from the EEWeather international package (via CDS) are accurate, API traffic on the CDS means that download speeds are poor compared to 'original' EEWeather. A typical 2-year interval call for US sites using the 'original' package can be expected to return temperature data at a mean of 0.12 seconds; the same call using EEWeather international might typically take __552 seconds__. \n", + "\n", + "Users in the United States should therefore continue to use 'original' EEWeather.\n", + "\n", + "Users seeking to use EEWeather outside of the United States (in combination with EEMeter international) should plan in advance to save weather files locally to avoid repetition. Where portfolios of multiple sites are concerned, users can make use of the `get_weather_intervals_for_similar_sites` function described below.\n", + "\n", + " Operation of the CDS \n", + "\n", + "The CDS uses a queue to handle traffic. EEWeather international calls are automatically placed in the queue; once approved a download will begin. Since the CDS assigns small-volume calls higher priority in the queue, EEWeather operates by making requests on a month-by-month basis. This means that for a request of January - April, CDS will be asked for January's, February's, March's and April's data in turn.\n", + " \n", + "Further details on the queue, including [queue times by request size](https://eqc.climate.copernicus.eu/monitoring#highlevel_view;year) and [KPIs](https://cds.climate.copernicus.eu/live/priorities), can be found [here](https://cds.climate.copernicus.eu/live/queue)." + ] + }, + { + "cell_type": "markdown", + "id": "f2714ddf", + "metadata": {}, + "source": [ + "### Portfolio weather calls - efficient handling of the CDS\n", + "In circumstances where weather data is required for portfolios of sites, it is possible that groups of sites will fall into the same reference grid used to pull data from the CDS. Whereas 'original' EEWeather operates on a discrete basis i.e. the package will call as many weather datasets as sites, irrespective of possible duplication, performance constraints on the CDS have required the development of an alternative approach. EEWeather international can identify unique sites to a precision of 0.25 decimal degrees (the same precision as ERA5 2m temperature data from the CDS) and make a single call for a group of sites in the same reference area corresponding to the first and the latest chronological points required for all calls. The resulting CSV file can then be saved and individual 'per-site' slices of weather data extracted.\n", + "\n", + "This facility is available via the `get_weather_intervals_for_similar_sites` function. In order for this function to work, data must be formatted into categories of start_date, end_date, latitude and longitude for each site. This format is detailed below." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4b4a4051", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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start_dateend_datelatitudelongitude
02018-04-102022-12-22 12:10:30.01435753.50-2.25
12018-06-292022-12-22 12:10:30.01435753.50-2.00
22019-08-192022-12-22 12:10:30.01435753.50-1.75
32019-02-252022-12-22 12:10:30.01435753.50-2.25
42020-03-292022-12-22 12:10:30.01435751.25-0.50
\n", + "
" + ], + "text/plain": [ + " start_date end_date latitude longitude\n", + "0 2018-04-10 2022-12-22 12:10:30.014357 53.50 -2.25\n", + "1 2018-06-29 2022-12-22 12:10:30.014357 53.50 -2.00\n", + "2 2019-08-19 2022-12-22 12:10:30.014357 53.50 -1.75\n", + "3 2019-02-25 2022-12-22 12:10:30.014357 53.50 -2.25\n", + "4 2020-03-29 2022-12-22 12:10:30.014357 51.25 -0.50" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv(str(p) + \"/samples/df.csv\", index_col=0).dropna()\n", + "\n", + "#NOTE start_date and end_date must be pd.datetime\n", + "df['start_date'] = pd.to_datetime(df['start_date'])\n", + "df['end_date'] = pd.to_datetime(df['end_date'])\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "064607d2", + "metadata": {}, + "source": [ + "Weather data can then be called using the following syntax. (NOTE `break` should be removed before actioning locally)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "9c3396da", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "for start_date, end_date, lat, long, in zip(\n", + " df['start_date'],\n", + " df['end_date'],\n", + " df['latitude'], \n", + " df['longitude'], \n", + "):\n", + " weather = eeweather.get_weather_intl(\n", + " start_date = start_date,\n", + " end_date = end_date,\n", + " latitude = lat,\n", + " longitude = long,\n", + " areacode = None,\n", + " )\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "ed9156ef", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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temp
Datetime
2018-04-10 00:00:006.726715
2018-04-10 01:00:006.603912
2018-04-10 02:00:006.604889
2018-04-10 03:00:006.564362
2018-04-10 04:00:006.396393
......
2022-12-22 08:00:004.327545
2022-12-22 09:00:004.508209
2022-12-22 10:00:005.460846
2022-12-22 11:00:005.892487
2022-12-22 12:00:006.352448
\n", + "

41221 rows × 1 columns

\n", + "
" + ], + "text/plain": [ + " temp\n", + "Datetime \n", + "2018-04-10 00:00:00 6.726715\n", + "2018-04-10 01:00:00 6.603912\n", + "2018-04-10 02:00:00 6.604889\n", + "2018-04-10 03:00:00 6.564362\n", + "2018-04-10 04:00:00 6.396393\n", + "... ...\n", + "2022-12-22 08:00:00 4.327545\n", + "2022-12-22 09:00:00 4.508209\n", + "2022-12-22 10:00:00 5.460846\n", + "2022-12-22 11:00:00 5.892487\n", + "2022-12-22 12:00:00 6.352448\n", + "\n", + "[41221 rows x 1 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "weather" + ] + } + ], + "metadata": { + "hide_input": false, + "kernelspec": { + "display_name": "'cc_jupyter'", + "language": "python", + "name": "cc" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/setup.py b/setup.py index 17b9008..842abf8 100644 --- a/setup.py +++ b/setup.py @@ -31,6 +31,7 @@ NAME = "eeweather" REQUIRED = ["click", "pandas", "pyproj>=1.9.6", "requests", "shapely"] +EXTRAS = ["cdsapi", "xarray", "cfgrib", "eccodes"] here = os.path.abspath(os.path.dirname(__file__)) @@ -89,6 +90,7 @@ def run(self): packages=find_packages(exclude=("tests",)), entry_points={"console_scripts": ["eeweather=eeweather.cli:cli"]}, install_requires=REQUIRED, + extras_require=EXTRAS, include_package_data=True, license=about["__license__"], classifiers=[ diff --git a/tests/snapshots/snap_test_database.py b/tests/snapshots/snap_test_database.py index f3beebd..4c35392 100644 --- a/tests/snapshots/snap_test_database.py +++ b/tests/snapshots/snap_test_database.py @@ -7,29 +7,29 @@ snapshots = Snapshot() -snapshots['test_isd_station_metadata_table_count count'] = 4921 +snapshots["test_isd_station_metadata_table_count count"] = 4921 -snapshots['test_isd_file_metadata_table_count count'] = 43141 +snapshots["test_isd_file_metadata_table_count count"] = 43141 -snapshots['test_isd_station_metadata_table_content data'] = { - 'ba_climate_zone': 'Hot-Dry', - 'ca_climate_zone': 'CA_14', - 'elevation': '+0625.1', - 'icao_code': 'KNXP', - 'iecc_climate_zone': '3', - 'iecc_moisture_regime': 'B', - 'latitude': '+34.300', - 'longitude': '-116.167', - 'name': 'TWENTY NINE PALMS', - 'quality': 'high', - 'recent_wban_id': '93121', - 'state': 'CA', - 'usaf_id': '690150', - 'wban_ids': '93121,99999' +snapshots["test_isd_station_metadata_table_content data"] = { + "ba_climate_zone": "Hot-Dry", + "ca_climate_zone": "CA_14", + "elevation": "+0625.1", + "icao_code": "KNXP", + "iecc_climate_zone": "3", + "iecc_moisture_regime": "B", + "latitude": "+34.300", + "longitude": "-116.167", + "name": "TWENTY NINE PALMS", + "quality": "high", + "recent_wban_id": "93121", + "state": "CA", + "usaf_id": "690150", + "wban_ids": "93121,99999", } -snapshots['test_isd_file_metadata_table_content data'] = { - 'usaf_id': '690090', - 'wban_id': '99999', - 'year': '2008' +snapshots["test_isd_file_metadata_table_content data"] = { + "usaf_id": "690090", + "wban_id": "99999", + "year": "2008", } diff --git a/tests/snapshots/snap_test_ranking.py b/tests/snapshots/snap_test_ranking.py index f234240..7e4f2a9 100644 --- a/tests/snapshots/snap_test_ranking.py +++ b/tests/snapshots/snap_test_ranking.py @@ -7,146 +7,102 @@ snapshots = Snapshot() -snapshots['test_rank_stations_no_filter df.shape'] = ( - 4921, - 15 -) +snapshots["test_rank_stations_no_filter df.shape"] = (4921, 15) -snapshots['test_rank_stations_match_climate_zones_null match_iecc_climate_zone'] = ( +snapshots["test_rank_stations_match_climate_zones_null match_iecc_climate_zone"] = ( 1539, - 15 + 15, ) -snapshots['test_rank_stations_match_climate_zones_null match_iecc_moisture_regime'] = ( +snapshots["test_rank_stations_match_climate_zones_null match_iecc_moisture_regime"] = ( 1974, - 15 + 15, ) -snapshots['test_rank_stations_match_climate_zones_null match_ba_climate_zone'] = ( +snapshots["test_rank_stations_match_climate_zones_null match_ba_climate_zone"] = ( 1544, - 15 + 15, ) -snapshots['test_rank_stations_match_climate_zones_null match_ca_climate_zone'] = ( +snapshots["test_rank_stations_match_climate_zones_null match_ca_climate_zone"] = ( 4688, - 15 + 15, ) -snapshots['test_rank_stations_match_state site_state=CA, match_state=False'] = ( +snapshots["test_rank_stations_match_state site_state=CA, match_state=False"] = ( 4921, - 15 + 15, ) -snapshots['test_rank_stations_match_state site_state=CA, match_state=True'] = ( - 306, - 15 -) +snapshots["test_rank_stations_match_state site_state=CA, match_state=True"] = (306, 15) -snapshots['test_rank_stations_match_state site_state=None, match_state=True'] = ( +snapshots["test_rank_stations_match_state site_state=None, match_state=True"] = ( 1113, - 15 + 15, ) -snapshots['test_rank_stations_is_tmy3 is_tmy3=True'] = ( - 1019, - 15 -) +snapshots["test_rank_stations_is_tmy3 is_tmy3=True"] = (1019, 15) -snapshots['test_rank_stations_is_tmy3 is_tmy3=False'] = ( - 3902, - 15 -) +snapshots["test_rank_stations_is_tmy3 is_tmy3=False"] = (3902, 15) -snapshots['test_rank_stations_is_cz2010 is_cz2010=True'] = ( - 86, - 15 -) +snapshots["test_rank_stations_is_cz2010 is_cz2010=True"] = (86, 15) -snapshots['test_rank_stations_is_cz2010 is_cz2010=False'] = ( - 4835, - 15 -) +snapshots["test_rank_stations_is_cz2010 is_cz2010=False"] = (4835, 15) -snapshots['test_rank_stations_minimum_quality minimum_quality=low'] = ( - 4921, - 15 -) +snapshots["test_rank_stations_minimum_quality minimum_quality=low"] = (4921, 15) -snapshots['test_rank_stations_minimum_quality minimum_quality=medium'] = ( - 1810, - 15 -) +snapshots["test_rank_stations_minimum_quality minimum_quality=medium"] = (1810, 15) -snapshots['test_rank_stations_minimum_quality minimum_quality=high'] = ( - 1662, - 15 -) +snapshots["test_rank_stations_minimum_quality minimum_quality=high"] = (1662, 15) -snapshots['test_rank_stations_minimum_tmy3_class minimum_tmy3_class=III'] = ( - 1019, - 15 -) +snapshots["test_rank_stations_minimum_tmy3_class minimum_tmy3_class=III"] = (1019, 15) -snapshots['test_rank_stations_minimum_tmy3_class minimum_tmy3_class=II'] = ( - 857, - 15 -) +snapshots["test_rank_stations_minimum_tmy3_class minimum_tmy3_class=II"] = (857, 15) -snapshots['test_rank_stations_minimum_tmy3_class minimum_tmy3_class=I'] = ( - 222, - 15 -) +snapshots["test_rank_stations_minimum_tmy3_class minimum_tmy3_class=I"] = (222, 15) -snapshots['test_rank_stations_max_distance_meters max_distance_meters=200000'] = ( +snapshots["test_rank_stations_max_distance_meters max_distance_meters=200000"] = ( 52, - 15 + 15, ) -snapshots['test_rank_stations_max_distance_meters max_distance_meters=50000'] = ( - 5, - 15 -) +snapshots["test_rank_stations_max_distance_meters max_distance_meters=50000"] = (5, 15) -snapshots['test_rank_stations_max_difference_elevation_meters max_difference_elevation_meters=200'] = ( - 4921, - 15 -) +snapshots[ + "test_rank_stations_max_difference_elevation_meters max_difference_elevation_meters=200" +] = (4921, 15) -snapshots['test_rank_stations_max_difference_elevation_meters site_elevation=0, max_difference_elevation_meters=200'] = ( - 2861, - 15 -) +snapshots[ + "test_rank_stations_max_difference_elevation_meters site_elevation=0, max_difference_elevation_meters=200" +] = (2861, 15) -snapshots['test_rank_stations_max_difference_elevation_meters site_elevation=0, max_difference_elevation_meters=50'] = ( - 1761, - 15 -) +snapshots[ + "test_rank_stations_max_difference_elevation_meters site_elevation=0, max_difference_elevation_meters=50" +] = (1761, 15) -snapshots['test_rank_stations_max_difference_elevation_meters site_elevation=1000, max_difference_elevation_meters=50'] = ( - 52, - 15 -) +snapshots[ + "test_rank_stations_max_difference_elevation_meters site_elevation=1000, max_difference_elevation_meters=50" +] = (52, 15) -snapshots['test_rank_stations_match_climate_zones_not_null match_iecc_climate_zone'] = ( +snapshots["test_rank_stations_match_climate_zones_not_null match_iecc_climate_zone"] = ( 745, - 15 + 15, ) -snapshots['test_rank_stations_match_climate_zones_not_null match_iecc_moisture_regime'] = ( - 711, - 15 -) +snapshots[ + "test_rank_stations_match_climate_zones_not_null match_iecc_moisture_regime" +] = (711, 15) -snapshots['test_rank_stations_match_climate_zones_not_null match_ba_climate_zone'] = ( +snapshots["test_rank_stations_match_climate_zones_not_null match_ba_climate_zone"] = ( 280, - 15 + 15, ) -snapshots['test_rank_stations_match_climate_zones_not_null match_ca_climate_zone'] = ( +snapshots["test_rank_stations_match_climate_zones_not_null match_ca_climate_zone"] = ( 10, - 15 + 15, ) -snapshots['test_select_station_with_second_level_dates station_id'] = '747020' +snapshots["test_select_station_with_second_level_dates station_id"] = "747020" -snapshots['test_select_station_with_empty_tempC station_id'] = '747020' +snapshots["test_select_station_with_empty_tempC station_id"] = "747020" diff --git a/tests/snapshots/snap_test_stations.py b/tests/snapshots/snap_test_stations.py index b7d541e..04ac794 100644 --- a/tests/snapshots/snap_test_stations.py +++ b/tests/snapshots/snap_test_stations.py @@ -7,86 +7,86 @@ snapshots = Snapshot() -snapshots['test_get_isd_filenames_single_year filenames'] = [ - '/pub/data/noaa/2007/722860-23119-2007.gz' +snapshots["test_get_isd_filenames_single_year filenames"] = [ + "/pub/data/noaa/2007/722860-23119-2007.gz" ] -snapshots['test_get_isd_filenames_multiple_year filenames'] = [ - '/pub/data/noaa/2006/722860-23119-2006.gz', - '/pub/data/noaa/2007/722860-23119-2007.gz', - '/pub/data/noaa/2008/722860-23119-2008.gz', - '/pub/data/noaa/2009/722860-23119-2009.gz', - '/pub/data/noaa/2010/722860-23119-2010.gz', - '/pub/data/noaa/2011/722860-23119-2011.gz', - '/pub/data/noaa/2012/722860-23119-2012.gz', - '/pub/data/noaa/2013/722860-23119-2013.gz', - '/pub/data/noaa/2014/722860-23119-2014.gz', - '/pub/data/noaa/2015/722860-23119-2015.gz', - '/pub/data/noaa/2016/722860-23119-2016.gz', - '/pub/data/noaa/2017/722860-23119-2017.gz', - '/pub/data/noaa/2018/722860-23119-2018.gz', - '/pub/data/noaa/2019/722860-23119-2019.gz' +snapshots["test_get_isd_filenames_multiple_year filenames"] = [ + "/pub/data/noaa/2006/722860-23119-2006.gz", + "/pub/data/noaa/2007/722860-23119-2007.gz", + "/pub/data/noaa/2008/722860-23119-2008.gz", + "/pub/data/noaa/2009/722860-23119-2009.gz", + "/pub/data/noaa/2010/722860-23119-2010.gz", + "/pub/data/noaa/2011/722860-23119-2011.gz", + "/pub/data/noaa/2012/722860-23119-2012.gz", + "/pub/data/noaa/2013/722860-23119-2013.gz", + "/pub/data/noaa/2014/722860-23119-2014.gz", + "/pub/data/noaa/2015/722860-23119-2015.gz", + "/pub/data/noaa/2016/722860-23119-2016.gz", + "/pub/data/noaa/2017/722860-23119-2017.gz", + "/pub/data/noaa/2018/722860-23119-2018.gz", + "/pub/data/noaa/2019/722860-23119-2019.gz", ] -snapshots['test_isd_station_get_isd_filenames filenames'] = [ - '/pub/data/noaa/2006/722860-23119-2006.gz', - '/pub/data/noaa/2007/722860-23119-2007.gz', - '/pub/data/noaa/2008/722860-23119-2008.gz', - '/pub/data/noaa/2009/722860-23119-2009.gz', - '/pub/data/noaa/2010/722860-23119-2010.gz', - '/pub/data/noaa/2011/722860-23119-2011.gz', - '/pub/data/noaa/2012/722860-23119-2012.gz', - '/pub/data/noaa/2013/722860-23119-2013.gz', - '/pub/data/noaa/2014/722860-23119-2014.gz', - '/pub/data/noaa/2015/722860-23119-2015.gz', - '/pub/data/noaa/2016/722860-23119-2016.gz', - '/pub/data/noaa/2017/722860-23119-2017.gz', - '/pub/data/noaa/2018/722860-23119-2018.gz', - '/pub/data/noaa/2019/722860-23119-2019.gz' +snapshots["test_isd_station_get_isd_filenames filenames"] = [ + "/pub/data/noaa/2006/722860-23119-2006.gz", + "/pub/data/noaa/2007/722860-23119-2007.gz", + "/pub/data/noaa/2008/722860-23119-2008.gz", + "/pub/data/noaa/2009/722860-23119-2009.gz", + "/pub/data/noaa/2010/722860-23119-2010.gz", + "/pub/data/noaa/2011/722860-23119-2011.gz", + "/pub/data/noaa/2012/722860-23119-2012.gz", + "/pub/data/noaa/2013/722860-23119-2013.gz", + "/pub/data/noaa/2014/722860-23119-2014.gz", + "/pub/data/noaa/2015/722860-23119-2015.gz", + "/pub/data/noaa/2016/722860-23119-2016.gz", + "/pub/data/noaa/2017/722860-23119-2017.gz", + "/pub/data/noaa/2018/722860-23119-2018.gz", + "/pub/data/noaa/2019/722860-23119-2019.gz", ] -snapshots['test_isd_station_get_isd_filenames_with_year filenames'] = [ - '/pub/data/noaa/2007/722860-23119-2007.gz' +snapshots["test_isd_station_get_isd_filenames_with_year filenames"] = [ + "/pub/data/noaa/2007/722860-23119-2007.gz" ] -snapshots['test_get_gsod_filenames_single_year filenames'] = [ - '/pub/data/gsod/2007/722860-23119-2007.op.gz' +snapshots["test_get_gsod_filenames_single_year filenames"] = [ + "/pub/data/gsod/2007/722860-23119-2007.op.gz" ] -snapshots['test_get_gsod_filenames_multiple_year filenames'] = [ - '/pub/data/gsod/2006/722860-23119-2006.op.gz', - '/pub/data/gsod/2007/722860-23119-2007.op.gz', - '/pub/data/gsod/2008/722860-23119-2008.op.gz', - '/pub/data/gsod/2009/722860-23119-2009.op.gz', - '/pub/data/gsod/2010/722860-23119-2010.op.gz', - '/pub/data/gsod/2011/722860-23119-2011.op.gz', - '/pub/data/gsod/2012/722860-23119-2012.op.gz', - '/pub/data/gsod/2013/722860-23119-2013.op.gz', - '/pub/data/gsod/2014/722860-23119-2014.op.gz', - '/pub/data/gsod/2015/722860-23119-2015.op.gz', - '/pub/data/gsod/2016/722860-23119-2016.op.gz', - '/pub/data/gsod/2017/722860-23119-2017.op.gz', - '/pub/data/gsod/2018/722860-23119-2018.op.gz', - '/pub/data/gsod/2019/722860-23119-2019.op.gz' +snapshots["test_get_gsod_filenames_multiple_year filenames"] = [ + "/pub/data/gsod/2006/722860-23119-2006.op.gz", + "/pub/data/gsod/2007/722860-23119-2007.op.gz", + "/pub/data/gsod/2008/722860-23119-2008.op.gz", + "/pub/data/gsod/2009/722860-23119-2009.op.gz", + "/pub/data/gsod/2010/722860-23119-2010.op.gz", + "/pub/data/gsod/2011/722860-23119-2011.op.gz", + "/pub/data/gsod/2012/722860-23119-2012.op.gz", + "/pub/data/gsod/2013/722860-23119-2013.op.gz", + "/pub/data/gsod/2014/722860-23119-2014.op.gz", + "/pub/data/gsod/2015/722860-23119-2015.op.gz", + "/pub/data/gsod/2016/722860-23119-2016.op.gz", + "/pub/data/gsod/2017/722860-23119-2017.op.gz", + "/pub/data/gsod/2018/722860-23119-2018.op.gz", + "/pub/data/gsod/2019/722860-23119-2019.op.gz", ] -snapshots['test_isd_station_get_gsod_filenames_with_year filenames'] = [ - '/pub/data/gsod/2007/722860-23119-2007.op.gz' +snapshots["test_isd_station_get_gsod_filenames_with_year filenames"] = [ + "/pub/data/gsod/2007/722860-23119-2007.op.gz" ] -snapshots['test_isd_station_get_gsod_filenames filenames'] = [ - '/pub/data/gsod/2006/722860-23119-2006.op.gz', - '/pub/data/gsod/2007/722860-23119-2007.op.gz', - '/pub/data/gsod/2008/722860-23119-2008.op.gz', - '/pub/data/gsod/2009/722860-23119-2009.op.gz', - '/pub/data/gsod/2010/722860-23119-2010.op.gz', - '/pub/data/gsod/2011/722860-23119-2011.op.gz', - '/pub/data/gsod/2012/722860-23119-2012.op.gz', - '/pub/data/gsod/2013/722860-23119-2013.op.gz', - '/pub/data/gsod/2014/722860-23119-2014.op.gz', - '/pub/data/gsod/2015/722860-23119-2015.op.gz', - '/pub/data/gsod/2016/722860-23119-2016.op.gz', - '/pub/data/gsod/2017/722860-23119-2017.op.gz', - '/pub/data/gsod/2018/722860-23119-2018.op.gz', - '/pub/data/gsod/2019/722860-23119-2019.op.gz' +snapshots["test_isd_station_get_gsod_filenames filenames"] = [ + "/pub/data/gsod/2006/722860-23119-2006.op.gz", + "/pub/data/gsod/2007/722860-23119-2007.op.gz", + "/pub/data/gsod/2008/722860-23119-2008.op.gz", + "/pub/data/gsod/2009/722860-23119-2009.op.gz", + "/pub/data/gsod/2010/722860-23119-2010.op.gz", + "/pub/data/gsod/2011/722860-23119-2011.op.gz", + "/pub/data/gsod/2012/722860-23119-2012.op.gz", + "/pub/data/gsod/2013/722860-23119-2013.op.gz", + "/pub/data/gsod/2014/722860-23119-2014.op.gz", + "/pub/data/gsod/2015/722860-23119-2015.op.gz", + "/pub/data/gsod/2016/722860-23119-2016.op.gz", + "/pub/data/gsod/2017/722860-23119-2017.op.gz", + "/pub/data/gsod/2018/722860-23119-2018.op.gz", + "/pub/data/gsod/2019/722860-23119-2019.op.gz", ] diff --git a/tests/snapshots/snap_test_summaries.py b/tests/snapshots/snap_test_summaries.py index 254a57b..88399cb 100644 --- a/tests/snapshots/snap_test_summaries.py +++ b/tests/snapshots/snap_test_summaries.py @@ -7,10 +7,10 @@ snapshots = Snapshot() -snapshots['test_get_zcta_ids n_zcta_ids'] = 33144 +snapshots["test_get_zcta_ids n_zcta_ids"] = 33144 -snapshots['test_get_zcta_ids_by_state n_zcta_ids'] = 1763 +snapshots["test_get_zcta_ids_by_state n_zcta_ids"] = 1763 -snapshots['test_get_isd_station_usaf_ids n_usaf_ids'] = 4921 +snapshots["test_get_isd_station_usaf_ids n_usaf_ids"] = 4921 -snapshots['test_get_isd_station_usaf_ids_by_state n_usaf_ids'] = 77 +snapshots["test_get_isd_station_usaf_ids_by_state n_usaf_ids"] = 77 diff --git a/tests/test_international.py b/tests/test_international.py new file mode 100644 index 0000000..7362333 --- /dev/null +++ b/tests/test_international.py @@ -0,0 +1,58 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" + + Copyright 2023 The Society for the Reduction of Carbon, Ltd (T/A Carbon Co-op). + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + +""" + +import datetime +from dateutil.relativedelta import relativedelta +import eeweather +import numpy as np +import pandas as pd +from pathlib import Path + +def test_get_weather_intervals_for_similar_sites(): + p = Path().resolve().parents[0] + df = pd.read_csv(str(p) + "/samples/sample_sites.csv", index_col=0) + df["end_date"] = pd.to_datetime(df["end_date"]) + df["start_date"] = pd.to_datetime(df["start_date"]) + ( + df_collated, + df_shorter_intervals, + ) = eeweather.get_weather_intervals_for_similar_sites(df) + for i in df_shorter_intervals.iterrows(): + shorter_interval = i[-1]["end_date"] - i[-1]["start_date"] + larger_interval = ( + df_collated[df_collated.index == i[-1]["matching_index"]]["end_date"] + - df_collated[df_collated.index == i[-1]["matching_index"]]["start_date"] + ) + larger_interval = larger_interval.iloc[0] + assert larger_interval > shorter_interval + + +def test_get_weather_intl(): + p = Path().resolve().parents[0] + df = pd.read_csv(str(p) + "/samples/test_locations.csv", index_col=0) + for lat, long in zip(df["latitude"], df["longitude"]): + weather = eeweather.get_weather_intl( + start_date=datetime.datetime.now() - relativedelta(years=1), + end_date=datetime.datetime.now(), + latitude=lat, + longitude=long, + ) + assert isinstance(weather["temp"].iloc[0], np.float32) + assert isinstance(weather.index, pd.DatetimeIndex) diff --git a/tests/test_stations.py b/tests/test_stations.py index bd72c01..1bbe2e0 100644 --- a/tests/test_stations.py +++ b/tests/test_stations.py @@ -337,7 +337,7 @@ def test_get_isd_file_metadata(): {"usaf_id": "722874", "wban_id": "93134", "year": "2016"}, {"usaf_id": "722874", "wban_id": "93134", "year": "2017"}, {"usaf_id": "722874", "wban_id": "93134", "year": "2018"}, - {"usaf_id": "722874", "wban_id": "93134", 'year': "2019"}, + {"usaf_id": "722874", "wban_id": "93134", "year": "2019"}, ] with pytest.raises(UnrecognizedUSAFIDError) as excinfo: @@ -361,7 +361,7 @@ def test_isd_station_get_isd_file_metadata(): {"usaf_id": "722874", "wban_id": "93134", "year": "2016"}, {"usaf_id": "722874", "wban_id": "93134", "year": "2017"}, {"usaf_id": "722874", "wban_id": "93134", "year": "2018"}, - {"usaf_id": "722874", "wban_id": "93134", 'year': "2019"}, + {"usaf_id": "722874", "wban_id": "93134", "year": "2019"}, ] @@ -598,21 +598,21 @@ def test_cached_gsod_daily_temp_data_is_expired_empty(monkeypatch_key_value_stor # station cache expired empty def test_isd_station_cached_isd_hourly_temp_data_is_expired_empty( - monkeypatch_key_value_store + monkeypatch_key_value_store, ): station = ISDStation("722874") assert station.cached_isd_hourly_temp_data_is_expired(2007) is True def test_isd_station_cached_isd_daily_temp_data_is_expired_empty( - monkeypatch_key_value_store + monkeypatch_key_value_store, ): station = ISDStation("722874") assert station.cached_isd_daily_temp_data_is_expired(2007) is True def test_isd_station_cached_gsod_daily_temp_data_is_expired_empty( - monkeypatch_key_value_store + monkeypatch_key_value_store, ): station = ISDStation("722874") assert station.cached_gsod_daily_temp_data_is_expired(2007) is True @@ -709,35 +709,35 @@ def test_validate_cz2010_hourly_temp_data_cache_empty(monkeypatch_key_value_stor # station validate cache empty def test_isd_station_validate_isd_hourly_temp_data_cache_empty( - monkeypatch_key_value_store + monkeypatch_key_value_store, ): station = ISDStation("722874") assert station.validate_isd_hourly_temp_data_cache(2007) is False def test_isd_station_validate_isd_daily_temp_data_cache_empty( - monkeypatch_key_value_store + monkeypatch_key_value_store, ): station = ISDStation("722874") assert station.validate_isd_daily_temp_data_cache(2007) is False def test_isd_station_validate_gsod_daily_temp_data_cache_empty( - monkeypatch_key_value_store + monkeypatch_key_value_store, ): station = ISDStation("722874") assert station.validate_gsod_daily_temp_data_cache(2007) is False def test_isd_station_validate_tmy3_hourly_temp_data_cache_empty( - monkeypatch_key_value_store + monkeypatch_key_value_store, ): station = ISDStation("722880") assert station.validate_tmy3_hourly_temp_data_cache() is False def test_isd_station_validate_cz2010_hourly_temp_data_cache_empty( - monkeypatch_key_value_store + monkeypatch_key_value_store, ): station = ISDStation("722880") assert station.validate_cz2010_hourly_temp_data_cache() is False @@ -987,7 +987,7 @@ def test_isd_station_deserialize_cz2010_hourly_temp_data(): # write read destroy def test_write_read_destroy_isd_hourly_temp_data_to_from_cache( - monkeypatch_key_value_store + monkeypatch_key_value_store, ): store = monkeypatch_key_value_store key = get_isd_hourly_temp_data_cache_key("123456", 1990) @@ -1007,7 +1007,7 @@ def test_write_read_destroy_isd_hourly_temp_data_to_from_cache( def test_write_read_destroy_isd_daily_temp_data_to_from_cache( - monkeypatch_key_value_store + monkeypatch_key_value_store, ): store = monkeypatch_key_value_store key = get_isd_daily_temp_data_cache_key("123456", 1990) @@ -1027,7 +1027,7 @@ def test_write_read_destroy_isd_daily_temp_data_to_from_cache( def test_write_read_destroy_gsod_daily_temp_data_to_from_cache( - monkeypatch_key_value_store + monkeypatch_key_value_store, ): store = monkeypatch_key_value_store key = get_gsod_daily_temp_data_cache_key("123456", 1990) @@ -1047,7 +1047,7 @@ def test_write_read_destroy_gsod_daily_temp_data_to_from_cache( def test_write_read_destroy_tmy3_hourly_temp_data_to_from_cache( - monkeypatch_key_value_store + monkeypatch_key_value_store, ): store = monkeypatch_key_value_store key = get_tmy3_hourly_temp_data_cache_key("123456") @@ -1067,7 +1067,7 @@ def test_write_read_destroy_tmy3_hourly_temp_data_to_from_cache( def test_write_read_destroy_cz2010_hourly_temp_data_to_from_cache( - monkeypatch_key_value_store + monkeypatch_key_value_store, ): store = monkeypatch_key_value_store key = get_cz2010_hourly_temp_data_cache_key("123456") @@ -1088,7 +1088,7 @@ def test_write_read_destroy_cz2010_hourly_temp_data_to_from_cache( # station write read destroy def test_isd_station_write_read_destroy_isd_hourly_temp_data_to_from_cache( - monkeypatch_key_value_store + monkeypatch_key_value_store, ): station = ISDStation("722874") store = monkeypatch_key_value_store @@ -1109,7 +1109,7 @@ def test_isd_station_write_read_destroy_isd_hourly_temp_data_to_from_cache( def test_isd_station_write_read_destroy_isd_daily_temp_data_to_from_cache( - monkeypatch_key_value_store + monkeypatch_key_value_store, ): station = ISDStation("722874") store = monkeypatch_key_value_store @@ -1130,7 +1130,7 @@ def test_isd_station_write_read_destroy_isd_daily_temp_data_to_from_cache( def test_isd_station_write_read_destroy_gsod_daily_temp_data_to_from_cache( - monkeypatch_key_value_store + monkeypatch_key_value_store, ): station = ISDStation("722874") store = monkeypatch_key_value_store @@ -1151,7 +1151,7 @@ def test_isd_station_write_read_destroy_gsod_daily_temp_data_to_from_cache( def test_isd_station_write_read_destroy_tmy3_hourly_temp_data_to_from_cache( - monkeypatch_key_value_store + monkeypatch_key_value_store, ): station = ISDStation("722880") store = monkeypatch_key_value_store @@ -1172,7 +1172,7 @@ def test_isd_station_write_read_destroy_tmy3_hourly_temp_data_to_from_cache( def test_isd_station_write_read_destroy_cz2010_hourly_temp_data_to_from_cache( - monkeypatch_key_value_store + monkeypatch_key_value_store, ): station = ISDStation("722880") store = monkeypatch_key_value_store